Mass spectrometry using time-encoded sample multiplexing

Abstract

Methods of multiplexing through temporal encoding are described. For example, a method including injecting multiple sample segments within a separation platform utilizing a liquid mobile phase, wherein each sample segment elutes at a pre-determined time point; allowing analytes of interest from sequential segments to elute in an overlapping eluate; analyzing, using a computer system, the overlapping eluate using a Mass Spectrometry (MS) technique to identify analytes of interest is provided. Systems for multiplexing through temporal encoding, and separation platforms therefor, are also described.

Description

[0001] Attorney Docket No.: 776866: PST-003PC

[0002] MASS SPECTROMETRY USING TIME-ENCODED SAMPLE MULTIPLEXING

[0003] CROSS-REFERENCE TO RELATED APPLICATION

[0004] This application claims the benefit of U.S. Provisional Patent Application Nos.

[0005] 63 / 791,304, filed April 18, 2025, 63 / 891,943, filed October 1, 2025, and 63 / 981,499, filed February 12, 2026, each of which is incorporated by reference herein in its entirety.

[0006] FIELD OF THE INVENTION

[0007] This disclosure relates to systems and techniques for increasing throughput of data acquisition for mass spectrometry (MS).

[0008] BACKGROUND

[0009] Advances in liquid chromatography (LC) have enabled robust and repeatable separations of chemical analytes from complex mixtures. Coupled with improvements in MS instrumentation and data-acquisition, and also data interpretation approaches, these advances have positioned LC-MS as a powerful tool in systems biology investigations. However, the scale at which such datasets can be generated remains constrained by time and costs associated with data-acquisition. As such, there is a need for increased throughput of data acquisition to generate datasets that will empower biological investigations.

[0010] SUMMARY

[0011] The present disclosure provides systems and methods for enhancing MS data acquisition through temporal encoding of analytical samples. As used herein “temporal encoding” or “time encoding” refers to strategies where the retention time (or migration time) of an analyte in a separation platform is systematically controlled to carry information about its identity, whereby the time at which an analyte elutes, or can be detected by a mass spectrometer detector, becomes a key part of its characterization. In certain embodiments, the separation periods of sample segments overlap. Samples can be separated independently and encoded in the time domain by slightly offsetting in time, their introduction to the MS for analysis. Advantageously, multiplexing in the time-domain can be combined with multiplexing techniques in the mass-domain, to provide a combinatorial increase in sample-throughput in MS analyses.

[0012] Accordingly, in one aspect, the present disclosure provides a method of multiplexing through temporal encoding (also referred to as “timePlex”), which comprises:

[0013] injecting multiple sample segments within a separation platform utilizing a liquid mobile phase, wherein each sample segment elutes at a pre-determined time point;

[0014] allowing analytes of interest from sequential segments to elute in an overlapping eluate; and

[0015] analyzing, on a computer system, the overlapping eluate using a Mass Spectrometry 1

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[0017] (MS) technique to identify analytes of interest.

[0018] In certain embodiments, the method further includes assigning, on the computer system, identified analytes of interest to a single sample segment based on elution time offset and predicted elution times for the analytes of interest.

[0019] In certain embodiments, the injection is performed sequentially.

[0020] In another aspect, the present disclosure provides a system for multiplexing through temporal encoding, which system comprises:

[0021] a separation platform utilizing a liquid mobile phase;

[0022] an injection system configured to inject multiple sample segments within the separation platform at a pre-determined time point; and

[0023] a mass spectrometry (MS) interface for connecting the separation platform to a mass spectrometer.

[0024] In certain embodiments, the system further comprises a computing system configured to assign identified analytes of interest to a single sample segment based on elution time offset and predicted elution times for the analytes of interest.

[0025] In certain embodiments, the injection system is configured to sequentially inject multiple sample segments with the separation platform at the per-determined time point.

[0026] In another aspect, the present disclosure provides a method of multiplexing through temporal encoding comprising:

[0027] sequentially injecting multiple sample segments within a separation platform utilizing a liquid mobile phase, wherein each sample segment is injected at a pre-determined time point;

[0028] allowing analytes of interest from sequential segments to elute in an overlapping eluate; analyzing, using a computer system, the overlapping eluate using an MS technique to identify analytes of interest; and

[0029] assigning, using a computer system, identified analytes of interest in the multiplexed sample to a single sample based on injection time offset and predicted elution times for the analytes of interest.

[0030] In certain embodiments, two or more samples are injected.

[0031] In certain embodiments, the liquid mobile phase comprises a composition that remains constant throughout elution of the analytes of interest.

[0032] In certain embodiments, the liquid mobile phase composition changes overtime.

[0033] In certain embodiments, the change in composition initiates elution of one or more analytes of interest; and assigning identified analytes of interest to a single sample segment is based on elution initiation time offset and predicted elution times for the analytes of interest.

[0034] In certain embodiments, the change comprises an increase in the proportion of organic solvent to aqueous solvent in the liquid mobile phase over time.

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[0037] In certain embodiments, the separation platform is configured for reversed-phase chromatography.

[0038] In certain embodiments, the change in composition occurs linearly over time.

[0039] In certain embodiments, the change in composition occurs in a multi-segment gradient comprising one or more of Logarithm-like, Exponential-like, or Step-linear gradients, and optionally comprises isocratic holds.

[0040] In certain embodiments, the composition changes over a period of time selected from the group consisting of:

[0041] (a) less than one minute;

[0042] (b) greater than or equal to one minute;

[0043] (c) about 1 minute to about 30 minutes;

[0044] (d) about 30 minutes to about 60 minutes;

[0045] (e) about 60 minutes to about 90 minutes;

[0046] (f) about 90 minutes to about 240 minutes; and

[0047] (g) about 4 or more hours.

[0048] In certain embodiments, the samples comprise proteomic, lipidomic, or metabolomic preparations.

[0049] In certain embodiments, the samples comprise proteomic, lipidomic, metabolomic, or small-molecule analytes.

[0050] In certain embodiments, the analytes of interest are label-free.

[0051] In certain embodiments, the analytes of interests are labeled, and at least one sample segment comprises one or more labeled samples.

[0052] In certain embodiments, the sample segment comprises a multiplexed sample obtained by separately labeling at least two individual samples with a mass tag unique to each sample to form respective sets of labeled samples and pooling the sets to form a mixture.

[0053] In certain embodiments, the separation platform is an ultra-high-performance liquid chromatography (UHPLC) system, a capillary electrophoresis (CE) system, or a size-exclusion chromatography (SEC) system.

[0054] In certain embodiments, the separation platform comprises a single column or capillary, or multiple columns.

[0055] In certain embodiments, the separation platform comprises a plurality of chromatography columns arranged in parallel, wherein each column of the plurality is fluidically connected to a transfer line, wherein the transfer lines are adapted to receive a sample segment and encode a time offset for injection of the sample segment into its connected column.

[0056] In certain embodiments, the transfer lines have different volumes.

[0057] In certain embodiments, each of the multiple sample segments is directed to and loaded onto a respective column of the plurality using one or more rotary valves or selector valves.

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[0060] In certain embodiments, the step of allowing analytes of interest from sequential segments to elute in an overlapping eluate comprises flowing each of the multiple sample segments onto a respective column of the plurality samples, wherein the plurality chromatography columns is configured to merge the eluate of the respective columns prior to introduction to a mass spectrometer inlet, or configured to separately introduce the eluate from each column to the mass spectrometer inlet.

[0061] In certain embodiments, the plurality of chromatography columns is configured to merge the flow using a junction or flow-joiner.

[0062] In certain embodiments, the liquid mobile phase comprises a composition in each of the plurality of chromatography columns that changes over time to form an active gradient that initiates elution of one or more analytes of interest, wherein the separation platform is configured to create a difference in the onset of the active gradient reaching each chromatography column of the plurality; and assigning identified analytes of interest to a single sample segment is based differences in the onset of the active gradient.

[0063] In certain embodiments, the composition change in each of the plurality of chromatography columns comprises an increase in the proportion of organic solvent to aqueous solvent in the liquid mobile phase over time thereby configuring the separation platform for reversed-phase chromatography.

[0064] In certain embodiments, the method further includes generating, for temporal encoding of respective samples, a differential spacer. The differential spacer being formed by a preformed gradient comprising different volumes of solvent between injected multiple sample segments.

[0065] In certain embodiments, the MS technique comprises multiple reaction monitoring (MRM), selected reaction monitoring (SRM), parallel reaction monitoring (PRM), data-dependent acquisition (DDA), or data-independent acquisition (DIA).

[0066] In another aspect, the present disclosure provides a system for multiplexing through temporal encoding, which includes:

[0067] a separation platform utilizing a liquid mobile phase;

[0068] an injection system configured to sequentially inject multiple sample segments within the separation platform at a pre-determined time point;

[0069] an MS interface for connecting the chromatography separation platform to a mass spectrometer; and

[0070] a data analysis system configured to assign identified analytes of interest in the multiplexed sample to a single sample based on injection time offset and predicted elution times for the analytes of interest.

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[0073] In certain embodiments, the separation platform is configured for isocratic flow chromatography.

[0074] In certain embodiments, separation platform is configured for gradient flow chromatography.

[0075] In certain embodiments, the separation platform is an ultra-high-performance liquid chromatography (UHPLC) system, a capillary electrophoresis (CE) system, or a size-exclusion chromatography (SEC) system.

[0076] In certain embodiments, the separation platform comprises a single column or capillary. In certain embodiments, the separation platform comprises a plurality of columns or capillaries.

[0077] In certain embodiments, the MS interface is configured for a separate flow approach or a joined flow approach.

[0078] In certain embodiments, the separation platform comprises a plurality of chromatography columns (C1-n, C'1-n) arranged in parallel, wherein each column of the plurality is fluidically connected to a transfer line, wherein the transfer lines are adapted to receive a sample segment and encode a time offset for injection of the sample segment into its connected column.

[0079] In certain embodiments, the system further includes an MS system operatively connected to the MS interface.

[0080] In another aspect, the present disclosure provides a computer-implemented method of deconvolving temporally-encoded multiplexed samples, the method comprising:

[0081] detecting precursors present in a temporally-encoded overlapping eluate obtained from a multiplexed sample containing n sample segments using an MS technique;

[0082] identifying analyte-like features in a plurality of the detected precursors; and assigning each detected precursor to a single sample segment according to a predicted retention-time.

[0083] In another aspect, the present disclosure provides a computer-implemented method of deconvolving temporally-encoding multiplexed samples, the method comprising:

[0084] identifying, in spectral data acquired from analysis of a temporally-encoded overlapping eluate obtained from a multiplexed sample containing n sample segments using an MS technique, analyte-like features in a plurality of precursors;

[0085] identifying a precursor of the plurality corresponding to the analyte-like features that corresponds to a set of precursors detected in each of the n sample segments;

[0086] filtering, from the spectral data, precursors that are not detected n times to provide an extracted data set; and

[0087] assigning each precursor of the extracted set to a single sample segment according to a predicted retention-time.

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[0090] In certain embodiments, the analyte-like features include spectral mass and fragmentation patterns.

[0091] In certain embodiments, the analyte-like features consist of spectral mass and fragmentation patterns.

[0092] In certain embodiments, the analyte-like features do not include retention times.

[0093] In another aspect, the present disclosure provides a separation platform comprising: an injection system operable to sequentially inject a plurality of samples into a fluid of a fluid handling system;

[0094] the fluid handling system comprising:

[0095] a first transfer line of a plurality of transfer lines in fluid connection with a first column of a plurality of columns; and

[0096] a second transfer line of the plurality of transfer lines in fluid connection with a second column of the plurality of columns,

[0097] wherein a first-time offset of a plurality of time offsets is encoded for a first sample of the plurality of samples in the first transfer line and a second-time offset of the plurality of time offsets is encoded for a second sample of the plurality of samples in the second transfer line; and

[0098] an interface configured to deliver the first sample and the second sample to a mass spectrometer.

[0099] Separation platforms comprising a plurality of three or more transfer lines, three or more columns, and three or more samples are expressly contemplated.

[0100] In certain embodiments, the first transfer line has a first volume and the second transfer line has a second volume, and wherein the first-time offset is defined by the first volume and the second-time offset is defined by the second volume.

[0101] In certain embodiments, the first volume is defined by a first diameter and a first length, and the second volume is defined by a second diameter and a second length.

[0102] In certain embodiments, the first-time offset is defined by a first position of the first sample within the first transfer line and the second-time offset is defined by a second position of the second sample within the second transfer line.

[0103] In certain embodiments, the plurality of samples includes more than two samples, the plurality of transfer lines includes more than two transfer lines, the plurality of columns includes more than two columns, and the plurality of time offsets includes more than two time offsets.

[0104] In certain embodiments, the fluid handling system further comprises a flow splitter to split flow received from a separation platform to the first transfer line and the second transfer line, or to any additional transfer lines.

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[0107] In certain embodiments, the flow splitter comprises at least one of a plurality of valves and a selector valve.

[0108] In certain embodiments, the fluid handling system further comprises a sample loop configured to provide a sample to at least one transfer line of the plurality of transfer lines.

[0109] In certain embodiments, the separation platform comprises at least one of Liquid Chromatography (LC), Capillary Electrophoresis (CE), and Solid Phase Extraction (SPE).

[0110] In certain embodiments, the interface comprises an emitter disposed at the end of each of the first column and the second column, or any additional columns, the emitter operable to convert the first sample and the second sample, or any additional samples, into a spray of charged droplets for ionization by electrospray ionization.

[0111] In certain embodiments, the interface comprises a flow joiner and an emitter, wherein the flow joiner is operable to join the flow of the first sample and the second sample, or any additional samples, after the first column and the second column, or any additional columns, respectively, and wherein the emitter is configured to convert the joined first sample and the second sample, or any additional samples, into a spray of charged droplets by electrospray ionization.

[0112] In certain embodiments, the fluid handling system further comprises a pump configured to move the fluid from the injection system to the first transfer line and the second transfer line and to the emitter.

[0113] In another aspect, the present disclosure provides a separation platform comprising: an injection system operable to sequentially inject a plurality of samples into a fluid of a fluid handling system;

[0114] the fluid handling system comprising:

[0115] a first transfer line of a plurality of transfer lines in fluid connection with a first column of a plurality of columns, the first transfer line having a first volume; and

[0116] a second transfer line of the plurality of transfer lines in fluid connection with a second column of the plurality of columns, the second transfer line having a second volume; and any additional transfer line(s) of the plurality of transfer lines in fluid connection with their respective column of the plurality of columns, the additional transfer lines having an additional respective volume,

[0117] wherein a first-time offset of a plurality of time offsets is encoded for a first sample of the plurality of samples in the first transfer line and defined by the first volume and a second-time offset of the plurality of time offsets is encoded for a second sample of the plurality of samples in the second transfer line and defined by the second volume and any additional time offsets of the plurality of time offsets is encoded for any additional samples of the plurality of samples by their respective transfer lines and defined by their respective volume; and

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[0120] an interface configured to deliver the first sample and the second sample, or any additional samples, to a mass spectrometer.

[0121] In another aspect, the present disclosure provides a separation platform comprising: an injection system operable to sequentially inject a plurality of samples into a fluid of a fluid handling system;

[0122] the fluid handling system comprising:

[0123] a first transfer line of a plurality of transfer lines in fluid connection with a first column of a plurality of columns; and

[0124] a second transfer line of the plurality of transfer lines in fluid connection with a second column of the plurality of columns; and

[0125] any additional transfer line of the plurality of transfer lines in fluid connection with any additional column of the plurality of columns,

[0126] wherein a first-time offset of a plurality of time offsets is defined by a first position of the first sample within the first transfer line and a second-time offset of the plurality of time offsets is defined by a second position of the second sample within the second transfer line, or any additional time offset of the plurality of time offsets, is defined by respective position of the additional sample within the respective additional transfer line; and

[0127] an interface configured to deliver the first sample and the second sample, or any additional sample, to a mass spectrometer.

[0128] In another aspect, the present disclosure provides a tandem separation platform comprising a plurality of the separation platform of either of the above two aspects, adapted or configured to operate simultaneously or sequentially.

[0129] In another aspect, the present disclosure provides a separation platform comprising: an injection system operable to inject a plurality of sample segments into a fluid of a fluid handling system and to inject a plurality of volumetric spacers between adjacent sample segments into a capillary;

[0130] the fluid handling system comprising a reservoir in fluid communication with the capillary; a power supply configured to create an electric field within the capillary; and

[0131] an interface configured to deliver eluting analytes of the plurality of sample segments to a mass spectrometer,

[0132] wherein the plurality of sample segments are injected into the capillary and each sample segment of the plurality of samples is encoded with a time-offset defined by the volume of the volumetric spacer, and

[0133] wherein the electric field is applied to the capillary to separate one or more analytes of each sample segment of the plurality of sample segments.

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[0136] In certain embodiments, the separation platform comprises two or more capillaries, in fluid communication with the fluid handling system.

[0137] In another aspect, the present disclosure provides a separation platform comprising: an injection system operable to inject a plurality of sample segments into a fluid of a fluid handling system;

[0138] the fluid handling system comprising a reservoir of a background electrolyte in fluid communication with a capillary;

[0139] a power supply configured to supply a voltage to the capillary; and

[0140] an interface configured to deliver the eluting analytes of the plurality of sample segments to a mass spectrometer,

[0141] wherein samples, of a plurality of samples, are sequentially injected into the capillary while the voltage is continuously applied to the capillary to allow the capillary to fill with the background electrolyte between sequential injections,

[0142] wherein each sample segment of the plurality is encoded with a time-offset defined by an interval between injections, and

[0143] wherein the applied voltage separates one or more analytes of each sample segment of the plurality of samples.

[0144] In certain embodiments, the separation platform comprises two or more capillaries, in fluid communication with the fluid handling system.

[0145] In another aspect, the present disclosure provides a method of multiplexing through temporal encoding comprising:

[0146] sequentially injecting multiple sample segments within at least one capillary of a separation platform, wherein each sample segment is physically separated by a volumetric spacer that encodes sample segment specificity;

[0147] applying a voltage to the at least one capillary to drive movement of analytes within each sample segment;

[0148] allowing the analytes within each sample segment to separate based on electrophoretic mobility, whereby at least a portion of the analytes of different sample segments co-migrate; analyzing, on a computer system, co-migrating analytes using an MS technique to identify analytes of interest;

[0149] assigning, on the computer system, identified analytes of interest to a single sample segment based on a time-offset created by the volume of a respective volumetric spacer and the predicted electrophoretic mobility of the analytes of interest.

[0150] In another aspect, the present disclosure provides a method of multiplexing through temporal encoding comprising:

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[0153] sequentially injecting multiple sample segments within at least one capillary of a separation platform, wherein each sample segment loaded at a defined interval to encode sample segment specificity;

[0154] applying a voltage to the at least one capillary to drive movement of analytes within each sample segment;

[0155] allowing the analytes within each sample segment to separate based on electrophoretic mobility, whereby at least a portion of the analytes of different sample segments within the at least one capillary co-migrate;

[0156] analyzing, on a computer system, co-migrating analytes using an MS technique to identify analytes of interest;

[0157] assigning, on the computer system, identified analytes of interest to a single sample segment based on a time-offset created by the defined interval between respective sample segments and the predicted electrophoretic mobility of the analytes of interest.

[0158] In certain embodiments of either of the two preceding aspects, the applied voltage is a constant voltage.

[0159] In certain embodiments of either of the two preceding aspects, the voltage is within a range of about 10-30 kV.

[0160] In certain embodiments of either of the two preceding aspects, the method further includes ionizing co-migrating analytes as they exit the at least one capillary.

[0161] In certain embodiments of either of the two preceding aspects, two or more sample segments are injected, optionally at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 sample segments are injected.

[0162] In certain embodiments of either of the two preceding aspects, the samples comprise proteomic, lipidomic, or metabolomic preparations. In certain embodiments, the samples comprise proteomic, lipidomic, metabolomic, or small-molecule analytes.

[0163] In certain embodiments of either of the two preceding aspects, the analytes of interest are label-free.

