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Get Started Free →Stem cell, iPSC, and organoid research — pluripotency markers, differentiation protocol pathways, lineage commitment factors, organoid model selection. Use for iPSC characterization, differentiation protocol design via developmental-pathway recapitulation, and organoid-model selection for disease modeling.
.claude/skills/mims-harvard-tooluniverse-stem-cell-organoid/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 111% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 253% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 66% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 157% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 198% | 0% |
Pipeline for investigating stem cell biology, iPSC characterization, organoid models, and cell differentiation using ToolUniverse tools.
Stem cell differentiation follows developmental biology — to make any target cell type from iPSCs, the protocol must mimic the embryonic signaling pathway that generates that cell type in vivo. For neural induction: inhibit BMP and TGF-beta (dual SMAD inhibition). For cardiomyocytes: activate WNT then inhibit WNT. For pancreatic beta cells: activate Activin/Nodal → FGF → Notch inhibition → BMP in sequence. The order and timing of growth factors matters critically — adding BMP4 during neural induction will redirect cells toward mesoderm. Mouse and human stem cells differ in their signaling requirements (LIF/STAT3 for mouse naive pluripotency; FGF/ERK for human primed pluripotency), so protocols are not interchangeable. Organoids recapitulate some but not all organ features — always assess maturation state (fetal vs. adult gene expression) before drawing disease-relevance conclusions.
LOOK UP DON'T GUESS: Do not assume which markers define a target cell type or which signaling pathway drives differentiation — query CellMarker_search_by_cell_type for markers and kegg_search_pathway for the relevant pathway. Do not assume organoid fidelity; look up published CellxGene or HCA atlas data for comparison.
Key principles:
| Tool | Use For | |------|---------| | CELLxGENE_get_census_versions | Discover CELLxGENE Census release versions; then use CELLxGENE_get_cell_metadata / CELLxGENE_get_expression_data for specific cells / genes. Requires cellxgene-census package (pip install cellxgene-census). May not be installed by default. | | CellMarker_search_by_cell_type | Cell type marker genes. Requires operation="search_by_cell_type", cell_name= (NOT cell_type=) | | CellMarker_search_by_gene | Which cell types express a gene. Requires operation="search_by_gene", gene_symbol= | | hca_search_projects | Human Cell Atlas organoid/development projects | | GEO_search_rnaseq_datasets | Find stem cell RNA-seq datasets | | kegg_search_pathway | Differentiation signaling pathways (WNT, Notch, Hedgehog) | | ReactomeAnalysis_pathway_enrichment | Pathway analysis of stem cell gene sets | | STRING_get_network | Pluripotency/differentiation gene networks | | OpenTargets_get_associated_targets_by_disease_efoId | Disease genes for organoid disease modeling | | PubMed_search_articles | Stem cell and organoid literature | | search_clinical_trials | iPSC-based clinical trials |
Phase 0: Define the Question
Pluripotency? Differentiation? Disease modeling? Drug screening?
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Phase 1: Cell Identity & Markers
CellMarker → pluripotency/lineage markers → verify identity
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Phase 2: Differentiation Pathways
KEGG/Reactome → WNT, Notch, BMP, FGF signaling
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Phase 3: Atlas & Dataset Discovery
CellxGene/HCA → reference datasets for target cell type
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Phase 4: Disease Modeling (if applicable)
OpenTargets → disease genes → organoid recapitulation assessment
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Phase 5: Report
Evidence-graded findings with clinical translation potentialPluripotency markers (must be co-expressed): OCT4 (POU5F1), SOX2, NANOG (essential); SSEA-4, TRA-1-60 (human surface markers). KLF4 and MYC are Yamanaka factors but also expressed in somatic cells — do not rely on them alone. Use CellMarker_search_by_cell_type to retrieve the full validated marker set for any target cell type.
Lineage markers: Ectoderm → PAX6/SOX1 (early), MAP2/TUBB3 (neurons); Mesoderm → TBXT/MIXL1 (early), CD34 (blood); Endoderm → SOX17/FOXA2 (early), PDX1/NKX6.1 (pancreas). Retrieve current marker lists from CellMarker rather than relying on memory.
Key signaling pathways for directed differentiation:
| Pathway | KEGG ID | Role in Stem Cells | Common Modulators | |---------|---------|-------------------|-------------------| | WNT signaling | hsa04310 | Pluripotency maintenance (canonical) vs differentiation (non-canonical) | CHIR99021 (activator), IWP-2 (inhibitor) | | Notch signaling | hsa04330 | Lateral inhibition, fate decisions | DAPT (gamma-secretase inhibitor) | | BMP/TGF-beta | hsa04350 | Mesoderm/trophectoderm induction | BMP4 (activator), Noggin (inhibitor) | | FGF signaling | hsa04010 | Self-renewal, neural induction | bFGF (activator), SU5402 (inhibitor) | | Hedgehog | hsa04340 | Patterning, organoid maturation | SAG (activator), cyclopamine (inhibitor) | | Hippo/YAP | hsa04390 | Mechanotransduction, organoid size | Verteporfin (YAP inhibitor) |
python# Find stem cell single-cell datasets CELLxGENE_get_census_versions() # discover available Census releases, then use CELLxGENE_get_cell_metadata / CELLxGENE_get_expression_data hca_search_projects(query="organoid") GEO_search_rnaseq_datasets(query="iPSC differentiation neural", organism="Homo sapiens")
Organoid fidelity scoring — how well does the organoid recapitulate the organ?
