Crosstalk between neighboring cells underlies many biological processes, including cell signaling, proliferation and differentiation. Current single-cell genomic technologies profile each cell separately after tissue dissociation, losing information on cell–cell interactions. In the present study, we present an approach for sequencing physically interacting cells (PIC-seq), which combines cell sorting of physically interacting cells (PICs) with single-cell RNA-sequencing. Using computational modeling, PIC-seq systematically maps in situ cellular interactions and characterizes their molecular crosstalk. We apply PIC-seq to interrogate diverse interactions including immune–epithelial PICs in neonatal murine lungs. Focusing on interactions between T cells and dendritic cells (DCs) in vitro and in vivo, we map T cell–DC interaction preferences, and discover regulatory T cells as a major T cell subtype interacting with DCs in mouse draining lymph nodes. Analysis of T cell–DC pairs reveals an interaction-specific program between pathogen-presenting migratory DCs and T cells. PIC-seq provides a direct and broadly applicable technology to characterize intercellular interaction-specific pathways at high resolution.
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RNA-seq data that support the findings of the present study were deposited in the Gene Expression Omnibus under accession code GSE135382. Source data for Figs. 3f and 4i and Supplementary Figs. 2 and 6 are presented with the paper. All other data supporting the findings of the present study are available from the corresponding author on reasonable request.
The PIC-seq algorithm, including scripts and auxiliary data needed to reconstruct analysis files from count matrices to full figures, is provided as Supplementary Software.
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We thank G. Brodsky for artwork. The research of I.A. and A.T. is supported by the Seed Networks for the Human Cell Atlas of the Chan Zuckerberg Initiative and by Merck KGaA, Darmstadt. I.A. is an Eden and Steven Romick Professorial Chair, supported by the HHMI International Scholar Award, the European Research Council Consolidator Grant (no. 724471-HemTree2.0), an MRA Established Investigator Award (no. 509044), DFG (no. SFB/TRR167), the Ernest and Bonnie Beutler Research Program for Excellence in Genomic Medicine, the Helen and Martin Kimmel awards for innovative investigation, and the SCA award of the Wolfson Foundation and Family Charitable Trust. The Thompson Family Foundation Alzheimer’s Research Fund and the Adelis Foundation also provided support. A.T.’s laboratory is supported by the European Research Council, the I-CORE for chromatin and RNA regulation, and a grant from the Israel Science Foundation. A.T. is a Kimmel investigator. A.G. is a recipient of the Clore fellowship. M.C. is supported by a postdoctoral fellowship in Applied and Engineering Science, Israeli Government, Ministry of Science and Technology.
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Integrated supplementary information
a, Experimental design of the in vitro model. OT-II CD4+ T cells were co-cultured with ovalbumin (OVA)-loaded, lipopolysaccharides (LPS)-exposed DC for 3, 20 and 44 hours. Mono-cultures of both cell types were used as controls. b, Projection of genes on the Metacell-generated 2d graph (Fig. 1c) allows identification of cell types by expression quantiles of Trbc2 (T cells) and Fscn1 (DC). c, Metacells distribution across MARS-seq technical replicates (rows), grouped by experimental condition and sorting scheme. Color bar indicates assignment of meta-cells to 9 T and DC subsets. d-g, summary of all MARS-seq plates and cells (Supplementary Table 1). Shown are (e) total number of Illumina reads, (f) total number of Unique Molecular Identifiers (UMI) per cell, and (g) fraction of QC-positive cells retained for further analysis per technical replicate. In (d), ‘Replicates’ indicate number of biological replicates (co-cultures or mice), ‘Batches’ indicate number of technical replicates, ‘%analyzed’ indicates fraction of QC-positive cells retained for downstream analysis.
