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Guo X, Keenan BT, Reiner BC, Lian J, Pack AI. Single-nucleus RNA-seq identifies one galanin neuronal subtype in mouse preoptic hypothalamus activated during recovery from sleep deprivation. Cell Rep 2024; 43:114192. [PMID: 38703367 DOI: 10.1016/j.celrep.2024.114192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/13/2024] [Accepted: 04/18/2024] [Indexed: 05/06/2024] Open
Abstract
The preoptic area of the hypothalamus (POA) is essential for sleep regulation. However, the cellular makeup of the POA is heterogeneous, and the molecular identities of the sleep-promoting cells remain elusive. To address this question, this study compares mice during recovery sleep following sleep deprivation to mice allowed extended sleep. Single-nucleus RNA sequencing (single-nucleus RNA-seq) identifies one galanin inhibitory neuronal subtype that shows upregulation of rapid and delayed activity-regulated genes during recovery sleep. This cell type expresses higher levels of growth hormone receptor and lower levels of estrogen receptor compared to other galanin subtypes. single-nucleus RNA-seq also reveals cell-type-specific upregulation of purinergic receptor (P2ry14) and serotonin receptor (Htr2a) during recovery sleep in this neuronal subtype, suggesting possible mechanisms for sleep regulation. Studies with RNAscope validate the single-nucleus RNA-seq findings. Thus, the combined use of single-nucleus RNA-seq and activity-regulated genes identifies a neuronal subtype functionally involved in sleep regulation.
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Affiliation(s)
- Xiaofeng Guo
- Circadian Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brendan T Keenan
- Circadian Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin C Reiner
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jie Lian
- Circadian Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allan I Pack
- Circadian Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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2
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Wu Y, Libby JB, Dumitrescu L, De Jager PL, Menon V, Schneider JA, Bennett DA, Hohman TJ. Association of 10 VEGF Family Genes with Alzheimer's Disease Endophenotypes at Single Cell Resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589221. [PMID: 38826287 PMCID: PMC11142115 DOI: 10.1101/2024.04.12.589221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The cell-type specific role of the vascular endothelial growth factors (VEGFs) in the pathogenesis of Alzheimer's disease (AD) is not well characterized. In this study, we utilized a single-nucleus RNA sequencing dataset from Dorsolateral Prefrontal Cortex (DLFPC) of 424 donors from the Religious Orders Study and Memory and Aging Project (ROS/MAP) to investigate the effect of 10 VEGF genes ( VEGFA, VEGFB, VEGFC, VEGFD, PGF, FLT1, FLT4, KDR, NRP1 , and NRP2 ) on AD endophenotypes. Mean age of death was 89 years, among which 68% were females, and 52% has AD dementia. Negative binomial mixed models were used for differential expression analysis and for association analysis with β-amyloid load, PHF tau tangle density, and both cross-sectional and longitudinal global cognitive function. Intercellular VEGF-associated signaling was profiled using CellChat. We discovered prefrontal cortical FLT1 expression was upregulated in AD brains in both endothelial and microglial cells. Higher FLT1 expression was also associated with worse cross-sectional global cognitive function, longitudinal cognitive trajectories, and β-amyloid load. Similarly, higher endothelial FLT4 expression was associated with more β-amyloid load. In contrast to the receptors, VEGFB showed opposing effects on β-amyloid load whereby higher levels in oligodendrocytes was associated with high amyloid burden, while higher levels in inhibitory neurons was associated with lower amyloid burden. Finally, AD cells showed significant reduction in overall VEGF signaling comparing to those from cognitive normal participants. Our results highlight key changes in VEGF receptor expression in endothelial and microglial cells during AD, and the potential protective role of VEGFB in neurons.
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3
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Patil AR, Schug J, Liu C, Lahori D, Descamps HC, Naji A, Kaestner KH, Faryabi RB, Vahedi G. Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets. Cell Rep Med 2024; 5:101535. [PMID: 38677282 PMCID: PMC11148720 DOI: 10.1016/j.xcrm.2024.101535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/22/2024] [Accepted: 04/07/2024] [Indexed: 04/29/2024]
Abstract
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.
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Affiliation(s)
- Abhijeet R Patil
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jonathan Schug
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Chengyang Liu
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Deeksha Lahori
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Hélène C Descamps
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ali Naji
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Klaus H Kaestner
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Robert B Faryabi
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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4
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Linna-Kuosmanen S, Schmauch E, Galani K, Ojanen J, Boix CA, Örd T, Toropainen A, Singha PK, Moreau PR, Harju K, Blazeski A, Segerstolpe Å, Lahtinen V, Hou L, Kang K, Meibalan E, Agudelo LZ, Kokki H, Halonen J, Jalkanen J, Gunn J, MacRae CA, Hollmén M, Hartikainen JEK, Kaikkonen MU, García-Cardeña G, Tavi P, Kiviniemi T, Kellis M. Transcriptomic and spatial dissection of human ex vivo right atrial tissue reveals proinflammatory microvascular changes in ischemic heart disease. Cell Rep Med 2024; 5:101556. [PMID: 38776872 PMCID: PMC11148807 DOI: 10.1016/j.xcrm.2024.101556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 11/27/2023] [Accepted: 04/16/2024] [Indexed: 05/25/2024]
Abstract
Cardiovascular disease plays a central role in the electrical and structural remodeling of the right atrium, predisposing to arrhythmias, heart failure, and sudden death. Here, we dissect with single-nuclei RNA sequencing (snRNA-seq) and spatial transcriptomics the gene expression changes in the human ex vivo right atrial tissue and pericardial fluid in ischemic heart disease, myocardial infarction, and ischemic and non-ischemic heart failure using asymptomatic patients with valvular disease who undergo preventive surgery as the control group. We reveal substantial differences in disease-associated gene expression in all cell types, collectively suggesting inflammatory microvascular dysfunction and changes in the right atrial tissue composition as the valvular and vascular diseases progress into heart failure. The data collectively suggest that investigation of human cardiovascular disease should expand to all functionally important parts of the heart, which may help us to identify mechanisms promoting more severe types of the disease.
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Affiliation(s)
- Suvi Linna-Kuosmanen
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland.
| | - Eloi Schmauch
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Kyriakitsa Galani
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Johannes Ojanen
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Carles A Boix
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tiit Örd
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Anu Toropainen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Prosanta K Singha
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Pierre R Moreau
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Kristiina Harju
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Adriana Blazeski
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Åsa Segerstolpe
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Veikko Lahtinen
- Heart Center, Turku University Hospital, 20521 Turku, Finland; MediCity Research Laboratories and InFLAMES Flagship, University of Turku, 20500 Turku, Finland
| | - Lei Hou
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kai Kang
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Elamaran Meibalan
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Leandro Z Agudelo
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hannu Kokki
- School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland
| | - Jari Halonen
- School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland; Heart Center, Kuopio University Hospital, 70200 Kuopio, Finland
| | - Juho Jalkanen
- MediCity Research Laboratories and InFLAMES Flagship, University of Turku, 20500 Turku, Finland
| | - Jarmo Gunn
- Heart Center, Turku University Hospital, 20521 Turku, Finland; Department of Medicine, University of Turku, 20500 Turku, Finland
| | - Calum A MacRae
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Cardiovascular Medicine and Network Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA
| | - Maija Hollmén
- MediCity Research Laboratories and InFLAMES Flagship, University of Turku, 20500 Turku, Finland
| | - Juha E K Hartikainen
- School of Medicine, University of Eastern Finland, 70211 Kuopio, Finland; Heart Center, Kuopio University Hospital, 70200 Kuopio, Finland
| | - Minna U Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Guillermo García-Cardeña
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA
| | - Pasi Tavi
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Tuomas Kiviniemi
- Heart Center, Turku University Hospital, 20521 Turku, Finland; Department of Medicine, University of Turku, 20500 Turku, Finland; Cardiovascular Medicine and Network Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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5
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Guo X, Ning J, Chen Y, Liu G, Zhao L, Fan Y, Sun S. Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies. Brief Funct Genomics 2024; 23:95-109. [PMID: 37022699 DOI: 10.1093/bfgp/elad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/09/2022] [Accepted: 03/10/2023] [Indexed: 04/07/2023] Open
Abstract
Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.
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Affiliation(s)
- Xiya Guo
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Jin Ning
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yuanze Chen
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Guoliang Liu
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Liyan Zhao
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yue Fan
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Shiquan Sun
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
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6
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Lin KZ, Qiu Y, Roeder K. eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings. BMC Bioinformatics 2024; 25:113. [PMID: 38486150 PMCID: PMC10941434 DOI: 10.1186/s12859-024-05724-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. RESULTS We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. CONCLUSIONS eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.
