ORCID Profile
0000-0001-7870-5703
Current Organisations
University of Queensland Institute for Molecular Bioscience
,
QIMR Berghofer Medical Research Institute
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Gene Expression (incl. Microarray and other genome-wide approaches) | Genomics | Bioinformatics | Genetics
Publisher: Cold Spring Harbor Laboratory
Date: 31-05-2020
DOI: 10.1101/2020.05.31.125658
Abstract: Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and erse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues.
Publisher: Cold Spring Harbor Laboratory
Date: 20-02-2023
DOI: 10.1101/2023.02.19.529128
Abstract: The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal s les, bronchoalveolar lavage fluid, and similar, but a robust platform for deep tissue characterisation of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterise and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected s les, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488500.V1
Abstract: (A) UMAP representation of dimensionally reduced data following batch-corrected graph-based clustering of all lymphocyte lineage cells annotated by cell types. (B) Heatmap depicting average expression of selected genes for each cell type. (C) UMAP representation annotated by condition. (D) Heatmap depicting cell states from CD8 T cells identified using EcoTyper NMF analysis, annotated by condition. (A-D) Data from seven mCRCs, five paired normal liver tissue and two paired PBMCs. (E) Violin plot depicting the expression of cytotoxicity gene signature in normal and tumor CD8 T cells with T-test p-value. Data from seven mCRCs, five paired normal liver tissue.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488491
Abstract: (A-C) Comparisons with Pearson correlation between (A) proportions of cell lineages in five s les with both scRNA-seq and CODEX data. (B) Average expression of LGALS3 and CD68 in macrophages across all 15 patients. (C) Average expression of COL4A1 and ACTA2 in CAFs across all 15 patients.
Publisher: Springer International Publishing
Date: 2015
DOI: 10.1007/82_2015_464
Abstract: Long noncoding RNAs (lncRNAs) perform erse regulatory functions in transcription, translation' chromatin modification, and cellular organization. Misregulation of lncRNAs is found linked to various human diseases. Compared to protein-coding RNAs' lncRNAs are more specific to organs, tissues, cell types, developmental stages, and disease conditions' making them promising candidates as diagnostic and prognostic biomarkers and as gene therapy targets. The functional annotation of mammalian genome (FANTOM) consortium utilizes cap analysis of gene expression (CAGE) method to quantify genome-wide activities of promoters and enhancers of coding and noncoding RNAs across a large collection of human and mouse tissues' cell types' diseases, and time-courses. The project discovered widespread transcription of major lncRNA classes, including lncRNAs derived from enhancers' bidirectional promoters' antisense lncRNAs' and repetitive elements. Results from FANTOM project enable assessment of lncRNA expression specificity across tissue and disease conditions' based on differential promoter and enhancer usage. More than 85 % of disease-related SNPs are within noncoding regions and are strikingly overrepresented in enhancer and promoter regions, suggestive of the importance of lncRNA loci at these SNP harboring regions to human diseases. In this chapter' we discuss lncRNA expression specificity' review erse functions of disease-associated lncRNAs' and present perspectives on their potential therapeutic applications for personalized medicine. The future development of lncRNA applications relies on technologies to identify and validate their functions' structures' and mechanisms. Comprehensive understanding of genome-wide interaction networks of lncRNAs with proteins, chromatins, and other RNAs in regulating cellular processes will allow personalized medicine to use lncRNAs as highly specific biomarkers in diagnosis' prognosis, and therapeutic targets.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488494
Abstract: Graphical representation of cell types identified in CODEX analysis in image data from respective patients.
Publisher: Cold Spring Harbor Laboratory
Date: 23-10-2017
DOI: 10.1101/207704
Abstract: ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. ascend is designed to work with scRNA-seq data generated by any high-throughput platform, and includes functions to convert data objects between software packages. The R package and associated vignettes are freely available at github.com/IMB-Computational-Genomics-Lab/ascend . joseph.powell@uq.edu.au An ex le dataset is available at ArrayExpress, accession number E-MTAB-6108
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488497
Abstract: (A) Dot plot depicting expression levels of respective ligands and receptors together with the percentage of cells expressing them. (B) Top 10 percent of interactions inferred between cell types in the TME. Edge weights are proportional to the number of interactions, circle sizes are proportional to the number of cells in each group, edge color represents the cell type as sender. For scale, autocrine interactions in CAFs are 68. (A-B) Data from seven mCRCs and five paired normal liver tissue.
Publisher: Elsevier BV
Date: 09-2013
DOI: 10.1016/J.VIROL.2013.06.004
Abstract: Knowledge of baculovirus-insect host interactions at a genome-scale level is important for developing a number of baculovirus-based applications, but the gathering of such knowledge is hindered by the lack of genomic sequences in most insect hosts. In this study, expression kinetics of 24,206 Helicoverpa zea insect transcripts and 134 Helicoverpa armigera nucleopolyhedrovirus (HearNPV) genes at 0, 12, 24 and 48 h post-infection (hpi) were simultaneously analyzed using microarrays, which were developed from sequences obtained by deep transcriptome sequencing. Host genes in pathways important for infection such as those for energy generation, anti-viral peptides, apoptosis, detoxification, DNA polymerase activities, RNA polymerase activities, translation initiation, protein processing and cell cycle arrest were identified. Differential expression was linked to changes in the number of intracellular and extracellular viral genomes and occlusion bodies. The first comprehensive elucidation of HearNPV-H. zea expression kinetics was obtained.
Publisher: Cold Spring Harbor Laboratory
Date: 11-05-2018
Abstract: Heterogeneity of cell states represented in pluripotent cultures has not been described at the transcriptional level. Since gene expression is highly heterogeneous between cells, single-cell RNA sequencing can be used to identify how in idual pluripotent cells function. Here, we present results from the analysis of single-cell RNA sequencing data from 18,787 in idual WTC-CRISPRi human induced pluripotent stem cells. We developed an unsupervised clustering method and, through this, identified four subpopulations distinguishable on the basis of their pluripotent state, including a core pluripotent population (48.3%), proliferative (47.8%), early primed for differentiation (2.8%), and late primed for differentiation (1.1%). For each subpopulation, we were able to identify the genes and pathways that define differences in pluripotent cell states. Our method identified four transcriptionally distinct predictor gene sets composed of 165 unique genes that denote the specific pluripotency states using these sets, we developed a multigenic machine learning prediction method to accurately classify single cells into each of the subpopulations. Compared against a set of established pluripotency markers, our method increases prediction accuracy by 10%, specificity by 20%, and explains a substantially larger proportion of deviance (up to threefold) from the prediction model. Finally, we developed an innovative method to predict cells transitioning between subpopulations and support our conclusions with results from two orthogonal pseudotime trajectory methods.
Publisher: Frontiers Media SA
Date: 05-01-2023
DOI: 10.3389/FIMMU.2022.1060438
Abstract: Robust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness. We conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 ( IFI27 ) in COVID-19 patients. We show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For ex le, in the pandemic H1N1/09 influenza virus infection, IFI27- like genes were highly upregulated in the blood s les of severely infected patients. These data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488491.V1
Abstract: (A-C) Comparisons with Pearson correlation between (A) proportions of cell lineages in five s les with both scRNA-seq and CODEX data. (B) Average expression of LGALS3 and CD68 in macrophages across all 15 patients. (C) Average expression of COL4A1 and ACTA2 in CAFs across all 15 patients.
