Abstract
Advances in cellular and molecular interrogation of kidney tissue have ushered a new era of understanding the pathogenesis of kidney disease and potentially identifying molecular targets for therapeutic intervention. Classifying cells in situ and identifying subtypes and states induced by injury is a foundational task in this context. High resolution Imaging-based approaches such as large-scale fluorescence 3D imaging offer significant advantages because they allow preservation of tissue architecture and provide a definition of the spatial context of each cell. We recently described the Volumetric Tissue Exploration and Analysis cytometry tool which enables an interactive analysis, quantitation and semiautomated classification of labeled cells in 3D image volumes. We also established and demonstrated an imaging-based classification using deep learning of cells in intact tissue using 3D nuclear staining with 4′,6-diamidino-2-phenylindole (DAPI). In this mini-review, we will discuss recent advancements in analyzing 3D imaging of kidney tissue, and how combining machine learning with cytometry is a powerful approach to leverage the depth of content provided by high resolution imaging into a highly informative analytical output. Therefore, imaging a small tissue specimen will yield big scale data that will enable cell classification in a spatial context and provide novel insights on pathological changes induced by kidney disease.
Highlights
Understanding the biology and function of an organ requires detailed assessment of various cells and structures in the intact tissue environment (Asp et al, 2019; Stewart et al, 2019; Barwinska et al, 2021)
Disease states are associated with alteration in tissue architecture and Tissue Cytometry and Machine Learning changes in cell distribution, activity, and/or state (Wilson et al, 2019; Lake et al, 2021; Muto et al, 2021)
Tissue cytometry refers to the process of surveying all cells within an image volume of a tissue, and transforming cells into “analysis-ready” objects with associated variables based on labels or spatial parameters
Summary
Understanding the biology and function of an organ requires detailed assessment of various cells and structures in the intact tissue environment (Asp et al, 2019; Stewart et al, 2019; Barwinska et al, 2021). Imaging in all 3 dimensions using optical sectioning has allowed faithful preservation of tissue architecture and spatial context (Puelles et al, 2016; Klingberg et al, 2017; Winfree et al, 2017b; Ferkowicz et al, 2021; Lake et al, 2021; Liu et al, 2021) These advancements were catalyzed by the availability of novel software tools that allow streamlined image processing and quantitative analysis (Dao et al, 2016; Winfree et al, 2017a; Czech et al, 2019; Stoltzfus et al, 2020). Developing deep neural networks that allow classification of cells independent of specific labels will increase the power and usefulness of cytometry in classifying cells based on imaging data (Woloshuk et al, 2020), but will enable unbiased and non-exhaustive discovery of cell subtypes in situ These novel subtypes can be visualized and mapped back in the image volumes, which will allow biological interpretation. When large scale 3D imaging is coupled with advanced computational tools that allow processing of large image volumes, hundreds thousand cells or more could be analyzed from a single tissue specimen, thereby allowing the generation of big data from these imaging experiments
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