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Affiliation(s)
- Kevin Z Lin
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Yixuan Qiu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, People's Republic of China
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
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7
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Lin KZ, Qiu Y, Roeder K. eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568369. [PMID: 38045428 PMCID: PMC10690270 DOI: 10.1101/2023.11.22.568369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Background Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. Results We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. Conclusions eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.
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Affiliation(s)
- Kevin Z Lin
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Yixuan Qiu
- School of Statistics & Management, Shanghai University of Finance and Economics, Shanghai,People's Republic of China
| | - Kathryn Roeder
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
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8
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De Solis AJ, Del Río-Martín A, Radermacher J, Chen W, Steuernagel L, Bauder CA, Eggersmann FR, Morgan DA, Cremer AL, Sué M, Germer M, Kukat C, Vollmar S, Backes H, Rahmouni K, Kloppenburg P, Brüning JC. Reciprocal activity of AgRP and POMC neurons governs coordinated control of feeding and metabolism. Nat Metab 2024; 6:473-493. [PMID: 38378998 DOI: 10.1038/s42255-024-00987-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/16/2024] [Indexed: 02/22/2024]
Abstract
Agouti-related peptide (AgRP)-expressing and proopiomelanocortin (POMC)-expressing neurons reciprocally regulate food intake. Here, we combine non-interacting recombinases to simultaneously express functionally opposing chemogenetic receptors in AgRP and POMC neurons for comparing metabolic responses in male and female mice with simultaneous activation of AgRP and inhibition of POMC neurons with isolated activation of AgRP neurons or isolated inhibition of POMC neurons. We show that food intake is regulated by the additive effect of AgRP neuron activation and POMC neuron inhibition, while systemic insulin sensitivity and gluconeogenesis are differentially modulated by isolated-versus-simultaneous regulation of AgRP and POMC neurons. We identify a neurocircuit engaging Npy1R-expressing neurons in the paraventricular nucleus of the hypothalamus, where activated AgRP neurons and inhibited POMC neurons cooperate to promote food consumption and activate Th+ neurons in the nucleus tractus solitarii. Collectively, these results unveil how food intake is precisely regulated by the simultaneous bidirectional interplay between AgRP and POMC neurocircuits.
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Affiliation(s)
- Alain J De Solis
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Policlinic for Endocrinology, Diabetes and Preventive Medicine (PEDP), University Hospital Cologne, Cologne, Germany
| | - Almudena Del Río-Martín
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Policlinic for Endocrinology, Diabetes and Preventive Medicine (PEDP), University Hospital Cologne, Cologne, Germany
| | - Jan Radermacher
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Institute for Zoology, Biocenter, University of Cologne, Cologne, Germany
| | - Weiyi Chen
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Policlinic for Endocrinology, Diabetes and Preventive Medicine (PEDP), University Hospital Cologne, Cologne, Germany
| | - Lukas Steuernagel
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Policlinic for Endocrinology, Diabetes and Preventive Medicine (PEDP), University Hospital Cologne, Cologne, Germany
| | - Corinna A Bauder
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Policlinic for Endocrinology, Diabetes and Preventive Medicine (PEDP), University Hospital Cologne, Cologne, Germany
| | - Fynn R Eggersmann
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Institute for Zoology, Biocenter, University of Cologne, Cologne, Germany
| | - Donald A Morgan
- Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Anna-Lena Cremer
- Multimodal Imaging of Brain Metabolism Group, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Michael Sué
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Maximilian Germer
- FACS & Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Christian Kukat
- FACS & Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Stefan Vollmar
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Heiko Backes
- Multimodal Imaging of Brain Metabolism Group, Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Kamal Rahmouni
- Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- Fraternal Order of Eagles Diabetes Research Center, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Peter Kloppenburg
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Institute for Zoology, Biocenter, University of Cologne, Cologne, Germany
| | - Jens C Brüning
- Department of Neuronal Control of Metabolism, Max Planck Institute for Metabolism Research, Cologne, Germany.
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD) and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.
- Policlinic for Endocrinology, Diabetes and Preventive Medicine (PEDP), University Hospital Cologne, Cologne, Germany.
- National Center for Diabetes Research (DZD), Neuherberg, Germany.
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9
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Islam MT, Liu Y, Hassan MM, Abraham PE, Merlet J, Townsend A, Jacobson D, Buell CR, Tuskan GA, Yang X. Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0029. [PMID: 38435807 PMCID: PMC10905259 DOI: 10.34133/bdr.0029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/28/2024] [Indexed: 03/05/2024] Open
Abstract
Plants are complex systems hierarchically organized and composed of various cell types. To understand the molecular underpinnings of complex plant systems, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity. Furthermore, scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology, which aims to modify plants genetically/epigenetically through genome editing, engineering, or re-writing based on rational design for increasing crop yield and quality, promoting the bioeconomy and enhancing environmental sustainability. In particular, data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects. To date, scRNA-seq has been demonstrated in a limited number of plant species, including model plants (e.g., Arabidopsis thaliana), agricultural crops (e.g., Oryza sativa), and bioenergy crops (e.g., Populus spp.). It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants. In this review, we summarize current technical advancements in plant scRNA-seq, including sample preparation, sequencing, and data analysis, to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples. We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research. Finally, we discuss the challenges and opportunities for the application of scRNA-seq in plants.
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Affiliation(s)
- Md Torikul Islam
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Yang Liu
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Md Mahmudul Hassan
- Department of Genetics and Plant Breeding,
Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jean Merlet
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Alice Townsend
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C. Robin Buell
- Center for Applied Genetic Technologies,
University of Georgia, Athens, GA 30602, USA
- Department of Crop and Soil Sciences,
University of Georgia, Athens, GA 30602, USA
- Institute of Plant Breeding, Genetics, and Genomics,
University of Georgia, Athens, GA 30602, USA
| | - Gerald A. Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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10
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Almeida MC, Eger SJ, He C, Audouard M, Nikitina A, Glasauer SMK, Han D, Mejía-Cupajita B, Acosta-Uribe J, Villalba-Moreno ND, Littau JL, Elcheikhali M, Rivera EK, Carrettiero DC, Villegas-Lanau CA, Sepulveda-Falla D, Lopera F, Kosik KS. Single-nucleus RNA sequencing demonstrates an autosomal dominant Alzheimer's disease profile and possible mechanisms of disease protection. Neuron 2024:S0896-6273(24)00093-X. [PMID: 38417436 DOI: 10.1016/j.neuron.2024.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 01/07/2024] [Accepted: 02/09/2024] [Indexed: 03/01/2024]
Abstract
Highly penetrant autosomal dominant Alzheimer's disease (ADAD) comprises a distinct disease entity as compared to the far more prevalent form of AD in which common variants collectively contribute to risk. The downstream pathways that distinguish these AD forms in specific cell types have not been deeply explored. We compared single-nucleus transcriptomes among a set of 27 cases divided among PSEN1-E280A ADAD carriers, sporadic AD, and controls. Autophagy genes and chaperones clearly defined the PSEN1-E280A cases compared to sporadic AD. Spatial transcriptomics validated the activation of chaperone-mediated autophagy genes in PSEN1-E280A. The PSEN1-E280A case in which much of the brain was spared neurofibrillary pathology and harbored a homozygous APOE3-Christchurch variant revealed possible explanations for protection from AD pathology including overexpression of LRP1 in astrocytes, increased expression of FKBP1B, and decreased PSEN1 expression in neurons. The unique cellular responses in ADAD and sporadic AD require consideration when designing clinical trials.
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Affiliation(s)
- Maria Camila Almeida
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Center for Natural and Humans Sciences, Federal University of ABC, Sao Bernardo do Campo, SP 09608020, Brazil
| | - Sarah J Eger
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Caroline He
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Morgane Audouard
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Arina Nikitina
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Stella M K Glasauer
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Dasol Han
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Barbara Mejía-Cupajita
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Grupo de Neurociencias de Antioquia, School of Medicine, Universidad de Antioquia, Medellín 050010, Colombia
| | - Juliana Acosta-Uribe
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Grupo de Neurociencias de Antioquia, School of Medicine, Universidad de Antioquia, Medellín 050010, Colombia
| | - Nelson David Villalba-Moreno
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Jessica Lisa Littau
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Megan Elcheikhali
- Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Erica Keane Rivera
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Daniel Carneiro Carrettiero
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Center for Natural and Humans Sciences, Federal University of ABC, Sao Bernardo do Campo, SP 09608020, Brazil
| | | | - Diego Sepulveda-Falla
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, School of Medicine, Universidad de Antioquia, Medellín 050010, Colombia.
| | - Kenneth S Kosik
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
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11
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Liu PW, Lin J, Hou R, Cai Z, Gong Y, He PA, Yang J. Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer. Comput Biol Med 2024; 169:107926. [PMID: 38183706 DOI: 10.1016/j.compbiomed.2024.107926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/09/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024]
Abstract
Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.
