Comparing the signaling and transcriptome profiling landscapes of human iPSC-derived and primary rat neonatal cardiomyocytes
Bibliographic record
Abstract
Abstract The inaccessibility of human cardiomyocytes significantly hindered years of cardiovascular research efforts. Post-mortem tissue or biopsies from diseased patients, which remain scarcely available, rendered it possible to study end-stage heart disease yet the inclusion of healthy human cardiac materials for basic science research was beyond reach. To overcome these limitations, non-human cell sources were used as proxies to study heart function and associated diseases. Rodent models became increasingly acceptable surrogates to model the human heart either in vivo or through in vitro cultures. More recently, due to concerns regarding animal to human translation, including cross-species differences, the use of human inducible stem cell derived cardiomyocytes presented a renewed opportunity. We think it necessary to conduct a comparative study, assessing cellular signalling through cardiac G protein-coupled receptors and bulk transcriptomics of traditional rat neonatal cardiomyocytes and human iPSC-CMs. Genetically-encoded biosensors were used to interrogate nuclear protein kinase A (PKA) and extracellular signal-regulated kinase 1/ 2 (ERK 1/2 ) in rat and human-derived cardiomyocyte populations. To increase data granularity, a single-cell analytical approach was conducted for an in-depth examination of existing differences between both in vitro cardiomyocyte models. Using automated high content microscopy, our analyses of nuclear PKA and ERK 1/2 signaling revealed distinct response clusters in rat and human CMs. In line with this, bulk RNA-seq demonstrated key differences regarding the expression patterns of GPCRs, G proteins and effectors. Overall, our study demonstrates that human stem cell derived models of the cardiomyocyte do provide significant advantages and should be taken advantage of.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".