Biology subjects

Kelly, K. J.

Publications and source records attributed to Kelly, K. J..

8 recordsLinked to original sources

bioRxiv2024

Integration of spatial multiplexed protein imaging and transcriptomics in the human kidney tracks the regenerative potential timeline of proximal tubules

The organizational principles of nephronal segments are based on longstanding anatomical and physiological attributes that are closely linked to the homeostatic functions of the kidney. Novel molecular approaches have recently uncovered layers of deeper signatures and states in tubular cells that arise at various timepoints on the spectrum between health and disease. For example, a dedifferentiated state of proximal tubular cells with mesenchymal stemness markers is frequently seen after injury. The persistence of such a state is associated with failed repair. Here, we introduce a novel analytical pipeline applied to highly multiplexed spatial protein imaging to characterize proximal tubular subpopulations and neighborhoods in reference and disease human kidney tissue. The results were validated and extended through integration with spatial and single cell transcriptomics. We demonstrate that, in reference tissue, a large proportion of S1 and S2 proximal tubular epithelial cells express THY1, a mesenchymal stromal and stem cell marker that regulates differentiation. Kidney disease is associated with loss of THY1 and transition towards expression of PROM1, another stem cell marker shown recently to be linked to failed repair. We demonstrate that the trajectory of proximal tubular cells to THY1 expression is clearly distinct from that of PROM1, and that a state with PROM1 expression is associated with niches of inflammation. Our data support a model in which the interplay between THY1 and PROM1 expression in proximal tubules associates with their regenerative potential and marks the timeline of disease progression.

bioinformatics↗

bioRxiv2024

A MEF2C transcripton factor network regulates proliferation of glomerular endothelial cells in diabetic kidney disease

The maintenance of a healthy epithelial-endothelial juxtaposition requires cross-talk within glomerular cellular niches. We sought to understand the spatially-anchored regulation and transition of endothelial and mesangial cells from health to injury in DKD. From 74 human kidney samples, an integrated multi-omics approach was leveraged to identify cellular niches, cell-cell communication, cell injury trajectories, and regulatory transcription factor (TF) networks in glomerular capillary endothelial (EC-GC) and mesangial cells. Data were culled from single nucleus RNA and ATAC sequencing and three orthogonal spatial transcriptomic technologies for correlation with histopathological and clinical trial data. We identified a cellular niche in diabetic glomeruli enriched in a proliferative endothelial cell subtype (prEC) and altered vascular smooth muscle cells (VSMCs). Cellular communication within this niche maintained pro-angiogenic signaling with loss of anti-angiogenic factors. We identified a TF network of MEF2C, MEF2A, and TRPS1 which regulated SEMA6A and PLXNA2, a receptor-ligand pair opposing angiogenesis. In silico knockout of the TF network accelerated the transition from healthy EC-GCs toward a degenerative (injury) endothelial phenotype, with concomitant disruption of EC-GC and prEC expression patterns. Glomeruli enriched in the prEC niche had histologic evidence of neovascularization. MEF2C activity was increased in diabetic glomeruli with nodular mesangial sclerosis. The gene regulatory network (GRN) of MEF2C was dysregulated in EC-GCs of patients with DKD, but sodium glucose transporter-2 inhibitor (SGLT2i) treatment reversed the MEF2C GRN effects of DKD. The MEF2C, MEF2A, and TRPS1 TF network carefully balances the fate of the EC-GC in DKD. When the TF network is "on" or over-expressed in DKD, EC-GCs may progress to a prEC state, while TF suppression leads to cell death. SGLT2i therapy may restore the balance of MEF2C activity.

cell biology↗

bioRxiv2023

The chromatin landscape of healthy and injured cell types in the human kidney

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. However, comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measured dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We established a comprehensive and spatially-anchored epigenomic atlas to define the kidneys active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we noted distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulated the transition between health and injury, while in thick ascending limb cells this transition was regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguished two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

molecular biology↗

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↗

bioRxiv2021

Alterations in protein translation and carboxylic acid catabolic processes in diabetic kidney disease

