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Related Experiment Video

Updated: Jun 16, 2026

Estimation of Structural Sensitivity of Intrinsically Disordered Regions in Response to Hyperosmotic Stress in Living Cells Using FRET

Estimation of Structural Sensitivity of Intrinsically Disordered Regions in Response to Hyperosmotic Stress in Living Cells Using FRET

Published on: January 12, 2024

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data.

Mingze Dong^1,2,3, David G Su^4,5,6,7, Harriet Kluger^4,5,6

¹Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA.

Nature Communications

|March 28, 2025

Summary

Spatial omics analysis is improved by SIMVI, a new deep learning framework. It accurately separates cell variability from spatial effects, enhancing understanding of tissue function and disease.

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Area of Science:

Computational biology
Genomics
Systems biology

Background:

Spatial omics technologies offer insights into gene expression within tissue context.
Current computational methods struggle to differentiate intrinsic cellular variability from intercellular spatial interactions.
This limitation hinders accurate capture of spatial regulatory mechanisms.

Purpose of the Study:

To introduce Spatial Interaction Modeling using Variational Inference (SIMVI), a novel annotation-free deep learning framework.
To disentangle cell-intrinsic and spatial-induced latent variables in spatial omics data.
To enable single-cell resolution estimation of spatial effects for downstream analyses.

Main Methods:

Developed SIMVI, a deep learning framework utilizing variational inference.
Employed an annotation-free approach to analyze spatial omics data.
Validated SIMVI's performance across diverse spatial omics platforms and tissue types.

Main Results:

SIMVI effectively disentangles cellular intrinsic variability and spatial effects.
Demonstrated superior performance of SIMVI compared to existing methods.
Revealed cyclical spatial dynamics of germinal center B cells in human tonsil.
Identified potential tumor epigenetic reprogramming states in melanoma multiome data.
Uncovered space-and-outcome-dependent macrophage states and cellular communication in melanoma microenvironments.

Conclusions:

SIMVI provides a robust computational framework for spatial omics data analysis.
The disentanglement approach allows for precise estimation of spatial effects at single-cell resolution.
SIMVI enhances the understanding of cellular interactions and spatial regulation in various biological contexts, including cancer.