Importance of evaluating protein glycosylation in pluripotent stem cell-derived cardiomyocytes for research and clinical applications
Bibliographic record
Abstract
Proper protein glycosylation is critical to normal cardiomyocyte physiology. Aberrant glycosylation can alter protein localization, structure, drug interactions, and cellular function. The in vitro differentiation of human pluripotent stem cells into cardiomyocytes (hPSC-CM) has become increasingly important to the study of protein function and to the fields of cardiac disease modeling, drug testing, drug discovery, and regenerative medicine. Here, we offer our perspective on the importance of protein glycosylation in hPSC-CM. Protein glycosylation is dynamic in hPSC-CM, but the timing and extent of glycosylation are still poorly defined. We provide new data highlighting how observed changes in hPSC-CM glycosylation may be caused by underlying differences in the protein or transcript abundance of enzymes involved in building and trimming the glycan structures or glycoprotein gene products. We also provide evidence that alternative splicing results in altered sites of glycosylation within the protein sequence. Our findings suggest the need to precisely define protein glycosylation events that may have a critical impact on the function and maturation state of hPSC-CM. Finally, we provide an overview of analytical strategies available for studying protein glycosylation and identify opportunities for the development of new bioinformatic approaches to integrate diverse protein glycosylation data types. We predict that these tools will promote the accurate assessment of protein glycosylation in future studies of hPSC-CM that will ultimately be of significant experimental and clinical benefit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".