ORCID Profile
0000-0003-1098-3138
Current Organisations
University of Sydney
,
Children's Medical Research Institute
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Publisher: Elsevier BV
Date: 10-2017
Publisher: Cold Spring Harbor Laboratory
Date: 16-06-2022
DOI: 10.1101/2022.06.13.495996
Abstract: Characterizing cell identity in complex tissues such as the human retina is essential for studying its development and disease. While retinal organoids derived from pluripotent stem cells have been widely used to model development and disease of the human retina, there is a lack of studies that have systematically evaluated molecular and cellular fidelity of the organoids derived from various culture protocols in recapitulating their in vivo counterpart. To this end, we performed an extensive meta-atlas characterisation of cellular identities of the human eye, covering a wide range of developmental stages. The resulting map uncovered previously unknown biomarkers of major retinal cell types and those associated with cell-type specific maturation. Using our retinal cell identity map from the fetal and adult tissues, we systematically assessed the fidelity of the retinal organoids to mimic the human eye, enabling us to comprehensively benchmark the current protocols for retinal organoid generation.
Publisher: Cold Spring Harbor Laboratory
Date: 06-12-2017
DOI: 10.1101/229815
Abstract: Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level are intrinsically stochastic and noisy. Yet, on cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how their expression stability can be assessed? We have developed a computational framework for ranking expression stability of genes in single cells. Here we evaluate the proposed framework and characterize SEGs derived from two scRNA-seq datasets that profile early human and mouse development. Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets are highly reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across 10 erse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in in idual cells. SEGs identified in this study have immediate utility both for understanding variation/stability of single-cell transcriptomes and for practical applications including scRNA-seq data normalization, the proposed framework can be applied to identify genes with stable expression in other scRNA-seq datasets.
Publisher: Public Library of Science (PLoS)
Date: 23-06-2016
Publisher: Springer Science and Business Media LLC
Date: 12-2021
DOI: 10.1186/S13059-021-02544-3
Abstract: Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. Here, we revisit feature selection techniques and summarise recent developments. We review their application to a range of single-cell data types generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions and finally consider their scalability and make general recommendations on each type of feature selection method. We hope this review stimulates future research and application of feature selection in the single-cell era.
Publisher: Cold Spring Harbor Laboratory
Date: 09-2020
DOI: 10.1101/2020.08.31.276329
Abstract: Mass spectrometry (MS)-based phosphoproteomics has revolutionised our ability to profile phosphorylation-based signalling in cells and tissues on a global scale. To infer the action of kinases and signalling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalisation using a novel set of ‘stably phosphorylated sites’, and in functional analysis for inferring active kinases and signalling pathways. In particular, we introduce a ‘signalome’ construction method for identifying a collection of signalling modules to summarise and visualise the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating novel biological knowledge from MS-based phosphoproteomic data.
Publisher: Springer Science and Business Media LLC
Date: 25-11-2021
DOI: 10.1038/S41467-021-27130-W
Abstract: Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational methods for analysing scRNA-seq data especially when ground truth is experimentally unattainable. The reliability of evaluation depends on the ability of simulation methods to capture properties of experimental data. However, while many scRNA-seq data simulation methods have been proposed, a systematic evaluation of these methods is lacking. We develop a comprehensive evaluation framework, SimBench, including a kernel density estimation measure to benchmark 12 simulation methods through 35 scRNA-seq experimental datasets. We evaluate the simulation methods on a panel of data properties, ability to maintain biological signals, scalability and applicability. Our benchmark uncovers performance differences among the methods and highlights the varying difficulties in simulating data characteristics. Furthermore, we identify several limitations including maintaining heterogeneity of distribution. These results, together with the framework and datasets made publicly available as R packages, will guide simulation methods selection and their future development.
Publisher: Cold Spring Harbor Laboratory
Date: 18-01-2023
DOI: 10.1101/2023.01.18.524481
Abstract: The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarised and incorporated into patient outcome prediction models in several ways, however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integration approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using each single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalisation when using multiple datasets as the model input. This work assesses and compares the performance of three categories of workflow consisting of 350 analytical combinations for outcome prediction using multi-s le, multi-conditions single-cell studies. We observed that using ensemble of feature types performs better than using in idual feature type We found that in the current data, all learning approaches including deep learning exhibit similar predictive performance. When combining multiple datasets as the input, our study found that integrating multiple datasets at the cell level performs similarly to simply concatenating the patient representation without modification.
Publisher: Oxford University Press (OUP)
Date: 09-2019
DOI: 10.1093/GIGASCIENCE/GIZ106
Abstract: Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the "Mouse Atlas" dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across erse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in in idual cells. SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, "scSEGIndex," is incorporated into the scMerge Bioconductor R package (sydneybiox.github.io/scMerge/reference/scSEGIndex.html) and can be used for identifying genes with stable expression in scRNA-seq datasets.
Publisher: The Company of Biologists
Date: 2017
DOI: 10.1242/JCS.205369
Abstract: Akt is a key node in a range of signal transduction cascades and plays a critical role in diseases such as cancer and diabetes. Fluorescently-tagged Akt reporters have been used to discern Akt localisation, yet it has not been clear how well these tools recapitulate the behaviour of endogenous Akt. Here we observed that fusion of eGFP to Akt impaired both insulin-stimulated plasma membrane recruitment and its phosphorylation. Endogenous-like responses were restored by replacing eGFP with TagRFP-T. The improved response magnitude and sensitivity afforded by TagRFP-T-Akt over eGFP-Akt enabled monitoring of signalling outcomes in single cells at physiological doses of insulin with subcellular resolution and revealed two previously unreported features of Akt biology. In 3T3-L1 adipocytes, stimulation with insulin resulted in recruitment of Akt to the plasma membrane in a polarised fashion. Additionally, we observed oscillations in plasma membrane localised Akt in the presence of insulin with a consistent periodicity of 2 minutes. Our studies highlight the importance of fluorophore choice when generating reporter constructs and shed light on new Akt signalling responses that may encode complex signalling information.
Publisher: Springer Science and Business Media LLC
Date: 08-02-2022
DOI: 10.1186/S13059-022-02622-0
Abstract: A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the s le, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by s ling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( github.com/PYangLab/scCCESS ).
Publisher: Bentham Science Publishers Ltd.
