Abstract
Cells secrete numerous bioactive molecules that are essential for the function of healthy organisms. However, scalable methods are needed to link individual cell secretions to their transcriptional state over time. Here, by developing and using secretion-encoded single-cell sequencing (SEC-seq), which exploits hydrogel particles with subnanolitre cavities (nanovials) to capture individual cells and their secretions, we simultaneously measured the secretion of vascular endothelial growth factor A (VEGF-A) and the transcriptome for thousands of individual mesenchymal stromal cells. Our data indicate that VEGF-A secretion is heterogeneous across the cell population and is poorly correlated with the VEGFA transcript level. The highest VEGF-A secretion occurs in a subpopulation of mesenchymal stromal cells characterized by a unique gene expression signature comprising a surface marker, interleukin-13 receptor subunit alpha 2 (IL13RA2), which allowed the enrichment of this subpopulation. SEC-seq enables the identification of gene signatures linked to specific secretory states, facilitating mechanistic studies, the isolation of secretory subpopulations and the development of means to modulate cellular secretion.
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Data availability
Sequencing data from this study can be found on the Gene Expression Omnibus with the accession number GSE223550. Source data are provided with this paper.
Code availability
R scripts used for generating the hypoxic gene signature can be found at https://github.com/Teneth/GEND_Scrip t.
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Acknowledgements
We acknowledge support from the National Institutes of Health grants NIDDK R21DK128730 to D.D.C. and P01GM099134 to K.P., as well as from the Broad Stem Cell Research Center (BSCRC) and California NanoSystems Institute (CNSI) Stem Cell Nano-Medicine Initiative Planning Award to K.P. and D.D.C. We also acknowledge Entelexo for providing the immortalized human mesenchymal stromal cells. Sorting experiments were performed in the UCLA Jonsson Comprehensive Cancer Center (JCCC) Flow Cytometry Shared Resource that is supported by the National Institutes of Health award P30CA016042 and by the JCCC and the David Geffen School of Medicine at UCLA. We thank the Broad Stem Cell Research Center (BSCRC) and UCLA Technology Center for Genomics and Bioinformatics (TCGB) facility for assisting with sequencing. Confocal laser scanning microscopy was performed at the CNSI Advanced Light Microscopy Shared Resource Facility at UCLA. We also thank the laboratory of A. Meyer for access to the Incucyte. Selected schematic figures were created using BioRender.com.
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S.U., J.L., J.d.R, K.P. and D.D.C. conceived the idea and contributed to the design of experiments. S.U., J.L., D.K., S.B., B.C., S.K. and C.S. performed the experiments/simulations and contributed to data analysis. S.U., J.L., K.P. and D.D.C. drafted the paper, and all the authors provided feedback. S.U. and J.L. contributed equally and have the right to list their name first in their CVs.
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S.U., J.L., D.K., J.d.R., K.P. and D.D.C. are inventors on a patent application assigned to the University of California. J.d.R. is an employee of Partillion Bioscience, which is commercializing nanovial technology. J.d.R., D.D.C. and the University of California have financial interests in Partillion Bioscience. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Cell loading into nanovials, enrichment of single cell-loaded nanovials by FACS, and VEGF-A nanovial secretion assay validation.
