101
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Takahama M, Patil A, Johnson K, Cipurko D, Miki Y, Taketomi Y, Carbonetto P, Plaster M, Richey G, Pandey S, Cheronis K, Ueda T, Gruenbaum A, Dudek SM, Stephens M, Murakami M, Chevrier N. Organism-Wide Analysis of Sepsis Reveals Mechanisms of Systemic Inflammation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.526342. [PMID: 36778287 PMCID: PMC9915512 DOI: 10.1101/2023.01.30.526342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Sepsis is a systemic response to infection with life-threatening consequences. Our understanding of the impact of sepsis across organs of the body is rudimentary. Here, using mouse models of sepsis, we generate a dynamic, organism-wide map of the pathogenesis of the disease, revealing the spatiotemporal patterns of the effects of sepsis across tissues. These data revealed two interorgan mechanisms key in sepsis. First, we discover a simplifying principle in the systemic behavior of the cytokine network during sepsis, whereby a hierarchical cytokine circuit arising from the pairwise effects of TNF plus IL-18, IFN-γ, or IL-1β explains half of all the cellular effects of sepsis on 195 cell types across 9 organs. Second, we find that the secreted phospholipase PLA2G5 mediates hemolysis in blood, contributing to organ failure during sepsis. These results provide fundamental insights to help build a unifying mechanistic framework for the pathophysiological effects of sepsis on the body.
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102
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Wang S, Sun ST, Zhang XY, Ding HR, Yuan Y, He JJ, Wang MS, Yang B, Li YB. The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives. Int J Mol Sci 2023; 24:ijms24032943. [PMID: 36769267 PMCID: PMC9918030 DOI: 10.3390/ijms24032943] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/01/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
As an emerging sequencing technology, single-cell RNA sequencing (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and cell heterogeneity by achieving high-throughput and multidimensional analysis of individual cells and circumventing the shortcomings of traditional sequencing for detecting the average transcript level of cell populations. It has been applied to life science and medicine research fields such as tracking dynamic cell differentiation, revealing sensitive effector cells, and key molecular events of diseases. This review focuses on the recent technological innovations in scRNA-Seq, highlighting the latest research results with scRNA-Seq as the core technology in frontier research areas such as embryology, histology, oncology, and immunology. In addition, this review outlines the prospects for its innovative application in traditional Chinese medicine (TCM) research and discusses the key issues currently being addressed by scRNA-Seq and its great potential for exploring disease diagnostic targets and uncovering drug therapeutic targets in combination with multiomics technologies.
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Affiliation(s)
| | | | | | | | | | | | | | - Bin Yang
- Correspondence: (B.Y.); (Y.-B.L.)
| | - Yu-Bo Li
- Correspondence: (B.Y.); (Y.-B.L.)
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103
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Khozyainova AA, Valyaeva AA, Arbatsky MS, Isaev SV, Iamshchikov PS, Volchkov EV, Sabirov MS, Zainullina VR, Chechekhin VI, Vorobev RS, Menyailo ME, Tyurin-Kuzmin PA, Denisov EV. Complex Analysis of Single-Cell RNA Sequencing Data. BIOCHEMISTRY (MOSCOW) 2023; 88:231-252. [PMID: 37072324 PMCID: PMC10000364 DOI: 10.1134/s0006297923020074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.
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Affiliation(s)
- Anna A Khozyainova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia.
| | - Anna A Valyaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mikhail S Arbatsky
- Laboratory of Artificial Intelligence and Bioinformatics, The Russian Clinical Research Center for Gerontology, Pirogov Russian National Medical University, Moscow, 129226, Russia
- School of Public Administration, Lomonosov Moscow State University, Moscow, 119991, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Sergey V Isaev
- Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Pavel S Iamshchikov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
- Laboratory of Complex Analysis of Big Bioimage Data, National Research Tomsk State University, Tomsk, 634050, Russia
| | - Egor V Volchkov
- Department of Oncohematology, Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia
| | - Marat S Sabirov
- Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, Moscow, 119334, Russia
| | - Viktoria R Zainullina
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Vadim I Chechekhin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Rostislav S Vorobev
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Maxim E Menyailo
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Pyotr A Tyurin-Kuzmin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Evgeny V Denisov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
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104
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Zhang Z, Sun H, Mariappan R, Chen X, Chen X, Jain MS, Efremova M, Teichmann SA, Rajan V, Zhang X. scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection. Nat Commun 2023; 14:384. [PMID: 36693837 PMCID: PMC9873790 DOI: 10.1038/s41467-023-36066-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023] Open
Abstract
Single cell data integration methods aim to integrate cells across data batches and modalities, and data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. Specifically, we show that integrated cell embedding combined with learned bio-markers lead to cell type annotations of higher quality or resolution compared to their original annotations.
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Affiliation(s)
- Ziqi Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Haoran Sun
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ragunathan Mariappan
- Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore
| | - Xi Chen
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Xinyu Chen
- Bioengineering Program, Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | | | - Vaibhav Rajan
- Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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105
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Duan M, Nguyen DC, Joyner CJ, Saney CL, Tipton CM, Andrews J, Lonial S, Kim C, Hentenaar I, Kosters A, Ghosn E, Jackson A, Knechtle S, Maruthamuthu S, Chandran S, Martin T, Rajalingam R, Vincenti F, Breeden C, Sanz I, Gibson G, Eun-Hyung Lee F. Human Bone Marrow Plasma Cell Atlas: Maturation and Survival Pathways Unraveled by Single Cell Analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524601. [PMID: 36711623 PMCID: PMC9882341 DOI: 10.1101/2023.01.18.524601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Human bone marrow (BM) plasma cells are heterogeneous, ranging from newly arrived antibody-secreting cells (ASC) to long-lived plasma cells (LLPC). We provide single cell transcriptional resolution of 17,347 BM ASC from 5 healthy adults. Fifteen clusters were identified ranging from newly minted ASC (cluster 1) expressing MKI67 and high MHC Class II that progressed to late clusters 5-8 through intermediate clusters 2-4. Additional clusters included early and late IgM-predominant ASC of likely extra-follicular origin; IFN-responsive; and high mitochondrial activity ASC. Late ASCs were distinguished by differences in G2M checkpoints, MTOR signaling, distinct metabolic pathways, CD38 expression, and utilization of TNF-receptor superfamily members. They mature through two distinct paths differentiated by the degree of TNF signaling through NFKB. This study provides the first single cell resolution atlas and molecular roadmap of LLPC maturation, thereby providing insight into differentiation trajectories and molecular regulation of these essential processes in the human BM microniche. This information enables investigation of the origin of protective and pathogenic antibodies in multiple diseases and development of new strategies targeted to the enhancement or depletion of the corresponding ASC. One Sentence Summary: The single cell transcriptomic atlas of human bone marrow plasma cell heterogeneity shows maturation of class-switched early and late subsets, specific IgM and Interferon-driven clusters, and unique heterogeneity of the late subsets which encompass the long-lived plasma cells.
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106
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Buratin A, Bortoluzzi S, Gaffo E. Systematic benchmarking of statistical methods to assess differential expression of circular RNAs. Brief Bioinform 2023; 24:6966517. [PMID: 36592056 PMCID: PMC9851295 DOI: 10.1093/bib/bbac612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/28/2022] [Accepted: 12/11/2022] [Indexed: 01/03/2023] Open
Abstract
Circular RNAs (circRNAs) are covalently closed transcripts involved in critical regulatory axes, cancer pathways and disease mechanisms. CircRNA expression measured with RNA-seq has particular characteristics that might hamper the performance of standard biostatistical differential expression assessment methods (DEMs). We compared 38 DEM pipelines configured to fit circRNA expression data's statistical properties, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq) and metagenomics DEMs. The DEMs performed poorly on data sets of typical size. Widely used DEMs, such as DESeq2, edgeR and Limma-Voom, gave scarce results, unreliable predictions or even contravened the expected behaviour with some parameter configurations. Limma-Voom achieved the most consistent performance throughout different benchmark data sets and, as well as SAMseq, reasonably balanced false discovery rate (FDR) and recall rate. Interestingly, a few scRNA-seq DEMs obtained results comparable with the best-performing bulk RNA-seq tools. Almost all DEMs' performance improved when increasing the number of replicates. CircRNA expression studies require careful design, choice of DEM and DEM configuration. This analysis can guide scientists in selecting the appropriate tools to investigate circRNA differential expression with RNA-seq experiments.
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Affiliation(s)
- Alessia Buratin
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | | | - Enrico Gaffo
- Corresponding author: Enrico Gaffo, Department of Molecular Medicine, University of Padova - Via G. Colombo, 3—35131 Padova, Italy. Phone +39 049 827 6502; Fax +39 049 827 6209; E-mail:
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107
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Dong X, Bacher R. Analysis of Single-Cell RNA-seq Data. Methods Mol Biol 2023; 2629:95-114. [PMID: 36929075 DOI: 10.1007/978-1-0716-2986-4_6] [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] [Indexed: 03/18/2023]
Abstract
As single-cell RNA sequencing experiments continue to advance scientific discoveries across biological disciplines, an increasing number of analysis tools and workflows for analyzing the data have been developed. In this chapter, we describe a standard workflow and elaborate on relevant data analysis tools for analyzing single-cell RNA sequencing data. We provide recommendations for the appropriate use of commonly used methods, with code examples and analysis interpretations.
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Affiliation(s)
- Xiaoru Dong
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Rhonda Bacher
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA.
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108
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Moroz LL, Mukherjee K, Romanova DY. Nitric oxide signaling in ctenophores. Front Neurosci 2023; 17:1125433. [PMID: 37034176 PMCID: PMC10073611 DOI: 10.3389/fnins.2023.1125433] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
Nitric oxide (NO) is one of the most ancient and versatile signal molecules across all domains of life. NO signaling might also play an essential role in the origin of animal organization. Yet, practically nothing is known about the distribution and functions of NO-dependent signaling pathways in representatives of early branching metazoans such as Ctenophora. Here, we explore the presence and organization of NO signaling components using Mnemiopsis and kin as essential reference species. We show that NO synthase (NOS) is present in at least eight ctenophore species, including Euplokamis and Coeloplana, representing the most basal ctenophore lineages. However, NOS could be secondarily lost in many other ctenophores, including Pleurobrachia and Beroe. In Mnemiopsis leidyi, NOS is present both in adult tissues and differentially expressed in later embryonic stages suggesting the involvement of NO in developmental mechanisms. Ctenophores also possess soluble guanylyl cyclases as potential NO receptors with weak but differential expression across tissues. Combined, these data indicate that the canonical NO-cGMP signaling pathways existed in the common ancestor of animals and could be involved in the control of morphogenesis, cilia activities, feeding and different behaviors.