[0164] In certain embodiments of either of the two preceding aspects, the separation platform comprises a single capillary, an array of capillaries, or a bundle of capillaries.

[0165] In certain embodiments of either of the two preceding aspects, the MS technique comprises multiple reaction monitoring (MRM), selected reaction monitoring (SRM), parallel reaction monitoring (PRM), data-dependent acquisition (DDA), or data-independent acquisition (DIA).

[0166] The details of one or more embodiments of the present disclosure are set forth in the accompanying drawings and the detailed description below. Other features, objects, and advantages of the methods and systems described herein will be apparent from the detailed description and drawings, and from the claims.

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[0169] BRIEF DESCRIPTION OF DRAWINGS

[0170] These and other objects and features of the present disclosure will be better understood from the following detailed description taken in conjunction with the drawings wherein:

[0171] FIG. 1 : Illustration of precursor elution curves for no multiplexing, a method of the present disclosure referred to as “timePlex”, plexDIA, and combined timePlex + plexDIA data acquisitions. Combinatorial timePlex & plexDIA enables quadratic scaling of the number of samples acquired / run.

[0172] FIG. 2: Costs associated with proteomics data acquisition predominantly stem from the high cost of operating LC-MS instrumentation. Making more efficient use of this instrumentation, by sample-multiplexing with plexDIA or timePlex, multiplicatively reduces costs per sample. Due to the orthogonality of timePlex and plexDIA, they can be combined to produce quadratically increased efficiency. The total cost per sample is estimated assuming $150 / hrfor LC-MS / MS, approximately $1k / column with an average 1,000-run lifespan, and the cost to label 1 pg of protein with commercially available mTRAQ (approximately $0.36).

[0173] FIG. 3: Design showing two implementations for mediating timePlex. The time-offsets are encoded through differences in volumes of the transfer lines.

[0174] FIG. 4: Conceptual illustration of repeated observations of peptides species systematically offset in time.

[0175] FIG. 5: Illustration of retention time (RT) predicting model. Peptide sequences were one-hot encoded, and a hybrid convolutional neural network and deep neural network architecture was trained to predict retention times for label-free (LF) and mTRAQ-labeled peptides. Models were trained on 91,657 LF peptides or 56,645 mTRAQ-labeled peptides.

[0176] FIG. 6: Test data, which were withheld from training, were used to evaluate the performance of the RT predictor. R-squared values were 0.9976 and 0.9978 for LF and mTRAQ-labeled peptides, respectively.

[0177] FIG. 7: To evaluate the model’s ability to fine-tune RTs from new data we computed residuals between the original RT predictions of the model (shown in blue), and after finetuning on approximately 3,200 peptides (shown in orange); the plotted data is the test-set withheld from fine-tuning. The 90th percentile boundaries are shown with orange and blue lines.

[0178] FIG. 8: The original library indexed retention times (iRTs) predicted during DIANN in-silico library generation of LF data were used to visualize a typical starting point of RT accuracies, as shown in the left panel. For each run, our model is reinitialized and re-trained on the initial search results to fine-tune RTs to be used in the final search. The final search output is shown in the right panel with the library RTs for the middle time-plex channel.

[0179] FIG. 9: LF 3-timePlex data was acquired for human only, human & yeast, and human & yeast samples present in A0 min, A4 min, and A8 min timePlex channels, respectively. The data was searched with a human, yeast, and Arabidopsis spectral library. The number of precursor 11

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[0181] identifications is shown in each sub-panel for each species, as well as overlapping histograms of MS1 intensities for all precursors identified. At 1% False Discovery Rate (FDR) 21,478, 28,030, and 16,942 human precursors, 205, 7,661, and 4,261 yeast precursors, and 112, 127, and 97 Arabidopsis precursors were identified in the three timePlexes.

[0182] FIG. 10: The total ion chromatogram (TIC) of the missing-species run shown in panel a is displayed, as well as extracted ion chromatograms for a human GAPDH precursor and a yeast homolog of GAPDH (TDH1 ). The data demonstrate comparable raw intensities for human precursors across all time-channels, and a relative absence of yeast signal in the A0 min timechannel.

[0183] FIG. 11: Sample composition for benchmarking protein coverage and quantitative accuracy.

[0184] FIG. 12: Protein data points identified per run at 1% FDR for LF no-plex, 3-plexDIA, LF 3-timePlex, and 3-timePlex & 3-plexDIA with the ‘separate emitters’ approach.

[0185] FIG. 13: Protein data points identified per run at 1% FDR for LF no-plex, LF 3-timePlex, and 3-timePlex & 3-plexDIA with the ‘single emitter & joined flow’ approach

[0186] FIG. 14: The relative abundances of precursor intensities may change across the gradient, though this change is systematic and correctable as shown in quantitative assessments before and after this correction was applied. The relative intensity panels correspond to a single timePlex channel in a single run, and the quantitative ratio panels compare that timePlex channel to another channel in that same run, in this case Sample B and C are shown for yeast (orange) and human (blue) precursors. All quantitation is from MS1-leveL FIG. 15: Distribution density plots for MS1 ratios of human and yeast precursors in common between samples B and C (n=9,884) are shown for both timePlex implementations to assess quantitative accuracy. Black horizontal lines mark the median of each respective distribution.

[0187] FIG. 16A-C: The left panel displays the number of quantified precursor ratios for each pairwise comparison between samples for each methodology, as well as the intersect between methodologies. A: A / B precursor ratios; B: A / C precursor ratios; and C: B / C precursor ratios. MS1 quantitation is compared for each methodology using this intersect. Precursor ratios are shown as scatter and box plots.

[0188] FIG. 17: Comparisons of MS1 and MS2-level quantitation are shown for intersected precursor ratios. This comparison computes the absolute-valued error between expected and observed fold-changes (FC) for each precursor. The distribution of these errors are visualized as density plots with black vertical lines indicating the median of each distribution.

[0189] FIG. 18: The throughput of combinatorial plexDIA and timePlex scales quadratically, while each individually only scales linearly. The dashed red line marks the throughput achieved in this implementation of 3x3 plexDIA & timePlex.

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[0192] FIG. 19: All data was acquired on an Orbitrap Exploris 240. Here, the duty cycle is illustrated, which consisted of 12 MS2 scans with an MS1 scan scheduled every 1.3 seconds.

[0193] FIG. 20: Single-cell data from Exploris 240 acquired with 3x3 plexDIA & timePlex identified a median of 632 and 483.5 precursors and 295 and 241.5 proteins per K562 and U937 cell, respectively.

[0194] FIG. 21: Averaged fold-changes from single-cells of K562 and U937 cell lines agrees with bulk quantitation acquired by LF 3-timePlex (p=0.84). Highlighted is a protein, FABP5, found to be differentially abundant between the cell lines, and a highly abundant protein, HMGA1.

[0195] FIG. 22: The raw MS1 extracted intensities from single-cells are plotted for a single 3x3 plexDIA & timePlex run, for precursors corresponding to HMGA1 and FABP5. The differential abundance of FABP5 is observed in the raw data of MS1 XICs, where it is higher on average in U937 cells.

[0196] FIG. 23: Principal Component Analysis (PCA) was performed and the subset of proteins in common between bulk and single-cell analyses. The bulk (diamond) and single cell (circle) data points show agreement in their cell-type specific separation along PC1.

[0197] FIG. 24: Implementing 3-timePlex on Dionex UltiMate 3000. The diagram illustrates how samples are loaded sequentially for each column and then run together. Flow is bifurcated from the NC pump, one line leads to the sample loop to push sample onto a given column, and the other line is bifurcated again to ports 1 and 6 of Valve 1. Plugs are utilized to maintain pressure to push sample through the loop during column loading. The valve-positions required to achieve this are noted on each panel of the diagram. Flow-rate of the methods are adjusted to account for the number of columns actively receiving flow. In this implementation, columns 1 and 2 are loaded at 300 nL / min, where only a single column is active at a time; this produces approximately 1.5x the backpressure as three columns run at 600 nL / min, which occurs during the ‘Load column 3 and run’ phase.

[0198] FIG. 25: Implementing 4-timePlex on Dionex UltiMate 3000: The diagram illustrates how samples can be loaded sequentially for each column and then run together; this would require pushing two flows against one another as an alternative to a plug; otherwise, the design is similar to the 3-column approach shown in FIG. 24.

[0199] FIG. 26: Assessing carryover between timePlex channels: Sample loading occurs through a shared sample-loop and transfer lines in some cases, which may result in unspecific binding of peptides onto these surfaces which will flow (unintended) into the third column during the active gradient. To mitigate this, we resuspend samples in 0.015% DDM for sample-loading. To assess the carryover, we searched LF non-multiplexed data as a 2-timePlex, attempting to quantify carryover (if any exists) in the unintended time channel. If signal was only detected in the intended time channel, that ratio was given a separable value of 1e-6, if it was only detected 13

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[0201] in the unintended channel (blue) the ratio was given a value of 1e6, otherwise the observed ratio was plotted. Log10-transformed ratios reflect mostly missing data in the unintended time channel, and low signal for non-missing values. The median intensity of the non-missing data was 5.7% of the intensity of the intended time-channel.

[0202] FIG. 27: Model architecture for predicting RTs: The model is a hybrid CNN and fully connected feedforward DNN which takes inputs of shape (30, 20) - 30 being the peptide length and 20 being placeholders for each amino acid. There are 4 1D convolutional layers with kernel size = 4. The number of filters applied at each convolutional layer was set to double, starting at 32 and ending with 256. Max pooling = 2 was used for the final two layers. The final convolutional layer was flattened and fed into a fully connected feed-forward deep neural network with 1000, 256, 128, 64, and 32 nodes at each layer. Swish activation functions were used at all layers, with the exception of the final layer which was linear for computing the final RT output.

[0203] FIG. 28A-B: describes setting RT tolerances. A: Fine-tuning is not necessary for data where the library has accurate RTs, as is the case here where the libraries were acquired shortly after the benchmarking runs and originate from the same columns used in the benchmarking data. B: However, in cases where the library RTs do not properly model the empirical RTs, fine-tuning is highly beneficial, allowing for a smaller range to consider a peptide eluting.

[0204] FIG. 29A-C: Investigating the effect of the 3-column approach (background spray) Here we investigate how the 3-column setup alone (not timePlex) impacts ion intensities and coverage relative to standard proteomics acquisition from a single emitter. The flow rate for the 3 column approach is 600 nL / min (approximately 200 nL / min / column) or 200 nL / min for a 1 column setup. A: Ratio of precursor MS1 intensities for the same samples acquired when 3 columns are spraying a total of 600 nL / min (however, only 1 column in active use), or 1 column is spraying 200 nL / min. The 3-column setup results in approximately 2-fold lower MS1 intensities. B: We investigate how the 3-column setup impacts precursors identified for non-timePlex applications. The 3-column setup reduces precursors identified by approximately 25%.

[0205] C: Likewise, the 3-column setup reduces proteins identified by approximately 20%.

[0206] FIG. 30A-B: Spectral library quality becomes increasingly important at high-plex. A: Number of precursors identified per run with 3-plexDIA and 3x3 plexDIA & timePlex. Triplicates of each plexDIA were acquired, resulting in 9 runs for 3-plexDIA and 3 runs for 3x3 plexDIA & timePlex. B: Change in the number of precursors identified as a result of searching data with the label-free spectral library (accounting for mass shifts of mTRAQ mass tag) instead of the appropriate mTRAQ library. Searching data with a poor spectral library (e.g., LF library) results in a greater percentage decrease of identifications at higher plex.

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[0209] FIG. 31A-C: Investigating column-specific differences with timePlex. A: Full width half max (FWHM) of precursor elution was intersected across columns and implementations for the ‘separate emitters’ approach using lonOpticks 25cm x 75pm columns, or the ‘single emitter’ approach using pPAC Neo 50 cm columns and plotted as density plots; a vertical black line marks the median of the distributions. B: The ‘separate emitters’ approach using lonOpticks columns produced similar precursor coverage across columns. In the figure, each point represents a replicate. C: Similar trends are observed for the ‘single emitter joined flow’ approach using Thermo pPAC Neo columns, however the 3rd column consistently under-performed the first two.

[0210] FIG. 32A-B: Investigating presence of mass-tag and timePlex batch effects in PCA. A:

[0211] PCA of single-cells and bulk samples colored by the mass tag label with which they were acquired. B: PCA of single-cells and bulk samples colored by the timePlex channel with which they were acquired. This also corresponds to the LC column which was used.

[0212] FIG. 33: illustrates a block flow diagram of a method of multiplexing samples in time through staggered and overlapped separation, according to one or more embodiments of the present disclosure.

[0213] FIG. 34 illustrates a schematic diagram of a separation platform and a mass spectrometer, according to one or more embodiments of the present disclosure.

[0214] FIG. 35A illustrates a schematic diagram of a fluid handling system and a mass spectrometer, according to one or more embodiments of the present disclosure.

[0215] FIG. 35B illustrates a schematic diagram of another fluid handling system and a mass spectrometer, according to one or more embodiments of the present disclosure.

[0216] FIG. 36 illustrates a schematic diagram of another fluid handling system, according to one or more embodiments of the present disclosure.

[0217] FIG. 37 illustrates a schematic diagram of another fluid handling system, according to one or more embodiments of the present disclosure.

[0218] FIG. 38 illustrates a schematic diagram of another fluid handling system, according to one or more embodiments of the present disclosure.

[0219] The various examples discussed herein are amenable to modifications and alternative forms. While aspects of these modifications have been shown by way of example in the drawings, and described in detail below, the intention is not to limit the disclosure to the particular examples described. Rather, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure, including aspects defined in the claims.

[0220] DETAILED DESCRIPTION

[0221] The present disclosure provides methods and systems for enhancing MS data acquisition through temporal encoding of analytical samples.

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[0224] Methods

[0225] Methods described herein improve sample-multiplexing by separating samples independently and, which are encoded in the time domain by slightly offsetting their introduction to a mass spectrometer for analysis, thereby improving throughput. As used herein “multiplexing” refers to a strategy to increase sample throughput and analytical efficiency by running multiple analyses or processing multiple samples concurrently on a single Mass Spectrometry (MS) system. Advantageously, multiplexing in the time-domain can be combined with multiplexing techniques in the mass-domain, to provide a combinatorial increase in samplethroughput in MS analyses. In one or more of the embodiments of the present disclosure, the methods described herein can increase throughput to about 1,000 samples / day or more.

[0226] Turning to the figures, FIG. 33 illustrates method 100 as an example of multiplexing through temporal encoding. In block 102, the method includes sequentially injecting multiple sample segments within a separation platform utilizing a liquid mobile phase. As used herein, a “sample segment” refers to an individual sample or a subset of a multiplexed sample. Sample preparation is described in detail below.

[0227] The liquid mobile phase can be flowing within a “liquid separation platform”. A liquid separation platform of the present disclosure can include any apparatus, module, or unit in an analytical apparatus for separating analytes of interest from a complex mixture using a liquid mobile phase. Non-limiting examples include Liquid Chromatography (LC), Capillary Electrophoresis (CE), and Solid Phase Extraction (SPE). The choice of separation platform depends on the chemical properties of the analytes of interest, the complexity of the sample matrix, and the specific research question, and is within the level of skill and knowledge of the ordinary artisan in the field of the invention. The liquid mobile phase comprises a composition that can be constant throughout the analysis or can vary at one or more stages of the analysis. In certain embodiments, the composition of the mobile phase varies throughout the analysis.

[0228] Returning to FIG. 33, in certain embodiments of block 102, each sample segment is injected at a pre-determined time point, in series (i.e. , sequentially), thereby time-encoding the sample segment. Time-encoding can include offsetting the relative times at which an analyte elutes. In certain embodiments, although the sample segments are introduced to the liquid mobile phase at different time points, the separation periods of sample segments overlap. In certain embodiments, the sample segments are injected to minimize or substantially eliminate defined volumes of the liquid mobile phase (e.g., running buffer) that would otherwise result in physical separation between the sample segments during separation. The interval between injection time points can be any amount of time that is sufficient to analytically separate eluting multiplexed sample analytes. In certain embodiments, the interval between injection time points can be less than about 1 minute, such as within about 1, 5, 10, 20, 30, 40, or 50 seconds of another injection time point. In certain embodiments, a sample segment can be injected about 16

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[0230] every second to every minute, such as about every 1-10 seconds, every 10-20 seconds, every 20-30 sections, every 30-40 seconds, every 40-50 seconds, or every 50-60 seconds. In certain embodiments, the interval between injection time points is about 1 minute or at least about 1 minute, such as at least about 1.5, 2, 2.5, 3, 3.5, 4 minutes, or more. In certain embodiments, the interval between injection time points can be at least about 1 minute to about 5 minutes, at least about 2 minutes to about 4 minutes, or at least about 1 minute to about 3 minutes. In a specific embodiment, each sample segment can be injected about every 2 minutes.

[0231] In a particular embodiment, block 102 includes sequentially injecting multiple sample segments within a separation platform that includes with two or more separation channels. The separation channels can be characterized by volume. A pump can be configured to flow buffer through the separation channels. The flow-rate generated by the pump (i.e., the volume of the mobile phase that passes through the system per unit of time) is split between the channels, and the proportion of the total flow that goes into each channel will depend on the resistance to flow within each channel. As the mobile phase is pumped through the channel, it encounters resistance due to the stationary phase, the narrowness of the line, and / or the viscosity of the liquid. Two or more channels having different volumes (and optionally different lengths and / or stationary phases), will offer different levels of resistance to the flow and analytes of interest will move through the channels at different speeds. The volume of a channel can be manipulated to provide a specific temporal offset to a sample loaded therein. In certain embodiments, the offset between channels can be less than about 1 minute, such as about 1, 5, 10, 20, 30, 40, or 50 seconds. In certain embodiments, the offset between channels can be about 1-10 seconds, about 10-20 seconds, about 20-30 sections, about 30-40 seconds, about 40-50 seconds, or about 50-60 seconds. In certain embodiments, the offset between channels can be at least about 1 minute, such as at least about 1.5, 2, 2.5, 3, 3.5, 4 minutes, or more. In certain embodiments, the offset between channels can be at least about 1 minute to about 5 minutes, at least about 2 minutes to about 4 minutes, or at least about 1 minute to about 3 minutes. In a specific embodiment, each channel offset can be about 2 minutes.

[0232] The transfer lines can be loaded, sequentially, to introduce a sample segment therein to the respective column or capillary of the channel. Thus, in certain embodiments, block 102 can include one or more steps for loading the liquid separation system. Non-limiting examples of loading are described in the Example section below. In certain embodiments, loading can include manual, semi-automatic or fully automatic loading. In certain embodiments, one transfer line can be loaded with a reference set sample for retention time prediction.

[0233] In block 104 of FIG. 33, the method includes allowing analytes of interest from sequential segments to elute in an overlapping eluate. An “overlapping eluate” refers to the effluent or the liquid that exits the separation platform where at least a portion of analytes of interest from two or more sample segments exit the separation platform at substantially the 17

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[0235] same time, or at times that are so close that their electropherogram or chromatographic peaks (electropherogram or chromatographic) are not fully resolved, and instead merge or significantly overlap. Each respective analyte is offset in time between samples. Sample segments can be a complex mixture containing thousands of analytes. The time frame during which all analytes of interest present in a sample segment elute can be referred to as the “separation window”.

[0236] As a non-limiting example of a 3-plex embodiment of the present method, in which individual sample segments have a separation window of greater than 8 minutes, if sample segment A is injected at 0 min, sample segment B is injected at 4 minutes, and sample segment C is injected at 8 minutes, the separation windows of these three sample segments will overlap. As a result, some analyte species from sample segment A elute during the separation window of sample segment B or both sample segments B and C. However, the same analyte species from each sample segment is systematically offset in time, and therefore observable as a resolvable peak in each separation window. Returning to the above example, if analyte “Peptide i” of sample segment A elutes from the separation platform at 10 minutes, the same peptide of sample segments B and C elutes at 14 and 18 minutes, respectively (see also, FIG. 4).

