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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Spatial Pattern Analysis using Closest Events (SPACE)-A Nearest Neighbor Point Pattern Analysis Framework for

Andrew M Soltisz¹, Peter F Craigmile², Rengasayee Veeraraghavan^1,3

¹Department of Biomedical Engineering, College of Engineering, 2124 Fontana Labs,140 W. 19th Ave, The Ohio State University, Columbus, OH 43210, USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|March 18, 2024

Summary

This summary is machine-generated.

Spatial Pattern Analysis using Closest Events (SPACE) offers a novel method for analyzing biological structures. This tool overcomes limitations of traditional colocalization techniques by using point pattern analysis for more accurate spatial relationship assessments.

Keywords:

colocalization complete spatial randomness empty space distribution fluorescence microscopy image analysis nearest neighbor distribution object-based analysis point pattern analysis spatial analysis spatial statistics

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Area of Science:

Life Sciences
Microscopy
Bioimage Analysis

Background:

Quantitative description of biological structures is challenging.
Traditional methods like Pearson's correlation and Manders' co-occurrence are flawed due to signal overlap and sensitivity to image quality.
These methods struggle to distinguish true from incidental colocalization.

Purpose of the Study:

Introduce a new image analysis tool, Spatial Pattern Analysis using Closest Events (SPACE).
Leverage point pattern analysis for accurate spatial relationship characterization in microscopy images.
Provide a superior alternative to traditional colocalization methods.

Main Methods:

Developed the Spatial Pattern Analysis using Closest Events (SPACE) tool.
Employed nearest neighbor-based point pattern analysis.
Applied SPACE to analyze spatial association between mRNA and cell nuclei in cardiac myocytes images.

Main Results:

SPACE demonstrated superior performance compared to traditional colocalization methods.
Assessed the spatial association of mRNA and cell nuclei in cardiac myocytes.
Evaluated SPACE's sensitivity to image segmentation, signal abundance, and resolution using synthetic and empirical data.

Conclusions:

SPACE provides a robust framework for quantitative spatial analysis in microscopy.
The tool overcomes critical limitations of intensity-based colocalization methods.
SPACE is a valuable addition to the microscopist's toolkit for accurate biological structure analysis.