Biology subjects

Khochare, S.

Publications and source records attributed to Khochare, S..

4 recordsLinked to original sources

bioRxiv2021

Integrated cytometry with machine learning applied to high-content imaging of human kidney tissue for in-situ cell classification and neighborhood analysis

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 applied to human kidney tissue to create single cell resolution datasets that are both spatially large and multi-dimensional. These single cell resolution high-content imaging datasets have a great potential to uncover the complex spatial organization and cellular make-up of the human kidney. Tissue cytometry is a novel approach used for quantitative analysis of imaging data, but the scale and complexity of such datasets 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, VTEAs integrated pipeline now includes enhanced analytical tools, such as machine learning, data visualization, and neighborhood analyses for hyperdimensional large-scale imaging datasets. These novel capabilities enable the analysis of mesoscale two and three-dimensional multiplexed human kidney imaging datasets (such as CODEX and 3D confocal multiplexed fluorescence imaging). We demonstrate the utility of this approach in identifying cell subtypes in the kidney based on labels, spatial association and their microenvironment or neighborhood membership. VTEA provides integrated and intuitive approach to decipher the cellular and spatial complexity of the human kidney and complement other transcriptomics and epigenetic efforts to define the landscape of kidney cell types.

physiology↗

bioRxiv2020

A wolf in another wolf's clothing. Post-genomic regulation dictates venom profiles of medically-important cryptic kraits in India

The Common Krait (Bungarus caeruleus) shares a distribution range with many other phenotypically-similar kraits across the Indian subcontinent. Despite several reports of fatal envenoming by other Bungarus species, commercial Indian antivenoms are only manufactured against B. caeruleus. It is, therefore, imperative to understand the distribution of genetically-distinct lineages of kraits, compositional differences in their venoms, and the consequent impact of venom variation on the (pre)clinical effectiveness of antivenom therapy. To address this knowledge gap, we conducted phylogenetic and comparative venomics investigations of kraits in Southern and Western India. Phylogenetic reconstructions using mitochondrial markers revealed a new species of krait, Romulus krait (B. romulusi), in Southern India. Additionally, we find that kraits with 17 mid-body dorsal scale rows in Western India do not represent a subspecies of the Sind Krait (B. sindanus walli) as previously believed, but are genetically very similar to B. sindanus in Pakistan. Furthermore, venom proteomics and comparative transcriptomics revealed completely contrasting venom profiles. While the venom gland transcriptomes of all three species were highly similar, venom proteomes and toxicity profiles differed significantly, suggesting the prominent role of post-genomic regulatory mechanisms in shaping the venoms of these cryptic kraits. In vitro venom recognition and in vivo neutralisation experiments revealed a strong negative impact of venom variability on the preclinical performance of commercial antivenoms. While the venom of B. caeruleus was neutralised as per the manufacturers claim, performance against the venoms of B. sindanus and B. romulusi was alarming, highlighting the need for regionally-effective antivenoms in India.

evolutionary biology↗

bioRxiv2020

In situ classification of cell types in human kidney tissue using 3D nuclear staining

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.

cell biology↗

bioRxiv2019

A precision medicine approach uncovers a unique signature of neutrophils in patients with brushite kidney stones

BackgroundWe 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. MethodsIn 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. ResultsBrushite 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 to those with CaOx stones. Large-scale 3D tissue cytometry on renal papillary biopsies showed an increase in the number and density of neutrophils in the papillae of brushite vs. CaOx patients, 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 brushite compared to CaOx patients. ConclusionsWe 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 brushite patients. Given the increasing prevalence of brushite stones, the role of neutrophil activation in brushite stone formation requires further study.

pathology↗