bioRxiv preprint doi: https://doi.org/10.1101/309831; this version posted October 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

A rapid and robust method for single cell chromatin accessibility profiling

1 1,4 1,5 Xi Chen ​, Ricardo J Miragaia ,​ Kedar Nath Natarajan ,​ Sarah A ​ ​ ​ Teichmann1,2,3,* ​

1 Wellcome​ Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, United Kingdom 2 EMBL-European​ Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom 3 Theory​ of Condensed Matter, Cavendish Laboratory, 19 JJ Thomson Ave, Cambridge, CB3 0HE, UK 4 Present​ address: MedImmune, Sir Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, United Kingdom 5 Present​ address: Functional Biology and Metabolism Unit, Biochemistry and Molecular Biology, SDU, 5230 Odense, Denmark * Correspondence​ should be addressed to S.A.T. ([email protected]) ​ ​

Xi Chen: [email protected] ​ Ricardo J Miragaia: [email protected] ​ Kedar Nath Natarajan: [email protected] ​ Sarah A Teichmann: [email protected] ​

Keywords: Single-cell genomics, scATAC-seq, gene regulation, chromatin accessibility, immune cells

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Abstract

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting. We demonstrated that our method worked robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3,000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors.

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Due to its simplicity and sensitivity, ATAC-seq1 has been widely used to map open ​ chromatin regions across different cell types in bulk. Recent technical developments have allowed chromatin accessibility profiling at the single cell level (scATAC-seq) and revealed distinct regulatory modules across different cell types within 2–9 heterogeneous samples .​ In these approaches, single cells are first captured by ​ 3 7 either a microfluidic device or a liquid deposition system ,​ followed by independent ​ ​ tagmentation of each cell. Alternatively, a combinatorial indexing strategy has been 2,4,9 reported to perform the assay without single cell isolation .​ However, these ​ approaches require either a specially engineered and expensive device, such as a 3 7 Fluidigm C1 or Takara ICELL8 ,​ or a large quantity of customly modified Tn5 ​ ​ 2,4,5,9 transposase .​ ​

Here, we overcome these limitations by performing upfront Tn5 tagging in the bulk cell population, prior to single nuclei isolation. It has been previously demonstrated that Tn5 transposase-mediated tagmentation contains two stages: (1) a tagging stage where the Tn5 transposome binds to DNA, and (2) a fragmentation stage where the Tn5 transposase is released from DNA using heat or denaturing agents, 10–12 such as sodium dodecyl sulfate (SDS) .​ Since the Tn5 tagging does not fragment ​ DNA, we reasoned that the nuclei would remain intact after incubation with the Tn5 transposome in an ATAC-seq experiment. Based on this idea, we developed a simple, robust and flexible plate-based scATAC-seq protocol, performing a Tn5 tagging reaction6,13 on a pool of cells (5,000 - 50,000) followed by sorting individual ​ nuclei into plates containing lysis buffer.

14 Tween-20 is subsequently added to quench the SDS in the lysis buffer ,​ which ​ otherwise will interfere the downstream reactions. Library indexing and amplification are done by PCR, followed by sample pooling, purification and sequencing. The whole procedure takes place in one single plate, without any intermediate purification or plate transfer steps (Fig. 1a). With this easy and quick workflow, it only ​ ​ takes a few hours to prepare sequencing-ready libraries, and the method can be implemented by any laboratory using standard equipment.

We first tested the accuracy of our sorting by performing a species mixing experiment, where equal amounts of HEK293T and NIH3T3 cells were mixed, and scATAC-seq was performed with our method. Using a stringent cut-off (Online Methods), we recovered 307 wells, among which 303 wells contain predominantly either mouse fragments (n = 136) or human fragments (n=167). Only 4 wells are categorised as doublets (Fig. 1b).

To compare our plate-based method to the existing Fluidigm C1 scATAC-seq approach, we performed side-by-side experiments, where cultured K562 and mouse embryonic stem cells (mESC) were tested by both approaches. We used three

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metrics to evaluate the quality of the data generated by both methods (Fig. 1c and ​ Supplementary Fig. 1a). Our plate-based method has higher library complexity ​ (library size estimated by the Picard tool), comparable or lower amount of mitochondrial DNA, and higher signal-to-noise ratio measured by fraction of reads in peaks (FRiP) (Fig. 1c). In addition, visual inspection of the read pileup from the ​ ​ aggregated single cells suggested both methods were successful, but data generated from our plate-based method exhibited higher signal (Fig. 1d and e). ​ ​ ​ ​

The main difference between our method and Fluidigm C1 is the Tn5 tagging strategies. The plate-based method performed Tn5 tagging using a population of cells, while it was done in individual microfluidic chambers in the Fluidigm C1. It is possible that the upfront Tn5 tagging is more efficient than tagging in microfluidic chambers.

To evaluate the generality of our method, we tested the plate-based method on cryopreserved cells from four tissues: human and mouse skin fibroblasts (hSF and mSF)15 and mouse cardiac progenitor cells (mCPC) at embryonic day E8.5 and ​ 16 E9.5 .​ Cells were revived from liquid nitrogen, and our plate-based method was ​ carried out immediately after revival. The library complexities varied among cell types (Fig. 2a). We obtained median library sizes ranging from 52,747 (mSF) to ​ ​ 104,608.5 (mCPC_E8.5) unique fragments (Fig. 2a). The amount of mitochondrial ​ ​ DNA also varied across cell types but was low in all samples (<13%). All four tested samples had very high signal-to-noise ratio, with a median FRiP ranging from 0.50 (mSF) to 0.60 (hSF) (Fig. 2a). The insert size distributions of the aggregated single ​ ​ cells from all four samples exhibited clear nucleosomal banding patterns (Fig. 2b), ​ ​ 1 which is a feature of high quality ATAC-seq libraries .​ Finally, visual inspection of ​ aggregate of single cell profiles showed clear open chromatin peaks around expected genes (Fig. 2c and d). Details of all tested cells/tissues are summarised in ​ ​ ​ Supplementary Table 1. ​

After this validation of the technical robustness of our plate-based method, we further tested it by generating the chromatin accessibility profiles of 3,648 splenocytes (after red blood cell removal) from two C57BL/6Jax mice. In total, we performed two 96-well plates and nine 384-well plates. By setting a stringent quality control threshold (>10,000 reads and >90% mapping rate), 3,385 cells passed the technical cut-off (>90% successful rate) (Supplementary Fig. 3b). The aggregated scATAC-seq profiles exhibited good coverage and signal and resembled the bulk data generated from 10,000 cells by the Immunological Genome Project (ImmGen)17 ​ (Fig. 3a). The library fragment size distribution before and after sequencing both ​ ​ displayed clear nucleosome banding patterns (Fig. 3b and Supplementary Fig. 2a). ​ ​ ​ In addition, sequencing reads showed strong enrichment around transcriptional start sites (TSS) (Fig. 3c), further demonstrating the quality of the data was high. ​ ​ 4 bioRxiv preprint doi: https://doi.org/10.1101/309831; this version posted October 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Importantly, for the majority of the cells, less than 10% (median 2.1%) of the reads were mapped to the mitochondrial genome (Supplementary Fig. 3a). Overall, we ​ ​ obtained a median of 643,734 reads per cell, while negative controls (empty wells) generated only ~ 100-1,000 reads (Supplementary Fig. 3b). In most cells, more than ​ ​ 98% of the reads were mapped to the mouse genome (Supplementary Fig. 3b), ​ ​ indicating low level of contamination. The median of estimated library sizes is 31,808.5 (Supplementary Fig. 3c). At the sequencing depth of this experiment, the ​ ​ duplication rate of each single cell library is ~ 95% (Supplementary Fig. 3d), ​ ​ indicating that the libraries were sequenced to near saturation. Downsampling the raw reads (from the fastq files) and repeating the analysis suggest that at 20 - 30% of our current sequencing depth, the majority of the fragments would have already been captured (Supplementary Fig. 4a and b). Therefore, in a typical scATAC-seq ​ ​ experiment, ~ 120,000 reads per cell are sufficient to capture most of the unique fragments, with higher sequencing depth still increasing the number of detected unique fragments (Supplementary Fig. 3e). ​ ​

Next, we examined the data to analyse signatures of different cell types in the mouse spleen. Reads from all cells were merged, and a total of 78,048 open chromatin regions were identified by peak calling with q values less than 0.0118 ​ (Online Methods). We binarised peaks as “open” or “closed” (Online Methods) and applied a Latent Semantic Indexing (LSI) analysis to the cell-peak matrix for 2 2 dimensionality reduction (Online Methods). Consistent with previous findings ,​ the ​ ​ first dimension is primarily influenced by sequencing depth (Supplementary Fig. 3f). ​ ​ Therefore, we only focused on the second dimension and upwards and visualised 19 the data by t-distributed stochastic neighbour embedding (t-SNE) .​ We did not ​ observe batch effects from the two profiled spleens, and several distinct populations of cells were clearly identified in the t-SNE plot (Fig. 3d). Read counts in peaks near ​ ​ key marker genes (e.g. Bcl11a and Bcl11b) suggested that the major populations are ​ ​ ​ ​ B and T lymphocytes, as expected in this tissue (Fig. 4a). In addition, we found a ​ ​ small number of antigen-presenting cell populations (Supplementary Fig. 5), ​ ​ 20 consistent with previous analyses of mouse spleen cell composition .​ ​

To systematically interrogate various cell populations captured in our experiments, we applied a spectral clustering technique21 which revealed 12 different cell clusters ​ (Fig. 4b). Reads from cells within the same cluster were merged together to form ​ ​ ‘pseudo-bulk’ samples and compared to the bulk ATAC-seq data sets generated by ImmGen (Supplementary Fig. 6 and 7). Cell clusters were assigned to the most ​ ​ ​ similar ImmGen cell type (Fig. 4b and Supplementary Fig. 7). In this way, we ​ ​ ​ identified most clusters as different subtypes of B, T and Natural Killer (NK) cells, as well as a small population of granulocytes (GN), dendritic cells (DC) and macrophages (MF) (Fig. 4b and Supplementary Table 2). An aggregate of all single ​ ​ 5 bioRxiv preprint doi: https://doi.org/10.1101/309831; this version posted October 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

cells within the same predicted cell type agrees well with the ImmGen bulk ATAC-seq profiles (Supplementary Fig. 8). Remarkably, the aggregate of as few as ​ ​ 55 cells (e.g. the predicted MF cell cluster) already exhibited typical bulk ATAC-seq ​ profiles (Supplementary Fig. 8). This finding opens the door for a novel ATAC-seq ​ ​ experimental design, where Tn5 tagging can be performed upfront on large populations of cells (e.g. 5,000 - 50,000 cells). Subsequently, cells of interest (for ​ example, marked by surface protein antibodies or fluorescent RNA/DNA probes) can be isolated by FACS, and libraries generated for subsets of cells only. This will be a simple and fast way of obtaining scATAC-seq profiles for rare cell populations.

To test the feasibility of this idea, we stained mouse splenocytes with an anti-CD4 antibody conjugated with PE and performed tagmentation afterwards. The PE signal remained after tagmentation (Supplementary Fig. 9), allowing us to specifically sort ​ ​ out CD4 positive T cells from the rest of the splenocytes for analysis (we named these “TagSort” libraries). As a control, we first purified CD4 T cells using an antibody-based depletion method (Online Method), and subsequently performed scATAC-seq on the purified CD4 T cells (we named these “SortTag” libraries). The data of CD4 T cells generated from these two strategies agree very well (Fig. 4c). The library complexity is comparable with median library sizes of 30,953 and 25,830 respectively (Fig. 4c, top left panel). The binding signals around open chromatin ​ ​ peaks are highly correlated (Pearson r=0.96) (Fig. 4c, top right panel). Visual ​ ​ inspection of read pileup profiles around the Cd4 gene locus from single cell ​ aggregates suggested the data are of good quality (Fig. 4c, bottom panel). ​ ​

This experiment serves as a proof-of-principle test where staining of a surface marker can be done before Tn5 tagging, and a specific population can be sorted by FACS afterwards for scATAC-seq analysis. It should be noted that we have only tested CD4 - an abundant marker in a subpopulation of splenocytes. Other surface markers in different tissues would need to be investigated individually. In addition, the ability to investigate rare cell populations using this approach is limited by the frequency of the rare cell types and the amount of cells that can be tagged upfront.

The spectral clustering was able to distinguish different cell subtypes, such as naïve and memory CD8 T cells, naïve and regulatory CD4 T cells and CD27+ and CD27- NK cells (Fig. 4b). Previous studies have identified many enhancers that are only ​ ​ accessible in certain cell subtypes, and these are robustly identified in our data. Examples are the Ilr2b and Cd44 loci in memory CD8 T cells22 and Ikzf2 and Foxp3 ​ ​ ​ ​ ​ in regulatory T cells23 (Supplementary Fig. 10a and b). Interestingly, our clustering ​ ​ ​ ​ approach successfully identified two subtle subtypes of NK cells (CD27- and CD27+ NK cells), as determined by their open chromatin profiles (Fig. 4b and d). It has been ​ ​ ​ 24 shown that, upon activation, NK cells can express CD83 ,​ a well-known marker for ​ 25 mature dendritic cells .​ In mouse spleen, Cd83 expression was barely detectable in ​ ​ ​ ​ 6 bioRxiv preprint doi: https://doi.org/10.1101/309831; this version posted October 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

the two NK subpopulations profiled by the ImmGen consortium (Supplementary Fig. 10c). However, in our data, the Cd83 locus exhibited different open chromatin states ​ in the two NK clusters (Fig. 4d). Multiple ATAC-seq peaks were observed around the ​ ​ Cd83 locus in the CD27+ NK cell cluster but not in the CD27- NK cluster (Fig. 4d). ​ ​ This suggests that Cd83 is in a transcriptionally permissive state in the Cd27+ NK ​ cells, and the CD27+ NK cells have a greater potential for rapidly producing CD83 upon activation. This may partly explain the functional differences between CD27+ 26 and CD27- NK cell states .​ ​

Finally, we investigated whether we could identify the regulatory regions that define each cell cluster. To this end, we trained a logistic regression classifier using the spectral clustering labels and the binarised scATAC-seq count data (Online Methods). From the classifier, we extracted the top 500 open chromatin peaks (marker peaks) that can distinguish each cell cluster from the others (Fig. 4e and ​ ​ Online Methods). By looking at genes in the vicinity of the top 50 marker peaks, we recapitulated known markers, such as Cd4 for the helper T cell cluster (cluster 3), ​ Cd8a and Cd8b1 for the cytotoxic T cell cluster (cluster 6) and Cd9 for marginal zone ​ ​ B cell cluster (cluster 4) (Supplementary Figure 11 and Supplementary Table 3). ​ ​ ​ These results are consistent with our correlation-based cell cluster annotation (Fig. ​ 4b). ​

While the peaks at TSS are useful for cell type annotation, the majority of the cluster-specific marker peaks are in intronic and distal intergenic regions, in line with the global peak distribution (Supplementary Fig. 12). To identify transcription factors ​ ​ that are important for the establishment of these marker peaks, we investigated 27 them in more detail by motif enrichment analysis using HOMER .​ The full results of ​ these motif enrichment analyses are included in Supplementary Table 4. As expected, different ETS motifs and ETS-IRF composite motifs were significantly enriched in marker peaks of many clusters (Fig. 4f), consistent with the notion that ​ ​ 28 ETS and IRF transcription factors are important for regulating immune activities .​ ​ Furthermore, we found motifs that were specifically enriched in certain cell clusters (Fig. 4f). Our motif discovery is consistent with previous findings, such as the ​ ​ importance of T-box (e.g. Tbx21) motifs in NK29 and CD8T memory cells30 and POU ​ ​ ​ 31 domain (e.g. Pou2f2) motifs in marginal zone B cell .​ This suggests that our ​ ​ scATAC-seq data is able to identify known gene regulation principles in different cell types within a tissue.

