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StereoSiTE: a framework to spatially and quantitatively profile the cellular neighborhood organized iTME.
Gigascience
January 2024
BGI Research, Chongqing 401329, PR China.
Article Synopsis
- Spatial transcriptome (ST) technologies are vital for understanding tumor biology, but existing analysis tools mainly focus on single-cell RNA sequencing, neglecting spatial data integration.
- This study introduces StereoSiTE, a new analytical framework that merges open-source bioinformatics with custom algorithms to assess spatial cell interaction intensity (SCII) in cellular neighborhoods, enhancing the understanding of intercellular interactions and cellular contexts.
- StereoSiTE helps researchers identify significant cellular regions, such as those dominated by neutrophils, and analyze their role in the immune tumor microenvironment after treatment, making the software user-friendly and accessible for a broad audience.
Cell behaviors that pattern developing tissues: the case of the vertebrate nervous system.
Curr Top Dev Biol
May 2024
Instituto Gulbenkian de Ciência, Oeiras, Portugal. Electronic address:
Morphogenesis from cells to tissue gives rise to the complex architectures that make our organs. How cells and their dynamic behavior are translated into functional spatial patterns is only starting to be understood. Recent advances in quantitative imaging revealed that, although highly heterogeneous, cellular behaviors make reproducible tissue patterns.
View Article and Find Full Text PDFGriddient: a microfluidic array to generate reconfigurable gradients on-demand for spatial biology applications.
Commun Biol
September 2023
Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA.
Biological tissues are highly organized structures where spatial-temporal gradients (e.g., nutrients, hypoxia, cytokines) modulate multiple physiological and pathological processes including inflammation, tissue regeneration, embryogenesis, and cancer progression.
View Article and Find Full Text PDFAnnotation of spatially resolved single-cell data with STELLAR.
Nat Methods
November 2022
Department of Computer Science, Stanford University, Stanford, CA, USA.
Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets.
View Article and Find Full Text PDFExploring the diversity of plant proteome.
J Integr Plant Biol
July 2021
Development Center of Plant Germplasm Resources, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China.
The tremendous functional, spatial, and temporal diversity of the plant proteome is regulated by multiple factors that continuously modify protein abundance, modifications, interactions, localization, and activity to meet the dynamic needs of plants. Dissecting the proteome complexity and its underlying genetic variation is attracting increasing research attention. Mass spectrometry (MS)-based proteomics has become a powerful approach in the global study of protein functions and their relationships on a systems level.
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