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Sci Rep. 2019 Mar 20;9(1):4836. doi: 10.1038/s41598-019-41235-9.

In situ 10-cell RNA sequencing in tissue and tumor biopsy samples.

Author information

1
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
2
Bioinformatics Core, University of Virginia, Charlottesville, VA, 22908, USA.
3
Department of Microbiology, Immunology & Cancer Biology, University of Virginia, Charlottesville, VA, 22908, USA.
4
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA.
5
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA. kjanes@virginia.edu.
6
Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA. kjanes@virginia.edu.
7
Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, 22908, USA. kjanes@virginia.edu.

Abstract

Single-cell transcriptomic methods classify new and existing cell types very effectively, but alternative approaches are needed to quantify the individual regulatory states of cells in their native tissue context. We combined the tissue preservation and single-cell resolution of laser capture with an improved preamplification procedure enabling RNA sequencing of 10 microdissected cells. This in situ 10-cell RNA sequencing (10cRNA-seq) can exploit fluorescent reporters of cell type in genetically engineered mice and is compatible with freshly cryoembedded clinical biopsies from patients. Through recombinant RNA spike-ins, we estimate dropout-free technical reliability as low as ~250 copies and a 50% detection sensitivity of ~45 copies per 10-cell reaction. By using small pools of microdissected cells, 10cRNA-seq improves technical per-cell reliability and sensitivity beyond existing approaches for single-cell RNA sequencing (scRNA-seq). Detection of low-abundance transcripts by 10cRNA-seq is comparable to random 10-cell groups of scRNA-seq data, suggesting no loss of gene recovery when cells are isolated in situ. Combined with existing approaches to deconvolve small pools of cells, 10cRNA-seq offers a reliable, unbiased, and sensitive way to measure cell-state heterogeneity in tissues and tumors.

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