Platform

Platform

The immune system is at the center of human health and disease.

Overview

Immunai leverages big data, single cell multi-omics, and ML to bridge the gap between causal immunology and translational-disease biology. We generate cell-specific perturbation signatures across multiple cohorts in order to inform drug discovery and development decisions.

Identify discovery cohorts

Inflammatory diseases and cancer

Reverse translational discovery

Identify systems-level immune dysregulation

Target validation

Nominate and validate targets to reverse imbalance

Therapeutics development

Development of agonist and antagonist modalities

Treatment cohorts

Inflammatory diseases and cancer

Immunai technology platform

  • Single cell technology
  • Data engineering
  • Functional genomics
  • AMICA™
  • Experimental immunology
  • Machine learning
  • In vitro model systems

Solutions

Immunai’s platform enables novel, data-driven insights that reveal actionable therapeutic targets and accelerates drug development for immune-modulating medicines.

Immunai links causal experimental biology and observational translational insights

Data generation and curation

ML-driven target ID and prioritization

Validation

AMICA™ – the world’s largest cell-level immune knowledge base

Immunai’s rich clinico-genomic data assets are harmonized in AMICA™, the – Annotated Multi-Omic Immune Cell Atlas. Fed by massive scale curated public -omics data and proprietary cohorts and experiments, AMICA™ covers 10s of millions of cells across hundreds of disease settings, and is growing rapidly. AMICA™ is the data foundation that powers our immune models and our therapeutics discovery and development efforts.

Immunai data foundation

  • Deep, multiomic

    Single cell profiling of clinical cohorts

  • Dynamic

    Functional genomics platform for multiplexed perturbations

  • Broad, curated

    Data from public sources

  • Unified

    Clinical and immunological annotations for integrative analysis

  • 5-6k

    Studies

    • 350+ Sc studies

  • 800+

    Cell types

    • 80 Immune cell types

  • 500+

    Diseases

  • 100K+

    Patient samples

Curated public transcriptomics data

Immunai has created the world’s largest compendium of deeply curated transcriptomic data, which covers a wide range of research and therapeutic areas. The use of controlled vocabularies, stringent quality control, accurate cell-type annotation, data normalization, and peer-review by domain experts, enables machine learning and interpretation of biological phenomena across organisms and diseases.

Single cell multiomic immune profiling

Immunai single cell profiling platform generates high quality and high scale data assets from biology, capturing key -omics at a single cell granularity that provides a comprehensive view into cell behavior and state. Our ML-driven pipeline translates this massive, high complexity data into standardized cell models that enable analysis across time, patients, and experiments.

Single-cell multiomic readout

A machine learning based immune profiling and discovery platform

  • Dehashing, Multiplet Removal, and Refined Cell Annotation

    Multi-task cell identity neural network and expert system to eliminate contamination and improve cell annotation

  • Single-Cell Anomaly Filtering

    Nearest neighbor graph analysis identifies rare populations

  • Robust DEG Computation

    Single-cell bootstrap and quantile smoothing improves sensitivity and specificity of DEG

  • Perturbation Prediction

    Composable single-cell autoencoder infers gene regulatory networks at scale

  • Experiment Planning

    Multi-armed bandit and optimistic phenotypic regression network

Immunai leverages transfer learning between our ever growing experimental and patient cohort datasets

By applying transfer learning to the immune system, Immunai is able to measure the commonalities that exist between different cell types and disease indications in order to uncover novel insights and accelerate drug discovery and development.

Single-cell analysis supported by big data and machine learning:

  • – Cell annotations
  • – Differential expression analysis
  • – Multi-cohort meta-analysis
  • – GRN inference

Functional genomics at single cell resolution

Immunai leverages ML-guided pooled CRISPR perturbations in primary cells to interrogate disease mechanisms and tease out causation at single cell resolution.

Immunai’s platform perturbs hundreds of genes across millions of cells per experiment in vitro with rich single cell multiomic readout.

We create libraries of causative perturbational signatures across a broad experimental landscape and a variety of immune model systems. When mapped against multiomic profiling of our longitudinal patient cohorts, we can connect molecular perturbations to clinical context, and vice versa, to generate novel, differentiated insights for discovery and development.

Predicting perturbations in silico to guide experiments

Systematic in vitro experimental perturbation to dissect immune cell gene regulation

We design optimized in vitro model systems to better recapitulate human physiology and evaluate drugs

Knowing if and how patients will respond to a new immunotherapy remains opaque.

Many current in vitro systems used for preclinical testing do not adequately model human physiology, which limits their predictive power to accurately evaluate drugs.

Our deeper understanding of the immune cascade allows us to quickly optimize in vitro models. Alongside our data-driven machine learning pipeline, we leverage patient-derived multi-omic signatures to identify those model systems with the greatest predictive power for therapeutic priorities. These precise patient-derived signatures can be translated into predictive biomarkers which inform clinical strategy decisions such as patient selection or indication selection.