[0237] The 3-plex example above is provided for illustration purposes only. The present disclosure expressly contemplates 2-plex to n-plex embodiments, where n can include 3, 4, 5, 6, 7, 8, 9, 10, 16, 25 or more distinct sample segments, and sequential sample segments can be injected at intervals other than 4 minute intervals (e.g., such as at least about 1.5, 2, 2.5, 3, 3.5, 4 minutes, or more, with intervals of less than 1.5 minutes also contemplated), as described in detail elsewhere. The skilled artisan can reasonably predict separation times and the extent of overlap using appropriate retention time libraries or determine these empirically. As discussed in the Example section below, advantages, and modifications are within the scope of the following claims, including label-free multiplexing and combinatorial scaling for throughput that exceeds 1,000 samples / day.

[0238] In block 106 of FIG. 33, the method includes analyzing the overlapping eluate using an MS technique. An MS technique can include a number of steps such as ionization of the analytes of interest and acquisition of a full spectrum (survey scan or MS1 scan). This approach is distinct over methods in which each sample is analyzed individually by liquid chromatographymass spectrometry (LC-MS). Here multiplexing is achieved through staggered and overlapped separations and deconvolved and interpreted in silico during data analysis by aligning the chromatographic retention times and mass-to-charge ratios (m / z) of the detected peptides across different LC-MS runs (e.g., Sequential Injection with Data Alignment) or CE-MS analyzing multiple samples sequentially with short injection segments separated by buffer spacers.

[0239] In certain embodiments, an MS technique can include selection of specific precursor ions to be fragmented, fragmentation, and acquisition of MS / MS spectra (product-ion spectra).

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[0242] An MS technique can include collision-induced dissociation (CID), electron transfer dissociation (ETD), high-energy collisional dissociation (HCD), matrix-assisted laser desorption / ionization (MALDI), and electro-spray ionization (ESI). The choice of MS technique can be based on a specific application. Systems for mass spectrometry are described in detail below.

[0243] In one or more embodiments, the MS technique includes Data-dependent acquisition (DDA). DDA can perform a precursor scan to determine the mass-to-charge ratio (m / z) and abundance of ions eluting from the LC column at a particular time (MS1 scan). The initial precursor scan allows for identification and screening of the most intense ion signals (precursor ions), which are then selected for subsequent fragmentation and selection in the second part of MS / MS. In MS / MS, this precursor scan is followed by isolation and fragmentation of selected peptide ions using sequence determining MS / MS scans (often referred to as MS2 scan) to generate a mass spectrum. As such, DDA generates a mass spectrum based on fragment ions from a subset of peaks detected during the precursor scan.

[0244] In one or more embodiments, the MS technique includes data-independent acquisition (DIA) strategies that allow fragmentation of all precursor ions within a certain range of m / z and retention time in an unbiased and untargeted fashion. DIA can be contrasted with DDA, as described above. DIA can also be contrasted with selected reaction monitoring (SRM) techniques. SRM generates mass spectra from selected precursor ions identified in precursor scanning (MS1 ). A mass spectra generated by DIA can yield a more complete record of all peptides that are present in a sample, including those with low abundance, since a range of precursor ions are selected, and fragment ions are generated from this range of precursor ions.

[0245] A method of the present disclosure can also include a step of storing the mass spectrometry data. The MS data can be stored, for example, as a mass spectrum or a plot of the ion signal as a function of the mass-to-charge ratio, a data table listing ion signal and related mass-to-charge ratio, a data string comprising pairs of ion signal and related mass-to-charge ratio, where values can be stored in corresponding data fields and data instances. The mass spectra data sets can be stored in various data structures for retrieval, transformation, and modification. Such data structures can be, for example, one or more tables, images, graphs, strings, maps, linked lists, arrays, other data structure, or a combination of same.

[0246] In block 108 of FIG. 33, the method includes deconvolving the MS spectra. As used herein, “deconvolving” refers to computationally disentangling the spectral data from step 106, the peaks of which include the combined signals obtained from the fragmented analytes originating from all the sample segments. Block 108 can include identifying analytes of interest; and assigning identified analytes of interest to a single sample segment based on the injection time offset and predicted elution times for the analytes of interest in the sample segments.

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[0249] Sample Preparation

[0250] In the context of the present disclosure, the term “sample” can refer to a biological material containing one or more analytes of interest. In certain embodiments, a sample of the present disclosure can be characterized as a proteomic, glycoproteomic, phosphoproteomic, peptidomic, metabolomic, fluxomic, lipidomic, pharmacometabolomic, glycomic, or epigenomic sample. Methods of the present disclosure can allow for parallel analysis of multiplexed samples, including highly multiplexed samples (e.g., comprising 4 or more samples). In certain embodiments, methods of the present disclosure can be used to analyze two or more samples (e.g., about 2, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 100 or more) in a single experimental assay.

[0251] A sample of the present disclosures can be derived from any biological source, such as a physiological fluid, including, blood, serum, plasma, saliva, ocular lens fluid, tears, cerebrospinal fluid, sweat, urine, milk, ascites fluid, mucous, synovial fluid, peritoneal fluid, amniotic fluid, tissue, cells, or lavage (e.g., lung, nasal, gargle, peritoneal lavages). The sample can be pretreated prior to use, such as preparing plasma from blood, diluting viscous fluids, lysis, or the like; methods of treatment can involve filtration, centrifugation, distillation, concentration, inactivation of interfering components, and the addition of reagents. A sample can be used directly as obtained from the source in some cases or following a pretreatment and / or sample preparation workflow to modify the composition of the sample, e.g., after adding an internal standard, after being diluted with another solution or after having being mixed with reagents e.g., to enable carrying out one or more in vitro diagnostic tests, or for enriching (extracting / separating / concentrating) analytes of interest and / or for removing matrix components potentially interfering with the detection of the analyte(s) of interest. The term “sample” can be used to indicate a sample before sample preparation whereas the term “prepared sample” can be used to refer to samples after sample preparation. In non-specified cases, the term “sample” may generally indicate either a sample before sample preparation or a sample after sample preparation or both.

[0252] In certain embodiments, a sample can be obtained from one or more of cell homogenates, cell fractions, tissue homogenates, and biological fluids, or purified fractions or semi-purified fractions thereof. A purified or semi-purified fraction of a homogenate or biological fluid can be obtained by precipitation, centrifugation, filtration, immuno-depletion, and / or chromatographic methods. A portion of highly abundant components can be removed during fractionation to enrich the relative concentration of analytes of interest in the sample.

[0253] Sample preparation workflows can be analyte specific. For example, there can be at least one sample workflow for whole blood, at least one sample workflow for plasma and / or serum, at least one sample workflow for urine, and the like. Different sample workflows can include different steps or a different number of steps that may require a different time for completion.

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[0256] An analyte of interest can include any biomolecule, or mixture thereof. In certain embodiments, the analyte of interest can be selected from proteins, lipids, carbohydrates, nucleic acids, and metabolites thereof. In certain embodiments, an analyte of interest can be a biological molecule associated with a clinical condition (e.g., a biomarker for a disease or condition), a response to internal or external stimuli, and / or a developmental stage.

[0257] In certain embodiments, a sample can include complex mixture or “preparations” containing one of more types of biologically derived molecules. For example, proteomic preparations can refer to samples containing proteins (e.g., cell lysate, plasma), lipidomic preparations can refer to samples containing lipids (e.g., lipid extracts, cell membranes), and metabolomic preparations can refer to Samples containing small molecule metabolites (e.g., urine, cell culture media).

[0258] In certain embodiments, a sample can be described based on the presence of individual compounds or a class thereof. In certain embodiments, a sample can include non-metabolite small molecules.

[0259] Sample segments can include any amount of analyte. In certain embodiments, a peptide containing sample can include microgram quantities or less. For example, a sample segment can include about 1, 2, 5, 10, 20, 50, 100, 200, 300, 500, 700, or 1000 nanograms quantities of peptides prepared by any of the methods described above. In certain embodiments, the method of the present disclosure can be used to identify one or more analytes of interest that are present in the sample segment in sub-nanogram quantities, e.g., about 50, 100, 200, 300, or 500 picograms.

[0260] In a particular embodiment, the analyte of interest is a peptide. The peptide can be obtained from any source, and the source can vary according to the purpose of an assay. In some embodiments, the peptide can be obtained from an animal (human or non-human animal), plant, bacteria, virus, fungus, or other organism, or a product made therefrom, such as a foodstuff. Other peptides can be obtained from environmental samples. In some cases, the peptide is obtained from a subject or patient sample. A patient or subject can be a human or non-human animal. A peptide can be obtained directly or indirectly from the source of the peptide. For example, a peptide-containing sample can be obtained from a depository, such as a blood or tissue bank, and / or directly from a patient.

[0261] A peptide analyte can include L- and / or D-amino acids, and / or amino acids other than the 20 gene-encoded, proteinogenic amino acids, which amino acids can be incorporated directly into an intact protein by an unusual mRNA translation step (e.g., selenocysteine), produced by metabolic conversions of free amino acids (e.g., ornithine and citrulline), modified by a post-transitional modification (e.g., acetylation, amidation, deamidation, biotinylation, C-mannosylation, flavinylation, farnesylation, formylation, geranyl-geranylation, lipidation, phosphorylation, glycosylation, hydroxylation, disulfide bond formation, methylation,

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[0264] myristoylation, sulphation, carboxylation, ADP-ribosylation, etc.), and / or modified by chemical modification techniques. A peptide analyte can include linear, cyclic, and / or branched chains of amino acid residues.

[0265] A peptide analyte can be obtained from an intact protein, or a mixture of different intact proteins, such as two or more different intact proteins, e.g., >5 intact proteins, >10 intact proteins, >20 intact proteins, >30 intact proteins, >40 intact proteins, >50 intact proteins, >60 intact proteins, >70, intact proteins, >80 intact proteins, >90 intact proteins, >100 intact proteins, >125 intact proteins, >150 intact proteins, >175 intact proteins, >200 intact proteins, >225 intact proteins, >250 intact proteins, >300 intact proteins, >400, intact proteins, >500 intact proteins, >600 intact proteins, >700 intact proteins, >800 intact proteins, >900 intact proteins, >1000 intact proteins, >1250 intact proteins, >1500 intact proteins, >1750 intact proteins, >2000 intact proteins, >2500, intact proteins, >3000 intact proteins, >3500 intact proteins, >4000 intact proteins, >4500 intact proteins, >5000 intact proteins, >6000 intact proteins, >7000 intact proteins, >8000 intact proteins, >9000 intact proteins, >10,000 intact proteins, etc. A peptide analyte can associate with one or more other peptide analytes to form a complex, such as a complex formed within a quaternary structure of a protein (e.g., a multi-subunit complex, a receptor-ligand complex, etc.), or a crosslinked complex.

[0266] A peptide analyte can be obtained from a proteome, i.e., a set of intact proteins encoded on a genome as expressed and / or as modified by a cell, tissue, or organism at a given sampling time. A proteome can include at least about 20 intact proteins, at least about 50 different intact proteins, at least about 100 different intact proteins, at least about 1000 intact proteins, at least about 10,000 different intact proteins, at least about 100,000 different intact proteins, or more. A proteome can include a set of intact proteins resulting from any translation event, expressed by a naturally-occurring or transgenic cell. A proteome can include a set of intact proteins expressed under normal physiological or pathological conditions, and / or in response to exposure to a chemical agent or environmental state. A proteome can include secreted proteins and / or proteins obtained by lysis / extraction. A peptide analyte can be obtained from a purified fraction of a proteome, such as a microsomal-, organelle-, mitochondrion-, or plasma membrane-fraction. A purified fraction of a proteome can be obtained by any technique for fractionating, separating, dividing cellular fractions including chromatographic methodologies.

[0267] In some cases, the proteome includes full-length proteins and / or truncated proteins (e.g., 1, 10, 100, 1,000, 10,000 proteins or more). A full-length protein can be an intact protein as it occurs in its natural state, i.e., a protein having all amino acids encoded by an intact genetic sequence, unaltered by heat, chemicals, enzyme action or protocols used to extract the protein from a cell amino acid sequence as expressed in vivo, or variants thereof. A truncated protein can be a genome-encoded variant of full-length protein that is shortened relative to a full-length protein due to premature termination of its synthesis during translation. A truncated protein can 22

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[0269] lack an amino acid from a N- or C-terminal end relative to a full-length protein. A full-length or truncated protein can be 10, 20, 30, 40, 50, 60, 70, 80, 100, 200, 300, 400, 500, 600, 700, 800, 1000, 5000 or more amino acids in length. A peptide analyte can be derived from one or more intact proteins or other protein source.

[0270] A peptide can be naturally-occurring, synthetic, formed due to the presence of naturally-occurring proteases, formed due to the addition of proteases or other chemicals, or formed due to fragmentation during isolation. In some embodiments, the peptide is synthesized in vivo, in cellulo, in vitro, etc. using labeled amino acids.

[0271] A peptide can be obtained from an intact protein by enzymatic or chemical proteolysis of a protein of interest. Proteolysis can result in cleavage of a preselected protein of interest into two or more (e.g., 2, 3, 4, 5 or more) peptide fragments. Suitable proteases include, without limitation: serine proteases (e.g., such as trypsin, hepsin, Stratum Corneum Chymotryptic Enzyme (SCCE), Tumor Antigen Derived Gene-12 (TADG12), Tumor Antigen Derived Gene-14 (TADG14)), metalloproteases (e.g., Matrix metalloproteinase-7, or PUMP-1), chymotrypsin, cathepsin, pepsin, elastase, pronase, endoproteinase Arg-C, endoproteinase Asp-N, endoproteinase Glu-C, endoproteinase Lys-C, carboxypeptidases A, B, and / or C, dispase, thermolysin, cysteine proteases, and the like.

[0272] In certain embodiments of the present disclosure, one or more sample segment(s) (and analytes therein) can be label-free. In a particular embodiment, all of the sample segments are label-free. As used herein, “label-free” refers to samples without an artificial label, such as a chemical, isotopic, or metabolic label. Label-free techniques can be particularly advantageous when cost, sample availability, or experimental design make labeling undesirable or impractical.

[0273] As discussed above, multiplexing through temporal encoding can be combined with multiplexing by mass encoding, to provide a combinatorial increase in sample-throughput. For example, samples or analytes can be modified in a way that results in subtle but resolvable mass shifts for the same molecule from different samples (e.g., through stable isotope labeling). Accordingly, in certain embodiments, the analytes of interest comprise a label. The label can be any chemical or isotopic modification introduced to the analytes of the sample to enable their differentiation and optionally for relative or absolute quantification by MS techniques (i.e., a mass tag).

[0274] In certain embodiments, samples can be separately isotopically labeled, e.g., by metabolic labeling (e.g., Stable Isotope Labeling by Amino acids in Cell culture (SILAC), incorporation of 0-18 during enzymatic digestion, dimethyl labeling) and / or chemically labeled (e.g., with Isotope-Coded Affinity Tags - ICAT). Non-limiting examples of chemical labels include isobaric tags and non-isobaric tags. Examples of commercially available mass tags include Isobaric Tags for Relative and Absolute Quantitation (iTRAQ), original tandem mass tags (TMT), TMTPro platform and reporter isotopes thereof, deuterium isobaric amine reactive 23

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[0276] tag (DiART), Thompson’s TMT (1st or 2nd generation), easily abstractable sulfoxide-based isobaric (EASI)-tag, and isotope-coded affinity tag (ICAT) tag, and isotopologues thereof for example. Commercially available non-isobaric mass tags include but are not limited to mTRAQ, mass difference TMT, and DiLeu, and isotopologues thereof. An isotopologue can include at least one of the following isotopes: H-2, C-13, N-15, 0-18, S-33, and S-34. In certain embodiments, the sample can be labeled with a mass tag, or isotopologue thereof, disclosed in International Patent Application No. PCT / US2026 / 014752, filed February 10, 2026, the entire contents of which are incorporated herein by reference.

[0277] In certain embodiments, individual samples can be chemically labeled with mass tags that differ from one another by any resolvable mass (also referred to as “mass labeled samples). The mass tags can differ by at least about 2, 4, 6, 8, 10, 20, 30, 35, 40, 45, 50, 60, 70, 80, or 100 mDalton (mDa).

[0278] In certain embodiments, mass labeled samples of the present disclosure can include a single peptide or a mixture of different peptides, such as two or more different labeled peptides, e.g., >5, >10, >20, >30, >40, >50, >60, >70, >80, >90, >100, >125, >150, >175, >200, >225, >250, >300, >400, >500, >600, >700, >800, >900, >1000, >1250, >1500, >1750, >2000, >2500, >3000, >3500, >4000, >4500, >5000, >6000, >7000, >8000, >9000, >10000, >30000, >50000 different peptides, etc. The mixture of peptides can be obtained from a mixture of proteins, such as proteomic samples from single-cells or whole cell lysates, as described above.

[0279] In certain embodiments, the separately labeled samples are combined (pooled) to form a pooled composition. The pooled composition can also be referred to as a multiplex sample. Pooling can include manual pooling or automated pooling (e.g., liquid handling robotics). A multiplexed sample can include two or more individual samples, such as a duplex or n-plex composition, where n can be an integer greater than 2. Two or more samples can include samples from a single subject or source and / or one or more samples obtained from two or more subjects or sources. A multiplex sample can include a test sample, a reference sample (such as a calibration reference) and / or a replicate sample (such as a preparation replicate or a measurement replicate). A multiplex sample can include 2 to 80 mass labeled samples (e.g., n can be 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 25 or more).

[0280] In certain embodiments, a time encoded sample segment includes a multiplex sample. A time-encoded, mass-encoded sample segment can be referred to as a “combinatorial multiplex sample”. The combination of time-encoded and mass-encoded data acquisitions enables quadratic scaling of the number of samples acquired / run. For example, FIG. 1 shows three combinatorial multiplex samples each including a multiplex sample of three individual samples: Two mass labeled samples and on label-free sample. As described in method 100 above, each combinatorial multiplex sample can be serially injected into the liquid mobile phase of a separation platform at a pre-determined time offset. Combinatorial multiplex samples can be 24

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[0282] analyzed using a MS technique as described above. In a particular embodiment, combinatorial multiplex samples are analyzed using a data-independent acquisition (DIA) method.

[0283] In a particular embodiment, a method of multiplexing through temporal encoding can be performed by injecting multiple sample segments within at least one capillary of a separation platform, wherein each sample segment is physically separated by a volumetric spacerthat encodes sample segment specificity;

[0284] applying a voltage to at least one capillary to drive movement of analytes within each sample segment;

[0285] allowing the analytes within each sample segment to separate based on electrophoretic mobility, whereby at least a portion of the analytes of different sample segments co-migrate; analyzing, on a computer system, co-migrating analytes using an MS technique to identify analytes of interest; and

[0286] assigning, on the computer system, identified analytes of interest to a single sample segment based on a time-offset created by the volume of a respective volumetric spacer and the predicted electrophoretic mobility of the analytes of interest.

[0287] As used herein, a “volumetric spacer” refers to a plug comprising electrolyte. The plug can comprise the same buffer as the rest of the capillary. In certain embodiments, the plug can include other components which affect the electric field experienced by the analytes (e.g., to create a focusing effect). The volumetric spacer acts as a separator to isolate the different sample segments and to define a specific, predictable time interval relative to the start of the injection sequence. The volume can be selected to limit, but not prevent, mixing of the sample segments during the separation process.

[0288] A volumetric spacer can be hydrodynamically or electrokinetically injected into the capillary between sample segments. In certain embodiments, assigning the identified analytes of interest utilizes the time-based pattern defined by sequence of sample segment and volumetric spacer injection to identify analytes of interest in complex samples.