32 33 In recent years, other methods, such as DNase-seq ,​ MNase-seq and ​ ​ 34,35 NOMe-seq ,​ have investigated chromatin status at the single cell level. However, ​ due to its simplicity and reliability, ATAC-seq currently remains the most popular technique for chromatin profiling. Several recent studies have demonstrated the power of using scATAC-seq for investigating regulatory principles, e.g. brain ​ 7 bioRxiv preprint doi: https://doi.org/10.1101/309831; this version posted October 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

4,9 36 37 development ,​ Mouse sci-ATAC-seq Atlas and pseudotime inference .​ The ​ ​ ​ 38 combined multi-omics approaches also began to emerge, such as sci-CAR-seq ,​ ​ 39 8 scCAT-seq and piATAC-seq .​ Our study added on top of those methods to ​ ​ provide a simple and easy-to-implement scATAC-seq approach that can successfully detect different cell populations, including subtle and rare cell subtypes, from a complex tissue. More importantly, it is able to reveal key gene regulatory features, such as cell-type specific open chromatin regions and transcription factor motifs, in an unbiased manner. Future studies can utilise this method to unveil the regulatory characteristics of novel and rare cell populations and the mechanisms behind their transcriptional regulation.

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Figure Legends Figure 1. Simple and robust analysis of chromatin status at the single cell level. (a) ​ ​ Schematic view of the workflow of the scATAC-seq method. Tagmentation is performed upfront on bulk cell populations, followed by sorting single-nuclei into 96/384 well plates containing lysis buffer. The lysis buffer contains a low concentration of proteinase K and SDS to denature the Tn5 transposase and 14 fragment the genome. Tween-20 is added to quench SDS .​ Subsequently, library ​ preparation by indexing PCR is performed, and the number of PCR cycles needed to amplify the library is determined by quantitative PCR (qPCR) (Supplementary Fig. ​ 2b). (b) Species mixing experiments to show the accuracy of FACS. Equal amounts ​ of HEK293T (Human) and NIH3T3 (Mouse) cells were mixed, and scATAC-seq was performed as described in (a). Successful wells with more than 90% of reads uniquely mapped to either human or mouse were categorised as singlets (n=303). Otherwise, they will be categorised as doublets (n=4) (see Online Methods). (c) Comparison of the median library size (estimated by the Picard tool), fraction of mitochondrial DNA (MT content) and fraction of reads in peaks (FRiP) in single cells from either C1 (blue) or plate-based (red) scATAC-seq approach. (d) UCSC genome browser tracks displaying the signal around the Nanog gene locus from the ​ aggregate of mESCs obtained from Fluidigm C1 (top) and plate (bottom). (e) The same type of tracks as (d) around the ZBTB32 gene locus in K562 cells. ​ ​

Figure 2. Plate-based scATAC-seq worked robustly on cryopreserved cells from primary tissues. (a) Comparison of the median library size (estimated by the Picard ​ tool), fraction of mitochondrial DNA (MT content) and fraction of reads in peaks (FRiP) in cryopreserved single cells from four different tissues: human skin fibroblasts (hSF), mouse skin fibroblasts (mSF), mouse cardiac progenitor cells (mCPC) at embryonic day E8.5 and E9.5. (b) Insert size frequencies from the aggregated data of the cells from the four tissues. (c and d) UCSC genome browser tracks displaying the signal around the RPS18 gene locus from the aggregate of hSFs (c) and around ​ the Gapdh gene locus from the aggregate of mSFs, mCPC_E8.5 and mECP_E9.5 (d). ​ ​

Figure 3. Plate-based scATAC-seq applied to over 3000 mouse splenocytes. (a) UCSC genome browser tracks displaying the signal around the Cxcr5 gene locus ​ from the aggregate of all single cells in this study. Bulk ATAC-seq profiles from the ImmGen consortium are also shown. Randomly selected 100 single cell profiles are show below the aggregated profile. (b and c) Insert size frequencies (b) and ​ ​ sequencing read distributions across transcriptional start sites (c) of libraries from the ​ ​ aggregated data (the red line) and individual single cells (grey lines, 24 examples are shown). (d) A two-dimensional projection of the scATAC-seq data using t-SNE.

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Colours represent two different batches, showing excellent agreement between batches. Sp: spleen.

Figure 4. Identification of different cell types and cell-type specific open chromatin ​ regions and transcription factor motifs. (a) The same t-SNE plot as in Figure 3d, ​ ​ ​ ​ coloured by the number of counts in the peaks near indicated gene locus. (b) The same t-SNE plot as in Figure 3d coloured by spectral clustering and cell type ​ annotation. (c) Comparisons of spleen CD4 T cells scATAC-seq obtained by two ​ ​ strategies. TagSort: cells were stained with anti-CD4-PE, tagged with Tn5 and CD4-PE positive cells were sorted for scATAC-seq; SortTag: CD4 T cells were purified first and scATAC-seq was performed on the purified cells. Top: comparison of library size and binding signal correlation (pearson r=0.96) around called peaks; bottom: UCSC genome browser tracks of the indicated single cell aggregates around the Cd4 gene locus. (d) UCSC genome browser tracks around Cd27 and ​ ​ Cd83 gene loci, displaying the aggregate (top panel) and single cell (bottom panel) signals of the two NK clusters. ATAC-seq peaks specific to the CD27+ NK cells are highlighted. For visual comparison reason, we randomly choose 65 out of 75 CD27- NK cells. (e) Z-score of normalised read counts in the top 500 peaks that distinguish ​ ​ each cell cluster based on the logistic regression classifier, across each peak (row) in each cell (column). Top 500 marker peaks were picked per cell cluster, so there are 500 x 12 = 6,000 peaks in the heatmap. Cells are ordered by cluster labels. (f) ​ ​ Heatmap representation of transcription factor motif (rows) enrichments (binomial test p-values) in the top 500 marker peaks in different cell clusters (columns). Some ​ ​ key motifs are enclosed by black rectangles and motif logos are shown to the right. Motif names are taken from the HOMER software suite.

Supplementary Figure 1. (a) Violin plots showing the comparisons of distributions of ​ the median library size (estimated by the Picard tool), fraction of mitochondrial DNA (MT content) and fraction of reads in peaks (FRiP) in single cells from either plate or C1 scATAC-seq approach. (b) the same metrics as in (a) showing data obtained from ​ ​ cryopreserved cells of four different primary tissues.

Supplementary Figure 2. (a) Bioanalyzer results of pools of 11 different plates (two ​ ​ spleens) of scATAC-seq in this study. (b) qPCR amplification plot of 64 different ​ ​ single cell libraries. The qPCR was performed after 8 cycles of pre-amplification. Dotted line indicates the number of cycles used for final amplification. A total of 8 + 10 = 18 cycles were performed in this study.

Supplementary Figure 3. Scatter plots of different quality control metrics. Single cells from different batches are indicated by different colours, and empty well controls are also indicated. We removed cells that have less than 10,000 reads or less than 90% mapping rate, as indicated by dotted lines in (b). Sp: spleen; MT

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content: fraction of mitochondrial reads; LSI: latent semantic indexing; Library size is estimated by the Picard tool.

Supplementary Figure 4. (a) The median library size after downsampling (at the fastq ​ ​ stage) to different fractions relative to the full data sets. (b) Violin Plot of the library ​ ​ size at the different level of downsampling.

Supplementary Figure 5. Number of counts from all peaks that assigned to the indicated genes by HOMER.

Supplementary Figure 6. Hierarchical clustering of the Pearson’s correlation ​ between aggregated single cell clusters and the bulk ATAC-seq data sets from ImmGen. The full matrix is shown here, and the ImmGen sample labels were taken directly from the ImmGen ATAC-seq data deposited at the European Nucleotide Archive (ENA) (https://www.ebi.ac.uk/ena/data/view/PRJNA392905). ​ ​

Supplementary Figure 7. The top correlated ImmGen bulk samples to each ​ aggregated single cell clusters. Top 5 pearson r scores for each clusters are shown.

Supplementary Figure 8. UCSC genome browser tracks showing ATAC-seq profiles ​ of indicated ImmGen bulk samples and aggregated single cell clusters around the Ptprc (Cd45) promoter region. ​ ​ ​

Supplementary Figure 9. FACS results showing the anti-CD4-PE and DAPI stain on ​ mouse splenocytes before (top) and after (bottom) Tn5 tagging. Note, all cells are DAPI negative before Tn5 tagging but become DAPI positive afterwards. CD4-PE signal remains after Tn5 tagging.

Supplementary Figure 10. (a and b) UCSC genome browser tracks showing ​ ​ ATAC-seq profiles of aggregate (top panel) and individual single cells (bottom panels). Known enhancers are highlighted. (c) Cd83 expression from the ImmGen ​ ​ bulk RNA-seq of the indicated sample.

Supplementary Figure 11. Nearest genes assigned to the top 50 marker peaks in each single cell cluster.

Supplementary Figure 12. Genomic distribution (by HOMER) of all peaks and the marker peaks in each single cell cluster.

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Author contributions X.C, K.N.N and S.A.T conceived the project. X.C designed the protocol. X.C, R.J.M and K.N.N performed the experiments. X.C carried out the computational analysis. S.A.T supervised the entire project. All authors contributed to the writing.

Acknowledgement We thank Jong-Eun Park, Johan Henriksson, Tzachi Hagai, Tomas Gomez, Kerstin Meyer, Roser Vento, Lira Mamanova and all others from the Teichmann group for the inspiring discussion of the method, the critical reading of the manuscript and the computational help. We also thank Natalia Kunowska, Qianxin Wu and Andrew Knights for the helpful discussion related to the Tn5 transposase. We thank Bee Ling Ng, Chris Hall, Jennie Graham and Sam Thompson for the excellent support of FACS. We thank the DNA pipeline from the Wellcome Sanger Institute for the Illumina sequencing support. We thank Guangshuai Jia and Jens Preussner from Thomas Braun’s lab for sharing the mouse cardiac progenitor cells. We thank Aik ​ Ooi for the initial help with the experimental setup.

Funding X.C is funded by the FET-OPEN grant MRG-GRAMMAR 664918, K.N.N by Wellcome Trust Strategic Award “Single cell genomics of mouse gastrulation” and S.A.T by the European Research Council grant ThDEFINE. Wellcome trust core facilities are supported by grant WT206194.

Data and materials availability

The sequencing data has been deposited at ArrayExpress (accession E-MTAB-6714). The code used for the analysis is available on the Github repository https://github.com/dbrg77/plate_scATAC-seq. ​

The UCSC genome browser tracks containing both the ImmGen bulk ATAC-seq and scATAC-seq from this study can be viewed via this link: http://genome-euro.ucsc.edu/cgi-bin/hgTracks?hgS_doOtherUser=submit&hgS_oth erUserName=dbrg77&hgS_otherUserSessionName=mSpleen_scATAC_cluster. ​

Competing financial interests None declared.

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Methods

Cell isolation For splenocytes, the spleen from a C57BL/6Jax mouse was mashed by a 2-ml syringe plunger through a 70 μm cell strainer (Fisher Scientific 10788201) into 30 ml 1X DPBS (ThermoFisher 14190169) supplied with 2 mM EDTA and 0.5% (w/v) BSA (Sigma A9418). Cells were centrifuged down, supernatant was removed, and the cell pellet was briefly vortexed. 5 ml 1X RBC lysis buffer (ThermoFisher 00-4300-54) was used to resuspend the cell pellet, and the cell suspension was vortexed again, and left on bench for 5 minutes to lyse red blood cells. Then 45 ml 1X DPBS was added, and cells were centrifuged down. 30 ml 1X DPBS were used to resuspend the cell pellet. The cell suspension was passed through a Miltenyi 30 μm Pre-Separation Filter (Miltenyi 130-041-407), and the cell number was determined using C-chip counting chamber (VWR DHC-N01). All centrifugations were done at 500 g, 4 °C, 5 minutes. For human and mouse skin fibroblasts, cells were extracted as previously 15 described .​ For mouse cardiac progenitor cells, cells were extracted as previously ​ 16 described .​ Cells were cryopreserved in 90% FBS and 10% DMSO and stored in ​ liquid nitrogen until experiments.

Plate-based single-cell ATAC-seq (scATAC-seq). A detailed step-by-step protocol can be found in the Supplementary Protocol. ​ ​ Briefly, 50,000 cells were centrifuged down at 500 g, 4 °C, 5 minutes. Cell pellets were resuspended in 50 μl tagmentation mix (33 mM Tris-acetate, pH 7.8, 66 mM potassium acetate, 10 mM magnesium acetate, 16% dimethylformamide (DMF), 0.01% digitonin and 5 μl of Tn5 from the Nextera kit from Illumina, Cat. No. FC-121-1030). The tagmentation reaction was done on a thermomixer (Eppendorf 5384000039) at 800 rpm, 37 °C, 30 minutes. The reaction was then stopped by adding equal volume (50 μl) of tagmentation stop buffer (10 mM Tris-HCl, pH 8.0, 20 mM EDTA, pH 8.0) and left on ice for 10 minutes. 200 μl 1X DPBS with 0.5% BSA was added and the nuclei suspension was transferred to a FACS tube. DAPI (ThermoFisher 62248) was added at a final concentration of 1 μg/μl to stain the nuclei.

