1
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Han S, Xu Q, Du Y, Tang C, Cui H, Xia X, Zheng R, Sun Y, Shang H. Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine. Genes Dis 2024; 11:101163. [PMID: 39224111 PMCID: PMC11367031 DOI: 10.1016/j.gendis.2023.101163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 10/17/2023] [Accepted: 10/29/2023] [Indexed: 09/04/2024] Open
Abstract
Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.
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Affiliation(s)
- Songjie Han
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Qianqian Xu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yawen Du
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Chuwei Tang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Herong Cui
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing 102488, China
| | - Xiaofeng Xia
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
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2
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Paryani F, Kwon JS, Ng CW, Jakubiak K, Madden N, Ofori K, Tang A, Lu H, Xia S, Li J, Mahajan A, Davidson SM, Basile AO, McHugh C, Vonsattel JP, Hickman R, Zody MC, Housman DE, Goldman JE, Yoo AS, Menon V, Al-Dalahmah O. Multi-omic analysis of Huntington's disease reveals a compensatory astrocyte state. Nat Commun 2024; 15:6742. [PMID: 39112488 PMCID: PMC11306246 DOI: 10.1038/s41467-024-50626-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
The mechanisms underlying the selective regional vulnerability to neurodegeneration in Huntington's disease (HD) have not been fully defined. To explore the role of astrocytes in this phenomenon, we used single-nucleus and bulk RNAseq, lipidomics, HTT gene CAG repeat-length measurements, and multiplexed immunofluorescence on HD and control post-mortem brains. We identified genes that correlated with CAG repeat length, which were enriched in astrocyte genes, and lipidomic signatures that implicated poly-unsaturated fatty acids in sensitizing neurons to cell death. Because astrocytes play essential roles in lipid metabolism, we explored the heterogeneity of astrocytic states in both protoplasmic and fibrous-like (CD44+) astrocytes. Significantly, one protoplasmic astrocyte state showed high levels of metallothioneins and was correlated with the selective vulnerability of distinct striatal neuronal populations. When modeled in vitro, this state improved the viability of HD-patient-derived spiny projection neurons. Our findings uncover key roles of astrocytic states in protecting against neurodegeneration in HD.
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Affiliation(s)
- Fahad Paryani
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Ji-Sun Kwon
- Department of Developmental Biology Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Christopher W Ng
- Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA
| | - Kelly Jakubiak
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Nacoya Madden
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Kenneth Ofori
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Alice Tang
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Hong Lu
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Shengnan Xia
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Juncheng Li
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Aayushi Mahajan
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Shawn M Davidson
- Northwestern Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | | | | | - Jean Paul Vonsattel
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Richard Hickman
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | | | - David E Housman
- Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA
| | - James E Goldman
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, New York, NY, USA
| | - Andrew S Yoo
- Department of Developmental Biology Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Vilas Menon
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, New York, NY, USA.
| | - Osama Al-Dalahmah
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, New York, NY, USA.
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3
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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4
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Thirman HL, Hayes MJ, Brown LE, Porco JA, Irish JM. Single Cell Profiling Distinguishes Leukemia-Selective Chemotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.591362. [PMID: 38826485 PMCID: PMC11142275 DOI: 10.1101/2024.05.01.591362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
A central challenge in chemical biology is to distinguish molecular families in which small structural changes trigger large changes in cell biology. Such families might be ideal scaffolds for developing cell-selective chemical effectors - for example, molecules that activate DNA damage responses in malignant cells while sparing healthy cells. Across closely related structural variants, subtle structural changes have the potential to result in contrasting bioactivity patterns across different cell types. Here, we tested a 600-compound Diversity Set of screening molecules from the Boston University Center for Molecular Discovery (BU-CMD) in a novel phospho-flow assay that tracked fundamental cell biological processes, including DNA damage response, apoptosis, M-phase cell cycle, and protein synthesis in MV411 leukemia cells. Among the chemotypes screened, synthetic congeners of the rocaglate family were especially bioactive. In follow-up studies, 37 rocaglates were selected and deeply characterized using 12 million additional cellular measurements across MV411 leukemia cells and healthy peripheral blood mononuclear cells. Of the selected rocaglates, 92% displayed significant bioactivity in human cells, and 65% selectively induced DNA damage responses in leukemia and not healthy human blood cells. Furthermore, the signaling and cell-type selectivity were connected to structural features of rocaglate subfamilies. In particular, three rocaglates from the rocaglate pyrimidinone (RP) structural subclass were the only molecules that activated exceptional DNA damage responses in leukemia cells without activating a detectable DNA damage response in healthy cells. These results indicate that the RP subset should be extensively characterized for anticancer therapeutic potential as it relates to the DNA damage response. This single cell profiling approach advances a chemical biology platform to dissect how systematic variations in chemical structure can profoundly and differentially impact basic functions of healthy and diseased cells.
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Affiliation(s)
- Hannah L. Thirman
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN, USA
- Chemical & Physical Biology Program, Vanderbilt University, Nashville, TN, USA
| | - Madeline J. Hayes
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lauren E. Brown
- Department of Chemistry and Center for Molecular Discovery (BU-CMD), Boston University, Boston, MA, USA
| | - John A. Porco
- Department of Chemistry and Center for Molecular Discovery (BU-CMD), Boston University, Boston, MA, USA
| | - Jonathan M. Irish
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN, USA
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5
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Karwański M, Grzybowska U, Mierzejewska E, Szamotulska K. Archetype analysis and the PHATE algorithm as methods to describe and visualize pregnant women's levels of physical activity knowledge. BMC Public Health 2024; 24:1054. [PMID: 38622561 PMCID: PMC11020919 DOI: 10.1186/s12889-024-18355-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 03/13/2024] [Indexed: 04/17/2024] Open
Abstract
The knowledge of physical activity (PA) recommended for pregnant women and practical application of it has positive impact on the outcome. Nevertheless, it is estimated that in high-income countries over 40% of pregnant women are insufficiently physically active. One of the reasons is insufficient knowledge pregnant women have about allowed effort during pregnancy and both recommended and not recommended physical activities. Description of knowledge about physical activity the women have and distinguishing patterns of their knowledge is becoming an increasingly important issue. A common approach to handle survey data that reflect knowledge involves clustering methods or Principal Component Analysis (PCA). Nevertheless, new procedures of data analysis are still being sought. Using survey data collected by the Institute of Mother and Child Archetypal analysis has been applied to detect levels of knowledge reflected by answers given in a questionnaire and to derive patterns of knowledge contained in the data. Next, PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) algorithm has been used to visualize the results and to get a deeper insight into the data structure. The results were compared with picture derived from PCA. Three archetypes representing three patterns of knowledge have been distinguished and described. The presentation of complex data in a low dimension was obtained with help of PHATE. The formations revealed by PHATE have been successfully described in terms of knowledge levels reflected by the survey. Finally, comparison of PHATE with PCA has been shown. Archetype analysis combined with PHATE provides novel opportunities in examining nonlinear structure of survey data and allows for visualization that captures complex relations in the data. PHATE has made it possible to distinguish sets of objects that have common features but were captured neither by Archetypal analysis nor PCA. Moreover, for our data, PHATE provides an image of data structure which is more detailed than interpretation of PCA.
