The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations1,2,3,4 as well as the makeup of the tumour microenvironment5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables the de novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model of metastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Open Access 10 August 2022
Nature Biotechnology Open Access 19 May 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Raw sequencing data are available from the Sequence Read Archive under accession PRJNA768453. Spatial barcode locations and counts matrices are available from the Broad Institute Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP1278). GC-content tracks for hg19 and mm10 were downloaded from the UC Santa Cruz Genome Browser. k36 mappability tracks for both genomes were downloaded from https://bismap.hoffmanlab.org/. Replication timing data were downloaded from Gene Expression Omnibus accession GSM923451 for hg19 and GSE137764 for mm10. Tn5 insertion bias tracks for both genomes were generated using the bias command from pyatac (https://nucleoatac.readthedocs.io/en/latest/pyatac/). Gene sets were downloaded from the Molecular Signatures Database Collections (http://www.gsea-msigdb.org/gsea/msigdb/collections.jsp).
Code for the in situ bead indexing is available from https://github.com/broadchenf/Slideseq. Code for all analyses is available from https://github.com/buenrostrolab/slide_dna_seq_analysis and archived at https://doi.org/10.5281/zenodo.5553305.
McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).
Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019).
Ramón y Cajal, S. et al. Clinical implications of intratumor heterogeneity: challenges and opportunities. J. Mol. Med. 98, 161–177 (2020).
Pogrebniak, K. L. & Curtis, C. Harnessing tumor evolution to circumvent resistance. Trends Genet. 34, 639–651 (2018).
Duan, Q., Zhang, H., Zheng, J. & Zhang, L. Turning cold into hot: firing up the tumor microenvironment. Trends Cancer Res. 6, 605–618 (2020).
Jin, M.-Z. & Jin, W.-L. The updated landscape of tumor microenvironment and drug repurposing. Signal Transduct. Target. Ther. 5, 166 (2020).
Tse, J. M. et al. Mechanical compression drives cancer cells toward invasive phenotype. Proc. Natl Acad. Sci. USA 109, 911–916 (2012).
Zhao, Y. et al. Selection of metastasis competent subclones in the tumour interior. Nat. Ecol. Evol. 5, 1033–1045 (2021).
Stylianopoulos, T., Munn, L. L. & Jain, R. K. Reengineering the physical microenvironment of tumors to improve drug delivery and efficacy: from mathematical modeling to bench to bedside. Trends Cancer Res. 4, 292–319 (2018).
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
Landau, D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).
Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).
Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).
Minussi, D. C. et al. Breast tumours maintain a reservoir of subclonal diversity during expansion. Nature 592, 302–308 (2021).
Casasent, A. K. et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205–217.e12 (2018).
McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016).
Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
Payne, A. C. et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371, eaay3446 (2020).
Rodriques, S. G. et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
McFadden, D. G. et al. Genetic and clonal dissection of murine small cell lung carcinoma progression by genome sequencing. Cell 156, 1298–1311 (2014).
Johnson, L. et al. Somatic activation of the K-ras oncogene causes early onset lung cancer in mice. Nature 410, 1111–1116 (2001).
LaFave, L. M. et al. Epigenomic state transitions characterize tumor progression in mouse lung adenocarcinoma. Cancer Cell 38, 212–228.e13 (2020).
Morishita, A. et al. HMGA2 is a driver of tumor metastasis. Cancer Res. 73, 4289–4299 (2013).
Thuault, S. et al. Transforming growth factor-β employs HMGA2 to elicit epithelial–mesenchymal transition. J. Cell Biol. 174, 175–183 (2006).
Kumar, M. S. et al. HMGA2 functions as a competing endogenous RNA to promote lung cancer progression. Nature 505, 212–217 (2014).
Cable, D. M., Murray, E., Zou, L. S., Goeva, A. & Macosko, E. Z. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00830-w (2021).
Dienstmann, R. et al. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat. Rev. Cancer 17, 268 (2017).
The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).
