The Data Layer Missing From AI for Biology

Tissue Has a Story to Tell.

And cells are its words. Early tools let us look up one word at a time. Newer ones count every word in the book — and all their meanings — at once. In the process, we lost the sentences and the chapters. A word inventory is not a novel. Without context, we can't interpret what the tissue is saying or predict what happens next. Terrain is building a new spatial proteomics platform to change that: hundreds of proteins, in spatial context, across whole tissue sections, with the sensitivity and scalability the field has been missing.

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Origin
From the origins of in situ multiplexing Terrain's founders helped develop the in situ methods that launched spatial transcriptomics. Now they're applying that same thinking to proteins.
Biology
Biology of form and function is spatial Cells communicate through direct contact and signals that ripple across tissue. Genomics tells you who and what is present, but it cannot follow the conversation — or what happens as a result.
Medicine
The foundation for predictive medicine Read the full conversation and you can predict what happens next — who responds, how drugs behave inside living tissue, how to rebuild what disease destroys.
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The rationale

If meaning depends on context, the measurement has to preserve context.

That is the central argument. Biology cannot be fully understood by reducing tissue to a list of molecules. The same cell state can mean something different depending on where it is, what surrounds it, and which protein programs are active nearby.

01

Cells are not standalone units.

A tumor cell, immune cell, or stromal cell only becomes biologically interpretable when its neighborhood is visible.

02

Proteins define form and function.

DNA can describe possibility. Protein expression and signaling reveal what cells are saying and doing in the tissue.

03

AI needs the full sentence.

Models trained on fragmented or low-context biology cannot learn tissue grammar. They need structured spatial protein data.

Why spatial proteomics

The disease signal is written in tissue, not in a spreadsheet.

Tissue sections have anchored clinical medicine for 150 years for a reason: the architecture is the information. Where cells sit, who they touch, how they organize — these are not metadata. They are the phenotype. Terrain reads that phenotype at protein resolution, across the whole section.

The company thesis begins with origin, rationale, and clinical purpose.

The case for Terrain is not a feature list. It is a scientific argument — about what biology requires, what existing tools miss, and what becomes possible when you measure it correctly.

Why Terrain
01

The question that founded Terrain

A graduate thesis on Turing-pattern cell organization asked how contact signals and receptor kinetics shape what a cell becomes. That question drove the development of spatial genomics. It still drives Terrain.

02

Real biology, limited measurement

Developmental biology — from flies to cancer — proved that biological grammar is real and conserved. But spatial technologies hit a hard ceiling — noise dominated location inference, and discovery gave way to denoising. The grammar exists. The measurement has to catch up.

03

Designed for medicine's next chapter

A clinical background and a front-row view of precision oncology's rise — kinase inhibitors, immunotherapy, ADCs — made one thing clear: the next generation of targeted therapies needs spatial proteomics at clinical scale. Terrain was built protein-first, for pathologists, not researchers.

Biological Grammar Tessera
The data layer

Tessera is built to learn from tissue as it is organized.

Tessera is Terrain’s emerging AI layer for converting spatial protein measurements into structured biological representations using our proprietary assay and software — learning from critical limitations in genomics and spatial biology. The intelligence layer is only credible after the measurement logic is clear.

The long-term aim is to help partners discover tissue states, biomarkers, and therapeutic response patterns, and to help clinicians match the right patients to the right drug.

Where it matters

Tissue-level data is the missing layer in precision medicine.

The hardest problems in drug development and patient care depend on information that dissociated and low-plex approaches cannot provide. That is the gap Terrain is built to fill.

01

Translational oncology

Understand disease in the tissue environment where therapies act.

02

Biomarker discovery

Search for patterns built from cells, proteins, and neighborhoods.

03

Therapeutic response

Connect tissue organization with the biology of response and resistance.

04

AI model development

Create structured spatial data that models can learn from directly.

For select collaborators

Tissue has a story. Help us tell it.

We are engaging partners who believe tissue context will become a central data layer for precision medicine, therapeutic discovery, and biological AI.

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