Single cell multiomics

Abstract

Provided herein are compositions and methods for accurate and scalable single cell multiomics methods, as well as their use for mutation analysis in research, diagnosis, and therapy. Also provided herein are multi-omics methods for parallel analysis of DNA, RNA and / or proteins from single cells using primary template directed amplification (PTA) nucleic acid amplification.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 335,949 filed on April 28, 2022 and U.S. Provisional Patent Application No. 63 / 403,213 filed on September 1, 2022, both of which are incorporated herein by reference in their entirety. Background Art

[0003] Research methods using nucleic acid amplification (e.g., next generation sequencing) provide a wealth of information about complex samples, genomes, and other nucleic acid sources. In some cases, these samples are obtained in small amounts from single cells. For research, diagnosis, and treatment involving small samples, highly accurate, scalable, and effective nucleic acid amplification and sequencing methods are needed, especially for simultaneous analysis of RNA, DNA, and protein. Summary of the invention

[0004] Provided herein is a method for preparing a multi-omics sample, the method comprising: isolating a single cell from a cell population, wherein the single cell comprises RNA and genomic DNA; amplifying RNA by RT-PCR to produce a cDNA library; contacting the genomic DNA with at least one amplification primer, at least one nucleic acid polymerase, and a nucleotide mixture, wherein the nucleotide mixture comprises at least one terminator nucleotide, the at least one terminator nucleotide terminating the nucleic acid replication of the polymerase to produce a genomic DNA library; isolating cDNA from the genomic DNA; and sequencing the cDNA library and the genomic DNA library. Provided herein is a method for preparing a multi-omics sample, the method comprising: isolating a single cell from a cell population, wherein the single cell comprises RNA and genomic DNA; amplifying RNA by RT-PCR to produce a cDNA library; contacting the genomic DNA with at least one amplification primer, at least one nucleic acid polymerase, and a nucleotide mixture, wherein the nucleotide mixture comprises at least one terminator nucleotide, the at least one terminator nucleotide terminating the nucleic acid replication of the polymerase to produce a genomic DNA library and at least one nucleotide configured for removal or digestion; isolating cDNA from the genomic DNA; and sequencing the cDNA library and the genomic DNA library. Provided herein are methods for multi-omics sample preparation, the methods comprising: isolating a single cell from a cell population, wherein the single cell comprises RNA and genomic DNA; amplifying the RNA by RT-PCR to produce a cDNA library; contacting the genomic DNA with at least one amplification primer, at least one nucleic acid polymerase, and a nucleotide mixture, wherein the nucleotide mixture comprises at least one terminator nucleotide, the at least one terminator nucleotide terminating nucleic acid replication of the polymerase to produce a genomic DNA library and dUTP; isolating cDNA from the genomic DNA; and sequencing the cDNA library and the genomic DNA library. Also provided herein are methods, wherein the nucleotide mixture comprises dUTP. Also provided herein are methods, wherein the nucleotide mixture comprises dATP, dCTP, dGTP, dTTP, and dUTP. Also provided herein are methods, wherein the nucleotide mixture comprises at least one base that is not dATP, dCTP, dGTP, dTTP. Also provided herein are methods, wherein at least some of the polynucleotides in the cDNA library comprise a barcode. Also provided herein are methods, wherein at least some of the polynucleotides in the cDNA library comprise a tag. Also provided herein are methods, wherein the cDNA is at least 90% free of genomic DNA libraries after purification. Also provided herein are methods wherein the cDNA is at least 95% free of genomic DNA libraries after purification. Also provided herein are methods wherein at least 90% of the polynucleotides of the cDNA library comprise a 5' to 3' bias of 0.8 to 1.2. Also provided herein are methods wherein the separation comprises capturing at least some of the cDNA library by binding to a tag.Methods are also provided herein, wherein separation includes contacting a cDNA library with an enzyme configured to digest or remove polynucleotides from a genomic DNA library. Methods are also provided herein, wherein separation includes contacting a cDNA library with a DNA glycosylase. Methods are also provided herein, wherein contact of the cDNA library with the enzyme occurs on a solid support. Methods are also provided herein, wherein a genomic DNA library is amplified before sequencing. Methods are also provided herein, wherein the genomic DNA library is amplified with uracil-tolerant polymerase. Methods are also provided herein, wherein uracil-tolerant polymerases include DNA polymerases ε and δ from Saccharomyces cerevisiae (S.cerevisiae), and Escherichia coli (E.coli) DNA polymerase III, PolA type polymerase, KAPA HiFi uracil+DNA polymerase (Q5U), KOD Multi&Epi DNA polymerase, Taq, Taq2000, FailSafe enzyme or PhusionU. Methods are also provided herein, wherein separation includes nuclear lysis / denaturation. Methods are also provided herein, wherein the cDNA library includes 50-300ng of DNA. Also provided herein is a method, wherein the cDNA library comprises a polynucleotide comprising a cell barcode or a sample barcode. Also provided herein is a method, wherein the cDNA library comprises a polynucleotide corresponding to at least 2000 genes. Also provided herein is a method, wherein amplifying the cDNA library comprises contacting with a labeled primer. Also provided herein is a method, wherein the method further comprises adding an adapter to one or more of the cDNA library and the genomic DNA library. Also provided herein is a method, wherein the addition of the adapter comprises contacting with a ligase. Also provided herein is a method, wherein the addition of the adapter comprises contacting with a transposase or a complex thereof. Also provided herein is a method, wherein the transposase or its complex comprises Tn5. Also provided herein is a method, wherein the addition of the adapter comprises contacting with a polymerase and one or more primers. Also provided herein is a method, wherein separation comprises contacting the cDNA library with a DNA glycosylase-lyase endonuclease VIII. Also provided herein is a method, wherein the genomic DNA library comprises 0.5-2.5ng of DNA. Also provided herein is a method, wherein the single cell comprises a NA12878 control. Also provided herein is a method, wherein the single cell is a primary cell. Also provided herein is a method, wherein the single cell is derived from liver, skin, kidney, blood or lung. Also provided herein is a method, wherein the single cell is a cancer cell, a neuron, a glial cell or a fetal cell. Also provided herein is a method, wherein the genomic DNA library is generated by 2-15 amplification cycles. Also provided herein is a method, wherein the genomic DNA library comprises polynucleotides of 250-1500 bases in length. Also provided herein is a method, wherein the genomic DNA library comprises 70%-95% allele balance.Also provided herein is a method, wherein the genomic DNA library comprises a SNV sensitivity of at least 0.85%. Also provided herein is a method, wherein the genomic DNA library comprises a SNV accuracy of at least 0.95%. Also provided herein is a method, wherein the method further comprises analyzing one or more expressed proteins in a single cell. Also provided herein is a method, wherein the method further comprises analyzing one or more genomic methylation patterns from a single cell. Also provided herein is a method, wherein at least 98% of the polynucleotides comprise terminator nucleotides. Also provided herein is a method, wherein the terminator nucleotides are attached to the 3' end of at least some of the polynucleotides. Also provided herein is a method, wherein the irreversible terminator is resistant to exonuclease activity. Also provided herein is a method, wherein the irreversible terminator is resistant to 3'-5 exonuclease activity. Also provided herein is a method, wherein the terminator nucleotides comprise adenine, guanine, cystine or thymine. Also provided herein is a method, wherein the terminator nucleotides do not comprise uridine. Also provided herein is a method, wherein the terminator nucleotide is selected from a nucleotide modified with an α group, a C3 spacer nucleotide, a locked nucleic acid (LNA), an inverted nucleic acid, a 2'fluoro nucleotide, a 3' phosphorylated nucleotide, a 2'-O-methyl modified nucleotide, and a trans nucleic acid. Also provided herein is a method, wherein the nucleotide modified with an α group is an α-thiodideoxy nucleotide. Also provided herein is a method, wherein the terminator nucleotide comprises a modification of the r group of the 3' carbon of the deoxyribose. Also provided herein is a method, wherein the terminator nucleotide is selected from a 3' blocked reversible terminator containing a nucleotide, a 3' unblocked reversible terminator containing a nucleotide, a terminator containing a T modification of a deoxynucleotide, a terminator containing a modification of a nitrogenous base of a deoxynucleotide, and a combination thereof. Also provided herein are methods, wherein the terminator nucleotides are selected from dideoxy nucleotides, inverted dideoxy nucleotides, 3' biotinylated nucleotides, 3' amino nucleotides, 3'-phosphorylated nucleotides, 3'-0-methyl nucleotides, 3' carbon spacer nucleotides including 3' C3 spacer nucleotides, 3' C18 nucleotides, 3' hexanediol spacer nucleotides, acyclic nucleotides, and combinations thereof.Also provided herein are methods, wherein the nucleic acid polymerase is bacteriophage phi29 (F29) polymerase, genetically modified phi29 (F29) DNA polymerase, Klenow fragment of DNA polymerase I, bacteriophage M2 DNA polymerase, bacteriophage phiPRD1 DNA polymerase, Bst DNA polymerase, Bst large fragment DNA polymerase, exo (-) Bst polymerase, exo (-) Bca DNA polymerase, Bsu DNA polymerase, VentR DNA polymerase, VentR (exo-) DNA polymerase, Deep Vent DNA polymerase, DeepVent (exo-) DNA polymerase, IsoPol DNA polymerase, DNA polymerase I, Therminator DNA polymerase, T5 DNA polymerase, Sequenase, T7 DNA polymerase, T7-Sequenase or T4 DNA polymerase. Also provided herein are methods, wherein the nucleic acid polymerase comprises 3'->5' exonuclease activity, and the at least one terminator nucleotide inhibits the 3'->5' exonuclease activity. Also provided herein are methods, wherein the nucleic acid polymerase does not comprise 3'->5' exonuclease activity. Also provided herein are methods, wherein the polymerase is Bst DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, Bsu DNA polymerase, VentR(exo-)DNA polymerase, Deep Vent(exo-)DNA polymerase, Klenow fragment (exo-)DNA polymerase or Therminator DNA polymerase.

[0005] Incorporation by reference

[0006] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The novel features of the present invention are particularly set forth in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by referring to the following detailed description and accompanying drawings which set forth illustrative embodiments in which the principles of the invention are utilized, in which:

[0008] Figure 1A An exemplary high-level workflow for simultaneous enrichment and preparation of RNA and DNA from single cells is shown. RNA is reverse transcribed using oligo dT primers and reverse transcriptase, followed by template switching and primer extension. Genomic DNA is then amplified using primary template amplification (PTA).

[0009] Figure 1B Nucleic acid yield graphs of DNA (top) and RNA (bottom) from various samples are shown (NTC = no template control). Yields (in ng) of RNA and DNA isolated per cell used in this study. Samples where purification by streptavidin beads was omitted are highlighted in orange.

[0010] Figure 2A Allelic balance plots using combined RNA+DNA multiomics (left) compared to a DNA-only approach (right) in a control (NA12878) are shown, displayed as deciles of observed allele frequency (AF) at known heterozygous positions. Each point represents the proportion of variants that display AF within the bin frequency of a given cell. The bar graph with error bars describes the overall trend of all cell replicates for each AF bin. When AF <0.1 or >0.9, it is called allele dropout.

[0011] Figure 2B Cumulative genome coverage plots for each sample type using multi-omics approaches (combined RNA+DNA multi-omics (left) versus DNA-only approaches (right)) are shown, showing the proportion of the entire genome covered (y-axis) at a given depth (x-axis). Each point represents a cell replicate within the dataset, and the error plots represent the variability of coverage at a given depth.

[0012] Figure 2C Shown is a sensitivity plot using combined RNA+DNA multi-omics (left) compared to a DNA-only approach (right). SNV calling sensitivity (y-axis) and accuracy (x-axis) relative to the GIAB NA12878 reference dataset are shown with minimum ranges of 0.9 and 0.99 for both axes, respectively.

[0013] Figure 3A A summary coverage plot of all detected transcripts in the full-length chemistry is shown (top). The x-axis is the normalized fraction of transcripts from 5' to 3', dividing the region into average depth per percentile of transcripts, and the y-axis is counts. Distribution of counts for coding sequences of two known housekeeping genes: GAPDH and ACTB (bottom).

[0014] Figure 3BThe ratio of the comparison reads of the RNA species matching specific transcript features or reported for each data set is shown (averaged in all biological samples of a group). Features and ratios are derived from the Qualimap summary of our transcriptome definition file. Except for MOLM / DCIS figure, NA12878 cells are fully utilized. Extract a large amount of data from the online knowledge base as a reference from typical RNA-Seq. The conditions on the x-axis are: a large amount, IsolatedBulkRNA-StandardPrep, SingleCellRNA-StandardPrep, IsolatedBulkRNA-ResolveOME (BioskrybGenomics, Inc.), SingleCell-ResolveOME (Bioskryb Genomics, Inc.), MOLM and DCIS. The region of each bar (top to bottom) is FivePrimeUTR_protein_coding, CDS_protein_coding, ThreePrimeUTR_protein_coding, intron_protein_coding, exon_IncRNA, intron_IncRNA, other and intergenic.

[0015] Figure 3C Plots showing various RNA quality control metrics displayed for the UHRR and HBRR RNA controls used in this study and the NA12878 control. Clockwise from top left, distribution of reads assigned to transcriptomes, coding region features, unique genes detected, range of counts per million (CPM), and median absolute deviation (MAD) for common housekeeping genes.

[0016] Figure 3D Multi-omics whole transcript performance is shown compared to a mixture of publicly available bulk RNA-Seq and 3' end count datasets, including expressed protein coding genes detected using multi-omics chemistries, compared to bulk preparations using the same workflow. Number of uniquely expressed genes in multiple cell line models and primary DCIS patient samples. All sample sets were downsampled to 75,000 reads.

[0017] Figure 4A Shown is the case size of 500kb using Ginkgo, the copy number change from the individual MOLM-13 cell (row) of parental generation (turquoise) and resistance (salmon color) cell.Based on the average multiple change of each case and the distance of 2N.b., a dendrogram was produced: the representative metaphase propagation of 25 total karyotype propagations.Red circle represents the chromosome of abnormal amplification.

[0018] Figure 4B A representative metaphase spread of 25 total karyotype spreads is shown. Red circles indicate abnormally amplified chromosomes.

[0019] Figure 5A Shown is a genomic view showing detection of reciprocal FLT3 ITD mutations in parental and quizartinib-resistant single cells.

[0020] Figure 5B A genomic view of the FLT3 secondary mutation N841K only in quizartinib-resistant cells is shown.

[0021] The missense mutation N841K was detected in all quizartinib-resistant cells.

[0022] Figure 5C qRT-PCR detection of mutant FLT3 K841 in treatment naive parental cells is shown. qPCR cycle traces of FLT3 N841 (blue) and K841 (red) in MOLM-13 parental cells and quizartinib-resistant cells.

[0023] Figure 6 The heat map of SNV is shown, showing the genotype prevalence with statistical significance (by multinomial logistic regression, p < 0.05) in MOLM-13 parental cells and resistant cells. Column represents cell and row represents SNV id. The color in the expansion diagram represents the genotype determined. Row and column both have undergone unsupervised hierarchical clustering.

[0024] Fig. 7A Shown is a scatter plot showing the principal coordinate projection (PCA) of 28,134 SNVs that showed statistically significant (chi-square test, p<0.05) differential prevalence in two MOLM-13 cohorts: parental (turquoise, left panel) and resistant (salmon, right panel).

[0025] Figure 7B Clustering of differentially expressed genes in the MOLM-13 drug resistance model is shown. Parental single cells (turquoise) and quizartinib resistance (salmon) single cells form columns; Gene Symbol / Ensembl transcript ID forms rows. Biotype and FDR are shown on the right side of the heat map; Red line represents q<0.1.

[0026] Figure 7C CEBPA / B transcript upregulation in single quizartinib-resistant MOLM-13 cells is shown. Each row corresponds to a single MOLM-13 cell. Resistant cells that also have 19q gain are also shown.

[0027] Fig.7DHeat map is shown, wherein the transcript on the y-axis shows the statistical (ZLM p<0.01) association of the ploidy level of all cells in the MOLM-13 data set. The color of the expansion diagram represents the average standardized expression value under a given ploidy level. The right figure shows the output of the ZLM model of test expression when given ploidy. The red line represents the p<0.05 critical value of the model. Based on the -log10 p value of the ZLM model of transcriptional differences between test parental cells and resistant cells, the bar is colored.

[0028] Fig. 7E Examples of differential transcript utilization (DTU) between MOLM-13 parental and drug-resistant single cells are shown.

[0029] Fig. 8A Shown is a bubble diagram (p<0.05) showing SNV-transcript expression association. Top: SNV within 5000 bases of the transcription start site. The y-axis shows candidate SNVs, and the x-axis shows genotypes. The size of the circle represents the genotype prevalence of variants in the MOLM-13 cell type set (parental or resistance). The color of the point represents the standardized average expression level of transcripts in the described concentration. The side bar represents the significance of the model associated between test transcript expression and genotype. The red line represents the p<0.1 critical value of the model. Based on the -log10 p value of the ZLM model of transcription differences between the test parental cells and the resistant cells, the bar is colored. PABPC4 and MYC are highlighted in yellow. CEBPA SNV is too far away from the transcription start site (>5kb) and is not significant in this figure.

[0030] Figure 8B Parental / quizartinib resistance SNVs near the CEBPA genomic locus are shown. Asterisks indicate mutation positions. Resistant cells showed the variant in 60% of cells, compared to 11% in the parental line variant "chr19:33,333,734–delA" (middle star). For "chr19:33,361,973–insA", we observed no mutation in parental cells and 50% of quizartinib-resistant cells.

[0031] Figure 8C An intronic SNV of the MYC gene "chr8:127,739,932G>A" is shown, which is associated with increased expression in drug-resistant MOLM-13 cells.

[0032] Fig.8D Putative promoter variants in PABPC4 "chr1:39,579,411T>G" and "chr1:39,579,413T>G" that were found only in half of the resistant cells and were also associated with differential expression between MOLM-13 parental and resistant cells are shown.

[0033] Fig. 9 Single cell copy number changes in the primary DCIS / IDC EpCAM cohort are shown. EpCAM high (yellow) and low (turquoise) EpCAM states are presented. Two different classifications of chromosome loss were observed in EpCAM high (yellow) cells: 1) combined 11q, 13q, 16q / 17p loss, and 2) combined 13q and 16q / 17p loss. In addition, 13p gain was identified in 10 / 20 EpCAM high cells, while Chr.X gain including centromere and flanking P&Q segments was observed in 3 single cells.

[0034] Fig. 10A Shown is a principal component analysis of EpCAM high (circles) and EpCAM low (diamonds) primary DCIS / IDC transcriptomes, where cells are colored based on the number of transcripts detected.

[0035] Fig. 10B PAM50 gene expression stratification of EpCAM high and EpCAM low DCIS / IDC transcriptomes is shown.

[0036] Fig. 10C Unsupervised clustering generates six main modules of differential gene expression between EpCAM high and EpCAM low branches. The average ploidy, PIK3CA genotype status (green = N345 wild type, pink = K345 heterozygous mutant) and cell identity determination of each single cell (column) are shown. The gene biotype and FDR (row) of each transcript are presented.

[0037] Fig. 10D Prediction of DCIS cell identity / status using Human Cell Atlas data is shown. Heatmap showing the identity scores of different cell types (rows) for EpCAM high and EpCAM low single cells (columns) used to identify cell annotation.

[0038] Fig.10E Overlay of cell annotations for principal component analysis of DCIS cells is shown. EpCAM high (circles) and EpCAM low (diamonds) single cell transcriptomes, with isoform counts overlaid with cell identity / state (color).

[0039] Fig.11 The relative growth rates of parental and quizartinib-resistant MOLM-13 cells are shown. Cell counts over culture days after introduction of different concentrations of quizartinib.

[0040] Fig.12Shown is the missense variant compared to the parent in resistance MOLM-13 cell.Shown is the variant (row) accredited as significantly relevant to drug resistance by logistic regression, and individual genotype (0 / 0=homozygous reference, 0 / 1=heterozygous, 1 / 1=homozygous alternative, NA=undetermined).Shown is the single cell (column) of parent (left) or resistance (right) queue.P value is shown along the right side.

[0041] Fig.13 Model for transcriptional bypass signaling through AXL following FLT3 inhibition is shown. Schematic illustration that following FLT3 inhibition by quizartinib, GAS6, a ligand for the receptor tyrosine kinase AXL, is upregulated in resistant MOLM-13 cells to drive growth and survival through PI3 kinase and AKT signaling, respectively.

[0042] Fig.14 Variants associated with the DCIS expression set are shown. Variants identified by logistic regression as significantly associated with the expression set within EpCAM-HDCIS cells are shown (rows), with individual genotypes shown (0 / 1 = heterozygous, 1 / 1 = homozygous alternative, NA = not determined). P values ​​are shown along the right.

[0043] Fig.15A An exemplary schematic diagram showing the multi-omics workflow and steps for dUTP and uracil DNA glycosylase (UDG) intervention.

[0044] Fig. 15B Shown are the number of genes observed with or without UDG treatment when dUTP was used in the PTA reaction of the multi-omics workflow.

[0045] Fig. 15C Intergenic background removal using dUTP+UDG modification of the PTA workflow is shown.

[0046] Fig.15D Shown is the modified allelic balance using dUTP+UDG for the PTA workflow compared to the PTA workflow without dUTP+UDG.

[0047] Fig.15E Shown are the SNV calling metrics (sensitivity and accuracy) modified using dUTP+UDG for the PTA workflow compared to the PTA workflow without dUTP+UDG. DETAILED DESCRIPTION

[0048] It is necessary to develop new scalable, accurate and effective nucleic acid amplification (including single cell and multi-cell genome amplification) and sequencing methods, which will overcome the limitations of current methods by increasing sequence presentation, uniformity and accuracy in a reproducible manner. Provided herein are compositions and methods for providing accurate and scalable primary template directional amplification (PTA) and sequencing in combination with other cell analysis techniques (multi-omics). Also provided herein are methods for multi-omics analysis, including combining with PTA to analyze proteins, DNA and RNA from single cells, and corresponding post-transcriptional or post-translational modifications. These methods and compositions promote the high-precision amplification of target (or "template") nucleic acids, which increases the accuracy and sensitivity of downstream applications (such as next-generation sequencing).

[0049] definition

[0050] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which these inventions belong.

[0051] Throughout this disclosure, numerical features are represented in range format. It should be understood that the description of range format is only for convenience and simplicity, and should not be interpreted as a strict limitation of the scope of any embodiment. Therefore, unless the context clearly indicates otherwise, it should be considered that the description of the range has specifically disclosed all possible sub-ranges and a single numerical value up to one-tenth of the lower limit unit within the range. For example, the description of the range such as from 1 to 6 should be considered as specifically disclosing a sub-range, such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and a single value within the range, for example, 1.1, 2, 2.3, 5 and 5.9. Regardless of the width of the range, this applies. The upper and lower limits of these intermediate ranges can be independently included in a smaller range, and are also included in the present invention, subject to any explicit exclusion of restrictions within the specified range. When the range includes one or two limits, unless the context clearly indicates otherwise, the scope that does not include one or two of the included limits is also included in the present invention.

[0052] The terms used herein are for the purpose of describing specific embodiments only and are not intended to limit any embodiments. As used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprises" and / or "comprising" are used in this specification, they specify the presence of the features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0053] Unless otherwise specified or apparent from the context, as used herein, the term "about" when referring to a numerical value or a range of numerical values ​​should be understood to mean the stated numerical value + / - 10% thereof, or, for values ​​listed in a range, from 10% below the listed lower limit to 10% above the listed upper limit.

[0054] As used herein, the term "subject" or "patient" or "individual" refers to animals, including mammals, such as humans, veterinary animals (e.g., cats, dogs, cows, horses, sheep, pigs, etc.) and disease experimental animal models (e.g., mice, rats). According to the present invention, conventional molecular biology, microbiology and recombinant DNA techniques can be used within the technical scope of the art. These techniques are fully explained in the literature. See, e.g., Sambrook, Fritsch and Maniatis, Molecular Cloning: A Laboratory Manual, 2nd Edition (1989) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York (herein "Sambrook et al., 1989"); DNA Cloning: A practical Approach, Volumes I and II (D. N. Glover, ed. 1985); Oligonucleotide Synthesis (M. J. Gait, ed. 1984); Nucleic Acid Hybridization (B. D. Hames and S. J. Higgins, eds. (1985); Transcription and Translation (B. D. Hames and S. J. Higgins, eds. (1984); Animal Cell Culture (R. I. Freshney, ed. (1986); Immobilized Cells and Enzymes (IRL Press, (1986); B. Perbal, A practical Guide To Molecular Cloning (1984); F. M. Ausubel et al. (eds.), Current Protocols in Molecular Biology, John Wiley & Sons, Inc. (1994); etc.

[0055] The term "nucleic acid" encompasses multi-stranded and single-stranded molecules. In double-stranded or triple-stranded nucleic acids, the nucleic acid chains do not need to be co-extended (i.e., double-stranded nucleic acids do not need to be double-stranded along the entire length of the two chains). Nucleic acid templates as described herein can have any size according to the sample (from small cell-free DNA fragments to the entire genome), including but not limited to a length of 50-300 bases, 100-2000 bases, 100-750 bases, 170-500 bases, 100-5000 bases, 50-10,000 bases, or 50-2000 bases. In some cases, the length of the template is at least 50, 100, 200, 500, 1000, 2000, 5000, 10,000, 20,000, 50,000, 100,000, 200,000, 500,000, 1,000,000 or more than 1,000,000 bases. The methods described herein provide amplification of nucleic acids such as nucleic acid templates. The methods described herein additionally provide the generation of isolated and at least partially purified nucleic acids and nucleic acid libraries. In some cases, the methods described herein provide extracted nucleic acids (e.g., extracted from tissues, cells or culture media). Nucleic acids include, but are not limited to, those including the following: DNA, RNA, circular RNA, mtDNA (mitochondrial DNA), cfDNA (cell-free DNA), cfRNA (cell-free RNA), siRNA (small interfering RNA), cffDNA (cell-free fetal DNA), mRNA, tRNA, rRNA, miRNA (microRNA), synthetic polynucleotides, polynucleotide analogs, any other nucleic acid that meets this specification, or any combination thereof. When provided, the length of a polynucleotide is described in bases and abbreviated as nt (nucleotides), bp (bases), kb (kilobases), or Gb (gigabases).

[0056] As used herein, the term "droplet" refers to a volume of liquid on a droplet actuator. In some cases, for example, the droplet is aqueous or non-aqueous, or can be a mixture or emulsion including aqueous and non-aqueous components. For non-limiting examples of droplet fluids that can be subjected to droplet operations, see, for example, International Patent Application Publication No. WO2007 / 120241. In the embodiments presented herein, any system suitable for forming and manipulating droplets can be used. For example, in some cases, a droplet actuator is used. For non-limiting examples of droplet actuators that can be used, see, e.g., U.S. Patent Nos. 6,911,132, 6,977,033, 6,773,566, 6,565,727, 7,163,612, 7,052,244, 7,328,979, 7,547,380, 7,641,779, U.S. Patent Application Publication Nos. US20060194331, US20030205632, US 20060164490, US20070023292, US20060039823, US20080124252, US20090283407, US20090192044, US20050179746, US20090321262, US20100096266, US20110048951, International Patent Application Publication No. WO2007 / 120241. In some cases, the bead is provided in a droplet, in a droplet operations gap, or on a droplet operations surface. In some cases, the bead is provided in a reservoir located outside the droplet operations gap or separate from the droplet operations surface, and the reservoir can be associated with a flow path that allows a droplet including the bead to enter the droplet operations gap or contact the droplet operations surface. Non-limiting examples of droplet actuator technology for immobilizing magnetically responsive beads and / or non-magnetically responsive beads and / or using beads for droplet manipulation protocols are described in U.S. Patent Application Publication No. US20080053205, International Patent Application Publication No. WO2008 / 098236, WO2008 / 134153, WO2008 / 116221, WO2007 / 120241. Bead properties can be employed in multiplexing embodiments of the methods described herein. Examples of beads with properties suitable for multiplexing, and methods for detecting and analyzing signals emitted from such beads, can be found in U.S. Patent Application Publication Nos. US20080305481, US20080151240, US20070207513, US20070064990, US20060159962, US20050277197, US20050118574.In some cases, the methods described herein utilize a transposon-based droplet / bead process, such as those described in U.S. Pat. Nos. 11473138, 10844372, 10590244, 10725027, 9771575, 10676736, 11479816, 10975371, 11180752, 11085036, 11111519, 11124830, and 11434530. In some cases, the methods described herein utilize droplet manipulation techniques and devices such as those found in U.S. Pat. Nos. 10633701, 10029256, 11517864, 11358105, 11000849, 11229911, 10569268, 10012592, 9573099, 11389800, 9475013, 11203787, 10589274, 10232373, 11312990, 11020736, 11111519, and 11142791. In some cases, the methods described herein utilize single cell manipulation techniques such as those found in U.S. Pat. Nos. 11124830 and 11365441.

