Biology subjects

Rankin, R.

Publications and source records attributed to Rankin, R..

5 recordsLinked to original sources

bioRxiv2025

Single-cell transcriptomic integrated with machine learning reveals retinal cell-specific biomarkers in diabetic retinopathy

Diabetic retinopathy (DR) remains a principal cause of vision impairment worldwide, involved complex retinal cellular pathophysiology that remains incompletely understood. To elucidate cell-type-specific molecular signatures underlying DR, we generated a high-resolution single-cell transcriptomic atlas of 297,121 retinal cells from 20 Chinese donors, including non-diabetic controls (26.4%), diabetic without retinopathy (23.4%) and DR (50.2%). Following rigorous quality control, batch-effect correction, and clustering and annotation, 10 major retinal cell populations were delineated. Differential expression analyses across disease states within each cell type yielded candidate gene sets, which were further refined via a multi-stage machine-learning pipeline combining L1-regularized logistic regression and recursive feature elimination with cross-validation, alongside bootstrap stability selection. Resulting cell-type-specific classifiers achieved high accuracy (79-95%) and AUCs (0.85-0.99) in distinguishing DR disease states. Enrichment analyses implicated immune activation, oxidative stress, neurodegeneration and synaptic dysfunction pathways across multiple cell types in retina. Integrating 567 unique marker genes from all cell types, a general multilayer perceptron classifier achieved 95.31% overall accuracy on held-out test data, demonstrating the translational potential of these signatures for non-diabetic controls, diabetic without retinopathy and DR classification. This high-resolution atlas and the accompanying analytic framework provide a robust computational framework for biomarker discovery, mechanistic insight and targeted intervention strategies in diabetic retinal diseases.

bioinformatics↗

bioRxiv2025

Cell Type Specific Aging Transcriptional Signatures of Human Retina Through Integrated Machine Learning and Single-Cell Transcriptomics

PurposeTo characterize cell type specific transcriptional changes during human retinal aging and develop machine learning model for cellular age discrimination in a Chinese cohort. DesignCross-sectional, laboratory-based observational study. ParticipantsEighteen unfrozen retinas from 12 Chinese donors (9 young, 34-55y; 9 old, 68-92 y). MethodsSingle-cell RNA sequencing (10x, v3.1) generated 223612 cells, batch-corrected with scVI; age-related signatures were defined by intersecting single-cell and pseudo-bulk differentially expressed genes, then cell-type-specific panels were rank-ordered with L1-regularised logistic regression plus recursive feature elimination and interpreted through hallmark-pathway enrichment and transcription-factor regulon mapping. Main Outcome MeasuresAge-related cellular composition shifts; cell-type-specific differentially expressed genes; machine-learning classifier accuracy and feature rankings; transcription factor regulon activity changes. ResultsEleven major retinal cell populations were identified. Aging showed declining rod-to-cone ratios, reduced bipolar cell proportions among interneurons, and increased astrocyte abundance. Muller glial cells exhibited the most pronounced transcriptional changes, followed by bipolar cells and rods. Machine-learning classifiers achieved 80-96% accuracy across cell types (microglia 96%, horizontal cells 93%, bipolar cells 91%, cones 90%, rods 89%). Shared aging signatures included mitochondrial dysfunction and inflammatory activation. Cell specific vulnerabilities emerged: mitochondria-centric stress in rods/bipolar cells, proteostasis-retinoid metabolism in cones, and structural-RNA maintenance in horizontal cells. ConclusionsThis study provides the first machine learning derived, cell-type specific aging signatures for human retina in a Chinese cohort, revealing both conserved molecular hallmarks and distinctive cellular vulnerabilities that inform targeted therapeutic strategies for retinal aging.

bioinformatics↗

bioRxiv2025

Single-cell transcriptomic atlas of human retina from Chinese donors reveals population-specific cellular diversity