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Affiliation(s)
- Pei-Wen Liu
- School of Science, Zhejiang Sci-Tech University, Hangzhou, China; Geneis Beijing Co., Ltd., Beijing, China
| | - Jun Lin
- Depatment of Pathology, The People's Hospital of QuZhou City, ZheJiang, China
| | - Rui Hou
- Geneis Beijing Co., Ltd., Beijing, China
| | - Zhe Cai
- Extendcity (Shanghai) Co., Ltd., Shanghai, China
| | - Yue Gong
- Geneis Beijing Co., Ltd., Beijing, China
| | - Ping-An He
- School of Science, Zhejiang Sci-Tech University, Hangzhou, China.
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12
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Boocock J, Alexander N, Tapia LA, Walter-McNeill L, Munugala C, Bloom JS, Kruglyak L. Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570640. [PMID: 38106186 PMCID: PMC10723400 DOI: 10.1101/2023.12.07.570640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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Affiliation(s)
- James Boocock
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Noah Alexander
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leslie Alamo Tapia
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Laura Walter-McNeill
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Chetan Munugala
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
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13
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Pouyabahar D, Chung SW, Pezzutti OI, Perciani CT, Wang X, Ma XZ, Jiang C, Camat D, Chung T, Sekhon M, Manuel J, Chen XC, McGilvray ID, MacParland SA, Bader GD. A rat liver cell atlas reveals intrahepatic myeloid heterogeneity. iScience 2023; 26:108213. [PMID: 38026201 PMCID: PMC10651689 DOI: 10.1016/j.isci.2023.108213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 08/20/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023] Open
Abstract
The large size and vascular accessibility of the laboratory rat (Rattus norvegicus) make it an ideal hepatic animal model for diseases that require surgical manipulation. Often, the disease susceptibility and outcomes of inflammatory pathologies vary significantly between strains. This study uses single-cell transcriptomics to better understand the complex cellular network of the rat liver, as well as to unravel the cellular and molecular sources of inter-strain hepatic variation. We generated single-cell and single-nucleus transcriptomic maps of the livers of healthy Dark Agouti and Lewis rat strains and developed a factor analysis-based bioinformatics analysis pipeline to study data covariates, such as strain and batch. Using this approach, we discovered transcriptomic variation within the hepatocyte and myeloid populations that underlie distinct cell states between rat strains. This finding will help provide a reference for future investigations on strain-dependent outcomes of surgical experiment models.
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Affiliation(s)
- Delaram Pouyabahar
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Sai W. Chung
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
- Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Olivia I. Pezzutti
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Catia T. Perciani
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Xinle Wang
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Xue-Zhong Ma
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Chao Jiang
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Damra Camat
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
- Department of Immunology, University of Toronto, Toronto, ON, Canada
| | - Trevor Chung
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Manmeet Sekhon
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Justin Manuel
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Xu-Chun Chen
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Ian D. McGilvray
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Sonya A. MacParland
- Ajmera Transplant Centre, Toronto General Hospital Research Institute, 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
- Lunenfeld-Tanenbaum Research Institute, Toronto, ON, Canada
- Princess Margaret Research Institute, University Health Network, Toronto, ON, Canada
- The Multiscale Human Program, Canadian Institute for Advanced Research, Toronto, ON, Canada
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14
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Paas-Oliveros E, Hernández-Lemus E, de Anda-Jáuregui G. Computational single cell oncology: state of the art. Front Genet 2023; 14:1256991. [PMID: 38028624 PMCID: PMC10663273 DOI: 10.3389/fgene.2023.1256991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.
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Affiliation(s)
- Ernesto Paas-Oliveros
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
- Investigadores por Mexico, Conahcyt, Mexico City, Mexico
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15
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Kearns NA, Iatrou A, Flood DJ, De Tissera S, Mullaney ZM, Xu J, Gaiteri C, Bennett DA, Wang Y. Dissecting the human leptomeninges at single-cell resolution. Nat Commun 2023; 14:7036. [PMID: 37923721 PMCID: PMC10624900 DOI: 10.1038/s41467-023-42825-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 10/23/2023] [Indexed: 11/06/2023] Open
Abstract
Emerging evidence shows that the meninges conduct essential immune surveillance and immune defense at the brain border, and the dysfunction of meningeal immunity contributes to aging and neurodegeneration. However, no study exists on the molecular properties of cell types within human leptomeninges. Here, we provide single nuclei profiling of dissected postmortem leptomeninges from aged individuals. We detect diverse cell types, including unique meningeal endothelial, mural, and fibroblast subtypes. For immune cells, we show that most T cells express CD8 and bear characteristics of tissue-resident memory T cells. We also identify distinct subtypes of border-associated macrophages (BAMs) that display differential gene expressions from microglia and express risk genes for Alzheimer's Disease (AD), as nominated by genome-wide association studies (GWAS). We discover cell-type-specific differentially expressed genes in individuals with Alzheimer's dementia, particularly in fibroblasts and BAMs. Indeed, when cultured, leptomeningeal cells display the signature of ex vivo AD fibroblasts upon amyloid-β treatment. We further explore ligand-receptor interactions within the leptomeningeal niche and computationally infer intercellular communications in AD. Thus, our study establishes a molecular map of human leptomeningeal cell types, providing significant insight into the border immune and fibrotic responses in AD.
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Affiliation(s)
- Nicola A Kearns
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Artemis Iatrou
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - Daniel J Flood
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Sashini De Tissera
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Zachary M Mullaney
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Jishu Xu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
- Department of Psychiatry, Upstate Medical University, Syracuse, NY, 13210, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA.
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, 60612, USA.
- Rush Graduate College, Rush University, Chicago IL, 60612, USA.
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16
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Wasdin PT, Abu-Shmais AA, Irvin MW, Vukovich MJ, Georgiev IS. Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.13.562258. [PMID: 37904915 PMCID: PMC10614817 DOI: 10.1101/2023.10.13.562258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Motivation LIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in LIBRA-seq antigen count data is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies. Results In this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model. This approach leverages the VRC01 negative control cells included in a recent LIBRA-seq study(Abu-Shmais et al.) to provide a data-driven means for identification of technical noise. We apply this method to a dataset of nine donors representing separate LIBRA-seq experiments and show that our approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method used in LIBRA-seq, despite variance in data size and noise structure across samples. This development will improve the ability of LIBRA-seq to identify antigen-specific B cells and contribute to providing more reliable datasets for future machine learning based approaches to predicting antibody-antigen binding as the corpus of LIBRA-seq data continues to grow.
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Affiliation(s)
- Perry T Wasdin
- Program in Chemical and Physical Biology, Vanderbilt University Medical Center; Nashville, TN, USA
- Program in Computational Microbiology and Immunology, Vanderbilt University Medical Center; Nashville, TN, 37232, USA
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Alexandra A Abu-Shmais
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Michael W Irvin
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, USA
| | - Matthew J Vukovich
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Ivelin S Georgiev
- Program in Chemical and Physical Biology, Vanderbilt University Medical Center; Nashville, TN, USA
- Program in Computational Microbiology and Immunology, Vanderbilt University Medical Center; Nashville, TN, 37232, USA
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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17
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Martinez-Valbuena I, Lee S, Santamaria E, Irigoyen JF, Forrest S, Li J, Tanaka H, Couto B, Reyes NG, Qamar H, Karakani AM, Kim A, Senkevich K, Rogaeva E, Fox SH, Tartaglia C, Visanji NP, Andrews T, Lang AE, Kovacs GG. 4R-Tau seeding activity unravels molecular subtypes in patients with Progressive Supranuclear Palsy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.28.559953. [PMID: 37808843 PMCID: PMC10557711 DOI: 10.1101/2023.09.28.559953] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Progressive Supranuclear palsy (PSP) is a 4-repeat (4-R) tauopathy. We hypothesized that the molecular diversity of tau could explain the heterogeneity seen in PSP disease progression. To test this hypothesis, we performed an extensive biochemical characterisation of the high molecular weight tau species (HMW-Tau) in 20 different brain regions of 25 PSP patients. We found a correlation between the HMW-Tau species and tau seeding capacity in the primary motor cortex, where we confirmed that an elevated 4R-Tau seeding activity correlates with a shorter disease duration. To identify factors that contribute to these differences, we performed proteomic and spatial transcriptomic analysis that revealed key mechanistic pathways, in particular those involving the immune system, that defined patients demonstrating high and low tau seeding capacity. These observations suggest that differences in the tau seeding activity may contribute to the considerable heterogeneity seen in disease progression of patients suffering from PSP.