Kang, H. et al. Many private mutations originate from the first few divisions of a human colorectal adenoma. J. Pathol. 237, 355–362 (2015).
Humphries, A. et al. Lineage tracing reveals multipotent stem cells maintain human adenomas and the pattern of clonal expansion in tumor evolution. Proc. Natl Acad. Sci. USA 110, E2490–E2499 (2013).
Brayer, K. J., Frerich, C. A., Kang, H. & Ness, S. A. Recurrent fusions in MYB and MYBL1 define a common, transcription factor-driven oncogenic pathway in salivary gland adenoid cystic carcinoma. Cancer Discov. 6, 176–187 (2016).
Wang, B., Matsuoka, S., Carpenter, P. B. & Elledge, S. J. 53BP1, a mediator of the DNA damage checkpoint. Science 298, 1435–1438 (2002).
Long, D. T., Räschle, M., Joukov, V. & Walter, J. C. Mechanism of RAD51-dependent DNA interstrand cross-link repair. Science 333, 84–87 (2011).
Lachaud, C. et al. Ubiquitinated Fancd2 recruits Fan1 to stalled replication forks to prevent genome instability. Science 351, 846–849 (2016).
Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).
Landrette, S. F. et al. Plag1 and Plagl2 are oncogenes that induce acute myeloid leukemia in cooperation with Cbfb-MYH11. Blood 105, 2900–2907 (2005).
Ren, B. et al. MCM7 amplification and overexpression are associated with prostate cancer progression. Oncogene 25, 1090–1098 (2006).
Farhad, M., Rolig, A. S. & Redmond, W. L. The role of Galectin-3 in modulating tumor growth and immunosuppression within the tumor microenvironment. Oncoimmunology 7, e1434467 (2018).
Karim, B. O., Rhee, K.-J., Liu, G., Yun, K. & Brant, S. R. Prom1 function in development, intestinal inflammation, and intestinal tumorigenesis. Front. Oncol. 4, 323 (2014).
Winkler, J., Abisoye-Ogunniyan, A., Metcalf, K. J. & Werb, Z. Concepts of extracellular matrix remodelling in tumour progression and metastasis. Nat. Commun. 11, 5120 (2020).
Wu, C.-Y. et al. Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer. Nat. Biotechnol. 39, 1259–1269 (2021).
Wu, S. et al. Circular ecDNA promotes accessible chromatin and high oncogene expression. Nature 575, 699–703 (2019).
Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project. Nature 569, 641–648 (2019).
van Haasteren, J., Li, J., Scheideler, O. J., Murthy, N. & Schaffer, D. V. The delivery challenge: fulfilling the promise of therapeutic genome editing. Nat. Biotechnol. 38, 845–855 (2020).
Goldman, N. et al. Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. Nature 494, 77–80 (2013).
Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019).
Liu, Y. et al. Bisulfite-free direct detection of 5-methylcytosine and 5-hydroxymethylcytosine at base resolution. Nat. Biotechnol. 37, 424–429 (2019).
J.D.B. and F.C. acknowledge funding from the Allen Institute Distinguished Investigator award and funding from the NIH R21HG009749. F.C. also acknowledges funding from NIH DP50D024583, and R33CA246455. F.C. and E.Z.M acknowledge funding from NIH R01HG010647. F.C. also acknowledges the Searle Scholars Award and the Burroughs Wellcome CASI award. J.D.B. acknowledges support from the NIH New Innovator Award (DP2HL151353). Z.D.C. acknowledges funding from NHGRI training grant T32HG002295 and the Harvard Quantitative Biology Initiative. We thank S. Nagaraja for assistance in making figures. Components of our figures were created with BioRender.com. We thank J. Strecker for the gift of Tn5 enzyme, and the Buenrostro and Chen laboratories for helpful discussions. We thank the cancer patients and their families for their invaluable donations to science, making this work possible.
