Virtual Embryo — atlas data + knowledge graph

Virtual Embryo — atlas data + knowledge graph

Virtual Embryo is an AI-augmented, multi-modal atlas of mouse (and human) embryonic development built in the Xiaojie Qiu lab. It unifies, under one anatomical reference frame: a knowledge graph (genes · anatomy · developmental stages · expression · disease · drugs · papers, ~1.8M facts in Neo4j) and a 3D data atlas (eMouseAtlas OPT/histology reference volumes + anatomy meshes, and 3D spatial-transcriptomics reconstructions).

This skill lets you answer developmental-biology questions from the KG and pull atlas datasets and render them in 3D with the volume3d / spatial3d LiveView viewers.

The public interfaces (read-only, no key)

All reads are open (no auth). The four write endpoints (/kg/cypher_write, /kg/submit_extraction, …) need an admin key and are not for general use. Quick health check: curl https://kg.virtualembryo.ai/healthz → {"ok":true}.

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1. Knowledge graph — query the developmental KG

Hit the endpoints with plain HTTP (requests). All return JSON.

import requests
KG = "https://kg.virtualembryo.ai/kg"

# Resolve a name → canonical entity (gene / anatomy / stage / disease …)
hits = requests.get(f"{KG}/search", params={"q": "Sox2", "limit": 5}).json()
# → {"results":[{"iri":"http://identifiers.org/mgi/MGI:98364","label":"Sox2",
#                "type":".../Gene","match_kind":"exact"}]}
iri = hits["results"][0]["iri"]

# One-hop details (type, synonyms, direct relations)
requests.get(f"{KG}/entity", params={"iri": iri}).json()

# Where is a gene expressed? (curated anatomy × stage, from paper extractions)
requests.get(f"{KG}/expression", params={"gene": "Sox2", "stage": "TS17"}).json()
# → {"expressions":[{"anatomy_label":"neural tube","stage":"TS17",
#                    "intensity":...,"paper_doi":...,"confidence":...}], ...}

# Neighbours of an entity, optionally one relation type
requests.get(f"{KG}/expand", params={"iri": iri, "rel": "EXPRESSED_IN"}).json()

# A subgraph for reasoning / a graph view (anchor on a stage or seed entity)
requests.get(f"{KG}/subgraph", params={"stage": "TS17", "limit": 200}).json()
requests.get(f"{KG}/subgraph", params={"seed": iri, "limit": 100}).json()

# Read-only Cypher escape hatch (write keywords are blocked server-side)
requests.post(f"{KG}/cypher", json={
    "cypher": "MATCH (g:Gene)-[:EXPRESSED_IN]->(a:Anatomy {name:'neural tube'}) "
              "RETURN DISTINCT g.name LIMIT 50"}).json()

Endpoints (all GET unless noted): search(q,limit,species) · entity(iri) · expand(iri,rel,limit) · subgraph(stage|seed|type|curated| staging,limit) · expression(gene,stage,limit) · dataset(id) · schema_graph · stats · resolve_entity(text,type) · known_papers · discover/pubmed(q,year_from) · discover/biorxiv(days_back, category) · POST cypher(cypher,params,limit).

KG schema (so you can write meaningful queries)

Node labels (every node also has :Entity; key props iri, name, synonyms, species):

Relationship types: EXPRESSED_IN (Gene→Anatomy/CellType, with qual_at_stage, qual_intensity), MARKER_FOR, REGULATES, CO_EXPRESSED_WITH, PART_OF/SUBCLASS_OF/DEVELOPS_FROM (anatomy hierarchy), AT_STAGE/COVERS_STAGES, IN_DATASET/USES_ASSAY, DESCRIBED_IN (→Paper), CAUSES_PHENOTYPE/CAUSES_DISEASE/DISEASE_MODEL_OF, EQUIVALENT_TO (cross-vocab). Edges carry paper_doi, confidence, evidence_span, qual_at_stage.

