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Related Experiment Video

Updated: Jul 6, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Space-feature measures on meshes for mapping spatial transcriptomics.

Michael I Miller1, Alain Trouvé2, Laurent Younes3

  • 1Center of Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, United States of America.

Medical Image Analysis
|January 4, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces novel algorithms for mapping gene expression data in mouse brains. It enables integration of molecular and cellular data into common coordinate systems for advanced neuroinformatics.

Keywords:
Atlas registrationLarge deformation diffeomorphic metric mappingMolecular imaging registration

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Genomics

Background:

  • Automated microscopy (e.g., MERFISH) provides high-resolution gene expression data in mouse brains.
  • Computational Anatomy (CA) excels at tissue-scale diffeomorphic mapping but struggles with integrating molecular/cellular data into common coordinates.
  • Statistical averaging of molecular and cellular populations via common coordinates remains a challenge.

Purpose of the Study:

  • To develop algorithms for calculating geodesics in the space of diffeomorphisms.
  • To extend Large Deformation Diffeomorphic Metric Mapping (LDDMM) for integrated spatial and molecular data analysis.
  • To enable cross-modality alignment of transcriptomic data to standard brain atlases.

Main Methods:

  • Introduced space-feature-measure LDDMM algorithms.
  • Developed algorithms for calculating geodesics in the space of diffeomorphisms.
  • Represented brain data as geometric "space-feature measures" with high-dimensional feature spaces.
  • Utilized diffeomorphic transformations to measure the shape of these brain spaces.
  • Employed a "chordal metric" derived from embedding measures in a linear space.

Main Results:

  • Successfully extended LDDMM to accommodate space-feature actions on marked particles.
  • Derived a novel cross-modality alignment algorithm for transcriptomic data.
  • Enabled consistent extension of mapping to tissue scales.
  • Provided a method for measuring the metric between complex brain data structures.

Conclusions:

  • The developed space-feature-measure LDDMM algorithms represent a significant advancement in neuroinformatics.
  • This work bridges the gap between molecular-level gene expression and macroscopic brain atlases.
  • It paves the way for more comprehensive spatial transcriptomic analyses in brain research.