Method for cell label classification

Abstract

This application relates to methods for classifying cell markers. Methods and systems for classifying cell markers, such as identifying signaling cell markers, are disclosed herein. In some embodiments, the method includes: obtaining sequencing data of a barcoded target created using a barcoded target within a cell, wherein the barcode contains both cellular and molecular markers. After classifying these cellular markers, a minimum value of the second derivative graph of the cumulative sum can be determined. Using these methods, based on the number of molecular markers with different sequences associated with the cellular marker and a cellular marker threshold, the cellular marker can be classified as a signaling cell marker or a noise cell marker.

Description

[0001] This application is a divisional application of application No. 202310996650.5, filed on November 7, 2017, entitled "Method for Cell Marker Classification". Application No. 202310996650.5, filed on November 7, 2017, entitled "Method for Cell Marker Classification", is a divisional application of application No. 201780068299.6, filed on November 7, 2017, entitled "Method for Cell Marker Classification".

[0002] Cross-reference to related applications This application claims priority to U.S. Provisional Patent Application No. 62 / 419194, filed November 8, 2016, and U.S. Provisional Patent Application No. 62 / 445546, filed January 12, 2017. The contents of each of these related applications are expressly incorporated herein by reference in their entirety. Background of the Invention Technical Field

[0003] This disclosure generally relates to the field of molecular barcode encoding, and more specifically to the identification and correction of noisy cell markers. Background Technology

[0004] Methods and techniques such as random barcoding are useful for cell analysis, particularly for using reverse transcription, polymerase chain reaction (PCR) amplification, and next-generation sequencing (NGS) to decipher gene expression profiles and determine cell state. However, these methods and techniques can introduce errors that, if not corrected, may lead to overestimation of cell counts. Summary of the Invention

[0005] This article discloses a method for identifying signal cell markers. In some embodiments, the method includes: (a) barcoding (e.g., random barcoding) a plurality of targets in a cell sample using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes contains a cellular marker and a molecular marker; (b) obtaining sequencing data of the plurality of barcoded targets; (c) determining the number of molecular markers with different sequences associated with each of the cellular markers in the plurality of barcodes; (d) determining a rank of each of the cellular markers in the plurality of barcodes based on the number of molecular markers with different sequences associated with each of the cellular markers; (e) generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cellular markers determined in (c) and the rank of each of the cellular markers determined in (d); (f) generating a second derivative graph of the cumulative sum graph; (g) determining a minimum value of the second derivative graph of the cumulative sum graph, wherein the minimum value of the second derivative graph corresponds to a cellular marker threshold; and (h) Based on the number of molecular markers with different sequences associated with each of the cell markers determined in (c) and the cell marker threshold determined in (g), each of the cell markers is identified as a signal cell marker or a noise cell marker.

[0006] In some embodiments, the method includes removing sequencing information associated with the identified cell markers from the sequencing data obtained in (b) if the cell markers of the plurality of barcodes are identified as noise cell markers in (h). The method may also include removing sequencing information associated with the molecular markers of the target with different sequences from the sequencing data obtained in (b) if the number of molecular markers with different sequences associated with a target among the plurality of targets exceeds a molecular marker occurrence threshold.

[0007] In some embodiments, determining the number of molecular markers with different sequences associated with each of the cell markers in step (c) includes removing sequencing information associated with non-unique molecular markers associated with each of the cell markers from the sequencing data. The cumulative sum graph can be a log-log graph. A log-log graph can be a log10-log10 graph.

[0008] In some embodiments, generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cell markers determined in (c) and the rank of each of the cell markers determined in (d) includes: determining a cumulative sum for each rank of the cell markers, wherein the cumulative sum for each rank includes the sum of the number of molecular markers with different sequences associated with each of the lower-rank cell markers. Generating a second derivative graph of the cumulative sum graph may include determining the difference between the cumulative sum of the first rank and the cumulative sum of the second rank relative to a first rank and a second rank of the cell markers. The difference between the first rank and the second rank may be 1.

[0009] In some embodiments, the minimum is a global minimum. Determining the minimum of the second derivative plot involves determining the minimum of the second derivative plot that is above a threshold for the minimum number of molecular markers associated with each of the cell markers.

[0010] In some embodiments, the threshold for the minimum number of molecular markers associated with each of the cell markers is a percentile threshold. The threshold for the minimum number of molecular markers associated with each of the cell markers is determined based on the number of cells in the cell sample.

[0011] In some embodiments, determining the minimum of the second derivative plot includes determining the minimum of the second derivative plot below a threshold for the maximum number of molecular markers associated with each of the cell markers. The threshold for the maximum number of molecular markers associated with each of the cell markers may be a percentile threshold. The threshold for the maximum number of molecular markers associated with each of the cell markers may be determined based on the number of cells in the cell sample.

[0012] In some embodiments, if the number of molecular markers with different sequences associated with each of the cell markers determined in (c) is greater than a cell marker threshold, then each of the cell markers is identified as a signal cell marker. If the number of molecular markers with different sequences associated with each of the cell markers determined in (c) is not greater than a cell marker threshold, then each of the cell markers may be identified as a noise cell marker.

[0013] In some embodiments, the method includes: (i) for one or more of the plurality of targets: (1) counting the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) estimating the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0014] This article discloses a method for identifying signal cell markers. In some embodiments, the method includes: (a) obtaining sequencing data of a plurality of barcoded targets (e.g., targets randomly barcoded), wherein the plurality of barcoded targets are created by barcoding (e.g., randomly barcoding) a plurality of targets in a cell sample using a plurality of barcodes (e.g., random barcodes), and wherein each of the plurality of barcodes includes a cell marker and a molecular marker; (b) determining a rank of each of the plurality of barcoded targets (or barcodes) cell markers based on the number of molecular markers with different sequences associated with each of the cell markers of the plurality of barcoded targets (or barcodes); (c) determining a cell marker threshold based on the number of molecular markers with different sequences associated with each of the cell markers and the rank of each of the cell markers of the plurality of barcoded targets (or barcodes) determined in (b); and identifying each of the cell markers as a signal cell marker or a noise cell marker based on the number of molecular markers with different sequences associated with each of the cell markers and the cell marker threshold determined in (c).

[0015] In some embodiments, the method includes determining the number of molecular markers with different sequences associated with each of the cell markers. Determining the number of molecular markers with different sequences associated with each of the cell markers may include removing sequencing information associated with non-unique molecular markers associated with each of the cell markers from the sequencing data.

[0016] In some embodiments, determining a cell marker threshold based on the number of molecular markers with different sequences associated with each of a plurality of barcoded cell markers in a target includes: determining the cell marker with the greatest variation in the cumulative sum of cell markers at level n and the cumulative sum of cell markers at the next level n+1, wherein the number of molecular markers with different sequences associated with that cell marker corresponds to the cell marker threshold.

[0017] In some embodiments, determining a cell marker threshold based on the number of molecular markers with different sequences associated with each of the plurality of barcoded target cell markers and the level of each of the plurality of barcoded target cell markers determined in (b) includes: determining a cumulative sum for each level of cell markers, wherein the cumulative sum for each level includes the sum of the number of molecular markers with different sequences associated with each of the lower-level cell markers; and determining the cumulative sum at level n and the next level. n+1 The rank n of the cell markers with the largest changes in the cumulative sum, where the changes occur in the cumulative sum and the next rank. n+1 The level n of the cell marker with the largest change in the cumulative sum corresponds to the cell marker threshold.

[0018] In some embodiments, determining a cell marker threshold based on the number of molecular markers with different sequences associated with each of a plurality of barcoded target cell markers and the level of each of the plurality of barcoded target cell markers determined in (b) includes: generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cell markers and the level of each of the cell markers determined in (b); generating a second derivative graph of the cumulative sum graph; and determining a minimum value of the second derivative graph of the cumulative sum graph, wherein the minimum value of the second derivative graph corresponds to the cell marker threshold. Generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cell markers and the level of each of the cell markers determined in (b) may include: determining a cumulative sum for each level of cell markers, wherein the cumulative sum for each level includes the sum of the number of molecular markers with different sequences associated with each of lower-level cell markers. Generating a second derivative graph of the cumulative sum graph may include determining a difference between the cumulative sum of the first level and the cumulative sum of the second level relative to a difference between a first level and a second level of cell markers.

[0019] In some embodiments, the difference between the first level and the second level is 1. In some embodiments, the method includes removing sequencing information associated with the identified cell marker from the sequencing data obtained in (a) if the cell markers of a plurality of barcoded targets are identified as noise cell markers in (d). The method may include removing sequencing information associated with the molecular markers of the target with different sequences from the sequencing data obtained in (a) if the number of molecular markers with different sequences associated with a target among the plurality of targets is higher than a molecular marker occurrence threshold. The cumulative sum graph may be a log-log graph. The log-log graph may be a log10-log10 graph.

[0020] In some embodiments, the minimum value is a global minimum value. Determining the minimum value of the second derivative plot may include determining a threshold for the minimum number of molecular markers associated with each of the cell markers. The threshold for the minimum number of molecular markers associated with each of the cell markers may be a percentile threshold. The threshold for the minimum number of molecular markers associated with each of the cell markers may be determined based on the number of cells in the cell sample.

[0021] In some embodiments, determining the minimum of the second derivative plot includes determining the minimum of the second derivative plot below a threshold for the maximum number of molecular markers associated with each of the cell markers. The threshold for the maximum number of molecular markers associated with each of the cell markers may be a percentile threshold. The threshold for the maximum number of molecular markers associated with each of the cell markers may be determined based on the number of cells in the cell sample.

[0022] In some embodiments, if the number of molecular markers with different sequences associated with each of the cell markers determined in (c) is greater than a cell marker threshold, then each of the cell markers is identified as a signal cell marker. If the number of molecular markers with different sequences associated with each of the cell markers determined in (c) is not greater than a cell marker threshold, then each of the cell markers may be identified as a noise cell marker.

[0023] In some embodiments, the method includes: (e) for one or more of the plurality of targets: (1) counting the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) estimating the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0024] This document discloses embodiments for identifying signaling cell markers. In some embodiments, the method includes: (a) obtaining sequencing data of a plurality of targets of a cell, wherein each target is associated with a number of molecular markers with different sequences associated with each of the plurality of cell markers; (b) determining a cell marker threshold based on the number of molecular markers with different sequences associated with each of the cell markers; and (c) identifying each of the cell markers as a signaling cell marker or a noise cell marker based on the number of molecular markers with different sequences associated with each of the cell markers and the cell marker threshold.

[0025] In some embodiments, obtaining sequencing data includes: barcoding multiple targets of a cell using multiple barcodes to create multiple barcoded targets, wherein each of the multiple barcodes includes a cellular marker and a molecular marker among multiple cellular markers; and determining the number of molecular markers with different sequences associated with each of the cellular markers of the multiple barcodes. In some embodiments, the method includes: for one or more of the multiple targets: (1) counting the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) estimating the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1). The method may include removing sequencing information associated with the identified cellular marker from the sequencing data if the cellular marker of the multiple barcodes is identified as a noise cellular marker. The method may include removing sequencing information associated with the molecular marker with different sequences associated with the target from the sequencing data if the number of molecular markers with different sequences associated with a target among the multiple targets is higher than a molecular marker occurrence threshold. In some embodiments, determining the number of molecular markers with different sequences associated with each of the cell markers in (c) includes removing sequencing information associated with non-unique molecular markers associated with each of the cell markers from the sequencing data.

[0026] In some embodiments, determining a cell marker threshold includes: determining an inflection point of a cumulative sum graph, wherein the cumulative sum graph is based on the number of molecular markers with different sequences associated with each of a plurality of cell markers and the rank of each of the cell markers, and wherein the inflection point corresponds to a cell marker threshold. Determining the inflection point of the cumulative sum graph may include: generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the plurality of cell markers and the rank of each of the cell markers; generating a second derivative graph of the cumulative sum graph; and determining a minimum value of the second derivative graph of the cumulative sum graph, wherein the minimum value of the second derivative graph corresponds to a cell marker threshold. Determining a cell marker threshold may include: determining the rank of each of a plurality of cell markers based on the number of molecular markers with different sequences associated with each of the cell markers. The cumulative sum graph may be a log-log graph, such as a log10-log10 graph.

[0027] In some embodiments, generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cell markers and the rank of each of the cell markers includes: determining a cumulative sum for each rank of the cell markers, wherein the cumulative sum for a rank includes the sum of the number of molecular markers with different sequences associated with each of lower-rank cell markers. Generating a second derivative graph of the cumulative sum graph may include determining the difference between the cumulative sum of the first rank and the cumulative sum of the second rank relative to a first rank and a second rank of the cell markers. The difference between the first rank and the second rank may be 1. The minimum value may be a global minimum. Determining the minimum value of the second derivative graph may include: determining that the minimum value of the second derivative graph is higher than a threshold for a minimum number of molecular markers associated with each of the cell markers. The threshold for the minimum number of molecular markers associated with each of the cell markers may be a percentile threshold. The threshold for the minimum number of molecular markers associated with each of the cell markers may be determined based on the number of multiple cells.

[0028] In some embodiments, determining the minimum of the second derivative plot includes determining the minimum of the second derivative plot below a threshold for the maximum number of molecular markers associated with each of the cell markers. The threshold for the maximum number of molecular markers associated with each of the cell markers may be a percentile threshold. The threshold for the maximum number of molecular markers associated with each of the cell markers may be determined based on the number of multiple cells.

[0029] In some embodiments, each of the cell markers can be identified as a signal cell marker if the number of molecular markers with different sequences associated with each of the cell markers is greater than a cell marker threshold. If the number of molecular markers with different sequences associated with each of the cell markers is not greater than a cell marker threshold, each of the cell markers can be identified as a noise cell marker.

[0030] This document discloses a method for identifying signaling cell markers. In some embodiments, the method includes: (a) barcoding (e.g., random barcoding) a plurality of targets in a cell sample using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes contains a cell marker and a molecular marker, wherein the barcoded targets created from targets of different cells in the plurality of cells have different cell markers, and wherein the barcoded targets created from targets of the same cells in the plurality of cells have different molecular markers; (b) obtaining sequencing data of the plurality of barcoded targets; (c) determining a feature vector for each cell marker of the plurality of barcodes (or barcoded targets), wherein the feature vector contains the number of molecular markers with different sequences associated with each cell marker; (d) determining a cluster of each cell marker of the plurality of barcodes (or barcoded targets) based on the feature vector; and (e) Based on the number of cell markers in the cluster and the cluster size threshold, each cell marker of the multiple random barcodes (or barcoded targets) is identified as a signal cell marker or a noise cell marker.

[0031] In some embodiments, determining the clustering of each cell marker of a plurality of barcoded targets based on feature vectors includes clustering each cell marker of the plurality of barcoded targets into clusters based on the distance between the feature vectors and the clusters in the feature vector space. Determining the clustering of each cell marker of a plurality of barcoded targets based on feature vectors may include: projecting the feature vectors from the feature vector space to a lower-dimensional space; and clustering each cell marker into clusters based on the distance between the feature vectors and the clusters in the lower-dimensional space.

[0032] In some embodiments, the lower-dimensional space is a two-dimensional space. Projecting feature vectors from the feature vector space to the lower-dimensional space may include using a t-distributed random neighborhood embedding (tSNE) method. Clustering each cell label into clusters based on the distance between the feature vectors and the clusters in the lower-dimensional space may include using a density-based method. The density-based method may include a density-based spatial clustering (DBSCAN) method with noisy applications.

[0033] In some embodiments, if the number of cell markers in a cluster is less than a cluster size threshold, the cell marker is identified as a signal cell marker. If the number of cell markers in a cluster is not less than a cluster size threshold, the cell marker is identified as a noise cell marker. The method may include: (f) for one or more of the plurality of targets: (1) counting the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) estimating the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0034] In some embodiments, the method includes determining a cluster size threshold based on the number of cell markers of a plurality of barcoded targets. The cluster size threshold may be a percentage of the number of cell markers of the plurality of barcoded targets. In some embodiments, the method includes determining a cluster size threshold based on the number of cell markers of a plurality of barcodes. The cluster size threshold is a percentage of the number of cell markers of the plurality of barcodes. In some embodiments, the method includes determining a cluster size threshold based on the number of molecular markers with different sequences associated with each cell marker of the plurality of barcodes.

[0035] This document discloses a method for identifying signaling cell markers. In some embodiments, the method includes: (a) obtaining sequencing data of a plurality of barcoded targets (e.g., randomly barcoded targets), wherein the plurality of barcoded targets are created from a plurality of targets in a cell sample, the plurality of targets being barcoded (e.g., randomly barcoded) using a plurality of barcodes, wherein each of the plurality of barcodes contains a cell marker and a molecular marker, wherein the barcoded targets created from targets of different cells in the plurality of cells have different cell markers, and wherein the barcoded targets created from targets of the same cells in the plurality of cells have different molecular markers; (b) determining a feature vector for each cell marker of the plurality of barcoded targets, wherein the feature vector contains the number of molecular markers with different sequences associated with each cell marker; (c) determining a cluster of each cell marker of the plurality of barcoded targets based on the feature vector; and (d) identifying each cell marker of the plurality of barcoded targets as a signaling cell marker or a noise cell marker based on the number of cell markers in the cluster and a cluster size threshold.

[0036] In some embodiments, determining the clustering of each cell marker of a plurality of barcoded targets based on feature vectors includes clustering each cell marker of the plurality of barcoded targets into clusters based on the distance between the feature vector and the cluster in the feature vector space. Determining the clustering of each cell marker of a plurality of barcoded targets based on feature vectors includes: projecting the feature vector from the feature vector space to a lower-dimensional space; and clustering each cell marker into clusters based on the distance between the feature vector and the cluster in the lower-dimensional space. The lower-dimensional space may be a two-dimensional space.

[0037] In some embodiments, projecting feature vectors from the feature vector space to a lower-dimensional space includes using a t-distributed random neighborhood embedding (tSNE) method. Clustering each cell label into clusters based on the distance between the feature vectors and the clusters in the lower-dimensional space can include using a density-based method. Density-based methods can include density-based spatial clustering (DBSCAN) methods with noisy applications.

[0038] In some embodiments, if the number of cell markers in a cluster is less than a cluster size threshold, the cell marker can be identified as a signal cell marker. If the number of cell markers in a cluster is not less than a cluster size threshold, the cell marker can be identified as a noise cell marker.

[0039] In some embodiments, the method includes determining a cluster size threshold based on the number of cell markers of a plurality of barcoded targets. The cluster size threshold may be a percentage of the number of cell markers of the plurality of barcoded targets. In some embodiments, the method includes determining a cluster size threshold based on the number of cell markers with different sequences associated with each cell marker of the plurality of barcodes.

[0040] In some embodiments, the method includes: (e) for one or more of the plurality of targets: (1) counting the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) estimating the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0041] This document discloses embodiments for identifying signaling cell markers. In some embodiments, the method includes: (a) obtaining sequencing data of a plurality of first targets of a cell, wherein each first target is associated with a number of molecular markers with different sequences associated with each of the plurality of cell markers; (b) identifying each of the cell markers as a signaling cell marker or a noise cell marker based on the number of molecular markers with different sequences associated with each of the cell markers and an identification threshold; and (c) re-identifying at least one of the plurality of cell markers identified as a noise cell marker in (b) as a signaling cell marker, or re-identifying at least one of the plurality of cell markers identified as a signaling cell marker as a noise cell marker. Identifying each of the cell markers, re-identifying at least one of the plurality of cell markers as a signaling cell marker, or re-identifying at least one of the plurality of cell markers as a noise cell marker can be based on the same cell marker identification method as in this disclosure or different cell marker identification methods. The identification threshold may include a cell marker threshold, a cluster size threshold, or any combination thereof. The method may include: removing one or more cell markers from the plurality of cell markers, each associated with a number of molecular markers with different sequences below a molecular marker number threshold.

[0042] In some embodiments, re-identifying at least one of the plurality of cell markers identified as a noise cell marker in (b) as a signal cell marker includes: identifying a plurality of second targets, each of the plurality of first targets having one or more variability indices above a variability threshold; and for each of the plurality of cell markers, re-identifying at least one of the plurality of cell markers identified as a noise cell marker in (b) as a signal cell marker based on the number of molecular markers with different sequences associated with the plurality of second targets and the identification threshold. The one or more variability indices of the second targets may include the average, maximum, median, minimum, dispersion, or any combination thereof of the number of molecular markers with different sequences associated with the second targets and the cell markers in the plurality of cell markers in the sequencing data. The one or more variability indices of the second targets may include standard deviation, normalized deviation, or any combination thereof, or variability indices of a subset of the plurality of second targets. The variability threshold may be less than or equal to the size of a subset of the plurality of second targets.

[0043] In some embodiments, re-identifying at least one of a plurality of cell markers identified as a signal cell marker in (b) as a noise cell marker includes: identifying a plurality of third targets, each of a plurality of first targets, having an association with a cell marker identified as a noise cell marker in (c) that is above an association threshold; and for each of the plurality of cell markers, re-identifying at least one cell marker identified as a signal cell marker in (b) as a noise cell marker based on the number of molecular markers with different sequences associated with the plurality of third targets and the identification threshold. Determining a plurality of third targets, each of a plurality of first targets, having an association with a cell marker identified as a noise cell marker in (c) that is above an association threshold, may include: after re-identifying at least one cell marker identified as a noise cell marker in (b) as a signal cell marker, identifying a plurality of remaining cell markers identified as signal cell markers; and for each of the plurality of cell markers, identifying a plurality of third targets based on the number of molecular markers with different sequences associated with the plurality of targets, and for each of the plurality of remaining cell markers, identifying a plurality of third targets based on the number of molecular markers with different sequences associated with the plurality of targets.

[0044] This document discloses a system for identifying signaling cell markers. In some embodiments, the system includes: a hardware processor; and a non-transitory memory having instructions stored thereon, which, when executed by the hardware processor, cause the processor to perform any of the methods disclosed herein. This document also discloses a computer-readable medium for identifying signaling cell markers. In some embodiments, the computer-readable medium contains code for implementing any of the methods disclosed herein.

[0045] The following projects are also disclosed in this article: 1. A method for identifying signal cell markers, the method comprising: (a) Using multiple barcodes to barcode multiple targets in multiple cells to create multiple barcode-coded targets, wherein each of the multiple barcodes contains a cellular marker and a molecular marker; (b) Obtain sequencing data of the multiple barcoded targets; (c) Determine the number of molecular markers with different sequences associated with each of the multiple barcode cell markers; (d) Determine the rank of each of the multiple barcode cell markers based on the number of molecular markers with different sequences associated with each of these cell markers; (e) Based on the number of molecular markers with different sequences associated with each of these cell markers as determined in (c) and the rank of each of these cell markers as determined in (d), generate a cumulative sum graph; (f) Generate the second derivative graph of the cumulative sum graph; (g) Determine the minimum of the second derivative plot of the cumulative sum plot, where the minimum of the second derivative plot corresponds to the cell labeling threshold; and (h) Based on the number of molecular markers with different sequences associated with each of these cell markers as determined in (c) and the cell marker threshold determined in (g), each of these cell markers is identified as a signal cell marker or a noise cell marker.

[0046] 2. The method of Project 1, wherein if the cell markers of the plurality of barcodes are identified as noise cell markers in (h), then the sequencing information associated with the identified cell markers is removed from the sequencing data obtained in (b).

[0047] 3. The method of any one of items 1-2, the method comprising: if the number of molecular markers with different sequences associated with a target among the plurality of targets is higher than a molecular marker occurrence threshold, then removing sequencing information associated with molecular markers with different sequences associated with the target among the plurality of targets from the sequencing data obtained in (b).

[0048] 4. The method of any one of items 1-3, wherein determining the number of molecular markers with different sequences associated with each of these cell markers in (c) includes removing sequencing information associated with non-unique molecular markers associated with each of these cell markers from the sequencing data.

[0049] 5. The method as described in any one of items 1-4, wherein the cumulative sum graph is a log-log graph.

[0050] 6. As described in Project 5, where the log-log plot is log 10 -log 10 picture.

[0051] 7. The method of any one of items 1-6, wherein generating the cumulative sum graph based on the number of molecular markers with different sequences associated with each of these cell markers as determined in (c) and the rank of each of these cell markers as determined in (d) comprises: Determine the cumulative sum for each of these cellular markers, where the cumulative sum for each level includes the sum of the number of molecular markers with different sequences associated with each of the lower-level cellular markers.

[0052] 8. The method of Item 7, wherein generating the second derivative plot of the cumulative sum plot includes determining the difference between the cumulative sum of the first level and the cumulative sum of the second level relative to the difference between a first level and a second level of the cell markers.

[0053] 9. The method described in Project 8, wherein the difference between the first level and the second level is 1.

[0054] 10. The method as described in any one of items 1-9, wherein the minimum value is the global minimum value.

[0055] 11. The method of any one of items 1-10, wherein determining the minimum value of the second derivative graph comprises determining the minimum value of the second derivative graph that is above a threshold number of molecular markers associated with each of these cellular markers.

[0056] 12. The method as described in item 11, wherein the threshold for the minimum number of molecular markers associated with each of these cellular markers is a percentile threshold.

[0057] 13. The method of any one of items 11-12, wherein the threshold for the minimum number of molecular markers associated with each of these cellular markers is determined based on the number of cells in the plurality of cells.

[0058] 14. The method of any one of items 1-13, wherein determining the minimum value of the second derivative plot comprises determining the minimum value of the second derivative plot below a threshold for the maximum number of molecular markers associated with each of these cellular markers.

[0059] 15. The method as described in Item 14, wherein the threshold for the maximum number of molecular markers associated with each of these cellular markers is a percentile threshold.

[0060] 16. The method of any one of items 14-15, wherein the threshold for the maximum number of molecular markers associated with each of these cellular markers is determined based on the number of cells in the plurality of cells.

