Single-cell high-content crispr screening for functional characterization of genetic regulators of cell fate

Abstract

Provided herein is a single-cell multi-omics screening platform for the investigation of the effects of large-scale genetic perturbations on the continuum of gene regulation, from chromatin remodeling to nascent transcription and ultimately to steady-state transcriptomic phenotypes, at single-cell resolution.

Description

Attorney Docket No.046531-5039-00WOSINGLE-CELL HIGH-CONTENT CRISPR SCREENING FOR FUNCTIONAL CHARACTERIZATION OF GENETIC REGULATORS OF CELL FATECROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 789,011, filed April 15, 2025, which is hereby incorporated by reference herein in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under Grant No. HG011014 awarded by the National Institutes of Health (NIH). The government has certain rights in the invention.REFERENCE TO SEQUENCE LISTING SUBMITTED ELECTRONICALLY

[0003] This application contains a Sequence Listing, which is submitted electronically via EFS-Web as an XML Document formatted sequence listing with a file name “046531-5039-00WO_SequenceListing.xml,” having a creation date of April 13, 2026, and having a size of 1,232,299 bytes. The sequence listing submitted via EFS-Web is part of the specification and is herein incorporated by reference in its entirety.BACKGROUND OF THE INVENTION

[0004] Dynamic changes in cell state play a pivotal role in diverse biological processes. In pathological contexts, aberrant cell state plasticity contributes to disease progression, particular in cancer, where cell state transitions can influence progression, therapeutic response, and prognosis (Davies et al. ,2023, Cancer Discov 13, 1771-1788). To identify genetic regulators that control cellular phenotypes, massively parallel perturbation screens (Coelho et al., 2024, Nat Genet 56, 2479-2492; Szlachta et al., 2018, Nature Communications 9, 1-13) have been widely employed. However, the broad biological functions of candidate genes, spanning epigenetic and transcriptional regulation as well as signal transduction (Parnaset al., 2015, Cell 162, 675-686), can confound identification of the core regulatory programs that drive phenotypic changes. Mapping these programs is critical for advancing understanding of cell biology and for developing therapeutic strategies to manipulate cell behavior.

[0005] CRISPR perturbations with single-cell high-dimensional readouts (e.g., Perturb-seq (Dixit et al., 2016, Cell 167, 1853-1866. el7)) offer powerful means to dissect genetic control of cell states at unprecedented resolution. Yet, most current approaches are limited to a single modality readout, typically gene expression (Dixit et al., 2016, Cell 167, 1853— 1866. el7; Schraivogel et al., 2020, Nature Methods 17, 629-635; Replogle et al., 2022, Cell 185, 2559-2575. e28) or chromatin accessibility (Pierce et al., 2021, Nat Commun 12, 2969; Liscovitch et al., 2021, Nat. Biotechnol. 39, 1270-1277), hindering comprehensive interpretation of perturbation effects. Although recent efforts have expanded single-cell CRISPR screens to incorporate additional modalities, such as both steady-state transcriptome and chromatin accessibility profiling (Yan et al., 2025, Nat Biotechnol, 43, 1628-1634;Metzner et al., 2025, Cell Syst 16, 101161; Martin-Rufino et al., 2025, Science 388, 52-59), these approaches are built on commercial droplet-based platforms with limited throughput, constraining the scale of perturbations. Moreover, their reliance on single-nucleus profiling hinders a comprehensive examination of transcriptomic dynamics and subtle regulatory processes, such as transcription, RNA processing, and transport of mature RNA to the cytoplasm.

[0006] There is thus a need in the art for improved methods for single-cell RNA sequencing. The present invention addresses this unmet need in the art.SUMMARY OF THE INVENTION

[0007] Provided herein is a single-cell multi-omics screening platform for the investigation of the effects of large-scale genetic perturbations on the continuum of gene regulation, from chromatin remodeling to nascent transcription and ultimately to steady-state transcriptomic phenotypes, at single-cell resolution.

[0008] In one embodiment, the invention relates to a method for preparing a sequencing library comprising nucleic acids from a plurality of single nuclei or cells, the method comprising:a) providing a plurality of nuclei or cells;b) transducing the nuclei or cells with a pooled sgRNA library;c) contacting the cells with 5-Ethynyl Uridine (5-EU);d) harvesting, fixing, and permeabilizing the nuclei or cells;e) tagging open chromatin regions using a Tn5-mediated transposition assay for transposase-accessible chromatin (ATAC);f) performing reverse transcription to generate cDNA fori) unlabeled pre-existing transcripts;ii) 5-EU labeled nascent transcripts; andiii) sgRNA transcriptsg) performing two rounds of split-and-pool index ligation;h) conjugating biotin to 5-EU-labeled nucleic acid molecules;i) generating single cell sequencing libraries for each ofi') nascent transcripts;ii') sgRNA transcripts;iii') pre-existing RNA transcripts; andiv') ATAC,wherein each of i') to iv1) in each single nuclei or cell is linked to a unique combination of DNA barcodes, with the barcodes added in the split pool labeling of step g) being cell or nuclei specific and a third barcode added in step i) which is library-type specific;j) sequencing the multiple libraries; andk) performing computational analysis of sequencing reads to identify transcriptional dynamics in a single cell.

[0009] In one embodiment, step h) further comprises using click chemistry-based in situ ligation to attach an azide-containing fluorophore to the nascent transcripts.

[0010] In one embodiment, step d) further comprises performing fluorescence-activated cell sorting (FACS) to enrich 5-EU labeled cells or nuclei.

[0011] In one embodiment, the method includes a step of FACS to enrich the 5-EU labeled cells or nuclei following step f).

[0012] In one embodiment, a first round of FACS is performed in step d) and a second round of FACS is performed following step f) to enrich the EdU+ cells.

[0013] In one embodiment, the reverse transcription after ATAC is performed at 50°C.

[0014] In one embodiment, the method of transducing the nuclei or cells with a pooled sgRNA library is performed by contacting the cells or nuclei with a vector comprising two sgRNA expression cassettes (sgRNA- 1 and sgRNA-2), driven by the hU6 and hHl promoters respectively, incorporated into the 3' long terminal repeat (LTR) of a lentiviral construct, and further wherein the vector comprises a sgRNA scaffold containing a capture sequence downstream of sgRNA- 1.

[0015] In one embodiment, biotin is conjugated to 5-EU using click chemistry.

[0016] In one embodiment, the reverse transcription includes contacting the cells or nuclei with a combination of primers including:a) sgRNA capture RT primers (5'- / 5Phos / CGCGCTGCATACTTGTG[8bp UMI]CTAGGACCGGCCTTAAAGC-3') (SEQ ID NO: 1);b) shortdT RT primer (5'- / 5Phos / CGCGCTGCATACTTGTG[8bp UMI]TTTTTTTTTTTTTTT-3') (SEQ ID NO:2); andc) randomN RT primers (5'- / 5Phos / CGCGCTGCATACTTGTG[8bp UMI]NNNNNN-3') (SEQ ID NO:3).

[0017] In one embodiment, the reverse transcription (RT) reaction is performed in PEG NaCl RT buffer.

[0018] In one embodiment, the method further comprises determining the effect of sgRNA on the temporal dynamics of transcription by integrating the temporal dynamics of all three molecular layers (pre-existing transcription, chromatin status and nascent transcription) following sgRNA administration.

[0019] In one embodiment, the method is used for determining the effects of large-scale genetic perturbations on the continuum of gene regulation, from chromatin remodeling to nascent transcription and ultimately to steady-state transcriptomic phenotypes, at single-cell resolution.BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The following detailed description of embodiments of the invention will be better understood when read in conjunction with the appended drawings. It should be understood that the invention is not limited to the precise arrangements and instrumentalities of the embodiments shown in the drawings.

[0021] Figure 1 A through Figure IK depict experimental results demonstrating that PerturbFate enables simultaneous capture of nascent and pre-existing transcriptomes, chromatin accessibility, and sgRNA identities from the same single cell. Figure 1A depicts a schematic of the PerturbFate library preparation workflow for co-capturing nascent and preexisting transcriptomes, chromatin accessibility, and sgRNA identities in individual cells. Figure IB depicts a box plot showing the percentage of labeled nascent gene count UMIs detected in 5-EU-labeled and unlabeled cells (HEK293T unlabeled n = 867, HEK293T labeled u = 651, NIH / 3T3 unlabeled n = 289, NIH / 3T3 labeled n = 206). Figure 1C depicts a box plot showing the exonic proportion of reads from single-cell labeled nascent RNA and unlabeled pre-existing RNA (HEK293T n = 1,246, NIH / 3T3 n = 395). Figure ID depicts box plots showing the top enriched Gene Ontology (GO) terms in genes with significantly higher (upper panel) or lower (lower panel) nascent counts, as identified by single-cell differential expression analysis comparing nascent and pre-existing transcriptomes. Figure IE depicts a line plot showing the normalized ATAC read coverage enrichment around TSS. For HEK293T cells profiled by PerturbFate, ATAC reads were aggregated in 100 bp bins within a 4 kb window and normalized by the mean read coverage in the first and last 100 bp bins. Figure IE depicts a 3D dot plot showing the consistency of species assignment across single-cell molecular modalities. Human HEK293T cells and mouse NIH / 3T3 cells were mixed for PerturbFate profiling, and sequencing reads from each layer were aligned to a human- mouse hybrid reference genome to examine the purity of single-cell data. Figure 1G depicts genome tracks displaying signal densities of different molecular modalities in HEK293T cells profiled by PerturbFate or by single-modality methods in a representative genomic region. Signals were normalized by counts per million (CPM) and visualized using the WashU Epigenome Browser (Li et al., 2022, Nucleic acids research, 5O(W1), W774-W781). Figure 1H depicts a schematic of the optimized PerturbFate dual-sgRNA expression construct. An mRNA encoding anti-Puromycin and EGFP tags, along with sgRNA sequences at the 3' end, is driven by the Pol II promoter; a pair of sgRNAs is separately driven by the hU6 and hHl promoters. Figure 1H depicts a box plot showing single-cell sgRNA UMIs obtained from human (HEK293T, n = 1,219) and mouse (NIH / 3T3, n = 827) cells. Figure 1J depicts genome tracks showing aggregated ATAC chromatin accessibility around the DROSHA TSS in NTC (non-targeting control) cells and in DROSHA -knockdown cells profiled by PerturbFate (normalized byCPM). Figure IK depicts bar plots showing normalized pseudobulk expression levels of DROSHA in NTC and DROSHA -knockdown cells. Single-cell nascent and pre-existing transcriptome counts were combined by perturbation and normalized by CPM; the expression levels of DROSHA in each layer were scaled to the expression level in NTC cells.

[0022] Figure 2A through Figure 2Q depict schematics of PerturbFate vector library construction, sequencing library preparation, and optimizations. Figure 2A depicts a sequencing library preparation schematic. ATAC / pre-existing RNA and nascent RNA fractions were processed separately. Multiplex PCR added matched cell barcodes and inner i7 barcodes, followed by sgRNA enrichment PCR. Figure 2B depicts post-sequencing cell barcode matching across tri-omics modalities. Figure 2C depicts ATAC and nascent RNA quality under different reaction temperatures. Bulk tagmentation was performed at 32°C or 37°C and reverse transcription at 50°C or 55°C. N cells in conditions from left to right = 602, 580, 460, 676. Figure 2D depicts nonspecific nascent read recovery under different streptavidin wash conditions (N cells from 50 °C wash = 1,780; on-ice wash = 1,705; Wilcoxon test, two-sided p-values). Figure 2E through Figure 2G depict the effects of fixation (PCR well n = 4 for each condition), Read2 Tn5 concentration (N cells in Tn5 conditions from left to right = 1,010, 1,856, 1,113), and reverse transcription chemistry on multimodal recovery and transcriptomic UMI (N cell in groups from top to bottom = 857, 1,387, 741, 1,025; two-sample t-tests, FDR-corrected two-sided p values). Figure 2H depicts species assignment consistency across multimodal single cells from mixed HEK293T and NIH / 3T3 samples. Figure 21 depicts data demonstrating that PerturbFate -AT AC Tn5 oligo structure; SNARE-seq2 and in-house adapters were tested. Figure 2J depicts an ATAC library gel image from 100,000-cell tagmentation. Three independent experiments were conducted. Figure 2K depicts an exemplary PerturbFate vector library construction. sgRNA oligo pools were amplified, BsmBI-digested, and ligated into the CROP-seq-OPTI backbone. The filler sequence was replaced by the sgRNA scaffold and hHl promoter via PacCI digestion and ligation. Figure 2L through Figure 2N depict bulk screen and dual-omics workflows and barcode matching strategies. Figure 20 and Figure 2P depict the pseudobulk ATAC and RNA concordance between EasySci and PerturbFate dual-omics (Pearson correlation coefficients are shown). Figure 2Q depicts representative genome tracks showing cross modality consistency. Boxes in box plots indicate the median and IQR, with whiskers indicating 1.5x IQR.

[0023] Figure 3A through Figure 3K depict data demonstrating the compatibility of PerturbFate with diverse designs. Figure 3A depicts a schematic of the single-sgRNA vector design used in the PerturbFate proof-of-concept experiment. Figure 3B depicts a dot plot with trend line showing cell loss during cross-modality matching and doublet removal. Figure 3C depicts box plots showing sgRNA distributions across cells assigned to different sgRNAs (N cells = 1,576, 880, 1,554, 1,005 for sgRNAl-4 singlets). Figure 3D depicts UMAPs computed from single-cell RNA (top) and single-cell ATAC (bottom) data, with cells colored by Leiden cluster identity. Figure 3E depicts a heatmap showing concordance of cell line identities assigned by RNA- and ATAC based Leiden clustering. Figure 3F depicts a heatmap showing concordance between RNA-based Leiden clustering and sgRNA assignments. Figure 3G depicts a schematic of the dual-sgRNA co-capture PerturbFate proof-of-concept experiment. Figure 3H depicts box plots showing sgRNA distributions across different sgRNA capture reverse-transcription conditions (N cells = 318, 392, 813 for each design). Figure 3K depicts a table summarizing sgRNA combinations and their corresponding interpretations. Boxes in box plots indicate the median and IQR, with whiskers indicating 1.5x IQR.

[0024] Figure 4A through Figure 4H depict data demonstrating the selection and evaluation of the sgRNA library for the PerturbFate screen. Figure 4A depicts a network graph of candidate Vemurafenib-resistant genes with cluster annotations based on protein-protein interactions. Each node represents a protein encoded by a candidate gene, and edges represent interactions; node size reflects the number of detected interactions. Clustering was performed using MCL clustering in the STRING database, and top functional annotations were extracted from STRING. Figure 4B depicts a histogram showing expression distributions of candidate Vemurafenib-resistant genes in wild-type A375 cells. Cells were profiled by single-cell RNA-seq, gene counts were aggregated across cells, normalized by total gene counts, and multiplied by le6. Genes with Log2(CPM) < 1 were excluded. Figure 4C depicts a dot plot with error bars showing melanoma cell growth inhibition following Vemurafenib treatment at different concentrations. A375 cells were seeded in 6-well plates and treated with DMSO, 500 nM, 1000 nM, or 2000 nM Vemurafenib. Cell numbers were measured after six days (n = 2 per condition), and relative cell numbers were calculated against regular media controls. Figure 4D depicts a representative flow cytometry gating strategy for selecting dCas9-BFP-KRAB-expressing and sgRNA-expressing (GFP+) cells. Figure 4E depicts a density plot showingdistributions of sgRNA abundance changes at Day 6 and Day 14 of the CRISPR screen.Normalized sgRNA abundance represents relative sgRNA pair abundance compared to NTCs. The Vemurafenib / DMSO ratio reflects perturbation effects on cell growth under drug treatment. Figure 4F depicts dot plots showing reproducibility of genetic perturbation effects across bulk CRISPR screen replicates at Day 14 (upper) and Day 6 (lower). Figure 4G and Figure 4H depict dot plots showing enrichment of top sgRNAs by comparing sgRNA abundance between Vemurafenib and DMSO conditions (Figure 4G) or the plasmid library (Figure 4H). Perturbations were ranked by their Day 14 enrichment, while Day 6 enrichment is shown in gray to indicate relatively unchanged sgRNA representation.

[0025] Figure 5 depicts a schematic of the experimental design. Published bulk CRISPR screens in A375 melanoma cells were used to nominate candidate genes. After evaluating gene expression levels and sgRNA performance, a dual-sgRNA library was constructed and introduced into cells by low MOI transduction. Cells were subsequently treated with either DMSO or IpM vemurafenib. Bulk and single-cell screening libraries were then prepared in parallel.

[0026] Figure 6A through Figure 6T depict data demonstrating the Quality control and benchmarking of the PerturbFate library. Figure 6A depicts the distribution of sgRNA numbers per cell. Figure 6B depicts a comparison of sgRNA-assigned cell populations across computational methods. Figure 6C depicts a schematic of lentiviral barcode swapping leading to sgRNA pair decoupling. Figure 6D depicts the distribution of swapped sgRNA pairs and correct sgRNA pair percentages assessed by bulk CRISPR sequencing. Figure 6E depicts the effect of cell number on pseudobulk stability for steady state expression, nascent expression, and AT AC peak counts; dashed line indicates the computationally identified elbow point. N repeated downsampling runs per cell-number level = 50. Figure 6F depicts the distribution of cell numbers per perturbation in PerturbFate, with a 50-cell filtering threshold. Figure 6G depicts the fold changes in steady-state expression of target genes relative to NTCs. Figure 6H depicts the consistency of target gene knockdown across modalities. For ATAC, promoter regions annotated in hg38 were evaluated. Figure 61 depicts the cumulative distributions of knockdown expression fold changes from PerturbFate (dual-sgRNA) and Perturb Sci-Kinetics (single-sgRNA). A two-sided p-value from a KS test is reported. Figure 6J through Figure 6L depict single-cell ATAC counts and TSS enrichment (Figure 61), transcriptomic UMI counts(Figure 6K), and gene numbers (Figure 6L) across platforms and conditions (N cells in Dual-omics DMSO condition = 25,671, Tri-omics DMSO condition = 60,905, Dual-omics Vem condition = 21,233, Tri-omics Vem condition = 49,253). Figure 6M through Figure 6P depict a comparison of per-cell UMIs, genes, ATAC fragments, and TSS enrichment across combinatorial-indexing RNA and ATAC platforms. N cells (left to right): 8,515, 6,093, 922, 43,406 for RNA benchmarking; 4,955, 2,799, 5,835, 46,904 for ATAC benchmarking. Figure 6Q depicts the percentage of sgRNAs detection rate across platforms. Figure 6R depicts the er-cell library preparation cost comparison across platforms. Figure 6S and Figure 6T depicts the numbers of differentially-expressed genes (Figure 6S) and differentially accessible peaks (Figure 6T). Pearson correlation coefficients are reported. Error bands reflect the 95% confidence interval of the fitted regression line. Boxes in box plots indicate the median and IQR, with whiskers indicating 1.5x IQR.

[0027] Figure 7A through Figure 7L depict data demonstrating that genetic perturbations induce extensive cell state transitions through epigenetic and transcriptional remodeling. Figure 7A depicts a weighted nearest neighbor (WNN)-based UMAP integrating RNA and ATAC modalities of pseudobulk perturbations. Figure 7B depicts a WNN UMAP of pseudobulk perturbations colored by melanoma phenotypic signature scores. Figure 7C depicts histograms benchmarking reference Dual-omics perturbed cells and projected Tri-omics cells from the DMSO condition along the trajectory. Figure 7D depicts a heatmap showing nascent gene expression along the trajectory, ordered identically to the steady-state heatmap in Figure 8F. Figure 7E depicts a heatmap of steady-state expression of TF shown in Figure 8H. Figure 7F depicts a schematic of temporal gene regulatory network reconstruction. TF-CRE-gene links were inferred by associating nascent expression with nearby ATAC peaks containing TF motifs, with temporal constraints applied to select activating links following state transitions. Figure 7G depicts bar plots showing significant enrichments of putative TF target genes in the regulons that were identified (top), and their overrepresentation over background based on shuffled genes (bottom). Figure 7H depicts representative genome tracks showing CRE-gene linkages for RAB38 with SOX 10 and MEF2C motifs during the Neural crest-like to Melanocytic transition (left), and CALM2 with AP-1 and TEAD motifs during the Neural crest-like to Undifferentiated- 1 transition (right). Empirical one-sided p-values over randomized gene-peak association backgrounds are reported. Figure 71 depicts examplecontingency tables showing significant TF co-regulation of state-specific genes, including SOX10-MEF2C in Melanocytic state and JUN-TEAD1 and KLF5-TCF4 in Undifferentiated states. Odds ratios and two-sided p-values of Fisher’s exact tests are reported. Figure 7J depicts a schematic of statistical examination to quantify perturbation effects on state transitions, including K-S tests for cell-order distributions and proportion tests for state enrichment. Figure 7K depicts a volcano plot showing differences in mean cell order between perturbed and NTC populations with FDR-corrected two-sided p-values of K-S tests. Figure 7L depicts a dot heatmap showing increases in multimodal regulon scores for representative perturbations relative to NTCs, with permutation-based FDR-corrected empirical one-sided p-values reported.

[0028] Figure 8A through Figure 8J depict data demonstrating perturbations of drugresistant candidate genes strongly drive melanoma phenotype switching toward an undifferentiated state. Figure 8A and Figure 8B depict diffusion maps showing heterogeneous cellular states of genetically perturbed A375 cells under DMSO treatment, colored by position along the trajectory (Figure 8A) or annotated cell state (Figure 8B). Arrows in (Figure 8A) indicate directions of transcriptomic state transitions inferred by RNA velocity analysis using nascent and whole-transcriptome data. Figure 8C depicts line plots showing dynamics of multimodal melanoma phenotypic gene program scores along the one-dimensional trajectory. Chromatin accessibility scores (upper) were computed by averaging normalized ATAC counts of peaks associated with nascent expression of program genes, while steady-state expression scores (lower) were calculated by averaging scaled expression values of program genes within each bin. Figure 8D and Figure 8E depict heatmaps with annotation bars showing phenotypic gene program scores and inferred cell states along the trajectory (Figure 8D) and scaled 584 steady-state gene expression dynamics (Figure 8E). Figure 8F depicts bar plots showing representative functional enrichments of state-associated gene programs. Figure 8G depicts box plots showing expression of cell state-specific gene programs in BRAFi or BRAFi+MEKi-sensitive and -resistant tumors from a published patient cohort (Sensitive n=13, Resistant n=34). One-sided Wilcoxon test p-values are shown. Figure 8H depicts a heatmap showing scaled transcription factor motif accessibility along the trajectory. Figure 81 depicts network diagrams of state-specific gene regulatory networks. Nodes represent transcription factor regulators, with node size proportional to the number of target genes. Edges indicate sharedtarget genes co-regulated by transcription factor pairs, with edge thickness corresponding to the number of co-targets. Figure 8J depicts a heatmap showing enrichment of genetic perturbations across melanoma cell states; tiles with black borders indicate statistically significant enrichment. Statistical significance was assessed using proportion tests, and two-sided p-values were FDR-corrected. Only perturbations with at least one significantly enriched state are displayed. Boxes in box plots indicate the median and IQR, with whiskers indicating 1.5x IQR.

[0029] Figure 9A through Figure 90 depict data demonstrating the identification of the gene regulatory programs driving drug resistance in melanoma cells. Figure 9A through Figure 9C depict Diffusion maps of perturbed A375 cells under DMSO or Vemurafenib, colored by trajectory order (Figure 9A), non-targeting control (NTC) density (Figure 9B), or treatment condition (Figure 9C). Arrows indicate state transitions inferred by nascent transcriptomebased RNA velocity. Figure 9D depicts data demonstrating that the steady-state melanoma phenotype program scores along the trajectory (upper) and distributions of representative perturbations (lower). Figure 9E depicts the relationship between perturbation-induced cell state shifts and growth advantages from bulk CRISPR screening (Pearson correlation two-sided p-value). Figure 9F through Figure 9H depict heatmaps showing phenotype program scores (Figure 9F), scaled gene expression dynamics (Figure 9G), and motif accessibility (Figure 9H) along the trajectory. Figure 91 depicts the Vemuraf enib-resistant gene regulatory network; node size reflects the number of TF targets, and edge thickness indicates shared targets. Figure 9 J depicts a correlation heatmap of multimodal regulon scores among Vem-resistant regulons under Vemurafenib. Figure 9K depicts the top 10 Vem-resistant regulons ranked by the number of perturbations with significantly increased scores relative to NTCs. Figure 9L depicts correlations between single-cell oncogenic signatures and trajectory order, with representative TF motifs enriched in YAP ChlP-seq peaks (FDR-corrected two-sided p-values). Figure 9M depicts cumulative distributions of perturbation growth advantages stratified by relative multimodal regulon score changes of perturbations to NTC (K-S test), and two-sided p-values are reported. Figure 9N depicts Kaplan-Meier overall survival analysis of TCGA SKCM patients stratified by key TF expression (log-rank test). Two-sided p-values are reported.Figure 90 depicts the validation of combined inhibition of candidate TFs on anti -Vemurafenib resistance. The upper panel illustrates the experimental design, in which additional genetic and / or chemical perturbations were conducted in parallel with the PerturbFate bulk CRISPRscreen. Box plots show reduced growth advantage of resistant perturbations (n = 24) under combinatorial perturbations, calculated relative to NTCs. FDR corrected two-sided p-values from one-sample Wilcoxon tests are used to assess global deviation from zero. Boxes in box plots indicate the median and IQR, with whiskers indicating 1.5x IQR.

[0030] Figure 10A through Figure ION depict data demonstrating the global perturbation effects and cell state transitions with epigenetic remodeling in response to Vemurafenib. Figure 10A depicts a schematic of transcriptomic state transitions between perturbed and NTC cells under DMSO and Vemurafenib, with a volcano plot showing differences in mean cell order under Vemurafenib and corresponding FDR-corrected K-S test two-sided p-values. Figure 10B and Figure 10C depict schematics and scatter plots showing perturbation-induced changes in cell cycle activity (S / G2M fraction) and relative dedifferentiation shifts under DMSO and Vemurafenib conditions. Figure 10D and Figure 10E depicts the distribution of NTC cells and melanoma phenotypic gene program scores along the Vemurafenib-treated trajectory. The trajectory was inferred from combined DMSO- and Vemurafenib-treated cells, with Vemurafenib treated cells extracted. Cell-order cutoffs define Melanocytic, Neural crest-like, and Undifferentiated states; trajectory was binned to ensure equal cell numbers per bin. Figure 10F depicts a heatmap showing global chromatin accessibility correlations between trajectory bins based on pseudobulk ATAC profiles normalized to le6. Figure 10G and Figure 10H depict significantly differentially accessible peaks and enriched TF motifs defining Vem-sensitive melanocytic (clusters 1378 1-3) and Vem-resistant undifferentiated (clusters 9-10) states. Motif enrichment was performed using HOMER, and one-sided p-values and FDR-corrected q-values from cumulative binomial tests against background are reported. Figure 101 and Figure 10J depict nascent transcription, promoter accessibility, and promoter differentially accessible peak dynamics along the trajectory, identifying a Transiently reactivating state characterized by promoter reopening. Figure 10K depicts the functional enrichment of genes associated with DA peaks across Melanocytic and Reactivating states; FDR corrected two-sided p-values from hypergeometric tests are reported. Figure 10L depicts a bar plot showing top significant enrichment of TF motifs in associated peaks of cluster 4 genes compared to cluster 1 genes shown in Figure 9G. Figure 10M and Figure 10N depict the chromVAR motif accessibility scores of FOSL1 andnascent expression trends of FOSL1 -regulated versus non regulated genes highlight a role for FOSL1 in melanoma dedifferentiation and drug resistance.

[0031] Figure 11 A through Figure 1 IM depict data demonstrating Characterization and validations of state-transiting gene regulatory networks under the Vemurafenib treatment. Figure 11A depicts a Venn diagram showing TF overlap between Vem-sensitive / Vem-resistant GRNs. Figure 1 IB depicts dot plots showing the fraction of Vem-sensitive GRN target genes under the same TF in the Vem-resistant GRN (FDR-corrected two-sided p-values; Fisher’s exact tests). Figure 11C depicts box plots comparing N target genes / regulon and the shared targets percentages between two GRNs. Shared TF regulons n = 44 (Two-sided p-values; two-sample wilcoxon tests). Reduced regulon size, weaker regulon collaboration, and strong target overlap support the interpretation that the Vem-sensitive GRN represents a partially activated sub-network of the Vem-resistant GRN. Figure 1 ID depicts a heatmap showing z scored multimodal regulon scores across bins. Figure HE depicts a representative genome track showing CRE- RREB gene links. Empirical one-sided p-values over randomized gene-peak association backgrounds are reported. Figure 1 IF depicts bar plots showing co-dependencies among melanoma drug resistance genes from DepMap within top 100 co-dependent genes per query. Figure 11G depicts a differential expression analysis workflow and volcano plot comparing baseline drug-sensitive and drug resistant patient tumors, revealing limited shared differentially expressed genes. FDR-corrected two-sided p-values from DESeq2 Wald tests under negative binomial models are reported. Figure 11H depicts a dot plot showing activation of Vem-resistant TF regulons in drug-resistant tumors (One-sided p-values; Wilcoxon tests on patient sample TF program scores). Figure 1 II depicts a histogram showing background expectations from randomized gene regulons. An empirical p-value over background activation from randomized gene programs is reported. Figure 11 J depicts a bar plot of top eight positive TF regulons in the penalized logistic regression model classifying patient tumor drug sensitivity, k, ROC curve of the TF regulon-based model. Figure 1 IL depicts a workflow for predictive gene feature selection and analytic strategy. Figure 1 IM depicts a dot plot showing enrichment of predictive genes within Vem-resistant TF regulons. One-sided p-values of Fisher’s exact tests are reported. Boxes in box plots indicate the median and IQR, with whiskers indicating 1.5x IQR.

[0032] Figure 12A through Figure 12Q depict data demonstrating perturbations of distinct Mediator Complex components drive drug resistance through divergent mechanisms. Figure 12A depicts the structure of the human Mediator Complex highlighting related components. Figure 12B depicts the relative dedifferentiation of Mediator perturbations in DMSO / Vemurafenib. Figure 12C depicts the relative global nascent transcription changes (FDR-corrected two-sided p-values; two-sample Wilcoxon tests). Figure 12D depicts the transcriptomic and motif accessibility similarity across Mediator perturbations. e,f, Relative changes of phenotypic gene signatures (Figure 12E) and TF motifs enrichment / depletion (Figure 12F); directionality and FDR-corrected two-sided hypergeometric p-values indicated. Figure 12G depicts the ATAC coverage at YAP- Core Mediator co-regulatory regions under DMSO. Figure 12H depicts data demonstrating that MED1 ChlP-seq signals at YAP-Core Mediator co-target genes (gene n = 295) in NTC and MED12-K0 cells (two-sided p-value of paired Wilcoxon test). Error bars represent mean ± s.d.. Figure 121 depicts the nascent expression of YAP-Core Mediator co-target genes (gene n = 75) under Vemurafenib treatment (one-way-ANOVA with Tukey’s test; FDR-corrected two-sided p-values). Figure 12J depicts the SOXIO target scores in DMSO and Vemurafenib (perturbations n = 120; two-sided p-value of paired Wilcoxon test). Figure 12K depicts the overlap and expression correlation between perturbation- and SOXIO-KD-induced genes. Figure 12L depicts the ATAC signal at MAPKi responsive SOXIO binding regions in Vemurafenib. Figure 12M depicts the nascent expression changes of MAPKi responsive SOXIO target genes in Vemurafenib (gene n = 46; FDR-corrected two-sided p-values; two-sample Wilcoxon tests). Figure 12N depicts the genome tracks showing SOXIO-motif-containing peak accessibility and nascent transcription of GAS7 in Mediator Kinase perturbations. Figure 120 depicts the expression changes relative toNTCs in DMSO / Vemurafenib; gray indicates maximum CPM < 20. Figure 12P depicts the genome tracks showing MED ! binding and accessibility loss at proximal CREs to VEGFC upon YAP / TAZ silencing or SOX10-KD. Figure 12Q depicts the functional validations on VEGFC. AXL expression by HCR flow cytometry on DMSO-treated perturbed A375 cells (two-sided p-value of K-S test), Vemurafenib resistance reduction upon VEGFC-KD (biological replicates n = 3; FDR-corrected two-sided p values; one-sample Wilcoxon tests; Error bars represent mean ± s.d.). Boxes in box plots indicate the median and IQR, with whiskers indicating 1.5x IQR.

[0033] Figure 13 A through Figure 13J depict data demonstrating Perturbation of Mediator Complex components in melanoma involves a convergent Hippo / YAP-mediated program. Figure 13A depicts a Venn diagram showing overlap of genetic perturbations exhibiting strong dedifferentiation shifts relative to NTCs under DMSO and Vemurafenib treatments. Figure 13B and Figure 13C depict UMAP embeddings of pseudobulk steady-state transcriptomes (Figure 13B) and ATAC (Figure 13C) of pseudobulk perturbations in DMSO condition. Mediator Complex perturbations cluster together, whereas Kinase Module-associated perturbations localize near Hippo / YAP inhibitor perturbations, indicating shared response features. Perturbed genes are colored by primary function. Figure 13D depicts a network graph showing co dependencies among strong perturbations identified from the DepMap pan-cancer CRISPR screen. Only the top 100 co-dependent genes per query are shown; edge width reflects correlation strength. Co-dependencies between Mediator Kinase Module genes and Hippo / YAP pathway genes support convergent functionality. e,f, Bar plots showing overlap of upregulated (Figure 13E) or downregulated (Figure 13F) steady-state transcriptome DEGs between Mediator Kinase Module perturbations and associated TF motif enrichments in promoter regions identified by RcisTarget. Shared upregulated DEGs between MED12 and MED19 show enrichment of AP-1 and NF-KB motifs, whereas shared downregulated DEGs show strong SOXIO motif enrichment. Figure 13G depicts a workflow for identifying YAP-Mediator co-regulatory regions and target genes. Published ChlP-seq datasets were used, and GREAT was applied to assign genes and functional annotations. Figure 13H depicts representative genome tracks showing co-occupancy of Core Mediator (MED1) and the Mediator Kinase Module, and derepression of Core Mediator binding upon Kinase Module knockout at YAP-Mediator co-target genes. Figure 131 depicts a schematic and volcano plot comparing cell-order transition distances between NTC and perturbed cells under Vemurafenib treatment, with relative resistance or hypersensitivity quantified by FDR-corrected two-sided Wilcoxon tests. Figure 13 J depicts bar plots showing averaged log2 fold changes of ATAC accessibility at NTC-upregulated and NTC downregulated peaks under Vemurafenib, highlighting attenuated epigenetic remodeling in MED 15- and MED24-perturbed cells.

[0034] Figure 14A through Figure 14E depict data demonstrating MED12 knockdown partially mimics SOXIO loss of function. Figure 14A depicts bar plots showing the topperturbations with the strongest depletion of S0X10 motifs and their statistical significance. Mediator Complex Kinase Module perturbations, particularly MED 12, result in the most significant depletion of SOXIO accessibility. Additionally, the persistent depletion of SOXIO activity following Vemurafenib -induced melanoma differentiation in MED12 knockdown cells strongly suggests impaired SOXIO function. Figure 14B depicts a heatmap showing the correlation of gene expression changes induced by SOXIO knockdown in reference melanoma cell lines and by all candidate drug-resistant gene knockdowns examined in this study.Hierarchical clustering identified high intra-category similarity among Mediator Complex Kinase Module and Hippo signaling YAP inhibitor perturbations. While most perturbations induced transcriptomic changes distinct from SOXIO knockdown, and perturbations driving melanoma dedifferentiation (e.g., Hippo signaling YAP inhibitor perturbations) did not cause complete SOXIO loss of function, MED12 perturbation closely mimicked the SOXIO knockdown phenotype. Figure 14C depicts a volcano plot showing gene expression changes upon SOXIO knockdown across multiple melanoma culture models. Differentially expressed gene criteria: FDR-corrected two-sided p-value < 0.01 (from DESeq2 Wald tests under negative binomial models) and an absolute fold change > 1. VEGFC was identified as a top shared upregulated gene upon SOXIO knockdown across melanoma models. Figure 14D depicts functional enrichment of oncogenic signatures among upregulated and downregulated genes following SOXIO knockdown. Figure 14E depicts a workflow illustrating the identification of genomic regions bound by SOXIO and SOXIO target genes induced by treatment with another MAPK inhibitor (AZD6244) in melanoma. In addition to a subset of housekeeping SOXIO binding sites, MAPK inhibitor treatment strongly induced SOXIO activity. By intersecting the nearest genes to SOXIO peaks with upregulated differentially expressed genes from paired RNA-seq data, direct SOXIO target genes induced upon MAPK inhibitor treatment were identified, including key melanogenesis genes.

