Browsing by Author "Khochare, Suraj"

Now showing 1 - 4 of 4
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    In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining
    (Wiley, 2021) Woloshuk, Andre; Khochare, Suraj; Almulhim, Aljohara F.; McNutt, Andrew T.; Dean, Dawson; Barwinska, Daria; Ferkowicz, Michael J.; Eadon, Michael T.; Kelly, Katherine J.; Dunn, Kenneth W.; Hasan, Mohammad A.; El-Achkar, Tarek M.; Winfree, Seth; Medicine, School of Medicine
    To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.
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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
    (Elsevier, 2023) Winfree, Seth; McNutt, Andrew T.; Khochare, Suraj; Borgard, Tyler J.; Barwinska, Daria; Sabo, Angela R.; Ferkowicz, Michael J.; Williams, James C., Jr.; Lingeman, James E.; Gulbronson, Connor J.; Kelly, Katherine J.; Sutton, Timothy A.; Dagher, Pierre C.; Eadon, Michael T.; Dunn, Kenneth W.; El-Achkar, Tarek M.; Medicine, School of Medicine
    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 types.
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    Large-scale, three-dimensional tissue cytometry of the human kidney: a complete and accessible pipeline
    (Elsevier, 2021) Ferkowicz, Michael J.; Winfree, Seth; Sabo, Angela R.; Kamocka, Malgorzata M.; Khochare, Suraj; Barwinska, Daria; Eadon, Michael T.; Cheng, Ying-Hua; Phillips, Carrie L.; Sutton, Timothy A.; Kelly, Katherine J.; Dagher, Pierre C.; El-Achkar, Tarek M.; Dunn, Kenneth W.; Kidney Precision Medicine Project; Anatomy, Cell Biology and Physiology, School of Medicine
    The advent of personalized medicine has driven the development of novel approaches for obtaining detailed cellular and molecular information from clinical tissue samples. Tissue cytometry is a promising new technique that can be used to enumerate and characterize each cell in a tissue and, unlike flow cytometry and other single-cell techniques, does so in the context of the intact tissue, preserving spatial information that is frequently crucial to understanding a cell's physiology, function, and behavior. However, the wide-scale adoption of tissue cytometry as a research tool has been limited by the fact that published examples utilize specialized techniques that are beyond the capabilities of most laboratories. Here we describe a complete and accessible pipeline, including methods of sample preparation, microscopy, image analysis, and data analysis for large-scale three-dimensional tissue cytometry of human kidney tissues. In this workflow, multiphoton microscopy of unlabeled tissue is first conducted to collect autofluorescence and second-harmonic images. The tissue is then labeled with eight fluorescent probes, and imaged using spectral confocal microscopy. The raw 16-channel images are spectrally deconvolved into 8-channel images, and analyzed using the Volumetric Tissue Exploration and Analysis (VTEA) software developed by our group. We applied this workflow to analyze millimeter-scale tissue samples obtained from human nephrectomies and from renal biopsies from individuals diagnosed with diabetic nephropathy, generating a quantitative census of tens of thousands of cells in each. Such analyses can provide useful insights that can be linked to the biology or pathology of kidney disease. The approach utilizes common laboratory techniques, is compatible with most commercially-available confocal microscope systems and all image and data analysis is conducted using the VTEA image analysis software, which is available as a plug-in for ImageJ.
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    A Precision Medicine Approach Uncovers a Unique Signature of Neutrophils in Patients With Brushite Kidney Stones
    (Elsevier, 2020-05) Makki, Mohammad Shahidul; Winfree, Seth; Lingeman, James E.; Witzmann, Frank A.; Worcester, Elaine M.; Krambeck, Amy E.; Coe, Fredric L.; Evan, Andrew P.; Bledsoe, Sharon; Bergsland, Kristin J.; Khochare, Suraj; Barwinska, Daria; Williams, James C.; El-Achkar, Tarek M.; Medicine, School of Medicine
    Introduction: We have previously found that papillary histopathology differs greatly between calcium oxalate and brushite stone formers (SF); the latter have much more papillary mineral deposition, tubular cell injury, and tissue fibrosis. Methods: In this study, we applied unbiased orthogonal omics approaches on biopsied renal papillae and extracted stones from patients with brushite or calcium oxalate (CaOx) stones. Our goal was to discover stone type-specific molecular signatures to advance our understanding of the underlying pathogenesis. Results: Brushite SF did not differ from CaOx SF with respect to metabolic risk factors for stones but did exhibit increased tubule plugging in their papillae. Brushite SF had upregulation of inflammatory pathways in papillary tissue and increased neutrophil markers in stone matrix compared with those with CaOx stones. Large-scale 3-dimensional tissue cytometry on renal papillary biopsies showed an increase in the number and density of neutrophils in the papillae of patients with brushite versus CaOx, thereby linking the observed inflammatory signatures to the neutrophils in the tissue. To explain how neutrophil proteins appear in the stone matrix, we measured neutrophil extracellular trap (NET) formation-NETosis-and found it significantly increased in the papillae of patients with brushite stones compared with CaOx stones. Conclusion: We show that increased neutrophil infiltration and NETosis is an unrecognized factor that differentiates brushite and CaOx SF and may explain the markedly increased scarring and inflammation seen in the papillae of patients with brushite stones. Given the increasing prevalence of brushite stones, the role of neutrophil activation in brushite stone formation requires further study.