Strand AI
Strand AI provides a platform that predicts missing biological data modalities from existing patient samples, primarily generating spatial proteomics maps from routine H&E pathology slides. Its Lattice model delivers virtual multiplex immunofluorescence channels at pixel resolution, enabling whole-cohort retrospective studies without consuming additional tissue. The company operates as a dry lab service, handling slide processing, prediction, and spatial analysis end-to-end for research teams.
- Artificial Intelligence
- Biotechnology
- Data & Analytics
- Drug Discovery & Therapeutics
- Healthcare Technology
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Researchers often lack complete multimodal biological data for patient cohorts because acquiring assays such as proteomics or transcriptomics is costly, invasive, or impossible, leading to dropped subjects and missed biomarker opportunities. Archived tissue samples were rarely profiled with spatial proteomics, limiting retrospective studies and patient stratification efforts.
Solution
Strand AI offers an AI platform that infers missing biological modalities from existing data sources such as H&E pathology slides. Its Lattice model predicts spatially resolved protein-marker maps as virtual multiplex immunofluorescence channels at the resolution of the underlying image, trained pan-cancer and pan-tissue across more than a hundred protein markers. The platform runs across entire cohorts, not just selected sections, making retrospective studies viable without consuming precious archival blocks. Strand operates as a dry lab service: clients send digitized slides, Strand runs Lattice predictions plus cell segmentation, phenotyping, and tumor microenvironment characterization, and returns structured spatial data with a written analysis. For cohorts that cannot leave an institution, Strand deploys and runs Lattice entirely within the client's environment. The company positions Lattice as the first model in a broader "missing data layer for biology," with other modalities such as genotype-to-expression imputation following.
Target Audience
Primary customers are pharmaceutical and biotech research teams conducting oncology or rare-disease studies, as well as diagnostics teams needing spatial biomarker data for patient stratification, target discovery, or triage before wet lab work.
Features
- Lattice predicts spatial proteomics from routine H&E slides, delivering virtual mIF channels at pixel resolution with pan-cancer and pan-tissue coverage
- Validated at four scales: pixel-level intensity correlation, cell-level segmentation and typing, region-level tissue organization, and population-level stratification and survival analysis
- Cohort-scale processing across entire archives, enabling retrospective studies on tissue never intended for spatial work
- End-to-end dry lab service: clients send digitized slides, Strand runs prediction, spatial analysis, and returns structured data with a written readout
- On-premise deployment option for cohorts that cannot leave the institution
- Custom model partnerships scoped to specific targets, indications, cohorts, and validation plans for biopharma and diagnostics teams