[0035] Figure 15A through Figure 15S depict data demonstrating the identification of VEGFC as a mediator of Vemurafenib resistance. Figure 15A and Figure 15B depict heatmaps showing steady-state cytokine expression. Figure 15C depicts a bar plot showing VEGFC expression in RNA-seq of A375 overexpressing YAP mutants. (Biological replicates in each condition n = 3; mean ± s.e.). Figure 15D depicts dot plots showing correlations between VEGFC and Hippo / YAP targets in the TCGA-SKCM cohort (two-sided Pearson correlation p-values). Figure 15E depicts a box plot comparing VEGFC expression in patient-derived melanoma lines within distinct states (N cell lines: not undifferentiated = 6; undifferentiated = 3; FDR-corrected two-sided p-values; two-sample t-tests among targeted genes). Figure 15F depicts a volcano plot showing expression changes in RNA-seq of Latsl / 2-KO mouse uveal melanoma relative to peripheral tissue. Figure 15G depicts a line plot showing perturbed A375 growth (Biological replicates in Vem / VEGFC-KD+Vem conditions n = 3; DMSO condition n = 2; FDR corrected p-values of two-sided t-tests; Error bars in the n=3 group represent mean ± s.d.). Figure 15H depicts images showing VEGFC-KD or control’s growth (Three independent biological replicates). Figure 151 and Figure 15 J depict bar plots showing potential noise in the original VEGFC-KD screen (n = 3). Figure 15K through Figure 15M depict bar plots showing replicate screen following Figure 12Q (n = 2). Figure 15N depicts a box plot showing Undifferentiated scores in melanoma cell lines (N cells from left to right: 5,375, 8,126, 2,877; post-one-way -ANOVA Dunnett tests; FDR-corrected two-sided p-values). Figure 150 and Figure 15P depict bar plots showing cell lines’ growth under perturbations (n = 2). Figure 15Q depicst a bar plot showing relative growth of perturbations-of-interest under treatments. Figure 15R depicts a line plot showing perturbed cells’ survival under IgG / VGX-100 following Vemurafenib treatment (FDR-corrected two-sided p-values of two-sample t-tests; Biological replicates of each condition n = 4; Error bars represent mean ± s.d.). Figure 15S depicts a scatter plot showing normalized sgRNA counts of drug-resistant perturbations (n = 24) across treatments (Biological replicates of each condition n = 3; two-sided t-tests, p = 0.05 cutoff; Error bars represent mean ± s.d.).

[0036] Figure 16 provides representative gel images. In the left image, lanes 2-3 show the sgRNA library prior to gel extraction. In the right image, lanes 2-6 show the nascent transcript library and lanes 7-11 show the pre-existing transcript + ATAC library, each prior to gel extraction. The thick ladder bands correspond to 2.5 kb, 800 bp, and 350 bp, and the interval between bands is 50 bp.

[0037] Figure 17 depicts a representative gel image. Lanes 2-5 show the sgRNA library prior to gel extraction, and lanes 6-9 show the steady-state transcript + ATAC library. The thick ladder bands correspond to 2.5 kb, 800 bp, and 350 bp, and the interval between bands is 50 bp.DETAILED DESCRIPTION

[0038] PerturbFate is a high-throughput, combinatorial -indexing-based single-cell platform that simultaneously profdes chromatin accessibility along with nascent and preexisting, or steady-state transcriptomes in response to multiplexed CRISPR perturbations. PerturbFate enables the investigation of the effects of large-scale genetic perturbations on the continuum of gene regulation, from chromatin remodeling to nascent transcription and ultimately to steady-state transcriptomic phenotypes, at single-cell resolution. The invention includes generating single-cell multi-omic profiles from a population of cells, and the use of the multi-omic profiles for identifying convergent phenotypic states which allows for identification of coordinated epigenetic and gene expression dynamics underlying cell state transitions, thus revealing the intertwined pathways in differentiation, cell fate determination or dedifferentiation.Definitions

[0039] 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 this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described.

[0040] As used herein, each of the following terms has the meaning associated with it in this section.

[0041] The articles “a” and “an” are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.

[0042] “About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20% or ±10%, more preferably ±5%, even more preferably ±1%, and still more preferably ±0.1% from the specified value, as such variations are appropriate to perform the disclosed methods.

[0043] The terms “cells” and “population of cells” are used interchangeably and generally refer to a plurality of cells, i.e., more than one cell. The population may be a purepopulation comprising one cell type. Alternatively, the population may comprise more than one cell type. In the present invention, there is no limit on the number of cell types that a cell population may comprise.

[0044] “Isolated” means altered or removed from the natural state. For example, a nucleic acid or a peptide naturally present in a living organism is not “isolated,” but the same nucleic acid or peptide partially or completely separated from the coexisting materials of its natural state is “isolated.” An isolated nucleic acid or protein can exist in substantially purified form, or can exist in a non-native environment such as, for example, a fixed nuclei.

[0045] The term “polynucleotide” as used herein is defined as a chain of nucleotides. Furthermore, nucleic acids are polymers of nucleotides. Thus, nucleic acids and polynucleotides as used herein are interchangeable. One skilled in the art has the general knowledge that nucleic acids are polynucleotides, which can be hydrolyzed into the monomeric “nucleotides.” The monomeric nucleotides can be hydrolyzed into nucleosides. As used herein polynucleotides include, but are not limited to, all nucleic acid sequences which are obtained by any means available in the art, including, without limitation, recombinant means, i.e., the cloning of nucleic acid sequences from a recombinant library or a cell genome, using ordinary cloning technology and PCR, and the like, and by synthetic means.

[0046] In the context of the present invention, the following abbreviations for the commonly occurring nucleic acid bases are used. “A” refers to adenosine, “C” refers to cytosine, “G” refers to guanosine, “T” refers to thymidine, and “U” refers to uridine.

[0047] Unless otherwise specified, a “nucleotide sequence encoding an amino acid sequence” includes all nucleotide sequences that are degenerate versions of each other and that encode the same amino acid sequence. The phrase nucleotide sequence that encodes a protein or an RNA may also include introns to the extent that the nucleotide sequence encoding the protein may in some version contain an intron(s).

[0048] As used herein, the terms “peptide,” “polypeptide,” and “protein” are used interchangeably, and refer to a compound comprised of amino acid residues covalently linked by peptide bonds. A protein or peptide must contain at least two amino acids, and no limitation is placed on the maximum number of amino acids that can comprise a protein’s or peptide’s sequence. Polypeptides include any peptide or protein comprising two or more amino acids joined to each other by peptide bonds. As used herein, the term refers to both short chains,which also commonly are referred to in the art as peptides, oligopeptides and oligomers, for example, and to longer chains, which generally are referred to in the art as proteins, of which there are many types. “Polypeptides” include, for example, biologically active fragments, substantially homologous polypeptides, oligopeptides, homodimers, heterodimers, variants of polypeptides, modified polypeptides, derivatives, analogs, fusion proteins, among others. The polypeptides include natural peptides, recombinant peptides, synthetic peptides, or a combination thereof.

[0049] As used herein, an “instructional material” includes a publication, a recording, a diagram, or any other medium of expression which can be used to communicate the usefulness of a compound, composition, vector, or delivery system of the invention in the kit for effecting alleviation of the various diseases or disorders recited herein. Optionally, or alternately, the instructional material can describe one or more methods of alleviating the diseases or disorders in a cell or a tissue of a mammal. The instructional material of the kit of the invention can, for example, be affixed to a container which contains the identified compound, composition, vector, or delivery system of the invention or be shipped together with a container which contains the identified compound, composition, vector, or delivery system. Alternatively, the instructional material can be shipped separately from the container with the intention that the instructional material and the compound be used cooperatively by the recipient.

[0050] The term “microarray” refers broadly to both “DNA microarrays” and “DNA chip(s),” and encompasses all art-recognized solid supports, and all art-recognized methods for affixing nucleic acid molecules thereto or for synthesis of nucleic acids thereon.

[0051] Ranges: throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges 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., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.Description

[0052] PerturbFate enables investigation of the effects of genetic perturbations on both chromatin landscape and genome-wide gene expression dynamics within the same cell. In brief, the method involves the following key steps: 1) Pooled sgRNA transduction: cells are transduced with a pooled sgRNA Lentivirus library, generating a heterogenous cell population in which every subpopulation harbors individual gene knockdowns. 2) Nascent transcript labeling: before harvesting cells, 5-Ethynyl Uridine (5-EU) is added to the culture medium to mark newly synthesized transcripts 3) Chromatin Tagging: After harvesting, fixation, and permeabilization, Tn5-mediated transposition assay for transposase-accessible chromatin (ATAC) is carried out in bulk, tagging open chromatin regions. 4) Reverse transcription is performed in bulk to capture both newly synthesized and pre-existing RNA transcripts, including sgRNA sequences. 5) Indexing ligations: cells undergo two rounds of split-and-pool steps for indexing ligation reactions, introducing cell barcode combinations to RNA and ATAC molecules. 6) Click chemistry is performed in bulk on permeabilized post-ligation cells to conjugate biotin to 5-EU-labeled cDNA-RNA hybrids. 7) Streptavidin pull-down: following cell lysis, biotin-conjugated nascent cDNA-RNA hybrids with the first two cell barcodes are isolated using streptavidin beads. 8) Library preparation: After second-strand synthesis of cDNA and Read2 tagmentation, multiplex indexing PCR and sgRNA enrichment PCR are performed sequentially to amplify and separate multi-omics sequencing libraries. 9) Single-cell multimodal assignment: Throughout this workflow, each modality in each individual cell is tagged with a unique combination of DNA barcodes. Computational assignment of sequencing reads and matching of multiple modalities are performed to recover single cells in silico.PerturbFate is a single-cell platform capable of simultaneous capture of comprehensive molecular modalities with CRISPR perturbations, with key advantages of low cost and scalability.

[0053] In one embodiment, the method of the invention can be used to dissect key regulators of transcriptome kinetics. In such an embodiment, a PerturbFate screen can be performed on dCas9 cells transduced with a library of dual-sgRNA vectors, each containing a pair of sgRNAs targeting a gene. In one embodiment, the dual-sgRNA library targets genes involved in a variety of biological processes including mRNA transcription, processing, degradation, and others. In one embodiment, the cloning and lentiviral packaging areperformed in a pooled fashion. In one embodiment, the dCas9 cell line is transfected with the dual-sgRNA vector library at a low multiplicity of infection to ensure most cells received only one dual-sgRNA vector. In some embodiments, cells receiving no dual-sgRNA vector are removed prior to bulk library preparation. In one embodiment, the dCas9 is inducible.

[0054] In some embodiments, the screening method of the invention can be used to uniquely capture multiple layers of information, including, but not limited to gene-specific synthesis and degradation rate in each perturbation, splicing information, the kinetics of genes targeted by CRISPRi, the impact of diverse genetic perturbations on the global dynamics (i.e., synthesis, splicing and degradation) of the transcriptome, and gene-specific synthesis and degradation regulation across all gene perturbations.CRISPR / sgRNA screening

[0055] In some embodiments, the method includes performing sgRNA / CRISPR screening of candidate genes to determine the effect of the perturbation on transcription at the cellular level. In one embodiment, the method of the invention can be used to dissect key regulators of transcription.

[0056] In some embodiments, the screening method of the invention can be used to uniquely capture multiple layers of information, including, but not limited to gene-specific synthesis and degradation rate in each perturbation, splicing information, the impact of diverse genetic perturbations on the global dynamics (i.e., synthesis, splicing and degradation) of the transcriptome, and gene-specific synthesis and degradation regulation across all gene perturbations.

[0057] In some embodiments, the methods of the invention involve the step of contacting a plurality of cells with an sgRNA library. In some embodiments, the sgRNA library comprises at least 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or more than 1000 plasmids for expression of unique sgRNA species.

[0058] In some embodiments, the methods of the invention involve the step of contacting a plurality of cells with an sgRNA library. In some embodiments, the sgRNA library comprises at least 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700,750, 800, 850, 900, 950, 1000, or more than 1000 plasmids for expression of unique sgRNA species.

[0059] In some embodiments, the plurality of cells are contacted with a dual sgRNA library in which each vector comprises two sgRNA expression cassettes (sgRNA- 1 and sgRNA-2), driven by the hU6 and hHl promoters respectively, incorporated into the 3' long terminal repeat (LTR) of a lentiviral construct, and further wherein each vector comprises a sgRNA scaffold containing a capture sequence downstream of sgRNA-1. Exemplary sgRNA-1 and sgRNA-2 paired sgRNA molecules are provided in Table 8. In one embodiment, the invention comprises a vector library wherein each vector comprises an oligonucleotide sequence as set forth in SEQ ID NO:1390 comprising a set of paired sgRNA from Table 8. In one embodiment, the vector library comprises the combination of each set of paired sgRNA as set forth in Table 8.

[0060] In some embodiments, an mRNA encoding anti-Puromycin and EGFP tags, along with sgRNA sequences at the 3' end, is driven by the Pol II promoter; a pair of sgRNAs is separately driven by the hU6 and hHl promoters.

[0061] In some embodiments, the plurality of cells are contacted with the sgRNA library at a concentration of at least about lOOOx coverage / sgRNA. In some embodiments, the plurality of cells are contacted with the sgRNA library at a concentration of at least about 2000x coverage / sgRNA. In some embodiments, the cells are contacted with the sgRNA library such that each cell is transduced with a single sgRNA. In some embodiments, the plasmids of the sgRNA library express a selectable marker (e.g., an antibiotic resistance gene) and transduced cells are selected by contacting the plurality of cells with selection compound (e.g., an antibiotic) for at least one day.

[0062] In some embodiments, the methods of the invention involve the use of a catalytically dead Cas9 protein. In some embodiments, the catalytically dead Cas9 protein is inducible. In one embodiment, the inducible catalytically dead Cas9 protein is dCas9-KRAB-MeCP2 which is inducible in the presence of doxycycline. In some embodiments, expression of the catalytically dead Cas9 protein is induced for at least 1 day by the addition of an induction agent (e.g., doxycycline) to the cell culture media. In some embodiments, the sgRNA library transfected cells are cultured for at least 2, 3, 4, 5, 6, 7, or more than days in the presence of the induction agent for inducing expression of the catalytically dead Cas9 protein.

[0063] In some embodiments, the sgRNA library transfected cells are cultured in media to sensitize the cells to perturbation. For example, in some embodiments, the cells are cultured in L-glutamine+, sodium pyruvate-, high glucose DMEM to sensitize the cells to perturbations of energy metabolism genes. In some embodiments, the cells are cultured for at least 2, 3, 4, 5, 6, 7, or more than days in the presence of the media to sensitize the cells to perturbation.

[0064] In some embodiments, the sgRNA library transfected cells are cultured in media comprising a combination of an inducing agent to induce expression of catalytically dead Cas9 as well as one or more agent or condition to sensitize the cells to perturbation. In some embodiments, the cells are cultured for at least 2, 3, 4, 5, 6, 7, or more than days in the presence of the media to sensitize the cells to perturbation further comprising an inducing agent to induce expression of the catalytically dead Cas9. In some embodiments, the cells are cultured for at least 7 days in L-glutamine+, sodium pyruvate-, high glucose DMEM further comprising an induction agent to induce expression of the catalytically dead Cas9. In some embodiments, the cells are cultured for at least 7 days in L-glutamine+, sodium pyruvate-, high glucose DMEM further comprising doxycycline.Temporal Dynamics

[0065] PerturbFate enables the investigation of the effects of large-scale genetic perturbations on the continuum of gene regulation, from chromatin remodeling to nascent transcription and ultimately to steady-state transcriptomic phenotypes, at single-cell resolution. By integrating the temporal dynamics at all three molecular layers, PerturbFate facilitates the reconstruction of intricate gene regulatory networks by mapping transcription factor modules based on their expression levels, motif accessibility, and target gene transcription, thereby providing deeper insights into the critical drivers of cell state transitions.

[0066] In some embodiments, the methods of the invention include a combination of (1) Ethynyl-2-deoxyuridine (EdU) labeling of nascent transcripts, with single-cell combinatorial indexing to profde the single-cell transcriptome and chromatin landscape of cells in vivo. In some embodiments, the methods of the invention allow for both transcriptome and chromatin accessibility profding. In some embodiments, the methods allow for tracking celltype-specific proliferation and differentiation dynamics across conditions, and foridentification of genetic and epigenetic signatures associated with the alteration of cellular dynamics.

[0067] In some embodiments, the method comprises the following steps: (i) label a cell, tissue or sample with 5-Ethynyl-2-deoxyuridine (EdU), a thymidine analog that can be incorporated into replicating DNA for labeling in vivo cellular proliferation, (ii) nuclei are extracted, fixed, and then subjected to click chemistry-based in situ ligation to an azide-containing fluorophore, followed by fluorescence-activated cell sorting (FACS) to enrich the EdU+ cells, (iii) indexed reverse transcription or transposition is used to introduce the first round of indexing, cells from all wells are pooled and then redistributed into multiple 96-well plates through FACS sorting to further purify the EdU+ cells, (iv) library preparation proceeds using protocols for multi-barcoding of polynucleotides as described in WO2024073412, which is incorporated herein by reference in its entirety, such that most cells pass through a unique combination of wells, such that their contents are marked by a unique combination of barcodes that can be used to group reads derived from the same cell. In some embodiments, the two sorting steps are essential for excluding contaminating cells and enriching extremely rare proliferating cell populations.Tagmentation

[0068] In some embodiments, barcoded Tn5 is added and Tagmentation is performed. All nuclei are then pooled, stained with DAPI, and sorted into multi-sell plates with the gating based on DAPI and Alexa647 such that singlets are discriminated from doublets and EdU+ cells are purified. After sorting, reverse crosslinking is performed. Then, indexed P5 primer (5'-(SEQ ID NO:2415) AATGATACGGCGACCACCGAGATCTACA[i5]CCCTACACGACGCTCTTCCGATCT-3' (SEQ ID NO:2449)), and indexed P7 primer (5’-(SEQ ID NO:2419) CAAGCAGAAGACGGCATACGAGAT[i7]GTGACTGGAGTTCAGACGTGTGCTCTTCC GATCT-3’ (SEQ ID NO:2420)) are added into each well and PCR amplification is carried out. Final PCR products are pooled and purified. The TrackerSci ATAC-seq library can then be sequenced.Barcoded Polynucleotides

[0069] In some embodiments, the invention includes methods of generating multibarcoded polynucleotide molecules. An exemplary method of generating multi-barcoded polynucleotide molecules are described in WO2024073412, which is incorporated herein by reference in its entirety. An example protocol is provided herein.

[0070] In some embodiments, the method of generating multi-barcoded polynucleotide molecules includes contacting a sample containing RNA molecules with at least one set of barcoded reverse transcription primers, performing reverse transcription to generate singly barcoded DNA molecules, and contacting the singly barcoded DNA molecules with a set of barcoded PCR primers, and performing PCR amplification to generate a set of double barcoded polynucleotides. In some embodiments, the number of unique double barcoded polynucleotides corresponds to the number of unique combinations of barcodes that can be generated.Therefore, in various embodiments, a set of double barcoded polynucleotides comprises 5 to 109 unique double barcoded polynucleotides.

[0071] In some embodiments, the method includes contacting a sample containing nucleic acid molecules with at least one set of barcoded transposases, performing tagmentation to generate singly barcoded DNA molecules, and contacting the singly barcoded DNA molecules with a set of barcoded PCR primers, and performing PCR amplification to generate a set of double barcoded polynucleotides. In some embodiments, the number of unique double barcoded polynucleotides corresponds to the number of unique combinations of barcodes that can be generated. Therefore, in various embodiments, a set of double barcoded polynucleotides comprises 5 to 109 unique double barcoded polynucleotides.

[0072] In some embodiments, the methods relate to contacting a sample containing RNA molecules with at least one set of barcoded reverse transcription primers, performing reverse transcription to generate singly barcoded DNA molecules, contacting the singly barcoded DNA molecules with at least one set of barcoded ligation oligonucleotides, ligating the barcoded ligation oligonucleotides to the nucleic acid molecules to generate double barcoded DNA molecules, and contacting the double barcoded DNA molecules a set of barcoded PCR primers, and performing PCR amplification to generate a set of triple barcoded polynucleotides. In some embodiments, the number of unique triple barcoded polynucleotides corresponds to the number of unique combinations of barcodes that can be generated.Therefore, in various embodiments, a set of triple barcoded polynucleotides comprises 5 to 109 unique triple barcoded polynucleotides.

[0073] A barcode sequence is a unique sequence that can be used to distinguish a barcoded polynucleotide in a biological sample from other barcoded polynucleotides in the same biological sample. The concept of “barcodes” and appending barcodes to nucleic acids and other proteinaceous and non-proteinaceous materials is known to one of ordinary skill in the art (see, e.g., Liszczak G et al. Angew Chem Int Ed Engl. 2019 Mar 22;58(13):4144-4162). Thus, it should be understood that the term “unique” is with respect to the molecules of a single biological sample and means “only one” of a particular molecule or subset of molecules of the sample.

[0074] The length of a barcode sequence may vary. For example, a barcode sequence may have a length of 5 to 50 nucleotides (e g., 5 to 40, 5 to 30, 5 to 20, 5 to 10, 10 to 50, 10 to 40, 10 to 30, or 10 to 20 nucleotides). In some embodiments, a barcode sequence may have a length of 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 nucleotides.

[0075] In some embodiments, the methods comprise delivering to a biological tissue a first set of barcoded polynucleotides. A first set may include any number of barcoded polynucleotides. In some embodiments, a first set include 5 to 1000 barcoded polynucleotides. For example, a first set may comprise 5 to 900, 5 to 800, 5 to 700, 5 to 600, 5 to 500, 5 to 400, 5 to 300, 5 to 200, 5 100, 10 to 1000, 10 to 900, 10 to 800, 10 to 700, 10 to 600, 10 to 500, 10 to 400, 10 to 300, 10 to 200, 20 to 1000, 20 to 900, 20 to 800, 20 to 700, 20 to 600, 20 to 500, 20 to 400, 20 to 300, 20 to 200, 50 to 1000, 50 to 900, 50 to 800, 50 to 700, 50 to 600, 50 to 500, 50 to 400, 50 to 300, or 50 to 200 barcoded polynucleotides. More than 1000 barcoded polynucleotides in a first set are contemplated herein.

[0076] In some embodiments, the methods comprise delivering to the biological sample a second set of barcoded polynucleotides. A second set may include any number of barcoded polynucleotides. In some embodiments, a second set include 5 to 1000 barcoded polynucleotides. For example, a second set may comprise 5 to 900, 5 to 800, 5 to 700, 5 to 600, 5 to 500, 5 to 400, 5 to 300, 5 to 200, 5 100, 10 to 1000, 10 to 900, 10 to 800, 10 to 700, 10 to 600, 10 to 500, 10 to 400, 10 to 300, 10 to 200, 20 to 1000, 20 to 900, 20 to 800, 20 to 700, 20 to 600, 20 to 500, 20 to 400, 20 to 300, 20 to 200, 50 to 1000, 50 to 900, 50 to 800, 50 to 700,50 to 600, 50 to 500, 50 to 400, 50 to 300, or 50 to 200 barcoded polynucleotides. More than 1000 barcoded polynucleotides in a second set are contemplated herein.

[0077] In some embodiments, the methods comprise delivering to the biological sample a third set of barcoded polynucleotides. A third set may include any number of barcoded polynucleotides. In some embodiments, a third set includes 5 to 1000 barcoded polynucleotides. For example, a third set may comprise 5 to 900, 5 to 800, 5 to 700, 5 to 600, 5 to 500, 5 to 400, 5 to 300, 5 to 200, 5 100, 10 to 1000, 10 to 900, 10 to 800, 10 to 700, 10 to 600, 10 to 500, 10 to 400, 10 to 300, 10 to 200, 20 to 1000, 20 to 900, 20 to 800, 20 to 700, 20 to 600, 20 to 500, 20 to 400, 20 to 300, 20 to 200, 50 to 1000, 50 to 900, 50 to 800, 50 to 700, 50 to 600, 50 to 500, 50 to 400, 50 to 300, or 50 to 200 barcoded polynucleotides. More than 1000 barcoded polynucleotides in a third set are contemplated herein.

[0078] In one embodiment, the invention provides a method of performing reverse transcription (RT) comprising contacting an RNA sample with a set of RT primers and a reverse transcriptase.

[0079] In some embodiments, the methods comprise joining barcoded polynucleotides of the first set to barcoded polynucleotides of the second set. In some embodiments, the methods comprise exposing the biological sample to a ligation reaction, thereby producing double barcoded polynucleotides, wherein the double barcoded polynucleotides comprises a unique combination of barcoded polynucleotides from the first set and the second set.

[0080] In one embodiment, the 1stligation adapter sequence comprises SEQ ID NO:5. In some embodiments the 1stligation adaptor further comprises a barcode sequence from SEQ ID NO: 16-111.

[0081] In one embodiment, the 2ndligation adapter sequence comprises SEQ ID NO:7. In some embodiments the 2ndligation adaptor further comprises a barcode sequence from SEQ ID NO: 112-495.

[0082] In some embodiments, the adaptor sequence for use in the ligation reaction comprises 5'- A*G*A*T*C*G*G* A*A*G* A*G*C*G*T*C*G*T*G*T*A*G*G*G* A*A*A*G*A*G*T*G *T* / 3ddC / (SEQ ID NO:6), wherein1*' represents phosphorothioate bonds between nucleotides, which prevents the tagmentation of the oligo, and wherein 73ddC / ' represents adideoxycytidine modification, which prevents the extension of the oligo on the 3' end by DNA polymerases.

[0083] In one embodiment, the method of the invention incorporates a step of combining two polynucleotide sequences into a single nucleic acid molecule using “tagmentation.” As used herein, the term “tagmentation” refers to the modification of DNA by a transposome complex comprising transposase enzyme complexed with adaptors comprising transposon end sequence. Tagmentation results in the simultaneous fragmentation of the target DNA molecule and ligation of a polynucleotide sequence (e.g. an adaptor or linker) to the 5' ends of both strands of duplex fragments. Following a purification step to remove the transposase enzyme, additional sequences (e.g., barcodes) can be added to the ends of the adapted fragments, for example by PCR, ligation, or any other suitable methodology known to those of skill in the art.

[0084] The method of the invention can use any transposase that can accept a transposase end sequence and fragment a target nucleic acid, attaching a transferred end, but not a non-transferred end. A “transposome” is comprised of at least a transposase enzyme and a transposase recognition site. In some such systems, termed “transposomes”, the transposase can form a functional complex with a transposon recognition site that is capable of catalyzing a transposition reaction. The transposase or integrase may bind to the transposase recognition site and insert the transposase recognition site into a target nucleic acid in a process sometimes termed “tagmentation”. In some such insertion events, one strand of the transposase recognition site may be transferred into the target nucleic acid.

[0085] Some embodiments can include the use of a barcoded Tn5 transposase to incorporate a barcode into DNA molecules for preparation of a multi-indexed library.

[0086] In some embodiments, the methods comprise performing PCR amplification of using a set of PCR primers comprising a set of barcoded polynucleotides.

[0087] In some embodiments, the indexing P7 PCR primer comprises SEQ ID NO: 9. In some embodiments the indexing P7 PCR Primer further comprises a barcode sequence from SEQ ID NO:496-879.

[0088] In some embodiments the multi-indexed library of the invention comprises a multitude of indexed nucleic acid products comprising two or more barcodes, wherein the combination of the two or more barcodes comprises a unique combination of barcodedpolynucleotides. Tn some embodiments, the unique combination is a unique combination of a first and second barcode. In some embodiments, the unique combination is a unique combination of a first, a second, and a third barcode.

[0089] In some embodiments, nascent and pre-existing transcriptomes from the same cell are amplified separately using distinct P7 PCR barcodes and processed as separate cells (two groups of reads with distinct cell barcodes). These are then matched after single-cell gene-to-cell raw count matrices are generated. Additionally, in some embodiments, the single-end Readl of ATAC fragments is processed for chromatin accessibility quantification. The sgRNA library from the same cell is amplified from both nascent and pre-existing transcriptome PCR wells, and share a unique cell barcode for matching, achieved by adding the same barcoded sgRNA inner P7-U6 primer to paired nascent and pre-existing transcriptome PCR wells.

[0090] In some embodiments, the sgRNA inner P7-U6 primer comprises SEQ ID NO:11. In some embodiments the sgRNA inner i7 primer further comprises a barcode sequence from SEQ ID NO:880-887.

[0091] In some embodiments, the sgRNA enrichment primer comprises a sequence as set forth in SEQ ID NO:888-983. In some embodiments the sgRNA enrichment primer comprises comprises a barcode sequence from SEQ ID NO:948-1079.Sequencing

[0092] In some embodiments, the methods include a sequencing step. For example, next generation sequencing (NGS) methods (or other sequencing methods) may be used to sequence the barcoded polynucleotide libraries. In some embodiments, the methods comprise preparing an NGS library in vitro. Thus, in some embodiments, the methods comprise sequencing the library of barcoded nucleic acid molecules to produce sequencing reads.Sequencing methods are known, and an example protocol is provided herein.Samples

[0093] In some embodiments, a sample is a biological sample. Non-limiting examples of biological samples include tissues, cells, and bodily fluids (e.g., blood, urine, saliva, cerebrospinal fluid, and semen). The biological sample may be adult tissue, embryonic tissue, or fetal tissue, for example. In some embodiments, a biological sample is from a human orother animal. For example, a biological sample may be obtained from a murine (e.g., mouse or rat), feline (e g., cat), canine (e.g., dog), equine (e.g., horse), bovine (e.g., cow), leporine (e.g., rabbit), porcine (e.g., pig), hircine (e.g., goat), ursine (e.g., bear), or piscine (e.g., fish). Other animals are contemplated herein.

[0094] In some embodiments, a biological sample is fixed, and thus is referred to as a fixed biological sample. Fixation (e.g., tissue fixation) refers to the process of chemically preserving the natural state of a biological sample, for example, for subsequent histological analysis. Various fixation agents are routinely used, including, for example, formalin (e g., formalin fixed paraffin embedded (FFPE) tissue), formaldehyde, paraformaldehyde and glutaraldehyde, any of which may be used herein to fix a biological sample. Other fixation reagents (fixatives) are contemplated herein.

[0095] In some embodiments, the biological sample is a tissue. In some embodiments, the biological sample is a cell. A biological sample, such as a tissue or a cell, in some embodiments, is sectioned and mounted on a surface, such as a slide. In such embodiments, the sample may be fixed before or after it is sectioned. In some embodiments, the fixation process involves perfusion of the animal from which the sample is collected.

[0096] Nucleic acid molecules suitable as templates for use in generating a multiindexed library of the invention include any nucleic acid molecule or population of nucleic acid molecules (e.g., DNA, RNA, mRNA, sgRNA), particularly those derived from a cell or tissue. In one aspect, a population of mRNA molecules (a number of different mRNA molecules, typically obtained from cells or tissue) are used to make a multi-indexed cDNA library, in accordance with the invention. Exemplary sources of nucleic acid templates include viruses, virally infected cells, bacterial cells, fungal cells, plant cells and animal cells.Reaction Solutions

[0097] Various reaction solutions can be used for performing the different reactions (RT, PCR, tagmentation, ligation, etc.) of the methods of the invention.

[0098] In some embodiments, one or more reaction solution comprises a buffering agent. The concentration of the buffering agent in the reaction solutions of the invention will vary with the particular buffering agent used. Typically, the working concentration (i.e., the concentration in the reaction mixture) of the buffering agent will be from about 5 mM to about500 mM (e.g., about 10 mM, about 15 mM, about 20 mM, about 25 mM, about 30 mM, about 35 mM, about 40 mM, about 45 mM, about 50 mM, about 55 mM, about 60 mM, about 65 mM, about 70 mM, about 75 mM, about 80 mM, about 85 mM, about 90 mM, about 95 mM, about 100 mM, from about 5 mM to about 500 mM, from about 10 mM to about 500 mM, from about 20 mM to about 500 mM, from about 25 mM to about 500 mM, from about 30 mM to about 500 mM, from about 40 mM to about 500 mM, from about 50 mM to about 500 mM, from about 75 mM to about 500 mM, from about 100 mM to about 500 mM, from about 25 mM to about 50 mM, from about 25 mM to about 75 mM, from about 25 mM to about 100 mM, from about 25 mM to about 200 mM, from about 25 mM to about 300 mM, etc.). When Tris (e.g., Tris-HCl) is used, the Tris working concentration will typically be from about 5 mM to about 100 mM, from about 5 mM to about 75 mM, from about 10 mM to about 75 mM, from about 10 mM to about 60 mM, from about 10 mM to about 50 mM, from about 25 mM to about 50 mM, etc.

[0099] The final pH of solutions of the invention will generally be set and maintained by buffering agents present in reaction solutions of the invention. The pH of reaction solutions of the invention, and hence reaction mixtures of the invention, will vary with the particular use and the buffering agent present but will often be from about pH 5.5 to about pH 9.0 (e.g., about pH 6.0, about pH 6.5, about pH 7.0, about pH 7.1, about pH 7.2, about pH 7.3, about pH 7.4, about pH 7.5, about pH 7.6, about pH 7.7, about pH 7.8, about pH 7.9, about pH 8.0, about pH 8.1, about pH 8.2, about pH 8.3, about pH 8.4, about pH 8.5, about pH 8.6, about pH 8.7, about pH 8.8, about pH 8.9, about pH 9.0, from about pH 6.0 to about pH 8.5, from about pH 6.5 to about pH 8.5, from about pH 7.0 to about pH 8.5, from about pH 7.5 to about pH 8.5, from about pH 6.0 to about pH 8.0, from about pH 6.0 to about pH 7.7, from about pH 6.0 to about pH 7.5, from about pH 6.0 to about pH 7.0, from about pH 7.2 to about pH 7.7, from about pH 7.3 to about pH 7.7, from about pH 7.4 to about pH 7.6, from about pH 7.0 to about pH 7.4, from about pH 7.6 to about pH 8.0, from about pH 7.6 to about pH 8.5, from about pH 7.7 to about pH 8.5, from about pH 7.9 to about pH 8.5, from about pH 8.0 to about pH 8.5, from about pH 8.2 to about pH 8.5, from about pH 8.3 to about pH 8.5, from about pH 8.4 to about pH 8.5, from about pH 8.4 to about pH 9.0, from about pH 8.5 to about pH 9.0, etc.)