E.Z.M. and F.C. are listed as inventors on a patent application related to slide-seq. T.J. is a member of the board of directors of Amgen and Thermo Fisher Scientific. He is also a co-founder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the scientific advisory board of Dragonfly Therapeutics, SQZ Biotech and Skyhawk Therapeutics, and is the president of Break Through Cancer. J.D.B. holds patents related to ATAC-seq and is on the scientific advisory board for Camp4, Seqwell and Celsee. F.C. is a paid consultant for Celsius Therapeutics and Atlas Bio. E.Z.M is a paid consultant for Atlas Bio. T.Z., E.Z.M., J.D.B. and F.C. have filed a patent application based on this work.
Peer review information Nature thanks Andrew Adey and Naveed Ishaque for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Library preparation steps. b–e, Library size comparisons for live vs fixed tissue (b); histone extraction protocols (c, d); and varying lengths of a bridge oligo used to connect the barcoded bead oligo to genomic fragments e, either hybridized after tagmentation (left bar) or pre-loaded onto the Tn5 transposase prior to tagmentation (rest). All values are normalized to control condition (first column). f, Rate of ligation of genomic fragments to barcoded oligo either ordered in solution from IDT (left) or cleaved off from beads (center, right). g–j, Frequency of Tn5 insertions in the genome relative to the nearest transcription start site (TSS) for slide-DNA-seq of mouse cerebellum (g), mouse liver metastases (h), human colon tumor (i), and for single-cell ATAC-seq of a mouse brain (j). Error bars, mean ± s.d; n, number of replicate comparisons (generated from 4 biological samples); dots represent values of each replicate.
Cerebellar sections are exposed to treatment as stated (with or without prior fixation) and stained with DAPI. Scale bars, 500 μm.
a, Nuclear (left) and mitochondrial (center) DNA fragments per bead obtained for tissues used in this study. Right, mitochondrial fraction of fragments. 4x, protocol variant with 4x tagmentation. Black lines on violin plots indicate the mean. b, slide-DNA-seq of the mouse cerebellum experiment in Fig. 1. Beads are colored by the number of nuclear fragments (left), mitochondrial DNA fragments (center), and fraction of mitochondrial DNA fragments (right). c, Visualization of representative convex hulls for different spatial bin values of k for k-nearest neighbor smoothing. Beads are colored by raw counts, insets show convex hulls for k = 1, 10, 25, and 50, centered on salmon colored beads. Hulls are generally circular except at the edge of the array. d, Distribution of mean fragments per 1 Mb genomic bin for different spatial bin values of k. The median diameter of the smoothed features is indicated in parentheses. e, Comparison of nuclear fragments (left) and effective diameter (right) per bead for different spatial bin values of k. Scale bars, 500 μm.
a, Interpolated image showing the nuclear fraction of fragments of a 10 μm mouse cerebellar section processed for slide-DNA-seq. Cyan box indicates magnified area (right). Smaller boxes indicate regions taken for linescans in b and e. b, Pseudo-intensity (representing nuclear fraction of fragments) of linescan as indicated by red box in a. Black dots, halfmax. Full Width at Half Maximum (FWHM) = 57.3 μm. c, 10 μm serial section of the same mouse cerebellum stained with DAPI. Blue box indicates magnified area (right). Smaller boxes indicate regions taken for linescans in d and f. d, Linescan of DAPI intensity as indicated in c. Black dots, halfmax. FWHM = 16.4 μm. e, Same as b, but for 3 different regions as indicated by the smaller non-red boxes in a. For the left (green) and middle (yellow) panel, FWHM is calculated as twice the distance between the peak and the halfmax (marked by black dots). f, Same as d, but for 3 different regions as indicated by the smaller non-red boxes in b. For the left (green) and middle (yellow) panel, FWHM is calculated as twice the distance between the peak and the halfmax (marked by black dots). g, Bar graph of average FWHM (n=4 regions). Error bars, mean ± s.d. Upper bound for the diffusion measurement is half of largest FWHM (not taking into account the DAPI measurement). Scale bars, 500 μm (a, c), 200 μm (b, d).
a, Top, raw sequencing reads per 1 Mb bin for mouse cerebellum slide-DNA-seq are plotted for GC-content, mappability, replication timing score, and Tn5 bias. Pearson’s r values are shown for each. Bottom, bias corrected coverage and correlation values after normalization. b, Same as a but for tagmentation-based bulk sequencing of mouse cerebellum (Methods). c, Same as a but for slide-DNA seq of mouse liver metastases. d, Same as b but for tagmentation-based bulk sequencing of mouse liver metastases. Blue points, bins from chrX (not included in the calculation of the fit).
