Reprogrammed Mouse Fibroblasts Differentiate into Cells of the Cardiovascular and Hematopoietic Lineages
Bibliographic record
Abstract
Forced expression of the four transcription factors Oct4, Sox2, c-Myc, and Klf4 is sufficient to confer a pluripotent state upon the murine fibroblast genome, generating induced pluripotent stem (iPS) cells. Although the differentiation potential of these cells is thought to be equivalent to that of embryonic stem (ES) cells, it has not been rigorously determined. In this study, we sought to identify the capacity of iPS cells to differentiate into Flk1-positive progenitors and their mesodermal progeny, including cells of the cardiovascular and hematopoietic lineages. Immunostaining of tissues from iPS cell-derived chimeric mice demonstrated that iPS cells could contribute in vivo to cardiomyocytes, smooth muscle cells, endothelial cells, and hematopoietic cells. To compare the in vitro differentiation potential of murine ES and iPS cells, we either induced embryoid body (EB) formation of each cell type or cultured the cells on collagen type IV (ColIV), an extracellular matrix protein that had been reported to direct murine ES cell differentiation to mesodermal lineages. EB formation and exposure to ColIV both induced iPS cell differentiation into cells that expressed cardiovascular and hematopoietic markers. To determine whether ColIV-differentiated iPS cells contained a progenitor cell with cardiovascular and hematopoietic differentiation potential, Flk1-positive cells were isolated by magnetic cell sorting and exposed to specific differentiation conditions, which induced differentiation into functional cardiomyocytes, smooth muscle cells, endothelial cells, and hematopoietic cells. Our data demonstrate that murine iPS cells, like ES cells, can differentiate into cells of the cardiovascular and hematopoietic lineages and therefore may represent a valuable cell source for applications in regenerative medicine. Disclosure of potential conflicts of interest is found at the end of this article.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".