| Feature | High Fidelity (3) | Moderate (2) | Low (1) | |---------|------------------|-------------|---------| | Cell type diversity | All major cell types present | Most cell types, missing rare ones | Only 1-2 cell types | | Architecture | Self-organized, correct spatial arrangement | Partial organization | Disorganized aggregate | | Function | Measurable organ function (secretion, contraction, electrophysiology) | Some functional markers | Marker expression only | | Maturation | Adult-like gene expression profile | Fetal-like | ESC-like (failed differentiation) | | Disease relevance | Recapitulates patient phenotype | Some disease features | No disease phenotype |
| Grade | Criteria | Example | |-------|---------|---------| | T1 | Clinical iPSC study or approved therapy | iPSC-derived RPE for macular degeneration (Mandai 2017) | | T2 | Functional validation (teratoma, engraftment, drug response) | Organoid drug screening with patient-specific response | | T3 | Marker expression + morphology | iPSC colony expressing OCT4/SOX2/NANOG | | T4 | Computational prediction or single-marker evidence | Predicted pluripotent by gene expression classifier |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-20 | fail→fail | 13,322 | 22,725 | +71% | 1 | 1 | 0% | 1,965 | 5,771 | +194% | 0 | 0 | — |
case-01 | fail→fail | 32,087 | 8,665 | -73% | 1 | 1 | 0% | 6,224 | 2,836 | -54% | 0 | 0 | — |
case-02 | fail→fail | 33,465 | 17,344 | -48% | 1 | 1 | 0% | 5,840 | 2,703 | -54% | 0 | 0 | — |
case-03 | fail→fail | 34,979 | 9,052 | -74% | 1 | 1 | 0% | 6,216 | 2,668 | -57% | 0 | 0 | — |
case-04 | fail→pass | 7,337 | 2,367 | -68% | 1 | 1 | 0% | 1,171 | 2,468 | +111% | 0 | 0 | — |
case-05 | pass→fail | 10,214 | 5,762 | -44% | 1 | 1 | 0% | 1,916 | 2,453 | +28% | 0 | 0 | — |
case-21 | pass→pass | 19,365 | 27,875 | +44% | 1 | 1 | 0% | 2,788 | 6,170 | +121% | 0 | 0 | — |
case-06 | fail→pass | 4,022 | 2,136 | -47% | 1 | 1 | 0% | 693 | 2,446 | +253% | 0 | 0 | — |
case-07 | pass→pass | 6,978 | 9,689 | +39% | 1 | 1 | 0% | 1,307 | 3,337 | +155% | 0 | 0 | — |
case-08 | pass→fail | 8,905 | 7,163 | -20% | 1 | 1 | 0% | 1,672 | 2,594 | +55% | 0 | 0 | — |
case-09 | pass→fail | 10,250 | 6,750 | -34% | 1 | 1 | 0% | 1,994 | 2,633 | +32% | 0 | 0 | — |
case-10 | pass→pass | 13,759 | 17,566 | +28% | 1 | 1 | 0% | 2,318 | 4,476 | +93% | 0 | 0 | — |
case-11 | pass→fail | 3,172 | 4,542 | +43% | 1 | 1 | 0% | 585 | 2,354 | +302% | 0 | 0 | — |
case-12 | pass→fail | 5,292 | 4,557 | -14% | 1 | 1 | 0% | 992 | 2,279 | +130% | 0 | 0 | — |
case-13 | fail→pass | 11,237 | 7,124 | -37% | 1 | 1 | 0% | 2,035 | 3,375 | +66% | 0 | 0 | — |
case-14 | pass→fail | 16,015 | 6,603 | -59% | 1 | 1 | 0% | 2,864 | 2,357 | -18% | 0 | 0 | — |
case-15 | pass→fail | 13,794 | 6,646 | -52% | 1 | 1 | 0% | 2,471 | 2,428 | -2% | 0 | 0 | — |
case-16 | pass→pass | 7,579 | 5,392 | -29% | 1 | 1 | 0% | 1,001 | 2,903 | +190% | 0 | 0 | — |
case-17 | fail→fail | 9,523 | 6,212 | -35% | 1 | 1 | 0% | 1,506 | 2,448 | +63% | 0 | 0 | — |
case-18 | fail→pass | 7,604 | 4,397 | -42% | 1 | 1 | 0% | 1,072 | 2,756 | +157% | 0 | 0 | — |
case-19 | fail→pass | 6,847 | 5,330 | -22% | 1 | 1 | 0% | 973 | 2,900 | +198% | 0 | 0 | — |
case-22 | fail→fail | 18,357 | 10,750 | -41% | 1 | 1 | 0% | 3,235 | 2,999 | -7% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 10 counted toward the lift figure. The other 12 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -9 percentage points is the difference between those two pass rates over the 10 comparable cases. 10 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.