Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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

ABSTRACT: 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.

INSTRUMENT(S): Illumina NovaSeq 6000

ORGANISM(S): Homo sapiens

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PROVIDER: E-MTAB-9844 | biostudies-arrayexpress |

SECONDARY ACCESSION(S): ERP125488

REPOSITORIES: biostudies-arrayexpress

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Project description:Barcode swapping results in the mislabeling of sequencing reads between multiplexed samples on the new patterned flow cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays, especially for single-cell studies where many samples are routinely multiplexed together. The severity and consequences of barcode swapping for single-cell transcriptomic studies remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in each of two plate-based single-cell RNA sequencing datasets. We found that approximately 2.5% of reads were mislabeled between samples on the HiSeq 4000 machine, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Further- more, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10X Genomics experiments, exploiting the combinatorial complexity present in the data. This permits the continued use of cutting-edge sequencing machines for droplet-based experiments while avoiding the confounding effects of barcode swapping. This data repository contains the sequencing files associated with the droplet based scRNA-seq dataset in Griffiths et al. (2018). The data presented here should purely used for technical analysis, the biological motivation is nonetheless briefly described in the following: The mammary gland is a unique organ as it undergoes most of its development during puberty and adulthood. Characterising the hierarchy of the various mammary epithelial cells and how they are regulated in response to gestation, lactation and involution is important for understanding how breast cancer develops. Recent studies have used numerous markers to enrich, isolate and characterise the different epithelial cell compartments within the adult mammary gland. However, in all of these studies only a handful of markers were used to define and trace cell populations. Therefore, there is a need for an unbiased and comprehensive description of mammary epithelial cells within the gland at different developmental stages. To this end we used single cell RNA sequencing (scRNAseq) to determine the gene expression profile of individual mammary epithelial cells across four adult developmental stages; nulliparous, mid gestation, lactation and post weaning (full natural involution).

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

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