Species mixing experiments 25,000 HEK293T (Human) and 25,000 NIH3T3 (Mouse) cells were mixed together, and scATAC-seq was performed as described in the Supplementary Protocol. The ​ ​ obtained sequencing reads were mapped to a concatenated genome of mouse and 40 human by hisat2 .​ One 384-well plate was performed. We first set a technical cutoff ​ where a successful well must contain more than 10,000 total reads and more than 90% of reads are mapped to the concatenated genome. 307 wells were marked as

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successful. Among the successful wells, we calculated the ratio of reads that mapped to the human genome and the mouse genome. If the ratio is larger than 10, the well is categorised as containing human single cells; if the ratio is less than 0.1, the well is categorised as containing mouse single cells; otherwise, the well is categorised as containing human-mouse doublets.

Plate scATAC-seq on CD4+ T cells (TagSort vs SortTag) For the “TagSort” strategy, 50,000 splenocytes were stained with anti-Mouse CD4 PE (eBioscience cat no. 12-0043-82) at room temperature for 30 minutes according to the manufacturer’s instructions. The stained cells were washed with ice-cold 1X PBS twice and pelleted down at 500 g, 4 °C, 5 minutes. Experimente were carried out following the procedures described in the Supplementary Protocol. DAPI and PE ​ ​ double positive cells were sorted into a 384-well plate for library construction. For the “SortTag” strategy, CD4+ T cells were purified first from mouse splenocytes using the Naive CD4 T Cell Isolation Kit, Mouse (Miltenyi, cat. no. 130-104-453) following the manufacturer’s instruction without the anti-CD44 depletion step. The purified CD4 T cells were processed according to the procedures described in the Supplementary Protocol. ​

scATAC-seq using Fluidigm C1 Experiments were performed as previously described3 using the medium-sized ​ (1862x) Open App chip. We followed the manufacturer’s instructions described in the “ATAC Seq No Stain (Rev C)” from the Fluidigm ScriptHub (https://www.fluidigm.com/c1openapp/scripthub), except that we replace the ​ ​ detergent NP-40 in the original protocol with digitonin so that the final concentration of digitonin in the reaction chamber is 0.005%. After collecting the pre-amplified material from the Fluidigm chip, the libraries were indexed by library 3 PCR for 14 cycles as previously described .​ ​

Costs involved in plate-based and Fluidigm C1 scATAC-seq For our plate-based scATAC-seq method, most reagents and buffers are available in a standard molecular biology lab. Exceptions are the Tn5 transposase, which can be purchased from Illumina (Cat No. FC-121-1030), and the PCR master mix, which can be purchased from various vendors (we used the 2X NEBNext® High-Fidelity 2X PCR Master Mix from NEB). Since the Tn5 tagging reaction was performed upfront at the bulk level, the Tn5 cost per cell depends on how many cells are sorted during the sorting. Based on our experience, when 50,000 cells are used at the beginning, two to eight 384-well plates can be sorted. Therefore, the cost of Tn5 is negligible. The major cost per unit for the plate-based scATAC-seq is the PCR master mix used during library amplification. Currently, 10 μl of PCR master mix are needed per cell in a 20 μl library amplification reaction, but we have been successfully and consistently generated libraries from half of the volume described in the protocol. 14 bioRxiv preprint doi: https://doi.org/10.1101/309831; this version posted October 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

For scATAC-seq using the Fluidigm C1, all the aforementioned reagents are needed, and a microfluidic chip is required per 96 cells.

Hands-on time for plate-based versus C1 scATAC-seq approaches For our plate-based scATAC-seq method, the most time-consuming part is the lysis plate preparation (mixing lysis buffer and indexing primers). For maximum efficiency, this can be done upfront in bulk, and the lysis plate is stable in -80 °C for a long time. Another time/labour-consuming step is the pooling of single cell libraries after PCR using a multi-channel pipette. We provide online advice to perform the whole procedure in minutes. This information is included in the accompanying GitHub page: https://github.com/dbrg77/plate_scATAC-seq. For scATAC-seq using the Fluidigm C1, an extra ~4 hours of C1 runtime are needed.

qPCR for library amplification After assembly of the 20 μl PCR reaction (see Supplementary Protocol), a ​ ​ pre-amplification step was performed on a PCR machine (Alpha Cycler 4, PCRmax) with 72 °C 5 minutes, 98 °C 5 minutes, 8 cycles of [98 °C 10 seconds, 63 °C 30 seconds, 72 °C 20 seconds]. Of the product, 19 μl of pre-amplified library were transferred to a 96 well qPCR plate, 1 μl 20X EvaGreen (Biotium #31000) was added, and qPCR was performed on an ABI StepOnePlus system with the following cycle conditions: 98 °C 1 minutes, 20 cycles of [98 °C 10 seconds, 63 °C 30 seconds, 72 °C 20 seconds]. Data was acquired at 72 °C. We qualitatively chose the cycle number to where the fluorescence signals just about to start going up (Supplementary Fig. 1b). In this study, a total of 18 cycles were used to amplify the ​ ​ libraries.

Sequencing data processing 41 All sequencing data were processed using a pipeline written in snakemake .​ The ​ software/packages and the exact flags used in this study can be found in the ‘Snakefile’ provided in the GitHub repository https://github.com/dbrg77/plate_scATAC-seq. Briefly, reads were trimmed with ​ cutadapt42 to remove the Nextera sequence at the 3’ end of short inserts. The ​ trimmed reads were mapped to the reference mouse genome (UCSC mm10) using 40 43 hisat2 .​ Reads with mapping quality less than 30 were removed by samtools (-q 30 ​ ​ flag) and deduplicated using the MarkDuplicates function of the Picard tool (http://broadinstitute.github.io/picard). All reads from single cells were merged ​ ​ together using samtools, and the merged BAM file was deduplicated again. Peak 18 calling was performed on the merged and deduplicated BAM file by MACS2 .​ For ​ bulk ATAC-seq and single cell aggregate coverage visualisation, bedGraph files generated from MACS2 callpeak were converted to bigWig files and visualised via UCSC genome browser. For individual single cell ATAC-seq visualisation, aligned

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reads from individual cells were converted to bigBed files. A count matrix over the union of peaks was generated by counting the number of reads from individual cells 44 that overlap the union peaks using coverageBed from the bedTools suite .​ ​

Public ATAC-seq data processing FASTQ files were all downloaded from the European Nucleotide Archive (ENA). The ImmGen bulk ATAC-seq data (study accession PRJNA392905) and the scATAC-seq data using Fluidigm C1 (study accessions PRJNA274006 and PRJNA299657) were processed in the same way as described in this study. The ‘Snakefile’ used to process the data can be found at the the same GitHub repository.

Bioinformatics analysis Codes used to carry out all the analyses were provided as Jupyter Notebook files, which can be found in the same GitHub repository. Briefly, downsampling was performed by randomly selecting a fraction of reads from the original FASTQ files using seqtk (https://github.com/lh3/seqtk), and the same pipeline was run on the ​ ​ sub-sampled FASTQ files. For binarising the scATAC-seq data, peak calling was performed on reads merged from all cells, and we labelled the peak ‘1’ (open) if there was at least one read overlapping the peak, and ‘0’ (closed) otherwise. Latent semantic indexing analysis was performed by first normalising the binarized count matrix by term frequency inverse document frequency (TF-IDF) and then performing a Singular-Value Decomposition (SVD) on the normalised count matrix. Only the 2nd - 50th dimensions after the SVD were passed to t-SNE for visualisation. To compare with ImmGen bulk ATAC-seq data, a reference peak set was created by taking the union of peaks from the peak calling results of aggregated scATAC-seq (this study) and different samples of ImmGen bulk ATAC-seq using mergeBed from the 44 bedTools suite .​ All comparisons were done based on this reference peak set. The ​ annotatePeaks.pl from HOMER27 was used to assign genes to peaks. Latent ​ semantic indexing, spectral clustering and logistic regression were carried out using 45 Scikit-learn .​ ​

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21

a ➛ ➛ Quench SDS 65 ○C Sequencing by adding 15 minutes TWEEN-20

➛ ➛ Single nuclei Single cells Tn5 Pooling Upfront bulk sorting into lysis Library PCR suspension (5,000 fragment + Tn5 “tagging” buffer (SDS + amplification – 50,000 cells) release Purification (30 minutes) proteinase K) (15 minutes) (1 hour) (1.5 hour) (>20 minutes)

b c

Scale 50 kb mm10 Scale 50 kb hg38 d chr6: 122,750,000 122,800,000 25 kb e chr19: 35,700,000 35,750,000 25 kb 1010 - 10 10 - c1_mESC C1 mESC (n=192) c1_K562 C1 K562 (n=192)

00 _ 0 0 _ 1010 - 10 10 - plate_mESC Plate mESC (n=192) plate_K562 Plate K562 (n=192)

00 _ 0 0 _ Nanog Foxj2 RBM42 UPK1A ZBTB32 IGFLR1 ARHGAP33 Nanog Mir7231 RBM42 UPK1A ZBTB32 U2AF1L4 ARHGAP33 Nanog ETV2 UPK1A-AS1 ZBTB32 U2AF1L4 Nanog ETV2 KMT2B HSPB6 Slc2a3 ETV2 U2AF1L4 Slc2a3 COX6B1 U2AF1L4 U2AF1L4 PSENEN PSENEN LIN37 PROSER3 Figure 1 a b c

hSF

Scale 50 kb hg38 chr6: 33,200,000 33,250,000 33,300,000 15 - hSF (n=384) hSkin

0 _ COL11A2 MIR219A1 HCG25 RPS18 PFDN6 TAPBP COL11A2 RING1 VPS52 MIR6873 TAPBP COL11A2 VPS52 PFDN6 ZBTB22 COL11A2 VPS52 PFDN6 ZBTB22 RXRB VPS52 PFDN6 DAXX RXRB B3GALT4 RGL2 DAXX RXRB WDR46 DAXX mSF SLC39A7 WDR46 DAXX SLC39A7 MIR6834 SLC39A7 RGL2 HSD17B8 TAPBP

d

Scale 50 kb mm10 mCPC_E8.5 chr6: 125,150,000 125,200,000

Frequency 15 - mSF (n=384)

mSkin fibr

0 _ 15 - mCPC_E8.5 (n=384) mCPC_E8.5

0 _ 15 - mCPC_E9.5 (n=384) mCPC_E9.5mCPC_E9.5

0 _ Chd4 Iffo1 Scarna10 Vamp1 Cd27 Chd4 Iffo1 Mrpl51 Vamp1 Cd27 Chd4 Nop2 Gapdh Mrpl51 Vamp1 Iffo1 Mir3098 Tapbpl Iffo1 E130112N10Rik Iffo1 AK080173 Gapdh Cd27 Ncapd2 Mir8113 Cd27 (n=384) (n=384) hSF (n=384) E8.5 (n=384) E9.5 mSF Insert size (kb) mCPC mCPC

Figure 2 b a Single splenocyte Singlesplenocyte Figure 3 Figure cellexamples mSp_scATAC_all ImmGen T.4.Nve.Sp ImmGen B,Fo.Sp mSp_scATAC_all ImmGen T.4.Nve.Sp ImmGen B,Fo.Sp scATAC-N4-131_S131 scATAC-N4-130_S130 scATAC-N4-129_S129 scATAC-N4-128_S128 scATAC-N4-127_S127 scATAC-N4-107_S107 scATAC-N4-106_S106 scATAC-N4-105_S105 scATAC-N4-104_S104 scATAC-N4-103_S103 scATAC-N2-365_S365 scATAC-N2-364_S364 scATAC-N2-363_S363 scATAC-N2-362_S362 scATAC-N2-361_S361 scATAC-N2-341_S341 scATAC-N2-340_S340 scATAC-N2-339_S339 scATAC-N2-338_S338 scATAC-N2-337_S337 scATAC-N2-318_S318 scATAC-N2-317_S317 scATAC-N2-316_S316 scATAC-N2-315_S315 scATAC-N2-314_S314 scATAC-N2-313_S313 scATAC-N2-294_S294 scATAC-N2-293_S293 scATAC-N2-292_S292 scATAC-N2-291_S291 scATAC-N2-290_S290 scATAC-N2-289_S289 scATAC-N2-270_S270 scATAC-N2-269_S269 scATAC-N2-268_S268 scATAC-N2-266_S266 scATAC-N2-265_S265 scATAC-N2-246_S246 scATAC-N2-245_S245 scATAC-N2-244_S244 scATAC-N2-243_S243 scATAC-N2-242_S242 scATAC-N2-241_S241 scATAC-N2-222_S222 scATAC-N2-221_S221 scATAC-N2-219_S219 scATAC-N2-218_S218 scATAC-N2-217_S217 scATAC-N2-198_S198 scATAC-N2-197_S197 scATAC-N2-196_S196 scATAC-N2-195_S195 scATAC-N2-194_S194 scATAC-N2-193_S193 scATAC-N2-174_S174 scATAC-N2-173_S173 scATAC-N2-172_S172 scATAC-N2-171_S171 scATAC-N2-169_S169 scATAC-N2-150_S150 scATAC-N2-149_S149 scATAC-N2-148_S148 scATAC-N2-145_S145 scATAC-N2-126_S126 scATAC-N2-125_S125 scATAC-N2-124_S124 scATAC-N2-123_S123 scATAC-N2-122_S122 scATAC-N2-121_S121 scATAC-N2-102_S102 scATAC-N2-101_S101 scATAC-N2-100_S100 scATAC-N4-131_S131 scATAC-N4-130_S130 scATAC-N4-129_S129 scATAC-N4-128_S128 scATAC-N4-127_S127 scATAC-N4-107_S107 scATAC-N4-106_S106 scATAC-N4-105_S105 scATAC-N4-104_S104 scATAC-N4-103_S103 scATAC-N2-365_S365 scATAC-N2-364_S364 scATAC-N2-363_S363 scATAC-N2-362_S362 scATAC-N2-361_S361 scATAC-N2-341_S341 scATAC-N2-340_S340 scATAC-N2-339_S339 scATAC-N2-338_S338 scATAC-N2-337_S337 scATAC-N2-318_S318 scATAC-N2-317_S317 scATAC-N2-316_S316 scATAC-N2-315_S315 scATAC-N2-314_S314 scATAC-N2-313_S313 scATAC-N2-294_S294 scATAC-N2-293_S293 scATAC-N2-292_S292 scATAC-N2-291_S291 scATAC-N2-290_S290 scATAC-N2-289_S289 scATAC-N2-270_S270 scATAC-N2-269_S269 scATAC-N2-268_S268 scATAC-N2-266_S266 scATAC-N2-265_S265 scATAC-N2-246_S246 scATAC-N2-245_S245 scATAC-N2-244_S244 scATAC-N2-243_S243 scATAC-N2-242_S242 scATAC-N2-241_S241 scATAC-N2-222_S222 scATAC-N2-221_S221 scATAC-N2-219_S219 scATAC-N2-218_S218 scATAC-N2-217_S217 scATAC-N2-198_S198 scATAC-N2-197_S197 scATAC-N2-196_S196 scATAC-N2-195_S195 scATAC-N2-194_S194 scATAC-N2-193_S193 scATAC-N2-174_S174 scATAC-N2-173_S173 scATAC-N2-172_S172 scATAC-N2-171_S171 scATAC-N2-169_S169 scATAC-N2-150_S150 scATAC-N2-149_S149 scATAC-N2-148_S148 scATAC-N2-145_S145 scATAC-N2-126_S126 scATAC-N2-125_S125 scATAC-N2-124_S124 scATAC-N2-123_S123 scATAC-N2-122_S122 scATAC-N2-121_S121 scATAC-N2-102_S102 scATAC-N2-101_S101 scATAC-N2-100_S100 100 single 100 scATAC-N4-12_S12 scATAC-N4-11_S11 scATAC-N4-10_S10 scATAC-N2-99_S99 scATAC-N2-98_S98 scATAC-N2-78_S78 scATAC-N2-77_S77 scATAC-N2-76_S76 scATAC-N2-75_S75 scATAC-N2-74_S74 scATAC-N2-73_S73 scATAC-N2-54_S54 scATAC-N2-53_S53 scATAC-N2-52_S52 scATAC-N2-51_S51 scATAC-N2-50_S50 scATAC-N2-49_S49 scATAC-N2-30_S30 scATAC-N2-29_S29 scATAC-N2-28_S28 scATAC-N2-27_S27 scATAC-N2-25_S25 scATAC-N4-12_S12 scATAC-N4-11_S11 scATAC-N4-10_S10 scATAC-N2-99_S99 scATAC-N2-98_S98 scATAC-N2-78_S78 scATAC-N2-77_S77 scATAC-N2-76_S76 scATAC-N2-75_S75 scATAC-N2-74_S74 scATAC-N2-73_S73 scATAC-N2-54_S54 scATAC-N2-53_S53 scATAC-N2-52_S52 scATAC-N2-51_S51 scATAC-N2-50_S50 scATAC-N2-49_S49 scATAC-N2-30_S30 scATAC-N2-29_S29 scATAC-N2-28_S28 scATAC-N2-27_S27 scATAC-N2-25_S25

