Purpose-built for

every modality,

and to read them together

Nucleai applies a dedicated analytical approach to each data type— H&E, IHC, multiplex imaging, and spatial transcriptomics — then co-registers them into one dataset per patient.

Co-Registered, Not Just Covered

The platform pairs features across available modalities, including H&E, IHC, multiplex imaging and spatial transcriptomics, into a unified patient-level model.

Case Study

In a study with Genmab across 103 patients with non-small cell lung cancer treated with second-line immunotherapy, we combined H&E, 5 IHC markers, and RNAseq profiling from the same patients into a single multivariate model. Patients with a positive combined-model score showed longer progression-free survival than those with a negative score (HR 0.48)¹.

1. Markovits E, et al. Predicting response to immune checkpoint inhibitors in NSCLC using multimodal spatial analysis and RNA sequencing. SITC 2022, Poster #1289.

Case Study · Genmab Study Results

H&E

H&E is the foundational pathology modality — routinely available for every patient.

AI-powered analysis detects and classifies cells, and segments tissue into regions, including tumor, stroma, necrosis, tumor core, tumor-stroma interface, and surrounding microenvironment. From these maps, Nucleai’s platform quantifies:

Immune context: TIL density and distribution, immune phenotypes, TLS-associated biology
Tumor microenvironment: cellular composition, stromal architecture, fibrosis, spatial niches
Tumor architecture & morphology: cellular and tissue-level phenotypes
Spatial biology: tumor–immune interactions, immune exclusion, cellular proximity
Foundation Model–Inferred Phenotypes: Infer molecular phenotypes and biological programs from H&E

Positioned for biomarker enrichment (TILs, TLS), mechanism-of-action and resistance-mechanism work.

IHC — Single-Plex

Quantitative, subcellular and spatial characterization of protein expression from conventional IHC.

Single-Plex IHC
The platform measures optical density (OD) at the individual-cell level across membrane, cytoplasm, and nuclear compartments, providing continuous expression readouts beyond conventional binned scores.

Feature set includes: median and 10th-percentile membrane/cytoplasmic OD, nmOD, percent positivity at OD 10/30/50, H-score, Spatial Proximity Score
Spatial Proximity Score quantifies proximity of target-positive and target-negative tumor cells, examining the bystander effect
Applied across dozens of IHC markers and thousands of slides, spanning many diseases and therapeutic targets

IHC — Multiplex / Duplex Chromogenic

Multiplex chromogenic enables protein co-expression analysis, though double-positive or co-localized cells are difficult for pathologists to read directly. Platform capabilities include:

Case Study

For a top-10 biopharma sponsor, co-expression algorithms were developed and validated under design control against pathologist scoring at >85% agreement for clinical cutoffs, and integrated into a CLIA lab viewer — 10 validated algorithms across 10 indications.

Multiplex Fluorescence

High-plex protein imaging and transcript-level in situ hybridization (ISH), applied across panel, plex, tissue source and imaging platform.

mIF pipeline
Nucleai’s platform integrates purpose-built deep learning models for whole-slide mIF analysis:

mIF classifier and results
A binary protein expression classifier, trained on 300,000+ expert annotations, learns marker expression patterns rather than relying on mean intensity. This enables more accurate protein quantification and cell typing (including rare populations) and generalizes across unseen markers, diseases, and imaging platforms.

A scalable, cloud-based platform and whole-slide viewer enables efficient analysis and exploration of large multiplex imaging datasets.

Reported performance is 92% versus a 65% average F1 against standard clustering, with the largest gains on rare cell types such as Tregs and dendritic cells. Whole-slide, tested across 10+ indications, 5+ mIF platforms and 100+ markers and clones, including Akoya PhenoImager and PhenoCycler, and Lunaphore COMET data.

Supported Platforms

RNA In Situ Hybridization (ISH)

Transcript-level ISH assays are supported. The platform detects signal at the single-molecule level, using a local- maxima algorithm that identifies intensity peaks above local background.

New

Spatial Transcriptomics

The newest modality on the platform: automated spatial profiling, built and internally validated, supported for integration alongside protein (mIF) and morphology (H&E) data on the same tissue for multiomic analysis.

Ingestion, Across Every Modality

The platform is cloud-native and built for high-throughput cohort analysis across studies and sites.

Supported whole-slide image formats include:

Ready to Unlock MOre from your Tissue Data?