Publisher: Cold Spring Harbor Laboratory
Date: 28-03-2023
DOI: 10.1101/2023.03.27.534291
Abstract: Emerging spatial transcriptomics technologies (e.g. Visium, Slide-seq, or MERFISH) have made it possible to keep the spatial information while profiling gene expression of every cell/spatial-spot. Integrating expression values, spatial coordinates, and imaging data type promises to bring more biological insights but is still technically challenging. A user-friendly software tool to enable interactive analysis of spatial transcriptomic data by the broader community is lacking. We present i-stLearn, an all-in-on web application with an analysis pipeline and interactive visualization for studying spatial heterogeneity using spatial transcriptomics data. i-stLearn can be used to gain biological insights from tissue through key analysis types cell-cell interaction analysis, clustering, and trajectory inference. Using functions, users can interactively segment the tissue and identify cellular state transition or cellular communications in a heterogeneous biological s le.
Publisher: Frontiers Media SA
Date: 19-07-2021
DOI: 10.3389/FGENE.2021.666771
Abstract: Finding cell states and their transcriptional relatedness is a main outcome from analysing single-cell data. In developmental biology, determining whether cells are related in a differentiation lineage remains a major challenge. A seamless analysis pipeline from cell clustering to estimating the probability of transitions between cell clusters is lacking. Here, we present Single Cell Global fate Potential of Subpopulations ( scGPS ) to characterise transcriptional relationship between cell states. scGPS decomposes mixed cell populations in one or more s les into clusters ( SCORE algorithm) and estimates pairwise transitioning potential ( scGPS algorithm) of any pair of clusters. SCORE allows for the assessment and selection of stable clustering results, a major challenge in clustering analysis. scGPS implements a novel approach, with machine learning classification, to flexibly construct trajectory connections between clusters. scGPS also has a feature selection functionality by network and modelling approaches to find biological processes and driver genes that connect cell populations. We applied scGPS in erse developmental contexts and show superior results compared to a range of clustering and trajectory analysis methods. scGPS is able to identify the dynamics of cellular plasticity in a user-friendly workflow, that is fast and memory efficient. scGPS is implemented in R with optimised functions using C++ and is publicly available in Bioconductor.
Publisher: Cold Spring Harbor Laboratory
Date: 19-03-2021
DOI: 10.1101/2021.03.18.436004
Abstract: Deep learning cancer classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches depends on prior annotation through a pathologist. This initial step relying on a manual, low-resolution, time-consuming process is highly variable and subject to observer variance. To address this issue, we developed a novel method, H& E Molecular neural network (HEMnet). This two-step process utilises immunohistochemistry as an initial molecular label for cancer cells on a H& E image and then we train a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, we show that HEMnet accurately distinguishes colorectal cancer from normal tissue at high resolution without the need for an initial manual histopathologic evaluation. Our validation study using histopathology images from TCGA s les accurately estimates tumour purity. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: github.com/BiomedicalMachineLearning/HEMnet
Publisher: Cold Spring Harbor Laboratory
Date: 22-09-2017
DOI: 10.1101/191395
Abstract: We used human embryonic stem cell-derived retinal ganglion cells (RGCs) to characterize the transcriptome of 1,174 cells at the single cell level. The human embryonic stem cell line BRN3B-mCherry A81-H7 was differentiated to RGCs using a guided differentiation approach. Cells were harvested at day 36 and subsequently prepared for single cell RNA sequencing. Our data indicates the presence of three distinct subpopulations of cells, with various degrees of maturity. One cluster of 288 cells upregulated genes involved in axon guidance together with semaphorin interactions, cell-extracellular matrix interactions and ECM proteoglycans, suggestive of a more mature phenotype.
Publisher: Elsevier BV
Date: 08-2011
DOI: 10.1016/J.JVIROMET.2011.05.011
Abstract: Baculovirus pesticides are increasingly being used as effective biological control agents against caterpillar pests worldwide. Increasing occlusion body (OB) yields per cell in culture is the main challenge to enable commercialization of in vitro production of baculovirus pesticides. Isolating clones from a heterogeneous cell population may allow development of a high virus producing cell clone. To date, the selection of insect clones has been based mainly on laborious cell serial dilution methods which create few viable clones. This work used an automated robotic clone picking system to establish over 250 insect clones of a Helicoverpa zea cell population to be screened for virus production. However, the higher producing clones only produced 10-30% higher OB yields than the original cell population. This study suggested that unless screening of thousands of clones is performed, obtaining a 2-fold increase in OB/cell yield compared to the parent population is unlikely. Nevertheless, it creates pure clones for manufacturing. In addition, two clones that were at least 2-3 times different in OB yields were isolated. Hence, this method can create a high contrast system (OB/cell yield basis), for comparative studies using a systems biology approach, which should inform a more targeted approach to engineer genetically a production cell line.
Publisher: Springer Science and Business Media LLC
Date: 27-06-2023
DOI: 10.1038/S41467-023-39341-4
Abstract: In vitro, ACE2 translocates to the nucleus to induce SARS-CoV-2 replication. Here, using digital spatial profiling of lung tissues from SARS-CoV-2-infected golden Syrian hamsters, we show that a specific and selective peptide inhibitor of nuclear ACE2 (NACE2i) inhibits viral replication two days after SARS-CoV-2 infection. Moreover, the peptide also prevents inflammation and macrophage infiltration, and increases NK cell infiltration in bronchioles. NACE2i treatment increases the levels of the active histone mark, H3K27ac, restores host translation in infected hamster bronchiolar cells, and leads to an enrichment in methylated ACE2 in hamster bronchioles and lung macrophages, a signature associated with virus protection. In addition, ACE2 methylation is increased in myeloid cells from vaccinated patients and associated with reduced SARS-CoV-2 spike protein expression in monocytes from in iduals who have recovered from infection. This protective epigenetic scarring of ACE2 is associated with a reduced latent viral reservoir in monocytes/macrophages and enhanced immune protection against SARS-CoV-2. Nuclear ACE2 may represent a therapeutic target independent of the variant and strain of viruses that use the ACE2 receptor for host cell entry.
Publisher: Springer Science and Business Media LLC
Date: 28-02-2018
DOI: 10.1038/S41467-017-02809-1
Abstract: Domestication fundamentally reshaped animal morphology, physiology and behaviour, offering the opportunity to investigate the molecular processes driving evolutionary change. Here we assess sheep domestication and artificial selection by comparing genome sequence from 43 modern breeds ( Ovis aries ) and their Asian mouflon ancestor ( O. orientalis ) to identify selection sweeps. Next, we provide a comparative functional annotation of the sheep genome, validated using experimental ChIP-Seq of sheep tissue. Using these annotations, we evaluate the impact of selection and domestication on regulatory sequences and find that sweeps are significantly enriched for protein coding genes, proximal regulatory elements of genes and genome features associated with active transcription. Finally, we find in idual sites displaying strong allele frequency ergence are enriched for the same regulatory features. Our data demonstrate that remodelling of gene expression is likely to have been one of the evolutionary forces that drove phenotypic ersification of this common livestock species.