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Affiliation(s)
- Leonid L. Moroz
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
- The Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, FL, United States
- *Correspondence: Leonid L. Moroz, ; orcid.org/0000-0002-1333-3176
| | - Krishanu Mukherjee
- The Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, FL, United States
| | - Daria Y. Romanova
- Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia
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109
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Garcia AX, Xu J, Cheng F, Ruppin E, Schäffer AA. Altered gene expression in excitatory neurons is associated with Alzheimer's disease and its higher incidence in women. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2023; 9:e12373. [PMID: 36873924 PMCID: PMC9983144 DOI: 10.1002/trc2.12373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 02/11/2023]
Abstract
Introduction Alzheimer's disease (AD) is a neurodegenerative disorder involving interactions between different cell types in the brain. Previous single-cell and bulk expression Alzheimer's studies have reported conflicting findings about the key cell types and cellular pathways whose expression is primarily altered in this disease. We re-analyzed these data in a uniform, coherent manner aiming to resolve and extend past findings. Our analysis sheds light on the observation that females have higher AD incidence than males. Methods We re-analyzed three single-cell transcriptomics datasets. We used the software Model-based Analysis of Single-cell Transcriptomics (MAST) to seek differentially expressed genes comparing AD cases to matched controls for both sexes together and each sex separately. We used the GOrilla software to search for enriched pathways among the differentially expressed genes. Motivated by the male/female difference in incidence, we studied genes on the X-chromosome, focusing on genes in the pseudoautosomal region (PAR) and on genes that are heterogeneous across individuals or tissues for X-inactivation. We validated findings by analyzing bulk AD datasets from the cortex in the Gene Expression Omnibus. Results Our results resolve a contradiction in the literature, showing that by comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types. Synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons. PAR genes and X-chromosome heterogeneous genes, including, for example, BEX1 and ELK1, may contribute to the difference in sex incidence of Alzheimer's disease. GRIN1, stood out as an overexpressed autosomal gene in cases versus controls in all three single-cell datasets and as a functional candidate gene contributing to pathways upregulated in cases. Discussion Taken together, these results point to a potential linkage between two longstanding questions concerning AD pathogenesis, involving which cell type is the most important and why females have a higher incidence than males. Highlights By reanalyzing three, published, single-cell RNAseq datasets, we resolved a contradiction in the literature and showed that when comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types.Further analysis of the published single-cell datasets showed that synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons.Combining analysis of single-cell datasets and publicly available bulk transcriptomics datasets revealed that X-chromosome genes, such as BEX1, ELK1, and USP11, whose X-inactivation status is heterogeneous may contribute to the higher incidence in females of Alzheimer's disease.
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Affiliation(s)
- A. Xavier Garcia
- Cancer Data Science LaboratoryCenter for Cancer ResearchNational Cancer InstituteNational Institutes of HealthBethesdaMarylandUSA
- Present address:
Weill Cornell/Rockefeller/Sloan Kettering Tri‐Institutional MD‐PhD ProgramNew YorkNYUSA
| | - Jielin Xu
- Genomic Medicine InstituteLerner Research InstituteCleveland ClinicClevelandOhioUSA
| | - Feixiong Cheng
- Genomic Medicine InstituteLerner Research InstituteCleveland ClinicClevelandOhioUSA
- Department of Molecular MedicineCleveland Clinic Lerner College of MedicineCase Western Reserve UniversityClevelandOhioUSA
- Case Comprehensive Cancer CenterCase Western Reserve University School of MedicineClevelandOhioUSA
| | - Eytan Ruppin
- Cancer Data Science LaboratoryCenter for Cancer ResearchNational Cancer InstituteNational Institutes of HealthBethesdaMarylandUSA
| | - Alejandro A. Schäffer
- Cancer Data Science LaboratoryCenter for Cancer ResearchNational Cancer InstituteNational Institutes of HealthBethesdaMarylandUSA
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110
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Aslanis I, Krokidis MG, Dimitrakopoulos GN, Vrahatis AG. Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1423:207-214. [PMID: 37525046 DOI: 10.1007/978-3-031-31978-5_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.
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Affiliation(s)
- Ioannis Aslanis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Marios G Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Georgios N Dimitrakopoulos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
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111
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Dharmaratne M, Kulkarni AS, Taherian Fard A, Mar JC. scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data. Gigascience 2022; 12:giac126. [PMID: 36691728 PMCID: PMC9871437 DOI: 10.1093/gigascience/giac126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 08/27/2022] [Accepted: 12/15/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell. RESULTS We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by overdispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically relevant genes that were not discovered through standard approaches. CONCLUSIONS This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into a Bioconductor R package (https://www.bioconductor.org/packages/release/bioc/html/scShapes.html).
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Affiliation(s)
- Malindrie Dharmaratne
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ameya S Kulkarni
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
- Department of Medicine, Division of Endocrinology, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jessica C Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia
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112
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Välikangas T, Suomi T, Chandler CE, Scott AJ, Tran BQ, Ernst RK, Goodlett DR, Elo LL. Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach. Nat Commun 2022; 13:7877. [PMID: 36550114 PMCID: PMC9780321 DOI: 10.1038/s41467-022-35564-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users.
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Affiliation(s)
- Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | | | - Alison J Scott
- University of Maryland - Baltimore, Baltimore, MD, 21201, USA
| | - Bao Q Tran
- US Army 20th Support Command CBRNE Analytical and Remediation Activity, Baltimore, MD, 21010-5424, USA
| | - Robert K Ernst
- University of Maryland - Baltimore, Baltimore, MD, 21201, USA
| | - David R Goodlett
- University of Victoria, Victoria, BC, V8P 3E6, Canada
- International Centre for Cancer Vaccine Science, Gdansk, Poland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland.
- Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
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113
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Schäbitz A, Hillig C, Mubarak M, Jargosch M, Farnoud A, Scala E, Kurzen N, Pilz AC, Bhalla N, Thomas J, Stahle M, Biedermann T, Schmidt-Weber CB, Theis F, Garzorz-Stark N, Eyerich K, Menden MP, Eyerich S. Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases. Nat Commun 2022; 13:7729. [PMID: 36513651 PMCID: PMC9747967 DOI: 10.1038/s41467-022-35319-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 11/28/2022] [Indexed: 12/15/2022] Open
Abstract
Abundant heterogeneous immune cells infiltrate lesions in chronic inflammatory diseases and characterization of these cells is needed to distinguish disease-promoting from bystander immune cells. Here, we investigate the landscape of non-communicable inflammatory skin diseases (ncISD) by spatial transcriptomics resulting in a large repository of 62,000 spatially defined human cutaneous transcriptomes from 31 patients. Despite the expected immune cell infiltration, we observe rather low numbers of pathogenic disease promoting cytokine transcripts (IFNG, IL13 and IL17A), i.e. >125 times less compared to the mean expression of all other genes over lesional skin sections. Nevertheless, cytokine expression is limited to lesional skin and presented in a disease-specific pattern. Leveraging a density-based spatial clustering method, we identify specific responder gene signatures in direct proximity of cytokines, and confirm that detected cytokine transcripts initiate amplification cascades of up to thousands of specific responder transcripts forming localized epidermal clusters. Thus, within the abundant and heterogeneous infiltrates of ncISD, only a low number of cytokine transcripts and their translated proteins promote disease by initiating an inflammatory amplification cascade in their local microenvironment.
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Affiliation(s)
- A Schäbitz
- Division of Dermatology and Venereology, Department of Medicine Solna, and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - C Hillig
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
| | - M Mubarak
- Center for Allergy and Environment (ZAUM), Technical University and Helmholtz Center Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
| | - M Jargosch
- Center for Allergy and Environment (ZAUM), Technical University and Helmholtz Center Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
- Department of Dermatology and Allergy, Technical University of Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
| | - A Farnoud
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
| | - E Scala
- Division of Dermatology and Venereology, Department of Medicine Solna, and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Dermatology and Venerology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - N Kurzen
- Center for Allergy and Environment (ZAUM), Technical University and Helmholtz Center Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
| | - A C Pilz
- Department of Dermatology and Allergy, Technical University of Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
- Department of Dermatology and Venerology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - N Bhalla
- Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - J Thomas
- Center for Allergy and Environment (ZAUM), Technical University and Helmholtz Center Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
| | - M Stahle
- Division of Dermatology and Venereology, Department of Medicine Solna, and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - T Biedermann
- Department of Dermatology and Allergy, Technical University of Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
| | - C B Schmidt-Weber
- Center for Allergy and Environment (ZAUM), Technical University and Helmholtz Center Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
| | - F Theis
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
| | - N Garzorz-Stark
- Division of Dermatology and Venereology, Department of Medicine Solna, and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Dermatology and Allergy, Technical University of Munich, Biedersteinerstrasse 29, 80802, Munich, Germany
| | - K Eyerich
- Division of Dermatology and Venereology, Department of Medicine Solna, and Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Dermatology and Venerology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Dermatology and Venereology, Unit of Dermatology, Karolinska University Hospital, Stockholm, Sweden
| | - M P Menden
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
- Department of Biology, Ludwig-Maximilians University, Goßhadernerstrasse 2, Martinsried, 82152, Germany
- German Center for Diabetes Research (DZD e.V.), Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany
| | - S Eyerich
- Center for Allergy and Environment (ZAUM), Technical University and Helmholtz Center Munich, Biedersteinerstrasse 29, 80802, Munich, Germany.
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McEvoy CM, Murphy JM, Zhang L, Clotet-Freixas S, Mathews JA, An J, Karimzadeh M, Pouyabahar D, Su S, Zaslaver O, Röst H, Arambewela R, Liu LY, Zhang S, Lawson KA, Finelli A, Wang B, MacParland SA, Bader GD, Konvalinka A, Crome SQ. Single-cell profiling of healthy human kidney reveals features of sex-based transcriptional programs and tissue-specific immunity. Nat Commun 2022; 13:7634. [PMID: 36496458 PMCID: PMC9741629 DOI: 10.1038/s41467-022-35297-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 11/27/2022] [Indexed: 12/13/2022] Open
Abstract
Knowledge of the transcriptional programs underpinning the functions of human kidney cell populations at homeostasis is limited. We present a single-cell perspective of healthy human kidney from 19 living donors, with equal contribution from males and females, profiling the transcriptome of 27677 cells to map human kidney at high resolution. Sex-based differences in gene expression within proximal tubular cells were observed, specifically, increased anti-oxidant metallothionein genes in females and aerobic metabolism-related genes in males. Functional differences in metabolism were confirmed in proximal tubular cells, with male cells exhibiting higher oxidative phosphorylation and higher levels of energy precursor metabolites. We identified kidney-specific lymphocyte populations with unique transcriptional profiles indicative of kidney-adapted functions. Significant heterogeneity in myeloid cells was observed, with a MRC1+LYVE1+FOLR2+C1QC+ population representing a predominant population in healthy kidney. This study provides a detailed cellular map of healthy human kidney, and explores the complexity of parenchymal and kidney-resident immune cells.
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Affiliation(s)
- Caitriona M McEvoy
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
- Department of Medicine, Division of Nephrology, University Health Network, Toronto, ON, Canada
| | - Julia M Murphy
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
- Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Lin Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Sergi Clotet-Freixas
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
| | - Jessica A Mathews
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
| | - James An
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
- Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Mehran Karimzadeh
- Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Delaram Pouyabahar
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Shenghui Su
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
| | - Olga Zaslaver
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Hannes Röst
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Rangi Arambewela
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
| | - Lewis Y Liu
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
- Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Sally Zhang
- Division of Urology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Keith A Lawson
- Division of Urology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Antonio Finelli
- Division of Urology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Sonya A MacParland
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada
- Department of Immunology, University of Toronto, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Gary D Bader
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Ana Konvalinka
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada.
- Department of Medicine, Division of Nephrology, University Health Network, Toronto, ON, Canada.
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
| | - Sarah Q Crome
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
- Ajmera Transplant Centre, University Health Network, Toronto, ON, Canada.
- Department of Immunology, University of Toronto, Toronto, ON, Canada.