Stages: mouse Theiler TS9–TS27, human Carnegie CS01–CS23 / PCW. Rough mouse age map: E7.5≈TS11, E8.5≈TS13, E9.5≈TS15, E10.5≈TS17, E11.5≈TS19, E12.5≈TS20, E13.5≈TS21, E14.5≈TS23 (use search/resolve_entity to confirm).

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2. Atlas catalog — find datasets to visualise

The KG describes facts; the renderable data is listed in the catalog.

import requests
cat = requests.get("https://kg.virtualembryo.ai/index.json").json()
cat.keys()  # stages, samples, spatial_datasets, singlecell_datasets, *_by_stage

• samples (72) — eMouseAtlas reference volumes + anatomy meshes. Fields: ema_code, theiler_stage, voxel_um, zarr_path (OME-NGFF volume, e.g. samples/ema10/images/reference.ome.zarr), mesh_glb_path, has_anatomy_mesh. → render with volume3d.
• spatial_datasets (64) — 3D / 2D spatial transcriptomics (Stereo-seq MOSTA sections, Spateo 3D reconstructions, digital embryos). Fields: name, species, stage_xref, n_cells, n_genes, technology, spatial_ndim (2 or 3), default_color_obs, default_spatial_key, path (the .spatial.zarr, e.g. datasets/spatial/digiembryo_e7_5_rep1.spatial.zarr), paper_doi. → render with spatial3d.

Filter the catalog in code, e.g. 3D spatial datasets for E9.5:

e95 = [s for s in cat["spatial_datasets"]
       if s["spatial_ndim"] == 3 and "e9" in s["name"].lower()]

https://kg.virtualembryo.ai/kg/stats gives headline counts (80k genes, 191 datasets, …).

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3. Visualise an atlas dataset in 3D

The viewers (volume3d, spatial3d) load a zarr the LiveView data server serves with CORS. The public data host (tiles.virtualembryo.org) has no CORS, so the browser can't fetch it directly — read it server-side and re-serve it locally. (On a machine that already has the atlas checked out locally, just serve_local_data the local path and skip the fetch.)

Spatial transcriptomics → spatial3d

A VE .spatial.zarr is already in spatial3d's format. Read it over HTTP with zarrita/zarr (no CORS needed server-side), write a local copy, serve, open. See the spatial3d skill for the full viewer + the write_spatial_zarr recipe; this fetches the source for it:

import json, requests, numpy as np, zarr, anndata as ad, pandas as pd
from scipy.sparse import csr_matrix

def fetch_ve_spatial(name, out="/workspace", max_cells=150_000):
    """Pull a VE spatial dataset → an AnnData (subsampled if huge)."""
    cat = requests.get("https://kg.virtualembryo.ai/index.json").json()
    rec = next(s for s in cat["spatial_datasets"] if s["name"] == name)
    url = f"https://tiles.virtualembryo.org/{rec['path']}"
    g = zarr.open_group(url, mode="r")              # remote read, no CORS needed
    N = rec["n_cells"]
    idx = np.arange(N)
    if N > max_cells:                               # subsample big reconstructions
        idx = np.sort(np.random.default_rng(0).choice(N, max_cells, replace=False))
    coords = np.asarray(g[f"obsm/{rec['default_spatial_key']}"][:])[idx].astype("float32")
    co = rec["default_color_obs"]
    codes = np.asarray(g[f"obs/{co}"][:])[idx]
    cats = list(g[f"obs/{co}"].attrs["categories"])
    X = csr_matrix((np.asarray(g["X/data"][:]), np.asarray(g["X/indices"][:]),
                    np.asarray(g["X/indptr"][:])), shape=(N, rec["n_genes"]))[idx]
    genes = requests.get(f"{url}/gene_symbols.json").json()
    a = ad.AnnData(X=X, obs=pd.DataFrame({"cell_type": pd.Categorical.from_codes(
        np.clip(codes, -1, len(cats) - 1), categories=cats)}), var=pd.DataFrame(index=genes))
    a.obsm["spatial"] = coords
    try:
        a.obsm["X_umap"] = np.asarray(g["obsm/X_umap"][:])[idx].astype("float32")
    except Exception:
        pass
    a.write_h5ad(f"{out}/{name}.h5ad")
    return f"{out}/{name}.h5ad"

