Biology subjects

Ouyang, J. F.

Publications and source records attributed to Ouyang, J. F..

9 recordsLinked to original sources

bioRxiv2025

ShinyCell2: An extended library for simple and sharable visualisation of spatial, peak-based and multi-omic single-cell data

Single-cell technologies now span multiple modalities, generating large, complex datasets that challenge analysis and sharing. We present ShinyCell2, an enhanced R package for interactive visualisation of single-cell multi-omics and spatial transcriptomics data. ShinyCell2 retains the simplicity and lightweight deployment of its predecessor while introducing advanced visualisations, cross-modality comparisons, and statistical tools tailored to spatial and multi-omic data. It enables intuitive, rapid exploration of high-dimensional data without requiring extensive computational expertise.

bioinformatics↗

bioRxiv2024

scPanel: A tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large the translation to clinical applications is challenging due to prohibitive sequencing costs. Here we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a minimal gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g., diseases/drugs). scPanel incorporates a data-driven way to automatically determine the number of selected genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel on scleroderma and COVID-19 datasets resulted in high patient classification accuracy using a small number (<20) of genes automatically selected from the entire transcriptome. We demonstrate 100% cross-dataset accuracy to predict COVID-19 disease state on an external dataset, illustrating the generalizability of the predicted genes. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify small sets of reliable biomarker candidates for clinical translation.

bioinformatics↗

bioRxiv2023

cellstruct: Metrics scores to quantify the biological preservation between two embeddings

Single-cell transcriptomics (scRNA-seq) is extensively applied in uncovering biological heterogeneity. There are different dimensionality reduction techniques, but it is unclear which method works best in preserving biological information when creating a two-dimensional embedding. Therefore, we implemented cellstruct, which calculates three metrics scores to quantify the global or local biological similarity between a two-dimensional and its corresponding higher-dimensional PCA embeddings at either single-cell or cluster level. These scores pinpoint cell populations with low biological information preservation, in addition to visualizing the cell-cell or cluster-cluster relationships in the PCA embedding. Two study cases illustrate the usefulness of cellstruct in exploratory data analysis.

bioinformatics↗

bioRxiv2022

Systems Level Identification Of A Matrisome-Associated Macrophage Polarization State In Multi-Organ Fibrosis

Tissue fibrosis affects multiple organs and involves a master-regulatory role of macrophages which respond to an initial inflammatory insult common in all forms of fibrosis. The recently unraveled multi-organ heterogeneity of macrophages in healthy and fibrotic human disease suggest that tissue resident macrophages, expressing osteopontin (SPP1), associate with lung and liver fibrosis. However, the conservation of this SPP1+ macrophage population across different tissues, and its specificity to fibrotic diseases with different etiologies remain unclear. Integrating 13 single cell RNA-sequencing datasets to profile 225,985 tissue macrophages from healthy and fibrotic heart, lung, liver, kidney, skin and endometrium, we extended the association of SPP1+ macrophages with fibrosis to all these tissues. We also identified a subpopulation expressing matrisome-associated genes (e.g., matrix metalloproteinases and their tissue inhibitors), functionally enriched for ECM remodeling and cell metabolism, representative of a matrisome-associated macrophage (MAM) polarization state within SPP1+ macrophages. Importantly, the MAM polarization state follows a differentiation trajectory from SPP1+ macrophages, which was conserved across all fibrotic tissues and driven by NFATC1 and HIVEP3 regulons. Unlike SPP1+ macrophages, the MAM polarization state shows a positive association with ageing in mice and humans, and across multiple tissues during homeostasis. These results suggest an advanced, age-dependent polarization state of SPP1+ macrophages in fibrotic tissues as a result of prolonged inflammatory cues within each tissue microenvironment.

systems biology↗

bioRxiv2020

ShinyCell: Simple and sharable visualisation of single-cell gene expression data

MotivationAs the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. ResultsIn order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customised in order to maximise their usability and can be easily uploaded to online platforms to facilitate wider access to published data. AvailabilityShinyCell is available at https://github.com/SGDDNB/ShinyCell. Contactowen.rackham@duke-nus.edu.sg

bioinformatics↗

bioRxiv2019

GeneSwitches: Ordering gene-expression and functional events in single-cell experiments

SummaryEmerging single-cell RNA-seq technologies has made it possible to capture and assess the gene expression of individual cells. Based on the similarity of gene expression profiles, many tools have been developed to generate an in silico ordering of cells in the form of pseudo-time trajectories. However, these tools do not provide a means to find the ordering of critical gene expression changes over pseudo-time. We present GeneSwitches, a tool that takes any single-cell pseudo-time trajectory and determines the precise order of gene-expression and functional-event changes over time. GeneSwitches uses a statistical framework based on logistic regression to identify the order in which genes are either switched on or off along pseudo-time. With this information, users can identify the order in which surface markers appear, investigate how functional ontologies are gained or lost over time, and compare the ordering of switching genes from two related pseudo-temporal processes.\n\nAvailabilityGeneSwitches is available at https://geneswitches.ddnetbio.com\n\nContactowen.rackham@duke-nus.edu.sg\n\nSupplementary Informationis available at http://www.ddnetbio.com/files/GeneSwitches_SI.pdf

bioinformatics↗

bioRxiv2019

Functional Annotation of Human Long Non-Coding RNAs via Molecular Phenotyping

Long non-coding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes and yet, their functions remain largely unknown. We systematically knockdown 285 lncRNAs expression in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNA exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest to-date lncRNA knockdown dataset with molecular phenotyping (over 1,000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.

genomics↗

bioRxiv2019

Mouse and human microglial phenotypes in Alzheimer’s disease are controlled by amyloid plaque phagocytosis through Hif1α

The important role of microglia, the brains resident immune cells, in Alzheimers disease (AD) is now well recognized, however their molecular and functional diversity and underlying mechanisms still remain controversial. To transcriptionally and functionally characterize the diversity of microglia in AD and aging, we isolated the amyloid plaque-containing (XO4+) and non-containing (XO4-) microglia from an AD mouse model. Transcriptomics analysis unveiled independent transcriptional trajectories in ageing and AD. XO4+ microglial transcriptomes linked plaque phagocytosis to altered [Formula] expression of bona fide late onset AD genetic risk factors. We further revealed that the XO4+ transcriptional program is present in a subset of human microglia from AD patients and is a direct and reversible consequence of A{beta} plaque phagocytosis. Conversely, XO4- microglia in AD displayed an accelerated ageing signature and contained more intracellular post synaptic material than plaque-containing microglia, despite reduced active synaptosome phagocytosis. Mechanistically, we predicted HIF1 as a core regulator of the XO4-/XO4+ axis, and further validated the mechanism in vitro using human stem cell-derived microglia like cells and primary human microglia. Together these findings unveiled the molecular mechanism underpinning the functional diversity of microglia in AD, providing opportunities to develop treatments targeted at subset specific manipulation of the microglial niche.

neuroscience↗

bioRxiv2019

A single cell brain atlas in human Alzheimer’s disease

Alzheimers disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimers patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.\n\nHighlightsO_LIWe generated the first human single cell transcriptome in AD patient brains\nC_LIO_LIOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)\nC_LIO_LIOur analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categories\nC_LIO_LIOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clusters\nC_LIO_LIWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clusters\nC_LIO_LIFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.\nC_LI\n\nData resource web interface:http://adsn.ddnetbio.com

neuroscience↗