Integrated Cytometry With Machine Learning Applied to High-Content Imaging of
Human Kidney Tissue for In Situ Cell Classification and Neighborhood Analysis.
Authors Winfree S, McNutt AT, Khochare S, Borgard TJ, Barwinska D, Sabo AR, Ferkowicz
MJ, Williams JC, Lingeman JE, Gulbronson CJ, Kelly KJ, Sutton TA, Dagher PC,
Eadon MT, Dunn KW, El-Achkar TM
Submitted By Submitted Externally on 8/28/2023
Status Published
Journal Laboratory investigation; a journal of technical methods and pathology
Year 2023
Date Published 6/1/2023
Volume : Pages 103 : 100104
PubMed Reference 36867975
Abstract 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-scale imaging data sets. These
novel capabilities enable the analysis of mesoscale 2- and 3-dimensional
multiplexed human kidney imaging data sets (such as co-detection by indexing and
3-dimensional confocal multiplexed fluorescence imaging). We demonstrate the
utility of this approach in identifying cell subtypes in the kidney on the basis
of labels, spatial association, and their microenvironment or neighborhood
membership. VTEA provides an integrated and intuitive approach to decipher the
cellular and spatial complexity of the human kidney and complements other
transcriptomics and epigenetic efforts to define the landscape of kidney cell