aggregate ImmGen bulk (n= 3,648) scATAC-N2-2_S2 scATAC-N2-1_S1 scATAC-N2-6_S6 scATAC-N2-5_S5 scATAC-N2-4_S4 scATAC-N2-3_S3 scATAC-N2-6_S6 scATAC-N2-5_S5 scATAC-N2-4_S4 scATAC-N2-3_S3 scATAC-N2-2_S2 scATAC-N2-1_S1 ATAC-seq B cells cellsB T cells cells T 16.186 - 16.186 - Ccdc84 Ccdc84 Ccdc84 Ccdc84 Ccdc84 Ccdc84 Ccdc84 Ccdc84 Scale Scale 115 - 115 - chr9: chr9: 44 - 44 - 0 _ 0 _ 0 _ 0 _ 0 _ 0 _ 25 kb 25 Foxr1 Foxr1 44,450,000 44,450,000 Upk2 Upk2 c AK078316 AK078316 Bcl9l Bcl9l 50 kb 50 kb Bcl9l Bcl9l 44,500,000 44,500,000 Bcl9l Bcl9l Cxcr5 Cxcr5 mm10 mm10 44,550,000 44,550,000 d

t-SNE dimension 2 Sp Sp #2 (n=2333) #2 (n=833) #1 t - SNE dimension 1 44,600,000 44,600,000 Ddx6 Ddx6 Ddx6 Ddx6 Ddx6 Ddx6 Ddx6 Ddx6 a Bcl11a locus Cd4 locus Csf3r locus b

count

Bcl11b locus Cd8a locus Gzma locus SNE SNE - dim2 t t-SNE dim1

c Scale 5 kb mm10 Scale 10 kb mm10 dchr6: 125,235,000 125,240,000 chr13: 43,780,000 43,790,000 43,800,000 r = 0.96 30 - 15 - CD27- NK cluster aggregate (n=65)

scATAC_NK_CD27- scATAC_NK_CD27- density

0 _ 0 _ 30 - 15 - CD27+ NK cluster aggregate (n=65)

scATAC_NK_CD27+ scATAC_NK_CD27+

0 _ 0 _ Vamp1 Cd83 Tapbpl Cd27 Cd83 E130112N10Rik Cd83 Scale 5 kb mm10 chr6: 125,235,000 125,240,000 30 - Scale 5 kb mm10 AK080173chr6: 125,235,000 125,240,000 scATAC_NK_CD27-30 - mSp_rep10_003 Scale 10 kb mm10 chr13:Scale 43,780,00010 kb 43,790,000 43,800,000mm10 chr13: 43,780,000 43,790,000 43,800,000 scATAC_NK_CD27- 15 - 0 _ 15 - 30 - Cd27 mSp_rep10_020 scATAC_NK_CD27- 0 _ scATAC_NK_CD27- 30 - scATAC_NK_CD27+ Cd27 0 _ mSp_rep10_028150 _- scATAC_NK_CD27+ 15 - 0 _ Vamp1 Tapbpl Cd27 E130112N10Rik scATAC_NK_CD27+ AK0801730 _ scATAC_NK_CD27+ Vamp1 Cd27 Tapbpl Cd27 Cd27 mSp_rep10_068 20 kb E130112N10Rik Cd27 Scale mm10 AK080173 Cd27 0 _ Cd27Mir8113 0 _ Cd83 mSp_rep10_003 Cd27 Cd83 mSp_rep10_020 Cd27 Cd83 mSp_rep10_028 Mir8113 Cd83 Mir8113 mSp_rep10_003 mSp_rep10_003mSp_rep10_068 mSp_rep10_078mSp_rep10_020mSp_rep10_003 mSp_rep10_020mSp_rep10_078 mSp_rep10_028mSp_rep10_020 mSp_rep10_028mSp_rep10_166 mSp_rep10_068mSp_rep10_028 chr6: 124,860,000 124,870,000 124,880,000 124,890,000 124,900,000 124,910,000 124,920,000 mSp_rep10_068mSp_rep10_183 mSp_rep10_078mSp_rep10_068 mSp_rep10_078mSp_rep10_257 mSp_rep10_166mSp_rep10_078 mSp_rep10_166mSp_rep10_276 mSp_rep10_183mSp_rep10_166 mSp_rep10_183mSp_rep10_283mSp_rep10_003 mSp_rep10_257mSp_rep10_183 mSp_rep10_309 mSp_rep10_166mSp_rep10_276mSp_rep10_257 mSp_rep10_257 mSp_rep10_276mSp_rep11_033 mSp_rep10_283mSp_rep10_276 mSp_rep10_283mSp_rep11_052 mSp_rep10_309mSp_rep10_283 mSp_rep10_309mSp_rep11_145 mSp_rep11_033mSp_rep10_309 20 - mSp_rep11_033mSp_rep11_150 mSp_rep11_052mSp_rep11_033 mSp_rep11_194mSp_rep11_052 mSp_rep11_145mSp_rep11_052 mSp_rep10_020 mSp_rep11_150mSp_rep11_145 mSp_rep11_227mSp_rep11_145 mSp_rep10_183mSp_rep11_194mSp_rep11_150 mSp_rep11_253mSp_rep11_150 mSp_rep11_270mSp_rep11_194 mSp_rep11_227mSp_rep11_194 mSp_rep11_282mSp_rep11_227 mSp_rep11_253mSp_rep11_227 mSp_rep11_300mSp_rep11_253 mSp_rep11_270mSp_rep11_253 mSp_rep11_270 mSp_rep11_282mSp_rep11_270 mSp_rep2_091NK mSp_rep11_282mSp_rep3_176 mSp_rep11_300mSp_rep11_282 mSp_rep10_028 mSp_rep11_300 mSp_rep11_300mSp_rep3_182 mSp_rep2_091 mSp_rep10_257mSp_rep2_091 mSp_rep3_196mSp_rep2_091 mSp_rep3_176 CD4 SortTag (n=384) mSp_rep3_176 mSp_rep3_182mSp_rep3_176 mSp_rep3_361 mSp_rep4_169mSp_rep3_182 mSp_rep3_196mSp_rep3_182 mSp_rep4_334mSp_rep3_196 mSp_rep3_361mSp_rep3_196 mSp_rep3_361 mSp_rep4_169mSp_rep3_361 mSp_rep5_007- mSp_rep4_169 mSp_rep10_068mSp_rep5_043mSp_rep4_169 mSp_rep4_334 mSp_rep4_334 mSp_rep5_007mSp_rep4_334 mSp_rep5_052 mSp_rep10_276mSp_rep5_007 mSp_rep5_057mSp_rep5_007 mSp_rep5_043 mSp_rep5_043 mSp_rep5_052mSp_rep5_043 mSp_rep5_052mSp_rep5_069 mSp_rep5_057mSp_rep5_052 mSp_rep5_057mSp_rep5_082 mSp_rep5_069mSp_rep5_057 mSp_rep5_069mSp_rep5_111 mSp_rep5_082mSp_rep5_069 mSp_rep5_082mSp_rep5_135 mSp_rep5_111mSp_rep5_082 mSp_rep10_078mSp_rep5_218 mSp_rep5_111 mSp_rep5_135mSp_rep5_111 mSp_rep5_297 mSp_rep10_283 cd4_st mSp_rep5_135 mSp_rep5_218mSp_rep5_135 mSp_rep5_218mSp_rep5_316 mSp_rep5_297mSp_rep5_218 mSp_rep5_297mSp_rep5_376 mSp_rep5_316mSp_rep5_297 mSp_rep5_316mSp_rep6_097 mSp_rep5_376mSp_rep5_316 mSp_rep5_376mSp_rep6_108 mSp_rep6_097mSp_rep5_376 mSp_rep10_166mSp_rep6_097mSp_rep6_132 mSp_rep6_108mSp_rep6_097 mSp_rep6_108mSp_rep6_227 mSp_rep10_309mSp_rep6_132mSp_rep6_108 mSp_rep6_132mSp_rep6_231 mSp_rep6_227mSp_rep6_132 mSp_rep6_227mSp_rep6_283 mSp_rep6_231mSp_rep6_227 mSp_rep6_231mSp_rep6_341 mSp_rep6_283mSp_rep6_231 mSp_rep6_283mSp_rep6_344 mSp_rep6_341mSp_rep6_283 mSp_rep6_341mSp_rep6_374 mSp_rep6_344mSp_rep6_341 mSp_rep10_183mSp_rep6_344mSp_rep7_032 mSp_rep6_374mSp_rep6_344 mSp_rep6_374mSp_rep7_271 mSp_rep11_033mSp_rep7_032mSp_rep6_374 mSp_rep7_032mSp_rep7_290 mSp_rep7_271mSp_rep7_032 mSp_rep8_122mSp_rep7_271 mSp_rep7_290mSp_rep7_271 mSp_rep8_136mSp_rep7_290 mSp_rep8_122mSp_rep7_290 mSp_rep8_205mSp_rep8_122 mSp_rep8_136mSp_rep8_122 mSp_rep8_259mSp_rep8_136 mSp_rep8_205mSp_rep8_136 mSp_rep10_257mSp_rep8_266mSp_rep8_205 mSp_rep8_259mSp_rep8_205 mSp_rep9_066mSp_rep8_259 mSp_rep11_052mSp_rep8_266mSp_rep8_259 mSp_rep9_077mSp_rep8_266 mSp_rep9_066mSp_rep8_266