[0289] In another particular embodiment, a method of multiplexing through temporal encoding can include:

[0290] sequentially injecting multiple sample segments within at least one capillary of a separation platform, wherein each sample segment loaded at a defined interval to encode sample segment specificity;

[0291] applying a voltage to the at least one capillary to drive movement of analytes within each sample segment;

[0292] allowing the analytes within each sample segment to separate based on electrophoretic mobility, whereby at least a portion of the analytes of different sample segments within the at least one capillary co-migrate;

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[0295] analyzing, on a computer system, co-migrating analytes using an MS technique to identify analytes of interest;

[0296] assigning, on the computer system, identified analytes of interest to a single sample segment based on a time-offset created by the defined interval between respective sample segments and the predicted electrophoretic mobility of the analytes of interest.

[0297] Sequential injection is timed to allow for a spacer of background electrolyte between each sample segment. For example, while applying an electric field, sample segments can be injected at specific time intervals, thereby causing the sample segments to migrate as zones defined by intervening background electrolyte. The interval between sample segment injections can be optimized to manipulate the extent of overlapping sample segments in adjacent zones as the zones migrate. This timing can vary based on the samples being analyzed (e.g., mobility of the analytes being separated) and / or the total length of the capillary to achieve the desired resolution (Rs).

[0298] For embodiments using volumetric spacers, the method can include two distinct injection steps. If electrokinetic injection is used, a voltage can be applied first to inject the sample and then again to inject the buffer plug. These injection voltages can be lower and applied for a shorter duration than the separation voltage. In certain embodiments, the voltage applied for separation is within a range of about 10-30 kV. The applied voltage for separation can be a constant voltage.

[0299] For embodiments using sequential injection (i.e., without a spacer plug) the injection can be performed using voltage or pressure, and separation of the migrating zones can include varying the applied voltage to control migration and separation.

[0300] In certain embodiments, the method further includes ionizing co-migrating analytes as they exit the capillary. The outlet of the capillary can be connected to an electrospray ionizer. A suitable electrospray ionizer can be selected based on a particular mass spectrometer interface, flow rate, background electrolyte, desired separation efficiency, or other parameters known to persons of skill in the art.

[0301] In certain embodiments, the number of sample segments injected can depend on various factors such as the capillary length, segment length, injection time, properties of the volumetric spacer, the number of analytes in each sample segment, and / or properties of the analytes in each sample segment. In certain embodiments, 2 or more sample segments are injected, such as at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 sample segments. The person or ordinary skill in the art of capillary electrophoresis is capable of adjusting injection parameters to balance throughput and analytical performance using routine experimentation, as guided by the present disclosure.

[0302] In certain embodiments, the separation platform can include a single capillary, an array of capillaries, or a bundle of capillaries.

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[0305] Identifying Analytes of Interest

[0306] The present disclosure also provides methods for interpreting data acquired using time-encoded multiplexing as described above. One or more steps of data interpretation can be automated (e.g., computer-implemented).

[0307] In certain embodiments, interpreting the data includes executing a computer-implemented method that deconvolves the temporally-encoded multiplexed samples. For example, the method can include identifying analyte-like features in a plurality of the precursors detected in the spectral data obtained from a multiplexed sample containing n sample segments; and identifying a precursor of the plurality that corresponds to the analyte-like features which is detected in each of the n sample segments. The method includes filtering out precursors that are not detected n times from the spectral data, thereby generating an extracted data set and then, assigning each precursor of the extracted data set to a single sample segment according to a predicted retention-time.

[0308] In certain embodiments, interpreting the data includes generating a timePlex library. One or more steps of data interpretation can be automated (e.g., computer-implemented). One example of automation includes the search software described in the Examples below as “JMod”.

[0309] A timePlex library can be generated by initially identifying analyte-like features in time encoded n-plex acquired MS data (also referred to as “time-channel identification”, and applicable to multi-channel and single column / capillary embodiments). Identifying these features can include determining the times when precursors can be expected to elute from the separation platform (e.g., each channel). These times can then be used to perform the main search.

[0310] The initial determination can include identifying analyte-like features that correspond to the same analyte detected in each of the n sample segments. An advantage of this method is that the time spacing between samples does not need to be specified. The temporal order of analyte detections can be used to build time-channel specific libraries. The initial identification can then be used to align and fine tune the retention-times of the library for the main search. The identifications made in this main search do not need to occur in all time-channels.

[0311] In certain embodiments, the method includes individually identifying a precursor that best fits each of the features identified above. Identification can be based solely on their mass and fragmentation patterns (i.e. , spectra-centric), without initially considering when the analyte eluted. Thus, multiple instances of the same precursor can be identified. In a particular embodiment for the analysis of proteomic data using DIA, the method can include finding peptide-like signals, and then identifying the peptides responsible for each signal based solely on their mass and fragmentation patterns.

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[0314] Time-encoding imparts systematic offsets that can be used to identify correctly matched precursor sets. Thus, the method can further include extracting precursors that have been identified n times (i.e. , at least once for each injected sample segment) and filtering out sets of precursors that are inconsistent with time encoding. In some embodiments, and without being bound by theory, time-encoding can allow for the identification of analytes in fewer than n samples. The accuracy of the refined retention time predictions to their empirical elution time can allow for accurate sample-assignment in cases where analytes have been identified fewer than n times.

[0315] While time-encoding as described herein results in the analyte precursors being systematically separated in time across time-channels, accurately assigning each identified precursor to a sample can be impacted by inaccuracies in library retention times. Thus, the method can include fine-tuning the retention times by developing a retention-time predictor. The retention-time predictor can be used for fine-tuning library retention times on a per run basis.

[0316] In certain embodiments, a retention-time predictor can include an artificial intelligence / machine learning (AI / ML) model, such as an artificial neural network, optionally a pre-trained neural network (e.g., a convolutional neural network (CNN), deep neural network (DNN), and combinations thereof). As used herein, “a pre-trained neural network” refers to an Al model that has been trained on a large dataset for a specific task, which is then used as a starting point for training a new model for a similar, but potentially different, task (i.e., by a transfer learning process). In certain embodiments, the artificial neural network can be a foundational model. As used herein, a “foundational model” refers to Al models pre-trained on vast, diverse datasets, capable of performing a wide range of tasks with fine-tuning, rather than being trained from scratch for specific application. In a non-limiting example, the retention-time predictor is an convolutional neural network (CNN) ending in fully connected layers of a feedforward deep neural network (DNN) as illustrated in FIG. 5 (see also FIG. 27).

[0317] Selection of the model that minimizes the retention time errors (i.e., best fit model) can include calculating (a) retention time errors based on an alignment of library retention time and the observed data, (b) cumulative distributions for both the empirical and predicted retention time errors, and (c) optimal retention time tolerance.

[0318] The best fit model can be duplicated for each sample segment, with an updated retention time based on the retention times and respective alignment identified using the same techniques described above or finetuned separately for each time-channel. Retention time tolerances can be made to be non-overlapping between sample segments to ensure that the same precursor in each sample segment will be considered independent of the other channels, leading to column and sample-specific identifications. After the timePlex library is created, the data can be searched using the same approach as any other experiment.

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[0321] Systems

[0322] The present disclosure provides systems for multiplexing through temporal encoding, which, according to one or more embodiments, can include: a separation platform utilizing a liquid mobile phase; an injection system configured to inject multiple sample segments in series within the separation platform at a pre-determined time point; an MS interface for connecting the chromatography separation platform to a mass spectrometer; and a data analysis system configured to assign identified analytes of interest in the multiplexed sample to a single sample based on injection time offset and predicted elution times for the analytes of interest. The system can be configured to inject multiple sample segments sequentially or simultaneously (e.g., at substantially the same time).

[0323] As discussed in the context of method 100 (FIG. 33), a liquid separation platform can include CE or LC platforms. CE can be used as a separation platform for polar and ionic metabolites that possess an intrinsic electrophoretic mobility under a specific buffer condition. A CE system can include a homogenous aqueous buffer in a CE capillary. For coupling with MS, an isocratic aqueous solution comprising an organic solvent can serve as a make-up flow in the sheath liquid. A CE platform can include a single capillary or a capillary array whereby separations are performed "in-parallel". The sample to be analyzed can be introduced (e.g., injected) by any known method, examples of which include hydrodynamic injection, electrokinetic injection, and multi-segment injection. The time at which a specific analyte elutes from the capillary is called the migration time or elution time and can be a reasonably identifying characteristic of a given analyte especially when combined with other analyzing characteristics.

[0324] In certain embodiments, the liquid separation platform comprises an LC platform. LC is a well-established analytical technique for separating components of a fluidic mixture for subsequent analysis and / or identification, in which a chromatography column, microfluidic chipbased channel, or tube is packed with a stationary phase material that typically is a finely divided solid or gel such as small particles with diameter of a few microns. The small particle size provides a large surface area that can be modified with various chemistries creating a stationary phase. A liquid eluent is pumped through the chromatography column (“LC column”) at a desired flow rate based on the column dimensions and particle size. This liquid eluent can be referred to as the mobile phase. The sample to be analyzed is introduced (e.g., injected) in a small volume into the stream of the mobile phase prior to the LC column. The analytes in the sample are retarded by specific chemical and / or physical interactions with the stationary phase as they traverse the length of the column. The amount of retardation depends on the nature of the analyte, stationary phase, and mobile phase composition.

[0325] In certain embodiments, the separation platform is a conventional high-performance LC system, an ultra-HPLC (UHPLC) system, a reversed-phase HPLC (RP-HPLC) system, a

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[0328] Hydrophilic Interaction Liquid Chromatography (HILIC) system, ora size-exclusion chromatography (SEC) system.

[0329] In certain embodiments, the LC platform is configured for reversed-phase chromatography, normal phase chromatography, ion exchange chromatography, size exclusion chromatography (SEC) by the selection of appropriate buffers and columns. In certain embodiments, the separation platform is configured for isocratic flow chromatography or gradient flow chromatography.

[0330] In certain embodiments, the separation platform is configured or adapted to deliver a change in liquid mobile phase composition. The liquid mobile phase composition typically includes a mixture of solvents, including one or more organic solvents and water. In certain embodiments, the change in composition of the liquid mobile phase involves varying the relative proportions of organic solvent and water to elute different analytes of interest from the sample segments to create a gradient. As used herein, the term “active gradient” refers to the period during a separation during which a change in mobile phase composition is used to elute and separate the target compounds, as distinguished from the total cycle time, which can include loading, washing and / or re-equilibration. Thus, the change in liquid mobile phase composition can initiate elution of one or more analytes of interest, which can be used to encode an elution initiation time offset. In certain embodiments, the separation platform can be configured or adapted to provide a gradient, such as a gradient formed by an increase in the relative proportion of organic solvent to aqueous solvent, an inverse gradient, a pH gradient, an electroosmotic flow gradient, or combinations thereof (e.g., multidimensional chromatography). Non-limiting examples of suitable organic solvents include acetonitrile, methanol, ethanol, propanol, isopropanol, and the like. Non-limiting examples of organic solvents for reversed phase chromatography can include acetonitrile and / or methanol. In certain embodiments, the liquid mobile phase composition further includes one or more additives such as, e.g., acetic acid, trifluoroacetic acid, heptafluorobutyric acid, formic acid, ammonium hydroxide, ammonium formate, and / or ammonium acetate; non-volatile salts; surfactants / detergents, and combinations thereof. The person of ordinary skill in liquid chromatography is capable of selecting and mixing the components of a liquid mobile phase based on the sample type, miscibility, temperature, viscosity, compressibility, etc.

[0331] In certain embodiments, the separation platform is configured or adapted to change the composition of the liquid mobile phase linearly overtime, to deliver a multi-segment gradient selected from the group consisting of Logarithm-like, Exponential-like, or Step-linear gradient, or to program a combination of different gradient curves to fine tune a time offset (e.g., a hybrid linear and exponential gradients).

[0332] In certain embodiments, the separation platform can be configured or adapted to change the composition over any period of time useful for the specific application, desired resolution,

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[0335] and / or the type of equipment being used. Suitable gradient durations can be determined empirically based on the desired resolution, e.g., to optimize the number of peaks that can be separated, and / or to optimize throughput. In certain embodiments, the separation platform is configured or adapted to change the composition of the liquid mobile phase over a period having a duration of less than one minute or greater than or equal to one minute, such as about 1 minute to about 30 minutes, about 30 minutes to about 60 minutes, about 60 minutes to about 90 minutes, 90 minutes to about 240 minutes, or about 4 or more hours. In certain embodiments, the separation platform is configured or adapted to program a multi-segmented gradient in which one or more segments has a distinct duration from another of the segments. In a non-limiting example of a multi-segmented gradient comprising a combination of a shallow linear gradient segment, an exponential gradient segment, a steep linear gradient segment, and an isocratic hold segment, each segment can be independently programmed to have a duration within about 1-10 minutes.

[0336] A gradient can be initiated at any time with respect to sample injection time. In certain embodiments, the gradient can be initiated after an isocratic hold. The gradient (or initial gradient in the case of multi-segmented gradient) can be initiated at a defined period after injection. In certain embodiments, the gradient can be initiated immediately after injection (i.e. , at a time point of zero).

[0337] A skilled artisan in liquid chromatography or capillary electrophoretic techniques would be capable of selecting equipment to configure or adapt a separation platform for gradient separations based on desired speed and resolution. Non-limiting examples of equipment can include one or more external pumps, one or more solvent reservoirs, one or more mixing chambers, and one or more degassers. The separation platform can further include columns or capillaries compatible with the desired gradient. In a non-limiting example, a separation platform for use with a gradient of less than one minute can be configured with one or more high pressure pump(s), low dwell volumes between the mixing chamber and the head of the column(s), which can be configured for high back-pressure (e.g., narrow-bore and / or short columns with small particles).

[0338] In certain embodiments, a pre-formed gradient is introduced to the separation platform to provide a differential spacer. As used in the context of a gradient, “differential spacer” can refer to a discrete plug of mobile phase having a different proportion of solvent than the adjacent mobile phase comprising a sample. The volume and / or composition of spacer plugs in can be determined experimentally to fine tune separation of different mobile phase segments while creating a reproducible gradient profile. For example, plugs of a weaker mobile phase (e.g., water) can be used as spacers to maintain a low concentration of the strong mobile phase to ensure that early-eluting compounds are focused and adequately resolved, and high-concentration solvent spacers can be used for steeper gradients. In certain embodiments, the 31

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[0340] total volume of a pre-formed gradient, including all spacer plugs can be scaled to the flow rate and the volume of the column or capillary.

[0341] In certain embodiments, a differential spacer can be based on volume. Different volumes of spacer can be introduced in a structured series of injections to process multiple samples without waiting for the complete elution of one sample before injecting the next. By separating multiple sample segments with spacer plugs of different volumes, the physical separation between sample segments can be varied, allowing for fine tuning of the time offsets of each sample. Unlike methods describing spacer volume optimized to allow for baseline resolution between loaded samples (e.g., US 9,490,110 B2), the methods described herein do not require baseline resolution to assign identified analytes of interest to a single sample segment, thereby further improving efficiency. In certain embodiments, the separation platform comprises a single column or capillary, or multiple columns or capillaries. In certain embodiments, the separation platform comprises a plurality of chromatography columns (C1-n, C'1-n) arranged in parallel (also referred to as a “parallel assembly”). Each chromatography column of the plurality can be fluidically connected to a transfer line. The transfer lines can be adapted to receive a sample segment and encode a time offset for injection of the sample segment into its connected column. Transfer lines can be composed of any suitable material for moving fluids or materials between different points in a system.

[0342] The combination of chromatography column and transfer line can be referred to as a fluid path. In a specific example, a separation platform comprises at least three fluid paths, a common fluid inlet to the parallel assembly, and a common fluid outlet from the parallel assembly, or a plurality of outlets from respective columns of the fluid path. The inlet fluid path can include a liquid handling unit comprising one or more pumps, valves, transfer lines, and sample loops. In certain embodiments, a liquid handling unit can include a metering type pump, hereby allowing for determination of delivered flow rate by calculation from a number of pump revolutions, a displaced volume or similar. In certain embodiments, the inlet fluid path includes one or more valves configured for blocking of a transfer line, opening of a transfer line, and / or connecting two transfer lines (e.g., rotary valves). The valve can be used to switch between transfer lines during sample loading. In certain embodiments, the fluid path further includes a sample loop. A sample loop can be configured for precise and reproducible introduction of a defined volume of sample into the transfer lines. The valve can be configured to switch to a position where the sample loop is isolated from the main flow path connecting the column. Sample can be introduced into the loop by injection from a sample vial or source, thereby filling the loop with the desired sample segment. Non-limiting examples of liquid handling units according to one or more embodiments of the present disclosure is shown in FIGs. 24 and 25.

[0343] A separation platform can include at least one transfer line with a larger internal volume and at least one with a smaller internal volume. Transfer line volume differences can encode 32

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[0345] specific elution time windows for identification of analytes of interest. In certain embodiments, injection time offset can be achieved using transfer lines that have different internal volumes. In certain embodiments, the internal volumes of any one of the transfer lines can differ by about 10%, 20%, 30%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 110%, 120%, 130%, 140%, 150%, 160%, 170%, 180%, 190%, 200%, 210%, 220%, 230%, 240%, 250%, 260%, 270%, 280%, 290%, 300%, 325%, 350%, 375%, 400%, 425%, 450%, 475%, 500%, 525%, 550%, 575%, 600%, 625%, 650%, 675%, 700%, 725%, 750%, 775%, 800%, 825%, 850%, 875%, 900%, 925%, 950%, 975%, 1000%, or more in comparison with another of the C1-n, C'1-n transfer lines.

[0346] Internal volume of respective transfer lines can be determined from the physical dimensions (the internal diameter (ID) and the length (e.g., mm)). Transfer lines can have a constant internal diameter or have sections with different internal diameters. In a particular embodiment, the internal diameters and the lengths of the transfer lines can range independently from about 20-50 pm ID and about 150 mm - 950 mm can be used in combination to encode time offsets between samples.

[0347] A separation platform can be configured for direct coupling with a mass spectrometry platform. In certain embodiments, a liquid separation platform of the present disclosure can include an emitter. As used herein, the term “emitter” refers to the physical tip or the end of a capillary, column, channel, or fluid path outlet through which the liquid sample eluent exits and is converted into a fine spray of charged droplets for ionization by electrospray ionization (ESI). The emitter is configured as an interface between the liquid-phase separation technique and the gas-phase mass spectrometer. The design and material of the emitter can vary to optimize sensitivity, stability, and / or robustness of the analysis. In certain embodiments, the emitter can be selected to enhance coverage of analyte specific data-points per run, as further described in the Examples.

[0348] In certain embodiments, the emitter is a multi-flow emitter (designed to generate multiple simultaneous electrosprays from a single liquid stream (e.g., common outlet of a parallel assembly) or from multiple independent separation channels (e.g., a plurality of outlets, capillaries, columns, chip-based systems). A separation platform can include an array of separate emitters. The array can be configured for introducing the eluent from different outlets of a parallel assembly to the mass spectrometer detector substantially simultaneously.

[0349] A mass spectrometer of the present disclosure can include any suitable instrumentation for converting the analyte molecules (e.g., peptides) to a charged (ionized) state, with subsequent analysis of the ions and any fragment ions that are produced during the ionization process, on the basis of their mass to charge ratio (m / z). Examples of mass spectrometry systems of the present disclosure include, without limitation, systems with one or more mass

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[0352] spectrometers optionally coupled with a system for electrophoretic or chromatographic separation.

[0353] A tandem mass spectrometer (MS / MS) involves two stages of mass spectrometry selection and fragmentation. The mass spectrometer can be a Quadrupole, Time of flight (TOF), quadrupole time of flight (Q-TOF), ion trap (IT), quadrupole ion trap (Q-IT), triple quadrupole (QQQ), hybrid quadrupole-time-of-flight, quadrupole-orbitrap, ion trap-orbitrap, or quadrupole-ion-trap-orbitrap (tribrid), Fourier transform mass spectrometry (FTMS) or Fourier transform ion cyclotron resonance (FTICR) mass analyzer. A mass analyzer can be configured for detection of low concentrations of a protein of interest, such as samples containing amounts below 10 femtomoles (fmoles) of an analyte of interest (e.g., less than 10, 8, 6, 4, 2, 1 fmoles, or attomole quantities). A mass spectrometer can be configured for any of various approaches, such as multiple reaction monitoring (MRM), selected reaction monitoring (SRM), parallel reaction monitoring (PRM), a data-dependent (DDA), or a data-independent acquisition (DIA) method.