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18
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Xiong X, James BT, Boix CA, Park YP, Galani K, Victor MB, Sun N, Hou L, Ho LL, Mantero J, Scannail AN, Dileep V, Dong W, Mathys H, Bennett DA, Tsai LH, Kellis M. Epigenomic dissection of Alzheimer's disease pinpoints causal variants and reveals epigenome erosion. Cell 2023; 186:4422-4437.e21. [PMID: 37774680 PMCID: PMC10782612 DOI: 10.1016/j.cell.2023.08.040] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/04/2023] [Accepted: 08/29/2023] [Indexed: 10/01/2023]
Abstract
Recent work has identified dozens of non-coding loci for Alzheimer's disease (AD) risk, but their mechanisms and AD transcriptional regulatory circuitry are poorly understood. Here, we profile epigenomic and transcriptomic landscapes of 850,000 nuclei from prefrontal cortexes of 92 individuals with and without AD to build a map of the brain regulome, including epigenomic profiles, transcriptional regulators, co-accessibility modules, and peak-to-gene links in a cell-type-specific manner. We develop methods for multimodal integration and detecting regulatory modules using peak-to-gene linking. We show AD risk loci are enriched in microglial enhancers and for specific TFs including SPI1, ELF2, and RUNX1. We detect 9,628 cell-type-specific ATAC-QTL loci, which we integrate alongside peak-to-gene links to prioritize AD variant regulatory circuits. We report differential accessibility of regulatory modules in late AD in glia and in early AD in neurons. Strikingly, late-stage AD brains show global epigenome dysregulation indicative of epigenome erosion and cell identity loss.
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Affiliation(s)
- Xushen Xiong
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Benjamin T James
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Carles A Boix
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Yongjin P Park
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Kyriaki Galani
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Matheus B Victor
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Na Sun
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Lei Hou
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Li-Lun Ho
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Julio Mantero
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Aine Ni Scannail
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vishnu Dileep
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hansruedi Mathys
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Li-Huei Tsai
- The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA; The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
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19
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Lara-Astiaso D, Goñi-Salaverri A, Mendieta-Esteban J, Narayan N, Del Valle C, Gross T, Giotopoulos G, Beinortas T, Navarro-Alonso M, Aguado-Alvaro LP, Zazpe J, Marchese F, Torrea N, Calvo IA, Lopez CK, Alignani D, Lopez A, Saez B, Taylor-King JP, Prosper F, Fortelny N, Huntly BJP. In vivo screening characterizes chromatin factor functions during normal and malignant hematopoiesis. Nat Genet 2023; 55:1542-1554. [PMID: 37580596 PMCID: PMC10484791 DOI: 10.1038/s41588-023-01471-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 07/11/2023] [Indexed: 08/16/2023]
Abstract
Cellular differentiation requires extensive alterations in chromatin structure and function, which is elicited by the coordinated action of chromatin and transcription factors. By contrast with transcription factors, the roles of chromatin factors in differentiation have not been systematically characterized. Here, we combine bulk ex vivo and single-cell in vivo CRISPR screens to characterize the role of chromatin factor families in hematopoiesis. We uncover marked lineage specificities for 142 chromatin factors, revealing functional diversity among related chromatin factors (i.e. barrier-to-autointegration factor subcomplexes) as well as shared roles for unrelated repressive complexes that restrain excessive myeloid differentiation. Using epigenetic profiling, we identify functional interactions between lineage-determining transcription factors and several chromatin factors that explain their lineage dependencies. Studying chromatin factor functions in leukemia, we show that leukemia cells engage homeostatic chromatin factor functions to block differentiation, generating specific chromatin factor-transcription factor interactions that might be therapeutically targeted. Together, our work elucidates the lineage-determining properties of chromatin factors across normal and malignant hematopoiesis.
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Affiliation(s)
- David Lara-Astiaso
- Department of Haematology, University of Cambridge, Cambridge, UK.
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK.
| | | | | | - Nisha Narayan
- Department of Haematology, University of Cambridge, Cambridge, UK
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK
| | - Cynthia Del Valle
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | | | - George Giotopoulos
- Department of Haematology, University of Cambridge, Cambridge, UK
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK
| | - Tumas Beinortas
- Department of Haematology, University of Cambridge, Cambridge, UK
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK
| | - Mar Navarro-Alonso
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | | | - Jon Zazpe
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Francesco Marchese
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Natalia Torrea
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Isabel A Calvo
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Cecile K Lopez
- Department of Haematology, University of Cambridge, Cambridge, UK
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK
| | - Diego Alignani
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Aitziber Lopez
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Borja Saez
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | | | - Felipe Prosper
- Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Nikolaus Fortelny
- Department of Biosciences & Medical Biology, University of Salzburg, Salzburg, Austria.
| | - Brian J P Huntly
- Department of Haematology, University of Cambridge, Cambridge, UK.
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK.
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20
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Fetahu IS, Esser-Skala W, Dnyansagar R, Sindelar S, Rifatbegovic F, Bileck A, Skos L, Bozsaky E, Lazic D, Shaw L, Tötzl M, Tarlungeanu D, Bernkopf M, Rados M, Weninger W, Tomazou EM, Bock C, Gerner C, Ladenstein R, Farlik M, Fortelny N, Taschner-Mandl S. Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis. Nat Commun 2023; 14:3620. [PMID: 37365178 DOI: 10.1038/s41467-023-39210-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 05/29/2023] [Indexed: 06/28/2023] Open
Abstract
Metastasis is the major cause of cancer-related deaths. Neuroblastoma (NB), a childhood tumor has been molecularly defined at the primary cancer site, however, the bone marrow (BM) as the metastatic niche of NB is poorly characterized. Here we perform single-cell transcriptomic and epigenomic profiling of BM aspirates from 11 subjects spanning three major NB subtypes and compare these to five age-matched and metastasis-free BM, followed by in-depth single cell analyses of tissue diversity and cell-cell interactions, as well as functional validation. We show that cellular plasticity of NB tumor cells is conserved upon metastasis and tumor cell type composition is NB subtype-dependent. NB cells signal to the BM microenvironment, rewiring via macrophage mgration inhibitory factor and midkine signaling specifically monocytes, which exhibit M1 and M2 features, are marked by activation of pro- and anti-inflammatory programs, and express tumor-promoting factors, reminiscent of tumor-associated macrophages. The interactions and pathways characterized in our study provide the basis for therapeutic approaches that target tumor-to-microenvironment interactions.
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Affiliation(s)
- Irfete S Fetahu
- St. Anna Children's Cancer Research Institute, Vienna, Austria.
| | - Wolfgang Esser-Skala
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
| | - Rohit Dnyansagar
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
| | - Samuel Sindelar
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
| | | | - Andrea Bileck
- University of Vienna, Department of Analytical Chemistry, Faculty of Chemistry, Vienna, Austria
- Joint Metabolomics Facility, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Lukas Skos
- University of Vienna, Department of Analytical Chemistry, Faculty of Chemistry, Vienna, Austria
| | - Eva Bozsaky
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Daria Lazic
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Lisa Shaw
- Medical University of Vienna, Department of Dermatology, Vienna, Austria
| | - Marcus Tötzl
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | | | - Marie Bernkopf
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Magdalena Rados
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Wolfgang Weninger
- Medical University of Vienna, Department of Dermatology, Vienna, Austria
| | - Eleni M Tomazou
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Medical University of Vienna, Institute of Artificial Intelligence, Center for Medical Data Science, Vienna, Austria
| | - Christopher Gerner
- University of Vienna, Department of Analytical Chemistry, Faculty of Chemistry, Vienna, Austria
- Joint Metabolomics Facility, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Ruth Ladenstein
- St. Anna Children's Hospital and St. Anna Children's Cancer Research Institute, Department of Studies and Statistics for Integrated Research and Projects, Vienna, Austria
- Medical University of Vienna, Department of Pediatrics, Vienna, Austria
| | - Matthias Farlik
- Medical University of Vienna, Department of Dermatology, Vienna, Austria
| | - Nikolaus Fortelny
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria.