Date: 12-2010
Publisher: Elsevier BV
Date: 11-2015
Publisher: Springer Science and Business Media LLC
Date: 27-02-2019
Publisher: Elsevier BV
Date: 2020
DOI: 10.1016/J.JID.2019.06.128
Abstract: Actinic keratosis, Bowen's disease and cutaneous squamous cell carcinoma (cSCC) are heterogeneous keratinocytic skin lesions. Biomarkers that can accurately stratify these lesion types are needed to support a new paradigm of personalized and precise management of skin neoplasia. In this paper, we used a data independent acquisition proteomics workflow, sequential window acquisition of all theoretical mass spectra, to analyze formalin-fixed paraffin-embedded s les of normal skin and keratinocytic skin lesions, including well-differentiated, moderately differentiated and poorly differentiated cSCC lesions. We quantified 3,574 proteins across the 93 s les studied. Differential abundance analysis identified 19, 5, and 6 protein markers exclusive to actinic keratosis, Bowen's disease and cSCC lesions, respectively. Among cSCC lesions of various levels of tumor differentiation, 118, 230, and 17 proteins showed a potential as biomarkers of well-differentiated, moderately differentiated and poorly differentiated cSCC lesions, respectively. Bioinformatics analysis revealed that actinic keratosis and cSCC lesions were associated with decreased apoptosis, and Bowen's disease lesions with over-representation of the DNA damage repair pathway. Differential expression of alternatively spliced FGFR2, Rho guanosine triphosphatase signaling, and RNA metabolism proteins were associated with the level of cSCC tumor differentiation. Proteome profiles also separated keratinocytic skin lesion subtypes on principal components analysis. Overall, protein markers have excellent potential to discriminate keratinocytic skin lesion subtypes and facilitate new diagnostic and therapeutic strategies.
Publisher: Cold Spring Harbor Laboratory
Date: 27-05-2022
DOI: 10.1101/2022.05.26.493198
Abstract: The failure of metabolic tissues to appropriately respond to insulin (“insulin resistance”) is an early marker in the pathogenesis of type 2 diabetes. Protein phosphorylation is central to the adipocyte insulin response, but how adipocyte signaling networks are dysregulated upon insulin resistance is unknown. Here we employed phosphoproteomics to delineate insulin signal transduction in adipocyte cells and adipose tissue. Across a range of insults triggering insulin resistance, we observed marked rewiring of the insulin signaling network. This included both attenuated insulin-responsive phosphorylation, and the emergence of phosphorylation uniquely insulin-regulated in insulin resistance. Identifying signaling changes common to multiple insults revealed subnetworks likely containing causal drivers of insulin resistance. Focusing on defective GSK3 signaling initially observed in a relatively small subset of well-characterized substrates, we employed a pipeline for identifying context-specific kinase substrates. This facilitated robust identification of widespread dysregulated GSK3 signaling. Pharmacological inhibition of GSK3 partially reversed insulin resistance in cells and tissue explants. These data highlight that insulin resistance is a multi-nodal signaling defect that encompasses dysregulated GSK3 activity.
Publisher: Oxford University Press (OUP)
Date: 30-08-2022
DOI: 10.1093/BIOINFORMATICS/BTAC590
Abstract: With the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into s les from in iduals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each s le (e.g. in iduals). Here, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the s le level. We demonstrate that summarizing a broad collection of features at the s le level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of in iduals. scFeatures is publicly available as an R package at github.com/SydneyBioX/scFeatures. All data used in this study are publicly available with accession ID reported in the Section 2. Supplementary data are available at Bioinformatics online.
Publisher: Cold Spring Harbor Laboratory
Date: 25-11-2022
DOI: 10.1101/2022.11.23.517747
Abstract: The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. The availability of multiple methods for detecting SVGs bears questions such as whether different methods select a similar set of SVGs, how reliable is the reported statistics significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. In this study, we address the above questions by systematically evaluating a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. Our results shed light on the performance of each method from multiple aspects and highlight the discrepancy among different methods especially on calling statistically significant SVGs across datasets. Taken together, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of such methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2009
DOI: 10.1109/MIS.2009.92
Publisher: Cold Spring Harbor Laboratory
Date: 23-09-2019
DOI: 10.1101/776948
Abstract: Cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, we present scClassify, a hierarchical classification framework based on ensemble learning. scClassify can identify cells from published scRNA-seq datasets more accurately and more finely than in the original publications. We also estimate the cell number needed for accurate classification anywhere in a cell type hierarchy.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 06-2008
Publisher: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S12859-019-3179-5
Abstract: Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of in idual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex s les or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification. Here, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets to generate clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k -means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metric used. Our results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from edcom/scCCESS
Publisher: Elsevier BV
Date: 06-2013
Publisher: Elsevier BV
Date: 2023
Publisher: Proceedings of the National Academy of Sciences
Date: 26-04-2019
Abstract: Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with in idual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.
Publisher: Elsevier BV
Date: 03-2014
Publisher: Oxford University Press (OUP)
Date: 14-02-2017
DOI: 10.1093/BIOINFORMATICS/BTX092
Abstract: DNA binding proteins such as chromatin remodellers, transcription factors (TFs), histone modifiers and co-factors often bind cooperatively to activate or repress their target genes in a cell type-specific manner. Nonetheless, the precise role of cooperative binding in defining cell-type identity is still largely uncharacterized. Here, we collected and analyzed 214 public datasets representing chromatin immunoprecipitation followed by sequencing (ChIP-Seq) of 104 DNA binding proteins in embryonic stem cell (ESC) lines. We classified their binding sites into those proximal to gene promoters and those in distal regions, and developed a web resource called Proximal And Distal (PAD) clustering to identify their co-localization at these respective regions. Using this extensive dataset, we discovered an extensive co-localization of BRG1 and CHD7 at distal but not proximal regions. The comparison of co-localization sites to those bound by either BRG1 or CHD7 alone showed an enrichment of ESC master TFs binding and active chromatin architecture at co-localization sites. Most notably, our analysis reveals the co-dependency of BRG1 and CHD7 at distal regions on regulating expression of their common target genes in ESC. This work sheds light on cooperative binding of TF binding proteins in regulating gene expression in ESC, and demonstrates the utility of integrative analysis of a manually curated compendium of genome-wide protein binding profiles in our online resource PAD. PAD is freely available at pad.victorchang.edu.au/ and its source code is available via an open source GPL 3.0 license at github.com/VCCRI/PAD/ Supplementary data are available at Bioinformatics online.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: Oxford University Press (OUP)
Date: 30-04-2020
DOI: 10.1093/BIOINFORMATICS/BTAA282
Abstract: Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and cell-surface proteins. Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for doublet detection, modality integration, clustering, differential RNA and protein expression analysis, antibody-derived tag evaluation, ligand–receptor interaction analysis and interactive web-based visualization of CITE-seq data. We demonstrate the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single-modality profiling using both simulations and real-world CITE-seq data. Furthermore, we illustrate a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Finally, we demonstrate CiteFuse for predicting ligand–receptor interactions by using multi-modal CITE-seq data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data. CiteFuse is freely available at shiny.maths.usyd.edu.au/CiteFuse/ as an online web service and at github.com/SydneyBioX/CiteFuse/ as an R package. pengyi.yang@sydney.edu.au Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 11-07-2005
DOI: 10.1038/S41420-021-00456-6
Abstract: Both tumour suppressive and oncogenic functions have been reported for dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A). Herein, we performed a detailed investigation to delineate the role of DYRK1A in glioblastoma. Our phosphoproteomic and mechanistic studies show that DYRK1A induces degradation of cyclin B by phosphorylating CDC23, which is necessary for the function of the anaphase-promoting complex, a ubiquitin ligase that degrades mitotic proteins. DYRK1A inhibition leads to the accumulation of cyclin B and activation of CDK1. Importantly, we established that the phenotypic response of glioblastoma cells to DYRK1A inhibition depends on both retinoblastoma (RB) expression and the degree of residual DYRK1A activity. Moderate DYRK1A inhibition leads to moderate cyclin B accumulation, CDK1 activation and increased proliferation in RB-deficient cells. In RB-proficient cells, cyclin B/CDK1 activation in response to DYRK1A inhibition is neutralized by the RB pathway, resulting in an unchanged proliferation rate. In contrast, complete DYRK1A inhibition with high doses of inhibitors results in massive cyclin B accumulation, saturation of CDK1 activity and cell cycle arrest, regardless of RB status. These findings provide new insights into the complexity of context-dependent DYRK1A signalling in cancer cells.
