tooluniverse-stem-cell-organoid

What this skill does

# Stem Cell & Organoid Research

Pipeline for investigating stem cell biology, iPSC characterization, organoid models, and cell differentiation using ToolUniverse tools.

## Reasoning Strategy

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**:
1. **Marker-based identity** — stem cell identity is defined by marker expression profiles (OCT4, SOX2, NANOG for pluripotency)
2. **Differentiation is a trajectory** — not a binary state; analyze intermediate progenitor stages
3. **Organoid ≠ organ** — organoids recapitulate some but not all organ features; always note limitations
4. **Species matters** — mouse and human stem cells differ in signaling requirements
5. **Evidence grading** — T1: validated in clinical iPSC study, T2: functional assay (teratoma, engraftment), T3: marker expression only, T4: computational prediction

---

## Core Tools

| 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 |

---

## Workflow

```
Phase 0: Define the Question
  Pluripotency? Differentiation? Disease modeling? Drug screening?
    |
Phase 1: Cell Identity & Markers
  CellMarker → pluripotency/lineage markers → verify identity
    |
Phase 2: Differentiation Pathways
  KEGG/Reactome → WNT, Notch, BMP, FGF signaling
    |
Phase 3: Atlas & Dataset Discovery
  CellxGene/HCA → reference datasets for target cell type
    |
Phase 4: Disease Modeling (if applicable)
  OpenTargets → disease genes → organoid recapitulation assessment
    |
Phase 5: Report
  Evidence-graded findings with clinical translation potential
```

### Phase 1: Cell Identity & Markers

**Pluripotency 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.

### Phase 2: Differentiation Pathways

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) |

### Phase 3: Atlas & Dataset Discovery

```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")
```

### Phase 4: Organoid Model Assessment

**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 |

---

## Evidence Grading

| 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 |

### Synthesis Questions

1. **Is the cell identity verified?** (co-expression of 3+ pluripotency markers, or lineage-appropriate markers)
2. **Is the differentiation protocol reproducible?** (published, peer-reviewed, with quantified efficiency)
3. **Does the organoid model the disease?** (patient-derived iPSC shows disease phenotype in organoid)
4. **What are the translational barriers?** (scalability, maturation, immune compatibility, tumorigenicity)
5. **What's the best reference dataset?** (CellxGene atlas for comparison)

---

## Limitations

- **No organoid protocol database** — protocols are scattered across publications; use PubMed search
- **Maturation gap** — most organoids resemble fetal, not adult tissue; always note maturation state
- **Batch variability** — iPSC-derived cells vary between passages and donor lines
- **No direct culture tools** — this skill analyzes published data and designs experiments; it does not contr