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Integration of whole transcriptome spatial profiling with protein markers

Nature Biotechnology volume 41, pages 788–793 (2023)Cite this article

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Abstract

Spatial transcriptomics and proteomics provide complementary information that independently transformed our understanding of complex biological processes. However, experimental integration of these modalities is limited. To overcome this, we developed Spatial PrOtein and Transcriptome Sequencing (SPOTS) for high-throughput simultaneous spatial transcriptomics and protein profiling. Compared with unimodal measurements, SPOTS substantially improves signal resolution and cell clustering and enhances the discovery power in differential gene expression analysis across tissue regions.

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Fig. 1: SPOTS.
Fig. 2: SPOTS reveals two spatially distinct macrophages in breast cancer TME.

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Data availability

All raw data generated in the present study have been deposited to the GEO with accession no. GSE198353. High-resolution images of the tissues used in the present study are available at the Figshare website (https://figshare.com/account/home#/projects/143019). Source data are provided with this paper.

Code availability

To enable easy access to the SPOTS computational pipeline, the auxiliary R package ‘spots’ can be found on the CRAN (https://CRAN.R-project.org/package=spots) and GitHub (https://github.com/stevexniu/spots) websites. The R markdown files for SPOTS data analysis are included in Supplementary Notes 3 and 4 and also on Github (https://github.com/stevexniu/SPOTS-paper).

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Acknowledgements

We thank J. Chew and Y. Yin from 10x Genomics Headquarters for critical discussions and K. Ganapathy for helping coordinate 10x Genomics and Weill Cornell interactions. X.N. can list himself first in authors in his CV. D.A.L. is supported by the Burroughs Wellcome Fund Career Award for Medical Scientists (no. 1014689-01), Valle Scholar Award (no. VS-2020-31), Leukemia Lymphoma Scholar Award (no. 1373-21), the Sontag Foundation Distinguished Scientist Award (no. SFI 203261-02), the Mark Foundation Emerging Leader Award (no. 21-042-ELA) and the National Institutes of Health Director’s New Innovator Award (no. DP2-CA239065). This work was supported by the CEGS award (no. RM1 HG011014) and Emerson (NPT Charitable grant no. 584001).

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Author notes

  1. These authors contributed equally: Nir Ben-Chetrit, Xiang Niu, Ariel D. Swett.

Authors and Affiliations

  1. Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

    Nir Ben-Chetrit, Xiang Niu, Ariel D. Swett, Jesus Sotelo, Caitlin M. Stewart, Catherine Potenski & Dan A. Landau

  2. New York Genome Center, New York, NY, USA

    Nir Ben-Chetrit, Xiang Niu, Ariel D. Swett, Jesus Sotelo, Caitlin M. Stewart, Catherine Potenski & Dan A. Landau

  3. Center of Comparative Medicine and Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Maria S. Jiao

  4. 10x Genomics, Stockholm, Sweden

    Paulius Mielinis, Patrick Roelli & Marlon Stoeckius

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Contributions

N.B.-C., X.N., M.S. and D.A.L. conceived the project and wrote the manuscript with input from all authors. N.B.-C., M.S., A.D.S., J.S., M.S.J., C.M.S., P.M. and P.R. performed the experiments. X.N. performed the bioinformatic analyses. C.P. edited the manuscript. D.A.L. supervised the study. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Marlon Stoeckius or Dan A. Landau.

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Competing interests

M.S., P.M. and P.R. are current employees of 10x Genomics, Sweden. D.A.L. has served as a consultant for Abbvie, AstraZeneca and Illumina, and is on the Scientific Advisory Board or equity holder of Mission Bio, Pangea, Alethiomics and C2i Genomics. D.A.L. has also received previous research funding from BMS, 10x Genomics, Ultima Genomics and Illumina unrelated to the current manuscript. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Workflow schematic of SPOTS.