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Affiliation(s)
- Marek Karwański
- Department of Applied Mathematics, University of Life Sciences-SGGW, Nowoursynowska 159, 02-776, Warsaw, Poland
| | - Urszula Grzybowska
- Department of Applied Mathematics, University of Life Sciences-SGGW, Nowoursynowska 159, 02-776, Warsaw, Poland.
| | - Ewa Mierzejewska
- Department of Epidemiology and Biostatistics, Institute of Mother and Child, Kasprzaka 17a, 01-211, Warsaw, Poland
| | - Katarzyna Szamotulska
- Department of Epidemiology and Biostatistics, Institute of Mother and Child, Kasprzaka 17a, 01-211, Warsaw, Poland
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6
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He S, Jin Y, Nazaret A, Shi L, Chen X, Rampersaud S, Dhillon BS, Valdez I, Friend LE, Fan JL, Park CY, Mintz RL, Lao YH, Carrera D, Fang KW, Mehdi K, Rohde M, McFaline-Figueroa JL, Blei D, Leong KW, Rudensky AY, Plitas G, Azizi E. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. Nat Biotechnol 2024:10.1038/s41587-024-02173-8. [PMID: 38514799 PMCID: PMC11415552 DOI: 10.1038/s41587-024-02173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/14/2024] [Indexed: 03/23/2024]
Abstract
Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.
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Affiliation(s)
- Siyu He
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Yinuo Jin
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Achille Nazaret
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Lingting Shi
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Xueer Chen
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Sham Rampersaud
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Bahawar S Dhillon
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Izabella Valdez
- The Graduate School of Biomedical Sciences at the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren E Friend
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Joy Linyue Fan
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Cameron Y Park
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Rachel L Mintz
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Yeh-Hsing Lao
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Pharmaceutical Sciences, University at Buffalo, the State University of New York, Buffalo, NY, USA
| | - David Carrera
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Kaylee W Fang
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Kaleem Mehdi
- Department of Computer Science, Fordham University, New York, NY, USA
| | | | - José L McFaline-Figueroa
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA
| | - David Blei
- Department of Computer Science, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Alexander Y Rudensky
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Ludwig Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - George Plitas
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Ludwig Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Surgery, Breast Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Elham Azizi
- Department of Biomedical Engineering, Columbia University, New York, NY, USA.
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA.
- Department of Computer Science, Columbia University, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA.
- Data Science Institute, Columbia University, New York, NY, USA.
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7
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Zhou Y, Cosentino J, Yun T, Biradar MI, Shreibati J, Lai D, Schwantes-An TH, Luben R, McCaw Z, Engmann J, Providencia R, Schmidt AF, Munroe P, Yang H, Carroll A, Khawaja AP, McLean CY, Behsaz B, Hormozdiari F. Utilizing multimodal AI to improve genetic analyses of cardiovascular traits. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304547. [PMID: 38562791 PMCID: PMC10984061 DOI: 10.1101/2024.03.19.24304547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.
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Affiliation(s)
| | | | | | - Mahantesh I Biradar
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | | | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Robert Luben
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | - Zachary McCaw
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jorgen Engmann
- Center for Translational Genomics, Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, UK
| | - Rui Providencia
- Institute of Health Informatics Research, University College London, London, UK
- Electrophysiology Department, Barts Heart Centre, St. Bartholomew's Hospital, London, UK
| | - Amand Floriaan Schmidt
- Department of Cardiology; Amsterdam University Medical Centres, Amsterdam, The Netherlands
- Institute of Cardiovascular Science; University College London, London, UK
- Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, Netherlands
| | - Patricia Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Howard Yang
- Google Research, San Francisco CA, 94105 USA
| | | | - Anthony P Khawaja
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
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8
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Yakimovich A. Toward the novel AI tasks in infection biology. mSphere 2024; 9:e0059123. [PMID: 38334404 PMCID: PMC10900907 DOI: 10.1128/msphere.00591-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024] Open
Abstract
Machine learning and artificial intelligence (AI) are becoming more common in infection biology laboratories around the world. Yet, as they gain traction in research, novel frontiers arise. Novel artificial intelligence algorithms are capable of addressing advanced tasks like image generation and question answering. However, similar algorithms can prove useful in addressing advanced questions in infection biology like prediction of host-pathogen interactions or inferring virus protein conformations. Addressing such tasks requires large annotated data sets, which are often scarce in biomedical research. In this review, I bring together several successful examples where such tasks were addressed. I underline the importance of formulating novel AI tasks in infection biology accompanied by freely available benchmark data sets to address these tasks. Furthermore, I discuss the current state of the field and potential future trends. I argue that one such trend involves AI tools becoming more versatile.
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Affiliation(s)
- Artur Yakimovich
- Center for Advanced Systems Understanding (CASUS), Görlitz, Germany
- Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR), Dresden, Germany
- Department of Renal Medicine, Division of Medicine, Bladder Infection and Immunity Group (BIIG), University College London, Royal Free Hospital Campus, London, United Kingdom
- Artificial Intelligence for Life Sciences CIC, Dorset, United Kingdom
- Institute of Computer Science, University of Wroclaw, Wroclaw, Poland
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9
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Joodaki M, Shaigan M, Parra V, Bülow RD, Kuppe C, Hölscher DL, Cheng M, Nagai JS, Goedertier M, Bouteldja N, Tesar V, Barratt J, Roberts IS, Coppo R, Kramann R, Boor P, Costa IG. Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT). Mol Syst Biol 2024; 20:57-74. [PMID: 38177382 PMCID: PMC10883279 DOI: 10.1038/s44320-023-00003-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/20/2023] [Accepted: 11/24/2023] [Indexed: 01/06/2024] Open
Abstract
Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.
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Affiliation(s)
- Mehdi Joodaki
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Mina Shaigan
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Victor Parra
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Roman D Bülow
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany
| | - Christoph Kuppe
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - David L Hölscher
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany
| | - Mingbo Cheng
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - James S Nagai
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
| | - Michaël Goedertier
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany
| | - Nassim Bouteldja
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany
| | - Vladimir Tesar
- Department of Nephrology, 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | - Jonathan Barratt
- John Walls Renal Unit, University Hospital of Leicester National Health Service Trust, Leicester, UK
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Ian Sd Roberts
- Department of Cellular Pathology, Oxford University Hospitals National Health Services Foundation Trust, Oxford, UK
| | - Rosanna Coppo
- Fondazione Ricerca Molinette, Regina Margherita Children's University Hospital, Torino, Italy
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
- Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, Netherlands
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Medical School, Aachen, Germany.
| | - Ivan G Costa
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen, Germany.
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10
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Rhodes JS, Aumon A, Morin S, Girard M, Larochelle C, Brunet-Ratnasingham E, Pagliuzza A, Marchitto L, Zhang W, Cutler A, Grand'Maison F, Zhou A, Finzi A, Chomont N, Kaufmann DE, Zandee S, Prat A, Wolf G, Moon KR. Gaining Biological Insights through Supervised Data Visualization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568384. [PMID: 38293135 PMCID: PMC10827133 DOI: 10.1101/2023.11.22.568384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE's prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.
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11
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Yi H, Plotkin A, Stanley N. Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets. Genome Biol 2024; 25:9. [PMID: 38172966 PMCID: PMC10762948 DOI: 10.1186/s13059-023-03143-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND To analyze the large volume of data generated by single-cell technologies and to identify cellular correlates of particular clinical or experimental outcomes, differential abundance analyses are often applied. These algorithms identify subgroups of cells whose abundances change significantly in response to disease progression, or to an experimental perturbation. Despite the effectiveness of differential abundance analyses in identifying critical cell-states, there is currently no systematic benchmarking study to compare their applicability, usefulness, and accuracy in practice across single-cell modalities. RESULTS Here, we perform a comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-art differential abundance testing methods. We benchmarked six single-cell testing methods on several practical tasks, using both synthetic and real single-cell datasets. The tasks evaluated include effectiveness in identifying true differentially abundant subpopulations, accuracy in the adequate handling of batch effects, runtime efficiency, and hyperparameter usability and robustness. Based on various evaluation results, this paper gives dataset-specific suggestions for the practical use of differential abundance testing approaches. CONCLUSIONS Based on our benchmarking study, we provide a set of recommendations for the optimal usage of single-cell DA testing methods in practice, particularly with respect to factors such as the presence of technical noise (for example batch effects), dataset size, and hyperparameter sensitivity.