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115
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Su M, Pan T, Chen QZ, Zhou WW, Gong Y, Xu G, Yan HY, Li S, Shi QZ, Zhang Y, He X, Jiang CJ, Fan SC, Li X, Cairns MJ, Wang X, Li YS. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res 2022; 9:68. [PMID: 36461064 PMCID: PMC9716519 DOI: 10.1186/s40779-022-00434-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
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Affiliation(s)
- Min Su
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Tao Pan
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiu-Zhen Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Wei-Wei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Yi Gong
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
- Department of Immunology, Nanjing Medical University, Nanjing, 211166 China
| | - Gang Xu
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Huan-Yu Yan
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Si Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiao-Zhen Shi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Ya Zhang
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Xiao He
- Department of Laboratory Medicine, Women and Children’s Hospital of Chongqing Medical University, Chongqing, 401174 China
| | | | - Shi-Cai Fan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110 Guangdong China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, Callaghan, NSW 2308 Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305 Australia
| | - Xi Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Yong-Sheng Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
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116
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Hou J, Liang S, Xu C, Wei Y, Wang Y, Tan Y, Sahni N, McGrail D, Bernatchez C, Davies M, Li Y, Chen R, Yi S, Chen Y, Yee C, Chen K, Peng W. Single-cell CRISPR immune screens reveal immunological roles of tumor intrinsic factors. NAR Cancer 2022; 4:zcac038. [PMID: 36518525 PMCID: PMC9732527 DOI: 10.1093/narcan/zcac038] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/15/2022] [Accepted: 11/16/2022] [Indexed: 12/14/2022] Open
Abstract
Genetic screens are widely exploited to develop novel therapeutic approaches for cancer treatment. With recent advances in single-cell technology, single-cell CRISPR screen (scCRISPR) platforms provide opportunities for target validation and mechanistic studies in a high-throughput manner. Here, we aim to establish scCRISPR platforms which are suitable for immune-related screens involving multiple cell types. We integrated two scCRISPR platforms, namely Perturb-seq and CROP-seq, with both in vitro and in vivo immune screens. By leveraging previously generated resources, we optimized experimental conditions and data analysis pipelines to achieve better consistency between results from high-throughput and individual validations. Furthermore, we evaluated the performance of scCRISPR immune screens in determining underlying mechanisms of tumor intrinsic immune regulation. Our results showed that scCRISPR platforms can simultaneously characterize gene expression profiles and perturbation effects present in individual cells in different immune screen conditions. Results from scCRISPR immune screens also predict transcriptional phenotype associated with clinical responses to cancer immunotherapy. More importantly, scCRISPR screen platforms reveal the interactive relationship between targeting tumor intrinsic factors and T cell-mediated antitumor immune response which cannot be easily assessed by bulk RNA-seq. Collectively, scCRISPR immune screens provide scalable and reliable platforms to elucidate molecular determinants of tumor immune resistance.
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Affiliation(s)
- Jiakai Hou
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Shaoheng Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Chunyu Xu
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Yanjun Wei
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yunfei Wang
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yukun Tan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nidhi Sahni
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Daniel J McGrail
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Chantale Bernatchez
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael Davies
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yumei Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - S Stephen Yi
- Department of Oncology, Livestrong Cancer Institutes, and Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Interdisciplinary Life Sciences Graduate Programs (ILSGP) and Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX, USA
| | - Yiwen Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cassian Yee
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Weiyi Peng
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
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117
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Yuan X, Ma S, Fa B, Wei T, Ma Y, Wang Y, Lv W, Zhang Y, Zheng J, Chen G, Sun J, Yu Z. A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data. Front Genet 2022; 13:1063130. [DOI: 10.3389/fgene.2022.1063130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/14/2022] [Indexed: 12/03/2022] Open
Abstract
Colorectal cancer is a highly heterogeneous disease. Tumor heterogeneity limits the efficacy of cancer treatment. Single-cell RNA-sequencing technology (scRNA-seq) is a powerful tool for studying cancer heterogeneity at cellular resolution. The sparsity, heterogeneous diversity, and fast-growing scale of scRNA-seq data pose challenges to the flexibility, accuracy, and computing efficiency of the differential expression (DE) methods. We proposed HEART (high-efficiency and robust test), a statistical combination test that can detect DE genes with various sources of differences beyond mean expression changes. To validate the performance of HEART, we compared HEART and the other six popular DE methods on various simulation datasets with different settings by two simulation data generation mechanisms. HEART had high accuracy (F1 score >0.75) and brilliant computational efficiency (less than 2 min) on multiple simulation datasets in various experimental settings. HEART performed well on DE genes detection for the PBMC68K dataset quantified by UMI counts and the human brain single-cell dataset quantified by read counts (F1 score = 0.79, 0.65). By applying HEART to the single-cell dataset of a colorectal cancer patient, we found several potential blood-based biomarkers (CTTN, S100A4, S100A6, UBA52, FAU, and VIM) associated with colorectal cancer metastasis and validated them on additional spatial transcriptomic data of other colorectal cancer patients.
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118
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Jiménez‐Santos MJ, García‐Martín S, Fustero‐Torre C, Di Domenico T, Gómez‐López G, Al‐Shahrour F. Bioinformatics roadmap for therapy selection in cancer genomics. Mol Oncol 2022; 16:3881-3908. [PMID: 35811332 PMCID: PMC9627786 DOI: 10.1002/1878-0261.13286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/22/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.
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Affiliation(s)
| | | | - Coral Fustero‐Torre
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Tomás Di Domenico
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Gonzalo Gómez‐López
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Fátima Al‐Shahrour
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
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119
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Bocci F, Zhou P, Nie Q. spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data. Mol Syst Biol 2022; 18:e11176. [PMID: 36321549 PMCID: PMC9627675 DOI: 10.15252/msb.202211176] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
Extracting dynamical information from single-cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory-oriented, bottom-up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single-cell RNA sequencing (scRNA-seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state-specific gene-gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial-mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre-existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.
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Affiliation(s)
- Federico Bocci
- Department of MathematicsUniversity of CaliforniaIrvineCAUSA
- NSF‐Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCAUSA
| | - Peijie Zhou
- Department of MathematicsUniversity of CaliforniaIrvineCAUSA
| | - Qing Nie
- Department of MathematicsUniversity of CaliforniaIrvineCAUSA
- NSF‐Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCAUSA
- Department of Developmental and Cell BiologyUniversity of CaliforniaIrvineCAUSA
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120
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Pita-Juarez Y, Karagkouni D, Kalavros N, Melms JC, Niezen S, Delorey TM, Essene AL, Brook OR, Pant D, Skelton-Badlani D, Naderi P, Huang P, Pan L, Hether T, Andrews TS, Ziegler CGK, Reeves J, Myloserdnyy A, Chen R, Nam A, Phelan S, Liang Y, Amin AD, Biermann J, Hibshoosh H, Veregge M, Kramer Z, Jacobs C, Yalcin Y, Phillips D, Slyper M, Subramanian A, Ashenberg O, Bloom-Ackermann Z, Tran VM, Gomez J, Sturm A, Zhang S, Fleming SJ, Warren S, Beechem J, Hung D, Babadi M, Padera RF, MacParland SA, Bader GD, Imad N, Solomon IH, Miller E, Riedel S, Porter CBM, Villani AC, Tsai LTY, Hide W, Szabo G, Hecht J, Rozenblatt-Rosen O, Shalek AK, Izar B, Regev A, Popov Y, Jiang ZG, Vlachos IS. A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.10.27.514070. [PMID: 36324805 PMCID: PMC9628199 DOI: 10.1101/2022.10.27.514070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.
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Affiliation(s)
- Yered Pita-Juarez
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dimitra Karagkouni
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nikolaos Kalavros
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Spatial Technologies Unit, HMS Initiative for RNA Medicine / Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Johannes C Melms
- Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
| | - Sebastian Niezen
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
| | - Toni M Delorey
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adam L Essene
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Boston Nutrition and Obesity Research Center Functional Genomics and Bioinformatics Core, Boston, MA, USA
| | - Olga R Brook
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Deepti Pant
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Boston Nutrition and Obesity Research Center Functional Genomics and Bioinformatics Core, Boston, MA, USA
| | - Disha Skelton-Badlani
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
| | - Pourya Naderi
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Pinzhu Huang
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
| | - Liuliu Pan
- NanoString Technologies, Inc., Seattle, WA, USA
| | | | - Tallulah S Andrews
- Ajmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, ON, Canada
| | - Carly G K Ziegler
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Health Sciences & Technology, Harvard Medical School & Massachusetts Institute of Technology, Boston, MA, USA
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
- Program in Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Immunology, Harvard Medical School, Boston, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Andriy Myloserdnyy
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
| | - Rachel Chen
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
| | - Andy Nam
- NanoString Technologies, Inc., Seattle, WA, USA
| | | | - Yan Liang
- NanoString Technologies, Inc., Seattle, WA, USA
| | - Amit Dipak Amin
- Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
| | - Jana Biermann
- Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
| | - Hanina Hibshoosh
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Molly Veregge
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Boston Nutrition and Obesity Research Center Functional Genomics and Bioinformatics Core, Boston, MA, USA
| | - Zachary Kramer
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Christopher Jacobs
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Boston Nutrition and Obesity Research Center Functional Genomics and Bioinformatics Core, Boston, MA, USA
| | - Yusuf Yalcin
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
| | - Devan Phillips
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Michal Slyper
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | | | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zohar Bloom-Ackermann
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Victoria M Tran
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - James Gomez
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexander Sturm
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shuting Zhang
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephen J Fleming
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Deborah Hung
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Mehrtash Babadi
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Robert F Padera
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sonya A MacParland
- Ajmera Transplant Centre, Toronto General Research Institute, University Health Network, Toronto, ON, Canada
- Department of Immunology, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Gary D Bader
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- The Donnelly Centre, Toronto, ON, Canada
| | - Nasser Imad
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Isaac H Solomon
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Eric Miller
- NanoString Technologies, Inc., Seattle, WA, USA
| | - Stefan Riedel
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Caroline B M Porter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexandra-Chloé Villani
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Linus T-Y Tsai
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Boston Nutrition and Obesity Research Center Functional Genomics and Bioinformatics Core, Boston, MA, USA
| | - Winston Hide
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Gyongyi Szabo
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
| | - Jonathan Hecht
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Alex K Shalek
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Health Sciences & Technology, Harvard Medical School & Massachusetts Institute of Technology, Boston, MA, USA
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
- Program in Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Immunology, Harvard Medical School, Boston, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benjamin Izar
- Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center, New York, NY, USA
- Columbia Center for Translational Immunology, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY, USA
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Yury Popov
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Z Gordon Jiang
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Gastroenterology, Hepatology and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, MA, USA
| | - Ioannis S Vlachos
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Spatial Technologies Unit, HMS Initiative for RNA Medicine / Beth Israel Deaconess Medical Center, Boston, MA, USA
- Cancer Research Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School Initiative for RNA Medicine, Boston, MA, USA
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Mukherjee P, Park SH, Pathak N, Patino CA, Bao G, Espinosa HD. Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows. ACS NANO 2022; 16:15653-15680. [PMID: 36154011 DOI: 10.1021/acsnano.2c05494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.
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Affiliation(s)
- Prithvijit Mukherjee
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
| | - So Hyun Park
- Department of Bioengineering, Rice University, 6500 Main Street, Houston, Texas 77030, United States
| | - Nibir Pathak
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
| | - Cesar A Patino
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Gang Bao
- Department of Bioengineering, Rice University, 6500 Main Street, Houston, Texas 77030, United States
| | - Horacio D Espinosa
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
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122
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Leng J, Wu LY. Interaction-based transcriptome analysis via differential network inference. Brief Bioinform 2022; 23:6768051. [PMID: 36274239 PMCID: PMC9677477 DOI: 10.1093/bib/bbac466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/13/2022] [Accepted: 09/28/2022] [Indexed: 12/14/2022] Open
Abstract
Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.
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Affiliation(s)
- Jiacheng Leng
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ling-Yun Wu
- Corresponding author. Ling-Yun Wu, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China. E-mail:
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123
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Huuhtanen J, Chen L, Jokinen E, Kasanen H, Lönnberg T, Kreutzman A, Peltola K, Hernberg M, Wang C, Yee C, Lähdesmäki H, Davis MM, Mustjoki S. Evolution and modulation of antigen-specific T cell responses in melanoma patients. Nat Commun 2022; 13:5988. [PMID: 36220826 PMCID: PMC9553985 DOI: 10.1038/s41467-022-33720-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/15/2022] [Indexed: 11/15/2022] Open
Abstract
Analyzing antigen-specific T cell responses at scale has been challenging. Here, we analyze three types of T cell receptor (TCR) repertoire data (antigen-specific TCRs, TCR-repertoire, and single-cell RNA + TCRαβ-sequencing data) from 515 patients with primary or metastatic melanoma and compare it to 783 healthy controls. Although melanoma-associated antigen (MAA) -specific TCRs are restricted to individuals, they share sequence similarities that allow us to build classifiers for predicting anti-MAA T cells. The frequency of anti-MAA T cells distinguishes melanoma patients from healthy and predicts metastatic recurrence from primary melanoma. Anti-MAA T cells have stem-like properties and frequent interactions with regulatory T cells and tumor cells via Galectin9-TIM3 and PVR-TIGIT -axes, respectively. In the responding patients, the number of expanded anti-MAA clones are higher after the anti-PD1(+anti-CTLA4) therapy and the exhaustion phenotype is rescued. Our systems immunology approach paves the way for understanding antigen-specific responses in human disorders. Previous studies have characterized the diversity and dynamics of the T cell receptor (TCR) repertoire in patients with solid cancer. Here, by analyzing TCR repertoire data from multiple datasets, the authors report that melanoma-associated antigen-specific TCRs can be used to separate metastatic melanoma patients from healthy controls and to follow anti-tumor responses in patients treated with immunotherapy.