mSp_rep9_091mSp_rep9_066CD27 mSp_rep9_077mSp_rep9_066 mSp_rep9_141mSp_rep9_077 mSp_rep9_091mSp_rep9_077 mSp_rep9_154mSp_rep9_091 mSp_rep9_141mSp_rep9_091 mSp_rep9_247mSp_rep9_141 mSp_rep9_154mSp_rep9_141 mSp_rep10_276mSp_rep9_154 mSp_rep9_247mSp_rep9_154 mSp_rep9_364 0 _ mSp_rep9_366mSp_rep9_247 mSp_rep11_145mSp_rep9_364mSp_rep9_247 mSp_rep10_040mSp_rep9_364 mSp_rep9_366mSp_rep9_364 mSp_rep10_042mSp_rep9_366 mSp_rep10_040mSp_rep9_366 mSp_rep10_043mSp_rep10_040 mSp_rep10_042mSp_rep10_040 mSp_rep10_052mSp_rep10_042 mSp_rep10_043mSp_rep10_042 mSp_rep10_153mSp_rep10_043 mSp_rep10_052mSp_rep10_043 mSp_rep10_052mSp_rep10_283 mSp_rep10_153mSp_rep10_052 mSp_rep10_210 mSp_rep10_153 mSp_rep10_261mSp_rep10_153 mSp_rep11_150mSp_rep10_210 20 - mSp_rep10_210 mSp_rep10_261mSp_rep10_210 mSp_rep10_261mSp_rep10_310 mSp_rep10_310mSp_rep10_261 mSp_rep10_310mSp_rep10_325 mSp_rep10_325mSp_rep10_310 mSp_rep10_325mSp_rep11_046 mSp_rep11_046mSp_rep10_325 mSp_rep11_046mSp_rep11_053 mSp_rep11_053mSp_rep11_046 mSp_rep11_112mSp_rep10_309 mSp_rep11_053 mSp_rep11_053 mSp_rep11_112 mSp_rep11_112mSp_rep11_133 mSp_rep11_194mSp_rep11_133mSp_rep11_112 mSp_rep11_133mSp_rep11_199 mSp_rep11_199mSp_rep11_133 mSp_rep11_199mSp_rep11_200 mSp_rep11_200mSp_rep11_199 mSp_rep11_200mSp_rep11_210 mSp_rep11_210mSp_rep11_200 mSp_rep11_210mSp_rep11_218 mSp_rep11_218mSp_rep11_210 mSp_rep11_218mSp_rep11_222 mSp_rep11_222mSp_rep11_218 mSp_rep11_383mSp_rep11_033 mSp_rep11_222 mSp_rep11_383mSp_rep11_222 mSp_rep11_383mSp_rep1_031 mSp_rep11_227mSp_rep11_383mSp_rep1_031 mSp_rep1_031mSp_rep1_064 mSp_rep1_064mSp_rep1_031 mSp_rep1_064mSp_rep2_062 mSp_rep2_062mSp_rep1_064 mSp_rep2_062mSp_rep3_107 mSp_rep3_107mSp_rep2_062 mSp_rep3_107mSp_rep3_217 mSp_rep3_217mSp_rep3_107 mSp_rep11_052mSp_rep3_281mSp_rep3_217 mSp_rep3_281mSp_rep3_217 mSp_rep3_326mSp_rep3_281 mSp_rep3_326mSp_rep3_281 CD4 TagSort (n=384) mSp_rep4_092mSp_rep3_326 mSp_rep11_253mSp_rep4_092mSp_rep3_326 mSp_rep4_130mSp_rep4_092 mSp_rep4_130mSp_rep4_092 mSp_rep4_178mSp_rep4_130 mSp_rep4_178mSp_rep4_130 mSp_rep4_255mSp_rep4_178 mSp_rep4_255mSp_rep4_178 cd4_ts5 mSp_rep5_037mSp_rep4_255 mSp_rep5_037mSp_rep4_255 mSp_rep11_145mSp_rep5_039mSp_rep5_037cells Single mSp_rep5_039mSp_rep5_037 mSp_rep5_096mSp_rep5_039 mSp_rep5_096mSp_rep5_039 mSp_rep5_110mSp_rep5_096 mSp_rep11_270mSp_rep5_110mSp_rep5_096 mSp_rep5_173mSp_rep5_110 mSp_rep5_173mSp_rep5_110 mSp_rep5_312mSp_rep5_173 mSp_rep5_312mSp_rep5_173 mSp_rep5_336mSp_rep5_312 mSp_rep5_336mSp_rep5_312 mSp_rep5_357mSp_rep5_336 mSp_rep5_357mSp_rep5_336 mSp_rep11_150mSp_rep5_378mSp_rep5_357 mSp_rep5_378mSp_rep5_357 mSp_rep6_287mSp_rep5_378 mSp_rep6_287mSp_rep5_378 mSp_rep6_287 mSp_rep11_282mSp_rep7_020mSp_rep6_287 mSp_rep7_020 mSp_rep7_063mSp_rep7_020 mSp_rep7_063mSp_rep7_020 mSp_rep7_084mSp_rep7_063 mSp_rep7_084mSp_rep7_063 mSp_rep7_132mSp_rep7_084 mSp_rep7_132mSp_rep7_084 mSp_rep7_190mSp_rep7_132 mSp_rep7_190mSp_rep7_132 mSp_rep11_194mSp_rep7_190 mSp_rep7_312mSp_rep7_190 mSp_rep7_312 mSp_rep8_041mSp_rep7_312 mSp_rep8_041 mSp_rep11_300mSp_rep8_054mSp_rep8_041 mSp_rep8_054 mSp_rep8_097mSp_rep8_054 mSp_rep8_097 mSp_rep8_129mSp_rep8_097 mSp_rep8_129 mSp_rep8_199mSp_rep8_129 mSp_rep8_199 mSp_rep8_224mSp_rep8_199 mSp_rep11_227mSp_rep8_224 mSp_rep8_234mSp_rep8_224 mSp_rep8_234 mSp_rep8_255mSp_rep8_234 mSp_rep8_255 mSp_rep2_091mSp_rep9_001mSp_rep8_255 mSp_rep9_001 mSp_rep9_054mSp_rep9_001 mSp_rep9_054 mSp_rep9_099mSp_rep9_054 0 _ mSp_rep9_099 mSp_rep9_129mSp_rep9_099 mSp_rep9_129 mSp_rep9_139mSp_rep9_129 mSp_rep11_253mSp_rep9_139 mSp_rep9_183mSp_rep9_139 mSp_rep9_183 mSp_rep9_205mSp_rep9_183 mSp_rep9_205 mSp_rep3_176mSp_rep9_219mSp_rep9_205 mSp_rep9_219 NK CD27+ mSp_rep9_222mSp_rep9_219 mSp_rep9_222 mSp_rep9_281mSp_rep9_222 P3h3 Cd4 Lag3 mSp_rep9_281 mSp_rep9_347mSp_rep9_281 mSp_rep11_270mSp_rep9_347 mSp_rep3_182mSp_rep9_347 Gpr162 Cd4 Ptms mSp_rep11_282 mSp_rep3_196 Gpr162 A230083G16Rik mSp_rep11_300 mSp_rep3_361 Cd4 mSp_rep2_091 mSp_rep4_169 Cd4 mSp_rep3_176 mSp_rep4_334 mSp_rep3_182 mSp_rep5_007 mSp_rep3_196 mSp_rep5_043 mSp_rep3_361 mSp_rep5_052 mSp_rep4_169 mSp_rep5_057 mSp_rep4_334 mSp_rep5_069 mSp_rep5_007 mSp_rep5_082 mSp_rep5_043NK e f Nve Nve mSp_rep5_111 Cells mSp_rep5_052- mSp_rep5_135 mSp_rep5_057 mSp_rep5_218 mSp_rep5_069 mSp_rep5_297 mSp_rep5_082 CD4T CD8T Mem CD27 CD8T CD27+ NK B.Fo Treg B.MZ B.T2 DC GN MF mSp_rep5_316 mSp_rep5_111 mSp_rep5_376 § GN § B.Fo § CD8T Mem § CD4T Nve § B.MZ § MF mSp_rep5_135 mSp_rep6_097 mSp_rep5_218 mSp_rep6_108 § CD8T Nve § B.T2 § CD27- NK § Treg § DC § CD27+ NK mSp_rep5_297 mSp_rep6_132 mSp_rep5_316 mSp_rep6_227 mSp_rep5_376 mSp_rep6_097 mSp_rep6_231 mSp_rep6_108 mSp_rep6_283 mSp_rep6_132 mSp_rep6_341 mSp_rep6_227 mSp_rep6_344 mSp_rep6_231 mSp_rep6_374 mSp_rep6_283 mSp_rep7_032 mSp_rep6_341 mSp_rep7_271 mSp_rep6_344 mSp_rep7_290 mSp_rep6_374 mSp_rep8_122 mSp_rep7_032 mSp_rep8_136 mSp_rep7_271 mSp_rep8_205 mSp_rep7_290 mSp_rep8_259 mSp_rep8_122 mSp_rep8_266 AC T T mSp_rep8_136 mSp_rep9_066 GG G C A CGG G GG TA AA T C mSp_rep8_205 mSp_rep9_077 mSp_rep8_259 mSp_rep9_091 mSp_rep8_266 mSp_rep9_141 mSp_rep9_066 mSp_rep9_154 mSp_rep9_077 mSp_rep9_247 mSp_rep9_091 mSp_rep9_364 mSp_rep9_141 mSp_rep9_366 mSp_rep9_154 mSp_rep10_040 mSp_rep9_247 mSp_rep10_042 mSp_rep9_364 mSp_rep10_043 mSp_rep9_366 mSp_rep10_052 mSp_rep10_040 mSp_rep10_153 AA mSp_rep10_210 G G C mSp_rep10_042 AA TC C G T TT T G TTGA T mSp_rep10_261 AC G mSp_rep10_043 C AAG mSp_rep10_052 mSp_rep10_310 mSp_rep10_153 mSp_rep10_325 mSp_rep10_210 mSp_rep11_046 mSp_rep10_261 mSp_rep11_053 mSp_rep10_310 mSp_rep11_112 mSp_rep10_325 mSp_rep11_133 mSp_rep11_046 mSp_rep11_199 mSp_rep11_053 mSp_rep11_200 mSp_rep11_112 mSp_rep11_210 mSp_rep11_133 mSp_rep11_218 AT mSp_rep11_222 CA T mSp_rep11_199 T C T A GG C T A G GA A A mSp_rep11_200 mSp_rep11_383 mSp_rep11_210 mSp_rep1_031 mSp_rep11_218 mSp_rep1_064 mSp_rep11_222 mSp_rep2_062 mSp_rep11_383 mSp_rep3_107 mSp_rep1_031 mSp_rep3_217 mSp_rep1_064 mSp_rep3_281 mSp_rep2_062 mSp_rep3_326 mSp_rep3_107 mSp_rep4_092 mSp_rep3_217 mSp_rep4_130 mSp_rep3_281 mSp_rep4_178

Top 6,000 peaks that define cell clusters cell define that peaks 6,000 Top mSp_rep3_326 mSp_rep4_255 AA CGC T mSp_rep5_037 G C G mSp_rep4_092 CG C A AA G G A TT AA mSp_rep4_130 mSp_rep5_039 mSp_rep4_178 mSp_rep5_096 G mSp_rep5_110 C A mSp_rep4_255 A GCC A C A T mSp_rep5_037 mSp_rep5_173 GG GCA T TTGAAAATTG mSp_rep5_039 mSp_rep5_312 mSp_rep5_096 mSp_rep5_336 mSp_rep5_110 mSp_rep5_357 mSp_rep5_173 mSp_rep5_378 mSp_rep5_312 mSp_rep6_287 GCGC C A T A AT T C AT G G

AC GC A A mSp_rep5_336 mSp_rep7_020 TTT A mSp_rep5_357 mSp_rep7_063 mSp_rep5_378 mSp_rep7_084 G GT mSp_rep6_287 mSp_rep7_132 GG CT AA CC A AAG G AA G A A G AA T mSp_rep7_020 mSp_rep7_190 mSp_rep7_063 mSp_rep7_312 mSp_rep7_084 mSp_rep8_041 mSp_rep7_132 mSp_rep8_054 mSp_rep7_190 mSp_rep8_097 mSp_rep7_312 mSp_rep8_129 mSp_rep8_041 mSp_rep8_199 mSp_rep8_054 mSp_rep8_224 mSp_rep8_097 mSp_rep8_234 mSp_rep8_129 mSp_rep8_255 mSp_rep8_199 mSp_rep9_001 mSp_rep8_224 mSp_rep9_054 mSp_rep8_234 mSp_rep9_099 mSp_rep8_255 mSp_rep9_129 mSp_rep9_001 mSp_rep9_139 mSp_rep9_054 mSp_rep9_183 mSp_rep9_099 mSp_rep9_205 mSp_rep9_129 mSp_rep9_219 Figure 4 mSp_rep9_139 mSp_rep9_222 mSp_rep9_183 mSp_rep9_281 mSp_rep9_205 mSp_rep9_347 mSp_rep9_219 mSp_rep9_222 mSp_rep9_281 mSp_rep9_347 a b

Supplementary Figure 1 a Sp #1 Rep1 (n = 96) Sp #1 Rep2 (n = 96) Sp #1 Rep3 (n = 384)

Sp #1 Rep4 (n = 384) Sp #2 Rep5 (n = 384) Sp #2 Rep6 (n = 384)

Sp #2 Rep7 (n = 384) Sp #2 Rep8 (n = 384) Sp #2 Rep9 (n = 384)

Sp #2 Rep10 (n = 384) Sp #2 Rep11 (n = 384)

b 140,000 130,000 120,000 110,000 100,000 90,000

Rn 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 0 2 4 6 8 10 12 14 16 18 20 Cycles

Supplementary Figure 2 a b Sp #1 (n=960)

Sp #2 (n=2688)

Empty wells

c d

e f

Supplementary Figure 3 a

b

Supplementary Figure 4 count SNE SNE - dim2 T

T-SNE dim1

Supplementary Figure 5 Supplementary Figure 6 Supplementary Figure 7 Scale 50 kb mm10 chr1: 138,150,000 138,200,000 138,250,000 5 - ImmGen_GN ImmGen GN 0 _ 20 - scATAC_cluster_0scATAC GN cluster (n=85) 0 _ 5 - ImmGen_MF ImmGen MF 0 _ 20 - scATAC_cluster_5scATAC MF cluster (n=55) 0 _ 5 - ImmGen_DC ImmGen DC 0 _ 20 - scATAC_cluster_10scATAC DC cluster (n=61) 0 _ 5 - ImmGen_BFo ImmGen B.Fo 0 _ 20 - scATAC_cluster_1scATAC B.Fo cluster (n=1,358) 0 _ 5 - ImmGen_BMZ ImmGen B.MZ 0 _ 20 - scATAC_cluster_4scATAC B.MZ cluster (n=254) 0 _ 5 - ImmGen_BT2 ImmGen B.T2 0 _ 20 - scATAC_cluster_7scATAC B.T2 cluster (n=223) 0 _ 5 - ImmGen_T4_Nve ImmGen CD4T Nve 0 _ 20 - scATAC_cluster_3scATAC CD4T Nve cluster (n=454) 0 _ 5 - ImmGen_Treg ImmGen Treg 0 _ 20 - scATAC_cluster_9scATAC Treg cluster (n=89) 0 _ 5 - ImGen_T8_Nve ImmGen CD8T Nve 0 _ 20 - scATAC_cluster_6scATAC CD8T Nve cluster(n=331) 0 _ 5 - ImmGen_T8_mem immGen CD8T Mem 0 _ 20 - scATAC_cluster_2scATAC CD8T Mem cluster (n=116) 0 _ 5 - ImmGen_NK_CD27- immGen CD27- NK 0 _ 20 - scATAC_cluster_8scATAC CD27- NK cluster (n=75) 0 _ 5 - ImmGen_NK_CD27+ immGen CD27+ NK 0 _ 20 - scATAC_cluster_11scATAC CD27+ NK cluster (n=65) 0 _ Ptprc AK040222 Ptprc Ptprc Ptprc Ptprc Ptprc

Supplementary Figure 8 Before Tn5 Tagging

After Tn5 tagging

Supplementary Figure 9 a b

Scale 100 kb mm10 Scale 10 kb mm10 Scale 10 kb mm10 chr1: 69,600,000 69,700,000 chrX: 7,590,000 7,600,000 Scale 5 kb mm10 20 - chr2: 102,850,000 102,855,000 chr15: 78,500,000 10 - 15 - 20 - scATAC CD8T Nve scATAC CD4T Nve scATAC_T8_Nve scATAC_T4_Nve scATAC_T4_Nve scATAC_T8_Nve cluster (n=116) cluster (n=89)