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21
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Patil AR, Kumar G, Zhou H, Warren L. scViewer: An Interactive Single-Cell Gene Expression Visualization Tool. Cells 2023; 12:1489. [PMID: 37296611 PMCID: PMC10253102 DOI: 10.3390/cells12111489] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/09/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer's disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.
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Affiliation(s)
- Abhijeet R. Patil
- Global Statistical and Data Sciences, Teva Pharmaceuticals, West Chester, PA 19380, USA
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22
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Gabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J, Ding Y, Mahoney JT, Dee N, Goldy J, Melief EJ, Brouner K, Campos J, Carr AJ, Casper T, Chakrabarty R, Clark M, Compos J, Cool J, Valera Cuevas NJ, Dalley R, Darvas M, Ding SL, Dolbeare T, Mac Donald CL, Egdorf T, Esposito L, Ferrer R, Gala R, Gary A, Gloe J, Guilford N, Guzman J, Ho W, Jarksy T, Johansen N, Kalmbach BE, Keene LM, Khawand S, Kilgore M, Kirkland A, Kunst M, Lee BR, Malone J, Maltzer Z, Martin N, McCue R, McMillen D, Meyerdierks E, Meyers KP, Mollenkopf T, Montine M, Nolan AL, Nyhus J, Olsen PA, Pacleb M, Pham T, Pom CA, Postupna N, Ruiz A, Schantz AM, Sorensen SA, Staats B, Sullivan M, Sunkin SM, Thompson C, Tieu M, Ting J, Torkelson A, Tran T, Wang MQ, Waters J, Wilson AM, Haynor D, Gatto N, Jayadev S, Mufti S, Ng L, Mukherjee S, Crane PK, Latimer CS, Levi BP, Smith K, Close JL, Miller JA, Hodge RD, Larson EB, Grabowski TJ, Hawrylycz M, Keene CD, Lein ES. Integrated multimodal cell atlas of Alzheimer's disease. RESEARCH SQUARE 2023:rs.3.rs-2921860. [PMID: 37292694 PMCID: PMC10246227 DOI: 10.21203/rs.3.rs-2921860/v1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia in older adults. Neuropathological and imaging studies have demonstrated a progressive and stereotyped accumulation of protein aggregates, but the underlying molecular and cellular mechanisms driving AD progression and vulnerable cell populations affected by disease remain coarsely understood. The current study harnesses single cell and spatial genomics tools and knowledge from the BRAIN Initiative Cell Census Network to understand the impact of disease progression on middle temporal gyrus cell types. We used image-based quantitative neuropathology to place 84 donors spanning the spectrum of AD pathology along a continuous disease pseudoprogression score and multiomic technologies to profile single nuclei from each donor, mapping their transcriptomes, epigenomes, and spatial coordinates to a common cell type reference with unprecedented resolution. Temporal analysis of cell-type proportions indicated an early reduction of Somatostatin-expressing neuronal subtypes and a late decrease of supragranular intratelencephalic-projecting excitatory and Parvalbumin-expressing neurons, with increases in disease-associated microglial and astrocytic states. We found complex gene expression differences, ranging from global to cell type-specific effects. These effects showed different temporal patterns indicating diverse cellular perturbations as a function of disease progression. A subset of donors showed a particularly severe cellular and molecular phenotype, which correlated with steeper cognitive decline. We have created a freely available public resource to explore these data and to accelerate progress in AD research at SEA-AD.org.
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Affiliation(s)
| | | | - Victoria M. Rachleff
- Allen Institute for Brain Science, Seattle, WA, 98109
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | | | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jeanelle Ariza
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Yi Ding
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Erica J. Melief
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | | | - John Campos
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | | | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Michael Clark
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jazmin Compos
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jonah Cool
- Chan Zuckerberg Initiative, Redwood City, CA 94063
| | | | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Martin Darvas
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Luke Esposito
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Rohan Gala
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Amanda Gary
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | - Windy Ho
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Tim Jarksy
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | - Lisa M. Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Sarah Khawand
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Mitch Kilgore
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Amanda Kirkland
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Michael Kunst
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Brian R. Lee
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Zoe Maltzer
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Naomi Martin
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Rachel McCue
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | - Kelly P. Meyers
- Kaiser Permanente Washington Research Institute, Seattle, WA, 98101
| | | | - Mark Montine
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Amber L. Nolan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Paul A. Olsen
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Maiya Pacleb
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Nadia Postupna
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Augustin Ruiz
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Aimee M. Schantz
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | | | - Brian Staats
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Matt Sullivan
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Jonathan Ting
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Amy Torkelson
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Tracy Tran
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Angela M. Wilson
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - David Haynor
- Department of Radiology, University of Washington, Seattle, WA 98014
| | - Nicole Gatto
- Kaiser Permanente Washington Research Institute, Seattle, WA, 98101
| | - Suman Jayadev
- Department of Neurology, University of Washington, Seattle, WA 98104
| | - Shoaib Mufti
- Allen Institute for Brain Science, Seattle, WA, 98109
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | - Paul K. Crane
- Department of Medicine, University of Washington, Seattle, WA 98104
| | - Caitlin S. Latimer
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Boaz P. Levi
- Allen Institute for Brain Science, Seattle, WA, 98109
| | | | | | | | | | - Eric B. Larson
- Department of Medicine, University of Washington, Seattle, WA 98104
| | | | | | - C. Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104
| | - Ed S. Lein
- Allen Institute for Brain Science, Seattle, WA, 98109
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23
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Li K, Sun YH, Ouyang Z, Negi S, Gao Z, Zhu J, Wang W, Chen Y, Piya S, Hu W, Zavodszky MI, Yalamanchili H, Cao S, Gehrke A, Sheehan M, Huh D, Casey F, Zhang X, Zhang B. scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing. BMC Genomics 2023; 24:228. [PMID: 37131143 PMCID: PMC10155351 DOI: 10.1186/s12864-023-09332-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/25/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.
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Affiliation(s)
- Kejie Li
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Yu H Sun
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | | | - Soumya Negi
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Zhen Gao
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Jing Zhu
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Wanli Wang
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Yirui Chen
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Sarbottam Piya
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Wenxing Hu
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Maria I Zavodszky
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Hima Yalamanchili
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Shaolong Cao
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Andrew Gehrke
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Mark Sheehan
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Dann Huh
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Fergal Casey
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Xinmin Zhang
- Data Science, BioInfoRx Inc., Madison, WI, 53719, USA
| | - Baohong Zhang
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA.
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24
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Brase L, You SF, D'Oliveira Albanus R, Del-Aguila JL, Dai Y, Novotny BC, Soriano-Tarraga C, Dykstra T, Fernandez MV, Budde JP, Bergmann K, Morris JC, Bateman RJ, Perrin RJ, McDade E, Xiong C, Goate AM, Farlow M, Sutherland GT, Kipnis J, Karch CM, Benitez BA, Harari O. Single-nucleus RNA-sequencing of autosomal dominant Alzheimer disease and risk variant carriers. Nat Commun 2023; 14:2314. [PMID: 37085492 PMCID: PMC10121712 DOI: 10.1038/s41467-023-37437-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/15/2023] [Indexed: 04/23/2023] Open
Abstract
Genetic studies of Alzheimer disease (AD) have prioritized variants in genes related to the amyloid cascade, lipid metabolism, and neuroimmune modulation. However, the cell-specific effect of variants in these genes is not fully understood. Here, we perform single-nucleus RNA-sequencing (snRNA-seq) on nearly 300,000 nuclei from the parietal cortex of AD autosomal dominant (APP and PSEN1) and risk-modifying variant (APOE, TREM2 and MS4A) carriers. Within individual cell types, we capture genes commonly dysregulated across variant groups. However, specific transcriptional states are more prevalent within variant carriers. TREM2 oligodendrocytes show a dysregulated autophagy-lysosomal pathway, MS4A microglia have dysregulated complement cascade genes, and APOEε4 inhibitory neurons display signs of ferroptosis. All cell types have enriched states in autosomal dominant carriers. We leverage differential expression and single-nucleus ATAC-seq to map GWAS signals to effector cell types including the NCK2 signal to neurons in addition to the initially proposed microglia. Overall, our results provide insights into the transcriptional diversity resulting from AD genetic architecture and cellular heterogeneity. The data can be explored on the online browser ( http://web.hararilab.org/SNARE/ ).