[0061] 17. The method of any one of items 1-16, wherein each of the cell markers is identified as a signal cell marker if the number of molecular markers with different sequences associated with each of these cell markers determined in (c) is greater than the cell marker threshold.

[0062] 18. The method of any one of items 1-17, wherein each of the cell markers is identified as a noise cell marker if the number of molecular markers with different sequences associated with each of these cell markers determined in (c) is not greater than the cell marker threshold.

[0063] 19. The method of any one of items 1-18, the method comprising: (i) For one or more of the multiple targets: (1) Count the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) Estimate the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0064] 20. A method for determining signal cell markers, the method comprising: (a) Obtaining sequencing data of multiple barcoded targets, wherein the multiple barcoded targets are created by barcoding multiple targets in multiple cells using multiple barcodes, and wherein each of the multiple barcodes includes a cellular marker and a molecular marker. (b) Determine the rank of each of the multiple barcode cell markers based on the number of molecular markers with different sequences associated with each of the multiple barcode cell markers; (c) Determine a cell marker threshold based on the number of molecular markers with different sequences associated with each of these cell markers and the rank of each of the plurality of barcode cell markers determined in (b); and (d) Based on the number of molecular markers with different sequences associated with each of these cell markers and the cell marker threshold determined in (c), each of these cell markers is identified as a signal cell marker or a noise cell marker.

[0065] 21. The method of item 20, the method comprising: determining the number of molecular markers with different sequences associated with each of these cellular markers.

[0066] 22. The method of item 21, wherein determining the number of molecular markers with different sequences associated with each of these cellular markers includes removing sequencing information associated with non-unique molecular markers associated with each of these cellular markers from the sequencing data.

[0067] 23. The method of any one of items 20-22, wherein determining the cell marker threshold based on the number of molecular markers with different sequences associated with each of the plurality of barcode cell markers and the level of each of the plurality of barcode cell markers determined in (b) comprises: Determine the cumulative sum for each level of these cellular markers, where the cumulative sum for each level includes the sum of the number of molecular markers with different sequences associated with each of the lower-level cellular markers; and Determine the cumulative sum at level n and the next level. n+1 The rank n of the cell markers with the largest changes in the cumulative sum, where the changes occur in the cumulative sum and the next rank. n+1The level n of the cell marker with the largest change in the cumulative sum corresponds to the cell marker threshold.

[0068] 24. The method of any one of items 20-22, wherein determining the cell marker threshold based on the number of molecular markers with different sequences associated with each of the plurality of barcode cell markers comprises: Determine the cumulative sum of cell markers at level n and the next level. n+1 The cumulative sum of cell markers is the cell marker with the greatest variation, wherein the number of molecular markers with different sequences associated with that cell marker corresponds to that cell marker threshold.

[0069] 25. The method of any one of items 20-22, wherein determining the cell marker threshold based on the number of molecular markers with different sequences associated with each of the plurality of barcode cell markers and the level of each of the plurality of barcode cell markers determined in (b) comprises: Based on the number of molecular markers with different sequences associated with each of these cell markers and the rank of each of the cell markers identified in (b), a cumulative sum graph is generated; Generate the second derivative graph of this cumulative sum graph; and Determine the minimum value of the second derivative plot of the cumulative sum plot, where the minimum value of the second derivative plot corresponds to the cell labeling threshold.

[0070] 26. The method of item 25, wherein generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of these cell markers and the rank of each of the cell markers determined in (b) comprises: Determine the cumulative sum for each of these cellular markers, where the cumulative sum for each level includes the sum of the number of molecular markers with different sequences associated with each of the lower-level cellular markers.

[0071] 27. The method of any one of items 25-26, wherein generating the second derivative plot of the cumulative sum plot includes determining the difference between the cumulative sum of the first level and the cumulative sum of the second level relative to the difference between a first level and a second level of the cell markers.

[0072] 28. The method described in Item 27, wherein the difference between the first level and the second level is 1.

[0073] 29. The method of any one of items 20-28, the method comprising: if the cell markers of the plurality of barcodes are identified as noise cell markers in (d), then removing sequencing information associated with the identified cell markers from the sequencing data obtained in (a).

[0074] 30. The method of any one of items 20-29, the method comprising: removing sequencing information associated with molecular markers having different sequences associated with a target of the plurality of targets from the sequencing data obtained in (a) if the number of such molecular markers with different sequences associated with a target of the plurality of targets is higher than a molecular marker occurrence threshold.

[0075] 31. The method of any one of items 20-30, wherein the cumulative sum graph is a log-log graph.

[0076] 32. As described in Project 31, where the log-log plot is log 10 -log 10 picture.

[0077] 33. The method as described in any one of items 25-32, wherein the minimum value is the global minimum value.

[0078] 34. The method of any one of items 25-33, wherein determining the minimum value of the second derivative plot comprises determining the minimum value of the second derivative plot that is above a threshold number of molecular markers associated with each of these cellular markers.

[0079] 35. The method as described in item 34, wherein the threshold for the minimum number of molecular markers associated with each of these cellular markers is a percentile threshold.

[0080] 36. The method of any one of items 34-35, wherein the threshold for the minimum number of molecular markers associated with each of these cellular markers is determined based on the number of cells in the plurality of cells.

[0081] 37. The method of any one of items 25-36, wherein determining the minimum of the second derivative plot comprises determining the minimum of the second derivative plot below a threshold for the maximum number of molecular markers associated with each of these cellular markers.

[0082] 38. The method as described in item 37, wherein the threshold for the maximum number of molecular markers associated with each of these cellular markers is a percentile threshold.

[0083] 39. The method of any one of items 37-38, wherein the threshold for the maximum number of molecular markers associated with each of these cellular markers is determined based on the number of cells in the plurality of cells.

[0084] 40. The method of any one of items 20-39, wherein each of the cell markers is identified as a signal cell marker if the number of molecular markers with different sequences identified as associated with each of these cell markers is greater than the cell marker threshold.

[0085] 41. The method of any one of items 20-40, wherein each of the cell markers is identified as a noise cell marker if the number of molecular markers with different sequences identified as associated with each of these cell markers is not greater than the cell marker threshold.

[0086] 42. The method of any one of items 20-41, the method comprising: (e) For one or more of the plurality of targets: (1) Count the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) Estimate the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0087] 43. A method for identifying signal cell markers, the method comprising: (a) Obtain sequencing data for multiple targets of the cell, wherein each target is associated with the number of molecular markers with different sequences that are associated with each of the multiple cell markers; (b) Determine the cell marker threshold based on the number of molecular markers with different sequences associated with each of these cell markers; and (c) Based on the number of molecular markers with different sequences associated with each of these cell markers and the cell marker threshold, each of these cell markers is identified as a signal cell marker or a noise cell marker.

[0088] 44. The method described in item 43, wherein obtaining sequencing data includes: Multiple targets of these cells are barcoded using multiple barcodes to create multiple barcoded targets, wherein each of the multiple barcodes includes a cellular marker and a molecular marker from the multiple cell markers; and Determine the number of molecular markers with different sequences associated with each of the multiple barcode cell markers.

[0089] 45. The method of any one of items 43-44, the method comprising: For one or more of the multiple targets: (1) Count the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) Estimate the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0090] 46. ​​The method of any one of items 43-45, the method comprising: if the cell markers of the plurality of barcodes are identified as noise cell markers, removing sequencing information associated with the identified cell markers from the sequencing data.

[0091] 47. The method of any one of items 43-46, the method comprising: removing sequencing information associated with molecular markers having different sequences associated with a target among the plurality of targets from the sequencing data if the number of such molecular markers with different sequences associated with a target among the plurality of targets is higher than a molecular marker occurrence threshold.

[0092] 48. The method of any one of items 43-47, wherein determining the number of molecular markers with different sequences associated with each of these cell markers in (c) includes removing sequencing information associated with non-unique molecular markers associated with each of these cell markers from the sequencing data.

[0093] 49. The method of any one of items 43-48, wherein determining the cell marker threshold comprises: Determine the inflection point of the cumulative sum graph. The cumulative sum graph is based on the number of molecular markers with different sequences associated with each of the plurality of cellular markers and the rank of each of these cellular markers, and The inflection point corresponds to the cell labeling threshold.

[0094] 50. The method described in item 49, wherein determining the inflection point of the cumulative sum graph includes: A cumulative sum graph is generated based on the number of molecular markers with different sequences associated with each of these multiple cell markers and the rank of each of these cell markers; Generate the second derivative graph of this cumulative sum graph; and Determine the minimum of the second derivative plot of the cumulative sum plot, where the minimum of the second derivative plot corresponds to the cell labeling threshold.

[0095] 51. The method of any one of items 49-50, wherein determining the cell marker threshold comprises: determining a class of each of the plurality of cell markers based on the number of molecular markers with different sequences associated with each of the cell markers.

[0096] 52. The method of any one of items 43-51, wherein the cumulative sum graph is a log-log graph.

[0097] 53. As described in Project 52, where the log-log plot is log 10 -log 10 picture.

[0098] 54. The method of any one of items 50-53, wherein generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of these cell markers and the rank of each of the cell markers comprises: Determine the cumulative sum for each of these cellular markers, where the cumulative sum for each level includes the sum of the number of molecular markers with different sequences associated with each of the lower-level cellular markers.

[0099] 55. The method of item 54, wherein generating the second derivative plot of the cumulative sum plot includes determining the difference between the cumulative sum of the first level and the cumulative sum of the second level relative to the difference between a first level and a second level of the cell markers.

[0100] 56. The method described in item 55, wherein the difference between the first level and the second level is 1.

[0101] 57. The method as described in any one of items 43-56, wherein the minimum value is the global minimum value.

[0102] 58. The method of any one of items 43-57, wherein determining the minimum value of the second derivative graph comprises determining the minimum value of the second derivative graph that is above a threshold number of molecular markers associated with each of these cellular markers.

[0103] 59. The method as described in item 58, wherein the threshold for the minimum number of molecular markers associated with each of these cellular markers is a percentile threshold.

[0104] 60. The method of any one of items 58-59, wherein the threshold for the minimum number of molecular markers associated with each of these cell markers is determined based on the number of the plurality of cells.

[0105] 61. The method of any one of items 43-60, wherein determining the minimum value of the second derivative graph comprises determining the minimum value of the second derivative graph below a threshold for the maximum number of molecular markers associated with each of these cellular markers.

[0106] 62. The method as described in item 61, wherein the threshold for the maximum number of molecular markers associated with each of these cellular markers is a percentile threshold.

[0107] 63. The method of any one of items 61-62, wherein the threshold for the maximum number of molecular markers associated with each of these cell markers is determined based on the number of the plurality of cells.

[0108] 64. The method of any one of items 43-63, wherein each of the cell markers is identified as a signal cell marker if the number of molecular markers with different sequences associated with each of these cell markers is greater than the cell marker threshold.

[0109] 65. The method of any one of items 43-64, wherein each of the cell markers is identified as a noise cell marker if the number of molecular markers with different sequences associated with each of these cell markers is not greater than the cell marker threshold.

[0110] 66. A method for identifying signal cell markers, the method comprising: (a) Using multiple barcodes to barcode multiple targets in multiple cells to create multiple barcode-coded targets, wherein each of the multiple barcodes includes a cell marker and a molecular marker, wherein the barcode-coded targets created from targets of different cells in the multiple cells have different cell markers, and wherein the barcode-coded targets created from targets of the same type of cells in the multiple cells have different molecular markers. (b) Obtain sequencing data of the multiple barcoded targets; (c) Determine the feature vector of each cell marker of the plurality of barcoded targets, wherein the feature vector contains the number of molecular markers with different sequences associated with each cell marker; (d) Based on the feature vector, determine the clustering of each cell marker of the multiple barcoded targets; and (e) Based on the number of cell markers in the cluster and the cluster size threshold, each cell marker of the multiple barcoded targets is identified as a signal cell marker or a noise cell marker.

[0111] 67. The method of Item 66, wherein determining the clustering of each cell marker of the plurality of barcoded targets based on the feature vector comprises clustering each cell marker of the plurality of barcoded targets into clusters based on the distance between the feature vector and the cluster in the feature vector space.

[0112] 68. The method of item 66, wherein determining the clustering of each cell marker of the plurality of barcoded targets based on the feature vector comprises: Project the eigenvector from the eigenvector space to a lower-dimensional space; and Each cell label is clustered into a cluster based on the distance between the feature vector and the cluster in a lower-dimensional space.

[0113] 69. The method described in Project 68, wherein the lower-dimensional space is a two-dimensional space.

[0114] 70. The method of any one of items 68-69, wherein projecting the feature vector from the feature vector space to the lower-dimensional space comprises projecting the feature vector from the feature vector space to the lower-dimensional space using the t-distributed random neighborhood embedding (tSNE) method.

[0115] 71. The method of any one of items 68-70, wherein clustering each cell marker into a cluster based on the distance between the feature vector and the cluster in the lower-dimensional space comprises using a density-based method to cluster each cell marker into a cluster based on the distance between the feature vector and the cluster in the lower-dimensional space.

[0116] 72. The method as described in item 71, wherein the density-based method includes a density-based spatial clustering (DBSCAN) method with noisy applications.

[0117] 73. The method of any one of items 66-72, wherein if the number of cell markers in the cluster is less than the cluster size threshold, the cell marker is identified as a signal cell marker.

[0118] 74. The method of any one of items 66-73, wherein a cell marker is identified as a noise cell marker if the number of cell markers in the cluster is not less than the cluster size threshold.

[0119] 75. The method of any one of items 66-74, the method comprising: determining the cluster size threshold based on the number of cell markers of the plurality of barcoded targets.

[0120] 76. The method of Item 75, wherein the cluster size threshold is a percentage of the number of cell markers of the plurality of barcoded targets.

[0121] 77. The method of any one of items 66-74, the method comprising: determining the cluster size threshold based on the number of cell markers of the plurality of barcoded targets.

[0122] 78. The method of Item 77, wherein the cluster size threshold is a percentage of the number of cell markers of the plurality of barcoded targets.

[0123] 79. The method of any one of items 66-78, the method comprising: determining the cluster size threshold based on the number of molecular markers with different sequences associated with each cell marker of the plurality of barcodes.

[0124] 80. The method of any one of items 66-79, the method comprising: (f) For one or more of the plurality of targets: (1) Count the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) Estimate the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0125] 81. A method for identifying signal cell markers, the method comprising: (a) Obtaining sequencing data of multiple barcoded targets, wherein the multiple barcoded targets are created by barcoding multiple targets in multiple cells using multiple barcodes, wherein each of the multiple barcodes contains a cellular marker and a molecular marker, wherein the barcoded targets created from targets in different cells of the multiple cells have different cellular markers, and wherein the barcoded targets created from targets in the same cells of the multiple cells have different molecular markers; (b) Determine the feature vector of each cell marker of the plurality of barcoded targets, wherein the feature vector contains the number of molecular markers with different sequences associated with each cell marker; (c) Based on the feature vector, determine the clustering of each cell marker of the plurality of barcoded targets; and (d) Based on the number of cell markers in the cluster and the cluster size threshold, each cell marker of the multiple barcoded targets is identified as a signal cell marker or a noise cell marker.

[0126] 82. The method of Item 80, wherein determining the clustering of each cell marker of the plurality of barcoded targets based on the feature vector comprises clustering each cell marker of the plurality of barcoded targets into clusters based on the distance between the feature vector and the cluster in the feature vector space.

[0127] 83. The method of item 80, wherein determining the clustering of each cell marker of the plurality of barcoded targets based on the feature vector comprises: Project the eigenvector from the eigenvector space to a lower-dimensional space; and Each cell label is clustered into a cluster based on the distance between the feature vector and the cluster in a lower-dimensional space.

[0128] 84. The method described in Project 83, wherein the lower-dimensional space is a two-dimensional space.

[0129] 85. The method of any one of items 83-84, wherein projecting the feature vector from the feature vector space to the lower-dimensional space comprises projecting the feature vector from the feature vector space to the lower-dimensional space using the t-distributed random neighborhood embedding (tSNE) method.

[0130] 86. The method of any one of items 83-85, wherein clustering each cell marker into a cluster based on the distance between the feature vector and the cluster in the lower-dimensional space comprises using a density-based method to cluster each cell marker into a cluster based on the distance between the feature vector and the cluster in the lower-dimensional space.

[0131] 87. The method described in item 86, wherein the density-based method includes a density-based spatial clustering (DBSCAN) method with noisy applications.

[0132] 88. The method of any one of items 83-87, wherein if the number of cell markers in the cluster is less than the cluster size threshold, the cell marker is identified as a signal cell marker.

[0133] 89. The method of any one of items 83-88, wherein a cell marker is identified as a noise cell marker if the number of cell markers in the cluster is not less than a cluster size threshold.

[0134] 90. The method of any one of items 83-89, the method comprising: determining the cluster size threshold based on the number of cell markers of the plurality of barcoded targets.

[0135] 91. The method of item 90, wherein the cluster size threshold is a percentage of the number of cell markers of the plurality of barcoded targets.

[0136] 92. The method of any one of items 83-91, the method comprising: determining the cluster size threshold based on the number of cell markers of the plurality of barcoded targets.

[0137] 93. The method of item 92, wherein the cluster size threshold is a percentage of the number of cell markers of the plurality of barcoded targets.

[0138] 94. The method of any one of items 83-93, the method comprising: determining the cluster size threshold based on the number of molecular markers with different sequences associated with each cellular marker of the plurality of barcoded targets.

[0139] 95. The method of any one of items 83-94, the method comprising: (e) For one or more of the plurality of targets: (1) Count the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) Estimate the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1).

[0140] 96. A method for identifying signal cell markers, the method comprising: (a) Obtain sequencing data for multiple first targets of the cell, wherein each first target is associated with the number of molecular markers with different sequences that are associated with each of the multiple cell markers; (b) Based on the number of molecular markers with different sequences associated with each of these cell markers and an identification threshold, each of these cell markers is identified as a signal cell marker or a noise cell marker; and (c) Re-identify at least one of the plurality of cell markers identified as noise cell markers in (b) as a signal cell marker, or re-identify at least one cell marker identified as a signal cell marker in (b) as a noise cell marker.

[0141] 97. The method as described in item 96, wherein the identification threshold includes a cell marker threshold, a cluster size threshold, or any combination thereof.

[0142] 98. The method of any one of items 96-97, the method comprising: removing one or more cell markers from the plurality of cell markers, each associated with a number of molecular markers having a different sequence below a molecular marker number threshold.

[0143] 99. The method of any one of items 96-98, wherein re-identifying at least one of the plurality of cell markers identified as a noise cell marker in (b) as a signal cell marker comprises: Among the plurality of first targets, identify a plurality of second targets, each having one or more variability indices above a variability threshold; and For each of the plurality of cell markers, at least one of the plurality of cell markers identified as a noise cell marker in (b) is re-identified as a signal cell marker based on the number of molecular markers with different sequences associated with the plurality of second targets and the identification threshold.

[0144] 100. The method of Item 99, wherein one or more variability indicators of the second target include the average, maximum, median, minimum, deviation, or any combination thereof of the number of molecular markers with different sequences associated with the second target and the cellular markers in the sequencing data.

[0145] 101. The method of any one of items 99-100, wherein one or more variability indicators of the second target include standard deviation, normalized deviation, or any combination thereof, or variability indicators of a subset of the plurality of second targets.

[0146] 102. The method as described in item 101, wherein the variability threshold is less than or equal to the size of a subset of the plurality of second targets.

[0147] 103. The method of any one of items 96-102, wherein re-identifying at least one of the plurality of cell markers identified as a signal cell marker in (b) as a noise cell marker comprises: Identify multiple third targets, each of which has an association with a cell marker identified as a noise cell marker in (c) above an association threshold; and For each of the plurality of cell markers, at least one cell marker identified as a signal cell marker in (b) is re-identified as a noise cell marker based on the number of molecular markers with different sequences associated with the plurality of third targets and the identification threshold.

[0148] 104. The method of claim 103, wherein identifying a plurality of third targets, each of the plurality of first targets having an association with a cell marker identified as a noise cell marker in (c) above an association threshold, comprises: After re-identifying at least one cell marker identified as a noise cell marker in (b) as a signal cell marker, a plurality of remaining cell markers identified as signal cell markers are determined; The multiple third targets were determined based on the following. For each of the multiple cellular markers, the number of molecular markers with different sequences associated with the multiple targets, and For each of the multiple remaining cellular markers, the number of molecular markers with different sequences associated with the multiple targets.

[0149] 105. A computer system for determining the number of targets, the computer system comprising: Hardware processor; and A non-transitory memory having instructions stored thereon, which, when executed by the hardware processor, cause the processor to perform the method as described in any one of items 1-104.

[0150] 106. A computer-readable medium comprising code for performing a method as described in any one of items 1-104. Brief description of the attached figures Figure 1 Non-limiting exemplary barcodes (e.g., random barcodes) are illustrated.

[0151] Figure 2 A non-limiting exemplary workflow for barcode generation and digit counting (e.g., random barcode generation and digit counting) is shown.

[0152] Figure 3 This is a schematic diagram illustrating a non-limiting exemplary process for generating an index library from multiple targets (e.g., targets that have been barcoded and then randomly re-barcoded).

[0153] Figure 4 This is a flowchart illustrating a non-limiting exemplary method for identifying cells as signal cell markers or noise cell markers.

[0154] Figure 5 This is a flowchart illustrating another non-limiting exemplary method for identifying cells as signal cell markers or noise cell markers.

[0155] Figure 6A This is a flowchart illustrating a non-limiting exemplary method for distinguishing markers associated with real cells from noise cells. Figure 6B This is a flowchart illustrating another non-limiting exemplary method for distinguishing markers associated with true cells from noise cells.

[0156] Figure 7 This is a non-limiting example diagram showing the identification of the most variable genes. Methods for distinguishing markers associated with true cells from noisy cells (e.g., see reference) Figure 6A The described method 600a (illustrated in Example 4) may include the identification of the most variable gene.

[0157] Figures 8A-8B This is a non-limiting example diagram illustrating gene identification, where the loss is greatest for each gene in terms of the number of associated molecular markers with different sequences. Methods for distinguishing markers associated with true cells from those of noisy cells (e.g., refer to...) Figure 6A The described method 600a (illustrated in Example 4) may include identifying genes that, for each gene, suffer the greatest loss in terms of the number of molecular markers associated with different sequences.

[0158] Figure 9 This is a block diagram of an exemplary computing system configured to perform the methods of this disclosure.

[0159] Figure 10 A non-limiting example cumulative sum graph is shown.

[0160] Figure 11 Showing Figure 10 The unrestricted second derivative plot of the cumulative sum graph.

[0161] Figure 12 Unrestricted tSNE plots of signal or noise cell markers are shown.

[0162] Figures 13A-13B This is a non-restrictive example diagram illustrating the use of BD. TM Samples processed with breast cancer gene plates (containing three different breast cancer cell lines and donor-isolated PBMCs) were prepared by a reference. Figure 4 Method 400 (explained) Figure 13A ) and by reference Figure 6A Method 600a (explained) Figure 13B (Comparison of cells identified) Figures 13A-13B The dots marked in blue are common cells detected by both methods. Figure 13A The dots marked in red are cells identified as noise by method 600a. Figure 13B The dots marked in red are additional true cells identified by method 600a.

[0163] Figure 14A This is a non-limiting example diagram showing cells identified by method 600a, where cells marked in red are additional cells identified (compared to those identified by reference). Figure 4 The illustrative method 400 identifies cells). By expressing PBMCs, for example, B cells ( Figure 14B NK cells ( Figure 14C ) and T cells ( Figure 14D ( ) to stain the cells. Figure 14B-14D The additional cells identified by method 600a were indeed true cells.

[0164] Figures 15A-15B This is a non-limiting example diagram illustrating the use of PBMCs with healthy donor separation in BD. TM Blood gene plate-treated samples, from reference Figure 4 Method 400 (explained) Figure 15A ) and by reference Figure 6A Method 600a (explained) Figure 15B (Comparison of cells identified) Figures 15A-15B The dots marked in blue are common cells detected by both methods. Figure 15A The dots marked in red are cells identified as noise by method 600a. Figure 15B The dots marked in red are additional cells identified by method 600a.

[0165] Figures 16A-16B This is a non-limiting example diagram showing cells identified by method 400. Figure 16A In the diagram, cells marked in red are identified as noise cells by method 600a. Figure 16BIn this study, cells were stained by expressing a set of monocyte marker genes, such as CD14 and S100A6. The "noise" cells identified by the improved algorithm were mostly monocytes with low expression levels.

[0166] Figure 17A This is a non-limiting example diagram showing cells identified by method 600a, where the labeled cells are additional cells identified. This is based on the expression of T cells (…). Figure 17B ), expression of the important gene LAT ( Figure 17C ) and IL7R expression ( Figure 17D ( ) to stain the cells. Detailed Implementation

[0167] The accompanying drawings, which form part of this document, are referenced in the following detailed description. In the drawings, similar symbols generally identify similar components unless the context otherwise indicates. The illustrative embodiments described in the detailed description, drawings, and claims are not intended to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter set forth herein. It will be readily understood that the aspects of this disclosure, as generally described herein and illustrated in the figures, can be arranged, replaced, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein and form part of this disclosure.

[0168] All patents, published patent applications, other publications, and sequences from GenBank, as well as other databases mentioned herein, relating to the relevant technology are incorporated herein by reference in their entirety.

[0169] Quantifying small numbers of nucleic acid or target molecules (e.g., messenger ribonucleotide (mRNA) molecules) is clinically important for identifying genes expressed in cells, for example, at different developmental stages or under different environmental conditions. However, determining the absolute number of nucleic acid molecules (e.g., mRNA molecules) is also very challenging, especially when the number of molecules is very small. One method for determining the absolute number of molecules in a sample is digital polymerase chain reaction (PCR). Ideally, PCR produces the same copy of the molecule in each cycle. However, PCR can have drawbacks such as the random probability of replication for each molecule, and this probability varies depending on the PCR cycle and the gene sequence, leading to amplification bias and inaccurate gene expression measurements.

