Biology subjects

Patrick, E.

Publications and source records attributed to Patrick, E..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Construction and optimization of multi-platform precision pathways for precision medicine

In the enduring challenge against disease, advancements in medical technology have empowered clinicians with novel diagnostic platforms. Whilst in some cases, a single test may provide a confident diagnosis, often additional tests are required. However, to strike a balance between diagnostic accuracy and cost-effectiveness, one must rigorously construct the clinical pathways. Here, we developed a framework to build multi-platform precision pathways in an automated, unbiased way, recommending the key steps a clinician would take to reach a diagnosis. We achieve this by developing a confidence score, used to simulate a clinical scenario, where at each stage, either a confident diagnosis is made, or another test is performed. Our framework provides a range of tools to interpret, visualize and compare the pathways, improving communication and enabling their evaluation on accuracy and cost, specific to different contexts. This framework will guide the development of novel diagnostic pathways for different diseases, accelerating the implementation of precision medicine into clinical practice.

bioinformatics↗

bioRxiv2023

MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial transcriptomics data in Bioconductor

Imaging-based spatial transcriptomics technologies have achieved subcellular resolution, enabling detection of individual molecules in their native tissue context. Data associated with these technologies promises unprecedented opportunity towards understanding cellular and subcellular biology. However, in R/Bioconductor there is a scarcity of existing computational infrastructure to represent such data, and particularly to summarise and transform it for existing widely adopted computational tools in single cell transcriptomics analysis, including SingleCellExperiment and SpatialExperiment classes. With the emergence of several commercial offerings of imaging-based spatial transcriptomics, there is a pressing need to develop consistent data structure standards for these technologies at the individual molecule level. To this end, we have developed MoleculeExperiment, an R/Bioconductor package, which i) stores molecule and cell segmentation boundary information at the molecule-level, ii) standardises this molecule-level information across different imaging-based ST technologies, including 10x Genomics Xenium, and iii) streamlines transition from a MoleculeExperiment object to a SpatialExperiment object. Overall, MoleculeExperiment is generally applicable as a data infrastructure class for consistent analysis of imaging-based spatial transcriptomics data.

bioinformatics↗

bioRxiv2023

Ensemble of similarity metrics with a multiview self-organizing map improves cell clustering in highly multiplexed imaging cytometry data.

Highly multiplexed in situ imaging cytometry assays have enabled researchers to scru-tinize cellular systems at an unprecedented level. With the capability of these assays to simultaneously profile the spatial distribution and molecular features of many cells, unsuper-vised machine learning, and in particular clustering algorithms, have become indispensable for identifying cell types and subsets based on these molecular features. However, the most widely used clustering approaches applied to these novel technologies were developed for cell suspension technologies and may not be optimal for in situ imaging assays. In this work, we systematically evaluated the performance of various similarity metrics used to quan-tify the similarity between cells when clustering. Our results demonstrate that performance in cell clustering varies significantly when different similarity metrics were used. Lastly, we propose FuseSOM, an ensemble clustering algorithm employing hierarchical multi-view learning of similarity metrics and self-organizing maps (SOM). Using a stratified subsam-pling analysis framework, FuseSOM exhibits superior clustering performance compared to the current best-practice clustering approaches for in situ imaging cytometry data analysis.

bioinformatics↗

bioRxiv2022

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allow researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalised scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

bioinformatics↗

bioRxiv2022

Spatial mapping of human lung tissue reveals granuloma diversity and histopathological superstructure in tuberculosis

The histopathological hallmark of tuberculosis (TB) is the formation of immune cell enriched aggregates called granulomas, but the scope of granuloma heterogeneity in human TB is unknown. By spatially mapping individual immune cells across large regions of TB lung tissue, we report that in addition to necrotizing granulomas, the human TB lung contains abundant non-necrotizing leukocyte aggregates surrounding areas of necrotizing tissue. These cellular lesions were more diverse in composition than necrotizing lesions and could be stratified into four general classes based on cellular composition and spatial distribution of B cells and macrophages, indicating there are foci of distinct immune reactions adjacent to necrotizing granulomas. We further show that the specific cellular composition of non-necrotizing structures correlates with their proximity to necrotizing lesions. Together, our study shows that during tuberculosis diseased lung tissue develops a histopathological superstructure comprising at least four different types of non-necrotizing cellular aggregates organized as satellites of necrotizing granulomas.