Publisher: BMJ Publishing Group Ltd
Date: 11-2020
Publisher: No publisher found
Date: 2020
Publisher: Cold Spring Harbor Laboratory
Date: 29-06-2023
DOI: 10.1101/2023.06.27.546665
Abstract: Dinoflagellates are a erse group of phytoplankton, ranging from harmful bloom-forming microalgae to photosymbionts that are critical for sustaining coral reefs. Genome and transcriptome data from dinoflagellates are revealing extensive genomic ergence and lineage-specific innovation of gene functions. However, most studies thus far have focused on protein-coding genes long non-coding RNAs (lncRNAs), known to regulate gene expression in eukaryotes, are largely unexplored. Here, using both genome and transcriptome data, we identified a combined total of 48,039 polyadenylated lncRNAs in the genomes of three dinoflagellate species: the coral symbionts of Cladocopium proliferum and Durusdinium trenchii , and the bloom-forming Prorocentrum cordatum . These putative lncRNAs are shorter, and have fewer introns and lower G+C-content when compared to protein-coding sequences. Although 37,768 (78.6%) lncRNAs shared no significant similarity with one another, we classified all lncRNAs based on conserved sequence motifs ( k -mers) into distinct clusters following properties of potential protein-binding and/or subcellular localisation. Interestingly, 3708 (7.7%) lncRNAs were differentially expressed in response to heat stress, lifestyle, and/or growth phases, and they shared co-expression patterns with protein-coding genes. Based on inferred triplex interactions between lncRNA and upstream (putative promoter) regions of protein-coding genes, we identified a combined 19,460 putative gene targets for 3,721 lncRNAs 907 genes exhibit differential expression under heat stress. These results reveal for the first time the functional ersity of lncRNAs in dinoflagellates, and demonstrate how lncRNAs, often overlooked in transcriptome data, could regulate gene expression as a molecular response to heat stress in these ecologically important organisms.
Publisher: Springer Science and Business Media LLC
Date: 02-03-2022
DOI: 10.1038/S41698-022-00252-0
Abstract: Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H& E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H& E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA s les accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at: github.com/BiomedicalMachineLearning/HEMnet
Publisher: Elsevier BV
Date: 11-2021
Publisher: Oxford University Press (OUP)
Date: 09-2016
Publisher: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S13059-019-1852-7
Abstract: A variety of methods have been developed to demultiplex pooled s les in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or s le genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled s les. scSplit also enables mapping clusters to original s les. Using simulated, merged, and pooled multi-in idual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to s les without external genotype information and is available at: on-xu/scSplit
Publisher: MDPI AG
Date: 12-11-2013
DOI: 10.3390/V5112721
Publisher: Cold Spring Harbor Laboratory
Date: 07-03-2019
DOI: 10.1101/570614
Abstract: A variety of experimental and computational methods have been developed to demultiplex s les from pooled in iduals in a single-cell RNA sequencing (scRNA-Seq) experiment which either require adding information (such as hashtag barcodes) or measuring information (such as genotypes) prior to pooling. We introduce scSplit which utilises genetic differences inferred from scRNA-Seq data alone to demultiplex pooled s les. scSplit also extracts a minimal set of high confidence presence/absence genotypes in each cluster which can be used to map clusters to original s les. Using a range of simulated, merged in idual-s le as well as pooled multi-in idual scRNA-Seq datasets, we show that scSplit is highly accurate and concordant with demuxlet predictions. Furthermore, scSplit predictions are highly consistent with the known truth in cell-hashing dataset. We also show that multiplexed-scRNA-Seq can be used to reduce batch effects caused by technical biases. scSplit is ideally suited to s les for which external genome-wide genotype data cannot be obtained (for ex le non-model organisms), or for which it is impossible to obtain unmixed s les directly, such as mixtures of genetically distinct tumour cells, or mixed infections. scSplit is available at: on-xu/scSplit
Publisher: Springer Science and Business Media LLC
Date: 30-09-2021
DOI: 10.1038/S41598-021-98935-4
Abstract: Chronic kidney disease (CKD) is a persistent impairment of kidney function. Genome-wide association studies (GWAS) have revealed multiple genetic loci associated with CKD susceptibility but the complete genetic basis is not yet clear. Since CKD shares risk factors with cardiovascular diseases and diabetes, there may be pleiotropic loci at play but may go undetected when using single phenotype GWAS. Here, we used multi-phenotype GWAS in the Norfolk Island isolate (n = 380) to identify new loci associated with CKD. We performed a principal components analysis on different combinations of 29 quantitative traits to extract principal components (PCs) representative of multiple correlated phenotypes. GWAS of a PC derived from glomerular filtration rate, serum creatinine, and serum urea identified a suggestive peak (p min = 1.67 × 10 –7 ) that mapped to KCNIP4 . Inclusion of other secondary CKD measurements with these three kidney function traits identified the KCNIP4 locus with GWAS significance (p min = 1.59 × 10 –9 ). Finally, we identified a group of two SNPs with increased minor allele frequencies as potential functional variants. With the use of genetic isolate and the PCA-based multi-phenotype GWAS approach, we have revealed a potential pleotropic effect locus for CKD. Further studies are required to assess functional relevance of this locus.
Publisher: Springer Science and Business Media LLC
Date: 22-03-2018
Abstract: Identification of important, functional small RNA (sRNA) species is currently h ered by the lack of reliable and sensitive methods to isolate and characterize them. We have developed a method, termed target-enrichment of sRNAs (TEsR), that enables targeted sequencing of rare sRNAs and erse precursor and mature forms of sRNAs not detectable by current standard sRNA sequencing methods. It is based on the lification of full-length sRNA molecules, production of biotinylated RNA probes, hybridization to one or multiple targeted RNAs, removal of nontargeted sRNAs and sequencing. By this approach, target sRNAs can be enriched by a factor of 500-30,000 while maintaining strand specificity. TEsR enriches for sRNAs irrespective of length or different molecular features, such as the presence or absence of a 5' cap or of secondary structures or abundance levels. Moreover, TEsR allows the detection of the complete sequence (including sequence variants, and 5' and 3' ends) of precursors, as well as intermediate and mature forms, in a quantitative manner. A well-trained molecular biologist can complete the TEsR procedure, from RNA extraction to sequencing library preparation, within 4-6 d.
Publisher: Springer Science and Business Media LLC
Date: 09-09-2016
DOI: 10.1007/S00253-016-7774-3
Abstract: Baculoviruses are recognized as viral workhorses of biotechnology, being used for production of vaccines, complex recombinant proteins, gene delivery vectors' and safe biological pesticides. Improving production yields and understanding the interactions of the virus and its host cell are important aspects of ensuring baculovirus-based processes are commercially competitive. This study aims at potential optimization of host cells used in in vitro virus production by systemically investigating changes in host gene expression in response to virus replication and transcription inside host cells. The study focuses on in vitro interactions of the Helicoverpa armigera virus with Helicoverpa zea insect cells. We used 22 genome-wide microarrays to simultaneously measure both virus and host genes in infected cells in multiple batch suspension cultures, representing high- and low-producing infection conditions. Among 661 differentially expressed genes, we identified a core set of 59 host genes consistently overexpressed post infection, with strong overrepresentation of genes involved in retrotransposition, protein processing in the endoplasmic reticulum, and ubiquitin-mediated proteolysis. Applying a whole genome correlation network analysis to link gene expression to productivity, we revealed 18 key genes significantly associated to virus yield. In addition, this study is among the first to perform a genome-wide expression study for a major baculovirus group II strain, the H. armigera virus, extending current understanding of baculovirus-insect interactions, which mainly focuses on group I viruses.