a, Anchorage of single cells on nanovials coated with gelatin via integrin binding. Cell loading into nanovials is achieved by simple mixing. b, Flow cytometry histogram of cell-loaded nanovials stained with calcein AM viability dye (0.4 cell per nanovial loading shown here). Cells are sorted via FACS (SONY SH800S) based on calcein signal into ‘Multiple Cells’ and ‘Single-cell’ gates. The distribution of the calcein signal has one peak with a tail at higher intensities, which represents nanovials containing more than one cell. n = 14,934 single cells events and 7,586 multiple cell events. c, We tested three cell loading concentrations (0.4, 0.7, and 1 cell per nanovial) and analysed the fraction of nanovials carrying zero, single or multiple cells using the gates described in (b). The graph quantifies cell loading into nanovials for these conditions. When loading cells at the 1:1 cell-to-nanovial ratio, we achieved 23% single-cell loaded nanovials which could be separated by sorting for downstream approaches and analyses. d, Fluorescence microscopy images of nanovials sorted for the indicated gates as described in (b). By sorting nanovials in the ‘Single-cell’ gate, we enriched for nanovials carrying single cells as confirmed by Hoechst nuclei staining, whereas nanovials in the tail (‘Multiple Cells Gate’) represented mostly two or more loaded cells. Following sorting, we estimated that 95% of the ‘Single-cell’ gate sorted nanovials contained one cell based on image analysis. e, To isolate single cells on single nanovials, the following gating strategy was used for cell-loaded nanovial samples: i) All nanovials were gated based on FSC/SSC (n = 93,263), followed by ii) a single nanovial gate based on FSC-Width (n = 80,253), and iii) the ‘Single-cell’ gate based on calcein signal intensity (n = 9,527). iv) Flow cytometry fluorescence histogram of the fluorescent (AF647) anti-VEGF-A detection signal in single cell-loaded nanovials and empty nanovials isolated from the same nanovial cell-loading experiment after 12 hours of secretion incubation. Single cell-loaded nanovials have higher fluorescent (AF647) anti-VEGF-A signal than empty nanovials, showing low crosstalk. f, Stability of recombinant VEGF-A on nanovials over 24 hours. There is a 23% decrease in AF647 Anti-VEGF-A signal from 0 to 12 hours, and a 10% decrease in signal from 12 to 24 hours. g, Level of autofluorescence and VEGF-A detection antibody signal for cell-loaded nanovials without VEGF-A capture/detection antibodies and cell-loaded nanovials without the VEGF-A capture antibodies, respectively. h, Image of one well in the ELISpot assay measuring VEGF-A secretion from MSCs. An average of 99% of cells seeded formed spots across 3 wells. The range of integrated intensity of the spots across 3 wells, quantified in the histogram on the right, indicates that there is heterogeneity in secretion level. Schematics in (a) and (b) created with BioRender.com.
Extended Data Fig. 2 Nanovials protect viability of MSCs during flow sorting.
a, The effect of surfactant and sorting on viability of MSCs in nanovials or suspended, as measured by live/dead stain imaging. Typically, nanovial samples are kept in buffer with a surfactant (Pluronic) at low concentration for handling and sorting steps, as it prevents nanovials from aggregating. Here, we exploit the surfactant as a stressor to test the effect of sorting on the viability of suspendered cells and cells in nanovials. For suspended samples, MSCs were dissociated from flasks, resuspended in FACS buffer with and without Pluronic surfactant, and viability was measured for MSCs with and without sorting. For MSC-loaded nanovial samples, MSCs were loaded on nanovials, resuspended in wash buffer with and without Pluronic surfactant, and viability was measured for MSCs after sorting. We found that viability decreased significantly when MSCs suspended in FACS buffer with Pluronic are sorted, but all other conditions maintained high viability. Surfactant, even at low concentrations, can likely induce some membrane damage, which is enhanced during sorting; however, nanovials seem to protect cells from this damage. Data presented as mean values +/- SD for 3 replicates. b, Finite element modelling using COMSOL results show that cells in nanovials are exposed to reduced levels of shear stress compared to cells in suspension when flowing through the nozzle of a flow sorter (see Methods). Here, the shear stress is plotted on the cell and nozzle geometry, and shows how the suspended cell (right) experiences greater shear stress than the cell inside nanovial (left). Schematic partially created with BioRender.com. c, Shear stress from the COMSOL model is plotted against position along the cell perimeter for suspended cells and cells adhered within a nanovial. The red arrow in each schematic (based on the model geometry) indicates the direction of arc length and shear stress measurement. d, Range of shear stress for suspended cells and cells adhered within a nanovial based on (c), with average shear stress plotted (red line). The average shear stress is 400-fold higher for suspended cells than cells in nanovials.
Extended Data Fig. 3 Transcript changes related to cell loading and adhesion in nanovials.
a, For the scRNA-seq experiment with MSCs loaded into nanovials or freely suspended shown in Fig. 2i, the graph shows the genes detected per cell for suspended and unsorted MSCs (unsorted), suspended FACS-sorted MSCs (sorted), and MSCs loaded on nanovials and sorted (Nanovial). b, Heatmap showing the average normalized transcript levels of known MSC markers (top) and markers from other cell types (bottom) in each condition from the experiment in (a). c, UMAPs of the combined transcriptome data from the scRNA-seq experiment described in (a). The cells from each condition are separately displayed and coloured. d, As in (c), showing the mean transcript level of genes significantly upregulated in cells adhered to nanovials relative to suspended MSCs (top) or upregulated in suspended MSCs (bottom). e, Gene ontology for the two gene sets from (d). f, Percent of cells with a nanovial associated oligo-barcode and the number of cells lacking such a barcode. Presumably, the cells with a barcode were still associated with a nanovial at the time of emulsification.