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Affiliation(s)
- Haidong Yi
- Department of Computer Science, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA
| | - Alec Plotkin
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA
| | - Natalie Stanley
- Department of Computer Science, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA.
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12
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Nazaret A, Fan JL, Lavallée VP, Cornish AE, Kiseliovas V, Masilionis I, Chun J, Bowman RL, Eisman SE, Wang J, Shi L, Levine RL, Mazutis L, Blei D, Pe'er D, Azizi E. Deep generative model deciphers derailed trajectories in acute myeloid leukemia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.11.566719. [PMID: 38014231 PMCID: PMC10680623 DOI: 10.1101/2023.11.11.566719] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Single-cell genomics has the potential to map cell states and their dynamics in an unbiased way in response to perturbations like disease. However, elucidating the cell-state transitions from healthy to disease requires analyzing data from perturbed samples jointly with unperturbed reference samples. Existing methods for integrating and jointly visualizing single-cell datasets from distinct contexts tend to remove key biological differences or do not correctly harmonize shared mechanisms. We present Decipher, a model that combines variational autoencoders with deep exponential families to reconstruct derailed trajectories ( https://github.com/azizilab/decipher ). Decipher jointly represents normal and perturbed single-cell RNA-seq datasets, revealing shared and disrupted dynamics. It further introduces a novel approach to visualize data, without the need for methods such as UMAP or TSNE. We demonstrate Decipher on data from acute myeloid leukemia patient bone marrow specimens, showing that it successfully characterizes the divergence from normal hematopoiesis and identifies transcriptional programs that become disrupted in each patient when they acquire NPM1 driver mutations.
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13
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Paryani F, Kwon JS, Ng CW, Madden N, Ofori K, Tang A, Lu H, Li J, Mahajan A, Davidson SM, Basile A, McHugh C, Vonsattel JP, Hickman R, Zody M, Houseman DE, Goldman JE, Yoo AS, Menon V, Al-Dalahmah O. Multi-OMIC analysis of Huntington disease reveals a neuroprotective astrocyte state. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.08.556867. [PMID: 37745577 PMCID: PMC10515780 DOI: 10.1101/2023.09.08.556867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Huntington disease (HD) is an incurable neurodegenerative disease characterized by neuronal loss and astrogliosis. One hallmark of HD is the selective neuronal vulnerability of striatal medium spiny neurons. To date, the underlying mechanisms of this selective vulnerability have not been fully defined. Here, we employed a multi-omic approach including single nucleus RNAseq (snRNAseq), bulk RNAseq, lipidomics, HTT gene CAG repeat length measurements, and multiplexed immunofluorescence on post-mortem brain tissue from multiple brain regions of HD and control donors. We defined a signature of genes that is driven by CAG repeat length and found it enriched in astrocytic and microglial genes. Moreover, weighted gene correlation network analysis showed loss of connectivity of astrocytic and microglial modules in HD and identified modules that correlated with CAG-repeat length which further implicated inflammatory pathways and metabolism. We performed lipidomic analysis of HD and control brains and identified several lipid species that correlate with HD grade, including ceramides and very long chain fatty acids. Integration of lipidomics and bulk transcriptomics identified a consensus gene signature that correlates with HD grade and HD lipidomic abnormalities and implicated the unfolded protein response pathway. Because astrocytes are critical for brain lipid metabolism and play important roles in regulating inflammation, we analyzed our snRNAseq dataset with an emphasis on astrocyte pathology. We found two main astrocyte types that spanned multiple brain regions; these types correspond to protoplasmic astrocytes, and fibrous-like - CD44-positive, astrocytes. HD pathology was differentially associated with these cell types in a region-specific manner. One protoplasmic astrocyte cluster showed high expression of metallothionein genes, the depletion of this cluster positively correlated with the depletion of vulnerable medium spiny neurons in the caudate nucleus. We confirmed that metallothioneins were increased in cingulate HD astrocytes but were unchanged or even decreased in caudate astrocytes. We combined existing genome-wide association studies (GWAS) with a GWA study conducted on HD patients from the original Venezuelan cohort and identified a single-nucleotide polymorphism in the metallothionein gene locus associated with delayed age of onset. Functional studies found that metallothionein overexpressing astrocytes are better able to buffer glutamate and were neuroprotective of patient-derived directly reprogrammed HD MSNs as well as against rotenone-induced neuronal death in vitro. Finally, we found that metallothionein-overexpressing astrocytes increased the phagocytic activity of microglia in vitro and increased the expression of genes involved in fatty acid binding. Together, we identified an astrocytic phenotype that is regionally-enriched in less vulnerable brain regions that can be leveraged to protect neurons in HD.
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Affiliation(s)
- Fahad Paryani
- Department of Neurology, Columbia University Irving Medical Center
| | - Ji-Sun Kwon
- Washington University School of Medicine in St. Louis
| | - Chris W Ng
- Massachusetts Institute of Technology, Department of Biological Engineering
| | - Nacoya Madden
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | - Kenneth Ofori
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | - Alice Tang
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | - Hong Lu
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | - Juncheng Li
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | - Aayushi Mahajan
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | - Shawn M. Davidson
- Princeton University, Lewis-Sigler Institute for Integrative Genomics
| | | | | | - Jean Paul Vonsattel
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | - Richard Hickman
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | | | - David E. Houseman
- Massachusetts Institute of Technology, Department of Biological Engineering
| | - James E. Goldman
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
| | - Andrew S. Yoo
- Washington University School of Medicine in St. Louis
| | - Vilas Menon
- Department of Neurology, Columbia University Irving Medical Center
| | - Osama Al-Dalahmah
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center
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14
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Baird S, Ashley CL, Marsh‐Wakefield F, Alca S, Ashhurst TM, Ferguson AL, Lukeman H, Counoupas C, Post JJ, Konecny P, Bartlett A, Martinello M, Bull RA, Lloyd A, Grey A, Hutchings O, Palendira U, Britton WJ, Steain M, Triccas JA. A unique cytotoxic CD4 + T cell-signature defines critical COVID-19. Clin Transl Immunology 2023; 12:e1463. [PMID: 37645435 PMCID: PMC10461786 DOI: 10.1002/cti2.1463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/04/2023] [Accepted: 08/11/2023] [Indexed: 08/31/2023] Open
Abstract
Objectives SARS-CoV-2 infection causes a spectrum of clinical disease presentation, ranging from asymptomatic to fatal. While neutralising antibody (NAb) responses correlate with protection against symptomatic and severe infection, the contribution of the T-cell response to disease resolution or progression is still unclear. As newly emerging variants of concern have the capacity to partially escape NAb responses, defining the contribution of individual T-cell subsets to disease outcome is imperative to inform the development of next-generation COVID-19 vaccines. Methods Immunophenotyping of T-cell responses in unvaccinated individuals was performed, representing the full spectrum of COVID-19 clinical presentation. Computational and manual analyses were used to identify T-cell populations associated with distinct disease states. Results Critical SARS-CoV-2 infection was characterised by an increase in activated and cytotoxic CD4+ lymphocytes (CTL). These CD4+ CTLs were largely absent in asymptomatic to severe disease states. In contrast, non-critical COVID-19 was associated with high frequencies of naïve T cells and lack of activation marker expression. Conclusion Highly activated and cytotoxic CD4+ T-cell responses may contribute to cell-mediated host tissue damage and progression of COVID-19. Induction of these potentially detrimental T-cell responses should be considered when developing and implementing effective COVID-19 control strategies.