immunology↗

bioRxiv2022

Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient Variables

The spatial architecture of the tumour microenvironment and phenotypic heterogeneity of tumour cells have been shown to be associated with cancer prognosis and clinical outcomes, including survival. Recent advances in highly multiplexed imaging, including imaging mass cytometry (IMC), capture spatially resolved, high-dimensional maps that quantify dozens of disease-relevant biomarkers at single-cell resolution, that contain potential to inform patient-specific prognosis. However, existing automated methods for predicting survival typically do not leverage spatial phenotype information captured at the single-cell level, and current methods tend to focus on a single modality, such as patient variables (PVs). There is no end-to-end method designed to leverage the rich information in whole IMC images and all marker channels, and aggregate this information with PVs in a complementary manner to predict survival with enhanced accuracy. We introduce a deep multimodal graph-based network (DMGN) that integrates entire IMC images and multiple PVs for end-to-end survival prediction of breast cancer. We propose a multimodal graph-based module that considers relationships between spatial phenotype information in all image regions and all PVs, and scales each region-PV pair based on its relevance to survival. We propose another module to automatically generate embeddings specialised for each PV to enhance multimodal aggregation. We show that our modules are consistently effective at improving survival prediction performance using two public datasets, and that DMGN can be applied to an independent validation dataset across the same antigens but different antibody clones. Our DMGN outperformed state-of-the-art methods at survival prediction.

bioinformatics↗

bioRxiv2022

An in situ quantitative map of initial human colorectal HIV transmission

The initial immune response to HIV is critical in determining transmission. However, due to technical limitations we still do not have a comparative map of early mucosal transmission events. We combined RNAscope, cyclic-immunofluorescence and novel image analysis tools to quantify HIV transmission dynamics in intact human colorectal tissue. We mapped HIV enrichment to mucosal dendritic cells (DC) and submucosal macrophages, but not CD4+ T-cells, the primary targets of downstream infection. DCs appeared to funnel virus to lymphoid aggregates which acted as early sanctuaries of high viral titres whilst facilitating HIV passage to the submucosa. Finally, HIV entry induced rapid recruitment and clustering of target cells, facilitating DC and macrophage mediated HIV transfer and enhanced infection of CD4+ T-cells. These data demonstrate a rapid response to HIV structured to maximise the likelihood of mucosal infection, and provide a framework for in situ studies of host pathogen interactions and immune mediated pathologies. Highlights- in situ quantification of host cellular microenvironment response to pathogen invasion in human colorectal tissue. - HIV first localises to mucosal DCs and submucosal macrophages, but not CD4+ T cells. - Viral enrichment first occurs in lymphoid aggregates which is associated with passage into the submucosa. - Early localisation of HIV to CD4+ T cells is associated with interactions with DCs and macrophages. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/490175v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@ef8000org.highwire.dtl.DTLVardef@1bc8ed7org.highwire.dtl.DTLVardef@45ddbdorg.highwire.dtl.DTLVardef@e0a57c_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

High-dimensional and spatial analysis reveals immune landscape dependent progression in cutaneous squamous cell carcinoma

PurposeThe tumour immune microenvironment impacts the biological behaviour of the tumour but its effect on clinical outcomes in head and neck cutaneous squamous cell carcinomas (HNcSCC) is largely unknown. Experimental DesignWe compared the immune milieu of high-risk HNcSCC that never progressed to metastasis with those that metastasised using multi-parameter imaging mass cytometry. The cohort included both immunosuppressed patients (IS) and patients with an absence of clinical immune-suppression (ACIS). Spatial analyses were used to identify cellular interactions that were associated with tumour behaviour. ResultsNon-progressing primary HNcSCC were characterised by higher CD8+ and CD4+ T cell responses, including numerically increased Regulatory T cells. By contrast, primary lesions from HNcSCC patients who progressed were largely devoid of T cells with lower numbers of innate immune cells and increased expression of checkpoint receptors and in the metastatic lesions were characterised by an accumulation of B cells. Spatial analysis reveals multiple cellular interactions associated with non-progressing primary tumours that were distinct in primary tumours of disease progressing patients. Cellular regional analysis of the tumour microenvironment also shows squamous cell-enriched tumour regions associated with primary non-progressing tumours. ConclusionsEffective responses from both CD8+ and CD4+ T cells in the tumour microenvironment are essential for immune control of primary HNcSCC. Our findings indicate that the early events that shape the immune responses in primary tumours dictate progression and disease outcomes in HNcSCC. Translational RelevanceThe ability to predict metastatic tumour progression at the time of initial diagnosis of primary HNcSCC could tailor personalised medical care including disease surveillance strategies and identifying patients who will benefit most from adjuvant therapy. One Sentence SummaryThe immune landscape of high-risk cutaneous squamous cell carcinoma differs in tumours that never progress compared to those that progress to metastasis.