[0354] Turning to FIG. 34, a schematic diagram of a system 3400 for multiplexing through temporal time encoding is shown. As previously described, the system 3400 can include: a separation platform 3402 utilizing a liquid mobile phase and a mass spectrometer 3410. The separation platform 3402 includes: an injection system 3404 configured to sequentially inject multiple sample segments in series into a fluid within the separation platform 3402 at a predetermined time point; a fluid handling system 3406 configured to move the fluid through the separation platform 3402; and an MS interface 3408 for connecting the separation platform 3402 to the mass spectrometer 3410. In an alternative embodiment, the injection system 3404 can be configured to inject multiple sample segments simultaneously or at substantially the same time.

[0355] The separation platform 3402 can include CE or LC platforms as previously described. In other embodiments, the separation platform 3402 can include any platform capable of separating compounds in a mixture. In certain embodiments, the separation platform 3402 comprises an LC platform. In certain embodiments, the separation platform 3402 is a conventional high-performance LC system, an ultra-HPLC (UHPLC) system, a reversed-phase HPLC (RP-HPLC) system, a Hydrophilic Interaction Liquid Chromatography (HILIC) system, or a size-exclusion chromatography (SEC) system.

[0356] In embodiments where the separation platform 3402 is an LC platform, the LC platform is configured for reversed-phase chromatography, normal phase chromatography, ion exchange chromatography, size exclusion chromatography (SEC) by the selection of appropriate buffers and columns. In certain embodiments, the separation platform 3402 is configured for isocratic flow chromatography or gradient flow chromatography.

[0357] The injection system 3404 is configured to inject or introduce the sample to be analyzed. The sample to be analyzed can be injected by any known method by the injection system 3404,

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[0360] examples of which include hydrodynamic injection, electrokinetic injection, and multi-segment injection.

[0361] The fluid handling system 3406 (discussed in detail for FIGS. 35A-38) is configured to move the fluid and sample through the separation platform 3402 and the interface 3408, and to the mass spectrometer 3410. As previously described, the mass spectrometer 3410 can include any suitable instrumentation for converting the analyte molecules (e.g., peptides) to a charged (ionized) state, with subsequent analysis of the ions and any fragment ions that are produced during the ionization process, on the basis of their mass to charge ratio (m / z). Examples of the mass spectrometry 3410 of the present disclosure include, without limitation, systems with one or more mass spectrometers optionally coupled with a system for electrophoretic or chromatographic separation.

[0362] Various embodiments and implementations of the fluid handling system 3406 will now be described in FIGS. 35A-38. It will be appreciated that the embodiments described below can include more or less components than described and components of one embodiment can be used in other embodiments and / or in combination with other components.

[0363] Turning to FIGS. 35A and 35B, a first embodiment and second embodiment of the fluid handling system 3406 according to embodiments of the present disclosure are respectively shown. As shown, the fluid handling system 3406 includes a flow splitter 3500, one or more transfer lines 3502, one or more columns or capillaries 3504, and an emitter 3508. In FIG. 35A, the samples from the one or more columns or capillaries 3504 are joined at a flow joiner 3506 and delivered to a single emitter 3508 that interfaces with the mass spectrometer 3410. In FIG.

[0364] 35B, the fluid handling system 3406 does not include the flow joiner 3506 and rather, each column or capillary of the one or more columns or capillaries 3504 has a corresponding emitter 3508 that interfaces with the mass spectrometer 3410. In other embodiments, the fluid handling system 3406 may include more or less components, as will be discussed in FIGS. 36-38.

[0365] The flow splitter 3500 is configured to split flow received from the separation platform 3402 to each transfer line of the one or more transfer lines 3502. The flow from the separation platform 3402 can be gradient flow, isocratic flow, or any other type of flow. The one or more transfer lines 3502 can be composed of any suitable material for moving fluids or materials between different points in a system. Each transfer line 3502 is in fluid communication with a corresponding column or capillary of a one or more columns or capillaries 3504. The one or more transfer lines 3502 can be adapted to receive a sample segment and encode a time offset for injection of the sample segment into its connected column or capillary 3504, as will be discussed in more detail below.

[0366] In the illustrated embodiments, the one or more transfer lines 3502 includes a first transfer line 3502(1), a second transfer line 3502(2), and transfer line 3502(N) representing any number of transfer lines. In other words, the one or more transfer lines 3502 can include two 35

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[0368] transfer lines or more than two transfer lines. Each transfer line of the one or more transfer lines 3502 has a volume defined by a length of the transfer line and a diameter of the transfer line. In the illustrated embodiment, the first transfer line 3502(1) has a first length L1 and a first diameter D1 (e.g., internal diameter) and the second transfer line 3502(2) has a second length L2 and a second diameter D2. In other embodiments, any transfer line represented by transfer line 3502(N) can have a length LN and a diameter DN. The lengths L1, L2, and LN may be the same lengths or may each be different lengths and the diameters D1 , D2, and DN may be the same diameters or may each be different diameters. It will be appreciated that in some embodiments, the diameter may also vary along the length of a transfer line. The lengths L1 , L2, and LN and the diameters D1 , D2, and DN define a volume for each transfer line 3502. Because the lengths L1, L2, and LN and the diameters D1, D2, and DN can vary in each transfer line 3502, the corresponding volume of each transfer line 3502 may be different depending on the length and diameter of each transfer line 3502.

[0369] The volumes of each transfer line 3502 can be beneficially used to encode time-offsets for each transfer line 3502. Such time-offsets encode a difference in time travel to each column or capillary 3504, and thus the mass spectrometer 3410. For example, the first transfer line 3502(1) may have a first-time offset defined by the first length L1 and the first diameter D1 and the second transfer line 3502(2) may have a second-time offset defined by the second length L2 and the second diameter D2. It will be appreciated that any number of time offsets can be encoded for any number of transfer lines (e.g. , transfer line 3502(N)) and defined by a length LN and a diameter DN.

[0370] As previously described, each transfer line 3502 is in fluid communication with a corresponding column or capillary of the one or more columns or capillaries 3504. As shown, the one or more columns or capillaries 3504 are arranged in parallel, though in other embodiments the one or more columns or capillaries 3504 may not be arranged in parallel. The one or more columns or capillaries 3504 also have the same length, diameter, and composition in the illustrated embodiment, though it will be appreciated that in other embodiments, the one or more columns or capillaries 3504 may have different lengths, diameters, and / or compositions. The columns or capillaries 3504 either are in fluid communication with the flow joiner 3506 (shown in FIG. 35A) or with corresponding emitters 3508 (shown in FIG. 35B). In FIG. 35A, the fluid flow joiner 3506 joins the fluid flow from each of the one or more columns or capillaries 3504 prior to the fluid flow entering the emitter 3508. In any embodiment, the fluid flow enters the emitter 3508 to be converted for the mass spectrometer 3410.

[0371] The emitter 3508 refers to the physical tip or the end of the column or capillary 3504, or fluid path outlet through which the fluid flow exits and is converted into a fine spray of charged droplets for ionization by electrospray ionization (ESI). The emitter 3508 is an interface between the liquid-phase separation technique (e.g., the separation platform 3402) and the gas-phase 36

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[0373] mass spectrometer (e.g., the mass spectrometer 3410). The design and material of the emitter 3508 can vary to optimize sensitivity, stability, and / or robustness of the analysis. In certain embodiments, the emitter 3508 is a multi-flow emitter (designed to generate multiple simultaneous electrosprays from a single liquid stream (e.g., common outlet of a parallel assembly) or from multiple independent separation channels (e.g., a one or more outlets, capillaries, columns, chip-based systems). The emitter 3508 can also include an array of separate emitters. The array can be configured for introducing the eluent from different outlets of a parallel assembly to the mass spectrometer detector substantially simultaneously.

[0374] T urning to FIG. 36, a schematic diagram of another embodiment of the fluid handling system 3406 is shown. The fluid handling system 3406 includes a pump 3510 configured to move the fluid from the separation platform 3402, through the fluid handling system 3406 (e.g., the transfer lines 3502 and the columns 3504) and to the emitter 3508. The pump 3510 may be any type of pump such as, for example, a mechanical pump, an electromechanical pump, a positive-displacement pump, a rotary pump, a gear pump, a piston pump, a lobe pump, or the like.

[0375] The fluid handling system 3406 also includes a sample loop 3512 configured to provide a sample to the transfer lines 3502. As shown in the illustrated embodiment, the sample loop 3512 is in fluid communication with a selector valve 3514. The selector valve 3514 is configured to control flow to the one or more transfer lines 3502 and can control flow to any number of transfer lines 3502. For example, in the illustrated embodiment the selector valve 3514 may provide flow to the first transfer line 3502(1) while blocking flow to the second transfer line 3502(2) and the transfer line 3502(N). In another example, the selector valve 3514 may provide flow to the first transfer line 3502(1) and the second transfer line 3502(2) while blocking flow to the transfer line 3502(N).

[0376] As previously described, the fluid flow moves from the one or more transfer lines (with samples time encoded in the one or more transfer lines) to the corresponding columns or capillaries 3504. Though not shown, the columns or capillaries 3504 are in fluid communication with corresponding emitters or the flow joiner.

[0377] T urning to FIG. 37, a schematic diagram of another embodiment of the fluid handling system 3406 is shown. The fluid handling system 3406 shown in FIG. 37 is similar to the fluid handling system 3406 shown in FIG. 36. The fluid handling system 3406 includes the pump 3510 configured to move the fluid from the separation platform 3402, through the fluid handling system 3406 (e.g., the transfer lines 3502 and the columns 3504) and to the emitter 3508.

[0378] The fluid handling system 3406 also includes the sample loop 3512 configured to provide a sample to the transfer lines 3502. As shown in the illustrated embodiment, fluid handling system 3406 also includes a fluid splitter 3516, supply lines 3518, and one or more valves 3520. The one or more valves 3520 are configured to control fluid flow to the one or 37

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[0380] more transfer lines 3502. The one or more valves 3520 may include any number of valves (and as previously described, the one or more transfer lines 3502 can include any number of transfer lines). In one example, the first valve 3520(1) may allow fluid flow to the first transfer line 3502(1) while the second valve 3520(2) restricts fluid flow to the second transfer line 3502(2) and the N-valve 3520(N) restricts fluid flow to the transfer line 3502(N). In another example, the first valve 3520(1) may allow fluid flow to the first transfer line 3502(1) and the second valve 3520(2) may allow fluid flow to the second transfer line 3502(2) while the N-valve 3520(N) restricts fluid flow to the transfer line 3502(N).

[0381] Specific embodiments and details of the fluid handling system 3406 with the valves 3520 are shown and described in FIGS. 24 and 25.

[0382] T urning to FIG. 38, a schematic diagram of another embodiment of the fluid handling system 3406 is shown. The fluid handling system 3406 shown in FIG. 38 is similar to the fluid handling system 3406 shown in FIG. 36. The fluid handling system 3406 includes the pump 3510 configured to move the fluid from the separation platform 3402, through the fluid handling system 3406 (e.g., the transfer lines 3502 and the columns 3504) and to the emitter 3508.

[0383] The fluid handling system 3406 also includes the sample loop 3512 that is configured to provide a sample to the transfer lines 3502. As shown in the illustrated embodiment, the sample loop 3512 is in fluid communication with the selector valve 3514. It will be appreciated that the sample loop 3512 can be in fluid communication with other flow controls such as one or more other valves (shown in, for example, FIG. 37).

[0384] The fluid handling system 3406 illustrates another embodiment of time-encoding the sample in a transfer line 3502. As shown, a first-time offset is defined by a first position of a first sample 3522(1) in the first transfer line 3502(1), a second-time offset is defined by a second position of a second sample 3522(2) in the second transfer line 3502(2), and an N-time offset is defined by an N-position of a N-sample 3522(N) in the transfer line 3502(N). The first sample 3522(1) is shown in a position closer to the selector valve 3514, the second sample 3522(2) is shown in a position closer to the second column 3504(2), and the N-sample is shown in between the first sample 3522(1) and the second sample 3522(2). Such positionings of the samples 3522 offset the timing of the sample entering a corresponding column or capillary 3504. Thus, the positioning of samples in the transfer lines 3502 (regardless of the dimensions of the transfer lines 3502) can be used to encode time-offsets. Additionally, in embodiments where the transfer lines 3502 have identical or similar dimensions such that time-offsets cannot be encoded based on the volume of the transfer lines 3502, the time-offsets can be encoded by positioning of the samples within the transfer lines 3502. Though, it will be appreciated that in some embodiments, the time-offsets can be defined by both the volume of the transfer line 3502 and the position of the sample within the transfer line.

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[0387] In at least one embodiment of the present disclosure, time-offsets for multiple samples can be encoded based on volumetric spacers injected into a capillary between sample segments and applying a voltage to the samples. More specifically, in such embodiments, the one or more columns or capillaries 3504 described above are capillaries and the separation platform 3402 is a CE platform. In such embodiments, a plurality of samples are loaded into a capillary 3504 and interspaced with defined volumetric spacers to encode a corresponding timeoffset for each sample segment within the capillary 3504. A voltage supplied by, for example, a power supply of the CE platform is then applied to the capillary 3504 to separate one or more analytes of each sample segment of the plurality of sample segments. The one or more analytes may be, for example, peptides or other biological molecules. The voltage causes each analyte to separate at a predictable rate based on each analyte’s electrophoretic mobility. Thus, the applied voltage and encoding the time-offset for multiple sample segments in the capillary 3504 allow for overlapping separations from multiple sample segments in a single capillary 3504. It will be appreciated that the embodiment described can include multiple capillaries in which each capillary has multiple sample segments. Further, each capillary 3504 is in fluid communication with a flow joiner 3506, which is in fluid communication with an emitter 3508, or is directly in fluid communication with the emitter(s) 3508. In such embodiments, the emitter(s) 3508 uses ESI.

[0388] In an alternative embodiment of the above, a plurality of sample segments are sequentially loaded into a capillary 3504 at defined intervals to encode sample-specificity within the capillary 3504. In such alternative embodiment, the voltage supplied by, for example, the power supply of the CE platform is continuously applied to the capillary 3504 as the samples are sequentially loaded into the capillary 3504. It will be appreciated that the embodiment described can also include multiple capillaries in which each capillary has multiple sample segments.

[0389] In an alternative embodiment of the above, the system can include a plurality of separation platform 3402. The plurality can be configured to operate in tandem. For example, two or more separation platforms having any of the features described above, can be configured to operate simultaneously or sequentially.

[0390] Instruments for Deconvolving

[0391] A system for performing mass spectrometry using time-encoded sample multiplexing as described herein can include any suitable instrumentation for deconvolving. Deconvolving includes analyzing the overlapping eluate to identify analytes of interest and to assign these identified analytes to a single sample segment. The instrumentation can include one or more computer processors for executing stand-alone or remote software or software stored directly or indirectly on the instrumentation.

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[0394] One or more embodiments may be implemented on a computing system or using instrumentation specifically designed to achieve an improved technological result. The use of computing system, as used herein, means any system or instrumentation that includes components or has functionality as described in this disclosure.

[0395] When implemented on a computing system or using instrumentation, the features and elements of the disclosure provide a significant technological advancement over systems that do not implement the features and elements of the disclosure. Any combination of embedded devices or other types of hardware may be improved by including the features and elements described in the disclosure.

[0396] For example, the computing system or instrumentation may include one or more computer processor(s), non-persistent storage device(s), persistent storage device(s), a communication interface (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and numerous other elements and functionalities that implement the features and elements of the disclosure. The computer processor(s) may be an integrated circuit for processing instructions. The computer processor(s) may be one or more cores, or micro-cores, of a processor. The computer processor(s) includes one or more processors. The computer processor(s) may include a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), combinations thereof, etc.

[0397] The input device(s) may include a touchscreen, keyboard, dial, button, mouse, microphone, touchpad, electronic pen, or any other type of input device. The input device(s) may receive inputs from a user that are responsive to data and messages presented by the output device(s). The inputs may include text input, audio input, video input, etc., which may be processed and transmitted by the computing system or instrumentation in accordance with one or more embodiments. The communication interface may include an integrated circuit for connecting the computing system or instrumentation to a network (not shown) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) or to another device, such as another computing device, and combinations thereof.

[0398] Further, the output device(s) may include a display device, a printer, external storage, or any other output device. One or more of the output device(s) may be the same or different from the input device(s). The input device(s) and output device(s) may be locally or remotely connected to the computer processor(s). Many different types of computing systems and instrumentation exist, and the aforementioned input device(s) and output device(s) may take other forms. The output device(s) may display data and messages that are transmitted and received by the computing system or instrumentation. The data and messages may include text, audio, video, etc., and include the data and messages described above in the other figures of the disclosure.

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[0401] Software instructions in the form of computer readable program code to perform embodiments may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a solid state drive (SSD), compact disk (CD), digital video disk (DVD), storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium. Specifically, the software instructions may correspond to computer readable program code that, when executed by the computer processor(s), is configured to perform one or more embodiments, which may include transmitting, receiving, identifying, analyzing, presenting, and displaying data and messages described in the other figures of the disclosure.

[0402] The computing system or instrumentation may be connected to, or be a part of, a network. For example, the network may include multiple nodes. Each node may correspond to a computing system or instrumentation or a group of nodes may be combined. By way of an example, embodiments may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments may be implemented on a distributed system that has multiple nodes, where each portion may be located on a different node within the distributed computing system.

[0403] The computing system or instrumentation may include functionality to present data (including raw data, processed data, and combinations thereof) such as results of comparisons and other processing. For example, presenting data may be accomplished through various presenting methods. Specifically, data may be presented by being displayed in a user interface, transmitted to a different computing system, and stored. The user interface may include a graphical user interface (GUI) that displays information on a display device.

[0404] The methods and systems for mass spectrometry using time-encoded sample multiplexing will be further described in the following examples, which do not limit the scope of the subject matter described in the claims.

[0405] EXAMPLES

[0406] This work demonstrates two or more embodiments of the methods and systems described above and provides a framework for multiplexing samples in time to higher plex which is generalizable to other implementations, and can allow for a combinatorial increase in proteomics throughput when paired with labeling approaches (see, e.g., Derks, J. et al.

[0407] (2022). The following examples demonstrate multiplexing in the time domain, “timePlex”, as a synergistic strategy that works by staggering and overlapping the separation periods of individual samples. Such an approach has been implemented for sample multiplexing based on multi-segment capillary electrophoresis (CE) and targeted analysis of relatively few analytes, (see, e.g., Kuehnbaum, et al. (2013), (2014), and (2015)).

[0408] Example I. Multiplexing in the time and mass domains is combinatorial

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[0411] The throughput of LC-MS proteomics data acquisition can be increased by acquiring multiple samples in parallel. This sample-multiplexing is commonly achieved through isobaric or isotopologous labeling, and pooling of samples to be run in the same LC-MS run. Previously we demonstrated this potential with 3-plexDIA enabling comparable quantitative accuracy and proteomic coverage with multiplicatively increased sample-throughput. Here we investigate an orthogonal approach which multiplexes samples in the time-dimension through chromatographic multiplexing, which we term “timePlex”, and the combination of 3x3 plexDIA & timePlex to enable a 9-plex. Shown in FIG. 1 is an illustration of precursor elution curves for the different multiplexing approaches. Costs associated with LC-MS proteomics data acquisition predominantly stem from the high cost of operating LC-MS instrumentation. Making more efficient use of this instrumentation, by sample-multiplexing with plexDIA or timePlex, multiplicatively reduces costs per sample as costs of mass-tags and LC-columns are small relative to LC-MS instrumentation. Due to their orthogonality, timePlex and plexDIA may be used in combination, increasing throughput and cost-saving quadratically with plex, as shown in FIG. 2. Here, we seek to develop an approach for timePlex proteomics, and benchmark its combinatorial potential when paired with plexDIA.