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Affiliation(s)
- Sarah Baird
- Sydney Infectious Diseases Institute, Faculty of Medicine and HealthThe University of SydneyNSWCamperdownAustralia
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
| | - Caroline L Ashley
- Sydney Infectious Diseases Institute, Faculty of Medicine and HealthThe University of SydneyNSWCamperdownAustralia
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
| | - Felix Marsh‐Wakefield
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
- Liver Injury and Cancer ProgramCentenary InstituteCamperdownNSWAustralia
- Human Cancer and Viral Immunology LaboratoryThe University of SydneyCamperdownNSWAustralia
| | - Sibel Alca
- Sydney Infectious Diseases Institute, Faculty of Medicine and HealthThe University of SydneyNSWCamperdownAustralia
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
| | - Thomas M Ashhurst
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
- Sydney Cytometry Core Research FacilityCharles Perkins Centre, Centenary Institute and The University of SydneyCamperdownNSWAustralia
| | - Angela L Ferguson
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
- Liver Injury and Cancer ProgramCentenary InstituteCamperdownNSWAustralia
| | - Hannah Lukeman
- Sydney Infectious Diseases Institute, Faculty of Medicine and HealthThe University of SydneyNSWCamperdownAustralia
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
| | - Claudio Counoupas
- Sydney Infectious Diseases Institute, Faculty of Medicine and HealthThe University of SydneyNSWCamperdownAustralia
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
- Tuberculosis Research ProgramCentenary InstituteSydneyNSWAustralia
| | - Jeffrey J Post
- Prince of Wales Clinical SchoolUNSWSydneyNSWAustralia
- School of Clinical Medicine, Medicine & HealthUNSWSydneyNSWAustralia
| | - Pamela Konecny
- Prince of Wales Clinical SchoolUNSWSydneyNSWAustralia
- St George HospitalSydneyNSWAustralia
| | - Adam Bartlett
- The Kirby Institute, UNSWSydneyNSWAustralia
- School of Biomedical Sciences, Medicine & HealthUNSWSydneyNSWAustralia
- Sydney Children's HospitalSydneyNSWAustralia
| | | | - Rowena A Bull
- The Kirby Institute, UNSWSydneyNSWAustralia
- School of Biomedical Sciences, Medicine & HealthUNSWSydneyNSWAustralia
| | - Andrew Lloyd
- The Kirby Institute, UNSWSydneyNSWAustralia
- School of Biomedical Sciences, Medicine & HealthUNSWSydneyNSWAustralia
| | - Alice Grey
- RPA Virtual Hospital, Sydney Local Health DistrictSydneyNSWAustralia
| | - Owen Hutchings
- RPA Virtual Hospital, Sydney Local Health DistrictSydneyNSWAustralia
| | - Umaimainthan Palendira
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
- Liver Injury and Cancer ProgramCentenary InstituteCamperdownNSWAustralia
| | - Warwick J Britton
- Tuberculosis Research ProgramCentenary InstituteSydneyNSWAustralia
- Department of Clinical ImmunologyRoyal Prince Alfred HospitalCamperdownNSWAustralia
| | - Megan Steain
- Sydney Infectious Diseases Institute, Faculty of Medicine and HealthThe University of SydneyNSWCamperdownAustralia
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
| | - James A Triccas
- Sydney Infectious Diseases Institute, Faculty of Medicine and HealthThe University of SydneyNSWCamperdownAustralia
- School of Medical Sciences and Charles Perkins CentreThe University of SydneyCamperdownNSWAustralia
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15
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Venkat A, Bhaskar D, Krishnaswamy S. Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data. Trends Immunol 2023; 44:551-563. [PMID: 37301677 DOI: 10.1016/j.it.2023.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 06/12/2023]
Abstract
Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally organized into hierarchical relationships, characterized at multiple levels. Such a multigranular structure corresponds to key geometric and topological features. Given that differences between an effective and ineffective immunological response may not be found at one level, there is vested interest in characterizing and predicting outcomes from such features. In this review, we highlight single cell methods and principles for learning geometric and topological properties of data at multiple scales, discussing their contributions to immunology. Ultimately, multiscale approaches go beyond classical clustering, revealing a more comprehensive picture of cellular heterogeneity.
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Affiliation(s)
- Aarthi Venkat
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | | | - Smita Krishnaswamy
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA; Department of Genetics, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA.
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16
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Pierce CA, Loh LN, Steach HR, Cheshenko N, Preston-Hurlburt P, Zhang F, Stransky S, Kravets L, Sidoli S, Philbrick W, Nassar M, Krishnaswamy S, Herold KC, Herold BC. HSV-2 triggers upregulation of MALAT1 in CD4+ T cells and promotes HIV latency reversal. J Clin Invest 2023; 133:e164317. [PMID: 37079384 PMCID: PMC10232005 DOI: 10.1172/jci164317] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 04/17/2023] [Indexed: 04/21/2023] Open
Abstract
Herpes simplex virus type 2 (HSV-2) coinfection is associated with increased HIV-1 viral loads and expanded tissue reservoirs, but the mechanisms are not well defined. HSV-2 recurrences result in an influx of activated CD4+ T cells to sites of viral replication and an increase in activated CD4+ T cells in peripheral blood. We hypothesized that HSV-2 induces changes in these cells that facilitate HIV-1 reactivation and replication and tested this hypothesis in human CD4+ T cells and 2D10 cells, a model of HIV-1 latency. HSV-2 promoted latency reversal in HSV-2-infected and bystander 2D10 cells. Bulk and single-cell RNA-Seq studies of activated primary human CD4+ T cells identified decreased expression of HIV-1 restriction factors and increased expression of transcripts including MALAT1 that could drive HIV replication in both the HSV-2-infected and bystander cells. Transfection of 2D10 cells with VP16, an HSV-2 protein that regulates transcription, significantly upregulated MALAT1 expression, decreased trimethylation of lysine 27 on histone H3 protein, and triggered HIV latency reversal. Knockout of MALAT1 from 2D10 cells abrogated the response to VP16 and reduced the response to HSV-2 infection. These results demonstrate that HSV-2 contributes to HIV-1 reactivation through diverse mechanisms, including upregulation of MALAT1 to release epigenetic silencing.
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Affiliation(s)
- Carl A. Pierce
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
| | - Lip Nam Loh
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
| | | | - Natalia Cheshenko
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
| | | | - Fengrui Zhang
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Leah Kravets
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
| | | | - William Philbrick
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Michel Nassar
- Department of Otorhinolaryngology–Head and Neck Surgery, Albert Einstein College of Medicine, New York, New York, USA
| | - Smita Krishnaswamy
- Department of Computational Biology
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kevan C. Herold
- Department of Immunobiology, and
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Betsy C. Herold
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
- Department of Pediatrics, Albert Einstein College of Medicine, New York, New York, USA
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17
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Tornini VA, Miao L, Lee HJ, Gerson T, Dube SE, Schmidt V, Kroll F, Tang Y, Du K, Kuchroo M, Vejnar CE, Bazzini AA, Krishnaswamy S, Rihel J, Giraldez AJ. linc-mipep and linc-wrb encode micropeptides that regulate chromatin accessibility in vertebrate-specific neural cells. eLife 2023; 12:e82249. [PMID: 37191016 PMCID: PMC10188112 DOI: 10.7554/elife.82249] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Thousands of long intergenic non-coding RNAs (lincRNAs) are transcribed throughout the vertebrate genome. A subset of lincRNAs enriched in developing brains have recently been found to contain cryptic open-reading frames and are speculated to encode micropeptides. However, systematic identification and functional assessment of these transcripts have been hindered by technical challenges caused by their small size. Here, we show that two putative lincRNAs (linc-mipep, also called lnc-rps25, and linc-wrb) encode micropeptides with homology to the vertebrate-specific chromatin architectural protein, Hmgn1, and demonstrate that they are required for development of vertebrate-specific brain cell types. Specifically, we show that NMDA receptor-mediated pathways are dysregulated in zebrafish lacking these micropeptides and that their loss preferentially alters the gene regulatory networks that establish cerebellar cells and oligodendrocytes - evolutionarily newer cell types that develop postnatally in humans. These findings reveal a key missing link in the evolution of vertebrate brain cell development and illustrate a genetic basis for how some neural cell types are more susceptible to chromatin disruptions, with implications for neurodevelopmental disorders and disease.