[0100] In some embodiments, one or more monovalent cationic salts (e.g., LiCl, NaCl, KC1, NH4CI, etc.) may be included in reaction solutions of the invention. In many instances,salts used in reaction solutions of the invention will dissociate in solution to generate at least one species which is monovalent (e.g., Li+, Na+, K+, NH4+, etc.) When included in reaction solutions of the invention, salts will often be present either individually or in a combined concentration of from about 0.5 mM to about 500 mM (e.g., about 1 mM, about 2 mM, about 3 mM, about 5 mM, about 10 mM, about 12 mM, about 15 mM, about 17 mM, about 20 mM, about 22 mM, about 23 mM, about 24 mM, about 25 mM, about 27 mM, about 30 mM, about 35 mM, about 40 mM, about 45 mM, about 50 mM, about 55 mM, about 60 mM, about 64 mM, about 65 mM, about 70 mM, about 75 mM, about 80 mM, about 85 mM, about 90 mM, about 95 mM, about 100 mM, about 120 mM, about 140 mM, about 150 mM, about 175 mM, about 200 mM, about 225 mM, about 250 mM, about 275 mM, about 300 mM, about 325 mM, about 350 mM, about 375 mM, about 400 mM, from about 1 mM to about 500 mM, from about 5 mM to about 500 mM, from about 10 mM to about 500 mM, from about 20 mM to about 500 mM, from about 30 mM to about 500 mM, from about 40 mM to about 500 mM, from about 50 mM to about 500 mM, from about 60 mM to about 500 mM, from about 65 mM to about 500 mM, from about 75 mM to about 500 mM, from about 85 mM to about 500 mM, from about 90 mM to about 500 mM, from about 100 mM to about 500 mM, from about 125 mM to about 500 mM, from about 150 mM to about 500 mM, from about 200 mM to about 500 mM, from about 10 mM to about 100 mM, from about 10 mM to about 75 mM, from about 10 mM to about 50 mM, from about 20 mM to about 200 mM, from about 20 mM to about 150 mM, from about 20 mM to about 125 mM, from about 20 mM to about 100 mM, from about 20 mM to about 80 mM, from about 20 mM to about 75 mM, from about 20 mM to about 60 mM, from about 20 mM to about 50 mM, from about 30 mM to about 500 mM, from about 30 mM to about 100 mM, from about 30 mM to about 70 mM, from about 30 mM to about 50 mM, etc.).

[0101] In some embodiments, one or more reaction solution comprises a buffering agent, one or more divalent cationic salts (e.g., MnCh, MgCk, MgSC , CaCh, etc.) may be included in reaction solutions of the invention. In many instances, salts used in reaction solutions of the invention will dissociate in solution to generate at least one species which is divalent (e.g., Mg++, Mn++, Ca++, etc.) When included in reaction solutions of the invention, salts will often be present either individually or in a combined concentration of from about 0.5 mM to about 500 mM (e.g., about 1 mM, about 2 mM, about 3 mM, about 4 mM, about 5 mM,about 6 mM, about 7 mM, about 8 mM, about 9 mM, about 10 mM, about 12 M, about 15 mM, about 17 mM, about 20 mM, about 22 mM, about 23 mM, about 24 mM, about 25 mM, about 27 mM, about 30 mM, about 35 mM, about 40 mM, about 45 mM, about 50 mM, about 55 mM, about 60 mM, about 64 mM, about 65 mM, about 70 mM, about 75 mM, about 80 mM, about 85 mM, about 90 mM, about 95 mM, about 100 mM, about 120 mM, about 140 mM, about 150 mM, about 175 mM, about 200 mM, about 225 mM, about 250 mM, about 275 mM, about 300 mM, about 325 mM, about 350 mM, about 375 mM, about 400 mM, from about 1 mM to about 500 mM, from about 5 mM to about 500 mM, from about 10 mM to about 500 mM, from about 20 mM to about 500 mM, from about 30 mM to about 500 mM, from about 40 mM to about 500 mM, from about 50 mM to about 500 mM, from about 60 mM to about 500 mM, from about 65 mM to about 500 mM, from about 75 mM to about 500 mM, from about 85 mM to about 500 mM, from about 90 mM to about 500 mM, from about 100 mM to about 500 mM, from about 125 mM to about 500 mM, from about 150 mM to about 500 mM, from about 200 mM to about 500 mM, from about 10 mM to about 100 mM, from about 10 mM to about 75 mM, from about 10 mM to about 50 mM, from about 20 mM to about 200 mM, from about 20 mM to about 150 mM, from about 20 mM to about 125 mM, from about 20 mM to about 100 mM, from about 20 mM to about 80 mM, from about 20 mM to about 75 mM, from about 20 mM to about 60 mM, from about 20 mM to about 50 mM, from about 30 mM to about 500 mM, from about 30 mM to about 100 mM, from about 30 mM to about 70 mM, from about 30 mM to about 50 mM, etc.).

[0102] When included in reaction solutions of the invention, reducing agents (e.g., dithiothreitol, P-mercaptoethanol, etc.) will often be present either individually or in a combined concentration of from about 0.1 mM to about 50 mM (e.g., about 0.2 mM, about 0.3 mM, about 0.5 mM, about 0.7 mM, about 0.9 mM, about 1 mM, about 2 mM, about 3 mM, about 4 mM, about 5 mM, about 6 mM, about 10 mM, about 12 mM, about 15 mM, about 17 mM, about 20 mM, about 22 mM, about 23 mM, about 24 mM, about 25 mM, about 27 mM, about 30 mM, about 35 mM, about 40 mM, about 45 mM, about 50 mM, from about 0.1 mM to about 50 mM, from about 0.5 mM to about 50 mM, from about 1 mM to about 50 mM, from about 2 mM to about 50 mM, from about 3 mM to about 50 mM, from about 0.5 mM to about 20 mM, from about 0.5 mM to about 10 mM, from about 0.5 mM to about 5 mM, from about 0.5 mM to about 2.5 mM, from about 1 mM to about 20 mM, from about 1 mM to about 10mM, from about 1 mM to about 5 mM, from about 1 mM to about 3.4 mM, from about 0.5 mM to about 3.0 mM, from about 1 mM to about 3.0 mM, from about 1.5 mM to about 3.0 mM, from about 2 mM to about 3.0 mM, from about 0.5 mM to about 2.5 mM, from about 1 mM to about 2.5 mM, from about 1.5 mM to about 2.5 mM, from about 2 mM to about 3.0 mM, from about 2.5 mM to about 3.0 mM, from about 0.5 mM to about 2 mM, from about 0.5 mM to about 1.5 mM, from about 0.5 mM to about 1.1 mM, from about 5.0 mM to about 10 mM, from about 5.0 mM to about 15 mM, from about 5.0 mM to about 20 mM, from about 10 mM to about 15 mM, from about 10 mM to about 20 mM, etc.).

[0103] Reaction solutions of the invention may also contain one or more ionic or nonionic detergent (e.g., TRITON X-100™, NONIDET P40™, sodium dodecyl sulfate, etc.). When included in reaction solutions of the invention, detergents will often be present either individually or in a combined concentration of from about 0.01% to about 5.0% (e.g., about 0.01%, about 0.02%, about 0.03%, about 0.04%, about 0.05%, about 0.06%, about 0.07%, about 0.08%, about 0.09%, about 0.1%, about 0.15%, about 0.2%, about 0.3%, about 0.5%, about 0.7%, about 0.9%, about 1%, about 2%, about 3%, about 4%, about 5%, from about 0.01% to about 5.0%, from about 0.01% to about 4.0%, from about 0.01% to about 3.0%, from about 0.01% to about 2.0%, from about 0.01% to about 1.0%, from about 0.05% to about 5.0%, from about 0.05% to about 3.0%, from about 0.05% to about 2.0%, from about 0.05% to about 1.0%, from about 0.1% to about 5.0%, from about 0.1% to about 4.0%, from about 0.1% to about 3.0%, from about 0.1% to about 2.0%, from about 0.1% to about 1.0%, from about 0.1% to about 0.5%, etc.). For example, reaction solutions of the invention may contain TRITON X-100™ at a concentration of from about 0.01% to about 2.0%, from about 0.03% to about 1.0%, from about 0.04% to about 1.0%, from about 0.05% to about 0.5%, from about 0.04% to about 0.6%, from about 0.04% to about 0.3%, etc.

[0104] Reaction solutions of the invention may also contain one or more stabilizing agents (e.g., PEG8000, trehalose, betaine, BSA, glycerol). In some embodiments, when included in reaction solutions of the invention, stabilizing agents are present either individually or in a combined concentration from 0.01 M to about 50 M (e.g., about 0.05M, about 0.1 M, 0.2 M, about 0.3 M, about 0.5 M, about 0.6 M, about 0.7 M, about 0.9 M, about 1 M, about 2 M, about 3 M, about 4 M, about 5 M, about 6 M, about 10 M, about 12 M, about 15 M, about 17 M, about 20 M, about 22 M, about 23 M, about 24 M, about 25 M, about 27 M, about 30 M,about 35 M, about 40 M, about 45 M, about 50 M, from about 0.1 M to about 1 M, from about 0.5 M to about 5 M, from about 0.2 M to about 2 M, from about 0.3 M to about 3 M, from about 0.4 M to about 4 M, from about 0.5 M to about 5 M, from about 0.2 M to about 0.8 M, from about 0.5 M to about 1 M, from about 0.05 M to about 1 M, from about 0.05 M to about 10 M, from about 0.05 M to about 20M, etc.). In some embodiments, when included in reaction solutions of the invention, such stabilizing agents are present either individually or in a combined concentration of from about 0.01 mg / ml to about 100 mg / ml (e.g., about 0.01 mg / ml, about 0.02 mg / ml, about 0.03 mg / ml, about 0.04 mg / ml, about 0.05 mg / ml, about 0.06 mg / ml, about 0.07 mg / ml, about 0.08 mg / ml, about 0.09 mg / ml, about 0.1 mg / ml, about 0.11 mg / ml, about 0.12 mg / ml, about 0.15 mg / ml, about 0.17 mg / ml, about 0.2 mg / ml, about 0.25 mg / ml, about 0.35 mg / ml, about 0.5 mg / ml, about 0.75mg / ml, about 1.0 mg / ml, about 1.5 mg / ml, about 2.0 mg / ml, about 2.5 mg / ml, about 3.0 mg / ml, about 3.5 mg / ml, about 4.0 mg / ml, about 5.0 mg / ml, about 6.0 mg / ml, about 7.0 mg / ml, about 8.0 mg / ml, about 9.0 mg / ml, about 10.0 mg / ml, from about 0.05 mg / ml to about 3.0 mg / ml, from about 0.1 mg / ml to about 5.0 mg / ml, from about 0.2 mg / ml to about 2.0 mg / ml, etc.). In some embodiments, when included in reaction solutions of the invention, such stabilizing agents are be present either individually or in a combined concentration of from about 0.1% to about 50% (e.g., about 0.1%, about 0.2%, about 0.3%, about 0.4%, about 0.5%, about 0.6%, about 0.7%, about 0.8%, about 0.9%, about 1.0%, about 1.5%, about 2.0%, about 3.0%, about 5.0%, about 7.0%, about 9.0%, about 10%, about 11%, about 12%, about 13%, about 14%, about 15%, about 20%, about 22%, about 25%, about 27%, about 30%, about 35%, about 40%, about 45%, about 50%, from about 0.1% to about 50%, from about 0.1% to about 40%, from about 0.1% to about 30%, from about 0.0% to about 20%, from about 0.1% to about 10%, etc.

[0105] Reaction solutions the invention may also contain one or more additional additives that improve enzymatic activity, including agents that improve primer utilization efficiency and improve product yield.

[0106] In many instances, nucleotides (e.g., dNTPs, such as dGTP, dATP, dCTP, dTTP, etc.) will be present in reaction mixtures of the invention. Typically, individual nucleotides will be present in concentrations of from about 0.05 mM to about 50 mM (e.g., about 0.07 mM, about 0.1 mM, about 0.15 mM, about 0.18 mM, about 0.2 mM, about 0.3 mM, about 0.5 mM, about 0.7 mM, about 0.9 mM, about 1 mM, about 2 mM, about 3 mM, about 4mM, about 5 mM, about 6 mM, about 10 mM, about 12 mM, about 15 mM, about 17 mM, about 20 mM, about 22 mM, about 23 mM, about 24 mM, about 25 mM, about 27 mM, about 30 mM, about 35 mM, about 40 mM, about 45 mM, about 50 mM, from about 0.1 mM to about 50 mM, from about 0.5 mM to about 50 mM, from about 1 mM to about 50 mM, from about 2 mM to about 50 mM, from about 3 mM to about 50 mM, from about 0.5 mM to about 20 mM, from about 0.5 mM to about 10 mM, from about 0.5 mM to about 5 mM, from about 0.5 mM to about 2.5 mM, from about 1 mM to about 20 mM, from about 1 mM to about 10 mM, from about 1 mM to about 5 mM, from about 1 mM to about 3.4 mM, from about 0.5 mM to about 3.0 mM, from about 1 mM to about 3.0 mM, from about 1.5 mM to about 3.0 mM, from about 2 mM to about 3.0 mM, from about 0.5 mM to about 2.5 mM, from about 1 mM to about 2.5 mM, from about 1.5 mM to about 2.5 mM, from about 2 mM to about 3.0 mM, from about 2.5 mM to about 3.0 mM, from about 0.5 mM to about 2 mM, from about 0.5 mM to about 1.5 mM, from about 0.5 mM to about 1.1 mM, from about 5.0 mM to about 10 mM, from about 5.0 mM to about 15 mM, from about 5.0 mM to about 20 mM, from about 10 mM to about 15 mM, from about 10 mM to about 20 mM, etc.). The combined nucleotide concentration, when more than one nucleotide is present, can be determined by adding the concentrations of the individual nucleotides together. When more than one nucleotide is present in reaction solutions of the invention, the individual nucleotides may not be present in equimolar amounts. Thus, a reaction solution may contain, for example, 1 mM dGTP, 1 mM dATP, 0.5 mM dCTP, and 1 mM dTTP.

[0107] Enzymes such as reverse transcriptases, ligases, polymerases, or transposases may also be present in reaction solutions. When present, enzymes will often be present in a concentration which results in about 0.01 to about 1,000 units of enzymatic activity / pl (e.g., about 0.01 unit / pl, about 0.05 unit / pl, about 0.1 unit / pl, about 0.2 unit / pl, about 0.3 unit / pl, about 0.4 unit / pl, about 0.5 unit / pl, about 0.7 unit / pl, about 1.0 unit / pl, about 1.5 unit / pl, about 2.0 unit / pl, about 2.5 unit / pl, about 5.0 unit / pl, about 7.5 unit / pl, about 10 unit / pl, about 20 unit / pl, about 25 unit / pl, about 50 unit / pl, about 100 unit / pl, about 150 unit / pl, about 200 unit / pl, about 250 unit / pl, about 350 unit / pl, about 500 unit / pl, about 750 unit / pl, about 1,000 unit / pl, from about 0.1 unit / pl to about 1,000 unit / pl, from about 0.2 unit / pl to about 1,000 unit / pl, from about 1.0 unit / pl to about 1,000 unit / pl, from about 5.0 unit / pl to about 1,000 unit / pl, from about 10 unit / pl to about 1,000 unit / pl, from about 20 unit / pl to about 1,000unit / pl, from about 50 unit / j l to about 1,000 unit / pl, from about 100 unit / pl to about 1,000 unit / pl, from about 200 unit / pl to about 1,000 unit / pl, from about 400 unit / pl to about 1,000 unit / pl, from about 500 unit / pl to about 1,000 unit / pl, from about 0.1 unit / pl to about 300 unit / pl, from about 0.1 unit / pl to about 200 unit / pl, from about 0.1 unit / pl to about 100 unit / pl, from about 0.1 unit / pl to about 50 unit / pl, from about 0.1 unit / pl to about 10 unit / l, from about 0.1 unit / pl to about 5.0 unit / pl, from about 0.1 unit / pl to about 1.0 unit / pl, from about 0.2 unit / pl to about 0.5 unit / pl, etc.

[0108] Reaction solutions of the invention may be prepared as concentrated solutions (e.g., 5* solutions) which are diluted to a working concentration for final use. With respect to a 5 x reaction solution, a 5 : 1 dilution is required to bring such a 5 * solution to a working concentration. Reaction solutions of the invention may be prepared, for examples, as a 2x, a 3*, a 4x, a 5x, a 6x, a 7x, a 8x, a 9x, a 10x, etc. solutions. One major limitation on the fold concentration of such solutions is that, when compounds reach particular concentrations in solution, precipitation occurs. Thus, concentrated reaction solutions will generally be prepared such that the concentrations of the various components are low enough so that precipitation of buffer components will not occur. As one skilled in the art would recognize, the upper limit of concentration which is feasible for each solution will vary with the particular solution and the components present.

[0109] In many instances, reaction solutions of the invention will be provided in sterile form. Sterilization may be performed on the individual components of reaction solutions prior to mixing or on reaction solutions after they are prepared. Sterilization of such solutions may be performed by any suitable means including autoclaving or ultrafiltration.Kits

[0110] The invention is also directed to kits for, sequencing or amplifying nucleic acid molecules (single- or double-stranded). Kits of the invention may comprise a carrier, such as a box or carton, having in close confinement therein one or more containers, such as vials, tubes, bottles and the like. In kits of the invention, a first container may contain one or more of the reverse transcriptase enzymes of the invention or one or more of the indexed reverse transcription primer sets and one or more additional container may contain one or more of the ligation enzymes of the invention or the indexed ligation primer set. Kits of the invention mayalso comprise, in the same or different containers, at least one component selected from one or more adaptor molecule, one or more indexed PCR primer, or other component for performing the library preparation method of the invention. In one embodiment, kits of the invention may also comprise, in the same or different containers, an optimized reaction buffer as described elsewhere herein, or components used to produce the optimized reaction buffer. Alternatively, the components of the kit may be divided into separate containers.In one embodiment, the kit includes instructional material that describes the use of the kit to generate a pooled CRISPR sequencing library, wherein the instructional material creates an increased functional relationship between the kit components and the individual using the kit. In one embodiment, the kit is utilized by one person or entity. In another embodiment, the kit is utilized by more than one person or entity. In one embodiment, the kit is used without any additional compositions or methods. In another embodiment, the kit is used with at least one additional composition or method.EXPERIMENTAL EXAMPLESThe invention is further described in detail by reference to the following experimental examples. These examples are provided for purposes of illustration only, and are not intended to be limiting unless otherwise specified. Thus, the invention should in no way be construed as being limited to the following examples, but rather, should be construed to encompass any and all variations which become evident as a result of the teaching provided herein.Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the compounds of the present invention and practice the claimed methods. The following working examples therefore, specifically point out the preferred embodiments of the present invention, and are not to be construed as limiting in any way the remainder of the disclosure.Example 1 : Mapping convergent regulators of melanoma drug resistance by PerturbFate

[0111] Here PerturbFate, a scalable and cost-efficient single-cell multi-omics CRISPR screening platform is presented. Applying PerturbFate to a BRAFV600E melanoma model ofVemurafenib resistance, prioritized genes were perturbed and transitions were reconstructed from a neural crest-like state toward either melanocytic differentiation or an undifferentiated state, recapitulating melanoma phenotypic plasticity. Joint profiling of chromatin accessibility with steady-state and nascent transcription revealed core transcriptional programs underlying these fate decisions, with SOXIO governing melanocytic identity and AP-1 and TEAD factors driving dedifferentiation. While BRAE inhibition promoted differentiation, perturbations derepressing MAPK or YAP signaling consistently redirected cells toward a dedifferentiated, invasive state. Diverse resistance-conferring perturbations converged on a shared chromatin and transcriptional state, defining a common pro-resistance program centered on FOSL1 and additional transcription factors, including KLF5, RREB1, and SMAD3. Combinatorial disruption of these modules neutralized most resistance-inducing perturbations, supporting their promising central roles within a convergent resistance network.

[0112] Despite convergence on a shared resistant state, the mechanisms linking individual perturbations to this fate were heterogeneous. Perturbations of distinct Mediator complex modules revealed condition-specific effects on dedifferentiation, with MED 12 uniquely promoting state transitions across conditions through loss of SOXIO activity. Across perturbations and conditions, unified activation of a Mediator Complex / YAP-coregulated transcriptional program was identified, including TGFBR2 (Huang et al., 2012, Cell 151, 937-950) and VEGFC (Malekan et al., 2024, Life Sci 345, 122563). Beyond melanoma, PerturbFate provides a generalizable framework for scalable, time-resolved dissection of gene-regulatory programs, enabling systematic discovery of transcriptional regulators, RNA-kinetic control, and cell-fate logic across diverse biological systems.The Materials and Methods are now describedCell culture

[0113] The A375 and HEK293T cell lines were obtained from the American Type Culture Collection. All cells were maintained at 37 °C with 5% CO2 in high-glucose DMEM medium supplemented with L-glutamine, sodium pyruvate (Gibco, 11995065), and 10% FBS (Sigma- Aldrich, F4135).Cell strain construction

[0114] The A375-dCas9-BFP-KRAB cell strain was constructed by transducing A375 cells with lentivirus produced using pHR-SFFV-dCas9-BFP (Addgene, 46910) at a multiplicity of infection (MOI) of 0.2. BFP+ cells with the top 15% fluorescence intensity were sorted for expansion. Enrichment sorting was performed regularly to maintain the Cas9 expression level in the cell strain.Plasmid and Lentivirus Construction

[0115] Single sgRNA expression construct cloning and lentivirus packaging were performed similarly to previous work (Xu et al., 2024, Nature Biotechnology, 42(8), 1218-1223). In brief, the CROPseqOPTI vector with an additional EGFP marker (CROPseqOPTI-GFP) was digested with BsmBI (NEB, ER0451) to remove the fdler sequence between the U6 promoter and the sgRNA scaffold. Pre-annealed phosphorylated double-stranded DNA with an sgRNA sequence and complementary sticky ends was ligated into the digested backbone. Lentivirus was packaged by co-transfecting the sgRNA-expressing plasmid, psPAX2 plasmid, and pMD2.G plasmid into HEK293T cells at high confluency. After washing the cells and changing the medium, virus-containing supernatants were harvested 24 hours later. The functional titer of the viruses was measured by assessing the proportion of fluorescent markerpositive cells after transducing target cell lines with varying amounts of lentivirus.

[0116] A two-step cloning strategy was adopted for the construction of the dual-sgRNA drug resistance library. The oligo library was designed with the following structure: BsmBI digestion site - sgRNAl sequence - PaqCI digestion site - filler - PaqCI digestion site -sgRNA2 sequence - BsmBI digestion site. The single-stranded library (IDT) was amplified into double-stranded DNA using primers complementary to both ends and then digested with BsmBI. The amplified library with sticky ends was ligated into the BsmBI-digested CROPseqOPTI-GFP backbone using Blunt / TA Ligase Master Mix (NEB, M0367S). The ligation product was purified with 0.75x AMPure beads (Beckman Coulter, A63881) and transformed into competent cells via electroporation. After recovery, cells were spread onto a bioassay agarose plate and incubated at 32 °C for 14 hours. A separate agarose plate at a 1 TOO, 000 dilution was inoculated to determine sgRNA coverage. All colonies were scrapped, and a Midiprep was performed to obtain the intermediate plasmid library.

[0117] The intermediate plasmid library and a gBlock (IDT) with the structure: PaqCI digestion site - sgRNA scaffold CR3 with a lOx capture sequence - Hl promoter - PaqCI digestion site were digested with PaqCI (NEB, R0745S). Ligation, transformation, and plasmid preparation were performed as described above.

[0118] The full structure of the sgRNA expression cassette in the final library is: U6 promoter - sgRNA 1 sequence - sgRNA scaffold CR3 with a lOx capture sequence - Hl promoter - sgRNA2 sequence - sgRNA scaffold. The accuracy of plasmid library construction and the abundance of each sgRNA pair were evaluated by amplicon sequencing.

[0119] Dual-sgRNA-expressing CROPseqOPTI-mCherry plasmids were constructed for specific gene knockdown experiments. In brief, gBlocks with the structure: homology arm to the U6 promoter sequence - sgRNAl sequence - sgRNA scaffold CR3 with a lOx capture sequence - Hl promoter - sgRNA2 sequence - homology arm to the second sgRNA scaffold were mixed with the BsmBI-digested CROPseqOPTI-mCherry backbone for Gibson assembly. After incubation at 50 °C for 30 minutes, transformation and plasmid preparation were carried out as described in previous work (Xu et al., 2024, Nature Biotechnology’, 42(8), 1218-1223).Gene selection and sgRNA evaluation

[0120] To identify potential gene candidates whose loss of function might confer resistance to Vemurafenib treatment in melanoma cells, previously published bulk CRISPR screens with similar experimental settings were analyzed (Shalem et al., 2014,Science, 343(6166), 84-87; Li et al., 2014, Genome biology, 15, 1-12; Goh et al., 2021, G3, 7 / (2), jkaa069; Doench et al., 2016, Nature biotechnology’, 34(2), 184-191). From these datasets, 183 genes with an FDR < 0.1 in at least one screen were identified. Candidate genes for knockdown were further filtered based on their endogenous expression levels; 143 genes with expression levels > 1 log2 counts per million (CPM) in the in-house A375 EasySci-RNA data were retained.

[0121] Reference was made to the sgRNA library from Genome-wide Perturb-seq, which also utilized a paired sgRNA expression system but employed a different chemistry. The sgRNAs were further examined and only those meeting the following criteria were retained: (1) unique STAR mapping to the reference genome (GRCh38.pl3, GENCODE), and (2) knockdown fold-change < 0.5 (i .e., the fold-change of target gene expression between cellswith a target sgRNA and cells without a target sgRNA), as reflected in the Genome-wide Perturb-seq gene expression data. For target genes not found in the Genome-wide Perturb-seq or in cases where sgRNA inefficiency was observed in the original dataset, newly designed paired sgRNA libraries from; Repl ogle et al., 2022, Elife, 11, e81856 were used.CRISPR screen experimental procedure

[0122] For each replicate, 5 million A375-dCas9-BFP-KRAB cells were seeded into a 10 cm dish. The next day, the drug-resistance lentivirus library was added to the dish at an MOI of 0.05 with 8 ng / ml Polybrene (Sigma-Aldrich, TR-1003). GFP+ BFP+ cells were sorted and reseeded into a 6 cm dish for expansion. After 48 hours, the medium was replaced with fresh DMEM containing either 0.1% DMSO (VWR, IC0219605525) or 1 pM Vemurafenib in DMSO (Selleck, SI 267) to initiate the screen (day 0). The culture medium was refreshed every other day.

[0123] On day 6, for the PerturbFate experiment, 2.5 ml of the original medium was removed from the dish, the remaining medium was aspirated, and the removed medium was mixed with 2.5 pl of 200 mM 5-EU (Vector Laboratories, CCT-1261). The mixture was then added back to the dish for a 1-hour incubation at 37°C. Cells were subsequently trypsinized and collected for single-cell library preparation. To minimize the impact of experimental handling on nascent transcriptomes, cells were processed on ice immediately after collection and fixed promptly.

[0124] For the bulk screen, cells were trypsinized and collected on days 6 and 14. Cell pellets were stored at -20 °C until further processing. Genomic DNA was extracted from each sample (Zymo, D3024) and split equally into multiple PCR reactions, with a maximum of 1 mg of DNA per reaction for sgRNA expression cassette amplification. After 20 cycles of amplification, the PCR products were purified (Zymo, D4014), and the target band was extracted from an agarose gel (Zymo, D4007) for amplicon sequencing.

[0125] Validation secondary screens were performed similarly to the method stated above. When additional genetic perturbations were involved, A375 cell lines with the target gene knocked down were established prior to the screen. Specific target sgRNAs were transduced using CROPseqOPTI-mCherry to prevent fluorescent conflicts with the PerturbFate sgRNA library, which expresses GFP. Additional chemicals included SIS3(Sigma- Aldrich, 566405- IMG), SRI 8662 (MCE, HY- 136530), and Lenvatinib (MCE, L125518-5MG). Chemicals were reconstituted in DMSO and added to the culture media at a 1 : 1000 dilution ratio in combination with Vemurafenib.Sequencing data processing

[0126] For the bulk CRISPR screens, BCL files were demultiplexed into FASTQ files based on index 7 barcodes. Reads from each sample were further extracted using index 5 barcodes. Each read pair was matched against two constant sequences (Readl : 11-25 nt;Read2: 11-25 nt) to retain reads with the correct library structure, allowing for a maximum of one mismatch. sgRNA sequences were then extracted from filtered read pairs (sgRNA sequence 1: 25-44 nt of Readl; sgRNA sequence 2: 84-103 nt of Read2). sgRNA identities were assigned based on the reference sgRNA sequences, permitting no mismatches. sgRNA count matrices were constructed using Python 3.8.

[0127] For PerturbFate (whole transcriptome + chromatin accessibility + sgRNA), BCL files were demultiplexed into FASTQ files using P7 PCR barcodes. Each FASTQ file contained all RNA and ATAC reads from the same indexing PCR well. During barcode extraction, reads from the oligo-dT / randomN RNA and ATAC modalities were split into separate files based on modality-specific adapter sequence matching. Readl matching the "TG" sequence at the 41-42 nt position was classified as transcriptome data. Oligo-dT-originated RNA reads required at least 10 consecutive T bases at positions 51-60 nt, while randomN-originated RNA reads did not exhibit this pattern. ATAC read pairs were identified by the Tn5 ME sequence at the 41-50 nt position of Readl.

[0128] For the RNA reads, cell barcodes and UMI sequences from Readl were appended to the headers of each Read2 FASTQ file, and only Read2 was used for subsequent steps. Poly(A) sequences, adapter sequences, and low-quality bases were trimmed from Read2 of oligo-dT reads, while 14 additional bases (potential priming hexamer and UMI sequences) were removed from randomN reads using Trim_Galore (0.6.7). After this step, oligo-dT and randomN reads were combined for further processing. STAR (2.7.9a) was used to map reads to the hg38 reference genome (GRCh38.pl3, GENCODE). Reads with a mapping score >30 were selected using SAMtools (1.13). Single-cell deduplication was performed based on UMI sequences and alignment locations, and retained reads were split into SAM files per cell.Finally, single-cell gene-to-cell raw count matrices were constructed by assigning reads to genes.

[0129] For the AT AC reads, cell barcodes from RNA Readl were appended to the headers of both ATAC Readl and Read2 FASTQ fdes. Adapter sequences were removed in paired-end mode using Trim Galore (0.6.7). STAR (2.7.9a) was used to map read pairs to the hg38 reference genome (GRCh38.pl3, GENCODE). Picard (2.27.4) was used to remove PCR duplicates, and TSV files containing genomic locations and full cell barcodes were generated for SnapATAC2 (2.6.1) (Zhang et al., 2024, Nature methods, 21(2), 217-227).

[0130] sgRNA reads were matched against constant sequences in Readl and Read2, allowing a maximum of one mismatch. For each filtered read pair, the cell barcode, sgRNA sequence, and UMI were extracted from designed positions. Extracted sgRNA sequences with at most one mismatch from the sgRNA library were corrected. UMI sequences were used for deduplication by collapsing identical UMIs for each individual sgRNA under a unique cell barcode. Cells with sgRNA UMI counts exceeding 5 were retained, and the sgRNA x cell count matrix was constructed.

[0131] For PerturbFate (nascent transcriptome + pre-existing transcriptome + chromatin accessibility + sgRNA), most computational steps were the same as described above, with minor modifications. In this protocol, nascent and pre-existing transcriptomes from the same cell were amplified separately using distinct P7 PCR barcodes and processed as separate cells (two groups of reads with distinct cell barcodes). These were matched after single-cell gene-to-cell raw count matrices were generated. Additionally, because ATAC DNA underwent secondary tagmentation, the pipeline was modified to process single-end Readl of ATAC fragments for chromatin accessibility quantification. The sgRNA library from the same cell was amplified from both nascent and pre-existing transcriptome PCR wells but shared a unique cell barcode for matching, achieved by adding the same barcoded sgRNA inner P7-U6 primer to paired nascent and pre-existing transcriptome PCR wells.

[0132] ATAC peaks were called for each treatment condition with a q-value cutoff of 0.01 using MACS3 (3.0.0b3) (Zhang et al., 2008, Genome biology), 9, 1-9) embedded in SnapATAC2 (2.6.1). Peaks were resized to 500 bp centered at the summit and iteratively merged into a union peak set, resulting in 758,365 peaks in the final set. A peak-to-cell rawcount matrix was generated using SnapATAC2 and converted into a sparse matrix in R for downstream analyses.Measurement of Growth Phenotypes in Bulk CRISPR Screens

[0133] Raw sgRNA counts from the plasmid library, as well as from cells in the DMSO and Vem conditions, were normalized by the total counts to account for sequencing depth. Perturbations relative to NTC counts were calculated by dividing the depth-normalized sgRNA counts by the sum of depth-normalized NTC counts. Relative counts from replicates were then averaged for growth phenotype calculations. To assess whether perturbed cells outgrew NTC cells under the Vem condition, fold changes in relative counts between the Vem library and the plasmid library were calculated and log2-transformed (i.e., Vem log2(sgRNA enrichment)). The Vem log2(sgRNA enrichment) value indicates whether perturbed cells exhibit a growth advantage over NTC cells in response to Vem treatment. To further compare changes in cell growth under Vem treatment, the ratio of fold changes in relative counts between the Vem library and the DMSO library was calculated and log2 -transformed (i.e., Vem - DMSO log2(sgRNA enrichment)). The Vem - DMSO log2(sgRNA enrichment) value reflects whether perturbed cells gained a greater growth advantage in the Vem condition compared to the DMSO condition.sgRNA singlets identification and knockdown efficiency examination

[0134] sgRNA singlets were identified using two additional cutoffs: a Max-to-Total proportion > 50% and a Sec-to-Max ratio < 0.3. The Max-to-Total proportion represents the percentage of UMI counts for the most abundant sgRNA relative to the total sgRNA UMI counts from a single cell. The Sec-to-Max ratio represents the ratio of UMI counts for the second most abundant sgRNA to the UMI counts for the dominant sgRNA in a single cell. The method was benchmarked against a recent Python package that implements multiple statistical inference methods for single-cell sgRNA identities (Braunger and Velten, 2024, Bioinformatics, 40(9), btae535) and observed extremely high consistency. Only knockdown populations with more than 50 cells in each treatment condition and those in which the pseudobulk expression level of the target gene was less than 60% of that observed in NTC cells were retained.Differential gene expression (DEGs) and differential accessible peaks (DAPs) analyses

[0135] Whole-transcriptome differential expression analysis was performed similarly to previous work (Sziraki et al., 2023, Nature Genetics, 55(12), 2104-2116; Xu et al., 2024, Nature Biotechnology, 42(8), 1218-1223; Lu et al., 2023, Cell, 186(20), 4345-4364). For each knockdown-to-NTC DEG test, cells with fewer than 100 detected genes and features present in fewer than 25 cells were fdtered out. The analysis was conducted using Monocle2 (2.34.0) (Qiu et al., 2017, Nature methods, 74(10), 979-982). The differentialGeneTest() function was implemented with the number of genes detected per cell as a covariate. The fold change was calculated by dividing the average normalized single-cell gene expression CPM in knockdown cells by that in NTC cells. Differentially accessible peaks were identified using the diff_test() function in snapATAC2 with default parameters.UMAP embedding on pseudobulk perturbations

[0136] Pseudobulk knockdown population whole-transcriptome matrices and ATAC peak count matrices were constructed by aggregating all cells with the same sgRNA identity within each treatment condition. Seurat (4.2.0) (Hao et al., 2021, Cell, 184(\8), 3573-3587) was used for normalization, scaling, PCA dimensionality reduction, and UMAP visualization. Single-cell level MAST differential expression analysis of knockdown cells versus NTC cells was performed to obtain gene features for dimensionality reduction. Perturbations with at least one significant DEG were retained, and the top 20 DEGs with the highest absolute fold changes from all tests were combined. The top 15 principal components were used for UMAP. For ATAC pseudobulk dimensionality reduction, 300,000 highly variable peaks were selected, as implemented in snapATAC2. The top 30 principal components were used as input for UMAP.ATAC peaks - nascent gene expression linkage identification

[0137] The FigR (1.0.1) package (Kartha et al., 2022, Cell genomics, 2(9)) in R was used for the analysis. To account for the potential effects of genetic and chemical perturbations on global transcriptional activity, a specialized normalization strategy to adjust for sequencing depth was employed. While NTC cells in the DMSO condition were normalized by counts per1 Ok, other perturbed cells were normalized using a scaling factor of 1 Ok x (mean UMIs in this population / mean UMIs in NTC cells in the DMSO condition). Given that gene regulation patterns of non-coding genes remain largely understudied, a focus exclusively on proteincoding genes was adopted. Only protein-coding genes annotated in the hg38 GTF fde (GRCh38.pl3, GENCODE) were retained. The ATAC peak-to-cell raw count matrix was filtered to include only peaks detected in at least 10 cells. The log-transformed, normalized nascent gene expression matrix and the filtered ATAC peak-to-cell raw count matrix were used as input for the association test. Gene-peak linkages with p-values (pvalZ) < 0.05, as specified in the FigR manual, were retained as significant linkages.Cell trajectory and nascent transcriptome-based RNA velocity inference

[0138] Since whole transcriptomes of single cells remain the most robust single-cell modality, they were used for diffusion map construction and cell order (pseudotime) inference using the destiny package (3.20.0) in R. To highlight the impact of genetic and chemical perturbations on the melanoma phenotype switch, well-established gene sets defining multiple melanoma phenotypic states (Tsoi et al., 2018, Cancer cell, 33(5), 890-904) were used as features. Genes expressed in at least 100 cells in each condition were retained for dimensionality reduction. The top 20 principal components were used to construct the diffusion map with the DiffusionMap() function, and cell order was subsequently calculated using the DPT() function. Phenotype gene set scores in single cells were calculated by averaging the scaled expression values of constituent genes, and cells were annotated according to the highest gene program score in each bin along the trajectory. To improve clarity and ensure the statistical robustness of downstream analyses, transient plastic phenotype labels were excluded when annotating cells in the DMSO condition. Instead, cells were categorized as one of “Melanocytic,” “Neural crest-like,” or “Undifferentiated.” For Vem-treated cells, no phenotype labels were assigned; instead, focus was on their transitions along the trajectory, reasoning that their transcriptomic states underwent complex and subtle changes after treatment, making them unsuitable for categorization into untreated phenotypes.

