Gradient Path — Rare stains. Routine tissue. Real biology.

01 The Problem

The pathology that matters most can't be stained at scale.

Hundreds of rare pathology markers hold biology that's locked behind expensive, slow staining protocols. The markers researchers and pharma care about most are the most expensive to generate, the least available to scale, and the least accessible in existing data.

Traditional immunofluorescence consumes rare human tissue, takes 24+ hours per protocol, and costs $30–$100 per slide. Staining protocols differ between institutions. And the petabytes of tissue images already sitting in archives like TCGA can never be re-stained.

The result: the most biologically interesting questions are exactly the ones that are hardest to answer at scale. Sample sizes are tiny, datasets are fragmented, and the underlying tissue is gone.

100+

rare markers of research
and pharmaceutical interest

$30–100

per slide for traditional
immunofluorescence

24hr+

turnaround per
staining protocol

of archived tissue can
never be re-stained

02 Our Approach

A virtual stain for
rare pathology markers.

Input: a routine H&E image. Output: tissue stained for the marker of interest. Our generative models learn the mapping from routine staining patterns to rare-marker signal, validated against board-certified pathologist review and benchmarked against ground-truth immunofluorescence.

Once trained, the model can be applied to archived imagery at scale — including public datasets like The Cancer Genome Atlas — unlocking biology that was previously frozen in petabytes of unrestainable tissue.

03 The Pipeline

From routine stain
to rare biology.

Five steps that move us from petabytes of unused archive imagery to validated rare-pathology insights — at a scale traditional staining can't reach.

Routine Imaging

Tissue is processed and imaged with standard H&E staining — the universal foundation of clinical pathology, available everywhere with consistent protocols.

Expert Annotation

Board-certified pathologists at academic partners provide ground-truth labels paired with H&E for the rare marker of interest. Quality is the data moat.

Virtual Staining Models

Generative models learn the mapping from routine stain to rare marker. Pre-trained on porcine tissue at large scale, then fine-tuned on human data per institution.

Inference at Scale

Trained models infer rare staining patterns directly from routine images — across petabytes of public archives like TCGA, displacing thousands of physical immunostains.

Biological Insight

Virtual stains feed downstream analysis — pathology research, drug development, clinical trial enrichment, biomarker discovery. Each new partner improves every model.

Stain less.
See more.

The pathology that matters most can't be stained at scale.

A virtual stain for
rare pathology markers.

From routine stain
to rare biology.

Advised across the bench.

We're not building a better stain. We're building the only scalable way to study rare pathology.

Stain less. See more.

The pathology that matters most can't be stained at scale.

A virtual stain for rare pathology markers.

From routine stainto rare biology.

Advised across the bench.

We're not building a better stain. We're building the only scalable way to study rare pathology.

Stain less.
See more.

A virtual stain for
rare pathology markers.

From routine stain
to rare biology.