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 06-2023
Publisher: Springer Science and Business Media LLC
Date: 11-08-2019
DOI: 10.1007/S12021-018-9395-8
Abstract: Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer's disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction (ii) a temporal signal viewer and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.
Publisher: Cold Spring Harbor Laboratory
Date: 14-04-2020
DOI: 10.1101/2020.04.14.040527
Abstract: Temporal changes in omics events can now be routinely measured, however current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method that can infer event ordering at better temporal resolution than the experiment, and integrates omic events into two concise visualizations (event maps and sparklines). Testing our method gave results well-correlated with prior knowledge and indicated it streamlines analysis of time-series data.
Publisher: Cold Spring Harbor Laboratory
Date: 14-10-2022
DOI: 10.1101/2022.10.12.511862
Abstract: This study develops a versatile cell multiplexing and data analysis platform to gain knowledge gain into mechanisms of cell differentiation. We engineer a cell barcoding system in human cells enabling multiplexed single-cell RNA sequencing for high throughput perturbation of customisable and erse experimental conditions. This is coupled with a new computational analysis pipeline that overcomes the limitations of conventional algorithms by using an unsupervised, genome-wide, orthogonal biological reference point to reveal the cell ersity and regulatory networks in the input scRNA-seq data set. We implement this pipeline by engineering transcribed barcodes into induced pluripotent stem cells and multiplex 62 independent experimental conditions comprising eight differentiation time points and nine developmental signalling perturbations in duplicates. We identify and deconstruct the temporal, signalling, and gene regulatory imperatives of iPSC differentiation into cell types of ectoderm, mesoderm, and endoderm lineages. This study provides a cellular and computational pipeline to study cell differentiation applicable to studies in developmental biology, drug discovery, and disease modelling.
Publisher: Oxford University Press (OUP)
Date: 19-12-2019
DOI: 10.1093/NAR/GKZ1179
Abstract: The developmental potential of cells, termed pluripotency, is highly dynamic and progresses through a continuum of naive, formative and primed states. Pluripotency progression of mouse embryonic stem cells (ESCs) from naive to formative and primed state is governed by transcription factors (TFs) and their target genes. Genomic techniques have uncovered a multitude of TF binding sites in ESCs, yet a major challenge lies in identifying target genes from functional binding sites and reconstructing dynamic transcriptional networks underlying pluripotency progression. Here, we integrated time-resolved ‘trans-omic’ datasets together with TF binding profiles and chromatin conformation data to identify target genes of a panel of TFs. Our analyses revealed that naive TF target genes are more likely to be TFs themselves than those of formative TFs, suggesting denser hierarchies among naive TFs. We also discovered that formative TF target genes are marked by permissive epigenomic signatures in the naive state, indicating that they are poised for expression prior to the initiation of pluripotency transition to the formative state. Finally, our reconstructed transcriptional networks pinpointed the precise timing from naive to formative pluripotency progression and enabled the spatiotemporal mapping of differentiating ESCs to their in vivo counterparts in developing embryos.
Publisher: Springer Science and Business Media LLC
Date: 20-12-2021
Publisher: Elsevier BV
Date: 06-2021
Publisher: Springer Science and Business Media LLC
Date: 2010
Publisher: Cold Spring Harbor Laboratory
Date: 16-08-2018
DOI: 10.1101/393280
Abstract: Concerted examination of multiple collections of single cell RNA-Seq (scRNA-Seq) data promises further biological insights that cannot be uncovered with in idual datasets. However, such integrative analyses are challenging and require sophisticated methodologies. To enable effective interrogation of multiple scRNA-Seq datasets, we have developed a novel algorithm, named scMerge, that removes unwanted variation by combining stably expressed genes and utilizing pseudo-replicates across datasets. Analysis of large collections of publicly available datasets demonstrates that scMerge performs well in multiple scenarios and enhances biological discovery, including inferring cell developmental trajectories.
Publisher: Springer Science and Business Media LLC
Date: 02-12-2019
DOI: 10.1038/S41467-019-13114-4
Abstract: Protein oxidation sits at the intersection of multiple signalling pathways, yet the magnitude and extent of crosstalk between oxidation and other post-translational modifications remains unclear. Here, we delineate global changes in adipocyte signalling networks following acute oxidative stress and reveal considerable crosstalk between cysteine oxidation and phosphorylation-based signalling. Oxidation of key regulatory kinases, including Akt, mTOR and AMPK influences the fidelity rather than their absolute activation state, highlighting an unappreciated interplay between these modifications. Mechanistic analysis of the redox regulation of Akt identified two cysteine residues in the pleckstrin homology domain (C60 and C77) to be reversibly oxidized. Oxidation at these sites affected Akt recruitment to the plasma membrane by stabilizing the PIP 3 binding pocket. Our data provide insights into the interplay between oxidative stress-derived redox signalling and protein phosphorylation networks and serve as a resource for understanding the contribution of cellular oxidation to a range of diseases.