(Step 1) Tissue sections are mounted onto Visium slide, fixed for 10 min, and immunostained (Step 2) with fluorescent and TotalSeq-A antibodies (same clones) for 90 min in the presence of poly-T blocking oligos that prevent non-specific ADTs binding to slide surface. After washes, tissue is scanned under a scanning microscope to capture the tissue structure and fiducial spots (Step 3). The tissue is then permeabilized (Step 4), and RNA diffuses onto the poly-T oligos on the spatial barcodes. Following tissue digestion (Step 5), and template switched RT-PCR (Step 6), gene expression and ADT libraries are generated to preserve associated spatial barcodes and RNA sequence/ADT antibody barcodes (Step 7). Gene expression and ADT libraries are sequenced, and data are integrated using our designated pipeline (Step 8).

Extended Data Fig. 2 SPOTS library preparation and optimizations.

(a) Illustration of the structure of a barcoded oligo, including Read 1, spatial barcode (SB), unique molecular identifier (UMI), and poly-T (T30) sequences, attached to the Visium slide. (b) Illustration of the structure of an ADT-conjugated antibody, including a PCR handle (Handle), an antibody barcode (AB), and poly-A (A30). (c) Schematic of SPOTS following second strand synthesis. ADT and mRNA location is recorded through spatial barcodes on the Visium slide. (d) After cDNA amplification, ADT and cDNA libraries can be separated and prepared for sequencing to produce indexed ADT libraries (top panel, BioA) and gene expression cDNA libraries (bottom panel, BioA). (e) IF analysis using CD29 antibodies (TotalSeq-A clone) in spleen tissues that were fixed for 10 min at 25 °C using 100% Methanol (MeOH, top) or 1% PFA (bottom). Scale bar 200 μm. (f) Normalized ADT levels of indicated antibodies (left) and cDNA libraries (right, BioA) using tissue permeabilization vs. tissue removal enzyme and SDS. (g) Poly-dT blocking oligo length and wash temperature titration. Titration of different poly-dT blocking oligo lengths and their ability to hinder binding of dual-tagged antibodies (ADT + fluorophore) to poly-A surface probes at either 4 °C or 37 °C. Note the binding differences at the different temperatures for dT20. (h) IF using dual-tagged antibodies (ADT + fluorophore) of CD29 (green) and CD4 (red) in mouse spleens in the presence or absence of 20 µM poly-dT20 blocking oligos. Note for reduction of non-specific bindings in tissue-free areas. Scale bar 600 μm.

Extended Data Fig. 3 Normalized ADT levels in spleens.

(a) Normalized ADT levels of all 21 lineage and functional markers surface proteins in two spleen samples. Markers include; B cells (CD19, CD20, CD220, IgD, IgM, CD38), T cells (CD3, CD8, CD4), scavenging and antigen-presenting macrophages (CD169, F4/80, CD163, CD68, CD11b), granulocytes (Ly6G, Ly6C), endothelial cells (CD31, MadCAM1), and fibroblasts (CD29, CD105). (b) Immunofluorescence staining for CD29 (green) and CD4 (red) in spleen tissues from a, using fluorophore-ADT dual-tagged antibodies. Scale bar 300 μm. (c) Serial histological sections of mouse spleen (unstimulated) stained for H&E, B cells (B220), and T cells (CD8), demonstrating the typical spatial cell organization. Scale bar 300 μm.

Extended Data Fig. 4 Reproducibility and quality controls of SPOTS spleen data.

(a) Correlation of normalized ADT levels and mRNA expressions between two biological replicates of mouse spleen. The blue lines represent linear regression lines and 95% confident intervals are shown as gray shades. (b) Correlation between mRNA and different ADT levels (UMI) at single spatial barcode level for B cell, T cell, and macrophage enriched clusters across spatial barcodes in two biological replicates. The blue lines represent linear regression lines and 95% confident intervals are shown as gray shades. (c) Boxplot showing mRNA expression levels of germinal center (GC) specific genes (GSE12366; n = 163 genes) as a function of the distance from the center of GCs. The boxes were colored by the physical distance from the center of GCs as shown in Fig. 1f. Each boxplot ranges from the first and third quartiles with median shown as middle line, and the whiskers represents 1.5 times the interquartile range. (d) Cell-type deconvolution based on ADTs (Z-score) of each spatial barcode overlaid onto the spleen tissue. Magnified areas are shown in Fig. 1h.

