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Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer.

ABSTRACT: Single-cell transcriptome profiling of tumour tissue isolates allows the characterization of heterogeneous tumour cells along with neighbouring stromal and immune cells. Here we adopt this powerful approach to breast cancer and analyse 515 cells from 11 patients. Inferred copy number variations from the single-cell RNA-seq data separate carcinoma cells from non-cancer cells. At a single-cell resolution, carcinoma cells display common signatures within the tumour as well as intratumoral heterogeneity regarding breast cancer subtype and crucial cancer-related pathways. Most of the non-cancer cells are immune cells, with three distinct clusters of T lymphocytes, B lymphocytes and macrophages. T lymphocytes and macrophages both display immunosuppressive characteristics: T cells with a regulatory or an exhausted phenotype and macrophages with an M2 phenotype. These results illustrate that the breast cancer transcriptome has a wide range of intratumoral heterogeneity, which is shaped by the tumour cells and immune cells in the surrounding microenvironment.

SUBMITTER: Chung W

PROVIDER: S-EPMC5424158 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer.

Chung Woosung W Eum Hye Hyeon HH Lee Hae-Ock HO Lee Kyung-Min KM Lee Han-Byoel HB Kim Kyu-Tae KT Ryu Han Suk HS Kim Sangmin S Lee Jeong Eon JE Park Yeon Hee YH Kan Zhengyan Z Han Wonshik W Park Woong-Yang WY

Nature communications 20170505

Single-cell transcriptome profiling of tumour tissue isolates allows the characterization of heterogeneous tumour cells along with neighbouring stromal and immune cells. Here we adopt this powerful approach to breast cancer and analyse 515 cells from 11 patients. Inferred copy number variations from the single-cell RNA-seq data separate carcinoma cells from non-cancer cells. At a single-cell resolution, carcinoma cells display common signatures within the tumour as well as intratumoral heterogen ...[more]

PMID: 28474673

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Project description:We report transcriptomes from 430 single glioblastoma cells isolated from 5 individual tumors and 102 single cells from gliomasphere cells lines generated using SMART-seq. In addition, we report population RNA-seq from the five tumors as well as RNA-seq from cell lines derived from 3 tumors (MGH26, MGH28, MGH31) cultured under serum free (CSC) and differentiated (FCS) conditions. This dataset highlights intratumoral heterogeneity with regards to the expression of de novo derived transcriptional modules and established subtype classifiers. Operative specimens from five glioblastoma patients (MGH26, MGH28, MGH29, MGH30, MGH31) were acutely dissociated, depleted for CD45+ inflammatory cells and then sorted as single cells (576 samples). Population controls for each tumor were isolated by sorting 2000-10000 cells and processed in parallel (5 population control samples). Single cells from two established cell lines, GBM6 and GBM8, were also sorted as single cells (192 samples). SMART-seq protocol was implemented to generate single cell full length transcriptomes (modified from Shalek, et al Nature 2013) and sequenced using 25 bp paired end reads. Single cell cDNA libraries for MGH30 were resequenced using 100 bp paired end reads to allow for isoform and splice junction reconstruction (96 samples, annotated MGH30L). Cells were also cultured in serum free conditions to generate gliomasphere cell lines for MGH26, MGH28, and MGH31 (CSC) which were then differentiated using 10% serum (FCS). Population RNA-seq was performed on these samples (3 CSC, 3 FCS, 6 total). The initial dataset included 875 RNA-seq libraries (576 single glioblastoma cells, 96 resequenced MGH30L, 192 single gliomasphere cells, 5 tumor population controls, 6 population libraries from CSC and FCS samples). Data was processed as described below using RSEM for quantification of gene expression. 5,948 genes with the highest composite expression either across all single cells combined (average log2(TPM)>4.5) or within a single tumor (average log2(TPM)>6 in at least one tumor) were included. Cells expressing less than 2,000 of these 5,948 genes were excluded. The final processed dataset then included 430 primary single cell glioblastoma transcriptomes, 102 single cell transcriptomes from cell lines(GBM6,GBM8), 5 population controls (1 for each tumor), and 6 population libraries from cell lines derived from the tumors (CSC and FCS for MGH26, MGH28 and MGH31). The final matrix (GBM_data_matrix.txt) therefore contains 5948 rows (genes) quantified in 543 samples (columns). Please note that the samples which are not included in the data processing are indicated in the sample description field.

Dataset Information

Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer.

Publications

Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer.

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