The human retina exhibits complex cellular heterogeneity which is critical for visual function, yet comprehensive ethnic-specific references are scarce in ophthalmic transcriptomics. The lack of single-cell RNA sequencing (scRNA-seq) data from Asian populations particularly Chinese donors imposes significant limitations in understanding population-specific retinal biology. We constructed the first comprehensive single-cell transcriptomic atlas of the human retina from Chinese donors, generated through high-throughput scRNA-seq of [~]290,000 viable cells obtained from 18 fresh retinal specimens (living donor and post-mortem specimens). Our multi-level analysis identified 10 distinct retinal cell types, encompassing all major neuronal lineages, Muller glia, astrocytes, microglia. Detailed subcluster analyses further revealed extensive heterogeneity, identifying distinct subtypes within several cell populations such as 7 amacrine cell subtypes and 14 bipolar cell subtypes. Concurrently, through systematic analysis of delineated subtype-specific molecular programs, we mapped their associated biological signaling pathways, functions, and mechanistic processes. This analysis explained the critical involvement of these subpopulations in core biological processes including synaptic organization, neurotransmission, and phototransduction cascades, potentially governing retinal homeostatic regulation and disease mechanisms. Single-cell transcriptomic atlas of the human retina from Chinese donors describes a comprehensive cellular landscape, encompassing major cell types and subtypes including neuronal, glial, and immune populations. This ethnic-specific atlas provided an important reference for understanding retinal development, cellular interaction and disease pathogenesis in Chinese populations, addressing a longstanding gap in ophthalmic transcriptomic resources.

genomics↗

bioRxiv2025

Single-Cell Transcriptomics Reveals Dynamic Microglial States and Neural-Immune Networks in Human Diabetic Retinopathy

Diabetic retinopathy (DR) is a major cause of vision loss worldwide. Here, we conduct single-cell RNA sequencing of twenty human retina samples (from living and post-mortem donors) across non-diabetic, diabetic, and DR states to create a comprehensive transcriptomic atlas. We identify two stable microglial populations--homeostatic and inflammatory--that exist along a functional continuum, plus a neutrophil cluster within C1QA+ myeloid cells with dynamic transitions occurring throughout disease progression. Module-level analysis reveals divergent transcriptional trajectories: homeostatic microglia maintain energetic programs while selectively upregulating stress elements, whereas inflammatory microglia layer additional pro-inflammatory programs onto preserved biosynthetic foundations. Eleven co-expression modules organize into two major axes: an inflammatory-stress axis, and a regulatory/metabolic-motility axis, with a stable translation module persisting across disease stages. Cell communication analysis further highlights sophisticated neural-immune interactions, particularly between photoreceptors and microglia. Our findings provide insights into the complex cellular dynamics of DR progression and suggest potential therapeutic targets for early intervention.

bioinformatics↗

bioRxiv2024

Fresh Human Retinal scRNA-seq Atlas Reveals a Novel Cone Subtype and Cellular Diversity

BackgroundCurrent human retina studies predominantly utilize post-mortem tissue, and the sample accessibility constraints make the characterization of the living human retina at single-cell resolution a challenge. Although single-nucleus RNA-seq expands the utility of frozen samples, it provides a nuclear-centric view, potentially missing key cytoplasmic information and transient biological processes. Thus, it is important to generate resources directly from living human retinal tissue to complement existing datasets. MethodsWe profiled 106,829 single cells from nine unfrozen human retina samples. Living samples were collected withinLJ10LJmin of therapeutic enucleation and four postmortem samples were collected within 6LJh. After standardized dissociation, single-cell transcriptomes were generated using 10x Genomics 3 RNA-seq and applied scVI to generate batch-corrected integrated atlas. Major cell types and subtypes were annotated through iterative Leiden clustering, canonical markers. Subsequent analyses included differential expression comparisons between cell states and regulon activity profiling to further characterize cellular identities and regulatory networks. Transcriptional dynamics were assessed using RNA velocity, and cell-cell signaling pathways were inferred with CellChat. Key findings were validated in independent samples from two additional donors (four samples) using the identical workflow. ResultsWe contribute to establishing a reference for retinal cell type proportions and cellular states. Our analysis revealed ELF1-mlCone, a distinct cluster of mlCone photoreceptors identified by distinct transcriptional features. The presence and transcriptional features of this cluster were validated in independent samples. Additionally, by comparing living and post-mortem samples, our study highlights differences in transcriptional dynamics: living tissue preserved coherent RNA velocity streams, enabling clear dynamic state transitions, while post-mortem tissue exhibited disorganized patterns. These findings suggest that using living tissue can improve the capture of active cellular states and transitions. ConclusionsOur atlas provides a single-cell reference contrasting living versus early post-mortem human retina, integrating cell type composition, transcriptional diversity, and functional insights. It may serve as a useful resource for retinal research and for understanding aspects of human retinal biology, particularly given its inclusion of living tissue and diverse pathological states.

bioinformatics↗