Publisher: Cold Spring Harbor Laboratory
Date: 07-12-2017
DOI: 10.1101/229294
Abstract: Differentiation into erse cell lineages requires the orchestration of gene regulatory networks guiding erse cell fate choices. Utilizing human pluripotent stem cells, we measured expression dynamics of 17,718 genes from 43,168 cells across five time points over a thirty day time-course of in vitro cardiac-directed differentiation. Unsupervised clustering and lineage prediction algorithms were used to map fate choices and transcriptional networks underlying cardiac differentiation. We leveraged this resource to identify strategies for controlling in vitro differentiation as it occurs in vivo . HOPX, a non-DNA binding homeodomain protein essential for heart development in vivo was identified as dys-regulated in in vitro derived cardiomyocytes. Utilizing genetic gain and loss of function approaches, we dissect the transcriptional complexity of the HOPX locus and identify the requirement of hypertrophic signaling for HOPX transcription in hPSC-derived cardiomyocytes. This work provides a single cell dissection of the transcriptional landscape of cardiac differentiation for broad applications of stem cells in cardiovascular biology.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488509.V1
Abstract: Supplemental Table 1: Antibodies, reporters, imaging order and exposure times used in CODEX imaging. EPCAM, SPP1, CD163 were excluded from downstream analysis due to non-specific staining Supplemental Table 2: Sequencing metrics for scRNA-seq Supplemental Table 3: Absolute number of cells detected from each s le per cell lineage Supplemental Table 4: Differentially expressed genes belonging to respective macrophage clusters compared by site of s le origin. pct.1, pct.2 indicate percentage of cells expressing given gene in the cluster of interest and all other comparison clusters respectively Supplemental Table 5: Enriched reactome pathways in differentially expressed genes in tumor macrophages Supplemental Table 6: Gene signatures for Foam cells (Fernandez et.al., 2020, PMID 31591603) and scar-associated macrophages (Ramachandran et. al., 2020, PMID 31597160) Supplemental Table 7: Annotation of differentially expressed genes in CAFs with the components of the matrisome expression program Supplemental Table 8: Spatial proximity analysis examining whether fibroblasts are more proximal to macrophages than any other cell type.
Publisher: American Association for Cancer Research (AACR)
Date: 14-10-2022
DOI: 10.1158/1078-0432.CCR-22-2041
Abstract: The liver is the most frequent metastatic site for colorectal cancer. Its microenvironment is modified to provide a niche that is conducive for colorectal cancer cell growth. This study focused on characterizing the cellular changes in the metastatic colorectal cancer (mCRC) liver tumor microenvironment (TME). We analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue, and peripheral blood mononuclear cells using single-cell RNA sequencing (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution. We identified TME-specific SPP1-expressing macrophages with altered metabolism features, foam cell characteristics, and increased activity in extracellular matrix (ECM) organization. SPP1+ macrophages and fibroblasts expressed complementary ligand–receptor pairs with the potential to mutually influence their gene-expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, which is a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of SPP1+ macrophages and fibroblasts using gene-expression data. An increased proportion of TME fibroblasts was associated with the worst prognosis in these patients. We demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports colorectal cancer growth in the immunosuppressed metastatic niche in the liver. These features can be used to target immune-checkpoint–resistant MSS tumors.
Publisher: Springer Nature Singapore
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 05-12-2017
DOI: 10.1101/229336
Abstract: The majority of genetic loci underlying common disease risk act through changing genome regulation, and are routinely linked to expression quantitative trait loci, where gene expression is measured using bulk populations of mature cells. A crucial step that is missing is evidence of variation in the expression of these genes as cells progress from a pluripotent to mature state. This is especially important for cardiovascular disease, as the majority of cardiac cells have limited properties for renewal postneonatal. To investigate the dynamic changes in gene expression across the cardiac lineage, we generated RNA-sequencing data captured from 43,168 single cells progressing through in vitro cardiac-directed differentiation from pluripotency. We developed a novel and generalized unsupervised cell clustering approach and a machine learning method for prediction of cell transition. Using these methods, we were able to reconstruct the cell fate choices as cells transition from a pluripotent state to mature cardiomyocytes, uncovering intermediate cell populations that do not progress to maturity, and distinct cell trajectories that terminate in cardiomyocytes that differ in their contractile forces. Second, we identify new gene markers that denote lineage specification and demonstrate a substantial increase in their utility for cell identification over current pluripotent and cardiogenic markers. By integrating results from analysis of the single cell lineage RNA-sequence data with population-based GWAS of cardiovascular disease and cardiac tissue eQTLs, we show that the pathogenicity of disease-associated genes is highly dynamic as cells transition across their developmental lineage, and exhibit variation between cell fate trajectories. Through the integration of single cell RNA-sequence data with population-scale genetic data we have identified genes significantly altered at cell specification events providing insights into a context-dependent role in cardiovascular disease risk. This study provides a valuable data resource focused on in vitro cardiomyocyte differentiation to understand cardiac disease coupled with new analytical methods with broad applications to single-cell data.
Publisher: Oxford University Press (OUP)
Date: 12-2019
DOI: 10.1093/BIOINFORMATICS/BTZ914
Abstract: Spatial transcriptomics (ST) technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for ST data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology. We developed a user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially barcoded spots in a tissue. We show the integration approach outperforms the use of gene-count data alone or imaging data alone to build deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy. The SpaCell package is open source under an MIT licence and it is available at github.com/BiomedicalMachineLearning/SpaCell. Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 05-03-2021
DOI: 10.1186/S13059-021-02293-3
Abstract: The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of in idual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression. Here, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of in idual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an in idual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming. This work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.
Publisher: Cold Spring Harbor Laboratory
Date: 16-02-2023
DOI: 10.1101/2023.02.15.528116
Abstract: Medulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single cell transcriptome studies have defined the cellular states underlying each MB subgroup, however the spatial organisation of these erse cell states and how this impacts response to therapy remains to be determined. Here, we used spatially resolved transcriptomics to define the cellular ersity within a sonic hedgehog (SHH) patient-derived model of MB and identify how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We integrated spatial gene expression with histological annotation and single cell gene expression data from MB, developing a analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. We distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. Our data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.
Publisher: Oxford University Press (OUP)
Date: 13-05-2020
Abstract: The libraries generated by high-throughput single cell RNA-sequencing (scRNA-seq) platforms such as the Chromium from 10× Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use these data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3′ libraries consisting of over 70 000 cells generated on the 10× Genomics Chromium platform. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and the detection of genes, cell barcodes, Unique Molecular Identifiers. The performance of the NextSeq 500 was also similarly comparable to the MGISEQ-2000 based on the same metrics. Data generated by both sequencing platforms yielded similar analytical outcomes for general single-cell analysis. The performance of the NextSeq 500 and MGISEQ-2000 were also comparable for the deconvolution of multiplexed cell pools via variant calling, and detection of guide RNA (gRNA) from a pooled CRISPR single-cell screen. Our study provides a benchmark for high-capacity sequencing platforms applied to high-throughput scRNA-seq libraries.
Publisher: Springer Science and Business Media LLC
Date: 02-06-2021
DOI: 10.1038/S41467-021-23143-7
Abstract: Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.
Publisher: Cold Spring Harbor Laboratory
Date: 13-02-2023
DOI: 10.1101/2023.02.11.527941
Abstract: Applying spatial transcriptomics (ST) to explore a vast amount of formalin-fixed paraffin-embedded (FFPE) archival cancer tissues has been highly challenging due to several critical technical issues. In this work, we optimised ST protocols to generate unprecedented spatial gene expression data for FFPE skin cancer. Skin is among the most challenging tissue types for ST due to its fibrous structure and a high risk of RNAse contamination. We evaluated tissues collected from ten years to two years ago, spanning a range of tissue qualities and complexity. Technical replicates and multiple patient s les were assessed. Further, we integrated gene expression profiles with pathological information, revealing a new layer of molecular information. Such integration is powerful in cancer research and clinical applications. The data allowed us to detect the spatial expression of non-coding RNAs. Together, this work provides important technical perspectives to enable the applications of ST on archived cancer tissues.