Extended Data Fig. 5 Comparative performance analyses between SPOTS, Visium- alone, and Guilliams et al.

(a) Total detected genes and UMIs across spatial barcodes in biological replicates of spleens using SPOTS or Visium alone (n = 2 each). Sequencing depth: SPOTS rep A ~175 K reads per spot, SPOTS rep B ~155 K reads per spot, Visium rep A ~124 K reads per spot, Visium rep B ~110 K reads per spot. Each boxplot ranges from the first and third quartiles with median shown as middle line, and the whiskers represents 1.5 times the interquartile range. (b) Sequencing saturation curves for RNA of the two replicates of mouse spleen using SPOTS or Visium alone. (c) Correlation between mRNA and ADT UMI counts (left panel) in spleens using SPOTS. Correlation of total mRNA UMI counts in spatial barcodes using SPOTS (Y-axis) and Visium only (X-axis). The blue lines represent linear regression lines and 95% confident intervals are shown as gray shades. (d) Correlation of gene expression signatures in the indicated cell clusters as detected by SPOTS (Y-axis) and Visium alone (X-axis). (e) Total ADT UMI counts across the tissue spatial barcodes using 93 or 21 TotalSeq-A antibodies (Guilliams in mouse liver or SPOTS in mouse spleen, respectively). Each boxplot ranges from the first and third quartiles with median shown as middle line, and the whiskers represents 1.5 times the interquartile range. (f) Correlation map between several ADT markers of the same cell type in mouse liver (Guilliams et al.) and mouse spleen (SPOTS). ADTs: T-cells (CD8, CD4, CD3), B-cells (CD19, B220, IgD), and macrophages (F4/80, CD68, CD163). (g) Visualization of normalized ADTs of macrophages from panel b in liver (Guilliams et al.) and spleen (SPOTS).

Extended Data Fig. 6 Performance comparison of SPOTS to SM-Omics.

(a) Total mRNA and ADT counts across spatial barcodes recovered in SM-Omics and SPOTS in biological replicates of spleens. (b) Total ADT counts of F4/80 (macrophages) and IgD (B cells) across biological replicates of spleens. (c) Correlations between IgD and F4/80 (normalized ADT counts) for each spatial barcode in spleen by SPOTS and SM-Omics. Note the anti-correlation between F4/80+ macrophage-enriched (Red pulp) and IgD+ B cell-enriched (B follicle) regions in the SPOTS analyses. (d) Differentially expressed ADT levels (Z-score) for each cluster of spatial barcodes in spleen. Left panel: SPOTS, right panel: SM-Omics (e) Heatmap showing differentially expressed mRNAs (Z-score) in each cluster of spatial barcodes in splenic tissues. Left panel: SPOTS, right panel: SM-Omics (f) Visualization of normalized ADT expression of macrophages (F4/80) and B-cells (IgD) in spleens using SPOTS (left) and SM-Omics (right).

Extended Data Fig. 7 Breast cancer tissue architecture and optimizations.

(a) Immunohistochemistry (IHC) analysis of CD8 (T cells) and IBA-1 (macrophages) in mammary tumor section from MMTV-PyMT model demonstrating the typical abundance of T cells and tumor-associated macrophages in breast tumors. Right, schematic representation of Visium spatial capture spot on tissue and cell type abundances within. (b) Fluorescence imaging of TRITC labeled cDNA from mouse breast tumors following 5 or 10 minutes with 5X or 10X saponin or 0.1% Triton pre-staining permeabilization conditions. Dotted lines outline tissue borders.

