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Updated: May 5, 2026

In vivo Imaging of Deep Cortical Layers using a Microprism

In vivo Imaging of Deep Cortical Layers using a Microprism

Published on: August 27, 2009

PRISM: a Python package for interactive and integrated analysis of multiplexed tissue microarrays.

Rafael Tubelleza^1,2, Aaron Kilgallon^1,2, Chin Wee Tan^1,3,4

¹Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia.

NAR Genomics and Bioinformatics

|August 27, 2025

Summary

This summary is machine-generated.

PRISM is a new Python package for analyzing multiplexed proteomic data from tissue microarrays (TMAs). It offers an end-to-end solution for spatial omics analysis, aiding biomarker discovery in translational cancer research.

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Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

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Published on: April 7, 2023

Area of Science:

Computational Biology
Bioinformatics
Proteomics

Background:

Tissue microarrays (TMAs) allow simultaneous analysis of multiple tissue samples, conserving resources and enabling efficient screening for clinical applications.
Multiplexed imaging provides spatial protein profiling at single-cell resolution, crucial for understanding tumor microenvironments and disease mechanisms.
High-plex spatial proteomic data analysis is vital for biomarker discovery but lacks comprehensive computational tools.

Purpose of the Study:

Introduce PRISM, a Python package for interactive, end-to-end analysis of TMAs using multiplexed proteomic data.
Facilitate translational and clinical research by simplifying the analysis of spatial omics data.
Provide an intuitive interface for researchers to translate raw multiplexed images into actionable clinical insights.

Main Methods:

PRISM utilizes the SpatialData framework for standardized data storage and interoperability.
Includes TMA Image Analysis for tissue masking, dearraying, cell segmentation, and feature extraction.
Features AnnData Analysis for quality control, clustering, cell-type annotation, and spatial analysis, integrated within napari for interactive use.

Main Results:

PRISM enables marker-based tissue masking, TMA dearraying, and single-cell feature extraction.
Facilitates quality control, clustering, cell-type annotation, and spatial analysis of proteomic data.
Offers efficient multi-resolution image processing and accelerates bioinformatics workflows via scalable data structures, parallelization, and GPU acceleration.

Conclusions:

PRISM provides a modular, computationally efficient, and interactive solution for spatial omics data analysis.
Simplifies the translation of raw multiplexed images into clinically relevant insights.
Empowers researchers to effectively explore and interact with complex spatial proteomic datasets for biomarker discovery.