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: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S13059-019-1862-5
Abstract: Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an in idual cell based on its transcriptional profile. Here, we present scPred , a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify in idual cells with high accuracy. The generalized method is available at owellgenomicslab/scPred/ .
Publisher: Cold Spring Harbor Laboratory
Date: 14-05-2023
DOI: 10.1101/2023.05.14.540710
Abstract: Spatial transcriptomic (ST) data enables us to link tissue morphological features with thousands of unseen gene expression values, opening a horizon for breakthroughs in digital pathology. Models to predict the presence/absence, high/low, or continuous expression of a gene using images as the only input have a huge potential clinical applications, but such models require improvements in accuracy, interpretability, and robustness. We developed STimage models to estimate parameters of gene expression as distributions rather than fixed data points, thereby allowing for the essential quantification of uncertainty in the predicted results. We assessed aleatoric and epistemic uncertainty of the models across a erse range of test cases and proposed an ensemble approach to improve the model performance and trust. STimage can train prediction models for one gene marker or a panel of markers and provides important interpretability analyses at a single-cell level, and in the histopathological annotation context. Through a comprehensive benchmarking with existing models, we found that STimage is more robust to technical variation in platforms, data types, and s le types. Using images from the cancer genome atlas, we showed that STimage can be applied to non-spatial omics data. STimage also performs better than other models when only a small training dataset is available. Overall, STimage contributes an important methodological advance needed for the potential application of spatial technology in cancer digital pathology.
Publisher: MDPI AG
Date: 28-06-2023
Abstract: Identifying tissue-specific molecular signatures of active regulatory elements is critical to understanding gene regulatory mechanisms. In this study, transcription start sites (TSS) and enhancers were identified using Cap analysis of gene expression (CAGE) across endometrial stromal cell (ESC) s les obtained from women with (n = 4) and without endometriosis (n = 4). ESC TSSs and enhancers were compared to those reported in other tissue and cell types in FANTOM5 and were integrated with RNA-seq and ATAC-seq data from the same s les for regulatory activity and network analyses. CAGE tag count differences between women with and without endometriosis were statistically tested and tags within close proximity to genetic variants associated with endometriosis risk were identified. Over 90% of tag clusters mapping to promoters were observed in cells and tissues in FANTOM5. However, some potential cell-type-specific promoters and enhancers were also observed. Regions of open chromatin identified using ATAC-seq provided further evidence of the active transcriptional regions identified by CAGE. Despite the small s le number, there was evidence of differences associated with endometriosis at 210 consensus clusters, including IGFBP5, CALD1 and OXTR. ESC TSSs were also located within loci associated with endometriosis risk from genome-wide association studies. This study provides novel evidence of transcriptional differences in endometrial stromal cells associated with endometriosis and provides a valuable cell-type specific resource of active TSSs and enhancers in endometrial stromal cells.
Publisher: Elsevier BV
Date: 09-2018
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488497.V1
Abstract: (A) Dot plot depicting expression levels of respective ligands and receptors together with the percentage of cells expressing them. (B) Top 10 percent of interactions inferred between cell types in the TME. Edge weights are proportional to the number of interactions, circle sizes are proportional to the number of cells in each group, edge color represents the cell type as sender. For scale, autocrine interactions in CAFs are 68. (A-B) Data from seven mCRCs and five paired normal liver tissue.
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 12-2021
DOI: 10.1016/J.MOLMED.2021.09.006
Abstract: Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.
Publisher: EMBO
Date: 22-08-2019
Publisher: Springer Science and Business Media LLC
Date: 31-05-2017
DOI: 10.1038/NCOMMS15656
Abstract: Of the many types of DNA damage, DNA double-strand breaks (DSBs) are probably the most deleterious. Mounting evidence points to an intricate relationship between DSBs and transcription. A cell system in which the impact on transcription can be investigated at precisely mapped genomic DSBs is essential to study this relationship. Here in a human cell line, we map genome-wide and at high resolution the DSBs induced by a restriction enzyme, and we characterize their impact on gene expression by four independent approaches by monitoring steady-state RNA levels, rates of RNA synthesis, transcription initiation and RNA polymerase II elongation. We consistently observe transcriptional repression in proximity to DSBs. Downregulation of transcription depends on ATM kinase activity and on the distance from the DSB. Our study couples for the first time, to the best of our knowledge, high-resolution mapping of DSBs with multilayered transcriptomics to dissect the events shaping gene expression after DSB induction at multiple endogenous sites.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488506
Abstract: (A) UMAP representation of dimensionally reduced data following batch-corrected graph-based clustering of all tumor epithelial cells annotated by s le. (B) Heatmap depicting expression of five highest significantly expressed genes (adjusted p-value .05) per patient. (C) Heatmap representation of inferred single-cell CNV profiles of all tumors and reference cells. (A-C) Data from seven mCRCs.
Publisher: Cold Spring Harbor Laboratory
Date: 16-02-2023
DOI: 10.1101/2023.02.15.528116
Abstract: Medulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single cell transcriptome studies have defined the cellular states underlying each MB subgroup, however the spatial organisation of these erse cell states and how this impacts response to therapy remains to be determined. Here, we used spatially resolved transcriptomics to define the cellular ersity within a sonic hedgehog (SHH) patient-derived model of MB and identify how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We integrated spatial gene expression with histological annotation and single cell gene expression data from MB, developing a analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. We distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. Our data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.
Publisher: Public Library of Science (PLoS)
Date: 18-05-2012
Publisher: Springer Science and Business Media LLC
Date: 11-05-2021
DOI: 10.1038/S41467-021-22863-0
Abstract: With age, hematopoietic stem cells (HSC) undergo changes in function, including reduced regenerative potential and loss of quiescence, which is accompanied by a significant expansion of the stem cell pool that can lead to haematological disorders. Elevated metabolic activity has been implicated in driving the HSC ageing phenotype. Here we show that nicotinamide riboside (NR), a form of vitamin B3, restores youthful metabolic capacity by modifying mitochondrial function in multiple ways including reduced expression of nuclear encoded metabolic pathway genes, d ing of mitochondrial stress and a decrease in mitochondrial mass and network-size. Metabolic restoration is dependent on continuous NR supplementation and accompanied by a shift of the aged transcriptome towards the young HSC state, more youthful bone marrow cellular composition and an improved regenerative capacity in a transplant setting. Consequently, NR administration could support healthy ageing by re-establishing a more youthful hematopoietic system.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488506.V1
Abstract: (A) UMAP representation of dimensionally reduced data following batch-corrected graph-based clustering of all tumor epithelial cells annotated by s le. (B) Heatmap depicting expression of five highest significantly expressed genes (adjusted p-value .05) per patient. (C) Heatmap representation of inferred single-cell CNV profiles of all tumors and reference cells. (A-C) Data from seven mCRCs.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488509
Abstract: Supplemental Table 1: Antibodies, reporters, imaging order and exposure times used in CODEX imaging. EPCAM, SPP1, CD163 were excluded from downstream analysis due to non-specific staining Supplemental Table 2: Sequencing metrics for scRNA-seq Supplemental Table 3: Absolute number of cells detected from each s le per cell lineage Supplemental Table 4: Differentially expressed genes belonging to respective macrophage clusters compared by site of s le origin. pct.1, pct.2 indicate percentage of cells expressing given gene in the cluster of interest and all other comparison clusters respectively Supplemental Table 5: Enriched reactome pathways in differentially expressed genes in tumor macrophages Supplemental Table 6: Gene signatures for Foam cells (Fernandez et.al., 2020, PMID 31591603) and scar-associated macrophages (Ramachandran et. al., 2020, PMID 31597160) Supplemental Table 7: Annotation of differentially expressed genes in CAFs with the components of the matrisome expression program Supplemental Table 8: Spatial proximity analysis examining whether fibroblasts are more proximal to macrophages than any other cell type.
