Isolating Nuclei From Frozen Human Heart Tissue for Single‐Nucleus RNA Sequencing
Bibliographic record
Abstract
Heart disease is the leading cause of global morbidity and mortality. This is in part because, despite an abundance of animal and in vitro models, it has been a challenge to date to study human heart tissue with sufficient depth and resolution to develop disease-modifying therapies for common cardiac conditions. Single-nucleus RNA sequencing (snRNA-seq) has emerged as a powerful tool capable of analyzing cellular function and signaling in health and disease, and has already contributed to significant advances in areas such as oncology and hematology. Employing snRNA-seq technology on flash-frozen human tissue has the potential to unlock novel disease mechanisms and pathways in any organ. Studying the human heart using snRNA-seq is a key priority for the field of cardiovascular sciences; however, progress to date has been slowed by numerous barriers. One key challenge is the fact that the human heart is very resistant to shearing and stress, making tissue dissociation and nuclear isolation difficult. Here, we describe a tissue dissociation method allowing the efficient and cost-effective isolation of high-quality nuclei from flash-frozen human heart tissue collected in surgical operating rooms. Our protocol addresses the challenge of nuclear isolation from human hearts, enables snRNA-seq of the human heart, and paves the way for an improved understanding of the human heart in health and disease. Ultimately, this will be key to uncovering signaling pathways and networks amenable to therapeutic intervention and the development of novel biomarkers and disease-modifying therapies. © 2022 Wiley Periodicals LLC. Basic Protocol: Human heart tissue dissociation and nuclear isolation for snRNA-seq.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.036 |
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".