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Affiliation(s)
| | - Liyun Miao
- Department of Genetics, Yale UniversityNew HavenUnited States
| | - Ho-Joon Lee
- Department of Genetics, Yale UniversityNew HavenUnited States
- Yale Center for Genome Analysis, Yale UniversityNew HavenUnited States
| | - Timothy Gerson
- Department of Genetics, Yale UniversityNew HavenUnited States
| | - Sarah E Dube
- Department of Genetics, Yale UniversityNew HavenUnited States
| | - Valeria Schmidt
- Department of Genetics, Yale UniversityNew HavenUnited States
| | - François Kroll
- Department of Cell and Developmental Biology, University College LondonLondonUnited Kingdom
| | - Yin Tang
- Department of Genetics, Yale UniversityNew HavenUnited States
| | - Katherine Du
- Department of Genetics, Yale UniversityNew HavenUnited States
- Department of Computer Science, Yale UniversityNew HavenUnited States
| | - Manik Kuchroo
- Department of Genetics, Yale UniversityNew HavenUnited States
- Department of Computer Science, Yale UniversityNew HavenUnited States
| | | | - Ariel Alejandro Bazzini
- Stowers Institute for Medical ResearchKansas CityUnited States
- Department of Molecular & Integrative Physiology, University of Kansas School of MedicineKansas CityUnited States
| | - Smita Krishnaswamy
- Department of Genetics, Yale UniversityNew HavenUnited States
- Department of Computer Science, Yale UniversityNew HavenUnited States
| | - Jason Rihel
- Department of Cell and Developmental Biology, University College LondonLondonUnited Kingdom
| | - Antonio J Giraldez
- Department of Genetics, Yale UniversityNew HavenUnited States
- Yale Stem Cell Center, Yale University School of MedicineNew HavenUnited States
- Yale Cancer Center, Yale University School of MedicineNew HavenUnited States
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18
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McCarthy MW. Interferon lambda as a potential treatment for COVID-19. Expert Opin Biol Ther 2023; 23:389-394. [PMID: 37147857 DOI: 10.1080/14712598.2023.2211709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
INTRODUCTION Pegylated interferon lambda substantially reduced the risk of COVID-19-related hospitalizations or emergency room visits in a recent phase 3, multi-center, randomized, double-blind, placebo-controlled study of high-risk, non-hospitalized adult patients with SARS-CoV-2 infection compared to treatment with placebo. AREAS COVERED Interferons are a family of signaling molecules produced as part of the innate immune response to viral infections. The administration of exogenous interferon may limit disease progression in patients with COVID-19. EXPERT OPINION Interferons have been used to treat viral infections, including hepatitis B and hepatitis C, and malignancies such as non-Hodgkin's lymphoma, as well as the autoimmune condition multiple sclerosis. This manuscript examines what is known about the role of interferon lambda in the treatment of COVID-19, including potential limitations, and explores how this approach may be used in the future.
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19
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Bogaert D, van Beveren GJ, de Koff EM, Lusarreta Parga P, Balcazar Lopez CE, Koppensteiner L, Clerc M, Hasrat R, Arp K, Chu MLJN, de Groot PCM, Sanders EAM, van Houten MA, de Steenhuijsen Piters WAA. Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites. Cell Host Microbe 2023; 31:447-460.e6. [PMID: 36893737 DOI: 10.1016/j.chom.2023.01.018] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/07/2022] [Accepted: 01/30/2023] [Indexed: 03/11/2023]
Abstract
Early-life microbiota seeding and subsequent development is crucial to future health. Cesarean-section (CS) birth, as opposed to vaginal delivery, affects early mother-to-infant transmission of microbes. Here, we assess mother-to-infant microbiota seeding and early-life microbiota development across six maternal and four infant niches over the first 30 days of life in 120 mother-infant pairs. Across all infants, we estimate that on average 58.5% of the infant microbiota composition can be attributed to any of the maternal source communities. All maternal source communities seed multiple infant niches. We identify shared and niche-specific host/environmental factors shaping the infant microbiota. In CS-born infants, we report reduced seeding of infant fecal microbiota by maternal fecal microbes, whereas colonization with breastmilk microbiota is increased when compared with vaginally born infants. Therefore, our data suggest auxiliary routes of mother-to-infant microbial seeding, which may compensate for one another, ensuring that essential microbes/microbial functions are transferred irrespective of disrupted transmission routes.
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Affiliation(s)
- Debby Bogaert
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK.
| | - Gina J van Beveren
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Emma M de Koff
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Paula Lusarreta Parga
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Carlos E Balcazar Lopez
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Lilian Koppensteiner
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Melanie Clerc
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Raiza Hasrat
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Kayleigh Arp
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Mei Ling J N Chu
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Pieter C M de Groot
- Department of Obstetrics and Gynaecology, Spaarne Gasthuis, 2035 RC Haarlem, the Netherlands
| | - Elisabeth A M Sanders
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | | | - Wouter A A de Steenhuijsen Piters
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands.
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20
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Fuda F, Chen M, Chen W, Cox A. Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress. Semin Diagn Pathol 2023; 40:120-128. [PMID: 36894355 DOI: 10.1053/j.semdp.2023.02.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/07/2023]
Abstract
There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases. Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.
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Affiliation(s)
- Franklin Fuda
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Mingyi Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Weina Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Andrew Cox
- Lyda Hill Department of Bioinformatics, University of Texas, Southwestern Medical Center, Dallas, Texas, USA; Department of Cell and Molecular Biology, University of Texas, Southwestern Medical Center, Dallas, Texas, USA.
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21
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Busch EL, Huang J, Benz A, Wallenstein T, Lajoie G, Wolf G, Krishnaswamy S, Turk-Browne NB. Multi-view manifold learning of human brain-state trajectories. NATURE COMPUTATIONAL SCIENCE 2023; 3:240-253. [PMID: 37693659 PMCID: PMC10487346 DOI: 10.1038/s43588-023-00419-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 02/14/2023] [Indexed: 09/12/2023]
Abstract
The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reduction methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they have not been used extensively for brain activity data such as those from functional magnetic resonance imaging (fMRI), primarily due to their inability to maintain dynamic structure. Here we introduce a nonlinear manifold learning method for time-series data-including those from fMRI-called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a low-dimensional intrinsic manifold geometry from time-series data, T-PHATE exploits the data's autocorrelative structure to faithfully denoise and unveil dynamic trajectories. We empirically validate T-PHATE on three fMRI datasets, showing that it greatly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality-reduction benchmarks. These improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally diffuse processes.
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Affiliation(s)
- Erica L. Busch
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Jessie Huang
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Andrew Benz
- Department of Mathematics, Yale University, New Haven, CT, USA
| | - Tom Wallenstein
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Guillaume Lajoie
- Department of Mathematics and Statistics, Université de Montréal, Montreal, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Canada
| | - Guy Wolf
- Department of Mathematics and Statistics, Université de Montréal, Montreal, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Canada
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, USA
- Department of Genetics, Yale University, New Haven, CT, USA
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- These authors contributed equally: Smita Krishnaswamy and Nicholas B. Turk-Browne
| | - Nicholas B. Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- These authors contributed equally: Smita Krishnaswamy and Nicholas B. Turk-Browne
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22
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Gazeau S, Deng X, Ooi HK, Mostefai F, Hussin J, Heffernan J, Jenner AL, Craig M. The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2023; 9:100021. [PMID: 36643886 PMCID: PMC9826539 DOI: 10.1016/j.immuno.2023.100021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/16/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.