[0139] To integrate single-cell nascent transcriptomes into the diffusion map, an assumption was made that cell state transitions were not completed within one hour. This assumption aligns with the observation that RNA half-lives in human cells are typically muchlonger than one hour (Schwanhausser et al., 2011, Nature, 473(1341), 337-342). Whole-transcriptome gene count matrices of cells were concatenated with pre-existing transcriptome gene count matrices of cells (which had matched single-cell nascent transcriptomes), and a joint diffusion map was constructed. Manual curation revealed that DC2 and DC3 captured the phenotypic state of cells across both chemistries. A k-nearest neighbor model using DC2 and DC3 values with the FNN package (1.1.4.1) was constructed in R. The inferred cell order value for each pre-existing transcriptome cell was assigned by averaging the real cell order values of its three nearest neighbor whole-transcriptome cells. This approach demonstrated highly consistent sgRNA abundance distributions (Figure 10C).

[0140] The scvelo (0.3.2) package (Bergen et al., 2020, Nature biotechnology, 3S(12), 1408-1414) in Python was used to infer RNA velocity from nascent transcriptomes. To match nascent transcriptomes to whole-transcriptome cells, integration was performed as described above. Each whole-transcriptome cell was matched to a nascent transcriptome by identifying the two nearest pre-existing transcriptome neighbors and averaging their log-transformed normalized nascent transcriptomes. Normalized whole and nascent transcriptome gene expression matrices, diffusion map coordinates of whole-transcriptome cells, and principal components computed with destiny were imported into the adata object. Instead of using the unspliced-to-spliced ratio, the new-to-total ratio of genes was used (Erhard et al., 2019, Nature, 571(1165), 419-423) to compute velocities. The top 3,000 genes were selected, and the top 20 principal components were used to compute moments and connectivity. Velocities were generated in stochastic mode.Significance examination of perturbed cells in the trajectory

[0141] The ks.test() function in R was used to identify perturbed cells that exhibited significant shifts along the trajectory compared to NTC cells in both conditions. Cell order values for each perturbation were compared to those of NTC cells in the corresponding treatment condition, and differences in mean cell order values between perturbations and NTC cells were used to determine the direction and extent of the shift. The computed p-values were adjusted using FDR correction. For most analyses of Vem resistance, only the perturbations in the Vem condition that displayed consistent growth and molecular phenotypes were retained (i.e., shift trends along the trajectory).

[0142] To compare the relative transition distance between NTC and other perturbations upon Vemurafenib treatment, the wilcox.test() function in R was used in a similar manner. For both DMSO and Vemurafenib treatment conditions, single-cell order values of perturbed cells were adjusted relative to the averaged cell order value of the NTC cell population, and the difference between these two distributions was examined using the wilcox.test() function followed by FDR correction.Identification of DEGs, DA motifs, and DAPs along the cell trajectory

[0143] The TradeSeq (1.20.0) package (Van den Berge et al., 2020, Nature communications, 77(1), 1201) in R was used to infer statistically significant DEGs, DA motifs, and DAPs along the melanoma phenotype switch trajectory. For whole-transcriptome and nascent-transcriptome DEG identification, raw gene count matrices (whole transcriptome and nascent transcriptome) and cell order values inferred by destiny (for whole transcriptome cells) or by the integration mentioned above (for nascent transcriptome cells) were used as input to the fitGAM() function. Lineage weights for all cells were set to 1, as only a single trajectory was analyzed. The statistical significance of associations between expression levels and cell order was calculated using the associationTestQ function. Each trajectory was divided into 40 bins based on cell order values. Genes with an FDR < 0.01 in both whole-transcriptome and nascent-transcriptome analyses were considered for gene regulatory network (GRN) construction. Genes with an FDR < 0.01 and an absolute log2 fold change > 1 were selected for heatmap visualization. Motif counts in single cells were obtained using the motifmatchr package (1.28.0) in R. The peak-to-cell raw matrix was used as input, sequences of peaks were scanned, and motifs from the JASPAR database (Rauluseviciute et al., 2024, Nucleic acids research, 52(D1), D174-D182) were matched against sequences in open peaks of cells.Significant motifs (FDR < 0.01) were included in GRN construction and heatmap visualization. For DAP analysis along the drug-resistance trajectory, data sparsity in single-cell ATAC and program memory requirements were addressed by binning the trajectory as follows: 1,000 cells on the leftmost (melanocytic) side of the trajectory were divided into 10 bins, 300 cells on the rightmost (undifferentiated) side were divided into 3 bins, and the remaining cells in the middle were divided into 10 bins. For bins in the middle containing more than 100 cells, 100 cells were randomly sampled. ATAC raw peak counts were aggregated for each bin, andonly peaks with non-zero counts in more than one-third of the bins were retained. Subsequent steps followed the methodology described above. Motif enrichment in DAPs was performed using HOMER (5.1) (Duttke et al., 2024, Nature, 637(8022), 891-898).Melanoma phenotype switch GRN inference

[0144] The GRN inference was conducted through the following steps. First, the initial and terminal states of each phenotype switch was determined, which defined the directionality of the temporal constraints and the cell order window to use. Cells in the DMSO condition initially exhibited a Neural crest-like phenotype and transdifferentiated into either melanocytic (DMSO Neural crest-like to Melanocytic trajectory) or undifferentiated phenotypes (DMSO Neural crest-like to Undifferentiated- 1 trajectory and DMSO Undifferentiated- 1 to Undifferentiated-2 trajectory). The bin containing the median cell order value of DMSO NTC cells was set as the root for both the DMSO Neural crest-like to Melanocytic trajectory and the DMSO Neural crest-like to Undifferentiated- 1 trajectory. Additionally, the bin where the threshold value between gene clusters 7 and 8 was reached was set as the root for the DMSO Undifferentiated- 1 to Undifferentiated-2 trajectory. For cells under Vemurafenib treatment, a strong trend of transdifferentiation toward the melanocytic side of the trajectory was observed, particularly from the normal WT Neural crest-like phenotype to the inactivated Neural crestlike phenotype (Vem-sensitive trajectory). Conversely, drug-resistant cells transitioned from the inactivated Neural crest-like phenotype to the undifferentiated phenotype (Vem-resi stance trajectory). The root of the Vem-sensitive trajectory was set at the bin containing the median cell order value of DMSO NTC cells, while the root of the Vem-resistant trajectory was set at the bin immediately following the one containing the median cell order value of Vem NTC cells.

[0145] K-means clustering (k = 10) was performed on all significant DE steady-state and nascent z-scored gene expressions along the trajectory. Genes were filtered by correlating steady-state gene expression with nascent gene expression at the cluster level along the trajectory. Steady-state and nascent gene clusters with correlations > 0.6 were matched, and genes present in both counterparts of matched clusters, with nascent gene expression peaking either before or simultaneously with steady-state gene expression in the direction of transdifferentiation, were retained as GRN target gene candidates. The association between TFmotifs and genes was then determined. If TF motifs identified by motifmatchr were found in peaks significantly associated with the nascent expression of a gene, a TF-gene linkage was considered. Significant z-scored TF motif counts along the trajectory were clustered using K-means (k = 10), and only TF motif counts that showed consistent trends with their steady-state gene expression (correlation > 0.4 and manual curation) were retained. Motif clusters and nascent gene expression clusters were matched in the same manner as described above. TF-gene linkages were retained only if they met the following criteria: (1) the associated peaks of a gene contained at least one motif of the TF from its matched counterpart motif cluster, and (2) the peak of motif opening along the trajectory occurred before or at the same time as the peak of nascent gene expression.

[0146] For each transdifferentiation trajectory segment (e.g., DMSO Neural crest-like to Undifferentiated- 1 trajectory, DMSO Undifferentiated-1 to Undifferentiated-2 trajectory, DMSO Neural crest-like to Melanocytic trajectory, Vem-sensitive trajectory, and Vem-resistant trajectory), only clusters from all modalities (i.e., steady-state, nascent transcriptome, and motif) that exhibit peak values within the segment were considered for segment-specific GRN construction.Multi-modal regulon scoring analysis

[0147] A regulon is defined as a group of target genes co-regulated by an upstream TF. An integrated score (Regulon Score) was developed to quantify the overall transcriptional activity of this gene group in a group of cells that received the same sgRNA:- n m.. 1WhereT(TX,T2, .. . 7}) is the log-transformed, normalized pseudobulk steady-state expression level of the regulator TF I in a perturbed population; G (G . 2, • • • , G!„ ) jsthe log-transformed, normalized pseudobulk nascent expression level of a target gene regulated by the TF in a perturbed population;6^2, • • • - is the log-transformed, normalized ATAC-seq counts at a peak associated with the target gene 7, which contains the motif of TF i in a perturbed population.

[0148] To identify the pattern of inter-regulon co-regulation in Vem resistance, perturbations in the Vem condition that exhibited a significant shift toward the undifferentiatedend of the trajectory with consistent growth phenotypes, as well as perturbations in the Vem condition that showed no significant shift in the trajectory were selected. The fold change in Regulon Scores between perturbations and NTC was calculated, and Pearson correlations of these fold changes between regulons across the selected perturbations were computed.Distances between regulons were calculated, and hierarchical clustering was performed on the resulting correlation coefficient matrix. Clusters of regulons were then defined using the cutree() function.

[0149] To assess the statistical significance of the Regulon Score fold changes for each perturbation compared to NTC in the Vem condition, a permutation strategy was implemented. For each regulon in each perturbation, the order of G was randomly permuted 500 times, and the permuted Regulon Scores were divided by the real Regulon Score of the NTC. This process generated a fold change background distribution for each regulon in each perturbation. Two-sided empirical p-values were calculated for each regulon in each perturbation and subjected to FDR correction. Regulon Scores with FDR < 0.1 were considered significant.Cell order - gene signature correlation analysis and YAP ChlP-seq analysis

[0150] To identify potential contributors to the cell state shift in the trajectory, oncogenic signatures from MSigDB (Liberzon et al., 2015, Cell systems, 1(6), 417-425) in Vem-treated single cells were scored using the AddModuleScore() function in Seurat. Pearson correlations between cell order values and gene signature scores were calculated. Gene signatures with significant correlations (FDR < 0.01) were further tested with 1,000 permutations, followed by FDR correction, to confirm that the observed correlations were not due to random chance. YAP indirect binding peaks were obtained from a previous report (Nardone et al., 2017, Nature communications, S(l), 15321). The PScan-ChIP tool (1.3) (Zambelli et al., 2013, Nucleic acids research, 41(\ '\), W535-W543) was used to analyze local motif enrichment within these peaks, identifying potential direct downstream signal transmitters of YAP.TF motif deviation scoring analysis

[0151] The ChromVAR (1.28.0) package (Schep et al., 2017, Nature methods, 14( ), 975-978) in R was used to compute TF motif deviation scores for individual cells. Motif scoresfor each cell were calculated using peaks that were open in at least 10 cells. GC-matched backgrounds were then added using the addGCBias() function, and the computeDeviations() function was executed to obtain motif deviation scores. For each treatment condition, the deviation score distribution of each motif was compared to that of NTC cells. Simultaneously, the steady-state DEG results between perturbed populations and NTC cells were intersected with the motif score tests. Only TFs that were significant in the DEG analysis and exhibited a minimum level of gene expression changes were retained. P-values for these motifs were further corrected for false discovery rate (FDR). Significant motifs were defined as those with an FDR < 0.1, with enrichment or depletion determined based on the median z-score differences between perturbed cells and NTC cells.Transcriptome and TF deviation score-based clustering

[0152] To cluster perturbed populations, the top 200 whole-transcriptome DEGs with the highest fold changes were extracted and combined into a unified gene set. Pairwise Pearson correlation coefficients between perturbations were calculated based on the fold changes of these populations compared to NTC cells. Perturbation clustering based on motifs was performed using the median deviation score differences between the perturbed populations and NTC cells. Deviation score differences were set to 0 if a motif was not present in the original analysis output.Mediator Complex perturbations associated gene program analyses

[0153] The following criteria to identify gene signatures in Oncogenic Signatures that might account for heterogeneous phenotypic responses to genetic perturbations in the Mediator Complex and Vem treatment was established: (1) Under the DMSO condition, compared to NTC cells, the scores of gene signatures in both MED 15 and MED24 showed no significant changes (based on Wilcoxon tests with FDR corrections); (2) Under the Vem condition, compared to NTC cells, the scores of gene signatures in both MED 15 and MED24 showed significant changes (based on Wilcoxon tests with FDR corrections).Published RNA-seq and ChlP-seq analyses

[0154] Genomic SOX10 binding profiles and paired bulk gene expression were analyzed in a previous study (Fufa et al., 2019, Epigenetics & Chromatin, 12, 1-18) using another melanoma cell line harboring the BRAFV600Emutation, the 501Mel cell line, treated with a different MAPK signaling inhibitor, AZD6244. SOXIO ChlP-seq and bulk RNA-seq FASTQ files were downloaded and reprocessed from NCBI BioProject PRJNA515302.

[0155] For bulk RNA-seq processing, adapter sequences were removed in paired-end mode using Trim Galore (0.6.7). Reads were mapped to the hg38 reference genome (GRCh38.pl3, GENCODE) using STAR (2.7.9a). Reads with mapping scores <10 were filtered out using SAMtools (1.13). PCR duplicates were removed with Picard (2.27.4), and gene counts were generated using featureCounts (2.0.1). For ChlP-seq processing, adapter sequences in FASTQ files were trimmed using cutadapt (3.4). Reads were aligned to the hg38 reference genome using Bowtie2. PCR duplicates were removed using Picard (2.27.4), and peaks were called using MACS2 (2.2.9.1) with a minimum q-value threshold of 0.01.

[0156] ChlP-seq peaks called from replicates were combined and resized to 500 bp, extending from the center. A total of 2,003 and 7,159 peaks were identified for the DMSO and AZD6244 conditions, respectively, with 1,878 overlapping peaks. Significantly upregulated DEGs with an FDR < 0.05 were identified using the DESeq2 package (Love et al., 2014, Genome biology, 15, 1-21) in R. To identify potent MAPK / ERK inhibitor-responsive SOXIO targets, the nearest genes within 10 kb of 5,281 AZD6244-specific SOXIO binding peaks were identified using the closest command from bedtools (2.30.0) and intersected with the significantly upregulated DEGs. Genes from this intersection that also met the following criteria were considered SOXIO direct targets responsive to Vem treatment: (1) the maximum steady-state expression CPM in either DMSO NTC cells or Vem NTC cells was > 20, (2) the gene was significantly upregulated in NTC cells upon Vem treatment, and (3) the fold change was > 1.25. Ratios of these genes between the Vem-to-DMSO fold change in perturbed cells and the Vem-to-DMSO fold change in NTC cells were calculated to assess the strength of SOXIO-mediated gene activation upon Vem treatment.

[0157] Likewise, to identify regulatory interactions between YAP and the Mediator complex, ChlP-seq data was reprocessed from different conditions originally described in Galli et al., (2015, Mol Cell, 60, 328-337). FASTQ fdes were preprocessed for peak calling as described above. MED1 ChlP-seq peaks that were present only under the control condition butabsent under the siYAP / TAZ condition, and that overlapped with YAP ChlP-seq peaks, were defined as YAP-Mediator complex co-regulatory genomic regions. GREAT (Hiller et al., 2013, Nucleic Acids Res 41, el 51) was used to link regulatory regions to putative target genes. Only target genes that were significantly upregulated in LATS2- and NF2-KD cells in this study were retained as YAP-Mediator complex co-regulated genes. Validation analyses and visualization of Mediator binding signals for representative genes were performed using normalized bigWig data originally from Freitas et al., (2022, Science 378, eabn5647).

[0158] Validation on patient tumor samples was conducted using RNA-seq data from MAPKi-sensitive and -resistant tumors (Hugo et al., 2015, Cell 162, 1271-1285). DESeq2 was used for initial DEG identification, with tumor drug sensitivity as the primary covariate and patient identity as a confounding covariate. The AddModuleScore() function in Seurat was used to score phenotype programs and TF modules. For TF regulons with large target sets, the top 500 genes with the highest average expression across tumor samples were selected for scoring. Penalized linear regression was performed using the glmnet (4.1-10) package in R with elastic net regularization (a = 0.3). The pROC (1.19.0.1) package in R was used to evaluate AUC. DESeq2 was also used to process RNA-seq data from A375 cells overexpressing multiple YAP mutants (Kanai et al., 2024, Cancers (Basel) Feb 20; 16(5):852).Hybridization Chain Reaction (HCR) staining on intracellular RNA

[0159] The HCR reaction was performed to quantify intracellular AXL expression levels. Probe pairs targeting AXL mRNA were designed in-house, and the following steps were carried out based on a published protocol (Reilly et al., 2021, Nature genetics, 53(8), 1166-1176). In brief, cells were fixed in 4% formaldehyde at room temperature for 1 hour, followed by ethanol permeabilization at -20°C overnight. Hybridization was performed at 37°C overnight using 40 nM per probe. Hairpins conjugated with an Alexa647 fluorophore (Molecular Instruments, HCR Gold Amplifier) were used for signal amplification at room temperature overnight. Flow cytometry analysis was performed to record intracellular signals.The Experimental Results are now DescribedPerturbFate for capturing multi-layer single-cell readout upon genetic perturbations

[0160] PerturbFate enables simultaneous profiling of chromatin accessibility and genome-wide gene expression dynamics in the same genetically perturbed cell. The workflow integrates pooled CRISPRi perturbation, metabolic labeling of nascent RNA, and combinatorial indexing-based multi -omic profiling (Sziraki et al., 2023, Nat Genet 55, 2104-2116; Xu et al., 2024, Nat Biotechnol 42, 1218-1223) (Figure 1A, Figure 2A). Briefly, cells constitutively expressing dCas9-BFP-KRAB are transduced with a pooled sgRNA library targeting gene promoters, and short-term labeling of newly synthesized transcripts (using 5-ethynyl uridine (5-EU)) was performed before cell harvesting. After fixation and permeabilization, bulk Tn5-mediated tagmentation is performed to tag chromatin accessibility, followed by bulk reverse transcription to capture full transcriptomes. Two rounds of split-and-pool ligation introduce multi-modal cell-specific barcodes. Click chemistry is then used to biotinylate 5-EU-labeled cDNA-RNA hybrids, which are selectively isolated by streptavidin pull-down after cell lysis. Following second-strand synthesis and tagmentation, modalityspecific libraries are generated through multiplex indexing PCR, enabling computational matching across modalities at single-cell resolution (Figure 2B). Of note, key steps of PerturbFate were extensively optimized to improve robustness and sensitivity. For example, lowering the reverse transcription temperature after ATAC markedly improved chromatin accessibility profiles, while systematic optimization of fixation and reverse transcription conditions substantially increased cell recovery and RNA capture efficiency. In addition, the Tn5 adapter design was optimized for maximal ligation efficiency (Figure 2C-J).

[0161] As a proof-of-concept, nascent RNA capture was first validated by profiling a mixture of human HEK293T and mouse NIH / 3T3 cells with or without 5-EU labeling.Background nascent reads were minimal in unlabeled controls (<0.4%), whereas labeled cells exhibited substantially higher fractions of nascent reads (27.2% in HEK293T and 13.5% in NIH / 3T3; Figure IB). Labeled transcriptomes showed reduced exonic and increased intronic coverage relative to steady-state RNA (Figure 1C), consistent with enrichment for newly synthesized, unspliced transcripts. Genes with high nascent signal were enriched for dynamic regulatory processes, whereas genes with low nascent signal were associated with housekeeping functions (Figure ID).

[0162] Moreover, chromatin accessibility profiles displayed strong enrichment at transcription start sites, indicating efficient ATAC capture by PerturbFate (Figure IE).Species-mixing experiments demonstrated high single-cell purity across modalities, with only 0.7% of cells exhibiting ambiguous species identity (Figure IF). Both RNA and ATAC profiles showed strong concordance with corresponding single-modality assays (Figure 1G). Notably, the labeled nascent RNA layer in PerturbFate exhibited a 1.6-fold increase in normalized intronic coverage and a 2.4-fold decrease in normalized exonic coverage relative to the nonlabeled transcriptome, consistent with selective capture of newly synthesized, unspliced transcripts.

[0163] To further enhance CRISPR knockdown efficiency and reduce sgRNA-specific variability in PerturbFate, a dual-sgRNA CROP-seq-based vector was engineered incorporating two sgRNA expression cassettes driven by distinct promoters (Li et al., 2023, Nature 621, 373-380) (Figure 1H, Figure 2K). Two sgRNA scaffolds containing different capture sequences enabled direct sequencing of sgRNA identity (Figure 1H). By mixing cells transduced with distinct sgRNAs, sgRNAs were detected in 98.1% of cells, with 90.5% classified as species-consistent singlets across all modalities (Figure 2H). Further validation using sgRNAs targeting DROSHA revealed robust epigenetic and transcriptional repression, including a 72.6% reduction in promoter accessibility and >85% suppression of both nascent and pre-existing RNA expression (Figure II- J). Moreover, PerturbFate is compatible with parallel bulk CRISPR screens and can be adapted to jointly profile steady-state transcriptomes and chromatin accessibility under pooled perturbations (Figure 2L-Q). Flexible sgRNA expression and capture designs, including both single- and dual-sgRNA configurations, further support the versatility of PerturbFate (Figure IK, Figure 3).

[0164] Together, these results establish PerturbFate as a scalable and cost-efficient single-cell multi-omics platform for high-content CRISPR screening, enabling integrated analysis of transcriptional dynamics, chromatin regulation, and genetic perturbations at scale The detailed protocol for PerturbFate is provided in Example 2.Single-cell multi-omics profiling of BRAFV600Emelanoma cells via PerturbFate

[0165] As a demonstration of PerturbFate, the molecular programs underlying Vemurafenib resistance in BRAFV600E melanoma were investigated. Vemurafenib targets hyperactive BRAFV600E, a major driver mutation in melanoma. Despite high initial response rates, resistance and relapse occur in nearly all patients within 6-12 months (Rossi et al., 2019,European Journal of Pharmacology 862, 172621). Although prior CRISPR screens have identified various genetic regulators of resistance (Rossi et al., 2019, European Journal of Pharmacology 862, 172621; Shalem et al., 2014, Science 343, 84-87; Goh et al., 2021, G3 (Bethesda) 11, jkaa069; Doench et al., 2016, Nat Biotechnol 34, 184-191), these genes span diverse biological processes (Figure 4A), and the mechanisms linking their functions to adaptive cell-state transitions remain poorly understood.

[0166] PerturbFate was applied to A375 melanoma cells, a BRAFV600E -mutant line widely used in resistance screens. Based on prior bulk CRISPR screens and A375 expression profiles, 143 candidate genes were selected (Figure 5, Figure 4A-B). A375 cells were highly sensitive to Vemurafenib, with 1 pM inhibiting -90% of cell growth (Figure 4C). Following low-MOI (0.05) lentiviral transduction of a dual-sgRNA CRISPRi library into dCas9-BFP-KRAB-expressing cells (Figure 5), cells were sorted (Figure 4D), expanded, and screened under DMSO or 1 pM Vemurafenib. Cells were harvested on day 6 for PerturbFate profiling to maintain even sgRNA representation and on day 6 and 14 for parallel bulk CRISPR screens (Figure 4E). Bulk screens were highly reproducible (Figure 4F) and recapitulated known resistance regulators (Figure 4G-H).

[0167] In total, 318,032 cells were profiled, including 218,678 with linked nascent / pre-existing transcriptomes and chromatin accessibility and 99,354 with linked steady-state transcriptomes and chromatin accessibility. Overall, 72.4% of cells were assigned to sgRNAs, with 71.4% confidently identified as singlets (Figure 6A-B). Assignment rates were lower than in proof-of-concept experiments, likely due to reduced fitness under drug treatment and incomplete selection. Because only the first sgRNA was captured in the dataset, lentiviral template switching could confound target assignment; however, bulk sequencing indicated that 68% of reads were correctly paired, consistent with prior examination (Replogle et al., 2022, Elife, 11 :e81856), supporting the validity of sgRNA population-level analyses, in which rare random sgRNA switching is effectively diluted (Figure 6C-D). After filtering low-efficiency sgRNAs and low-cell-number perturbations (Figure 6E), 119 perturbations and No-Targeting Controls (NTC) were retained, with averages of 438 Tri-omics and 214 Dual-omics cells per perturbation under DMSO condition, and 353 Tri-omics and 177 Dual-omics cells under Vemurafenib treatment (Figure 6F). Most perturbations showed consistent knockdown across chromatin accessibility, nascent, and steady-state transcriptomes (Figure 6G-H), with improvedefficiency relative to single-sgRNA designs (Figure 61). Quality-control benchmarking confirmed robust, low-cost multi-modal profiling (Figure 6J-R), enabling detection of extensive perturbation effects at both transcriptional and chromatin levels (Figure 6S-T).

[0168] To characterize perturbation-induced phenotypic responses, unsupervised weighted nearest neighbor (WNN) integration of RNA and AT AC data was performed for each pseudobulk perturbation, followed by UMAP visualization and melanoma phenotype scoring. This analysis grouped functionally related genes (e.g., SWI / SNF, SAGA, and Mediator complexes; Figure 7A) and ordered perturbations along melanoma phenotypic transition (Figure 7B), indicating that modulation of cell state is a dominant perturbation response. To further characterize melanoma phenotypic state transitions, Diffusion Map analysis with melanoma phenotypic signatures (Tsoi et al., 2018, Cancer Cell 33, 890-904. e5) was applied and nascent RNA-based velocity was incorporated (Figure 8A, Figure 7C). Cells formed a trajectory spanning Neural crest-like, Melanocytic, and two Undifferentiated states (Methods), with nascent transcription indicating primary transitions from Neural crest-like toward Undifferentiated and Melanocytic states (Figure 8B). This trajectory correctly identified the Neural crest-like state origin of A375 and aligned with melanoma plasticity observed during therapy adaptation (Marine et al., 2020, Nat Rev Cancer 20, 743-756; Rambow et al., 2018, Cell 174, 843-855. el9; Smith et al., 2016, Cancer Cell 29, 270-284; Pozniak et al., 2025, Trends Cell Biol 35, 911-921). ATAC peak-nascent expression association analysis further identified cis-regulatory elements linked to phenotypic programs and confirmed coordinated epigenetic remodeling along the trajectory (Figure 8C).

[0169] Gene expression dynamics along the trajectory revealed distinct state-specific programs (Figure 8D-E). Dedifferentiation markers (e.g., NGFR, WNT5A, AXL) were enriched in Undifferentiated states, whereas melanocytic states showed elevated antigenpresentation markers (e.g., HLA-DMB, HLA-DOA, CD74, CIITA). A pronounced ZEB2Hi-to-ZEBlHi transition, a reported melanoma dedifferentiation switch (Denecker at al., 2014, Cell Death Differ 21, 1250-1261), was observed (Figure 8E). Functional enrichment distinguished melanocytic clusters associated with pigmentation and immunogenicity, a Neural crest-like cluster enriched for neuronal development terms, and two Undifferentiated states enriched for cell-cycle and adhesion programs (Figure 8D-F). As an independent validation, similar phenotypic dynamics were observed in patient tumors exhibiting distinct responses toMAPKi treatment (Hugo et al., 2015, Cell 162, 1271-1285) (Figure 8G). Notably, nascent transcriptomic patterns closely mirrored steady-state dynamics despite higher noise (Figure 7D), indicating that transcriptional regulation predominates during state transitions.

[0170] Next, epigenetic regulators of these transitions were examined by identifying TF motifs whose accessibility correlated with gene expression (Figure 8H, Figure 7E). SOXIO motifs were most accessible in the Melanocytic state, consistent with its role in melanocyte differentiation, while STAT motifs opening supported IFN-response activation detected at the gene expression level. In contrast, dedifferentiation was accompanied by increased accessibility of TEAD family motifs (e.g., TEAD1, TEAD4) and AP-1 family motifs (e.g., JUN, FOSL2) (Figure 8H), consistent with prior reports implicating stepwise SOXIO loss alongside AP-1 and TEAD activation in epigenetic reprogramming associated with Vemurafenib resistance (Shaffer et al., 2017, Nature 546, 431-435).

[0171] To obtain an integrative view of multi-modal temporal dynamics and to elucidate the gene regulatory principles underlying melanoma state transitions, an analytical framework was developed that leverages single-cell multi-modal data to infer TF-gene regulatory linkages (Figure 7F). Candidate TF-target gene pairs were nominated by jointly considering (i) correlations between nascent gene expression and chromatin accessibility at nearby AT AC peaks, (ii) the presence of TF binding motifs within gene-associated accessible regions, and (iii) concordance between TF expression and motif accessibility along the inferred trajectory. Building on the principle that transcriptional activation proceeds through TF binding-mediated chromatin opening, followed by induction of nascent transcription and subsequent accumulation of steady-state mRNA, these candidate interactions were refined by retaining only TF-target pairs in which TF motif accessibility, nascent transcript abundance, and steady-state gene expression exhibited sequential peak activation in this order along the transition axis. This yielded a temporally ordered and mechanistically grounded set of regulatory interactions, defining GRNs that drive transitions toward Melanocytic, Undifferentiated- 1, and Undifferentiated-2 states.

[0172] The SOXIO regulon (i.e., a group of genes regulated by SOXIO) emerged as the dominant regulon in the melanocytic GRN. It shared a substantial number of target genes with the MEF2C regulon, consistent with prior evidence that MEF2C cooperates with SOXIO to direct neural crest cells toward a melanocytic fate (Agarwal et al., 2011, Development 138,2555-2565) (Figure 7G, I). In contrast, the Undifferentiated- 1 state was primarily driven by AP-1 family factors (e.g., JUN, JUND, FOSL2) downstream of MAPK signaling and TEAD TFs (e.g., TEAD1, TEAD4) downstream of Hippo / YAP signaling (Ramsdale et al., 2015, Sci Signal 8, ra82; Verfaillie et al., 2015, Nat Commun 6, 6683). (Figure 81). The substantial overlap between AP-1 and TEAD regulons within the Undifferentiated- 1 GRN (Figure 7H, I) further supports their established cooperative roles in promoting tumor invasion and metastasis (Zanconato et al., 2015, Nat Cell Biol 17, 1218-1227). The Undifferentiated-2 state GRN was characterized by regulons of multiple TFs implicated in tumor progression and metastasis, including KLF5, KLF16, ZEB1, MYC, and TCF4 (Durand et al., 2024, Oncogene 43, 1489-1505; Pozniak et al., 2024, Cell 187, 166-183. e25), with FOSL1 as the sole AP-1 family member (Figure 31). This pattern is consistent with prior observations of more concentrated FOSL1 activation in undifferentiated melanoma states (Marques et al., 2021, Elife 10, e64846; Comandante-Lou et al., 2022, Cell Rep 40, 111147).

[0173] Finally, perturbation-specific effects were quantified on cell-state distributions relative to controls by comparing perturbation-specific distributions along the trajectory and their enrichment across cell states relative to NTC (Figure 7J). 52 perturbations significantly altered trajectory positioning, of which 46 shifted cells toward undifferentiated states; 33 of them also showed significant enrichment in undifferentiated states (Figure 8J, Figure 7K). Perturbations targeting genes encoding Hippo signaling YAP inhibitors (e.g., NF2, LATS2, PTPN14, TAOK1) induced the strongest dedifferentiation, consistent with activated TEAD transcriptional regulation revealed in GRN analysis. Moreover, perturbations targeting genes encoding components of the Mediator complex (e.g., MED12, MED13, CCNC, MED19), the SAGA complex (e.g., SUPT20H, TAF6L, TADA2B), and the ubiquitin-proteasome system (e g., KCTD10, CAND1, RNF7, KEAP1) also robustly promoted dedifferentiation (Figure 8J). Multi-modal regulon scoring confirmed convergent activation of AP-1 and TEAD programs across perturbations spanning diverse functional classes (Figure 7L), indicating that melanoma dedifferentiation under genetic perturbation is mediated through shared regulatory programs.Identification of convergent gene regulatory programs driving Vemurafenib resistance

[0174] To investigate how Vemurafenib alters melanoma cell-state dynamics, Vemurafenib- and DMSO-treated cells were projected onto a shared trajectory (Figure 9A-B).Vemurafenib induced a global shift toward a melanocytic state, as confirmed by nascent transcriptome-based RNA velocity and phenotypic signature scoring (Figure 9C-D).

[0175] How genetic perturbations modulate cellular responses to Vemurafenib were next assessed (Figure 10A). Relative to NTC, perturbations displayed variable resistance to Vemurafenib-induced differentiation on day 6, which strongly correlated with their growth advantage measured by the bulk CRISPR screen on day 14 (Pearson r = 0.594, p = l.le-12; Figure 9E). Cell-cycle activity inferred from PertiirbFate single-cell data showed a similarly strong correlation with growth phenotypes (Pearson r = 0.66, p = 6.8e-l 1; Figure 10B), reinforcing a tight coupling between melanoma cell-state plasticity and drug-resistant proliferation. 24 perturbations were identified, including NF2, MED 12, and SMARCE1, that were resistant to both Vemurafenib-induced growth arrest (Figure 4H) and differentiation (Figure 9E, Figure 10A). Most of these perturbations (20 / 24) also promoted significant dedifferentiation under DMSO conditions, though with condition-dependent magnitudes (Figure 10C). Perturbations targeting Hippo / YAP inhibitor genes (e.g., NF2, TAOK1, KEAP1, PTPN14) strongly induced dedifferentiation in DMSO but showed attenuated effects under Vemurafenib, consistent with MAPK signaling amplifying Hippo / YAP downstream effects (Pham et al., 2021, Cancer Discov 11, 778-793; Paul et al., 2025, Nat Commun 16, 1743). In contrast, perturbations targeting MAPK inhibitors (e.g., NF1, DUSP6) had minimal effects in DMSO but promoted proliferation and dedifferentiation under Vemurafenib treatment.Perturbations affecting Mediator complex components (e.g., MED 12, MED 19) consistently induced dedifferentiation under both conditions (Figure 10C).

[0176] Similar to DMSO-treated cells, Vemurafenib-treated cells exhibited continuous phenotypic transitions and extensive chromatin remodeling, revealing melanocytic, neural crest-like, and undifferentiated states (Figure 9F-H, Figure 10D-E). These states were marked by elevated SOX10 activity in melanocytic cells and increased FOSL1 motif accessibility in undifferentiated cells (Figure 9F-H). Notably, a transiently reactivating state was identified during transitions toward melanocytic differentiation, characterized by promoter-transcription decoupling: chromatin exhibited a sharp wave of global promoter opening, accompanied by limited activation of undifferentiated-associated TF motifs, while induction of global nascent transcription and undifferentiated-associated gene expression were limited (Figure 9F-H, Figure 10F-J). Limited activation of MAPK-downstream serum response factors (ELK1,ELK3, ELK4), RREB1, and especially F0SL1 likely constrained the transcription activation (Figure 10K-N).

