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Microenvironments

Microenvironments

Microorganisms inhabit highly localized spaces known as microenvironments, which are defined by distinct physical and chemical characteristics. These include oxygen concentration, pH, temperature, light availability, and nutrient levels. The conditions within a microenvironment can differ markedly from those in the surrounding area and significantly influence microbial growth, metabolism, and community structure.Microenvironments often display sharp physicochemical gradients over small spatial...

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

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Published on: April 8, 2015

Microenvironment-aware spatial modeling for accurate inference of cell identity.

Qi Liu^1,2, Yu Wang^1,2, Chih-Yuan Hsu^1,2

¹Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37203, United States.

Nucleic Acids Research

|January 7, 2026

Summary

MEcell, a new computational method, accurately identifies cell identities by integrating spatial context with molecular data. This approach improves understanding of microenvironment-dependent cell states in spatial transcriptomics.

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Area of Science:

Spatial biology
Computational biology
Genomics

Background:

Spatial omics technologies provide insights into cellular organization and tissue architecture at single-cell resolution.
Existing computational methods for spatial omics primarily focus on domain detection, not cell identity inference.
Traditional methods neglect the impact of the local microenvironment on cell states.

Purpose of the Study:

To develop a computational method that integrates spatial context for improved cell identity modeling in spatial transcriptomics.
To address the limitations of existing methods that rely solely on intrinsic molecular features.

Main Methods:

Introduction of MEcell, a parameter-free computational method.
MEcell explicitly incorporates and adaptively weights spatial context for cell identity inference.
Validation across simulated and diverse real-world spatial transcriptomics datasets.

Main Results:

MEcell accurately infers cell identities by leveraging spatial context.
The method consistently outperformed existing approaches across multiple platforms (MERFISH/Vizgen, Xenium, CosMx, Visium HD, Slide-seqV2, open-ST) and tissue types.
Demonstrated the critical role of the microenvironment in defining cell identity.

Conclusions:

MEcell effectively captures spatially informed cellular heterogeneity.
The findings underscore the importance of integrating spatial information for accurate cell type deconvolution.
MEcell offers a powerful tool for analyzing complex spatial omics data.