Diabetic kidney disease (DKD) remains the leading cause of end stage kidney disease despite decades of study. Alterations in the glomerulus and kidney tubules both contribute to the pathogenesis of DKD although the majority of investigative efforts have focused on the glomerulus. We sought to examine the differential expression signature of human DKD in the glomerulus and proximal tubule and corroborate our findings in the db/db mouse model of diabetes. A transcriptogram network analysis of RNAseq data from laser microdissected (LMD) human glomerulus and proximal tubule of DKD and reference nephrectomy samples revealed enriched pathways including rhodopsin-like receptors, olfactory signaling, and ribosome (protein translation) in the proximal tubule of human DKD biopsy samples. The translation pathway was also enriched in the glomerulus. Increased translation in diabetic kidneys was validated using polyribosomal profiling in the db/db mouse model of diabetes. Using single nuclear RNA sequencing (snRNAseq) of kidneys from db/db mice, we prioritized additional pathways identified in human DKD. The top overlapping pathway identified in the murine snRNAseq proximal tubule clusters and the human LMD proximal tubule compartment was carboxylic acid catabolism. Using ultra-performance liquid chromatography-mass spectrometry, the fatty acid catabolism pathway was also found to be dysregulated in the db/db mouse model. The Acetyl-CoA metabolite was down-regulated in db/db mice, aligning with the human differential expression of the genes ACOX1 and ACACB. In summary, our findings demonstrate that proximal tubular alterations in protein translation and carboxylic acid catabolism are key features in both human and murine DKD.

systems biology↗

bioRxiv2021

Integration of spatial transcriptomic and single cell sequencing identifies expression patterns underlying immune and epithelial cell cross-talk in acute kidney injury

Despite important advances in studying experimental and clinical acute kidney injury (AKI), the pathogenesis of this disease remains incompletely understood. Single cell sequencing studies have closed this knowledge gap by characterizing the transcriptomic signature of different cell types within the kidney. However, the spatial distribution of injury can be regional and affect cells heterogeneously. We first optimized coordination of spatial transcriptomics and single nuclear sequencing datasets, mapping 30 dominant cell types to a human nephrectomy sample. The predicted cell type spots corresponded with the underlying hematoxylin and eosin histopathology. To study the implications of acute kidney injury on the distribution of transcript expression, we then characterized the spatial transcriptomic signature of two murine AKI models: ischemia reperfusion injury (IRI) and cecal ligation puncture (CLP). Localized regions of reduced overall expression were found associated with tissue injury pathways. Using single cell sequencing, we deconvoluted the signature of each spatial transcriptomic spot, identifying patterns of colocalization between immune and epithelial cells. As expected, neutrophils infiltrated the renal medullary outer stripe in the ischemia model. Atf3 was identified as a chemotactic factor in S3 proximal tubule cells. In the CLP model, infiltrating macrophages dominated the outer cortical signature and Mdk was identified as a corresponding chemotactic factor. The regional distribution of these immune cells was validated with multiplexed CO-Detection by inDEXing (CODEX) immunofluorescence. Spatial transcriptomic sequencing can aid in uncovering the mechanisms driving immune cell infiltration and allow detection of relevant subpopulations in single cell sequencing. The complementarity of these technologies facilitates the development of a transcriptomic kidney atlas in health and disease.

bioinformatics↗

bioRxiv2020

Irradiance modulates thermal niche in a previously undescribed low-light and cold-adapted nano-diatom

Diatoms have well-recognized roles in fixing and exporting carbon and supplying energy to marine ecosystems, but only recently have we begun to explore the diversity and importance of nano- and pico-diatoms. Here we describe a small (~5 m) diatom from the genus Chaetoceros Isolated from a wintertime temperate estuary (2{degrees} C, Narragansett Bay, RI), with a unique obligate specialization for low-light environments (< 120 mol photons m-2 sec-1). This diatom exhibits a striking interaction between irradiance and thermal responses whereby as temperatures increase, so does its susceptibility to light stress. Historical 18S rRNA amplicon data from our study site show this isolate was abundant throughout a six-year period, and its presence strongly correlates with winter and early spring months when light and temperature are low. Two ASVs matching this isolate had a circumpolar distribution in Tara Polar Ocean Circle samples, indicating its unusual light and temperature requirements are adaptations to life in a cold, dark environment. We expect this isolates low light, psychrophilic niche to shrink as future warming-induced stratification increases both light and temperature levels experienced by high latitude marine phytoplankton.

microbiology↗

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↗