[0412] Example II. Implementing timePlex proteomics data acquisition

[0413] Multiplexing through temporal encoding can be implemented through several distinct approaches. Here, we aim for a proof of principle with two accessible implementations illustrated in FIG. 3, expecting significant improvements in future implementations. In both approaches, the gradient flow originating from a single LC-system is split to separate columns. Time offsets are encoded through capillary transfer lines with different volumes, thus encoding a difference in travel time to each column, and thus the mass spectrometer. In the first implementation, termed ‘separate-emitters’, the flow from each column remains separate and they undergo separate ESI from their respective emitters. In the second approach, termed ‘single-emitter’, the flow is rejoined post-column to a single emitter. The resulting MS data will include multiple observations of the same peptides, separated by systematic time offsets, encoded by the different travel paths FIG. 4.

[0414] In these implementations, samples are sequentially loaded and travel through the same sample-loop and parts of the same transfer lines to reach their designated columns during sample-loading. Schematics for implementing a 3-timePlex are shown in FIG. 24; a 4-timePlex may be theoretically achievable with the schematics shown in FIG. 25, although it is not demonstrated in this work. During sample loading, peptides may bind unspecifically to surfaces in the common flowpath, leading to sample-carryover into unintended columns. To mitigate or prevent this all together, samples were resuspended in 0.015% dodecyl maltopyranoside (DDM) prior to sample-loading. Of two separate instances, carryover was entirely undetectable, as the timePlex search would not identify two separate time channels (as expected in a non-timePlex 42

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[0416] acquisition), or the carryover was mostly undetectable, and of the non-missing data, carryover was limited to a median of 5.7% of the intensity of the intended time-channel, FIG. 26.

[0417] Example III. Interpreting timePlex proteomics data

[0418] Interpretation of timePlex data capitalizes on highly predictable elution profiles of chemical analytes (e.g., peptides) anticipating the potential for multiple observations of such analytes separated by systematic time-offsets. While RT offsets are less precise and reproducible than m / z offsets, if they are engineered to be large enough for the precision of RT prediction, they can afford reliable sample assignment.

[0419] To analyze such data, we developed a specialized timePlex module in JMod (manuscript in progress). The module implements (1) time-channel identification and (2) main search. The time-channel identification determines the times when precursors from each channel are expected. Then, these times are used to perform the main search. The time-channel detection starts by identifying peptide-like features that correspond to the same peptide sequence detected in each of the n samples. The temporal order of these observations is then used to build time-channel specific libraries. These initial identifications are then used to align and finetune the retention-times of the library for the main search. This approach remains flexible to differences in time-offsets between multiple time-channels, as this process is automated. As a result, the user does not need to specify the time spacing between samples, only the number of samples multiplexed in time to expect.

[0420] While precursors across time-channels are systematically separated in time, accurately assigning each identified precursor to a sample becomes challenging without fully complete observations. This difficulty stems from inaccuracies in library retention times, which we improved by creating a retention-time predictor, as others have done, to be used for fine-tuning library retention times on a per run basis. The model architecture implemented in this work is a convolutional neural network (CNN) ending in fully connected layers of a feedforward deep neural network (DNN) as illustrated in FIG. 5. A summary of the model architecture is shown in FIG. 27. LF and mTRAQ models were trained on fewer than 100k peptides each; test-data was withheld to evaluate the accuracy of the models for unseen mTRAQ and LF peptides. We observed strong agreement between predicted and empirical iRTs (R2 > 0.997) for both label-free and mTRAQ-labeled peptides, as shown in FIG. 6, suggesting these models accurately predict retention times, and may be suitable for transfer learning.

[0421] Data generated outside of the training and test sets was used to evaluate the potential for these models to refine library retention times given new data acquired under different conditions (different gradient, instruments, and column batch). We found that the original model was able to predict 90% of the data within 30.0 seconds of the empirical RT, and after fine-tuning it improved to within 8.8 seconds, as shown in FIG. 7. The improved RT accuracy stands

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[0424] to benefit DIA search performance due to increased specificity of retention time search spaces, even in non-timePlex applications, as previously shown by Wallmann et al.

[0425] Fine-tuning retention times on a per run basis is of even greater importance with timePlex data where inaccurate library retention times will lead to ambiguous time channel assignment of peptides, as shown in the left panel of FIG. 8. To evaluate fine-tuning in a real timePlex applications, we compared the iRTs generated from DIANN in-silico library prediction to the empirical RTs in a LF 3-timePlex experiment. The 3-timePlex data display overlap between timePlex channels, despite there being a systematic retention time offset. This ambiguity is due to inaccuracies in the library RTs, which we resolve with through updating library RTs based on initial search results using transfer learning. Using the distribution of residuals between predicted and empirical RTs of this initial search, we dynamically set thresholds to confine the RT-space where a precursor will be considered, shown in FIG. 28A-B.

[0426] This fine-tuning step, enhances the ability of properly assigning sample-origins to each precursor. The resulting fine-tuned and searched data produce clear temporal separation between the timePlex channels, as shown in the right panel of FIG. 8.

[0427] Example IV. Benchmarking timePlex proteomic coverage through mixed-species standards

[0428] To investigate the potential for false identifications in 3-timePlex, we acquired data where human protein digests were present at 10 ng in all three timePlexes, but yeast digests were present at 5 ng in only the last two. These data were searched with a human, yeast, and Arabidopsis spectral library, searching for peptides from all species in each sample. At 1% FDR, we identify very few yeast precursors in the time channel with missing yeast digest, and very few Arabidopsis precursors across all three time channels, as shown in FIG. 9. Extracted ion chromatograms were plotted for a charged peptide sequence corresponding to GAPDH in humans and the yeast homolog TDH1. Signal was present in each time-channel for each human precursor, while the yeast precursors had a relative absence in the AO min time channel, FIG. 10.

[0429] Next, we sought to benchmark the depth of coverage achieved with 3-timePlex and combinatorial 3x3 plexDIA & timePlex, relative to LF No-plex. To accomplish this, we created mixed-species standards of human and yeast proteomic digests, where the human composition was held constant, and yeast was variably spiked at know differential abundances, as commonly done to benchmark quantitative accuracy. The sample compositions are shown in FIG. 11. Before benchmarking timePlex to established methods, we acquired non-timePlex data with and without the timePlex setup to directly evaluate the impact of the ‘separate emitters’ timePlex implementation on ion intensities and proteome coverage. Results in FIG. 29A-C show that the 3-column timePlex ‘separate emitters’ approach results in a ion intensity loss of approximately 2-fold and the identification of approximately 20% fewer proteins. To control for 44

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[0431] this implementation and evaluate the timePlex data-type (not its implementation), the benchmarking experiments were performed with identical LC-MS setups regardless of the methodology used for acquisition.

[0432] For the implementation with separate emitters, LF no-plex quantified an average of 4,559 proteins per run between samples A, B, and C. 3-PlexDIA quantified 11 ,224 protein data points / run and 3-timePlex quantified 11 ,624 protein data-points per run, corresponding to an average of 3,741 and 3,875 proteins / sample respectively (FIG. 12). Therefore, both multiplexing approaches quantified 2.5-fold more sample-specific protein data-points / run than LF no-plex. The combination of 3x3 plex-DIA & timePlex identified 25,253 sample-specific proteins per run for an average of 2,806 proteins / sample. Thus, the current implementation of multi-emitters results in 5.5-fold more sample-specific protein data-points per run, compared to LF no-plex.

[0433] We observed similar proportional scaling of coverage for the single emitter (merged flow) approach with uPAC columns. With this setup, LF no-plex identified an average of 3,270 proteins between samples A, B, and C per run. 3-timePlex identified 7,919 protein data points per run, for an average of 2,640 proteins / sample, producing approximately 2.4-fold as many protein data points per run compared to LF no-plex (FIG. 13). The combination of 3-timePlex and 3-plexDIA identified a total of 17,550 sample-specific proteins per run on average, thus approximately 1 ,950 sample-specific proteins per run for a 5.4-fold increase in the number of sample-specific proteins identified per run compared to LF no-plex.

[0434] Search approaches that jointly model fragments, as JMod and other tools do, heavily rely of accurate spectral libraries. The quality of the spectral library likely becomes increasingly important as spectral complexity increases. To investigate this potential, we searched 3-plexDIA and 3x3 plexDIA & timePlex data with our label-free library, keeping the appropriate RTs and accounting for the mass-shifts of the mTRAQ mass-tag. Because the label-free fragment intensities do not accurately reflect mTRAQ-labeled peptide fragmentation, we identified 5% fewer precursors when using the label-free library, FIG. 30A-B. Similar search of the 3x3 plexDIA & timePlex data with a label-free library identified even fewer precursors on average, with a 14% decrease. The larger decrease for the 3x3 data suggests that spectral library quality becomes increasingly important at increasingly high-plex; this partially explains the proportionally decreased coverage per sample in 3x3 plexDIA & timePlex, which may be mitigated through improved spectral libraries.

[0435] Example V. Investigating and correcting biases introduced by timePlex implementation We evaluated column-specific differences in elution profiles, shown in FIG. 31A, as differences have the potential to impact timePlex coverage per time-channel. Aside from one column from the ‘single emitter’ implementation which produced 2-fold broader elution peaks, the columns within each implementation performed comparably. As a result, the number of

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[0438] precursor identifications were similar within each implementation, except for the third column with the single-emitter approach, FIG. 31C.

[0439] Additionally, we observed RT-dependent differences in relative precursor ion intensities for timePlex data. Fortunately, these differences are systematic and thus correctable. This correction is conceptually similar to normalizations performed in other search softwares. To stabilize relative precursor intensities across the gradient for each time channel, we applied a LOESS fit to correct relative precursor intensities with respect to retention time. Results before and after the correction are shown for a single 3-timePlex run in FIG. 14, using the mixed species standards to benchmark the change in quantitative accuracy. Next, we sought to evaluate the quantitative performance of both timePlex implementations (‘separate emitters’ and ‘single emitter, merged flow’). The data indicate accurate precursor-level quantitation for both approaches, as shown in FIG. 15, suggesting both timePlex implementations produce quantitatively accurate data.

[0440] Example VI. Benchmarking timePlex quantitative accuracy

[0441] We continued to use these mixed species standards to benchmark the quantitative performance of 3-plexDIA, 3-timePlex, and combinatorial 3x3 timePlex & plexDIA to LF no-plex data. The number of quantified precursor ratios between sample pairs for the four methods is shown in the left sub-panels of FIG. 16A-C, in addition to the number of intersected precursor pairs across the methods. To fairly compare quantitative accuracy between methods, only the subset of precursor ratios in common was used. All multiplexed approaches produced comparable quantitative accuracy to LF no-plex data, albeit with slightly decreased precision for the 9-plex combination of 3x3 timePlex & plexDIA.

[0442] We summarize these quantitative accuracy investigations at MS1 and MS2-levels by computing absolute errors between observed and expected fold-changes for intersected precursors (n=16,026) quantified across all methods. Quantitative accuracy remains similar across all methods at MS1 and MS2-levels, but with MS1 quantitation being most accurate for all methods, FIG. 17. Interestingly, we observe MS2 quantitation to be less affected by high-plex than MS1 -level, although the accuracy is generally lower for all methods.

[0443] Example VI. Combinatorial multiplexing of single-cell proteomics

[0444] Due to the orthogonality of plexDIA and timePlex, the number of samples acquired per run scales quadratically with plex when utilized together and linearly alone, as shown in FIG. 18.

[0445] Given that the combination of 3x3 timePlex & plexDIA enables comparable quantitative accuracy at combinatorially higher throughput, we sought to evaluate its potential in enabling quantification of proteins at high throughput in human single cells.

[0446] Single cells from K562 and U937 cell-lines were prepared by nPOP49,50 using mTRAQ to isotopically label single-cells to be pooled into single-cell plexDIA sets. The plexDIA sets were

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[0449] acquired with 3-timePlex using 4-minute offsets on an Exploris 240 using a duty-cycle of 12 MS2 scans of variable length and MS1 scans interspaced every 1.3 seconds as shown in FIG.

[0450] 19. These data quantified a median of 274 proteins per cell at 1% FDR, FIG. 20.

[0451] To benchmark the reliability of our single-cell measurements, bulk lysate was diluted to 100-cell amounts and acquired as LF 3-timePlex. Fold-changes between cell-types indicate strong quantitative agreement between averaged single-cells and the diluted bulk lysate, with a Pearson correlation of 0.84, FIG. 21. Of the 3,276 proteins quantified in common between U937 and K562 100-cell lysate, 791 were found to be differentially abundant at 5% FDR. Among the most differentially abundant proteins which intersect with our single-cell data was FABP5, highlighted in FIG. 22. To investigate the differential abundance of FABP5 in raw single-cell data, MS1 ion chromatograms were extracted for each single cell in a single 3x3 timePlex & plexDIA run. The total ion chromatogram (TIC) and MS1 traces are shown for each plexDIA set from each of the 4-minute separated timePlex channels. Indeed, the differential abundance of FABP5 from the averaged single-cells shown in panel FIG. 20 is likewise reflected in raw MS1-level single-cell data shown in FIG. 21, as K562 cells exhibit lower abundance of a FABP5 precursor than U937 cells. To verify this was not an artifact of sample preparation or acquisition, we plotted XICs for a highly abundant precursor in HMGA1 for each of the 9 single-cells, which show strong signal across all cells.

[0452] Dimensionality reduction by PCA of 100x diluted bulk and single-cell data from LF 3-timePlex and 3x3 timePlex & plexDIA is shown in FIG. 23. Single cell 3x3 plexDIA & timePlex data clustered with 100x bulk cell lysates acquired with LF 3-timePlex. The two cell-types separate along PC1, where 57% of the variance is explained. Data that is instead colored by mass tag, and timePlex is shown in FIG. 32A-B to evaluate the impact of unresolved batch effects on dimensionality reduction; the data indicate appropriate correction for any batch effects related to plexDIA and timePlex. These results highlight the potential for timePlex and plexDIA combinatorial multiplexing to increase the throughput of single-cell proteomics.

[0453] Methods

[0454] 1. timePlex implementation

[0455] timePlex implemented in this work utilized a Dionex UltiMate 3000 HPLC. Schematics and details for sample loading are shown in FIG. 24. Flow from the NC pump is forked using a ZenFit tee (700011518, Waters), and nanoViper™ (Thermo Fisher) transfer lines ranging from 20-50 pm ID and 150 mm - 950 mm were used in combination to encode time offsets between samples.

[0456] Sample-loading occurred in the following way: The first sample was injected with valves in the 1_2 and 1_2 position for the first and second valves. At this stage, flow is actively being pushed through the sample-loop into port 1 of Valve 2, which flows out of Port 2 and into Port 8 of Valve 1, and out of Port 7 to ‘Column T. The flows which were split from the NC pump push 47

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[0458] against plugs; as a result, all net flow is directed through the sample-loop. At this step, only ‘Column T is actively receiving flow. To prevent over-pressurization, the flow rate at the step is set to 300 nL / min. Immediately after injection, the sample is run isocratically for 7 minutes with 2% ACN and 0.1% formic acid, for the sole-purpose of pushing the sample out of the loop and onto the first column. This process takes approximately 10 minutes total.

[0459] The second column is loaded in a similar way, except Valve 2 is switched to the 1_10 position, directing the second sample onto ‘Column 2’. This process also takes approximately 10 minutes total. In the last 10 seconds of pushing the sample onto the column, the flowrate is ramped to 600 nL / min in anticipation of switching the valves which occurs during the loading of the third column.

[0460] The third column is loaded by switching the positions of both valves to 1_10 and 1_2 positions, respectively for the first and second valves. At this point, all three columns are actively receiving flow. The flowrate is set to 600 nL / min, as it is split between three columns; therefore, the flowrate across each column is approximately 200 nL / min although differences in back pressure of the columns will change the flowrate across each column, and thus the encoded time-differential. As a result, the volume differences which encode the time-offsets may need to be adjusted to achieve the desired time-differential between timePlexes. The third sample does not need to be pushed separately like the first two samples. Instead, the gradient can start immediately. Additionally, because the third sample will receive its gradient through the sample-loop, which in this case holds a 1 pL volume, it was most efficient to have this column be the latest eluting one.

[0461] All samples, including non-timePlex data acquisitions (such as LF no plex and plexDIA), were acquired with the timePlex setup so we could directly evaluate the impact on timePlex data on downstream interpretation rather than the combination of interpretation and implementation, as the implementation for timePlex is flexible. This was to allow us to more directly evaluate the impact of timePlex data on performance.

[0462] 1-1. Differences between single and multi-emitter approaches:

[0463] Both timePlex implementations demonstrated in this work utilize the same sampleloading scheme, and a Nanospray Flex™ Ion Source on an Exploris 240 (Thermo Fisher). The difference between the multi-emitter (separate flow) approach and the single-emitter (joined flow) approach only pertains to how the flow is treated post-column separation, and how the ESI occurs.

[0464] In the case of the multiple emitters approach, voltage (+2.2kV) is applied from the MS source to two columns via an alligator clip, and an auxiliary power supply (PS350, Stanford Research Systems) applies voltage (+2.2kV) to the third column. A PEEK cone, kindly provided by lonOpticks, holds the columns in place, pointing spray at the mass-spec inlet of an Exploris 240.

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[0467] For the merged flow approach, flow out of each pPAC column was merged through a 50 pm bore cross (C360TS62, VICI Valeo) to an emitter tip (TIP360002010-10-5, MS WIL).

[0468] Utilizing narrow-bore crosses for merging flow post-column is critically important to mitigate peak broadening. Voltage (+2.2kV) was applied to the emitter via alligator clip.

[0469] 2. MS data acquisition:

[0470] All samples were resuspended in 0.015% n-Dodecyl p-d-maltoside (DDM) 54 with 0.1% formic acid. Each benchmarking sample, which consisted of 8 ng human digest and variable yeast digest, was acquired with the timePlex multiplexing setups shown in FIG. 3, whether it was timePlexed or non-timePlexed. This means LF no-plex and 3-plexDIA benchmarks were acquired with the same setup as the timePlex experiments (3 columns active at a time) so we could directly benchmarking the timePlex data-type, rather than its implementation and datatype. Effects of the implementation are shown in FIGs. 31A-C.

[0471] All data were acquired on an Exploris 240 using either 25cm x 75pm lonOpticks columns (AUR3-25075C18) or 50 cm pPAC™ Neo columns (CGL-NANO050NEGB) with a Dionex Ultimate 3000. 8 ng LF samples were acquired in DIA mode with an MS1 scan at 120k resolving power and 300% AGC conducted every 1.3 seconds. The MS2 duty cycle consisted of 24 variable length windows coveraging 378 - 1356 m / z for LF and 478-1356 m / z for mTRAQ-labeled peptides. The MS2 windows were designed to equally distribute the ion current over the MS1 m / z range.

[0472] 2-1. LF Benchmarking

[0473] For LF benchmarks, the MS2 windows were as follows: 378-387, 386.5-393, 392.5-401.5, 401-409.5, 409-418, 417.5-427.5, 427-438.5, 438-448.5, 448-460.5, 460-473.5, 473-487, 486.5-501.5, 501-517, 516.5-536.5, 536-558.5, 558-580.5, 580-607, 606.5-639, 638.5-675.5, 675-723.5, 723-784.5, 784-867, 866.5-986, and 985.5-1356 m / z.

[0474] Gradient for lonOpticks columns ‘separate emitters’ approach: 600 nL / min ramping from 2% B buffer to 5.8% 2.25 minute into the gradient, 8.8% 6.3 minutes into gradient, 11.5% (11.25 min), 18.5% (31 min), 25.5% (45 min), 34.2% (54.9 min), 37.7% (56.25 min), 45% (60 min), 48% (68 min), 95% (68.2 min), 95% (72 min), 2% (72.1 min), maintaining 2% B until end (90 min). The full LC method is 90 minutes.