[0170] Barcodes with unique molecular markers (MLs, also known as molecular indices (MIs)) (e.g., random barcodes) can be used to count the number of molecules. Barcodes with molecular markers, unique to each cell, can be used to count the number of molecules in each cell. Non-limiting example assays for barcode digitization include Precise. TMMeasurement method (Cellular Research, Inc. (Palo Alto, CA)), Resolve TM Measurement method (Cellular Research, Inc. (Palo Alto, CA)), or Rhapsody TM Assay methods (Cellular Research, Inc. (Palo Alto, CA)). However, these methods and techniques can introduce errors, which, if not corrected, may lead to overestimation of cell counts.

[0171] Rhapsody TM The assay utilizes a non-depleted pool of unique molecular markers on poly(T) oligonucleotides with a large number (e.g., 6561 to 65536) barcodes (e.g., random barcodes) to hybridize with all poly(A)-mRNAs in the sample during the RT step. In addition to molecular markers, barcode-based cellular markers can be used to identify each single cell in each well of a microplate. Barcodes may include universal PCR initiation sites. During RT, target gene molecules react randomly with barcodes. Each target molecule can hybridize with a barcode (e.g., random barcodes) to produce barcoded complementary ribonucleotide (cDNA) molecules (e.g., randomly barcoded cDNA molecules). After labeling, the barcoded cDNA molecules from the microplate wells can be combined into a single tube for PCR amplification and sequencing. The raw sequencing data can be analyzed to determine the number of barcodes with unique molecular markers.

[0172] This article discloses methods and systems for identifying signal cell markers. In some embodiments, the method includes: (a) barcoding (e.g., random barcoding) a plurality of targets in a cell sample using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes contains a cellular marker and a molecular marker; (b) obtaining sequencing data of the plurality of barcoded targets; (c) determining the number of molecular markers with different sequences associated with each of the cellular markers in the plurality of barcodes; (d) determining a rank of each of the cellular markers in the plurality of barcodes based on the number of molecular markers with different sequences associated with each of the cellular markers; (e) generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cellular markers determined in (c) and the rank of each of the cellular markers determined in (d); (f) generating a second derivative graph of the cumulative sum graph; (g) determining a minimum value of the second derivative graph of the cumulative sum graph, wherein the minimum value of the second derivative graph corresponds to a cellular marker threshold; and (h) Based on the number of molecular markers with different sequences associated with the cell marker determined in (c) and the cell marker threshold, the cell marker is identified as a signal cell marker or a noise cell marker.

[0173] In some embodiments, the method includes: (a) obtaining sequencing data of a plurality of barcoded targets (e.g., randomly barcoded targets), wherein the sequencing data of the plurality of barcoded targets are derived from a plurality of targets in a cell sample, the plurality of targets being barcoded (e.g., randomly barcoded) using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes contains a cell marker and a label; (b) determining a rank of each of the cell markers in the plurality of barcodes based on the number of molecular markers with different sequences associated with each of the cell markers; (c) determining a minimum of a second derivative plot of a cumulative sum plot, wherein the cumulative sum plot is based on the number of molecular markers with different sequences associated with each of the cell markers and the rank of each of the cell markers determined in (b), and wherein the minimum of the second derivative plot corresponds to a cell marker threshold; and (d) Based on the number of molecular markers with different sequences associated with the cell marker and the cell marker threshold, the cell marker is identified as a signal cell marker (associated with the cell) or a noise cell marker (not associated with the cell).

[0174] This document discloses a method for identifying signaling cell markers. In some embodiments, the method includes: (a) barcoding (e.g., random barcoding) multiple targets in a cell sample using multiple barcodes (e.g., random barcodes) to create multiple barcoded targets (e.g., randomly barcoded targets), wherein each of the multiple barcodes contains a cell marker and a molecular marker, wherein the barcoded targets created from the targets of the multiple cells have different cell markers, and wherein the barcoded targets created from the target of one of the multiple cells have different molecular markers; (b) obtaining sequencing data of these barcoded targets; (c) determining a feature vector of the cell marker, wherein the feature vector contains the number of molecular markers with different sequences associated with the cell marker; (d) determining a cluster of the cell marker based on the feature vector; and (e) identifying the cell marker as a signaling cell marker or a noise cell marker based on the number of cells in the cluster and a cluster size threshold.

[0175] This document discloses a system for identifying signaling cell markers. In some embodiments, the system includes: a hardware processor; and a non-transitory memory having instructions stored thereon, which, when executed by the hardware processor, cause the processor to perform any of the methods disclosed herein. This document also discloses a computer-readable medium for identifying signaling cell markers. In some embodiments, the computer-readable medium contains code for implementing any of the methods disclosed herein.

[0176] definition Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. See, for example, Singleton et al., Dictionary of Microbiology and Molecular Biology, 2nd ed., J. Wiley & Sons (New York, NY 1994); Sambrook et al., Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Laboratory Press (Cold Spring Harbor, NY 1989). For the purposes of this disclosure, the following terms are defined as follows.

[0177] As used herein, the term "adaptor" may refer to a sequence that facilitates the amplification or sequencing of an associated nucleic acid. The associated nucleic acid may include a target nucleic acid. The associated nucleic acid may contain one or more of spatial markers, target markers, sample markers, index markers, barcodes, random barcodes, or molecular markers. The adaptor may be linear. The adaptor may be a pre-adenosylated adaptor. The adaptor may be double-stranded or single-stranded. One or more adaptors may be located at the 5' or 3' end of the nucleic acid. When the adaptor includes known sequences at the 5' and 3' ends, the known sequences may be the same or different sequences. Adaptors located at the 5' and / or 3' ends of a polynucleotide are capable of hybridizing with one or more oligonucleotides immobilized on a surface. In some embodiments, the adaptor may include a universal sequence. The universal sequence may be a region of a nucleotide sequence common to two or more nucleic acid molecules. Two or more nucleic acid molecules may have regions with different sequences. Thus, for example, the 5' adaptor may include the same and / or universal nucleic acid sequence, and the 3' adaptor may include the same and / or universal sequence. Universal sequences that can be present in different members of multiple nucleic acid molecules allow for the replication or amplification of multiple different sequences using a single universal primer complementary to the universal sequence. Similarly, at least one, two (e.g., a pair) or more universal sequences that can be present in different members of a collection of nucleic acid molecules allow for the replication or amplification of multiple different sequences using at least one, two (e.g., a pair) or more single universal primers complementary to the universal sequence. Therefore, universal primers include sequences that can hybridize with such universal sequences. Molecules carrying target nucleic acid sequences can be modified to attach universal adaptors (e.g., non-target nucleic acid sequences) to one or both ends of different target nucleic acid sequences. One or more universal primers attached to the target nucleic acid can provide sites for universal primer hybridization. One or more universal primers attached to the target nucleic acid can be the same as or different from each other.

[0178] As used herein, the term "association" or "associated with" may mean that two or more species can be identified as co-located at a point in time. Association may mean that two or more species are or were in similar containers. Association can be an informatics association, where, for example, digital information about two or more species is stored and can be used to determine that one or more of the species are co-located at a point in time. Association can be a physical association. In some embodiments, two or more associated species are "connected," "attached," or "fixed" to each other or to a common solid or semi-solid surface. Association can refer to a covalent or non-covalent manner used to attach a marker to a solid or semi-solid support (such as a bead). Association can be a covalent bond between a target and a marker.

[0179] As used herein, the term "complementarity" can refer to the ability of two nucleotides to pair precisely. For example, if a nucleotide at a given position of a nucleic acid can bind to a nucleotide of another nucleic acid via a hydrogen bond, the two nucleic acids are considered complementary to each other at that position. Complementarity between two single-stranded nucleic acid molecules can be "partial," meaning only some of the nucleotides are bound, or it can be complete when there is complete complementarity between the single-stranded molecules. If a first nucleotide sequence is complementary to a second nucleotide sequence, the first nucleotide sequence can be considered a "complement" of the second sequence. If a first nucleotide sequence is complementary to a sequence opposite to the second sequence (i.e., the nucleotide order is reversed), the first nucleotide sequence can be considered an "inverse complement" of the second sequence. As used herein, the terms "complement," "complement," and "inverse complement" are used interchangeably. It will be understood from this disclosure that if a molecule can hybridize with another molecule, it can be a complement of the hybridized molecule.

[0180] As used herein, the term "numerical counting" can refer to a method used to estimate the number of target molecules in a sample. Numerical counting may include steps to determine the number of unique markers that have been associated with the target in the sample. This stochastic approach transforms the problem of counting molecules from one of locating and identifying the same molecules to a series of yes / no numerical questions about the detection of a predefined set of markers.

[0181] As used herein, the term "tag(s)" can refer to a nucleic acid code associated with a target in a sample. A tag can be, for example, a nucleic acid tag. A tag can be a fully or partially amplifiable tag. A tag can be a fully or partially sequenceable tag. A tag can be a portion of a naturally occurring nucleic acid that can be identified as distinct. A tag can be a known sequence. A tag can include junctions of nucleic acid sequences, such as junctions between natural and non-natural sequences. As used herein, the term "tag" can be used interchangeably with the terms "index," "tag," or "tag-tag." A tag can convey information. For example, in various embodiments, a tag can be used to determine the identity of a sample, the origin of the sample, the identity of the cells, and / or the target.

[0182] As used herein, the term "non-depleting reservoir" can refer to a pool of random barcodes consisting of many different labels. A non-depleting reservoir can include a large number of different random barcodes, such that when the non-depleting reservoir is associated with a target pool, each target may be associated with a unique random barcode. The uniqueness of each labeled target molecule can be determined by statistically selected randomness and depends on the copy number of the same target molecule in the set compared to the diverse labels. The size of the resulting set of labeled target molecules can be determined by the randomness of the barcode processing, and analysis of the number of detected random barcodes allows for the calculation of the number of target molecules present in the original set or sample. When the ratio of the copy number of present target molecules to the number of unique random barcodes is low, the labeled target molecules are highly unique (i.e., the probability of labeling more than one target molecule with a given label is very low).

[0183] As used herein, the term "nucleic acid" refers to a polynucleotide sequence or a fragment thereof. Nucleic acids may include nucleotides. Nucleic acids may be exogenous or endogenous to cells. Nucleic acids may be present in cell-free environments. Nucleic acids may be genes or fragments thereof. Nucleic acids may be DNA. Nucleic acids may be RNA. Nucleic acids may include one or more analogs (e.g., modified backbones, sugars, or nucleobases). Some non-limiting examples of analogs include: 5-bromouracil, peptide nucleic acids, exogenous nucleic acids, morpholino, locked nucleic acids, diol nucleic acids, threonic acid, dideoxynucleotides, cordycepin, 7-denitro-GTP, fluorophores (e.g., rhodamine or sugar-linked fluorescein), thiols containing nucleotides, biotin-linked nucleotides, fluorescent analogs, CpG islands, methyl-7-guanosine, methylated nucleotides, inosine, thiouridine, pseudouridine, dihydrouridine, piracetamidine, and woyoside. "Nucleic acid," "polynucleotide," "target polynucleotide," and "target nucleic acid" are used interchangeably.

[0184] Nucleic acids can include one or more modifications (e.g., base modifications, backbone modifications) to provide new or enhanced characteristics (e.g., improved stability). Nucleic acids can include nucleic acid affinity tags. Nucleosides can be base-sugar combinations. The base moiety of a nucleoside can be a heterocyclic base. The two most common classes of such heterocyclic bases are purines and pyrimidines. Nucleotides can also include a phosphate group covalently linked to the sugar moiety of the nucleoside. For those nucleosides that include furanopentoses, the phosphate group can be linked to the 2', 3', or 5' hydroxyl moiety of the sugar. In the formation of nucleic acids, phosphate groups can covalently link adjacent nucleosides to each other to form a linear polymer. The respective ends of this linear polymer can then further bond to form a cyclic compound; however, linear compounds are generally preferred. Furthermore, linear compounds can have internal nucleotide base complementarity and can therefore fold in a manner that produces a fully or partially double-stranded compound. In nucleic acids, this phosphate group can generally be referred to as the internucleotide backbone that forms the nucleic acid. The link or backbone can be a 3' to 5' phosphodiester bond.

[0185] Nucleic acids may include modified backbones and / or modified internucleotide bonds. Modified backbones may include those that retain phosphorus atoms in the backbone and those that do not have phosphorus atoms in the backbone. Suitable modified nucleic acid backbones containing phosphorus atoms may contain, for example, thiophosphates; chiral thiophosphates; dithiophosphates; phosphate triesters; aminoalkyl phosphate triesters; methylphosphonates and other alkylphosphonates, such as 3'-alkylphosphonene esters, 5'-alkylphosphonene esters; chiral phosphonates; phosphites; phosphatidyl esters including 3'-aminophosphatidyl esters and aminoalkylphosphatidyl esters; phosphadiamide esters; thiocarbonylphosphatidyl esters; thiocarbonylalkylphosphonates; thiocarbonylalkyl phosphate triesters; selenophosphates; and boron phosphates having normal 3'-5' bonds, 2'-5' linked analogs, and those with reverse polarity, wherein one or more internucleotide bonds are 3' to 3', 5' to 5', or 2' to 2' bonds.

[0186] Nucleic acids may include polynucleotide backbones formed by short-chain alkyl or cycloalkyl nucleoside bonds, mixed heteroatoms, and alkyl or cycloalkyl nucleoside bonds or one or more short-chain heteroatoms or heterocyclic nucleoside bonds. These may include those having the following structures: morpholino bonds (partially formed from the sugar moiety of the nucleoside); siloxane backbones; sulfide, sulfoxide, and sulfone backbones; formylacetyl and thioformylacetyl backbones; methyleneformylacetyl and thioformylacetyl backbones; ribose acetyl backbones; alkene-containing backbones; aminosulfonate backbones; methyleneimino and methylenehydrazine backbones; sulfonate and sulfonamide backbones; amide backbones; and other backbones having mixed N, O, S, and CH2 component moieties.

[0187] Nucleic acids can include nucleic acid mimics. The term "mimic" can be intended to include polynucleotides in which only the furanose ring or both the furanose ring and the intermolecular bond are replaced by non-furanose groups; replacement of only the furanose ring can be called a sugar substitute. The heterocyclic base moiety or modified heterocyclic base moiety can be retained for hybridization with a suitable target nucleic acid. One such nucleic acid can be a peptide nucleic acid (PNA). In a PNA, the sugar backbone of the polynucleotide can be replaced by an amide-containing backbone (particularly an aminoethylglycine backbone). The nucleotide can be retained and directly or indirectly bound to the aza-nitrogen atom of the amide moiety of the backbone. The backbone in a PNA compound can include two or more linked aminoethylglycine units that give the PNA an amide-containing backbone. The heterocyclic base moiety can be directly or indirectly bound to the aza-nitrogen atom of the amide moiety of the backbone.

[0188] Nucleic acids may include a morpholine backbone structure. For example, nucleic acids may include a 6-membered morpholine ring instead of a ribose ring. In some of these embodiments, a phosphodiacetate or other non-phosphodiester nucleoside bond may replace the phosphodiester bond.

[0189] Nucleic acids can include morpholino units (i.e., morpholinonucleotides) with heterocyclic bases attached to a morpholino ring. Linking groups can connect the morpholino monomer units within morpholinonucleotides. Nonionic morpholino-based oligomers can exhibit fewer undesirable interactions with cellular proteins. Morpholino-based polynucleotides can serve as nonionic mimics of nucleic acids. Various compounds within the morpholino class can be linked using different linking groups. Another class of polynucleotide mimics can be called cyclohexenyl nucleic acids (CeNA). The furanose ring, typically present in nucleic acid molecules, can be replaced by a cyclohexenyl ring. CeNA DMT-protected phosphoramidite monomers can be prepared and used in the synthesis of oligomers using phosphoramidite chemistry. Incorporating CeNA monomers into nucleic acid chains can increase the stability of DNA / RNA hybrids. CeNA oligoadenylates can form complexes with nucleic acid complements exhibiting similar stability to the natural complex. Other modifications may include locked nucleic acids (LNAs), in which a 2'-hydroxy group is attached to the 4' carbon atom of the sugar ring, thereby forming a 2'-C,4'-C-oxomethylene bond, thus forming a bicyclic sugar moiety. This bond can be a methylene (-CH2-) group bridging the 2' oxygen atom and the 4' carbon atom. n It is 1 or 2. LNA and LNA analogs can exhibit very high double-stranded thermal stability (Tm = +3°C to +10°C) with complementary nucleic acids, stability against 3′-exonuclease degradation, and good solubility properties.

[0190] Nucleic acids can also include nucleobase (usually referred to simply as "bases") modifications or substitutions. As used herein, "unmodified" or "natural" nucleobases can include purine bases (e.g., adenine (A) and guanine (G)) and pyrimidine bases (e.g., thymine (T), cytosine (C), and uracil (U)). Modified nucleobases can include other synthetic and natural nucleobases such as 5-methylcytosine (5-me-C), 5-hydroxymethylcytosine, xanthine, hypoxanthine, 2-aminoadenine, 6-methyl and other alkyl derivatives of adenine and guanine, 2-propyl and other alkyl derivatives of adenine and guanine, 2-thiouracil, 2-thiothymine and 2-thiocytosine, 5-halouracil and cytosine, 5-propynyl (-C=C-CH3)uracil and cytosine and other alkynyl derivatives of pyrimidine bases, 6-azouracil. Cytosine and thymine, 5-uracil (pseudouracil), 4-thiouracil, 8-halogen, 8-amino, 8-mercapto, 8-thioalkyl, 8-hydroxy and other 8-substituted adenine and guanine, 5-halogen, especially 5-bromo, 5-trifluoromethyl and other 5-substituted uracil and cytosine, 7-methylguanine and 7-methyladenine, 2-F-adenine, 2-aminoadenine, 8-azaguanine and 8-azaadenine, 7-deazoguanine and 7-deazoadenine, and 3-deazoguanine and 3-deazoadenine. Modified nucleobases may include tricyclic pyrimidines such as phenoxazincytidine (1H-pyrimido(5,4-b)(1,4)benzoxazin-2(3H)-one), phenthiazincytidine (1H-pyrimido(5,4-b)(1,4)benzoxazin-2(3H)-one), G-clamps such as substituted phenoxazincytidines (e.g., 9-(2-aminoethoxy)-H-pyrimido(5,4-(b)(1,4)benzoxazin-2(3H)-one), phenthiazincytidine ( 1H-pyrimidino(5,4-b)(1,4)benzothiazine-2(3H)-one), G-clamp such as substituted phenoxazine cytidine (e.g. 9-(2-aminoethoxy)-H-pyrimidino(5,4-(b)(1,4)benzothiazine-2(3H)-one), carbazole cytidine (2H-pyrimidino(4,5-b)indole-2-one), pyridinoindole cytidine (H-pyridino(3',2':4,5)pyrrolo[2,3-d]pyrimidino-2-one).

[0191] As used herein, the term "sample" can refer to a composition including a target. Suitable samples for analysis using the disclosed methods, apparatus, and systems include cells, tissues, organs, or organisms.

[0192] As used herein, the term "sampling device" or "device" can refer to a device that can take a portion of a sample and / or place said portion on a substrate. Sampling devices can refer to, for example, fluorescence activated cell sorting (FACS) machines, cell sorters, biopsy needles, biopsy devices, tissue sectioning devices, microfluidic devices, leaf cascades, and / or ultramicrotome.

[0193] As used herein, the term "solid support" can refer to a discrete solid or semi-solid surface on which multiple random barcodes can be attached. Solid supports can include any type of solid, porous, or hollow spheres, balls, supports, cylinders, or other similar configurations made of plastic, ceramic, metal, or polymeric materials (e.g., hydrogels) on which nucleic acids (e.g., covalently or non-covalently) can be immobilized. Solid supports can include discrete particles that can be spherical (e.g., microspheres) or have non-spherical or irregular shapes, such as cubic, rectangular, conical, cylindrical, elliptical, or disk-shaped. Multiple solid supports spaced apart in an array may not include a substrate. The term "solid support" is used interchangeably with the term "beads."

[0194] Solid support can refer to a “substrate”. A substrate can be a solid support. A substrate can refer to a continuous solid or semi-solid surface on which the methods disclosed herein can be performed. For example, a substrate can refer to an array, a cassette, a chip, a device, and a glass slide.

[0195] As used in this article, the term "spatial marker" can refer to a marker that can be associated with a location in space.

[0196] As used herein, the term "random barcode" can refer to a polynucleotide sequence containing a label. A random barcode can be a polynucleotide sequence that can be randomly barcoded. Random barcodes can be used for target quantification in a sample. Random barcodes can be used to control for errors that may occur after a label is associated with a target. For example, random barcodes can be used to assess amplification or sequencing errors. A random barcode associated with a target can be referred to as a random barcode-target or a random barcode-tag-target.

[0197] As used herein, the term "gene-specific random barcode" can refer to a polynucleotide sequence containing a marker and a gene-specific target-binding region. A random barcode can be a polynucleotide sequence that can be randomly barcoded. Random barcodes can be used to quantify the target in a sample. Random barcodes can be used to control for errors that may occur after a marker is associated with a target. For example, random barcodes can be used to assess amplification or sequencing errors. A random barcode associated with a target can be referred to as a random barcode-target or a random barcode-tag-target.

[0198] As used herein, the term “random barcoding” can refer to the random labeling (e.g., barcoding) of nucleic acids. Random barcoding can utilize a recursive Poisson strategy to associate and quantify the label associated with the target. As used herein, the term “random barcoding” can be used interchangeably with “gene-specific random barcoding”.

[0199] As used herein, the term "target" can refer to a composition that can be associated with a random barcode. Exemplary suitable targets for analysis using the disclosed methods, apparatus, and systems include oligonucleotides, DNA, RNA, mRNA, microRNA, tRNA, etc. Targets can be single-stranded or double-stranded. In some embodiments, a target can be a protein. In some embodiments, a target is a lipid.

[0200] As used herein, the term "reverse transcriptase" can refer to a group of enzymes that possess reverse transcriptase activity (i.e., catalyze the synthesis of DNA from an RNA template). Generally, such enzymes include, but are not limited to, retroviral reverse transcriptases, retrotransposon reverse transcriptases, reverse plasmid reverse transcriptases, reverse transcripton reverse transcriptases, bacterial reverse transcriptases, type II intron-derived reverse transcriptases, and their mutants, variants, or derivatives. Non-retroviral reverse transcriptases include non-LTR retrotransposon reverse transcriptases, reverse plasmid reverse transcriptases, reverse transcripton reverse transcriptases, and type II intron reverse transcriptases. Examples of type II intron reverse transcriptases include *Lactococcus lactis* LI.LtrB intron reverse transcriptase and *Synechococcus slenderus* (…). Thermosynechococcus TeI4c intron reverse transcriptase or GsI-IIC intron reverse transcriptase from Bacillus stearothermophilus. Other classes of reverse transcriptases may include many types of non-retroviral reverse transcriptases (i.e., reverse transcripts, type II introns, and diversity-generating reverse transcription elements, etc.).

[0201] This article discloses systems and methods for identifying signal cell markers. In some embodiments, the method includes: (a) randomly barcoding a plurality of target bars in a cell sample using a plurality of random barcodes to create a plurality of randomly barcoded targets, wherein each of the plurality of random barcodes contains a cellular marker and a molecular marker; (b) obtaining sequencing data of the plurality of randomly barcoded targets; (c) determining the number of molecular markers with different sequences associated with each of the cellular markers of the plurality of random barcodes; (d) determining a rank of each of the cellular markers of the plurality of random barcodes based on the number of molecular markers with different sequences associated with each of the cellular markers; (e) generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cellular markers determined in (c) and the rank of each of the cellular markers determined in (d); (f) generating a second derivative graph of the cumulative sum graph; (g) determining a minimum value of the second derivative graph of the cumulative sum graph, wherein the minimum value of the second derivative graph corresponds to a cellular marker threshold; and (h) based on (c) The number of molecular markers with different sequences associated with each of the cell markers determined in (g) and the cell marker threshold determined in (g) identify each of the cell markers as a signal cell marker or a noise cell marker.

[0202] barcode Barcoding (e.g., random barcoding) has been described in, for example, US 20150299784, WO 2015031691, and Fu et al., Proc Natl Acad Sci [Proceedings of the National Library of America] USA May 31, 2011; 108(22):9026-31 and Fan et al., Science[Science] (2015) 347(6222):1258367; the contents of these publications are incorporated herein by reference in their entirety. In some embodiments, the barcode disclosed herein may be a random barcode, which may be a polynucleotide sequence that can be used to randomly label a target (e.g., a barcode, a tag). A barcode can be called a random barcode if the ratio of the number of distinct barcode sequences in a random barcode to the number of occurrences of any target to be labeled is, or approximately 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1, 100:1, or a number or range between any two of these values. The target can be, for example, a variety of mRNAs including mRNA molecules with the same or nearly identical sequences. A barcode can be called a random barcode if the ratio of the number of distinct barcode sequences in a random barcode to the frequency of occurrence of any target to be labeled is at least 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1, or 100:1. The barcode sequences of a random barcode can be called molecular markers.