Publisher: Springer Science and Business Media LLC
Date: 2009
Publisher: Cold Spring Harbor Laboratory
Date: 25-11-2019
DOI: 10.1101/854299
Abstract: Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and surface marker proteins (ADT). Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for pre-processing, modality integration, clustering, differential RNA and ADT expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualization of CITE-seq data. We show the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single modality profiling. Furthermore, we illustrate the pre-processing steps in CiteFuse and in particular a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data.
Publisher: Springer Science and Business Media LLC
Date: 11-07-2019
DOI: 10.1038/S41467-019-10905-7
Abstract: Faithful transcription initiation is critical for accurate gene expression, yet the mechanisms underlying specific transcription start site (TSS) selection in mammals remain unclear. Here, we show that the histone-fold domain protein NF-Y, a ubiquitously expressed transcription factor, controls the fidelity of transcription initiation at gene promoters in mouse embryonic stem cells. We report that NF-Y maintains the region upstream of TSSs in a nucleosome-depleted state while simultaneously protecting this accessible region against aberrant and/or ectopic transcription initiation. We find that loss of NF-Y binding in mammalian cells disrupts the promoter chromatin landscape, leading to nucleosomal encroachment over the canonical TSS. Importantly, this chromatin rearrangement is accompanied by upstream relocation of the transcription pre-initiation complex and ectopic transcription initiation. Further, this phenomenon generates aberrant extended transcripts that undergo translation, disrupting gene expression profiles. These results suggest NF-Y is a central player in TSS selection in metazoans and highlight the deleterious consequences of inaccurate transcription initiation.
Publisher: Cold Spring Harbor Laboratory
Date: 19-10-2021
Abstract: Bivalent chromatin is characterized by the simultaneous presence of H3K4me3 and H3K27me3, histone modifications generally associated with transcriptionally active and repressed chromatin, respectively. Prevalent in embryonic stem cells (ESCs), bivalency is postulated to poise rime lineage-controlling developmental genes for rapid activation during embryogenesis while maintaining a transcriptionally repressed state in the absence of activation cues however, this hypothesis remains to be directly tested. Most gene promoters DNA hypermethylated in adult human cancers are bivalently marked in ESCs, and it was speculated that bivalency predisposes them for aberrant de novo DNA methylation and irreversible silencing in cancer, but evidence supporting this model is largely lacking. Here, we show that bivalent chromatin does not poise genes for rapid activation but protects promoters from de novo DNA methylation. Genome-wide studies in differentiating ESCs reveal that activation of bivalent genes is no more rapid than that of other transcriptionally silent genes, challenging the premise that H3K4me3 is instructive for transcription. H3K4me3 at bivalent promoters—a product of the underlying DNA sequence—persists in nearly all cell types irrespective of gene expression and confers protection from de novo DNA methylation. Bivalent genes in ESCs that are frequent targets of aberrant hypermethylation in cancer are particularly strongly associated with loss of H3K4me3/bivalency in cancer. Altogether, our findings suggest that bivalency protects reversibly repressed genes from irreversible silencing and that loss of H3K4me3 may make them more susceptible to aberrant DNA methylation in diseases such as cancer. Bivalency may thus represent a distinct regulatory mechanism for maintaining epigenetic plasticity.
Publisher: Cold Spring Harbor Laboratory
Date: 02-06-2021
DOI: 10.1101/2021.06.01.446157
Abstract: Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational methods for analysing scRNA-seq data especially when ground truth is experimentally unattainable. The reliability of evaluation depends on the ability of simulation methods to capture properties of experimental data. However, while many scRNA-seq data simulation methods have been proposed, a systematic evaluation of these methods is lacking. We developed a comprehensive evaluation framework, SimBench, including a novel kernel density estimation measure to benchmark 12 simulation methods through 35 scRNA-seq experimental datasets. We evaluated the simulation methods on a panel of data properties, ability to maintain biological signals, scalability and applicability. Our benchmark uncovered performance differences among the methods and highlighted the varying difficulties in simulating data characteristics. Furthermore, we identified several limitations including maintaining heterogeneity of distribution. These results, together with the framework and datasets made publicly available as R packages, will guide simulation methods selection and their future development.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2012
DOI: 10.1109/TCBB.2012.86
Publisher: Oxford University Press (OUP)
Date: 13-01-2022
Abstract: Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.
Publisher: Oxford University Press (OUP)
Date: 06-2023
DOI: 10.1093/BIOINFORMATICS/BTAD382
Abstract: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in in idual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. We propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterization of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. SnapCCESS is implemented as a Python package and is freely available from github.com/PYangLab/SnapCCESS under the open-source license of GPL-3. The data used in this study are publicly available (see section ‘Data availability’).
Publisher: Cold Spring Harbor Laboratory
Date: 25-02-2021
DOI: 10.1101/2021.02.23.432610
Abstract: Mass spectrometry (MS)-based phosphoproteomics enables the quantification of proteome-wide phosphorylation in cells and tissues. A major challenge in MS-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. By leveraging large-scale phosphoproteomics data, machine learning has become an increasingly popular approach for computationally predicting substrates of kinases. However, the small number of high-quality experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together impact the performance of existing approaches. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, including six published datasets and a new muscle differentiation dataset, and both traditional and deep learning models, we first demonstrate that a ‘pseudo-positive’ learning strategy for alleviating small s le size is effective at improving model predictive performance. We next show that a data re-s ling based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model (‘SnapKin’) incorporating the above two learning strategies into a ‘snapshot’ ensemble learning algorithm. We demonstrate that the SnapKin model achieves overall the best performance in kinase-substrate prediction. Together, we propose SnapKin as a promising approach for predicting substrates of kinases from large-scale phosphoproteomics data. SnapKin is freely available at github.com/PYangLab/SnapKin .
Publisher: Oxford University Press (OUP)
Date: 11-04-2022
DOI: 10.1093/BFGP/ELAC008
Abstract: Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain including the inefficiency in the proportion of reprogrammed cells of current experiments, and the requirement of a significant amount of time and resources. To this end, several computational algorithms have been developed with the goal of guiding the hypotheses to be experimentally validated. These approaches can be broadly categorized into two main types: transcription factor identification methods which aim to identify candidate transcription factors for a desired cell conversion, and transcription factor perturbation methods which aim to simulate the effect of a transcription factor perturbation on a cell state. The transcription factor perturbation methods can be broken down into Boolean networks, dynamical systems and regression models. We summarize the contributions and limitations of each method and discuss the innovation that single cell technologies are bringing to these approaches and we provide a perspective on the future direction of this field.