[0177] Using the GRN reconstruction framework, regulatory networks driving transitions toward the Vemurafenib-resistant undifferentiated state were inferred (Figure 7F). These networks revealed a dense, cooperative TF architecture (Figure 91), which was partially activated in the transiently reactivating state (Figure 11 A-D), consistent with the prior qualitative observations from individual modalities. The resistant undifferentiated state was marked by concurrent activation of FOSL1, TEAD1, SMAD2 / 3 / 4, TCF4, ZEB1, and less-characterized TFs such as RREB1 and KLF5 (Figure 9K).

[0178] To dissect regulatory coordination among these TFs, multi-modal regulon activities were quantified across perturbations. Pairwise regulon correlation analysis identified a tightly co-varying cluster (Figure 9J), suggestive of a synergistic regulatory hub underlying drug resistance. In addition to the cooperative FOSL1 and TEAD1 regulons, ELK3 and ZEB1, which have been reported to collaborate in EMT regulation (Williams et al., 2019, Dev Cell 51, 255-276. e7), also clustered closely. Strong co-variation was observed between SMADs and RREB1, consistent with recent evidence of their joint roles in EMT (Su et al., 2020, Nature 577, 566-571; Lee et al., 2024, Cell 187, 6182-6199. e29). Among 24 resistant perturbations, KLF5, SMAD3, RREB1, and FOSL1 emerged as the most broadly activated regulons across perturbations (Figure 9K), which might represent key convergent gene programs. For example, the existence of KLF5, SMAD3, and FOSL1 motifs within accessible peaks associated with RREB1 implies a potential direct regulatory relationship among these TFs (Figure HE).

[0179] Correlating single-cell oncogenic signatures with their orders in the trajectory revealed that YAP and KRAS dependency signatures were most strongly associated with Vemurafenib-resistant dedifferentiation (Figure 9L), consistent with the prior perturbation- and molecular-level analyses (Figure 8G, Figure 9E-H). Further validation using YAP ChlP-seq (Nardone et al., 2017, Nat Commun 8, 15321) indicates the enrichment of RREB1, KLF5, SMAD3, and FOSL1 in its binding peaks (Figure 9L); their broad co-dependencies were captured in DepMap CRISPR screens (Figure 1 IF). Stratifying perturbations by relative regulon activation demonstrated that activation of these TFs conferred significantly enhanced Vemurafenib resistance (Figure 9M). Their expression was also negatively associated with patient overall survival in the TCGA SKCM cohort (Figure 9N). Despite strong inter-tumorheterogeneity, activation of these TF regulons in MAPKi-resistant patient tumors was robustly identified (Hugo et al., 2015, Cell 162, 1271-1285) (Figure 11G-M). All these validations further underscore the potential essentiality of these TFs in mediating melanoma malignant transformation.

[0180] To further examine the functional relevance of these TF modules, combinatorial perturbations were performed in the presence of Vemurafenib using the pooled screening framework. Inhibition of SMAD3 or RREB1 alone produced limited effects, whereas their combined inhibition was associated with a reduction in the global growth advantage observed across resistant perturbations (Figure 90). Inhibition of KLF5 alone also reduced resistance, and combined inhibition of RREB1, SMAD3, and KLF5 resulted in the largest reduction in relative growth advantage, with a mean 3.1 -fold decrease (Figure 90). Given the combinatorial application of multiple inhibitors, potential off-target effects or nonspecific additive fitness costs could not be excluded. Accordingly, these results should be interpreted as suggestive of context-dependent functional convergence among the implicated pathways, to support the regulatory analyses, rather than as definitive evidence of synergistic regulation in melanoma drug resistance.Perturbation of Mediator Complex genes results in structure-concordant, heterogeneous melanoma cell state transitions

[0181] Next, distinct genetic perturbations that modulate melanoma cell-state dynamics under DMSO and Vemurafenib treatment were investigated, focusing on the Mediator Complex, a central regulator of transcription (Jeronimo et al., 2017, Trends Cell Biol 27, 765-783), whose role in melanoma phenotypic transitions remains poorly defined.

[0182] The Mediator Complex comprises the Head, Middle, Tail, and Kinase Modules (Chao et al., 2024, Mol Cell 84, 3932-3949.el0) (Figure 12A). The Kinase Module (MED12, MED13, CCNC, CDK8) negatively regulates transcription by preventing RNA polymerase II association with the Core Mediator, whereas the Core Mediator (Head, Middle, Tail) facilitates transcription by bridging transcription factors to the pre-initiation complex. Perturbations of distinct Mediator modules produced divergent phenotypic outcomes associated with their structural composition (Figure 12A-B, Figure 13A). Under DMSO conditions, knockdown of Kinase Module genes (MED 12, MED 13, CCNC) and MED 19, a Middle Module componentinteracting with MED12, induced strong melanoma dedifferentiation (Figure 12B). In contrast, knockdown of Tail Module genes (MED15, MED24) promoted dedifferentiation and drug resistance under Vemurafenib treatment (Figure 12B). Global nascent transcriptome analysis revealed that MED 15 and MED24 perturbations caused the most severe global transcriptional impairment (Figure 12C), whereas Kinase Module perturbations showed extensive multimodal perturbation outcome similarity (Figure 12D). UMAP embeddings of transcriptomic and chromatin data further highlighted functional divergence between modules (Figure 13B-C).

[0183] To identify convergent molecular programs underlying these responses, perturbation-specific transcriptomic phenotyping and motif activity analyses was performed. Under the DMSO condition, Kinase Module perturbations consistently activated inflammatory gene signatures and TF motifs (Figure 12E-F, Figure 13E-F), consistent with established roles of the Kinase Module in suppressing inflammation via inhibiting Core Mediator activity on chromatin (Freitas et al., 2022, Science 378, eabn5647). Strikingly, Hippo / YAP signaling signatures showed the strongest activation following Kinase Module perturbations (Figure 12E-F). This association was supported by the proximity of Kinase Module perturbations with YAP inhibitor perturbations in UMAP space (Figure 13B-C) and by their co-dependencies detected in DepMap CRISPR screens (Figure 13D).

[0184] To further examine the Kinase Module’s participation in YAP -mediated transcription, external ChlP-seq (Galli et al., 2015, Mol Cell 60, 328-337) was analyzed and extensive Core Mediator-YAP co-regulating genomic regions were identified (Figure 13G), and they exhibited increased chromatin accessibility upon Kinase Module perturbation (Figure 12G). Furthermore, as confirmed by independent ChlP-seq data (Freitas et al., 2022, Science 378, eabn5647), MED12-K0 increased Core Mediator binding at consensus Mediator binding sites (Figure 12H). Target genes that were identified, including VEGFC, TGFBR2, and GLIS3, showed coordinated activation of transcription upon MED12-K0 (Figure 13H). Together, these data support that the Kinase Module suppresses melanoma dedifferentiation by restraining Core Mediator activity at YAP-regulated enhancers.

[0185] In contrast, only upon Vemurafenib treatment, MED 15- and MED24-perturbed cells retained elevated undifferentiated gene signatures and motif accessibility relative to NTC (Figure 12E). Despite severe global transcriptional impairment (Figure 12C), these cells displayed pronounced resistance to Vemurafenib-induced differentiation and relativelyminimal chromatin remodeling (Figure 13I-J). Nascent transcription analysis further confirmed sustained expression of YAP-Core Mediator co-target genes under Vemurafenib treatment (Figure 121), supporting that the loss of Tail Module helps maintain YAP-dependent transcriptional programs during drug exposure.

[0186] Among all Mediator perturbations, MED 12 knockdown uniquely promoted strong melanoma dedifferentiation under both DMSO and Vemurafenib conditions and conferred the highest resistance to Vemurafenib-induced differentiation, which could not be fully explained by Kinase Module disruption alone (Figure 12B, Figure 131). The most profound loss of SOXIO activity was observed at both chromatin and transcriptomic levels MED12 knockdown, even under Vemurafenib treatment, which normally enhances SOX10-driven differentiation (Figure 12F, J, Figure 14A). Consistently, MED12-perturbed transcriptomes closely resembled those of reference SOXIO-KD melanoma cells (Wouters et al., 2020, Nat Cell Biol 22, 986-998) (Figure 12K, Figure 14B-D). Analysis of published paired ChlP-seq and RNA-seq profding further showed that MED 12 knockdown selectively impaired MAPKi-induced SOXIO binding and target gene activation (Figure 12L-M). Notably, chromatin accessibility and nascent transcription at a validated SOXIO binding site regulating GAS7 were strongly reduced only in MED12-KD cells (Figure 12N), indicating a unique loss of SOXIO activity in MED 12 perturbation that likely contributed to persistent melanoma dedifferentiation and drug resistance.

[0187] As regulating inflammatory factors’ transcription is the established role of Mediator Complex across biological contexts (Freitas et al., 2022, Science 378, eabn5647), potential convergent downstream effectors were sought that mediate dedifferentiation and drug resistance across Mediator perturbations. VEGFC emerged as the only factor consistently activated in dedifferentiated states under both treatments (Figure 120, Figure 15A-B). VEGFC was identified as a co-target of YAP and Core Mediator (Figure 12P, Figure 13G-H), and its transcriptional regulation responded directly to YAP functional mutations (Figure 15C).Besides, both VEGFC’s proximal CREs activity and expression were induced following SOXIO knockdown in melanoma cells (Figure 12P, Figure 14C). Its correlated expression with Hippo / Y AP target genes in TCGA SKCM samples, strong induction in external undifferentiated melanoma cells (Wouters et al., 2020, Nat Cell Biol 22, 986-998), andactivation in YAP-activated mouse melanoma tumors (Li et al., 2019, Cell Rep 29, 3200-321 l.e4) (Figure 15D-F) support its potential central role.

[0188] Functional validation confirmed VEGFC as a key mediator of resistance.VEGFC knockdown in perturbed A375 cells under the DMSO condition reduced expression of the undifferentiated marker AXL, and suppressed overall growth under Vemurafenib treatment (Figure 12Q, Figure 15G-H). At the perturbation level, VEGFC knockdown significantly reduced the growth advantage of MED12, MED19, MED15, and MED24 perturbations, but not CCNC or MED13 perturbations (Figure 12Q). It also suppressed the growth of YAP inhibitor perturbations and reduced resistance across multiple functional assays (Figure 15LS), supporting the proposed transcriptional regulatory model in which VEGFC co-regulated by Hippo / Y AP-Mediator Complex contributed to melanoma dedifferentiation.

[0189] In summary, PerturbFate enables systematic identification of shared resistant cell states and convergent gene regulatory programs across diverse genetic perturbations, while also resolving perturbation-specific mechanisms. These capabilities highlight PerturbFate as a powerful framework for dissecting key gene regulatory principles governing cell-state transitions across diverse biological contexts.Example 2: PerturbFate library preparation protocolPrimers and oligos

[0190] All primers were ordered from IDT. Barcoded primer plates (ligation primer plate, EasySci-RNA / EasySci-ATAC P7 primer plates) were ordered in the solution form (lOOpM in TE buffer). Other primers are dissolved and diluted as stated in the protocol.Materials• Nuclease free water (Corning, 46-000-CM)• lx PBS (Corning, 21 -040-CV)• 10cm cell culture dish (Genesee, 25-202)• PluriStrainer Mini 40pM (PluriSelect 43-10040-70)• PluriStrainer Mini 20pM (PluriSelect 43-10020-70)• Pierce 16% Formaldehyde (w / v), Methanol-free (Thermo Fisher Scientific, 28908) • SUPERase In RNase Inhibitor 20 U / pL (Thermo Fisher Scientific, AM2696)• RNaseOUT Recombinant Ribonuclease Inhibitor (Thermo Fisher Scientific, 10777019) • Recombinant Albumin, Molecular Biology Grade (NEB, B9200S)• IM Tris-HCl, pH 7.5 (Thermo Fisher Scientific, 15567027)• IM Tris-HCl, pH 8.0 (Thermo Fisher Scientific, 15568025)• IM Tris-HCl, pH 8.3 (Teknova, T1083)• 50% (W / V) PEG8000 (Rigaku)• 5M NaCl (Thermo Fisher Scientific, AM9759)• IM MgCh (Thermo Fisher Scientific, AM9530G)• 5M KAc (Sigma, 95843-100ML-F)• IM MgAc (Sigma, 63052- 100ML)• Dimethylformamide, 99.8% (Fisher Scientific, AC327175000)• Acetic acid (Sigma, A6283-100ML)• 5-Ethynyl Uridine (Vector Laboratories, CCT-1261)• Biotin Picolyl Azide (Vector Laboratories, CCT-1167)• Click-iT Plus Alexa Fluor 647 Picolyl Azide Toolkit (Thermo Fisher Scientific, C10643) • cOmplete Protease Inhibitor Cocktail (Roche, 11697498001)• Glycine (Sigma, 50046-50G)• Glycerol (Thermo Fisher Scientific, 15514011)• DTT (dithiothreitol) (Thermo Fisher Scientific, R0861)• Phenylmethylsulfonyl Fluoride (Sigma, 10837091001)• Dimethyl Sulfoxide (VWR, 97063-136)• Triton X-100 for molecular biology (Sigma, 93443-100ML)• lOmM dNTP (Thermo Fisher Scientific, R0192)• Maxima H Minus Reverse Transcriptase with Buffer (Thermo Fisher Scientific, EP0753) • T4 DNA Ligase (NEB, M0202L)• EDTA 0.5M Solution (VWR, 97062-656)• Elution buffer (Qiagen, 19086)• DAPI (lOmg, Thermo Fisher Scientific, D1306)• Falcon Tubes, 15 ml (VWR, 21008-936)• Falcon Tubes, 50 ml (VWR, 21008-940)• NEBNext® Ultra II Non-Directional RNA Second Strand Synthesis Module (NEB, E6111L)• DNA binding buffer (Zymo Research, D4004- 1 -L)• AMPure XP beads (Beckman Coulter, A63882)• RNAClean XP beads (Beckman Coulter, A63987)• SDS, 20% Solution, RNase Free (Thermo Fisher Scientific, AM9820)• Tween-20 (Sigma, P9416-100ML)• Ethanol (Sigma, 459844-4L)• Dynabeads MyOne Streptavidin C l (Thermo Fisher Scientific, 65002)• Proteinase K Solution (20mg / mL), RNA grade (Thermo Fisher Scientific, 25530049) • NEBNext High-Fidelity 2X PCR Master Mix (NEB, M0541L)• Qubit dsDNA HS kit (Invitrogen, Q32854)• Qubit tubes (Invitrogen, Q32856)• E-Gel EX Agarose Gel, 2% (Thermo Fisher Scientific, G402002)• E-Gel 50bp DNA Ladder (Thermo Fisher Scientific, 10488099)• 4-Chip Disposable Hemacytometers, Bulldog Bio (VWR, 102966-632)• DNA LoBind Tube 1.5 ml, PCR clean (Eppendorf North America, 22431021)• l.OmL Self-Standing Cryovial (GeneSeeSci, catalog number: 24-200P)• LoBind clear, 96-well PCR Plate (Eppendorf North America, 30129512) • 0.2mL 8-Strip Tubes with Individual Caps (PCR Tubes) (Genesee, 27-125U)• Reagent reservoirs (Fisher Scientific, 07-200-127)• Falcon® 5mL Round Bottom w / Cell Strainer (Fisher Scientific, 352235)• eXTReme Foil Seal Film (Genesee, 12-156)• eXTReme Clear Sealing Film (Genesee, 12-157)• DNA Clean & Concentrator- 5 (Zymo Research, D4014)• Zymoclean Gel DNA Recovery Kit (Zymo Research, D4008)Buffer and reagents preparationDAPI stock (Stored -20°C)

[0191] Dissolve lOmg DAPI in 2ml of nuclease-free water with a final concentration of 5mg / ml Split the DAPI solution into multiple tubes (lOOul per tube).

[0192] Take out one tube (lOOul, 5mg / ml DAPI), add 1.9ml nuclease-free water. Split the diluted DAPI solution into multiple tubes (lOOul per tube, 0.25mg / ml DAPI).

[0193] Store the stock in the dark in -20°C.1% (V / V) SDS (Stored at room temperature)

[0194] Dilute 20% SDS into 1% by mixing 500uL 20% SDS with 9.5mL Molecular Biology Grade Water.

[0195] Filter through a 0.2-micron strainer and store at room temperature.10% (V / V) Triton-X-100 in nuclease-free water (Stored at 4°C)

[0196] Add 2mL Triton X-100 to 18mL nuclease-free water.

[0197] Then rigorously vortex until a uniform solution is formed. The mix can be stored in 4°C for up to 1 year.10% (V / V) Tween-20 in nuclease-free water (Stored at 4°C)

[0198] Add 2mL Tween-20 to 18mL nuclease-free water.

[0199] Then rigorously vortex until a uniform solution is formed. The mix can be stored in 4°C for up to 1 year.lOOmM PMSF (Stored at -20°C)

[0200] Dissolve 17.4mg PMSF (MW=174.19) in lOmL 100% ethanol.

[0201] Aliquot into 1 ,5mL tubes and the reagent is stable for months.4x Tagmentation Buffer for in-situ ATAC (Stored in -20°C)Reagents Volume Final concentrations IM Tris-Acetate pH7.8 132pL 132mM5M KAc 52.8pL 264mMIM Mg Ac 40pL 40mMDMF 640pL 64% (V / V)Molecular Biology Grade 135.2pL \WaterTotal ImL \

[0202] Aliquot the solution into 1 5mL tubes and store at -20°C.2x Tagmentation Buffer for Read! tagmentation (Stored in -20°C)Reagents VolumeIM TrisHCl (pH 7.5) 4mlIMMgCh 2mlDMF 40mlNuclease-free water 154ml

[0203] Aliquot the solution into 15mL or 1.5mL tubes and store at -20°C.5x PEG NaCl Reverse Transcription Buffer for whole cell assays (Stored in -20°C)Reagents Volume Final concentrations IM Tris-HCl pH8.3 125pL 250mM5M NaCl lOpL lOOmMIM MgCh 7.5pL 15mM1MDTT 25 pL 50mM50% (W / V) PEG8000 250pL 25%Molecular Biology Grade 82.5pL \WaterTotal 500pL \

[0204] Caution: The concentration of NaCl in this buffer is adjusted. For each RT well, whole cells are expected to be in 2 pL of PBSI, which already contains 137 mM NaCl. To achieve a final concentration of 75 mM NaCl in the 5 pL RT reaction, the concentration of NaCl was reduced to 100 M in the 5x RT buffer.Protease Inhibitor reconstitution

[0205] Dissolve one tablet of cOmplete Protease Inhibitor Cocktail in 2mL Molecular Biology Grade Water, the solution is 25x stock.

[0206] Aliquot the solution into 1.5mL tubes and store at -20°C.Buffers for Streptavidin beads pulldown

[0207] 2xBW buffer: lOmM Tris-HCl 7.5, ImM EDTA, 2M NaClReagents Volum Final concentrationseIM Tris-HCl pH7.5 lOOpL lOmM0.5MEDTA 20pL ImM5M NaCl 4mL 2MMolecular Biology Grade Water 5.88m \LTotal lOmL \

[0208] IxBW buffer: 5mL 2xBW buffer + 5mL Water

[0209] IxBW-T buffer: 5mL IxBW buffer + 25pL 10% tween-20 (the final concentration is 0.05%)Reagents for metabolic labeling and in-situ Click Chemistry

[0210] Reconstitute 5-EU powder to 0.25mg / pL in DMSO: add 400pL DMSO to the vial and heat the vial in a metal bath at 60°C until the powder fully dissolves.

[0211] Dilute to 200mM: mix 21.46pl 0.25mg / pL 5EU stock with 78.54pl DMSO.

[0212] Prepare lOx Click Chemistry Additive buffer: add 2mL Molecular Biology Grade Water to the Component E vial of the Click-iT kit containing 400mg Additive powder.

[0213] Aliquot in 1 ,5mL tubes and store at -20°C. Renew the reagent when the color of the solution turns brown.

[0214] Prepare lOmM Biotin-Picolyl-Azide (MW=622.74) in DMSO:

[0215] Shortly spin down the vial to make sure powder is at the bottom.

[0216] Add 803 pL of DMSO to the tube containing 5mg Biotin-Picolyl-Azide (50mM stock).

[0217] Vortex well.

[0218] Dilute to lOmM: mix lOpL 50mM stock with 40pL DMSO and vortex well.

[0219] Store dissolved Biotin-Picolyl-Azide stock in -20°C. It is stable for up to 1 year. Aliquot into multiple tubes to reduce freeze-and-thaw cycles.Preparation of ligation adapter plates

[0220] In each well of an empty 96-well plate, add 5pL of lOOpM Ligation Adaptor Primer and 5 pL 100 pM Barcoded Ligation Primers. Make sure to add the Barcoded Ligation Primers to their correct wells.

[0221] Anneal the adaptor and ligation primers together by running the following thermocycler program:Temperature Time95°C 2 minutesRamp down to 20°C at the \rate of -l°C / minute4°C Hold

[0222] The concentration of the annealed primer in each well is 50 pM.

[0223] Dilute the primers to 3.125 pM by adding 150 pl of EB buffer to each well. The resulting product is in stable, double-stranded form and can be stored at 4°C or frozen. In 4°C, the annealed primers should be stable for roughly three months and are suitable for short-term testing experiments.Preparation of PerturbFate-ATAC Tn5

[0224] This Tn5 loading protocol is derived from Hennig et al. 2018, Large-Scale Low-Cost NGS Library Preparation Using a Robust Tn5 Purification and Tagmentation Protocol -purified Tn5 protein is also from this publication.

[0225] Prepare Tn5 Dilution Buffer: 25mM Tris pH 7.5, 800mM NaCl, 0. ImM EDTA, ImM DTT and 50% glycerol.

[0226] Prepare double-stranded DNA adapters: Mix 20pL lOOmM Tn5 adapter for ligation oligo ( / 5’Phos / CGCGCTGCATACTTGGAGATGTGTATAAGAGACAG-3’, in EB buffer), 20pL lOOmM blocked Tn5Merev oligo ( / 5’Phos / CTGTCTCTTATACACATCT / 3’ddC / , in EB buffer) with 85 pL Tn5 Dilution Buffer; mix 20pL lOOmM Tn5 Nextera R2 oligo (5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3’, in EB buffer), 20pL lOOmM blocked Tn5Merev with 85 pL Tn5 Dilution Buffer.

[0227] Anneal adapters: both tubes are incubated in the thermocycler following the program: 95°C for 5 minutes, slowly cooled to 65°C (-O.lC / sec), 65°C for 5 minutes, slowly cooled to 4°C (-0.1°C / sec).

[0228] Mix adapters from both tubes together: to obtain 250pL adapters mix (8pM for each adapter).

[0229] Dilute Tn5: thaw Tn5 protein (4mg / mL) on ice and mix 75pL Tn5 protein with 75 pL Tn5 Dilution Buffer, 150pL pure glycerol and additional 2mM DTT.

[0230] Tn5 assembly: mix 275pL diluted Tn5 to 250pL annealed adapter mix, pipette gently to mix, and incubate in the thermomixer at 23°C, 300rpm for 30min.Preparation of Nextera-R2 Tn5

[0231] The protocol is derived from Hennig et al. 2018, Large-Scale Low-Cost NGS Library Preparation Using a Robust Tn5 Purification and Tagmentation Protocol - purified Tn5 protein is also from this publication.

[0232] 150pL of lOOpM Tn5-ME-B oligo (5’- GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3’, in TE buffer) is mixed with 150pL of lOOpM Tn5Merev oligo ( / 5’Phos / CTGTCTCTTATACACATCT-3’, in TE buffer) reaching a final concentration of 50pM.

[0233] Then, the mixture is split into aliquots and performed the following thermocycler conditions: 95°C for 5 minutes, slowly cooled to 65°C (-0.1C / sec), 65°C for 5 minutes, slowly cooled to 4°C (-0.1°C / sec).

[0234] The mixture is further diluted to 35pM by mixing lOpL of the oligo mixture with 4.28pL of TE buffer. Then, IpL of the Tn5 enzyme at 4mg / mL is combined with 19pL of Tn5 Dilution Buffer (25mM Tris pH 7.5, 800mM NaCl, 0. ImM EDTA, ImM DTT and 50% glycerol) and 2pL of the 35pM Tn5-ME-B / Tn5-Merev oligo mixture.

[0235] Incubate on a thermomixer at 23°C for 30 minutes and dilute Tn5 with 22pL of glycerol. The final solution is stored at -20°C for future usage.Library preparationCell labeling

[0236] For cells growing in a 6cm dish:

[0237] Quickly take 2.5mL culture media to a 15mL tube.

[0238] Add 2.5pL of 200mM EU stock to the tube to reach a final concentration of 200pM EU with 0.1% DMSO. Pipette several times to mix well.

[0239] Suction the remaining media in the dish. Gently add the media containing 5-EU back to the dish.

[0240] Incubate in the 37°C cell culture incubator for Ih.Harvest cells from the dish

[0241] For a 6cm dish:

[0242] Suction the culture medium, and rinse the dish with ImL PBS.

[0243] Digest cells with ImL 0.25% trypsin for 3min.

[0244] Neutralize the trypsin by 3mL DMEM complete medium, and quickly transfer cells into a 15mL tube and keep it on ice.

[0245] Spin down cells at 300xg, 4°C for 5min.

[0246] Wash cells again using 3mL ice-cold PBS, then spinning down at 300xg, 4°C for 5min.Cell fixation

[0247] All steps are performed on ice.

[0248] For every IM cells, prepare:

[0249] PBSI:Reagents Volumes Final concentrations IxPBS 750pL \SUPERase In 7.5pL 1% V / VBSA (NEB) 7.5pL 1% V / V25x Protease Inhibitor 30pL lxTotal 795jiL \

[0250] lOOmM Tris-HCl pH8:Reagents Volumes Final concentrations IM Tris-HCl pH8 100pL lOOmMMolecular Biology Grade 900pL \WaterTotal ImL \

[0251] 1% (W / V) Formaldehyde in PBS:Reagents Volumes Final concentrations 16% Formaldehyde (W / V) 62.5pL 1% (W / V)IxPBS 937.5pL \Total ImL \

[0252] After washing cells, IM cells in each tube are fixed on ice with 1ml 1% FA for 10 min.

[0253] Quickly add 20pL of 10% Triton X-100 (0.2% final) to the tube and gently pipette a few times. Cells are permeabilized for 3 min on ice.

[0254] Spin down tubes at 500g for 3 min at 4°C and quickly aspirate the supernatant. Cells are resuspended in 500pL of PBSI.

[0255] Add 500 pL of cold 100 mM Tris-HCl pH8 and 5 pL of 10% Triton-XlOO to each tube, gently pipette to mix. Spin down tubes at 500g for 3 min at 4 °C.

[0256] Cells of each tube are resuspended in lOOpL of ice-cold PBSI.

[0257] Count cells: prepare DAPI working solution by diluting 0.25mg / ml DAPI in PBS at 1:100 ratio, and mix DAPI working solution and cell suspension at 1:1 ratio for cell counting. Cells should either be cryopreserved or immediately processed.ATAC in bulk

[0258] Prepare ATAC reactions on ice. For every 50,000 cells:Reagents Volumes Final concentrations 4xTD buffer 12.5pL lx50,000 cells in lOpL 50,000 cells / rxnPBSIMolecular Biology 19.25pL \Grade WaterSUPERase In 0.5pL 1% V / VRNaseOUT 0.5pL 1% V / V25x Protease 2pL lxInhibitor10% Triton-XlOO 0.25pL 0.05%PerturbFate-Kl AC 5pL \Tn5Total 50pL \

[0259] Incubate on the thermomixer at 37°C, 500rpm for 30min.Wash cells

[0260] Prepare 10ml PBB-0.05:Reagents Volumes Final concentrations IxPBS lOmL10% Triton-XlOO 50uL 0.05% V / VBSA (NEB) lOOpL 1% V / VTotal ~10mL \

[0261] Prepare 3 ml PBB-0.1 :Reagents Volumes Final concentrations IxPBS 3mL \10% Triton-XlOO 30pL 0.1% V / VBSA (NEB) 30uL 1% V / V\Total ~3mL

[0262] Prepare 250uL PSBI for each RT plate:Reagents Volumes F i nal concentrate oris PBS 250pL \SUPERase In 2.5uL 1% V / VBSA (NEB) 2.5pL 1% V / V25x Protease Inhibitor lOpL lxTotal 250uL \

[0263] Add 450pL PBB-0.05 to each tube. Combine all tubes of each condition. Spin down at 500g, 4°C for 5min.

[0264] Aspirate the supernatant, and resuspend cells in ImL PBB-0.05.

[0265] Spin down once more.

[0266] Resuspend cells for reverse transcription: resuspend up to 150,000 cells (cells from 3 ATAC tubes) in 210pL PSBI, and they will be distributed to a 96-well plate for RT. (To control the intercellular contamination, bulk RT is still performed in 96-well plates.) The number of cells to be included in each RT well can be adjusted: based on experience, 500-5000 cells / RT well works equally well.Reverse transcription in bulk

[0267] For every 210pL cell suspension, a master reaction mix is prepared and is then distributed to a 96-well plate:

[0268] Prepare the RT reaction:Reagents Per well Per 96~well plate Cell suspension 2pL 210pLdNTP (lOmM each) 0.25 pL 2625 pLdT primer (TOOpM) 0.25pL 26.25pLrN primer (lOOpM) 0.25 pL 26.25 pLsgRNA capture primer 0.2pL 21 pL(iOpM)EB buffer 0.3 pL 31 5 pl.Total 3.25pL 341.25pL

[0269] Split 42.5pL premixed reaction to each well of an 8-well PCR strip for a RT plate.

[0270] Split 3.25pL reaction to each well of the plate using a multi-channel pipette, and seal the plate with a plastic film.

[0271] After pre-heating the PCR thermocycler, incubate the plate at 50°C for 5 min. Then transfer the RT plate to ice immediately.

[0272] Prepare the reverse transcription reaction mix during the incubation:Reagents Per well Per 96-well plate 5x PEG NaCI RT buffer IpL HOpLMaxima H Minus RTase 0.125pL 13.75pLSUPERase In 0.125pL 13.75pLMolecular Biology Grade 0.5pL 55pLWaterTotal 1.75pL 192.5pL

[0273] Split 23.4pL to each well of an 8-well PCR strip. Then add 1.75pL to each well of the plate containing cells, and gently mix for 5 times on ice.

[0274] Pre-cool the thermocycler, and start the RT program:Temperature Time4°C 2min10°C 2min20°C 2min30°C 2min40°C 2min50°C 17min

[0275] After RT, put the RT plate back on ice.Wash cells

[0276] Prepare PBB-0.1-EDTA: 644pL PBB-0.1+56pL 500mM EDTA

[0277] Split 5pL PBB-0.1-EDTA to each well of the RT plate. Then collect all cells from the same condition together.

[0278] Spin down at 4°C, 500xg for 5min.

[0279] Aspirate the supernatant and resuspend cells of each tube in 500pL PBB-0.05.

[0280] Spin down at 4°C, 500xg for 5min.

[0281] Resuspend cells in an appropriate volume of PBB-0.05 for the first indexing ligation.

[0282] For example:

[0283] 255pL PBB-0.05 is needed for cells to be distributed to a full 96-well plate.The 1st ligation

[0284] For each row of the ligation plate, split 32.5 pl of the cell suspension into each well of an 8-well PCR tube strip. Ensure the treatment condition of the cells distributed to each row is recorded, as the barcode of each ligation well will be used for cell demultiplexing.

[0285] For example:

[0286] Rowl-4: Vemurafenib treated cells

[0287] Row5-8: DMSO treated cells

[0288] Distribute 2pL cell suspension into each well of the 96-well plate using a multichannel pipette.

[0289] Add IpL barcoded DNA ligation adapter (PerturbFate multi-modal ligation plate, 3.125pM) to each ligation plate. Use either the eppendorf 96-channel pipette machine or a multichannel pipette.

[0290] Prepare the ligation reaction mix for each well of 96 well plates:Reagents Per well Per 96-well plate lOx T4 ligation buffer 0.5pL 55pLSUPERase In 0.05pL 5.5pLT4 ligase 0.5pL 55pLMolecular Biology Grade 0.45pL 49.5pLWaterTotal 1.5pL 165pL

[0291] Split 20.6pL to each well of the 8-well PCR strip, and split 1.5pL into each well of the ligation plate using a multi-channel pipette.

[0292] Incubate at room temperature with gentle shaking (50rpm) for 30min.

[0293] Prepare EDTA-PBB-0.1 : 56pL 500mM EDTA + 644pL PBB-0.1

[0294] Split 5pL EDTA-PBB-0.1 into each well of the ligation plate.Wash cells

[0295] Pool all cells together using a multi-channel pipette with wide-bore tips.

[0296] Pellet cells (500g, 5min, 4°C). Aspirate the supernatant, and resuspend cells in lOOOpL PBB-0.05.

[0297] Pellet the cells / nuclei (500g, 5min, 4°C), aspirate the supernatant as much as possible while keeping the cell pellet undisturbed.

[0298] Resuspend cells in an appropriate volume of PBB-0.05 for the 2nd indexing ligation.

[0299] For example: resuspend cells in 255pL PBB-0.05 for one 96-well ligation plate.The 2nd ligation

[0300] For each row of the ligation plate: split 32.5pL of the cell suspension into each well of an 8-well PCR tube strip.

[0301] Distribute 2pL cell suspension into each well of the 96-well plate using a multichannel pipette.

[0302] Add IpL barcoded DNA ligation adapter (Easysci ligation plate 1-4,3. 125pM) to each ligation plate. Use either the eppendorf 96-channel pipette machine or a multichannel pipette.

[0303] Prepare the ligation reaction mix for each well of 96 well plates:Reagents Per well Per 96-well plate lOx T4 ligation buffer 0.5pL 55pLSUPERase In 0.05pL 5.5pLT4 ligase 0.5pL 55pLMolecular Biology Grade 0.45pL 49.5pLWaterTotal 1.5pL 165pL

[0304] Split 20.6pL to each well of the 8-well PCR strip, and split 1.5pL into each well of the ligation plate using a multi-channel pipette.

[0305] Incubate at room temperature with gentle shaking (50rpm) for 30min.

[0306] Prepare EDTA-PBB-0.1 : 56pL 500mM EDTA + 644pL PBB-0.1

[0307] Split 5pL EDTA-PBB-0.1 into each well of the ligation plate.Wash cells

[0308] Pool all cells together using a multi-channel pipette with wide-bore tips.

[0309] Pellet cells (500g, 5min, 4°C). Aspirate the supernatant, and resuspend cells in lOOOpL PBB-0.05.

[0310] Pellet cells (500g, 5min, 4°C), aspirate the supernatant as much as possible while keeping the cell pellet undisturbed.

[0311] For cells from one ligation plate, resuspend in one volume of PBS for the click chemistry reaction. Scale up the reaction according to the number of ligation plates used.Biotinylation Click Chemistry on nascent RNA in cells

[0312] For cells in each ligation plate, prepare one reaction mix in the following order:

[0313] Prepare lx Click chemistry additive:

[0314] Mix 5pL lOx Click Chemistry Additive with 45pL Molecular Biology Grade Water to obtain 50pL lx additive.Reagents Volumes Final concentrations Cells in IxPBS 437pL \lOmM Biotin-Picolyl-Azide in 2.5pL 50pMDMSOCopper protectant lOpL \lx Click Chemistry Additive 50pL \SUPERase In 5pL 1% V / VTotal ~500pL \

[0315] Incubate at room temperature for 30min. Gently pipette several times to resuspend cells at the 15min time point.Wash cells

[0316] Pool all cells together and add an equal volume of PBB-0.1 , mix gently.

[0317] Pellet cells (500g, 5min, 4°C). Aspirate the supernatant, and resuspend cells in lOOOpL PBB-0.05.