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124
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Shi Q, Shen Q, Liu Y, Shi Y, Huang W, Wang X, Li Z, Chai Y, Wang H, Hu X, Li N, Zhang Q, Cao X. Increased glucose metabolism in TAMs fuels O-GlcNAcylation of lysosomal Cathepsin B to promote cancer metastasis and chemoresistance. Cancer Cell 2022; 40:1207-1222.e10. [PMID: 36084651 DOI: 10.1016/j.ccell.2022.08.012] [Citation(s) in RCA: 98] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 07/06/2022] [Accepted: 08/15/2022] [Indexed: 12/24/2022]
Abstract
How glucose metabolism remodels pro-tumor functions of tumor-associated macrophages (TAMs) needs further investigation. Here we show that M2-like TAMs bear the highest individual capacity to take up intratumoral glucose. Their increased glucose uptake fuels hexosamine biosynthetic pathway-dependent O-GlcNAcylation to promote cancer metastasis and chemoresistance. Glucose metabolism promotes O-GlcNAcylation of the lysosome-encapsulated protease Cathepsin B at serine 210, mediated by lysosome-localized O-GlcNAc transferase (OGT), elevating mature Cathepsin B in macrophages and its secretion in the tumor microenvironment (TME). Loss of OGT in macrophages reduces O-GlcNAcylation and mature Cathepsin B in the TME and disrupts cancer metastasis and chemoresistance. Human TAMs with high OGT are positively correlated with Cathepsin B expression, and both levels predict chemotherapy response and prognosis of individuals with cancer. Our study reports the biological and potential clinical significance of glucose metabolism in tumor-promoting TAMs and reveals insights into the underlying mechanisms.
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Affiliation(s)
- Qingzhu Shi
- Department of Immunology, Institute of Basic Medical Research, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100005, China; National Key Laboratory of Medical Immunology, Institute of Immunology, Naval Medical University, Shanghai 200433, China
| | - Qicong Shen
- National Key Laboratory of Medical Immunology, Institute of Immunology, Naval Medical University, Shanghai 200433, China
| | - Yanfang Liu
- Department of Pathology, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Yang Shi
- Department of Immunology, Institute of Basic Medical Research, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100005, China; National Key Laboratory of Medical Immunology, Institute of Immunology, Naval Medical University, Shanghai 200433, China
| | - Wenwen Huang
- Institute of Immunology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Xi Wang
- Institute of Immunology, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zhiqing Li
- National Key Laboratory of Medical Immunology, Institute of Immunology, Naval Medical University, Shanghai 200433, China
| | - Yangyang Chai
- Department of Immunology, Institute of Basic Medical Research, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100005, China
| | - Hao Wang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xiangjia Hu
- National Key Laboratory of Medical Immunology, Institute of Immunology, Naval Medical University, Shanghai 200433, China
| | - Nan Li
- National Key Laboratory of Medical Immunology, Institute of Immunology, Naval Medical University, Shanghai 200433, China
| | - Qian Zhang
- National Key Laboratory of Medical Immunology, Institute of Immunology, Naval Medical University, Shanghai 200433, China.
| | - Xuetao Cao
- Department of Immunology, Institute of Basic Medical Research, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100005, China; National Key Laboratory of Medical Immunology, Institute of Immunology, Naval Medical University, Shanghai 200433, China; Frontier Research Center for Cell Response, Institute of Immunology, College of Life Sciences, Nankai University, Tianjin 300071, China.
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125
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Su Y, Wang F, Zhang S, Liang Y, Wong KC, Li X. scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information. Bioinformatics 2022; 38:4537-4545. [PMID: 35984287 DOI: 10.1093/bioinformatics/btac570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 08/09/2022] [Accepted: 08/18/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects. RESULTS In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values. Thus, to impute more precisely the dropout events in scRNA-seq data, we develop a regularization for leveraging that imperfect prior information to estimate the true underlying prior subspace and then embed it in a typical low-rank matrix completion-based framework, named scWMC. To evaluate the performance of the proposed method, we conduct comprehensive experiments on simulated and real scRNA-seq data. Extensive data analysis, including simulated analysis, cell clustering, differential expression analysis, functional genomic analysis, cell trajectory inference and scalability analysis, demonstrate that our method produces improved imputation results compared to competing methods that benefits subsequent downstream analysis. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/XuYuanchi/scWMC and test data is available at https://doi.org/10.5281/zenodo.6832477. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yanchi Su
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, Hong Kong 999077, Hong Kong SAR
| | - Shixiong Zhang
- School of Computer Science and Technology, Xidian University, Xian 710000, China
| | - Yanchun Liang
- Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Science and Technology, Zhuhai 519041, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong 999077, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
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126
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Zhao C, Biondic S, Vandal K, Björklund ÅK, Hagemann-Jensen M, Sommer TM, Canizo J, Clark S, Raymond P, Zenklusen DR, Rivron N, Reik W, Petropoulos S. Single-cell multi-omics of human preimplantation embryos shows susceptibility to glucocorticoids. Genome Res 2022; 32:1627-1641. [PMID: 35948369 PMCID: PMC9528977 DOI: 10.1101/gr.276665.122] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/04/2022] [Indexed: 11/25/2022]
Abstract
The preconceptual, intrauterine, and early life environments can have a profound and long-lasting impact on the developmental trajectories and health outcomes of the offspring. Given the relatively low success rates of assisted reproductive technologies (ART; ∼25%), additives and adjuvants, such as glucocorticoids, are used to improve the success rate. Considering the dynamic developmental events that occur during this window, these exposures may alter blastocyst formation at a molecular level, and as such, affect not only the viability of the embryo and the ability of the blastocyst to implant, but also the developmental trajectory of the first three cell lineages, ultimately influencing the physiology of the embryo. In this study, we present a comprehensive single-cell transcriptome, methylome, and small RNA atlas in the day 7 human embryo. We show that, despite no change in morphology and developmental features, preimplantation glucocorticoid exposure reprograms the molecular profile of the trophectoderm (TE) lineage, and these changes are associated with an altered metabolic and inflammatory response. Our data also suggest that glucocorticoids can precociously mature the TE sublineages, supported by the presence of extravillous trophoblast markers in the polar sublineage and presence of X Chromosome dosage compensation. Further, we have elucidated that epigenetic regulation-DNA methylation and microRNAs (miRNAs)-likely underlies the transcriptional changes observed. This study suggests that exposures to exogenous compounds during preimplantation may unintentionally reprogram the human embryo, possibly leading to suboptimal development and longer-term health outcomes.
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Affiliation(s)
- Cheng Zhao
- Department of Clinical Science, Intervention and Technology, Division of Obstetrics and Gynecology, Karolinska Institutet, 14186 Stockholm, Sweden
| | - Savana Biondic
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Axe Immunopathologie, H2X 0A9 Montréal, Canada
- Département de Médecine, Université de Montréal, H3T 1J4 Montréal, Canada
| | - Katherine Vandal
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Axe Immunopathologie, H2X 0A9 Montréal, Canada
- Département de Médecine, Université de Montréal, H3T 1J4 Montréal, Canada
| | - Åsa K Björklund
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, SE-752 37 Uppsala, Sweden
| | | | - Theresa Maria Sommer
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Jesica Canizo
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Axe Immunopathologie, H2X 0A9 Montréal, Canada
- Département de Médecine, Université de Montréal, H3T 1J4 Montréal, Canada
| | - Stephen Clark
- Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, United Kingdom
| | - Pascal Raymond
- Département de Biochimie et Médecine Moléculaire, Université de Montréal, H3T 1J4 Montréal, Canada
| | - Daniel R Zenklusen
- Département de Biochimie et Médecine Moléculaire, Université de Montréal, H3T 1J4 Montréal, Canada
| | - Nicolas Rivron
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Wolf Reik
- Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, United Kingdom
- Wellcome Sanger Institute, Cambridge CB10 1RQ, United Kingdom
- Center for Trophoblast Research, University of Cambridge, Cambridge CB2 3EG, United Kingdom
| | - Sophie Petropoulos
- Department of Clinical Science, Intervention and Technology, Division of Obstetrics and Gynecology, Karolinska Institutet, 14186 Stockholm, Sweden
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Axe Immunopathologie, H2X 0A9 Montréal, Canada
- Département de Médecine, Université de Montréal, H3T 1J4 Montréal, Canada
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127
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Park A, Croset V, Otto N, Agarwal D, Treiber CD, Meschi E, Sims D, Waddell S. Gliotransmission of D-serine promotes thirst-directed behaviors in Drosophila. Curr Biol 2022; 32:3952-3970.e8. [PMID: 35963239 PMCID: PMC9616736 DOI: 10.1016/j.cub.2022.07.038] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/04/2022] [Accepted: 07/15/2022] [Indexed: 12/13/2022]
Abstract
Thirst emerges from a range of cellular changes that ultimately motivate an animal to consume water. Although thirst-responsive neuronal signals have been reported, the full complement of brain responses is unclear. Here, we identify molecular and cellular adaptations in the brain using single-cell sequencing of water-deprived Drosophila. Water deficiency primarily altered the glial transcriptome. Screening the regulated genes revealed astrocytic expression of the astray-encoded phosphoserine phosphatase to bi-directionally regulate water consumption. Astray synthesizes the gliotransmitter D-serine, and vesicular release from astrocytes is required for drinking. Moreover, dietary D-serine rescues aay-dependent drinking deficits while facilitating water consumption and expression of water-seeking memory. D-serine action requires binding to neuronal NMDA-type glutamate receptors. Fly astrocytes contribute processes to tripartite synapses, and the proportion of astrocytes that are themselves activated by glutamate increases with water deprivation. We propose that thirst elevates astrocytic D-serine release, which awakens quiescent glutamatergic circuits to enhance water procurement.
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Affiliation(s)
- Annie Park
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK
| | - Vincent Croset
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK; Department of Biosciences, Durham University, Durham DH1 3LE, UK.
| | - Nils Otto
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK
| | - Devika Agarwal
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK; MRC Computational Genomics Analysis and Training Programme (CGAT), MRC Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, Headington, Oxford OX3 9DS, UK
| | - Christoph D Treiber
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK
| | - Eleonora Meschi
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK
| | - David Sims
- MRC Computational Genomics Analysis and Training Programme (CGAT), MRC Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, Headington, Oxford OX3 9DS, UK
| | - Scott Waddell
- Centre for Neural Circuits & Behaviour, University of Oxford, Oxford OX1 3TA, UK.