0 _ 0 _ 15 - 20 - 0 _ 0 _ 20 - 10 - scATAC CD8T Mem scATAC_T8_mem scATAC_T8_mem cluster (n=116) scATAC Treg cluster scATAC_Treg scATAC_Treg(n=89) 0 _ 0 _ Cd44 Il2rb

Scale 5 kb mm10 Cd44chr2: 102,850,000 102,855,000 15 - mSp_rep10_033 Scale 5 kb mm10 chr2: 102,850,000 102,855,000 scATAC_T8_NveCd4415 - Scale 10 kb mm10 chr15: 78,500,000 Scale 10 kb mm10 mSp_rep10_03420 - 20 - chrX: 7,590,000 7,600,000 10 - scATAC_T8_Nve0 _ Cd4415 - Scale 100 kb mm10 scATAC_T8_Nve 0 _ 0 _ mSp_rep10_058 chr1: 69,600,000 69,700,000 20 - 0 _ scATAC_T8_mem15 - scATAC_T4_Nve Cd44 0 _ mSp_rep10_12720 - Ikzf2 Ppp1r3fos Ccdc22 scATAC_T8_mem scATAC_T4_Nve 0 _ Cd44 scATAC_T8_mem 0 _ Cd44 Scale 10 kb mm10 Cd44 mSp_rep10_139 chrX:10 - 7,590,000 7,600,000 Cd440 _ 10 - Cd44 0 _ Ikzf2 Foxp3 Cd44 0 _ 20 - Cd44 Cd44 Il2rb mSp_rep10_033Cd44 mSp_rep10_033 Cd44 scATAC_Treg mSp_rep10_034 mSp_rep10_157mSp_rep10_034 mSp_rep10_058Cd44 mSp_rep10_058 scATAC_T4_Nve mSp_rep10_127 Cd44 mSp_rep10_127 mSp_rep10_139mSp_rep10_033 mSp_rep10_139 scATAC_Treg Foxp3 mSp_rep10_034 mSp_rep10_157 mSp_rep10_318 mSp_rep10_157mSp_rep10_033 mSp_rep10_181mSp_rep10_058 mSp_rep10_181 mSp_rep10_127 mSp_rep10_181mSp_rep10_212 mSp_rep10_212 mSp_rep10_212 0 _ mSp_rep10_139 mSp_rep10_234 mSp_rep10_234mSp_rep10_157 Ppp1r3fos10 - Ccdc22 mSp_rep10_275 mSp_rep10_275 mSp_rep10_181 mSp_rep10_282 0 _ Foxp3 mSp_rep10_282mSp_rep10_034mSp_rep10_212 0 _ mSp_rep10_364 mSp_rep10_364mSp_rep10_234 mSp_rep10_364 Ikzf2 Foxp3Foxp3 mSp_rep11_095 mSp_rep10_319 mSp_rep11_095mSp_rep10_275 mSp_rep10_212 Ikzf2 Foxp3 Ikzf2 mSp_rep11_115mSp_rep10_282 mSp_rep11_115 mSp_rep11_122mSp_rep10_364 mSp_rep11_122 mSp_rep10_318 mSp_rep10_318scATAC_Treg mSp_rep11_183mSp_rep11_095 mSp_rep11_183 mSp_rep10_319 mSp_rep10_319 mSp_rep11_216mSp_rep10_058 mSp_rep11_216 mSp_rep11_115 mSp_rep10_207 mSp_rep10_207 mSp_rep11_240mSp_rep11_122 mSp_rep11_240 mSp_rep10_234mSp_rep11_266 mSp_rep10_051 mSp_rep10_051 mSp_rep11_266mSp_rep11_183 mSp_rep11_267 mSp_rep10_318 mSp_rep11_267 mSp_rep10_207mSp_rep10_152 mSp_rep11_216 mSp_rep10_152 mSp_rep10_152 mSp_rep11_294mSp_rep11_240 mSp_rep11_294 0 _ mSp_rep11_303 mSp_rep10_161 mSp_rep10_161Ppp1r3fos Ccdc22 mSp_rep11_303mSp_rep10_127mSp_rep11_266 mSp_rep10_360 mSp_rep11_315mSp_rep11_267 mSp_rep11_315 mSp_rep10_360 mSp_rep10_360 Foxp3 mSp_rep11_321mSp_rep11_294 mSp_rep11_321 mSp_rep11_100 mSp_rep10_275mSp_rep11_340 mSp_rep11_100 Foxp3 mSp_rep11_340mSp_rep11_303 mSp_rep11_340 mSp_rep10_214 Foxp3 mSp_rep11_315mSp_rep1_009 mSp_rep1_009 mSp_rep10_214 mSp_rep1_033 mSp_rep11_134 mSp_rep10_319mSp_rep10_318 mSp_rep11_321mSp_rep1_033 mSp_rep10_051 mSp_rep11_134 mSp_rep10_139mSp_rep11_340 mSp_rep1_043 mSp_rep11_136 mSp_rep10_319 mSp_rep1_043 mSp_rep11_136 mSp_rep1_080mSp_rep1_009 mSp_rep1_080 mSp_rep11_288 mSp_rep10_207 mSp_rep2_003mSp_rep1_033 mSp_rep10_282mSp_rep2_003 mSp_rep11_288 mSp_rep2_089 mSp_rep11_325 mSp_rep10_051 mSp_rep2_089mSp_rep1_043 mSp_rep2_089 mSp_rep11_325 mSp_rep1_080 mSp_rep3_030 mSp_rep2_010 mSp_rep10_152 mSp_rep3_030 mSp_rep3_088 mSp_rep2_010 mSp_rep10_157mSp_rep3_088mSp_rep2_003 mSp_rep11_107 mSp_rep10_161 mSp_rep2_089 mSp_rep3_099 mSp_rep10_207 mSp_rep3_099 mSp_rep10_152mSp_rep1_034 mSp_rep10_360mSp_rep11_107 mSp_rep3_105 mSp_rep1_034 mSp_rep3_105mSp_rep3_030 mSp_rep3_088 mSp_rep10_364mSp_rep3_185 mSp_rep1_045 mSp_rep11_100mSp_rep1_034 mSp_rep3_185 mSp_rep3_187 mSp_rep10_214 mSp_rep3_187mSp_rep3_099 mSp_rep3_155 mSp_rep1_045 mSp_rep3_105 mSp_rep3_193 mSp_rep3_219 mSp_rep11_134mSp_rep3_155 mSp_rep10_181mSp_rep3_193mSp_rep3_185 mSp_rep3_204 mSp_rep11_136 mSp_rep3_204mSp_rep3_187 mSp_rep3_212 mSp_rep3_293 mSp_rep3_219 mSp_rep3_212mSp_rep3_193 mSp_rep3_224 mSp_rep3_299 mSp_rep10_051mSp_rep11_288 mSp_rep3_224 mSp_rep10_161 mSp_rep3_293 mSp_rep11_095 mSp_rep11_325 mSp_rep3_204 mSp_rep3_248 mSp_rep3_308 mSp_rep3_248mSp_rep3_212 mSp_rep3_285 mSp_rep3_308 mSp_rep2_010mSp_rep3_299 mSp_rep3_285mSp_rep3_224 mSp_rep3_325 mSp_rep3_365 mSp_rep11_107mSp_rep3_308 mSp_rep10_212mSp_rep3_325mSp_rep3_248 mSp_rep3_327 mSp_rep3_327 mSp_rep4_004 mSp_rep1_034mSp_rep3_365 mSp_rep3_327mSp_rep3_285 mSp_rep3_341 mSp_rep10_009 mSp_rep3_341mSp_rep3_325 mSp_rep11_115mSp_rep3_376 mSp_rep4_004mSp_rep1_045 mSp_rep3_376mSp_rep3_327 mSp_rep4_036 mSp_rep10_023 mSp_rep10_152mSp_rep10_009mSp_rep3_155 mSp_rep4_036 mSp_rep10_360 mSp_rep3_341 mSp_rep4_168 mSp_rep4_021 mSp_rep10_023mSp_rep3_219 mSp_rep10_234mSp_rep4_168mSp_rep3_376 mSp_rep4_223 mSp_rep4_044 mSp_rep4_223mSp_rep4_036 mSp_rep4_227 mSp_rep4_021mSp_rep3_293 mSp_rep4_227mSp_rep4_168 mSp_rep4_256 mSp_rep4_194 mSp_rep3_299 Nve mSp_rep4_044 mSp_rep4_256mSp_rep4_223 mSp_rep11_122mSp_rep4_263 mSp_rep4_217 mSp_rep3_308 mSp_rep4_263mSp_rep4_227 mSp_rep4_270 mSp_rep4_228 mSp_rep3_365mSp_rep4_194 mSp_rep4_270mSp_rep4_256 mSp_rep4_283 mSp_rep10_161mSp_rep4_217 mSp_rep4_268 mSp_rep4_004 mSp_rep10_275mSp_rep4_283mSp_rep4_263 mSp_rep4_358 mSp_rep11_100mSp_rep4_268 mSp_rep4_358mSp_rep4_270 mSp_rep4_364 mSp_rep4_278 mSp_rep10_009mSp_rep4_228 mSp_rep4_364mSp_rep4_283 mSp_rep5_036 mSp_rep4_290 mSp_rep10_023mSp_rep4_268 mSp_rep4_358 mSp_rep11_183 mSp_rep5_036 mSp_rep5_089 mSp_rep4_326 mSp_rep4_278mSp_rep4_021 mSp_rep5_089mSp_rep4_364 mSp_rep5_112 mSp_rep4_290mSp_rep4_044 mSp_rep10_282mSp_rep5_112mSp_rep5_036 mSp_rep5_125 mSp_rep4_328 mSp_rep5_125mSp_rep5_089 mSp_rep5_128 mSp_rep4_341 mSp_rep10_360mSp_rep4_326mSp_rep4_194 mSp_rep5_128mSp_rep5_112 mSp_rep5_131 mSp_rep10_214 mSp_rep4_217 mSp_rep4_345 mSp_rep4_328 mSp_rep5_131mSp_rep5_125 mSp_rep11_216mSp_rep5_134 mSp_rep4_228 mSp_rep5_134mSp_rep5_128 mSp_rep5_161 mSp_rep4_355 mSp_rep4_341 mSp_rep5_131 mSp_rep5_123 mSp_rep4_268 mSp_rep5_161 mSp_rep5_174 mSp_rep5_123Nve mSp_rep4_345 mSp_rep10_364mSp_rep5_134 mSp_rep5_256 mSp_rep4_278 mSp_rep5_174 mSp_rep5_161 mSp_rep5_182 mSp_rep4_290mSp_rep4_355 mSp_rep5_256 mSp_rep5_260 mSp_rep5_250 mSp_rep5_260mSp_rep5_174 mSp_rep5_277 mSp_rep11_100mSp_rep4_326mSp_rep5_123 mSp_rep5_277mSp_rep5_256 mSp_rep11_240mSp_rep5_301 mSp_rep11_134mSp_rep5_252 mSp_rep5_260 mSp_rep4_328mSp_rep5_182 mSp_rep5_301 mSp_rep5_342 mSp_rep5_356 mSp_rep5_277 mSp_rep5_379 mSp_rep5_250mSp_rep4_341 mSp_rep11_095mSp_rep5_342mSp_rep5_301 mSp_rep6_031 mSp_rep6_003 mSp_rep5_252mSp_rep4_345 mSp_rep5_379mSp_rep5_342 mSp_rep6_241 mSp_rep6_003 mSp_rep6_012 mSp_rep6_241 mSp_rep5_356mSp_rep4_355 mSp_rep5_379 mSp_rep6_036 mSp_rep6_252 mSp_rep6_012mSp_rep6_003 mSp_rep11_266 mSp_rep6_031mSp_rep5_123 mSp_rep6_036mSp_rep6_012 mSp_rep6_059 mSp_rep7_001 mSp_rep10_214mSp_rep5_182 mSp_rep6_059 mSp_rep6_067 mSp_rep11_136 mSp_rep6_241 mSp_rep6_036 mSp_rep11_115 mSp_rep6_090 mSp_rep7_067 mSp_rep5_250 mSp_rep6_067mSp_rep6_059 mSp_rep6_252 mSp_rep6_090mSp_rep6_067 mSp_rep6_091 mSp_rep7_095 mSp_rep5_252 mSp_rep6_091mSp_rep6_090 mSp_rep6_239 mSp_rep7_134 mSp_rep5_356mSp_rep7_001 mSp_rep6_239mSp_rep6_091 mSp_rep11_267mSp_rep6_259 mSp_rep6_031mSp_rep7_067 mSp_rep6_262 mSp_rep7_149 mSp_rep6_259mSp_rep6_239 mSp_rep7_152 mSp_rep7_095mSp_rep6_241 mSp_rep11_122mSp_rep6_262mSp_rep6_259 mSp_rep6_274 mSp_rep11_134 CD8T mSp_rep6_252 mSp_rep6_274mSp_rep6_262 mSp_rep7_135 mSp_rep11_288mSp_rep7_181 mSp_rep7_134 mSp_rep7_135mSp_rep6_274 mSp_rep7_150 mSp_rep7_231 mSp_rep7_149mSp_rep7_001 mSp_rep7_150mSp_rep7_135 mSp_rep7_176 mSp_rep7_067 mSp_rep11_294mSp_rep7_216 mSp_rep7_255 mSp_rep7_152 mSp_rep7_176mSp_rep7_150 mSp_rep7_095 mSp_rep7_216mSp_rep7_176 mSp_rep7_218 mSp_rep7_383 mSp_rep7_181 mSp_rep11_183 mSp_rep7_241 mSp_rep7_134 mSp_rep7_218mSp_rep7_216 mSp_rep8_027 mSp_rep7_231 mSp_rep7_241mSp_rep7_218 mSp_rep7_259 mSp_rep8_080 mSp_rep11_136mSp_rep7_149 mSp_rep8_080 mSp_rep7_255 mSp_rep7_259mSp_rep7_241 mSp_rep8_010 mSp_rep11_325 mSp_rep7_152 mSp_rep8_056 mSp_rep8_138 mSp_rep8_010mSp_rep7_259 mSp_rep11_303 mSp_rep7_181mSp_rep7_383 mSp_rep8_056mSp_rep8_010 mSp_rep8_099 mSp_rep8_159 mSp_rep7_231mSp_rep8_027 mSp_rep11_216mSp_rep8_099mSp_rep8_056 mSp_rep8_117 mSp_rep8_268 mSp_rep8_117mSp_rep8_099 mSp_rep8_170 mSp_rep8_080mSp_rep7_255 mSp_rep8_117 mSp_rep8_185 mSp_rep8_348 mSp_rep7_383 mSp_rep8_170 mSp_rep8_206 mSp_rep8_138 mSp_rep8_185mSp_rep8_170 mSp_rep9_007 mSp_rep11_288mSp_rep8_027 mSp_rep8_159 mSp_rep8_185 mSp_rep11_315mSp_rep8_223 CD4T mSp_rep8_206 mSp_rep2_010mSp_rep9_017 mSp_rep8_080 mSp_rep8_206 mSp_rep8_246 mSp_rep8_268 mSp_rep11_240mSp_rep8_223 mSp_rep8_265 mSp_rep9_023 mSp_rep8_138 mSp_rep8_246 mSp_rep8_283 mSp_rep9_048 mSp_rep8_159mSp_rep8_348 mSp_rep8_265 mSp_rep8_285 mSp_rep9_107 mSp_rep8_268mSp_rep9_007 mSp_rep8_283 mSp_rep8_314 mSp_rep9_017 mSp_rep8_285 mSp_rep11_321mSp_rep8_334 mSp_rep9_153 mSp_rep8_348 mSp_rep8_314 mSp_rep11_325mSp_rep9_023 mSp_rep8_314 mSp_rep8_360 mSp_rep9_194 mSp_rep9_007 mSp_rep11_266mSp_rep8_334 mSp_rep11_107 mSp_rep8_334 mSp_rep8_378 mSp_rep9_237 mSp_rep9_048mSp_rep9_017 mSp_rep8_360 mSp_rep9_035 mSp_rep8_378 mSp_rep9_274 mSp_rep9_107mSp_rep9_023 mSp_rep8_378 mSp_rep9_089 mSp_rep9_048 mSp_rep9_035 mSp_rep11_340mSp_rep9_235 mSp_rep9_283 mSp_rep9_153 mSp_rep9_089 mSp_rep9_252 mSp_rep9_284 mSp_rep9_194mSp_rep9_107 mSp_rep11_267mSp_rep9_235 mSp_rep9_269 mSp_rep9_153 mSp_rep9_252 mSp_rep9_298 mSp_rep2_010mSp_rep9_237 mSp_rep9_271 mSp_rep9_194 mSp_rep9_269 mSp_rep9_290 mSp_rep1_034mSp_rep9_322 