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Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas

An Author Correction to this article was published on 23 November 2020

This article has been updated

Abstract

Single-cell RNA sequencing (scRNA-seq) enables the systematic identification of cell populations in a tissue, but characterizing their spatial organization remains challenging. We combine a microarray-based spatial transcriptomics method that reveals spatial patterns of gene expression using an array of spots, each capturing the transcriptomes of multiple adjacent cells, with scRNA-Seq generated from the same sample. To annotate the precise cellular composition of distinct tissue regions, we introduce a method for multimodal intersection analysis. Applying multimodal intersection analysis to primary pancreatic tumors, we find that subpopulations of ductal cells, macrophages, dendritic cells and cancer cells have spatially restricted enrichments, as well as distinct coenrichments with other cell types. Furthermore, we identify colocalization of inflammatory fibroblasts and cancer cells expressing a stress-response gene module. Our approach for mapping the architecture of scRNA-seq-defined subpopulations can be applied to reveal the interactions inherent to complex tissues.

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Fig. 1: scRNA-seq analysis of two tumors from patients with PDAC.
Fig. 2: ST of PDAC and mapping of cell types.
Fig. 3: MIA mapping of ductal subpopulations across tissue regions.
Fig. 4: Cancer subregions reveal differential cell type and subpopulation enrichments.
Fig. 5: Mapping the relationship between cancer cell states and stromal subtypes using MIA.

Data availability

The scRNA-seq and ST data reported in this manuscript have been deposited to the Gene Expression Omnibus under accession number GSE111672.

Change history

  • 23 November 2020

    A Correction to this paper has been published: https://doi.org/10.1038/s41587-019-0392-8.

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Acknowledgements

We thank C. Loomis, Z. Dewan and B. Dabovic from the NYU Experimental Pathology Core, and B. Zeck and L. Chiriboga from the NYU Center for Biospecimen Research and Development for technical assistance, A. Weil from the NYU CBRD for sample acquisition, and members of the Yanai laboratory for constructive comments.

Author information

Authors and Affiliations

Authors

Contributions

R.M. performed the spatial transcriptomics and scRNA-seq as well as the data analysis. F.W. contributed to scRNA-seq and spatial transcriptomics analysis. M.C. contributed to spatial transcriptomics and scRNA-seq processing, and immunofluorescence experiments. M.B. and D.B. contributed expertise in scRNA-seq processing and analysis. J.C.D. contributed to the spatial transcriptomics analysis. C.H.H. contributed histology analysis. D.M.S. contributed sample acquisition. I.Y. conceived the project, and contributed to the data analysis and interpretation of the results. R.M. and I.Y. drafted the manuscript.

Corresponding author

Correspondence to Itai Yanai.

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Competing interests

I.Y. is a shareholder of OneCell Medical Ltd.

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Integrated supplementary information

Supplementary Fig. 1 scRNA-Seq data statistics.

(a-b) Histogram of unique transcripts detected per cell (log10) in PDAC-A (a) and PDAC-B (b). (c-d) Histogram of unique genes expressed per cell (log10) in PDAC-A (c) and PDAC-B. (e-f) Histogram of unique transcripts expressed per gene across all cells in PDAC-A (e) and PDAC-B (f). (g-h) PDAC-A (g) and PDAC-B (h) cells colored by UMIs per cell (log10) in tSNE space.

Supplementary Fig. 2 Identification and validation of multiple cancer populations.

(a) PDAC-A (top) and PDAC-B (bottom) CNV profiles inferred from scRNA-Seq (same as Fig. 1d, e). A subset of 200 randomly selected ductal cells were chosen as a negative control for the analysis and analyzed together with cancer clusters. (b) Expression levels of KRT19 (marker of malignant and non-malignant ductal epithelial cells), TM4SF1, and S100A4 projected onto t-SNE of PDAC-A (top) and PDAC-B (bottom). Note specificity of TM4SF1 expression for PDAC-A cancer cluster 1, and S100A4 expression for PDAC-A cancer cluster 2. In PDAC-B, TM4SF1 is expressed primarily by cancer cells whereas an S100A4 expressing cancer population is absent. (c) Double immunofluorescence staining (n = 2) for KRT19 and TM4SF1 in PDAC-A FFPE tissue. Note co-localization of KRT19 and TM4SF1 signal in malignant ducts (top panel, white arrowheads), but not in non-malignant ducts (bottom panel, white arrowheads). Scale bar, 250 µm. (d) Double immunofluorescence staining (n = 2) for KRT19 and S100A4 in PDAC-A FFPE tissue. Note co-localization of KRT19 and S100A4 signal in malignant ducts (top panel, white arrowheads), but not in non-malignant ducts (bottom panel, white arrowheads). Scale bar, 250 µm.