Publisher: Cold Spring Harbor Laboratory
Date: 20-02-2023
DOI: 10.1101/2023.02.19.529128
Abstract: The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal s les, bronchoalveolar lavage fluid, and similar, but a robust platform for deep tissue characterisation of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterise and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected s les, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.
Publisher: Cold Spring Harbor Laboratory
Date: 13-02-2023
DOI: 10.1101/2023.02.11.527941
Abstract: Applying spatial transcriptomics (ST) to explore a vast amount of formalin-fixed paraffin-embedded (FFPE) archival cancer tissues has been highly challenging due to several critical technical issues. In this work, we optimised ST protocols to generate unprecedented spatial gene expression data for FFPE skin cancer. Skin is among the most challenging tissue types for ST due to its fibrous structure and a high risk of RNAse contamination. We evaluated tissues collected from ten years to two years ago, spanning a range of tissue qualities and complexity. Technical replicates and multiple patient s les were assessed. Further, we integrated gene expression profiles with pathological information, revealing a new layer of molecular information. Such integration is powerful in cancer research and clinical applications. The data allowed us to detect the spatial expression of non-coding RNAs. Together, this work provides important technical perspectives to enable the applications of ST on archived cancer tissues.
Publisher: American Association for Cancer Research (AACR)
Date: 15-06-2022
DOI: 10.1158/1538-7445.AM2022-3817
Abstract: Skin cancer is by far the most common cancer, encompassing squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. The ersity of cell types and tissue organisation in skin cancer remains poorly understood yet is required to improve diagnosis and treatment. In this work, we integrated six imaging and sequencing technologies to build the first spatial single cell reference for all three major skin cancer types and create a comprehensive skin cancer interactome. Using single-cell RNA-Seq (RNA) of & ,000 cells from 11 paired patient biopsies, we identified 28 SCC cell types, including 10 immune cell types, and found core suites of 39 cancer genes and 222 healthy genes shared across ≥80% patient s les. Using independent Nanostring Digital Spatial Profiling (RNA, protein), we validated most immune cell types and gene markers at protein and RNA levels. The enrichment of an immune signalling signature in SCC was further revealed by spatial Nanostring Single Molecular Imaging - SMI (RNA). Strikingly, we found the high consistency in mapping cell types in scRNAseq data and the independent SMI data, for ex le, the distribution of the three keratinocyte layers (basal, cycling and differentiated). This observation suggested the power of combining scRNAseq data with spatial SMI data. Furthermore, we implemented three approaches to validate the spatial distribution and cell type co-localisation by both Visium Spatial Transcriptomics (RNA), SMI (RNA) and Opal Multiplex Polaris (protein). Finally, cell-cell interactions were inferred at the global level using scRNAseq data (no spatial information) and Visium data (with spatial dimension), which were then validated at high throughput (517 ligands/receptors) and single-cell resolution using SMI. These in situ interaction maps were built across all three cancer types to create a comprehensive spatial interaction atlas of skin cancer. We also used targeted approaches with Polaris (protein) and RNAScope (RNA) to confirm and visualise clinically-important ligand-receptor pairs, including checkpoint inhibitor drug targets PD-1 and PD-L1. By integrating six distinct yet complementary spatial and single cell technologies, this study highlights the power of a spatial multi-omics approach for understanding cell types and their activities in cancer tissues. Citation Format: Laura Grice, Guiyan Ni, Xinnan Jin, Minh Tran, Emily Killingbeck, Mark Gregory, Onkar Mulay, Siok-Min Teoh, Arutha Kulasinghe, Michael Leon, Sarah Murphy, Sarah Warren, Youngmi Kim, Quan Nguyen. A single-cell, spatial multiomics atlas and cellular interactome of all major skin cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022 2022 Apr 8-13. Philadelphia (PA): AACR Cancer Res 2022 (12_Suppl):Abstract nr 3817.
Publisher: Oxford University Press (OUP)
Date: 06-2022
DOI: 10.1093/NEUONC/NOAC079.381
Abstract: Medulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups on the basis of gene expression and genomic features. Single cell transcriptome studies have defined the cellular states underlying each MB subgroup, however the spatial organisation of these erse cell states and how this impacts response to therapy remains to be determined. Here, we used spatially resolved transcriptomics to define the cellular ersity within a sonic hedgehog (SHH) patient-derived model of MB and identify how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in the majority of the tumours. Additionally, we resolve in cellular resolution a distinct tumour “interface” where the tumour contacts neighbouring mouse brain consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. Our data highlight the power of this approach to characterise the response of a tumour to targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB
Publisher: Oxford University Press (OUP)
Date: 09-2016
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: Springer Science and Business Media LLC
Date: 13-02-2018
Abstract: We used single cell sequencing technology to characterize the transcriptomes of 1,174 human embryonic stem cell-derived retinal ganglion cells (RGCs) at the single cell level. The human embryonic stem cell line BRN3B-mCherry (A81-H7), was differentiated to RGCs using a guided differentiation approach. Cells were harvested at day 36 and prepared for single cell RNA sequencing. Our data indicates the presence of three distinct subpopulations of cells, with various degrees of maturity. One cluster of 288 cells showed increased expression of genes involved in axon guidance together with semaphorin interactions, cell-extracellular matrix interactions and ECM proteoglycans, suggestive of a more mature RGC phenotype.
Publisher: Frontiers Media SA
Date: 29-07-2022
DOI: 10.3389/FIMMU.2022.911873
Abstract: The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed, especially for cancer immunotherapy development, which requires understanding of molecular mechanisms and discovery of more druggable targets. In this work, we assembled and evaluated an integrated experimental framework and analytical process to enable genome-wide scale discovery of ligand-receptors potentially used for cellular crosstalks, followed by targeted validation. We assessed the complementarity of four different technologies: single-cell RNA sequencing and Spatial transcriptomic (measuring over & ,000 genes), RNA In Situ Hybridization (RNAscope, measuring 4-12 genes) and Opal Polaris multiplex protein staining (4-9 proteins). To utilize the multimodal data, we implemented existing methods and also developed STRISH (Spatial TRanscriptomic In Situ Hybridization), a computational method that can automatically scan across the whole tissue section for local expression of gene (e.g. RNAscope data) and/or protein markers (e.g. Polaris data) to recapitulate an interaction landscape across the whole tissue. We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We showed that inference of cell-cell interactions using scRNA-seq data can misdetect or detect false positive interactions. Spatial transcriptomics still suffers from misdetecting lowly expressed ligand-receptor interactions, but reduces false discovery. RNAscope and Polaris are sensitive methods for defining the location of potential ligand receptor interactions, and the STRISH program can determine the probability that local gene co-expression reflects true cell-cell interaction. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.
Publisher: Springer Science and Business Media LLC
Date: 02-08-2021
DOI: 10.1038/S41592-021-01203-6
Abstract: Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between in idual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.
