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Articles by Ellis Patrick
AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology
Keywords digital pathology artificial intelligence deep learning breast cancer spatial transcriptomics spatial biomarkers histopathology treatment response prediction HER2 tumor microenvironment Get full text access Log in, subscribe or purchase for full access. References 1. Wu, S.Z. ∙ Al-Eryani, G. ∙ Roden, D.L. ... A single-cell and spatially resolved atlas of human breast cancers Nat. Genet. 2021; 53:1334-1347 2. Wang, X. ∙ Wang, Z. ∙ Liao, Q. ...
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST - Nature Methods
Abstract The increased use of spatially resolved transcriptomics provides new biological insights into disease mechanisms. However, the high cost and complexity of these methods are barriers to broader application. Consequently, methods have been created to predict spot-based gene expression from routinely collected histology images. Recent benchmarking showed that current methodologies have limited accuracy and spatial resolution, constraining translational capacity.
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST
Gender testing returns to track, stirring up harsh memories and doubts that date to the 1980s TOKYO (AP) — The woman whose genetic test in the 1980s led sports leaders to conclude it was too intrusive, not definitive enough and, ultimately, not worth keeping, said she was faced with “a sadness that I could not share” after results from that test upended her life. The man who discovered the …
Benchmarking the translational potential of spatial gene expression prediction from histology - Nature Communications
Abstract Spatial transcriptomics has enabled the quantification of gene expression at spatial coordinates across a tissue, offering crucial insights into molecular underpinnings of diseases. In light of this, several methods predicting spatial gene expression from paired histology images have provided the opportunity to enhance the utility of obtainable and cost-effective haematoxylin-and-eosin-stained histology images.
A highly adaptable protocol for mapping spatial features of cellular aggregates in tissues
Abstract Multiplex imaging technologies have developed rapidly over the past decades. The advancement of multiplex imaging has been driven in part by the recognition the spatial organisation of cells can represent important prognostic biomarkers and that simply studying the composition of cells in diseased tissue is often insufficient.
Spatial mapping reveals unique cellular interactions and enhanced tertiary lymphoid structures in responders to anti-PD-1 therapy in mucosal head and neck cancers.
Abstract Survival in recurrent/metastatic head and neck mucosal squamous cell carcinoma (HNmSCC) remains poor. Anti-programmed death (PD)-1 therapies have demonstrated improved survival with lower toxicity when compared to standard chemotherapy. However, response to anti-PD-1 therapy remains modest, at 13-17%. We evaluated the tumor microenvironment (TME) using Imaging Mass Cytometry (IMC) on 27 tumor specimens from 24 advanced HNmSCC patients prior to receiving anti-PD-1 based treatment.
BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data - Nature Communications
Abstract Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression.
Non‐invasive diagnosis of early cutaneous squamous cell carcinoma
AK actinic keratosis BD Bowen's disease cSCC cutaneous squamous cell carcinoma DIA-MS data independent acquisition mass spectrometry 1 INTRODUCTION Cutaneous squamous cell carcinoma (cSCC) is the second most frequent cancer in humans.1 Surgical excision is frequently curative but sometimes cSCC metastasize. It is a significant cause of mortality due to skin cancer, particularly in solid organ transplant patients and patients on immunosuppressants.
Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2 - Nature Communications
scMerge2 Single-cell grouping within one batch Following the same principals as scMerge, the scMerge2 approach begins by grouping the cells that share similar biological signals within each dataset or batch. We can approach this in two ways: one way is to perform unsupervised clustering; the other way is using results from supervised cell type classification. Clustering-based grouping: This is performed by default when no cell type label is used as input.
The impact of similarity metrics on cell type clustering in highly multiplexed in situ imaging cytometry data
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MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial transcriptomics data in Bioconductor
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Spatial mapping reveals granuloma diversity and histopathological superstructure in human tuberculosis
Tuberculosis (TB) remains a major cause of mortality, with 1.5 million deaths in 2020 (World Health Organization, 2021). Development of new TB vaccines and therapeutics remains a global priority and depends on a deep understanding of mechanisms mediating resistance to Mycobacterium tuberculosis (M.tb), the causative agent of TB (O’Garra et al., 2013; Orme et al., 2015; Libardo et al., 2018).
Ensemble of similarity metrics with a multiview self-organizing map improves cell clustering in highly multiplexed imaging cytometry data.
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Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2
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Research trends in Australia and New Zealand Nephrology: basic science and gender equity in peril
Abstract Basic science research remains fundamental to progress in clinical care, understanding of disease pathophysiology and underpinning the evolution of personalised medicine. Exposure to research is pivotal to educating students, but a declining profile of basic science research has the potential to erode research capacity further.
Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient Variables
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Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient Variables
New Results doi: https://doi.org/10.1101/2022.07.19.500604 Abstract 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.
Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient Variables
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scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction
Abstract Recent advances in single-cell technologies enable scientists to measure molecular data at high-resolutions and hold the promise to substantially improve clinical outcomes through personalised medicine. However, due to a lack of tools specifically designed to represent each sample (e.g. patient) from the collection of cells sequenced, disease outcome prediction on the sample level remains a challenging task.
treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data - Genome Biology
Adam Chan1,2, Wei Jiang3,4, Emily Blyth3,4,5, Jean Yang1,2,6 & Ellis Patrick ORCID: orcid.org/0000-0002-5253-47471,3,6 Genome Biology volume 22, Article number: 324 (2021) Cite this article Abstract High-throughput single-cell technologies hold the promise of discovering novel cellular relationships with disease.
Identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data
Identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data Adam S Chan, Wei Jiang, Emily Blyth, Jean Yee Hwa Yang, Ellis Patrick bioRxiv 2021.07.08.451609; doi: https://doi.org/10.1101/2021.07.08.451609
Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement for Multiplexed Images
Yunhao Bai1,2†, Bokai Zhu1,3†, Xavier Rovira-Clave1, Han Chen1, Maxim Markovic1, Chi Ngai Chan4, Tung-Hung Su1,5, David R. McIlwain1, Jacob D. Estes4, Leeat Keren1,6, Garry P.
Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement for Multiplexed Images
Introduction High-dimensional tissue imaging approaches such as CODetection by indEXing (CODEX), Multiplexed Ion Beam Imaging (MIBI), Cyclic Immunofluorescence (CyCIF), and imaging mass cytometry have contributed to our understanding of tissue biology and microenvironmental changes in disease (1–7). These methods, which are based on either fluorescence or isotope detection, retain the tissue context of single cells while enabling deep phenotyping capabilities (8).
Investigating higher-order interactions in single-cell data with scHOT
Abstract Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered ‘pseudotime’ offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT—single-cell higher-order testing—which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes.
A transcriptomic atlas of aged human microglia
The biological specimens used in this study originated from two prospective studies of aging: the Religious Orders Study (ROS)6 and the Memory and Aging Project (MAP)7. Participants have to be at least 53 (ROS) or 55 (MAP) years old and non-demented at enrollment and sign an Anatomical Gift Act agreeing to donate their brain and spinal cord at the time of death. Thus, they represent a sample of the older population from the Chicago, IL metropolitan area.
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