Supplementary Fig. 3 Histology for pancreatic cancer tissue used for spatial transcriptomics.

(a) Overview of PDAC-A tissue histology. Scale bar, 1mm. (b) Inset of pancreatic cancer ducts and surrounding desmoplasia. White arrowheads indicate cancer cells. Black arrowheads show the surrounding stroma and desmoplasia. Scale bar, 100 µm. (c) Inset of pancreatic tissue. Arrowheads indicate the acini. Scale bar, 100 µm. (d) Inset of duct epithelium and inflamed tissue. White arrowheads indicate the pancreatic ducts and the black arrowheads point to inflammatory cells with smaller nuclei. Scale bar, 100 µm. (e) Overview of PDAC-B tissue histology. Scale bar, 1mm. 3 ST replicates were generated from this tumor. (f) Inset of PDAC-B tissue showing normal ducts (white arrow) surrounded by interstitial space (black arrows). Scale bar, 100 µm.

Supplementary Fig. 4 Estimating the total number of cells captured by an ST spot.

ST spots were mapped back onto the H&E stained tissue, and brightfield images were extracted from the location of each ST spot. In each enlarged spot, the dark purple dots are nuclei, and the purple background is the cytoplasm and extracellular space. Enlarged spots shown demonstrate that ST spots can capture as few as ten or less cells and as many as a few dozen cells. Scale bar, 1 mm.

Supplementary Fig. 5 Spatial Transcriptomics (ST) and MIA statistics.

(a-b) Histogram of unique transcripts detected per spot (log10) in PDAC-A (a) and PDAC-B (b). (c-d) Histogram of unique genes detected per spot (log10) in PDAC-A (c) and PDAC-B (d). (e-f) Distribution of unique transcripts detected across ST spots in PDAC-A (e) and PDAC-B (f). (g-h) PC scores for the first six components projected onto ST data for PDAC-A (g) and PDAC-B (h). (i) Heatmap of MIA enrichment values with the indicated range of p-value thresholds for selecting cell type (scRNA-Seq) and tissue region (ST) specific genes after a two-sided t-test for marker gene selection (see Methods). Asterisk indicates the enrichment of fibroblast-specific genes in the PDAC-A cancer region with the corresponding p-value thresholds used in Fig. 2h. The values plotted in the heatmap were calculated using the hypergeometric distribution with 19,738 genes used as the background. (j) Same as (i), for varied p-value thresholds for cancer cluster 1 markers and PDAC-A pancreatic tissue region markers. Asterisk indicates the depletion of cancer cluster 1 specific genes in the PDAC-A pancreatic tissue region with the corresponding p-value thresholds used in Fig. 2h. (k) Boxplots of hypergeometric enrichment (P-value) of the fibroblast-specific genes with different sized down-samplings of the ST cancer-region gene set. Downsampling of cancer region-specific genes (of the 1734 genes with P< 0.05) was for 750, 500, 250, 100, 50, 25, and 5 genes. Each box plot represents the enrichment p-value of the fibroblast-specific genes with 500 independent random subsets of the cancer region-specific genes. The red central mark for each box plot represents the median enrichment.

Supplementary Fig. 6 Immunofluorescence staining of ductal subpopulation markers in PDAC tissue.

FFPE tissue was co-stained for KRT19 (duct marker) and subpopulation markers CA9 (a), AQP3 (b), HLA-DR (c), and TFF1 (d), as shown in Fig. 3. Individual signals are shown here in addition to the merged signal to better demonstrate marker co-localization (n = 2). Scale bar, 250 µm.

Supplementary Fig. 7 MIA maps of macrophage and dendritic cell subpopulations across PDAC-A tissue.

(a) Expression of M1 (top row) and M2 (bottom row) marker genes (two-tailed Student’s t-test, P < 10−5). Center of violin plots show the median expression level, and grey lines show interquartile range. Violin plots for M1 and M2 macrophages represent 19 and 21 data points, respectively. (b) Enrichment of macrophage subpopulations across PDAC-A ST regions. Indicated are the number of genes used for MIA. Values plotted are calculated using the hypergeometric distribution with 19,738 genes in the background. The number of subpopulation and region-specific genes used in the calculation are shown. (c) Expression of dendritic cells A (top panel) or B (bottom panel) marker genes (two-tailed Student’s t-test, P < 10−5). Center of violin plots show the median expression level, and grey lines show interquartile range. Violin plots for A and B dendritic cell subpopulations represent 12 and 33 data points, respectively. (d) Enrichment of dendritic cell subpopulations across PDAC-A ST regions. Indicated are the number of genes used for MIA. Values plotted are calculated using the hypergeometric distribution with 19,738 genes in the background. The number of subpopulation and region-specific genes used in the calculation are shown.