Extended Data Fig. 4 Effect of hypoxia inducers on VEGF-A secretion by MSCs.
a, ELISA for VEGF-A levels in conditioned media collected from MSCs grown on tissue culture plates under normoxic condition (normal growth media) or treated with indicated concentrations of cobalt chloride (CoCl2) and deferoxamine (DFX) hypoxia mimicking agents for 24 hours to induce hypoxic conditions, as indicated. Error bars are for standard deviation. Data presented as mean values +/- SD for 3 replicates. b, Flow cytometry histograms for two fluorescence channels indicating calcein positive MSC-loaded nanovials with anti-VEGF-A labelling on nanovials for MSCs treated with indicated concentrations of cobalt chloride (CoCl2) and deferoxamine (DFX) hypoxia mimicking agents for 14 hours total. Normoxia and control without VEGF-A capture antibody are also shown. 500 µM DFX yielded the largest increase in VEGF-A secretion (fluorescent (AF647) anti-VEGF signal) without compromising cell metabolic activity/viability (calcein).
Extended Data Fig. 5 Oligo-barcoded Anti-VEGF-A binding specificity on nanovials.
a, An anti-VEGF-A antibody (used as detection antibody for VEGF-A secretion in our SEC-seq approach) was conjugated with a 10x Chromium compatible oligo-barcode along with additional sequences necessary for library preparation. The schematic shows the sequence composition of the oligo attached to the VEGF-A detection antibody, along with the 10x Chromium primer sequence which hybridizes to the antibody-derived oligo and adds the unique molecular identifier (UMI) upon reverse transcription. b, (Left) Schematic showing the attachment of recombinant VEGF-A and the detection immunoassay via the oligo-barcoded antibody described in (a) which was quantified with a fluorescently-labelled secondary antibody in nanovials by flow cytometry. (Right) Flow cytometry histograms showing outcome of the experiment on the left with recombinant VEGF-A with and without the oligo-barcoded VEGF-A detection antibody, demonstrating antibody specificity of the detection assay and the validity of the oligo-barcoded VEGF-A detection antibody to the presence of VEGF-A protein. The numbers on the left indicate the mean of both histograms. c, As in (b), except that no biotinylated anti-VEGF-A capture antibody and recombinant VEGF-A was used in the assay. Schematics in created with BioRender.com.
Extended Data Fig. 6 Analysis of the SEC-seq experiments for normoxic and hypoxic MSCs.
a, Proportion per cluster of hypoxic and normoxic MSCs from the SEC-seq experiments described in Fig. 3, as percent of each sample (top) or as a percent of each cluster (bottom). b, Scatter plot showing the correlation between VEGFA transcript and VEGF-A secretion for individual cells comprising hypoxic and normoxic conditions, from Fig. 3. c, Violin plot showing the log10 total transcript count per cell, for each cluster in Fig. 3. An overlaid box plot shows the median and first and third quartiles, in addition to the lower and upper bounds of the data. Outliers are labelled as dots.
Extended Data Fig. 7 Identification of a high-VEGF-A secreting MSC subpopulation in a replicate experiment.
a, UMAPs showing the normalized transcript level of the indicated genes for the combined normoxic/hypoxic MSC SEC-seq experiments from Fig. 3. The five genes shown belong to the top 10 transcripts correlating highest with VEGF-A secretion. b, As in (a), for the replicate normoxic MSC SEC-seq experiment from Fig. 4d,e. c, UMAP with cluster information (also shown in Fig. 4e) and violin plots showing VEGF-A secretion and VEGFA transcript levels for all cells in each cluster in the SEC-seq experiment for normoxic MSCs from (b). Box plot shows median, 2nd and 3rd quartile, and outlier range whiskers. d, Violin plots showing the average normalized transcript level of the 10 highest correlating genes with VEGF-A secretion from Fig. 4b for the SEC-seq experiments with normoxic and hypoxic MSCs from Fig. 3 and the normoxic replicate from Fig. 4d,e. The UMAPs with cluster information are repeated here from the main figures for ease of interpretation. Box plot shows median, 2nd and 3rd quartile, and outlier range whiskers. e, For the SEC-seq experiment with normoxic MSCs in (b), all detected genes were ranked by the correlation of their transcript levels to the VEGF-A secretion level. Each gene is plotted by its rank and correlation. The ranks of the VEGFA and IGFBP6 genes are highlighted and the correlation values are given. f-g, Scatter plots showing f, the correlation between VEGFA transcript and VEGF-A secretion for individual cells and g, the expression of IGFBP6 normalized transcripts versus the log transformed VEGF-A secretion values in the replicate normoxic MSC experiment (from Fig. 4d,e). The correlation value and linear regression line are shown in each graph. h, UMAP showing VEGFA transcript levels per cell for the replicate normoxic MSC SEC-seq experiment (from Fig. 4d,e).