a, Spurious PIC rate is quantified by mixing two parallel cultures, one stained for TCRβ-FITC and CD11c-APC, and the other to TCRβ-PE and CD11c-APC.Cy7. PICs from FITC+APC.Cy7+ and PE+APC+ gates are considered spurious (~1% of all PICs). b, PICs share genes from T and DC. Shown is expression of top 10 differential DC genes (y axis) plotted against top 10 differential T genes (x axis) in single cells (left) and in PICs (right). c, Performance of the linear regression model, used to estimate the mixing factor of 20,000 synthetic PICs. d-e, Performance of the (d) T cell and (e) DC metacell assignments of PIC-seq over 5,000 synthetic PICs. Each row summarizes all synthetic PICs originating from one metacell and their assignments to metacells by PIC-seq (columns. Data is row-normalized). f, Estimation of triplet frequency in PIC-seq data. Triplets can be composed of two T cells and a DC (left), or of one T cell and two DCs (right). Each PIC was assigned a triplet score, indicating the gain in likelihood when instead of modelling doublets, the MLE models heterotypic triplets. Plotted are the cumulative distributions of triplet score of synthetic doublets (gray), triplets (blue), and empirical PIC (green). g, Pearson correlation between genes observed and expected values across all empirical PICs, in a leave 10% out cross-validation. Expected values are calculated (i) from the full PIC-seq algorithm, (ii) when incorporating the meta-cell assignments or (iii) the mixing factor only, or (iv) when mixing factor and metacell assignments are arbitrary. n=2,725 PICs. h, Differential gene expression between singlets from three DC subsets against all other DCs (log2 fold change, x axis), plotted against differential expression between PICs assigned to the same DC subsets and the activated T cell subset, calculated against all other PICs assigned to activated T cells (log2 fold change, y axis). Source data
a-b, Gene modules analysis. Shown are five and four correlated gene modules derived from mono- and co-cultured single T cells (a) and single DCs (b), respectively. Left – pairwise spearman correlation. Right – z-normalized pooled expression across different conditions. n=3,642 T cells (a) and 4,558 DCs (b). c, Pooled expression of five gene modules across single T cells derived from mono-cultures, 3h, 20h and 44h of co-culture. Dots represent single cells and dot colors indicate assignment to T subsets. Representative genes (black) and transcription factors (red) from each module are depicted (Supplementary Table 3). d, Pooled expression of four gene modules across DCs derived from mono-cultures and 3, 20 and 44h of co-culture (Supplementary Table 4). The central mark in box plot is median, with 5/95 percentiles at the whiskers and 25/75 percentiles at the box. Two-tailed Kolmogorov-Smirnov test; n=3,891 DCs. e, Projection of T cells and DCs after 20 hours of mono-culture (top), co-culture (bottom), or co-culture disrupted by an insert preventing cell contact but allowing passage of soluble material (transwell; middle).
a, Schematics: expected PIC gene expression values are calculated by mixing the multinomial probability vectors of the contributing metacells by the inferred mixing factor, while preserving total UMI count. b, Differences between observed and expected gene expression pooled over all PICs (log2 fold change). Expected values are calculated over the maximum likelihood doublet assignments (x axis), or the maximum likelihood triplet assignments (y axis; of two T cells and one DC; Methods). c, Mean observed and expected gene-expression levels in PIC grouped by their T and DC contributor subsets along the different time points of the co-culture experiment, shown for selected genes. Expected values are decomposed by the contribution from the T (green) and DC (red) PIC components. Bottom panel shows T and DC subset identities. Error bars indicate binomial 95% confidence intervals of the observed values. d, Log2 fold change values of 348 genes exhibiting significant differences between their observed and expected values across PICs grouped by their T and DC contributor subsets. χ2 test; q < 10-6. c-d, n=2,389 PICs.
a, FACS plot of EPCAM+ (CD326) epithelial cells, CD45+ immune cells and PIC derived from postnatal murine lung. Results are representative of two independent experiments. b, Gene expression profiles of 1,071 CD45+ or EPCAM+ single cells from postnatal lungs, grouped into 13 cell types. Top panel indicates UMI count. c, CD45+ and EPCAM+ cell subset identity of single cells in a. d, Gene expression profiles of 543 PICs, grouped by their contributing CD45+ and EPCAM+ identities, as determined by PIC-seq algorithm. Top panels as in a. e, CD45+ and EPCAM+ subset identities of PIC contributing cells in b, as determined using the PIC-seq algorithm. f, Estimation of the relative UMI count from epithelial cells (green), and immune cells (red) contributing to each PIC in b, as inferred using the PIC-seq algorithm (mixing factor). g-h, Differences in metacell composition between PIC contributing epithelial cells (g) and PIC contributing immune cells (h) in postnatal lungs. Each bar represents one metacell, and shows log2 fold change of its frequency between the PIC and the EPCAM+ (g) or CD45+ (h) single-cell populations. Bar colors relate to metacell subset identity. i, Distribution of Epcam+ and CD45+ subsets in non-conjugated populations and in PICs. Two-tailed FDR adjusted Fisher’s exact test; n= 621 Epcam+, 441 CD45+ single cells, and 543 PICs. j, Mean observed (gray bar) and expected (colored bar) gene expression of Alveolar type 1 (AT1)-C45+ PICs grouped by subsets of C45+ contributing cells (x-axis). Error bars indicate binomial 95% confidence intervals of the observed values. n=543 PICs.
