Pannexin 1 Influences Lineage Specification of Human iPSCs
Bibliographic record
Abstract
Every single cell in the body communicates with nearby cells to locally organize activities with their neighbors and dysfunctional cell-cell communication can be detrimental during cell lineage commitment, tissue patterning and organ development. Pannexin channels (PANX1, PANX2, and PANX3) facilitate purinergic paracrine signaling through the passage of messenger molecules out of cells. PANX1 is widely expressed throughout the body and has recently been identified in human oocytes as well as 2 and 4-cell stage human embryos. Given its abundance across multiple adult tissues and its expression at the earliest stages of human development, we sought to understand whether PANX1 impacts human induced pluripotent stem cells (iPSCs) or plays a role in cell fate decisions. Western blot, immunofluorescence and flow cytometry reveal that PANX1 is expressed in iPSCs as well as all three germ lineages derived from these cells: ectoderm, endoderm, and mesoderm. PANX1 demonstrates differential glycosylation patterns and subcellular localization across the germ lineages. Using CRISPR-Cas9 gene ablation, we find that loss of PANX1 has no obvious impact on iPSC morphology, survival, or pluripotency gene expression. However, PANX1 gene knockout iPSCs exhibit apparent lineage specification bias under 3-dimensional spontaneous differentiation into the three germ lineages. Indeed, loss of PANX1 increases representation of endodermal and mesodermal populations in PANX1 knockout cells. Importantly, PANX1 knockout iPSCs are fully capable of differentiating toward each specific lineage when exposed to the appropriate external signaling pressures, suggesting that although PANX1 influences germ lineage specification, it is not essential to this process.
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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 | 0.001 |
| 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; 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".