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128
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Otero-Garcia M, Mahajani SU, Wakhloo D, Tang W, Xue YQ, Morabito S, Pan J, Oberhauser J, Madira AE, Shakouri T, Deng Y, Allison T, He Z, Lowry WE, Kawaguchi R, Swarup V, Cobos I. Molecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer's disease. Neuron 2022; 110:2929-2948.e8. [PMID: 35882228 PMCID: PMC9509477 DOI: 10.1016/j.neuron.2022.06.021] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 03/08/2022] [Accepted: 06/27/2022] [Indexed: 01/01/2023]
Abstract
Tau aggregation in neurofibrillary tangles (NFTs) is closely associated with neurodegeneration and cognitive decline in Alzheimer's disease (AD). However, the molecular signatures that distinguish between aggregation-prone and aggregation-resistant cell states are unknown. We developed methods for the high-throughput isolation and transcriptome profiling of single somas with NFTs from the human AD brain, quantified the susceptibility of 20 neocortical subtypes for NFT formation and death, and identified both shared and cell-type-specific signatures. NFT-bearing neurons shared a marked upregulation of synaptic transmission-related genes, including a core set of 63 genes enriched for synaptic vesicle cycling. Oxidative phosphorylation and mitochondrial dysfunction were highly cell-type dependent. Apoptosis was only modestly enriched, and the susceptibilities of NFT-bearing and NFT-free neurons for death were highly similar. Our analysis suggests that NFTs represent cell-type-specific responses to stress and synaptic dysfunction. We provide a resource for biomarker discovery and the investigation of tau-dependent and tau-independent mechanisms of neurodegeneration.
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Affiliation(s)
- Marcos Otero-Garcia
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sameehan U Mahajani
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Debia Wakhloo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weijing Tang
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yue-Qiang Xue
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Samuel Morabito
- Mathematical, Computational and Systems Biology Program, University of California, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA 92697, USA
| | - Jie Pan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jane Oberhauser
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Angela E Madira
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tamara Shakouri
- Department of Pathology, University of California, Los Angeles, CA 90095, USA
| | - Yongning Deng
- Department of Pathology, University of California, Los Angeles, CA 90095, USA; Department of Neurology, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Thomas Allison
- Department of Biological Chemistry, University of California, Los Angeles, CA 90095, USA
| | - Zihuai He
- Department Neurology and Neurological Sciences and Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - William E Lowry
- Department of Molecular Cell and Developmental Biology, Broad Center for Regenerative Medicine and Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA
| | - Riki Kawaguchi
- Department of Psychiatry and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Vivek Swarup
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA 92697, USA; Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
| | - Inma Cobos
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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129
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Junttila S, Smolander J, Elo LL. Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data. Brief Bioinform 2022; 23:6649780. [PMID: 35880426 PMCID: PMC9487674 DOI: 10.1093/bib/bbac286] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/07/2022] [Accepted: 06/23/2022] [Indexed: 12/13/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.
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Affiliation(s)
| | | | - Laura L Elo
- Corresponding author: Laura L. Elo, Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland. Tel.: +358504680795; E-mail:
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130
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Cheng S, Liu Y, Jing Y, Jiang B, Wang D, Chu X, Jia L, Xin S. Identification of key monocytes/macrophages related gene set of the early-stage abdominal aortic aneurysm by integrated bioinformatics analysis and experimental validation. Front Cardiovasc Med 2022; 9:950961. [PMID: 36186997 PMCID: PMC9515382 DOI: 10.3389/fcvm.2022.950961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 08/25/2022] [Indexed: 11/29/2022] Open
Abstract
Objective Abdominal aortic aneurysm (AAA) is a lethal peripheral vascular disease. Inflammatory immune cell infiltration is a central part of the pathogenesis of AAA. It’s critical to investigate the molecular mechanisms underlying immune infiltration in early-stage AAA and look for a viable AAA marker. Methods In this study, we download several mRNA expression datasets and scRNA-seq datasets of the early-stage AAA models from the NCBI-GEO database. mMCP-counter and CIBERSORT were used to assess immune infiltration in early-stage experimental AAA. The scRNA-seq datasets were then utilized to analyze AAA-related gene modules of monocytes/macrophages infiltrated into the early-stage AAA by Weighted Correlation Network analysis (WGCNA). After that, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis for the module genes was performed by ClusterProfiler. The STRING database was used to create the protein-protein interaction (PPI) network. The Differentially Expressed Genes (DEGs) of the monocytes/macrophages were explored by Limma-Voom and the key gene set were identified. Then We further examined the expression of key genes in the human AAA dataset and built a logistic diagnostic model for distinguishing AAA patients and healthy people. Finally, real-time quantitative polymerase chain reaction (RT-qPCR) and Enzyme Linked Immunosorbent Assay (ELISA) were performed to validate the gene expression and serum protein level between the AAA and healthy donor samples in our cohort. Results Monocytes/macrophages were identified as the major immune cells infiltrating the early-stage experimental AAA. After pseudocell construction of monocytes/macrophages from scRNA-seq datasets and WGCNA analysis, four gene modules from two datasets were identified positively related to AAA, mainly enriched in Myeloid Leukocyte Migration, Collagen-Containing Extracellular matrix, and PI3K-Akt signaling pathway by functional enrichment analysis. Thbs1, Clec4e, and Il1b were identified as key genes among the hub genes in the modules, and the high expression of Clec4e, Il1b, and Thbs1 was confirmed in the other datasets. Then, in human AAA transcriptome datasets, the high expression of CLEC4E, IL1B was confirmed and a logistic regression model based on the two gene expressions was built, with an AUC of 0.9 in the train set and 0.79 in the validated set. Additionally, in our cohort, we confirmed the increased serum protein levels of IL-1β and CLEC4E in AAA patients as well as the increased expression of these two genes in AAA aorta samples. Conclusion This study identified monocytes/macrophages as the main immune cells infiltrated into the early-stage AAA and constructed a logistic regression model based on monocytes/macrophages related gene set. This study could aid in the early diagnostic of AAA.
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Affiliation(s)
- Shuai Cheng
- Department of Vascular and Thyroid Surgery, The First Hospital, China Medical University, Shenyang, China
- Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China
- Regenerative Medicine Research Center of China Medical University, Shenyang, China
| | - Yuanlin Liu
- Department of Vascular and Thyroid Surgery, The First Hospital, China Medical University, Shenyang, China
- Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China
- Regenerative Medicine Research Center of China Medical University, Shenyang, China
| | - Yuchen Jing
- Department of Vascular and Thyroid Surgery, The First Hospital, China Medical University, Shenyang, China
- Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China
- Regenerative Medicine Research Center of China Medical University, Shenyang, China
| | - Bo Jiang
- Department of Vascular and Thyroid Surgery, The First Hospital, China Medical University, Shenyang, China
- Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China
- Regenerative Medicine Research Center of China Medical University, Shenyang, China
| | - Ding Wang
- Department of Vascular and Thyroid Surgery, The First Hospital, China Medical University, Shenyang, China
- Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China
- Regenerative Medicine Research Center of China Medical University, Shenyang, China
| | - Xiangyu Chu
- Department of Vascular and Thyroid Surgery, The First Hospital, China Medical University, Shenyang, China
- Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China
- Regenerative Medicine Research Center of China Medical University, Shenyang, China
| | - Longyuan Jia
- Department of Vascular and Thyroid Surgery, The First Hospital, China Medical University, Shenyang, China
- Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China
- Regenerative Medicine Research Center of China Medical University, Shenyang, China
| | - Shijie Xin
- Department of Vascular and Thyroid Surgery, The First Hospital, China Medical University, Shenyang, China
- Key Laboratory of Pathogenesis, Prevention, and Therapeutics of Aortic Aneurysm in Liaoning Province, Shenyang, China
- Regenerative Medicine Research Center of China Medical University, Shenyang, China
- *Correspondence: Shijie Xin,
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131
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Menassa DA, Muntslag TAO, Martin-Estebané M, Barry-Carroll L, Chapman MA, Adorjan I, Tyler T, Turnbull B, Rose-Zerilli MJJ, Nicoll JAR, Krsnik Z, Kostovic I, Gomez-Nicola D. The spatiotemporal dynamics of microglia across the human lifespan. Dev Cell 2022; 57:2127-2139.e6. [PMID: 35977545 PMCID: PMC9616795 DOI: 10.1016/j.devcel.2022.07.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 04/22/2022] [Accepted: 07/26/2022] [Indexed: 11/07/2022]
Abstract
Microglia, the brain's resident macrophages, shape neural development and are key neuroimmune hubs in the pathological signatures of neurodevelopmental disorders. Despite the importance of microglia, their development has not been carefully examined in the human brain, and most of our knowledge derives from rodents. We aimed to address this gap in knowledge by establishing an extensive collection of 97 post-mortem tissues in order to enable quantitative, sex-matched, detailed analysis of microglia across the human lifespan. We identify the dynamics of these cells in the human telencephalon, describing waves in microglial density across gestation, infancy, and childhood, controlled by a balance of proliferation and apoptosis, which track key neurodevelopmental milestones. These profound changes in microglia are also observed in bulk RNA-seq and single-cell RNA-seq datasets. This study provides a detailed insight into the spatiotemporal dynamics of microglia across the human lifespan and serves as a foundation for elucidating how microglia contribute to shaping neurodevelopment in humans.
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Affiliation(s)
- David A Menassa
- School of Biological Sciences, University of Southampton, Southampton, United Kingdom; Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
| | - Tim A O Muntslag
- School of Biological Sciences, University of Southampton, Southampton, United Kingdom
| | - Maria Martin-Estebané
- School of Biological Sciences, University of Southampton, Southampton, United Kingdom
| | - Liam Barry-Carroll
- School of Biological Sciences, University of Southampton, Southampton, United Kingdom
| | - Mark A Chapman
- School of Biological Sciences, University of Southampton, Southampton, United Kingdom
| | - Istvan Adorjan
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
| | - Teadora Tyler
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
| | - Bethany Turnbull
- School of Biological Sciences, University of Southampton, Southampton, United Kingdom
| | | | - James A R Nicoll
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Zeljka Krsnik
- Croatian Institute for Brain Research, University of Zagreb Medical School, Zagreb, Croatia
| | - Ivica Kostovic
- Croatian Institute for Brain Research, University of Zagreb Medical School, Zagreb, Croatia
| | - Diego Gomez-Nicola
- School of Biological Sciences, University of Southampton, Southampton, United Kingdom.
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132
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Li Z, Wang Y, Ganan-Gomez I, Colla S, Do KA. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data. Bioinformatics 2022; 38:4885-4892. [PMID: 36083008 PMCID: PMC9801963 DOI: 10.1093/bioinformatics/btac617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 01/07/2023] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) has been widely used to decompose complex tissues into functionally distinct cell types. The first and usually the most important step of scRNA-seq data analysis is to accurately annotate the cell labels. In recent years, many supervised annotation methods have been developed and shown to be more convenient and accurate than unsupervised cell clustering. One challenge faced by all the supervised annotation methods is the identification of the novel cell type, which is defined as the cell type that is not present in the training data, only exists in the testing data. Existing methods usually label the cells simply based on the correlation coefficients or confidence scores, which sometimes results in an excessive number of unlabeled cells. RESULTS We developed a straightforward yet effective method combining autoencoder with iterative feature selection to automatically identify novel cells from scRNA-seq data. Our method trains an autoencoder with the labeled training data and applies the autoencoder to the testing data to obtain reconstruction errors. By iteratively selecting features that demonstrate a bi-modal pattern and reclustering the cells using the selected feature, our method can accurately identify novel cells that are not present in the training data. We further combined this approach with a support vector machine to provide a complete solution for annotating the full range of cell types. Extensive numerical experiments using five real scRNA-seq datasets demonstrated favorable performance of the proposed method over existing methods serving similar purposes. AVAILABILITY AND IMPLEMENTATION Our R software package CAMLU is publicly available through the Zenodo repository (https://doi.org/10.5281/zenodo.7054422) or GitHub repository (https://github.com/ziyili20/CAMLU). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ziyi Li
- To whom correspondence should be addressed. or
| | - Yizhuo Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Irene Ganan-Gomez
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Simona Colla
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kim-Anh Do
- To whom correspondence should be addressed. or
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133
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Wang C, Yu Q, Song T, Wang Z, Song L, Yang Y, Shao J, Li J, Ni Y, Chao N, Zhang L, Li W. The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing. Signal Transduct Target Ther 2022; 7:289. [PMID: 36008393 PMCID: PMC9411197 DOI: 10.1038/s41392-022-01130-8] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 05/23/2022] [Accepted: 07/25/2022] [Indexed: 11/30/2022] Open
Abstract
A thorough interrogation of the immune landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in non-small-cell lung cancer (NSCLC) patients. Single-cell RNA sequencing (scRNA-seq) techniques have prompted the opportunity to dissect the distinct immune signatures between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of NSCLC. Here, we performed scRNA-seq on 72,475 immune cells from 40 samples of tumor and matched adjacent normal tissues spanning 19 NSCLC patients, and drew a systematic immune cell transcriptome atlas. Joint analyses of the distinct cellular compositions, differentially expressed genes (DEGs), cell–cell interactions, pseudotime trajectory, transcriptomic factors and prognostic factors based on The Cancer Genome Atlas (TCGA), revealed the central roles of cytotoxic and effector T and NK cells and the distinct functional macrophages (Mφ) subtypes in the immune microenvironment heterogeneity between LUAD and LUSC. The dominant subtype of Mφ was FABP4-Mφ in LUAD and SPP1-Mφ in LUSC. Importantly, we identified a novel lymphocyte-related Mφ cluster, which we named SELENOP-Mφ, and further established its antitumor role in both types, especially in LUAD. Our comprehensive depiction of the immune heterogeneity and definition of Mφ clusters could help design personalized treatment for lung cancer patients in clinical practice.