[0203] Barcodes (e.g., random barcodes) may include one or more markers. Exemplary markers may include universal markers, cell markers, barcode sequences (e.g., molecular markers), sample markers, plate markers, spatial markers, and / or pre-spatial markers. Figure 1 An exemplary barcode 104 with spatial markers is illustrated. Barcode 104 may include a 5' amine that can connect the barcode to a solid support 105. The barcode may include universal markers, dimensional markers, spatial markers, cellular markers, and / or molecular markers. The order of different markers in the barcode (including, but not limited to, universal markers, dimensional markers, spatial markers, cellular markers, and molecular markers) may be changed. For example, as... Figure 1As shown, the universal marker can be a 5'-terminal marker, and the molecular marker can be a 3'-terminal marker. Spatial markers, dimensional markers, and cellular markers can be in any order. In some embodiments, the universal marker, spatial marker, dimensional marker, cellular marker, and molecular marker are in any order. The barcode may include a target-binding region. The target-binding region can interact with a target in the sample (e.g., target nucleic acid, RNA, mRNA, DNA). For example, the target-binding region may include an oligomeric (dT) sequence that can interact with the poly(A) tail of mRNA. In some cases, the markers of the barcode (e.g., universal markers, dimensional markers, spatial markers, cellular markers, and barcode sequences) may be separated by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 or more nucleotides.

[0204] Tagwords (e.g., cell markers) may comprise a unique set of nucleic acid subsequences of defined length, such as seven nucleotides each (equivalent to the number of bits used in some Hamming error-correcting codes), which may be designed to provide error-correcting capabilities. Sets of error-correcting subsequences comprising seven-nucleotide sequences may be designed such that any pairwise combination of sequences in the set exhibits a defined “genetic distance” (or number of mismatched bases); for example, a set of error-correcting subsequences may be designed to exhibit a genetic distance of three nucleotides. In this case, review of error-correcting sequences in a set of sequence data of a tagged target nucleic acid molecule (described more fully below) allows for the detection or correction of amplification or sequencing errors. In some embodiments, the length of the nucleic acid subsequences used to generate error-correcting codes may vary; for example, they may be, or a number or range of nucleotides of length of about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 31, 40, 50, or any two of these values. In some embodiments, nucleic acid subsequences of other lengths may be used to generate error-correcting codes.

[0205] The barcode may include a target-binding region. This target-binding region can interact with a target in the sample. The target may be, or include, ribonucleic acid (RNA), messenger RNA (mRNA), microRNA, small interfering RNA (siRNA), RNA degradation products, RNA each containing a poly(A) tail, or any combination thereof. In some embodiments, multiple targets may include deoxyribonucleic acid (DNA).

[0206] In some embodiments, the target binding region may include an oligo(dT) sequence that can interact with the poly(A) tail of mRNA. One or more markers of the barcode (e.g., universal markers, dimensional markers, spatial markers, cellular markers, and barcode sequences (e.g., molecular markers)) may be separated from one or two of the remaining markers of the barcode by spacers. Spacers may be, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 or more nucleotides. In some embodiments, no markers in the barcode are separated by spacers.

[0207] General tags Barcodes may include one or more universal markers. In some embodiments, one or more universal markers may be identical for all barcodes in a group of barcodes (attached to a given solid support). In some embodiments, one or more universal markers may be identical for all barcodes attached to multiple beads. In some embodiments, the universal marker may include a nucleic acid sequence capable of hybridizing with sequencing primers. Sequencing primers may be used to sequence barcodes including universal markers. Sequencing primers (e.g., universal sequencing primers) may include sequencing primers associated with a high-throughput sequencing platform. In some embodiments, the universal marker may include a nucleic acid sequence capable of hybridizing with PCR primers. In some embodiments, the universal marker may include a nucleic acid sequence capable of hybridizing with both sequencing primers and PCR primers. The nucleic acid sequence of the universal marker capable of hybridizing with sequencing or PCR primers may be referred to as a primer binding site. The universal marker may include a sequence that can be used to initiate barcode transcription. The universal marker may include a sequence that can be used to extend the barcode or a region within the barcode. The length of the universal marker can be approximately 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 nucleotides, or a number or range of nucleotides between any two of these values. For example, the universal marker may include at least about 10 nucleotides. The length of the universal marker can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides. In some embodiments, the cleavable linker or modified nucleotides may be part of the universal marker sequence to allow the barcode to be cut from the support.

[0208] Dimension Marking A barcode may include one or more dimension markers. In some embodiments, a dimension marker may include a nucleic acid sequence that provides information about the dimension at which the marking (e.g., random marking) occurred. For example, a dimension marker may provide information about the timing of random barcoding of a target. Dimension markers may be associated with the timing of barcoding (e.g., random barcoding) in a sample. Dimension markers may be activated at the time of marking. Different dimension markers may be activated at different times. The dimension marker provides information about the order in which the target, target group, and / or sample was randomly barcoded. For example, a cell population may be randomly barcoded during the G0 phase of the cell cycle. During the G1 phase of the cell cycle, these cells may be pulsed again with barcodes (e.g., random barcodes). During the S phase of the cell cycle, the cells may be pulsed again with barcodes, and so on. The barcode at each pulse (e.g., each phase of the cell cycle) may include a different dimension marker. In this way, the dimension marker provides information about which targets were marked at which phase of the cell cycle. The dimension marker can probe many different biological phases. Exemplary biological timeframes may include, but are not limited to, the cell cycle, transcription (e.g., transcription initiation), and transcript degradation. In another instance, samples (e.g., cells, cell populations) may be randomly labeled before and / or after treatment with drugs and / or therapies. Changes in copy number of different targets may indicate a sample’s response to drugs and / or therapies.

[0209] Dimension markers can be activated. Activatable dimension markers can be activated at specific time points. Activatable markers can be, for example, constitutively activated (e.g., not turned off). The activatable dimension marker can be, for example, reversibly activated (e.g., the activatable dimension marker can be turned on and off). The dimension marker can be, for example, reversibly activated at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more times. In some embodiments, the dimension marker can be activated by fluorescence; light; chemical events (e.g., cleavage, linkage to another molecule, addition of modifications (e.g., PEGylation, sumoation, acetylation, methylation, deacetylation, demethylation); photochemical events (e.g., photolocking); and the introduction of a non-natural nucleotide.

[0210] In some embodiments, the dimension marker may be the same for all barcodes (e.g., random barcodes) attached to a given solid support (e.g., beads), but different for different solid supports (e.g., beads). In some embodiments, at least 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, or 100% of the barcodes on the same solid support may include the same dimension marker. In some embodiments, at least 60% of the barcodes on the same solid support may include the same dimension marker. In some embodiments, at least 95% of the barcodes on the same solid support may include the same dimension marker.

[0211] Multiple solid supports (e.g., beads) can exhibit up to 10 6 One or more unique dimension marker sequences. The length of a dimension marker can be about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides, or a number or range of nucleotides between any two of these values. The length of a dimension marker can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides. Dimension markers can include between about 5 and about 200 nucleotides. Dimension markers can include between about 10 and about 150 nucleotides. Dimension markers can include nucleotides with a length between about 20 and about 125 nucleotides.

[0212] Spatial markers A barcode may include one or more spatial markers. In some embodiments, the spatial marker may include a nucleic acid sequence that provides information about the spatial orientation of a target molecule associated with the barcode. The spatial marker may be associated with coordinates in a sample. These coordinates may be fixed coordinates, for example, referenced to a substrate. The spatial marker may reference a two-dimensional or three-dimensional grid. It may reference a landmark fixed coordinate. The landmark is identifiable in space. The landmark may be an imageable structure. The landmark may be a biological structure, such as an anatomical landmark. The landmark may be a cellular landmark, such as an organelle. The landmark may be a non-natural landmark, such as a structure with identifiable identifiers (e.g., color codes, barcodes, magnetism, fluorescence, radioactivity, or unique size or shape). The spatial marker may be associated with physical partitions (e.g., pores, containers, or droplets). In some embodiments, multiple spatial markers are used together to encode one or more locations in space.

[0213] The spatial marker can be the same for all barcodes attached to a given solid support (e.g., a bead), but different for different solid supports (e.g., beads). In some embodiments, the percentage of barcodes on the same solid support including the same spatial marker can be, or about 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, 100%, or a number or range between any two of these values. In some embodiments, the percentage of barcodes on the same solid support including the same spatial marker can be at least, or at most, 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, or 100%. In some embodiments, at least 60% of the barcodes on the same solid support may include the same spatial marker. In some embodiments, at least 95% of the barcodes on the same solid support may include the same spatial marker.

[0214] Multiple solid supports (e.g., beads) can exhibit up to 10 6 One or more unique spatial marker sequences. The length of the spatial marker can be about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides, or a number or range of nucleotides between any two of these values. The length of the spatial marker can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides. The spatial marker may include between about 5 and about 200 nucleotides. The spatial marker may include between about 10 and about 150 nucleotides. The spatial marker may include nucleotides with a length between about 20 and about 125 nucleotides.

[0215] Cell markers Barcodes may include one or more cell markers. In some embodiments, the cell markers may include nucleic acid sequences that provide information for determining which target nucleic acid originates from which cell. In some embodiments, the cell marker is the same for all barcodes attached to a given solid support (e.g., beads), but different for different solid supports (e.g., beads). In some embodiments, the percentage of barcodes on the same solid support including the same cell marker may be, or about 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, 100%, or a number or range between any two of these values. In some embodiments, the percentage of barcodes on the same solid support including the same cell marker may be, or about 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, or 100%. For example, at least 60% of the barcodes on the same solid support may include the same cell marker. As another example, at least 95% of the barcodes on the same solid support may include the same cell marker.

[0216] Multiple solid supports (e.g., beads) can exhibit up to 10 6 One or more unique cell marker sequences. The length of the cell marker can be about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nucleotides, or a number or range of nucleotides between any two of these values. The length of the cell marker can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides. For example, a cell marker may include about 5 to about 200 nucleotides. As another example, a cell marker may include about 10 to about 150 nucleotides. Also as another example, a cell marker may include nucleotides with a length of about 20 to about 125 nucleotides.

[0217] Barcode sequence A barcode may include one or more barcode sequences. In some embodiments, the barcode sequence may include a nucleic acid sequence that provides identification information for a specific type of target nucleic acid species that hybridizes with the barcode. The barcode sequence may include a nucleic acid sequence that provides a counter (e.g., provides a rough approximation) for a specific occurrence of a target nucleic acid species that hybridizes with the barcode (e.g., a target binding region).

[0218] In some embodiments, a set of different barcode sequences are attached to a given solid support (e.g., beads). In some embodiments, there may be, or about 10 2 10 3 10 4 105 10 6 10 7 10 8 10 9 A unique molecular marker sequence, consisting of a number or range of values, or any two of these values. For example, multiple barcodes may include approximately 6,561 barcode sequences with different sequences. As another example, multiple barcodes may include approximately 65,536 barcode sequences with different sequences. In some embodiments, there may be at least or at most 10 2 10 3 10 4 10 5 10 6 10 7 10 8 , or 10 9 A unique barcode sequence. A unique molecular marker sequence can be attached to a given solid support (e.g., beads).

[0219] The length of a barcode can be, or approximately 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, or any number or range of nucleotides between these values. The length of a barcode can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides.

[0220] Molecular markers A barcode may include one or more molecular markers. A molecular marker may include a barcode sequence. In some embodiments, a molecular marker may include a nucleic acid sequence that provides identification information for a specific type of target nucleic acid species that hybridizes with a random barcode. A molecular marker may include a nucleic acid sequence that provides a counter for the specific occurrence of a target nucleic acid species that hybridizes with a random barcode (e.g., a target binding region).

[0221] In some embodiments, a set of different molecular markers are attached to a given solid support (e.g., beads). In some embodiments, there may be, or about 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9One, many, or a range of unique molecular marker sequences. For example, multiple random barcodes may include approximately 6,561 molecular markers with different sequences. As another example, multiple random barcodes may include approximately 65,536 molecular markers with different sequences. In some embodiments, there may be at least or at most 10 2 10 3 10 4 10 5 10 6 10 7 10 8 , or 10 9 A unique molecular marker sequence. A random barcode with a unique molecular marker sequence can be attached to a given solid support (e.g., a bead).

[0222] For random barcoding using multiple random barcodes, the ratio of the number of different molecular marker sequences to the number of occurrences of any target can be, or approximately 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1, 100:1, or a number or range between any two of these values. The target can be a class of mRNAs including mRNA molecules having the same or nearly identical sequences. In some embodiments, the ratio of the number of different molecular marker sequences to the number of occurrences of any target is at least 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:1, 14:1, 15:1, 16:1, 17:1, 18:1, 19:1, 20:1, 30:1, 40:1, 50:1, 60:1, 70:1, 80:1, 90:1, or 100:1.

[0223] The length of a molecular marker can be approximately 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, or any number or range of nucleotides between these values. The length of a molecular marker can be at least, or at most, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200, or 300 nucleotides.

[0224] Target binding region A barcode may include one or more target-binding regions, such as capture probes. In some embodiments, the target-binding region may hybridize with a target. In some embodiments, the target-binding region may include a nucleic acid sequence that specifically hybridizes with a target (e.g., a target nucleic acid, a target molecule, such as the cellular nucleic acid to be analyzed) (e.g., hybridizes with a specific gene sequence). In some embodiments, the target-binding region may include a nucleic acid sequence that can attach (e.g., hybridize) to a specific location on a specific target nucleic acid. In some embodiments, the target-binding region may include a nucleic acid sequence capable of specifically hybridizing with a restriction enzyme site overhang (e.g., an EcoRI sticky end overhang). The barcode can then be linked to any nucleic acid molecule that includes a sequence complementary to the restriction site overhang.

[0225] In some embodiments, the target binding region may include a nonspecific target nucleic acid sequence. A nonspecific target nucleic acid sequence can refer to a sequence independent of a specific target nucleic acid that can bind to multiple target nucleic acids. For example, the target binding region may include a random multimeric sequence or oligomeric (dT) sequence that hybridizes to a poly(A) tail on an mRNA molecule. The random multimeric sequence may be, for example, a random dimer, trimer, tetramer, pentamer, hexamer, heptamer, octamer, nonamer, decamer, or any higher multimeric sequence of any length. In some embodiments, the target binding region is identical for all barcodes attached to a given bead. In some embodiments, for multiple barcodes attached to a given bead, the target binding region may include two or more different target binding sequences. The length of the target binding region may be approximately 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 nucleotides, or a number or range of nucleotides between any two of these values. The length of the target binding region can be up to about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more nucleotides.

[0226] In some embodiments, the target binding region may include an oligomer (dT) that can hybridize with mRNA including a polyadenylated terminus. The target binding region may be gene-specific. For example, the target binding region may be configured to hybridize with a specific region of a target. The length of the target binding region may be about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides, or a number or range of nucleotides between any two of these values. The length of the target binding region can be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides. The length of the target binding region can be approximately 5-30 nucleotides. When a barcode includes a gene-specific target binding region, it can be called a gene-specific barcode.

[0227] Orientation characteristics Barcodes may include one or more orientation properties that can be used to orient (e.g., align) the barcode. Barcodes may include portions for isoelectric focusing. Different barcodes may include different isoelectric focusing points. When these barcodes are introduced into a sample, the sample may undergo isoelectric focusing to align the barcode in a known manner. In this way, the orientation property can be used to develop a known mapping of the barcode within the sample. Exemplary orientation properties may include electrophoretic mobility (e.g., based on barcode size), isoelectric point, spin, conductivity, and / or self-assembly. For example, a barcode may have a self-assembling orientation property that, when activated, can self-assemble into a specific orientation (e.g., nucleic acid nanostructures).

[0228] Affinity characteristics Barcodes may include one or more affinity properties. For example, spatial markers may include affinity properties. Affinity properties may be included in the chemical and / or biological portions that facilitate the binding of the barcode to another entity, such as a cell receptor. For example, affinity properties may include antibodies, such as antibodies specific to a specific portion of a sample, such as a receptor. In some embodiments, antibodies may direct the barcode to a specific cell type or molecule. Targets at and / or near a specific cell type or molecule may be randomly labeled. In some embodiments, in addition to the nucleotide sequence of the spatial marker, affinity properties may provide spatial information because the antibody may direct the barcode to a specific location. Antibodies may be therapeutic antibodies, such as monoclonal or polyclonal antibodies. Antibodies may be humanized or chimeric. Antibodies may be naked antibodies or fusion antibodies.

[0229] Antibodies can be full-length (i.e., naturally occurring or formed through normal immunoglobulin gene fragment recombination processes) immunoglobulin molecules (e.g., IgG antibodies) or immunologically active (i.e., specifically binding) portions of immunoglobulin molecules (like antibody fragments).

[0230] Antibody fragments can be, for example, portions of an antibody, such as F(ab')2, Fab', Fab, Fv, sFv, etc. In some embodiments, antibody fragments can bind to the same antigen recognized by a full-length antibody. Antibody fragments can include separate fragments composed of variable regions of an antibody, such as an "Fv" fragment composed of variable regions of a heavy chain and a recombinant single-chain polypeptide molecule ("scFv protein") in which the light chain and heavy chain variable regions are linked by peptide linkers. Exemplary antibodies can include, but are not limited to, cancer cell antibodies, viral antibodies, antibodies that bind to cell surface receptors (CD8, CD34, CD45), and therapeutic antibodies.

[0231] Universal adapter primers Barcodes may include one or more universal adaptor primers. For example, gene-specific barcodes (e.g., gene-specific random barcodes) may include universal adaptor primers. Universal adaptor primers may refer to nucleotide sequences that are common across all barcodes. Universal adaptor primers can be used to construct gene-specific barcodes. The length of a universal adaptor primer may be approximately 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides, or a number or range of nucleotides between any two of these values. Universal adaptor primers can be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides in length. The length of universal adaptor primers can range from 5 to 30 nucleotides.

[0232] connector When a barcode includes more than one type of marker (e.g., more than one cellular marker or more than one barcode sequence, such as a molecular marker), these markers can be interspersed with adapter marker sequences. The length of the adapter marker sequence can be at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more nucleotides. The length of the adapter marker sequence can be at most about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more nucleotides. In some cases, the length of the adapter marker sequence is 12 nucleotides. The adapter marker sequence can be used to facilitate barcode synthesis. The adapter marker can include error correction (e.g., Hamming) codes.

[0233] solid support In some embodiments, the barcodes disclosed herein (such as random barcodes) may be associated with a solid support. For example, the solid support may be synthetic particles. In some embodiments, some or all of the barcode sequences (e.g., molecular markers of random barcodes (e.g., the first barcode sequence) of a plurality of barcodes on a solid support (e.g., a first plurality of barcodes) have at least one nucleotide difference. The cell markers of barcodes on the same solid support may be identical. The cell markers of barcodes on different solid supports may have at least one nucleotide difference. For example, the first cell markers of a first plurality of barcodes on a first solid support may have the same sequence, and the second cell markers of a second plurality of barcodes on a second solid support may have the same sequence. The first cell markers of the first plurality of barcodes on a first solid support and the second cell markers of the second plurality of barcodes on a second solid support may have at least one nucleotide difference. The cell markers may, for example, be about 5-20 nucleotides long. The barcode sequences may, for example, be about 5-20 nucleotides long. The synthetic particles may, for example, be beads.

[0234] Beads can be, for example, silicone beads, controllable aperture glass beads, magnetic beads, Dynabeads, cross-linked dextran / agarose beads, beaded cellulose, polystyrene beads, or any combination thereof. Beads may include materials such as polydimethylsiloxane (PDMS), polystyrene, glass, polypropylene, agarose, gelatin, hydrogel, paramagnetic materials, ceramics, plastics, glass, methylstyrene, acrylic polymers, titanium, latex, agarose gel, cellulose, nylon, silicone, or any combination thereof.

[0235] In some embodiments, the beads may be polymeric microspheres (e.g., deformable beads or gel beads) functionalized with barcodes or random barcodes (such as gel beads from 10X Genomics (San Francisco, California)). In some implementations, the gel beads may comprise polymer-based gels. For example, gel beads can be generated by encapsulating one or more polymer precursors into droplets. Gel beads can be generated after exposing the polymer precursor to a promoter (e.g., tetramethylethylenediamine (TEMED)).

[0236] In some embodiments, the particles may be biodegradable. For example, polymeric microspheres may dissolve, melt, or degrade, for instance, under desired conditions. Desired conditions may include environmental conditions. Desired conditions may cause the polymeric microspheres to dissolve, melt, or degrade in a controlled manner. Gel beads may dissolve, melt, or degrade due to chemical stimulation, physical stimulation, biological stimulation, thermal stimulation, magnetic stimulation, electrical stimulation, light stimulation, or any combination thereof.

[0237] Analytes and / or reagents (such as oligonucleotide barcodes) may be coupled / immobilized, for example, to the inner surface of the gel beads (the diffusely accessible interior of the oligonucleotide barcode and / or the material used to generate the oligonucleotide barcode) and / or the outer surface of the gel beads or any other microcapsules described herein. Coupling / immobilization can be via any form of chemical bond (e.g., covalent, ionic) or physical phenomenon (e.g., van der Waals forces, dipole-dipole interactions, etc.). In some embodiments, the coupling / immobilization of the reagent with the gel beads or any other microcapsules described herein can be reversible, for example, via an unstable portion (e.g., via a chemical crosslinking agent, including those described herein). Upon application of stimulation, the unstable portion can be cleaved and the immobilized reagent released. In some embodiments, the unstable portion is a disulfide bond. For example, in the case of immobilizing an oligonucleotide barcode to a gel bead via a disulfide bond, exposing the disulfide bond to a reducing agent can cleave the disulfide bond and release the oligonucleotide barcode from the bead. The unstable portion may be part of a gel bead or microcapsule, part of a chemical connector linking a reagent or analyte to a gel bead or microcapsule, and / or included as part of a reagent or analyte. In some embodiments, at least one barcode of a plurality of barcodes may be affixed to the particle, partially affixed to the particle, encapsulated in the particle, partially encapsulated in the particle, or any combination thereof.

[0238] In some embodiments, gel beads may comprise a wide variety of different polymers, including but not limited to: polymers, thermosensitive polymers, photosensitive polymers, magnetic polymers, pH-sensitive polymers, salt-sensitive polymers, chemically sensitive polymers, polyelectrolytes, polysaccharides, peptides, proteins, and / or plastics. The polymer may include, but is not limited to, the following materials: such as poly(N-isopropylacrylamide) (PNIPAAm), poly(styrene sulfonate) (PSS), poly(allylamine) (PAAm), poly(acrylic acid) (PAA), poly(ethyleneimine) (PEI), poly(diallyldimethylammonium chloride) (PDADMAC), poly(pyrrole) (PPy), poly(vinylpyrrolidone) (PVPON), poly(vinylpyridine) (PVP), poly(methyl methacrylate) (PMAA), poly(methyl methacrylate) (PMMA), polystyrene (PS), poly(tetrahydrofuran) (PTHF), poly(phthalaldehyde) (PTHF), poly(hexyl viologen) (PHV), poly(L-lysine) (PLL), poly(L-arginine) (PARG), and poly(lactic acid-polyhydroxyacetic acid) (PLGA).

[0239] Many chemical stimuli can be used to trigger the destruction, dissolution, or degradation of beads. Examples of these chemical alterations include, but are not limited to, pH-mediated bead wall alteration, bead wall decomposition via chemical cleavage of cross-links, triggered depolymerization of the bead wall, and bead wall conversion reactions. Batch alterations can also be used to trigger bead destruction.

[0240] The ability to induce batch or physical changes in microcapsules through various stimuli also offers numerous advantages in designing capsules for reagent release. These batch or physical changes occur on a macroscopic scale, where bead rupture is the result of mechanical-physical forces induced by stimuli. These processes can include, but are not limited to, pressure-induced rupture, bead wall melting, or changes in bead wall porosity.

[0241] Biostimuli can also be used to trigger the destruction, dissolution, or degradation of beads. Typically, biotriggers are similar to chemical triggers, but many instances use biomolecules, or molecules common in living systems, such as enzymes, peptides, sugars, fatty acids, and nucleic acids. For example, beads may comprise polymers with peptide crosslinks that are sensitive to cleavage by specific proteases. More specifically, one example may include microcapsules containing GFLGK peptide crosslinks. Upon addition of a biotrigger (such as the protease cathepsin B), the peptide crosslinks in the shell pores are cleaved, and the contents of the bead are released. In other cases, the protease may be thermally activated. In another example, the beads comprise a shell wall containing cellulose. The addition of the hydrolase chitosan acts as a biotrigger for cellulose bond cleavage, shell wall depolymerization, and release of the internal contents.

[0242] Applying heat can also induce the beads to release their contents. Temperature changes can cause various changes in the beads. Changes in heat can cause the beads to melt, leading to the disintegration of the bead wall. In other cases, heat may increase the internal pressure of the bead's components, causing the bead to rupture or explode. Still in some cases, heat can cause the beads to shrink and become dehydrated. Heat can also act on the heat-sensitive polymers within the bead wall, thus causing the bead to break.

[0243] Incorporating magnetic nanoparticles within the bead walls of microcapsules allows for triggered breakage of the beads and the guidance of the beads into an array. The apparatus disclosed herein may include a magnetic bead for any of interest. In one example, Fe3O4 nanoparticles are incorporated into a polyelectrolyte-containing bead, and breakage is triggered in the presence of an oscillating magnetic field stimulus.

[0244] As a result of electrical stimulation, the beads may also be broken, dissolved, or degraded. Similar to the magnetic particles described in the previous section, electrosensitive beads can allow for triggered breakage and other functions, such as alignment, conductivity, or redox reactions in an electric field. In one example, beads containing electrosensitive materials are aligned in an electric field, thereby allowing control over the release of internal reagents. In other examples, the electric field can induce redox reactions within the bead wall itself, which can increase porosity.