# then: write_spatial_zarr(anndata.read_h5ad(path), out_zarr)  # from spatial3d.md
#       url = serve_local_data(out_zarr)
#       open_live_view(view_type="spatial3d", state={"url": url, "colorBy":"cluster"})

Note: big reconstructions (e9_5_embryo 646k, e11_5_embryo 7M cells) are slow to pull whole over HTTP — keep max_cells modest, or prefer a digiembryo_* (3D, ~8–27k cells) or a MOSTA section for a quick render.

Reference volume → volume3d

import requests, numpy as np, zarr
def fetch_ve_volume(ema_code, out="/workspace"):
    cat = requests.get("https://kg.virtualembryo.ai/index.json").json()
    rec = next(s for s in cat["samples"] if s["ema_code"].lower() == ema_code.lower())
    g = zarr.open_group(f"https://tiles.virtualembryo.org/{rec['zarr_path']}", mode="r")
    vol = np.asarray(g["0"][:])     # finest level [Z,Y,X]; coarsen if very large
    # then write_ome_zarr_v2(vol, out_zarr, voxel=rec.get("voxel_um", (1,1,1)))  # volume3d.md
    return vol
# open_live_view(view_type="volume3d", state={"url": serve_local_data(out_zarr), "mode":"iso"})

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4. End-to-end examples

"What is Sox2 and where is it expressed in the mouse embryo?" search("Sox2") → IRI → entity(iri) (what it is) → expression(gene="Sox2") (anatomy × stage rows). Summarise the expression domains + cite paper_doi.

"Show me an E9.5 mouse embryo in 3D coloured by cell type, then by Sox2." Pick a 3D dataset near E9.5 from spatial_datasets (e.g. a digiembryo_* or e9_5_embryo) → fetch_ve_spatial → write_spatial_zarr → serve_local_data → open_live_view("spatial3d", state={url, colorBy:"cluster"}) → then live_view_update(view_id, {"colorBy":"gene","gene":"Sox2","colormap":"plasma"}).

"Which genes are expressed in the neural tube?" resolve_entity("neural tube", type="anatomy") → EMAPA IRI → POST cypher MATCH (g:Gene)-[:EXPRESSED_IN]->(a:Anatomy {iri:$iri}) RETURN DISTINCT g.name (params {iri}), or include sub-structures with -[:PART_OF*0..2]->.

"Render the EMA10 reference embryo volume." fetch_ve_volume("EMA10") → write_ome_zarr_v2 → serve_local_data → open_live_view("volume3d", state={url, mode:"iso"}).

Gotchas

• Data host has no CORS — never give a tiles.virtualembryo.org URL straight to a viewer; read it server-side (zarr/requests) and serve_local_data a local copy (which adds CORS + Range). The KG API host does send CORS but you call it server-side anyway.
• KG ≠ raw matrices. /kg/expression returns curated anatomy×stage annotations (with paper provenance), not an expression matrix. Quantitative per-cell expression lives in the dataset .spatial.zarr (the viewer reads it).
• Stages: the KG keys on Theiler/Carnegie codes, not "E9.5" — map the age to a stage code (or search the stage) before filtering.
• Big datasets: subsample when fetching 3D reconstructions; the viewer also strides rendering, but the HTTP pull is the slow part.
• An MCP server with the same read tools also exists at https://kg.virtualembryo.ai/mcp (streamable-HTTP) if a ve-curator MCP profile is configured — but the HTTP calls above need no setup.