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Affiliation(s)
- Chengdi Wang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China.
| | - Qiuxiao Yu
- National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 518116, Shenzhen, China
| | - Tingting Song
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China
| | - Zhoufeng Wang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China
| | - Lujia Song
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China
| | - Ying Yang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China
| | - Jun Shao
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China
| | - Jingwei Li
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China
| | - Yinyun Ni
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China
| | - Ningning Chao
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China
| | - Li Zhang
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China.
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, Med-X Center for Manufacturing, Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, West China Medical School, Sichuan University, 610041, Chengdu, China.
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Apelblat D, Roethler O, Bitan L, Keren-Shaul H, Spiegel I. Meso-seq for in-depth transcriptomics in ultra-low amounts of FACS-purified neuronal nuclei. CELL REPORTS METHODS 2022; 2:100259. [PMID: 36046622 PMCID: PMC9421536 DOI: 10.1016/j.crmeth.2022.100259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/17/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022]
Abstract
Profiling of gene expression in sparse populations of genetically defined neurons is essential for dissecting the molecular mechanisms that control the development and plasticity of neural circuits. However, current transcriptomic approaches are ill suited for detailed mechanistic studies in sparse neuronal populations, as they either are technically complex and relatively expensive (e.g., single-cell RNA sequencing [RNA-seq]) or require large amounts of input material (e.g., traditional bulk RNA-seq). Thus, we established Meso-seq, a meso-scale protocol for identifying more than 10,000 robustly expressed genes in as little as 50 FACS-sorted neuronal nuclei. We demonstrate that Meso-seq works well for multiple neuroscience applications, including transcriptomics in antibody-labeled cortical neurons in mice and non-human primates, analyses of experience-regulated gene programs, and RNA-seq from visual cortex neurons labeled ultra-sparsely with viruses. Given its simplicity, robustness, and relatively low costs, Meso-seq is well suited for molecular-mechanistic studies in ultra-sparse neuronal populations in the brain.
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Affiliation(s)
- Daniella Apelblat
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Ori Roethler
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Lidor Bitan
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Hadas Keren-Shaul
- Life Science Core Facility, Weizmann Institute of Science, Rehovot, Israel
- The Nancy & Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Ivo Spiegel
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
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135
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Zhang S, Xie L, Cui Y, Carone BR, Chen Y. Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance. Biomolecules 2022; 12:biom12081130. [PMID: 36009024 PMCID: PMC9405875 DOI: 10.3390/biom12081130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/12/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
The detection of differentially expressed genes (DEGs) is one of most important computational challenges in the analysis of single-cell RNA sequencing (scRNA-seq) data. However, due to the high heterogeneity and dropout noise inherent in scRNAseq data, challenges in detecting DEGs exist when using a single distribution of gene expression levels, leaving much room to improve the precision and robustness of current DEG detection methods. Here, we propose the use of a new method, DEGman, which utilizes several possible diverse distributions in combination with Bhattacharyya distance. DEGman can automatically select the best-fitting distributions of gene expression levels, and then detect DEGs by permutation testing of Bhattacharyya distances of the selected distributions from two cell groups. Compared with several popular DEG analysis tools on both large-scale simulation data and real scRNA-seq data, DEGman shows an overall improvement in the balance of sensitivity and precision. We applied DEGman to scRNA-seq data of TRAP; Ai14 mouse neurons to detect fear-memory-related genes that are significantly differentially expressed in neurons with and without fear memory. DEGman detected well-known fear-memory-related genes and many novel candidates. Interestingly, we found 25 DEGs in common in five neuron clusters that are functionally enriched for synaptic vesicles, indicating that the coupled dynamics of synaptic vesicles across in neurons plays a critical role in remote memory formation. The proposed method leverages the advantage of the use of diverse distributions in DEG analysis, exhibiting better performance in analyzing composite scRNA-seq datasets in real applications.
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Affiliation(s)
- Shaoqiang Zhang
- Department of Computer Science, College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Linjuan Xie
- Department of Computer Science, College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Yaxuan Cui
- Department of Computer Science, College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Benjamin R. Carone
- Department of Biology and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
| | - Yong Chen
- Department of Biology and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
- Correspondence: ; Tel.: +1-856-256-4500
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136
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Mallick H, Chatterjee S, Chowdhury S, Chatterjee S, Rahnavard A, Hicks SC. Differential expression of single-cell RNA-seq data using Tweedie models. Stat Med 2022; 41:3492-3510. [PMID: 35656596 PMCID: PMC9288986 DOI: 10.1002/sim.9430] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 12/13/2022]
Abstract
The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.
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Affiliation(s)
- Himel Mallick
- Biostatistics and Research Decision Sciences, Merck &
Co., Inc., Rahway, NJ 07065, USA
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population
Health Research, Eunice Kennedy Shriver National Institute of Child
Health and Human Development, National Institutes of Health, Bethesda, MD 20892,
USA
| | - Shrabanti Chowdhury
- Department of Genetics and Genomic Sciences and Icahn
Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount
Sinai, New York, NY 10029, USA
| | - Saptarshi Chatterjee
- Department of Statistics, Data and Analytics, Eli Lilly
& Company, Indianapolis, IN 46225, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of
Biostatistics and Bioinformatics, Milken Institute School of Public Health, The
George Washington University, Washington, DC 20052, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School
of Public Health, Baltimore, MD 21205, USA
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137
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Wang Y, Wang Q, Xu D. New insights into macrophage subsets in atherosclerosis. J Mol Med (Berl) 2022; 100:1239-1251. [PMID: 35930063 DOI: 10.1007/s00109-022-02224-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 05/27/2022] [Accepted: 06/15/2022] [Indexed: 12/11/2022]
Abstract
Macrophages in atherosclerotic patients are notably plastic and heterogeneous. Single-cell RNA sequencing (Sc RNA-seq) can provide information about all the RNAs in individual cells, and it is used to identify cell subpopulations in atherosclerosis (AS) and reveal the heterogeneity of these cells. Recently, some findings from Sc RNA-seq experiments have suggested the existence of multiple macrophage subsets in atherosclerotic plaque lesions, and these subsets exhibit significant differences in their gene expression levels and functions. These cells affect various aspects of plaque lesion development, stabilization, and regression, as well as plaque rupture. This article aims to review the content and results of current studies that used RNA-seq to explore the different types of macrophages in AS and the related molecular mechanisms as well as to identify the potential roles of these macrophage types in the pathogenesis of atherosclerotic plaques. Also, this review listed some new therapeutic targets for delaying atherosclerotic lesion progression and treatment based on the experimental results.
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Affiliation(s)
- Yurong Wang
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Qiong Wang
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China
| | - Danyan Xu
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.
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138
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Peng L, Fang Z, Renauer PA, McNamara A, Park JJ, Lin Q, Zhou X, Dong MB, Zhu B, Zhao H, Wilen CB, Chen S. Multiplexed LNP-mRNA vaccination against pathogenic coronavirus species. Cell Rep 2022; 40:111160. [PMID: 35921835 PMCID: PMC9294034 DOI: 10.1016/j.celrep.2022.111160] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 05/07/2022] [Accepted: 07/13/2022] [Indexed: 12/02/2022] Open
Abstract
Although COVID-19 vaccines have been developed, multiple pathogenic coronavirus species exist, urging on development of multispecies coronavirus vaccines. Here we develop prototype lipid nanoparticle (LNP)-mRNA vaccine candidates against SARS-CoV-2 Delta, SARS-CoV, and MERS-CoV, and we test how multiplexing LNP-mRNAs can induce effective immune responses in animal models. Triplex and duplex LNP-mRNA vaccinations induce antigen-specific antibody responses against SARS-CoV-2, SARS-CoV, and MERS-CoV. Single-cell RNA sequencing profiles the global systemic immune repertoires and respective transcriptome signatures of vaccinated animals, revealing a systemic increase in activated B cells and differential gene expression across major adaptive immune cells. Sequential vaccination shows potent antibody responses against all three species, significantly stronger than simultaneous vaccination in mixture. These data demonstrate the feasibility, antibody responses, and single-cell immune profiles of multispecies coronavirus vaccination. The direct comparison between simultaneous and sequential vaccination offers insights into optimization of vaccination schedules to provide broad and potent antibody immunity against three major pathogenic coronavirus species.
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Affiliation(s)
- Lei Peng
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA
| | - Zhenhao Fang
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA
| | - Paul A Renauer
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA; Molecular Cell Biology, Genetics and Development Program, Yale University, New Haven, CT 06516, USA
| | - Andrew McNamara
- Department of Immunobiology, Yale University, New Haven, CT 06510, USA; Department of Laboratory Medicine, Yale University, New Haven, CT 06510, USA
| | - Jonathan J Park
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT 06516, USA
| | - Qianqian Lin
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA
| | - Xiaoyu Zhou
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA
| | - Matthew B Dong
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA; Department of Immunobiology, Yale University, New Haven, CT 06510, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT 06516, USA; Immunobiology Program, Yale University, New Haven, CT 06510, USA
| | - Biqing Zhu
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA
| | - Hongyu Zhao
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA; Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06510, USA; Yale Center for Biomedical Data Science, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Craig B Wilen
- Department of Immunobiology, Yale University, New Haven, CT 06510, USA; Department of Laboratory Medicine, Yale University, New Haven, CT 06510, USA
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA; System Biology Institute, Yale University, West Haven, CT 06516, USA; Center for Cancer Systems Biology, Yale University, West Haven, CT 06516, USA; Molecular Cell Biology, Genetics and Development Program, Yale University, New Haven, CT 06516, USA; M.D.-Ph.D. Program, Yale University, West Haven, CT 06516, USA; Immunobiology Program, Yale University, New Haven, CT 06510, USA; Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA; Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT 06510, USA; Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT 06510, USA; Yale Center for Biomedical Data Science, Yale University School of Medicine, New Haven, CT 06510, USA.
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139
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Algabri YA, Li L, Liu ZP. scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms. Bioengineering (Basel) 2022; 9:bioengineering9080353. [PMID: 36004879 PMCID: PMC9405199 DOI: 10.3390/bioengineering9080353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/27/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene–gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.