[0318] Pellet cells (500g, 5min, 4°C), aspirate the supernatant as much as possible while keeping the cell pellet undisturbed.Post-ligation sort

[0319] Resuspend cells in 200pL PBB-0.05, stain cells with DAPI by adding 1 / 100 volume of 0.25mg / ml DAPI solution. Sort DAPI singlets into 30pL PBS+0.05% triton-XlOO by flow cytometry to remove debris and clumped cells.Distribute cells

[0320] Measure the cell concentration under the microscope, and adjust cell concentration using PBS+0.05% triton-XlOO if needed.

[0321] Distribute cells into 8-well PCR strips, reaching 2,000 cells per 4pL suspension in each well.

[0322] Store strips in -80°C or process immediately.After this step, the cells in PCR strips can proceed with either the Tri-omics or Dual-omics protocol.Tri-omics downstream protocolCell lysis

[0323] Prepare cell lysis buffer:Reagents Per well Per stripProteinase K (20mg / mL) 0.25pL 2.5pLSDS (1% V / V) 0.25pL 2.5pLWater 0.5pL 5pLTotal 2pL 20pL

[0324] Add 2pL to each well of PCR strips. (V=6pL / well)

[0325] Incubate strips at 37°C for 15min with gentle shaking (300rpm).

[0326] During the incubation, prepare stop buffer:Reagents Per strip10% Tween-20 20pLlOOmM PMSF in 100% Ethanol 2.5pLSUPERase In 0.3pL

[0327] After the incubation, transfer strips onto the ice immediately. Add 2pL stop buffer to each well, mix well by vortexing and following spinning down.

[0328] Incubate strips on ice for 5min.Nascent RNA pulldown

[0329] The following steps are for an 8-well PCR strip, scale up according to the number of strips being processed.

[0330] Wash the Streptavidin beads Cl : Vortex the bottle for 30s to resuspend beads completely. For each strip, take 24pL beads (6pL beads suspension per well) to a new 1.5mL EP tube.• Add 240pL of IxBW-T buffer to the tube, and pipette 10 times to mix beads well.• Put the tube on the magnetic stand for 2min. Aspirate the supernatant.• Wash beads with 240pL IxBW-T + 2.4pL SUPERase In (1% V / V) once more.• Put the tube on the magnetic stand for 2min. Aspirate the supernatant.• Wash beads with 200pL IxBW-T + 2pL SUPERase In (1% V / V) once more.• Put the tube on the magnetic stand for 2min. Aspirate the supernatant.• Resuspend beads in 64 pL 2xBW buffer + 1.5pL SUPERase In.

[0331] For each well of the strip: add 8pL beads suspension to each well of the strip. Mix well by pipetting for several times.

[0332] Install the strip to the rotator with a PCR tube adaptor, and rotate at room temperature for Ih.

[0333] During the incubation, prepare IxBW-T-SUPERase In and water-SUPERase In:• To wash beads of each strip:• Prepare IxBW-T- SUPERase In: 560pL IXBW-T + 2.8pL SUPERase In (0.5% V / V), split 65 pE into each well of an 8-well PCR stripe.• Prepare water-SUPERase In: 450pL Molecular Biology Grade Water + 2.3 pL SUPERase In (0.5% V / V), then split 55pL to each well of a PCR stripe. • Keep them on ice.

[0334] Keep the pulldown strip on the magnetic stand on ice for 2min. Then transfer the supernatant to another PCR strip (~16pL). Take as much as possible.

[0335] Wash beads on ice:• Add 20pL of IxBW-T- SUPERase In buffer to each well of the pulldown strip, and gently pipette up and down for 10 times.• Put the strip back on the magnetic stand and keep on ice for 2min. Then take the supernatant, and combine them with 16pL 1st round supernatant in each well (pre-existing tx+ ATAC tubes).• Repeat the washing step once more using a 40pL IxBW-T-SUPERase In buffer. Discard the supernatant.

[0336] Add 50pL Water-SUPERase In to each well, quickly spin down, and put the strip back to the magnetic stand on ice. Aspirate the supernatant after 2min. Finally, resuspend the beads well in 8pL Molecular Biology Grade Water (nascent tx tubes).

[0337] Now having a strip with nascent RNA-cDNA hybrid on beads (in 8pL water / well), and a strip with pre-existing RNA-cDNA hybrid as well as ATAC fragments in the supernatant to be purified (36pL / well).Second Strand Synthesis (SSS) on beads

[0338] Thaw the second strand synthesis buffer at room temperature (~5min).

[0339] Prepare second strand synthesis mix for cDNA on beads:Reagents Per well Per strip Second Strand Synthesis Buffer 4 / 3 pL 12pLSecond Strand Synthesis Enzyme 2 / 3 pL 6pLTotal 2pL 18pL

[0340] Add 2pL SSS reaction mix to each well of the nascent tx strip with resuspended beads.

[0341] Then gently pipette several times using a P20 pipette on ice to fully mix beads with the reaction.

[0342] Install the nascent tx strip on the rotator at 16°C, and rotate for 2h.Pre-existing tx and ATAC purification and SSS

[0343] Purify the product using 1 ,5x RNAclean XP beads:

[0344] Add 54pL beads to each well of the strip containing combined supernatant.

[0345] Gently vortex and quickly spin down, then incubate at room temperature for 5min.

[0346] Put the strip on the magnetic stand for 5min.

[0347] Discard the supernatant, keep the strip on the magnetic stand, and wash beads twice with lOOpL 80% Ethanol.

[0348] Quickly spin down and put the strip back to the magnetic stand, aspirate residual ethanol at the bottom of tubes.

[0349] Finally, elute pre-existing RNA / DNA hybrid and ATAC DNA in 8pL EB buffer.

[0350] Prepare second strand synthesis mix:Reagents Per well Per strip Second Strand Synthesis Buffer 4 / 3 pL 12pL Second Strand Synthesis Enzyme 2 / 3 pL 6pLTotal 2pL 18pL

[0351] Add 2pL SSS reaction mix to each well of the pre-existing tx+ATAC strip.

[0352] Gently vortex and briefly spin down to fully mix beads with the reaction.

[0353] Install the strip on the rotator at 16°C, and rotate for 2h.Read! tagmentation by Nextera-R2 Tn5

[0354] After SSS, put nascent tx strips on the magnetic stand for 2min, and take the supernatant containing nascent cDNA. (~10pL / well).

[0355] For nascent tx, use 1 / 100 Nextera-R2 Tn5 per well:Reagents Per well Per strip 2x TD buffer for Read2 tagmentation 9.9pL 90pLNextera-R2 Tn5 0.1 pL 0.9pLTotal lOpL 90.9pL

[0356] For pre-existing tx, use 1 / 50 Nextera-R2 Tn5 per well:Reagents Per well Per strip 2x TD buffer for Read2 tagmentation 9.8pL 90pLNextera-R2 Tn5 0.2pL 1.8pLTotal lOpL 91.8pL

[0357] Add lOpL master mix to corresponding strips. Gently vortex and briefly spin down to mix the reaction well.

[0358] Incubate on 55°C for 5min.

[0359] During incubation, prepare inactivation buffer:Reagents Per well Per strip B SA (NEB) 0.4pL 4pL1% SDS 0.4pL 4pLTotal 0.8pL 8pL

[0360] After the tagmentation, quickly transfer all strips to ice.

[0361] Add 0.8pL inactivation buffer to each well and mix well by gentle vortexing.

[0362] Add 1 pL universal P5 primer (20pM) into each well.

[0363] Incubate at 55°C for 15min.

[0364] Add 1.2pL 10% tween-20 to each well to quench SDS.Indexing multiplex PCR

[0365] Add 1 pL of indexed Nextera P7 primer (20pM) to each well: make sure barcodes for both tubes split from the same pool of cells are well recorded.• For example:• nascent tx strip welll-8: EasySci P7 platel - columnl A-H • pre-existing tx+ATAC strip welll-8: EasySci P7 platel - column2 A-H

[0366] Add 1 pL barcoded inner U6 primer, make sure the order of barcodes is the same between corresponding nascent tx strip and pre-existing tx+ATAC strip:• For example:• nascent tx strip welll-8: inner U6 primer 1-8• pre-existing tx+ATAC strip welll-8: inner U6 primer 1-8

[0367] Add 25 pL NEBnext Hifi 2x master mix to each well of all strips. Gently vortex and briefly spin down to mix well.

[0368] Run the PCR program:Temperatures Time Cycles72°C 5min \98°C 30s \98°C 10s 1466°C 30s 1472°C 30s 1472°C 2min \4°C Hold \sgRNA enrichment PCR

[0369] Take lOpL multiplex PCR product from each well and combine the PCR product from 8 wells of both nascent tx and pre-existing tx+ATAC strip (V=160pL).

[0370] Purify 160pL combined PCR product with double size selection using 0.8-1. Ox AMPURE beads:• Mix 160pL combined PCR product with 128pL AMPURE XP beads, incubate at room temperature for 5min.• Put the tube on the magnetic stand for 5min.• Take the supernatant, and mix it with another 32pL beads, incubate at room temperature for 5min.• Rinse beads with 300pL 80% ethanol twice. Spin down and put back the tube to the magnetic stand, and aspirate residual ethanol at the bottom of the tube.• Elute combined cDNA of each strip pair in 16pL EB buffer: add 16pL EB buffer to the tube, gently vortex to fully resuspend beads, then incubate at room temperature for 5min. Finally, take the eluate.

[0371] Assemble sgRNA enrichment PCR reactions:• For each strip pair:• Add 2pL lOpM universal P5 primer + 2pL indexed EasySci-ATAC P7 primers (with TruSeq R2, lOpM) + 20pL 2x NEBnext PCR master mix to each well. Record the indices used for each strip pair.

[0372] Run the following PCR program:Temperatures Time Cycles98°C Imin \98°C 10s 1566°C 30s 1572°C 20s 1572°C 2min \4°C Hold \sgRNA library purification

[0373] For each sgRNA enrichment PCR reaction, perform the column purification using the Zymo DNA-concentrator5 kit. Elute each column in 20pL EB buffer.

[0374] Load all eluate to the 2% e-gel to check the sgRNA library amplification.

[0375] If the strongest band at ~276bp could be seen on the gel, perform gel extraction on this band. Finally, elute sgRNA libraries in 20pL EB buffer.Main libraries purification

[0376] Following amplification of the sgRNA libraries, combine the rest of the PCR product from nascent tx strips, and pre-existing tx + ATAC strips, respectively.

[0377] Take lOOpL of combined PCR product from each part, and perform column purification.

[0378] Finally, elute libraries containing either nascent cDNA or pre-existing cDNA+ATAC DNA from all single cells in the 20pL EB buffer.

[0379] Load libs onto 2% e-gel and cut 200bp to ~800bp band for zymo gel extraction.Figure 16 provides representative gel images

[0380] Left image: lane 2-3 sgRNA library before the gel extraction

[0381] Right image: lane 2-6 nascent tx library, line7-l 1 pre-existing tx + ATAC library before the gel extraction

[0382] Thick ladder bands correspond to 2.5kb, 800bp, and 350bp. The interval between bands is 50bp.Dual-omics downstream protocolSecond strand synthesis

[0383] Thaw the second strand synthesis buffer at room temperature (~5min).

[0384] Prepare second strand synthesis mix:Reagents Per well Per stripSecond Strand Synthesis Buffer 2 / 3 pL 6pLSecond Strand Synthesis Enzyme 1 / 3 pL 3pLTotal IpL 9pL

[0385] Distribute 1 pL SSS reaction mix to each well using a single-channel pipette. Briefly centrifuge using the benchtop centrifuge and then quickly spin down. (V = 5pL)

[0386] Perform second strand synthesis on the Thermomixer at 16°C, 300rpm for 2 hours.Read2 tagmentation on cDNA

[0387] Prepare the tagmentation mix, and add 5pL of mix to each well.Reagents Per well Per strip 2x TD buffer for Read2 4.9pL 45 pLtagmentationNextera-R2 Tn 5 0.1 pL 0.9pLTotal 5pL 45.9pL

[0388] Mix well by gentle vortexing. After spinning down, incubate in the thermocycler at 55°C for 5min.

[0389] During the incubation, prepare inactivation and uncrosslinking buffer:Reagents Per well Per strip Proteinase K (20mg / mL) 0.5pL 4.5pL1% SDS 0.5pL 4.5pLWater IpL 9pLTotal 2pL 18pL

[0390] Add 2pL to each well of PCR strips (V=12pL / well).

[0391] Perform uncrosslinking at 65°C overnight (16h).

[0392] Then purify the product using 1.5x AMPURE XP beads (18pL beads / well):• Add 18pL beads to each well of the.• Gently vortex and quickly spin down, then incubate at room temperature for 5 min.• Put the strip on the magnetic stand for 5min.• Discard the supernatant, keep the strip on the magnetic stand, and wash beads twice with lOOpL 80% Ethanol.• Quickly spin down and put the strip back to the magnetic stand, aspirate residual ethanol at the bottom of tubes.• Finally, elute cDNA and ATAC DNA in 16pL EB buffer.Indexing multiplex PCR

[0393] Add IpL universal P5 primer (20pM) into each well.

[0394] Add 1 pL indexed inner U6 primer (20pM) to each well of the strip:• For example: Welll-8 - inner U6 primer 1-8

[0395] Add 2pL of indexed EasySci P7 primer (lOpM) to each well:• For example: Welll-8: EasySci P7 plate3 - columnl (A-H)

[0396] Add 20pL NEBnext Hifi 2x master mix to each well of the stripe. Gently vortex and briefly spin down.

[0397] Run the PCR program:Temperatures Time Cycles72°C 5min \98°C 30s \98°C 10s 1465°C 30s 1472°C 30s 1472°C 2min \4°C Hold \

[0398] Collect 8pL / well of each strip for sgRNA enrichment PCR.sgRNA enrichment PCR

[0399] Take 8pL multiplex PCR product from each well and combine the PCR product from 8 wells of the strip (V=64pL).

[0400] Purify 64pL combined PCR product with double size selection using 0.8-1 ,0x AMPURE beads:Mix 64pL combined PCR product with 1 ,2pL AMPURE XP beads, incubate at room temperature for 5min.Put the tube on the magnetic stand for 5min.• Take the supernatant, and mix it with another 12.8pL beads, incubate at room temperature for 5min.• Rinse beads with 150pL 80% ethanol twice. Spin down and put back the tube to the magnetic stand, and aspirate residual ethanol at the bottom of the tube.• Elute combined cDNA of each strip pair in 16pL EB buffer: add 16pL EB buffer to the tube, gently vortex to fully resuspend beads, then incubate at room temperature for 5min. Finally, take the eluate.

[0401] For sgRNA enrichment PCR:• Add 2pL lOpM universal P5 primer + 2pL indexed EasySci-ATAC P7 primers (with TruSeq R2, lOpM) + 20pL 2x NEBnext PCR master mix to each well.

[0402] Mix the reaction as stated above, then run the following PCR program:Temperatures Time Cycles98°C Imin \98°C 10s 1566°C 30s 1572°C 20s 1572°C 2min \4°C Hold \sgRNA library purification

[0403] For each sgRNA enrichment PCR reaction, perform the column purification using the Zymo DNA-concentrator5 kit. Elute each column in 20pL EB buffer.

[0404] Load all eluate to the 2% e-gel to check the sgRNA library amplification.

[0405] If the strongest band at ~276bp could be seen on the gel, perform gel extraction on this band. Finally, elute sgRNA libraries in 20pL EB buffer.Main libraries purification

[0406] If the amplification of sgRNA libraries is successful, combine the rest of the PCR product.

[0407] Take lOOpL of combined PCR, and perform column purification.

[0408] Finally, elute libraries containing cDNA and ATAC DNA from all single cells in the 20 pL EB buffer.

[0409] Load libs onto 2% e-gel and cut 200bp to ~800bp band for zymo gel extraction.Figure 17 depicts a representative gel image

