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Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer

Author

Listed:
  • Ruli Gao

    (UT MD Anderson Cancer Center)

  • Charissa Kim

    (UT MD Anderson Cancer Center
    Graduate School of Biological Sciences, UT MD Anderson Cancer Center)

  • Emi Sei

    (UT MD Anderson Cancer Center)

  • Theodoros Foukakis

    (Karolinska Institutet)

  • Nicola Crosetto

    (Karolinska Institutet)

  • Leong-Keat Chan

    (Wafergen Biosystems, Inc)

  • Maithreyan Srinivasan

    (Wafergen Biosystems, Inc)

  • Hong Zhang

    (UT MD Anderson Cancer Center)

  • Funda Meric-Bernstam

    (UT MD Anderson Cancer Center)

  • Nicholas Navin

    (UT MD Anderson Cancer Center
    Graduate School of Biological Sciences, UT MD Anderson Cancer Center)

Abstract

Single cell RNA sequencing has emerged as a powerful tool for resolving transcriptional diversity in tumors, but is limited by throughput, cost and the ability to process archival frozen tissue samples. Here we develop a high-throughput 3′ single-nucleus RNA sequencing approach that combines nanogrid technology, automated imaging, and cell selection to sequence up to ~1800 single nuclei in parallel. We compare the transcriptomes of 485 single nuclei to 424 single cells in a breast cancer cell line, which shows a high concordance (93.34%) in gene levels and abundance. We also analyze 416 nuclei from a frozen breast tumor sample and 380 nuclei from normal breast tissue. These data reveal heterogeneity in cancer cell phenotypes, including angiogenesis, proliferation, and stemness, and a minor subpopulation (19%) with many overexpressed cancer genes. Our studies demonstrate the utility of nanogrid single-nucleus RNA sequencing for studying the transcriptional programs of tumor nuclei in frozen archival tissue samples.

Suggested Citation

  • Ruli Gao & Charissa Kim & Emi Sei & Theodoros Foukakis & Nicola Crosetto & Leong-Keat Chan & Maithreyan Srinivasan & Hong Zhang & Funda Meric-Bernstam & Nicholas Navin, 2017. "Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer," Nature Communications, Nature, vol. 8(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00244-w
    DOI: 10.1038/s41467-017-00244-w
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    Cited by:

    1. Kang Wang & Ioannis Zerdes & Henrik J. Johansson & Dhifaf Sarhan & Yizhe Sun & Dimitris C. Kanellis & Emmanouil G. Sifakis & Artur Mezheyeuski & Xingrong Liu & Niklas Loman & Ingrid Hedenfalk & Jonas , 2024. "Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    2. Cheng-Kai Shiau & Lina Lu & Rachel Kieser & Kazutaka Fukumura & Timothy Pan & Hsiao-Yun Lin & Jie Yang & Eric L. Tong & GaHyun Lee & Yuanqing Yan & Jason T. Huse & Ruli Gao, 2023. "High throughput single cell long-read sequencing analyses of same-cell genotypes and phenotypes in human tumors," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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