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140
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Baßler K, Fujii W, Kapellos TS, Dudkin E, Reusch N, Horne A, Reiz B, Luecken MD, Osei-Sarpong C, Warnat-Herresthal S, Bonaguro L, Schulte-Schrepping J, Wagner A, Günther P, Pizarro C, Schreiber T, Knoll R, Holsten L, Kröger C, De Domenico E, Becker M, Händler K, Wohnhaas CT, Baumgartner F, Köhler M, Theis H, Kraut M, Wadsworth MH, Hughes TK, Ferreira HJ, Hinkley E, Kaltheuner IH, Geyer M, Thiele C, Shalek AK, Feißt A, Thomas D, Dickten H, Beyer M, Baum P, Yosef N, Aschenbrenner AC, Ulas T, Hasenauer J, Theis FJ, Skowasch D, Schultze JL. Alveolar macrophages in early stage COPD show functional deviations with properties of impaired immune activation. Front Immunol 2022; 13:917232. [PMID: 35979364 PMCID: PMC9377018 DOI: 10.3389/fimmu.2022.917232] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 06/24/2022] [Indexed: 12/13/2022] Open
Abstract
Despite its high prevalence, the cellular and molecular mechanisms of chronic obstructive pulmonary disease (COPD) are far from being understood. Here, we determine disease-related changes in cellular and molecular compositions within the alveolar space and peripheral blood of a cohort of COPD patients and controls. Myeloid cells were the largest cellular compartment in the alveolar space with invading monocytes and proliferating macrophages elevated in COPD. Modeling cell-to-cell communication, signaling pathway usage, and transcription factor binding predicts TGF-β1 to be a major upstream regulator of transcriptional changes in alveolar macrophages of COPD patients. Functionally, macrophages in COPD showed reduced antigen presentation capacity, accumulation of cholesteryl ester, reduced cellular chemotaxis, and mitochondrial dysfunction, reminiscent of impaired immune activation.
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Affiliation(s)
- Kevin Baßler
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Wataru Fujii
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Theodore S. Kapellos
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Erika Dudkin
- Computational Life Sciences, Life & Medical Sciences (LIMES) Institute, University of Bonn, Neuherberg, Germany
| | - Nico Reusch
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Ari Horne
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | | | - Malte D. Luecken
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
| | - Collins Osei-Sarpong
- Immunogenomics & Neurodegeneration, German Center for Neurodegenerative Diseases and the University of Bonn, Bonn, Germany
| | - Stefanie Warnat-Herresthal
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Lorenzo Bonaguro
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Jonas Schulte-Schrepping
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Allon Wagner
- Department of electrical engineering and computer science, University of California, Berkeley, CA, United States
- Center for computational biology, University of California, Berkeley, CA, United States
| | - Patrick Günther
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
| | - Carmen Pizarro
- Department of Internal Medicine II, University Hospital Bonn, Section of Pneumology, Bonn, Germany
| | - Tina Schreiber
- Department of Internal Medicine II, University Hospital Bonn, Section of Pneumology, Bonn, Germany
| | - Rainer Knoll
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Prevention, Aging & Systems Immunology, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Lisa Holsten
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Charlotte Kröger
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- Prevention, Aging & Systems Immunology, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Elena De Domenico
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Matthias Becker
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Kristian Händler
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | | | | | | | - Heidi Theis
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Michael Kraut
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Marc H. Wadsworth
- Institute for Medical Engineering & Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Broad Institute of MIT and Harvard; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, United States
| | - Travis K. Hughes
- Institute of Structural Biology, University Hospital, University of Bonn, Bonn, Germany
| | - Humberto J. Ferreira
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
| | - Emily Hinkley
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Ines H. Kaltheuner
- Institute of Structural Biology, University Hospital, University of Bonn, Bonn, Germany
| | - Matthias Geyer
- Biochemistry & Cell Biology of Lipids, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Christoph Thiele
- University Clinics for Radiology, University Hospital Bonn, Bonn, Germany
| | - Alex K. Shalek
- Institute for Medical Engineering & Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Broad Institute of MIT and Harvard; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, United States
| | - Andreas Feißt
- University Clinics for Radiology, University Hospital Bonn, Bonn, Germany
| | - Daniel Thomas
- University Clinics for Radiology, University Hospital Bonn, Bonn, Germany
| | | | - Marc Beyer
- Immunogenomics & Neurodegeneration, German Center for Neurodegenerative Diseases and the University of Bonn, Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Patrick Baum
- Translational Medicine & Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Nir Yosef
- Department of electrical engineering and computer science, University of California, Berkeley, CA, United States
- Center for computational biology, University of California, Berkeley, CA, United States
- Chan-Zuckerberg Biohub, San Francisco, CA, United States
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, United States
| | - Anna C. Aschenbrenner
- Prevention, Aging & Systems Immunology, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, United States
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | - Thomas Ulas
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Jan Hasenauer
- Computational Life Sciences, Life & Medical Sciences (LIMES) Institute, University of Bonn, Neuherberg, Germany
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
| | - Fabian J. Theis
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany, Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Dirk Skowasch
- Department of Internal Medicine II, University Hospital Bonn, Section of Pneumology, Bonn, Germany
| | - Joachim L. Schultze
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
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141
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Hamed AA, Kunz DJ, El-Hamamy I, Trinh QM, Subedar OD, Richards LM, Foltz W, Bullivant G, Ware M, Vladoiu MC, Zhang J, Raj AM, Pugh TJ, Taylor MD, Teichmann SA, Stein LD, Simons BD, Dirks PB. A brain precursor atlas reveals the acquisition of developmental-like states in adult cerebral tumours. Nat Commun 2022; 13:4178. [PMID: 35853870 PMCID: PMC9296666 DOI: 10.1038/s41467-022-31408-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 06/16/2022] [Indexed: 01/01/2023] Open
Abstract
Human cerebral cancers are known to contain cell types resembling the varying stages of neural development. However, the basis of this association remains unclear. Here, we map the development of mouse cerebrum across the developmental time-course, from embryonic day 12.5 to postnatal day 365, performing single-cell transcriptomics on >100,000 cells. By comparing this reference atlas to single-cell data from >100 glial tumours of the adult and paediatric human cerebrum, we find that tumour cells have an expression signature that overlaps with temporally restricted, embryonic radial glial precursors (RGPs) and their immediate sublineages. Further, we demonstrate that prenatal transformation of RGPs in a genetic mouse model gives rise to adult cerebral tumours that show an embryonic/juvenile RGP identity. Together, these findings implicate the acquisition of embryonic-like states in the genesis of adult glioma, providing insight into the origins of human glioma, and identifying specific developmental cell types for therapeutic targeting.
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Affiliation(s)
- Akram A Hamed
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Daniel J Kunz
- The Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, UK
- Cavendish Laboratory, Department of Physics, JJ Thomson Avenue, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Ibrahim El-Hamamy
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Quang M Trinh
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Omar D Subedar
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Laura M Richards
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Warren Foltz
- STTARR Innovation Centre, Department of Radiation Oncology, University Health Network, Toronto, ON, Canada
| | - Garrett Bullivant
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Matthaeus Ware
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Maria C Vladoiu
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Jiao Zhang
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Antony M Raj
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Trevor J Pugh
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Michael D Taylor
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Division of Neurosurgery, University of Toronto, Toronto, ON, Canada
- Department of Surgery and Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Sarah A Teichmann
- Cavendish Laboratory, Department of Physics, JJ Thomson Avenue, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Lincoln D Stein
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Ontario Institute for Cancer Research, Toronto, ON, Canada.
| | - Benjamin D Simons
- Cavendish Laboratory, Department of Physics, JJ Thomson Avenue, Cambridge, UK.
- Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, UK.
- Wellcome Trust-Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, UK.
| | - Peter B Dirks
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada.
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada.
- Division of Neurosurgery, University of Toronto, Toronto, ON, Canada.
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142
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Das S, Rai A, Rai SN. Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges. ENTROPY 2022; 24:e24070995. [PMID: 35885218 PMCID: PMC9315519 DOI: 10.3390/e24070995] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/25/2022] [Accepted: 07/09/2022] [Indexed: 01/11/2023]
Abstract
With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.
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Affiliation(s)
- Samarendra Das
- ICAR-Directorate of Foot and Mouth Disease, Arugul, Bhubaneswar 752050, India
- International Centre for Foot and Mouth Disease, Arugul, Bhubaneswar 752050, India
- Correspondence: or (S.D.); (S.N.R.)
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India;
| | - Shesh N. Rai
- School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY 40292, USA
- Biostatistics and Bioinformatics Facility, Brown Cancer Center, University of Louisville, Louisville, KY 40202, USA
- Biostatisitcs and Informatics Facility, Center for Integrative Environmental Health Sciences, University of Louisville, Louisville, KY 40202, USA
- Data Analysis and Sample Management Facility, The University of Louisville Super Fund Center, University of Louisville, Louisville, KY 40202, USA
- Hepatobiology and Toxicology Center, University of Louisville, Louisville, KY 40202, USA
- Christina Lee Brown Envirome Institute, University of Louisville, Louisville, KY 40202, USA
- Correspondence: or (S.D.); (S.N.R.)
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143
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Zhu X, Li J, Lin Y, Zhao L, Wang J, Peng X. Dimensionality Reduction of Single-Cell RNA Sequencing Data by Combining Entropy and Denoising AutoEncoder. JOURNAL OF COMPUTATIONAL BIOLOGY : A JOURNAL OF COMPUTATIONAL MOLECULAR CELL BIOLOGY 2022; 29:1074-1084. [PMID: 35834604 DOI: 10.1089/cmb.2022.0118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
ABSTRACT Single-cell RNA sequencing (scRNA-seq) can present cellular heterogeneity at higher resolution when measuring the gene expression in an individual cell. However, there are still some computational problems in scRNA-seq data, including high dimensionality, high sparseness, and high noise. To solve them, dimensionality reduction is essential as it reduces dimensions and also removes most of the zeros and noise. Therefore, we propose a hybrid dimensionality reduction algorithm for scRNA-seq data by integrating binning-based entropy and a denoising autoencoder, named ScEDA. In ScEDA, a novel binning-based entropy estimation method is performed to select efficient genes, while removing noise. For each gene, binning-based entropy is designed to describe the differences in its expression across all cells, that is, the distribution of expression of each gene in all cells. Genes are regarded as inefficient and removed when they achieve low binning-based entropy. Moreover, by combining Kullback-Leibler (KL) divergence with the autoencoder, the objective function is reconstructed to maximize the similarity in distribution between input data and reconstructed data. Furthermore, by adding Poisson-distributed noise to the original input data, the denoising autoencoder is used to improve robustness. Compared with three other clustering methods, ScEDA provides superior average performance on 16 real scRNA-seq datasets, with obvious enhancement in large-scale datasets.
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Affiliation(s)
- Xiaoshu Zhu
- School of Computer Science and Engineering, Yulin Normal University, Yulin, China
| | - Jian Li
- School of Computer Science and Engineering, Yulin Normal University, Yulin, China
| | - Yongchang Lin
- School of Computer Science and Engineering, Yulin Normal University, Yulin, China
| | - Liquan Zhao
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Xiaoqing Peng
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
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144
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Selvin T, Fasterius E, Jarvius M, Fryknäs M, Larsson R, Andersson CR. Single-cell transcriptional pharmacodynamics of trifluridine in a tumor-immune model. Sci Rep 2022; 12:11960. [PMID: 35831404 PMCID: PMC9279337 DOI: 10.1038/s41598-022-16077-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 07/04/2022] [Indexed: 11/09/2022] Open
Abstract
Understanding the immunological effects of chemotherapy is of great importance, especially now that we have entered an era where ever-increasing pre-clinical and clinical efforts are put into combining chemotherapy and immunotherapy to combat cancer. Single-cell RNA sequencing (scRNA-seq) has proved to be a powerful technique with a broad range of applications, studies evaluating drug effects in co-cultures of tumor and immune cells are however scarce. We treated a co-culture comprised of human colorectal cancer (CRC) cells and peripheral blood mononuclear cells (PBMCs) with the nucleoside analogue trifluridine (FTD) and used scRNA-seq to analyze posttreatment gene expression profiles in thousands of individual cancer and immune cells concurrently. ScRNA-seq recapitulated major mechanisms of action previously described for FTD and provided new insight into possible treatment-induced effects on T-cell mediated antitumor responses.