[0245] Beads can also be disrupted by light stimulation. Many photo-initiating mechanisms are possible and can include systems using various molecules, such as nanoparticles and chromophores capable of absorbing photons in specific wavelength ranges. For example, metal oxide coatings can be used as capsule triggers. UV irradiation of polyelectrolyte capsules coated with SiO2 can cause the bead walls to disintegrate. In yet another example, photoswitching materials, such as azophenyl groups, can be incorporated into the bead walls. Upon application of UV or visible light, these chemicals undergo reversible cis-trans isomerization after absorbing photons. In this respect, the incorporation of photoswitching causes the bead walls to either disintegrate or become more porous upon application of a photo-initiating agent.

[0246] For example, in Figure 2 In a non-limiting example of barcoding (random barcoding) described herein, after a cell (such as a single cell) is introduced into a plurality of wells of a microwell array at box 208, a bead may be introduced onto a plurality of wells of the microwell array at box 212. Each well may include one bead. The bead may include multiple barcodes. The barcode may include a 5' amine region attached to the bead. The barcode may include a universal marker, a barcode sequence (e.g., a molecular marker), a target-binding region, or any combination thereof.

[0247] The barcodes disclosed herein can be associated (e.g., attached) to a solid support (e.g., beads). Each barcode associated with a solid support may include a barcode sequence selected from the group consisting of at least 100 or 1000 barcode sequences having unique sequences. In some embodiments, different barcodes associated with a solid support may include barcode sequences with different sequences. In some embodiments, the percentage of the barcodes associated with the solid support includes the same cell marker. For example, the percentage may be, or about 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, 100%, or a number or range between any two of these values. As another example, the percentage may be at least, or at most, 60%, 70%, 80%, 85%, 90%, 95%, 97%, 99%, or 100%. In some embodiments, the barcodes associated with the solid support may have the same cell marker. Barcodes associated with different solid supports may have different cell markers selected from the group consisting of at least 100 or 1000 cell markers with unique sequences.

[0248] The barcodes disclosed herein can be associated (e.g., attached) to a solid support (e.g., beads). In some embodiments, multiple targets in a sample can be randomly barcoded using a solid support comprising multiple synthetic particles associated with multiple barcodes. In some embodiments, the solid support may comprise multiple synthetic particles associated with multiple barcodes. The spatial markers of multiple barcodes on different solid supports may have a difference of at least one nucleotide. The solid support may, for example, comprise multiple barcodes in two or three dimensions. The synthetic particles may be beads. Beads may be silica beads, controlled-aperture glass beads, magnetic beads, Dynabeads, cross-linked dextran / agarose beads, beaded cellulose, polystyrene beads, or any combination thereof. The solid support may comprise polymers, matrices, hydrogels, needle array devices, antibodies, or any combination thereof. In some embodiments, the solid support may be free-floating. In some embodiments, the solid support may be embedded in a semi-solid or solid array. The barcode may not be associated with the solid support. The barcode may be a single nucleotide. The barcode may be associated with a substrate.

[0249] As used herein, the terms “tethering,” “attaching,” and “fixing” are used interchangeably and can refer to covalent or non-covalent methods for attaching barcodes to a solid support. Any of a variety of different solid supports can be used as a solid support for attaching pre-synthesized barcodes or for in-situ solid-phase synthesis of barcodes.

[0250] In some embodiments, the solid support is a bead. Beads may include one or more types of solid, porous, or hollow spheres, balls, supports, cylinders, or other similar configurations on which nucleic acids (e.g., covalently or non-covalently) may be immobilized. Beads may be made of, for example, plastic, ceramic, metal, polymeric materials, or any combination thereof. Beads may be, or include, spherical (e.g., microspheres) or discrete particles with non-spherical or irregular shapes, such as cubic, rectangular, conical, cylindrical, elliptical, or disk-shaped. In some embodiments, the shape of the bead may be non-spherical.

[0251] Beads can contain a variety of materials, including but not limited to paramagnetic materials (such as magnesium, molybdenum, lithium, and tantalum), superparamagnetic materials (such as ferrite (Fe3O4; magnetite) nanoparticles), ferromagnetic materials (such as iron, nickel, cobalt, some alloys thereof, and some rare earth metal compounds), ceramics, plastics, glass, polystyrene, silica, methylstyrene, acrylic polymers, titanium, latex, cross-linked agarose, agarose, hydrogels, polymers, cellulose, nylon, or any combination thereof.

[0252] In some embodiments, the beads (e.g., the beads to which the marker is attached) are hydrogel beads. In some embodiments, the beads comprise hydrogel.

[0253] Some embodiments disclosed herein include one or more particles (e.g., beads). Each particle may include multiple oligonucleotides (e.g., barcodes). Each of the multiple oligonucleotides may include a barcode sequence (e.g., a molecular marker), a cell marker, and a target-binding region (e.g., an oligomeric (dT) sequence, a gene-specific sequence, a random multimer, or a combination thereof). The cell marker sequence of each of the multiple oligonucleotides may be identical. The cell marker sequences of oligonucleotides on different particles may be different, allowing identification of oligonucleotides on different particles. In different implementations, the number of different cell marker sequences may be different. In some embodiments, the number of cell marker sequences may be 10, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 10 6 10 7 10 8 10 9The number or range between any two of these values, or more. In some embodiments, the number of cell marker sequences may be at least, or at most 10, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 10 6 10 7 10 8 , or 10 9 In some embodiments, no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or more of the plurality of particles comprising oligonucleotides having the same cell sequence. In some embodiments, the plurality of particles comprising oligonucleotides having the same cell sequence may be up to 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, or more. In some embodiments, no particles among the plurality of particles have the same cell marker sequence.

[0254] Multiple oligonucleotides on each particle can include different barcode sequences (e.g., molecular markers). In some embodiments, the number of barcode sequences can be 10, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 10 6 10 7 10 8 10 9Or, a number or range between any two of these values. In some embodiments, the number of barcode sequences may be at least, or at most, 10, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 10 6 10 7 10 8 , or 10 9 For example, at least 100 of the plurality of oligonucleotides include different barcode sequences. As another example, in a single particle, at least 100, 500, 1000, 5000, 10000, 15000, 20000, 50000, or more of the plurality of oligonucleotides, numbers or ranges between any two of these values, include different barcode sequences. Some embodiments provide multiple particles including barcodes. In some embodiments, the ratio of the occurrence (or copies or number) of the target to be labeled and the different barcode sequences can be at least 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:11, 1:12, 1:13, 1:14, 1:15, 1:16, 1:17, 1:18, 1:19, 1:20, 1:30, 1:40, 1:50, 1:60, 1:70, 1:80, 1:90, or higher. In some embodiments, each of the plurality of oligonucleotides further includes a sample label, a universal label, or both. The particles can be, for example, nanoparticles or microparticles.

[0255] The size of the beads can vary. For example, the diameter of the beads can range from 0.1 micrometers to 50 micrometers. In some embodiments, the diameter of the beads can be about 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50 micrometers, or between any two of these values, or a number or range.

[0256] The diameter of the bead can be related to the diameter of the pores in the substrate. In some embodiments, the diameter of the bead can be longer or shorter than the diameter of the pore, or about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, or a number or range between any two of these values. The diameter of the bead can also be related to the diameter of a cell (e.g., a single cell trapped by the pores in the substrate). In some embodiments, the diameter of the bead can be at least, or at most, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% longer or shorter than the diameter of the pore. The diameter of the bead can also be related to the diameter of a cell (e.g., a single cell trapped by the pores in the substrate). In some embodiments, the diameter of the bead may be longer or shorter than the diameter of the cell, or approximately 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, 300%, or a number or range between any two of these values. In some embodiments, the diameter of the bead may be at least, or at most, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 250%, or 300% longer or shorter than the diameter of the pore.

[0257] Beads can be attached to and / or embedded in a substrate. Beads can be attached to and / or embedded in gels, hydrogels, polymers, and / or matrices. The spatial location of the beads in a matrix (e.g., gel, matrix, scaffold, or polymer) can be identified using spatial markers on barcodes present in the beads that can serve as location addresses.

[0258] Examples of beads may include, but are not limited to, streptavidin beads, agarose beads, magnetic beads, Dynabeads®, MACS® microbeads, antibody-conjugated beads (e.g., anti-immunoglobulin microbeads), protein A-conjugated beads, protein G-conjugated beads, protein A / G-conjugated beads, protein L-conjugated beads, oligomeric (dT)-conjugated beads, silica beads, silica-like beads, anti-biotin microbeads, anti-fluorescent dye microbeads, and BcMag™ carboxyl-terminated magnetic beads.

[0259] Beads may be associated with (e.g., impregnated with) quantum dots or fluorescent dyes to make them fluorescent in one or more light channels. Beads may be associated with iron oxide or chromium oxide to make them paramagnetic or ferromagnetic. Beads are identifiable. For example, beads can be imaged using a camera. Beads may have a detectable code associated with them. For example, beads may include barcodes. The size of beads may vary, for example, due to swelling in organic or inorganic solutions. Beads may be hydrophobic. Beads may be hydrophilic. Beads may be biocompatible.

[0260] Solid supports (e.g., beads) can be visualized. Solid supports may include visual labels (e.g., fluorescent dyes). Solid supports (e.g., beads) can be etched with markings (e.g., numbers). These markings can be visualized by imaging the beads.

[0261] Solid supports can include insoluble, semi-soluble, or insoluble materials. A solid support may be described as "functionalized" when it includes a connector, scaffold, structural unit, or other reactive portion attached thereto, and as "unfunctionalized" when it lacks such a reactive portion attached thereto. Solid supports can be used freely in solution, such as in microburette wells; in flow-through forms, such as in columns; or in dipsticks.

[0262] Solid supports may include membranes, paper, plastics, coated surfaces, flat surfaces, glass, glass slides, chips, or any combination thereof. Solid supports may take the form of resins, gels, microspheres, or other geometries. Solid supports may include silica chips; microparticles; nanoparticles; plates; arrays; capillaries; flat supports such as glass fiber filters, glass surfaces, metal surfaces (steel, gold, silver, aluminum, silicon, and copper), glass supports, plastic supports, silicon supports, chips, filters, membranes, microplates, glass slides; plastic materials including porous plates or membranes (e.g., formed from polyethylene, polypropylene, polyamide, polyvinylidene fluoride); and / or wafers; combs; needles or needles (e.g., needle arrays suitable for combined synthesis or analysis); or beads in arrays of recesses or nanopores on flat surfaces such as wafers (e.g., silicon wafers), wafers with or without a filter bed.

[0263] The solid support may include a polymer matrix (e.g., a gel, hydrogel). This polymer matrix may be able to permeate intracellular spaces (e.g., around organelles). This polymer matrix may be able to be pumped through the circulatory system.

[0264] Solid supports can be biomolecules. For example, solid supports can be nucleic acids, proteins, antibodies, histones, cellular compartments, lipids, carbohydrates, etc. Solid supports as biomolecules can be amplified, translated, transcribed, degraded, and / or modified (e.g., PEGylated, sumolated, acetylated, methylated). In addition to spatial markers attached to biomolecules, solid supports as biomolecules can provide spatial and temporal information. For example, a biomolecule may include a first conformation when unmodified, but can change to a second conformation upon modification. These different conformations can expose the barcodes (e.g., random barcodes) disclosed herein to a target. For example, biomolecules may include barcodes that are inaccessible due to the folding of the biomolecule. When biomolecules are modified (e.g., acetylated), these biomolecules can change conformation to expose these barcodes. The temporal setting of the modification can provide another temporal dimension to the barcoding methods disclosed herein.

[0265] In some embodiments, biomolecules including the barcode reagents disclosed herein can be located in the cytoplasm of a cell. Upon activation, the biomolecule can move to the cell nucleus, where it can be barcoded. In this way, the modification of the biomolecule can encode additional spatial-temporal information about the target identified by the barcode.

[0266] Substrate and microporous array As used herein, a substrate can refer to a solid support. A substrate can refer to a solid support that may include the barcodes and random barcodes disclosed herein. For example, a substrate may include a plurality of microwells. For example, a substrate may be a pore array comprising two or more microwells. In some embodiments, the microwells may include small reaction chambers having a defined volume. In some embodiments, the microwells may retain one or more cells. In some embodiments, the microwells may retain only one cell. In some embodiments, the microwells may retain one or more solid supports. In some embodiments, the microwells may retain only one solid support. In some embodiments, the microwells retain a single cell and a single solid support (e.g., a bead). The microwells may include the combined barcode reagents disclosed herein.

[0267] Barcode methods This disclosure provides methods for estimating the number of distinct targets at different locations in a body sample (e.g., tissue, organ, tumor, cell). These methods may include placing a barcode (e.g., a random barcode) close to the sample, lysing the sample, associating distinct targets with the barcode, amplifying the targets, and / or digitally counting the targets. The method may further include analyzing and / or visualizing information obtained from spatial markings on the barcode. In some embodiments, the method includes visualizing multiple targets in the sample. Mapping multiple targets onto a map of the sample may include generating a two-dimensional or three-dimensional map of the sample. The two-dimensional and three-dimensional maps may be generated before or after barcoding (e.g., random barcoding) the multiple targets in the sample. Visualizing multiple targets in the sample includes mapping multiple targets onto a map of the sample. Mapping multiple targets onto a map of the sample may include generating a two-dimensional or three-dimensional map of the sample. The two-dimensional and three-dimensional maps may be generated before or after barcoding the multiple targets in the sample. In some embodiments, the two-dimensional and three-dimensional maps may be generated before or after lysing the sample. Before or after generating a two-dimensional or three-dimensional map, pyrolyzing the sample may include heating the sample, contacting the sample with a detergent, changing the pH of the sample, or any combination thereof.

[0268] In some embodiments, barcoding multiple targets includes hybridizing multiple barcodes with multiple targets to create barcoded targets (e.g., targets with random barcodes). Barcoding multiple targets may include generating an index library of barcoded targets. Generating an index library of barcoded targets can be done using a solid support comprising multiple barcodes (e.g., random barcodes).

[0269] Make the sample and barcode come into contact This disclosure provides methods for contacting a sample (e.g., cells) with a substrate disclosed herein. Samples comprising, for example, thin slices of cells, organs, or tissues may be contacted with a barcode (e.g., a random barcode). For example, these cells may be contacted by gravity flow, whereby the cells may precipitate and form a monolayer of cells. The sample may be a thin slice of tissue. The slice may be placed on a substrate. The sample may be one-dimensional (e.g., forming a plane). The sample (e.g., cells) may be coated onto the substrate, for example, by growing / culturing these cells on the substrate.

[0270] When a barcode is brought close to a target, the target can hybridize with the barcode. The barcode can contact at an inexhaustible rate, allowing each different target to be associated with a different barcode disclosed herein. To ensure effective association between the target and the barcode, the target and barcode can be crosslinked.

[0271] Cell lysis Following the distribution of cells and barcodes, cells can be lysed to release target molecules. Cell lysis can be accomplished by any of a variety of means, such as chemical or biochemical methods, osmotic shock, or thermal, mechanical, or optical lysis. Cells can be lysed by adding cell lysis buffers containing detergents (e.g., SDS, lithium dodecyl sulfate, Triton X-100, Tween-20, or NP-40), organic solvents (e.g., methanol or acetone), or digestive enzymes (e.g., proteinase K, pepsin, or trypsin), or any combination thereof. To increase the association between the target and the barcode, the diffusion rate of the target molecules can be altered, for example, by decreasing the temperature of the lysate and / or increasing the viscosity of the lysate.

[0272] In some embodiments, filter paper can be used to lyse the sample. The filter paper can be impregnated with a lysis buffer applied to its upper portion. The filter paper can be applied to the sample under pressure, which can promote sample lysis and hybridization of the sample's target and substrate.

[0273] In some embodiments, lysis can be performed by mechanical lysis, thermal lysis, optical lysis, and / or chemical lysis. Chemical lysis may include the use of digestive enzymes such as proteinase K, pepsin, and trypsin. Lysis can be performed by adding a lysis buffer to the substrate. The lysis buffer may include Tris HCl. The lysis buffer may include at least about 0.01 M, 0.05 M, 0.1 M, 0.5 M, or 1 M or more Tris HCl. The lysis buffer may include up to about 0.01 M, 0.05 M, 0.1 M, 0.5 M, or 1 M or more Tris HCl. The lysis buffer may include about 0.1 M Tris HCl. The pH of the lysis buffer may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or higher. The pH of the lysis buffer may be up to about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or higher. In some embodiments, the pH of the lysis buffer is about 7.5. The lysis buffer may include a salt (e.g., LiCl). The salt concentration in the lysis buffer can be at least about 0.1 M, 0.5 M, or 1 M, or higher. The salt concentration in the lysis buffer can be at most about 0.1 M, 0.5 M, or 1 M, or higher. In some embodiments, the salt concentration in the lysis buffer is about 0.5 M. The lysis buffer may include a detergent (e.g., SDS, lithium dodecyl sulfate, Triton X, Tween, NP-40). The detergent concentration in the lysis buffer can be at least about 0.0001%, 0.0005%, 0.001%, 0.005%, 0.01%, 0.05%, 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, or 7%, or higher. The concentration of detergent in the lysis buffer can be up to about 0.0001%, 0.0005%, 0.001%, 0.005%, 0.01%, 0.05%, 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, or 7%, or higher. In some embodiments, the concentration of detergent in the lysis buffer is about 1% lithium dodecyl sulfate. The time taken in this lysis method can depend on the amount of detergent used. In some embodiments, the more detergent used, the shorter the lysis time required. The lysis buffer may include a chelating agent (e.g., EDTA, EGTA). The concentration of the chelating agent in the lysis buffer can be at least about 1 mM, 5 mM, 10 mM, 15 mM, 20 mM, 25 mM, or 30 mM, or higher. The concentration of the chelating agent in the lysis buffer can be up to about 1, 5, 10, 15, 20, 25, or 30 mM, or higher. In some embodiments, the concentration of the chelating agent in the lysis buffer is about 10 mM. The lysis buffer may include a reducing agent (e.g., β-mercaptoethanol, DTT).The concentration of the reducing agent in the lysis buffer can be at least about 1, 5, 10, 15, or 20 mM or higher. The concentration of the reducing agent in the lysis buffer can be at most about 1, 5, 10, 15, or 20 mM or higher. In some embodiments, the concentration of the reducing agent in the lysis buffer is about 5 mM. In some embodiments, the lysis buffer may include about 0.1 M Tris HCl, about pH 7.5, about 0.5 M LiCl, about 1% lithium dodecyl sulfate, about 10 mM EDTA, and about 5 mM DTT.

[0274] Lysis can be performed at temperatures of approximately 4°C, 10°C, 15°C, 20°C, 25°C, or 30°C. Lysis can take approximately 1 minute, 5 minutes, 10 minutes, 15 minutes, or 20 minutes or more. Lysed cells may contain at least approximately 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, or 700,000 target nucleic acid molecules, or more. Lysed cells may contain up to approximately 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, or 700,000 target nucleic acid molecules, or more.

[0275] Attaching barcodes to target nucleic acid molecules Following cell lysis and the release of nucleic acid molecules, the nucleic acid molecules can be randomly associated with barcodes on a co-localized solid support. Association may include hybridization of the target recognition region of the barcode with a complementary portion of the target nucleic acid molecule (e.g., the oligomeric (dT) portion of the barcode may interact with the polymeric (A) tail of the target). Assay conditions (e.g., buffer pH, ionic strength, temperature, etc.) for hybridization can be selected to promote the formation of specific, stable hybrids. In some embodiments, nucleic acid molecules released from lysed cells can be associated with multiple probes on a substrate (e.g., hybridization with probes on the substrate). When the probe includes an oligomeric (dT) portion, mRNA molecules can be hybridized to the probe and reverse transcribed. The oligomeric (dT) portion of an oligonucleotide can be used as a primer for the first-strand synthesis of cDNA molecules. For example, Figure 2 In the non-limiting example of barcoding illustrated in box 216, an mRNA molecule can hybridize with a barcode on a bead. For example, a single-stranded nucleotide fragment can hybridize with the target binding region of the barcode.

[0276] The attachment may further include linking the target recognition region of the barcode to a portion of the target nucleic acid molecule. For example, the target binding region may include a nucleic acid sequence capable of specifically hybridizing to restriction site overhangs (e.g., EcoRI sticky end overhangs). The assay procedure may also include treating the target nucleic acid with a restriction enzyme (e.g., EcoRI) to generate restriction site overhangs. The barcode can then be ligated to any nucleic acid molecule including a sequence complementary to the restriction site overhang. A ligase (e.g., T4 DNA ligase) may be used to ligate the two fragments.

[0277] For example, in Figure 2 In the non-limiting example of barcoding illustrated (at box 220), labeled targets (e.g., target-barcode molecules) from multiple cells (or multiple samples) can then be pooled into a tube, for example. The labeled targets can be pooled by, for example, recovering barcodes and / or beads attached to target-barcode molecules.

[0278] Retrieval of a solid-support-based ensemble of attached target-barcode molecules can be achieved using magnetic beads and an externally applied magnetic field. Once the target-barcode molecules have been pooled, all further processing can be performed in a single reaction vessel. Further processing may include, for example, reverse transcription, amplification, cleavage, dissociation, and / or nucleic acid extension reactions. These further processing reactions can be carried out within microwells, i.e., without first pooling the labeled target nucleic acid molecules from multiple cells.

[0279] Reverse transcription This disclosure provides a method for generating target-barcode conjugates using reverse transcription (in... Figure 2 (See box 224). Target-barcode conjugates may include a barcode and all or part of the complementary sequence of the target nucleic acid (i.e., a barcoded cDNA molecule, such as a randomly barcoded cDNA molecule). Reverse transcription of the associated RNA molecule can occur by adding a reverse transcription primer along with reverse transcriptase. The reverse transcription primer can be an oligo(dT) primer, a random hexanucleotide primer, or a target-specific oligonucleotide primer. Oligo(dT) primers may be 12-18 nucleotides in length and bind to an endogenous poly(A) tail at the 3' end of mammalian mRNA. Random hexanucleotide primers can bind to mRNA at multiple complementary sites. Target-specific oligonucleotide primers typically selectively induce the target mRNA.

[0280] In some embodiments, reverse transcription of the labeled RNA molecule can be performed by adding a reverse transcription primer. In some embodiments, the reverse transcription primer is an oligomeric (dT) primer, a random hexanucleotide primer, or a target-specific oligonucleotide primer. Typically, oligomeric (dT) primers are 12-18 nucleotides in length and bind to an endogenous poly(A)+ tail at the 3' end of mammalian mRNA. Random hexanucleotide primers can bind to mRNA at multiple complementary sites. Target-specific oligonucleotide primers typically selectively induce the target mRNA.

[0281] Reverse transcription can occur repeatedly to produce multiple labeled cDNA molecules. The methods disclosed herein may include performing at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 reverse transcription reactions. The methods may also include performing at least about 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 reverse transcription reactions.

[0282] Amplification One or more nucleic acid amplification reactions can be performed (e.g., in...) Figure 2 (as shown in box 228) to produce multiple copies of a labeled target nucleic acid molecule. Amplification can be performed in a multiplexed manner, where multiple target nucleic acid sequences are amplified simultaneously. The amplification reaction can be used to add a sequencing adaptor to the nucleic acid molecule. The amplification reaction may include at least a portion of an amplified sample tag (if present). The amplification reaction may include at least a portion of an amplified cellular tag and / or barcode sequence (e.g., a molecular tag). The amplification reaction may include at least a portion of an amplified sample tag, cellular tag, spatial tag, barcode (e.g., a molecular tag), target nucleic acid, or a combination thereof. The amplification reaction may include amplifying multiple nucleic acids at 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 100%, or a number or range between any two of these values. The method may further include performing one or more cDNA synthesis reactions to produce one or more cDNA copies of a target-barcode molecule that includes a sample marker, cell marker, spatial marker, and / or barcode sequence (e.g., a molecular marker).

[0283] In some embodiments, amplification may be performed using polymerase chain reaction (PCR). As used herein, PCR can refer to a reaction used to amplify a specific DNA sequence in vitro by simultaneous primer extension of the complementary strand of DNA. As used herein, PCR may include derivative forms of the reaction, including but not limited to RT-PCR, real-time PCR, nested PCR, quantitative PCR, multiplex PCR, digital PCR, and assembly PCR.

[0284] Amplification of labeled nucleic acids can include non-PCR-based methods. Examples of non-PCR-based methods include, but are not limited to, multiple substitution amplification (MDA), transcription-mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), strand substitution amplification (SDA), real-time SDA, rolling circle amplification, or circle-to-circle amplification. Other non-PCR-based amplification methods include DNA-dependent RNA polymerase-driven RNA transcriptional amplification or multiple cycles of RNA-directed DNA synthesis and transcription to amplify DNA or RNA targets, ligase chain reaction (LCR) and Qβ replicase (Qβ) methods, the use of palindromic probes, strand substitution amplification, oligonucleotide-driven amplification using restriction endonucleases, amplification methods that hybridize primers to nucleic acid sequences and cleave the resulting duplexes before extension and amplification, strand substitution amplification using nucleic acid polymerases lacking 5' exonuclease activity, rolling circle amplification, and branch extension amplification (RAM). In some embodiments, amplification does not produce circularized transcripts.

[0285] In some embodiments, the methods disclosed herein further include performing a polymerase chain reaction on a labeled nucleic acid (e.g., labeled RNA, labeled DNA, labeled cDNA) to generate a labeled amplicon (e.g., randomly labeled amplicon). The labeled amplicon may be a double-stranded molecule. A double-stranded molecule may include a double-stranded RNA molecule, a double-stranded DNA molecule, or an RNA molecule that hybridizes to a DNA molecule. One or both strands of the double-stranded molecule may include a sample marker, a spatial marker, a cellular marker, and / or a barcode sequence (e.g., a molecular marker). The labeled amplicon may be a single-stranded molecule. A single-stranded molecule may include DNA, RNA, or a combination thereof. The nucleic acids disclosed herein may include synthetic or modified nucleic acids.