mSp_rep9_274 mSp_rep9_237 mSp_rep9_271 mSp_rep9_292 mSp_rep9_333 mSp_rep9_283 mSp_rep9_290 mSp_rep1_009mSp_rep9_303 mSp_rep9_274 mSp_rep9_367 mSp_rep9_292 mSp_rep9_349 mSp_rep9_367 mSp_rep9_283mSp_rep9_284 mSp_rep11_294 mSp_rep9_349 mSp_rep3_201 mSp_rep9_303 mSp_rep10_017 mSp_rep9_284mSp_rep9_298 mSp_rep9_349 mSp_rep10_065 mSp_rep2_046 mSp_rep10_017 mSp_rep11_107mSp_rep9_322mSp_rep9_298 mSp_rep10_149 mSp_rep2_083 mSp_rep10_065 mSp_rep10_151 mSp_rep1_045 mSp_rep9_333mSp_rep9_322 mSp_rep10_149 mSp_rep10_163mSp_rep1_033 mSp_rep2_020 mSp_rep9_333 mSp_rep9_367 mSp_rep10_151mSp_rep11_303 mSp_rep10_165 mSp_rep3_055 mSp_rep9_367 mSp_rep10_163 mSp_rep10_184 mSp_rep3_201 mSp_rep10_165 mSp_rep1_078 mSp_rep3_201 mSp_rep10_165 mSp_rep10_227 mSp_rep3_150 mSp_rep2_046 mSp_rep10_184 mSp_rep10_242 mSp_rep2_083mSp_rep2_046 mSp_rep10_227 mSp_rep10_263 mSp_rep11_326 mSp_rep1_034mSp_rep2_083 mSp_rep10_227 mSp_rep10_263 mSp_rep10_242 mSp_rep1_043 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mSp_rep5_128 mSp_rep5_174 mSp_rep7_001 mSp_rep6_252 mSp_rep5_131 mSp_rep5_256 mSp_rep7_067 mSp_rep5_134 mSp_rep5_260 mSp_rep7_095 mSp_rep7_001 mSp_rep5_161 mSp_rep5_277 mSp_rep7_134 mSp_rep7_067 mSp_rep5_174 ImmGenmSp_rep5_301 NK cell RNA-seq sample Cd83 expression (tpmmSp_rep7_095) mSp_rep5_256 mSp_rep5_342 mSp_rep7_149 mSp_rep7_134 mSp_rep5_260 mSp_rep5_379 mSp_rep7_152 mSp_rep5_277 mSp_rep6_003 mSp_rep7_181 mSp_rep7_149 mSp_rep5_301 mSp_rep6_012 mSp_rep7_152 mSp_rep5_342 mSp_rep6_036 mSp_rep7_231 mSp_rep7_181 mSp_rep5_379 mSp_rep6_059 mSp_rep7_255 mSp_rep7_231 mSp_rep6_003 mSp_rep6_067 mSp_rep7_383 mSp_rep6_012 mSp_rep6_090 mSp_rep7_255 mSp_rep6_036 mSp_rep6_091 DC.4+.Sp#1 mSp_rep8_027 498.9 mSp_rep7_383 mSp_rep6_059 mSp_rep6_239 mSp_rep8_080 mSp_rep8_027 mSp_rep6_067 mSp_rep6_259 mSp_rep8_138 mSp_rep8_080 mSp_rep6_090 mSp_rep6_262 mSp_rep8_159 mSp_rep6_091 mSp_rep6_274 mSp_rep8_138 mSp_rep6_239 mSp_rep7_135 mSp_rep8_268 mSp_rep8_159 mSp_rep6_259 mSp_rep7_150 mSp_rep8_348 mSp_rep8_268 mSp_rep6_262 mSp_rep7_176 mSp_rep9_007 mSp_rep8_348 mSp_rep6_274 mSp_rep7_216 DC.4+.Sp#2 mSp_rep9_017 712.3 mSp_rep7_135 mSp_rep7_218 mSp_rep9_007 mSp_rep7_150 mSp_rep7_241 mSp_rep9_023 mSp_rep9_017 mSp_rep7_176 mSp_rep7_259 mSp_rep9_048 mSp_rep7_216 mSp_rep9_023 mSp_rep8_010 mSp_rep9_107 mSp_rep9_048 mSp_rep7_218 mSp_rep8_056 mSp_rep9_153 mSp_rep7_241 mSp_rep8_099 mSp_rep9_107 mSp_rep7_259 mSp_rep8_117 mSp_rep9_194 mSp_rep9_153 mSp_rep8_010 mSp_rep8_170 DC.4+.Sp#3 mSp_rep9_237 1007.9 mSp_rep9_194 mSp_rep8_056 mSp_rep8_185 mSp_rep9_274 mSp_rep8_099 mSp_rep8_206 mSp_rep9_237 mSp_rep8_117 mSp_rep8_223 mSp_rep9_283 mSp_rep9_274 mSp_rep8_170 mSp_rep9_284 mSp_rep8_185 mSp_rep8_246 mSp_rep9_283 mSp_rep8_265 mSp_rep9_298 mSp_rep9_284 mSp_rep8_206 mSp_rep8_283 mSp_rep9_322 mSp_rep8_223 mSp_rep8_285 mSp_rep9_298 mSp_rep8_246 mSp_rep9_333 mSp_rep9_322 mSp_rep8_265 mSp_rep8_314 NK.27-11b+.Sp#2 mSp_rep9_367 0 mSp_rep8_283 mSp_rep8_334 mSp_rep9_333 mSp_rep8_285 mSp_rep8_360 mSp_rep3_201 mSp_rep9_367 mSp_rep8_314 mSp_rep8_378 mSp_rep2_046 mSp_rep3_201 mSp_rep8_334 mSp_rep9_035 mSp_rep2_083 mSp_rep8_360 mSp_rep9_089 mSp_rep2_046 mSp_rep8_378 mSp_rep9_235 mSp_rep2_020 mSp_rep2_083 mSp_rep9_035 mSp_rep9_252 mSp_rep3_055 mSp_rep2_020 mSp_rep9_089 mSp_rep9_269NK.27+11b+.Sp#1 mSp_rep1_078 0 mSp_rep3_055 mSp_rep9_235 mSp_rep9_271 mSp_rep3_150 mSp_rep9_252 mSp_rep9_290 mSp_rep1_078 mSp_rep9_269 mSp_rep9_292 mSp_rep11_326 mSp_rep3_150 mSp_rep9_271 mSp_rep9_303 mSp_rep1_010 mSp_rep11_326 mSp_rep9_290 mSp_rep9_349 mSp_rep10_316 mSp_rep9_292 mSp_rep10_017 mSp_rep1_010 mSp_rep9_303 mSp_rep10_065 mSp_rep10_355 mSp_rep10_316 mSp_rep9_349 mSp_rep10_149 mSp_rep6_014 mSp_rep10_355 mSp_rep10_017 mSp_rep10_151NK.27+11b+.Sp#2 mSp_rep6_296 3.5 mSp_rep6_014 mSp_rep10_065 mSp_rep10_163 mSp_rep6_321 mSp_rep10_149 mSp_rep10_165 mSp_rep6_296 mSp_rep10_151 mSp_rep10_184 mSp_rep7_025 mSp_rep6_321 mSp_rep10_163 mSp_rep10_227 mSp_rep7_228 mSp_rep7_025 mSp_rep10_165 mSp_rep10_242 mSp_rep6_329 mSp_rep7_228 mSp_rep10_184 mSp_rep10_263 mSp_rep10_227 mSp_rep10_281 mSp_rep6_023 mSp_rep6_329 mSp_rep10_242 mSp_rep10_288 mSp_rep7_233 mSp_rep6_023 mSp_rep10_263 mSp_rep10_299 mSp_rep6_348 mSp_rep7_233 mSp_rep10_281 mSp_rep10_340 mSp_rep7_334 mSp_rep6_348 mSp_rep10_288 mSp_rep11_010 mSp_rep10_299 mSp_rep11_013 mSp_rep7_349 mSp_rep7_334 mSp_rep10_340 mSp_rep11_029 mSp_rep8_050 mSp_rep7_349 mSp_rep11_010 mSp_rep11_051 mSp_rep8_144 mSp_rep8_050 mSp_rep11_013 mSp_rep11_087 mSp_rep8_203 mSp_rep8_144 mSp_rep11_029 mSp_rep11_103 mSp_rep11_051 mSp_rep11_123 mSp_rep8_301 mSp_rep8_203 mSp_rep11_087 mSp_rep11_142 mSp_rep8_303 mSp_rep8_301 mSp_rep11_103 mSp_rep11_176 mSp_rep8_326 mSp_rep8_303 mSp_rep11_123 mSp_rep11_193 mSp_rep8_338 mSp_rep8_326 mSp_rep11_142 mSp_rep11_224 mSp_rep11_176Supplementary Figure 10 mSp_rep8_338 mSp_rep11_237 mSp_rep8_339 mSp_rep11_193 mSp_rep9_147 mSp_rep8_339 mSp_rep11_224 mSp_rep11_324 mSp_rep11_237 mSp_rep11_370 mSp_rep9_191 mSp_rep9_147 mSp_rep11_324 mSp_rep1_019 mSp_rep9_037 mSp_rep9_191 mSp_rep11_370 mSp_rep2_009 mSp_rep2_047 mSp_rep7_144 mSp_rep9_037 mSp_rep1_019 mSp_rep7_144 mSp_rep2_009 mSp_rep2_086 mSp_rep7_060 mSp_rep2_047 mSp_rep3_004 mSp_rep5_273 mSp_rep7_060 mSp_rep2_086 mSp_rep3_020 mSp_rep10_054 mSp_rep5_273 mSp_rep3_004 mSp_rep3_101 mSp_rep10_081 mSp_rep10_054 mSp_rep3_020 mSp_rep3_116 mSp_rep3_101 mSp_rep3_142 mSp_rep9_210 mSp_rep10_081 mSp_rep3_116 mSp_rep3_173 mSp_rep9_212 mSp_rep9_210 mSp_rep3_142 mSp_rep3_175 mSp_rep9_251 mSp_rep9_212 mSp_rep3_173 mSp_rep3_188 mSp_rep9_251 mSp_rep3_175 mSp_rep3_189 mSp_rep9_309 mSp_rep3_188 mSp_rep3_202 mSp_rep9_350 mSp_rep9_309 mSp_rep3_189 mSp_rep3_242 mSp_rep10_150 mSp_rep9_350 mSp_rep3_202 mSp_rep3_260 mSp_rep10_150 mSp_rep3_242 mSp_rep3_300 mSp_rep10_185 mSp_rep3_260 mSp_rep3_375 mSp_rep10_258 mSp_rep10_185 mSp_rep3_300 mSp_rep4_016 mSp_rep10_268 mSp_rep10_258 mSp_rep3_375 mSp_rep4_046 mSp_rep10_346 mSp_rep10_268 mSp_rep4_016 mSp_rep4_087 mSp_rep10_346 mSp_rep4_046 mSp_rep4_089 mSp_rep10_347 mSp_rep4_087 mSp_rep4_098 mSp_rep10_361 mSp_rep10_347 mSp_rep4_089 mSp_rep4_134 mSp_rep11_066 mSp_rep10_361 mSp_rep4_098 mSp_rep4_170 mSp_rep11_311 mSp_rep11_066 mSp_rep4_134 mSp_rep4_184 mSp_rep11_311 mSp_rep4_170 mSp_rep4_209 mSp_rep11_366 mSp_rep4_184 mSp_rep4_220 mSp_rep11_378 mSp_rep11_366 mSp_rep4_209 mSp_rep4_254 mSp_rep1_001 mSp_rep11_378 mSp_rep4_220 mSp_rep4_258 mSp_rep1_023 mSp_rep1_001 mSp_rep4_254 mSp_rep4_288 mSp_rep1_023 mSp_rep4_258 mSp_rep4_331 mSp_rep2_023 mSp_rep4_288 mSp_rep4_350 mSp_rep2_045 mSp_rep2_023 mSp_rep4_331 mSp_rep4_368 mSp_rep2_059 mSp_rep2_045 mSp_rep4_350 mSp_rep5_055 mSp_rep3_024 mSp_rep2_059 mSp_rep4_368 mSp_rep5_083 mSp_rep3_024 mSp_rep5_055 mSp_rep5_085 mSp_rep3_048 mSp_rep5_083 mSp_rep5_113 mSp_rep3_096 mSp_rep3_048 mSp_rep5_085 mSp_rep5_116 mSp_rep3_144 mSp_rep3_096 mSp_rep5_113 mSp_rep5_121 mSp_rep5_116 mSp_rep3_180 mSp_rep3_144 mSp_rep5_180 mSp_rep3_180 mSp_rep5_121 mSp_rep5_196 mSp_rep3_186 mSp_rep5_180 mSp_rep5_225 mSp_rep3_235 mSp_rep3_186 mSp_rep5_196 mSp_rep3_235 mSp_rep5_225 mSp_rep5_262 mSp_rep3_236 mSp_rep5_262 mSp_rep5_278 mSp_rep3_292 mSp_rep3_236 mSp_rep5_278 mSp_rep5_307 mSp_rep3_292 mSp_rep5_307 mSp_rep5_329 mSp_rep3_317 mSp_rep5_335 mSp_rep3_324 mSp_rep3_317 mSp_rep5_329 mSp_rep3_324 mSp_rep5_335 mSp_rep5_341 mSp_rep3_343 mSp_rep5_341 mSp_rep5_368 mSp_rep3_345 mSp_rep3_343 mSp_rep5_368 mSp_rep5_372 mSp_rep3_345 mSp_rep5_372 mSp_rep6_030 mSp_rep3_369 mSp_rep3_369 mSp_rep6_030 mSp_rep6_038 mSp_rep4_032 mSp_rep6_038 mSp_rep6_243 mSp_rep4_058 mSp_rep4_032 mSp_rep6_243 mSp_rep6_254 mSp_rep4_076 mSp_rep4_058 mSp_rep6_254 mSp_rep6_284 mSp_rep4_076 mSp_rep6_284 mSp_rep6_307 mSp_rep4_108 mSp_rep4_108 mSp_rep6_307 mSp_rep6_343 mSp_rep4_123 mSp_rep6_343 mSp_rep6_363 mSp_rep4_198 mSp_rep4_123 mSp_rep6_363 mSp_rep7_089 mSp_rep4_198 mSp_rep7_089 mSp_rep7_122 mSp_rep4_207 mSp_rep4_207 mSp_rep7_122 mSp_rep7_164 mSp_rep4_212 mSp_rep4_212 mSp_rep7_164 mSp_rep7_172 mSp_rep4_222 mSp_rep7_172 mSp_rep7_179 mSp_rep4_222 mSp_rep7_179 mSp_rep7_209 mSp_rep4_247 mSp_rep4_247 mSp_rep7_209 mSp_rep7_226 mSp_rep4_248 mSp_rep4_248 mSp_rep7_226 mSp_rep7_239 mSp_rep4_284 mSp_rep4_284 mSp_rep7_239 mSp_rep7_257 mSp_rep4_302 mSp_rep7_257 mSp_rep7_293 mSp_rep4_302 mSp_rep7_293 mSp_rep7_306 mSp_rep4_305 mSp_rep4_305 mSp_rep7_306 mSp_rep7_316 mSp_rep4_324 mSp_rep4_324 mSp_rep7_316 mSp_rep7_321 mSp_rep4_340 mSp_rep4_340 mSp_rep7_321 mSp_rep8_018 mSp_rep4_348 mSp_rep8_018 mSp_rep8_022 mSp_rep4_348 mSp_rep8_022 mSp_rep8_123 mSp_rep5_087 mSp_rep5_087 mSp_rep8_123 mSp_rep8_251 mSp_rep5_138 mSp_rep8_251 mSp_rep5_138 mSp_rep8_273 mSp_rep5_145 mSp_rep5_145 mSp_rep8_273 mSp_rep8_300 mSp_rep5_200 mSp_rep8_300 mSp_rep8_362 mSp_rep5_200 mSp_rep8_362 mSp_rep9_043 mSp_rep5_224 mSp_rep5_224 mSp_rep9_043 mSp_rep9_080 mSp_rep5_228 mSp_rep5_228 mSp_rep9_080 mSp_rep9_122 mSp_rep5_244 mSp_rep9_122 mSp_rep9_164 mSp_rep5_244 mSp_rep9_164 mSp_rep5_258 mSp_rep5_258 mSp_rep9_241 mSp_rep9_241 mSp_rep5_361 mSp_rep9_244 mSp_rep9_244 mSp_rep5_361 mSp_rep9_285 mSp_rep9_285 mSp_rep7_109 mSp_rep7_109 mSp_rep9_355 mSp_rep9_355 mSp_rep9_372 mSp_rep9_372 Supplementary Figure 11 Supplementary Figure 12 Supplementary Protocol