[0057] Primers and / or template switching oligonucleotides may also be attached to a solid substrate to facilitate reverse transcription and template switching of mRNA polynucleotides. In this arrangement, a portion of the RT or template switching reaction occurs in the bulk solution of the device, wherein the second step of the reaction is carried out close to the surface. In other arrangements, the primers of the template switching oligonucleotides are released from the solid substrate to allow the entire reaction to proceed above the surface in the solution. In multi-omics methods, in some cases, primers for multi-stage reactions are attached to a solid substrate or combined with beads to achieve a combination of multi-stage primers.

[0058] Some microfluidic devices also support multi-omics methods. For example, devices made of PDMS usually have continuous chambers for each reaction step. Such multi-chambered devices are usually isolated using microvalve structures, which can be controlled by pressure with air or fluids such as water or inert hydrocarbons (i.e., fluorinert). In multi-omics methods, each level of the reaction can be isolated and allowed to proceed discretely. When a specific level is completed, the valve between adjacent chambers can be released, and the substrate for subsequent reactions can be added in series. The result is that individual cells can be used as input template materials to simulate the sequence group of reactions, such as the multi-omics (protein / RNA / DNA / epigenomics) group of reactions. Various microfluidic platforms can be used to analyze single cells. In some cases, cells are manipulated by fluid dynamics (droplet microfluidics, inertial microfluidics, vortex, microvalve, microstructure (e.g., micropore, microtrap)), electrical methods (electrophoresis (DEP), electroosmosis), optical methods (optical tweezers, optically induced dielectrophoresis (ODEP), optical thermocapillary), acoustic methods or magnetic methods. In some cases, the microfluidic platform comprises microwells. In some cases, the microfluidic platform comprises a PDMS (polydimethylsiloxane) based device. Non-limiting examples of single-cell analysis platforms compatible with the methods described herein are: ddSEQ single-cell isolator (Bio-Rad, Hercules, CA, USA and Illumina, San Diego, CA, USA); Chromium (10x Genomics, Pleasanton, CA, USA); Rhapsody single-cell analysis system (BD, Franklin Lakes, NJ, USA); Tapestri platform (MissionBio, San Francisco, CA, USA); Nadia Innovate (Dolomite Bio, Royston, UK); C1 and Polaris (Fluidigm, South San Francisco, CA, USA); ICELL8 single-cell system (Takara); MSND (Wafergen); Puncher platform (Vycap); CellRaft AIR system (CellMicrosystems); DEPArray NxT and DEPArray system (Menarini Silicon Labs, Inc.); Biosystems); AVISOCellCelector (ALS); and InDrop system (1CellBio), TrapTx (Celldom), PipSeq (FluentBio), RNA sequencing kit (Scale Bio), and Single Cell 3.0 (Parse Bio).

[0059] As used herein, the term "unique molecular identifier (UMI)" refers to a unique nucleic acid sequence attached to each of a plurality of nucleic acid molecules. When incorporated into a nucleic acid molecule, in some cases, the UMI is used to correct for subsequent amplification bias by directly counting the UMIs sequenced after amplification. The design, incorporation, and application of UMIs are described, for example, in International Patent Application Publication No. WO 2012 / 142213; Islam et al. Nat. Methods (2014) 11: 163-166; Kivioja, T. et al. Nat. Methods (2012) 9: 72-74; Brenner et al. (2000) PNAS 97 (4), 1665; and Hollas and Schuler, (2003) Conference: 3rd International Workshop on Algorithms in Bioinformatics, Vol. 2812.

[0060] As used herein, the term "barcode" refers to a nucleic acid tag that can be used to identify a sample or source of a nucleic acid material. Therefore, in the case where the nucleic acid sample is from multiple sources, in some cases, the nucleic acid in each nucleic acid sample is labeled with different nucleic acid tags, so that the source of the sample can be identified. Barcodes, also commonly referred to as indexes, labels, etc., are well known to those skilled in the art. Any suitable barcode or barcode set can be used. See, for example, the non-limiting examples provided in U.S. Patent No. 8,053,192 and International Patent Application Publication No. WO2005 / 068656. The barcoding of a single cell can be performed, for example, as described in U.S. Patent Application Publication No. 2013 / 0274117.

[0061] The terms "solid surface," "solid support," and other grammatical equivalents herein refer to any material that is suitable or can be modified to be suitable for attachment of primers, barcodes, and sequences described herein. Exemplary substrates include, but are not limited to, glass and modified or functionalized glass, plastics (including acrylic, polystyrene, and copolymers of styrene with other materials, polypropylene, polyethylene, polybutylene, polyurethane, Teflon, and polyvinyl alcohol). TM The solid support may be a polymer, such as polysaccharides, nylon, nitrocellulose, ceramics, resins, silica, silica-based materials (e.g., silicon or modified silicon), carbon, metals, inorganic glass, plastics, fiber optic bundles, and various other polymers. In some embodiments, the solid support comprises a patterned surface suitable for immobilizing primers, barcodes, and sequences in an ordered pattern.

[0062] As used herein, the term "biological sample" includes, but is not limited to, tissues, cells, biological fluids and isolates thereof. In some cases, the cells or other samples used in the methods described herein are isolated from human patients, animals, plants, soil or other samples including microorganisms such as bacteria, fungi, protozoa. In some cases, the biological sample is derived from humans. In some cases, the biological sample is not derived from non-humans. In some cases, cells undergo PTA methods and sequencing as described herein. Variants detected in the entire genome or a specific position can be compared with all other cells isolated from the object to track the history of cell lineages for research or diagnostic purposes. In some cases, variants are confirmed by other analytical methods such as direct PCR sequencing.

[0063] Single cell analysis

[0064] Methods and compositions for analyzing single cells are described herein. The analysis of a large number of cells provides general information about cell populations, but low-frequency mutants cannot usually be detected on background. Such mutants may include important characteristics, such as drug resistance or mutations associated with cancer. In some cases, DNA, RNA and / or protein from the same single cell are analyzed in parallel. Analysis may include identification of epigenetic post-translational modifications (e.g., glycosylation, phosphorylation, acetylation, ubiquitination, histone modifications) and / or post-transcriptional modifications (e.g., methylation, hydroxymethylation). Such methods may include "primary template directed amplification" (PTA) to obtain a nucleic acid library for sequencing. In some cases, PTA is combined with other steps or methods, such as RT-PCR or proteome / protein quantitative techniques (e.g., mass spectrometry, antibody staining, etc.). In some cases, during each analysis step, the various components of the cell are physically or spatially separated from each other. In addition, in some cases, the multi-omics method of genomic DNA / RNA analysis requires that genomic DNA be purified from RNA (or cDNA after reverse transcription). Residual contamination of genomic DNA in cDNA libraries may lead to inaccurate transcriptome sequencing results.

[0065] In an exemplary workflow, first the protein is labeled with an antibody. In some cases, at least some antibodies include a label or marker (e.g., nucleic acid / oligonucleotide label, mass label or fluorescent label). In some cases, a part of the antibody includes an oligonucleotide label. In some cases, a part of the antibody includes a fluorescent marker. In some cases, the antibody is labeled with two or more labels or markers. In some cases, a part of the antibody is sorted based on a fluorescent marker. After RT-PCR, the first strand mRNA product is produced, and then taken out for analysis. The library is then produced by the RT-PCR product and the barcode present on the protein-specific antibody, and then sequenced. At the same time, PTA is performed to the genomic DNA from the same cell, a library is produced, and sequenced. In some cases, the sequencing results from the genome, methylation group, proteome and transcriptome are merged using bioinformatics methods. In some cases, the methods described herein include any combination of labeling, cell sorting, affinity separation / purification, cracking of specific cell components (e.g., outer membrane, core, etc.), RNA amplification, DNA amplification (e.g., PTA) or other steps related to protein, RNA or DNA separation or analysis. In some cases, the methods described herein include one or more enrichment steps, such as exome enrichment.

[0066] The first method of single cell analysis is described herein, which includes analyzing RNA and DNA from single cells. In some cases, the method includes separating single cells, cracking single cells and reverse transcription (RT). In some cases, reverse transcription is performed with template switching oligonucleotides (TSO). In some cases, TSO includes molecular tags such as biotin, thereby allowing the cDNA RT product to be subsequently pulled down, and PCR amplification of the RT product to produce a cDNA library. Alternatively or in combination, centrifugation is used to separate the RNA in the supernatant from the cDNA in the cell pellet. In some cases, a solid support is used to bind to the label. In some cases, the solid support includes a substantially flat surface, a hole or a bead. In some cases, TSO is attached to a solid support. In some cases, the cDNA amplicon can be purified using a solid support including TSO. In some cases, the purification of cDNA includes a washing step. In some cases, the remaining cDNA is fragmented and removed with UDG (uracil DNA glycosylase), and alkaline lysis is used to degrade RNA and denature the genome. After neutralization, adding primers and PTA, in some cases, the amplified product is purified on SPRI (solid phase reversible fixation) beads, and connected to adapters to produce gDNA libraries. In some cases, the PTA reaction occurs in the presence of the cDNA library produced. In some cases, the PTA reaction includes the use of bases that can be cut or removed by an enzyme. In some cases, the enzyme includes a glycosylase. In some cases, the PTA reaction is carried out with a variety of dNTPs, and the dNTPs include nucleotides except A, T, G or C. In some cases, the PTA reaction is carried out with a variety of dNTPs including uracil. gDNA is purified on SPRI (solid phase reversible fixation) beads, and connected to adapters to produce gDNA libraries. After PTA amplification, cDNA is purified or separated in some cases. In some cases, the RT product is separated by pulling down, such as with the pull-down separation of streptavidin beads. In some cases, the RT product is separated from the reaction mixture by physical separation (for example, on beads or magnetic beads). In some cases, enzymes are used to remove (or digest) the residual genomic library amplicon produced by PTA. In some cases, glycosylases are used to remove the residual genomic library amplicon produced by PTA. In some cases, the residual genomic library amplicon produced by uracil-containing PTA is removed by digestion. After purification, the cDNA library is at least 80%, 85%, 90%, 95%, 97%, 98%, 99%, 99.5% or at least 99.9% free of genomic DNA amplicon (e.g., those amplicon produced by PTA).

[0067] The second method of single cell analysis is described herein, which includes analyzing RNA and DNA from single cells. In some cases, the method includes separating single cells, cracking single cells and reverse transcription (RT). In some cases, reverse transcription is performed with template switching oligonucleotides (TSO). In some cases, TSO includes molecular tags such as biotin, thereby allowing the cDNA RT product to be subsequently pulled down, and PCR amplification of the RT product to produce a cDNA library. In some cases, a solid support is used to bind to the label. In some cases, the solid support includes a substantially flat surface, a hole or a bead. In some cases, TSO is attached to a solid support. In some cases, the cDNA amplicon can be purified using a solid support including TSO. In some cases, the purification of cDNA includes a washing step. In some cases, the solid support is used to bind to the label. In some cases, the solid support includes a substantially flat surface, a hole or a bead. In some cases, TSO is attached to a solid support. In some cases, the cDNA amplicon can be purified using a solid support including TSO. In some cases, the purification of cDNA includes a washing step. In some cases, alkaline lysis is then used to degrade RNA and denature the genome. After neutralization, adding random primers and PTA, in some cases, the amplified product is purified on SPRI (solid phase reversible fixation) beads, and is connected to adapters to produce gDNA libraries. In some cases, PTA reactions occur in the presence of the cDNA libraries produced. In some cases, the PTA reaction includes the use of bases that can be cut or removed by enzymes. In some cases, the enzyme includes glycosylase. In some cases, the PTA reaction is carried out with a variety of dNTPs, and the dNTPs include nucleotides except A, T, G or C. In some cases, the PTA reaction is carried out with a variety of dNTPs including uracil. gDNA is purified on SPRI (solid phase reversible fixation) beads, and is connected to adapters to produce gDNA libraries. After PTA amplification, cDNA is purified or separated in some cases. In some cases, the RT product is separated by pulling down, such as with the pull-down separation of streptavidin beads. In some cases, the RT product is separated from the reaction mixture by physical separation (for example, on a bead or magnetic bead). In some cases, the residual genomic library amplicon produced by PTA is removed (or digested) using an enzyme. In some cases, the residual genomic library amplicon produced by PTA is removed using a glycosylase. In some cases, the residual genomic library amplicon produced by uracil-containing PTA is removed by digestion. After purification, the cDNA library is at least 80%, 85%, 90%, 95%, 97%, 98%, 99%, 99.5% or at least 99.9% free of genomic DNA amplicon (for example, those amplicons produced by PTA).

[0068] The third method of single cell analysis is described herein, and the third method includes analyzing RNA and DNA from single cells. In some cases, the method includes separating single cells, cracking single cells and reverse transcription (RT). In some cases, reverse transcription is performed with template switching oligonucleotides (TSO) in the presence of terminator nucleotides. In some cases, TSO includes molecular tags such as biotin, thereby allowing the cDNA RT product to be subsequently pulled down, and PCR amplification RT product to produce a cDNA library. In some cases, alkaline lysis is then used to degrade RNA and denature the genome. After neutralization, adding random primers and PTA, in some cases, the amplified product is purified on SPRI (solid phase reversible fixation) beads, and connected to adapters to produce DNA libraries. In some cases, PTA reactions occur in the presence of the cDNA library produced. In some cases, PTA reactions include the use of bases that can be cut or removed by enzymes. In some cases, the enzyme includes glycosylases. In some cases, PTA reactions are performed with a variety of dNTPs, and the dNTPs include nucleotides except A, T, G or C. In some cases, the PTA reaction is carried out with a variety of dNTPs including uracil. gDNA is purified on SPRI (solid phase reversible fixation) beads, and is connected to adapters to produce gDNA libraries. After PTA amplification, cDNA is purified or separated in some cases. In some cases, the RT product is separated by pull-down, such as with the pull-down separation of streptavidin beads. In some cases, the RT product is separated from the reaction mixture by physical separation (for example, on beads or magnetic beads). In some cases, enzymes are used to remove (or digest) the residual genomic library amplicon produced by PTA. In some cases, glycosylase is used to remove the residual genomic library amplicon produced by PTA. In some cases, the residual genomic library amplicon produced by PTA containing uracil is removed by digestion. After purification, cDNA library in some cases at least 80%, 85%, 90%, 95%, 97%, 98%, 99%, 99.5% or at least 99.9% does not contain genomic DNA amplicon (for example, those amplicons produced by PTA).

[0069] Nucleotide mixture can comprise at least one nucleotide, and it is configured to digest (or remove or react) by enzyme or chemical process.In some cases, the nucleotide that is configured to digest comprises dUTP.In some cases, the nucleotide that is configured to digest is relative to another kind of nucleotide in mixture with about 1000:1,500:1,100:1,50:1,25:1,20:1,15:1,10:1,5:1,2:1,1:1,1:1.5,1:2,1:5,1:10,1:20,1:25,1:50,1:100,1:500 or about 1:1000 ratio existence. In some cases, the nucleotide configured for digestion is present in a ratio of at least 1000:1, 500:1, 100:1, 50:1, 25:1, 20:1, 15:1, 10:1, 5:1, 2:1, 1:1, 1:1.5, 1:2, 1:3, 1:5, 1:10, 1:25, 1:50, 1:100, 1:500, or at least 1:1000 relative to another nucleotide in the mixture. In some cases, the nucleotide configured for digestion is present in a ratio of no more than 1000: 1, 500: 1, 100: 1, 50: 1, 25: 1, 20: 1, 15: 1, 10: 1, 5: 1, 2: 1, 1: 1, 1: 1.5, 1:2, 1:3, 1:5, 1: 10, 1:20, 1:25, 1:50, 1: 100, 1:500, or no more than 1: 1000 relative to another nucleotide in the mixture. In some cases, the nucleotide configured for digestion is present in a ratio of about 1000:1-1:1000, 100:1-1:100, 50:1-1:50, 50:1-1:20, 20:1-1:20, 10:1-1:10, 5:1-1:5, 3:1-1:3, 2:1-1:1, 3:1-1:1, 5:1-1:2, 5:1-1:1, 10:1-1:1, 10:1-1:2, 20:1-1:1, 20:1-1:2, 50:1-1:1, or 100:1-1:1. In some cases, the dUTP is present at a ratio of about 1000: 1, 500: 1, 100: 1, 50: 1, 25: 1, 20: 1, 15: 1, 10: 1, 5: 1, 2: 1, 1: 1, 1: 1.5, 1:2, 1:5, 1: 10, 1:25, 1:50, 1: 100, 1:500, or about 1: 1000 relative to another nucleotide in the mixture. In some cases, the dUTP is present at a ratio of at least 1000: 1, 500: 1, 100: 1, 50: 1, 25: 1, 20: 1, 15: 1, 10: 1, 5: 1, 2: 1, 1: 1, 1: 1.5, 1:2, 1:5, 1: 10, 1:25, 1:50, 1: 100, 1:500, or at least 1: 1000 relative to another nucleotide in the mixture.In some cases, the dUTP is present in a ratio of no more than 1000: 1, 500: 1, 100: 1, 50: 1, 25: 1, 20: 1, 15: 1, 10: 1, 5: 1, 2: 1, 1: 1, 1: 1.5, 1:2, 1:5, 1: 10, 1:25, 1:50, 1: 100, 1:500, or no more than 1: 1000 relative to another nucleotide in the mixture. In some cases, the dUTP is present at a ratio of about 1000: 1-1: 1000, 100: 1-1: 100, 50: 1-1:50, 50: 1-1:20, 20: 1-1:20, 10: 1-1: 10, 5: 1-1:5, 3: 1-1:3, 2: 1-1: 1, 3: 1-1: 1, 5: 1-1:2, 5: 1-1: 1, 10: 1-1: 1, 10: 1-1:2, 20: 1-1: 1, 20: 1-1:2, 50: 1-1: 1, or 100: 1-1: 1. In some cases, the mixture comprises a ratio of dTTP to dUTP of about 1000: 1, 500: 1, 100: 1, 50: 1, 25: 1, 20: 1, 15: 1, 10: 1, 5: 1, 2: 1, 1: 1, 1: 1.5, 1:2, 1:5, 1: 10, 1:25, 1:50, 1: 100, 1:500, or about 1: 1000. The mixture comprises a ratio of dTTP to dUTP of at least 1000: 1, 500: 1, 100: 1, 50: 1, 25: 1, 20: 1, 15: 1, 10: 1, 5: 1, 2: 1, 1: 1, 1: 1.5, 1:2, 1:5, 1: 10, 1:25, 1:50, 1: 100, 1:500, or at least 1: 1000. The mixture comprises a ratio of dTTP to dUTP of no more than 1000: 1, 500: 1, 100: 1, 50: 1, 25: 1, 20: 1, 15: 1, 10: 1, 5: 1, 2: 1, 1: 1, 1: 1.5, 1:2, 1:3, 1:5, 1: 10, 1:25, 1:50, 1: 100, 1:500, or 1: 1000. The mixture comprises dTTP to dUTP in an amount of 1000: 1-1: 1000, 100: 1-1: 100, 50: 1-1: 50, 50: 1-1: 20, 20: 1-1: 20, 10: 1-1: 10, 5: 1-1: 5, 3: 1-1: 3, 2: 1-1: 1, 3: 1-1: 1, 5: 1-1: 2, 5: 1-1: 1, 10: 1-1: 1, 10: 1-1: 2, 20: 1-1: 1, 20: 1-1: 2, 50: 1-1: 1, or 100: 1-1: 1. In some cases, the ratio of dTTP to dUTP is selected such that the PTA reaction completes at least 5 amplification cycles in no more than 0.1, 0.5, 1, 1.5, 2, 3, 4, 5, 8, 10, or no more than 12 hours.In some cases, the ratio of dTTP to dUTP is selected such that the PTA reaction completes at least 9 amplification cycles in no more than 0.1, 0.5, 1, 1.5, 2, 3, 4, 5, 8, 10, or no more than 12 hours.

[0070] The fourth method of single cell analysis is described herein, and the fourth method includes analyzing RNA and DNA from single cells. In some cases, the method includes separating single cells, cracking single cells and reverse transcription (RT). In some cases, reverse transcription is performed with template switching oligonucleotides (TSO). In some cases, TSO includes molecular tags such as biotin, thereby allowing the cDNA RT product to be subsequently pulled down, and PCR amplification RT product is to produce a cDNA library. In some cases, a solid support is used to bind to the label. In some cases, the solid support includes a substantially flat surface, a hole or a bead. In some cases, TSO is attached to a solid support. In some cases, the cDNA amplicon can be purified using a solid support including TSO. In some cases, the purification of cDNA includes a washing step. In some cases, alkaline lysis is then used to degrade RNA and denature the genome. After neutralization, adding random primers and PTA, in some cases, amplification products are subjected to RNA enzymes and cDNA amplification using closed and labeled primers. In some cases, PTA reactions occur under the presence of the cDNA library produced. In some cases, the PTA reaction includes the use of bases that can be cut or removed by an enzyme. In some cases, the enzyme includes a glycosylase. In some cases, the PTA reaction is carried out with a variety of dNTPs, and the dNTPs include nucleotides except A, T, G or C. In some cases, the PTA reaction is carried out with a variety of dNTPs including uracil. The gDNA is purified on SPRI (solid phase reversible fixation) beads, and is connected to an adapter to produce a gDNA library. After PTA amplification, in some cases, the cDNA is purified or separated. In some cases, the RT product is separated from the reaction mixture by physical separation (for example, on a bead or magnetic bead). In some cases, an enzyme is used to remove (or digest) the residual genomic library amplicon produced by PTA. In some cases, a glycosylase is used to remove the residual genomic library amplicon produced by PTA. In some cases, the residual genomic library amplicon produced by the PTA containing uracil is removed by digestion. After purification, the cDNA library is in some cases at least 80%, 85%, 90%, 95%, 97%, 98%, 99%, 99.5%, or at least 99.9% free of genomic DNA amplicons (e.g., those produced by PTA).

[0071] The fifth method of single cell analysis is described herein, and the fifth method includes analyzing RNA and DNA from single cells. The cell colony is contacted with an antibody library, wherein the antibody is labeled. In some cases, the antibody is labeled with a fluorescent marker, a nucleic acid barcode, or both. The labeled antibody is combined with at least one cell in the colony, and such cells are sorted, and each container (e.g., a tube, a vial, a micropore, etc.) is placed with a cell. In some cases, the container includes a solvent. In some cases, the region of the container surface is coated with a capture portion. In some cases, the capture portion is a small molecule, antibody, protein, or other agent that can be combined with one or more cells, organelles, or other cell components. In some cases, at least one cell, a single cell, or its component is combined with the region of the container surface. In some cases, the core is combined with the region of the container. In some cases, the outer membrane of the cell is lysed, thereby releasing mRNA into the solution in the container. In some cases, the core of the cell containing genomic DNA is combined with the region of the container surface. Next, RT is usually performed using the mRNA in the solution as a template to produce cDNA. In some cases, the template switching primer comprises TSS region (transcription start site), anchor region, RNA BC region and poly-dT tail from 5' to 3'. In some cases, the poly-dT tail is combined with the poly-A tail of one or more mRNAs. In some cases, the template switching primer comprises TSS region, anchor region and poly-G region from 3' to 5'. In some cases, the poly-G region includes ribose G. In some cases, the poly-G region is combined with the poly-C region on the mRNA transcript. In some cases, ribose G is added to the mRNA transcript by terminal transferase. After taking out the RT PCR product for subsequent sequencing, any remaining RNA in the cell is removed by UNG. Then the nuclear lysis is carried out, and the genomic DNA released is subjected to PTA method using random primers and isothermal polymerase. In some cases, the length of the primer is 6-9 bases. In some cases, PTA produces a genomic amplicon having a length of 100-5000, 200-5000, 500-2000, 500-2500, 1000-3000 or 300-3000 bases. In some cases, PTA produces a genomic amplicon having an average length of 100-5000, 200-5000, 500-2000, 500-2500, 1000-3000 or 300-3000 bases. In some cases, PTA produces a genomic amplicon having a length of 250-1500 bases. In some cases, the method described herein produces a short fragment cDNA pool having about 500, about 750, about 1000, about 5000 or about 10,000 times of amplification. In some cases, the methods described herein produce short fragment cDNA pools having 500-5000, 750-1500, or 250-10,000 fold amplification.The PTA products are optionally subjected to additional amplification and sequencing.

[0072] Sample preparation and isolation of single cells

[0073] Methods described herein may need to separate single cells for analysis. Any single cell separation method can be used together with PTA, such as oral pipetting, micro-pipetting, flow cytometry / FACS, microfluidics, methods for sorting nuclear (tetraploid or other) or manual dilution. Such methods are assisted by other reagents and steps, such as antibody-based enrichment (e.g., circulating tumor cells), enrichment methods or fluorescent labels based on other small molecules or proteins. In some cases, multi-omics analysis methods described herein include mechanical or enzymatic dissociation of cells from larger tissues.

[0074] Preparation and analysis of cell fractions

[0075] The polyomics analysis method including PTA described herein can include one or more methods for processing cell components such as DNA, RNA and / or protein. In some cases, the core (comprising genomic DNA) is physically separated from the cytosol (comprising mRNA), followed by a membrane selective lysis buffer to dissolve the membrane, but keep the core intact. The cytosol is then separated from the core using a method including micro-pipetting, centrifugation or antibody-conjugated magnetic microbeads. In another case, the coated magnetic beads of oligodT primers are combined with polyadenylated mRNA to separate from DNA. In another case, preamplify DNA and RNA simultaneously, and then separate to analyze. In another case, the single cell is split into two equal parts, wherein mRNA is processed from one half and genomic DNA is processed from the other half.

[0076] Multi-omics

[0077] Provided herein are methods for multi-omics sample preparation and / or analysis. In some cases, a method includes one or more steps of separating a single cell from a cell population, wherein the single cell comprises RNA and genomic DNA; RNA is amplified by RT-PCR to produce a cDNA library; cDNA is separated from the genomic DNA; genomic DNA is contacted with at least one amplification primer, at least one nucleic acid polymerase and a nucleotide mixture; cDNA is separated from a genomic library, and the cDNA library and the genomic DNA library are sequenced. In some cases, the nucleotide mixture comprises at least one nucleotide, which is configured to be digested (or removed or reacted) by an enzyme or chemical process. In some cases, the nucleotide mixture comprises dUTP. In some cases, the nucleotide mixture comprises at least one terminator nucleotide, and the at least one terminator nucleotide terminates the nucleic acid replication of the polymerase to produce a genomic DNA library. In some cases, the terminator nucleotide comprises an irreversible terminator. In some cases, the irreversible terminator suppresses or resists 3' to 5' exonuclease activity.

[0078] The methods described herein (e.g., PTA) can be used as an alternative to any number of other known methods in the art for single cell sequencing (multi-omics, etc.). PTA can replace genomic DNA sequencing methods such as MDA, PicoPlex, DOP-PCR, MALBAC, or target-specific amplification. In some cases, PTA replaces standard genomic DNA sequencing methods in multi-omics methods, including DR-seq (Dey et al., 2015), G&T seq (MacAulay et al., 2015), scMT-seq (Hu et al., 2016), sc-GEM (Cheow et al., 2016), scTrio-seq (Hou et al., 2016), RNA and protein simultaneous multiplexed measurement (Darmanis et al., 2016), scCOOL-seq (Guo et al., 2017), CITE-seq (Stoeckius et al., 2017), REAP-seq (Peterson et al., 2017), scNMT-seq (Clark et al., 2018) or SIDR-seq (Han et al., 2018). In some cases, the methods described herein include methods of PTA and polyadenylated mRNA transcripts. In some cases, the methods described herein include methods of PTA and non-polyadenylated mRNA transcripts. In some cases, the methods described herein include methods of PTA and total (polyadenylated and non-polyadenylated) mRNA transcripts.

[0079] In some cases, PTA is combined with standard RNA sequencing methods to obtain genomic and transcriptome data. In some cases, the multi-omics methods described herein include PTA and one of the following: Drop-seq (Macosko et al. 2015), mRNA-seq (Tang et al., 2009), InDrop (Klein et al., 2015), MARS-seq (Jaitin et al., 2014), Smart-seq2 (Hashimshony et al., 2012; Fish et al., 2016), CEL-seq (Jaitin et al., 2014), STRT -seq (Islam et al., 2011), Quartz-seq (Sasagawa et al., 2013), CEL-seq2 (Hashimshony et al., 2016), cytoSeq (Fan et al., 2015), SuPeR-seq (Fan et al., 2011), RamDA-seq (Hayashi et al., 2018), MATQ-seq (Sheng et al., 2017), or SMARTer (Verboom et al., 2019).

