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Related Experiment Video

Updated: Jun 29, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

SHEST: single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell-type prediction and

Hoyeon Jeong^1,2, Junghan Oh³, Donggeon Lee⁴

¹Medical Research Institute, Sungkyunkwan University, 81 Irwon-Ro, 06351 Seoul, Republic of Korea.

Briefings in Bioinformatics

|February 17, 2026

Summary

SHEST integrates tissue morphology with spatial molecular profiles to predict cell types and reconstruct gene expression. This framework enhances understanding of the tumour microenvironment for precision oncology.

Keywords:

cell-type prediction computational pathology digital pathology gene expression reconstruction haematoxylin and eosin lung adenocarcinoma spatial transcriptomics tumour microenvironment

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Published on: April 8, 2015

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Published on: September 5, 2025

Area of Science:

Computational Biology
Genomics
Cancer Research

Background:

Cancer progression understanding requires integrating tissue morphology and spatial molecular data.
Existing methods lack a unified approach for cell-type prediction and spatial gene expression reconstruction.

Purpose of the Study:

To introduce SHEST, a multi-task framework for predicting cellular composition and reconstructing spatial gene expression from haematoxylin and eosin morphology.
To bridge histopathology and spatial transcriptomics for comprehensive tissue characterization.

Main Methods:

SHEST utilizes a quadruple-tile input and a neighbourhood-informed clustering algorithm.
A shared morphological encoder with task-specific heads for cell-type classification and gene expression reconstruction.
Multi-task optimization with cross-entropy and zero-inflated negative binomial losses for sparse spatial transcriptomic data.

Main Results:

High accuracy in predicting tumour cells (F1: 0.97) and lymphocytes (F1: 0.91) in lung adenocarcinoma.
Successful reconstruction of spatially resolved, cell-type-specific gene expression patterns.
Preservation of spatial relationships and gene-level autocorrelation, reflecting tumour microenvironment structure.

Conclusions:

SHEST provides a synergistic and cost-efficient method to integrate histopathology and spatial transcriptomics.
Enables comprehensive tissue characterization and cell-level insights into tumour-immune ecosystems.
Forms a foundation for precision oncology through spatially informed analysis.