Supplementary Fig. 8 Additional PDAC-A replicates and cross-sample MIA map.

(a) Tissue histology for PDAC-A-2 and PDAC-A-3 used for ST. Highlighted in red are the cancer cells in the tissue. Scale bar, 1 mm. 3 ST replicates were generated from this PDAC-A. (b) Distribution of UMIs across the PDAC-A-2 and PDAC-A-3 ST tissue. (c) ST data statistics for PDAC-A-2 and PDAC-A-3 samples. Top, histogram of UMIs per spot. Bottom, histogram of genes expressed per spot. (d) MIA map of the PDAC-A-2 tissue regions. Color bar above MIA map indicates the clustering assignments. The hypergeometric distribution was used to calculate p-values shown with 19,738 genes in the background. (e) Hypergeometric between gene sets specific to PDAC-A-1 and PDAC-A-2 tissue regions. Color bar on left corresponds to PDAC-A-1 ST clustering assignments, and color bar on top corresponds to PDAC-A-2 ST clustering assignments.

Supplementary Fig. 9 Identification of cancer cells in additional PDAC scRNA-Seq dataset.

(a) Histogram of unique transcripts (left), genes expressed (middle), and unique transcripts per gene per cell in PDAC-C. (b) t-SNE visualization of PDAC-C cell type annotations by hierarchical clustering. (c) Standardized expression of KRT19 (left) and TM4SF1 (right) projected onto t-SNE. (d) CNV profiles inferred from scRNA-Seq. A subset of 200 randomly selected ductal cells were chosen as a negative control for the analysis and analyzed together with cancer clusters. All remaining cells in the dataset were used as the reference for CNV inference.

Supplementary Fig. 10 Additional PDAC ST histology and data statistics.

(a) H&E staining of two additional ST samples from PDAC-B tumor, and four additional ST samples each originating from different patients. Brightfield images were annotated for the presence of cancer cells (outlined in red). Scale bar, 1 mm. (b) Histograms of unique transcripts detected per spot (log10) for the indicated ST datasets. (c) Histograms of genes expressed per spot (log10) for the indicated ST datasets.

Supplementary Fig. 11 MIA maps of ST samples for identifying cancer regions.

(a-f) ST spots were analyzed using PCA followed by hierarchical clustering of PC scores, and genes specific to each region were identified for MIA (see Methods). MIA was performed using scRNA-Seq defined marker genes to determine which ST cluster represented the cancer region in each respective ST dataset. Color bars above each MIA map represent ST clustering assignments. Asterisks indicate the ST cancer cluster used for analysis in Fig. 5c. The hypergeometric distribution was used to calculate p-values using the number of region specific genes shown and with 19,738 background genes.

Supplementary Fig. 12 MIA maps of metastatic melanoma.

(a) t-SNE visualization of metastatic melanoma scRNA-Seq data from Tirosh et al 2016b6. Plots are colored by patient (left panel) and by clustering assignments (right panel). (b) Marker genes of T-cell subsets. Shown are expression levels of marker genes for CD8+ T-cells (top row), CD4+ regulatory T-cells (middle row) and helper T-cells (bottom row). Center of violin plots show the median expression level, and grey lines show interquartile range. Violin plots for CD8+, CD4+ T-regs, and T-helper cells represent 553, 193, and 368 data points, respectively. (c-d) Metastatic melanoma ST data from two patients published in Thrane et al 201828. Top left panels show histological annotations of H&E stained tissue, and top right panels show clustering assignments of the ST data. For the ST data shown, the ST spots are colored according to their clustering assignments. Bottom panels show MIA maps of the respective metastatic melanoma ST datasets. Marker genes were identified using the scRNA-Seq data from Tirosh et al 2016b6, and MIA was applied to generate cell population – tissue regions maps of ST data from Thrane et al 201828.

Supplementary information

Supplementary information

Supplementary Figs. 1–12.

Reporting Summary

Supplementary Table 1

Average number of transcripts per ST spot reported in other publications. The table indicates the number of transcripts detected per spot in other studies using the ST method. Included are only publications that report the average number of detected transcripts

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Moncada, R., Barkley, D., Wagner, F. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol 38, 333–342 (2020). https://doi.org/10.1038/s41587-019-0392-8

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