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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Atlas-scale metabolic activities inferred from single-cell and spatial transcriptomics.

Erick Armingol1, James Ashcroft1, Magda Mareckova2

  • 1Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.

Biorxiv : the Preprint Server for Biology
|June 4, 2025
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Summary
This summary is machine-generated.

Researchers developed scCellFie, a computational tool to map cell metabolism from transcriptomic data. This framework reveals organ-specific metabolic activities and disease-related changes in conditions like endometriosis.

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Area of Science:

  • Computational biology
  • Metabolomics
  • Single-cell analysis

Background:

  • Direct measurement of cellular metabolism at single-cell or spatial resolutions is challenging.
  • Transcriptomic data offers a high-throughput alternative for inferring metabolic states.
  • Computational tools are crucial for analyzing complex transcriptomic datasets.

Purpose of the Study:

  • To present scCellFie, a computational framework for inferring metabolic activities from transcriptomic data.
  • To enable single-cell and spatial resolution analysis of metabolism.
  • To create a comprehensive metabolic atlas across human organs.

Main Methods:

  • Development of the scCellFie computational framework.
  • Application of scCellFie to ~30 million human and mouse single-cell and spatial transcriptomic profiles.
  • Generation of a metabolic atlas across human organs.

Main Results:

  • Identification of organ- and cell-type-specific metabolic activities.
  • Uncovering metabolic programs in endometrial tissue remodeling during the menstrual cycle.
  • Revealing disease-associated metabolic alterations in endometriosis and endometrial carcinoma.

Conclusions:

  • scCellFie provides a scalable toolbox for extracting metabolic functionalities from transcriptomic data.
  • The framework enables the study of metabolism in health and disease at high resolution.
  • Metabolic insights can be gained from transcriptomic data for various biological contexts.