Xyzeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

Youjin Lee, Derek Bogdanoff, Yutong Wang, George C. Hartoularos, Jonathan M. Woo, Cody T. Mowery, Hunter M. Nisonoff, David S. Lee, Yang Sun, James Lee, Sadaf Mehdizadeh, Joshua Cantlon, Eric Shifrut, David N. Ngyuen, Theodore L. Roth, Yun S. Song, Alexander Marson, Eric D. Chow, Chun Jimmie Ye

Research output: Contribution to journalArticlepeer-review

Abstract

Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-Associated transcriptomic program in tumor-Associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.

Original languageEnglish
Article numbereabg4755
JournalScience Advances
Volume7
Issue number17
DOIs
StatePublished - 21 Apr 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General

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