[0080] Various reaction conditions and mixtures can be used to generate cDNA libraries for transcriptome analysis. In some cases, RT reaction mixtures are used to generate cDNA libraries. In some cases, RT reaction mixtures include crowding reagents, at least one primer, template switching oligonucleotide (TSO), reverse transcriptase and dNTP mixtures. In some cases, RT reaction mixtures include RNAse inhibitors. In some cases, RT reaction mixtures include one or more surfactants. In some cases, RT reaction mixtures include Tween-20 and / or Triton-X. In some cases, RT reaction mixtures include betaine. In some cases, RT reaction mixtures include one or more salts. In some cases, RT reaction mixtures include magnesium salts (e.g., magnesium chloride) and / or tetramethylammonium chloride. In some cases, RT reaction mixtures include gelatin. In some cases, RT reaction mixtures include PEG (PEG1000, PEG2000, PEG4000, PEG6000, PEG8000 or other lengths of PEG).

[0081] The multi-omics methods described herein can provide both genomic information and RNA transcript information from a single cell (e.g., a combination or dual scheme). In some cases, the genomic information from a single cell is obtained from a PTA method, and the RNA transcript information is obtained from reverse transcription for generating a cDNA library. In some cases, a full transcript method is used to obtain a cDNA library. In some cases, a cDNA library is obtained using 3' or 5' end counting. In some cases, a cDNA library is not obtained using UMI. In some cases, the multi-omics method provides RNA transcript information about at least 500, 1000, 2000, 5000, 8000, 10,000, 12,000 or at least 15,000 genes from a single cell. In some cases, the multi-omics method provides RNA transcript information about about 500, 1000, 2000, 5000, 8000, 10,000, 12,000 or about 15,000 genes from a single cell. In some cases, the multi-omics method provides RNA transcript information about 100-12,000, 1000-10,000, 2000-15,000, 5000-15,000, 10,000-20,000, 8000-15,000 or 10,000-15,000 genes from a single cell. In some cases, the multi-omics method provides at least 80%, 90%, 92%, 95%, 97%, 98% or at least 99% of the genome sequence information about the genome of a single cell. In some cases, the multi-omics method provides about 80%, 90%, 92%, 95%, 97%, 98% or about 99% of the genome sequence information about the genome of a single cell. RNA can be amplified in the multi-omics method described herein. In some cases, RNA is amplified to separate mRNA transcripts. In some cases, template switching polynucleotides is used. In some cases, the amplification of RNA uses a labeled primer. In some cases, the label includes biotin. In some cases, at least some cDNA polynucleotides are separated by affinity binding with a label. In some cases, multi-omics methods include amplifying RNA to produce a cDNA library. In some cases, a cDNA library with at least 10, 20, 30, 50, 75, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400 or at least 500ng of DNA is produced. In some cases, a cDNA library is generated having 10-500, 20-500, 30-500, 50-500, 50-400, 50-300, 100-500, 100-400, 100-300, 100-200, 200-500, 300-500, or 400-750 ng of DNA. In some cases, at least some of the polynucleotides in the cDNA library comprise a barcode.In some cases, the cDNA comprises polynucleotides corresponding to at least 100, 500, 1000, 1500, 2000, 2500, 3000, 3500, or at least 4000 genes. In some cases, the cDNA comprises a 5' to 3' transcription bias of 0.5-1.5, 0.6-1.5, 0.7-1.5, 0.8-1.5, 0.9-1.5, 0.8-1.5, 1-1.5, 1-2.0, 1.2-2.0, 0.5-2.0.

[0082] Multi-omics methods can include analysis of single cells from a cell population. In some cases, at least 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000 or at least 8000 cells are analyzed. In some cases, about 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000 or about 8000 cells are analyzed. In some cases, 5-100, 10-100, 50-500, 100-500, 100-1000, 50-5000, 100-5000, 500-1000, 500-10000, 1000-10000 or 5000-20,000 cells are analyzed.

[0083] Multi-omics methods can produce the yield of genomic DNA from PTA reaction based on the type of single cell. In some cases, the amount of DNA produced from a single cell is about 0.1, 1, 1.5, 2, 3, 5 or about 10 micrograms. In some cases, the amount of DNA produced from a single cell is about 0.1, 1, 1.5, 2, 3, 5 or about 10 femtograms. In some cases, the amount of DNA produced from a single cell is at least 0.1, 1, 1.5, 2, 3, 5 or at least 10 micrograms. In some cases, the amount of DNA produced from a single cell is at least 0.1, 1, 1.5, 2, 3, 5 or at least 10 femtograms. In some cases, the amount of DNA produced from a single cell is about 0.1-10, 1-10, 1.5-10, 2-20, 2-50, 1-3 or 0.5-3.5 micrograms. In some cases, the amount of DNA produced from a single cell is about 0.1-10, 1-10, 1.5-10, 2-20, 2-4, 1-3, or 0.5-4 femtograms. In some cases, the amount of DNA produced from a single cell is about 0.5-2.5, 0.5-3, 0.5-5, 0.2-5, 1-2.5, or 1-5 ng of DNA. In some cases, the amount of DNA produced from a single cell is at least 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3, 3.25, 3.5, 4, or at least 5 ng of DNA.

[0084] The DNA library can comprise allelic balance. In some cases, the allelic balance is 50%-100%, 60%-100%, 70%-100%, 80%-100%, 60%-95%, 70%-95%, 80%-95%, 85%-95%, 90%-95%, 90%-98%, 90%-99%, 85%-99% or 95%-99%. In some cases, the allelic balance is at least 50%, 60%, 70%, 80%, 83%, 85%, 87%, 90%, 92%, 95%, 98% or at least 99%.

[0085] The DNA library may comprise a sensitivity of one or more SNVs. In some cases, the sensitivity is 0.50-1, 0.60-1, 0.70-1, 0.80-1, 0.60-0.95, 0.70-0.95, 0.80-0.95, 0.85-0.95, 0.90-0.95, 0.90-0.98, 0.90-0.99, 0.85-0.99, or 0.95-0.99. In some cases, the sensitivity is at least 0.50, 0.60, 0.70, 0.80, 0.83, 0.85, 0.87, 0.90, 0.92, 0.95, 0.98, or at least 0.99.

[0086] The DNA library may comprise one or more SNVs with an accuracy. In some cases, the accuracy is 0.50-1, 0.60-1, 0.70-1, 0.80-1, 0.60-0.95, 0.70-0.95, 0.80-0.95, 0.85-0.95, 0.90-0.95, 0.90-0.98, 0.90-0.99, 0.85-0.99, or 0.95-0.99. In some cases, the accuracy is at least 0.50, 0.60, 0.70, 0.80, 0.83, 0.85, 0.87, 0.90, 0.92, 0.95, 0.98, or at least 0.99.

[0087] Methylome analysis

[0088] This paper describes a method including PTA, wherein the site of methylated DNA in a single cell is determined using the PTA method. In some cases, methylome analysis includes identifying the position of methylated bases (e.g., methyl C, hydroxymethyl C). In some cases, these methods also include parallel analysis of the transcriptome, methylome and / or proteome of the same cell. The method for detecting methylated genomic bases includes selective restriction with methylation-sensitive endonucleases, and then processing with the PTA method. Determine the site cut by this type of enzyme according to sequencing, and identify the methylated bases. In another case, the bisulfite treatment of a genomic DNA library converts unmethylated cytosine into uracil. Then in some cases, the library is amplified with methylation-specific primers selectively annealed to the methylated sequence. Alternatively, non-methylation-specific PCR is performed, followed by one or more methods for distinguishing the bases of bisulfite reactions, including direct pyrophosphate sequencing, MS-SnuPE, HRM, COBRA, MS-SSCA or base-specific cutting / MALDI-TOF. In some cases, the genomic DNA sample is split for parallel analysis of genome (or its enriched part) and methylome analysis. In some cases, the analysis of genome and methylome includes enriched genome fragments (e.g., exome or other targets) or whole genome sequencing. In some cases, the methylated bases in the genomic sample are identified by (a) methylated bases being converted to different bases or (b) non-methylated bases being converted to different bases. In some cases, such conversion is carried out on the whole genome or genomic fragment. The resulting sequence is then compared with the reference sequence (obtained in the absence of conversion / treatment) to identify which bases are methylated. In some cases, the conversion method (or process) includes treatment with a deamination reagent. In some cases, the conversion method includes treatment with bisulfate. In some cases, one or more enzymes are used to selectively distinguish methylated and unmethylated bases. In some cases, the enzyme includes TET (ten eleven translocation) family enzymes. In some cases, the TET family enzyme includes TET2. In some cases, the enzyme includes T4-BGT. In some cases, the conversion method includes treatment with a reagent (e.g., TET2, for oxidation) protecting methylcytosine, followed by treatment with an enzyme (e.g., APOBEC) that deaminates unprotected cytosine. Other reagents that distinguish methylated and unmethylated bases are also consistent with methods disclosed herein. In some cases, unmethylated cytosine is converted into uracil. In some cases, the amplification of these modified genomes containing uracil causes uracil to be converted into thymine. In some cases, amplification includes use of uracil-tolerant polymerases as described herein. In some cases, adapters as described herein are modified to replace cytosine with methylcytosine or other bases that resist conversion.

[0089] Bioinformatics

[0090] The data obtained from the single cell analysis method utilizing PTA as described herein can be compiled into a database. The method and system of bioinformatics data integration are described herein. In some cases, the data from proteome, genome, transcriptome, methylation group or other data are combined / integrated into a database and analyzed. In some cases, the bioinformatics data integration method and system include one or more of protein detection (FACS and / or NGS), mRNA detection and / or genome variation detection. In some cases, the data are related to disease state or disease. In some cases, the data from multiple single cells are compiled to describe the characteristics of a larger cell population (such as cells from a specific sample, region, organism or tissue). In some cases, protein data is obtained from fluorescently labeled antibodies selectively combined with proteins on cells. In some cases, the method of protein detection includes grouping cells based on fluorescent markers and reporting sample positions after sorting. In some cases, the method of protein detection includes detecting sample barcodes, detecting protein barcodes, comparing with the designed sequence and grouping cells based on barcodes and copy numbers. In some cases, protein data is obtained from barcoded antibodies selectively combined with proteins on cells. In some cases, transcriptome data is obtained from sample and RNA specific barcode. In some cases, the method for mRNA detection includes detection sample and RNA specific barcode, comparison with genome, comparison with RefSeq / Encode, reporting exon / intron / intergenic sequence, analyzing exon-exon junction, cell grouping and cluster analysis variation and top variable gene based on barcode and expression variation. In some cases, genome data is obtained from sample and DNA specific barcode. In some cases, the method for genomic variation detection includes detection sample and DNA specific barcode, comparison with genome, determination of genome recovery rate and SNV mapping rate, filtering reads on exon-exon junction, generation of variant determination file (VCF) and cluster analysis variation and top variable mutation.

[0091] mutation

[0092] In some cases, the method described herein (for example, multi-omics PTA) leads to higher detection sensitivity and / or lower false positive rate of mutation detection.In some cases, mutation is the difference between analysis sequence (for example, using method described herein) and reference sequence.In some cases, reference sequence is obtained from other individuals, organism colonies or other regions of the same genome of other organisms, identical or similar species.In some cases, mutation is identified on plasmid or chromosome.In some cases, mutation is SNV (single nucleotide variation), SNP (single nucleotide polymorphism) or CNV (copy number variation or CNA / copy number abnormality).In some cases, mutation is base substitution, insertion or deletion.In some cases, mutation is conversion, transversion, nonsense mutation, silent mutation, synonymous or non-synonymous mutation, non-pathogenic mutation, missense mutation or frameshift mutation (deletion or insertion). In some cases, PTA results in higher detection sensitivity and / or lower false positive rate for mutation detection when compared to methods such as prediction via computer simulation, ChIP-seq, GUIDE-seq, circular-seq, HTGTS (high-throughput whole-genome translocation sequencing), IDLV (integration-defective lentivirus), Digenome-seq, FISH (fluorescence in situ hybridization), or DISCOVER-seq.

[0093] Directed amplification of primary template

[0094] Nucleic acid amplification methods, such as "primary template directed amplification (PTA)", are described herein. In some cases, PTA is combined with other analytical workflows for multi-omics analysis. For example, one embodiment of the PTA method described herein is schematically represented in Figure 1AIn the PTA method, polymerase (e.g., strand displacement polymerase) is used to preferentially generate amplicons from primary templates ("direct copies"). Therefore, compared with MDA, in the subsequent amplification process, errors are propagated from subamplicons at a lower rate. The result is a method that is easy to perform, which is different from the existing WGA scheme, and the method can amplify low input DNA (including the genome of a single cell) in an accurate and reproducible manner, and has high coverage and uniformity. In addition, the terminated amplification product can be directed connected after removing the terminator, allowing the cell barcode to be attached to the amplification primer, so that the products from all cells can be merged after performing parallel amplification reactions. In some cases, the template nucleic acid is not bound to a solid support. In some cases, a direct copy of the template nucleic acid is not bound to a solid support. In some cases, one or more primers are not bound to a solid support. In some cases, no primer is not bound to a solid support. In some cases, a primer is attached to a first solid support, and the template nucleic acid is attached to a second solid support, wherein the first solid support and the second solid support are not identical. In some cases, PTA is used to analyze single cells from a larger cell population. In some cases, PTA is used to analyze more than one cell from a larger cell body, or an entire cell population.

[0095] This article describes a method for amplification using a nucleic acid polymerase with strand displacement activity. In some cases, this polymerase has strand displacement activity and a low error rate. In some cases, this polymerase has strand displacement activity and proofreading exonuclease activity, such as 3'->5' proofreading activity. In some cases, nucleic acid polymerase is used in combination with other components, such as reversible or irreversible terminators, or other strand displacement factors. In some cases, polymerase has strand displacement activity, but does not have exonuclease proofreading activity. For example, in some cases, these polymerases include bacteriophage phi29 (Φ29) polymerase, which also has a very low error rate, which is the result of 3'->5' proofreading exonuclease activity (see, e.g., U.S. Patent Nos. 5,198,543 and 5,001,050). In some cases, non-limiting examples of strand-displacing nucleic acid polymerases include, for example, genetically modified phi29 (Φ29) DNA polymerase, Klenow fragment of DNA polymerase I (Jacobsen et al., Eur. J. Biochem. 45:623-627 (1974)), bacteriophage M2 DNA polymerase (Matsumoto et al., Gene 84:247 (1989)), bacteriophage phiPRD1 DNA polymerase (Jung et al., Proc. Natl. Acad. Sci. USA 84:8287 (1987); Zhu and Ito, Biochim. Biophys. Acta. 1219:267-276 (1994)), Bst DNA polymerases (e.g., Bst large fragment DNA polymerase (Exo(-)Bst; Aliotta et al., Genet. Anal. (Netherlands) 12:185-195 (1996)), exo(-)Bca DNA polymerase (Walker and Linn, Clinical Chemistry 42:1604-1608 (1996)), Bsu DNA polymerase, including Vent R Vent of (exo-)DNA polymerase RDNA polymerase (Kong et al., J. Biol. Chem. 268: 1965-1975 (1993)), Deep Vent DNA polymerase including Deep Vent (exo-) DNA polymerase, IsoPol DNA polymerase, DNA polymerase I, Therminator DNA polymerase, T5 DNA polymerase (Chatterjee et al., Gene 97: 13-19 (1991)), Sequenase (USBiochemicals), T7 DNA polymerase, T7-sequenase, T7 gp5 DNA polymerase, PRDI DNA polymerase, T4 DNA polymerase (Kaboord and Benkovic, Curr. Biol. 5: 149-157 (1995)). Additional strand displacement nucleic acid polymerases are also compatible with the methods described herein. The ability of a given polymerase to perform strand displacement replication can be determined, for example, by using the polymerase in a strand displacement replication assay (e.g., as disclosed in U.S. Pat. No. 6,977,148). In some cases, such assays are performed at a temperature suitable for the optimal activity of the enzyme used, for example, 32°C for phi29 DNA polymerase, 46°C to 64°C for exo(-)Bst DNA polymerase, or about 60°C to 70°C for enzymes from hyperthermogenic organisms. Another useful assay for selecting polymerases is the primer blocking assay described in Kong et al., J.Biol.Chem.268:1965-1975 (1993). The assay includes a primer extension assay using an M13 ssDNA template in the presence or absence of an oligonucleotide that hybridizes upstream of the extension primer to block its progress. In this assay, other enzymes that can displace the blocking primer are useful for the disclosed method in some cases. In some cases, the polymerase incorporates dNTPs and terminators at approximately equal ratios. In some cases, the incorporation ratio of dNTPs and terminators of the polymerase described herein is about 1: 1, about 1.5: 1, about 2: 1, about 3: 1, about 4: 1, about 5: 1, about 10: 1, about 20: 1, about 50: 1, about 100: 1, about 200: 1, about 500: 1, or about 1000: 1. In some cases, the incorporation ratio of dNTPs and terminators of the polymerase described herein is 1: 1 to 1000: 1, 2: 1 to 500: 1, 5: 1 to 100: 1, 10: 1 to 1000: 1, 100: 1 to 1000: 1, 500: 1 to 2000: 1, 50: 1 to 1500: 1, or 25: 1 to 1000: 1. In some cases, a nucleobase or nucleobase analog that can be selectively removed is added. In some cases, an enzyme is used to remove the nucleobase. In some cases, the enzyme comprises UDG. In some cases, the nucleobase comprises dU.In some cases, the core base is present in a certain ratio relative to another nucleotide in the mixture. In some cases, the core base is present in the mixture at a ratio of no more than 0.2: 1, 0.5: 1, 0.7: 1, 0.8: 1, 1: 1, 1: 1.5, 1: 2, 1: 2.5, 1: 3 or no more than 1: 5. In some cases, the core base is present in the mixture at a ratio of at least 0.2: 1, 0.5: 1, 0.7: 1, 0.8: 1, 1: 1, 1: 1.5, 1: 2, 1: 2.5, 1: 3 or at least 1: 5. In some cases, dU is present in the mixture at a ratio of no more than 0.2: 1, 0.5: 1, 0.7: 1, 0.8: 1, 1: 1, 1: 1.5, 1: 2, 1: 2.5, 1: 3 or no more than 1: 5 to dT. In some cases, dU is present in the mixture at a ratio to dT of at least 0.2:1, 0.5:1, 0.7:1, 0.8:1, 1:1, 1:1.5, 1:2, 1:2.5, 1:3, or at least 1:5.

[0096] Amplification methods are described herein, wherein chain displacement can be promoted by using a chain displacement factor such as a helicase. In some cases, these factors are used in combination with other amplification components, such as polymerases, terminators or other components. In some cases, the chain displacement factor is used together with a polymerase that does not have chain displacement activity. In some cases, the chain displacement factor is used together with a polymerase with chain displacement activity. Not subject to theoretical constraints, the chain displacement factor can increase the speed at which smaller double-stranded amplicon is reinitiated. In some cases, any DNA polymerase that can carry out chain displacement replication in the presence of a chain displacement factor is suitable for the PTA method, even if the DNA polymerase cannot carry out chain displacement replication in the absence of this factor. In some cases, strand displacement factors that can be used for strand displacement replication include, but are not limited to, BMRF1 polymerase auxiliary subunit (Tsurumi et al., J. Virology 67(12):7648-7653 (1993)), adenovirus DNA binding protein (Zijderveld and van der Vliet, J. Virology 68(2):1158-1164 (1994)), herpes simplex virus protein ICP8 (Boehmer and Lehman, J. Virology 67(2):711-715 (1993); Skaliter and Lehman, Proc. Natl. Acad. Sci. USA 91(22):10665-10669 (1994)); single-stranded DNA binding protein (SSB; Rigler and Romano, J. Biol. Chem. 270:8910-8919 (1995)); bacteriophage T4 gene 32 protein (Villemain and Giedroc, Biochemistry 35:14395-14404 (1996); T7 helicase-primase; T7gp2.5 SSB protein; Tte-UvrD (from Thermoanaerobacter tengcongensis), calf thymus helicase (Siegel et al., J.Biol.Chem.267:13629-13635 (1992)); bacterial SSB (e.g., E. coli SSB), replication protein A (RPA) in eukaryotes, human mitochondrial SSB (mtSSB) and recombinases (e.g., recombinase A (RecA) family proteins, T4 UvsX, T4 UvsY, Sak4 of bacteriophage HK620, Rad51, Dmc1 or Radb). Combinations of factors that promote strand displacement and priming are also consistent with the methods described herein. For example, a helicase is used in combination with a polymerase.In some cases, the PTA method includes the use of a single-stranded DNA binding protein (SSB, T4 gp32 or other single-stranded DNA binding protein), a helicase and a polymerase (e.g., SauDNA polymerase, Bsu polymerase, Bst2.0, GspM, GspM2.0, GspSSD or other suitable polymerases). In some cases, a reverse transcriptase is used in combination with a strand displacement factor as described herein. In some cases, a reverse transcriptase is used in combination with a strand displacement factor as described herein. In some cases, a polymerase and a nickase (e.g., "NEAR") (such as those described in US 9,617,586) are used for amplification. In some cases, the nicking enzyme is Nt.BspQI, Nb.BbvCi, Nb.BsmI, Nb.BsrDI, Nb.BtsI, Nt.AlwI, Nt.BbvCI, Nt.BstNBI, Nt.CviPII, Nb.BpulOI, or Nt.BpulOI.

[0097] Amplification methods are described herein, which include the use of terminator nucleotides, polymerases, and additional factors or conditions. For example, in some cases, these factors are used to fragment one or more nucleic acid templates or amplicons during amplification. In some cases, these factors include endonucleases. In some cases, factors include transposases. In some cases, mechanical shearing is used to fragment nucleic acids during amplification. In some cases, nucleotides are added during amplification, and fragmentation can be achieved by adding other proteins or conditions. For example, uracil is incorporated into the amplicon; nucleic acids are fragmented at uracil-containing positions by treatment with uracil D-glycosylase. In some cases, other systems of selective nucleic acid fragmentation are also used, for example, engineered DNA glycosylases (Kwon et al. ChemBiol. 2003, 10 (4), 351) that cut modified cytosine-pyrene base pairs. In some cases, uracil-tolerant polymerases are also used. In some cases, the use of uracil-tolerant polymerases can improve the results of multi-omics methods, such as those described herein.

[0098] Transposase-based library preparation (i.e., "tag-ification") can be used with the methods and compositions described herein. In some cases, after PTA, the library is exposed to one or more transpososomes. In some cases, the transpososome comprises a transposase (e.g., Tn5, MuA or other enzymes). In some cases, the transposase simultaneously cuts and tags the polynucleotides in the library. In some cases, the tag comprises a polynucleotide. In some cases, the tag comprises one or more of a barcode, an adapter, a primer site, or other regions. In some cases, the transpososome is connected to a solid support. In some cases, the solid support comprises beads, a flat surface, or other structures.

[0099] Nanoball sequencing can be used in combination with multi-omics methods (e.g., PTA) described herein. In some cases, rolling circle amplification (RCA) is used to amplify genomic DNA fragments into DNA nanoballs. In some cases, amplification uses uracil-tolerant polymerase. DNA nanoballs are adsorbed onto a flow cell, and the fluorescence at each position is determined and used to identify bases. In some cases, a library is prepared with a desired insertion size and sequenced using nanoball sequencing. Circular adapters are compatible with nanoball sequencing. In some cases, the library preparation method described herein uses a transposition complex formed by a highly active Tn5 transposase and a Tn5 type transposon end. In some cases, the library preparation method described herein uses a transposition complex formed by a MuA transposase and a Mu transposon end comprising R1 and R2 terminal sequences. In some cases, a transposition system is used, which inserts a 5' tag and fragments the target DNA in a random or pseudo-random manner into the transposon end. In some cases, the transposition system includes Staphylococcus aureus Tn552, Ty1, transposons Tn7, Tn10 and IS10, Mariner transposase, Tc1, Tn3, bacterial insertion sequences, retroviruses, or yeast retrotransposons. In some cases, the transposase described herein includes wild-type or mutant transposase, wild-type or mutant Tn5 transposase (e.g., EZ-Tn5 TM Transposase, HYPERMU TM MuA transposase). In some cases, the transposase or a complex thereof comprises Nextera TM Tag DNA enzyme 1 (TDE1, Illumina). In some cases, the transposase includes a mutant or variant of a wild-type transposase. In some cases, the variant comprises a sequence having at least 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98% or at least 99% identity with the wild-type sequence. In some cases, the transposase comprises a Tn5 variant having at least 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98% or at least 99% identity with the wild-type sequence. In some cases, the Tn5 variant comprises one or more mutations at positions 42, 54, 56, 372, 450, 451 or 454. In some cases, the Tn5 variant comprises two or more mutations at positions 42, 54, 56, 372, 450, 451 or 454. In some cases, the Tn5 variant comprises three or more mutations at positions 42, 54, 56, 372, 450, 451, or 454.

[0100] The preparation of the library based on connection can be used together with the methods and compositions described herein (for example, sequencing by synthesis). In some cases, an adapter (for example, a Y adapter) is connected to the end of the amplicon obtained herein, to produce a library for sequencing. In some cases, the library is amplified before sequencing by using a uracil-tolerant polymerase. In some cases, the adapter includes one or more of a yoke area, a first non-complementary region, an index area, a unique molecule identifier code area, a second non-complementary region, a primer area, and a graft area. In some cases, the graft area is configured to be combined with a sequencing instrument flow cell. In some cases, the adapter includes a truncated (or "stubby" / universal) adapter. In some cases, the adapter of truncation includes one or more of a yoke area, a first non-complementary region, a unique molecule identifier code area, a second non-complementary region, and a primer area. In some cases, after the adapter is connected to the amplicon, one or more of the index area and the graft area are added to the adapter of truncation by amplification. In some cases, truncated adaptors are used, such as those described in Glenn et al. Peer J. 2019; 7: e7786.

[0101] Amplification methods including the use of terminator nucleotides are described herein, and the terminator nucleotides terminate nucleic acid replication, thereby reducing the size of the amplified product. In some cases, these terminators are used in combination with polymerases, strand displacement factors or other amplification components as described herein. In some cases, terminator nucleotides reduce or reduce the efficiency of nucleic acid replication. In some cases, these terminators reduce the elongation rate by at least 99.9%, 99%, 98%, 95%, 90%, 85%, 80%, 75%, 70% or at least 65%. In some cases, these terminators reduce the elongation rate by 50%-90%, 60%-80%, 65%-90%, 70%-85%, 60%-90%, 70%-99%, 80%-99% or 50%-80%. In some cases, terminators reduce the average amplicon product length by at least 99.9%, 99%, 98%, 95%, 90%, 85%, 80%, 75%, 70% or at least 65%. In some cases, the terminator reduces the average amplicon length by 50%-90%, 60%-80%, 65%-90%, 70%-85%, 60%-90%, 70%-99%, 80%-99%, or 50%-80%. In some cases, amplicons including terminator nucleotides form loops or hairpins, thereby reducing the ability of polymerases to use these amplicons as templates. In some cases, the use of terminators slows the rate of amplification at the initial amplification site by incorporating terminator nucleotides (e.g., dideoxynucleotides modified to make them resistant to exonucleases to terminate DNA extension), thereby producing smaller amplification products. By producing smaller amplification products than currently used methods (e.g., the average length of the PTA method is 50-2000 nucleotides, while the average product length of the MDA method is >10,000 nucleotides), the PTA amplification products can in some cases be directly ligated to adapters without fragmentation, thereby allowing efficient incorporation of cellular barcodes and unique molecular identifiers (UMIs).

