Back

Dissociation protocols used for sarcoma tissues bias the transcriptome observed in single-cell and single-nucleus RNA sequencing

Truong, D. D.; Lamhamedi-Cherradi, S.-E.; Porter, R. W.; Krishnan, S.; Swaminathan, J.; Gibson, A. L.; Lazar, A. J.; Livingston, J. A.; Gopalakrishnan, V.; Gordon, N.; Daw, N. C.; Gorlick, R.; Ludwig, J. A.

2022-01-22 cancer biology
10.1101/2022.01.21.476982 bioRxiv
Show abstract

BackgroundSingle-cell RNA-seq has emerged as an innovative technology used to study complex tissues and characterize cell types, states, and lineages at a single-cell level. Classification of bulk tumors by their individual cellular constituents has also created new opportunities to generate single-cell atlases for many organs, cancers, and developmental models. Despite the tremendous promise of this technology, recent evidence studying epithelial tissues and diverse carcinomas suggests the methods used for tissue processing, cell disaggregation, and preservation can significantly bias gene expression and alter the observed cell types. To determine whether sarcomas - tumors of mesenchymal origin - are subject to the same technical artifacts, we profiled patient-derived tumor explants (PDXs) propagated from three aggressive subtypes: osteosarcoma, Ewing sarcoma (ES), desmoplastic small round cell tumor (DSRCT). Given the rarity of these sarcoma subtypes, we explored whether single-nuclei RNA-seq from more widely available archival frozen specimens could accurately be identified by gene expression signatures linked to tissue phenotype or pathognomonic fusion proteins. ResultsWe systematically assessed dissociation methods across different sarcoma subtypes. We compared gene expression from single-cell and single-nucleus RNA-sequencing of 125,831 whole-cells and nuclei from ES, DSRCT, and osteosarcoma PDXs. We detected warm dissociation artifacts in single-cell samples and gene length bias in single-nucleus samples. Classic sarcoma gene signatures were observed regardless of dissociation method. In addition, we showed that dissociation method biases can be computationally corrected. ConclusionsWe highlighted transcriptional biases, including warm dissociation and gene-length biases, introduced by the dissociation method for various sarcoma subtypes. This work is the first to characterize how the dissociation methods used for sc/snRNA-seq may affect the interpretation of the molecular features in sarcoma PDXs.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Laboratory Investigation
13 papers in training set
Top 0.1%
17.5%
2
Scientific Reports
3102 papers in training set
Top 8%
9.1%
3
PLOS ONE
4510 papers in training set
Top 28%
6.4%
4
Frontiers in Oncology
95 papers in training set
Top 0.6%
6.3%
5
Cancers
200 papers in training set
Top 2%
3.6%
6
Journal of Translational Medicine
46 papers in training set
Top 0.3%
2.7%
7
PLOS Computational Biology
1633 papers in training set
Top 12%
2.6%
8
Nature Communications
4913 papers in training set
Top 44%
2.6%
50% of probability mass above
9
PeerJ
261 papers in training set
Top 4%
2.4%
10
Biology Methods and Protocols
53 papers in training set
Top 0.6%
2.1%
11
Genome Medicine
154 papers in training set
Top 4%
1.9%
12
Cancer Research Communications
46 papers in training set
Top 0.3%
1.9%
13
Molecular Cancer Research
42 papers in training set
Top 0.3%
1.7%
14
eLife
5422 papers in training set
Top 42%
1.7%
15
npj Genomic Medicine
33 papers in training set
Top 0.4%
1.7%
16
Cell Reports Medicine
140 papers in training set
Top 4%
1.5%
17
npj Precision Oncology
48 papers in training set
Top 0.7%
1.5%
18
NAR Cancer
36 papers in training set
Top 0.1%
1.2%
19
Modern Pathology
21 papers in training set
Top 0.3%
1.1%
20
Acta Neuropathologica Communications
81 papers in training set
Top 0.9%
0.9%
21
Cancer Research
116 papers in training set
Top 3%
0.9%
22
Communications Biology
886 papers in training set
Top 21%
0.8%
23
Disease Models & Mechanisms
119 papers in training set
Top 2%
0.8%
24
Bone
22 papers in training set
Top 0.3%
0.8%
25
Cell Reports
1338 papers in training set
Top 32%
0.8%
26
Epigenetics
43 papers in training set
Top 1.0%
0.7%
27
Nucleic Acids Research
1128 papers in training set
Top 17%
0.7%
28
iScience
1063 papers in training set
Top 32%
0.7%
29
BMC Bioinformatics
383 papers in training set
Top 7%
0.7%
30
Cancer Epidemiology, Biomarkers & Prevention
17 papers in training set
Top 0.6%
0.7%