Left (all panels), copy number profiles at 1 Mb genomic resolution of the mouse cerebellum for the sequencing modality and processing indicated. For this diploid sample, each copy number distribution is normalized to a median of 2. Right (all panels), histogram of the number of bins per copy number. a, Raw coverage profile of slide-DNA-seq. b, Coverage profile of slide-DNA-seq normalized by GC-content and mappability. c, Coverage profile for bulk tagmentation-based sequencing. d, Coverage profile of bulk sequencing normalized by GC-content and mappability. e, Coverage profile of slide-DNA-seq normalized by the GC-content and mappability divided by bulk sequencing normalized by GC-content and mappability.
a, Principal components calculated from smoothed slide-DNA-seq beads, ordered by the percentage of variance explained for the mouse liver metastases array shown in Fig. 1. b, Weights per 1 Mb genomic bin for principal components 1 and 2. Red points indicate bins from chromosomes with an odd number, blue from chromosomes with an even number (and chrX). c, slide-DNA-seq array for the mouse liver metastases array shown in Fig. 1 with points colored by raw PC 1 scores (top left), smoothed PC 1 scores (top right), raw PC 2 scores (bottom left), smoothed PC 2 scores (bottom right). d, Calinski-Harabasz criterion values used to select the optimal value of k for k-means clustering. e, slide-DNA-seq array colored by cluster assignment using the value of k selected in d. f–j, Same as a–e, but for the mouse liver metastases array shown in Fig. 2.
a, Raw copy number profiles for four tumor cell lines profiled using tagmentation-based bulk sequencing. b, Representative 10,000 fragments samples of the cell lines shown in a. c, Clonal assignment accuracy for 10,000 fragment samples (n=5,000 samples of each cell line) using the analysis workflow shown in Extended Data Fig. 7. d, Same as c but for 1,000 fragment samples.
a, Immunofluorescence (IF) against tumor marker HMGA2 (top) and two slide-DNA-seq replicates (center, bottom) were performed on two serial sections of a mouse liver metastasis. Beads colored by PC1 scores (left) and cluster assignment (right) show similar spatial architecture between replicates. Scale bars, 500 μm. b, Aggregate copy number profiles of normal and tumor beads show high correlation (Pearson’s r = 0.986 and 0.992) between the two replicates.
Each column represents normalized copy number profiles aggregated across the number of slide-DNA-seq beads indicated (10,000; 1,000; or 100), while each row indicates the genomic bin size (10 Mb, 5 Mb, 2.5 Mb, 1 Mb, and 500 kb) for the mouse cerebellum array.
a, H&E stain (left), IHC against tumor marker HMGA2 (center), and Hmga2 expression from slide-RNA-seq (right) of three serial sections of mouse liver metastases. b, UMAP of unsupervised clustering of single nucleus RNA-seq performed on nuclei from mouse liver metastasis sample. c, Dot plot showing the expression of marker genes used to annotate clusters in b. d, Spatial projection of cell types from b onto the slide-RNA-seq array, colored in the same fashion. Black lines indicate spatial tumor clusters. e, Differential localization of cell types between clone A, clone B and normal regions. Heatmap shows signed (positive, enrichment; negative, depletion) log10(p-value) from permutation testing (two-sided, not adjusted for multiple comparisons). f, Spatial plot of monocyte localization on the array, which is significantly enriched for clone B. Black lines indicate spatial tumor clusters. VSMC, vascular smooth muscle cell; LSEC, liver sinusoidal endothelial cells.