pathology↗

bioRxiv2022

scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

Recent advances in single-cell technologies enable scientists to measure molecular data at high-resolutions and hold the promise to substantially improve clinical outcomes through personalised medicine. However, due to a lack of tools specifically designed to represent each sample (e.g. patient) from the collection of cells sequenced, disease outcome prediction on the sample level remains a challenging task. Here, we present scFeatures, a tool that creates interpretable molecular representation of single-cell and spatial data using 17 types of features motivated by current literature. The feature types span across six distinct categories including cell type proportions, cell type specific gene expressions, cell type specific pathway scores, cell type specific cell-cell interaction scores, overall aggregated gene expressions and spatial metrics. By generating molecular representation using scFeatures for single-cell RNA-seq, spatial proteomic and spatial transcriptomic data, we demonstrate that different types of features are important for predicting different disease outcomes in different datasets and the downstream analysis of features uncover novel biological discoveries.

bioinformatics↗

bioRxiv2021

Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments

Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organisation of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarising the information-rich data generated from these technologies.

bioinformatics↗

bioRxiv2021

Identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

High-throughput single cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data -- as failing to do so can lead to missing important biological insights.

bioinformatics↗

bioRxiv2021

spicyR: Spatial analysis of in situ cytometry data in R

MotivationHigh parameter histological techniques have allowed for the identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue environment. This allows the complex cellular architecture and environment of diseased tissue to be explored. While spatial analysis techniques have revealed how cell-cell interactions are important within the disease pathology, there remains a gap in exploring changes in these interactions within the disease process. Specifically, there are currently no established methods for performing inference on cell localisation changes across images, hindering an understanding of how cellular environments change with a disease pathology. ResultsWe have developed the spicyR R package to perform inference on changes in the spatial localisation of cell types across groups of images. Application to simulated data demonstrates a high sensitivity and specificity. We demonstrate the utility of spicyR by applying it to a type 1 diabetes imaging mass cytometry dataset, revealing changes in cellular associations that were relevant to the disease progression. Ultimately, spicyR allows changes in cellular environments to be explored under different pathologies or disease states. Availability and ImplementationR package freely available at http://bioconductor.org/packages/release/bioc/html/spicyR.html and shiny app implementation at http://shiny.maths.usyd.edu.au/spicyR/ Contactellis.patrick@sydney.edu.au Supplementary informationCode for reproducing key figures available at https://github.com/nickcee/spicyRPaper.

bioinformatics↗

bioRxiv2020

Cross-Platform Omics Prediction procedure enables precision medicine in patients with stage-III melanoma

There is no consensus methodology that can account for the variation in omics signatures when they are acquired across different platforms and times. This poses a significant barrier to the implementation of valuable biomarkers into clinical practice. We present a novel procedure (Cross-Platform Omics Prediction) that accounts for these variations and demonstrate its utility in three risk models for different diseases that is suitable for prospective and multi-centre clinical implementation.

bioinformatics↗

bioRxiv2020

Identification of HIV-Transmitting Sub-Epithelial Mononuclear Phagocytes in Human Anogenital and Colorectal Tissues

