build 2026-09-04 18:54
Or paste x, y, z into the x field — it splits automatically.
Volumetric CLEM of hippocampal CA1 in a one-year-old female 3×Tg Alzheimer's mouse — 245 × 238 × 23 µm at 4 × 4 × 30 nm, with pTau, Aβ, CD11b and GFAP nanobody channels registered to the EM (Han et al., bioRxiv 2023.10.24.563674, PMID 37961104).
This dataset publishes no cell types. There is no classifier to agree or disagree with, so here the community is the classifier. What the panel shows instead is a molecular-marker measurement, which is evidence towards an identity but never an identity itself.
Three caveats worth knowing before you classify. Nuclei come from blob detection on the Hoechst channel, so positions are automatic and imperfect — correct them from the panel. Only about half the imaged block is segmented, so many nuclei have no segment and no 3D mesh; that gap is anatomically biased towards one side of the pyramidal layer. And the slab is only ~25 µm thick, so a quarter of nuclei are cut by a face and their volumes are underestimates.
Cortical layers are meaningless here, so each nucleus instead carries a signed distance from the fitted stratum pyramidale. Which side is oriens and which is radiatum is deliberately not named — that needs confirmation against a known landmark, and this project does not invent criteria it cannot source.
Most filters run against the 220 nuclei embedded in this page — no network needed. There is no MICrONS-style cell-type classifier for this dataset (see the Jump tab), so most filtering here is on geometry, segmentation coverage, and the molecular-marker measurements. Current community identity, below, is the one exception: it reads the Master cell list sheet, since that is the only place any nucleus's community-agreed name is recorded.
Each box has its own “Colab notebook” button, which writes a
.ipynb you upload to Google Colab and run to download that box’s EM imagery
(em_clahe, the measured 8 nm layer — not the GAN-enhanced one), its
segmentation, and the meshes of the cells matched inside it. Nothing is downloaded from this page.
Meshes need a current preview, since they are the cells the filter matched; EM and segmentation are
a plain spatial cutout and do not.
The dataset. Han, Wei et al., volumetric correlative light and electron microscopy (vCLEM) of hippocampal CA1 in a one-year-old 3×Tg Alzheimer’s mouse — bioRxiv 2023.10.24.563674, PMID 37961104. Every nucleus position, segment ID, and pTau/Aβ/CD11b/GFAP marker reading in this tool comes from that release. Synapses are also segmented in this dataset (a “Synapse_supervised_learning” layer, ~3.5 million instances), but published only as a Neuroglancer segmentation with no query API or synapse→cell partner table — unlike µJump’s CAVE-backed Connectivity panel, there is currently no way to ask this dataset “which synapses does cell X have,” only to look at them by eye.
This dataset publishes no cell types. There is no classifier to agree or disagree with here — every identity in the Filter/Dashboard tabs is a community call, made through the same guided decision tree µJump uses. Perisomatic ultrastructure as a classification method: Elabbady et al., “Perisomatic ultrastructure efficiently classifies cells in mouse cortex,” Nature 640, 478–486 (2025).
Brain ultrastructure overview: “Brain Ultrastructure: Putting the Pieces Together,” PMC7930431.
Neurovascular volume EM: “Public Volume Electron Microscopy Data: An Essential Resource to Study the Brain Microvasculature,” Frontiers in Cell and Developmental Biology 10, 849469 (2022).
EM ultrastructure teaching atlas (organelle and immune-cell morphology, not a peer-reviewed source — use it to learn to recognise structures, not to cite a claim): Jastrow’s EM Atlas.
Hippocampal CA1 anatomy. This volume has no cortical layers to classify by (see the Jump tab) — each nucleus instead carries a signed distance from the fitted stratum pyramidale, used throughout the Filter and Dashboard tabs. Deep-vs-superficial CA1 pyramidal cell diversity: Soltesz & Losonczy, “CA1 pyramidal cell diversity enabling parallel information processing in the hippocampus,” Nature Neuroscience 21, 484–493 (2018), PMID 29593317. This dataset’s own oriens/radiatum sidedness is deliberately left unconfirmed — see the guided-ID tree’s own note on that.
Hippocampal interneuron classification by axon target (basket/axo-axonic, O-LM, bistratified, etc.), the framework this tool’s guided tree uses for CA1: Klausberger & Somogyi, “Neuronal Diversity and Temporal Dynamics: The Unity of Hippocampal Circuit Operations,” Science 321, 53–57 (2008), free full text at PMC4487503.
Read these before trusting a number. Nuclei come from automatic blob detection on the Hoechst channel — positions are approximate, correctable from each cell’s own panel. Only about half the imaged block is segmented (secgan16), so many nuclei have no segment and no 3D mesh; that gap is anatomically biased towards one side of the pyramidal layer. The slab is only ~25 µm thick, so roughly a quarter of nuclei are cut by a top/bottom face and their volumes are underestimates. Cell contacts are mesh surface proximity, not synapses — see the note on synapse access above.
Charts built from the nuclei embedded in this page, plus one bulk read of the community’s current identifications (the same read the Filter tab’s identity dropdown uses). The charting library only loads once this tab is opened.
This dataset has no published cell types, so unlike µJump’s dashboard every chart here is keyed on current community identity — a live consensus that can still change as more people classify. The six most-identified names get their own bucket; anything else named folds into “Other”, and every nucleus nobody has classified yet is “Unclassified”.
Percentile rank of the shell marker reading among all 220 nuclei (see the Jump tab’s own note on why the shell, not the nucleus, reading is the informative one). A marker is evidence towards an identity, never an identity itself.
Which side is oriens and which is radiatum is not confirmed for this volume (see the References tab) — read the sign as a geometric fact, not an anatomical label.
For each identity (rows), what its single nearest neighbouring nucleus turns out to be (columns), as a percentage of that row. All 220 nuclei are used — no sampling needed at this size.
Each panel is one identity’s own distribution as a percentage of that identity, so a rarely-used identity is still readable next to a common one — same form as ηJump’s cortical depth profile, with distance from the fitted stratum pyramidale standing in for cortical depth.