Publisher: Oxford University Press (OUP)
Date: 02-07-2022
DOI: 10.1093/BIB/BBAC252
Abstract: Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs not included in the array (imputed SNPs) can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the array. It is those functional SNPs that are most likely associated with traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited. $\\textrm{LmTag}$ is available at: atngu/LmTag.
Publisher: Cold Spring Harbor Laboratory
Date: 13-02-2023
DOI: 10.1101/2023.02.10.527955
Abstract: Immune checkpoint inhibitor (ICI) modality has had a limited success ( %) in treating metastatic recurrent Head & Neck Oropharyngeal Squamous cell carcinomas (OPSCCs). To improve response rates to ICIs, tailored approaches capable to capture the tumor complexity and dynamics of each patient’s disease are needed. Here, we performed advanced analyses of spatial proteogenomic technologies to demonstrate that: (i) compared to standard histopathology, spatial transcriptomics better-identified tumor cells and could specifically classify them into two different metabolic states with therapeutic implications (ii) our new method (Spatial Proteomics-informed cell deconvolution method or SPiD ) improved profiling of local immune cell types relevant to disease progression, (iii) identified clinically relevant alternative treatments and a rational explanation for checkpoint inhibitor therapy failure through comparative analysis of pre- and post-failure tumor data and, (iv) discovered ligand-receptor interactions as potential lead targets for personalized drug treatments. Our work establishes a clear path for incorporating spatial-omics in clinical settings to facilitate treatment personalization.
Publisher: Springer Science and Business Media LLC
Date: 02-08-2012
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488494.V1
Abstract: Graphical representation of cell types identified in CODEX analysis in image data from respective patients.
Publisher: Oxford University Press (OUP)
Date: 11-2022
DOI: 10.1093/BIB/BBAC459
Abstract: Most polygenic risk score (PRS)models have been based on data from populations of European origins (accounting for the majority of the large genomics datasets, e.g. & % in the UK Biobank and & % in the GTEx project). Although several large-scale Asian biobanks were initiated (e.g. Japanese, Korean, Han Chinese biobanks), most other Asian countries have little or near-zero genomics data. To implement PRS models for under-represented populations, we explored transfer learning approaches, assuming that information from existing large datasets can compensate for the small s le size that can be feasibly obtained in developing countries, like Vietnam. Here, we benchmark 13 common PRS methods in meta-population strategy (combining in idual genotype data from multiple populations) and multi-population strategy (combining summary statistics from multiple populations). Our results highlight the complementarity of different populations and the choice of methods should depend on the target population. Based on these results, we discussed a set of guidelines to help users select the best method for their datasets. We developed a robust and comprehensive software to allow for benchmarking comparisons between methods and proposed a computational framework for improving PRS performance in a dataset with a small s le size. This work is expected to inform the development of genomics applications in under-represented populations. PRSUP framework is available at: github.com/BiomedicalMachineLearning/VGP
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488500
Abstract: (A) UMAP representation of dimensionally reduced data following batch-corrected graph-based clustering of all lymphocyte lineage cells annotated by cell types. (B) Heatmap depicting average expression of selected genes for each cell type. (C) UMAP representation annotated by condition. (D) Heatmap depicting cell states from CD8 T cells identified using EcoTyper NMF analysis, annotated by condition. (A-D) Data from seven mCRCs, five paired normal liver tissue and two paired PBMCs. (E) Violin plot depicting the expression of cytotoxicity gene signature in normal and tumor CD8 T cells with T-test p-value. Data from seven mCRCs, five paired normal liver tissue.
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 12-2021
Publisher: MDPI AG
Date: 07-10-2022
Abstract: Background. Gastric cancer (GC) is the fifth most common type of cancer and the fourth most common cause of cancer-related mortality. Although the risk of GC and peptic ulcer disease (PUD) is known to be increased by H. pylori infection, evidence regarding the direct relationship between PUD and GC across ethnicities is inconclusive. Therefore, we investigated the association between PUD and GC in the Stomach cancer Pooling (StoP) consortium. Methods. History of peptic ulcer disease was collected using a structured questionnaire in 11 studies in the StoP consortium, including 4106 GC cases and 6922 controls. The two-stage in idual-participant data meta-analysis approach was adopted to generate a priori. Unconditional logistic regression and Firth’s penalized maximum likelihood estimator were used to calculate study-specific odds ratios (ORs) and 95% confidence intervals (CIs) for the association between gastric ulcer (GU)/duodenal ulcer (DU) and risk of GC. Results. History of GU and DU was thoroughly reported and used in association analysis, respectively, by 487 cases (12.5%) and 276 controls (4.1%), and 253 cases (7.8%) and 318 controls (6.0%). We found that GU was associated with an increased risk of GC (OR = 3.04, 95% CI: 2.07–4.49). No association between DU and GC risk was observed (OR = 1.03, 95% CI: 0.77–1.39). Conclusions. In the pooled analysis of 11 case–control studies in a large consortium (i.e., the Stomach cancer Pooling (StoP) consortium), we found a positive association between GU and risk of GC and no association between DU and GC risk.
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1016/J.JID.2018.06.169
Abstract: Persistent human papillomavirus (HPV) infection is responsible for at least 5% of human malignancies. Most HPV-associated cancers are initiated by the HPV16 genotype, as confirmed by detection of integrated HPV DNA in cells of oral and anogenital epithelial cancers. However, single-cell RNA sequencing may enable prediction of HPV involvement in carcinogenesis at other sites. We conducted single-cell RNA sequencing on keratinocytes from a mouse transgenic for the E7 gene of HPV16 and showed sensitive and specific detection of HPV16-E7 mRNA, predominantly in basal keratinocytes. We showed that increased E7 mRNA copy number per cell was associated with increased expression of E7 induced genes. This technique enhances detection of active viral transcription in solid tissue and may clarify possible linkage of HPV infection to development of squamous cell carcinoma.
