Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE85755
PROVIDER: PRJNA339266 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR4042550_1.fastq.gz | Fastqsanger.gz | |||
| SRR4042550_2.fastq.gz | Fastqsanger.gz | |||
| SRR4042560_1.fastq.gz | Fastqsanger.gz | |||
| SRR4042560_2.fastq.gz | Fastqsanger.gz | |||
| SRR4042570_1.fastq.gz | Fastqsanger.gz |
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Project description:In many gene expression studies, cells are extracted by tissue dissociation and Fluorescence-Activated Cell Sorting (FACS), but the effect of these protocols on cellular transcriptomes is not well characterized and often ignored. Here, we applied single-cell mRNA sequencing (scRNA-seq) to muscle stem cells, and unexpectedly found a subpopulation that is strongly affected by the widely-used dissociation protocol that we employed. One implication of this finding is that several published transcriptomics studies may need to be reinterpreted. Importantly, we detected similar subpopulations in other single-cell datasets, suggesting that cells from other tissues might be affected by this artefact as well.
2017-09-28 | GSE85755 | GEO
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.
2023-06-30 | E-MTAB-9844 | biostudies-arrayexpress
Project description:Single Cell Sequencing Reveals Artifactual Gene Expression in Microglia in Response to Tissue Dissociation
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Project description:Single Cell Sequencing of FACS purified microglia/myeloid cells isolated with different dissociation protocols
2020-12-01 | GSE152183 | GEO
Project description:Tissue dissociation, mechanical and/or enzymatic, is a method of disaggregating 3D connective tissue to release cells for downstream single cell analysis. Without proper controls for tissue dissociation experiments, potential artifacts can be mistaken for baseline tissue data. We hypothesized that enzymatic (collagenase/hyaluronidase) compared to mechanical dissociation leads to dissociated related artifacts not found in endogenous tumor tissue. To distinguish artifacts between enzymatic and mechanical dissociation, we compared separate enzymatic and mechanical dissociated samples with baseline samples from healthy and tumor patient whole blood, melanoma and head and neck squamous cell carcinoma (HNSCC) tumor tissues in flow cytometry, RNA sequencing and CODEX experiments. In bulk sorted tumor tissue, there is a significant difference between enzymatic and mechanical digested samples in cell stress, heat shock proteins and cell death related genes. These genes were also significantly upregulated in enzymatic over mechanical samples in the single cell RNA sequencing HNSCC tissue data. Fibroblasts and CD39+CD103+ CD8 T cells from tumor tissue, appear to require enzymatic digestion for efficient release from tissue compared to the mechanical digested samples. CD39+CD103+ CD8 tumor infiltrating lymphocytes (TILs) show different phenotypes depending on dissociation technique and tumor tissue. In tumor tissue, CD3+CD8+CD69+ T cells and gene expression were elevated in the enzymatic samples over the mechanical and baseline frozen tissue. Tissue dissociation techniques and tissue analysis from downstream applications have the potential for dissociation related artifacts that can mislead both protein and RNA sequencing data analysis in cancer research.
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Project description:Background: Single-cell RNA sequencing has been widely adopted to estimate the cellular composition of heterogeneous tissues and obtain transcriptional profiles of individual cells. Multiple approaches for optimal sample dissociation and storage of single cells have been proposed as have single-nuclei profiling methods. What has been lacking is a systematic comparison of their relative biases and benefits. Results: Here, we compare gene expression and cellular composition of single cell suspensions prepared from adult mouse kidney using two tissue dissociation protocols. For each sample we also compare fresh cells to cryopreserved and methanol-fixed cells. Lastly, we compare this single-cell data to that generated using three single-nucleus RNA sequencing workflows. Our data confirms prior reports that digestion on ice avoids the stress response observed with 37°C dissociation. It also reveals cell types more abundant either in the cold or warm dissociations that may represent populations that require gentler or harsher conditions to be released intact. For cell storage, cryopreservation of dissociated cells results in a major loss of epithelial cell types; in contrast, methanol fixation maintains the cellular composition but suffers from ambient RNA leakage. Finally, cell type composition differences are observed between single-cell and single-nucleus RNA sequencing libraries. In particular, we note an under-representation of T, B and NK lymphocytes in the single-nucleus libraries. Conclusions: Systematic comparison of recovered cell types and their transcriptional profiles across the workflows has highlighted protocol-specific biases and thus enables researchers starting single cell experiments to make an informed choice.
2020-05-11 | GSE141115 | GEO
Project description:The use of single-cell technologies for clinical applications requires disconnecting sampling from downstream processing steps. Early sample preservation can further increase robustness and reproducibility by avoiding artifacts introduced during specimen handling. We present FixNCut, a methodology for the reversible fixation of tissue followed by dissociation that overcomes current limitations. We applied FixNCut to human and mouse tissues to demonstrate the preservation of RNA integrity, sequencing library complexity, and cellular composition, while diminishing stress-related artifacts. Besides single-cell RNA sequencing, FixNCut is compatible with multiple single-cell and spatial technologies, making it a versatile tool for robust and flexible study designs.
2024-03-11 | GSE229944 | GEO
Project description:ACME dissociation: a versatile cell fixation-dissociation method for single-cell transcriptomics
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Project description:ACME dissociation: a versatile cell fixation-dissociation method for single-cell transcriptomics
| PRJNA631686 | ENA
Project description:Tissue dissociation, mechanical and/or enzymatic, is a method of disaggregating 3D connective tissue to release cells for downstream single cell analysis. Without proper controls for tissue dissociation experiments, potential artifacts can be mistaken for baseline tissue data. We hypothesized that enzymatic (collagenase/hyaluronidase) compared to mechanical dissociation leads to dissociated related artifacts not found in endogenous tumor tissue. To distinguish artifacts between enzymatic and mechanical dissociation, we compared separate enzymatic and mechanical dissociated samples with baseline samples from healthy and tumor patient whole blood, melanoma and head and neck squamous cell carcinoma (HNSCC) tumor tissues in flow cytometry, RNA sequencing and CODEX experiments. In bulk sorted tumor tissue, there is a significant difference between enzymatic and mechanical digested samples in cell stress, heat shock proteins and cell death related genes. These genes were also significantly upregulated in enzymatic over mechanical samples in the single cell RNA sequencing HNSCC tissue data. Fibroblasts and CD39+CD103+ CD8 T cells from tumor tissue, appear to require enzymatic digestion for efficient release from tissue compared to the mechanical digested samples. CD39+CD103+ CD8 tumor infiltrating lymphocytes (TILs) show different phenotypes depending on dissociation technique and tumor tissue. In tumor tissue, CD3+CD8+CD69+ T cells and gene expression were elevated in the enzymatic samples over the mechanical and baseline frozen tissue. Tissue dissociation techniques and tissue analysis from downstream applications have the potential for dissociation related artifacts that can mislead both protein and RNA sequencing data analysis in cancer research.
2023-09-19 | GSE243360 | GEO