<em>In Vitro</em> Differentiation of Human Mesenchymal Stem Cells into Functional Cardiomyocyte-like Cells
Bibliographic record
Abstract
Myocardial infarction and the subsequent ischemic cascade result in the extensive loss of cardiomyocytes, leading to congestive heart failure, the leading cause of mortality worldwide. Mesenchymal stem cells (MSCs) are a promising option for cell-based therapies to replace current, invasive techniques. MSCs can differentiate into mesenchymal lineages, including cardiac cell types, but complete differentiation into functional cells has not yet been achieved. Previous methods of differentiation were based on pharmacological agents or growth factors. However, more physiologically relevant strategies can also enable MSCs to undergo cardiomyogenic transformation. Here, we present a differentiation method using MSC aggregates on cardiomyocyte feeder layers to produce cardiomyocyte-like contracting cells. Human umbilical cord perivascular cells (HUCPVCs) have been shown to have a greater differentiation potential than commonly investigated MSC types, such as bone marrow MSCs (BMSCs). As an ontogenetically younger source, we investigated the cardiomyogenic potential of first-trimester (FTM) HUCPVCs compared to older sources. FTM HUCPVCs are a novel, rich source of MSCs that retain their in utero immunoprivileged properties when cultured in vitro. Using this differentiation protocol, FTM and term HUCPVCs achieved significantly increased cardiomyogenic differentiation compared to BMSCs, as indicated by the increased expression of cardiomyocyte markers (i.e., myocyte enhancer factor 2C, cardiac troponin T, heavy chain cardiac myosin, signal regulatory protein α, and connexin 43). They also maintained significantly lower immunogenicity, as demonstrated by their lower HLA-A expression and higher HLA-G expression. Applying aggregate-based differentiation, FTM HUCPVCs showed increased aggregate formation potential and generated contracting cells clusters within 1 week of co-culture on cardiac feeder layers, becoming the first MSC type to do so. Our results demonstrate that this differentiation strategy can effectively harness the cardiomyogenic potential of young MSCs, such as FTM HUCPVCs, and suggests that in vitro pre-differentiation could be a potential strategy to increase their regenerative efficacy in vivo.
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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) | 0.004 | 0.003 |
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".