[0102] In some cases, the terminator nucleotides are present in various concentrations, depending on factors such as polymerase, template or other factors. For example, in some cases, in the methods described herein, the amount of terminator nucleotides is expressed as the ratio of non-terminator nucleotides to terminator nucleotides. In some cases, these concentrations allow the length of the amplicon to be controlled. In some cases, the ratio of terminator nucleotides to non-terminator nucleotides is changed for the size of the template amount or template present. In some cases, the ratio of terminator nucleotides to non-terminator nucleotides is reduced for smaller sample sizes (e.g., femtogram to picogram range). In some cases, the ratio of non-terminator nucleotides to terminator nucleotides is about 2:1, 5:1, 7:1, 10:1, 20:1, 50:1, 100:1, 200:1, 500:1, 1000:1, 2000:1 or 5000:1. In some cases, the ratio of non-terminator nucleotides to terminator nucleotides is 2: 1-10: 1, 5: 1-20: 1, 10: 1-100: 1, 20: 1-200: 1, 50: 1-1000: 1, 50: 1-500: 1, 75: 1-150: 1 or 100: 1-500: 1. In some cases, at least one nucleotide present during amplification using the methods described herein is a terminator nucleotide. Each terminator does not have to be present at about the same concentration; in some cases, the ratio of various terminators present in the methods described herein can be optimized for a particular set of reaction conditions, sample types or polymerases. Without being bound by theory, each terminator can be incorporated into the growing polynucleotide chain of the amplicon in response to pairing with the corresponding nucleotide on the template strand. For example, in some cases, the concentration of terminators paired with cytosine is about 3%, 5%, 10%, 15%, 20%, 25% or 50% higher than the average terminator concentration. In some cases, the concentration of the terminator paired with thymine is about 3%, 5%, 10%, 15%, 20%, 25% or 50% higher than the average terminator concentration. In some cases, the concentration of the terminator paired with guanine is about 3%, 5%, 10%, 15%, 20%, 25% or 50% higher than the average terminator concentration. In some cases, the concentration of the terminator paired with adenine is about 3%, 5%, 10%, 15%, 20%, 25% or 50% higher than the average terminator concentration. In some cases, the concentration of the terminator paired with uracil is about 3%, 5%, 10%, 15%, 20%, 25% or 50% higher than the average terminator concentration. In some cases, any nucleotide that can terminate the nucleic acid extension by nucleic acid polymerase is used as a terminator nucleotide in the method described herein. In some cases, reversible terminators are used to terminate nucleic acid replication. In some cases, irreversible terminators are used to terminate nucleic acid replication.In some cases, the non-limiting examples of terminators include reversible and irreversible nucleic acids and nucleic acid analogs, for example, including 3' sealed reversible terminators of nucleotides, including 3' unsealed reversible terminators of nucleotides, including 2' modified terminators of deoxynucleotides, including terminators of the modification of the nitrogenous bases of deoxynucleotides or any combination thereof. In one embodiment, terminator nucleotides are dideoxynucleotides. Nucleic acid replication is terminated and other nucleotide modifications applicable to the implementation of the present invention include but are not limited to any modification of the r group of the 3' carbon of deoxyribose, such as reverse dideoxynucleotides, 3' biotinylated nucleotides, 3' aminonucleotides, 3'-phosphorylated nucleotides, 3'-O-methyl nucleotides, including 3'C3 spacer nucleotides, 3'C18 nucleotides, 3' hexanediol spacer nucleotides 3' carbon spacer nucleotides, acyclic nucleotides, and combinations thereof. In some cases, terminators are polynucleotides with a length of 1, 2, 3, 4 or more bases. In some cases, terminators do not include detectable parts or labels (for example, mass labels, fluorescent labels, dyes, radioactive atoms or other detectable parts). In some cases, the terminator does not include a chemical moiety that allows a detectable portion or tag to be attached (e.g., "click" azide / alkyne, conjugate addition agent, or other chemical treatment for tag attachment). In some cases, all terminator nucleotides include the same modification that reduces amplification at a certain region of the nucleotide (e.g., sugar moiety, base moiety, or phosphate moiety). In some cases, at least one terminator has a different modification that reduces amplification. In some cases, all terminators have substantially similar fluorescence excitation or emission wavelengths. In some cases, terminators of unmodified phosphate groups are used with polymerases that do not have exonuclease proofreading activity. When terminators are used with polymerases (e.g., phi29) that have 3'->5' proofreading exonuclease activity that can remove terminator nucleotides, in some cases, further modification is required to make them resistant to exonucleases. For example, dideoxynucleotides are modified by α-sulfur groups to produce phosphorothioate bonds, making these nucleotides resistant to the 3'->5' proofreading exonuclease activity of nucleic acid polymerases. In some cases, the modification reduces the exonuclease proofreading activity of the polymerase by at least 99.5%, 99%, 98%, 95%, 90%, or at least 85%.In some cases, non-limiting examples of other terminator nucleotide modifications that provide resistance to 3'->5' exonuclease activity include: nucleotides with alpha group modifications, such as alpha-thiodideoxynucleotides that produce phosphorothioate bonds, C3 spacer nucleotides, locked nucleic acids (LNA), inverted nucleic acids, 2'fluoro bases, 3' phosphorylation, 2'-O-methyl modifications (or other 2'-O-alkyl modifications), propyne-modified bases (e.g., deoxycytosine, deoxyuridine), L-DNA nucleotides, L-RNA nucleotides, nucleotides with inverted linkages (e.g., 5'-5' or 3'-3'), 5' inverted bases (e.g., 5' inverted 2',3'-dideoxy dT), methylphosphonate backbones, and trans nucleic acids. In some cases, nucleotides with modifications include base-modified nucleic acids with free 3'OH groups (e.g., 2-nitrobenzyl alkylated HOMedU triphosphates, modified bases containing large chemical groups (such as solid supports or other larger moieties)). In some cases, a polymerase having strand displacement activity but not 3'->5' exonuclease proofreading activity is used with a terminator nucleotide that has or has not undergone modification to render it resistant to exonucleases. Such nucleic acid polymerases include, but are not limited to, Bst DNA polymerase, Bsu DNA polymerase, Deep Vent (exo-) DNA polymerase, Klenow fragment (exo-) DNA polymerase, Therminator DNA polymerase, and Vent. R (exo-).

[0103] Primers and Amplicon Libraries

[0104] Amplicon libraries produced by amplification of at least one target nucleic acid molecule are described herein. In some cases, these libraries are generated using methods described herein, such as libraries using terminators. In some cases, terminators are used in combination with A, C, T, G and U nucleotides. In some cases, the amplicon produced by methods described herein includes uracil. These methods include using strand displacement polymerases or factors, terminator nucleotides (reversible or irreversible) or other features and embodiments described herein. In some cases, the amplicon library produced using terminators described herein is further amplified in subsequent amplification reactions (e.g., PCR). In some cases, subsequent amplification reactions do not include terminators. In some cases, the amplicon library includes polynucleotides, wherein at least 50%, 60%, 70%, 80%, 90%, 95% or at least 98% of the polynucleotides include at least one terminator nucleotide. In some cases, the amplicon library includes target nucleic acid molecules derived from the amplicon library. The amplicon library includes a variety of polynucleotides, wherein at least some of the polynucleotides are direct copies (e.g., directly copied from a target nucleic acid molecule such as genomic DNA, RNA or other target nucleic acids). For example, at least 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or more than 95% of the amplicon polynucleotides are direct copies of at least one target nucleic acid molecule. In some cases, at least 5% of the amplicon polynucleotides are direct copies of at least one target nucleic acid molecule. In some cases, at least 10% of the amplicon polynucleotides are direct copies of at least one target nucleic acid molecule. In some cases, at least 15% of the amplicon polynucleotides are direct copies of at least one target nucleic acid molecule. In some cases, at least 20% of the amplicon polynucleotides are direct copies of at least one target nucleic acid molecule. In some cases, at least 50% of the amplicon polynucleotides are direct copies of at least one target nucleic acid molecule. In some cases, 3%-5%, 3-10%, 5%-10%, 10%-20%, 20%-30%, 30%-40%, 5%-30%, 10%-50% or 15%-75% of the amplicon polynucleotides are direct copies of at least one target nucleic acid molecule. In some cases, at least some polynucleotides are direct copies or progeny (first copy of target nucleic acid) of target nucleic acid molecules. For example, at least 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or more than 95% of the amplicon polynucleotides are direct copies or progeny of at least one target nucleic acid molecule. In some cases, at least 5% of the amplicon polynucleotides are direct copies or progeny of at least one target nucleic acid molecule. In some cases, at least 10% of the amplicon polynucleotides are direct copies or progeny of at least one target nucleic acid molecule. In some cases, at least 20% of the amplicon polynucleotides are direct copies or progeny of at least one target nucleic acid molecule.In some cases, at least 30% of the amplicon polynucleotides are direct copies or progeny of at least one target nucleic acid molecule. In some cases, 3%-5%, 3%-10%, 5%-10%, 10%-20%, 20%-30%, 30%-40%, 5%-30%, 10%-50%, or 15%-75% of the amplicon polynucleotides are direct copies or progeny of at least one target nucleic acid molecule. In some cases, the length of the direct copy of the target nucleic acid is 50-2500, 75-2000, 50-2000, 25-1000, 50-1000, 500-2000, or 50-2000 bases. In some cases, the length of the progeny is 1000-5000, 2000-5000, 1000-10,000, 2000-5000, 1500-5000, 3000-7000 or 2000-7000 bases. In some cases, the average length of the PTA amplification product is 25-3000 nucleotides, 50-2500, 75-2000, 50-2000, 25-1000, 50-1000, 500-2000 or 50-2000 bases. In some cases, the length of the amplicon produced from PTA is no more than 5000, 4000, 3000, 2000, 1700, 1500, 1200, 1000, 700, 500 or no more than 300 bases. In some cases, the length of the amplicon generated from PTA is 1000-5000, 1000-3000, 200-2000, 200-4000, 500-2000, 750-2500, or 1000-2000 bases. In some cases, the amplicon library generated using the methods described herein includes at least 1000, 2000, 5000, 10,000, 100,000, 200,000, 500,000, or more than 500,000 amplicons including unique sequences. In some cases, the library includes at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 2000, 2500, 3000 or at least 3500 amplicons. In some cases, at least 5%, 10%, 15%, 20%, 25%, 30% or more than 30% of the amplicon polynucleotides less than 1000 bases in length are direct copies of at least one target nucleic acid molecule. In some cases, at least 5%, 10%, 15%, 20%, 25%, 30% or more than 30% of the amplicon polynucleotides less than 2000 bases in length are direct copies of at least one target nucleic acid molecule. In some cases, at least 5%, 10%, 15%, 20%, 25%, 30%, or more than 30% of the amplicon polynucleotides of 3000-5000 bases in length are direct copies of at least one target nucleic acid molecule.In some cases, the ratio of direct copy amplicon to target nucleic acid molecule is at least 10: 1, 100: 1, 1000: 1, 10,000: 1, 100,000: 1, 1,000,000: 1, 10,000,000: 1, or greater than 10,000,000: 1. In some cases, the ratio of direct copy amplicon to target nucleic acid molecule is at least 10: 1, 100: 1, 1000: 1, 10,000: 1, 100,000: 1, 1,000,000: 1, 10,000,000: 1, or greater than 10,000,000: 1, wherein the direct copy amplicon is no more than 700-1200 bases in length. In some cases, the ratio of direct-copy amplicon and subamplifier to target nucleic acid molecule is at least 10: 1, 100: 1, 1000: 1, 10,000: 1, 100,000: 1, 1,000,000: 1, 10,000,000: 1, or greater than 10,000,000: 1. In some cases, the ratio of direct-copy amplicon and subamplifier to target nucleic acid molecule is at least 10: 1, 100: 1, 1000: 1, 10,000: 1, 100,000: 1, 1,000,000: 1, 10,000,000: 1, or greater than 10,000,000: 1, wherein the direct-copy amplicon is 700-1200 bases in length and the subamplifier is 2500-6000 bases in length. In some cases, the library includes about 50-10,000, about 50-5,000, about 50-2500, about 50-1000, about 150-2000, about 250-3000, about 50-2000, about 500-2000 or about 500-1500 amplicon, which are direct copies of the target nucleic acid molecule. In some cases, the library includes about 50-10,000, about 50-5,000, about 50-2500, about 50-1000, about 150-2000, about 250-3000, about 50-2000, about 500-2000 or about 500-1500 amplicon, which are direct copies or subamplicons of the target nucleic acid molecule. In some cases, the direct copy number can be controlled by the pcr amplification cycle number. In some cases, no more than 30, 25, 20, 15, 13, 11, 10, 9, 8, 7, 6, 5, 4, or 3 PCR cycles are used to generate copies of the target nucleic acid molecule. In some cases, about 30, 25, 20, 15, 13, 11, 10, 9, 8, 7, 6, 5, 4, or about 3 PCR cycles are used to generate copies of the target nucleic acid molecule. In some cases, 3, 4, 5, 6, 7, or 8 PCR cycles are used to generate copies of the target nucleic acid molecule.In some cases, 2-4, 2-5, 2-7, 2-8, 2-10, 2-15, 3-5, 3-10, 3-15, 4-10, 4-15, 5-10 or 5-15 PCR cycles are used to generate copies of the target nucleic acid molecule. In some cases, the amplicon library generated using the methods described herein is subjected to additional steps, such as adapter ligation and further PCR amplification. In some cases, these additional steps are prior to the sequencing step.

[0105] Methods described herein may additionally include one or more enrichment or purification steps. In some cases, one or more polynucleotides (such as cDNA, PTA amplicon or other polynucleotides) are enriched during the method described herein. In some cases, one or more polynucleotides are captured using polynucleotide probes. In some cases, probes are configured to capture one or more genomic exons. In some cases, the probe library includes at least 1000, 2000, 5000, 10,000, 50,000, 100,000, 200,000, 500,000 or more than 1 million different sequences. In some cases, the probe library includes sequences that can be combined with at least 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10,000 or more than 10,000 genes. In some cases, the probe includes a part for the capture of a solid support, such as biotin. In some cases, the enrichment step is carried out after the PTA step. In some cases, the enrichment step is performed before the PTA step. In some cases, the probe is configured to bind to a genomic DNA library. In some cases, the probe is configured to bind to a cDNA library.

[0106] In some cases, the amplicon library of the polynucleotide produced by the PTA method and composition (terminator, polymerase, etc.) as described herein has an increased uniformity. In some cases, uniformity is described using Lorentz curves or other such methods. In some cases, this increase makes the sequencing reads required for covering the desired target nucleic acid molecule (for example, genomic DNA, RNA or other target nucleic acid molecules) less. For example, no more than 50% of the cumulative score of the polynucleotide includes at least 80% of the sequence of the cumulative score of the sequence of the target nucleic acid molecule. In some cases, no more than 50% of the cumulative score of the polynucleotide includes at least 60% of the sequence of the cumulative score of the sequence of the target nucleic acid molecule. In some cases, no more than 50% of the cumulative score of the polynucleotide includes at least 70% of the sequence of the cumulative score of the sequence of the target nucleic acid molecule. In some cases, no more than 50% of the cumulative score of the polynucleotide includes at least 90% of the sequence of the cumulative score of the sequence of the target nucleic acid molecule. In some cases, uniformity is described using the Gini index (wherein index 0 represents the complete equality of the library, and index 1 represents completely unequal). In some cases, the Gini index of the amplicon library described herein does not exceed 0.55, 0.50, 0.45, 0.40 or 0.30. In some cases, the Gini index of the amplicon library described herein does not exceed 0.50. In some cases, the Gini index of the amplicon library described herein does not exceed 0.40. In some cases, this uniformity metric depends on the number of reads obtained. For example, the reads obtained do not exceed 100 million, 200 million, 300 million, 400 million or do not exceed 500 million. In some cases, the length of the reads is about 50, 75, 100, 125, 150, 175, 200, 225 or about 250 bases. In some cases, the uniformity metric depends on the coverage depth of the target nucleic acid. For example, the average coverage depth is about 10X, 15X, 20X, 25X or about 30X. In some cases, the average coverage depth is 10-30X, 20-50X, 5-40X, 20-60X, 5-20X or 10-20X. In some cases, the Gini index of the amplicon library described herein does not exceed 0.55, wherein about 300 million reads are obtained. In some cases, the Gini index of the amplicon library described herein does not exceed 0.50, wherein about 300 million reads are obtained. In some cases, the Gini index of the amplicon library described herein does not exceed 0.45, wherein about 300 million reads are obtained. In some cases, the Gini index of the amplicon library described herein does not exceed 0.55, wherein no more than 300 million reads are obtained. In some cases, the Gini index of the amplicon library described herein does not exceed 0.50, wherein no more than 300 million reads are obtained. In some cases, the Gini index of the amplicon library described herein does not exceed 0.45, wherein no more than 300 million reads are obtained.In some cases, the Gini index of the amplicon library described herein does not exceed 0.55, wherein the average depth of sequencing coverage is about 15X. In some cases, the Gini index of the amplicon library described herein does not exceed 0.50, wherein the average depth of sequencing coverage is about 15X. In some cases, the Gini index of the amplicon library described herein does not exceed 0.45, wherein the average depth of sequencing coverage is about 15X. In some cases, the Gini index of the amplicon library described herein does not exceed 0.55, wherein the average depth of sequencing coverage is at least 15X. In some cases, the Gini index of the amplicon library described herein does not exceed 0.50, wherein the average depth of sequencing coverage is at least 15X. In some cases, the Gini index of the amplicon library described herein does not exceed 0.45, wherein the average depth of sequencing coverage is at least 15X. In some cases, the Gini index of the amplicon library described herein does not exceed 0.55, wherein the average depth of sequencing coverage does not exceed 15X. In some cases, the Gini index of the amplicon library described herein does not exceed 0.50, wherein the average depth of sequencing coverage does not exceed 15X. In some cases, the Gini index of the amplicon library described herein is no more than 0.45, wherein the average depth of sequencing coverage is no more than 15X. In some cases, the uniform amplicon library generated using the methods described herein needs to be subjected to additional steps, such as adapter ligation and further PCR amplification. In some cases, these additional steps are prior to the sequencing step.

[0107] Primers include nucleic acids for initiating amplification reactions as described herein. In some cases, these primers include, but are not limited to, random deoxynucleotides of any length with or without modification that makes it resistant to exonucleases, random ribonucleotides of any length with or without modification that makes it resistant to exonucleases, modified nucleic acids, such as locked nucleic acids, DNA or RNA primers targeting specific genomic regions, and reactions initiated by enzymes such as priming enzymes. In the case of whole genome PTA, a group of primers with random or partially random nucleotide sequences are preferably used. In very complex nucleic acid samples, it is not necessary to know the specific nucleic acid sequence present in the sample, and it is not necessary to design primers to be complementary to any particular sequence. On the contrary, the complexity of nucleic acid samples results in a large number of different hybridization target sequences in the sample, which will be complementary to various primers of random or partially random sequences. In some cases, the complementary portion of the primer used for PTA is completely random, including only random portions, or selectively random. In some cases, for example, the number of random base positions in the complementary portion of the primer is 20% to 100% of the total number of nucleotides in the complementary portion of the primer. In some cases, the number of random base positions in the complementary part of primers is 10% to 90%, 15-95%, 20%-100%, 30%-100%, 50%-100%, 75-100% or 90-95% of the total number of nucleotides in the complementary part of primers. In some cases, the number of random base positions in the complementary part of primers is at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% or at least 90% of the total number of nucleotides in the complementary part of primers. In some cases, the primer sets with random or partially random sequences are synthesized using standard techniques, which is by allowing any nucleotide to be randomly added at each position. In some cases, primer sets are composed of primers with similar length and / or hybridization properties. In some cases, the term "random primer" refers to a primer that can show four times of degeneracy at each position. In some cases, the term "random primer" refers to a primer that can show three times of degeneracy at each position. In some cases, the random primers used in the methods described herein include random sequences of 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more bases in length. In some cases, primers include random sequences of 3-20, 5-15, 5-20, 6-12 or 4-10 bases in length. Primers can also include non-extendable elements that limit the subsequent amplification of the amplicon produced. For example, in some cases, primers with non-extendable elements include terminators. In some cases, primers include terminator nucleotides, such as 1, 2, 3, 4, 5, 10 or more than 10 kinds of terminator nucleotides. Primers do not need to be limited to the components added to the amplification reaction from the outside. In some cases, primers are produced in situ by adding nucleotides and proteins that promote initiation.For example, in some cases, the priming enzyme combined with nucleotides is used to generate random primers of methods described herein. In some cases, the priming enzyme is a member of the DnaG or AEP enzyme superfamily. In some cases, the priming enzyme is TthPrimPol. In some cases, the priming enzyme is T7 gp4 helicase-priming enzyme. In some cases, these priming enzymes are used together with polymerases or strand displacement factors as described herein. In some cases, the priming enzyme starts priming with deoxyribonucleotides. In some cases, the priming enzyme starts priming with ribonucleotides.

[0108] After PTA amplification, a specific subset of amplicon can be selected. In some cases, this selection depends on size, affinity, activity, hybridization with probe or other known selection factors in the art. In some cases, selection is performed before or after other steps as described herein such as adapter connection and / or library amplification. In some cases, selection is based on the size (length) of amplicon. In some cases, select a smaller amplicon, which is unlikely to experience exponential amplification, thereby enriching the product derived from the primary template, while further converting amplification from an exponential form into a quasi-linear amplification process ( Figure 1A). In some cases, the amplicon of 50-2000, 25-5000, 40-3000, 50-1000, 200-1000, 300-1000, 400-1000, 400-600, 600-2000 or 800-1000 bases in length is selected. In some cases, size selection is performed by using a scheme, for example, solid phase reversible immobilization (SPRI) is utilized on carboxylated paramagnetic beads to enrich for nucleic acid fragments of a specific size, or other schemes known to those skilled in the art. Optionally or in combination, selection is performed by preferentially connecting and amplifying smaller fragments during PCR while preparing a sequencing library, and also as a result of preferentially forming clusters by smaller sequencing library fragments during sequencing (e.g., sequencing by synthesis, nanopore sequencing or other sequencing methods). Other strategies for selecting smaller fragments also conform to the methods described herein and include but are not limited to separating nucleic acid fragments of a specific size after gel electrophoresis, using silica gel columns that bind nucleic acid fragments of a specific size, and using other PCR strategies that can more strongly enrich smaller fragments. Any number of library preparation schemes can be used together with the PTA method described herein. In some cases, library preparation includes amplification with uracil-tolerant polymerase. In some cases, the amplicon produced by PTA is connected to an adapter (optionally with the removal of terminator nucleotides). In some cases, the amplicon produced by PTA comprises a homologous region produced by transposase-based fragmentation, which is used as a priming site. In some cases, a library is prepared by mechanically or enzymatically fragmenting nucleic acids. In some cases, a library is prepared using labeling via a transposome. In some cases, a library is prepared via an adapter, such as the connection of a Y-adapter, a universal adapter, or a circular adapter. The non-complementary portion of the primer used in PTA can include a sequence that can be used to further manipulate and / or analyze the amplified sequence. An example of such a sequence is a "detection tag". The detection tag has a sequence complementary to the detection probe, and is detected using its homologous detection probe. There may be one, two, three, four or more detection tags on the primer. There is no basic limit to the number of detection tags that may be present on the primer, except for the size of the primer. In some cases, there is only one detection tag on the primer. In some cases, there are two detection tags on the primer. When there are multiple detection tags, they may have the same sequence or different sequences, each different sequence being complementary to a different detection probe. In some cases, multiple detection tags have the same sequence. In some cases, multiple detection tags have different sequences.

[0109] Another example of a sequence that can be included in the non-complementary portion of a primer is an "address tag," which can encode other details of the amplicon, such as the location in a tissue section. In some cases, the cell barcode includes an address tag. The address tag has a sequence that is complementary to the address probe. The address tag is incorporated into the end of the amplified chain. If present, there may be one or more than one address tag on the primer. There is no basic limit to the number of address tags that may be present on the primer, except for the size of the primer. When there are multiple address tags, they may have the same sequence or different sequences, each of which is complementary to a different address probe. The address tag portion may be any length that supports specific and stable hybridization between the address tag and the address probe. In some cases, nucleic acids from more than one source may be incorporated into a variable tag sequence. The length of the tag sequence may be up to 100 nucleotides, preferably 1 to 10 nucleotides in length, most preferably 4, 5 or 6 nucleotides, and includes a combination of nucleotides. In some cases, the length of the tag sequence is 1-20, 2-15, 3-13, 4-12, 5-12 or 1-10 nucleotides. For example, if six base pairs are chosen to form the tag and arrangements of four different nucleotides are used, a total of 4096 nucleic acid anchors (eg, hairpins) can be made, each with a unique 6-base tag.

[0110] Primers described herein may be present in solution or immobilized on a solid support. In some cases, primers with sample barcodes and / or UMI sequences may be immobilized on a solid support. For example, the solid support may be one or more beads. In some cases, individual cells are contacted with one or more beads having a unique set of sample barcodes and / or UMI sequences to identify individual cells. In some cases, lysates from individual cells are contacted with one or more beads having a unique set of sample barcodes and / or UMI sequences to identify individual cell lysates. In some cases, extracted nucleic acids from individual cells are contacted with one or more beads having a unique set of sample barcodes and / or UMI sequences to identify extracted nucleic acids from individual cells. The beads may be manipulated in any suitable manner known in the art, for example, using a droplet actuator described herein. The beads may be of any suitable size, including, for example, microbeads, microparticles, nanobeads, and nanoparticles. In some embodiments, the beads are magnetically responsive; in other embodiments, the beads have no apparent magnetic response. Non-limiting examples of suitable beads include flow cytometry microbeads, polystyrene microparticles and nanoparticles, functionalized polystyrene microparticles and nanoparticles, coated polystyrene microparticles and nanoparticles, silica microbeads, fluorescent microspheres and nanospheres, functionalized fluorescent microspheres and nanospheres, coated fluorescent microspheres and nanospheres, color-dyed microspheres and nanoparticles, magnetic microspheres and nanoparticles, superparamagnetic microspheres and nanoparticles (e.g., ATCC®, available from Invitrogen Group, Carlsbad, CA). ), fluorescent microparticles and nanoparticles, coated magnetic microparticles and nanoparticles, ferromagnetic microparticles and nanoparticles, coated ferromagnetic microparticles and nanoparticles, and described in U.S. Patent Application Publication Nos. US20050260686, US20030132538, US20050118574, 20050277197, 20060159962. The beads may be pre-coupled with antibodies, proteins or antigens, DNA / RNA probes, or any other molecule with affinity for the desired target. In some embodiments, primers with sample barcodes and / or UMI sequences may be in solution. In certain embodiments, a plurality of droplets may be provided, wherein each droplet in the plurality of droplets has a sample barcode unique to the droplet and a UMI unique to the molecule, such that the UMI is repeated multiple times within a collection of droplets. In some embodiments, individual cells are contacted with droplets having a unique set of sample barcodes and / or UMI sequences to identify the individual cells. In some implementations, a lysate from an individual cell is contacted with a droplet having a unique set of sample barcodes and / or UMI sequences to identify the individual cell lysate. In some implementations, an extracted nucleic acid from an individual cell is contacted with a droplet having a unique set of sample barcodes and / or UMI sequences to identify the extracted nucleic acid from the individual cell.

[0111] PTA primers may include sequence-specific or random primers, cellular barcodes, and / or unique molecular identifiers (UMIs) (see, e.g., Fig. 10A (linear primer) and Fig. 10B In some cases, the primers include sequence-specific primers. In some cases, the primers include random primers. In some cases, the primers include cell barcodes. In some cases, the primers include sample barcodes. In some cases, the primers include unique molecular identifiers. In some cases, the primers include two or more cell barcodes. In some cases, the barcodes identify a unique sample source or a unique workflow. In some cases, the barcodes or UMIs are 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 25, 30, or more than 30 bases in length. In some cases, the primers include at least 1000, 10,000, 50,000, 100,000, 250,000, 500,000, 10 6 , 10 7 , 10 8 , 10 9 or at least 10 10In some cases, the primers include at least 8, 16, 96, or 384 unique barcodes or UMIs. In some cases, standard adapters are then ligated to the amplified products prior to sequencing; after sequencing, reads are first assigned to specific cells based on the cell barcodes. Suitable adapters that can be used with the PTA method include, for example, the adapters available from Integrated DNA Technologies (IDT). DualIndex UMI adapters. UMIs are then used to group reads from each cell and reads with the same UMI are merged into a common read. Using cell barcodes allows all cells to be merged before library preparation, as they can then be identified by their cell barcodes. In some cases, using UMIs to form a common read corrects for PCR bias, thereby improving copy number variation (CNV) detection ( Fig.11 A and Fig.11 B). In addition, sequencing errors can be corrected by requiring a fixed percentage of reads from the same molecule to have the same detected base changes at each position. This approach has been used to improve CNV detection and correct sequencing errors in a large number of samples. In some cases, UMIs are used with the methods described herein, for example, U.S. Pat. No. 8,835,358 discloses a digital counting principle after attaching a random amplifiable barcode. Schmitt et al. and Fan et al. disclose similar methods for correcting sequencing errors. In some cases, a library is generated for sequencing using primers. In some cases, the library includes fragments of 200-700 bases, 100-1000, 300-800, 300-550, 300-700, or 200-800 bases in length. In some cases, the library includes fragments of at least 50, 100, 150, 200, 300, 500, 600, 700, 800, or at least 1000 bases in length. In some cases, the library includes fragments that are about 50, 100, 150, 200, 300, 500, 600, 700, 800, or about 1000 bases in length.