[0286] Amplification may involve the use of one or more non-natural nucleotides. Non-natural nucleotides may include light-labile or triggerable nucleotides. Examples of non-natural nucleotides may include, but are not limited to, peptide nucleic acids (PNAs), morpholino and locked nucleic acids (LNAs), and gamma-hydroxyl nucleic acids (GNAs) and threonine nucleic acids (TNAs). Non-natural nucleotides may be added to one or more cycles of the amplification reaction. The addition of non-natural nucleotides may also be used to identify the products at specific cycles or time points in the amplification reaction.

[0287] Performing one or more amplification reactions may include using one or more primers. One or more primers may include, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 or more nucleotides. One or more primers may include at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 or more nucleotides. One or more primers may include fewer than 12-15 nucleotides. One or more primers may anneal to at least a portion of a plurality of labeled targets (e.g., randomly labeled targets). One or more primers may anneal to the 3' or 5' end of a plurality of labeled targets. One or more primers may anneal to the internal regions of a plurality of labeled targets. The internal region can be at least about 50, 100, 150, 200, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 650, 700, 750, 800, 850, 900, or 1000 nucleotides from the 3' end of the plurality of labeled targets. One or more primers may include a fixed set of primers. One or more primers may include at least one or more custom primers. One or more primers may include at least one or more control primers. One or more primers may include at least one or more gene-specific primers.

[0288] One or more primers may include universal primers. Universal primers may be annealed to universal primer binding sites. One or more custom primers may be annealed to a first sample marker, a second sample marker, a spatial marker, a cellular marker, a barcode sequence (e.g., a molecular marker), a target, or any combination thereof. One or more primers may include both universal and custom primers. Custom primers may be designed to amplify one or more targets. Targets may include a subset of total nucleic acids in one or more samples. Targets may include a subset of total labeled targets in one or more samples. One or more primers may include at least 96 or more custom primers. One or more primers may include at least 960 or more custom primers. One or more primers may include at least 9600 or more custom primers. One or more custom primers may be annealed to two or more different labeled nucleic acids. Two or more different labeled nucleic acids may correspond to one or more genes.

[0289] Any amplification protocol can be used in the methods disclosed herein. For example, in one protocol, the first round of PCR can amplify the molecule attached to the bead using gene-specific primers and primers targeting the universal Illumina sequencing primer 1 sequence. The second round of PCR can amplify the first PCR product using nested gene-specific primers flanking the Illumina sequencing primer 2 sequence and primers targeting the universal Illumina sequencing primer 1 sequence. The third round of PCR adds P5 and P7, as well as a sample index, to allow the PCR product to enter the Illumina sequencing library. Sequencing using 150 bp x 2 can reveal cellular markers and barcode sequences (e.g., molecular markers) on read 1, genes on read 2, and the sample index on index 1.

[0290] In some embodiments, nucleic acids can be removed from the substrate using chemical cleavage. For example, chemical groups or modified bases present in the nucleic acids can be used to facilitate their removal from the solid support. Enzymes can be used to remove nucleic acids from the substrate. For example, nucleic acids can be removed from the substrate by digestion with restriction endonucleases. For example, nucleic acids containing dUTPs or ddUTPs can be removed from the substrate by treatment with uracil-d-glycosylation enzymes (UDG). For example, nucleic acids can be removed from the substrate using enzymes for nucleotide excision (e.g., base excision repair enzymes (e.g., depurine / depyrimidine (AP) endonucleases)). In some embodiments, photocleavable groups and light can be used to remove nucleic acids from the substrate. In some embodiments, cleavable adapters can be used to remove nucleic acids from the substrate. For example, cleavable adapters can include at least one of the following: biotin / avidin, biotin / streptavidin, biotin / neutral streptavidin, Igprotein A, photoinstability adapters, acid or base instability adapter groups, or aptamers.

[0291] When the probe is gene-specific, these molecules can be hybridized to the probe and reverse transcribed and / or amplified. In some embodiments, the nucleic acid can be amplified after it has been synthesized (e.g., reverse transcribed). Amplification can be performed in multiple ways, where multiple target nucleic acid sequences are amplified simultaneously. Amplification can add sequencing adaptors to the nucleic acid.

[0292] In some embodiments, for example, bridging amplification can be performed on a substrate. The cDNA can be a homopolymer tail, using an oligomeric (dT) probe on the substrate to generate compatible ends for bridging amplification. In bridging amplification, the primer complementary to the 3' end of the template nucleic acid can be the first primer of each pair of primers covalently attached to the solid particle. When the sample containing the template nucleic acid is contacted with the particle and subjected to a single thermal cycle, the template molecule can be annealed to the first primer, and the first primer is extended forward by adding nucleotides to form a double-stranded molecule consisting of the template molecule and a newly formed DNA strand complementary to the template. In the heating step of the next cycle, the double-stranded molecule can denature, releasing the template molecule from the particle and attaching the complementary DNA strand to the particle via the first primer. In the annealing phase of the subsequent annealing and extension steps, the complementary strand can hybridize with a second primer, which is complementary to a fragment of the complementary strand at the site removed from the first primer. This hybridization results in the formation of a bridge between the first and second primers, which are covalently attached to the first primer, and the formation of a second primer through hybridization. In the extension phase, the second primer can be extended in the opposite direction by adding nucleotides to the same reaction mixture, thus converting the bridge into a double-stranded bridge. The next cycle then begins, and the double-stranded bridge can be denatured to produce two single-stranded nucleic acid molecules, each with one end attached to the particle surface via the first and second primers, respectively, while the other end of each single-stranded nucleic acid molecule remains unattached. In the annealing and extension steps of this second cycle, each strand can hybridize with an additional, previously unused complementary primer on the same particle to form a new single-stranded bridge. The two previously unused primers now hybridizing are extended, thus converting the two new bridges into double-stranded bridges.

[0293] The amplification reaction may include amplifying at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of multiple nucleic acids.

[0294] Amplification of labeled nucleic acids can include PCR-based or non-PCR-based methods. Amplification of labeled nucleic acids can include exponential amplification of labeled nucleic acids. Amplification of labeled nucleic acids can include linear amplification of labeled nucleic acids. Amplification can be performed using polymerase chain reaction (PCR). PCR can refer to the reaction used to amplify specific DNA sequences in vitro via simultaneous primer extension of the complementary strand of DNA. PCR can encompass derived forms of this reaction, including but not limited to RT-PCR, real-time PCR, nested PCR, quantitative PCR, multiplex PCR, digital PCR, suppression PCR, semi-suppression PCR, and assembly PCR.

[0295] In some embodiments, the amplification of the labeled nucleic acid includes non-PCR-based methods. Examples of non-PCR-based methods include, but are not limited to, multiple displacement amplification (MDA), transcription-mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), strand displacement amplification (SDA), real-time SDA, rolling circle amplification, or circle-to-circle amplification. Other non-PCR-based amplification methods include DNA-dependent RNA polymerase-driven RNA transcriptional amplification or multiple cycles of RNA-directed DNA synthesis and transcription to amplify DNA or RNA targets, ligase chain reaction (LCR), Qβ replicase (Qβ), the use of palindromic probes, strand displacement amplification, oligonucleotide-driven amplification using restriction endonucleases, amplification methods that hybridize primers to nucleic acid sequences and cleave the resulting duplexes before extension and amplification, strand displacement amplification using nucleic acid polymerases lacking 5' exonuclease activity, rolling circle amplification, and branch extension amplification (RAM).

[0296] In some embodiments, the methods disclosed herein further include a nested polymerase chain reaction (PCR) of the amplified amplicon (e.g., a target). The amplicon may be a double-stranded molecule. A double-stranded molecule may include a double-stranded RNA molecule, a double-stranded DNA molecule, or an RNA molecule hybridized to a DNA molecule. One or both strands of the double-stranded molecule may include a sample tag or molecular identification marker. Alternatively, the amplicon may be a single-stranded molecule. A single-stranded molecule may include DNA, RNA, or a combination thereof. The nucleic acids of the present invention may include synthetic or modified nucleic acids.

[0297] In some embodiments, the method includes repeatedly amplifying a labeled nucleic acid to generate multiple amplicones. The methods disclosed herein may include performing at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 amplification reactions. Alternatively, the method includes performing at least about 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 amplification reactions.

[0298] The amplification may further include adding one or more control nucleic acids to one or more samples comprising multiple nucleic acids. The control nucleic acid may include a control marker.

[0299] Amplification may involve the use of one or more non-natural nucleotides. Non-natural nucleotides may include light-labile and / or triggerable nucleotides. Examples of non-natural nucleotides include, but are not limited to, peptide nucleic acids (PNAs), morpholino and locked nucleic acids (LNAs), and gamma-hydroxyl nucleic acids (GNAs) and threonine nucleic acids (TNAs). Non-natural nucleotides may be added to one or more cycles of the amplification reaction. The addition of non-natural nucleotides may also be used to identify the products at specific cycles or time points in the amplification reaction.

[0300] Performing one or more amplification reactions may involve using one or more primers. One or more primers may include one or more oligonucleotides. One or more oligonucleotides may include at least about 7 to 9 nucleotides. One or more oligonucleotides may include fewer than 12-15 nucleotides. One or more primers may anneal to at least a portion of a plurality of labeled nucleic acids. One or more primers may anneal to the 3' and / or 5' ends of a plurality of labeled nucleic acids. One or more primers may anneal to the internal regions of a plurality of labeled nucleic acids. The internal region can be at least about 50, 100, 150, 200, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 650, 700, 750, 800, 850, 900, or 1000 nucleotides from the 3' end of the plurality of labeled nucleic acids. One or more primers may include a fixed set of primers. One or more primers may include at least one or more custom primers. One or more primers may include at least one or more control primers. One or more primers may include at least one or more housekeeping gene primers. One or more primers may include universal primers. Universal primers may be annealed to universal primer binding sites. One or more custom primers may be annealed to a first sample tag, a second sample tag, a molecular identification marker, a nucleic acid, or a product thereof. One or more primers may include both universal and custom primers. Custom primers may be designed to amplify one or more target nucleic acids. Target nucleic acids may include a subset of total nucleic acids in one or more samples. In some embodiments, these primers are probes attached to an array disclosed herein.

[0301] In some embodiments, barcoding (e.g., randomly barcoding) multiple targets in a sample further includes an index library that generates barcoded fragments. The barcode sequences of different barcodes (e.g., molecular markers of different random barcodes) may be different from each other. Generating an index library of barcoded targets (e.g., randomly barcoded targets) includes generating multiple index polynucleotides from multiple targets in the sample. For example, for an index library of barcoded targets including a first index target and a second index target, the labeled region of the first index polynucleotide and the labeled region of the second index polynucleotide may have a difference of about, at least, or at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50 nucleotides, or a number or range of nucleotide differences between any two of these values. In some embodiments, generating an index library of barcoded targets includes contacting a plurality of targets (e.g., mRNA molecules) with a plurality of oligonucleotides including a poly(T) region and a labeled region; and performing first-strand synthesis using reverse transcriptase to generate single-stranded labeled cDNA molecules (each including a cDNA region and a labeled region), wherein the plurality of targets comprise at least two mRNA molecules of different sequences, and the plurality of oligonucleotides comprise at least two oligonucleotides of different sequences. Generating an index library of barcoded targets may further include amplifying the single-stranded labeled cDNA molecules to generate double-stranded labeled cDNA molecules; and performing nested PCR on the double-stranded labeled cDNA molecules to generate labeled amplicons. In some embodiments, the method may include generating amplicons labeled with adaptor tags.

[0302] Random barcoding can use nucleic acid barcodes or tags to label individual nucleic acid (e.g., DNA or RNA) molecules. In some embodiments, this involves adding DNA barcodes or tags to cDNA molecules, since they are generated from mRNA. Nested PCR can be performed to minimize PCR amplification bias. Adaptors can be added for sequencing, for example, using next-generation sequencing (NGS). Figure 2 At box 232, sequencing results can be used to determine the sequence of one or more copies of the target, including cellular markers, barcode sequences (e.g., molecular markers), and nucleotide fragments.

[0303] Figure 3This is a schematic diagram illustrating a non-limiting exemplary process for generating an index library of barcoded targets (e.g., randomly barcoded targets), such as mRNA. As shown in step 1, the reverse transcription process can encode each mRNA molecule having a unique barcode sequence (e.g., a molecular marker), a cellular marker, and a universal PCR site. For example, RNA molecule 302 can be reverse transcribed to produce a labeled cDNA molecule 304 (including a cDNA region 306) by hybridizing (e.g., random barcodes) 310 to the poly(A) tail region 308 of RNA molecule 302. Each barcode 310 may include a target binding region, such as a poly(dT) region 312, a barcode sequence or molecular marker 314, and a universal PCR region 316.

[0304] In some embodiments, the cell marker may comprise 3 to 20 nucleotides. In some embodiments, the barcode sequence (e.g., a molecular marker) may comprise 3 to 20 nucleotides. In some embodiments, each of the plurality of random barcodes further comprises one or more of a universal marker and a cell marker, wherein the universal marker is identical for the plurality of random barcodes on the solid support and the cell marker is identical for the plurality of random barcodes on the solid support. In some embodiments, the universal marker may comprise 3 to 20 nucleotides. In some embodiments, the cell marker comprises 3 to 20 nucleotides.

[0305] In some embodiments, the marker region 314 may include a barcode sequence or molecular marker 318 and a cell marker 320. In some embodiments, the marker region 314 may include one or more of a general marker, a dimensional marker, and a cell marker. The length of the barcode sequence or molecular marker 318 may be, may be about, may be at least, or may be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or a number or range of nucleotides between any of these values. The length of the cell marker 320 may be, may be about, may be at least, or may be at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or a number or range of nucleotides between any of these values. The length of the universal marker can be approximately, at least, or at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or a number or range of nucleotides between any of these values. For multiple random barcodes on a solid support, the universal marker can be the same, and for multiple random barcodes on a solid support, the cell marker is the same. The length of the dimensional marker can be approximately, at least, or at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or a number or range of nucleotides between any of these values.

[0306] In some embodiments, the marker region 314 may contain, contain about, contain at least, or contain up to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 different markers, or numbers or ranges between any of these values, such as barcode sequences or molecular markers 318 and cell markers 320. The length of each marker may be, may be about, may be at least, or may be up to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 nucleotides, or numbers or ranges between any of these values. A set of barcodes or random barcodes 310 may contain, contain about, contain at least, or may be at most 10, 20, 40, 50, 70, 80, 90, 10 2 10 3 10 4 10 5 10 6 10 710 8 10 9 10 10 10 11 10 12 10 13 10 14 10 15 10 20 A single barcode or random barcode 310, or a barcode or random barcode 310 of any number or range between these values. And the group of barcodes or random barcodes 310 may, for example, each contain a unique labeled region 314. The labeled cDNA molecule 304 can be purified to remove excess barcodes or random barcodes 310. Purification may include Ampure bead purification.

[0307] As shown in step 2, the product from the reverse transcription process can be pooled into a tube in step 1 and PCR amplified using a first PCR primer pool and a first universal PCR primer. Pooling is possible because of the unique labeled region 314. Specifically, labeled cDNA molecules 304 can be amplified to generate nested PCR-labeled amplicons 322. Amplification may include multiplex PCR amplification. Amplification may include multiplex PCR amplification using 96 multiplex primers in a single reaction volume. In some embodiments, multiplex PCR amplification in a single reaction volume may utilize, utilize about, utilize at least, or utilize up to 10, 20, 40, 50, 70, 80, 90, 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 10 10 11 10 12 10 13 10 14 10 15 10 20 Multiple primers, or multiple primers of any number or range between these values. Amplification may include a first PCR primer pool 324 of custom primers 326A-C targeting a specific gene and universal primers 328. Custom primers 326 can hybridize with a region within the cDNA portion 306' of the labeled cDNA molecule 304. Universal primers 328 can hybridize with the universal PCR region 316 of the labeled cDNA molecule 304.

[0308] like Figure 3As shown in step 3, the product from the PCR amplification in step 2 can be amplified using a nested PCR primer pool and a second universal PCR primer. Nested PCR can minimize PCR amplification bias. For example, the amplicon 322 labeled with nested PCR can be further amplified by nested PCR. Nested PCR can include multiplex PCR in a single reaction volume using a nested PCR primer pool 330 of nested PCR primers 332a-c and a second universal PCR primer 328'. The nested PCR primer pool 328 may contain, contain about, contain at least, or contain up to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 different nested PCR primers 330, or a number or range of different nested PCR primers 330 between any of these values. Nested PCR primer 332 may contain adaptor 334 and hybridizes with a region within the cDNA portion 306'' of the labeled amplicon 322. Universal primer 328' may contain adaptor 336 and hybridizes with the universal PCR region 316 of the labeled amplicon 322. Thus, step 3 produces an adaptor-labeled amplicon 338. In some embodiments, nested PCR primer 332 and the second universal PCR primer 328' may not contain adaptors 334 and 336. Instead, adaptors 334 and 336 may be ligated to the nested PCR product to produce an adaptor-labeled amplicon 338.

[0309] As shown in step 4, the PCR product from step 3 can be amplified by PCR using library amplification primers for sequencing. Specifically, adaptors 334 and 336 can be used to perform one or more additional assays on the adaptor-tagged amplicon 338. Adaptors 334 and 336 can hybridize with primers 340 and 342. One or more primers 340 and 342 can be PCR amplification primers. One or more primers 340 and 342 can be sequencing primers. One or more adaptors 334 and 336 can be used for further amplification of the adaptor-tagged amplicon 338. One or more adaptors 334 and 336 can be used to sequence the adaptor-tagged amplicon 338. Primer 342 may contain a plate index 344, allowing amplicon generated using the same set of barcodes or random barcodes 310 to be sequenced in a single sequencing reaction using next-generation sequencing (NGS).

[0310] Errors in cell marker identification Barcoding, such as random barcoding, for example, Rhapsody TMAssays (Cellular Research, Inc. (Palo Alto, CA)) can be bead-based. Molecules or targets, such as mRNA from different cells, can hybridize with barcodes (e.g., random barcodes) on different beads. Barcodes on different beads may have different cell markers, while barcodes on the same bead may have cell markers. For example, single cells and single beads can be added to the wells of a microplate before pairing a bead with a cell. In this way, the cell markers are the same for all oligonucleotides on the beads but different between different beads, making it possible to identify all molecules from a single cell using the same cell markers in the sequencing data. In some embodiments, the raw sequencing data from barcoding (e.g., random barcoding) may contain a higher number of cell markers than the number of cells in the experimental input. For example, some molecules from 1000 cells may be barcoded (e.g., random barcoded); however, the raw sequencing data may indicate 20,000–200,000 cell markers.

[0311] In different implementations, the sources of the higher number of molecular markers can be different. Without being bound by any particular theory, it is thought that in some embodiments, cells not paired with beads can be lysed, and their nucleic acid contents can diffuse and associate with beads not paired with any cells, thereby generating a false cell marker signal. In some embodiments, mutations may occur in the cell markers during bead fabrication, transforming one cell marker into another. In this case, molecules from the same cell may appear to originate from two different cells (e.g., they appear to come from two different beads because the cell markers have mutated). Furthermore, substitution and non-substitution errors may occur in the cell markers during PCR amplification prior to sequencing. In some embodiments, exonuclease treatment (e.g., Figure 2 Step 216 in the process may not be effective in allowing the single-stranded DNA on the beads to hybridize and form PCR chimeras during the PCR process.

[0312] If not corrected, excessive cell markers in the raw sequencing data can lead to an overestimation of cell counts. The method disclosed in this paper can separate or distinguish signal cell markers (also known as true cell markers) from noise cell markers.

[0313] Cell markers are identified as signal cell markers or noise cell markers based on the second derivative. This article discloses a method for identifying signal cell markers. In some embodiments, the method includes: (a) randomly barcoding a plurality of target bars in a cell sample using a plurality of random barcodes to create a plurality of randomly barcoded targets, wherein each of the plurality of random barcodes contains a cellular marker and a molecular marker; (b) obtaining sequencing data of the plurality of randomly barcoded targets; (c) determining the number of molecular markers with different sequences associated with each of the cellular markers of the plurality of random barcodes; (d) determining a rank of each of the cellular markers of the plurality of random barcodes based on the number of molecular markers with different sequences associated with each of the cellular markers; (e) generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cellular markers determined in (c) and the rank of each of the cellular markers determined in (d); (f) generating a second derivative graph of the cumulative sum graph; (g) determining a minimum value of the second derivative graph of the cumulative sum graph, wherein the minimum value of the second derivative graph corresponds to a cellular marker threshold; and (h) based on (c) The number of molecular markers with different sequences associated with each of the cell markers determined in (g) and the cell marker threshold determined in (g) identify each of the cell markers as a signal cell marker (associated with the cell) or a noise cell marker (not associated with the cell).

[0314] In different implementations, the causes of noisy cell markers can vary. In some embodiments, noisy cell markers may originate from one or more PCR or sequencing errors. In some embodiments, noisy cell markers may originate from RNA molecules released from dead cells. In some embodiments, noisy cell markers may originate from RNA molecules released when a cell not associated with a bead attaches to a bead not associated with a cell.

[0315] In some embodiments, the method includes: (a) obtaining sequencing data of a plurality of barcoded targets (e.g., targets randomly barcoded), wherein the plurality of barcoded targets are created by barcoding (e.g., randomly barcoding) a plurality of targets in a cell sample using a plurality of barcodes (e.g., random barcodes), and wherein each of the plurality of barcodes includes a cellular marker and a molecular marker; (b) determining a rank of each of the cellular markers of the plurality of barcoded targets (or barcodes) based on the number of molecular markers with different sequences associated with each of the cellular markers of the plurality of barcoded targets (or barcodes); (c) determining a cell marker threshold based on the number of molecular markers with different sequences associated with each of the cellular markers and the rank of each of the cellular markers of the plurality of barcoded targets (or barcodes) determined in (b); and identifying each of the cellular markers as a signal cellular marker or a noise cellular marker based on the number of molecular markers with different sequences associated with each of the cellular markers and the cell marker threshold determined in (c).

[0316] Figure 4 This is a flowchart illustrating a non-limiting exemplary method 400 for identifying cells as signal cell markers or noise cell markers. At block 404, method 400 may optionally use barcodes (e.g., random barcodes) to barcode (e.g., randomly barcode) a target in the cell to create a barcoded target (e.g., a randomly barcoded target), as referenced. Figure 2-3 Described. Each barcode may contain cellular markers and molecular markers. Barcoded targets created from targets of different cells within the multiple cell range may have different cellular markers. Barcoded targets created from targets of the same cells within the multiple cell range may have different molecular markers.

[0317] At box 408, method 400 may obtain sequencing data of a barcoded target (e.g., a randomly barcoded target), as described herein in the section entitled Sequencing. At box 412, method 400 may optionally determine the number of molecular markers with different sequences associated with each of the barcoded (or barcoded target) cellular markers. Determining the number of molecular markers with different sequences associated with each of the barcoded (or barcoded target) cellular markers may include: (1) counting the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) estimating the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1). In some embodiments, the sequencing data obtained at box 408 contains the number of molecular markers with different sequences associated with each of the barcoded (or barcoded target) cellular markers.

[0318] In some embodiments, the method may include removing sequencing information associated with molecular markers having different sequences associated with a target among the plurality of targets from the sequencing data obtained in block 408 if the number of such molecular markers is higher or lower than a molecular marker occurrence threshold. In different implementations, the molecular marker occurrence threshold may be different. In some embodiments, the molecular marker occurrence threshold may be, or approximately 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, or a number or range between any two of these values. In some embodiments, the molecular marker occurrence threshold may be at least, or at most, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, or 100000. In some embodiments, the molecular marker occurrence threshold may be, or about 1%, 2%, 3%, 4%, 5%, 6%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or a number or range between any two of these values. In some embodiments, the molecular marker occurrence threshold may be at least 10%, 20%, 30%, 40%, 50%, 60%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0319] At box 416, method 400 may determine the rank of each of the cell markers in the barcode (or barcoded target). The rank of each of the cell markers in the barcode (or barcoded target) is determined based on the number of molecular markers with different sequences associated with each of the cell markers in the plurality of barcodes (or barcoded targets).

[0320] At box 420, method 400 may determine a cell marker threshold associated with each of the multiple barcodes (or barcoded targets) and a level of each of the multiple barcodes (or barcoded targets) determined at box 416. In some embodiments, determining the cell marker threshold based on the number of molecular markers with different sequences associated with each of the multiple barcodes (or barcoded targets) includes: determining the cumulative sum of cell markers at level n and the next level. n+1The cumulative sum of cell markers is the cell marker with the greatest variation, where the number of molecular markers with different sequences associated with that cell marker corresponds to the cell marker threshold.

[0321] In some embodiments, determining a cell marker threshold based on the number of molecular markers with different sequences associated with each of the cell markers of the plurality of barcodes (or barcode-coded targets) and the level of each of the cell markers of the plurality of barcodes (or barcode-coded targets) determined at box 416 includes: determining a cumulative sum for each level of cell markers, wherein the cumulative sum for a level includes the sum of the number of molecular markers with different sequences associated with each of the cell markers of a lower level; and determining the cumulative sum at level n and the next level. n+1 The rank n of the cell markers with the largest changes in the cumulative sum, where the changes occur in the cumulative sum and the next rank. n+1 The level n of the cell marker with the largest change in the cumulative sum corresponds to the cell marker threshold.

[0322] In some embodiments, determining the cell marker threshold may include: generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cell markers and the rank of each of the cell markers determined in 416; determining the cell marker threshold may further include: generating a second derivative graph of the cumulative sum graph and determining a minimum value of the second derivative graph of the cumulative sum graph. The minimum value of the second derivative graph may correspond to the cell marker threshold.

