Abstract 4706: Temperature-dependent transcription artifacts and cell population biases in scRNAseq data are minimized by tissue dissociation at low temperatures
Bibliographic record
Abstract
Abstract Single cell RNA sequencing (scRNAseq) is a powerful tool, particularly for studying complex biological systems, such as tumor heterogeneity and the tumor microenvironment, which may not be resolved by sequencing of bulk material. Nonetheless, it is not without limitations, which include the technical challenges of generating a high quality single cell suspension. Dissociation of tissue to single cell suspension requires mechanical and enzymatic disruption, and the effect of these methods on gene expression or cellular population bias has not been established. In this study, we examined the effects of enzymatic dissociation on cell population capture and transcriptional changes at single cell resolution in breast and ovarian cancer patient samples, patient-derived breast cancer xenografts and cultured cell lines. scRNAseq data showed that enzymatic dissociation of tissues at 37oC with collagenase resulted in significant induction of heat shock, stress and immediate response genes, which was conserved across all tissues. This gene expression induction was not observed when tissues were dissociated at 6oC with a protease derived from a Himalayan glacier soil bacterium. Moreover, dissociation of patient tumors at low temperature enhanced the abundance of rare cell populations, including B-cells, T-Cells and cytotoxic T-cells, which were significantly depleted following dissociation at 37oC. These biases resulting from standard sample preparation methods could significantly affect biological interpretation of scRNAseq data, and can be minimized by dissociation of tissues at low temperature. Note: This abstract was not presented at the meeting. Citation Format: Ciara H. O'Flanagan, Kieran R. Campbell, Farhia Kabeer, Allen Zhang, Jamie Lim, Sohrab P. Shah, Samuel Aparicio. Temperature-dependent transcription artifacts and cell population biases in scRNAseq data are minimized by tissue dissociation at low temperatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4706.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.052 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".