Extended Data Fig. 8 The high VEGF-A secretion cluster is not affected by cell cycle regression.
a, UMAP showing the clusters in the normoxic MSC SEC-seq replicate experiment (replicated from Fig. 4e) for easy comparison with the cell cycle-regressed data below. b, VEGF-A secretion per cell for the normoxic MSC SEC-seq replicate experiment (replicated from Fig. 4d) is shown for comparison with the cell cycle-regressed data below. c, New UMAP coordinates and clustering of normoxic cells from (a) post cell cycle regression, with the original cluster information marked. Note that Cluster C5’s spatial separation from other clusters is preserved with low mixing. d, VEGF-A secretion shown on the new UMAP coordinates post cell cycle regression. The cells in the newly arranged cluster C5 remain highly enriched for high VEGF-A secretion. e, New clustering of cells post cell cycle regression displayed on the UMAP from (c). While the borders between other clusters has shifted, the majority of cells that made up cluster C5 still distinctly form their own cluster (new cluster #6), demonstrating that the highly secretion cluster’s special transcriptional profile is unaffected by cell cycle information.
Extended Data Fig. 9 Purification and characterization of the IL13RA2+ MSC population.
a, Dot plot showing the percent of cells expressing the indicating surface marker gene as well as average normalized transcripts, for each cluster from the SEC-seq experiment in Fig. 4d,e. All surface markers shown are contained in the VRS gene list. b, Violin showing the normalized transcripts of IL13RA2 for cells per cluster as labelled in Fig. 4e. The dashed line represents the mean across all cells for each plot. Box plot shows median, 2nd and 3rd quartile, and outlier range whiskers. c, FACS gating used for the isolation of IL13RA2+/- MSCs from three replicate experiments for downstream bulk RNA-seq. d, Normoxic MSCs were sorted for IL13RA2+/- subpopulations as indicated in the FACS histogram on the top. Sorted cells were expanded for 4 (IL13RA2-) or 7 (IL13RA2+) days, to account for growth differences, and subsequently stained for IL13RA2 again and analysed by flow cytometry (bottom).
Extended Data Fig. 10 VEGFA splicing from bulk RNAseq of IL13RA2+/− sorted cells.
a, Integrated Genome Viewer capture of the RNA-seq reads across the VEGFA transcript from the triplicate bulk-RNA-seq experiments of IL13RA2-positive (red) and -negative (blue) cell populations described in Extended Data Fig. 9c. The exons of the VEGFA transcript are annotated below b, Alternative splicing output from computational evaluation of IL13RA2+ vs IL13RA2- RNA-seq libraries, grading the splicing exclusion events using only reads that span exon-exon junctions for maximum accuracy. The same exon is shown multiple times if its size varied, or it had different donor/acceptor exon pairs. Given is exon location, exclusion p-value, exclusion false discovery rate, inclusion rates and inclusion difference between samples. No exons are significantly alternatively spliced (significance threshold is p<0.05 & FDR<0.10). c, Top: Differential exon expression plot comparing the expression difference in VEGFA exon levels between the three IL13RA2+ and the three IL13RA2- samples, displayed as the log10 fold change (logFC) between the samples, where positive values indicate higher inclusion in IL13RA2+ samples. The exons are labelled. Bottom: As on the top, except for GATAD2A as an example of a gene with significant alternative splicing. Dots indicate the exons of this transcript.
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Confocal microscopy of MSCs loaded in fluorescent streptavidin-coated nanovials.
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Udani, S., Langerman, J., Koo, D. et al. Associating growth factor secretions and transcriptomes of single cells in nanovials using SEC-seq. Nat. Nanotechnol. 19, 354–363 (2024). https://doi.org/10.1038/s41565-023-01560-7
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DOI: https://doi.org/10.1038/s41565-023-01560-7