Extended Data Fig. 8 Gene expression analysis of housekeeping genes in SPOTS and Visium cDNA libraries and comparative performance analysis between SPOTS and Visium-alone in breast cancer tissue.

(a) Images of serial sections of 3 biological replicates of mammary tumors using Visium protocol (H&E staining images) or SPOTS protocol (IF images of EpCAM). (b) Gene expression cycle threshold (CT) values of the indicated housekeeping genes using cDNA generated by Visium or SPOTS protocols from panel a. Each boxplot ranges from the first and third quartiles with median shown as middle line, and the whiskers represents 1.5 times the interquartile range. (c) Total detected genes across spatial barcodes in biological replicates of mammary tumors using SPOTS (n = 3) or Visium alone (n = 4). Sequencing depths for all Visium and SPOTS samples ~50 K reads per spot. Each boxplot ranges from the first and third quartiles with median shown as middle line, and the whiskers represents 1.5 times the interquartile range. (d) Sequencing saturation curves for RNA of the biological replicates from panel c.

Source data

Extended Data Fig. 9 Spatial mRNA and ADT levels in breast cancer TME.

(a) Normalized ADT levels (Z-score) of selected markers in a tissue section of MMTV-PyMT breast cancer. (b) Correlation between normalized mRNA expression and ADT levels of the indicated surface markers (EpCAM, PDPN, SCA-1, MHC-II). Pearson’s correlation coefficients were calculated on cluster-level and are indicated in the boxed labels. The blue lines represent linear regression lines, and 95% confident intervals are shown as gray shades. (c) Average normalized mRNA expressions of key cell-type marker genes (GSE158677) in each spatial cluster. (d) H&E staining and IHC analysis of KRT18 (tumor cells), CD8 (T cells) and IBA1 (macrophages) in serial sections of the MMTV-PyMT breast cancer model, demonstrating the typical abundances of tumor cells, T cells, and macrophages in mammary tumors. Scale bar 500 μm. (e) Normalized mRNA and ADT expression levels of Epcam and Ptprc along with their corresponding IF signals in breast cancer tissue. (f) Pearson’s correlation analysis between total UMI mRNA or UMI ADT counts and IF intensities of the indicated genes across the spatial barcodes. Pearson’s correlation coefficients are shown; mRNA/IF (top) and ADT/IF (bottom). The blue lines represent linear regression lines, and 95% confident intervals are shown as gray shades. P-values were calculated with two-sided Pearson’s correlation tests and exact p-values were labeled on each panel.

Extended Data Fig. 10 IF analysis using TotalSeq-A clones in breast cancer.

(a) IF analysis of EpCAM and DAPI to visualize tissue architecture along with F4/80 and MHC-II to highlight tumor-associated macrophages (TAMs). Scale bar 500 μm. (b) IF analysis of EpCAM and DAPI to visualize tissue architecture along with CCR2 and MHC-II to highlight two populations of TAMs (MHC-II + CCR2-/MHC-II + CCR2 + ). Scale bar 500 μm. (c) IF analysis of EpCAM and DAPI to visualize tissue architecture along with CD4, CD8 to mark the two populations of tumor-infiltrating T cells. Scale bar 500 μm. (d) IF analysis of EpCAM and DAPI to visualize tissue architecture and CD31 to visualize blood vessels. Scale bar 500 μm.

Supplementary information

Supplementary Information

Supplementary Notes 1–4.

Reporting Summary

Supplementary Table

Supplementary Tables 1–5.

Source data

Source Data Extended Data Fig. 8

Quantitastive PCR CT values of housekeeping genes in Visium_vs_SPOTS (breast cancer).

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Ben-Chetrit, N., Niu, X., Swett, A.D. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat Biotechnol 41, 788–793 (2023). https://doi.org/10.1038/s41587-022-01536-3

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