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The stem cell niche is the dynamic microenvironment where stem cells reside. Inside these niches, the cells may remain undifferentiated, undergo high self-renewal, or become lineage-specific progenitors. Stem cells coexist with other niche cells, such as stromal cells. They also interact closely with the ECM. Cell-cell and cell-matrix communication occur via adhesion molecules or soluble factors that signal the stem cells and determine their fate. Stromal cells also provide survival signals to...
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Updated: Jan 12, 2026

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
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Nicheformer: a foundation model for single-cell and spatial omics.

Alejandro Tejada-Lapuerta1,2, Anna C Schaar1,2, Robert Gutgesell2,3

  • 1TUM School of Computation, Information & Technology, Technical University of Munich, Garching, Germany.

Nature Methods
|October 31, 2025
PubMed
Summary
This summary is machine-generated.

Nicheformer, a new AI model, decodes cellular neighborhoods by integrating spatial and single-cell data. This approach accurately predicts cell locations, advancing spatial transcriptomics and single-cell RNA sequencing analysis.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Tissue structure and function are dictated by the cellular microenvironment.
  • Spatial single-cell genomics offers a powerful method for studying cell interactions.
  • Understanding spatial context is crucial for interpreting single-cell data.

Purpose of the Study:

  • To introduce Nicheformer, a transformer-based foundation model for spatial single-cell analysis.
  • To develop a model capable of learning spatial context from diverse transcriptomic data.
  • To enable the prediction of spatial microenvironments for dissociated cells.

Main Methods:

  • Training Nicheformer on a large dataset (SpatialCorpus-110M) of human and mouse single-cell and spatial transcriptomics data.
  • Utilizing a transformer architecture to capture spatial relationships.
  • Evaluating model performance on downstream tasks like spatial composition and label prediction.

Main Results:

  • Nicheformer successfully learns cell representations that incorporate spatial context.
  • The model excels in predicting spatial composition and labels.
  • Demonstrated that models trained solely on dissociated data cannot fully capture spatial microenvironment complexity.
  • Nicheformer can transfer spatial information to single-cell RNA sequencing datasets.

Conclusions:

  • Nicheformer represents a significant advancement in machine learning for spatial single-cell analysis.
  • Integrating multiscale data is essential for comprehensive understanding of spatial biology.
  • The model facilitates the prediction of spatial context for dissociated cells, enhancing scRNA-seq data interpretation.