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Updated: Jun 6, 2026

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities.

Haoran Zhang¹, Jeffrey F Quinn²,

¹Department of Computer Science, University of Texas at Austin.

Biorxiv : the Preprint Server for Biology

|June 5, 2026

Summary

MultiTME integrates diverse spatial and single-cell data, overcoming technical challenges to reveal biological insights. This multimodal framework enables accurate cell typing and spatial mapping, advancing cancer research.

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Current spatial and single-cell technologies provide incomplete views of cellular states.
Integrating transcriptomic, proteomic, and spatial data is challenging due to unpaired measurements and modality biases.

Purpose of the Study:

To present MultiTME, a novel multimodal framework for integrating heterogeneous spatial and single-cell data.
To enable cross-modal translation and harmonization of biological data without paired observations.

Main Methods:

Utilized a spatially-regularized, cycle-consistent deep generative model.
Enforced consistency of bidirectional mappings to learn a shared latent representation.
Applied the framework to benchmark datasets and multimodal colorectal cancer data.

Main Results:

MultiTME outperforms existing methods in data integration and cross-modal cell typing.
Improved spatial transcriptomic panel completion and generated high-resolution spatially resolved maps.
Revealed a proliferative-invasive tumor axis in colorectal cancer and corrected platform biases between Xenium and CosMx.

Conclusions:

MultiTME effectively integrates multimodal spatial and single-cell data, overcoming key technical limitations.
The framework facilitates cross-dataset harmonization and enables comprehensive pan-cancer spatial studies.
MultiTME advances the understanding of spatial biology and disease mechanisms.