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Affiliation(s)
- Sonia Gazeau
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Xiaoyan Deng
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Hsu Kiang Ooi
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Canada
| | - Fatima Mostefai
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Julie Hussin
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Jane Heffernan
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane Australia
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
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23
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Yi H, Plotkin A, Stanley N. Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.24.529894. [PMID: 36909641 PMCID: PMC10002703 DOI: 10.1101/2023.02.24.529894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Modern single-cell data analysis relies on statistical testing (e.g. differential expression testing) to identify genes or proteins that are up-or down-regulated in relation to cell-types or clinical outcomes. However, existing algorithms for such statistical testing are often limited by technical noise and cellular heterogeneity, which lead to false-positive results. To constrain the analysis to a compact and phenotype-related cell population, differential abundance (DA) testing methods were employed to identify subgroups of cells whose abundance changed significantly in response to disease progression, or experimental perturbation. Despite the effectiveness of DA testing algorithms of identifying critical cell-states, there are no systematic benchmarking or comparative studies to compare their usages in practice. Herein, we performed the first comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-art DA testing methods. We benchmarked six DA testing methods on several practical tasks, using both synthetic and real single-cell datasets. The task evaluated include, recognizing true DA subpopulations, appropriate handing of batch effects, runtime efficiency, and hyperparameter usability and robustness. Based on various evaluation results, this paper gives dataset-specific suggestions for the usage of DA testing methods.
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Affiliation(s)
- Haidong Yi
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alec Plotkin
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Natalie Stanley
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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24
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Brown B, Ojha V, Fricke I, Al-Sheboul SA, Imarogbe C, Gravier T, Green M, Peterson L, Koutsaroff IP, Demir A, Andrieu J, Leow CY, Leow CH. Innate and Adaptive Immunity during SARS-CoV-2 Infection: Biomolecular Cellular Markers and Mechanisms. Vaccines (Basel) 2023; 11:408. [PMID: 36851285 PMCID: PMC9962967 DOI: 10.3390/vaccines11020408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/01/2023] [Accepted: 02/04/2023] [Indexed: 02/16/2023] Open
Abstract
The coronavirus 2019 (COVID-19) pandemic was caused by a positive sense single-stranded RNA (ssRNA) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, other human coronaviruses (hCoVs) exist. Historical pandemics include smallpox and influenza, with efficacious therapeutics utilized to reduce overall disease burden through effectively targeting a competent host immune system response. The immune system is composed of primary/secondary lymphoid structures with initially eight types of immune cell types, and many other subtypes, traversing cell membranes utilizing cell signaling cascades that contribute towards clearance of pathogenic proteins. Other proteins discussed include cluster of differentiation (CD) markers, major histocompatibility complexes (MHC), pleiotropic interleukins (IL), and chemokines (CXC). The historical concepts of host immunity are the innate and adaptive immune systems. The adaptive immune system is represented by T cells, B cells, and antibodies. The innate immune system is represented by macrophages, neutrophils, dendritic cells, and the complement system. Other viruses can affect and regulate cell cycle progression for example, in cancers that include human papillomavirus (HPV: cervical carcinoma), Epstein-Barr virus (EBV: lymphoma), Hepatitis B and C (HB/HC: hepatocellular carcinoma) and human T cell Leukemia Virus-1 (T cell leukemia). Bacterial infections also increase the risk of developing cancer (e.g., Helicobacter pylori). Viral and bacterial factors can cause both morbidity and mortality alongside being transmitted within clinical and community settings through affecting a host immune response. Therefore, it is appropriate to contextualize advances in single cell sequencing in conjunction with other laboratory techniques allowing insights into immune cell characterization. These developments offer improved clarity and understanding that overlap with autoimmune conditions that could be affected by innate B cells (B1+ or marginal zone cells) or adaptive T cell responses to SARS-CoV-2 infection and other pathologies. Thus, this review starts with an introduction into host respiratory infection before examining invaluable cellular messenger proteins and then individual immune cell markers.
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Affiliation(s)
| | | | - Ingo Fricke
- Independent Immunologist and Researcher, 311995 Lamspringe, Germany
| | - Suhaila A Al-Sheboul
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
- Department of Medical Microbiology, International School of Medicine, Medipol University-Istanbul, Istanbul 34810, Turkey
| | | | - Tanya Gravier
- Independent Researcher, MPH, San Francisco, CA 94131, USA
| | | | | | | | - Ayça Demir
- Faculty of Medicine, Afyonkarahisar University, Istanbul 03030, Turkey
| | - Jonatane Andrieu
- Faculté de Médecine, Aix–Marseille University, 13005 Marseille, France
| | - Chiuan Yee Leow
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, USM, Penang 11800, Malaysia
| | - Chiuan Herng Leow
- Institute for Research in Molecular Medicine, (INFORMM), Universiti Sains Malaysia, USM, Penang 11800, Malaysia
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25
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Verschoor CP, Picard E, Andrew MK, Haynes L, Loeb M, Pawelec G, Kuchel GA. NK- and T-cell granzyme B and K expression correlates with age, CMV infection and influenza vaccine-induced antibody titres in older adults. FRONTIERS IN AGING 2023; 3:1098200. [PMID: 36685324 PMCID: PMC9849551 DOI: 10.3389/fragi.2022.1098200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 12/20/2022] [Indexed: 01/06/2023]
Abstract
Granzymes are a family of serine-proteases that act as critical mediators in the cytolytic and immunomodulatory activities of immune cells such as CD8+ T-cells and natural killer (NK) cells. Previous work indicates that both granzyme B (GZB) and K (GZK) are increased with age in CD8+ T-cells, and in the case of GZB, contribute to dysfunctional immune processes observed in older adults. Here, we sought to determine how GZB and GZK expression in NK-cells, and CD4+, CD8+, and gamma-delta T-cells, quantified in terms of positive cell frequency and mean fluorescence intensity (MFI), differed with age, age-related health-traits and the antibody response to high-dose influenza vaccine. We found that the frequency and MFI of GZB-expressing NK-cells, and CD8+ and Vδ1+ T-cells, and GZK-expressing CD8+ T-cells was significantly higher in older (66-97 years old; n = 75) vs. younger (24-37 years old; n = 10) adults by up to 5-fold. There were no significant associations of GZB/GZK expression with sex, frailty or plasma levels of TNF or IL-6 in older adults, but those who were seropositive for cytomegalovirus (CMV) exhibited significantly higher frequencies of GZB+ NK-cells, and CD4+, CD8+ and Vδ1+ T-cells, and GZK+ CD8+ T-cells (Cohen's d = .5-1.5). Pre-vaccination frequencies of GZB+ NK-cells were positively correlated with vaccine antibody responses against A/H3N2 (d = .17), while the frequencies of GZK+ NK and CD8+ T-cells were inversely associated with A/H1N1 (d = -0.18 to -0.20). Interestingly, GZK+ NK-cell frequency was inversely correlated with pre-vaccination A/H1N1 antibody titres, as well as those measured over the previous 4 years, further supporting a role for this subset in influencing vaccine antibody-responses. These findings further our understanding of how granzyme expression in different lymphoid cell-types may change with age, while suggesting that they influence vaccine responsiveness in older adults.
