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Strand AI turns a single H&E-stained whole-slide image into per-pixel predictions for a panel of protein markers: biology you would otherwise need multiplex immunofluorescence, mass cytometry, or a dedicated antibody panel to capture. We trained Strand AI Lattice on paired H&E ⇄ multiplex datasets so you can recover spatial protein signal directly from the H&E you already have. The platform accepts a slide, runs inference on our infrastructure, and returns model-validated marker channels you can drop into the rest of your pipeline.

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Why Strand AI

H&E in, markers out

You don’t need antibody panels, IF capacity, or a second slide. Submit the H&E and request the markers you need.

Spatial outputs

Predictions are returned as multi-channel OME-Zarr you can open as AnnData (Python) or SpatialExperiment (R) and treat like real multiplex data.

REST + SDKs

Python and R clients, a documented REST API, and a credits ledger you can estimate against before you submit.

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Who this is for

Researchers and biotech teams who have H&E at scale and want spatial proteomics signal, typically for:
  • biomarker hypothesis generation in retrospective cohorts,
  • enriching slides where IF or mIF was not collected,
  • batch-level QC of multiplex panels against an orthogonal predictor.
Lattice is trained primarily on oncology data. Predictions outside an oncology context are exploratory; if you have a non-oncology use case, email us.

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What’s next

Quickstart

Mint an API key and run your first prediction in 5 minutes.

Supported markers

The 52 markers we predict today, with the model versions behind them.
The platform is currently invite-only. If you don’t have access yet, request access.
For research use only. Strand AI predictions are model outputs intended for research and hypothesis generation. They are not validated for, and must not be used in, clinical diagnosis, treatment selection, or patient care decisions.