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Affiliation(s)
- Logan Brase
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Shih-Feng You
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ricardo D'Oliveira Albanus
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | | | - Yaoyi Dai
- Baylor College of Medicine, Houston, TX, USA
| | - Brenna C Novotny
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Carolina Soriano-Tarraga
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Taitea Dykstra
- Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Center for Brain Immunology and Glia (BIG), Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Maria Victoria Fernandez
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - John P Budde
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Kristy Bergmann
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - John C Morris
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Richard J Perrin
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Eric McDade
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Knight Alzheimer Disease Research Center, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Alison M Goate
- Ronald M. Loeb Center for Alzheimer's Disease, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Martin Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Greg T Sutherland
- School of Medical Sciences and Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Jonathan Kipnis
- Department of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Center for Brain Immunology and Glia (BIG), Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Celeste M Karch
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Bruno A Benitez
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Oscar Harari
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
- Hope Center for Neurological Disorders, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
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25
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Sahu A, Wang X, Munson P, Klomp JP, Wang X, Gu SS, Han Y, Qian G, Nicol P, Zeng Z, Wang C, Tokheim C, Zhang W, Fu J, Wang J, Nair NU, Rens JA, Bourajjaj M, Jansen B, Leenders I, Lemmers J, Musters M, van Zanten S, van Zelst L, Worthington J, Liu JS, Juric D, Meyer CA, Oubrie A, Liu XS, Fisher DE, Flaherty KT. Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha. Cancer Discov 2023; 13:672-701. [PMID: 36745048 PMCID: PMC9975674 DOI: 10.1158/2159-8290.cd-22-0244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 09/13/2022] [Accepted: 11/23/2022] [Indexed: 02/07/2023]
Abstract
Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms. SIGNIFICANCE BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.
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Affiliation(s)
- Avinash Sahu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, Colorado
- Corresponding Authors: Keith T. Flaherty, Developmental Therapeutics, Massachusetts General Hospital Cancer Center, 55 Fruit Street, Boston, MA 02114. Phone: 617-724-4000; E-mail: ; David E. Fisher, Charlestown Navy Yard Building 149, 149 13th Street, Charlestown, MA 02129. Phone: 617-643-5428; E-mail: ; and Avinash Sahu, Department of Data Sciences, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115. Phone: 240-391-8125; E-mail:
| | - Xiaoman Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Phillip Munson
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | | | - Xiaoqing Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Shengqing Stan Gu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ya Han
- School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Gege Qian
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Phillip Nicol
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Zexian Zeng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Chenfei Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jingxin Fu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jin Wang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | | | | | - Bas Jansen
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | | | - Jaap Lemmers
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | - Mark Musters
- Lead Pharma, Kloosterstraat, Oss, the Netherlands
| | | | | | | | - Jun S. Liu
- Department of Statistics, Harvard University, Cambridge, Massachusetts
| | - Dejan Juric
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Clifford A. Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | - X. Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - David E. Fisher
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
- Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts
- Corresponding Authors: Keith T. Flaherty, Developmental Therapeutics, Massachusetts General Hospital Cancer Center, 55 Fruit Street, Boston, MA 02114. Phone: 617-724-4000; E-mail: ; David E. Fisher, Charlestown Navy Yard Building 149, 149 13th Street, Charlestown, MA 02129. Phone: 617-643-5428; E-mail: ; and Avinash Sahu, Department of Data Sciences, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115. Phone: 240-391-8125; E-mail:
| | - Keith T. Flaherty
- Department of Medicine and Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts
- Corresponding Authors: Keith T. Flaherty, Developmental Therapeutics, Massachusetts General Hospital Cancer Center, 55 Fruit Street, Boston, MA 02114. Phone: 617-724-4000; E-mail: ; David E. Fisher, Charlestown Navy Yard Building 149, 149 13th Street, Charlestown, MA 02129. Phone: 617-643-5428; E-mail: ; and Avinash Sahu, Department of Data Sciences, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115. Phone: 240-391-8125; E-mail:
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26
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Mostly natural sequencing-by-synthesis for scRNA-seq using Ultima sequencing. Nat Biotechnol 2023; 41:204-211. [PMID: 36109685 PMCID: PMC9931582 DOI: 10.1038/s41587-022-01452-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 08/01/2022] [Indexed: 11/08/2022]
Abstract
Here we introduce a mostly natural sequencing-by-synthesis (mnSBS) method for single-cell RNA sequencing (scRNA-seq), adapted to the Ultima genomics platform, and systematically benchmark it against current scRNA-seq technology. mnSBS uses mostly natural, unmodified nucleotides and only a low fraction of fluorescently labeled nucleotides, which allows for high polymerase processivity and lower costs. We demonstrate successful application in four scRNA-seq case studies of different technical and biological types, including 5' and 3' scRNA-seq, human peripheral blood mononuclear cells from a single individual and in multiplex, as well as Perturb-Seq. Benchmarking shows that results from mnSBS-based scRNA-seq are very similar to those using Illumina sequencing, with minor differences in results related to the position of reads relative to annotated gene boundaries, owing to single-end reads of Ultima being closer to gene ends than reads from Illumina. The method is thus compatible with state-of-the-art scRNA-seq libraries independent of the sequencing technology. We expect mnSBS to be of particular utility for cost-effective large-scale scRNA-seq projects.
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27
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Gratuze M, Schlachetzki JCM, D'Oliveira Albanus R, Jain N, Novotny B, Brase L, Rodriguez L, Mansel C, Kipnis M, O'Brien S, Pasillas MP, Lee C, Manis M, Colonna M, Harari O, Glass CK, Ulrich JD, Holtzman DM. TREM2-independent microgliosis promotes tau-mediated neurodegeneration in the presence of ApoE4. Neuron 2023; 111:202-219.e7. [PMID: 36368315 PMCID: PMC9852006 DOI: 10.1016/j.neuron.2022.10.022] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/21/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
In addition to tau and Aβ pathologies, inflammation plays an important role in Alzheimer's disease (AD). Variants in APOE and TREM2 increase AD risk. ApoE4 exacerbates tau-linked neurodegeneration and inflammation in P301S tau mice and removal of microglia blocks tau-dependent neurodegeneration. Microglia adopt a heterogeneous population of transcriptomic states in response to pathology, at least some of which are dependent on TREM2. Previously, we reported that knockout (KO) of TREM2 attenuated neurodegeneration in P301S mice that express mouse Apoe. Because of the possible common pathway of ApoE and TREM2 in AD, we tested whether TREM2 KO (T2KO) would block neurodegeneration in P301S Tau mice expressing ApoE4 (TE4), similar to that observed with microglial depletion. Surprisingly, we observed exacerbated neurodegeneration and tau pathology in TE4-T2KO versus TE4 mice, despite decreased TREM2-dependent microgliosis. Our results suggest that tau pathology-dependent microgliosis, that is, TREM2-independent microgliosis, facilitates tau-mediated neurodegeneration in the presence of ApoE4.
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Affiliation(s)
- Maud Gratuze
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Johannes C M Schlachetzki
- Department of Cellular and Molecular Medicine, University of California, San Diego, San Diego, CA 92093, USA
| | - Ricardo D'Oliveira Albanus
- Department of Psychiatry, NeuroGenomics and Informatics Center, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Nimansha Jain
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Brenna Novotny
- Department of Psychiatry, NeuroGenomics and Informatics Center, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Logan Brase
- Department of Psychiatry, NeuroGenomics and Informatics Center, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Lea Rodriguez
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Clayton Mansel
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Michal Kipnis
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Sydney O'Brien
- Department of Cellular and Molecular Medicine, University of California, San Diego, San Diego, CA 92093, USA
| | - Martina P Pasillas
- Department of Cellular and Molecular Medicine, University of California, San Diego, San Diego, CA 92093, USA
| | - Choonghee Lee
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Melissa Manis
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Marco Colonna
- Department of Pathology and Immunology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Oscar Harari
- Department of Psychiatry, NeuroGenomics and Informatics Center, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Christopher K Glass
- Department of Cellular and Molecular Medicine, University of California, San Diego, San Diego, CA 92093, USA
| | - Jason D Ulrich
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David M Holtzman
- Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA.