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Affiliation(s)
- Chris P. Verschoor
- Health Sciences North Research Institute, Sudbury, ON, Canada,Northern Ontario School of Medicine, Sudbury, ON, Canada,*Correspondence: Chris P. Verschoor,
| | - Emilie Picard
- Health Sciences North Research Institute, Sudbury, ON, Canada
| | | | - Laura Haynes
- UConn Center on Aging, University of Connecticut School of Medicine, Farmington, CT, United States
| | - Mark Loeb
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Graham Pawelec
- Health Sciences North Research Institute, Sudbury, ON, Canada,Department of Immunology, University of Tübingen, Tübingen, Germany
| | - George A. Kuchel
- UConn Center on Aging, University of Connecticut School of Medicine, Farmington, CT, United States
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26
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Schreibing F, Hannani MT, Kim H, Nagai JS, Ticconi F, Fewings E, Bleckwehl T, Begemann M, Torow N, Kuppe C, Kurth I, Kranz J, Frank D, Anslinger TM, Ziegler P, Kraus T, Enczmann J, Balz V, Windhofer F, Balfanz P, Kurts C, Marx G, Marx N, Dreher M, Schneider RK, Saez-Rodriguez J, Costa I, Hayat S, Kramann R. Dissecting CD8+ T cell pathology of severe SARS-CoV-2 infection by single-cell immunoprofiling. Front Immunol 2022; 13:1066176. [PMID: 36591270 PMCID: PMC9800604 DOI: 10.3389/fimmu.2022.1066176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction SARS-CoV-2 infection results in varying disease severity, ranging from asymptomatic infection to severe illness. A detailed understanding of the immune response to SARS-CoV-2 is critical to unravel the causative factors underlying differences in disease severity and to develop optimal vaccines against new SARS-CoV-2 variants. Methods We combined single-cell RNA and T cell receptor sequencing with CITE-seq antibodies to characterize the CD8+ T cell response to SARS-CoV-2 infection at high resolution and compared responses between mild and severe COVID-19. Results We observed increased CD8+ T cell exhaustion in severe SARS-CoV-2 infection and identified a population of NK-like, terminally differentiated CD8+ effector T cells characterized by expression of FCGR3A (encoding CD16). Further characterization of NK-like CD8+ T cells revealed heterogeneity among CD16+ NK-like CD8+ T cells and profound differences in cytotoxicity, exhaustion, and NK-like differentiation between mild and severe disease conditions. Discussion We propose a model in which differences in the surrounding inflammatory milieu lead to crucial differences in NK-like differentiation of CD8+ effector T cells, ultimately resulting in the appearance of NK-like CD8+ T cell populations of different functionality and pathogenicity. Our in-depth characterization of the CD8+ T cell-mediated response to SARS-CoV-2 infection provides a basis for further investigation of the importance of NK-like CD8+ T cells in COVID-19 severity.
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Affiliation(s)
- Felix Schreibing
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Renal and Hypertensive Disorders, Rheumatological and Immunological Diseases (Medical Clinic II), Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Monica T. Hannani
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Hyojin Kim
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - James S. Nagai
- Institute for Computational Genomics, Medical Faculty, RWTH Aachen University, Aachen, Germany,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Fabio Ticconi
- Institute for Computational Genomics, Medical Faculty, RWTH Aachen University, Aachen, Germany,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Eleanor Fewings
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Tore Bleckwehl
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Matthias Begemann
- Institute of Human Genetics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Natalia Torow
- Institute of Medical Microbiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Christoph Kuppe
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Renal and Hypertensive Disorders, Rheumatological and Immunological Diseases (Medical Clinic II), Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Ingo Kurth
- Institute of Human Genetics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Jennifer Kranz
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Urology and Pediatric Urology, RWTH Aachen University, Aachen, Germany,Department of Urology and Kidney Transplantation, Martin Luther University (Saale), Halle, Germany
| | - Dario Frank
- Department of Medicine, St Antonius Hospital, Eschweiler, Germany
| | - Teresa M. Anslinger
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Renal and Hypertensive Disorders, Rheumatological and Immunological Diseases (Medical Clinic II), Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Patrick Ziegler
- Institute for Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Thomas Kraus
- Institute for Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Jürgen Enczmann
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Vera Balz
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Frank Windhofer
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Paul Balfanz
- Department of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Christian Kurts
- Institute of Molecular Medicine and Experimental Immunology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Gernot Marx
- Department of Intensive and Intermediate Care, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Nikolaus Marx
- Department of Cardiology, Angiology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Michael Dreher
- Department of Pneumology and Intensive Care Medicine, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Rebekka K. Schneider
- Institute of Cell and Tumor Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Developmental Biology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Ivan Costa
- Institute for Computational Genomics, Medical Faculty, RWTH Aachen University, Aachen, Germany,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Sikander Hayat
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Renal and Hypertensive Disorders, Rheumatological and Immunological Diseases (Medical Clinic II), Medical Faculty, RWTH Aachen University, Aachen, Germany,Department of Internal Medicine, Erasmus Medical Center (MC), Rotterdam, Netherlands,*Correspondence: Rafael Kramann,
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27
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Dutrow EV, Serpell JA, Ostrander EA. Domestic dog lineages reveal genetic drivers of behavioral diversification. Cell 2022; 185:4737-4755.e18. [PMID: 36493753 PMCID: PMC10478034 DOI: 10.1016/j.cell.2022.11.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/06/2022] [Accepted: 10/31/2022] [Indexed: 12/13/2022]
Abstract
Selective breeding of domestic dogs has generated diverse breeds often optimized for performing specialized tasks. Despite the heritability of breed-typical behavioral traits, identification of causal loci has proven challenging due to the complexity of canine population structure. We overcome longstanding difficulties in identifying genetic drivers of canine behavior by developing a framework for understanding relationships between breeds and the behaviors that define them, utilizing genetic data for over 4,000 domestic, semi-feral, and wild canids and behavioral survey data for over 46,000 dogs. We identify ten major canine genetic lineages and their behavioral correlates and show that breed diversification is predominantly driven by non-coding regulatory variation. We determine that lineage-associated genes converge in neurodevelopmental co-expression networks, identifying a sheepdog-associated enrichment for interrelated axon guidance functions. This work presents a scaffold for canine diversification that positions the domestic dog as an unparalleled system for revealing the genetic origins of behavioral diversity.
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Affiliation(s)
- Emily V Dutrow
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - James A Serpell
- Department of Clinical Sciences and Advanced Medicine, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA 19104, USA
| | - Elaine A Ostrander
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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29
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Perdigoto AL, Deng S, Du KC, Kuchroo M, Burkhardt DB, Tong A, Israel G, Robert ME, Weisberg SP, Kirkiles-Smith N, Stamatouli AM, Kluger HM, Quandt Z, Young A, Yang ML, Mamula MJ, Pober JS, Anderson MS, Krishnaswamy S, Herold KC. Immune cells and their inflammatory mediators modify β cells and cause checkpoint inhibitor-induced diabetes. JCI Insight 2022; 7:e156330. [PMID: 35925682 PMCID: PMC9536276 DOI: 10.1172/jci.insight.156330] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 07/28/2022] [Indexed: 11/17/2022] Open
Abstract
Checkpoint inhibitors (CPIs) targeting programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) have revolutionized cancer treatment but can trigger autoimmune complications, including CPI-induced diabetes mellitus (CPI-DM), which occurs preferentially with PD-1 blockade. We found evidence of pancreatic inflammation in patients with CPI-DM with shrinkage of pancreases, increased pancreatic enzymes, and in a case from a patient who died with CPI-DM, peri-islet lymphocytic infiltration. In the NOD mouse model, anti-PD-L1 but not anti-CTLA-4 induced diabetes rapidly. RNA sequencing revealed that cytolytic IFN-γ+CD8+ T cells infiltrated islets with anti-PD-L1. Changes in β cells were predominantly driven by IFN-γ and TNF-α and included induction of a potentially novel β cell population with transcriptional changes suggesting dedifferentiation. IFN-γ increased checkpoint ligand expression and activated apoptosis pathways in human β cells in vitro. Treatment with anti-IFN-γ and anti-TNF-α prevented CPI-DM in anti-PD-L1-treated NOD mice. CPIs targeting the PD-1/PD-L1 pathway resulted in transcriptional changes in β cells and immune infiltrates that may lead to the development of diabetes. Inhibition of inflammatory cytokines can prevent CPI-DM, suggesting a strategy for clinical application to prevent this complication.
