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

Author

Listed:
  • Tongtong Zhao

    (Broad Institute of MIT and Harvard
    Harvard University)

  • Zachary D. Chiang

    (Broad Institute of MIT and Harvard
    Harvard University
    Broad Institute of MIT and Harvard)

  • Julia W. Morriss

    (Broad Institute of MIT and Harvard
    Harvard University)

  • Lindsay M. LaFave

    (Harvard University
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Evan M. Murray

    (Broad Institute of MIT and Harvard
    Harvard University)

  • Isabella Del Priore

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Kevin Meli

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Caleb A. Lareau

    (Broad Institute of MIT and Harvard
    Harvard University)

  • 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
    Harvard University
    Broad Institute of MIT and Harvard)

  • Evan Z. Macosko

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital)

  • Tyler Jacks

    (Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Jason D. Buenrostro

    (Broad Institute of MIT and Harvard
    Harvard University
    Broad Institute of MIT and Harvard)

  • Fei Chen

    (Broad Institute of MIT and Harvard
    Harvard University
    Broad Institute of MIT and Harvard)

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.

Suggested Citation

  • 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. M, 2022. "Spatial genomics enables multi-modal study of clonal heterogeneity in tissues," Nature, Nature, vol. 601(7891), pages 85-91, January.
    • Handle: RePEc:nat:nature:v:601:y:2022:i:7891:d:10.1038_s41586-021-04217-4
    DOI: 10.1038/s41586-021-04217-4
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    Cited by:

    1. Shadi Shafighi & Agnieszka Geras & Barbara Jurzysta & Alireza Sahaf Naeini & Igor Filipiuk & Alicja Ra̧czkowska & Hosein Toosi & Łukasz Koperski & Kim Thrane & Camilla Engblom & Jeff E. Mold & Xinsong, 2024. "Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Bohan Li & Feng Bao & Yimin Hou & Fengji Li & Hongjue Li & Yue Deng & Qionghai Dai, 2024. "Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Beibei Ru & Jinlin Huang & Yu Zhang & Kenneth Aldape & Peng Jiang, 2023. "Estimation of cell lineages in tumors from spatial transcriptomics data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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