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Integrated Cytometry With Machine Learning Applied to High-Content Imaging of Human Kidney Tissue for In Situ Cell Classification and Neighborhood Analysis by Seth Winfree; Andrew T. McNutt; Suraj Khochare; Tyler J. Borgard; Daria Barwinska; Angela R. Sabo; Michael J. Ferkowicz; James C. Williams; James E. Lingeman; Connor J. Gulbronson; Katherine J. Kelly; Timothy A. Sutton; Pierre C. Dagher; Michael T. Eadon; Kenneth W. Dunn; Tarek M. El-Achkar is a Medicine article available to read on EtoBox.

What is Integrated Cytometry With Machine Learning Applied to High-Content Imaging of Human Kidney Tissue for In Situ Cell Classification and Neighborhood Analysis about?

The human kidney is a complex organ with various cell types that are intricately organized to perform key physiological functions and maintain homeostasis. New imaging modalities, such as mesoscale and highly multiplexed fluorescence microscopy, are increasingly being applied to human kidney tissue to create single-cell resolution data sets that are both spatially large and multidimensional. These single-cell resolution high-content imaging data sets have great potential to uncover the complex spatial organization and cellular makeup of the human kidney. Tissue cytometry is a novel approach used for the quantitative analysis of imaging data; however, the scale and complexity of such data sets pose unique challenges for processing and analysis. We have developed the Volumetric Tissue Exploration and Analysis (VTEA) software, a unique tool that integrates image processing, segmentation, and interactive cytometry analysis into a single framework on desktop computers. Supported by an extensible and open-source framework, VTEA’s integrated pipeline now includes enhanced analytical tools, such as machine learning, data visualization, and neighborhood analyses, for hyperdimensional large-

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Author

Seth Winfree; Andrew T. McNutt; Suraj Khochare; Tyler J. Borgard; Daria Barwinska; Angela R. Sabo; Michael J. Ferkowicz; James C. Williams; James E. Lingeman; Connor J. Gulbronson; Katherine J. Kelly; Timothy A. Sutton; Pierre C. Dagher; Michael T. Eadon; Kenneth W. Dunn; Tarek M. El-Achkar

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Health Sciences)