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28
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Straeten F, Zhu J, Börsch AL, Zhang B, Li K, Lu IN, Gross C, Heming M, Li X, Rubin R, Ouyang Z, Wiendl H, Mingueneau M, Meyer zu Hörste G. Integrated single-cell transcriptomics of cerebrospinal fluid cells in treatment-naïve multiple sclerosis. J Neuroinflammation 2022; 19:306. [PMID: 36536441 PMCID: PMC9764586 DOI: 10.1186/s12974-022-02667-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
Multiple sclerosis (MS) is a chronic and often disabling autoimmune disease of the central nervous system (CNS). Cerebrospinal fluid (CSF) surrounds and protects the CNS. Analysis of CSF can aid the diagnosis of CNS diseases, help to identify the prognosis, and underlying mechanisms of diseases. Several recent studies have leveraged single-cell RNA-sequencing (scRNA-seq) to identify MS-associated changes in CSF cells that are considerably more altered than blood cells in MS. However, not all alterations were replicated across all studies. We therefore integrated multiple available scRNA-seq datasets of CSF cells from MS patients with early relapsing-remitting (RRMS) disease. We provide a searchable and interactive resource of this integrated analysis ( https://CSFinMS.bxgenomics.com ) facilitating diverse visualization and analysis methods without requiring computational skills. In the present joint analysis, we replicated the known expansion of B lineage and the recently described expansion of natural killer (NK) cells and some cytotoxic T cells and decrease of monocytes in the CSF in MS. The previous observation of the abundance of Th1-like Th17 effector memory cells in the CSF was not replicated. Expanded CSF B lineage cells resembled class-switched plasmablasts/-cells (e.g., SDC1/CD138, MZB1) as expected. Our integrative analysis thus validates increased cell type diversity and B cell maturation in the CSF in MS and improves accessibility of available data.
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Affiliation(s)
- Frederike Straeten
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, Medical Faculty, University Hospital Münster, Münster, Germany
| | - Jing Zhu
- grid.417832.b0000 0004 0384 8146Department of Research, Biogen, Cambridge, MA USA
| | - Anna-Lena Börsch
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, Medical Faculty, University Hospital Münster, Münster, Germany
| | - Baohong Zhang
- grid.417832.b0000 0004 0384 8146Department of Research, Biogen, Cambridge, MA USA
| | - Kejie Li
- grid.417832.b0000 0004 0384 8146Department of Research, Biogen, Cambridge, MA USA
| | - I-Na Lu
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, Medical Faculty, University Hospital Münster, Münster, Germany
| | - Catharina Gross
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, Medical Faculty, University Hospital Münster, Münster, Germany
| | - Michael Heming
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, Medical Faculty, University Hospital Münster, Münster, Germany
| | - Xiaolin Li
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, Medical Faculty, University Hospital Münster, Münster, Germany
| | - Rebekah Rubin
- grid.417832.b0000 0004 0384 8146Department of Research, Biogen, Cambridge, MA USA
| | | | - Heinz Wiendl
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, Medical Faculty, University Hospital Münster, Münster, Germany
| | - Michael Mingueneau
- grid.417832.b0000 0004 0384 8146Department of Research, Biogen, Cambridge, MA USA
| | - Gerd Meyer zu Hörste
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, Medical Faculty, University Hospital Münster, Münster, Germany
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29
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Su C, Xu Z, Shan X, Cai B, Zhao H, Zhang J. Cell-type-specific co-expression inference from single cell RNA-sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.12.13.520181. [PMID: 36561173 DOI: 10.1101/2022.04.07.487499] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The inference of gene co-expressions from microarray and RNA-sequencing data has led to rich insights on biological processes and disease mechanisms. However, the bulk samples analyzed in most studies are a mixture of different cell types. As a result, the inferred co-expressions are confounded by varying cell type compositions across samples and only offer an aggregated view of gene regulations that may be distinct across different cell types. The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. However, the high sequencing depth variations and measurement errors in scRNA-seq data present significant challenges in inferring cell-type-specific gene co-expressions, and these issues have not been adequately addressed in the existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, built on a general expression-measurement model that explicitly accounts for sequencing depth variations and measurement errors in the observed single cell data. Systematic evaluations show that most existing methods suffer from inflated false positives and biased co-expression estimates and clustering analysis, whereas CS-CORE has appropriate false positive control, unbiased co-expression estimates, good statistical power and satisfactory performance in downstream co-expression analysis. When applied to analyze scRNA-seq data from postmortem brain samples from Alzheimer’s disease patients and controls and blood samples from COVID-19 patients and controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from other methods.
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30
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Raulin AC, Doss SV, Trottier ZA, Ikezu TC, Bu G, Liu CC. ApoE in Alzheimer’s disease: pathophysiology and therapeutic strategies. Mol Neurodegener 2022; 17:72. [PMID: 36348357 PMCID: PMC9644639 DOI: 10.1186/s13024-022-00574-4] [Citation(s) in RCA: 85] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 10/08/2022] [Accepted: 10/13/2022] [Indexed: 11/10/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common cause of dementia worldwide, and its prevalence is rapidly increasing due to extended lifespans. Among the increasing number of genetic risk factors identified, the apolipoprotein E (APOE) gene remains the strongest and most prevalent, impacting more than half of all AD cases. While the ε4 allele of the APOE gene significantly increases AD risk, the ε2 allele is protective relative to the common ε3 allele. These gene alleles encode three apoE protein isoforms that differ at two amino acid positions. The primary physiological function of apoE is to mediate lipid transport in the brain and periphery; however, additional functions of apoE in diverse biological functions have been recognized. Pathogenically, apoE seeds amyloid-β (Aβ) plaques in the brain with apoE4 driving earlier and more abundant amyloids. ApoE isoforms also have differential effects on multiple Aβ-related or Aβ-independent pathways. The complexity of apoE biology and pathobiology presents challenges to designing effective apoE-targeted therapeutic strategies. This review examines the key pathobiological pathways of apoE and related targeting strategies with a specific focus on the latest technological advances and tools.
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Gandal MJ, Haney JR, Wamsley B, Yap CX, Parhami S, Emani PS, Chang N, Chen GT, Hoftman GD, de Alba D, Ramaswami G, Hartl CL, Bhattacharya A, Luo C, Jin T, Wang D, Kawaguchi R, Quintero D, Ou J, Wu YE, Parikshak NN, Swarup V, Belgard TG, Gerstein M, Pasaniuc B, Geschwind DH. Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD. Nature 2022; 611:532-539. [PMID: 36323788 PMCID: PMC9668748 DOI: 10.1038/s41586-022-05377-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 09/21/2022] [Indexed: 11/17/2022]
Abstract
Neuropsychiatric disorders classically lack defining brain pathologies, but recent work has demonstrated dysregulation at the molecular level, characterized by transcriptomic and epigenetic alterations1-3. In autism spectrum disorder (ASD), this molecular pathology involves the upregulation of microglial, astrocyte and neural-immune genes, the downregulation of synaptic genes, and attenuation of gene-expression gradients in cortex1,2,4-6. However, whether these changes are limited to cortical association regions or are more widespread remains unknown. To address this issue, we performed RNA-sequencing analysis of 725 brain samples spanning 11 cortical areas from 112 post-mortem samples from individuals with ASD and neurotypical controls. We find widespread transcriptomic changes across the cortex in ASD, exhibiting an anterior-to-posterior gradient, with the greatest differences in primary visual cortex, coincident with an attenuation of the typical transcriptomic differences between cortical regions. Single-nucleus RNA-sequencing and methylation profiling demonstrate that this robust molecular signature reflects changes in cell-type-specific gene expression, particularly affecting excitatory neurons and glia. Both rare and common ASD-associated genetic variation converge within a downregulated co-expression module involving synaptic signalling, and common variation alone is enriched within a module of upregulated protein chaperone genes. These results highlight widespread molecular changes across the cerebral cortex in ASD, extending beyond association cortex to broadly involve primary sensory regions.
