VistoSeg

VistoSeg processes and integrates high-resolution histological and immunofluorescence images with spatial transcriptomics data from 10x Genomics Visium and Visium-IF to extract image-derived metrics that contextualize spatially resolved gene expression.

Key Features:

• MATLAB-based pipeline: Implements image processing, analysis, and visualization steps within a MATLAB computational framework.
• Integration of image and gene expression data: Maps high-resolution histology or immunofluorescence images to genome-wide gene expression profiles for combined morphological and molecular analysis.
• Compatibility with 10x Genomics Visium and Visium-IF: Processes images and associated spatial transcriptomics data generated by Visium and Visium-IF, including analysis of thousands of spots (each 2,375 µm²) within a 6.5 mm² cryosection.
• Image-derived metric extraction: Performs image processing to derive metrics such as cell counts and cell type identification from histological or immunofluorescent images.
• Visualization capabilities: Generates visual overlays and representations that link morphological features with spatial gene expression patterns.
• Downstream interoperability: Produces outputs that can be integrated with analytical workflows in R and Python for further analysis.

Scientific Applications:

• Spatial tissue organization: Enables analysis of transcriptional landscapes within their anatomical context to study tissue organization at the molecular level.
• Cellular heterogeneity: Facilitates identification and spatial mapping of cellular heterogeneity through combined image and gene expression metrics.
• Tissue-specific gene regulation: Supports investigations into spatial patterns of gene regulation within distinct tissue regions.
• Developmental biology and disease pathology: Applies integrated morphological and molecular data to studies ranging from developmental processes to disease-related tissue alterations.

Methodology:

Implements a MATLAB-based pipeline that processes and visualizes high-resolution histology and immunofluorescence images alongside 10x Genomics Visium and Visium-IF spatial transcriptomics data, extracting image-derived metrics (e.g., cell counts, cell type identification) and producing outputs compatible with R and Python.