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Updated: Jan 9, 2026

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

Multimodal AI generates virtual population for tumor microenvironment modeling.

Jeya Maria Jose Valanarasu¹, Hanwen Xu², Naoto Usuyama¹

¹Microsoft Research, Redmond, WA, USA.

|December 10, 2025

Summary

This summary is machine-generated.

GigaTIME uses AI to create virtual multiplex immunofluorescence images from standard H&E slides, enabling large-scale tumor immune microenvironment (TIME) analysis across many cancer types and patients.

Keywords:

digital pathology multimodal AI precision health real-world data real-world evidence spatial proteomics tumor microenvironment virtual population

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A Mimic of the Tumor Microenvironment: A Simple Method for Generating Enriched Cell Populations and Investigating Intercellular Communication

A Mimic of the Tumor Microenvironment: A Simple Method for Generating Enriched Cell Populations and Investigating Intercellular Communication

Published on: September 20, 2016

Area of Science:

Computational pathology
Artificial intelligence in oncology
Cancer immunology

Background:

The tumor immune microenvironment (TIME) influences cancer progression and treatment outcomes.
Multiplex immunofluorescence (mIF) is valuable for TIME analysis but is costly and low-throughput.
Existing methods struggle with large-scale TIME characterization due to data limitations.

Purpose of the Study:

To develop GigaTIME, an AI framework for population-scale TIME modeling.
To bridge cell morphology and states using a cross-modal translator.
To generate virtual mIF images from H&E slides for extensive analysis.

Main Methods:

Trained a cross-modal translator on 40 million cells with paired H&E and mIF data (21 proteins).
Applied GigaTIME to 14,256 patients across 24 cancer types, generating 299,376 virtual mIF slides.
Validated findings on 10,200 TCGA patients.

Main Results:

Generated a large virtual cohort of TIME data.
Uncovered 1,234 significant associations between proteins, biomarkers, staging, and survival.
Demonstrated the feasibility of large-scale TIME analysis previously limited by mIF data scarcity.

Conclusions:

GigaTIME enables scalable, cost-effective TIME analysis using standard H&E slides.
The AI framework significantly expands the scope of TIME research.
This approach facilitates discovery of novel biomarkers and therapeutic targets.