Publisher: Elsevier BV
Date: 07-2021
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488503
Abstract: Heatmap depicting cell states from monocytes and macrophages identified using Ecotyper NMF analysis, annotated by condition and genes with highest fold change in each state. Data from seven mCRCs, five paired normal liver tissue and two paired PBMCs.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.C.6532742
Abstract: AbstractPurpose: The liver is the most frequent metastatic site for colorectal cancer. Its microenvironment is modified to provide a niche that is conducive for colorectal cancer cell growth. This study focused on characterizing the cellular changes in the metastatic colorectal cancer (mCRC) liver tumor microenvironment (TME). Experimental Design: We analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue, and peripheral blood mononuclear cells using single-cell RNA sequencing (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution. Results: We identified TME-specific i SPP1 /i -expressing macrophages with altered metabolism features, foam cell characteristics, and increased activity in extracellular matrix (ECM) organization. i SPP1 sup + /sup /i macrophages and fibroblasts expressed complementary ligand–receptor pairs with the potential to mutually influence their gene-expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, which is a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of i SPP1 /i sup + /sup macrophages and fibroblasts using gene-expression data. An increased proportion of TME fibroblasts was associated with the worst prognosis in these patients. Conclusions: We demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports colorectal cancer growth in the immunosuppressed metastatic niche in the liver. These features can be used to target immune-checkpoint–resistant MSS tumors. /
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.C.6532742.V1
Abstract: AbstractPurpose: The liver is the most frequent metastatic site for colorectal cancer. Its microenvironment is modified to provide a niche that is conducive for colorectal cancer cell growth. This study focused on characterizing the cellular changes in the metastatic colorectal cancer (mCRC) liver tumor microenvironment (TME). Experimental Design: We analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue, and peripheral blood mononuclear cells using single-cell RNA sequencing (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution. Results: We identified TME-specific i SPP1 /i -expressing macrophages with altered metabolism features, foam cell characteristics, and increased activity in extracellular matrix (ECM) organization. i SPP1 sup + /sup /i macrophages and fibroblasts expressed complementary ligand–receptor pairs with the potential to mutually influence their gene-expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, which is a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of i SPP1 /i sup + /sup macrophages and fibroblasts using gene-expression data. An increased proportion of TME fibroblasts was associated with the worst prognosis in these patients. Conclusions: We demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports colorectal cancer growth in the immunosuppressed metastatic niche in the liver. These features can be used to target immune-checkpoint–resistant MSS tumors. /
Publisher: Springer Science and Business Media LLC
Date: 27-02-2017
DOI: 10.1038/NCOMMS13980
Abstract: The DNA damage response (DDR) is a set of cellular events that follows the generation of DNA damage. Recently, site-specific small non-coding RNAs, also termed DNA damage response RNAs (DDRNAs), have been shown to play a role in DDR signalling and DNA repair. Dysfunctional telomeres activate DDR in ageing, cancer and an increasing number of identified pathological conditions. Here we show that, in mammals, telomere dysfunction induces the transcription of telomeric DDRNAs (tDDRNAs) and their longer precursors from both DNA strands. DDR activation and maintenance at telomeres depend on the biogenesis and functions of tDDRNAs. Their functional inhibition by sequence-specific antisense oligonucleotides allows the unprecedented telomere-specific DDR inactivation in cultured cells and in vivo in mouse tissues. In summary, these results demonstrate that tDDRNAs are induced at dysfunctional telomeres and are necessary for DDR activation and they validate the viability of locus-specific DDR inhibition by targeting DDRNAs.
Publisher: Oxford University Press (OUP)
Date: 16-02-2018
Publisher: Cold Spring Harbor Laboratory
Date: 05-06-2017
DOI: 10.1101/143990
Abstract: Genome sequences for hundreds of mammalian species are available, but an understanding of their genomic regulatory regions, which control gene expression, is only beginning. A comprehensive prediction of potential active regulatory regions is necessary to functionally study the roles of the majority of genomic variants in evolution, domestication, and animal production. We developed a computational method to predict regulatory DNA sequences (promoters, enhancers and transcription factor binding sites) in production animals (cows and pigs) and extended its broad applicability to other mammals. The method utilizes human regulatory features identified from thousands of tissues, cell lines, and experimental assays to find homologous regions that are conserved in sequences and genome organization and are enriched for regulatory elements in the genome sequences of other mammalian species. Importantly, we developed a filtering strategy, including a machine learning classification method, to utilize a very small number of species-specific experimental datasets available to select for the likely active regulatory regions. The method finds the optimal combination of sensitivity and accuracy to unbiasedly predict regulatory regions in mammalian species. Furthermore, we demonstrated the utility of the predicted regulatory datasets in cattle for prioritizing variants associated with multiple production and climate change adaptation traits, and identifying potential genome editing targets.
Publisher: Wiley
Date: 2021
DOI: 10.1002/CTM2.272
Publisher: Springer Science and Business Media LLC
Date: 20-10-2022
DOI: 10.1038/S41598-022-22215-Y
Abstract: Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using erse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: ools/saa/ .
Publisher: Cold Spring Harbor Laboratory
Date: 13-02-2023
DOI: 10.1101/2023.02.10.527955
Abstract: Immune checkpoint inhibitor (ICI) modality has had a limited success ( %) in treating metastatic recurrent Head & Neck Oropharyngeal Squamous cell carcinomas (OPSCCs). To improve response rates to ICIs, tailored approaches capable to capture the tumor complexity and dynamics of each patient’s disease are needed. Here, we performed advanced analyses of spatial proteogenomic technologies to demonstrate that: (i) compared to standard histopathology, spatial transcriptomics better-identified tumor cells and could specifically classify them into two different metabolic states with therapeutic implications (ii) our new method (Spatial Proteomics-informed cell deconvolution method or SPiD ) improved profiling of local immune cell types relevant to disease progression, (iii) identified clinically relevant alternative treatments and a rational explanation for checkpoint inhibitor therapy failure through comparative analysis of pre- and post-failure tumor data and, (iv) discovered ligand-receptor interactions as potential lead targets for personalized drug treatments. Our work establishes a clear path for incorporating spatial-omics in clinical settings to facilitate treatment personalization.
Publisher: American Association for Cancer Research (AACR)
Date: 04-2023
DOI: 10.1158/1078-0432.22488503.V1
Abstract: Heatmap depicting cell states from monocytes and macrophages identified using Ecotyper NMF analysis, annotated by condition and genes with highest fold change in each state. Data from seven mCRCs, five paired normal liver tissue and two paired PBMCs.
Publisher: Cold Spring Harbor Laboratory
Date: 17-10-2022
DOI: 10.1101/2022.10.12.512003
Abstract: Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from erse bio-s les to identify genes demarcating cell ersity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across erse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell ersification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell ersity and study gene regulation of scRNA-seq data.
Publisher: Cold Spring Harbor Laboratory
Date: 17-10-2022
DOI: 10.1101/2022.10.12.512003
Abstract: Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from erse bio-s les to identify genes demarcating cell ersity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across erse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell ersification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell ersity and study gene regulation of scRNA-seq data.
Publisher: Oxford University Press (OUP)
Date: 25-10-2022
DOI: 10.1093/HMG/DDAC262
Abstract: Mayer–Rokitansky–Küster–Hauser (MRKH) syndrome is a congenital condition characterized by aplasia or hypoplasia of the uterus and vagina in women with a 46,XX karyotype. This condition can occur as type I when isolated or as type II when associated with extragenital anomalies including kidney and skeletal abnormalities. The genetic basis of MRKH syndrome remains unexplained and several candidate genes have been proposed to play a role in its etiology, including HNF1B, LHX1 and WNT4. Here, we conducted a microarray analysis of 13 women affected by MRKH syndrome, resulting in the identification of chromosomal changes, including the deletion at 17q12, which contains both HNF1B and LHX1. We focused on HNF1B for further investigation due to its known association with, but unknown etiological role in, MRKH syndrome. We ablated Hnf1b specifically in the epithelium of the Müllerian ducts in mice and found that this caused hypoplastic development of the uterus, as well as kidney anomalies, closely mirroring the MRKH type II phenotype. Using single-cell RNA sequencing of uterine tissue in the Hnf1b-ablated embryos, we analyzed the molecules and pathways downstream of Hnf1b, revealing a dysregulation of processes associated with cell proliferation, migration and differentiation. Thus, we establish that loss of Hnf1b function leads to an MRKH phenotype and generate the first mouse model of MRKH syndrome type II. Our results support the investigation of HNF1B in clinical genetic settings of MRKH syndrome and shed new light on the molecular mechanisms underlying this poorly understood condition in women’s reproductive health.
Location: Australia
Location: Viet Nam
Start Date: 2019
End Date: 2021
Funder: Australian Research Council
View Funded ActivityStart Date: 2021
End Date: 2024
Funder: National Health and Medical Research Council
View Funded ActivityStart Date: 2019
End Date: 12-2021
Amount: $415,737.00
Funder: Australian Research Council
View Funded Activity