[0323] In some embodiments, generating a cumulative sum graph based on the number of molecular markers with different sequences associated with each of the cell markers and the rank of each of the cell markers determined at box 416 may include: determining a cumulative sum for each rank of the cell markers, wherein the cumulative sum for each rank includes the sum of the number of molecular markers with different sequences associated with each of the lower-rank cell markers. Generating a second derivative graph of the cumulative sum graph may include determining the difference between the cumulative sum of the first rank and the cumulative sum of the second rank relative to a first rank and a second rank of the cell markers. In some embodiments, the difference between the first rank and the second rank is 1. The cumulative sum graph may be a log-log graph. A log-log graph may be a log10-log10 graph.

[0324] In some embodiments, the minimum value is a global minimum value. Determining the minimum value of the second derivative plot may include determining that the minimum value of the second derivative plot is higher than a threshold for the minimum number of molecular markers associated with each of the cell markers. The threshold for the minimum number of molecular markers associated with each of the cell markers may be a percentile threshold. The threshold for the minimum number of molecular markers associated with each of the cell markers may be determined based on the number of cells in the cell sample. For example, the larger the number of cells in the cell sample, the larger the threshold for the minimum number of molecular markers associated with each of the cell markers.

[0325] In different implementations, the threshold for the minimum number of molecular markers associated with each of the cell markers may differ. In some embodiments, the threshold for the minimum number of molecular markers associated with each of the cell markers may be, or approximately 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, or a number or range between any two of these values. In some embodiments, the threshold for the minimum number of molecular markers associated with each of the cell markers may be at least, or at most, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, or 100000. In some embodiments, the threshold for the minimum number of molecular markers associated with each of the cell markers may be, or about 1%, 10%, 20%, 30%, 40%, 45%, 50%, 60%, 80%, 90%, or a number or range between any two of these values. In some embodiments, the threshold for the presence of molecular markers may be at least, or at most, 1%, 10%, 20%, 30%, 40%, 45%, 50%, 60%, 80%, or 90%.

[0326] In some embodiments, determining the minimum of the second derivative plot includes determining the minimum of the second derivative plot below a threshold for the maximum number of molecular markers associated with each of the cell markers. The threshold for the maximum number of molecular markers associated with each of the cell markers may be a percentile threshold. The threshold for the maximum number of molecular markers associated with each of the cell markers may be determined based on the number of cells in the cell sample. For example, a larger number of cells in the cell sample corresponds to a larger threshold for the maximum number of molecular markers associated with each of the cell markers.

[0327] In different implementations, the threshold for the maximum number of molecular markers associated with each of the cell markers may differ. In some embodiments, the threshold for the maximum number of molecular markers associated with each of the cell markers may be, or approximately 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, or a number or range between any two of these values. In some embodiments, the threshold for the maximum number of molecular markers associated with each of the cell markers may be at least, or at most, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, or 100000. In some embodiments, the threshold for the maximum number of molecular markers associated with each of the cell markers may be, or about 10%, 20%, 30%, 40%, 45%, 50%, 60%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or a number or range between any two of these values. In some embodiments, the molecular marker occurrence threshold may be at least 10%, 20%, 30%, 40%, 45%, 50%, 60%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0328] At box 432, based on the number of molecular markers with different sequences associated with a cell marker and a cell marker threshold, method 400 can identify a cell marker as a signal cell marker or a noise cell marker. If the number of molecular markers with different sequences associated with each of the cell markers determined in (c) is greater than the cell marker threshold, then each of the cell markers is identified as a signal cell marker. If the number of molecular markers with different sequences associated with each of the cell markers determined in (c) is not greater than the cell marker threshold, then each of the cell markers can be identified as a noise cell marker. In some embodiments, the method includes removing sequencing information associated with the identified cell marker from the sequencing data obtained at box 408 if a plurality of barcode (or barcoded target) cell markers are identified as noise cell markers in 432.

[0329] Cell markers were identified as signal cell markers or noise cell markers based on clustering. This document discloses a method for identifying signaling cell markers. In some embodiments, the method includes: (a) barcoding (e.g., random barcoding) a plurality of targets in a cell sample using a plurality of barcodes (e.g., random barcodes) to create a plurality of barcoded targets (e.g., randomly barcoded targets), wherein each of the plurality of barcodes contains a cell marker and a molecular marker, wherein the barcoded targets created from targets of different cells in the plurality of cells have different cell markers, and wherein the barcoded targets created from targets of the same cells in the plurality of cells have different molecular markers; (b) obtaining sequencing data of the plurality of barcoded targets; (c) determining a feature vector for each cell marker of the plurality of barcodes (or barcoded targets), wherein the feature vector contains the number of molecular markers with different sequences associated with each cell marker; (d) determining a cluster of each cell marker of the plurality of barcodes (or barcoded targets) based on the feature vector; and (e) Based on the number of cell markers in the cluster and the cluster size threshold, each cell marker of the multiple barcodes (or barcoded targets) is identified as a signal cell marker or a noise cell marker.

[0330] This article discloses a method for identifying signal cell markers. In some embodiments, the method includes: (a) obtaining sequencing data of a plurality of barcoded targets (e.g., randomly barcoded targets), wherein the plurality of barcoded targets (e.g., randomly barcoded targets) are created from a plurality of targets in a cell sample, the plurality of targets being barcoded (e.g., randomly barcoded) using a plurality of barcodes, wherein each of the plurality of barcodes contains a cell marker and a molecular marker, wherein the barcoded targets created from targets of different cells in the plurality of cells have different cell markers, and wherein the barcoded targets created from targets of the same cells in the plurality of cells have different molecular markers; (b) determining a feature vector for each cell marker of the plurality of barcoded targets, wherein the feature vector contains the number of molecular markers with different sequences associated with each cell marker; (c) determining a cluster of each cell marker of the plurality of barcoded targets based on the feature vector; and (d) identifying each cell marker of the plurality of barcoded targets as a signal cell marker or a noise cell marker based on the number of cell markers in the cluster and a cluster size threshold.

[0331] Figure 5 This is a flowchart illustrating another non-limiting exemplary method for identifying cells as signal cell markers or noise cell markers. At block 504, method 500 may optionally use random barcoding to barcode (e.g., random barcoding) a target in the cell to create a barcoded target (e.g., a randomly barcoded target), as referenced. Figure 2-3Described. Each barcode contains cellular and molecular markers. Barcoded targets created from targets of different cells within the multiple cell range may have different cellular markers. Barcoded targets created from targets of the same cells within the multiple cell range may have different molecular markers.

[0332] At box 508, method 500 may obtain sequencing data of a barcoded target. At box 508, method 500 may optionally determine the number of molecular markers with different sequences associated with each of the barcoded (or barcoded target) cellular markers. Determining the number of molecular markers with different sequences associated with each of the barcoded (or barcoded target) cellular markers may include: (1) counting the number of molecular markers with different sequences associated with the target in the sequencing data; and (2) estimating the number of targets based on the number of molecular markers with different sequences associated with the target in the sequencing data counted in (1). In some embodiments, the sequencing data obtained at box 508 contains the number of molecular markers with different sequences associated with each of the barcoded (or barcoded target) cellular markers.

[0333] At box 512, method 500 may determine a feature vector of the cell marker. The feature vector may contain the number of molecular markers with different sequences associated with the cell marker. For example, each element of the feature vector may contain the number of molecular markers associated with the cell marker. As another example, one element of the feature vector may contain the number of molecular markers associated with the cell marker, while another element of the feature vector may contain the number of another molecular marker associated with the cell marker.

[0334] At box 516, method 500 may determine clusters of cell markers based on feature vectors. In some embodiments, determining the clustering of each cell marker of a barcode or barcoded target based on feature vectors includes clustering each cell marker of the barcode or barcoded target into clusters based on the distance between the feature vector and the cluster in the feature vector space. Determining the clustering of each cell marker of multiple barcoded targets based on feature vectors includes: projecting the feature vector from the feature vector space to a lower-dimensional space; and clustering each cell marker into clusters based on the distance between the feature vector and the cluster in the lower-dimensional space. The lower-dimensional space may be a two-dimensional space.

[0335] In some embodiments, projecting feature vectors from the feature vector space to a lower-dimensional space includes using a t-distributed random neighborhood embedding (tSNE) method. Clustering each cell label into clusters based on the distance between the feature vectors and the clusters in the lower-dimensional space can include using a density-based method. Density-based methods can include density-based spatial clustering (DBSCAN) methods with noisy applications.

[0336] At box 520, method 500 may identify cell markers as signal cell markers or noise cell markers based on the number of cells in a cluster and a cluster size threshold. In some embodiments, if the number of cell markers in a cluster is less than the cluster size threshold, the cell marker may be identified as a signal cell marker. If the number of cell markers in a cluster is not less than the cluster size threshold, the cell marker may be identified as a noise cell marker.

[0337] In some embodiments, the method includes determining a cluster size threshold based on the number of cell markers of a plurality of barcodes (or barcoded targets). The cluster size threshold may be a percentage of the number of cell markers of the plurality of barcoded targets. In some embodiments, the method includes determining a cluster size threshold based on the number of molecular markers with different sequences associated with each cell marker of the plurality of barcodes (or barcoded targets).

[0338] Distinguishing between cell markers associated with true cells and cell markers associated with noise cells. This document discloses embodiments of a method for reliably distinguishing between markers (e.g., cell markers) associated with true cells and noise cells. Cell markers associated with true cells are referred to herein as signal cell markers. Noise cells are referred to herein as noise cell markers. Corresponding to different cell types / clusters in some embodiments, the method can detect or identify most true cells (or signal cell markers). The method can automatically remove noise cells that are low in expression in certain cell types (such as monocytes and plasma).

[0339] Figure 6A This is a flowchart illustrating a non-limiting exemplary method 600a for distinguishing markers associated with true cells from noisy cells. Method 600a may be based on one or more cell marker identification or classification methods (e.g., referring to…). Figure 4(or method 400 or 500 as described in 5). In some embodiments, method 600a can improve upon these cell marker identification methods. This method can be used to... TM Cell marker classification in automated production lines.

[0340] Method 600a includes multiple steps or actions. At box 604, method 600a includes implementing (or running) a cell marker identification method (e.g., referring to…) Figure 4 Or the method described in 5 (400 or 500) to identify multiple true cells (or signal cell markers, in) Figures 6A-6B The cells in the curve are referred to as filtered cells (A). For example, cell marker identification methods can be based on a cumulative reading curve transformed by log10. Cell marker identification methods can be used to determine the inflection point where the curve begins to plateau. For example, the major inflection point could be the separation between true cells and noise cells.

[0341] Method 600a may include removing noisy cells by, for example, defining highly variable (e.g., most variable) genes across most cells (e.g., all cells) and implementing a cell marker identification method. For example, method 600a may include re-running the cell marker identification method run at box 604 on the most variable gene across all cells. Method 600a may include identifying highly variable genes across most cells (e.g., all cells) at box 608. At box 612, the cell marker identification method may be implemented on the most variable gene identified at box 608 to identify one or more true cell (or signal cell) markers, wherein... Figures 6A-6B The cell is referred to as a noise cell (B). To identify highly variable genes, method 600a may optionally include: logarithmically transforming the reading counts of each gene in each cell (e.g., the number of molecular markers with different sequences associated with each gene for each cell marker) to determine gene expression. For example, the reading counts may be logarithmically transformed using the following equation [1]. At box 608, method 600a may include: determining one or more measures or indicators of expression for each gene, such as mean expression (or maximum, median, or minimum expression) and deviation (e.g., variance / mean). Method 600a may include: assigning each gene (or the expression profile of each gene) to one of a plurality of bins. For example, genes may be assigned to 20 bins based on mean (or maximum, median, or minimum) expression for each gene. The number of bins may vary in different implementations. In some embodiments, the number of bins may be, or approximately 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or a number or range between any two of these values. In some embodiments, the number of warehouses may be at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, or 1000.

[0342] At box 608, method 600a may include: within each bin, determining one or more measures or indicators of deviation for all genes. For example, the mean and standard deviation (STD) of deviation for all genes may be determined. Method 600a may include using, for example, equation [2] to determine the normalized deviation measure for each gene. At box 608, method 600a may include: applying one or more different cutoff values ​​to normalized deviations to identify genes whose expression values ​​are highly variable (e.g., have variability above a threshold) (even when compared to genes with similar average expression). The number of cutoff values ​​may vary in different implementations. In some embodiments, the number of truncation values ​​may be, or approximately 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or a number or range between any two of these values. In some embodiments, the number of cutoff values ​​may be at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, or 1000.

[0343] In some embodiments, method 600a may identify a cell as a noise cell (or cell marker or noise cell marker) if the cell is identified as a noise cell in the number of threshold values ​​or the percentage of threshold values ​​for all threshold values ​​(e.g., minority, majority, or all threshold values). In some embodiments, the number of threshold values ​​for the cutoff values ​​may be, or approximately 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or a number or range between any two of these values. In some embodiments, the number of threshold values ​​for the cutoff values ​​may be at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700, 800, 900, or 1000. In some embodiments, the threshold percentage for all cutoff values ​​may be, or approximately 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 5 2%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.9%, 100%, or a number or range between any two of these values.In some embodiments, the threshold percentage for all cutoff values ​​may be at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%. %, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.9%, or 100%. In some embodiments, this type of noise cell identification can improve the accuracy of the identified noise cells (e.g., reduce the likelihood of identifying true cells as noise cells). Figure 7 This is a non-limiting example diagram showing the identification of the most variable gene.

[0344] Reference Figure 6A At box 616, method 600a may include: identifying or characterizing true cells (or signal cell markers) that may have been incorrectly identified (e.g., unidentified) at box 604, for example by determining whether any genes are missing. If so, method 600a may include, at box 620, running or re-running a cell marker identification method (e.g., the cell marker identification method used at box 604 or 612) to identify one or more missing true cells (or missing signal cell markers) that were not identified at box 604. The missing true cells identified at 620 are... Figure 6A The missing cells (D) are referred to in the diagram. Identifying missing genes may include: for each gene, determining the total count of readings from all cells and from the cleanup cells identified at box 625. Cleanup cells may be determined using equation [3a] or equation [3b], where C indicates cleanup cells, A indicates filtered or true cells identified at box 604, and B indicates lost cells identified at box 612. Identifying missing genes may include identifying genes that have a large loss (e.g., maximum loss) in terms of the number of cells cleared compared to the total count from all cells. For example, genes with the maximum loss can be determined by plotting the total count and finding the best-fit line that identifies genes with large residuals (e.g., maximum residuals), for example, by at least one threshold number of standard deviations from the median of the residuals from all genes (see [link to relevant documentation]). Figures 8A-8B In some embodiments, the median can be used instead of the mean to minimize the impact of outliers. The number of standard deviation thresholds can vary in different implementations. In some embodiments, the number of standard deviation thresholds can be 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, or numbers or ranges between any two of these values. In some embodiments, the number of standard deviation thresholds may be at least 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, or 10.

[0345] At box 624, method 600a may include combining the cells (or cell markers) identified at boxes 620 and 624 to determine true cells (in Figures 6A-6B The final collection of cells (referred to as filters in the text).

[0346] Figure 6B This is a flowchart illustrating another non-limiting exemplary method 600b for distinguishing markers associated with true cells from noise cells. Figure 6B The actions taken by the Chinese side at locations 604-628 can be compared with those at reference. Figure 6AThe behavior implemented in the corresponding box of method 600a described is similar. Method 600b may include running the algorithm at the inflection point where the cumulative reading curve based on the log10 transformation begins to plateau at box 604. The main inflection point is the separation between cells and noise. Method 600b may include one or more of the following steps. Starting with all cells, the most variable genes are obtained by truncating the z-score using the gene deviation metric. Focusing only on the most variable genes, the current algorithm is run to infer true cells, and this set is denoted as B. Cells that are detected by other cell marker identification methods using all genes in the panel but not by the algorithm using only the most variable genes (i.e., setdiff(A, B)) are identified as noise cells. In some embodiments, more conservatively, multiple deviation z-truncation values ​​are tried, and a cell is identified as noise only if it is classified as noise for some, most, or all of the truncation values. Noise cells are removed from set A using the above equation [3a] or [3b] to obtain an updated set of cells.

[0347] Method 600b may include removing noisy cells by defining the most variable or highly variable gene across all cells at box 608 and rerunning the algorithm at box 612 (e.g., running at box 604). For example, the method may include one or more of the following steps: Obtain true cells. For each gene, calculate the total read count for all cells and the total read count from cells in set C. Identify the gene that is mostly missing in set C. Focus on the missing gene and run the method run at box 604 to retrieve any true cells that may have been missing, designating the cells identified in this step as D.

[0348] Method 600b may include recovering true cells that may have been misdetected or incorrectly identified at box 604 by checking at box 616 for the presence of any missing genes. If so, method 600b may include limiting to the missing gene (also known as the gene with insufficient representation) and rerunning the algorithm (e.g., running at box 604) to pick up the lost true cells at box 620. The final list of cells F can be determined using equation [4]. In some embodiments, at block 632, cells from block 628 can be cleaned or improved by removing cells that do not carry a sufficiently high number of molecules. For example, a minimum threshold for the molecule count can be determined by the following rule: Step (a) Identify the largest gaps in the total molecule counts of the cells in the bottom quarter (e.g., the largest gap, the second largest gap, the third largest gap, etc.) and determine a cutoff value as the value of that gap. Step (b) Identify cells with molecule counts less than the cutoff value determined in step (a) and optionally, calculate the percentage of cells removed due to the low molecule count. Step (c) Instead of using the adaptive cutoff value determined above, use a fixed cutoff value of, for example, 20 molecules, under one or both of the following conditions: condition (i) the percentage of cells removed due to the low molecule count is greater than or at least a threshold percentage (e.g., 20%) and / or the gap is less than a threshold number (e.g., 500); and condition (ii) the largest gap in the total molecule counts of all cells is, for example, 1. The cleaned cells are part of the final set of filtered cells detected by method 600b.

[0349] In different implementations, the fixed cutoff value in step (c) can be different. In some embodiments, the cutoff value can be, or about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. In some embodiments, the cutoff value can be at least, or at most, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, or 100. In different implementations, the threshold percentage in condition (i) can be different. In some embodiments, the threshold percentage can be, or approximately 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, or a number or range between any two of these values. In some embodiments, the threshold percentage can be at least 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, or 50%. In different implementations, the number of threshold values ​​for the gap in condition (i) can be different. In some embodiments, the threshold number of the gap can be, or approximately 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or a number or range between any two of these values.In some embodiments, the threshold number of the gap can be at least, or at most, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or a number or range between any two of these values. In different implementations, the maximum gap in condition (ii) can be different. In some embodiments, the maximum gap can be, or approximately 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any two of these values. In some embodiments, the maximum gap may be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100.

[0350] This document discloses embodiments for identifying signaling cell markers. In some embodiments, the method includes: (a) obtaining sequencing data of a plurality of first targets of a cell, wherein each first target is associated with a number of molecular markers with different sequences associated with each of the plurality of cell markers; (b) identifying each of the cell markers as a signaling cell marker or a noise cell marker, for example at block 604 of method 600a or 600b, using method 400 or 500, based on the number of molecular markers with different sequences associated with each of the cell markers and an identification threshold; and (c) re-identifying at least one of the plurality of cell markers identified as a noise cell marker in (b) as a signaling cell marker, for example at blocks 608, 612 of method 600a or 600b, using method 400 or 500, or re-identifying at least one of the plurality of cell markers identified as a signaling cell marker in (b) as a noise cell marker, for example at blocks 616, 620 of method 600a or 600b, using method 400 or 500. Identifying each of the cell markers, re-identifying at least one of the plurality of cell markers as a signal cell marker, or re-identifying at least one of the plurality of cell markers as a noise cell marker, can be based on the provisions of this disclosure (as referenced). Figure 4 or Figure 5 The described method (400 or 500) may be the same cell marker identification method or a different cell marker identification method. Identification thresholds may include cell marker thresholds, cluster size thresholds, or any combination thereof. The method may include: removing one or more cell markers from a plurality of cell markers, each associated with a number of molecular markers with different sequences below a molecular marker number threshold, for example, in a reference... Figure 6A The method described is at box 628 of 600b.

[0351] In some embodiments, re-identifying at least one of the plurality of cell markers identified as a noise cell marker in (b) as a signal cell marker includes: identifying a plurality of second targets, each of the plurality of first targets having one or more variability indices above a variability threshold, for example at block 608 of method 600a or 600b; and for each of the plurality of cell markers, re-identifying at least one of the plurality of cell markers identified as a noise cell marker in (b) as a signal cell marker based on the number of molecular markers with different sequences associated with the plurality of second targets and the identification threshold, for example at block 612 of method 600a or 600b. The one or more variability indices of the second targets may include the average, maximum, median, minimum, dispersion, or any combination thereof of the number of molecular markers with different sequences associated with the second targets and the cell markers in the plurality of cell markers in the sequencing data. The one or more variability indices of the second targets may include standard deviation, normalized deviation, or any combination thereof, or variability indices of a subset of the plurality of second targets. The variability threshold may be less than or equal to the size of a subset of the plurality of second targets.

[0352] In some embodiments, re-identifying at least one of a plurality of cell markers identified as a signal cell marker in (b) as a noise cell marker includes: identifying a plurality of third targets, each of a plurality of first targets, having an association with a cell marker identified as a noise cell marker in (c) having an association threshold above a certain threshold, for example at block 616 of method 600a or 600b; and for each of the plurality of cell markers, re-identifying at least one cell marker identified as a signal cell marker in (b) as a noise cell marker based on the number of molecular markers with different sequences associated with the plurality of third targets and the identification threshold, for example at block 620 of method 600a or 600b. Identifying multiple third targets, each having an association with a cell marker identified as a noise cell marker in (c) above an association threshold, may include: identifying multiple remaining cell markers identified as signal cell markers after re-identifying at least one cell marker identified as a noise cell marker in (b) as a signal cell marker; and identifying multiple third targets for each of the multiple cell markers based on the number of molecular markers with different sequences associated with the multiple targets, and for each of the multiple remaining cell markers based on the number of molecular markers with different sequences associated with the multiple targets.

[0353] sequencing In some embodiments, estimating the number of different barcoded targets (e.g., randomly barcoded targets) may include determining the sequence of a labeled target, spatial marker, molecular marker, sample marker, cell marker, or any product thereof (e.g., a labeled amplicon, or a labeled cDNA molecule). The amplified target may be sequenced. Determining the sequence of a barcoded target (e.g., a randomly barcoded target) or any product thereof may include performing a sequencing reaction to determine the sequence of at least a portion of a sample marker, spatial marker, cell marker, molecular marker, a labeled target (e.g., a randomly labeled target), its complement, at least a portion of its inverse complement, or any combination thereof.

[0354] Various sequencing methods can be used to determine the sequences of barcoded or randomly barcoded targets (e.g., amplified nucleic acids, labeled nucleic acids, cDNA copies of labeled nucleic acids, etc.). These methods include, but are not limited to, hybridization sequencing (SBH), ligation sequencing (SBL), quantitative incremental fluorescent nucleotide addition sequencing (QIFNAS), fragmentation ligation and breakage, fluorescence resonance energy transfer (FRET), molecular beacons, TaqMan reporter probe digestion, pyrosequencing, fluorescence in situ sequencing (FISSEQ), FISSEQ beads, wobble sequencing, multiplex sequencing, polymerized colony (POLONY) sequencing; nanogrid rolling circle sequencing (ROLONY); and allele-specific oligo ligation assays (e.g., oligonucleotide ligation assay (OLA), readout using ligated linear probes and rolling circle amplification (RCA), and single-template molecules with ligated locking probes). OLA molecules, or single-template molecules read out using linked circular locking probes and rolling circle amplification (RCA), etc.

[0355] In some embodiments, determining the sequence of a barcoded target (e.g., a randomly barcoded target) or any of its products includes paired-end sequencing, nanopore sequencing, high-throughput sequencing, shotgun sequencing, dye-terminated sequencing, multiplex primer DNA sequencing, primer walking, Sanger dideoxy sequencing, Maxim-Gilbert sequencing, pyrosequencing, true single-molecule sequencing, or any combination thereof. Alternatively, the sequence of a barcoded target or any of its products can be determined by electron microscopy or by a chemically sensitive field-effect transistor (chemFET) array.

[0356] High-throughput sequencing methods can be used, such as cyclic array sequencing using platforms (e.g., Roche 454, Illumina Solexa, ABI-SOLiD, ION Torrent, Complete Genomics, Pacific Bioscience, Helicos, or Polonator platforms). In some embodiments, sequencing may include MiSeq sequencing. In some embodiments, sequencing may include HiSeq sequencing.

[0357] The labeled target (e.g., a randomly labeled target) may include nucleic acids representing approximately 0.01% to approximately 100% of the genome genes from an organism. For example, a target complementary region comprising multiple multimers can be used to sequence approximately 0.01% to approximately 100% of the genome genes by capturing genes containing complementary sequences from the sample. In some embodiments, the barcoded target includes nucleic acids representing approximately 0.01% to approximately 100% of the transcriptome transcripts from an organism. For example, a target complementary region comprising a poly(T) tail can be used to sequence approximately 0.501% to approximately 100% of the transcriptome transcripts from the sample by capturing mRNA.

[0358] Sequences for determining spatial and molecular markers of multiple barcodes (e.g., random barcodes) may include sequencing values ​​of 0.00001%, 0.0001%, 0.001%, 0.01%, 0.1%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 99%, 100%, or any number or range between any two of these values. Sequences for determining markers (e.g., sample markers, spatial markers, and molecular markers) of multiple barcodes may include sequencing values ​​of 1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 10 3 10 410 5 10 6 10 7 10 8 10 9 10 10 10 11 10 12 10 13 10 14 10 15 10 16 10 17 10 18 10 19 10 20 Sequencing some or all of a plurality of barcodes may include generating a sequence having, having about, having at least, or having at most 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, or a number or range between any two of these values, of nucleotide or base length.

