IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v601y2022i7891d10.1038_s41586-021-04217-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-021-04217-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-021-04217-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:601:y:2022:i:7891:d:10.1038_s41586-021-04217-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.