[0112] The methods described herein may also include additional steps, including steps performed on the sample or template. In some cases, these samples or templates are subjected to one or more steps before PTA. In some cases, a pretreatment step is performed on the sample including cells. For example, a combination of freeze-thaw, Triton X-100, Tween 20 and proteinase K is used to lyse and proteolyse the cells to increase the accessibility of chromatin. Other lysis strategies are also applicable to the implementation of the methods described herein. These strategies include but are not limited to the use of detergents and / or lysozyme and / or protease treatments, and / or other combinations of cell physical destruction such as ultrasound and / or alkaline lysis and / or hypotonic lysis for lysis. In some cases, a pretreatment step is performed on the primary template or one or more target molecules. In some cases, sodium hydroxide is used to denature the primary template (or target), and then the solution is neutralized. Other denaturation strategies may also be applicable to the implementation of the methods described herein. These strategies may include but are not limited to combining alkaline lysis with other alkaline solutions, increasing the sample temperature and / or changing the salt concentration in the sample, adding additives such as solvents or oils, other modifications or any combination thereof. In some cases, additional steps include sorting, filtering or separating samples, templates or amplicons by size. In some cases, cells are mechanically (e.g., high pressure homogenizer, bead grinding) or non-mechanically (physical, chemical or biological) lysed. In some cases, physical lysis methods include heating, osmotic shock and / or cavitation. In some cases, chemical lysis includes alkali and / or detergent. In some cases, biological lysis includes the use of enzymes. The combination of lysis methods is also compatible with the methods described herein. Non-limiting examples of lysing enzymes include recombinant lysozymes, serine proteases and bacterial lysins. In some cases, lysis with enzymes includes the use of lysozymes, lysostaphin, digestive enzymes, cellulose, proteases or polysaccharidase. For example, after amplification with the methods described herein, the amplicon library is enriched with amplicons of desired length. In some cases, the amplicon library is enriched in amplicons of 50-2000, 25-1000, 50-1000, 75-2000, 100-3000, 150-500, 75-250, 170-500, 100-500, or 75-2000 bases in length. In some cases, the amplicon library is enriched in amplicons of no more than 75, 100, 150, 200, 500, 750, 1000, 2000, 5000, or no more than 10,000 bases in length. In some cases, the amplicon library is enriched in amplicons of at least 25, 50, 75, 100, 150, 200, 500, 750, 1000, or at least 2000 bases in length.

[0113] The methods and compositions described herein may include buffers or other preparations. In some cases, such buffers are used for PTA, RT or other methods described herein. In some cases, these buffers include surfactants / detergents or denaturants (Tween-20, DMSO, DMF, PEGylated polymers including hydrophobic groups or other surfactants), salts (potassium phosphate or sodium phosphate (mono or dibasic), sodium chloride, potassium chloride), TrisHCl, magnesium chloride or magnesium sulfate, ammonium salts, such as phosphates, nitrates or sulfates, EDTA), reducing agents (DTT, THP, DTE, β-mercaptoethanol, TCEP or other reducing agents) or other components (glycerol, hydrophilic polymers, such as PEG). In some cases, buffers are used in combination with components such as polymerase, chain displacement factor, terminator or other reaction components described herein. In some cases, buffers are used in combination with components such as polymerase, chain displacement factor, terminator or other reaction components described herein. Buffers may include one or more crowding agents. In some cases, crowding agents include polymers. In some cases, crowding agents include polymers such as polyols. In some cases, the crowding agent includes a polyethylene glycol polymer (PEG). In some cases, the crowding agent includes a polysaccharide. Without limitation, examples of crowding agents include ficoll (e.g., ficoll PM 400, ficoll PM 70 or other molecular weight ficoll), PEG (e.g., PEG1000, PEG 2000, PEG4000, PEG6000, PEG8000 or other molecular weight PEG), dextran (dextran 6, dextran 10, dextran 40, dextran 70, dextran 6000, dextran 138k or other molecular weight dextran).

[0114] Nucleic acid molecules amplified according to the methods described herein (e.g., by uracil-tolerant polymerase) can be sequenced and analyzed using methods known to those skilled in the art. In some cases, non-limiting examples of sequencing methods used include, for example, hybridization sequencing (SBH), ligation sequencing (SBL) (Shendure et al. (2005) Science 309: 1728), quantitative incremental fluorescent nucleotide addition sequencing (QIFNAS), stepwise ligation and cleavage, fluorescence resonance energy transfer (FRET), molecular beacons, TaqMan reporter gene probe digestion, pyrophosphate sequencing, fluorescence in situ sequencing (FISSEQ), FISSEQ beads (U.S. Patent No. 7,425,431), wobble sequencing (International Patent Application Publication No. WO2006 / 073504), multiple sequencing (U.S. Patent Application Publication No. US2008 / 0269068; Porreca et al., 2007, Nat. Methods 4: 931), polyclonal cloning (POLONY) sequencing (U.S. Pat. Nos. 6,432,360, 6,485,944, and 6,511,803, and International Patent Application Publication No. WO2005 / 082098), nanogrid rolling circle sequencing (ROLONY) (U.S. Pat. No. 9,624,538), allele-specific oligonucleotide ligation assays (e.g., oligonucleotide ligation assays (OLA), single template molecule OLA using ligated linear probes and rolling circle amplification (RCA) readout, ligated padlock probes and / or single template molecule OLA using ligated circular padlock probes and rolling circle amplification (RCA) readout), high-throughput sequencing methods, such as, for example, using Roche 454, Illumina Solexa, AB-SOLiD, Helicos, Polonator platform, and light-based sequencing technologies (Landegren et al. (1998) Genome Res. 8: 769-76; Kwok (2000) Pharmacogenomics 1: 95-100; and Shi (2001) Clin. Chem. 47: 164-172). In some cases, the amplified nucleic acid molecules are subjected to shotgun sequencing. In some cases, sequencing of the sequencing library is performed using any appropriate sequencing technology, including but not limited to single molecule real-time (SMRT) sequencing, Polony sequencing, ligation sequencing, reversible terminator sequencing, proton detection sequencing, ion semiconductor sequencing, nanopore sequencing, electronic sequencing, pyrophosphate sequencing, Maxam-Gilbert sequencing, chain termination (e.g., Sanger) sequencing, +S sequence or synthetic sequencing (array / colony-based or nanoball-based).

[0115] The sequencing library produced using the methods described herein (e.g., PTA or RNAseq) can be sequenced to obtain the desired number of sequencing reads. In some cases, a library (alone or part of a multi-omics workflow) is produced from a single cell or a sample comprising a single cell. In some cases, the library is sequenced to obtain at least 0.1, 0.2, 0.4, 0.5, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.5, 2, 5 or at least 10 million reads. In some cases, the library is sequenced to obtain no more than 0.1, 0.2, 0.4, 0.5, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.5, 2, 5 or no more than 10 million reads. In some cases, the library is sequenced to obtain about 0.1, 0.2, 0.4, 0.5, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.5, 2, 5 or about 10 million reads. In some cases, the library is sequenced to obtain 0.1-10, 0.1-5, 0.1-1, 0.2-1, 0.3-1.5, 0.5-1, 1-5 or 0.5-5 million reads per sample. In some cases, the number of reads depends on the size of the genome. In some cases, the sample comprising the bacterial genome is sequenced to obtain 0.5-1 million reads. In some cases, the library is sequenced to obtain at least 2, 4, 10, 20, 50, 100, 200, 300, 500, 700 or at least 900 million reads. In some cases, the library is sequenced to obtain no more than 2, 4, 10, 20, 50, 100, 200, 300, 500, 700 or no more than 900 million reads. In some cases, the library is sequenced to obtain about 2, 4, 10, 20, 50, 100, 200, 300, 500, 700 or about 900 million reads. In some cases, the sample comprising the mammalian genome is sequenced to obtain 500-600 million reads. In some cases, the type of sequencing library (cDNA library or genomic library) is identified during sequencing. In some cases, cDNA libraries and genomic libraries are identified with unique barcodes during sequencing.

[0116] When used with respect to polymerase-mediated amplification reaction, the term "cycle" is used in this article to describe the following steps: dissociation (denaturation) of at least a portion of a double-stranded nucleic acid (e.g., a template or double-stranded template from an amplicon), hybridization (annealing) of at least a portion of a primer with a template, and extension of the primer to produce an amplicon. In some cases, the temperature remains constant (e.g., isothermal reaction) during the amplification cycle. In some cases, the number of cycles is directly related to the number of amplicon produced. In some cases, the number of cycles of isothermal reaction is controlled by the amount of time that the reaction is carried out.

[0117] Methods and Applications

[0118] Described herein is the method for the mutation in the PTA method such as single cell identification cell with multi-omics analysis.Using the PTA method in some cases can cause the improvement to known method (for example, MDA).In some cases, compared with the MDA method, the false positive and false negative variant determination rate of PTA is lower.In some cases, by genome, such as NA12878 platinum genome, whether the larger genome coverage and uniformity of PTA can cause lower false negative variant determination rate.Not subject to theoretical constraints, it can be determined that the missing of error propagation in PTA reduces the false positive variant determination rate.In some cases, the amplification balance between alleles with these two methods is estimated by comparing the allele frequency of heterozygous mutation determined at known positive locus.In some cases, the amplicon library produced by PTA is further amplified by PCR.In some cases, PTA is used in workflow together with other analytical methods, such as RNAseq, methylation group analysis or additives as described herein.

[0119] In some cases, the cells analyzed using the methods described herein include tumor cells. For example, circulating tumor cells can be separated from body fluids taken from patients, such as but not limited to blood, bone marrow, urine, saliva, cerebrospinal fluid, pleural fluid, pericardial fluid, ascites or aqueous humor. Then, cells are subjected to the methods described herein (e.g., PTA) and sequenced to determine the mutation load and mutation combination of each cell. In some cases, these data are used to diagnose specific diseases or as a tool for predicting treatment response. Similarly, in some cases, cells with unknown malignant potential are separated from body fluids taken from patients, such as but not limited to blood, bone marrow, urine, saliva, cerebrospinal fluid, pleural fluid, pericardial fluid, ascites, aqueous humor, blastocyst cavity fluid or cultured cells around the collection medium. In some cases, samples are obtained from the collection medium around embryonic cells. After using the methods described herein and sequencing, these methods are also used to determine the mutation load and mutation combination of each cell. In some cases, these data are used to diagnose specific diseases or as a tool for predicting that pre-malignant states develop into dominant malignancies. In some cases, cells can be separated from primary tumor samples. The cells can then be subjected to PTA and sequencing to determine the mutation load and mutation combination of each cell. These data can be used to diagnose a specific disease or as a tool to predict the resistance of a patient's malignant tumor to available anticancer drugs. By exposing the sample to different chemotherapeutic drugs, it has been found that the main and secondary clones have different sensitivities to specific drugs, and these sensitivities are not necessarily related to the presence of known "driver mutations", which indicates that the combination of mutations in the clonal population determines its sensitivity to specific chemotherapeutic drugs. Without being bound by theory, these findings suggest that if a precancerous lesion that has not yet expanded and can evolve into a clone with an increased number of genomic modifications that makes it more likely to be resistant to treatment is detected, it may be easier to eradicate the malignancy. See, Ma et al., 2018, "Pan-cancer genome and transcriptome analyses of 1,699pediatric leukemias and solid tumors". In some cases, single-cell genomics protocols are used to detect somatic genetic variant combinations in a single cancer cell or clonal type within a mixture of normal and malignant cells isolated from a patient sample. In some cases, the technology is also used to identify clonal types that have undergone positive selection after exposure to drugs in vitro and / or in vivo in patients. By comparing surviving clones exposed to chemotherapy with clones identified at diagnosis, a catalog of cancer clonotypes can be created that documents their resistance to specific drugs. In some cases, the PTA method detects the sensitivity of a specific clone in a sample composed of multiple clonotypes to existing drugs or new drugs and combinations thereof, where the method can detect the sensitivity of a specific clone to a drug.In some cases, this approach shows the efficacy of a drug against a specific clone, whereas current drug sensitivity measurements take into account the sensitivity of all cancer clones in one measurement and therefore may not detect such efficacy. When the PTA described herein is applied to patient samples collected at diagnosis to detect cancer clonal types in a given patient's cancer, a drug sensitivity catalog can then be used to find these clones, thereby informing oncologists which drugs or drug combinations are ineffective and which drugs or drug combinations are most likely to be effective against the patient's cancer. PTA can be used to analyze samples containing clusters of cells. In some cases, the sample contains neurons or glial cells. In some cases, the sample contains nuclei.

[0120] This paper describes the method of gene expression change when measuring and environmental factor mutagenicity combination.For example, cell (single cell or cell colony) is exposed to potential environmental conditions.For example, in some cases, the method uses cells such as being derived from organs (liver, pancreas, lung, colon, thyroid or other organs), tissues (skin or other tissues), blood or other biological sources.In some cases, environmental conditions include heat, light (for example, ultraviolet), radiation, chemical substances or any combination thereof.After a certain amount of environmental conditions are exposed, in some cases, this is a few minutes, a few hours, a few days or longer, separates single cells and performs PTA method.In some cases, molecular barcodes and unique molecular identifiers are used to mark samples.Samples are sequenced and then analyzed to identify gene expression changes and or exposure to mutations caused by environmental conditions.In some cases, this mutation is compared with control environmental conditions, such as known non-mutagenic substances, vehicles / solvents or lack of environmental conditions.In some cases, this analysis not only provides the total number of mutations caused by environmental conditions, but also provides the position and properties of this mutation.In some cases, pattern is identified from data, and can be used to diagnose disease or illness. In some cases, patterns are used to predict future disease states or conditions. In some cases, the methods described herein measure the mutation load, location, and pattern of cells after exposure to environmental agents, e.g., potential mutagens or teratogens. In some cases, the method is used to assess the safety of a given agent, including the likelihood that it induces mutations that may lead to disease development. For example, the method can be used to predict the carcinogenicity or teratogenicity of a particular agent to a particular cell type after exposure to a particular concentration of a particular agent.

[0121] Described herein is the method for identifying the gene expression changes during the combination of mutations in animals, plants or microbial cells that have undergone genome editing (e.g., using CRISPR technology). In some cases, these cells can be separated and PTA and sequencing can be performed to determine the mutation load and mutation combination of each cell. In some cases, the mutation rate per cell and the mutation position produced by the genome editing scheme are used to assess the safety of a given genome editing method.

[0122] Methods are described herein for determining changes in gene expression when combined with mutations in cells for cell therapy, such as, but not limited to, transplantation of induced pluripotent stem cells, transplantation of hematopoietic cells or other cells that have not been manipulated, or transplantation of hematopoietic cells or other cells that have undergone genome editing. The cells can then be subjected to PTA and sequencing to determine the mutation load and combination of mutations per cell. The mutation rate and mutation location per cell in a cell therapy product can be used to assess the safety and potential efficacy of the product.

[0123] The cell used for use with the PTA method can be a fetal cell, such as an embryonic cell. In some embodiments, PTA is used in combination with non-invasive pre-implantation genetic testing (NIPGT). In another embodiment, cells can be separated from blastomeres produced by in vitro fertilization. Then, cells can undergo PTA and sequencing to determine the load and combination of potential disease-susceptible genetic variants in each cell. Then, the combination of gene expression changes in cells and mutation spectrum can be used to infer the genetic susceptibility of blastomeres to specific diseases before implantation. In some cases, the embryo in the culture sheds nucleic acid, and the nucleic acid is used to assess the health status of the embryo using low-throughput genome sequencing. In some cases, the embryo is frozen and thawed. In some cases, nucleic acid is obtained from embryonic cell culture conditioned medium (BCCM), blastocyst cavity fluid (BF) or its combination. In some cases, chromosome aberrations are detected using the PTA analysis of fetal cells, such as fetal aneuploidy. In some cases, PTA is used to detect diseases such as Down's or Patau syndrome. In some cases, before obtaining nucleic acid for analysis (e.g., culture medium, BF or cell biopsy), frozen embryonic cells are thawed and cultured for a period of time. In some cases, before obtaining nucleic acid for analysis, embryonic cells are cultured for no more than 4, 6, 8, 12, 16, 24, 36, 48 hours or no more than 64 hours.

[0124] In another embodiment, microbial cells (e.g., bacteria, fungi, protozoa) can be isolated from plants or animals (e.g., microbiota samples [e.g., GI microbiota, skin microbiota, etc.] or body fluids, e.g., blood, bone marrow, urine, saliva, cerebrospinal fluid, pleural fluid, pericardial fluid, ascites, or aqueous humor). In addition, microbial cells can be isolated from indwelling medical devices, such as, but not limited to, intravenous catheters, urinary catheters, cerebrospinal shunts, prosthetic valves, artificial joints, or endotracheal catheters. The cells can then undergo PTA and sequencing to determine the identity of the specific microorganism and detect the presence of microbial genetic variants that predict response (or resistance) to specific antimicrobial agents. These data can be used to diagnose specific infectious diseases and / or as a tool to predict treatment response.

[0125] This article describes the method of using the PTA method described herein to produce an amplicon library from a sample comprising short nucleic acids. In some cases, PTA can improve the fidelity and uniformity of short nucleic acid amplification. In some cases, the length of the nucleic acid is no more than 2000 bases. In some cases, the length of the nucleic acid is no more than 1000 bases. In some cases, the length of the nucleic acid is no more than 500 bases. In some cases, the length of the nucleic acid is no more than 200, 400, 750, 1000, 2000 or 5000 bases. In some cases, the sample comprising the short nucleic acid fragment includes but is not limited to ancient DNA (age is hundreds of years, thousands of years, millions or even billions of years), FFPE (formalin-fixed paraffin-embedded) sample, cell-free DNA or other samples comprising short nucleic acids.

[0126] Described herein is a method for amplifying a target nucleic acid molecule, the method comprising: a) contacting a sample comprising a target nucleic acid molecule, one or more amplification primers, a nucleic acid polymerase, and a nucleotide mixture comprising one or more terminator nucleotides, the terminator nucleotides terminating nucleic acid replication by the polymerase, and b) incubating the sample under conditions that promote replication of the target nucleic acid molecule to obtain a plurality of terminated amplification products, wherein replication is performed by strand displacement replication. In one embodiment of any of the above methods, the method further comprises separating products having a length of about 50 to about 2000 nucleotides from a plurality of terminated amplification products. In one embodiment of any of the above methods, the method further comprises separating products having a length of about 400 to about 600 nucleotides from a plurality of terminated amplification products. In one embodiment of any of the above methods, the method further comprises: c) repairing the ends and adding A-tails, and d) connecting the molecules obtained in step (c) to adapters, thereby producing a library of amplification products. In some embodiments, the method further comprises removing terminator nucleotides from the terminated amplification products. In one embodiment of any of the above methods, the method further comprises sequencing the amplification products. In one embodiment of any of the above methods, the amplification is performed under substantially isothermal conditions. In one embodiment of any of the methods above, the nucleic acid polymerase is a DNA polymerase.

[0127] In one embodiment of any of the above methods, the DNA polymerase is a strand displacement DNA polymerase. In one embodiment of any of the above methods, the nucleic acid polymerase is selected from bacteriophage phi29 (Φ29) polymerase, genetically modified phi29 (Φ29) DNA polymerase, Klenow fragment of DNA polymerase I, bacteriophage M2 DNA polymerase, bacteriophage phiPRD1 DNA polymerase, Bst DNA polymerase, Bst large fragment DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, BsuDNA polymerase, Vent R DNA polymerase, Vent R(exo-)DNA polymerase, Deep Vent DNA polymerase, Deep Vent (exo-)DNA polymerase, IsoPol DNA polymerase, DNA polymerase I, Therminator DNA polymerase, T5 DNA polymerase, Sequenase, T7 DNA polymerase, T7-sequenase and T4 DNA polymerase. In one embodiment of any of the above methods, the nucleic acid polymerase has 3'->5' exonuclease activity, and the terminator nucleotide inhibits this 3'->5' exonuclease activity. In a specific embodiment, the terminator nucleotide is selected from a nucleotide modified with an α group (e.g., an α-thiodideoxynucleotide that produces a thiophosphate bond), a C3 spacer nucleotide, a locked nucleic acid (LNA), a reverse nucleic acid, a 2'fluoro nucleotide, a 3'phosphorylated nucleotide, a 2'-O-methyl modified nucleotide and a trans nucleic acid. In one embodiment of any of the above methods, the nucleic acid polymerase does not have 3'->5' exonuclease activity. In a specific embodiment, the polymerase is selected from Bst DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, Bsu DNA polymerase, Vent R(exo-)DNA polymerase, Deep Vent (exo-)DNA polymerase, Klenow fragment (exo-)DNA polymerase and Therminator DNA polymerase. In a specific embodiment, the terminator nucleotide includes a modification of the r group of the 3' carbon of the deoxyribose. In a specific embodiment, the terminator nucleotide is selected from a reversible terminator including a 3' sealed terminator of a nucleotide, a reversible terminator including a 3' unsealed terminator of a nucleotide, a terminator including a 2' modified terminator of a deoxynucleotide, a terminator including a modification of the nitrogenous base of a deoxynucleotide and a combination thereof. In a specific embodiment, the terminator nucleotide is selected from dideoxynucleotides, reverse dideoxynucleotides, 3' biotinylated nucleotides, 3' aminonucleotides, 3'-phosphorylated nucleotides, 3'-O-methyl nucleotides, 3' C3 spacer nucleotides, 3' C18 nucleotides, 3' hexanediol spacer nucleotides 3' carbon spacer nucleotides, acyclic nucleotides, and combinations thereof. In one embodiment of any of the above methods, the length of the amplification primer is 4 to 70 nucleotides. In one embodiment of any of the above methods, the length of the amplified product is about 50 to about 2000 nucleotides. In one embodiment of any of the above methods, the target nucleic acid is DNA (e.g., cDNA or genomic DNA). In one embodiment of any of the above methods, the amplification primer is a random primer. In one embodiment of any of the above methods, the amplification primer includes a barcode. In a specific embodiment, the barcode includes a cell barcode. In a specific embodiment, the barcode includes a sample barcode. In one embodiment of any of the above methods, the amplification primer includes a unique molecular identifier (UMI). In one embodiment of any of the above methods, the method includes denaturing the target nucleic acid or genomic DNA before the initial primer annealing. In a specific embodiment, the denaturation is performed under alkaline conditions and then neutralized. In one embodiment of any of the above methods, the sample, amplification primer, nucleic acid polymerase and nucleotide mixture are contained in a microfluidic device. In one embodiment of any of the above methods, the sample, amplification primer, nucleic acid polymerase and nucleotide mixture are contained in a droplet. In one embodiment of any of the above methods, the sample is selected from tissue samples, cells, biological fluid samples (e.g., blood, urine, saliva, lymph, cerebrospinal fluid (CSF), amniotic fluid, pleural fluid, pericardial fluid, ascites, aqueous humor), bone marrow samples, semen samples, biopsy samples, cancer samples, tumor samples, cell lysate samples, forensic samples, archaeological samples, paleontological samples, infection samples, production samples, whole plants, plant parts, microbiome samples, viral preparations, soil samples, marine samples, freshwater samples, household or industrial samples, and combinations and isolates thereof. In one embodiment of any of the above methods, the sample is a cell (e.g., animal cells [e.g., human cells], plant cells, fungal cells, bacterial cells, and protozoan cells).In a specific embodiment, the cell is lysed before replication. In a specific embodiment, cell lysis is accompanied by proteolysis. In a specific embodiment, the cell is selected from cells from preimplantation embryos, stem cells, fetal cells, tumor cells, suspected cancer cells, cancer cells, cells subjected to gene editing procedures, cells from pathogenic organisms, cells obtained from forensic samples, cells obtained from archaeological samples, and cells obtained from paleontological samples. In one embodiment of any of the above methods, the sample is a cell from a preimplantation embryo (e.g., blastomere [e.g., blastomere obtained from an eight-cell embryo produced by in vitro fertilization]). In a specific embodiment, the method also includes determining the presence of disease-susceptible germline or somatic variants in embryonic cells. In one embodiment of any of the above methods, the sample is a cell from a pathogenic organism (e.g., bacteria, fungi, protozoa). In a specific embodiment, pathogenic organism cells are obtained from body fluids, microbial samples (e.g., GI microbial samples, vaginal microbial samples, skin microbial samples, etc.) or indwelling medical devices (e.g., intravenous catheters, urinary catheters, cerebrospinal shunts, prosthetic valves, artificial joints, tracheal tubes, etc.) taken from patients. In a specific embodiment, the method further includes a step of determining the identity of the pathogenic organism. In a specific embodiment, the method further includes determining the presence of genetic variants that cause the pathogenic organism to be resistant to treatment. In one embodiment of any of the above methods, the sample is a tumor cell, a suspected cancer cell, or a cancer cell. In a specific embodiment, the method further includes determining the presence of one or more diagnostic or prognostic mutations. In a specific embodiment, the method further includes determining the presence of germline or somatic variants that cause resistance to treatment. In one embodiment of any of the above methods, the sample is a cell that has undergone a gene editing procedure. In a specific embodiment, the method further includes determining the presence of unplanned mutations caused by the gene editing process. In one embodiment of any of the above methods, the method further includes determining the cell lineage history. In a related aspect, the present invention provides the use of any of the above methods in identifying low-frequency sequence variants (e.g., variants that constitute ≥0.01% of the total sequence).

[0128] In a related aspect, the present invention provides a kit comprising a nucleic acid polymerase, one or more amplification primers, a nucleotide mixture including one or more terminator nucleotides, and optional instructions for use. In one embodiment of the kit of the present invention, the nucleic acid polymerase is a strand displacement DNA polymerase. In one embodiment of the kit of the present invention, the nucleic acid polymerase is selected from bacteriophage phi29 (Φ29) polymerase, genetically modified phi29 (Φ29) DNA polymerase, Klenow fragment of DNA polymerase I, bacteriophage M2 DNA polymerase, bacteriophage phiPRD1 DNA polymerase, Bst DNA polymerase, Bst large fragment DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, Bsu DNA polymerase, Vent R DNA polymerase, Vent R (exo-)DNA polymerase, Deep Vent DNA polymerase, Deep Vent (exo-)DNA polymerase, IsoPol DNA polymerase, DNA polymerase I, Therminator DNA polymerase, T5 DNA polymerase, Sequenase, T7 DNA polymerase, T7-sequenase and T4 DNA polymerase. In one embodiment of the kit of the present invention, the nucleic acid polymerase has 3'->5' exonuclease activity, and the terminator nucleotide inhibits this 3'->5' exonuclease activity (e.g., nucleotides modified with an α group [e.g., α-thiodideoxynucleotides], C3 spacer nucleotides, locked nucleic acids (LNA), reverse nucleic acids, 2' fluoronucleotides, 3' phosphorylated nucleotides, 2'-O-methyl modified nucleotides, trans nucleic acids). In one embodiment of the kit of the present invention, the nucleic acid polymerase does not have 3'->5' exonuclease activity (e.g., Bst DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, Bsu DNA polymerase, Vent R(exo-)DNA polymerase, Deep Vent (exo-)DNA polymerase, Klenow fragment (exo-)DNA polymerase, Therminator DNA polymerase). In a specific embodiment, the terminator nucleotide includes a modification of the r group of the 3' carbon of the deoxyribose. In a specific embodiment, the terminator nucleotide is selected from a reversible terminator including a 3' blocked terminator of the nucleotide, a reversible terminator including a 3' unblocked terminator of the nucleotide, a terminator including a 2' modified terminator of the deoxynucleotide, a terminator including a modification of the nitrogenous base of the deoxynucleotide, and a combination thereof. In a specific embodiment, the terminator nucleotide is selected from dideoxynucleotides, reverse dideoxynucleotides, 3'biotinylated nucleotides, 3'aminonucleotides, 3'-phosphorylated nucleotides, 3'-O-methyl nucleotides, 3'C3 spacer nucleotides, 3'C18 nucleotides, 3'hexanediol spacer nucleotides 3'carbon spacer nucleotides, acyclic nucleotides, and combinations thereof.

[0129] Described herein is a method for amplifying a genome, the method comprising: a) contacting a sample comprising a genome, a plurality of amplification primers (e.g., two or more primers), a nucleic acid polymerase, and a nucleotide mixture comprising one or more terminator nucleotides, the terminator nucleotides terminating nucleic acid replication of the polymerase, and b) incubating the sample under conditions that promote genome replication to obtain a plurality of terminated amplification products, wherein replication is performed by strand displacement replication. In one embodiment of any of the above methods, the method further comprises separating a product having a length of about 50 to about 2000 nucleotides from a plurality of terminated amplification products. In one embodiment of any of the above methods, the method further comprises separating a product having a length of about 400 to about 600 nucleotides from a plurality of terminated amplification products. In one embodiment of any of the above methods, the method further comprises: c) repairing the ends and adding A-tails, and d) connecting the molecules obtained in step (c) to adapters, thereby producing an amplification product library. In one embodiment of any of the above methods, the method further comprises sequencing the amplification products. In one embodiment of any of the above methods, the amplification is performed under substantially isothermal conditions. In one embodiment of any of the above methods, the nucleic acid polymerase is a DNA polymerase.