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Affiliation(s)
- Tove Selvin
- Department of Medical Sciences, Uppsala University, 75185, Uppsala, Sweden.
| | - Erik Fasterius
- National Bioinformatics Infrastructure Sweden (NBIS), Stockholm University, Stockholm, Sweden
| | - Malin Jarvius
- Department of Medical Sciences, Uppsala University, 75185, Uppsala, Sweden.,Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Mårten Fryknäs
- Department of Medical Sciences, Uppsala University, 75185, Uppsala, Sweden
| | - Rolf Larsson
- Department of Medical Sciences, Uppsala University, 75185, Uppsala, Sweden
| | - Claes R Andersson
- Department of Medical Sciences, Uppsala University, 75185, Uppsala, Sweden.
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145
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Ellis D, Wu D, Datta S. SAREV: A review on statistical analytics of single-cell RNA sequencing data. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2022; 14:e1558. [PMID: 36034329 PMCID: PMC9400796 DOI: 10.1002/wics.1558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/09/2021] [Indexed: 06/15/2023]
Abstract
Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.
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Affiliation(s)
- Dorothy Ellis
- Department of Biostatistics, University of Florida, School of Public Health and Health Professions, Gainesville, FL
| | - Dongyuan Wu
- Department of Biostatistics, University of Florida, School of Public Health and Health Professions, Gainesville, FL
| | - Susmita Datta
- Department of Biostatistics, University of Florida, School of Public Health and Health Professions, Gainesville, FL
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146
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Gleeson JP, Chaudhary N, Fein KC, Doerfler R, Hredzak-Showalter P, Whitehead KA. Profiling of mature-stage human breast milk cells identifies six unique lactocyte subpopulations. SCIENCE ADVANCES 2022; 8:eabm6865. [PMID: 35767604 PMCID: PMC9242445 DOI: 10.1126/sciadv.abm6865] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Breast milk is chock-full of nutrients, immunological factors, and cells that aid infant development. Maternal cells are the least studied breast milk component, and their unique properties are difficult to identify using traditional techniques. Here, we characterized the cells in mature-stage breast milk from healthy donors at the protein, gene, and transcriptome levels. Holistic analysis of flow cytometry, quantitative polymerase chain reaction, and single-cell RNA sequencing data identified the predominant cell population as epithelial with smaller populations of macrophages and T cells. Two percent of epithelial cells expressed four stem cell markers: SOX2, TRA-1-60, NANOG, and SSEA4. Furthermore, milk contained six distinct epithelial lactocyte subpopulations, including three previously unidentified subpopulations programmed toward mucosal defense and intestinal development. Pseudotime analysis delineated the differentiation pathways of epithelial progenitors. Together, these data define healthy human maternal breast milk cells and provide a basis for their application in maternal and infant medicine.
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Affiliation(s)
- John P. Gleeson
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Namit Chaudhary
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Katherine C. Fein
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Rose Doerfler
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | | | - Kathryn A. Whitehead
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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147
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Crooks BA, Mckenzie D, Cadd LC, McCoy CJ, McVeigh P, Marks NJ, Maule AG, Mousley A, Atkinson LE. Pan-phylum In Silico Analyses of Nematode Endocannabinoid Signalling Systems Highlight Novel Opportunities for Parasite Drug Target Discovery. Front Endocrinol (Lausanne) 2022; 13:892758. [PMID: 35846343 PMCID: PMC9283691 DOI: 10.3389/fendo.2022.892758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
The endocannabinoid signalling (ECS) system is a complex lipid signalling pathway that modulates diverse physiological processes in both vertebrate and invertebrate systems. In nematodes, knowledge of endocannabinoid (EC) biology is derived primarily from the free-living model species Caenorhabditis elegans, where ECS has been linked to key aspects of nematode biology. The conservation and complexity of nematode ECS beyond C. elegans is largely uncharacterised, undermining the understanding of ECS biology in nematodes including species with key importance to human, veterinary and plant health. In this study we exploited publicly available omics datasets, in silico bioinformatics and phylogenetic analyses to examine the presence, conservation and life stage expression profiles of EC-effectors across phylum Nematoda. Our data demonstrate that: (i) ECS is broadly conserved across phylum Nematoda, including in therapeutically and agriculturally relevant species; (ii) EC-effectors appear to display clade and lifestyle-specific conservation patterns; (iii) filarial species possess a reduced EC-effector complement; (iv) there are key differences between nematode and vertebrate EC-effectors; (v) life stage-, tissue- and sex-specific EC-effector expression profiles suggest a role for ECS in therapeutically relevant parasitic nematodes. To our knowledge, this study represents the most comprehensive characterisation of ECS pathways in phylum Nematoda and inform our understanding of nematode ECS complexity. Fundamental knowledge of nematode ECS systems will seed follow-on functional studies in key nematode parasites to underpin novel drug target discovery efforts.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Louise E. Atkinson
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
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148
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Chattopadhyay A, Guan P, Majumder S, Kaw K, Zhou Z, Zhang C, Prakash SK, Kaw A, Buja LM, Kwartler CS, Milewicz DM. Preventing Cholesterol-Induced Perk (Protein Kinase RNA-Like Endoplasmic Reticulum Kinase) Signaling in Smooth Muscle Cells Blocks Atherosclerotic Plaque Formation. Arterioscler Thromb Vasc Biol 2022; 42:1005-1022. [PMID: 35708026 PMCID: PMC9311463 DOI: 10.1161/atvbaha.121.317451] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Vascular smooth muscle cells (SMCs) undergo complex phenotypic modulation with atherosclerotic plaque formation in hyperlipidemic mice, which is characterized by de-differentiation and heterogeneous increases in the expression of macrophage, fibroblast, osteogenic, and stem cell markers. An increase of cellular cholesterol in SMCs triggers similar phenotypic changes in vitro with exposure to free cholesterol due to cholesterol entering the endoplasmic reticulum, triggering endoplasmic reticulum stress and activating Perk (protein kinase RNA-like endoplasmic reticulum kinase) signaling.
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Affiliation(s)
- Abhijnan Chattopadhyay
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School The University of Texas Health Science Center at Houston (A.C., P.G., S.M., K.K., Z.Z., A.K., C.S.K., D.M.M.)
| | - Pujun Guan
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School The University of Texas Health Science Center at Houston (A.C., P.G., S.M., K.K., Z.Z., A.K., C.S.K., D.M.M.).,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center and UTHealth, Houston (P.G.)
| | - Suravi Majumder
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School The University of Texas Health Science Center at Houston (A.C., P.G., S.M., K.K., Z.Z., A.K., C.S.K., D.M.M.)
| | - Kaveeta Kaw
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School The University of Texas Health Science Center at Houston (A.C., P.G., S.M., K.K., Z.Z., A.K., C.S.K., D.M.M.)
| | - Zhen Zhou
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School The University of Texas Health Science Center at Houston (A.C., P.G., S.M., K.K., Z.Z., A.K., C.S.K., D.M.M.)
| | - Chen Zhang
- Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX (C.Z.).,Department of Cardiovascular Surgery, Texas Heart Institute, Houston (C.Z.)
| | | | - Anita Kaw
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School The University of Texas Health Science Center at Houston (A.C., P.G., S.M., K.K., Z.Z., A.K., C.S.K., D.M.M.)
| | - L Maximillian Buja
- Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center at Houston (L.M.B.)
| | - Callie S Kwartler
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School The University of Texas Health Science Center at Houston (A.C., P.G., S.M., K.K., Z.Z., A.K., C.S.K., D.M.M.)
| | - Dianna M Milewicz
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School The University of Texas Health Science Center at Houston (A.C., P.G., S.M., K.K., Z.Z., A.K., C.S.K., D.M.M.)
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149
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Gaydosik AM, Stonesifer CJ, Khaleel AE, Geskin LJ, Fuschiotti P. Single-Cell RNA Sequencing Unveils the Clonal and Transcriptional Landscape of Cutaneous T-Cell Lymphomas. Clin Cancer Res 2022; 28:2610-2622. [PMID: 35421230 PMCID: PMC9197926 DOI: 10.1158/1078-0432.ccr-21-4437] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/24/2022] [Accepted: 04/11/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE Clonal malignant T lymphocytes constitute only a fraction of T cells in mycosis fungoides skin tumors and in the leukemic blood of Sézary syndrome, the classic types of cutaneous T-cell lymphomas. However, lack of markers specific for malignant lymphocytes prevents distinguishing them from benign T cells, thus delaying diagnosis and the development of targeted treatments. Here we applied single-cell methods to assess the transcriptional profiles of both malignant T-cell clones and reactive T lymphocytes directly in mycosis fungoides/Sézary syndrome patient samples. EXPERIMENTAL DESIGN Single-cell RNA sequencing was used to profile the T-cell immune repertoire simultaneously with gene expression in CD3+ lymphocytes from mycosis fungoides and healthy skin biopsies as well as from Sézary syndrome and control blood samples. Transcriptional data were validated in additional advanced-stage mycosis fungoides/Sézary syndrome skin and blood samples by immunofluorescence microscopy. RESULTS Several nonoverlapping clonotypes are expanded in the skin and blood of individual advanced-stage mycosis fungoides/Sézary syndrome patient samples, including a dominant malignant clone as well as additional minor malignant and reactive clones. While we detected upregulation of patient-specific as well as mycosis fungoides- and Sézary syndrome-specific oncogenic pathways within individual malignant clones, we also detected upregulation of several common pathways that included genes associated with cancer cell metabolism, cell-cycle regulation, de novo nucleotide biosynthesis, and invasion. CONCLUSIONS Our analysis unveils new insights into mycosis fungoides/Sézary syndrome pathogenesis by providing an unprecedented report of the transcriptional profile of malignant T-cell clones in the skin and blood of individual patients and offers novel prospective targets for personalized therapy.
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Affiliation(s)
- Alyxzandria M. Gaydosik
- Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA
| | | | | | | | - Patrizia Fuschiotti
- Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh PA 15261, USA,Correspondence to: Patrizia Fuschiotti, Department of Medicine, Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, S709 BST, 200 Lothrop Street, Pittsburgh PA 15261, USA. Tel.: +1-412-648-9385;
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150
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Gagnon J, Pi L, Ryals M, Wan Q, Hu W, Ouyang Z, Zhang B, Li K. Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking. LIFE (BASEL, SWITZERLAND) 2022; 12:life12060850. [PMID: 35743881 PMCID: PMC9225332 DOI: 10.3390/life12060850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/31/2022] [Accepted: 06/04/2022] [Indexed: 12/13/2022]
Abstract
To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.
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Affiliation(s)
- Jake Gagnon
- Analytics and Data Sciences, Biogen, Inc., 225 Binney St., Cambridge, MA 02142, USA;
| | - Lira Pi
- PharmaLex, 1700 District Ave., Burlington, MA 01803, USA; (L.P.); (M.R.); (Q.W.)
| | - Matthew Ryals
- PharmaLex, 1700 District Ave., Burlington, MA 01803, USA; (L.P.); (M.R.); (Q.W.)
| | - Qingwen Wan
- PharmaLex, 1700 District Ave., Burlington, MA 01803, USA; (L.P.); (M.R.); (Q.W.)
| | - Wenxing Hu
- Research Department, Biogen, Inc., 225 Binney St., Cambridge, MA 02142, USA;
| | - Zhengyu Ouyang
- BioInfoRx, Inc., 510 Charmany Dr., Suite 275A, Madison, WI 53719, USA;
| | - Baohong Zhang
- Research Department, Biogen, Inc., 225 Binney St., Cambridge, MA 02142, USA;
- Correspondence: (B.Z.); (K.L.)
| | - Kejie Li
- Research Department, Biogen, Inc., 225 Binney St., Cambridge, MA 02142, USA;
- Correspondence: (B.Z.); (K.L.)
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