a, Assignment of beads to normal tissue, clone A, and clone B based on k-means clustering. b–d, Histogram of DNA content of single cells measured by propidium iodide (PI) fluorescence intensity through flow cytometry. (b) bone marrow cells (normal control); (c) clone A; (d) clone B. Diploid G1 (2N) and G2 (4N) gates are determined on bone marrow histogram and applied to clones A and B, revealing that the clone A genome is triploid; and the clone B genome is diploid with some amplifications (e.g. of chr. 15 and 19, see Fig. 2d). e, Aggregate copy number profiles of beads assigned to clone A. f, Aggregate copy number profiles of beads assigned to clone B.
a, Genomic copy number profiles for 2,274 single cells obtained using scWGS, with cluster annotations colored. b, Top left: projection of scWGS clusters onto slide-DNA-seq. All other: three genomic regions of differential CNA profiles between the three projected clusters, shown are spatial heatmaps of signed p-value differences from the average profile (two-sided permutation test, not adjusted for multiple comparisons). c, Normalized copy number profiles for the three scWGS clusters, and the corresponding spatial clusters. Vertical lines denote variable regions from b. Single-cell cluster 2 (blue) shows complex CNA patterns that obscure cluster ploidy, nevertheless, copy number values are normalized to 2 for easy comparison to other clusters.
a, H&E stain of normal colon (left) and colon tumor (right) tissue from the same patient. Scale bars, 200 μm. b, Serial sections processed for H&E stain (left), slide-DNA-seq (center), and slide-RNA-seq (right). Scale bars, 500 μm. Yellow and black boxes indicate magnified areas in c, d, respectively. c, Magnified views of H&E stain, slide-DNA-seq and slide-RNA-seq reconstructions show concordant spatial tissue architecture across three modalities; scale bar, 200 μm. d, Magnified view of H&E stain of three regions that are assigned low, medium, and high tumor density by slide-RNA-seq transcriptomic analysis (b, right). Arrows indicate regions of high tumor density identified through H&E stain. Scale bar, 100 μm.
a, Subclone-associated pathways identified through gene set enrichment analysis. b, Hallmark E2F target genes (n=200) plotted according to percent variance explained by clonal identity (x-axis) and tumor density (y-axis). Included genes colored by normalized density on the scatter plot, all other genes are shown in grey. c, Expression of highly subclone-associated E2F target genes (n=11, listed in a), plotted for spatial tumor regions of the slide-RNA-seq array from Fig. 4. d, MYC target genes (n=200) plotted according to percent variance explained by clonal identity (x-axis) and tumor density (y-axis). Included genes are colored by normalized density on the scatter plot, MYC is colored red, all other genes are shown in grey. e, Expression of highly subclone-associated MYC target genes (n=16, listed in a), plotted for spatial tumor regions (left). Box plot showing normalized MYC target gene expression by subclone assignment; each point represents a spatial tumor cluster (right). Red line, mean, red box, 95% confidence interval for mean, blue box, standard deviation. f, MYC expression plotted for spatial tumor regions (left). Scatter plot showing normalized MYC expression by tumor cell density; each point represents a spatial tumor cluster (right). g, Subclone-associated pathways identified through gene set enrichment analysis. h, Cell adhesion molecule binding genes (n=514) plotted according to percent variance explained by clonal identity (x-axis) and tumor density (y-axis). Included genes are colored by normalized density on the scatter plot, all other genes are shown in grey (reproduced from Fig. 4i). i, Expression of highly density-associated cell adhesion molecule binding genes (n=14, listed in g), plotted for spatial tumor regions. Scale bars, 500 μm.
This file contains Supplementary Materials and Methods, Supplementary text, legends for Extended Data Figures and Supplementary References
Mouse Clone DEGs
Human colon variance exp
About this article
Cite this article
Zhao, T., Chiang, Z.D., Morriss, J.W. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022). https://doi.org/10.1038/s41586-021-04217-4
This article is cited by
Nature Methods (2022)
Nature Methods (2022)
Nature Biotechnology (2022)
Nature Cancer (2022)
Nature Reviews Cancer (2022)