[0130] In one embodiment of any of the above methods, the DNA polymerase is a strand displacement DNA polymerase. In one embodiment of any of the above methods, the nucleic acid polymerase is selected from bacteriophage phi29 (Φ29) polymerase, genetically modified phi29 (Φ29) DNA polymerase, Klenow fragment of DNA polymerase I, bacteriophage M2 DNA polymerase, bacteriophage phiPRD1 DNA polymerase, Bst DNA polymerase, Bst large fragment DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, BsuDNA polymerase, Vent R DNA polymerase, Vent R (exo-)DNA polymerase, Deep Vent DNA polymerase, Deep Vent (exo-)DNA polymerase, IsoPol DNA polymerase, DNA polymerase I, Therminator DNA polymerase, T5 DNA polymerase, Sequenase, T7 DNA polymerase, T7-sequenase and T4 DNA polymerase. In one embodiment of any of the above methods, the nucleic acid polymerase has 3'->5' exonuclease activity, and the terminator nucleotide inhibits this 3'->5' exonuclease activity. In a specific embodiment, the terminator nucleotide is selected from a nucleotide modified with an α group (e.g., an α-thiodideoxynucleotide that produces a thiophosphate bond), a C3 spacer nucleotide, a locked nucleic acid (LNA), a reverse nucleic acid, a 2'fluoro nucleotide, a 3'phosphorylated nucleotide, a 2'-O-methyl modified nucleotide and a trans nucleic acid. In one embodiment of any of the above methods, the nucleic acid polymerase does not have 3'->5' exonuclease activity. In a specific embodiment, the polymerase is selected from Bst DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, Bsu DNA polymerase, Vent R(exo-)DNA polymerase, Deep Vent (exo-)DNA polymerase, Klenow fragment (exo-)DNA polymerase and Therminator DNA polymerase. In a specific embodiment, the terminator nucleotide includes a modification of the r group of the 3' carbon of the deoxyribose. In a specific embodiment, the terminator nucleotide is selected from a reversible terminator including a 3' sealed terminator of a nucleotide, a reversible terminator including a 3' unsealed terminator of a nucleotide, a terminator including a 2' modified terminator of a deoxynucleotide, a terminator including a modification of the nitrogenous base of a deoxynucleotide and a combination thereof. In a specific embodiment, the terminator nucleotide is selected from dideoxynucleotides, reverse dideoxynucleotides, 3' biotinylated nucleotides, 3' aminonucleotides, 3'-phosphorylated nucleotides, 3'-O-methyl nucleotides, 3' C3 spacer nucleotides, 3' C18 nucleotides, 3' hexanediol spacer nucleotides 3' carbon spacer nucleotides, acyclic nucleotides, and combinations thereof. In one embodiment of any of the above methods, the length of the amplification primer is 4 to 70 nucleotides. In one embodiment of any of the above methods, the length of the amplified product is about 50 to about 2000 nucleotides. In one embodiment of any of the above methods, the target nucleic acid is DNA (e.g., cDNA or genomic DNA). In one embodiment of any of the above methods, the amplification primer is a random primer. In one embodiment of any of the above methods, the amplification primer includes a barcode. In a specific embodiment, the barcode includes a cell barcode. In a specific embodiment, the barcode includes a sample barcode. In one embodiment of any of the above methods, the amplification primer includes a unique molecular identifier (UMI). In one embodiment of any of the above methods, the method includes denaturing the target nucleic acid or genomic DNA before the initial primer annealing. In a specific embodiment, the denaturation is performed under alkaline conditions and then neutralized. In one embodiment of any of the above methods, the sample, amplification primer, nucleic acid polymerase and nucleotide mixture are contained in a microfluidic device. In one embodiment of any of the above methods, the sample, amplification primer, nucleic acid polymerase and nucleotide mixture are contained in a droplet. In one embodiment of any of the above methods, the sample is selected from tissue samples, cells, biological fluid samples (e.g., blood, urine, saliva, lymph, cerebrospinal fluid (CSF), amniotic fluid, pleural fluid, pericardial fluid, ascites, aqueous humor), bone marrow samples, semen samples, biopsy samples, cancer samples, tumor samples, cell lysate samples, forensic samples, archaeological samples, paleontological samples, infection samples, production samples, whole plants, plant parts, microbiome samples, viral preparations, soil samples, marine samples, freshwater samples, household or industrial samples, and combinations and isolates thereof. In one embodiment of any of the above methods, the sample is a cell (e.g., animal cells [e.g., human cells], plant cells, fungal cells, bacterial cells, and protozoan cells).In a specific embodiment, the cell is lysed before replication. In a specific embodiment, cell lysis is accompanied by proteolysis. In a specific embodiment, the cell is selected from cells from preimplantation embryos, stem cells, fetal cells, tumor cells, suspected cancer cells, cancer cells, cells subjected to gene editing procedures, cells from pathogenic organisms, cells obtained from forensic samples, cells obtained from archaeological samples, and cells obtained from paleontological samples. In one embodiment of any of the above methods, the sample is a cell from a preimplantation embryo (e.g., blastomere [e.g., blastomere obtained from an eight-cell embryo produced by in vitro fertilization]). In a specific embodiment, the method also includes determining the presence of disease-susceptible germline or somatic variants in embryonic cells. In one embodiment of any of the above methods, the sample is a cell from a pathogenic organism (e.g., bacteria, fungi, protozoa). In a specific embodiment, pathogenic organism cells are obtained from body fluids, microbial samples (e.g., GI microbial samples, vaginal microbial samples, skin microbial samples, etc.) or indwelling medical devices (e.g., intravenous catheters, urinary catheters, cerebrospinal shunts, prosthetic valves, artificial joints, tracheal tubes, etc.) taken from patients. In a specific embodiment, the method further includes a step of determining the identity of the pathogenic organism. In a specific embodiment, the method further includes determining the presence of genetic variants that cause the pathogenic organism to be resistant to treatment. In one embodiment of any of the above methods, the sample is a tumor cell, a suspected cancer cell, or a cancer cell. In a specific embodiment, the method further includes determining the presence of one or more diagnostic or prognostic mutations. In a specific embodiment, the method further includes determining the presence of germline or somatic variants that cause resistance to treatment. In one embodiment of any of the above methods, the sample is a cell that has undergone a gene editing procedure. In a specific embodiment, the method further includes determining the presence of unplanned mutations caused by the gene editing process. In one embodiment of any of the above methods, the method further includes determining the cell lineage history. In a related aspect, the present invention provides the use of any of the above methods in identifying low-frequency sequence variants (e.g., variants that constitute ≥0.01% of the total sequence).

[0131] In a related aspect, the invention provides a kit comprising a reverse transcriptase, a nucleic acid polymerase, one or more amplification primers, a nucleotide mixture comprising one or more terminator nucleotides, and optional instructions for use. In one embodiment of the kit of the invention, the nucleic acid polymerase is a strand displacement DNA polymerase. In some cases, the reverse transcriptase performs template switching. In some cases, the reverse transcriptase is a variant of MMLV (Moloney murine leukemia virus), HIV-1, AMV (avian myeloblastosis virus), telomerase RT, FIV (feline immunodeficiency virus) or XMRV (heterophilic murine leukemia virus-related virus). Non-limiting examples of reverse transcriptases include SuperScript I (Thermo), SuperScript II (Thermo), SuperScript III (Thermo), SuperScript IV (Thermo), OmniScript (Qiagen), SensiScript (Qiagen), PrimeScript (Takara), Maxima H- (Thermo), AcuuScript Hi-Fi (Agilent), iScript (Bio-Rad), eAMV (Merck KGaA), qScript (Quanta Biosciences), SmartScribe (Clontech), or GoScript (Promega). In some embodiments, the kit comprises dNTPs and uracil. In one embodiment of the kit of the present invention, the nucleic acid polymerase is selected from bacteriophage phi29 (Φ29) polymerase, genetically modified phi29 (Φ29) DNA polymerase, Klenow fragment of DNA polymerase I, bacteriophage M2 DNA polymerase, bacteriophage phiPRD1 DNA polymerase, Bst DNA polymerase, Bst large fragment DNA polymerase, exo (-) Bst polymerase, exo (-) Bca DNA polymerase, Bsu DNA polymerase, Vent R DNA polymerase, Vent R(exo-)DNA polymerase, Deep Vent DNA polymerase, DeepVent (exo-)DNA polymerase, IsoPol DNA polymerase, DNA polymerase I, Therminator DNA polymerase, T5 DNA polymerase, Sequenase, T7 DNA polymerase, T7-Sequenase and T4 DNA polymerase. In one embodiment of the kit of the present invention, the nucleic acid polymerase has 3'->5' exonuclease activity, and the terminator nucleotide inhibits this 3'->5' exonuclease activity (e.g., nucleotides modified with an α group [e.g., α-thiodideoxynucleotides], C3 spacer nucleotides, locked nucleic acids (LNA), reverse nucleic acids, 2' fluoronucleotides, 3' phosphorylated nucleotides, 2'-O-methyl modified nucleotides, trans nucleic acids). In one embodiment of the kit of the present invention, the nucleic acid polymerase does not have 3'->5' exonuclease activity (e.g., Bst DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, Bsu DNA polymerase, Vent R In one embodiment, the terminator nucleotide comprises a modification of the r group of the 3' carbon of the deoxyribose. In one embodiment, the terminator nucleotide is selected from a reversible terminator including a 3' sealed terminator of the nucleotide, a reversible terminator including a 3' unsealed terminator of the nucleotide, a terminator including a 2' modified terminator of the deoxynucleotide, a terminator including a modification of the nitrogenous base of the deoxynucleotide, and a combination thereof. In one embodiment, the terminator nucleotide is selected from dideoxynucleotides, reverse dideoxynucleotides, 3' biotinylated nucleotides, 3' aminonucleotides, 3'-phosphorylated nucleotides, 3'-O-methyl nucleotides, 3' C3 spacer nucleotides, 3' C18 nucleotides, 3' hexanediol spacer nucleotides, acyclic nucleotides, and combinations thereof. In some cases, the test kit comprises at least one enzyme stabilizer, a neutralizing buffer, a denaturing buffer, or a combination thereof. In some cases, the test kit comprises one or more modules. In some cases, a kit comprises a genome module and a transcriptome module.

[0132] Methods described herein (e.g., PTA multi-omics) can include chromatin analysis. In some cases, chromatin analysis includes chromatin accessibility analysis (mapping). In some cases, chromatin analysis includes ATAC, mChIP, ChiP-MS, ChroP, HiC or other chromatin analysis methods. In some cases, the method for measuring chromatin accessibility includes using a transposase such as Tn5 (see, Buenrostro et al., Curr Protoc Mol Biol.2015; 109: 21.29.1-21.29-9). In some cases, the genomic DNA bound to chromatin is treated with a transposase to produce a fragment. In some cases, PTA amplification is performed on the genomic DNA of transposase fragmentation. In some cases, such methods are combined with other multi-omics analyses (e.g., transcriptome, methylation group, proteome or other technologies described herein). In some cases, chromatin analysis is included in the genomic DNA bound to chromatin before being fragmented with a transposase or other fragmentation methods (e.g., ultrasonic treatment, digestion), and cross-linking (e.g., formaldehyde) is performed.

[0133] Example

[0134] The following examples are presented to more clearly illustrate the principles and practices of the embodiments disclosed herein to those skilled in the art, and should not be construed as limiting the scope of any claimed embodiments. Unless otherwise indicated, all parts and percentages are by weight.

[0135] Example 1: Design and execution of multi-omics workflows

[0136] Overview

[0137] Discovering genomic variants in the absence of information about the transcriptional consequences of the variants, or conversely, discovering transcriptional signatures without understanding the underlying genomic contribution, has hampered the understanding of the molecular mechanisms of disease. To assess this genomic and transcriptomic coordination, a multi-omics approach was developed to extract this information from individual cells. The workflow unifies template-switched whole-transcript RNA-Seq chemistry and whole genome amplification (WGA), followed by affinity purification of the first-strand cDNA and subsequent separation of RNA / DNA fractions for sequencing library preparation. In the multi-omics approach, the properties of primary template directed amplification (PTA) are exploited to accurately assess single nucleotide variations as DNA signatures, which is not possible with other workflows, to assess DNA+RNA information in the same cell.

[0138] Design and perform single-well integration of single-cell transcriptome and genome amplification, wherein the standard PTA reaction is modified to include a reverse transcription (RT) step before single-cell genome amplification, and is designated as multi-omics enrichment (ResolveOME, Bioskryb Genomics, Inc.). In this workflow, PTA amplifies the genome of a single cell immediately after the RT reaction ends in a single-well reaction. Using reverse transcription based on template switching, a first-strand cDNA molecule with a barcode is produced, which is affinity purified and pre-amplified before the RNA-Seq sequencing library is produced. The final result of the combined amplification reaction is a biotin-labeled cDNA pool mainly derived from cytoplasmic transcripts, which can be used for streptavidin purification, and a pool of amplified genomic material from a single cell. In an alternative embodiment, magnetic beads with attached RT primers can be used to directly remove cDNA amplicon libraries. At the end of the genome amplification reaction, cDNA fractions are separated from amplified genomic material, thereby producing a library from each pool. The resulting sequencing data provides the ability to define both genome and transcriptome plasticity at single-cell resolution. Specifically, the description of isoform expression combines the ability to annotate the genome of the same cell with potential structural variation and single nucleotide changes ( Figure 1A ), allowing for the assessment of genomic “penetrance” and definition of the mechanisms driving single-cell fate.

[0139] Previous multi-omics efforts pioneered pairing genomic and transcriptomic information from the same single cell, but with the major drawbacks of incomplete genome coverage and associated coverage heterogeneity—leaving uncovered genomic valleys that may harbor deleterious single nucleotide variants that remain undetected. Indeed, multiple displacement amplification (MDA) drives genome amplification for G&T-seq, and DR-Seq has genome amplification uniformity comparable to MALBAC, both of which outperform PTA in terms of genome coverage, allelic balance, and SNV calling metrics. In one example, definition of clonal evolution at the SNV / CNV level in primary patient samples was accomplished with G&T-seq, but candidate gene investigation was limited to exome-level data, whereby clusters defined by 59 cancer genes and another study employing G&T-seq limited its analysis to the RNA workflow of the method to exploit the low input requirements, without assessing genome-level data. Thus, this paper addresses the unmet need to add genome-wide, high-sensitivity, and high-accuracy SNV calling capabilities to joint DNA / RNA single-cell methods. Furthermore, the importance of these measurements was demonstrated, whereby single nucleotide variations fundamentally impact cellular states and tumor progression.

[0140] Here we provide insights into the utility of these unified “-omics” layers, highlighting heterogeneous genomic variation and corresponding phenotypic alterations in single cells, both of which are associated with the development of resistance to targeted therapies in acute myeloid leukemia cell line models and oncogenic mechanisms in primary breast cancer cells, yielding insights that cannot be inferred from a single dataset (genomic or transcriptomic) alone.

[0141] Amplification Product Yields for RNA+DNA Multi-omics Workflows

[0142] Prior to demonstrating the biological utility of the multi-omics approach described herein, the technical performance of the approach was examined in cell line drug resistance models and primary patient samples using the benchmark cell line 1000 Genomes cell line NA12878. The RNA and DNA arms of the protocol were first evaluated separately using metrics from either template-switched RNA-Seq chemistry or PTA chemistry to compare with metrics when the chemistry was unified in the combined multi-omics protocol.

[0143] use Figure 1A The workflow shown here generates multi-omics data from FACS-sorted NA12878 single cells using purified total NA12878 RNA or genomic DNA as amplification controls. The yields of PTA products and cDNA products from the unified protocol were as high as Figure 1B Approximately 1-1.5 μg of DNA amplification product from the single-cell genome and approximately 100-200 ng of cDNA product representing the single-cell transcriptome were obtained. Importantly, the no-template control (NTC) reaction showed a lack of detectable product, and in addition, the yield of the DNA fraction obtained from the control RNA input using a Qubit fluorometer (ThermoFisher) was negligible (<50 ng). Due to the known promiscuity of reverse transcriptase in the absence of mRNA template, low levels of background amplification of the genomic DNA control input in the cDNA fraction were observed. In contrast, this background amplification does not occur in reactions with single cells because the genomic material is isolated in unlysed nuclei in the multi-omics reverse transcription workflow.

[0144] PTA modification

[0145] Modification of the PTA method for multi-omics workflows ( Figures 15A-15D ). After reverse transcription is completed, during phi29 amplification, dUTP is added to the normal nucleotide mixture (dATP, dCTP, dGTP, dTTP) (red dots), resulting in PTA amplification products derived from original single cells or low input template DNA being "labeled" with dUTP ( Fig.15A). After affinity purification and washing of the cDNA, a UDG incubation step was performed on the beads to digest background dUTP-labeled PTA products (green dots) prior to cDNA pre-amplification. For library preparation, the cDNA library utilized a normal high-fidelity polymerase, however, the PTA-derived library representing the DNA arms of the multi-omics workflow used a uracil-tolerant polymerase to amplify library ligation products of uracil-containing PTA products (yellow dots). After UDG treatment, the number of expressed genes detected decreased; this suggests that in the absence of UDG treatment, transcript counts may be compounded by the DNA (PTA) background. IGV visualization of intergenic read background removal under the UDG protocol (700kb region containing 3 genes) ( Fig. 15C ). Each row is a single cell (NA12878) multi-omics RNA fraction library. When PTA was performed in the absence of dUTP, DNA background reads were seen in the first two control RNA libraries and these background reads gradually decreased as more dUTP was added during PTA. The PTA reaction was kinetically slower with a 1:1 nucleotide ratio of dUTP:dTTP; the PTA reaction containing only dUTP without dTTP was kinetically slower. The DNA background removal benefit of the addition of dUTP in the PTA reaction (C) did not adversely affect allelic balance ( Fig.15D ) and SNV determination accuracy and sensitivity metrics ( Fig.15E ).

[0146] The reagents can be used with the methods and compositions described herein to identify

[0147] Some polymerases stagnate or reduce efficiency when amplifying a template containing uracil. Uracil-tolerant polymerases can be used together with the methods described herein to amplify a template containing uracil (e.g., with PTA). In some cases, compared with a template that does not contain uracil, a uracil-tolerant polymerase maintains at least 50%, 60%, 70%, 80%, 85%, 90%, 95%, 97% or 99% polymerase activity when amplifying a template containing uracil. In some cases, uracil-tolerant polymerases are derived from archaea, yeast or bacterial species. In some cases, uracil-tolerant polymerases include DNA polymerases ε and δ from Saccharomyces cerevisiae, and Escherichia coli DNA polymerase III, PolA type polymerases, such as Taq, KAPA HiFi uracil + DNA polymerase (Kapa biosystems, Q5U), KOD Multi & Epi DNA polymerase, FastStart Taq (Roche), Taq2000 (Agilent Technologies), FailSafe enzyme (Epicenter) or Thermo PhusionU. In some cases, the uracil-tolerant polymerase comprises a sequence having at least 70%, 75%, 80%, 85%, 90%, 95%, 97% or 99% identity to DNA polymerases ε and δ from Saccharomyces cerevisiae, and Escherichia coli DNA polymerase III, PolA-type polymerases, such as Taq, KAPA HiFi uracil+DNA polymerase (Kapa biosystems, Q5U), KOD Multi & EpiDNA polymerase, FastStart Taq (Roche), Taq2000 (Agilent Technologies), FailSafe enzyme (Epicenter) or Thermo Phusion U. In some cases, the uracil-tolerant polymerase comprises a modification of one or more amino acid residues in the dUTP binding pocket.

[0148] Genomic performance comparison of multi-omics workflows

[0149] As a default practice before passing single cell samples to deep sequencing for SNV analysis, low-pass QC sequencing was performed and library complexity estimates using the PreSeq count algorithm were determined as part of the analysis workflow. The QC criteria set for genomic DNA only (the product solution of PTA) resulted in PreSeq count values ​​of >3.0E9 when low-pass sequencing, an empirically defined surrogate for genome coverage and uniformity that predicts that high-depth sequencing will produce strong allelic balance and high sensitivity and accuracy for single nucleotide variant calling. The average PreSeq count for single cells from Table 1A was 3.76E9 with a standard deviation of + / -2.27E8. The overall robust performance of the single cell and genomic DNA controls warranted subsequent deep sequencing for metric comparison of classic PTA and PTA from multi-omics workflows.

[0150] Table 1A

[0151]

[0152]

[0153]

[0154] Allelic balance (the ability to represent both alleles by strength of enrichment and genomic PTA methods) was reviewed after high depth sequencing (2X150bp, downsampled to 4.5E8 total reads, ~20x genome depth) and processing by our pipeline. The inverse of allelic dropout (ADO) is allelic balance, which is the proportion of known heterozygous loci that are called heterozygous after sequencing. Variants within these loci have allele frequencies between 10% and 90% at each locus. Review of allelic balance for the multi-omics workflow showed 85.5% (+ / -3.4%) in every 10 replicates, which is very comparable to 88.2% (+ / -4%) for the genomic DNA-only workflow ( Figure 2A There were no significant differences in genome coverage across depth ranges between workflows ( Figure 2B ). Finally, it is critical to demonstrate that the allelic balance and coverage obtained from a multi-omics workflow ultimately allow for confident SNV calls. Figure 2C Individual multi-omics NA12878 cells are highlighted, with SNV calling sensitivities ranging from 0.90-0.95 and precisions >0.99, similar to genomic DNA-only data. Collectively, these data demonstrate that the performance of PTA for amplification of single-cell genomes persists despite upstream modifications to reverse transcription chemistry to generate transcriptome data.

[0155] Comparison of transcriptomic performance across multi-omics workflows

[0156] In selecting a transcriptomic protocol to combine with PTA, one goal was to capture as comprehensively as possible the diversity of RNA-based modes of carcinogenesis and drug resistance mechanisms and, equally important, to be able to identify genomic lesions manifested at the RNA level. A template-switching reverse transcription protocol was designed for multi-omics workflows that captures whole-transcript information, rather than 5' or 3' end counting, to improve the ability to detect isoforms and identify fusions. This chemistry results in uniform coverage between transcripts and, as Figure 3A , which shows an increase in coverage of the 5' region (top), which is typically affected by degradation (or reverse transcriptase performance) proportional to the distance from the 3'-poly A. This confirms the behavior of the template switching chemistry in the RNA arm workflow. Figure 3A (Bottom) shows the distribution of read depth across the gene bodies of a set of housekeeping genes, with all exons equally represented. The features we defined in the transcriptome were quantified in Figure 3B , highlighting the ability to identify various transcripts. The performance progress observed in the bulk dataset (bar 1, aggregated dataset) is shown in this figure compared to features such as bulk separation for library preparation methods (bars 2 and 4): independent mRNA strands (bars 2 and 3) and multi-omics combinatorial library preparation (bars 4 and 5). Most notably, an increase in 5' coding and intronic regions was observed in the multi-omics chemistry overall, while intergenic background was generally less than 5% of the aligned reads, providing a wider space for isoform detection.

[0157] As a further performance benchmark after cell quality mapping to a reference transcriptome, the performance pattern of the universal metric was established, where the well-characterized human brain reference RNA (HBRR) and the universal human reference RNA (UHRR) were complemented as the NA12878 cell line and Figure 3CComposite features are shown in . Read segments and genomic feature mapping percentages were identified, as well as the total genes found as criteria for evaluating sequencing quality. The expression and dynamic range of expression patterns of well-known housekeeping genes were also examined, and various markers of DNA contamination, sample degradation and / or bias were calculated as percentages of exons (more than 55%), as well as intergenic mapping (less than 5%) as multi-omics RNA fraction features. Another important metric for measuring the quality of single-cell experiments is the number of genes found per cell (>0 counts). For NA12878 cells, there are an average of approximately 2500, while the average number of HBRR and UHRR genes found is approximately 6000 and 7000, respectively. Finally, the median absolute deviation (MAD) and percentage coefficient of variation (CV) scores of the normalized CPM values ​​of universal housekeeping genes in cross-tissue studies are calculated. These metrics measure reproducibility and are a robust method for measuring sample variability. Overall, comparable monotonic expression metrics were observed between the housekeeping genes examined, and MAD values ​​for our HBRR and UHRR benchmarks ranged from 0.25 to 1, indicating that these genes exhibit little expression variability between cells. NA12878 showed slightly more irregularity, which, without being bound by theory, could mean higher variability or an inappropriate housekeeping gene. Accordingly, CV rates ranged from 14% to 30%, although NA12878 exhibited more variation. The dynamic range of expressed genes was approximately 1300 (HBRR), 1400 (UHRR), and 1900 (NA12878) CPM per cell.

[0158] Figure 3D Multi-omics whole transcript performance is shown compared to a mixture of publicly available bulk RNA-Seq and 3' end counting datasets (see Methods), highlighting the increased 5'UTR and gene body coverage that occurs by definition relative to 3' end counting. The relative types of other RNA species detected with multi-omics chemistries are shown, including lncRNAs, snRNAs, and pseudogenes. In multi-omics, the relative proportions of features were consistent between the isolated template-switched RT chemistry and the combined RNA / DNA workflow, and overall consistency was observed between the purified RNA input template and single cells, except that single cells revealed more intronic reads for protein-coding genes than purified RNA input. In all single cells analyzed in Tables 1B-1 and 1B-2, the mitochondrial read percentage was <10%, with most cells averaging less than 5%, indicating that single-cell lysis is optimal for capturing mRNA and other polyadenylated transcripts and that the amplified cells are healthy.

[0159] Table 1B-1

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167] Table 1B-2

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177] Example 2: Multi-omics approach to analyze carcinogenesis and drug resistance mechanisms

[0178] Overview

[0179] Cancer is a disease with significant variation and heterogeneity between individual cells comprising the bulk of tumor tissue. Despite a large body of research describing these changes during cancer evolution, the etiology of most cancers remains driven by speculation. This is demonstrated in the molecular complexity underlying the resilience of cancer cell drug resistance, where single nucleotide variations (SNVs) and copy number variations (CNVs) at the genome level cooperate with transcriptional adaptation to promote resistance. While one of these modes may be the primary driver, increasing evidence suggests that these modes are not mutually exclusive but can work together to alter the cell state leading to resistance. Therefore, assaying these multiple “-omics” layers (genome and transcriptome) in single cells will become important as bulk sequencing provides an incomplete view of the inherent heterogeneity of each of these layers. Cancer evolution is driven by complex molecular coordination, where the interdependence of genomic and transcriptomic changes occurring in each cell conveys a few major adaptive advantages that drive expansion and drug resistance. The nature of current genomic and transcriptomic assays obfuscates the underlying clonal architecture by reducing genomic data to tissue-based averages. Recent approaches designed to simultaneously monitor both RNA and DNA in single cells have made this connection possible, but uneven genome coverage and low allelic balance limit the ability to accurately assess single nucleotide variation genome-wide.

[0180] To overcome this challenge, the PTA workflow has been enhanced and a second way of transcriptome enrichment has been expanded. The method is distinguished by enhanced genome coverage and uniformity and allele balance, in which both copies of the genome are amplified equally and evenly. This is a potential attribute that allows both CNV and SNV detection to be performed with high accuracy from the amplified genome of the sample, which is limited as a single cell. The ability of PTA to provide this degree of uniformity and accuracy stems from the unwelcome re-replication of the synthetic chain, driven by nucleotide terminators that limit the size of the amplicon, and coincidentally this amplicon size distribution (500-1500bp) is suitable for the natural distribution of transcript lengths.

[0181] NA12878 cells are relatively transcriptionally quiescent. Following the general multi-omics procedure of Example 1, genes uniquely expressed in single cells from our DCIS and MOLM-13 material were also evaluated ( Figure 3D). First, by sampling the RNA library down to 75k reads for rarefaction analysis, it was found that for the detected genes, doubling the number of reads had only nominal benefits. Isotype detection and coverage still increased in proportion to the reads. Under 75K reads per cell, the benchmark cell line NA12878 detected an average of about 4500 expressed genes, while MOLM-13AML cells were an average of about 5000-5500 expressed genes. The single cells enriched by FACS from primary DCIS / IDC tumor samples produced less expressed genes than the cell line model, an average of about 3500, which is not subject to theoretical constraints, which may be due to the sample integrity of the primary isolated cells and the increase in the number of workflow steps from surgical resection to FACS.