Publisher: Wiley
Date: 31-05-2023
Abstract: Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low‐throughput biochemical assays. The advancement of mass spectrometry (MS)‐based phosphoproteomics transformed the field by enabling measurement of proteome‐wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large‐scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS‐based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and ex le studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast‐growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.
Publisher: Elsevier BV
Date: 06-2022
Publisher: EMBO
Date: 06-2020
Publisher: American Chemical Society (ACS)
Date: 30-03-2012
DOI: 10.1021/PR300072J
Abstract: A key step in the analysis of mass spectrometry (MS)-based proteomics data is the inference of proteins from identified peptide sequences. Here we describe Re-Fraction, a novel machine learning algorithm that enhances deterministic protein identification. Re-Fraction utilizes several protein physical properties to assign proteins to expected protein fractions that comprise large-scale MS-based proteomics data. This information is then used to appropriately assign peptides to specific proteins. This approach is sensitive, highly specific, and computationally efficient. We provide algorithms and source code for the current version of Re-Fraction, which accepts output tables from the MaxQuant environment. Nevertheless, the principles behind Re-Fraction can be applied to other protein identification pipelines where data are generated from s les fractionated at the protein level. We demonstrate the utility of this approach through reanalysis of data from a previously published study and generate lists of proteins deterministically identified by Re-Fraction that were previously only identified as members of a protein group. We find that this approach is particularly useful in resolving protein groups composed of splice variants and homologues, which are frequently expressed in a cell- or tissue-specific manner and may have important biological consequences.
Publisher: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S12864-019-6305-X
Abstract: Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from github.com/SydneyBioX/scReClassify
Publisher: IEEE
Date: 06-2008
Publisher: Cold Spring Harbor Laboratory
Date: 11-01-2021
DOI: 10.1101/2021.01.10.426138
Abstract: We present Cepo, a method to generate cell-type-specific gene statistics of differentially stable genes from single-cell RNA-sequencing (scRNA-seq) data to define cell identity. Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells, and lineage inference of single cells.
Publisher: No publisher found
Date: 2013
Publisher: Oxford University Press (OUP)
Date: 09-10-2023
Publisher: Wiley
Date: 13-06-2019
Abstract: The increasing role played by liquid chromatography-mass spectrometry (LC-MS)-based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC-MS systems. While numerous quality control tools have been developed to track the performance of LC-MS systems based on a pre-defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological s les are not as well characterized. Here, a web-based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC-MS systems across different biological s le types. Leveraging on a standardized HeLa cell s le run as QC within a multi-user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. It is demonstrated that the learned model can be applied to predict LC-MS system performance for brain s les generated from an independent study. By compiling these predictive models into our web-application, QCMAP allows users to benchmark the performance of their LC-MS systems using their own s les and identify key factors for instrument optimization. QCMAP is freely available from: shiny.maths.usyd.edu.au/QCMAP/.
Publisher: IEEE
Date: 05-2008
Publisher: Elsevier BV
Date: 08-2010
Publisher: Cold Spring Harbor Laboratory
Date: 22-01-2022
DOI: 10.1101/2022.01.20.476845
Abstract: Recent advances in single-cell technologies enable scientists to measure molecular data at high-resolutions and hold the promise to substantially improve clinical outcomes through personalised medicine. However, due to a lack of tools specifically designed to represent each s le (e.g. patient) from the collection of cells sequenced, disease outcome prediction on the s le level remains a challenging task. Here, we present scFeatures, a tool that creates interpretable molecular representation of single-cell and spatial data using 17 types of features motivated by current literature. The feature types span across six distinct categories including cell type proportions, cell type specific gene expressions, cell type specific pathway scores, cell type specific cell–cell interaction scores, overall aggregated gene expressions and spatial metrics. By generating molecular representation using scFeatures for single-cell RNA-seq, spatial proteomic and spatial transcriptomic data, we demonstrate that different types of features are important for predicting different disease outcomes in different datasets and the downstream analysis of features uncover novel biological discoveries.
Publisher: Elsevier BV
Date: 12-2016
DOI: 10.1016/J.COMPBIOLCHEM.2016.09.005
Abstract: Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the correctness of annotations. Due to resources limitations, only a small portion of annotations are manually added/checked by GO curators, and a large portion of available annotations are computationally inferred. While computationally inferred annotations provide greater coverage of known genes, they may also introduce annotation errors (noise) that could mislead the interpretation of the gene functions and their roles in cellular and biological processes. In this paper, we investigate how to identify noisy annotations, a rarely addressed problem, and propose a novel approach called NoisyGOA. NoisyGOA first measures taxonomic similarity between ontological terms using the GO hierarchy and semantic similarity between genes. Next, it leverages the taxonomic similarity and semantic similarity to predict noisy annotations. We compare NoisyGOA with other alternative methods on identifying noisy annotations under different simulated cases of noisy annotations, and on archived GO annotations. NoisyGOA achieved higher accuracy than other alternative methods in comparison. These results demonstrated both taxonomic similarity and semantic similarity contribute to the identification of noisy annotations. Our study shows that annotation errors are predictable and removing noisy annotations improves the performance of gene function prediction. This study can prompt the community to study methods for removing inaccurate annotations, a critical step for annotating gene and pathway functions.
Publisher: Wiley
Date: 16-12-2014
Publisher: eLife Sciences Publications, Ltd
Date: 06-02-2018
DOI: 10.7554/ELIFE.32111
Abstract: Insulin resistance in muscle, adipocytes and liver is a gateway to a number of metabolic diseases. Here, we show a selective deficiency in mitochondrial coenzyme Q (CoQ) in insulin-resistant adipose and muscle tissue. This defect was observed in a range of in vitro insulin resistance models and adipose tissue from insulin-resistant humans and was concomitant with lower expression of mevalonate/CoQ biosynthesis pathway proteins in most models. Pharmacologic or genetic manipulations that decreased mitochondrial CoQ triggered mitochondrial oxidants and insulin resistance while CoQ supplementation in either insulin-resistant cell models or mice restored normal insulin sensitivity. Specifically, lowering of mitochondrial CoQ caused insulin resistance in adipocytes as a result of increased superoxide/hydrogen peroxide production via complex II. These data suggest that mitochondrial CoQ is a proximal driver of mitochondrial oxidants and insulin resistance, and that mechanisms that restore mitochondrial CoQ may be effective therapeutic targets for treating insulin resistance.
