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

Ruli Gao, Charissa Kim, Emi Sei, Theodoros Foukakis, Nicola Crosetto, Leong-Keat Chan, Maithreyan Srinivasan, Hong Zhang, Funda Meric-Bernstam and Nicholas Navin ()
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Ruli Gao: UT MD Anderson Cancer Center
Charissa Kim: 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

Nature Communications, 2017, vol. 8, issue 1, 1-12

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-00244-w

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