[0182] Establishment of a drug resistance model in MOLM-13 acute myeloid leukemia cells

[0183] DNA and RNA performance metrics from control cell multi-omics were expanded to generate unified genomic and transcriptomic information from drug resistance models. Chemicals were evaluated to confirm that they reproduced known genomic features of MOLM-13 before investigating heterogeneous effects on drug resistance. Cells were first assessed for karyotype to match published reports and to provide context for interpretation of CNV analysis. Combined copy number analysis of all MOLM-13 cells used in this study is available at Figure 4A Prior to drug resistance modeling, the MOLM-13 line exhibited hallmarks of initial cell line establishment, including trisomy of Chr.6 and Chr.13(49,,2n.,XY,+6,+8,+13, 49,,2n.,XY,+6,+8, ins(11;9)(q23;p22p23), ins(11;9)(q23;p22p23), del(14)(q23.3;q31.3). The MOLM-13 line exhibited additional gain( Figure 4B ), including trisomy 5 and pentasomy 8 with other translocations (52, XY, +5, +6, +8, +8, +del(8p), add(11q), +13, add(17p)).

[0184] To demonstrate the utility of simultaneous genomic and transcriptomic information in single cells in the context of drug resistance, a model was created by exploiting the presence of internal tandem duplication (ITD) mutations in MOLM-13 cells. Because ITD mutations found in approximately 20% of AML patients overactivate FLT3 signaling and lead to poor prognosis and relapse, non-resistant, drug-sensitive cells were treated with continuous doses of 2 nM quizartinib. The drug is a selective type II kinase inhibitor targeting FLT3. Resistance emerges after initial apparent growth inhibition / apoptosis (see Methods, Fig.11 ).

[0185] Differences in single-cell CNV profiles in parental and quizartinib-resistant MOLM-13 cells

[0186] As an initial assessment of single-cell genomic variation in the MOLM-13 quizartinib-resistant model, CNV analysis was performed on 9 parental "P" and 10 quizartinib-resistant "R" cells following a multi-omics workflow. Using sequencing data to generate approximately 25x coverage and a 500kb window size, there was a significant increase in the copy number of chromosomes 5, 6, 8, and 13 ( Figure 4A ), and is consistent with our parental cell karyotype data ( Figure 4B ).

[0187] Single-cell CNV heterogeneity immediately emerged from the data. In the "P" cohort, Chr.5 of 9 / 9 cells was observed to increase to 3N, however, 5 / 9 cells showed additional 5p gain. The most relevant heterogeneous copy number variation between "P" and "R" single cells was observed. No resistant cells showed additional 5p gain found in the parental cohort, and in addition, 7 / 10 resistant cells did not have any Chr.5 amplification as a diploid 2n state, indicating that this was selected for mediating drug resistance in part through the expression results of multiple Chr.5 resident genes. In addition to this general meaning of Chr.5 as a candidate factor for quizartinib resistance, 19q unique gain was observed in 4 / 10 resistant cells. In summary, a CNV paradigm for the MOLM-13 resistance model was defined, which was used as the background of the SNV and transcriptional layers to be subsequently defined by the multi-omics approach described herein.

[0188] Acquisition of secondary FLT3 mutations as a key driver of drug resistance

[0189] At the increased level of SNV genomic resolution, candidate key drivers of quizartinib resistance beyond total CNVs were identified. All parental and resistant single cells carried FLT3ITD ( Figure 5A In contrast, the missense mutation N841K ( Figure 5B ). FLT3 N841K has been previously detected in AML patients, is located in the activation loop of FLT3, and furthermore, mutations in the closely related receptor tyrosine kinase KIT at a residue corresponding to N841 are activating. Without being bound by theory, this suggests that N841K is the major secondary mutation in the ITD and may contribute to quizartinib resistance in this model by preventing the efficiency of drug binding.

[0190] To assess whether the N841K FLT3 secondary mutation might have arisen de novo or was a clone of existing genetic variation in the parental population, a custom quantitative PCR-based genotyping assay was used to distinguish between these two scenarios. This probe set, which emits fluorescence at different wavelengths upon probe binding and dequenching for allelic discrimination between N841 and K841, ​​was used in a qPCR assay of genomic DNA isolated from parental or quizartinib-resistant MOLM-13 cells. In parental cells, K841 was present at low but detectable levels when amplification of N841 was dominant ( Figure 5C ). Resistant cells showed the opposite situation, with equal signals from N841 and K841. These data suggest that FLT3 K841 existed as an extremely rare clone in the original MOLM-13 cell line and that under the selective pressure of quizartinib, this clone was enriched to a dominant position in the resistant cell line, likely due to its ability to influence drug binding – thus highlighting that our cell line model mimics clonal selection in patient tumors. While this variant makes a compelling case in isolation, with the improvement of biomarker resolution, it will be necessary to further characterize the role of FLT3 K841 in resistant cell lines. Figure 6 The heatmap in identified well-defined groups, demonstrating the different genotypes between the two groups.

[0191] Heteroplasmic SNVs in MOLM-13 quizartinib resistance

[0192] A candidate list of genes representing multiple functional categories (signaling, epigenetic, tumor suppressor, spliceosomal, cohesion complex genes) previously implicated in AML pathogenesis due to SNVs was interrogated. With the exception of FLT3 secondary mutations, no resistance-specific coding sequence changes were identified in single cells using this candidate approach, so an unbiased search was performed for mutations that could confer quizartinib resistance and those that represented subclones and were not found in all resistant cells. Due to the increased likelihood of deleterious functional consequences, the variant call file was first stratified by rare mutational functional categories, stop codon gain, and frameshift mutations. A heterozygous nonsense mutation in the splicing and mRNA stabilization factor CELF4 was identified in 7 / 10 quizartinib-resistant cells, whereas this change was not identified in any single cell of the parental cohort. A frameshift mutation at K291 (c.870dupC) of the metabolic enzyme ADSS1 was identified in 8 / 10 quizartinib-resistant cells and 0 / 9 parental cells, and a frameshift mutation at A57 (c.167dupG) of the GTP-binding protein RRAGC was identified in 5 / 10 resistant cells and 0 / 9 parental cells. Although these variants were initially prioritized, expression of their cognate transcripts was not detected ( Figure 7B). This suggests that these genes are underexpressed in MOLM-13 cells, not expressed at the time of cell capture and extraction, and / or are beyond the limits of our detection using multi-omics. These findings prompted us to more comprehensively quantify single nucleotide variation in our model, as well as prioritize genomic variation associated with gene expression, a unique capability of multi-omics for single cells.

[0193] A variant filtering / prioritization strategy was then employed to identify single nucleotide variations present in quizartinib-resistant single cells but not in parental single cells. Based on this analysis (see Methods), multinomial logistic regression analysis and Wald tests were used to derive 6444 SNVs that were differentially prevalent between parental and resistant single cells (p < 0.05). Figure 6 This statistically significant genotypic variation is displayed in a heatmap and allows visualization of the conversion of the homozygous reference (0 / 0) to the heterozygous (1 / 0, 0 / 1) or homozygous alternative (1 / 1) allele in resistant cells and, conversely, the loss of the heterozygous genotype to the homozygous reference in resistant cells. Additional filtering allows us to focus on Fig.12 Different missense variants in parental compared to resistant lines. A109V, a prioritized missense mutation with validated biological significance in mRNA expression, was found in the E3 ubiquitin ligase gene RNF167 and was found in all 10 quizartinib-resistant cells but was absent in cells of the parental cohort.

[0194] In addition to prioritizing coding sequence variation as described above, variant filtering (see Methods for details) enabled us to discover a significant degree of single nucleotide variation occurring in the intergenic space in our quizartinib resistance model. 8601 intergenic SNVs were catalogued in parental cells, while 2167 intergenic SNVs were present in at least 25% of all cells within the group in our quizartinib-resistant cell cohort. This group-specific variation demonstrates the context of selection for both existing genomic variation and de novo mutations in response to drug treatment, as well as exemplifying a high degree of plasticity in the genome ( Figure 6 ).

[0195] MOLM-13 quizartinib-resistant cells exhibit unique transcriptional signatures including adaptive bypass

[0196] At the SNV level, there were differences between parental and resistant MOLM-13 single cells in principal coordinate analysis (p < 0.05, Fig. 7A The same trend was observed in the multi-omics transcriptomes of two MOLM-13 single-cell cohorts (data not shown). Figure 7BA dendrogram is shown that highlights the transcripts differentially expressed between P and R single cells and is labeled with biotype, indicating the categorical nature of the up- or down-regulated transcripts. In this model, two specific examples of the contribution of DNA and RNA levels to drug resistance are highlighted.

[0197] First, from the differentially expressed gene set, GAS6, a ligand for the receptor tyrosine kinase AXL, was upregulated. The AXL pathway, particularly through downstream STAT3 cell proliferation and PI3K / ALT survival signaling, has been shown to be a bypass pathway for FLT3 inhibition ( Fig.13 ). Concurrent transcriptional upregulation of the small GTPase RAC1 was also observed, which may synergize with upregulation of the AXL-STAT3 and AXL-PI3K / AKT signaling axes. Collectively, these transcriptional responses suggest a pattern of adaptive transcriptional bypass that occurs in the same cells carrying secondary FLT3 mutations at the DNA level driving drug resistance. Interestingly, transcriptional upregulation of the pioneer transcription factor CEBPA CCAAT / enhancer binding protein alpha (C / EBPα) was also noted in quizartinib-resistant cells ( Figure 7B Truncating mutations of CEBPA are found in approximately 10%-15% of AML patients, resulting in the expression of the N-terminal fragment of CEBPA, p30, with potential dominant-negative activity. As CEBPA is located on Chr.19q13.11, Chr.19q gain (Chr.19q gain) is observed in a subset of quizartinib-resistant cells, accompanied by transcriptional upregulation of CEBPA. Figure 7C ), thus suggesting a potential genomic mechanism for upregulated CEBPA expression and exemplifying the power of unifying single-cell genomic and transcriptomic data.

[0198] Although plausible, no positive correlation was observed between copy number gain when CEBPA was upregulated in individual cells, suggesting that the pattern of transcriptional upregulation was epigenetic in nature. Zero-inflated linear models were then used to evaluate the relationship between ploidy and genome-wide gene expression. Using a 500 kb window size, there was no direct correlation between ploidy and gene expression, except for a set of genes for which statistically significant associations (p < 0.05) were identified by this model ( Fig.7D Table 4 shows each gene identified and summarizes copy number and expression correlations. This highlights the importance of concurrent transcriptome assessment when interpreting copy number changes in single cells, and also highlights the significant single-cell heterogeneity that occurs in ploidy at sub-megabase chromosomal intervals.

[0199] Table 4

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207]

[0208]

[0209] In addition to these examples of transcriptional drug resistance mechanism hypotheses provided by the combination of single-cell genomic and transcriptomic data, differential transcript usage (DTU) analysis was also employed ( Fig. 7E ) as full-length (compared to 3' end count) data-supported transcript isoform insights. Isoforms of HADHA were identified, whereby their expression was unique to the quizartinib-resistant population and absent in all cells except one parental cell - whereby the isoform biased in resistant cells was shorter (about 2688bp) than the parental isoform (2943bp). Similarly, 7 / 10 quizartinib-resistant single cells expressed only isoforms of PPP1R14B containing an additional 5' exon, while 7 / 10 parental cells did not express any isoforms. Overall, the multi-omics approach identified six examples of isoform specificity of additional genes RPS3, HSPA4, SUGT1, and CAPNS1 between parental and quizartinib-resistant populations.

[0210] Identification of candidate regulatory SNVs that modulate transcript levels in resistant cells

[0211] The occurrence of genomic lesions of interest that were not associated with predicted transcriptional output was identified, leading to further analysis to identify single nucleotide variants that would affect proximal gene expression as Fig. 8A Although early experiments failed to identify a correlation between Chr.19q gain and CEBPA mRNA upregulation in resistant cells ( Figure 7C ), but a candidate distal promoter / enhancer SNV approximately 20 kb 5' of the CEBPA transcription start site was identified in the variant call file defining the SNVs, which had a genotype bias between parental and resistant cells ( Figure 8B). An unbiased approach was then employed, whereby ZLM (zero-inflated linear model) modeling of gene transcript abundance across cohort genotypes was performed. For preliminary analysis, SNV detection was limited to intragenic or promoter regions (0 to -5000 relative to the transcription start site). Upregulation of MYC expression was observed in resistant cells compared to parental cells, and candidate intronic regulatory variants were identified that had a genotype bias toward the reference 0 / 0 allele in resistant cells, whereas all but one parental single cell contained a 0 / 1 genotype of the candidate regulatory variant ( Figure 8C Another example of a candidate proximal regulatory SNV with a parental / resistant genotype bias and concomitant expression dichotomy between parental and resistant cells includes a candidate promoter mutation in the PABPC4 gene, encoding a poly(A) binding protein, located within 5'kb upstream of the transcription start site ( Fig.8D ). All variants identified with this analysis certainly warrant functional studies of efficacy, but highlight the power of multi-omics to generate candidate regulatory SNVs through pairwise analysis of genotype shift and transcriptional regulation in individual cells. Extending this analysis to all intergenic space, and correlating SNVs with ENCODE ChIP-Seq data, will be a powerful tool to generate a greater number of candidates that influence drug resistance and tumorigenesis.

[0212] Primary DCIS / IDC single cells show different types of chromosome loss

[0213] After demonstrating the utility of the unification of the multi-omics workflow of genomic and transcriptomic data in elucidating the single-cell drug resistance mechanism in the cell line model, the utility of similar multi-omics in elucidating the single-cell carcinogenic mechanism in primary human cancer was demonstrated. For this reason, the contribution of the genome and transcriptome to the transition of precancerous ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) was evaluated. Single cells dissociated from tumor tissue from mastectomy were first enriched by FACS (Duke University Medical Center). The tumor pathology of the patient showed that ER / PR (estrogen receptor / progesterone receptor) was positive, but the lack of HER2 expression excluded HER2 antibodies for FACS enrichment. Therefore, a FACS strategy was adopted to enrich ductal epithelial cells by epithelial cell adhesion molecule (EpCAM) epitope enrichment, and simultaneously capture "EpCAM low" cells as enrichment controls.

[0214] As with the MOLM-13 resistance model, CNVs in primary DCIS / IDC single cells were first evaluated. The multi-omics workflow was performed on 16 single cells with significant EpCAM expression and 4 single cells with negligible EpCAM expression. The same genomic coverage as MOLM was used (25x), and 500kb window CNVs were evaluated in the “EpCAM high” single cell cohort. Distinct classes of CNV emerged, with single cells exhibiting discrete chromosomal losses. As a class, 5 / 20 cells had almost complete loss of Chr.13 with concomitant loss of 16q / 17p, Fig. 9 . The most abundant class (12 / 20 cells) contained these copy number alterations, plus a third discrete loss of Chr.11q. Two EpCAM high cells did not have any obvious copy number alterations, and one EpCAM high cell had a more aberrant series of genome-wide chromosome losses. The observed losses of Chr.13 and 16q / 17p are consistent with reported copy number alterations in multiple stages of DCIS progression and are consistent with losses of the prototypical tumor suppressor genes BRCA2, RB1, and TP53. Interestingly, gains of Chr.13p, a gene-deficient metachromatic “stalk,” were observed in 10 / 20 EpCAM high cells, as well as gains of Chr.X of unknown significance in 2 EpCAM high cells and 1 EpCAM low cell, which included the centromere and flanking p and q arm fragments. Even for this relatively small cohort of single cells, these data highlight the copy number heterogeneity of the original sample.

[0215] Identification of oncogenic PIK3CA mutations

[0216] Prior to the genome-wide unbiased assessment of SNVs, the exons of the PIK3CA gene, one of the most frequently mutated genes in different molecular subtypes of breast cancer, were evaluated. The missense mutation N345K ( Fig. 10C ). N345K is second only to H1047R among the PIK3CA hotspot mutations catalogued by TCGA and is known to affect the p85 (PIK3R1) regulatory / p110 (PIK3CA) catalytic subunit interaction by disrupting the C2 / iSH2 domain interface. The oncogenic N345K mutation was detected only in single cells where the CNV was observed; initially it was shown that the relevant ductal epithelial cells were stratified using a FACS strategy and that the two cells lacking the CNV+PIK3CA N345K either contained additional genomic variants or were different cell types – the RNA arm of a multi-omics approach was required to further distinguish possibilities.

[0217] Single nucleotide variants in DCIS / IDC

[0218] Variant filtering was performed to identify novel candidate oncogenic SNVs. As validation of our filtering strategy, PIK3CA N345K was identified in 14 / 16 cells carrying 11q, 13, 16q / 17p copy number losses. No coding sequence mutations were detected in other candidate genes known to affect ER+ breast cancer ( Fig.14 ). Using a strategy that resolved SNVs by CNV status, variants present in EpCAM high cells but absent in EpCAM low cells were catalogued. Similar to the MOLM-13 model of quizartinib resistance, extensive intergenic genomic SNVs were observed in EpCAM high cells compared to EpCAM low cells.

[0219] Cell identity and transcriptional status of DCIS / IDC isolated cells

[0220] A noteworthy utility in combined genomic / transcriptomic single-cell assays is the ability to link genotype to cell type identity and inference of cell state. This is critical in interpreting observed CNV and PIK3CA E345 single-cell DCIS / IDC genotypes given the difficulty in designing FACS labeling patterns that unambiguously identify ductal epithelial cells of interest from surrounding stromal cells and infiltrating immune cells. Gene expression profiles of EpCAM high and EpCAM low cells separated by principal component analysis ( Fig. 10A ), using the PAM50 gene panel of genes that are influential in different subtypes of breast cancer ( Fig. 10B Differential gene expression analysis highlighted a block of genes between two major branches: one cluster of exclusively EpCAM high cells and another cluster consisting of all EpCAM low cells mixed with the four EpCAM high cells ( Fig. 10C Initial identification of transcripts defining EpCAM low cells revealed enrichment for IL-2 and CD4 T cell-defining gene sets, suggesting that these cells were likely tumor infiltrating lymphocytes present in this patient's isolated tumor sample. However, further rigor of transcriptome-based cell annotation using Human Cell Atlas data (see Methods) resolved EpCAM low cells into stem cell-like, endothelial, fibroblastic, and monocytic identities / states ( Figures 10B-10E ), which is independent of transcript counts ( Figure 1Aa). Four aberrant EpCAM high cells exhibited gene expression signatures that placed them in the same branch of the dendrogram as EpCAM low cells. Cells were identified as having two distinct identities / states: epithelial and monocytic. Interestingly, while all EpCAM low cells lacked PIK3CA N345K or characteristic DCIS copy number loss, EpCAM high cells in the branch of the EpCAM low gene expression signature with epithelial properties contained both genomic alterations. Without being bound by theory, this suggests plasticity of the cell state of ductal epithelial cells and the acquisition of a phenotype with stemness attributes as suggested by cell annotation profiles that more closely matched tissue stem cell or fibroblast identities ( Fig. 10D ). One aberrant EpCAM-high cell in the EpCAM-low branch lacked oncogenic PIK3CA mutations and prototypical DCIS chromosome loss and displayed a monocytic gene expression profile. For this case, this is suggestive of monocytic infiltration in the sample, although it does not formally exclude the possibility of cell state variation in either malignant or benign ductal epithelial cells or monocytic infiltration in the sample. Furthermore, within this aberrant EpCAM-high category, one putative epithelial cell, while distinct from the typical DCIS chromosome loss observed in the main EpCAM-high branch, had a grossly abnormal CNV profile and likely represented a malignant cell. Our example of putative plasticity of a phenotypic cell state with respect to oncogenicity warrants multi-omic analysis of additional cells to determine the frequency of this cell state in the sample or whether it represents random genomic variation that is not sustained or selected for in the population. Collectively, these data suggest that analyzing cells solely at the transcriptome level may lead to incorrect cell classification and emphasize that understanding both the RNA and DNA-omic layers is critical to providing a correct classification.

[0221] A holistic view of single-cell molecular signatures of MOLM-13 and DCIS / IDC

[0222] After sequentially identifying CNVs, SNVs, and transcriptional insights in both the MOLM-13 drug resistance model and primary DCIS / IDC, it was critical to begin collecting and graphically presenting the interrelationships between the “-omics” data layers. For MOLM-13, a secondary driver mutation was identified that likely affected drug binding in all single cells but provided evidence for a simultaneous transcriptional bypass of FLT3 signaling, highlighting the importance of identifying DNA- and RNA-driven resistance mechanisms in the same cell.

[0223] For primary DCIS / IDC, unification of DNA-level and RNA-level data allows interpretation of genotype in the context of expression signatures that define cell type and cell state. Using these layers of molecular information in heatmaps / dendrograms, we quickly conveyed the finding that ductal epithelial cells expressing EpCAM harbor both prototypical copy number loss and oncogenic PIK3CA mutations, whereas EpCAM low cells with alternative identities via transcriptome profiling from the same isolated cell sample lacked chromosomal loss and this mutation ( Fig. 10D ). However, cell identity cannot be definitively assessed by EpCAM FACS protein levels alone, but rather utilizing more modern cell annotation methods; IDs that match the known biological origin of the cell or reflect cell state transitions can be objectively identified.

[0224] discuss

[0225] Each “-omics” layer of molecular information allows for greater ability to comprehensively define the molecular mechanisms of tumorigenesis and drug resistance in tumors. In the field of single-cell tumor biology, most work to date has been performed at the transcriptome level, thanks to the large-scale adoption of droplet-based approaches that have facilitated workflow simplification and single-cell throughput. Although droplet-based RNA-Seq studies have made unquestionable progress in defining the diversity and heterogeneity of transcriptional states, including those defined longitudinally, a gap remains because few studies provide simultaneous genomic and gene expression data. This is critical for a number of reasons. First, in the absence of DNA-level information, it is impossible to discern genomic contributions to transcriptional or phenotypic states, such as genomic mutations or variations in regulatory elements, transcription factors, or chromosome copy number, each of which has the potential to define transcriptional states. Therefore, previous studies have had significant limitations in addressing the critical link between DNA and transcriptional changes. Second, while transcript-level information is frequently used for molecular subtyping of tumors, pharmacology decisions are primarily driven by genomic variation due to technical and informatics challenges determined by transcriptional state. This may partly explain why tumor DNA molecular data are not perfect predictors of treatment sensitivity.

[0226] The combination of single-cell genomic and transcriptomic information has been limited to date due to technical challenges in integrating RNA and DNA amplification steps. Additionally, existing methods for amplifying single-cell genomes have been adapted to overcome this incompatibility, and the drawbacks that accompany these combined RNA / DNA protocols are incomplete genome coverage, poor coverage uniformity, and poor allelic balance. For example, G&Tseq provides researchers with transcriptional data from single cells combined with multiple displacement amplification of DNA-level information. This facilitates multi-omics insights primarily at the transcriptome + copy number alteration level, precluding SNV analysis due to the inherent incomplete genome amplification of MDA or PicoPLEX. Multi-omics chemistry could overcome this limitation by unifying primary template-directed amplification in single cells with RNA sequencing, and its utility has been shown by cataloging putative regulatory SNVs that affect gene expression.

[0227] The ability to define cell identity and cell state at the single-cell level is a major advantage of multi-omics. While some FACS strategies can adequately stratify cell types in heterogeneous samples, one does not always have such biomarker knowledge a priori, and even in the presence of such knowledge, aberrantly sorted cells have been observed with no consistent mRNA levels detected, despite being gated for high levels of the corresponding protein biomarker. Thus, combined RNA / DNA single-cell profiling here enabled us to uncover the landscape of distinct non-epithelial cell types in our primary breast cancer samples, thereby preventing the misinterpretation of ductal epithelial cells lacking prototypical copy number alterations or key oncogenic missense mutations when in fact the lack of genomic variation was due to the cell type being assayed. With combined genomic and transcriptomic information at hand, the cell type tumor heterogeneity revealed in FACS can now be exploited, for example, to understand the contribution of genomic variation in monocytes to the interaction of malignant epithelial cells in a given microenvironment, rather than assuming in this case that monocytes contaminate the epithelial population of interest.

[0228] In addition to characterizing cellular identity with multi-omics, the multi-omics approach described herein enables the continuum and heterogeneity of cellular states within breast tumor samples to be captured at unprecedented resolution. An intermediate transcriptional profile emerged between the EpCAM low single cell cohort and the core cohort of EpCAM high epithelial cells. Interestingly, this profile was observed in EpCAM high cells, which contained PIK3CA N345K and chromosomal losses characteristic of DCIS and therefore had the core genomic changes of the main epithelial cell cohort. However, it exhibited a distinct transcriptional stem cell-like state - thereby suggesting potential state transitions and highlighting inherent transcriptional single cell heterogeneity, even in relatively small sampling of isolated tumor samples. As more cells from this sample are sequenced, it will be critical to determine the prevalence of this cell state, as well as define the diversity of other novel transcriptional states that may contribute to the progression of DCIS to invasive cancer. Multi-omics approaches importantly provide the ability to link these distinct transcriptional cell states to genotype ( Fig. 8A ).

[0229] The second major advantage of the multi-omics workflow is the property of providing targeted amplification of primary templates to allow comprehensive genomic assessment, rather than determining only a few candidate loci or copy number alterations at a broad resolution level. This approach is effective for more than 95% of 1 The realization of highly sensitive and accurate SNV detection across the entire genome opens up a new area of ​​discovery. PTA in a multi-omics workflow opens up a new source of pharmacological targets with whole genome data and non-exon space, which is not possible with existing WGA methods with low genome coverage and uniformity. It is worth noting that single nucleotide variants (6444 differentially prevalent SNVs) present in parental compared to quizartinib-resistant MOLM-13 cells, Figure 6 ), which further emphasizes that while transcriptional plasticity is dogmatic, it is equally important to recognize the genomic plasticity observed in this model. Furthermore, while there will be a background of passenger mutations or currently non-pharmacologically targetable mutations, this diversity can ultimately be identified and represents the co-evolution of variants with functional, biologically relevant phenotypic outputs. Efforts to estimate intergenic variation at putative functional elements (promoters, enhancers, splicing enhancers) are a cutting-edge and underappreciated aspect of drug resistance research. Candidate regulatory SNVs that are proximal to differentially expressed genes of interest in our parental cells compared to resistant cells will likely require dedicated functional characterization, but as the cost of genome sequencing begins to decline, these data and their associated biological insights will inevitably begin to accumulate. For discovery, determining dual genomes / transcriptomes from single cells not only accelerates the generation of candidate regulatory SNVs linked to transcriptional regulation, but also reveals associations that are obscured by large amounts of sequencing data.

[0230] Both our engineered models of drug resistance in AML and analysis of primary DCIS / IDC samples yielded single nucleotide variants that were predicted at the outset to have deleterious effects on protein function. The frameshift and stop codon gain mutations observed in the single-cell genomes of our samples represent an unbiased starting point for discovering novel oncogenic and drug resistance loci, rather than just identifying known candidate genes. However, combined transcriptional information from the same cells revealed that for some of these novel genomic variants with purported deleterious effects, single cells did not express the corresponding transcripts—thus suggesting that the genomic changes were passenger or random in nature and not functional. Understanding this “penetrance” of genomic variants from their expression at the transcriptional level is a fundamental power of multi-omics and redirected or rejected multiple hypotheses in our initial sample set.

[0231] In addition to binary “express or not express” decisions, dual DNA / RNA information helps guide hypotheses about molecular mechanisms. 42 , which was significantly upregulated in our quizartinib-resistant single MOLM-13 cohort) is present on Chr.19q, with four resistant cells harboring 19q genomic gains ranging from 2n to 3n. A simple initial hypothesis is that genomic amplification of 19q contributes to the observed transcript upregulation, however CEBPA transcript upregulation was observed in all resistant cells and did not show correlation with single cells harboring 19q genomic amplification ( Figure 7C ). This suggests that alternative mechanisms of epigenetic control act on this upregulated gene, perhaps via regulation of transcription factors or phenomena at the enhancer level, as inferred by the SNVs between parental and resistant cells near the CEBPA gene. More broadly, although statistically significant associations between ploidy and expression of specific gene cohorts were identified ( Fig.7D ), but no such association was observed for most loci. Overall, these examples illustrate the importance of paired RNA information when mapping mechanisms based on genomic data alone and remind us of the need to determine the “penetrance” of variation. In contrast, significant correlations were found between SNVs and expression of neighboring genes, such as the oncogenic driver MYC ( Fig. 8A and Figure 8C ), highlighting instances in which DNA and RNA messages may be functionally linked.

[0232] The availability of simultaneous genomic and transcriptomic data from the same somatic cells has greatly increased the complexity of putative mechanisms of drug resistance and tumorigenesis. This will only increase with the addition of additional “-omics” layers, including determination of extracellular protein expression, as the nature of multi-omics template-switched cDNA chemistry allows for the integration of CITE-seq-like oligonucleotide-tagged antibodies. These data are complex and require the development of novel sophisticated bioinformatics tools. However, mechanistic insights similar to those presented here are accumulating from the research community with the newfound ability to accurately assess single nucleotide genomic variation in conjunction with transcriptional profiling – aiding discovery efforts to generate new wealth and generate pharmacological targets.

