Spatial genomics enables multi-modal study of clonal heterogeneity in tissues

Tongtong Zhao, Zachary D. Chiang, Julia W. Morriss, Lindsay M. LaFave, Evan M. Murray, Isabella Del Priore, Kevin Meli, Caleb A. Lareau, Naeem M. Nadaf, Jilong Li, Andrew S. Earl, Evan Z. Macosko, Tyler Jacks, Jason D. Buenrostro () and Fei Chen ()
Additional contact information
Tongtong Zhao: Broad Institute of MIT and Harvard
Zachary D. Chiang: Broad Institute of MIT and Harvard
Julia W. Morriss: Broad Institute of MIT and Harvard
Lindsay M. LaFave: Harvard University
Evan M. Murray: Broad Institute of MIT and Harvard
Isabella Del Priore: Massachusetts Institute of Technology
Kevin Meli: Massachusetts Institute of Technology
Caleb A. Lareau: Broad Institute of MIT and Harvard
Naeem M. Nadaf: Broad Institute of MIT and Harvard
Jilong Li: Broad Institute of MIT and Harvard
Andrew S. Earl: Broad Institute of MIT and Harvard
Evan Z. Macosko: Broad Institute of MIT and Harvard
Tyler Jacks: Broad Institute of MIT and Harvard
Jason D. Buenrostro: Broad Institute of MIT and Harvard
Fei Chen: Broad Institute of MIT and Harvard

Nature, 2022, vol. 601, issue 7891, 85-91

Abstract: Abstract 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 aberrations1–4 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.

Date: 2022
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DOI: 10.1038/s41586-021-04217-4

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