Human Multipotent Stromal Cell Secreted Effectors Accelerate Islet Regeneration
Bibliographic record
Abstract
Abstract Human multipotent stromal cells (hMSC) can induce islet regeneration after transplantation via the secretion of proteins that establish an islet regenerative niche. However, the identity of hMSC-secreted signals and the mechanisms by which pancreatic islet regeneration is induced remain unknown. Recently, mammalian pancreatic α-cells have been shown to possess considerable plasticity, and differentiate into β-like cells after near complete β-cell loss or overexpression of key transcriptional regulators. These studies have generated new excitement that islet regeneration during diabetes may be possible if we can identify clinically applicable stimuli to modulate these key regulatory pathways. Herein, we demonstrate that intrapancreatic-injection of concentrated hMSC-conditioned media (CM) stimulated islet regeneration without requiring cell transfer. hMSC CM-injection significantly reduced hyperglycemia, increased circulating serum insulin concentration, and improved glucose tolerance in streptozotocin-treated mice. The rate and extent of endogenous β-cell mass recovery was dependent on total protein dose administered and was further augmented by the activation of Wnt-signaling using GSK3-inhibition during CM generation. Intrapancreatic hMSC CM-injection immediately set in motion a cascade of regenerative events that included the emergence of proliferating insulin+ clusters adjacent to ducts, NKX6.1 expression in glucagon+ cells at days 1–4 suggesting the acquisition of β-cell phenotype by α-cells, and accelerated β-cell maturation with increased MAFA-expression for >1 month postinjection. Discovery and validation of islet regenerative hMSC-secreted protein may lead to the development of cell-free regenerative therapies able to tip the balance in favor of β-cell regeneration versus destruction during diabetes. Stem Cells 2019;37:516–528
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".