[0233] method

[0234] Cell culture

[0235] NA12878 cells (CEPH / Utah lineage 1463) were purchased from Coriell Institute for Medical Research (Camden, NJ) Cells were maintained in RPMI 1640 (Gibco 11875-093) supplemented with 15% FBS and penicillin / streptomycin and subcultured every 2-3 days while maintaining a density range of 1.0-3.0E6 / ml.

[0236] MOLM-13 acute myeloid leukemia cells with heterozygous FLT3 internal tandem repeats (ITD) were obtained from the DSMZ-German Collection of Microorganisms and Cell Cultures (ACC 554). Cells were maintained in RPMI 1640 (Gibco 11875-093) supplemented with 10% FBS and penicillin / streptomycin, and subcultured every 2-3 days while maintaining a density range of 2.5E5–1.5E6 cells / ml. In order to produce quizartinib-resistant MOLM-13 lines, cells were continuously treated with 2nM quizartinib (Selleckchem AC220) or DMSO vehicle controls for matching parental control lines, and drugs were supplemented at each subculture until resistant clones appeared in the 5-week duration of culture. Genomic DNA (Zymo Research Quick-DNA Microprep w\Plus Kit, D3020) or total RNA (Qiagen RNeasy Plus Kit, 74034) was isolated from quizartinib-resistant and matched parental MOLM-13 cells during FACS sorting to generate large sequencing control libraries for comparison with single-cell datasets and for quantitative PCR templates.

[0237] Multi-omics workflow

[0238] The multi-omics workflow starts with template-switching-based RNA-Seq chemistry to generate biotin-dT-primed first-strand cDNA, followed by termination of the reaction and nuclear lysis, at which point directed amplification of the primary template is performed. mRNA-derived cDNA is affinity-purified from the combined pool of cDNA and amplified genome using streptavidin beads. The cDNA is then further purified, followed by two stringent streptavidin bead washes and on-bead preamplification of the first-strand cDNA to generate double-stranded cDNA. In parallel, the PTA fraction of the same cell containing the genomic amplification product separated from the cDNA is purified. Separate and distinct fractions of the pre-amplified mRNA cDNA and genomic-derived DNA amplification fractions undergo SPRI cleanup prior to NGS library generation.

[0239] Karyotype analysis

[0240] MOLM-13 cells (KaryoLogic, Inc, Durham, NC) were analyzed within 2 weeks after thawing, using the workflow of the complex hyperdiploid karyotype of 25 mid-term propagations. Before mid-term propagation was produced, live cultures were delivered to the on-site service provider, and cultures were recovered in 5% CO2 37C incubators on site for one week.

[0241] FACS

[0242] Prior to FACS, cell lines were first counted and overall viability assessed using either Trypan Blue staining using a Countess II FL instrument (ThermoFisher Scientific) or Acridine Orange + Propidium Iodide using a Luna FL instrument (Logos Biosystems). Cell line cultures following the FACS protocol exhibited >90% viability.

[0243] MOLM-13

[0244] For single cell analysis, approximately 2.0E6 MOLM-13 quizartinib-resistant or matched parental cells were rinsed twice in staining buffer (0.2 μm filtered Dulbecco's phosphate buffered saline (Gibco 14190) lacking calcium and magnesium supplemented with 2% FBS) and kept on ice until sorting by a BD FACSAria III at the UNC School of Medicine Flow Cytometry Core Facility. After Calcein AM (BioLegend425201), propidium iodide (Millipore Sigma P4864) and DAPI staining, singlet (FSC-A / FSH-H, SSC-A / SSC-W) and live cell (DAPI / PI negative, top 70% Calcein-AM positive) gates were established, and single cells were sorted (130 μm nozzle assembly) into a low binding 96-well PCR plate (Eppendorf twin.tec LoBind, semi-skirted, 0030129504) containing cell buffer and immediately frozen on dry ice after brief mixing (1400 rpm, 10 sec) and centrifugation.

[0245] NA12878

[0246] Approximately 2.5E6 NA12878 (NA12878 / HG001) cells were prepared as above and sorted using a Sony SH800 130 micron chip. Single cells were sorted into a low binding 96-well PCR plate preloaded with cell buffer as described above using single line (FSC-A / FSC-H, BSC-A / BSC-W) and live cell (PI negative, top 70% Calcein-AM positive) gating.

[0247] Primary DCIS / IDC

[0248] The tissue used for the single-cell DCIS / IDC study was obtained according to the clinical trial PRO00034242 "Biological characteristics of the breast cancer tumor microenvironment" of the Duke University Medical Center IRB. The cryopreserved isolated cells (about 4.2E5) derived from mastectomy tissue were thawed at 37°C and centrifuged at 350x g for 5 min to separate the cryopreservation medium. The cells were rinsed once in the staining buffer and incubated with 2 μg / ml of anti-human CD326 conjugated to AlexaFluor 700 (ThermoFisher 56-9326-42) at 4°C in the dark for 1 h. After this,

[0249] About 8.4E4 cells are retained for the parallel negative control mock staining lacking any antibody, for assessing the background fluorescence level of viability and EpCAM staining. The cells are then washed 3 times with staining buffer, centrifuged for 5 min at 350 x g between washes, and passed through a 35 micron filter before loading for FACS. Single line (FSC-A / FSC-H, BSC-A / BSC-W) and live cell (calcein AM) gating are defined, followed by progeny EpCAM high and EpCAM low gating. EpCAM high and low cells are sorted into the same 96-well plate as described above, to minimize the potential batch effect of downstream genome / transcriptome amplification.

[0250] Quantitative RT-PCR

[0251] As described above, 10 ng of genomic DNA was isolated from cell pools of quizartinib-resistant or matched parental cells and subjected to custom Taqman sequencing using the manufacturer's recommended reaction setup and cycling conditions on the QuantStudio 6 instrument. TM Genotyping assay, #ANMF9C4 (Invitrogen-Applied Biosystems). The assay is designed to distinguish between human N841 and K841 with a C / A nucleotide polymorphism at GRCh38 / hg38 coordinates Chr13:28,018,485, respectively.

[0252] Combined genomic / transcriptomic analysis

[0253] First, biotin-conjugated oligo dT primers (Integrated DNA Technologies) are used in template switching reverse transcription reactions to produce the first strand cDNA from a single cell. After reverse transcription, primary template directional amplification (PTA) is continuously performed with reagents (Bioskryb Genomics, Inc.). The first strand cDNA is then affinity purified using streptavidin beads, and two high salt washes are performed, followed by a low salt wash. 24 cycles of pre-amplification are performed to produce the second strand cDNA, and RNA library preparation modules are used to prepare RNA sequencing libraries. In order to prepare PTA libraries, beads are used to purify the PTA products that are not bound to streptavidin beads, and the DNA library preparation module is used to connect it to the full-length IDT of the Illumina TruSeq adapter. The size of both RNA and DNA amplification products is determined by D5000 TapeStation electrophoresis (Agilent Technologies), and the size of library preparation is determined by HSD1000 electrophoresis. Amplification and library yield were assessed by Qubit 3 or Qubit Flex instruments (ThermoFisher Scientific).

[0254] Sequencing

[0255] First, low-pass sequencing was performed on the DNA fraction library using Illumina MiniSeq (2.3 pM library flow cell load concentration) or NextSeq1000 (640 pM library flow cell load concentration), 2X75 targeted

[0256] >2.0E6 total reads per library. For RNA fraction libraries, 2X75MiniSeq or NextSeq1000 sequencing was used, with the goal of an average of >1.0E6 reads per library to achieve flexibility in data downsampling. For joint clustering of DNA and RNA fraction libraries, a 10:1 molar ratio of [DNA arm]: [RNA arm] library was used. After low-pass sequencing, the DNA arm library was sequenced 2X150 on an IlluminaNovaSeq6000 S4 flow cell, with a goal of 5.5E8 total reads to provide downsampling flexibility at the Vanderbilt Technologies for Advanced Genomics (VANTAGE) core facility or the Duke University Genomics and Computational Biology (GCB) core facility.

[0257] Bioinformatics methods

[0258] Prediction sequence quality control

[0259] Single-cell libraries were evaluated using an in-house pre-sequencing pipeline that uses low-pass sequencing data to create multiple quality control metrics to help assess the readiness of single-cell libraries for high-throughput sequencing. Of note, PreSeq counts were retrieved to estimate library complexity. The pipeline features additional QC metrics for genome coverage, percentage of reads mapped to chimeras, percentage of reads aligned to a reference genome, and percentage of nucleotides mismatched to a reference genome. In addition, the pipeline implements MultiQC for supplementary QC metrics, including read length, percentage of duplicate reads, number of mapped reads, and total number of mapped reads.

[0260] Benchmark RNA-Seq Results

[0261] To establish an overall benchmark score for the multi-omics amplification method, pre- and post-sequencing quality control was performed on human brain reference RNA (HBRR), universal human reference RNA (UHRR), and NA12878 B lymphocytes. Several metrics were considered: percentage mapped, genes detected, dynamic range of expression, and coefficient of variation to measure DNA leakage, accuracy, and robustness of the method. For each cell, a Qualimap 44 (v2.2.2) platform quantifies total alignment, read alignment and genomic feature alignment to report QC metrics and bias estimates for whole transcriptome sequencing data. In addition, the platform is able to detect abnormal cells, relatively consistent performance patterns between these cells, and potential batch or other system artifacts that are not obvious when evaluating individual cells alone. Using the metrics generated by the Qualimap results, the mapping percentage of the total alignment and the exon and intergenic percentages of the genomic alignment are calculated. Thereafter, the number of genes identified, the dynamic range, and the housekeeping gene variability metrics are defined using the per million count (CPM) normalized gene expression counts, and the expression patterns of the housekeeping genes of each reference cell line are observed. The detected genes are defined as the number of genes with non-zero counts in each cell. The dynamic range of all expressed genes is then estimated to be 10%-90%. As an estimate of sample dispersion and reproducibility, the coefficient of variation percentage (CV) is calculated as the ratio of the standard deviation to the mean: CV =. The median absolute deviation (MAD) is calculated as a robust measure of variability between housekeeping genes. It is defined as the median of absolute deviations from the median (m): MAD = median(|xi-m|).

[0262] Secondary Analysis Process

[0263] For DNA-based analysis of genomic fractions from the multi-omics workflow, an in-house analysis pipeline modified from Sentieon's driver-based tools was utilized. Initial FASTQ pairs were trimmed for low quality and library artifacts using fastp (v0.20.1). Alignments were performed using BWA (Sentieon-202112), followed by deduplication of identically aligned reads (locus_collector v202112 / dedup v202112). Alignment-based QC and coverage determination were performed (driver_metrics v202010). ginko 46(GitHub commit: 892b2e9f851f71a491cade6297f74f09f17acf4c) was used for copy number determination with a window size of 500kb. Cellular variant determination was performed using Haplotype Analyzer (v202010). Features of all variants were provided to recalibrate variant quality scores based on VARcall, GVCFtyper (v202010). All variants were identified and annotated for gene / coding effects using snpEFF / SnpSIFT (5.0e). Further variant-based tertiary analysis used filtered genomic loci for candidate SNVs with sequencing depth >4 and >1 variant reads. All candidate SNVs were classified according to allele frequency.

[0264] The RNA-Seq process implemented here is used to generate characteristic quantification metrics at the transcript and gene levels. For detailed information on the number and length of reads produced by DNA arms (a) and RNA arms (b), see Table 1. Unless specified as down sampling (using seqtk v1.3), all reads were used for each analysis. In order to remove low-quality sections and sequencing artifacts, all cells were analyzed using fastp before alignment. Read alignment was performed with STAR (v 2.7.6a), and compared with the transcript reference formed by combining Ensembl (version 104) known transcripts and non-coding. The regional distribution of gene level metrics and the counting of alignment reads were performed with HTSeq4949 (v0.13.5) and Salmon5050 (v1.6.0). In addition, the pseudo-alignment algorithm implemented in Salmon was used to perform both transcript level and gene level quantification. The characteristic expression matrix was constructed using Bioconductor package tximport.

[0265] Level 3 Analysis

[0266] Large dataset identification

[0267] Several datasets were identified in the Short Read Archive (SRA) that had a high number of NA12878 in the mRNA-stranded RNA library preparation method that was most similar to our own multi-omics approach. To address the variation in individual datasets, at least 10 datasets were targeted for capture that could represent the transcriptome coverage of NA12878.

[0268] Variant evaluation in NA12878 cells

[0269] NA12878 cells were first genotyped jointly using the GVCFTyper, VarCal, and ApplyVarCal modules from Sentieon. Then, the recalibrated variants were input and the variant quality score log odds (VQSLOD) was evaluated by using the vcfeval module from RTG tools using the Genomes in a Bottle (GIAB) consortium. 52 NA12878 / HG001 genome v.3.3.2 51 As a reference, the accuracy and sensitivity of the called SNPs were determined.

[0270] Allelic balance in NA12878 cells

[0271] Allelic balance of NA12878 cells was calculated using an ad hoc developed module based on a series of bcftools commands that extracts a priori defined highly confident heterozygous sites from all sequenced NA12878 cells, reported in the GIABNA12878 / HG001 genome v.3.3.2. Then, for each cell and each heterozygous site, the variant allele depth was extracted and converted to a ratio. For the final report, heterozygous sites with at least a total depth >1 were used.

[0272] RNA arm: matrix normalization

[0273] For MOLM-13 and DCIS cells, their corresponding Salmon-based transcript and gene matrices were normalized across features using a logarithmic normalization method. Briefly, the feature counts for each cell were divided by the total counts for that cell and multiplied by a scaling factor (10 4 ), whose products were finally log2 transformed. These normalized matrices were used as input for downstream analyses including principal component analysis (PCA), differential transcript expression (DTE), differential gene expression (DGE), differential transcript usage (DTU), heatmap reconstruction including unsupervised clustering of cells and transcripts / genes, and zero-inflated linear models linking transcript expression to CNVs and SNVs.

[0274] Principal component analysis

[0275] MOLM-13 and DCIS normalized transcript level and gene level matrices were centered within samples using the R function scale. In addition, principal component analysis was calculated using the oh.pca function from the ohchibi R package, taking the centered normalized matrices as input.

[0276] Differential expression

[0277] Differential transcript expression was estimated and the expression of 53Differential gene expression from the zero-inflated linear model (ZLM) implemented in the R package was used as input to the log-normalized feature matrix described above. For the MOLM-13 dataset, the following model was fit to identify transcripts / genes with differentially expressed features in parental and resistant cells: Transcript / gene expression ~ cell type (parental / resistant) + number of features (transcripts / genes) detected per cell

[0278] For the DCIS dataset, principal component analysis was performed using the top 500 highly variable genes across the dataset, and the cells were then divided into three groups using the PCA projection as a guide. This three-group scheme was used to discretize the cellular heterogeneity in the EpCAM high and EpCAM low treatments in an unbiased manner. After dividing the cells into three groups, the following ZLM was suitable to identify transcripts / genes with differential expression signatures in each of the aforementioned groups: Transcript / gene expression ~ cell group + number of signatures (transcripts / genes) detected per cell

[0279] Cell typing

[0280] Using the R package SingleR 54 Perform transcriptome-based cell typing on DCIS datasets. The package uses the 54 The Human Primary Cell Atlas expression reference dataset is available in an R package and takes a salmon-based matrix of gene-level normalized expression as input.

[0281] Differential transcript usage

[0282] For the MOLM-13 dataset, differential transcript usage was performed. Briefly, the scaledTPM metric output from tximport was taken and reconstructed into a transcript abundance matrix across cells. Next, transcript expression was modeled using the Dirichlet multinomial distribution model implemented in the DRIMSeq R package.

[0283] Linking transcript expression to CNVs

[0284] For the MOLM-13 dataset, a zero-inflated linear model framework was used to relate expressed transcript level variation to changes in locus ploidy. Briefly, for each quantified transcript, its ploidy was extracted across cells from Ginkgo-based estimates using genomic coordinate intersections using the GenomicRanges R package. Next, the following ZLM design using the MASTR package was fitted: transcript expression ~ estimated ploidy at a given locus.

[0285] Linking transcript expression to genomic polymorphisms

[0286] For the MOLM-13 dataset, a zero-inflated linear model framework was used to link expressed transcript-level variations to single nucleotide variations across the genome. In short, the genomic coordinates of the SNVs were first paired with transcripts using genomic coordinate crossover via the GenomicRanges R package. Regarding transcript coordinates, the transcript start and transcript end reported by Ensembl were used to define the gene body of the transcript, and 5000bp upstream of the transcription start site (TSS) reported by Ensembl were used to define potential cis-regulatory regions affecting transcripts. After defining the corresponding SNV-transcript pairs, a matrix of expression and genotype loci (SNVs) for all cells was constructed. Finally, using this matrix, a zero-inflated linear model was fitted using the MAST R package with the following design: transcript expression ~ genotype

[0287] The GSEA-R tool was used in conjunction with the Molecular Signature Database (MSigDB) to systematically examine the enriched gene sets associated with genes differentially expressed in Molm-13 parental and resistant cells and significant SNVs. In addition, the Reactome Pathways database was used to find relevant pathways between these genes using the default adjusted p-value of 0.10.

[0288] Important variant testing

[0289] In order to identify the different SNVs between Molm13 P and R cells, the categorical variables of diploid state were generated and compared with chi-square test. The two-sided p value less than 0.05 was considered as significant. In addition, multinomial logistic regression was fitted to identify the difference of SNV universality in parental generation and resistance MOLM-13 type. Specifically, for each SNP, three state genotypes (0 / 0, 0 / 1, 1 / 1) were coded as dependent variables, and MOLM-13 type (parental generation, resistance) was coded as independent variable. The significance of the model was tested using Wald test.

[0290] Multi-omics is applied in the context of two major phenomena in oncology: tumor heterogeneity (leading to cancer progression) and treatment resistance.Material from primary patient breast cancer and acute myeloid leukemia (AML) cell line MOLM-13 is used to emphasize the multi-omics biomarker paradigm achieved by this chemical method.The performance of the genome amplification initiated by PTA is largely unaffected by the addition of RNA enrichment, wherein the control WGS result shows>95% genome coverage, accuracy>0.99, and allele dropout<15%.In the RNA fraction of chemistry, full-length transcripts are routinely obtained, proving that the ratio of 5' / 3' bias is 1, while the coverage of intronic regions and 5' regions increases, indicating novel transcripts, thereby showing that the strength of the template switching mechanism captures isoform information with a sparse rate of<75%.Despite the use of a relatively small number of individual cells, the cell variability of the biomarkers revealed has been observed in both the genome and the transcriptome. In our primary patient samples of ductal carcinoma in situ (DCIS) / invasive ductal carcinoma (IDC), oncogenic PIK3CA driver mutations were discovered and archetypal DCIS copy number alterations were classified as heterogeneous single-cell categories of genomic lesions. In our quizartinib-treated MOLM-13 cells, multiple potential resistance mechanisms were identified in seemingly sporadic changes and specific mutations, copy number, and expression were significantly associated with treatment were linked. In the latter case, the DNA arm of our combined workflow discovered secondary FLT3 (non-internal tandem duplication (ITD)) mutations as candidate major drivers of drug resistance, while the RNA arm showed upregulation of matching transcripts regulated by AXL signaling as well as enhancer factors. Importantly, proximal candidate regulatory SNVs outside of the CDS were identified and associated with cis-upregulated transcripts. This study highlights that both the genome and transcriptome are dynamic, resulting in a series of combinatorial changes that affect cellular evolution and that fate can be identified by applying multi-omics to individual cells.

[0291] Example 3: Use of uracil-tolerant polymerases to improve multi-omics

[0292] Follow the general method of embodiment 1-2, use reverse transcription to produce cDNA from single cell RNA.Use biotinylated poly-dT primer to produce cDNA amplicon.Next, use PTA method to amplify the genomic DNA from cell, wherein the mixture of dNTP comprises uracil.Then use streptavidin to purify cDNA from mixture, and further use uracil DNA glycosylase (UDG) and DNA glycosylase-lyase endonuclease VIII to process, to remove any residual genomic amplicon from cDNA.Then purify the genomic fragment produced by PTA, and use adapter to connect cDNA and genomic DNA two kinds of fractions to be converted into sequencing ready library.Uracil tolerant polymerase is used to amplify the genomic fragment produced by PTA.

[0293] Example 4: Preparation of a transposon library using uracil-tolerant polymerase

[0294] The general procedure of Example 3 was modified: Sequencing-ready libraries were prepared by labeling genomic and / or cDNA fragments with transposon complexes as described herein (e.g., TDE1). After labeling the adapters with the transposon complexes, the libraries were amplified. For uracil-containing libraries (e.g., genomic PTA libraries), a uracil-tolerant polymerase was used. The two adapter-labeled libraries were then sequenced.

Claims

1. A method for preparing a multi-omics sample, the method comprising: a. separating a single cell from a cell population, wherein the single cell comprises RNA and genomic DNA; b. amplifying the RNA by RT-PCR to produce a cDNA library; c. contacting the genomic DNA with at least one amplification primer, at least one nucleic acid polymerase, and a nucleotide mixture, wherein the nucleotide mixture comprises at least one terminator nucleotide that terminates nucleic acid replication of the polymerase to produce a genomic DNA library and dUTP; and d. isolating cDNA from the genomic DNA library; e. Sequencing the cDNA library and the genomic DNA library.

2. The method of claim 1, wherein the nucleotide mixture comprises at least two of dATP, dCTP, dGTP and dTTP.

3. The method of claim 1, wherein the nucleotide mixture comprises dATP, dCTP, dGTP, dTTP and dUTP.

4. The method of claim 2, wherein the ratio of dTTP to dUTP is 50:1 to 1:

20.

5. The method of claim 1, wherein at least some of the polynucleotides in the cDNA library comprise barcodes.

6. The method of claim 1, wherein at least some of the polynucleotides in the cDNA library comprise a tag.

7. The method of claim 1, wherein at least 90% of the polynucleotides of the cDNA library comprise a 5' to 3' bias of 0.8 to 1.

2.

8. The method of claim 1, wherein separating comprises capturing at least some of the cDNA library by binding to a label.

9. The method of claim 1, wherein the cDNA is at least 90% free of the genomic DNA library after purification.

10. The method of claim 1, wherein the cDNA is at least 95% free of the genomic DNA library after purification.

11. The method of claim 1, wherein isolating comprises contacting the cDNA library with an enzyme configured to digest or remove the genomic DNA library.

12. The method of claim 11, wherein isolating comprises contacting the cDNA library with a DNA glycosylase.

13. The method of claim 12, wherein isolating comprises contacting the cDNA library with DNA glycosylase-lyase endonuclease VIII.

14. The method of claim 11, wherein contacting the cDNA library with the enzyme occurs on a solid support.

15. The method of claim 1, wherein the method further comprises adding adaptors to one or more of the cDNA library and the genomic DNA library.

16. The method of claim 15, wherein adding an adaptor comprises contacting with a ligase.

17. The method of claim 15, wherein adding the adaptor comprises contacting with a transposase or a complex thereof.

18. The method of claim 17, wherein the transposase or complex thereof comprises Tn5.

19. The method of claim 15, wherein adding an adaptor comprises contacting with a polymerase and one or more primers.

20. The method of claim 1, wherein the genomic DNA library is amplified prior to sequencing.

21. The method of claim 1, wherein the genomic DNA library is amplified using a uracil-tolerant polymerase.

22. The method of claim 21, wherein the uracil-tolerant polymerase comprises DNA polymerases ε and δ from Saccharomyces cerevisiae, and Escherichia coli DNA polymerase III, PolA-type polymerase, KAPA HiFi Uracil+DNA polymerase (Q5U), KOD Multi&EpiDNA polymerase, Taq, Taq2000, FailSafe enzyme or PhusionU.

23. The method of claim 1, wherein separation comprises nuclear lysis / denaturation.

24. The method of claim 1, wherein the cDNA library comprises 50-300 ng of DNA.

25. The method of claim 1, wherein the cDNA library comprises polynucleotides comprising a cell barcode or a sample barcode.

26. The method of claim 1, wherein the cDNA library comprises polynucleotides corresponding to at least 2000 genes.

27. The method of claim 1, wherein amplifying the cDNA library comprises contacting with labeled primers.

28. The method of claim 1, wherein the genomic DNA library comprises 0.5-2.5 ng of DNA.

29. The method of claim 1, wherein the single cell comprises a NA12878 control.

30. The method of claim 1, wherein the single cell is a primary cell.

31. The method of claim 1, wherein the single cell is derived from liver, skin, kidney, blood, or lung.

32. The method of claim 1, wherein the single cell is a cancer cell, a neuron, a glial cell, or a fetal cell.

33. The method of claim 1, wherein the genomic DNA library is generated by 2-15 amplification cycles.

34. The method of claim 1, wherein the genomic DNA library comprises polynucleotides of 250-1500 bases in length.

35. The method of claim 1, wherein the genomic DNA library comprises 70%-95% allelic balance.

36. The method of claim 1, wherein the genomic DNA library comprises a SNV sensitivity of at least 0.85%.

37. The method of claim 1, wherein the genomic DNA library comprises a SNV accuracy of at least 0.95%.

38. The method of claim 1, wherein the method further comprises analyzing one or more expressed proteins in the single cell.

39. The method of claim 1, wherein the method further comprises analyzing one or more genomic methylation patterns from the single cell.

40. The method of claim 1, wherein at least 98% of the polynucleotides comprise terminator nucleotides.

41. The method of claim 1, wherein the terminator nucleotide is attached to the 3' end of the at least some of the polynucleotides.

42. The method of claim 1, wherein the terminator comprises an irreversible terminator.

43. The method of claim 1, wherein the irreversible terminator is resistant to exonuclease activity.

44. The method of claim 1, wherein the irreversible terminator is resistant to 3'-5' exonuclease activity.

45. The method of claim 1, wherein the terminator nucleotide comprises adenine, guanine, cystine, or thymine.

46. ​​The method of claim 1, wherein the terminator nucleotide does not comprise uridine.

47. The method of claim 1, wherein the terminator nucleotide is selected from nucleotides with alpha group modification, C3 spacer nucleotides, locked nucleic acids (LNA), inverted nucleic acids, 2' fluoro nucleotides, 3' phosphorylated nucleotides, 2'-O-methyl modified nucleotides and trans nucleic acids.

48. The method of claim 47, wherein the nucleotide modified with an alpha group is an alpha-thiodideoxynucleotide.

49. The method of claim 1, wherein the terminator nucleotide comprises a modification of the r group of the 3' carbon of the deoxyribose sugar.

50. The method of claim 1, wherein the terminator nucleotide is selected from a 3' blocked reversible terminator containing a nucleotide, a 3' unblocked reversible terminator containing a nucleotide, a terminator containing a T modification of a deoxynucleotide, a terminator containing a modification of a nitrogenous base of a deoxynucleotide, and combinations thereof.

51. The method of claim 1, wherein the terminator nucleotide is selected from dideoxy nucleotides, reversed dideoxy nucleotides, 3' biotinylated nucleotides, 3' amino nucleotides, 3'-phosphorylated nucleotides, 3'-O-methyl nucleotides, 3' carbon spacer nucleotides including 3' C3 spacer nucleotides, 3' C18 nucleotides, 3' hexanediol spacer nucleotides, acyclic nucleotides, and combinations thereof.

52. The method of claim 1, wherein the nucleic acid polymerase is bacteriophage phi29 polymerase, genetically modified phi29 (F29) DNA polymerase, Klenow fragment of DNA polymerase I, bacteriophage M2 DNA polymerase, bacteriophage phiPRDl DNA polymerase, Bst DNA polymerase, Bst large fragment DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, Bsu DNA polymerase, VentR DNA polymerase, VentR (exo-) DNA polymerase, Deep Vent DNA polymerase, DeepVent (exo-) DNA polymerase, IsoPol DNA polymerase, DNA polymerase I, Therminator DNA polymerase, T5 DNA polymerase, Sequenase, T7 DNA polymerase, T7-Sequenase or T4 DNA polymerase.

53. The method of claim 1, wherein the nucleic acid polymerase comprises a 3'->5' exonuclease activity and the at least one terminator nucleotide inhibits the 3'->5' exonuclease activity.

54. The method of claim 1, wherein the nucleic acid polymerase does not comprise 3'->5' exonuclease activity.

55. The method of claim 1, wherein the polymerase is Bst DNA polymerase, exo(-)Bst polymerase, exo(-)Bca DNA polymerase, Bsu DNA polymerase, VentR (exo-)DNA polymerase, Deep Vent (exo-)DNA polymerase, Klenow fragment (exo-)DNA polymerase or Therminator DNA polymerase.

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