Publisher: Elsevier BV
Date: 10-2022
Publisher: Springer Science and Business Media LLC
Date: 17-08-2020
Publisher: Cold Spring Harbor Laboratory
Date: 07-12-2020
DOI: 10.1101/2020.12.07.415257
Abstract: Trafficking regulator of GLUT4-1, TRARG1, positively regulates insulin-stimulated GLUT4 trafficking and insulin sensitivity. However, the mechanism(s) by which this occurs remain(s) unclear. Using biochemical and mass spectrometry analyses we found that TRARG1 is dephosphorylated in response to insulin in a PI3K/Akt-dependent manner and is a novel substrate for GSK3. Priming phosphorylation of murine TRARG1 at serine 84 allows for GSK3-directed phosphorylation at serines 72, 76 and 80. A similar pattern of phosphorylation was observed in human TRARG1, suggesting that our findings are translatable to human TRARG1. Pharmacological inhibition of GSK3 increased cell surface GLUT4 in cells stimulated with a submaximal insulin dose, and this was impaired following Trarg1 knockdown, suggesting that TRARG1 acts as a GSK3-mediated regulator in GLUT4 trafficking. These data place TRARG1 within the insulin signaling network and provide insights into how GSK3 regulates GLUT4 trafficking in adipocytes.
Publisher: Cold Spring Harbor Laboratory
Date: 17-08-2021
DOI: 10.1101/2021.08.16.456469
Abstract: Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organisation of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarising the information-rich data generated from these technologies.
Publisher: Cold Spring Harbor Laboratory
Date: 13-10-2020
DOI: 10.1101/2020.10.13.338103
Abstract: RNA polymerase II (RNAPII) pausing at gene promoters is a rate-limiting step in transcription regulation. Previous studies have elucidated the coordinated actions of pausing and releasing factors that collectively modulate RNAPII pausing. In general, the involvement of chromatin remodellers in RNAPII pausing has not been well documented. Whilst LSD1 is well-known for its role in decommissioning enhancers during ESC differentiation, its role at the promoters of genes remains poorly understood despite their widespread presence at these sites. Here, we report that LSD1 is associated with RNAPII pausing at the promoter-proximal region of genes in mouse embryonic stem cells (ESCs). We demonstrate that the knockdown of LSD1 preferentially affects genes with higher RNAPII pausing than those with lower pausing and, importantly, show that the co-localization of LSD1 and MYC, a factor known to regulate pause-release, is associated with the enrichment of other RNAPII pausing factors compared to their independent counterparts. Moreover, we found that genes co-occupied by LSD1 and MYC are significantly enriched for housekeeping genes that are involved in metabolic processes and globally depleted of transcription factors compared to those bound only by LSD1. These findings reveal a pleiotropic role of LSD1 in regulating housekeeping program besides its previously known role in regulating cell identity programs. Our integrative analysis presents evidence for a previously unanticipated role of LSD1 in RNAPII pausing through its association with pause release factors in modulating cell-type specific and cell-type invariant genes.
Publisher: Springer Science and Business Media LLC
Date: 18-02-2023
DOI: 10.1038/S41467-023-36549-2
Abstract: The failure of metabolic tissues to appropriately respond to insulin (“insulin resistance”) is an early marker in the pathogenesis of type 2 diabetes. Protein phosphorylation is central to the adipocyte insulin response, but how adipocyte signaling networks are dysregulated upon insulin resistance is unknown. Here we employ phosphoproteomics to delineate insulin signal transduction in adipocyte cells and adipose tissue. Across a range of insults causing insulin resistance, we observe a marked rewiring of the insulin signaling network. This includes both attenuated insulin-responsive phosphorylation, and the emergence of phosphorylation uniquely insulin-regulated in insulin resistance. Identifying dysregulated phosphosites common to multiple insults reveals subnetworks containing non-canonical regulators of insulin action, such as MARK2/3, and causal drivers of insulin resistance. The presence of several bona fide GSK3 substrates among these phosphosites led us to establish a pipeline for identifying context-specific kinase substrates, revealing widespread dysregulation of GSK3 signaling. Pharmacological inhibition of GSK3 partially reverses insulin resistance in cells and tissue explants. These data highlight that insulin resistance is a multi-nodal signaling defect that includes dysregulated MARK2/3 and GSK3 activity.
Publisher: American Chemical Society (ACS)
Date: 13-09-2013
DOI: 10.1021/BM401040V
Abstract: A new biologically compatible Zn(II) sensor was fabricated by embedding a Zn(II) sensing spiropyran within the surface of a liposome derived from Escherichia coli lipids (LSP2). Solution-based experiments with increasing Zn(II) concentrations show improved aqueous solubility and sensitivity compared to the isolated spiropyran molecule (SP2). LSP2 is capable of sensing Zn(II) efflux from dying cells with preliminary data indicating that sensing is localized near the surface membrane of HEK 293 cells. Finally, LSP2 is suitable for development into a nanoliter-scale dip-sensor for Zn(II) using microstructured optical fiber as the sensing platform to detect Zn(II) in the range of 100 ρM with minimal photobleaching. Existing spiropyran based sensing molecules can thus be made biologically compatible, with an ability to operate with improved sensitivity using nanoscale liquid s le volumes. This work represents the first instance where photochromic spiropyran molecules and liposomes are combined to create a new and multifunctional sensing entity for Zn(II).
Publisher: Springer Science and Business Media LLC
Date: 17-08-2021
DOI: 10.1038/S41467-021-25210-5
Abstract: Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological s le replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma s les run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.
Publisher: Oxford University Press (OUP)
Date: 13-03-2023
DOI: 10.1093/NAR/GKAD157
Abstract: Multimodal single-cell omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in in idual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of multimodal single-cell omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of multimodal single-cell omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular multimodal single-cell omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative multimodal single-cell omics data analysis. Matilda is implemented in Pytorch and is freely available from github.com/PYangLab/Matilda.
