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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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Related Experiment Video

Updated: Sep 15, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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A computational framework for mapping isoform landscape and regulatory mechanisms from spatial transcriptomics data.

Jiayu Su, Yiming Qu, Megan Schertzer

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    Summary
    This summary is machine-generated.

    This study introduces SPLISOSM, a computational tool for analyzing spatial transcriptomics. It reveals how transcript diversity in brain regions is linked to neuropsychiatric disorders and cancer.

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    Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
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    Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

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    Area of Science:

    • Genomics
    • Computational Biology
    • Neuroscience

    Background:

    • Transcript diversity, including splicing and alternative 3' end usage, is vital for cellular function but its spatial regulation is unclear.
    • Understanding spatial transcriptomic patterns is crucial for deciphering cellular identity and adaptation in complex tissues.
    • Existing methods lack the resolution to capture isoform-level spatial variations effectively.

    Purpose of the Study:

    • To develop a computational framework, SPLISOSM (SpatiaL ISOform Statistical Modeling), for detecting isoform-resolution patterns in spatial transcriptomics data.
    • To investigate the spatial coordination of transcript diversity in the mouse and human brain.
    • To analyze transcript diversity in human glioblastoma and its relationship with the tumor microenvironment.

    Main Methods:

    • Development of SPLISOSM, a computational framework utilizing multivariate testing for spatial transcriptomics.
    • Application of SPLISOSM to analyze spatial transcript diversity in mouse and human brain samples.
    • Integration of RNA binding protein expression data to identify regulatory relationships.
    • Analysis of spatial transcriptomic data from human glioblastoma samples.

    Main Results:

    • Identification of over 1,000 spatially variable transcript diversity events in the mouse brain, enriched in synaptic signaling pathways.
    • Discovery of conserved transcript diversity patterns between mouse and human prefrontal cortex, particularly in synaptic pathways.
    • Uncovering of region-specific RNA binding proteins regulating spatial transcript diversity in the brain.
    • Characterization of pervasive transcript diversity in human glioblastoma, linked to antigen presentation and adhesion genes and influenced by the tumor microenvironment.

    Conclusions:

    • SPLISOSM provides a robust method for analyzing spatial transcript diversity at the isoform level, even with sparse data.
    • Spatial transcript diversity plays a significant role in brain function, neuropsychiatric disorders, and cancer.
    • Evolutionary conservation of spatial transcript diversity highlights fundamental biological processes.
    • The tumor microenvironment significantly shapes spatial transcriptomic landscapes in glioma, impacting key cellular functions.