[0410] Lane 2-5 sgRNA library before the gel extraction

[0411] Lane 6-9 Steady-state tx+ATAC library

[0412] Thick ladder bands correspond to 2.5kb, 800bp, and 3 0bp. The interval between bands is 50bp.Example 3: General oligo design for the library preparationTable 1: OligonucleotidesNames DesignsOligo pool c esignSingle 5’-GTGTAACCCGTAGGGCACCTCGTCTCACACC[20bp sgRNA plus strand stranded sequence starting withsgRNA G]GTTTAGCTGCAGGTGTACAGGTACCTAGCGTGACGCACCTGCCTGACCCC oligo [20bp sgRNA plus strand sequence starting withlibrary for G]GTTTCGAGACGGTCGAAGGACTGCTCTCGAC-3’ (SEQ ID NO: 1390) synthesisOpool GTGTAACCCGTAGGGCACCT (SEQ ID NO: 1391)amplification FwdOpool GTCGAGAGCAGTCCTTCGAC (SEQ ID NO: 1392)amplification RevPertiirhbaie-KX Tn5 adapter designTn5 5’- / 5Phos / CGCGCTGCATACTTGGAGATGTGTATAAGAGACAG-3’ (SEQ ID NO: adapter for 1393)ligationoligoTn5 5 -GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3’ (SEQ ID NO: 1394) NexteraR2blocked / 5Phos / CTGTCTCTTATACACATCT / 3ddC / (SEQ ID NO: 1395)Tn5MerevPerturbFate RT primer designsgRNA 5'- / 5Phos / CGCGCTGCATACTTGTG[8bp UMI]CTAGGACCGGCCTTAAAGC capture -3' (SEQ ID NO:1)RT primershortdT 5'- / 5Phos / CGCGCTGCATACTTGTG[8bp UMI]TTTTTTTTTTTTTTT-3' (SEQRT primer ID NO:2)randomN 5'- / 5Phos / CGCGCTGCATACTTGTG[8bp UMI]NNNNNN-3' (SEQ ID NO:3) RT primerPerturbFate 1stindexed ligation oligo designLigation 5 -CAAGTATGCAGCGCGCTCAAGCACGTGGAT / 3InvdT / -3' (SEQ ID NO:4) adaptor- 1PerturbFa 5'- / 5Phos / ACGACGCTCTTCCGATCT[10bp ligationte indexed barcode] ATCCACGTGCTTGAG-3' (SEQ ID NO:5)1stligationoligosPerturbFate 2ndindexed ligation oligo design (the same as EasySci and PerturbSci-Kinetics ligation design)Ligation A*G*A*T*C*G*G*A*A*G*A*G*C*G*T*C*G*T*G*T*A*G*G*G*A*A*A*G*A* adaptor-2 G*T*G*T* / 3ddC / ('*' represents phosphorothioate bonds between nucleotides; 73ddC / ' represents a dideoxvcytidine modification) (SEQ ID NO:6)PerturbFa 5 -AATGATACGGCGACCACCGAGATCTACAC[ 1 Obp ligationte indexed barcode]ACACTCTTTCCCTAC-3' (SEQ ID NO:7)2ndligationoligosPerturbFate P5 / P7 primer designUniversal AATGATACGGCGACCACCGAGATCTACAC (SEQ ID NO: 8)P5 primerEasySci- 5'-CAAGCAGAAGACGGCATACGAGAT[10bp index 7]GTCTCGTGGGCTCGG-3' RNA (SEQ ID NO:9)indexedP7 primersEasySci- 5 -CAAGCAGAAGACGGCATACGAGAT[ 1 Obp indexATAC 7]GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3' (SEQ ID NO: 10) indexedP7 primersPerturbFate sgRNA inner i7 primerMultiplex 5'-CGTGTGCTCTTCCGATCT(10bp innerPCR index7]ATCTTGTGGAAAGGACGAAACACCG-3' (SEQ ID NO: 11)sgRNAenrichment indexedprimerBulk CRISPR screen genomic DNA amplification primersP5 primer 5'-AATGATACGGCGACCACCGAGATCTACAC[l Obp index 5]ACACTCTTTCCCTACACGACGCTCTTCCGATCTATCTTGTGGAAAGGACGA AACACCG-3' (SEQ ID NO: 12)P7 primer 5 -CAAGCAGAAGACGGC ATACGAGAT[ 1 Obp index7JGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCGACTCGGTGCCACTTTTTCAA-3' (SEQ ID NO: 13)5 nt PerterbFate sequence: ATCCACGTGCTTGAG (SEQ ID NO: 14)4nt SNARE seq2 sequence: ACGTACTGCAGACTATGTCTACAG (SEQ ID NO: 15)Example 4: PerturbFate costs estimationTable 2: Cost EstimatesSteps Purposes Reagents Unit cost Overall cost in (USD / unit) experiment (USD) Upstream processing stepsFixation PBSI SUPERase In 8.43 / IM cells 16.86 18.525x Protease 0.1255 / IM 0.251 Inhibitor cellsBSA (NEB) 0.6375 / IM 1.275cellsCell fixation 16% Formaldehyde 0.054 / IM cells 0.10810% Triton-XlOO 0.0014 / IM 0.0028cellsATAC in 4xTD buffer DMF 0.0062 / 50k 0.074 26.63 bulk cellsKAc 0.00081 / 50k 0.0097cellsMgAc 0.00067 / 50k 0.008cellsOther reagents SUPERase In 0.562 / 50k cells 6.744 in the reaction RNaseOUT 0.948 / 50k cells 11.37610% Triton-XlOO 0.000014 / 50k 0.000168cells25x Protease 0.0083 / 50k 0.1 Inhibitor cellsPerturbFate- Tn5 0.693 / 50k cells 8.32 ATAC Tn5Cell washing PBB-0.05 BSA (NEB) 0.85 / mL buffer 8.5 23.3110% Triton-XlOO 0.00028 / mL 0.0028bufferPBB-0.1 BSA (NEB) 0.85 / mL buffer 2.5510% Triton-XlOO 0.00056 / mL 0.0017bufferPSBI SUPERase In 11.24 / mL 11.24bufferBSA (NEB) 0.85 / mL buffer 0.8525x Protease 0.1672 / mL 0.1672 Inhibitor bufferReverse Primers dNTP (lOmM each) 1.932 / 96well 7.728 330.37 Transcription master mix platein bulk dT primers (lOOuM) 1.261 / 96well 5.043platerN primers (lOOuM) L229 / 96well 4.916platesgRNA primer 0.227 / 96well 0.909 (lOuM) plateRT reaction 5xPEGNaCl RT 0.614 / 96well 2.456 master mix buffer plateMaxima H Minus 6L875 / 96well 247.5 RTase plateSUPERase In 15.455 / 96well 61.82plateCell washing Reaction EDTA (500mM) 0.024 / 96well 0.0962 0.0962 quenching plateThe 1stLigation The 1stligation 0.919 / 96well 3.676 275.684 ligation barcoding adapters plateT4 ligase T4 Ligase 61.82 / 96well 247.28 reaction mix plateSUPERase In 6.182 / 96well 24.728plateThe 2ndLigation The 2ndligation 0.678 / 96well 2.713 274.721 ligation barcoding adapters plateT4 ligase T4 Ligase 61.82 / 96well 247.28 reaction mix plateSUPERase In 6.182 / 96well 24.728plateIn-situ Click Click Copper protectant 12.52 / rxn 50.08 72.99 Chemistry Chemistry Biotin-picolyLazide 0.108 / rxn 0.431Reaction (lOmM)SUPERase In 5.62 / rxn 22.48Total cost of PerturbFate upstream protocol: 1022.3 USDCells processed in total: 384,000 cellsCost per cell: 0.00266 / cellTri-omics downstream processing stepsCell lysis Cell lysis Proteinase K 0.1135 / PCR 1.816 1.8541 buffer (20mg / ml) strip1% SDS 0.00074 / PCR 0.01184stripStop buffer 10% Tween-20 0.000784 / PCR 0.012544stripPMSF (lOOmM) 0.000858 / PCR 0.013728stripStrep-biotin Pulldown MyOne Cl strep 11.04 / PCR 176.64 375.33 pulldown reaction beads stripIxBW-T buffer 0.021 / PCR 0.3371strip2xBW buffer 0.006125 / PCR 0.098stripSUPERase In 6.6316 / PCR 106.106stripBeads IxBW-T buffer 0.0268 / PCR 0.4291 washing stripSUPERase In 5.7325 / PCR 91.72stripSecond SSS master SSS buffer 18.975 / PCR 303.6 770.7 Strand mix stripSynthesis on SSS enzymebeadsSecond Purification RNAclean XP 10.22 / PCR 163.5 Strand on dsDNA beads stripSynthesis in and cDNA- supernatant RNA hybridSSS master SSS buffer 18.975 / PCR 303.6mix SSS enzyme stripRead2 Read2 Nextera Read2 Tn 5 O.O35 / PCR 0.56 12.546 tagmentation tagmentation stripon nascent txRead2 Nextera Read2 Tn5 0.069 / PCR 1.104 tagmentation stripon preexisting txRxn BSA (NEB) 0.34 / PCR strip 10.88 inactivation 1% SDS 0.00006 / PCR 0.0019stripMultiplex PCR rxn Universal P5 primer 0.1571 / PCR 5.0272 425.203 PCR stripInner i7 U6 primer 0.0896 / PCR 2.8672stripIndexed Nextera 0.0809 / PCR 2.5888R2-P7 primer stripNEB next 2x PCR 12.96 / PCR 414.72MM stripsgRNA PCR rxn Universal P5 primer 0.0197 / PCR 0.3152 26.397 enrichment wellPCR Indexed TruSeq R2- 0.0101 / PCR 0.1616P7 primer wellNEBnext 2x PCR 1.62 / PCR well 25.92MM DNA sgRNA DNA 1.716 / column 1.716 65.646 purification library cl ean& concentrator- column 5purificationMain libraries DNA 3.431 column cl ean& concentrator- purification 5sgRNA Zymoclean gel 1.93 / column 1.93library gel DNA recoveryextractionMain libraries Zymoclean gel 3.86gel extraction DNA recoveryTotal cost of Tri-omics downstream protocol: 1623 USD Cells processed by Tri-omics downstream protocol: 256,000 cellsCost per cell: 0.00634 USD / cellDual-omics downstream processing stepsSecond SSS master SSS buffer 9.4875 / PCR 75.9 75.9 Strand mix stripSSS enzymeSynthesisRead2 Read2 Nextera Read2 Tn5 0.069 / PCR 0.552 2.197 tagmentation tagmentation stripon steadystate txRxn Proteinase K 0.2043 / PCR 1.6344 inactivation (20mg / ml) stripand 1% SDS 0.001332 / PCR 0.0107 uncrosslinking stripMultiplex PCR rxn Universal P5 primer 0.1571 / PCR 1.2568 106.301 PCR stripInner i7 U6 primer 0.0896 / PCR 0.7168stripIndexed Nextera 0.0809 / PCR 0.6472R2-P7 primer stripNEB next 2x PCR 12.96 / PCR 103.68MM stripsgRNA PCR rxn Universal P5 primer 0.0197 / PCR 0.1576 13.20 enrichment wellPCR Indexed TruSeq R2- 0.0101 / PCR 0.0808P7 primer wellNEBnext 2x PCR 1.62 / PCR well 12.96MM DNA sgRNA DNA 1.716 / column 1.716 7.292 purification library cl ean& concentrat or- column 5purificationMain libraries DNA 1.716 column cl ean& concentrat or-purification 5sgRNA Zymoclean gel 1.93 / column 1.93 library gel DNA recoveryextractionMain libraries Zymoclean gel 1.93 gel extraction DNA recoveryTotal cost of Dual-omics downstream protocol: 204.89 USD Cells processed by Tri-omics downstream protocol: 128, 0( 30 cellsCost per cell: 0.0016 USD / cellFinal cost for each cell of PerturbFate (Tri-omics): 0.00266+0.00634=0.009 USD Final cost for each cell of PerturbFate (Dual-omics): 0.00266+0.0016=0.00426 USDAttorney Docket No.046531-5039-00WOExample 5: PerterbFate ligation adapter barcode sequences and indexing PCR barcode sequences1stligation adapter sequence: ACGACGCTCTTCCGATCTNNNNNNNNNNATC’CACGTGCTTGAG (SEQ ID NO:5) Table 3: Barcodes for 1stligation.Name Barcode SEQ sci_coassay_1 st J ig_p late 1 _21 ACTCAATAGG ID 36 NO: sci_coassay_1 st_l i g_plate 1 _22 CTTGCGCCGC 37 sci_coassay_1 st_l ig_plate 1 _01 TTCTCGCATG 16 sci_coassay_1 st_l ig_p late 1 _23 AATCGTAGCG 38 sci_coassay_1 st_l i g_plate 1 _02 TCCTACCAGT 17 sci_coassay_1 st_l ig_plate 1 _24 GGTACTGCCT 39 sci_coassay_1 st_l i g_plate 1 _03 GCGTTGGAGC 18 sci_coassay_1 stj ig_plate 1 _25 TAGAATTAAC 40 sci_coassay_1 stj i g_plate 1 _04 GATCTTACGC 19 sci_coassay_1 st I ig plate 1 26 GCCATTCTCC 41 sci_coassay_1 st_l i g_plate 1 _05 CTGATGGTCA 20 sci_coassay_1 stj i g_plate 1 _27 TGCCGGCAGA 42 sci_coassay_1 stj i g_plate 1 _06 CCGAGAATCC 21 sci_coassay_1 stj i g_plate 1 _28 TTACCGAGGC 43 sci_coassay_1 st_l ig_plate 1 _07 GCCGCAACGA 22 sci_coassay_1 stj i g_plate 1 _29 ATCATATTAG 44 sci_coassay_1 stj ig_pl ate 1 _08 TGAGTCTGGC 23 sci_coassay_1 stj ig_p late 1 _30 TGGTCAGCCA 45 sci_coassay_1 stj i g_plate 1 _09 TGCGGACCTA 24 sci_coassay_1 st J I g plate 1 _31 ACTATGCAAT 46 sci_coassay_1 st_l i g_plate 1 _10 ACCTCGTTGA 25 sci_coassay_1 stj ig_plate 1 _32 CGACGCGACT 47 sci_coassay_1 st_l ig_p late 1 _11 ACGGAGGCGG 26 sci_coassay_1 stj i g_plate 1 _33 GATACGGAAC 48 sci_coassay_1st_lig_plate1_12 TAGATCTACT 27 sci_coassay_1 st_lig_plate 1 _34 TTATCCGGAT 49 sci_coassay_1 st_l i g_plate 1 _13 AATTAAGACT 28 sci_coassay_1 stj ig_plate 1 _35 TAGAGTAATA 50 sci_coassay_1st_lig_plate1_14 CCATTGCGTT 29 sci_coassay_1 st J ig plate 1 _36 GCAGGTCCGT 51 sci_coassay_1 st_l i g_plate 1 _15 TTATTCATTC 30 sci_coassay_1 stj i g_plate 1 _37 TCGGCCTTAC 52 sci_coassay_1st_lig_plate1_16 ATCTCCGAAC 31 sci_coassay_1st_lig_plate1_38 AGAACGTCTC 53 sci_coassay_1 st_l i g_plate 1 _17 TTGACTTCAG 32 sci_coassay_1 stj i g_plate 1 _39 CCAGTTCCAA 54 sci_coassay_1 st_l ig_plate 1 _18 GGCAGGTATT 33 sci_coassay_1 stj ig_plate 1 _40 GGCGTTAAGG 55 sci_coassay_1 stj i g_plate 1 _19 AGAGCTATAA 34 sci_coassay_1 st J I g plate 1 _41 ACTTAACCTT 56 sci_coassay_1st_lig_plate1_20 CTAAGAGAAG 35 sci_coassay_1 st_lig_plate1_42 CAACCGCTAA 57sci_coassay_1 st_l ig_pl ate 1 _43 GACCTTGATA 58 sci_coassay_1st_lig_plate1_70 GGTATCCGCC 85 sci_coassay_1 st_l i g_plate 1 _ 44 TCTGATACCA 59 sci_coassay_1st_lig_plate1_71 AACTAGGCGC 86 sci_coassay_1 st_lig_plate 1 _ 45 GAAGATCGAG 60 sci_coassay_1st_lig_plate1_72 TCGCTAAGCA 87 sci_coassay_1 st_l ig_plate 1 _46 AGGAGCGGTA 61 sci_coassay_1 st_l i g_plate 1 _ 73 TATATACTAA 88 sci_coassay_1 st_lig_plate1_47 AAGAAGCTAG 62 sci_coassay_1st_lig_plate1_74 ACTTGCTAGA 89 sci_coassay_1 st_lig_plate1_48 TCCGGCCTCG 63 sci_coassay_1 st_lig_plate1_75 AACCATTGGA 90 sci_coassay_1 st_l ig_plate 1 _49 AGAGAAGGTT 64 sci_coassay_1 st_l ig_p late 1 _ 76 TCGCGGTTGG 91 sci_coassay_1 st_l i g_plate 1 _50 CATACTCCGA 65 sci_coassay_1 st_l i g_plate 1 _ 77 CGTAGTTACC 92 sci_coassay_1st_lig_plate1_51 GCTAACTTGC 66 sci_coassay_1st_lig_plate1_78 TCCAATCATC 93 sci_coassay_1 st_lig_plate 1 _52 AATCCATCTT 67 sci_coassay_1 st_l ig_plate 1 _79 AATCGATAAT 94 sci_coassay_1 st_l I g_plate 1 _53 GGCTGAGCTC 68 sci_coassay_1 st_l i g_p I ate 1 _80 CCATTATCTA 95 sci_coassay_1st_lig_plate1_54 CCGATTCCTG 69 sci_coassay_1st_lig_plate1_81 TCAACGTAAG 96 sci_coassay_1st_lig_plate1_55 ACCGCCAACC 70 sci_coassay_1st_lig_plate1_82 TCTAATAGTA 97 sci_coassay_1 st_l i g_plate 1 _56 TGGCCTGAAG 71 sci_coassay_1 st_lig_plate 1 _83 AACCGCTGGT 98 sci_coassay_1st_lig_plate1_57 AACCTCATTC 72 sci_coassay_1st_lig_plate1_84 GATCGCTTCT 99 sci_coassay_1 st_l ig_p late 1 _58 ATAAGGAGCA 73 sci_coassay_1 st_l i g_p I ate 1 _85 CTAACTAGAT 100 sci_coassay_1st_lig_plate1_59 CGAACGCCGG 74 sci_coassay_1 st_lig_plate 1_ 86 GCTGGAACTT 101 sci_coassay_1st_lig_plate1_60 GGTATGCTTG 75 sci_coassay_1st_lig_plate1_87 AGGTTAGTTC 102 sci_coassay_1 st_l ig_plate 1 _61 AACCTGCGTA 76 sci_coassay_1 st_l ig_p late 1 _ 88 CATTCGACGG 103 sci_coassay_1 st_l ig_plate 1 _62 GGCAGACGCC 77 sci_coassay_1 st_l i g_plate 1 _89 CATTCAATCA 104 sci_coassay_1st_lig_plate 1_63 TAGCCGTCAT 78 sci_coassay_1st_lig_plate1_90 CGGATTAGAA 105 sci_coassay_1 st_l ig_plate 1 _64 CCTGGAAGAG 79 sci_coassay_1 st_l i g_plate 1 _91 ATCGGCTATC 106 sci_coassay_1 st_lig_plate 1 _65 GGAGGTTCTA 80 sci_coassay_1 st_l i g_plate 1 _92 CCTTGATCGT 107 sci_coassay_1 st_lig_plate1_66 CTAGTAGTCT 81 sci_coassay_1st_lig_plate1_93 ACGAAGTCAA 108 sci_coassay_1st_lig_plate1_67 ATCATCAACG 82 sci_coassay_1 st_lig_plate1_94 TTACCTCGAC 109 sci_coassay_1 st_l I g_plate 1 _68 ACGCGAGATT 83 sci_coassay_1 st_l I g_pl ate 1 _95 GGAGGATAGC 110 sci_coassay_1st_lig_plate1_69 GAAGAGGCAT 84 sci_coassay_1st_lig_plate1_96 GGCTCTCTAT 1112ndligation adapter: AATGATACGGCGACCACCGAGATCTACACNNNNNNNNNNACACTCTTTCCCTAC (SEQ IDN0:7) Table 4. 2ndligation adapter sequence barcodesName Barcode SEQ Name Barcode SEQ ID Name Barcode SEQ ID ID NO: NO: NO:ligationl_01 CCGCGGCTCA 112 ligation2_01 AGAGAACTTG 208 ligation3_01 GTTACTTAGC 304 ligationl 02 GGCTCCTCGT 113 Ugation2 02 AGTCGGCTCC 209 ligation3 02 ACCGCCGCAA 305 ligationl_03 GTTACGCAAG 114 Ugation2_03 TACCAGAGTA 210 Ugation3_03 CTCAAGTCCT 306 ligationl_04 AGCCGGTACC 115 ligation2_04 ACCGACCTCA 211 ligation3_04 CGGTCGACTA 307 ligationl_05 ACCTCTATCT 116 ligation2_05 ATCCTACCTC 212 ligation3_05 TTCGCCGTAA 308 ligationl_06 GGACTACTAC 117 ligation2_06 TGCAAGGCGT 213 ligation3_06 GCTCCGCTTG 309 ligationl 07 GTATCATCGA 118 Ugation2 07 TTGCTGCGCC 214 Ugation3 07 ACTTAAGATA 310 ligationl_08 CCGCGATTAT 119 Ugation2_08 CCGCGCTATA 215 Ugation3_08 GGCATGGCCA 311 ligationl_09 ATTCAGGTAC 120 ligation2_09 GGACGGAGCC 216 ligation3_09 CTTCGGTATA 312 ligationl_10 ATGGAATTGG 121 ligation2_10 AATACTTGCG 217 ligation3_10 GAGATTCGCC 313 ligationl_ll GACGAAGCGT 122 ligation2_ll GGATTGACTC 218 ligation3_ll CTAGGCCGTT 314 ligationl 12 CTTGCAGTAG 123 Ligation2 12 AGCTTACGAA 219 Ligation3 12 GGCCAACGAT 315 ligationl_13 CTTGGTAATG 124 ligation2_13 TGATGCATCG 220 ligation3_13 ACGGAACCTG 316 ligationl_14 CAAGTCGACC 125 ligation2_14 ATAATCTCGC 221 ligation3_14 TGATTCTCGT 317 ligationl_15 TAACGAATTG 126 ligation2_15 CAGCAGTATC 222 ligation3_15 TTGCGTCAAC 318 ligationl_16 TGAGAACCAA 127 ligation2_16 ACGACCAATA 223 ligation3_16 GAATGCAACC 319 ligationl 17 TTATTCTGAG 128 Ligation2 17 CGGATAGGTA 224 Ligation3 17 TGCGGTTCAG 320 ligationl_18 TTATTATGGT 129 ligation2_18 TCGAAGCGCG 225 ligation3_18 TTGGCCAACC 321 ligationl_19 ATATGAGCCA 130 ligation2_19 GGTAAGCTCT 226 ligation3_19 TTGGTTAAGC 322 ligationl_20 CAACCAGTAC 131 ligation2_20 AGGTAATTCC 227 ligation3_20 CTTAAGTTCG 323 ligationl_21 CATCCGACTA 132 ligation2_21 AGACCATTCA 228 ligation3_21 GTCCTCAGAA 324 ligationl 22 ATCATGGCTG 133 Ugation2 22 CTGATCGACC 229 UgationS 22 GGAGTCGTCT 325 ligationl_23 CCGCAAGTTC 134 Ugation2_23 GCAATTACTC 230 Ugation3_23 GGTACCTCTA 326ligationl_24 CTTCTCATTG 135 Ligation2_24 GAGGAGTTCG 231 Ligation3_24 GATCGCTGAG 327 ligationl_25 CAGGAGGAGA 136 Ligation2_25 TAGTACTATC 232 Ligation3_25 AGAGTACTCC 328 ligationl_26 GATATCGGCG 137 Ligation2_26 CGACTTGGCG 233 Ligation3_26 TCATTCTATT 329 ligationl_27 CCAGTCCTCT 138 Ligation2_27 CTATTCGGCC 234 ligation3_27 GTTACTACCA 330 ligationl_28 CATAGTTCGG 139 Ligation2_28 CTTCCAAGAA 235 Ligation3_28 CCAGCTCGCC 331 ligationl_29 CGTAATGCAG 140 Ligation2_29 CGATCCTGGA 236 Ligation3_29 CGCCGGTATG 332 (igationl_30 CCGTTCGGAT 141 Ugation2_30 TATTCCGTTA 237 Ugation3_30 TTAATTCGTA 333 ligationl_31 CCATAAGTCC 142 Ugation2_31 TTAGTACGCC 238 Ugation3_31 GAAGGCTCCA 334 ligationl_32 GGCAATGAGA 143 ligation2_32 TCGTAGCATC 239 ligation3_32 GAGACGTACG 335 ligationl_33 CGGTTATGCC 144 Ligation2_33 GTATTAAGTT 240 Ligation3_33 GAAGAGCCTC 336 ligationl_34 TGGCCGGCCT 145 Ligation2_34 CATTCTAGAA 241 Ligation3_34 CCGATGCATA 337 ligationl_35 AGCTGCAATA 146 Ugation2_35 GGTAGATCAA 242 Ugation3_35 GTAATGGTAT 338 ligationl_36 TGGCCATGCA 147 Ugation2_36 ATCTCCTACG 243 Ugation3_36 TTCTATCTCA 339 ligationl_37 TGACGCTCCG 148 ligation2_37 ACGAAGAAGC 244 ligation3_37 GCAGCAGCTA 340 8 ligationl_38 AACTGCTGCC 149 Ligation2_38 CCGATCAGCC 245 Ligation3_38 TTGCTCGATT 341 ligationl_39 TGCGCGATGC 150 Ligation2_39 CTGGCTTCCT 246 Ligation3_39 CCTCATCGGC 342 ligationl_40 ATTGAGATTG 151 Ligation2_40 TTCATAATGG 247 Ligation3_40 ACTTCAGCAA 343 ligationl_41 TTGATATATT 152 Ligation2_41 GTTGAACGCA 248 Ligation3_41 AGGTCATCCT 344 ligationl_42 CGGTAGGAAT 153 Ligation2_42 TAACGGCTGA 249 ligation3_42 AACGCGTCAG 345 ligationl_43 ACCAGCGCAG 154 Ligation2_43 GAAGTCCGTC 250 Ligation3_43 CTATGCTTAC 346 ligationl_44 CGAATGAGCT 155 Ligation2_44 ATACGCCGCC 251 Ligation3_44 GTTGCCGTTC 347 ligationl_45 AGTTCGAGTA 156 Ligation2_45 ACTGGATGCT 252 Ligation3_45 GCTTACCGCC 348 ligationl_46 TTGGACGCTG 157 Ugation2_46 GAGCGAATAT 253 Ugation3_46 TGGCAAGTCA 349 ligationl_47 ATAGACTAGG 158 ligation2_47 TATATGAAGT 254 ligation3_47 CATCGAAGGA 350 ligationl_48 TATAGTAAGC 159 Ligation2_48 ACGATACCGG 255 Ligation3_48 AGAATCCTCG 351 ligationl_49 CGGTCGTTAA 160 Ligation2_49 TCATACCGCT 256 Ligation3_49 GCAATCGGTT 352 ligationl_50 ATGGCGGATC 161 Ugation2_50 CGCTAACCGT 257 Ugation3_50 CCTAAGATTC 353 ligationl_51 CTCTGATCAG 162 Ugation2_51 CGCATCCATC 258 Ugation3_51 CCTGCGCGCG 354 ligationl_52 GGCCAGTCCG 163 ligation2_52 CGTCTTCCTT 259 ligation3_52 ATCAGCGCGA 355ligationl_53 CGGAAGATAT 164 Ligation2_53 AACGCTATTA 260 ugation3_53 GTACGATTCT 356 ligationl_54 TGGCTGATGA 165 Ugation2_54 TAAGATAGGT 261 Ugation3_54 TTACCTTGCA 357 ligationl_55 GAAGGTTGCC 166 Ugation2_55 TGATAATAGC 262 Ugation3_55 CCGGCTCAGC 358 ligationl_56 GTTGAAGGAT 167 ligation2_56 GGCCTCCATT 263 ligation3_56 TTCTGCAAGA 359 ligationl_57 CCATTCGTAA 168 Ligation2_57 TGCCGCCGAT 264 Ligation3_57 ATATACGCTT 360 ligationl_58 TGCGCCAGAA 169 ligation2_58 TGCCTATTAT 265 ligation3_58 CTCAGCAACC 361 ligationl_59 CGAATAATTC 170 Ugation2_59 CTGATACGTC 266 Ugation3_59 CAATTCTAGG 362 ligationl_60 GCGACGCCTT 171 Ugation2_60 GACCTGGAAT 267 Ugation3_60 ATCAGTCTCG 363 ligationl_61 ATCAACGATT 172 ligation2_61 TCAGATCGGA 268 ligation3_61 AATCCGCAAC 364 ligationl_62 GTTCTGAATT 173 Ligation2_62 GAGGCGGAAT 269 Ligation3_62 CGGTTACCTT 365 ligationl_63 GCTAACCTCA 174 Ligation2_63 CAGCGCATCC 270 Ligation3_63 ACGTTAAGAC 366 ligationl_64 CAAGCAACTG 175 Ugation2_64 AATGCCAAGA 271 Ugation3_64 CTATCCAACC 367 ligationl_65 GGAGCGGCCG 176 Ugation2_65 TGGTCTACGT 272 Ugation3_65 ATAAGCGAAT 368 ligationl_66 CGCGTACGAC 177 ligation2_66 GGTCGCCGCT 273 ligation3_66 CTTATATCGG 369 2 ligationl_67 CGATGGCGCC 178 Ligation2_67 AGCAAGTAGT 274 Ligation3_67 ATATGACGAC 370 ligationl_68 TGGTATTCAT 179 Ligation2_68 AAGAAGTTCA 275 Ligation3_68 TTACCGCATA 371 ligationl_69 GATAAGGCAA 180 Ugation2_69 CGGCGCTGGC 276 Ugation3_69 ATTCATCGCC 372 ligationl_70 GCCGGTCGAG 181 Ugation2_70 TCGTCAACTT 277 Ugation3_70 AGAAGCAGAA 373 ligationl_71 TGCGCCATCT 182 ligation2_71 CAACTTGGAT 278 ligation3_71 GTTCGTCGTT 374 ligationl_72 AAGTCTTCCG 183 Ligation2_72 TTGGAGCTCA 279 Ligation3_72 CATGCTTCCA 375 ligationl_73 AGACTCAAGC 184 Ligation2_73 CTTAGTTCAA 280 Ligation3_73 TCGGTACCAG 376 ligationl_74 GCAGGCGACG 185 Ugation2_74 TTGAATTATA 281 Ugation3_74 TTGAGCCAAT 377 ligationl_75 AATACTCTTC 186 Ugation2_75 CTTCAGCTTC 282 Ugation3_75 AGATGACTGA 378 ligationl_76 CCAACTAACC 187 ligation2_76 GTATACCGAA 283 ligation3_76 ACGCTAGAAG 379 ligationl_77 TATCCTCAAT 188 Ligation2_77 GGATATAATA 284 Ligation3_77 GTTCAATTGC 380 ligationl_78 GCCGTCGCGT 189 Ligation2_78 GAATCGACGT 285 Ligation3_78 GGACCGTCAA 381 ligationl_79 CCGCTGCTTC 190 Ugation2_79 TGAACGGTAA 286 Ugation3_79 CATTAACGGA 382 ligationl_80 TGACCGAATC 191 Ugation2_80 AAGTCGCGCG 287 Ugation3_80 TAAGCAGTCC 383 ligationl_81 GTCTCCAGAG 192 ligation2_81 TCCGCCTACT 288 ligation3_81 CCGGTCAGTT 384ligationl_82 AATGCTAGTC 193 Ligation2_82 TTAATAGTTC 289 Ligation3_82 ATAACGGACT 385 ligationl_83 GACGACCTGC 194 Ugation2_83 CAACCGGATC 290 Ugation3_83 ACGAGAAGAT 386 ligationl_84 AGAGCCAGCC 195 Ugation2_84 TTAGAGCAAC 291 Ugation3_84 ATCCTCTTAA 387 ligationl_85 CCAGGCCGCA 196 Iigation2_85 CGTCATTCCA 292 ligation3_85 AATCCAATAA 388 ligationl_86 CAGGTATGGA 197 Ligation2_86 TAGGAAGGCA 293 Ligation3_86 CTAGCAGGAT 389 ligationl_87 CCGGAGTTGC 198 Ligation2_87 TTGGCCTATA 294 Ligation3_87 TGGTCTCGGA 390 ligationl_88 TTAATTATTG 199 Ugation2_88 GCGTCTATTC 295 Ugation3_88 CCGAGTACTA 391 ligationl_89 AATCAGCTGC 200 Ugation2_89 CAGAGTAGAC 296 Ugation3_89 GATGACGAAG 392 ligationl_90 CCGTTGACTT 201 Iigation2_90 ATGCCGGACG 297 ligation3_90 GGCAGTCTTC 393 ligationl_91 GCCAGGATCA 202 ligation2_91 TATTCGATCT 298 ligation3_91 AATACGAATA 394 ligationl_92 CTTCGGCGCA 203 Ligation2_92 ATGGATCCGA 299 Ligation3_92 ACCTAGGAGA 395 ligationl_93 CAAGGCATTC 204 Ugation2_93 ATAATGCATT 300 Ligation3_93 GAAGCGCCAA 396 ligationl_94 AAGAATGGAA 205 Ugation2_94 AAGTAGACTA 301 Ligation3_94 CGTTACGTTG 397 ligationl_95 CGGATGAAGG 206 Iigation2_95 ATGGAAGCAT 302 ligation3_95 GTCGCGAATA 398 ligationl_96 TATCGTCGGC 207 Ligation2_96 TGGATCAGGC 303 Ligation3_96 TTAGAGCCTG 399Table 4 continued:Name SEQID Ligation4_10 TCGGCCGATG 409 Ugation4_21 CCGCCTCGTT 420 Barcodes NO: Ugation4_ll AGCGACTGCT 410 Ugation4_22 TTATAATCAA 421 ligation4_01 ACGGTCATCA 400 ligation4_12 CTTAATGAGC 411 ligation4_23 CCATTGAACG 422 ligation4_02 ACGTAGCAGG 401 Ligation4_13 AATTCCTCTC 412 ligation4_24 ACTCCAACGG 423 Ugation4_03 CGACCGAGAG 402 Ligation4_14 GCTGGTCTCC 413 ligation4_25 ACCTCCTGAA 424 Ugation4 04 AAGCGGTTCT 403 Ugation4_15 AGTATTGCTA 414 Ugation4_26 AGAGGCCGGC 425 Ugation4_05 TCGGAATAAC 404 Ugation4_16 TCTAGGATAA 415 Ugation4_27 CTGCCTCTTC 426 ligation4_06 AAGTTCGCTG 405 ligation4_17 GGTCCTGCAA 416 Ugation4_28 CAGTATCCTT 427 ligation4_07 AATAATCGGT 406 Ligation4_18 CGCTTCAATT 417 Ugation4_29 GTCAACTAGC 428 Ugation4_08 AGGCGAAGGC 407 Ligation4_19 GGATTATTAT 418 ligation4_30 TGACGCAGTC 429 Iigation4 09 AAGCCGCCGC 408 Iigation4_20 TCCGGCTGAT 419 ligation4_31 GTCAATACGA 430Iigation4_32 TGAACTTCGA 431 Ligation4_54 CCGGTTAAGT 453 ligation4_76 TGGCAGCGAA 475 Ugation4_33 CGTACCAACG 432 Ligation4_55 ATATTCTACC 454 Ugation4_77 CCGATACTCT 476 Ugation4_34 AGAGATGAAT 433 Ligation4_56 TACGGATCGT 455 Ugation4_78 CAATATAGGC 477 ligation4_35 TATTCCAATT 434 ligation4_57 TTCTCTCCAG 456 Ligation4_79 ACCGGCCGAC 478 ligation4_36 GGATGCGATT 435 ligation4_58 CCAAGAGCAA 457 ligation4_80 AATAAGGCTC 479 ligation4_37 GTAACCAGGT 436 Ligation4_59 TTGGTTCGAG 458 ligation4_81 CATCATAGCA 480 Ugation4_38 CCTCGTCATA 437 Ugation4_60 AACGGATTAC 459 Ugation4_82 GATGATCCAT 481 ligation4_39 AATGGTCTTA 438 Ugation4_61 CATCTTCAGA 460 Ugation4_83 ATGGCAATAC 482 ligation4_40 ATGAATGCCT 439 ligation4_62 TTGAACCTCC 461 ligation4_84 ACCAGAACCA 483 Ugation4_41 GTCCGTAGAT 440 Ligation4_63 GGAATTCCAA 462 ligation4_85 GGTTCGACCT 484 ligation4_42 CCATCCTAGT 441 Ligation4_64 ATAGGTCCAA 463 ligation4_86 CTTGGACGGA 485 Ugation4_43 TGGTTCCTAC 442 Ligation4_65 GCCATGGTAC 464 Ugation4_87 CGGTCTCATA 486 Ugation4_44 GCGCCTTCCG 443 Ligation4_66 GAGCTCTTCA 465 Ugation4_88 AATCAGAGCC 487 ligation4_45 CGTACTACGC 444 ligation4_67 CCGAGGCAAC 466 Ligation4_89 CCTGAATACT 488 ligation4_46 GGCCGCGGTT 445 Ligation4_68 GTCTCTAGTT 467 Ligation4_90 CTTGGAGACT 489 ligation4_47 TGGATAGTTG 446 Ligation4_69 GCTGGTTATA 468 ligation4_91 AAGACCTTAC 490 Ugation4_48 CGGCGCCAGG 447 Ligation4_70 TCGTAGGTCA 469 Ugation4_92 GCGAGCGCTC 491 ligation4_49 CAAGCTCAGG 448 ligation4_71 AACTCAGACG 470 ligation4_93 TCGCAAGACG 492 ligation4_50 ATCATCCTTC 449 ligation4_72 TGCTGCCGGA 471 ligation4_94 CAATCTCGGA 493 Ugation4_51 CCTCCGGAGT 450 Ligation4_73 TGGAGGCAAG 472 ligation4_95 TCGACCTACC 494 ligation4_52 CCATTGCTGG 451 Ligation4_74 ACTGATGCGA 473 ligation4_96 TTATAGGCAT 495 Ugation4_53 TATTCGCAGT 452 Ugation4_75 ACGACTCCTC 474Indexing PCR platesBarcoded Indexing P7 PCR Primer: CAAGCAGAAGACGGCATACGAGATNNNNNNNNNNGTCTCGTGGGCTCGG(SEQ ID NO:9)Table 5. barcodes for Indexing:Name Barcode SEQ Name Barcode SEQ ID Name Barcode SEQ ID ID NO: NO: NO:P7-l_01 TCGGATTCGG 496 P7-2_01 TAGGCCAGAA 592 P7-3_01 CGGTAACCAT 688 P7-l_02 TCCGGCTTAT 497 P7-2_02 ATCGCCAATT 593 P7-3_02 ATAACCTAAG 689 P7-l_03 TCGCCGCCGG 498 P7-2_03 GCGGTTGCCT 594 P7-3_03 GGCTAAGGCG 690 P7-l_04 TTGGCAAGCC 499 P7-2_04 ATCATTGCGT 595 P7-3_04 TACGTTACGT 691 P7-l_05 CAGCTAGCGG 500 P7-2_05 AGTAACGAGT 596 P7-3_05 TAGAGAATAA 692 P7-l_06 GTAGGATAAG 501 P7-2_06 TGCTGACTTG 597 P7-3_06 TATACTATGC 693 P7-l_07 AAGTAGCTCA 502 P7-2_07 TATCCGTACC 598 P7-3_07 TATGCGCATG 694 P7-l_08 TAAGTCCTGA 503 P7-2_08 GACCATTGAG 599 P7-3_08 GAATTGGTAC 695 P7-l_09 GCGGCTGCGG 504 P7-2_09 TCTTCGGAGT 600 P7-3_09 TGATCTCGTT 696 P7-l_10 ACCAGGCGCA 505 P7-2_10 GCCTTGGATT 601 P7-3_10 TTAATCGAGA 697 P7-l_ll CCGTATGATT 506 P7-2_ll GCGATGGACG 602 P7-3_ll CCGGTCGACG 698 P7-l_12 TTGATTGGCG 507 P7-2_12 TCGTACCTAA 603 P7-3_12 AGCTATCAAC 699 P7-l_13 CTAAGCCTTG 508 P7-2_13 AGATGGTAAG 604 P7-3 L3 ACCAACGCTG 700 P7-l_14 CGTCGAGCGC 509 P7-2_14 TCTTATCTCG 605 P7-3_14 CAATGCCGAT 701 P7-l_15 TATTGCTGGA 510 P7-2_15 TAGAAGATCG 606 P7-3_15 TAGGACTACC 702 P7-l_16 AGTTCTCATG 511 P7-2_16 GGATCGGAAC 607 P7-3_16 CGCGATGCTA 703 P7-l_17 AGATTACGTT 512 P7-2_17 TTCTTATGGC 608 P7-3_17 GGTTACCAGT 704 P7-l_18 AGAGGAGAAT 513 P7-2_18 TTATGAATCT 609 P7-3 L8 GGTCAGTAGA 705 P7-l_19 AACGCGCAGA 514 P7-2_19 TGACGCTCTG 610 P7-3_19 ATAACTCTTG 706 P7-l_20 GCATATGAGC 515 P7-2_20 CTGGCTCCTC 611 P7-3_20 TTCCTAATTG 707 P7-l_21 CGTTACCGCT 516 P7-2_21 GGATCCTCAT 612 P7-3_21 GGTTCCGCAA 708 P7-l_22 ACTATTCATT 517 P7-2_22 TTGAGCGCCG 613 P7-3_22 TACTTCGGCT 709 P7-l_23 CCAGATACGG 518 P7-2_23 TGACGTAAGG 614 P7-3_23 CGTACGGACC 710 P7-l_24 CAGACTAAGG 519 P7-2_24 TGACTGAGAA 615 P7-3_24 CTTGCATGCT 711 P7-l_25 CTAACTAGGT 520 P7-2_25 AAGCTCCTAT 616 P7-3_25 GCAACGAAGA 712 P7-l_26 GTACTCCTAT 521 P7-2_26 GAGTCGGCTA 617 P7-3_26 TTCTTCCAAC 713P7-l_27 CTCGTCGTAG 522 P7-2_27 CGGAGTTCTT 618 P7-3_27 TCCGTATTCC 714 P7-l_28 GAACTCGACT 523 P7-2_28 CGGTCTCAAG 619 P7-3_28 TACCGTCGGT 715 P7-l_29 GACTGGACCA 524 P7-2_29 GTATGGCGTT 620 P7-3_29 AATCAAGTAT 716 P7-l_30 TTGCTTAGAT 525 P7-2_30 GTTGAGTAGG 621 P7-3_30 GGCGAACGAC 717 P7-l_31 AACGAATTCG 526 P7-2_31 GGAGGCTAGA 622 P7-3_31 TGCAATCATT 718 P7-l_32 TCTATCGCTG 527 P7-2_32 GATCTAAGCG 623 P7-3_32 CTGCTAATGG 719 P7-l_33 CATAGCGACC 528 P7-2_33 ATGATTAAGC 624 P7-3_33 CATTAGTCAG 720 P7-l_34 GCTAACGGAT 529 P7-2_34 TTGACCTGCT 625 P7-3_34 GTCCGTAAGG 721 P7-l_35 CTAATTGCGA 530 P7-2_35 TTATAAGCGG 626 P7-3_35 TAGTTATGCG 722 P7-l_36 CTAGCCAGCC 531 P7-2_36 TTCCATTAGG 627 P7-3_36 TCCTCCGGTT 723 P7-l_37 GCAAGACCGT 532 P7-2_37 GGAACCAATG 628 P7-3_37 CCGCTGCATT 724 P7-l_38 CGAATGGAGC 533 P7-2_38 AATAACTGGT 629 P7-3_38 TTGCCGACTG 725 P7-l_39 CGCTTATCGT 534 P7-2_39 TAAGCGAGTC 630 P7-3_39 TGGATTCTAT 726 P7-l_40 GCGCTATGGT 535 P7-2_40 CGGAGCAATT 631 P7-3_40 ATATCTGAGA 727 P7-l_41 CCGCTAAGAG 536 P7-2_41 GGCATTCGCA 632 P7-3_41 AATACTTCGT 728 P7-l_42 AGTTGAATCA 537 P7-2_42 GCTGGAGGCA 633 P7-3_42 TGATAAGTCT 729 P7-l_43 CGCGGCCATA 538 P7-2_43 TCAAGCCTAA 634 P7-3_43 TACGGCGCGA 730 P7-l_44 TGCGACCTCT 539 P7-2_44 AGCTCAGAGT 635 P7-3_44 TCTTC AGCT 731 P7-l_45 TCAATCTCCT 540 P7-2_45 GTCGTTCGAA 636 P7-3_45 GTAGCTACCG 732 P7-l_46 CTATATAGCC 541 P7-2_46 CCGAGCAGTA 637 P7-3_46 CGCATGCCGC 733 P7-l_47 AAGTACGCGA 542 P7-2_47 GTCTTCTAGA 638 P7-3_47 AGCCAGTCTT 734 P7-l_48 CATTATTATT 543 P7-2_48 TATTCCAATA 639 P7-3_48 TAAGAATGCG 735 P7-l_49 ATGGAACGAA 544 P7-2_49 TTGATATCTT 640 P7-3_49 GGAGACGGTA 736 P7-l_50 TGATTAGGTA 545 P7-2_50 ACCATGGACT 641 P7-3_50 ATACTATATT 737 P7-l_51 AATATTACTT 546 P7-2_51 GTATTCAACC 642 P7-3_51 ATCTTATGAT 738 P7-l_52 ATTCTTAGCT 547 P7-2_52 CCATCGCTAG 643 P7-3_52 GGATATTGAT 739 P7-l_53 ATACCTCGAC 548 P7-2J53 GGCGTATTGG 644 P7-3_53 GTTGGTAATA 740 P7-l_54 CTACTAATAA 549 P7-2_54 GAGGTATCTC 645 P7-3_54 TGGTCTTCTC 741 P7-l_55 GATCTTCGCA 550 P7-2_55 GCTCCTGAGC 646 P7-3_55 GAGAGCGCCA 742P7-l_56 AGCCGTAGTT 551 P7-2_56 TGCAACTAGT 647 P7-3_56 TTATCGTCGC 743 P7-l_57 GCGTTCCGTT 552 P7-2J57 TGGATAGCGG 648 P7-3_57 TCGCGACGTT 744 P7-l_58 TCGTAGCATC 553 P7-2_58 TAAGTTGCAT 649 P7-3_58 GAAGCTTCAT 745 P7-l_59 ATTGGCAGAT 554 P7-2_59 ACTTGGTCCT 650 P7-3_59 ACTCTATGGC 746 P7-l_60 TTGATACGAT 555 P7-2_60 TGAGGACGCC 651 P7-3_60 TTCTCAGTCA 747 P7-l_61 TAGAGGCGTT 556 P7-2_61 GCGAGCTTCC 652 P7-3_61 TTCGGAGCCG 748 P7-l_62 TGGTTGCCGT 557 P7-2J52 TATAACGATT 653 P7-3_62 CAATTGGCTG 749 P7-l_63 TCTTAGCCTG 558 P7-2_63 CCTCTCTCGG 654 P7-3_63 AACTGGTCGC 750 P7-l_64 TGATATTGCG 559 P7-2_64 TGAGTTCCTC 655 P7-3_64 CGTCGTTAGG 751 P7-l_65 GGTTATCGAA 560 P7-2_65 TGCTCAGACC 656 P7-3_65 GATTGCGAAG 752 P7-l_66 TCTTGAGGTT 561 P7-2_66 GCTTAATGGT 657 P7-3_66 AACGGAGTCA 753 P7-l_67 ATGGCGCCTG 562 P7-2J57 TGGTAGAGTT 658 P7-3_67 AGCGACTATC 754 P7-l_68 TATAATAGTT 563 P7-2_68 GAGTTGAGCA 659 P7-3_68 TTAGTACCTT 755 _ P7-l_69 TAGGTAACTT 564 P7-2_69 AGGCTGCCGG 660 P7-3_69 CCTCATGCAA 756 oo P7-l_70 TCCTCTGCCG 565 P7-2_70 TGGTCGCGAG 661 P7-3_70 TATATAATAC 757 P7-l_71 TTATTGAGGC 566 P7-2_71 CTAAGGTCCA 662 P7-3_71 AGCCAAGAAT 758 P7-l_72 ACGGCGTTAA 567 P7-2_72 CGGTCATTCC 663 P7-3_72 CCAAGATGCA 759 P7-l_73 GCATCGTATG 568 P7-2_73 CATCATACTG 664 P7-3_73 TTGAGCCAAC 760 P7-l_74 AGGTCAGCTT 569 P7-2_74 TATTCATGAG 665 P7-3_74 TAATGATATT 761 P7-l_75 ATAAGGACTC 570 P7-2_75 TACTACGCAG 666 P7-3_75 GACTTAGTTA 762 P7-l_76 TTGCCGTAGG 571 P7-2_76 AGGTTGCTCC 667 P7-3_76 TTGGTTATTG 763 P7-l_77 TTCGAATATT 572 P7-2_77 GTATTACTGA 668 P7-3_77 TGCAGTATAA 764 P7-l_78 GATTCTTGAA 573 P7-2_78 ATGCAGCGTC 669 P7-3_78 GCTCTGCTCA 765 P7-l_79 TTGCGAGCAT 574 P7-2_79 AGCTGGAGAT 670 P7-3_79 TTGGCTTGCA 766 P7-l_80 GGCTTACTCC 575 P7-2_80 GCAGTTCTAA 671 P7-3_80 TCGTTCTCCA 767 P7-l_81 AATACGATAA 576 P7-2_81 GGCGTTACGG 672 P7-3_81 TGAATCCGAT 768 P7-l_82 TATTAGACTT 577 P7-2_82 AGGTATAGTC 673 P7-3_82 TCGGTCTAGT 769 P7-l_83 TAGTAAGCCG 578 P7-2_83 ACCTCTCGCA 674 P7-3_83 AAGCATCTCG 770 P7-l_84 AGACCATATC 579 P7-2_84 TGAGGCCTAT 675 P7-3_84 ATTAATAGAA 771P7-l_85 TGGACGACTA 580 P7-2_85 GTTGAATTGA 676 P7-3_85 TGGACTAATT 772P7-l_86 GTAGCAGCTA 581 P7-2_86 CCAGGTAGCC 677 P7-3_86 GGCTTGGAGC 773P7-l_87 GCTTGAAGAG 582 P7-2_87 GTTCAAGAAG 678 P7-3_87 TTAGGAAGAG 774P7-l_88 GCGCGTTCAT 583 P7-2_88 TGCTGCGTAA 679 P7-3_88 GTTAACGCGG 775P7-l_89 TTCCGTCGAC 584 P7-2_89 TGGCGGTTAT 680 P7-3_89 CTATTCCGTC 776P7-l_90 CTCAATTAGT 585 P7-2_90 ATTCGACTGG 681 P7-3_90 CAACTCTGAG 777P7-l_91 ATTATGCAAG 586 P7-2_91 TAGTTGGTTA 682 P7-3_91 TCATACGTCC 778P7-l_92 TTGGTAAGGA 587 P7-2_92 TCGCCAGCTA 683 P7-3_92 GACTCATCTT 779P7-l_93 GTAGGCTGCA 588 P7-2_93 CAAGTCATTG 684 P7-3_93 GGTAGCGCAT 780P7-l_94 CCTCAGCTCC 589 P7-2_94 GATCGCAGAG 685 P7-3_94 TTAGGCGTCC 781P7-l_95 AGTCCGCTGC 590 P7-2_95 ATTGGTATGC 686 P7-3_95 GGTCTAACTG 782P7-l_96 GAGCGCGATG 591 P7-2_96 CCTATCAGGT 687 P7-3_96 ATTGAGACGG 783Table 5 continued:Name SEQ P7-4_13 ACCTGAGGTC 796 P7-4_27 TTCTTGGTCT 810IDBarcodes NO: P7-4_14 TTGCAATGAG 797 P7-4_28 GTATAGATTA 811 P7-4_01 AACGTACCAT 784 P7-4_15 ACTAACTGAC 798 P7-4_29 GACTATGACT 812 P7-4_02 GTTCAGTTAC 785 P7-4_16 GGTAAGAGGT 799 P7-4_30 GCTCCGCGAA 813 P7-4_03 GTTAACCTTA 786 P7-4_17 GTAATCGCAA 800 P7-4_31 GGAACGATTC 814 P7-4_04 GGAGTAGTAG 787 P7-4_18 ATATGATGAA 801 P7-4_32 GTACCTTCGT 815 P7-4_05 CAGGTTGAGA 788 P7-4_19 CCTACGGAAG 802 P7-4_33 TTATGCGACT 816 P7-4_06 AACCAATAAC 789 P7-4_20 GACTCTCCGA 803 P7-4_34 AACGGTTGGT 817 P7-4_07 GGTACCTATT 790 P7-4_21 ATAGATACGT 804 P7-4_35 CTAGAAGGAA 818 P7-4_08 AGCTGCCTCA 791 P7-4_22 TATGAAGCAA 805 P7-4_36 GTATGGAGGA 819 P7-4_09 TTGGTTGGTA 792 P7-4_23 GTAGAGACGA 806 P7-4_37 CGTAAGTAGC 820 P7-4_10 CTGATATCGG 793 P7-4_24 TTCGCATAAC 807 P7-4_38 TAGTCGTCAA 821 P7-4_ll TTGATGGAAC 794 P7-4_25 TAGATTCGCC 808 P7-4_39 GTAGTATGGA 822 P7-4_12 GGACCAGAAG 795 P7-4_26 TTCTTCGCGG 809 P7-4_40 AATGGACGTA 823P7-4_41 CCGTTCGCTG 824 P7-4_60 AGAGTCCGAG 843 P7-4_79 GCATATCTGG 862 P7-4.42 CAGTCAATAT 825 P7-4_61 CTATGGTTCG 844 P7-4_80 ACTCTAGAAG 863 P7-4_43 CTAAGGTCCA 826 P7-4_62 AACTATTGGC 845 P7-4_81 CAAGAATATG 864 P7-4_44 TTCCATGCGC 827 P7-4_63 CTTGCGAGGT 846 P7-4_82 CTGCTGCGCG 865 P7-4_45 GGACGGCATG 828 P7-4 64 TTCGGCTCGT 847 P7-4_83 CATATTACGG 866 P7-4_46 GGAGCAACGT 829 P7-4_65 CTCAAGTCTG 848 P7-4_84 CGGCGGAATG 867 P7-4 47 CGTCCTAGCT 830 P7-4_66 ATTAGGCCTG 849 P7-4_85 TGCTTCTGAA 868 P7-4_48 ATATTAGTAG 831 P7-4_67 TGGCTAACGT 850 P7-4_86 GTATTCTTCT 869 P7-4_49 AGTTCCTTCT 832 P7-4_68 AGCTAATATT 851 P7-4_87 CAGTAGTAAT 870 P7-4_50 GCCTTCAAGG 833 P7-4_69 AGGAATCTTC 852 P7-4_88 CCGCGGAGAC 871 P7-4_51 TAACGCCTCA 834 P7-4_70 AAGACCAGAC 853 P7-4_89 GCTCGTCGGC 872 P7-4.52 GAATACGTCT 835 P7-4_71 ACCATAAGCG 854 P7-4_90 CTTAGAGGCA 873 P7-4_53 GATGAGCCTT 836 P7-4_72 TGCTTATTAC 855 P7-4_91 GACCAAGACG 874 P7-4_54 TACGGAGACT 837 P7-4_73 ATAGAGCATT 856 P7-4_92 AGCAGATGGT 875 P7-4_55 AGAGGTCATT 838 P7-4 74 AATTATCTTG 857 P7-4_93 AATTAACCAT 876 P7-4_56 CGGCCAGTTA 839 P7-4_75 GTTCCGCGCG 858 P7-4_94 CAGACTCGCT 877 P7-4.57 TAGCGCTTAA 840 P7-4_76 CCGAAGTCCG 859 P7-4_95 CTTCGCCGTT 878 P7-4_58 CAACCTTATG 841 P7-4_77 TTACCATGAT 860 P7-4_96 CCTGCCAACC 879 P7-4_59 CTTCGACGAA 842 P7-4_78 TATAGATGGC 861Indexing PCR for sgRNAsgRNA_inner_i7_primer: CGTGTGCTCTTCCGATCTNNNNNNNNNNatcttgtggaaaggacgaaaCACCG (SEQ ID NO: 11)Table 6: barcodes for indexing PCR for sgRNANames BarcodesEQID NE) :sgRNA_inner_i7_primer_l TCGGATTCGG 880sgRNA_inner_i7_primer_2 CTAAGCCTTG 881sgRNA_inner_i7_primer_3 CTAACTAGGT 882sgRNA_inner_i7_primer_4 GCAAGACCGT 883sgRNA_inner_i7_primer_5 ATGGAACGAA 884sgRNA_inner_i7_primer_6 TAGAGGCGTT 885sgRNA_inner_i7_primer_7 GCATCGTATG 886sgRNA_inner_i7_primer_8 TGGACGACTA 887Table 7: sgRNA enrichment PCR P7 primersNames Sequences SEQ ID NO: Barcodes SEQ ID NO: sgRNA_P7-l_01 CAAGCAGAAGACGGCATACGAGATacctCCtgaaGT 888 TTCAGGAGGT 984GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_02 CAAGCAGAAGACGGCATACGAGATgtagatgCgcGT 889 GCGCATCTAC 985GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_03 CAAGCAGAAGACGGCATACGAGATccgttgacttGT 890 AAGTCAACGG 986GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_04 CAAGCAGAAGACGGCATACGAGATCtgaagagacGT 891 GTCTCTTCAG 987GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_05 CAAGCAGAAGACGGCATACGAGATtggCCatgcaGT 892 TGCATGGCCA 988GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_06 CAAGCAGAAGACGGCATACGAGATggtacgaaccGT 893 GGTTCGTACC 989GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_07 CAAGCAGAAGACGGCATACGAGATggactagattGT 894 AATCTAGTCC 990GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_08 CAAGCAGAAGACGGCATACGAGATtaagactaccGT 895 GGTAGTCTTA 991GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_09 CAAGCAGAAGACGGCATACGAGATgttaactctcGT 896 GAGAGTTAAC 992GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_10 CAAGCAGAAGACGGCATACGAGATaggtaattCCGT 897 GGAATTACCT 993GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_ll CAAGCAGAAGACGGCATACGAGATctCCggatagGT 898 CTATCCGGAG 994GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_12 CAAGCAGAAGACGGCATACGAGATttacctCgacGT 899 GTCGAGGTAA 995GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_13 CAAGCAGAAGACGGCATACGAGATttaccgaggcGT 900 GCCTCGGTAA 996 GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_14 CAAGCAGAAGACGGCATACGAGATaagaccttacGT 901 GTAAGGTCTT 997GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_15 CAAGCAGAAGACGGCATACGAGATaataattagaGT 902 TCTAATTATT 998GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_16 CAAGCAGAAGACGGCATACGAGATtgOtgCCggaGT 903 TCCGGCAGCA 999GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_17 CAAGCAGAAGACGGCATAGGAGATgCCaacgCCaGT 904 TGGCGTTGGC 1000GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_18 CAAGCAGAAGACGGCATACGAGATaggtagagCtGT 905 AGCTCTACCT 1001GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_19 CAAGCAGAAGACGGCATACGAGATggctCgagatGT 906 ATCTCGAGCC 1002GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_20 C AAGC AG AAGACGGC ATACG AG AT a acctatgCCGT 907 GGCATAGGTT 1003GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_21 CAAGCAGAAGACGGCATACGAGATcggttgCgCgGT 908 CGCGCAACCG 1004GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_22 CAAGCAGAAGACGGCATACGAGATa Cga Ct C Ct CGT 909 GAGGAGTCGT 1005GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_23 CAAGCAGAAGACGGCATACGAGATgtctctagttGT 910 AACTAGAGAC 1006GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_24 CAAGCAGAAGACGGCATACGAGATcgttctCCaaGT 911 TTGGAGAACG 1007GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_25 CAAGCAGAAGACGGCATACGAGATgctcatgCttGT 912 AAGCATGAGC 1008GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_26 CAAGCAGAAGACGGCATACGAGATgacctctctgGT 913 CAGAGAGGTC 1009GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_27 CAAGCAGAAGACGGCATACGAGATataagaggCgGT 914 CGCCTCTTAT 1010GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_28 CAAGCAGAAGACGGCATACGAGATggcggacgttGT 915 AACGTCCGCC 1011GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_29 CAAGCAGAAGACGGC ATACGAGATc cat C Ct agtGT 916 ACTAGGATGG 1012GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_30 CAAGCAGAAGACGGCATACGAGATactgatgCgaGT 917 TCGCATCAGT 1013GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_31 CAAGCAGAAGACGGCATACGAGATcgtcaaggcaGT 918 TGCCTTGACG 1014GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_32 CAAGCAGAAGACGGCATACGAGATttOtgatgatGT 919 ATCATCAGAA 1015GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_33 CAAGCAGAAGACGGCATACGAGATCgCggcatatGT 920 ATATGCCGCG 1016GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_34 CAAGCAGAAGACGGCATACGAGATagCgtCgtatGT 921 ATACGACGCT 1017GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_35 CAAGCAGAAGACGGCATACGAGATagttcagtCgGT 922 CGACTGAACT 1018GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_36 CAAGCAGAAGACGGCATACGAGATtaaccttCggGT 923 CCGAAGGTTA 1019GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_37 CAAGCAGAAGACGGCATACGAGATcgacgCgactGT 924 AGTCGCGTCG 1020GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_38 CAAGCAGAAGACGGCATACGAGATctCCaagttaGT 925 TAACTTGGAG 1021 I— ‘ GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_39 CAAGCAGAAGACGGCATACGAGATgctggttataGT 926 TATAACCAGC 1022GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_40 CAAGCAGAAGACGGCATACGAGATaaggCCgaacGT 927 GTTCGGCCTT 1023GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_41 CAAGCAGAAGACGGCATACGAGATggcggttgacGT 928 GTCAACCGCC 1024GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_42 CAAGCAGAAGACGGCATACGAGATt catataccgGT 929 CGGTATATGA 1025GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_43 CAAGCAGAAGACGGCATACGAGATt agt aga CggGT 930 CCGTCTACTA 1026GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_44 CAAGCAGAAGACGGCATACGAGATt CtgataccaGT 931 TGGTATCAGA 1027GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_45 CAAGCAGAAGACGGCATACGAGATgaatCgacgtGT 932 ACGTCGATTC 1028GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_46 CAAGCAGAAGACGGCATACGAGATgaagcaatcaGT 933 TGATTGCTTC 1029GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_47 CAAGCAGAAGACGGCATACGAGATcagaattaggGT 934 CCTAATTCTG 1030 GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_48 CAAGCAGAAGACGGCATACGAGATaataaggCtcGT 935 GAGCCTTATT 1031GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_49 CAAGCAGAAGACGGCATACGAGATgttCtgaattGT 936 AATTCAGAAC 1032GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_50 CAAGCAGAAGACGGCATACGAGATtgCggttcagGT 937 CTGAACCGCA 1033GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_51 CAAGCAGAAGACGGCATACGAGATtaatcagCOtGT 938 AGGCTGATTA 1034GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_52 CAAGCAGAAGACGGCATACGAGATcttCggCgcaGT 939 TGCGCCGAAG 1035GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_53 CAAGCAGAAGACGGCATACGAGATtctctaggCgGT 940 CGCCTAGAGA 1036GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_54 CAAGCAGAAGACGGCATACGAGATtggcagCgaaGT 941 TTCGCTGCCA 1037GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_55 CAAGCAGAAGACGGCATACGAGATtCgtaggtcaGT 942 TGACCTACGA 1038GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_56 CAAGCAGAAGACGGCATACGAGATccgatactctGT 943 AGAGTATCGG 1039GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_57 CAAGCAGAAGAGGGCATACGAGATggtatCCgCCGT 944 GGCGGATACC 1040GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_58 CAAGCAGAAGACGGCATACGAGATatagactaggGT 945 CCTAGTCTAT 1041GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_59 CAAGCAGAAGACGGCATACGAGATtggCCgcaatGT 946 ATTGCGGCCA 1042GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_60 CAAGCAGAAGACGGCATACGAGATcatgCtggttGT 947 AACCAGCATG 1043GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_61 CAAGCAGAAGACGGCATACGAGAT cgCCgaggCgGT 948 CGCCTCGGCG 1044GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_62 CAAGCAGAAGACGGCATACGAGATgctatgcagcGT 949 GCTGCATAGC 1045GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_63 CAAGCAGAAGACGGCATACGAGATcgactCgtagGT 950 CTACGAGTCG 1046GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_64 CAAGCAGAAGACGGCATACGAGATttataggcatGT 951 ATGCCTATAA 1047 GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_65 CAAGCAGAAGACGGCATACGAGATgttaaccggcGT 952 GCCGGTTAAC 1048GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_66 CAAGCAGAAGACGGCATACGAGATactatOCaggGT 953 CCTGGATAGT 1049GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_67 CAAGCAGAAGACGGCATACGAGATttggagt CggGT 954 CCGACTCCAA 1050GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_68 CAAGCAGAAGACGGCATAGGAGATtggCCggCCtGT 955 AGGCCGGCCA 1051GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_69 CAAGCAGAAGACGGCATACGAGATtCgtaggCttGT 956 AAGCCTACGA 1052GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_70 CAAGCAGAAGACGGCATACGAGATctagacgtaaGT 957 TTACGTCTAG 1053GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_71 CAAGCAGAAGACGGCATACGAGATaactcagacgGT 958 CGTCTGAGTT 1054GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_72 GAAGCAGAAGACGGCATACGAGATgagCCttCgcGT 959 GCGAAGGCTC 1055GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_73 CAAGCAGAAGACGGCATACGAGATgattCCggaaGT 960 TTCCGGAATC 1056GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_74 CAAGCAGAAGACGGCATACGAGATtcaacgtaagGT 961 CTTACGTTGA 1057GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_75 CAAGCAGAAGACGGCATACGAGATacgCCgttcaGT 962 TGAACGGCGT 1058GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_76 CAAGCAGAAGACGGCATACGAGATtggatagttgGT 963 CAACTATCCA 1059GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_77 CAAGCAGAAGACGGCATACGAGATctaggCCgttGT 964 AACGGCCTAG 1060GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_78 CAAGCAGAAGACGGCATACGAGATccaaggagCgGT 965 CGCTCCTTGG 1061GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_79 CAAGCAGAAGACGGCATACGAGATtaccgataacGT 966 GTTATCGGTA 1062GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_80 CAAGCAGAAGACGGC ATACGAGATggtt Cga C CtGT 967 AGGTCGAACC 1063GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_81 CAAGCAGAAGACGGCATACGAGATttgCCttggcGT 968 GCCAAGGCAA 1064 GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_82 GAAGCAGAAGACGGCATACGAGATagaatCCtCgGT 969 CGAGGATTCT 1065GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_83 CAAGCAGAAGACGGCATACGAGATcgtaaggagtGT 970 ACTCCTTACG 1066GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_84 CAAGGAGAAGACGGCATACGAGATCCtCCttcatGT 971 ATGAAGGAGG 1067GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_85 CAAGCAGAAGACGGCATACGAGATCtcaagtCOtGT 972 AGGACTTGAG 1068GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_86 CAAGCAGAAGACGGCATACGAGATtggaggcaagGT 973 CTTGCCTCCA 1069GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_87 CAAGCAGAAGACGGCATACGAGATaccttaactcGT 974 GAGTTAAGGT 1070GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_88 CAAGCAGAAGACGGCATACGAGATcggaccgCgcGT 975 GCGCGGTCCG 1071GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_89 CAAGCAGAAGACGGCATACGAGATggatgCgattGT 976 AATCGCATCC 1072GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_90 CAAGCAGAAGACGGCATACGAGATacgtcagaacGT 977 GTTCTGACGT 1073GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_91 CAAGCAGAAGACGGCATACGAGATaatCCgcaacGT 978 GTTGCGGATT 1074GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_92 CAAGCAGAAGACGGCATACGAGATcaaccggatcGT 979 GATCCGGTTG 1075GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_93 CAAGCAGAAGACGGCATACGAGATagCgtaagttGT 980 AACTTACGCT 1076GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_94 CAAGCAGAAGACGGCATACGAGATacgCgcaagcGT 981 GCTTGCGCGT 1077GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_95 CAAGCAGAAGACGGCATACGAGATtacggttCCaGT 982 TGGAACCGTA 1078GACTGGAGTTCAGACGTGTGCTCTTCCGATCTSgRNA_P7-l_96 CAAGCAGAAGACGGCATACGAGATgacggcaattGT 983 AATTGCCGTC 1079GACTGGAGTTCAGACGTGTGCTCTTCCGATCTPaired sgRNA for dual-sgRNA libraryGTGTAACCCGTAGGGCACCTCGTCTCACACCNNNNNNNNNNNNNNNNNNNNGTTTAGCTGCAGGTGTACAGGTACCTAGCGTGAC GCAC’CTGC’CTGACCCCNNNNNNNNNNNNNNNNNNNNGTTTCGAGACGGTCGAAGGACTGCTCTCGAC-3' (SEQ ID NO: 1390) Table 8: Paired sgRNA for dual-sgRNA librarygene_symbol sgRNA_l_seq SEQ ID NO: sgRNA_2_seq SEQ ID NO:ACTN4 GAGCTGAGGCGGGAGCGGAC 1080 GCGGGCTGAAGCAGCTGAAG 1235AHNAK GGCGGGCGGTGCAGTCCCCG 1081 GGCGGTGCTGCGGCTACTGG 1236AHR GGACCGCCAGCTCAGAACAG 1082 GGTTCTGAGCTGGCGGTCCG 1237ALG3 GCTGGGCCGCGGAACCGGAC 1083 GCGGCGCACCGTTAAGATGG 1238ANAPC11 GGACTCGGCGGGCGCTGTTG 1084 GGCCCTTCCGTTGGCAGCGC 1239ANP32B GCGGGAGCGGAGAGAAACCA 1085 GGAGCTGAGGAACCGGACCC 1240ARHGAP12 GCGCGGGTCCTGGCGGACGG 1086 GAAGAGCGTTCACACGGCTA 1241ARIH2 GGGACCGGATTGGGGCGAAG 1087 GGCTGTGGGGACGACTCTTC 1242ARRDC3 GGTGGTCTCCTTACAAAGAC 1088 GTTAGCCGCCCGTCTTTGTA 1243ASAHI GACTCCCCGGACTCCAGCAG 1089 GAGGCGACGCAACTCCGGCC 1244C7orf26 GCCGCCGCGCAGCCGAACAG 1090 GGCGCCCGAAGGTCCGACTG 1245CAN DI GCGCGGCTGCGAGAACCCCG 1091 GGCGAGAGGAGGAGCTCCAG 1246CCDC117 GCGTGACGTGGGGTCGAGAG 1092 GTTCTAGAAGGCGTGACGTG 1247CCNC GGAGGAGCGCGGTTACCGGA 1093 GGCTCGACTCCGCTCCGCGG 1248CDK8 GAAGGAGAGCCGAGGGACGA 1094 GCCCGGCGAAGGAGAGCCGA 1249CHEK2 GCGAGAGGGGAGGACGGATC 1095 GCTGCAGCGCTCGGCACTGT 1250CLTA GCGCCCGGCAGCCACCGCTG 1096 GAGGAGGACAAGCGCCAGGA 1251CREBBP GTTGTGGGGCCCGGGACCGG 1097 GGCTTCGAGCGCGATCTACT 1252CSK GGCTCGCCCAGGGCTCCGCT 1098 GCGGGTCGCGGAGGGAAGAA 1253CST3 GAGGCTGGAGCTAGATAGAG 1099 GAGGACCCGCTGCGATACCG 1254CUL3 GGTCCCCCTCACGTCCGGCT 1100 GCGAGATCCGGCCCAGAGGG 1255CUL5 GCCCAAAGAGCCGAACCCGG 1101 GGCGGCGGGTGCAACCACAA 1256CYC1 GCAGGGGCCGACGGGAGTGG 1102 GCCCCAGGGGCCGACGGGAG 1257CYP2R1 GGCAAGACGCCCGCACCTGA 1103 GCACCCTGGACCTGAAGTGG 1258DCAF17 GGCCGGAGCCGCTCTCTCAC 1104 GGAAGCCGGGCTGCCTCACG 1259 DGKD GTGGCCCGGCAGCATGGCGG 1105 GTGTCGGACGACTCCTCGGG 1260 DPH2 GGGCTGAAGGGGATACTCAC 1106 GGCGCTGCAGCGAGAGACCG 1261 EP300 GGCTCCGACGGCACCCGAGG 1107 GCTACCGCTAGCCTGTAAGG 1262 FHOD1 GTTGAGTAGAAGCTGCACTG 1108 GTCCGGCCGGGCCCGAACCG 1263 FLYWCH1 GCACGGGGCTCGCTCCCGAG 1109 GCGCCGCCGCCACCCCGACC 1264 FOXD3 GGGCAGGGACAGTAGCGGGG 1110 GTAGTACCGAGCGCCAGGGA 1265 FRYL GACTAGGACTGGGAACGGGT 1111 GCTCTCTCCCAGAGCCCGAC 1266 GATA4 GGCGGCGACGAAGCCTCTCG 1112 GGTTCCGCGCACGCTCCCCT 1267 GATA6 GAGGCGCGGAGAGAAGCCGA 1113 GCGGAGGGTGCTCCCAGGCG 1268 GFM1 GACCGGCAGCTGAACCCACC 1114 GAAGTCCCGTGGCGCCGGGT 1269 GLMN GCGGGCTAAGACGCCGGAGG 1115 GCGAGTCAGCCCGGTCCTCA 1270 HIRA GAGGAGCGGCGCAGGGGATG 1116 GGCCCGGGAGCCGCCTGAGG 1271 HOXA4 GGTCCGCGGCGCGTAGTAGG 1117 GGCTGCGCGTACTCCTCGAA 1272 HPRT1 GCGGGTCGCCATAACGGAGC 1118 GGGGCTGCGGGTCGCCATAA 1273 KCTD10 GTCCAAACCCGGACTGAGAG 1119 GGCCTCTCTCAGTCCGGGTT 1274 KCTD5 GAGTGGGTCCGACTCAACGT 1120 GCGCAACAGCTCCCTTCCAC 1275 KEAP1 GGTCCTGGCGGGGAACTCGG 1121 GGCCCTGGCCTCAGGCGGTA 1276 LRRFIP2 GCGCGGCGCAGAGGGCACAT 1122 GGTTCCCGCTGTCTCAGCCG 1277 MAP4K4 GAGGACGGGCCCGGAGCGTG 1123 GGCCGGCAGAGAGCCCCGAG 1278 MARS2 GCGTCCTAGCAGGCGGAGGA 1124 GCGTGCGTCCTAGCAGGCGG 1279 MAU2 GTGCTTCCGCCTCCCTGTGG 1125 GACAAGCCGCCGCCACAGGG 1280 MEDIO GAAAGGGGCCTCAAAGAGGT 1126 GGCTGGCGGGGCACGAGTAG 1281 MED12 GCGGCGGCCGAGAGACAACA 1127 GGCAGGGCCAGGCTGACTGG 1282 MED13 GGCCCCGTTCGGCACGAAGG 1128 GTCGGAGGCGGCGGTAATGG 1283 MED15 GTGGCGGCCAAGCGGGATAC 1129 GGGCTCTGTGACTGAGGCGG 1284 MED16 GTTGTTACCGAGGAAGATGG 1130 GAGGCGACTGGTTGTTACCG 1285 MED19 GAGACACGGAGACAGCGCCG 1131 GGCCGGGGCGGAGGGTACGA 1286 MED23 GGCTCCAGGTAAAGCGCTGG 1132 GACTCGAGCTCTGGGAATAT 1287MED24 GCACCTAGAACTGGATTGTG 1133 GTACCGCACAATCCAGTTCT 1288 METTL3 GGTGAGAATTGGCTATATCC 1134 GACCGAGGCCCAGCGTGAGG 1289 MICU2 GGGAGGGAGGAGCTTGACTG 1135 GTGCGCTTCCGCAAAGATGG 1290 M0RN2 GCCGCAGAGCTGGCCGCCCA 1136 GCGGGAGAGAACTAGGCGCT 1291 MRPS12 GCTCACAGGACCTAAAGGTG 1137 GCCGACCTCACAGGACCTAA 1292 NF1 GTGGGGACAGAGTAGGTGAG 1138 GGGATCCTTTCCACGGCCCG 1293 NF2 GGACTCCGCGCGCCTCTCAG 1139 GACCCTAGTCGGCCGCTGAG 1294 NIPBL GAGAGTTCGCTCCTCTCTGG 1140 GCGCCTGTGGAAGAGACCTG 1295 PBRM1 GGCGGCACCGGGAGGGGTAA 1141 GGGTGCCGAAGCGACGGCAG 1296 PDCD10 GGACGCCGGAAGGACCCAAG 1142 GTTGATCCGGGAGTTGAAGA 1297 PELO GGCCAGCGGGAACTGTGTAG 1143 GACCCCTACACAGTTCCCGC 1298 PGD GACGCGCGGAGGACGAGTGA 1144 GGTCACTCACTGGGCCATGG 1299 PGRMC2 GCTGGGGGCCTACCGGCTGT 1145 GCGCTGGCGGGCAGTGATGG 1300 PICALM GTCGGCTTCACTCACAGTCC 1146 GCTCGTGTCACCCGCGGAGT 1301 PLXND1 GGCGGCGCGAGGAGCCATCC 1147 GGCGGCGCGGCGAGTGCATG 1302 POLR3K GAGTGGGAGCTGCCGGGAGT 1148 GGTAGGCCCTGGGAGAACTC 1303 PPT1 GCCCGAGAAGCGCAGGTCCA 1149 GAGAAGCGCAGGTCCATGGC 1304 PTEN GGTTAGAAAAGACGAAGAGG 1150 GCGCCTGTGAGCAGCCGCGG 1305 PTPN12 GGCGGCTCGCCTGGTACTGT 1151 GCCGCAGCCGCCGCCTAGGG 1306 RAB35 GTCGTAGTCCCGGGCCATGG 1152 GGCGATCCGCAGCTCCCTTC 1307 RALGAPB GGACTCTCCCAGGGCGCCAA 1153 GGGGCGCCAAGGGACGACGA 1308 RBI GGAGCTGTGGAGGAGCGCCG 1154 GGGGAGCCAGCGAGCTGTGG 1309 RNF146 GAAGCGGGCTCAGGCGGAAG 1155 GGAGAGAAAAGACTGCGAGG 1310 RNF7 GGGCGCCGCGGAGCCGACGG 1156 GGCCTCCGGGAGCCGACCTC 1311 SAV1 GGTTATTCGGCGGCCCGCGG 1157 GGGTAGTGGTAGCGGTTATT 1312 SDSL GCAGGGAACCAGCTCTTTCC 1158 GGACCCTGAAGGGCGAGAGT 1313 SLC35A1 GGGGGGCTGTCGGGGAACCA 1159 GGCCCCGAGAGGTGAGAACT 1314 SMARCA4 GGCAGCCGCTGTAATTGGGA 1160 GGGAGGCGCCGGGAAGTCGA 1315 SMARCB1 GTGCGGACCGGGCCGGGTAC 1161 GCGGCTGAGGCGCCAGTACC 1316SMARCD1 GGCCGGCCGGCCGGCACGGG 1162 GGACATGGGAGTGACAGGGG 1317 SMARCE1 GCTTGGAAACACTCACCCGC 1163 GGGCCTAGCAGAGCGAGGGT 1318 SNRNP25 GGGGCAGAGCCCGGCTGAGA 1164 GTGGCCTCCCTAGTGCGGGC 1319 S0X2 GACAGCCCCCGTCACATGGA 1165 GCTCAGTCCTAGTCTTAAAG 1320 SPRED1 GCTCGCGGCTCCCCTCTCTC 1166 GGCTCCTCACGCCCGAGAGA 1321 SPRY2 GTGCGGCGCGCAGCTCTCGG 1167 GCGCGCCGCAGCGCTAGCGG 1322 STK24 GCTGGCCGCTGGAGCCGGAG 1168 GGCCCGGCCGGGACCCGCCA 1323 SUPT20H GTCGCCGCTAGGCCCCAAGA 1169 GGAAGTGCGGCCGCCGGAAC 1324 SUPT7L GAATGCCCAAGTCCCTCCAG 1170 GCGGCTGTCTGGACCTCGAG 1325 TADA1 GCCCTCTGGCTGGACGCGTA 1171 GGGCCGGGCGGTCTGCGGTC 1326 TADA2B GCTGGTAGCCGTGGTAGCGG 1172 GACGGCGCCTGCGTACTGAG 1327 TADA3 GCCCATAGCCAGTTCCGGGG 1173 GAGCGCCAGGAGCTGCGGGT 1328 TAF5L GGCTCCATGACTGACTGACA 1174 GGCTCCGTGTCAGTCAGTCA 1329 TAF6L GCCTGCTCACCTGAAGACCC 1175 GTGAGTGTGAGCTCGTGAGT 1330 TA0K1 GCGGTAGCCGCTACCCTGGA 1176 GCAAGCTCTGCAACAAGGGG 1331 TP53 GAGTCTAGAGCCACCGTCCA 1177 GCAGGGAGCAGGTAGCTGCT 1332 TRAF3IP1 GCCGGCCCGGGCCCCGACTC 1178 GGCGTCCTGGCAGGACCGGG 1333 UBE2F GCAGCCGCACCGGCTCCCGG 1179 GGAGCCGGTGCGGCTGTGAG 1334 UBE2G1 GGGCGGGACGCTTCCCAAGT 1180 GACTCACGCCCGGAAGGGGA 1335 UBE2R2 GTCGGGCCGGGCGAGAGGAG 1181 GCTCGGCTCCGTTGCTCCCG 1336 VBP1 GCGGCCGTTAAGGACAGTTG 1182 GCGCGCTCGCATCCCCAAGA 1337 WT1 GACTGCGCCGCGCCGCTCAC 1183 GTAGGCACCCAGGCCCGGAG 1338 ZBTB7A GTGGGCTGGCTCCCTCCGAG 1184 GGTCTCCCGCCCGGGAGGTA 1339 ZC3H15 GGCAGGGCCAGAACCCCTGA 1185 GTTGCGGAGACAGATGGCAC 1340 ZNF592 GCGACTGCAGGCCAGAGTCT 1186 GGGCTGCGGGAGCCACAACA 1341 ZNF781 GCAGCGACTACTAGGTCAGG 1187 GTGCCAGCGACTACTAGGTC 1342 SPRED2 GGAGACTGAATCATATATGG 1188 GAAGGAGACTGAATCATATA 1343 ARHGAP35 GGATGTGTCGACGATGATGA 1189 GCAGTGTGGTGGGATTATCT 1344 FBXO11 GCGGCTGAAGAGACAGATCC 1190 GCCTCGGGATCCCCAAGCGG 1345RXRA GGCAACAACTCGCCGCGCCG 1191 GACAAGTAGTTTACATTGTT 1346 ARID1A GAGCGAGCGCAGCGCAGCAG 1192 GAAAGCGGAGAGTCACAGCG 1347 SBF1 GAAGATGGCGGCGGGAACCT 1193 GGCTGAGGGCCGAGGACCGG 1348 CDKN2C GGCCATCATGCAGCCTGGTT 1194 GGCAGCCTGGTTAGGAGCAA 1349 SESN1 GGCCCTTCGCCCAGCCCCAT 1195 GGGGCTGATCCCCACCGATG 1350 CCM2 GGGAGCCGCACGCGGCGATA 1196 GGGCCCGGAAGTGCGGGTGT 1351 PTPN14 GGCAGCGGGCGCAGACTCGG 1197 GCTCGGCGGAGTGAGCGCAG 1352 NRP1 GAGACACCCGGACCTCCCCT 1198 GTGGCGCCCAGGGGAGGTCC 1353 ATP5G3 GCCTCTCTGCGGAGGAAAAG 1199 GCTGCGCGGCTGGAGATATT 1354 TYSND1 GGCTTCAGCCGGACCGGGTA 1200 GCGAGGACCCCTACCCGGTC 1355 LRP11 GGCGCTGGGTGGCGACGAGT 1201 GCCATGGCGACGAGAGCCAA 1356 DUSP6 GGCGCGAGGCAGCTCCTCAA 1202 GTCCGGGGAGAAAGACGCCC 1357 CTC1 GACTCACGGAGGAAGGGACC 1203 GGACGTAATAGCAGCACTCA 1358 M0SPD1 GGACGCTGACAAAGGAGCGG 1204 GCAGTCGCAGCTGCTTTCCG 1359 FRMD6 GCCGGTAGGAAGAGTCAGAG 1205 GGACCAGAGAGCCCAACGCC 1360 CCDC101 GCCCCTAGAGCGCCGCCTCA 1206 GACCTGCGTTCGAAGATTGG 1361 CDH13 GTGTCCCAGGTAGGGAAGAG 1207 GGACAGATCGGGGCTCGGTA 1362 SGK223 GTGCGAACAATGCTCCGGCC 1208 GGAACAATGCTCCGGCCGGG 1363 TMEM132D GTCCAGGCGCAGGGCTCCGG 1209 GCGCTCCGGCACCCAAAGTT 1364 CCDC122 GATCAGGCGGGGAATCTGTG 1210 GATCCTGGGACGGTATCAGG 1365 AMOTL2 GGCGCGAACAGCCAGAGCGT 1211 GAGTCAGACACCACAACCTC 1366 HECA GCAGCGAAGGCGGATCCCAG 1212 GCTTCACGCAGGGCCGGGAA 1367 CCDC173 GGTACGAGTAACAACTCCAC 1213 GTCCAGATCTAGGAGCATCA 1368 SUFU GAGACTCCCCGAGGCGATGG 1214 GGCGGCGGCGACAGCCTGGG 1369 CYLD GAGGTAAATACTAGGGCGGT 1215 GCGCGTCGGAGGTAAATACT 1370 DCUN1D3 GCTGGAAACGCGAGCGGGGA 1216 GCGGTGGGCCGCAGATGAAG 1371 LATS2 GCAGGGCGCAGGGCTCCGTC 1217 GCGGCTCCGCGGGCTCCGTA 1372 SENP8 GCGCTTTCCGGCCAACACAG 1218 GGCCGAGACAAACCGAAGTG 1373 DSCR3 GCACGCGCAGCCGGAAGTGG 1219 GGGTCACGCGCAGCCGGAAG 1374CASCIO GGCGCGACCCTGGTCCTCAA 1220 GGGTCGCGCCGAGAGGCCGG 1375 KIRREL GCAGAGGGCGAGGGTCGGTA 1221 GGAGAGTGAGGATCCAGACG 1376 SP0CK2 GGTTCGGGGATTGAGCTCCG 1222 GTCCTCCATGAAATTGCCGG 1377 NO-TARGET GTTCGCACCGGCCACCACAC 1223 GCCCGCCCCGCAGCCTAAGG 1378 NO-TARGET GGCGTTGGCTTAAGCTGTAG 1224 GTAGTTCCGCCAGCATTTAG 1379 NO-TARGET GAGCCCTGACCCCCACCGGG 1225 GTACCGGGGTCTTGCGTTGG 1380 NO-TARGET GGGCCAGGGCCCATCTCAAC 1226 GTATGCCTCGAATGCCCGAA 1381 NO-TARGET GGTGATATCCTACGGGATTA 1227 GTGTGGGGTACGGCAGACGT 1382 NO-TARGET GAACGGTGGTGCTTGTGTTT 1228 GGGCGGGGGCAACAATCCTG 1383 NO-TARGET GCCCCCCCCTAAGACTGGAA 1229 GACTACCAACCGCCGTTCCG 1384 NO-TARGET GACACCGTGCGGATGATCTA 1230 GACGCAGGAACGGCCTTTGG 1385 NO-TARGET GGGACCCGGACCATGGTTTG 1231 GAATTGCAGGGTTTAGACCT 1386 NO-TARGET GGCCAAAGACCGGTCATCGA 1232 GATGAGCGCGAACGCTTAGG 1387 NO-TARGET GGTGGTGCTGACAACAAGAA 1233 GCCTTACGTGGGATTCGGTT 1388 NO-TARGET GGATGACCGGAACTCCACTA 1234 GTTTCAGAAGGCGGCCATAT 1389