[0359] Sequencing may include sequencing at least or at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 or more nucleotides or base pairs of a barcoded target. For example, sequencing may include generating sequencing data by polymerase chain reaction (PCR) amplification of multiple barcoded targets, wherein the sequences have read lengths of 50, 75, or 100 or more nucleotides. Sequencing may also include sequencing at least or at least about 200, 300, 400, 500, 600, 700, 800, 900, 1,000 or more nucleotides or base pairs of a barcoded target. Sequencing may include sequencing at least or at least about 1,500, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000 or more nucleotides or base pairs of a barcoded target.

[0360] Sequencing may include at least about 200, 300, 400, 500, 600, 700, 800, 900, 1,000 or more sequencing reads / runs. In some embodiments, sequencing includes sequencing at least or at least about 1,500, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000 or more sequencing reads per run. Sequencing may include less than or equal to about 1,600,000,000 sequencing reads / runs. Sequencing may include less than or equal to about 200,000,000 readings / runs.

[0361] sample In some embodiments, multiple targets may be contained in one or more samples. The samples may contain one or more cells, or nucleic acids derived from one or more cells. The samples may be single cells or nucleic acids derived from single cells. The one or more cells may be one or more cell types. At least one of the one or more cell types may be brain cells, heart cells, cancer cells, circulating tumor cells, organ cells, epithelial cells, metastatic cells, benign cells, primary cells, circulating cells, or any combination thereof.

[0362] Samples used in the methods disclosed herein may include one or more cells. A sample may refer to one or more cells. In some embodiments, the multiple cells may include one or more cell types. At least one of the one or more cell types may be brain cells, heart cells, cancer cells, circulating tumor cells, organ cells, epithelial cells, metastatic cells, benign cells, primary cells, circulating cells, or any combination thereof. In some embodiments, the cell is a cancer cell removed from cancerous tissue, such as breast cancer, lung cancer, colon cancer, prostate cancer, ovarian cancer, pancreatic cancer, brain cancer, melanoma, and non-melanoma skin cancer. In some embodiments, the cell is derived from cancer but collected from bodily fluids (e.g., circulating tumor cells). Non-limiting examples of cancer may include adenoma, adenocarcinoma, squamous cell carcinoma, basal cell carcinoma, small cell carcinoma, large cell undifferentiated carcinoma, chondrosarcoma, and fibrosarcoma. Samples may include tissue, monolayer cells, fixed cells, tissue sections, or any combination thereof. Samples may include biological samples, clinical samples, environmental samples, biological fluids, or tissue or cells from a subject. Samples can be obtained from humans, mammals, dogs, rats, mice, fish, flies, worms, plants, fungi, bacteria, viruses, vertebrates, or invertebrates.

[0363] In some embodiments, the cell is a cell that has been infected with a virus and contains viral oligonucleotides. In some embodiments, the viral infection may be caused by a virus such as a single-stranded (+strand or "sense") DNA virus (e.g., parvovirus) or a double-stranded RNA virus (e.g., respiratory enterovirus). In some embodiments, the cell is a bacterium. These may include Gram-positive or Gram-negative bacteria. In some embodiments, the cell is a fungus. In some embodiments, the cell is a protozoan or other parasite.

[0364] As used herein, the term "cell" can refer to one or more cells. In some embodiments, the cell is a normal cell, such as a human cell at a different developmental stage, or a human cell derived from a different organ or tissue type. In some embodiments, the cell is a non-human cell, such as other types of mammalian cells (e.g., mouse, rat, pig, dog, cow, or horse). In some embodiments, the cell is other types of animal or plant cells. In other embodiments, the cell can be any prokaryotic or eukaryotic cell.

[0365] In some embodiments, the cells are sorted before being associated with the beads. For example, the cells may be sorted by fluorescence-activated cell sorting or magnetic activation cell sorting, or more generally by flow cytometry. Cells may be filtered by size. In some embodiments, the retentate contains cells to be associated with the beads. In some embodiments, the flow through contains cells to be associated with the beads.

[0366] A sample can refer to multiple cells. A sample can refer to a single layer of cells. A sample can refer to a thin section (e.g., a tissue section). A sample can refer to a solid or semi-solid collection of cells that can be placed in one dimension of an array.

[0367] Execution environment This disclosure provides programmed methods for performing this disclosure (e.g., method 400, method 500, method 600a, or method 600b, see reference ). Figure 4 , 5 The computer system described in 6A and 6B. Figure 9 A computer system 900 is shown, which is programmed or otherwise configured to perform any of the methods disclosed herein. The computer system 900 may be a user's electronic device or a computer system remotely located relative to an electronic device. The electronic device may be a mobile electronic device.

[0368] Computer system 900 includes a central processing unit (CPU, also referred to herein as a “processor” and “computer processor”) 905, which may be a single-core or multi-core processor, or multiple processors for parallel processing. Computer system 900 also includes memory or memory locations 910 (e.g., random access memory, read-only memory, flash memory), electronic storage units 915 (e.g., hard disks), a communication interface 920 for communicating with one or more other systems (e.g., a network adapter), and peripheral devices 925, such as caches, other memories, data storage, and / or electronic display adapters. Memory 910, storage units 915, interface 920, and peripheral devices 925 communicate with CPU 905 via a communication bus (solid line) such as a motherboard. Storage unit 915 may be a data storage unit (or data repository) for storing data. Computer system 900 may be operatively coupled to computer network (“network”) 930 by means of communication interface 920. Network 930 may be the Internet, the Internet and / or an extranet, or an intranet and / or extranet communicating with the Internet. In some cases, network 930 is a telecommunications and / or data network. Network 930 may include one or more computer servers that can enable distributed computing, such as cloud computing. In some cases, with the help of computer system 900, network 930 can implement a peer-to-peer network, which allows devices coupled to computer system 900 to act as clients or servers.

[0369] CPU 905 can execute a series of machine-readable instructions, which can be embodied in a program or software. The instructions can be stored in a memory location, such as memory 910. Instructions can be directed to CPU 905, which can then be programmed or otherwise configured to perform the methods of this disclosure. Examples of operations performed by CPU 905 can include fetching, decoding, executing, and writing back. CPU 905 can be part of circuitry, such as an integrated circuit. One or more other components of system 900 can be included in the circuitry. In some cases, the circuitry is an application-specific integrated circuit (ASIC).

[0370] Storage unit 915 can store files, such as drivers, libraries, and saved programs. Storage unit 915 can store user data, such as user preferences and user programs. In some cases, computer system 900 may include one or more additional data storage units external to computer system 900, such as those located on a remote server communicating with computer system 900 via an intranet or the Internet.

[0371] Computer system 900 can communicate with one or more remote computer systems via network 930. For example, computer system 900 can communicate with a user's (e.g., a microbiologist's) remote computer system. Examples of remote computer systems include personal computers (e.g., portable PCs), touchscreen computers or tablets (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone, Android-enabled devices, Blackberry®), or personal digital assistants. Users can access computer system 900 via network 930.

[0372] Computer system 900 may include or communicate with electronic display 935, electronic display 935 including user interface (UI) 940 for providing output, such as strings, indicating the co-occurrence or interactions of multiple microbial groups. Examples of UI include, but are not limited to, graphical user interface (GUI) and web-based user interface.

[0373] The methods described herein can be implemented using machine-executable code (e.g., a computer processor) stored in an electronic storage location of computer system 900, such as memory 910 or electronic storage unit 915. The machine-executable code or machine-readable code can be provided in software form. During use, the code can be executed by processor 905. In some cases, the code can be retrieved from storage unit 915 and stored in memory 910 for access by processor 905 at any time. In some cases, electronic storage unit 915 can be excluded, and machine-executable instructions are stored in memory 910.

[0374] Code can be pre-compiled and configured for use on machines with processors suitable for executing the code, or it can be compiled during runtime. The code can be provided in a programming language, which can be selected to enable the code to be executed either pre-compiled or compiled.

[0375] The aspects of the systems and methods provided herein, such as computer system 900, can be embodied in programming. These aspects of the technology can be considered as “products” or “artifacts” typically carried or embodied in a type of machine-readable medium containing machine (or processor) executable code and / or associated data. Machine-executable code can be stored in electronic storage units, such as memory (e.g., read-only memory, random access memory, flash memory) or hard disks. “Storage” media can include any or all of a computer’s tangible memory, processor, etc., or related modules thereof, such as any one or all of various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage for software programming at any time. All or part of the software can sometimes be communicated via the Internet or various other telecommunications networks. For example, such communication can load software from one computer or processor to another, e.g., from a management server or host computer to a computer platform for an application server. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves, such as physical interfaces between local devices, used via wired and optical terrestrial networks, and via various air links. Physical components carrying such waves, such as wired or wireless links, optical links, etc., can also be considered as media carrying software. As used herein, unless limited to non-transitory tangible "storage" media, terms such as "computer or machine-readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0376] Therefore, machine-readable media, such as computer-executable code, can take many forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical discs or disks, any storage device such as any computer, which can be used to implement a database as shown in the accompanying drawings. Volatile storage media include dynamic memory, such as the main memory of such computer platforms. Tangible transmission media include coaxial cables; copper wires and optical fibers, including lines that form a bus within a computer system. Carrier transmission media can take the form of electrical or electromagnetic signals, or sound waves or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Therefore, common forms of computer-readable media include, for example: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punched cardstock tapes, any other physical storage media with perforated patterns, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chips or cassette tapes, carrier waves for transmitting data or instructions, cables or links for transmitting such carrier waves, or any other media from which a computer can read programming code and / or data. Many of these forms of computer-readable media may involve transmitting one or more sequences of one or more instructions to a processor for execution.

[0377] In some embodiments, some or all of the analytical capabilities of the computer system 900 may be packaged within a single software package. In some embodiments, a complete set of data analysis capabilities may include a suite of software packages. In some embodiments, the data analysis software may be a standalone package made available to the user independent of the measurement instrument system. In some embodiments, the software may be web-based and may allow users to share data. In some embodiments, commercially available software may be used to perform all or part of the data analysis; for example, Seven Bridges (https: / / www.sbgenomics.com / ) software may be used to compile a table of copy numbers of one or more genes appearing in each cell of the entire cell collection.

[0378] The methods and systems of this disclosure can be executed by one or more algorithms or methods. Methods can be implemented in software when executed by the central processing unit 905. Exemplary applications of algorithms or methods executed in software include bioinformatics methods for sequence read processing (e.g., merging, filtering, pruning, clustering), alignment and calling, and processing of string data and optical density data (e.g., determination of most probable numbers and culturable abundance).

[0379] In an exemplary embodiment, computer system 900 can perform data analysis on sequence datasets generated by performing single-cell, random barcode assays. Examples of data analysis functions include, but are not limited to: (i) algorithms for decoding / multiplexing sample markers, cell markers, spatial markers, molecular markers, and target sequence data provided by sequencing random barcode libraries generated during the assay; (ii) algorithms for determining the number of reads per gene per cell and the number of unique transcript molecules per gene per cell based on the data, and creating summary tables; (iii) statistical analysis of sequence data, such as for clustering cells based on gene expression data, or for predicting confidence intervals for determining the number of transcript molecules per gene per cell; (iv) algorithms for identifying rare cell subpopulations, such as using principal component analysis, hierarchical clustering, k-means clustering, self-organizing maps, neural networks, etc.; (v) sequence alignment capabilities for comparing gene sequence data with known reference sequences and detecting mutations, polymorphic markers, and splice variants; and (vi) automatic clustering of molecular markers to compensate for amplification or sequencing errors. In some embodiments, the computer system 900 may output sequencing results in a useful graphical format, such as a heatmap indicating the copy number of one or more genes present in each cell of a cell collection. In some embodiments, the computer system 900 may execute algorithms for extracting biological meaning from the sequencing results, such as by associating the copy number of one or more genes present in each cell of the cell collection with a type of cell, a type of rare cell, or cells derived from a subject with a specific disease or condition. In some embodiments, the computer system 900 may execute algorithms for comparing cell populations across different biological samples.

[0380] Example Some aspects of the embodiments discussed above are further disclosed in detail in the following examples, which are not intended to limit the scope of this disclosure in any way.

[0381] Example 1 Separation of signal cell markers and noise cell markers—second derivative This example describes the separation of signal cell markers (also known as true cell markers) from noise cell markers based on the number of readings (or molecules) associated with the cell marker.

[0382] In some cases, noise cell markers may have fewer associated readings (or molecules) than signal cell markers. For example, noise cell markers can be generated by the lysis of cells not paired with beads, causing their nucleic acid contents to diffuse and associate with beads not paired with any cells. This type of noise cell marker may contain a portion of the cell's total nucleic acid contents. Therefore, molecules from the same cell may appear to originate from two different cells (e.g., they may appear to come from two different beads because the cell marker has mutated).

[0383] For example, noise cell markers can be caused by mutations during bead manufacturing. Furthermore, noise cell markers can be caused by insufficient exonuclease treatment (e.g., in...). Figure 2 Step 216 shown allows the single-stranded DNA on the beads to hybridize during the PCR process and form PCR chimeras. Both types of noise cell markers can occur randomly and infrequently.

[0384] Figure 10 A non-limiting exemplary cumulative sum plot is shown. The number of cumulative readings on a log-log scale is relative to the sorted cell marker indices. The red line shows the truncation between true cell markers and noisy cell markers. Figure 10 In the study, a sudden change in the slope of the cumulative number of readings (or molecules) was observed when all cell markers were sorted based on the number of readings. To find the cutoff between true cell markers and noisy cell markers, the second derivative of the log-log plot was calculated. Figure 11 Showing Figure 10 The unrestricted second derivative plot of the cumulative sum graph. The second derivative of the cumulative number of readings of the log10 transform relative to the sorted cell label index of the log10 transform. The global minimum is inferred as the truncation between true cell labels and noisy cell labels.

[0385] In some embodiments, the inferred cell number may not match the cell number input and the cell number observed in image analysis. Instead, using Figure 11 A defined cutoff can reflect the separation between signal cells with high and low expression levels, or between different types of noise markers. To accurately infer cell numbers in these cases, a constraint is set on the percentage of readings (or molecules) in signal cell markers within the range of 45% to 92%, based on empirical data. Optionally, this value can be set as a constraint when the number of cells observed from image analysis is available.

[0386] In summary, these data demonstrate that the identification of true cell markers (also known as signal cell markers) and noise cell markers can be achieved by determining a minimum value of the second derivative plot, which corresponds to a cell marker threshold used to distinguish between true cell markers and noise cell markers.

[0387] Example 2 Separation of signal cell markers and noise cell markers – clustering This example describes the separation of signal cell markers (also known as true cell markers) from noise cell markers based on their expression patterns (also known as feature vectors).

[0388] In some embodiments, samples used for random barcoding experiments may contain cell types with a wide range of expression levels. In such experiments, some cell types may have a number of molecules very similar to those of noisy cell markers. When the number of related molecules is difficult to distinguish, clustering techniques can be used to classify noisy cell markers and each cell type with low expression levels in order to separate true cell markers from noisy cell markers. This method may be based on the assumption that cell markers within the same cell type will have more similar expression patterns than cell markers between different cell types, and that noisy cell markers will also have feature vectors that are more similar to each other than to true cell markers.

[0389] Figure 12 Unrestricted tSNE plots of signal or noise cell markers are shown. PBMC cells were randomly barcoded. Figure 12 The 5450 cell markers included 240 true cell markers with low expression levels and 5210 noisy cell markers. Specifically, classification was achieved by first projecting the expression vectors into a two-dimensional (2D) space using t-distributed random neighborhood embedding (tSNE), and then clustering the 2D coordinates using density-based spatial clustering (DBScan) with noise. Knowing that the majority of the 5450 cell markers were noisy cell markers, the dominant cluster was inferred to be the noisy marker cluster, while the other three compact clusters were inferred to be true cell markers representing three different cell types.

[0390] In summary, these data confirm that identifying true cell markers and noisy cell markers can be achieved by clustering expression patterns associated with cell markers.

[0391] Example 3 Identification of True Cell and Noise Cell Markers—Second Derivative This example describes the separation of true cells (also known as signal cell markers or true cell markers) from noise (also known as noise cells or noise cell markers) based on the number of readings (or molecules) associated with cells (or cell markers).

[0392] Example dataset 1. BD was used with three different breast cancer cell lines and donor-isolated PBMCs (peripheral blood mononuclear cells). TM The Breast Cancer Genetic Panel (BrCa400) was used to process this dataset. (Reference) Figure 4The described method 400b identified 8017 cells, of which, by reference... Figures 6A-6B Method 600a identified 186 of these cells as noise cells. Method 600a detected an additional 1263 cells, which were confirmed to be predominantly PBMCs (see [link to relevant documentation]). Figures 13A-13B 14A-14D. Figures 13A-13B This is a non-restrictive example diagram illustrating the use of BD. TM Samples processed with breast cancer gene plates (containing three different breast cancer cell lines and donor-isolated PBMCs) were prepared by a reference. Figure 4 Method 400 (explained) Figure 13A ) and by reference Figure 6A Method 600a (explained) Figure 13B (Comparison of cells identified) Figures 13A-13B The dots marked in blue are common cells detected by both methods. Figure 13A The dots marked in red are cells identified as noise by method 600a. Figure 13B The dots marked in red are additional true cells identified by method 600a. Figure 14A This is a non-limiting example diagram showing cells identified by method 600a, where cells marked in red are additional cells identified (compared to those identified by reference). Figure 4 The illustrative method 400 identifies cells). By expressing PBMCs, for example, B cells ( Figure 14B NK cells ( Figure 14C ) and T cells ( Figure 14D ( ) to stain the cells. Figure 14B-14D The additional cells identified by method 600a were indeed true cells.

[0393] Example dataset 2. BD using PBMCs with healthy donor separation TM The Blood Genetic Panel (Blood500) was used to process this dataset. (Reference) Figure 4 The described method 400b identified 13,950 cells, of which, by reference... Figures 6A-6B Method 600a identified 1,333 of these cells as noise cells. Method 600a detected an additional 3,842 cells, which were identified as predominantly T cells, and expressed important genes such as LAT (linker for activation of T cells) and IL7R (interleukin-7 receptor). See [link to relevant documentation]. Figures 15A-15B 16A-16B and 17A-17D. Figures 15A-15B This is a non-limiting example diagram illustrating the use of PBMCs with healthy donor separation in BD. TM Blood gene plate-treated samples, from reference Figure 4 Method 400 (explained) Figure 15A ) and by reference Figure 6A Method 600a (explained) Figure 15B (Comparison of cells identified) Figures 15A-15B The dots marked in blue are common cells detected by both methods. Figure 15A The dots marked in red are cells identified as noise by method 600a. Figure 15B The dots marked in red are additional cells identified by method 600a. Figures 16A-16B This is a non-limiting example diagram showing cells identified by method 400. Figure 16A In the diagram, cells marked in red are identified as noise cells by method 600a. Figure 16B In this study, cells were stained by expressing a set of monocyte marker genes, such as CD14 and S100A6. The "noise" cells identified by the improved algorithm were mostly monocytes with low expression levels. Figure 17A This is a non-limiting example diagram showing cells identified by method 600a, where cells marked in red are additional cells identified. This is based on T cell expression ( Figure 17B ), expression of the important gene LAT ( Figure 17C ) and IL7R expression ( Figure 17D ( ) to stain the cells.

[0394] In summary, the data from different embodiments of methods for identifying cell markers or true cells have different performance and may be complementary to each other.

[0395] In at least some of the previously described embodiments, one or more elements used in one embodiment may be used interchangeably in another embodiment, unless such substitution is technically impractical. Those skilled in the art will understand that various other omissions, additions, and modifications may be made to the methods and structures described above without departing from the scope of the claimed subject matter. All such modifications and changes are intended to fall within the scope of the subject matter defined by the appended claims.

[0396] Regarding the use of substantially any plural and / or singular terms herein, those skilled in the art can convert from plural to singular and / or from singular to plural where appropriate for the context and / or application. For clarity, various singular / plural arrangements may be explicitly set forth herein. As used in this specification and the appended claims, unless the context clearly indicates otherwise, the singular forms “a / an” and “the” include plural references. Unless otherwise stated, any reference to “or” herein is intended to cover “and / or”.

[0397] Those skilled in the art will understand that, in general, the terminology used herein, particularly in the appended claims (e.g., the body of the appended claims), is typically intended as “open-ended” terms (e.g., the term “including” should be interpreted as “including but not limited to”, the term “having” should be interpreted as “having at least”, the term “includes” should be interpreted as “including but not limited to”, etc.). Those skilled in the art will further understand that if a particular number of claims is anticipated, such anticipation will be explicitly stated in the claims, and no such intention exists in the absence of such a statement. For example, to aid understanding, the appended claims may include the use of the introductory phrases “at least one” and “one or more” to introduce the claims. However, the use of such phrases should not be interpreted as meaning that introducing a claim statement with the indefinite article "a" or "an" would limit any specific claim containing such an introductory claim statement to an embodiment containing only one such statement, even when the same claim includes the introductory phrase "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" and / or "an" should be interpreted as meaning "at least one" or "one or more"); the same applies to the use of definite articles to introduce claim statements. Furthermore, even when a specific number of introductory claim statements is explicitly stated, those skilled in the art will recognize that such a statement should be interpreted as meaning at least the number stated (e.g., simply stating "two statements" without other modifiers means at least two statements, or two or more statements). Moreover, in those cases where conventions such as "at least one of A, B, and C" are used, this syntactic structure is generally expected in the sense that those skilled in the art would understand the convention (e.g., ，The phrase "a system having at least one of A, B, and C" will include, but is not limited to, systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, etc. In those cases where the convention of "at least one of A, B, or C" is used, this syntactic structure is generally expected in the sense that a person skilled in the art would understand the convention to be (e.g., "a system having at least one of A, B, or C" will include, but is not limited to, systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). A person skilled in the art will further understand that, in practice, any disjunctive words and / or phrases presenting two or more alternative terms, whether in the specification, claims, or drawings, should be understood to account for the possibility of including one, any, or both terms. For example, the phrase "A or B" will be understood to include the possibility of "A" or "B" or "A and B".

[0398] Furthermore, when features or aspects of this disclosure are described in the name of the Markush group, those skilled in the art will recognize that this disclosure is also described in the name of any individual member or subgroup of the Markush group.

[0399] As those skilled in the art will understand, for any and all purposes, such as in providing a written description, all scopes disclosed herein also include any and all possible subscopes and combinations thereof. Any listed scope can be readily identified as sufficiently descriptive and such scope can be decomposed into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each scope discussed herein can be readily decomposed into lower thirds, middle thirds, and upper thirds, etc. As those skilled in the art will also understand, all language such as “more than,” “at least,” “greater than,” “less than,” etc., includes the stated numbers and refers to a scope that can subsequently be decomposed into subscopes as discussed above. Finally, as those skilled in the art will understand, a scope includes each individual member. Thus, for example, a group having 1-3 items refers to a group having 1, 2, or 3 items. Similarly, a group having 1-5 items refers to a group having 1, 2, 3, 4, or 5 items, and so on.

[0400] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for illustrative purposes and are not intended to limit the true scope and spirit as set forth by the appended claims.

Claims

1. A method for identifying signal cell markers, the method comprising: (a) Using multiple barcodes to barcode multiple targets in multiple cells to create multiple barcode-coded targets, wherein each of the multiple barcodes contains a cellular marker and a molecular marker; (b) Obtain sequencing data of the multiple barcoded targets; (c) Determine the number of molecular markers with different sequences associated with each of the multiple barcode cell markers; (d) Determine the rank of each of the multiple barcode cell markers based on the number of molecular markers with different sequences associated with each of these cell markers; (e) Based on the number of molecular markers with different sequences associated with each of these cell markers as determined in (c) and the rank of each of these cell markers as determined in (d), generate a cumulative sum graph; (f) Generate the second derivative graph of the cumulative sum graph; (g) Determine the minimum of the second derivative plot of the cumulative sum plot, where the minimum of the second derivative plot corresponds to the cell labeling threshold; and (h) Based on the number of molecular markers with different sequences associated with each of these cell markers as determined in (c) and the cell marker threshold determined in (g), each of these cell markers is identified as a signal cell marker or a noise cell marker.

2. The method of claim 1, wherein the method comprises: If the cell markers of the multiple barcodes are identified as noisy cell markers in (h), then the sequencing information associated with the identified cell markers is removed from the sequencing data obtained in (b).

3. The method according to any one of claims 1-2, the method comprising: If the number of molecular markers with different sequences associated with a target among the multiple targets is higher than the molecular marker occurrence threshold, then the sequencing information associated with the molecular markers with different sequences associated with that target among the multiple targets is removed from the sequencing data obtained in (b).

4. The method of any one of claims 1-3, wherein determining the number of molecular markers with different sequences associated with each of these cell markers in (c) comprises removing sequencing information associated with non-unique molecular markers associated with each of these cell markers from the sequencing data.

5. The method of any one of claims 1-4, wherein the cumulative sum graph is a log-log graph.

6. The method of claim 5, wherein the log-log plot is log 10 -log 10 picture.

7. The method of any one of claims 1-6, wherein generating the cumulative sum graph based on the number of molecular markers with different sequences associated with each of these cell markers as determined in (c) and the rank of each of these cell markers as determined in (d) comprises: Determine the cumulative sum for each of these cellular markers, where the cumulative sum for each level includes the sum of the number of molecular markers with different sequences associated with each of the lower-level cellular markers.

8. The method of claim 7, wherein generating the second derivative plot of the cumulative sum plot includes determining the difference between the cumulative sum of the first level and the cumulative sum of the second level relative to the difference between a first level and a second level of the cell markers.

9. The method of claim 8, wherein the difference between the first level and the second level is 1.

10. The method of any one of claims 1-9, wherein the minimum value is a global minimum value.

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