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Related Experiment Video

Updated: Jun 17, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Evaluating integrative strategies for incorporating phenotypic features in spatial transcriptomics.

Levin M Moser1,2, Ahmad Kamal Hamid1, Esteban Miglietta1

  • 1Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

Journal of Microscopy
|June 16, 2026
PubMed
Summary

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Spatial transcriptomics (ST) combined with variational autoencoders (VAEs) effectively integrates imaging and gene expression data. VAEs extract meaningful biological signals from morphological data, improving spatial context analysis even with limited datasets.

Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Bioimaging

Background:

  • Spatial transcriptomics (ST) enables spatially informed interrogation of biological samples and integration with imaging modalities.
  • Exploiting spatial context and integrating ST with morphological imaging data remains a challenge, especially under experimental constraints like limited data and class imbalance.

Purpose of the Study:

  • To evaluate variational autoencoders (VAEs) for extracting informative low-dimensional representations from cell crops in spatial transcriptomics data.
  • To assess the utility of VAE-derived latent spaces (LSs) for biological variation capture and multi-modal integration, particularly under constrained experimental conditions.

Main Methods:

  • Utilized a murine ileum Multiplexed Error-Robust Fluorescence In Situ Hybridisation (MERFISH) dataset.
Keywords:
cell type deconvolutionmorphological featuresmulti‐modal integrationspatial transcriptomicsvariational autoencoder

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Related Experiment Videos

Last Updated: Jun 17, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

  • Extracted low-dimensional representations using a minimally tuned VAE from spot counts, nuclear/membrane stains, or combinations.
  • Assessed VAE embeddings via PERMANOVA, cross-validated classification, unsupervised clustering, and compared them to CellProfiler features.

    • Main Results:

    • VAE-derived latent spaces captured meaningful biological variation and improved cell type label recovery.
    • Morphology-trained VAEs showed predictive power for gene expression.
    • Combining transcript counts with VAE latent spaces enhanced clustering outcomes, improving homogeneity or label recovery depending on the clustering approach.

    Conclusions:

    • Variational autoencoders can extract biologically meaningful signals from imaging data, even under constrained conditions.
    • Learned representations from VAEs outperform handcrafted features from tools like CellProfiler for spatial transcriptomics analysis.
    • VAEs offer a promising strategy for multi-modal integration in spatial transcriptomics.