Tissue mononuclear phagocytes (MNP) are specialised in pathogen detection and antigen presentation. They are the first cells of the immune system to encounter HIV and play a key role in transmission as they deliver the virus to CD4 T cells, which are the primary HIV target cell in which the virus undergoes replication. Most studies have investigated the role that epithelial MNPs play in HIV transmission but, as mucosal trauma and inflammation are strongly associated with HIV transmission, it is also important to examine the role that sub-epithelial MNPs play. Sub-epithelial MNPs are present in a diverse array of subsets which differ in their function and the pathogens they detect. Understanding how specific subsets interact with HIV and deliver the virus to CD4 T cells is therefore of key importance to vaccine and microbicide development. In this study we have shown that, after topical application, HIV can penetrate to interact with sub-epithelial resident myeloid cells in anogenital explants and defined the full array of MNP subsets that are present in all the human anogenital and colorectal sub-epithelial tissues that HIV may encounter during sexual transmission. In doing so we have identified two subsets that preferentially take up HIV, become infected and transmit the virus to CD4 T cells; CD14+CD1c+CD11c+ monocyte-derived dendritic cells and langerin-expressing dendritic cells 2 (DC2).

immunology↗

bioRxiv2019

Investigating higher order interactions in single cell data with scHOT

Single-cell RNA-sequencing has transformed our ability to examine cell fate choice. For example, in the context of development and differentiation, computational ordering of cells along pseudotime enables the expression profiles of individual genes, including key transcription factors, to be examined at fine scale temporal resolution. However, while cell fate decisions are typically marked by profound changes in expression, many such changes are observed in genes downstream of the initial cell fate decision. By contrast, the genes directly involved in the cell fate decision process are likely to interact in subtle ways, potentially resulting in observed changes in patterns of correlation and variation rather than mean expression prior to cell fate commitment. Herein, we describe a novel approach, scHOT - single cell Higher Order Testing - which provides a flexible and statistically robust framework for identifying changes in higher order interactions among genes. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. We demonstrate the utility of scHOT by studying embryonic development of the liver, where we find coordinated changes in higher order interactions of programs related to differentiation and liver function. We also demonstrate its ability to find subtle changes in gene-gene correlation patterns across space using spatially-resolved expression data from the mouse olfactory bulb. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.

bioinformatics↗

bioRxiv2019

PGRMC1 phosphorylation status and cell plasticity 1: glucose metabolism, mitochondria, and mouse xenograft tumorigenesis

Progesterone Receptor Membrane Component 1 (PGRMC1) is expressed in many cancer cells, where it is associated with detrimental patient outcomes. It contains phosphorylated tyrosines which evolutionarily preceded deuterostome gastrulation and tissue differentiation mechanisms. Here, we demonstrate that manipulating PGRMC1 phosphorylation status in MIA PaCa-2 (MP) cells imposes broad pleiotropic effects. Relative to parental cells over-expressing hemagglutinin-tagged wild-type (WT) PGRMC1-HA, cells expressing a PGRMC1-HA-S57A/S181A double mutant (DM) exhibited reduced levels of proteins involved in energy metabolism and mitochondrial function, and altered glucose metabolism suggesting modulation of the Warburg effect. This was associated with increased PI3K/Akt activity, altered cell shape, actin cytoskeleton, motility, and mitochondrial properties. An S57A/Y180F/S181A triple mutant (TM) indicated the involvement of Y180 in PI3K/Akt activation. Mutation of Y180F strongly attenuated mouse xenograft tumor growth. An accompanying paper demonstrates altered metabolism, mutation incidence, and epigenetic status in these cells, indicating that PGRMC1 phosphorylation strongly influences cancer biology.

cancer biology↗

bioRxiv2019

Deconvolving the contributions of cell-type heterogeneity on cortical gene expression

Complexity of cell-type composition has created much skepticism surrounding the interpretation of brain bulk-tissue transcriptomic studies. We generated paired tissue genome-wide gene expression data and immunohistochemistry data, enabling us to assess statistical methods for modeling and estimating cellular heterogeneity in the brain. We demonstrate that several algorithms that rely on single-cell and cell-sorted data to define cell marker gene sets yield accurate relative and absolute estimates of constituent cell-type proportions.

neuroscience↗

bioRxiv2019

Digital removal of autofluorescence from microscopy images

Autofluorescence is a long-standing problem that has hindered fluorescence microscopy image analysis. To address this, we have developed a method that identifies and removes autofluorescent signals from multi-channel images post acquisition. We demonstrate the broad utility of this algorithm in accurately assessing protein expression in situ through the removal of interfering autofluorescent signals. Availability and implementationhttps://ellispatrick.github.io/AFremover Contactellis.patrick@sydney.edu.au Supplementary informationSupplementary Figs. 1-13

bioinformatics↗