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Single-cell analysis of dissociation-induced compositional and transcriptional bias in human breast tissue samples

ABSTRACT: Single-cell analysis of dissociation-induced compositional and transcriptional bias in human breast tissue samples

PROVIDER: PRJEB41675 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR4904490_1.fastq.gz	Fastqsanger.gz
	ERR4904490_2.fastq.gz	Fastqsanger.gz
	ERR4904487_1.fastq.gz	Fastqsanger.gz
	ERR4904487_2.fastq.gz	Fastqsanger.gz
	ERR4904488_1.fastq.gz	Fastqsanger.gz

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Project description:The use of single cell transcriptomics provides previously inaccessible insights into cellular heterogeneity and lineage dynamics of the mammary gland allowing for a better understanding of normal mammary gland function as well as breast cancer initiation and progression. Especially for human mammary gland research, limited tissue accessibility and restriction to ex vivo techniques reinforce the importance of reliable cross-study comparison of single-cell transcriptomic data. However, it is unclear to what extent differences in breast tissue dissociation influence composition and transcriptomic profiles of isolated cells. Here, we used single-cell RNA sequencing to compare human mammary cell populations isolated from a single mammoplasty patient by varying enzymatic dissociation protocols differing in duration (3 or 16 hours) and agitation speed (10 rpm or 100 rpm). Protocol A (3 hours, 100 rpm), protocol B (16 hours, 100 rpm) and protocol C (16 hours, 10 rpm) were used to extract cell fragments from tissue which were either frozen down directly or further dissociated into single cells prior to cryopreservation. Samples were then prepared together for 10x scRNA-sequencing, where the fragments were defrosted and freshly dissociated and were loaded together with the defrosted single cells. From each of the protocols we sequenced similar numbers of cells isolated from fragments (Protocol A: 3,586 cells, Protocol B: 2,809 cells and Protocol C: 4,796 cells), finding an average of 7,427 unique molecular identifiers and 2,445 genes detected per cell. Overall, we detected a greater abundance and heterogeneity of stromal cell types, such as fibroblasts and endothelial cells at a lower agitation speed. Moreover, an extended duration of tissue dissociation governed an overall cellular oxidative stress response together with a downregulation of breast cancer associated genes and a cell-type specific downregulation of lineage markers. Thus, our systematic analysis of dissociation-induced compositional and transcriptional bias in human breast tissue samples yields useful information to avoid misinterpretation of cellular heterogeneity and lineage composition.

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Single-cell analysis of dissociation-induced compositional and transcriptional bias in human breast tissue samples

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