[0475] Gradient for uPAC columns ‘single emitter’ approach: 600 nL / min ramping from 2.5% B buffer to 4% 1 minute into the gradient, 8% 5.5 minutes into gradient, 15% (26 min), 18.5% (31.2 min), 25% (37.4 min), 32% (41 min), 34% (51.5 min), 95% (52 min), 95% (58 min), 2% (64 min), maintaining 2% B until end (75 min), with the last 10 minutes run at 1,200 nL / min. The full LC method is 75 minutes.

[0476] 2-2. mTRAQ-labeled benchmarks

[0477] For mTRAQ-labeled benchmarks, the MS2 windows were as follows: 478-485.5, 485-493, 492.5-501.5, 501-510.5, 510-518, 517.5-526.5, 526-535.5, 535-544.5, 544-553, 552.5-563,

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[0480] 562.5-574.5, 574-586.5, 586-598.5, 598-612.5, 612-627, 626.5-643.5, 643-662, 661.5-683, 682.5-707.5, 707-739, 738.5-778, 777.5-834, 833.5-940, and 939.5-1356 m / z.

[0481] Gradient for lonOpticks columns ‘separate emitters’ approach: 600 nL / min ramping from 2% B buffer to 6.5% 2.25 minute into the gradient, 9.5% 6.3 minutes into gradient, 12% (11.25 min), 19.4% (31 min), 26.4% (45 min), 35.1% (54.9 min), 46% (60 min), 49% (68 min), 95% (68.2 min), 95% (72 min), 2% (72.1 min), maintaining 2% B until end (90 min). The full LC method is 90 minutes.

[0482] Gradient for uPAC columns ‘single emitter’ approach: 600 nL / min ramping from 4% B buffer to 8% 5.5 minute into the gradient, 16% 26 minutes into gradient, 15% (26 min), 19.5% (31.2 min), 26% (37.4 min), 33.3% (41 min), 36.5% (51.5 min), 95% (52 min), 95% (58 min), 2% (58.1 min), maintaining 2% B until end (75 min), with the last 10 minutes run at 1 ,200 nL / min. The full LC method is 76 minutes.

[0483] 2-3. plexDIA and timePlex single-cells:

[0484] For plexDIA and timePlex single-cell acquisition, the MS2 windows were as follows: 478-493, 492.5-510.5, 510-526.5, 526-544.5, 544-563, 562.5-586.5, 586-612.5, 612-643.5, 643-683, 682.5-739, 738.5-834, and 833.5-1356 m / z.

[0485] Gradient for lonOpticks columns ‘separate emitters’ approach: 600 nL / min ramping from 2% B buffer to 6.5% 1.5 minute into the gradient, 9.5% 4.16 minutes into gradient, 12% (7.425 min), 19.4% (20.46 min), 26.4% (29.7 min), 35.1% (36.6 min), 46% (40 min), 49% (48 min), 95% (48.2 min), 95% (52 min), 2% (52.1 min), maintaining 2% B until end (70 min). The full LC method is 70 minutes.

[0486] 3. Spectral library generation with DIA-NN

[0487] Data was generated to create empirical libraries for searching the human and yeast benchmarking data. The library-generating data consisted of 250 ng injections of label-free and mTRAQ-labeled human (K562) and yeast standards, acquired on the Exploris 240 using 25cm x 75pm lonOpticks columns (AUR3-25075C18) and a Dionex UltiMate 3000. The data was searched with DIA-NN version 1.914 using predicted library generation of in-silico digested H. sapiens and S. cerevisiae FASTAs. To generate an empirical library for searching single-cells, 8 ng injections of mTRAQ-labeled K562 and U937 protein digests were similarly searched.

[0488] 4. Retention time prediction and transfer learning:

[0489] Empirical libraries output from DIA-NN, which were generated from label-free and mTRAQ-labeled data, were used to train a model to predict peptide iRTs. Precursors from the empirical libraries were collapsed to peptide-level, averaging the iRT between charge-states, if there was any reported differences. The peptides were one-hot encoded as a 30x20 array for each peptide, where 30 is the maximum length for a peptide and 20 is the number of possible amino acids. A hybrid convolutional and fully connected feedforward neural network was trained to predict peptide RTs, using TensorFlow. The model consisted of four 1D convolutional layers,

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[0492] with the number of filters doubling at each successive layer: 32, 64, 128, and 256. Each layer used a kernel size of 4. No pooling was applied after the first two convolutional layers. Max pooling with a pool size of 2 was applied after each of the final two convolutional layers. The output of the final convolutional layer was flattened and passed into a fully connected feedforward neural network with five layers consisting of 1000, 256, 128, 64, and 32 nodes, respectively. All layers used the 'Swish' activation function, except for the final output layer, which used a linear activation to produce the final RT value. An ensemble of five models was created and used for transfer learning; predictions were the average of the five model outputs. The models were trained with early stopping (monitor='val loss', patience = 20, restore best weights=True), epochs = 150, batch size = 32, validation split = 0.1, with Adam as the optimizer (learning rate=0.001) and mean absolute error as the loss function.

[0493] Transfer learning was applied to fine-tune RT libraries fortimePlex applications. This involved re-initializing the five models and training on new data stemming from the initial search which occurs in JMod for m / z and RT alignment. This initial search seeks to identify peptides based on the peptide-like features output by Biosaur. These features are only used for the initial search to update m / z and RT alignments, and refine library RTs. The transfer learning occurs under the following parameters: early stopping was enabled (monitor='val loss', patience = 10, restore best weights=True), epochs = 50, batch size=24, optimizer='Adam '(learning rate=0.0001), loss = 'mae', validation split = 0.1. The average RT output across the five models is used as the final RT prediction in the library. Finally, we compare the RT predictions from transfer learning to the original library RTs using the validation set of data. The strategy with the greatest RT accuracy is used for the full-search in JMod.

[0494] 5. Searching with JMod

[0495] JMod is a spectrum-centric proteomics search software, which leverages joint-modeling of spectra to enable interpretation of proteomics data acquired by DIA. It begins by identifying peptide-like features which are output by feature detection software. JMod then attempts to identify the precursor that best fits each of these features individually. This first search does not utilize any retention time information and, due to JMod's spectrum centric approach, can identify multiple instances of the same precursor. For n-timePlex, precursors identified n times are extracted. These are assumed to have one identification for each column. The time differences encoded by the timePlex setup remain relatively constant across the gradient. Therefore, correctly matched precursor sets will all have systematic offsets. Importantly, this does not require the same offset to be encoded between different pairs of successive columns. However, the consistency between each pair of columns allows us to further filter our set of precursors to those that agree on the time encoding. This provides a set of high-confidence identifications across the gradient which are separated into different timePlexes. A LOWESS regression is fit to each individual timePlex channel to align the library retention times to the observed data. The 51

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[0497] first timePlex channel is also used as the reference set of identifications for retention time prediction. The peptides sequence and retention time pairs are used to finetune a retention time prediction model. LOWESS regressions are then also fit for the finetuned predictions vs the observed retention times for each timePlex.

[0498] The differences between these fits and the observed retention times are used to calculate retention time errors. Cumulative distributions are calculated for both the empirical and predicted retention time errors. The cumulative distribution with the largest area under the curve is selected as the “best fit” FIG. 28A-B. This is the model that minimizes the errors. Then, to calculate the optimal retention time tolerance, both exponential and Gaussian cumulative distributions are compared to the observed cumulative distribution. The distribution that matches the observed data best is used to calculate the 95th percentile which will specify the retention time tolerance. Once a best model is selected, JMod duplicates the library for n-timePlex. Each will have an updated retention time based on the retention times and respective LOWESS fit of the best approach identified previously. These tolerances are also made to be non-overlapping between timePlex channels. This ensures that the same precursor in each channel will be considered independent of the other channels, leading to column and sample-specific identifications. Once the library is created, the data are searched the same as any other experiment.

[0499] 5-1. Human and yeast benchmarks

[0500] LF no-plex human and yeast benchmarking data was searched with JMod using the following commands: {-iso}, {-num iso 2}, {-lib frac 0.5}. 3-timePlex data was searched using the following commands: {-iso}, {-num iso 2}, {-lib frac 0.5}, {-timeplex}, {-num timeplex 3}. The 3-timePlex data with missing species additionally used the following command to avoid using mutual information across channels for target decoy classification: {-no transfer}. The 3-plexDIA human and yeast benchmarking data was searched with JMod using the following commands: {-iso}, {-num iso 2}, {-plexDIA}, {-tag mTRAQ}, {-lib frac 0.5}. And finally, combinatorial 3-timePlex and 3-plexDIA human and yeast benchmarking data was searched with JMod using the following commands: {-iso}, {-num iso 2}, {-plexDIA}, {-tag mTRAQ}, {-lib frac 0.5}, {-timeplex}, {-num timeplex 3}.

[0501] Because the empirical library was generated using lonOpticks columns at the same time the samples were generated, there was no need for RT fine-tuning on the benchmarking samples which used lonOpticks columns (FIG. 28A). In these cases, we additionally used the following command {-use emp rt} to use the original library RTs for searching rather than performing RT fine-tuning. However, fine-tuning was applied for benchmarking data acquired with pPAC columns, as the library RTs are not suitable (FIG. 28B).

[0502] 5-2. Single Cell

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[0505] Single cell combinatorial 3-timePlex and 3-plexDIA data was searched with JMod using an empirical spectral library generated from 8 ng of K562 and U927 cell digests using the following commands: {-iso}, {-num iso 2}, {-plexDIA}, {-tag mTRAQ}, {-lib frac 0.5}, {-timeplex}, {-num timeplex 3}, {-lib frac 0.20}, {-score lib frac 0.33}, {-user rt tol}, {-rt tol 0.45}, {-initial percentile 20}, {-user percentile}. These additional commands reduce the heuristic filters that are applied by default and specify the RT tolerance window that is used to consider sample-specific precursors.

[0506] 6. Sample preparation

[0507] 6-1. Creating a mixed species standard for benchmarking

[0508] Benchmarking samples consisted of commercial Yeast (V7461 , Promega) and Human K562 (V6951, Promega) protein digests. The dried digests were resuspended in 100 mM TEAB, pooled using their appropriate ratios to create Samples A, B, and C, each sample consisted of 25 pg of total, human material for each sample. The samples were split in two, one to remain label-free and the other to be chemically labeled with mTRAQ. The label-free samples were dried, and the other set of samples were labeled with mTRAQ A0 (SCIEX 4440015), A4 (SCIEX 4427698), and A8 (SCIEX 4427700) at a ratio of 2:1 ratio of 500 ng / uL sample in 100 mM TEAB:stock concentration of tag. After addition of the label, the samples were left at room temperature to react for 2 hours. Hydroxylamine was added to a final concentration of 0.2% to quench the reaction at room temperature for 30 minutes. The samples were then dried with a speed vacuum, and then resuspended in 0.015% DDM and 0.1% formic acid to their appropriate concentrations.

[0509] 6-2. Cell culture

[0510] K562 (CCL-243, ATCC) and U937 (CRL-1593.2, ATCC) cells were cultured in RPMI-1640 Medium (R8758, Sigma-Aldrich) supplemented with 10% fetal bovine serum (10439016, Gibco) and 1% penicillin-streptomycin (15140122, Gibco), grown at 37° C and 5% CO2, and harvested within 15 passes.

[0511] 6-3. 100x bulk K562 and U937

[0512] K562 and U937 human cells were harvested from culture, washed 2x in PBS, and resuspended in water to a concentration of approximately 2000 cells / pL. The cell suspensions were subjected to mPOP. In brief, the cell suspensions were frozen at -80°C, thawed and heated at 90°C in a thermal-cycler for 10 minutes. TEAB was then added to the cell lysate to a concentration of 100 mM, Benzonase nuclease (E1014, Millipore) was added to 0.1 U / pL, and Trypsin Platinum (VA9000, Promega) was added to a concentration of 15 ng / pL.

[0513] 6-4. Single Cell

[0514] Single cells were prepared for proteomic analysis by nPOP as previously described by Leduc et al. In short, single-cells (in a volume of 0.3 nL) were sorted by CellenOne into 9 nL droplets of 100% DMSO on a fluorocarbon-coated glass slide to lyse. The lysed cells were 53

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[0516] digested with a mixture containing Trypsin Platinum (VA9000, Promega) at a concentration of 120 ng / pL and 0.015% DDM (w / v) and 5 mM HEPES (pH 8.5), 13.5 nL of digestion mixture was dispensed onto each droplet. Peptides were then chemically labeled with mTRAQ AO, A4, or A8 (20 nL / droplet) and subsequent addition of 100 mM TEAB buffer (13.5 nL / droplets, pH 8.5) was added for a 1 hr incubation period. Single cells were automatically pooled by the cellenOne into 3-plex sets, and dispensed into wells of a 384 well-plate. The pooled samples in the 384 wellplate were then dried in a speed-vacuum then stored at -80°C until use.

[0517] 7. Computation Data Analysis

[0518] 7-1. Quantification

[0519] Precursors were quantified by MS1 Area for LF no-plex and LF 3-timePlex data, or by a custom algorithm for 3-plexDIA and combinatorial 3-plexDIA & 3-timePlex acquisitions. The algorithm identifies the best fitting MS1 scan across the channels in a plexDIA set by computing a correlation of the theoretical isotopic envelopes of the precursor group to the empirically observed envelope. The MS1 scan with the highest correlation was considered the MS1 apex. Quantities at this scan are extracted for single-cell data; for bulk data, this scan and adjacent scans (radius = 1) were summed for MS1 quantitation.

[0520] 7-2. Correcting RT biases in relative quantitation

[0521] Precursor quantities were normalized to account for sample-loading differences by dividing each sample by its respective median, then relative quantities for each precursor were computed by dividing each normalized value by the mean of across samples. In the case of mixed-species spike-ins, the resulting data were filtered for human precursors only. These relative values were fit with a LOESS regression as a function of RT, and the fit was subtracted from the relative values to correct them. These corrected values were used for all downstream analyses.

[0522] 7-3. Computing full width half max (FHWM) of precursor elution profiles

[0523] Outputs from JMod list the sequence of MS1 intensities for each precursor. Using this list, the FWHM for each precursor was computed by fitting a gaussian of their MS1 elution profiles. The interpolated apex and surrounding interpolated profile was used to find the range (in number of scans) where the profile was at half the maximum. Given that the duty cycle is scheduled to perform an MS1-scan every 1.3 seconds, the FWHM in scans was multiplied by this scan rate to compute the final FWHM in seconds.

[0524] 7-4. Entrapment to assess FDR of timePlex

[0525] An empirical library generated by DIA-NN was created for human and yeast LF peptides, as a well as a predicted library for A. thaliana. Precursors from the A. thaliana predicted library were randomly sampled to have the same total number as in the empirical human and yeast library. The two equivalently sized libraries were merged, and this merged library was used to

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[0528] search LF 3-timePlex data which had missing yeast in one time channel, and A. thaliana missing from all time channels.

[0529] 7-5. Single-cell protein quantification, imputation, batch correction, and PCA

[0530] To remove cells which failed during preparation or acquisition, single-cells were excluded from downstream analyses if they quantified fewer than 250 precursors. Precursors were quantified at MS1-level at the scan where the theoretical isotopic envelope distribution most highly correlated to the empirically observed envelopes across the 3-plexDIA precursor group. Only this apex was extracted for quantitation. Precursor abundances were corrected for RT-dependent changes in relative abundances as described above. These relative values were then scaled by the mean of the original values across cells to scale them back into their original intensity ranges. These values were then used for MaxLFQ quantitation using the DIANN R package. Protein quantities were normalized by their median abundances for each cell to account for absolute differences, then normalized across cells by the mean. The Iog2-transformed data was then imputed using a weighted kNN approach (k=5), where the weights are scaled based on euclidean distances to its nearest neighbors. And finally, the data was batch-corrected using ComBat to account for mTRAQ label and column biases. Single cell and 100x bulk data were filtered to have intersected proteins, and principal components were computed from imputed data whose variance was weighted by protein correlations.

[0531] 7-6. Evaluating agreement between single-cell and bulk timePlex quantitation Precursor quantities were filtered for 1% FDR at precursor and protein level, while allowing additional precursor identifications for single-cells if at least one channel passed 1% FDR (BestChannel Qvalue < 0.01). All relative precursor abundances were normalized to resolve retention-time associated biases in precursor intensities, as described above. The median relative values were taken across single-cells was used for each protein, requiring at least 20% data-completeness for a protein to be included in the comparison relative to the 100x bulk. A Pearson correlation was then computed for the averaged single-cell fold-changes to the averaged 100x bulk fold-changes.

[0532] 7-7. Differential protein abundance analysis

[0533] Precursor quantities were filtered for Qvalue < 0.01 and Protein QValue < 0.01 for all data. The dynamic changes in relative precursor intensities which change as a function of retention time were normalized as previously described. These corrected precursors intensities were used for differential protein abundance analysis calculated through the MS-Empire workflow. In total, there were 6 replicates for each cell-type, two from each column. Precursors used for analysis required to be quantified in at least two replicates per condition. Data normalization was performed using default MS-Empire workflow operations, as described by Ammar et al.

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[0536] References'.

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[0552] Bouwmeester, R., Gabriels, R., Hulstaert, N., Martens, L. & Degroeve, S. DeepLC can predict retention times for peptides that carry as-yet unseen modifications. Nature Methods 18, 1363-1369. (2025) (Nov. 2021).

[0553] Zeng, W.-F. et al. AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nature Communications 13, 7238. (2025) (Nov. 2022). Wallmann, G. et al. AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics en. June 2024. (2025).

[0554] Navarro, P. et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nature Biotechnology 34, 1130-1136 (Nov. 2016).

[0555] Leduc, A., Huffman, R. G., Cantion, J., Khan, S. & Slavov, N. Exploring functional protein covariation across single cells using nPOP. Genome Biology 23, 261. (2023) (Dec.

[0556] 2022).

[0557] Leduc, A., Koury, L., Cantion, J. & Slavov, N. Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP en. Nov. 2023. (2024).

[0558] Ramachandran, P., Zoph, B. & Le, Q. V. Searching for Activation Functions Oct. 2017.

[0559] (2025).

[0560] Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization Jan. 2017. (2025). Abdrakhimov, D. A. et al. Biosaur: An open-source Python software for liquid chromatography-mass spectrometry peptide feature detection with ion mobility support. Rapid Communications in Mass Spectrometry, e9045. (2025).

[0561] Specht, H. et al. Automated sample preparation for high-throughput single-cell proteomics en. Aug. 2018. (2022).

[0562] Cox, J. et al. Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ. Molecular & Cellular Proteomics: MCP 13, 2513-2526. (2022) (Sept. 2014).

[0563] Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics (Oxford, England) 8, 118-127 (Jan. 2007).

[0564] Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882-883. (2024) (Mar. 2012).

[0565] Ammar, C., Gruber, M., Csaba, G. & Zimmer, R. MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins[S], Molecular & Cellular Proteomics 18, 1880-1892. (2024) (Sept. 2019).

[0566] Having described the present disclosure in detail and by reference to specific embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the disclosure defined in the appended claims. More specifically,

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[0569] although some aspects of the present disclosure are identified herein as particularly advantageous, the present disclosure is not necessarily limited to these aspects.

[0570] The methods and techniques of the present disclosure are generally performed, unless otherwise indicated, according to conventional methods well known in the art and as described in various general and more specific references that are cited and discussed throughout this specification. See, e.g., “Principles of Neural Science”, McGraw-Hill Medical, New York, N.Y. (2000); Motulsky, “Intuitive Biostatistics,” Oxford University Press, Inc. (1995); Lodish et aL, “Molecular Cell Biology,” 4th ed., W. H. Freeman & Co., New York (2000); Griffiths et aL, “Introduction to Genetic Analysis,” 7th ed.”, W. H. Freeman & Co., N.Y. (1999); and Gilbert et aL, “Developmental Biology,” 6th ed., Sinauer Associates, Inc., Sunderland, MA (2000).

[0571] In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

[0572] Chemistry terms used herein are used according to conventional usage in the art, as exemplified by “The McGraw-Hill Dictionary of Chemical Terms,” Parker S., Ed., McGraw-Hill, San Francisco, C.A. (1985).