Protocol for plate-based scATAC-seq using FACS Timestamp: 15-Feb-2018

1. One day before the experiment, prepare the plates by aliquoting 2 µl 2X Lysis Buffer to each well of the plates (either 96-well or 384-well plate). Then add 2 µl of 10 µM S5xx/N7xx Nextera Index Primer Mix (5 µM each) to each well. Seal the plate and store in -80 ºC.

Recipe for 2X Lysis Buffer: 100 mM Tris.HCl, pH 8.0 100 mM NaCl 40 µg/ml Proteinase K (Ambion, AM2546, 20 mg/ml stock) 0.4% SDS

2. On the day of the experiment, thaw plates at room temperature. 3. Pre-coat all tubes with 500 µl 0.5% BSA (prepared in 1X PBS) for a few minutes to reduce sample loss. Count or sort 5k - 50k cells into 1.5-ml eppendorf tubes. DO NOT use DNA LoBind tubes for pelleting cells, which does not work well especially when cell numbers are limited. 4. Pellet cells at 500 g, 4 ºC, 5 minutes. 5. Wash the cell pellet with 100 µl ice-cold PBS, twice, 500 g, 4 ºC, 5 minutes, and carefully remove the supernatant. 6. Resuspend the cell pellet in 50 µl tagmentation mix. The recipe for the tagmentation mix is as follows (THS-seq recipe): 12.5 µl 4X THS-seq TD buffer 5 µl 10X Digitonin

27. 5 µl H2O ​ ​ 5 µl Illumina Tn5 (Nextera kit, Illumina Cat No. FC-121-1030)

Recipe for 4X THS-seq TD buffer: 132 mM Tris-acetate, pH 7.8 264 mM Potassium acetate 40 mM Magnesium acetate 64% Dimethylformamide (DMF)

Recipe for 10X Digitonin: 1 µl Digitonin (Promega, G9441, 2% stock)

19 µl H2O ​ ​

7. Put the tagmentation reaction (50 µl) on a thermomixer, 37 ºC, 800 rpm, 30 minutes. 8. Stop the reaction by adding 50 µl tagmentation stop buffer (TSB). Recipe for TSB: 10 mM Tris-HCl, pH 8.0 20 mM EDTA, pH 8.0

9. Leave on ice for 10 minutes. 10.Add 100 - 300 µl PBS/0.5% BSA to the 100 µl stopped tagmentation mix, and transfer to a FACS tube. 11.Optional: add DAPI to stain nuclei based on manufacturer’s instruction. 12.Sort DAPI positive single nuclei into the plates prepared the day before. 13.Quickly spin down and seal the plate well (can be stored in -80 ºC for a few weeks from here), and put the plate on a PCR machine, with lid temperature set to 100 ºC. 14.Incubate the plate at 65 ºC for 15 minutes to perform Tn5 release. 15.Add equal volume (4 µl) of 10% TWEEN-20 to each well to quench SDS. Briefly vortex to mix.

16.Add 2 µl H2O to each well. ​ ​ 17.Add 10 µl 2X NEBNext® High-Fidelity 2X PCR Master Mix (NEB M0541L) to each well 18.At this stage, each well contains 20 µl PCR reaction. 19.Perform library amplification PCR: 72 ºC 10 minutes 98 ºC 5 minutes [98 ºC 10 seconds, 63 ºC 30 seconds, 72 ºC 20 seconds] x 18 10 ºC hold 20.Combine all reactions into a 50-ml falcon, which yields about 20 µl x 384 = 7.68 ml. Normally, the yield will be ~ 7.2 ml. 21. Add 5 volumes (~ 36 ml) Buffer PB (Qiagen), mix well, and pass reaction volume through a single column from a Qiagen MinElute PCR Purification Kit by connecting the column to a vacuum. 22.To wash the column, pass through 40 ml Column Wash Buffer (10 mM Tris-HCl, pH 7.5, 80% ethanol). 23.Spin down the column at top speed on a table top centrifuge to remove all traces of ethanol, and remember to use a pipette to remove the ethanol leftover on the rim of the Qiagen column. 24.Elute the library in 12.5 µl Buffer EB. Perform the elution three times and combine the three elutes to a final volume of ~ 36 µl. 25.Do a final fragment size selection using 0.5X SPRI upper cutoff, followed by 1.2X SPRI lower cutoff, and elute in 30 µl 10 mM Tris-HCl, pH 8.0. 26.Run Nanodrop to obtain a rough estimate of the concentration, and then dilute the library to a range suitable for Bioanalyzer/TapeStation etc. 27. Check for expected results (see Supplementary Fig. 2a). ​ ​ 28.Sequencing: we sequenced each 384 pool on one lane of Hiseq 2000 or one rapid run of Hiseq 2500, which nearly saturated the library. From the data obtained, each cell was sequenced to about 1 million reads, but only ~30,000 unique reads were obtained per cell. Further reads were redundant, which is comparable (if not better) to published scATAC-seq by other methods. Theoretically, 30,000 reads per cell should be sufficient to profile the unique reads. However, considering the presence of mitochondrial DNA, non-mapped and non-uniquely mapped reads, it is safer to aim for at least 100,000 reads per cell. Oligonucleotides sequence

N701 CAAGCAGAAGACGGCATACGAGATTCGCCTTAGTCTCGTGGGCTCGG N702 CAAGCAGAAGACGGCATACGAGATCTAGTACGGTCTCGTGGGCTCGG N703 CAAGCAGAAGACGGCATACGAGATTTCTGCCTGTCTCGTGGGCTCGG N704 CAAGCAGAAGACGGCATACGAGATGCTCAGGAGTCTCGTGGGCTCGG N705 CAAGCAGAAGACGGCATACGAGATAGGAGTCCGTCTCGTGGGCTCGG N706 CAAGCAGAAGACGGCATACGAGATCATGCCTAGTCTCGTGGGCTCGG N707 CAAGCAGAAGACGGCATACGAGATGTAGAGAGGTCTCGTGGGCTCGG N710 CAAGCAGAAGACGGCATACGAGATCAGCCTCGGTCTCGTGGGCTCGG N711 CAAGCAGAAGACGGCATACGAGATTGCCTCTTGTCTCGTGGGCTCGG N712 CAAGCAGAAGACGGCATACGAGATTCCTCTACGTCTCGTGGGCTCGG N714 CAAGCAGAAGACGGCATACGAGATTCATGAGCGTCTCGTGGGCTCGG N715 CAAGCAGAAGACGGCATACGAGATCCTGAGATGTCTCGTGGGCTCGG N716 CAAGCAGAAGACGGCATACGAGATTAGCGAGTGTCTCGTGGGCTCGG N718 CAAGCAGAAGACGGCATACGAGATGTAGCTCCGTCTCGTGGGCTCGG N719 CAAGCAGAAGACGGCATACGAGATTACTACGCGTCTCGTGGGCTCGG N720 CAAGCAGAAGACGGCATACGAGATAGGCTCCGGTCTCGTGGGCTCGG N721 CAAGCAGAAGACGGCATACGAGATGCAGCGTAGTCTCGTGGGCTCGG N722 CAAGCAGAAGACGGCATACGAGATCTGCGCATGTCTCGTGGGCTCGG N723 CAAGCAGAAGACGGCATACGAGATGAGCGCTAGTCTCGTGGGCTCGG N724 CAAGCAGAAGACGGCATACGAGATCGCTCAGTGTCTCGTGGGCTCGG N726 CAAGCAGAAGACGGCATACGAGATGTCTTAGGGTCTCGTGGGCTCGG N727 CAAGCAGAAGACGGCATACGAGATACTGATCGGTCTCGTGGGCTCGG N728 CAAGCAGAAGACGGCATACGAGATTAGCTGCAGTCTCGTGGGCTCGG N729 CAAGCAGAAGACGGCATACGAGATGACGTCGAGTCTCGTGGGCTCGG S502 AATGATACGGCGACCACCGAGATCTACACCTCTCTATTCGTCGGCAGCGTC S503 AATGATACGGCGACCACCGAGATCTACACTATCCTCTTCGTCGGCAGCGTC S505 AATGATACGGCGACCACCGAGATCTACACGTAAGGAGTCGTCGGCAGCGTC S506 AATGATACGGCGACCACCGAGATCTACACACTGCATATCGTCGGCAGCGTC S507 AATGATACGGCGACCACCGAGATCTACACAAGGAGTATCGTCGGCAGCGTC S508 AATGATACGGCGACCACCGAGATCTACACCTAAGCCTTCGTCGGCAGCGTC S510 AATGATACGGCGACCACCGAGATCTACACCGTCTAATTCGTCGGCAGCGTC S511 AATGATACGGCGACCACCGAGATCTACACTCTCTCCGTCGTCGGCAGCGTC S513 AATGATACGGCGACCACCGAGATCTACACTCGACTAGTCGTCGGCAGCGTC S515 AATGATACGGCGACCACCGAGATCTACACTTCTAGCTTCGTCGGCAGCGTC S516 AATGATACGGCGACCACCGAGATCTACACCCTAGAGTTCGTCGGCAGCGTC S517 AATGATACGGCGACCACCGAGATCTACACGCGTAAGATCGTCGGCAGCGTC S518 AATGATACGGCGACCACCGAGATCTACACCTATTAAGTCGTCGGCAGCGTC S520 AATGATACGGCGACCACCGAGATCTACACAAGGCTATTCGTCGGCAGCGTC S521 AATGATACGGCGACCACCGAGATCTACACGAGCCTTATCGTCGGCAGCGTC S522 AATGATACGGCGACCACCGAGATCTACACTTATGCGATCGTCGGCAGCGTC