Selection for CD49A+ and CD26- cells in pancreatic islet-like clusters differentiated from human pluripotent stem cells improves their therapeutic activity in diabetic mice
Bibliographic record
Abstract
Abstract BackgroundCell therapy of diabetes aims at restoring the physiological control of blood glucose by transplantation of functional pancreatic islet cells. Human islets from post-mortem donations have shown efficiency but the demand for islets vastly exceeds the availability of donations. A potentially unlimited source of cells for such transplantations would be islet cells derived from in vitro differentiation of human pluripotent stem cells (hPSC), such as embryonic stem cells (hESC). The islet-like clusters (ILC) produced by the known differentiation protocols contain various cell populations. Among these, the beta cells that express both insulin and the transcription factor Nkx6.1 seem to be the most efficient to restore normoglycemia in diabetes animal models. Our aim was to find markers allowing selection of these efficient cells.MethodsFunctional Cell-Capture Screening (FCCS), using an array of antibodies to cell surface proteins, was used to identify markers that preferentially capture the cells expressing insulin, or expressing both insulin and Nkx6.1, from hESC-derived ILC cells. In order to test whether selection for such markers could improve cell therapy in diabetic mouse models, we used ILC produced from a clinical-grade line of hESC by a refined differentiation protocol adapted to up-scalable bioreactors. The ILC, dissociated to single cells, were fractionated by Magnetic Activated Cell Sorting (MACS) for presence of the marker. The sorted cells, re-aggregated into clusters, were encapsulated in microspheres made of alginate modified to reduce foreign body reaction. Implantation was done intraperitoneally in C57BL/6 immuno-competent mice that were made diabetic by prior injections of Streptozotocin (STZ).ResultsCD49A (integrin alpha1) was identified by FCCS as a marker for cells double positive (DP) for insulin (and C-peptide) as well as Nkx6.1 in ILC derived by hESC differentiation. After sorting by MACS with CD49A antibodies, the ILC fraction enriched in CD49A+ cells rapidly reduced glycemia when implanted in the diabetic mice, whereas mice receiving the CD49A depleted population remained highly diabetic. CD49A-enriched ILC cells also produced significantly higher levels of human C-peptide in mouse blood. Another marker, CD26 (DPP4, dipeptidyl peptidase-4), was identified by FCCS as binding insulin-expressing cells which are Nkx6.1-negative. Depletion of CD26+ cells followed by enrichment for CD49A+ cells increased DP cells to over 70%. After this double selection, the CD26 depleted/CD49A enriched ILC were more active than non-sorted ILC to reduce glycemia in the diabetic mice.ConclusionsRefining the composition of ILC differentiated from hPSC by negative selection to remove cells expressing CD26 and positive selection for CD49A expressing cells can enable more effective cell therapy of diabetes.
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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 | 0.001 |
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; 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".