Biology subjects

Chen, R. J.

Publications and source records attributed to Chen, R. J..

7 recordsLinked to original sources

bioRxiv2025

Mapping galectin-3 ligands in tear fluid establishes spliceoform-dependent lacritin binding

Galectin-3 (Gal-3) is a carbohydrate-binding protein which plays crucial roles in inflammation, immune response, cell migration, autophagy, and signaling. At the ocular surface, Gal-3 is also known to crosslink transmembrane mucins across the epithelial cell glycocalyx, forming lattice structures important for barrier function. However, the biological role of Gal-3 in circulating tear fluid remains largely unexplored. Similarly, whether Gal-3 engages extracellular glycoproteins in tears to affect downstream biological processes has yet to be investigated. As increased Gal-3 levels in tears are known to correlate with ocular pathologies such as dry eye disease (DED), we sought to elucidate the Gal-3 interactome in tear fluid and uncover biological insights into the function of Gal-3 beyond adhesion to the epithelial cell surface. Here, we combined ELISA, lectin affinity enrichment, mass spectrometry (MS)-based glycoproteomics, and lectin blotting to uncover Gal-3 interactors and their associated glycoepitopes. Overall, we report nearly 100 proteins enriched from tear fluid across 3 different patients, identifying proteins involved in immune response, inflammation, and antimicrobial activity. Most notably, we report lacritin as a novel ligand for Gal-3 and demonstrate that specific glycoforms of lacritin bearing core 2 O-glycans preferentially engage with Gal-3. Lastly, we show that the Gal-3-lacritin axis is spliceoform-specific and dependent on lacritin multimerization. Taken together, this study elucidates new ligands for Gal-3 in tear film and reveals mRNA splicing and multimerization as new biochemical mechanisms that fine-tune Gal-3 binding events.

biochemistry↗

bioRxiv2025

Human-specific features of the cerebellum and ZP2-regulated synapse development

Understanding the unique features of the human brain compared to non-human primates has long intrigued humankind. The cerebellum refines motor coordination and cognitive functions, contributing to the evolutionary development of human adaptability and dexterity. To identify shared and divergent features across primates, we conducted single-nucleus transcriptomic and chromatin accessibility profiling of the adult cerebellar cortex in humans, chimpanzees, macaques, and marmosets. We revealed human-specific transcriptomic and regulatory features, particularly those involved in synaptogenesis. Notably, we identified an enrichment of the sperm receptor zona pellucida glycoprotein 2 (ZP2) and its potential interactors, known for their roles in gamete interaction, in human granule cells. Experimental data show that ZP2 expression in human granule cells is induced by pontine mossy fibers, reducing synaptic proteins at pontocerebellar glomerular synapses, and decreasing cerebellar neuron electrophysiological activity. This unexpected co-option of ZP2 in human-specific synapse regulation provides insights into the evolutionary specialization of the human cerebellum.

neuroscience↗

bioRxiv2025

Molecular and cellular processes disrupted in the early postnatal Down syndrome prefrontal cortex

Down syndrome is the most common genetic cause of intellectual disability and is characterized by early-onset delays in motor, cognitive, and language development. The molecular mechanisms underlying these neurodevelopmental impairments remain poorly understood. Here, we utilized single-nucleus multiomic sequencing to simultaneously profile gene expression and chromatin accessibility in the Down syndrome prefrontal cortex during early postnatal development, a critical period for synaptogenesis, neural maturation, and developmental neuroimmune interactions. Our findings reveal widespread dysregulation of chromatin accessibility and gene expression, with deficits spanning metabolic and synaptic pathways, oligodendrocyte lineage progression, and a pronounced neuroinflammatory signature. We present a molecular atlas of Down syndrome neuropathology at a critical stage of brain development, highlighting convergent neurodevelopmental and neurodegenerative pathways and informing potential targeted therapies for Down syndrome-associated neuroinflammation.

neuroscience↗

bioRxiv2024

AI-driven Discovery of Morphomolecular Signatures in Toxicology

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers. Live demo: https://mahmoodlab.github.io/tox-discovery-ui/

bioinformatics↗

bioRxiv2024

Deep Learning-based Modeling for Preclinical Drug Safety Assessment

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a timeintensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity. Here, we introduce TRACE, a model designed for toxicologic liver histopathology assessment capable of tackling a range of diagnostic tasks across multiple scales, including situations where labeled data is limited. TRACE was trained on 15 million histopathology images extracted from 46,734 digitized tissue sections from 157 preclinical studies conducted on Rattus norvegicus. We show that TRACE can perform various downstream toxicology tasks spanning histopathological response assessment, lesion severity scoring, morphological retrieval, and automatic dose-response characterization. In an independent reader study, TRACE was evaluated alongside ten board-certified veterinary pathologists and achieved higher concordance with the consensus opinion than the average of the pathologists. Our study represents a substantial leap over existing computational models in toxicology by offering the first framework for accelerating and automating toxicological pathology assessment, promoting significant progress with faster, more consistent, and reliable diagnostic processes. Live Demo: https://mahmoodlab.github.io/tox-foundation-ui/

bioinformatics↗

bioRxiv2024

Comprehensive single-cell atlas of the mouse retina

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity at the single-cell resolution by classifying and characterizing cell types in multiple tissues and species. While several mouse retinal scRNA-seq reference datasets have been published, each dataset either has a relatively small number of cells or is focused on specific cell classes, and thus is suboptimal for assessing gene expression patterns across all retina types at the same time. To establish a unified and comprehensive reference for the mouse retina, we first generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse whole retinas. This dataset was generated through the targeted enrichment of rare population cells via antibody-based magnetic cell sorting. By integrating this new dataset with public datasets, we conducted an integrated analysis to construct the Mouse Retina Cell Atlas (MRCA) for wild-type mice, which encompasses over 330,000 single cells. The MRCA characterizes 12 major classes and 138 cell types. It captured consensus cell type characterization from public datasets and identified additional new cell types. To facilitate the public use of the MRCA, we have deposited it in CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal for visualization and gene expression exploration. The comprehensive MRCA serves as an easy-to-use, one-stop data resource for the mouse retina communities.

genomics↗

bioRxiv2019

Epigenetically bistable regions across neuron-specific genes govern neuron eligibility to a coding ensemble in the hippocampus

Episodic memories are stored in distributed neurons but how eligibility of individual neurons to coding ensembles is determined remains elusive. We identified thousands of predominantly bistable (CpG methylated or unmethylated) regions within neuronal gene bodies, established during the development of the mouse hippocampal dentate gyrus. Reducing DNA methylation and the proportion of the methylated epialleles at bistable regions compromised novel context-induced neuronal activation and spatial memory. Conversely, increasing methylation and the frequency of the methylated epialleles at bistable regions enhanced intrinsic excitability and spatial memory but impaired spatial working memory, indicating that the developmentally established methylated-unmethylated epiallelic balance at bistable regions is essential for proper neuronal excitability and hippocampal cognitive functions. Single-nucleus profiling revealed the enrichment of specific epialleles from a subset of bistable regions, primarily exonic, in encoding neurons. We propose a model in which epigenetically bistable regions create neuron heterogeneity, and specific constellations of exonic epialleles dictate, via modulating neuronal excitability, eligibility to a coding ensemble.

neuroscience↗