Publisher: Oxford University Press (OUP)
Date: 24-10-2014
DOI: 10.1093/BIOINFORMATICS/BTT616
Abstract: Motivation: With the advancement of high-throughput techniques, large-scale profiling of biological systems with multiple experimental perturbations is becoming more prevalent. Pathway analysis incorporates prior biological knowledge to analyze genes roteins in groups in a biological context. However, the hypotheses under investigation are often confined to a 1D space (i.e. up, down, either or mixed regulation). Here, we develop direction pathway analysis (DPA), which can be applied to test hypothesis in a high-dimensional space for identifying pathways that display distinct responses across multiple perturbations. Results: Our DPA approach allows for the identification of pathways that display distinct responses across multiple perturbations. To demonstrate the utility and effectiveness, we evaluated DPA under various simulated scenarios and applied it to study insulin action in adipocytes. A major action of insulin in adipocytes is to regulate the movement of proteins from the interior to the cell surface membrane. Quantitative mass spectrometry-based proteomics was used to study this process on a large-scale. The combined dataset comprises four separate treatments. By applying DPA, we identified that several insulin responsive pathways in the plasma membrane trafficking are only partially dependent on the insulin-regulated kinase Akt. We subsequently validated our findings through targeted analysis of key proteins from these pathways using immunoblotting and live cell microscopy. Our results demonstrate that DPA can be applied to dissect pathway networks testing erse hypotheses and integrating multiple experimental perturbations. Availability and implementation: The R package ‘directPA’ is distributed from CRAN under GNU General Public License (GPL)-3 and can be downloaded from: eb ackages/directPA/index.html Contact: jean.yang@sydney.edu.au Supplementary Information: Supplementary data are available at Bioinformatics online.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 21-10-2010
Publisher: Springer Science and Business Media LLC
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 19-10-2023
Publisher: Cold Spring Harbor Laboratory
Date: 22-12-2020
DOI: 10.1101/2020.12.21.423723
Abstract: Liquid chromatography-mass spectrometry based metabolomics studies are increasingly applied to large population cohorts, running for several weeks to months, even extending to years of data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalization approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we designed an experiment with an arrangement to embed biological s le replicates to measure the variance within and between batches for over 1,000 human plasma s les run over 44 days. We integrate these replicates in a novel workflow to remove unwanted variation in a hierarchical structure (hRUV) by progressively merging the adjustments in neighbouring batches. We demonstrate significant improvement of hRUV over existing methods in maintaining biological signals whilst removing unwanted variation for large scale metabolomics studies.
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Cold Spring Harbor Laboratory
Date: 23-02-2023
DOI: 10.1101/2023.02.22.529627
Abstract: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in in idual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterisation of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. We propose SnapCCESS for clustering cells by integrating data modalities in multimodal singlecell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterisation of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. SnapCCESS is implemented as a Python package and is freely available from ulijia/SnapCCESS .
Publisher: Springer Science and Business Media LLC
Date: 15-02-2011
Publisher: AIP Publishing
Date: 2017
DOI: 10.1063/1.4974904
Abstract: Mixing fluid s les or reactants is a paramount function in the fields of micro total analysis system (μTAS) and microchemical processing. However, rapid and efficient fluid mixing is difficult to achieve inside microchannels because of the difficulty of diffusive mass transfer in the laminar regime of the typical microfluidic flows. It has been well recorded that the mixing efficiency can be boosted by migrating from two-dimensional (2D) to three-dimensional (3D) geometries. Although several 3D chaotic mixers have been designed, most of them offer a high mixing efficiency only in a very limited range of Reynolds numbers (Re). In this work, we developed a 3D fine-threaded lemniscate-shaped micromixer whose maximum numerical and empirical efficiency is around 97% and 93%, respectively, and maintains its high performance (i.e., & %) over a wide range of 1 & Re & 1000 which meets the requirements of both the μTAS and microchemical process applications. The 3D micromixer was designed based on two distinct mixing strategies, namely, the inducing of chaotic advection by the presence of Dean flow and diffusive mixing through thread-like grooves around the curved body of the mixers. First, a set of numerical simulations was performed to study the physics of the flow and to determine the essential geometrical parameters of the mixers. Second, a simple and cost-effective method was exploited to fabricate the convoluted structure of the micromixers through the removal of a 3D-printed wax structure from a block of cured polydimethylsiloxane. Finally, the fabricated mixers with different threads were tested using a fluorescent microscope demonstrating a good agreement with the results of the numerical simulation. We envisage that the strategy used in this work would expand the scope of the micromixer technology by broadening the range of efficient working flow rate and providing an easy way to the fabrication of 3D convoluted microstructures.
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Research Square Platform LLC
Date: 04-02-2021
DOI: 10.21203/RS.3.RS-156243/V1
Abstract: Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological s le replicates to quantify variance within and between batches and a novel workflow that uses these replicates to remove unwanted variation in a hierarchical (hRUV) manner. We use this design to produce a dataset of more than 1,000 human plasma s les run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our novel tools not only provide a strategy for large scale data normalization, but also provides guidance on the design strategy for large omics studies.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Informa UK Limited
Date: 10-2014
DOI: 10.1128/MCB.00583-14
Publisher: Cold Spring Harbor Laboratory
Date: 19-09-2019
DOI: 10.1101/773903
Abstract: Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of in idual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex s les or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification. Here, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets for generating clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k -means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metrics used. Our results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from edcom/autoencoder_cluster_ensemble
Publisher: Cold Spring Harbor Laboratory
Date: 02-01-2021
DOI: 10.1101/2020.12.30.424907
Abstract: Genetic heterogeneity of tumor is closely related to clonal evolution, phenotypic ersity and treatment resistance. Such heterogeneity has been characterized in liver cancer at single-cell sub-chromosomal scale, and a more precise single-variant resolution analysis is lacking. Here we employed a strategy to analyze both the single-cell genomic mutations and transcriptomic changes in 5 patients with liver cancer. Target sequencing was done for a total of 480 single cells in a patient-specific manner. DNA copy number status of point mutations was obtained from single-cell mutational profiling. The clonal structures of liver cancers were then uncovered at single-variant resolution, and mutation combinations in single cells enabled reconstruction of their evolutionary history. A common origin but independent evolutionary fate was revealed for primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. The mutational signature suggested early evolutionary process may be related to specific etiology like aristolochic acids. By parallel single-cell RNA-Seq, the transcriptomic phenotype was found to be related with genetic heterogeneity in liver cancer. We reconstructed the single-cell and single-variant resolution clonal evolutionary history of liver cancer, and dissection of both genetic and phenotypic heterogeneity provides knowledge for mechanistic understanding of liver cancer initiation and progression.
Publisher: Oxford University Press (OUP)
Date: 22-08-2018
DOI: 10.1093/BIB/BBY076
Abstract: Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (scRNA-seq)] have enabled transcriptome profiling on in idual cells from complex s les. A common goal in scRNA-seq data analysis is to discover and characterise cell types, typically through clustering methods. The quality of the clustering therefore plays a critical role in biological discovery. While numerous clustering algorithms have been proposed for scRNA-seq data, fundamentally they all rely on a similarity metric for categorising in idual cells. Although several studies have compared the performance of various clustering algorithms for scRNA-seq data, currently there is no benchmark of different similarity metrics and their influence on scRNA-seq data clustering. Here, we compared a panel of similarity metrics on clustering a collection of annotated scRNA-seq datasets. Within each dataset, a stratified subs ling procedure was applied and an array of evaluation measures was employed to assess the similarity metrics. This produced a highly reliable and reproducible consensus on their performance assessment. Overall, we found that correlation-based metrics (e.g. Pearson’s correlation) outperformed distance-based metrics (e.g. Euclidean distance). To test if the use of correlation-based metrics can benefit the recently published clustering techniques for scRNA-seq data, we modified a state-of-the-art kernel-based clustering algorithm (SIMLR) using Pearson’s correlation as a similarity measure and found significant performance improvement over Euclidean distance on scRNA-seq data clustering. These findings demonstrate the importance of similarity metrics in clustering scRNA-seq data and highlight Pearson’s correlation as a favourable choice. Further comparison on different scRNA-seq library preparation protocols suggests that they may also affect clustering performance. Finally, the benchmarking framework is available at www.maths.usyd.edu.au/u/SMS/bioinformatics/software.html.
