Biology subjects

Lucarelli, N.

Publications and source records attributed to Lucarelli, N..

4 recordsLinked to original sources

bioRxiv2025

Cellular and Spatial Drivers of Unresolved Injury and Functional Decline in the Human Kidney

Building upon a foundational Human Kidney resource, we present a comprehensive multi-modal atlas that defines spatially resolved versus unresolved repair states and mechanisms in human kidney disease. Homeostatic interactions between injured kidney epithelium and its surrounding milieu determine successful repair outcomes, while pathogenic signaling promotes unresolved inflammation and fibrosis leading to chronic disease. We integrated multiple single-cell and spatial modalities across [~]700 samples from >350 patients ([~]250 research biopsies), analyzing [~]1.7 million cells alongside complementary mouse multi-omic profiles spanning acute-to-chronic injury and aging (>300,000 cells) and spatial transcriptomic analysis of >150 human biopsies. This cross-species atlas delineates functional pathways and druggable targets across the nephron and defines gene regulatory networks and chromatin landscapes governing tubular, fibroblast, and immune cell transitions from injury to either recovery or failed repair states. We identified distinct cellular states associated with specific pathological features that show dynamic distributions between acute kidney injury (AKI) and chronic kidney disease (CKD), organized within unique spatial niches that reveal progression mechanisms from early injury to unresolved disease. Gene regulatory analyses prioritized key transcription factor activities (SOX4, SOX9, NFKB1, REL, KLFs) and their target networks establishing disease states and tissue microenvironments. These regulatory programs were directly linked to clinical outcomes, identifying molecular signatures of recovery and secreted biomarkers predictive of AKI-to-CKD progression, providing a key resource for therapeutic development and precision medicine approaches in kidney disease.

molecular biology↗

bioRxiv2024

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics

pathology↗

bioRxiv2024

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology

Digital pathology using whole slide imaging (WSI) and artificial intelligence (AI) has the potential to transform diagnostic workflows, but adoption remains limited by technical complexity and scalability. We developed the Computational Renal Pathology Suite (ComPRePS), a scalable cloud-based platform that automates WSI ingestion, compartmental segmentation, feature extraction, and AI-assisted interpretation through an integrated high-performance architecture. ComPRePS was evaluated in two use cases. First, using 213 procurement biopsies, we compared conventional assessments with automated AI analyses and a hybrid AI-assisted expert workflow. ComPRePS AI-assisted methods achieved higher precision and significantly improved interobserver agreement for key lesions, including global glomerulosclerosis, interstitial fibrosis and tubular atrophy, and arterial intimal thickening. Second, ComPRePS enabled high-throughput quantitative profiling of glomerular and tubular features across minimal change disease, diabetic nephropathy, and amyloid nephropathy revealing disease-specific phenotypic patterns inaccessible to manual evaluation. Overall, ComPRePS improves reproducibility, interpretability, and objectivity in renal pathology, bridging computation with clinical practice.

pathology↗

bioRxiv2023

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

BackgroundReference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine (SCr) in a multinational set of reference kidney tissue sections. MethodsA panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid-Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the relationship of histomorphometric parameters with age, sex, and SCr. ResultsOur deep-learning model achieved high segmentation performance for all test compartments. The size and density of nephrons and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Nephron size was significantly dependent on SCr. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of age. ConclusionsUsing deep learning, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics and SCr. Deep learning tools can increase the efficiency and rigor of histomorphometric analysis. Significance statementAlthough the importance of kidney morphometry is well explored in disease contexts, the definition of variance in reference tissue is not. Advancements in digital and computational pathology have rendered quantitative analysis of unprecedented tissue volumes via the single press of a button. The authors leverage the unique benefits of panoptic segmentation to perform the largest ever quantitation of reference kidney morphometry. Regression analysis identified several kidney morphometric features that varied significantly with patient age and sex, and the results suggested that the set size of nephrons might depend more intricately on creatinine than previously thought.

cell biology↗