[0573] All of the above, and any other publications, patents and published patent applications referred to in this application are specifically incorporated by reference herein. In case of conflict, the present specification, including its specific definitions, will control.

[0574] Unless otherwise noted, technical terms, and abbreviations thereof, are used according to conventional usage. Definitions of common terms in molecular biology can be found in Benjamin Lewin, Genes VII, published by Oxford University Press, 2000 (ISBN 019879276X); Kendrew et aL (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Publishers, 1994 (ISBN 0632021829); and Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published by Wiley, John & Sons, Inc., 1995 (ISBN 0471186341); and other similar references. Additional and alternative abbreviations can be found in: (1) The Journal of Organic Chemistry- Standard Abbreviations and Acronyms; (2) ACS Division of Organic Chemistry - Hans Reich's Collection Organic Acronyms; (3) Imperial College of London - Common Abbreviations in Organic Chemistry, for example.

[0575] Terms like “preferably,” “commonly,” and “typically” are not utilized herein to limit the scope of the claimed embodiments or to imply that certain features are critical, essential, or even important to the structure or function of the claimed embodiments. Rather, these terms are merely intended to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the present disclosure.

[0576] Further, terms of degree such as "substantially", "about" and "approximately" as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree should be construed as including a deviation

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[0579] of at least ±5% of the modified term if this deviation would not negate the meaning of the word it modifies.

[0580] As used herein, the singular terms "a," "an," and "the" include plural referents unless context clearly indicates otherwise. In addition, reference to an element by the indefinite article "a" or "an" does not exclude the possibility that more than one of the elements is present, unless the context clearly requires that there is one and only one of the elements. The indefinite article "a" or "an" thus usually means "at least one". Similarly, the word "or" is intended to include "and" unless the context clearly indicates otherwise. Also, as used herein, the term "comprises" means "includes." Hence "comprising A or B" means including A, B, or A and B. It is further to be understood that all nucleotide sizes or amino acid sizes, and all molecular weight or molecular mass values, given for nucleic acids or polypeptides or other compounds are approximate, and are provided for description.

[0581] The verb "comprise" as is used in this description and in the claims, and its conjugations are used in a non-limiting sense to mean that items following the verb are included, but items not specifically mentioned are not excluded. The word “comprising” in the claims may be replaced by “consisting essentially of’ or by “consisting of,” according to standard practice in patent law.

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[0583] 82558346v1

Claims

1. Attorney Docket No.: 776866: PST-003PCCLAIMS1. A method of multiplexing through temporal encoding comprising:sequentially injecting multiple sample segments within a separation platform utilizing a liquid mobile phase, wherein each sample segment is injected at a pre-determined time point;allowing analytes of interest from sequential segments to elute in an overlapping eluate; analyzing, on a computer system, the overlapping eluate using a Mass Spectrometry (MS) technique to identify analytes of interest; andassigning, on the computer system, identified analytes of interest to a single sample segment based on injection time offset and predicted elution times for the analytes of interest.

2. The method of claim 1 , wherein 2 or more samples are injected.

3. The method of claim 1 or 2, wherein the liquid mobile phase composition remains constant throughout elution of the analytes of interest.

4. The method of any one of claims 1-3, wherein the liquid mobile phase composition changes overtime.

5. The method of claim 4, wherein the change in composition initiates elution of one or more analytes of interest; and assigning identified analytes of interest to a single sample segment is based on elution initiation time offset and predicted elution times for the analytes of interest.

6. The method of claim 4 or 5, wherein the composition change comprises increasing the proportion of organic solvent relative to aqueous solvent for reversed-phase chromatography.

7. The method of any one of claims 4-6, wherein the change in composition occurs linearly overtime.

8. The method of any one of claims 4-7, wherein the change in composition occurs in a multi-segment gradient comprising one or more of Logarithm-like, Exponential-like, or Step-linear gradients, and optionally wherein the multi-segment gradient comprises an isocratic hold.

9. The method of any one of claims 4-8, wherein the composition changes over a period selected from the group consisting of:(a) less than one minute;(b) greater than or equal to one minute;6082558346v1Attorney Docket No.: 776866: PST-003PC(c) about 1 minute to about 30 minutes;(d) about 30 to about 60 minutes;(e) about 60 minutes to about 90 minutes;(f) about 90 minutes to about 240 minutes; and(g) about 4 or more hours.

10. The method of any one of claims 1-9, wherein the samples comprise proteomic, lipidomic, or metabolomic preparations and / or comprise proteomic, lipidomic, metabolomic, or small-molecule analytes.

11. The method of any one of claims 1-10, wherein the analytes of interest are label-free.

12. The method of any one of claims 1-10, wherein the analytes of interests are labeled, and at least one sample segment comprises one or more labeled samples.

13. The method of claim 12, wherein the sample segment comprises a multiplexed sample obtained by separately labeling at least two individual samples with a mass tag unique to each sample to form respective sets of labeled samples and pooling the sets to form a mixture.

14. The method of any one of claims 1-13, wherein the separation platform is an ultra-high-performance liquid chromatography (UHPLC) system, a capillary electrophoresis (CE) system, or a size-exclusion chromatography (SEC) system.

15. The method of claim 14, wherein the separation platform comprises a single column or capillary, or multiple columns.

16. The method of claim 14 or 15, wherein the separation platform comprises a plurality of chromatography columns arranged in parallel, wherein each column of the plurality is fluidically connected to a transfer line, wherein the transfer lines are adapted to receive a sample segment and encode a time offset for injection of the sample segment into its connected column.

17. The method of claim 16, wherein the transfer lines have different volumes.

18. The method of claim 16 or 17, wherein each of the multiple sample segments is directed to and loaded onto a respective column of the plurality using one or more rotary valves or selector valves.6182558346v1Attorney Docket No.: 776866: PST-003PC19. The method of any one of claims 16-18, wherein allowing analytes of interest from sequential segments to elute in an overlapping eluate comprises flowing each of the multiple sample segments onto a respective column of the plurality samples, wherein the plurality chromatography columns is configured to merge the eluate of the respective columns prior to introduction to a mass spectrometer inlet, or configured to separately introduce the eluate from each column to the mass spectrometer inlet.

20. The method of claim 19, wherein the plurality of chromatography columns is configured to merge the flow using a junction or flow-joiner.

21. The method of any one of claims 16-20, wherein the liquid mobile phase composition changes over time to form an active gradient that initiates elution of one or more analytes of interest, wherein the separation platform is configured to create a difference in the onset of the active gradient reaching each chromatography column of the plurality; and assigning identified analytes of interest to a single sample segment is based differences in the onset of the active gradient.

22. The method of claim 21, wherein the composition change comprises an increase in the relative proportion of organic solvent to aqueous solvent.

23. The method of claim 21 or 22, further comprising generating a differential spacer for temporal encoding of the analytes of respective sample segments.

24. The method of claim 23, wherein the spacer comprises a pre-formed gradient comprising different volumes of solvent between injected multiple sample segments.

25. The method of any one of claims 1-24, wherein the MS technique comprises multiple reaction monitoring (MRM), selected reaction monitoring (SRM), parallel reaction monitoring (PRM), data-dependent acquisition (DDA), or data-independent acquisition (DIA).

26. A system for multiplexing through temporal encoding comprising:a separation platform utilizing a liquid mobile phase;an injection system configured to sequentially inject multiple sample segments within the separation platform at a pre-determined time point;a mass spectrometry (MS) interface for connecting the separation platform to a mass spectrometer; and6282558346v1Attorney Docket No.: 776866: PST-003PCa computing system configured to assign identified analytes of interest to a single sample segment based on injection time offset and predicted elution times for the analytes of interest.

27. The system of claim 26, wherein the separation platform is configured for isocratic flow chromatography, for gradient flow chromatography, or a combination thereof.

28. The system of claim 26, wherein the separation platform is an ultra-high-performance liquid chromatography (UHPLC) system, a capillary electrophoresis (CE) system, or a sizeexclusion chromatography (SEC) system.

29. The system of any one of claims 26-28, wherein the separation platform comprises a single column or capillary.

30. The system of any one of claims 26-28, wherein the separation platform comprises a plurality of columns or capillaries.

31. The system of claim 30, wherein the MS interface is configured for a multi-emitter (separate flow) approach or a single-emitter (joined flow) approach.

32. The system of claim 29 or 30, wherein the separation platform comprises a plurality of chromatography columns arranged in parallel, wherein each column of the plurality is fluidically connected to a transfer line, wherein the transfer lines are adapted to receive a sample segment and encode a time offset for injection of the sample segment into its connected column.

33. The system of any one of claims 26-32, further comprising an MS system operatively connected to the MS interface.

34. A computer-implemented method of deconvolving temporally-encoded multiplexed samples, the method comprising:identifying, in spectral data acquired from analysis of a temporally-encoded overlapping eluate obtained from a multiplexed sample containing n sample segments using a Mass Spectrometry (MS) technique, analyte-like features in a plurality of detected precursors;identifying a precursor of the plurality corresponding to the analyte-like features that is detected in each of the n sample segments;filtering, from the spectral data, detected precursors that are not detected n times to provide an extracted data set; and6382558346v1Attorney Docket No.: 776866: PST-003PCassigning each precursor of the extracted data set to a single sample segment according to a predicted retention-time.

35. The method of claim 34, wherein analyte-like features include spectral mass and fragmentation patterns.

36. The method of claim 34, wherein analyte-like features do not include retention times.

37. A separation platform comprising:an injection system operable to sequentially inject a plurality of samples into a fluid of a fluid handling system;the fluid handling system comprising:a first transfer line of a plurality of transfer lines in fluid connection with a first column of a plurality of columns; anda second transfer line of the plurality of transfer lines in fluid connection with a second column of the plurality of columns; andwherein a first-time offset of a plurality of time offsets is encoded for a first sample of the plurality of samples in the first transfer line and a second-time offset of the plurality of time offsets is encoded for a second sample of the plurality of samples in the second transfer line; andan interface configured to deliver the first sample and the second sample to a mass spectrometer.

38. The separation platform of claim 37, wherein the first transfer line has a first volume and the second transfer line has a second volume, and wherein the first-time offset is defined by the first volume and the second-time offset is defined by the second volume.

39. The separation platform of claim 38, wherein the first volume is defined by a first diameter and a first length, and wherein the second volume is defined by a second diameter and a second length.

40. The separation platform of claim 39, wherein the first-time offset is defined by a first position of the first sample within the first transfer line and the second-time offset is defined by a second position of the second sample within the second transfer line.

41. The separation platform of any one of claims 37-40, wherein the plurality of samples includes more than two samples, the plurality of transfer lines includes more than two transfer6482558346v1Attorney Docket No.: 776866: PST-003PClines, the plurality of columns includes more than two columns, and the plurality of time offsets includes more than two time offsets.

42. The separation platform of any one of claims 37-41 , wherein the fluid handling system further comprises a flow splitter to split flow received from a separation platform to the first transfer line and the second transfer line.

43. The separation platform of claim 42, wherein the flow splitter comprises at least one of a plurality of valves and at least one selector valve.

44. The separation platform of any one of claims 37-43, wherein the fluid handling system further comprises a sample loop configured to provide a sample to at least one transfer line of the plurality of transfer lines.

45. The separation platform of any one of claims 37-44, wherein the separation platform comprises at least one of Liquid Chromatography (LC), Capillary Electrophoresis (CE), and Solid Phase Extraction (SPE).

46. The separation platform of any one of claims 37-45, wherein the interface comprises an emitter disposed at the end of each of the first column and the second column, the emitter operable to convert the first sample and the second sample into a spray of charged droplets for ionization by electrospray ionization.

47. The separation platform of any one of claims 37-46, wherein the interface comprises a flow joiner and an emitter, wherein the flow joiner is operable to join the flow of the first sample and the second sample after the first column and the second column, respectively, and wherein the emitter is configured to convert the joined first sample and the second sample into a spray of charged droplets for ionization by electrospray ionization.

48. The separation platform of claim 46 or 47, wherein the fluid handling system further comprises a pump configured to move the fluid from the injection system to the first transfer line and the second transfer line and to the emitter.

49. A separation platform comprising:an injection system operable to sequentially inject a plurality of samples into a fluid of a fluid handling system;the fluid handling system comprising:6582558346v1Attorney Docket No.: 776866: PST-003PCa first transfer line of a plurality of transfer lines in fluid communication with a first column of a plurality of columns, the first transfer line having a first volume; anda second transfer line of the plurality of transfer lines in fluid communication with a second column of the plurality of columns, the second transfer line having a second volume,wherein a first-time offset of a plurality of time offsets is encoded for a first sample of the plurality of samples in the first transfer line and defined by the first volume and a second-time offset of the plurality of time offsets is encoded for a second sample of the plurality of samples in the second transfer line and defined by the second volume; andan interface configured to deliver the first sample and the second sample to a mass spectrometer.

50. A separation platform comprising:an injection system operable to sequentially inject a plurality of samples into a fluid of a fluid handling system;the fluid handling system comprising:a first transfer line of a plurality of transfer lines in fluid communication with a first column of a plurality of columns; anda second transfer line of the plurality of transfer lines in fluid communication with a second column of the plurality of columns,wherein a first-time offset of a plurality of time offsets is defined by a first position of the first sample within the first transfer line and a second-time offset of the plurality of time offsets is defined by a second position of the second sample within the second transfer line; andan interface configured to deliver the first sample and the second sample to a mass spectrometer.

51. A separation platform comprising:an injection system operable to inject a plurality of sample segments into a fluid of a fluid handling system and to inject a plurality of volumetric spacers between adjacent sample segments into a capillary;the fluid handling system comprising a reservoir in fluid communication with the capillary; a power supply configured to create an electric field within the capillary; andan interface configured to deliver eluting analytes of the plurality of sample segments to a mass spectrometer,6682558346v1Attorney Docket No.: 776866: PST-003PCwherein the plurality of sample segments are injected into the capillary and each sample segment of the plurality of samples is encoded with a time-offset defined by the volume of the volumetric spacer, andwherein the electric field is applied to the capillary to separate one or more analytes of each sample segment of the plurality of sample segments.

52. The separation platform of claim 51 , comprising two or more capillaries, in fluid communication with the fluid handling system.

53. A separation platform comprising:an injection system operable to inject a plurality of sample segments into a fluid of a fluid handling system;the fluid handling system comprising a reservoir of a background electrolyte in fluid communication with a capillary;a power supply configured to supply a voltage to the capillary; andan interface configured to deliver separated analytes of the plurality of sample segments to a mass spectrometer,wherein samples of a plurality of samples are sequentially injected into the capillary while the voltage is continuously applied to the capillary to allow the capillary to fill with the background electrolyte between sequential injections,wherein each sample segment of the plurality is encoded with a time-offset defined by an interval between injections, andwherein the applied voltage separates one or more analytes of each sample segment of the plurality of samples.

54. The separation platform of claim 53, comprising two or more capillaries.

55. A method of multiplexing through temporal encoding comprising:sequentially injecting multiple sample segments within at least one capillary of a separation platform, wherein each sample segment is physically separated by a volumetric spacer that encodes sample segment specificity;applying a voltage to the at least one capillary to drive movement of analytes within each sample segment;allowing the analytes within each sample segment to separate based on electrophoretic mobility, whereby at least a portion of the analytes of different sample segments co-migrate; analyzing, on a computer system, co-migrating analytes using a Mass Spectrometry (MS) technique to identify analytes of interest; and6782558346v1Attorney Docket No.: 776866: PST-003PCassigning, on the computer system, identified analytes of interest to a single sample segment based on a time-offset created by the volumetric spacer and electrophoretic mobility of the analytes of interest.

56. A method of multiplexing through temporal encoding comprising:sequentially injecting multiple sample segments within at least one capillary of a separation platform, wherein each sample segment loaded at a defined interval to encode sample segment specificity;applying a voltage to the at least one capillary to drive movement of analytes within each sample segment;allowing the analytes within each sample segment to separate based on electrophoretic mobility, whereby at least a portion of the analytes of different sample segments within the at least one capillary co-migrate;analyzing, on a computer system, co-migrating analytes using a Mass Spectrometry (MS) technique to identify analytes of interest; andassigning, on the computer system, identified analytes of interest to a single sample segment based on a time-offset created by the defined interval between sample segments and predicted electrophoretic mobility of the analytes of interest.

57. A tandem separation platform comprising at least two separation platforms independently selected from claim 49, claim 50, and combinations thereof, wherein the at least two separation platforms are configured to operate simultaneously or sequentially.

58. A method of multiplexing through temporal encoding comprising:injecting multiple sample segments within a separation platform utilizing a liquid mobile phase, wherein each sample segment elutes at a pre-determined time point;allowing analytes of interest from sequential segments to elute in an overlapping eluate; andanalyzing, on a computer system, the overlapping eluate using a Mass Spectrometry (MS) technique to identify analytes of interest.

59. The method of claim 58, further comprising assigning, on the computer system, identified analytes of interest to a single sample segment based on elution time offset and predicted elution times for the analytes of interest.

60. The method of claim 58 or 59, wherein the multiple sample segments are injected simultaneously or sequentially.6882558346v1Attorney Docket No.: 776866: PST-003PC61. A system for multiplexing through temporal encoding comprising:a separation platform utilizing a liquid mobile phase;an injection system configured to inject multiple sample segments within the separation platform at a pre-determined time point; andan mass spectrometry (MS) interface for connecting the separation platform to a mass spectrometer.

62. The system of claim 61 , further comprising a computing system configured to assign identified analytes of interest to a single sample segment based on elution time offset and predicted elution times for the analytes of interest.

63. The system of claim 61 or 62, wherein the injection system is configured to inject multiple sample segments within the separation platform at the pre-determined time point sequentially.

64. A computer-implemented method of deconvolving temporally-encoded multiplexed samples, the method comprising:detecting precursors present in a temporally-encoded overlapping eluate obtained from a multiplexed sample containing n sample segments using a Mass Spectrometry (MS) technique;identifying analyte-like features in a plurality of the detected precursors; and assigning each detected precursor to a single sample segment according to a predicted retention-time.

65. A separation platform comprising:an injection system operable to inject a plurality of samples into a fluid of a fluid handling system; the fluid handling system comprising:a first transfer line of a plurality of transfer lines in fluid connection with a first column of a plurality of columns; anda second transfer line of the plurality of transfer lines in fluid connection with a second column of the plurality of columns; andwherein a first-time offset of a plurality of time offsets is encoded for a firstsample of the plurality of samples in the first transfer line and a second-time offset of the plurality of time offsets is encoded for a second sample of the plurality of samples in the second transfer line; andan interface configured to deliver the first sample and the second sample to a mass spectrometer.6982558346v1Attorney Docket No.: 776866: PST-003PC66. A separation platform comprising:an injection system operable to inject a plurality of samples into a fluid of a fluid handling system; the fluid handling system comprising:a first transfer line of a plurality of transfer lines in fluid communication with a first column of a plurality of columns, the first transfer line having a first volume; anda second transfer line of the plurality of transfer lines in fluid communication with a second column of the plurality of columns, the second transfer line having a second volume, wherein a first-time offset of a plurality of time offsets is encoded for a firstsample of the plurality of samples in the first transfer line and defined by the first volume and a second-time offset of the plurality of time offsets is encoded for a second sample of the plurality of samples in the second transfer line and defined by the second volume; andan interface configured to deliver the first sample and the second sample to a mass spectrometer.

67. A separation platform comprising:an injection system operable to inject a plurality of samples into a fluid of a fluid handling system; the fluid handling system comprising:a first transfer line of a plurality of transfer lines in fluid communication with a first column of a plurality of columns; anda second transfer line of the plurality of transfer lines in fluid communication with a second column of the plurality of columns,wherein a first-time offset of a plurality of time offsets is defined by a first position of the first sample within the first transfer line and a second-time offset of the plurality of time offsets is defined by a second position of the second sample within the second transfer line; andan interface configured to deliver the first sample and the second sample to a mass spectrometer.

68. The separation platform of any one of claims 65-67, wherein the injection system is operable to inject a plurality of samples into the fluid sequentially.7082558346v1

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