Publisher: No publisher found
Date: 2017
Publisher: Elsevier BV
Date: 05-2019
Publisher: Oxford University Press (OUP)
Date: 29-03-2012
DOI: 10.1093/BIOINFORMATICS/BTS150
Abstract: Motivation: Mass spectrometry-based iTRAQ protein quantification is a high-throughput assay for determining relative protein expressions and identifying disease biomarkers. Processing and analysis of these large and complex data involves a number of distinct components and it is desirable to have a pipeline to efficiently integrate these together. To date, there are limited public available comprehensive analysis pipelines for iTRAQ data and many of these existing pipelines have limited visualization tools and no convenient interfaces with downstream analyses. We have developed a new open source comprehensive iTRAQ analysis pipeline, OCAP, integrating a wavelet-based preprocessing algorithm which provides better peak picking, a new quantification algorithm and a suite of visualizsation tools. OCAP is mainly developed in C++ and is provided as a standalone version (OCAP_standalone) as well as an R package. The R package (OCAP) provides the necessary interfaces with downstream statistical analysis. Availability: OCAP is freely available and can be downloaded at www.maths.usyd.edu.au/u enghao Contact: penghao.wang@sydney.edu.au
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Publisher: Oxford University Press (OUP)
Date: 13-07-2010
DOI: 10.1093/BIOINFORMATICS/BTQ403
Abstract: Motivation: Mass spectrometry (MS)-based proteomics is one of the most commonly used research techniques for identifying and characterizing proteins in biological and medical research. The identification of a protein is the critical first step in elucidating its biological function. Successful protein identification depends on various interrelated factors, including effective analysis of MS data generated in a proteomic experiment. This analysis comprises several stages, often combined in a pipeline or workflow. The first component of the analysis is known as spectra pre-processing. In this component, the raw data generated by the mass spectrometer is processed to eliminate noise and identify the mass-to-charge ratio (m/z) and intensity for the peaks in the spectrum corresponding to the presence of certain peptides or peptide fragments. Since all downstream analyses depend on the pre-processed data, effective pre-processing is critical to protein identification and characterization. There is a critical need for more robust pre-processing algorithms that perform well on tandem mass spectra under a variety of different conditions and can be easily integrated into sophisticated data analysis pipelines for practical wet-lab applications. Result: We have developed a new pre-processing algorithm. Based on wavelet theory, our method uses a dynamic peak model to identify peaks. It is designed to be easily integrated into a complete proteomic analysis workflow. We compared the method with other available algorithms using a reference library of raw MS and tandem MS spectra with known protein composition information. Our pre-processing algorithm results in the identification of significantly more peptides and proteins in the downstream analysis for a given false discovery rate. Availability: Software available at: www.maths.usyd.edu.au/u enghao/index.html Contact: penghao.wang@sydney.edu.au
Publisher: Cold Spring Harbor Laboratory
Date: 12-10-2021
DOI: 10.1101/2021.10.11.463798
Abstract: Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Cold Spring Harbor Laboratory
Date: 20-01-2023
DOI: 10.1101/2023.01.18.524659
Abstract: Highly multiplexed in situ imaging cytometry assays have enabled researchers to scru-tinize cellular systems at an unprecedented level. With the capability of these assays to simultaneously profile the spatial distribution and molecular features of many cells, unsuper-vised machine learning, and in particular clustering algorithms, have become indispensable for identifying cell types and subsets based on these molecular features. However, the most widely used clustering approaches applied to these novel technologies were developed for cell suspension technologies and may not be optimal for in situ imaging assays. In this work, we systematically evaluated the performance of various similarity metrics used to quan-tify the similarity between cells when clustering. Our results demonstrate that performance in cell clustering varies significantly when different similarity metrics were used. Lastly, we propose FuseSOM, an ensemble clustering algorithm employing hierarchical multi-view learning of similarity metrics and self-organizing maps (SOM). Using a stratified subsam-pling analysis framework, FuseSOM exhibits superior clustering performance compared to the current best-practice clustering approaches for in situ imaging cytometry data analysis.
Publisher: Springer Science and Business Media LLC
Date: 16-07-2020
DOI: 10.1038/S41540-020-0141-0
Abstract: Temporal changes in omics events can now be routinely measured however, current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method that can infer event ordering at better temporal resolution than the experiment, and integrates omic events into two concise visualizations (event maps and sparklines). Testing our method gave results well-correlated with prior knowledge and indicated it streamlines analysis of time-series data.
Publisher: Oxford University Press (OUP)
Date: 07-01-2022
DOI: 10.1093/BIOINFORMATICS/BTAC015
Abstract: The advance of mass spectrometry-based technologies enabled the profiling of the phosphoproteomes of a multitude of cell and tissue types. However, current research primarily focused on investigating the phosphorylation dynamics in specific cell types and experimental conditions, whereas the phosphorylation events that are common across cell/tissue types and stable regardless of experimental conditions are, so far, mostly ignored. Here, we developed a statistical framework to identify the stable phosphoproteome across 53 human phosphoproteomics datasets, covering 40 cell/tissue types and 194 conditions/treatments. We demonstrate that the stably phosphorylated sites (SPSs) identified from our statistical framework are evolutionarily conserved, functionally important and enriched in a range of core signaling and gene pathways. Particularly, we show that SPSs are highly enriched in the RNA splicing pathway, an essential cellular process in mammalian cells, and frequently disrupted by cancer mutations, suggesting a link between the dysregulation of RNA splicing and cancer development through mutations on SPSs. The source code for data analysis in this study is available from Github repository github.com/PYangLab/SPSs under the open-source license of GPL-3. The data used in this study are publicly available (see Section 2.8). Supplementary data are available at Bioinformatics online.
Start Date: 2017
End Date: 2020
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 2020
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 2019
Funder: National Health and Medical Research Council
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