Claims

1. Attorney Docket No.046531-5039-00WOCLAIMS1. A method for preparing a sequencing library comprising nucleic acids from a plurality of single nuclei or cells, the method comprising:a) providing a plurality of nuclei or cells;b) transducing the nuclei or cells with a pooled sgRNA library;c) contacting the cells with 5-Ethynyl Uridine (5-EU);d) harvesting, fixing, and permeabilizing the nuclei or cells;e) tagging open chromatin regions using a Tn5-mediated transposition assay for transposase-accessible chromatin (ATAC);f) performing reverse transcription to generate cDNA fori) unlabeled pre-existing transcripts;ii) 5-EU labeled nascent transcripts; andiii) sgRNA transcriptsg) performing two rounds of split-and-pool index ligation;h) conjugating biotin to 5-EU-labeled nucleic acid molecules;i) generating single cell sequencing libraries for each of:i') nascent transcripts;ii1) sgRNA transcripts;iii1) pre-existing RNA transcripts; andiv') ATAC,wherein each of i') to iv') in each single nuclei or cell is linked to a unique combination of DNA barcodes, with the barcodes added in the split pool labeling of step g) being cell or nuclei specific and a third barcode added in step i) which is library-type specific;j) sequencing the multiple libraries; andk) performing computational analysis of sequencing reads to identify transcriptional dynamics in a single cell.

2. The method of claim 1, wherein step h) further comprises using click chemistry-based in situ ligation to attach an azide-containing fluorophore to the nascent transcripts.

3. The method of claim 1, wherein step d) further comprises performing fluorescence-activated cell sorting (FACS) to enrich 5-EU labeled cells or nuclei.

4. The method of claim 1, wherein the method includes a step of FACS to enrich the 5-EU labeled cells or nuclei following step f).

5. The method of claim 1, wherein a first round of FACS is performed in step d) and a second round of FACS is performed following step f) to enrich the EdU+ cells.

6. The method of claim 1, wherein reverse transcription after ATAC is performed at 50°C.

7. The method of claim 1, in which the method of transducing the nuclei or cells with a pooled sgRNA library is performed by contacting the cells or nuclei with a vector comprising two sgRNA expression cassettes (sgRNA-1 and sgRNA-2), driven by the hU6 and hHl promoters respectively, incorporated into the 3' long terminal repeat (LTR) of a lentiviral construct, and further wherein the vector comprises a sgRNA scaffold containing a capture sequence downstream of sgRNA-1.

8. The method of claim 1, wherein biotin is conjugated to 5-EU using click chemistry.

9. The method of claim 1, wherein the reverse transcription includes contacting the cells or nuclei with a combination of primers including:a) a sgRNA capture RT primer comprising SEQ ID NO: 1;b) a shortdT RT primer comprising SEQ ID NO:2; andc) a randomN RT primer comprising SEQ ID NO:3.

10. The method of claim 1, wherein the reverse transcription (RT) reaction is performed in PEG NaCl RT buffer.

11. The method of claim 1, further comprising determining the effect of sgRNA on the temporal dynamics of transcription by integrating the temporal dynamics of all three molecular layers (pre-existing transcription, chromatin status and nascent transcription) following sgRNA administration.

12. The method of claim 1, wherein the method is used for determining the effects of large-scale genetic perturbations on the continuum of gene regulation, from chromatin remodeling to nascent transcription and ultimately to steady-state transcriptomic phenotypes, at single-cell resolution.

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