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Affiliation(s)
| | | | | | | | | | | | - Gary Israel
- Department of Radiology and Biomedical Imaging, and
| | - Marie E. Robert
- Department of Pathology, Yale University, New Haven, Connecticut, USA
| | - Stuart P. Weisberg
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, New York, USA
| | | | - Angeliki M. Stamatouli
- Division of Endocrinology, Diabetes, and Metabolism, Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
| | | | - Zoe Quandt
- Department of Medicine and
- Diabetes Center, University of California, San Francisco, San Francisco, California, USA
| | - Arabella Young
- Diabetes Center, University of California, San Francisco, San Francisco, California, USA
- Huntsman Cancer Institute, University of Utah Health Sciences Center, Salt Lake City, Utah, USA
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | | | | | | | - Mark S. Anderson
- Department of Medicine and
- Diabetes Center, University of California, San Francisco, San Francisco, California, USA
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30
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Wang X, Almet AA, Nie Q. Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics. Front Genet 2022; 13:948508. [PMID: 36105110 PMCID: PMC9465179 DOI: 10.3389/fgene.2022.948508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022] Open
Abstract
Cell–cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focus on direct comparisons between individual CCI networks of individual samples from patients, rather than “group-level” comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease statuses, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease statuses. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network level. By applying these analysis to a large-scale single-cell RNA-sequencing dataset of coronavirus disease 2019 (COVID-19), we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19.
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Affiliation(s)
- Xinyi Wang
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States
| | - Axel A. Almet
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United States
- *Correspondence: Axel A. Almet , ; Qing Nie,
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United States
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, United States
- *Correspondence: Axel A. Almet , ; Qing Nie,
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31
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Burkhardt DB, San Juan BP, Lock JG, Krishnaswamy S, Chaffer CL. Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning. Cancer Discov 2022; 12:1847-1859. [PMID: 35736000 PMCID: PMC9353259 DOI: 10.1158/2159-8290.cd-21-0282] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/16/2022] [Accepted: 05/11/2022] [Indexed: 01/09/2023]
Abstract
ABSTRACT Phenotypic plasticity describes the ability of cancer cells to undergo dynamic, nongenetic cell state changes that amplify cancer heterogeneity to promote metastasis and therapy evasion. Thus, cancer cells occupy a continuous spectrum of phenotypic states connected by trajectories defining dynamic transitions upon a cancer cell state landscape. With technologies proliferating to systematically record molecular mechanisms at single-cell resolution, we illuminate manifold learning techniques as emerging computational tools to effectively model cell state dynamics in a way that mimics our understanding of the cell state landscape. We anticipate that "state-gating" therapies targeting phenotypic plasticity will limit cancer heterogeneity, metastasis, and therapy resistance. SIGNIFICANCE Nongenetic mechanisms underlying phenotypic plasticity have emerged as significant drivers of tumor heterogeneity, metastasis, and therapy resistance. Herein, we discuss new experimental and computational techniques to define phenotypic plasticity as a scaffold to guide accelerated progress in uncovering new vulnerabilities for therapeutic exploitation.
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Affiliation(s)
- Daniel B. Burkhardt
- Department of Genetics, Yale University, New Haven, Connecticut
- Cellarity, Somerville, Massachusetts
| | - Beatriz P. San Juan
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- St Vincent's Clinical School, UNSW Medicine, UNSW Sydney, Darlinghurst, New South Wales, Australia
| | - John G. Lock
- School of Medical Sciences, Faculty of Medicine and Health, UNSW Sydney, Kensington, New South Wales, Australia
| | - Smita Krishnaswamy
- Department of Genetics, Yale University, New Haven, Connecticut
- Department of Computer Science, Computational Biology Bioinformatics Program, Applied Math Program, Yale University, New Haven, Connecticut
| | - Christine L. Chaffer
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
- St Vincent's Clinical School, UNSW Medicine, UNSW Sydney, Darlinghurst, New South Wales, Australia
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32
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Gonzalez JC, Chakraborty S, Thulin NK, Wang TT. Heterogeneity in IgG-CD16 signaling in infectious disease outcomes. Immunol Rev 2022; 309:64-74. [PMID: 35781671 PMCID: PMC9539944 DOI: 10.1111/imr.13109] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
In this review, we discuss how IgG antibodies can modulate inflammatory signaling during viral infections with a focus on CD16a-mediated functions. We describe the structural heterogeneity of IgG antibody ligands, including subclass and glycosylation that impact binding by and downstream activity of CD16a, as well as the heterogeneity of CD16a itself, including allele and expression density. While inflammation is a mechanism required for immune homeostasis and resolution of acute infections, we focus here on two infectious diseases that are driven by pathogenic inflammatory responses during infection. Specifically, we review and discuss the evolving body of literature showing that afucosylated IgG immune complex signaling through CD16a contributes to the overwhelming inflammatory response that is central to the pathogenesis of severe forms of dengue disease and coronavirus disease 2019 (COVID-19).
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Affiliation(s)
- Joseph C. Gonzalez
- Department of Medicine, Division of Infectious DiseasesStanford University School of MedicineStanfordCaliforniaUSA,Program in ImmunologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Saborni Chakraborty
- Department of Medicine, Division of Infectious DiseasesStanford University School of MedicineStanfordCaliforniaUSA
| | - Natalie K. Thulin
- Department of ImmunologyUniversity of WashingtonSeattleWashingtonUSA
| | - Taia T. Wang
- Department of Medicine, Division of Infectious DiseasesStanford University School of MedicineStanfordCaliforniaUSA,Program in ImmunologyStanford University School of MedicineStanfordCaliforniaUSA,Department of Microbiology and ImmunologyStanford University School of MedicineStanfordCaliforniaUSA
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33
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Oishi K, Horiuchi S, Frere J, Schwartz RE, tenOever BR. A diminished immune response underlies age-related SARS-CoV-2 pathologies. Cell Rep 2022; 39:111002. [PMID: 35714615 PMCID: PMC9181267 DOI: 10.1016/j.celrep.2022.111002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/27/2022] [Accepted: 06/03/2022] [Indexed: 12/02/2022] Open
Abstract
Morbidity and mortality in response to SARS-CoV-2 infection are significantly elevated in people of advanced age. To understand the underlying biology of this phenotype, we utilize the golden hamster model to compare how the innate and adaptive immune responses to SARS-CoV-2 infection differed between younger and older animals. We find that while both hamster cohorts showed similar virus kinetics in the lungs, the host response in older animals was dampened, with diminished tissue repair in the respiratory tract post-infection. Characterization of the adaptive immune response also revealed age-related differences, including fewer germinal center B cells in older hamsters, resulting in reduced potency of neutralizing antibodies. Moreover, older animals demonstrate elevated suppressor T cells and neutrophils in the respiratory tract, correlating with an increase in TGF-β and IL-17 induction. Together, these data support that diminished immunity is one of the underlying causes of age-related morbidity.
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Affiliation(s)
- Kohei Oishi
- Grossman School of Medicine, New York University, New York, NY 10016, USA
| | - Shu Horiuchi
- Grossman School of Medicine, New York University, New York, NY 10016, USA
| | - Justin Frere
- Grossman School of Medicine, New York University, New York, NY 10016, USA
| | - Robert E Schwartz
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA; Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
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