a, Comparison between percentages of singlet T (TCRβ+) and B (CD19+) cells, and T-B PICs achieved by mild and strong dissociation conditions with the enzymes liberase and DNaseI. Results are representative of two tested digestion protocols with similar results. b, Quantification of spurious T-DC PIC rates, by mixing two LNs, one stained for TCRβ-PE and CD11c-APC, and the other to TCRβ-PerCP.Cy7 and CD11c-APC.Cy7. PICs from PE+APC.Cy7+ and PerCP.Cy7+APC+ gates are spurious (summing to <25% of all PICs). Bars indicate mean values; n=2 LNs. c, Full sorting schemes for the Nb immunization experiment. d, Confocal microscopy images of a PIC triplet composed of two T cells and one DC. Scale bar=10μm. Image is representative of four mice from two independent experiments with similar results. e, Metacells distribution across MARS-seq technical replicates (rows), grouped by experimental condition, sorting scheme and biological replicate. Color bar indicates association of meta-cells to 10 cell subsets. f-g, Cell type distributions across different TCRβ+ (f) and CD11c+ (g) sub-sorts and experimental conditions. h, Sorting scheme for Nb-presenting Ag+CD11c+ single cells and Ag+TCRβ+CD11c+ PICs. Source data
a, Performance of the linear regression model on the in vivo infection dataset, used to estimate the mixing factor of 20,000 synthetic PICs. b-c, Performance of the MLE over 5,000 synthetic PICs in T (b) and APC (c) metacell assignments. Each row summarizes all synthetic PICs originating from one metacell, and their assignments to metacells by PIC-seq (columns. Data is row-normalized). d-e, Estimation of triplet frequency in PIC-seq data. Triplets are either composed of two T cells and an APC (d), or one T cell and two APCs (e), as in Supplementary Figure 2. Right panels display performance of triplet filtering method over mixes of doublets with 4% triplets with two T cells (d), or doublets with 3% triplets with two APCs (e). False negative rate (y axis) is plotted against total fraction of discarded cells (x axis). Vertical lines indicate empirical filtering threshold. f, Differential gene expression between singlets from five T subsets against all other T cells (log2 fold change, x axis), plotted against differential expression between PICs assigned to the same T subsets and the migratory DC subset, calculated against all other PICs assigned to migratory DCs (log2 fold change, y axis).
a-b, Cell frequencies of different (a) T cell subsets and (b) APC subsets in the non-conjugated state and in PICs, following PBS injection or Nb infection. Bars depict the mean values across all cells; error bars indicate binomial 95% confidence intervals; lines connect non-conjugated cells and PICs from the same sample and line colors indicate significant differences between single cells and PICs; n=3 (PBS T cells), 4 (Nb T cells), 2 (PBS APCs), 4 (Nb APCs), 3 (PBS PICs) and 5 (Nb PICs) mice; Two-tailed FDR-adjusted Fisher exact test, q < 0.05; for binomial estimation: n=1015 PBS and 1579 Nb T cells, 978 PBS and 1700 Nb DCs, and 977 PBS and 1357 Nb PICs. c, Log2 fold change of 146 genes exhibiting significant differences between their observed and expected values across PICs assigned to Treg, grouped by their assignment to APC subsets. χ2 test; q < 10-6; n=504 PICs d, Frequencies of different T subsets in PICs from Nb infected LN, and from Nb-presenting PIC (Ag+TCRβ+CD11c+). n=Nb-injected PICs from 5 mice, and Nb-presenting PICs from 3 mice. e, Log2 fold change of 216 genes exhibiting significant differences between their observed and expected values across PICs assigned to migratory DC, grouped by whether they were sorted from PBS-injected, Nb-infected (Ag-), or Nb-presenting (Ag+) PIC populations. χ2 test; q < 10-6. f, Mean observed and expected gene-expression levels in PICs grouped as in e, shown for selected genes. Expected values are decomposed by the contribution from the T (green) and DC (red) PIC components. Error bars indicate binomial 95% confidence intervals of the observed values. g, Expression of costimulatory genes across 20h single cells from the coculture experiment. Line colors indicate whether a cell is from the mono-culture, transwell, or co-culture experiments. f-h, n=711 PICs. h, FACS gating scheme for pathogen presenting (Ag+) APCs (CD11c+) and PICs, and non-presenting (Ag-) PICs. i, Histogram of DLL4 expression for the different populations in h. h-i, data is from two mice processed in one experiment.
Supplementary Figs. 1–8.
PIC-seq algorithm and auxiliary scripts.
Summary of all technical replicates.
Gene lists used for analysis.
Correlated gene modules across single T cells from in vitro experiment.
Correlated gene modules across single DCs from in vitro experiment.
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Giladi, A., Cohen, M., Medaglia, C. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat Biotechnol 38, 629–637 (2020). https://doi.org/10.1038/s41587-020-0442-2
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