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Affiliation(s)
- Michael J Gandal
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Lifespan Brain Institute at Penn Medicine and The Children's Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jillian R Haney
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Brie Wamsley
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Chloe X Yap
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
| | - Sepideh Parhami
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Prashant S Emani
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Nathan Chang
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
| | - George T Chen
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Gil D Hoftman
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Diego de Alba
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Gokul Ramaswami
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Christopher L Hartl
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Chongyuan Luo
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ting Jin
- Waisman Center and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Daifeng Wang
- Waisman Center and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Riki Kawaguchi
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Diana Quintero
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jing Ou
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Ye Emily Wu
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Neelroop N Parikshak
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Vivek Swarup
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | | | - Mark Gerstein
- Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Center for Autism Research and Treatment, Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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Blanchard JW, Akay LA, Davila-Velderrain J, von Maydell D, Mathys H, Davidson SM, Effenberger A, Chen CY, Maner-Smith K, Hajjar I, Ortlund EA, Bula M, Agbas E, Ng A, Jiang X, Kahn M, Blanco-Duque C, Lavoie N, Liu L, Reyes R, Lin YT, Ko T, R'Bibo L, Ralvenius WT, Bennett DA, Cam HP, Kellis M, Tsai LH. APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes. Nature 2022; 611:769-779. [PMID: 36385529 PMCID: PMC9870060 DOI: 10.1038/s41586-022-05439-w] [Citation(s) in RCA: 136] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 10/12/2022] [Indexed: 11/17/2022]
Abstract
APOE4 is the strongest genetic risk factor for Alzheimer's disease1-3. However, the effects of APOE4 on the human brain are not fully understood, limiting opportunities to develop targeted therapeutics for individuals carrying APOE4 and other risk factors for Alzheimer's disease4-8. Here, to gain more comprehensive insights into the impact of APOE4 on the human brain, we performed single-cell transcriptomics profiling of post-mortem human brains from APOE4 carriers compared with non-carriers. This revealed that APOE4 is associated with widespread gene expression changes across all cell types of the human brain. Consistent with the biological function of APOE2-6, APOE4 significantly altered signalling pathways associated with cholesterol homeostasis and transport. Confirming these findings with histological and lipidomic analysis of the post-mortem human brain, induced pluripotent stem-cell-derived cells and targeted-replacement mice, we show that cholesterol is aberrantly deposited in oligodendrocytes-myelinating cells that are responsible for insulating and promoting the electrical activity of neurons. We show that altered cholesterol localization in the APOE4 brain coincides with reduced myelination. Pharmacologically facilitating cholesterol transport increases axonal myelination and improves learning and memory in APOE4 mice. We provide a single-cell atlas describing the transcriptional effects of APOE4 on the aging human brain and establish a functional link between APOE4, cholesterol, myelination and memory, offering therapeutic opportunities for Alzheimer's disease.
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Affiliation(s)
- Joel W Blanchard
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Nash Family Department of Neuroscience, Black Family Stem Cell Institute, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Icahn School of Medicine at Mt Sinai, New York, NY, USA
| | - Leyla Anne Akay
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
| | - Jose Davila-Velderrain
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
- Human Technopole, Milan, Italy
| | - Djuna von Maydell
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
| | - Hansruedi Mathys
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shawn M Davidson
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Audrey Effenberger
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chih-Yu Chen
- Department of Medicine, Emory Integrated Metabolomics and Lipidomics Core, Emory University School of Medicine, Atlanta, GA, USA
| | - Kristal Maner-Smith
- Department of Medicine, Emory Integrated Metabolomics and Lipidomics Core, Emory University School of Medicine, Atlanta, GA, USA
| | - Ihab Hajjar
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Eric A Ortlund
- Department of Medicine, Emory Integrated Metabolomics and Lipidomics Core, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Michael Bula
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Emre Agbas
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ayesha Ng
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xueqiao Jiang
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martin Kahn
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cristina Blanco-Duque
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicolas Lavoie
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Liwang Liu
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ricardo Reyes
- Nash Family Department of Neuroscience, Black Family Stem Cell Institute, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Icahn School of Medicine at Mt Sinai, New York, NY, USA
| | - Yuan-Ta Lin
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tak Ko
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lea R'Bibo
- Nash Family Department of Neuroscience, Black Family Stem Cell Institute, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Icahn School of Medicine at Mt Sinai, New York, NY, USA
| | - William T Ralvenius
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Hugh P Cam
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Manolis Kellis
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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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: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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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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Stogsdill JA, Kim K, Binan L, Farhi SL, Levin JZ, Arlotta P. Pyramidal neuron subtype diversity governs microglia states in the neocortex. Nature 2022; 608:750-756. [PMID: 35948630 PMCID: PMC10502800 DOI: 10.1038/s41586-022-05056-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 06/30/2022] [Indexed: 12/14/2022]
Abstract
Microglia are specialized macrophages in the brain parenchyma that exist in multiple transcriptional states and reside within a wide range of neuronal environments1-4. However, how and where these states are generated remains poorly understood. Here, using the mouse somatosensory cortex, we demonstrate that microglia density and molecular state acquisition are determined by the local composition of pyramidal neuron classes. Using single-cell and spatial transcriptomic profiling, we unveil the molecular signatures and spatial distributions of diverse microglia populations and show that certain states are enriched in specific cortical layers, whereas others are broadly distributed throughout the cortex. Notably, conversion of deep-layer pyramidal neurons to an alternate class identity reconfigures the distribution of local, layer-enriched homeostatic microglia to match the new neuronal niche. Leveraging the transcriptional diversity of pyramidal neurons in the neocortex, we construct a ligand-receptor atlas describing interactions between individual pyramidal neuron subtypes and microglia states, revealing rules of neuron-microglia communication. Our findings uncover a fundamental role for neuronal diversity in instructing the acquisition of microglia states as a potential mechanism for fine-tuning neuroimmune interactions within the cortical local circuitry.
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Affiliation(s)
- Jeffrey A Stogsdill
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kwanho Kim
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Loïc Binan
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Optical Profiling Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samouil L Farhi
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Optical Profiling Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua Z Levin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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36
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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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37
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Abondio P, De Intinis C, da Silva Gonçalves Vianez Júnior JL, Pace L. SINGLE CELL MULTIOMIC APPROACHES TO DISENTANGLE T CELL HETEROGENEITY. Immunol Lett 2022; 246:37-51. [DOI: 10.1016/j.imlet.2022.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/16/2022] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
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38
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He L, Loika Y, Kulminski AM. Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer's disease-associated genetic variants. Transl Psychiatry 2022; 12:163. [PMID: 35436980 PMCID: PMC9016079 DOI: 10.1038/s41398-022-01913-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 01/20/2023] Open
Abstract
Elucidating regulatory effects of Alzheimer's disease (AD)-associated genetic variants is critical for unraveling their causal pathways and understanding the pathology. However, their cell-type-specific regulatory mechanisms in the brain remain largely unclear. Here, we conducted an analysis of allele-specific expression quantitative trait loci (aseQTLs) for 33 AD-associated variants in four brain regions and seven cell types using ~3000 bulk RNA-seq samples and >0.25 million single nuclei. We first develop a flexible hierarchical Poisson mixed model (HPMM) and demonstrate its superior statistical power to a beta-binomial model achieved by unifying samples in both allelic and genotype-level expression data. Using the HPMM, we identified 24 (~73%) aseQTLs in at least one brain region, including three new eQTLs associated with CA12, CHRNE, and CASS4. Notably, the APOE ε4 variant reduces APOE expression across all regions, even in AD-unaffected controls. Our results reveal region-dependent and exon-specific effects of multiple aseQTLs, such as rs2093760 with CR1, rs7982 with CLU, and rs3865444 with CD33. In an attempt to pinpoint the cell types responsible for the observed tissue-level aseQTLs using the snRNA-seq data, we detected many aseQTLs in microglia or monocytes associated with immune-related genes, including HLA-DQB1, HLA-DQA2, CD33, FCER1G, MS4A6A, SPI1, and BIN1, highlighting the regulatory role of AD-associated variants in the immune response. These findings provide further insights into potential causal pathways and cell types mediating the effects of the AD-associated variants.
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Affiliation(s)
- Liang He
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA.
| | - Yury Loika
- grid.26009.3d0000 0004 1936 7961Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC USA
| | - Alexander M. Kulminski
- grid.26009.3d0000 0004 1936 7961Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC USA
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Scott AM, Yan JL, Baxter CM, Dworkin I, Dukas R. The genetic basis of variation in sexual aggression: evolution versus social plasticity. Mol Ecol 2022; 31:2865-2881. [DOI: 10.1111/mec.16437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Andrew M. Scott
- Animal Behaviour Group Department of Psychology, Neuroscience & Behaviour McMaster University 1280 Main Street West Hamilton Ontario L8S 4K1 Canada
| | - Janice L. Yan
- Animal Behaviour Group Department of Psychology, Neuroscience & Behaviour McMaster University 1280 Main Street West Hamilton Ontario L8S 4K1 Canada
| | - Carling M. Baxter
- Animal Behaviour Group Department of Psychology, Neuroscience & Behaviour McMaster University 1280 Main Street West Hamilton Ontario L8S 4K1 Canada
| | - Ian Dworkin
- Department of Biology McMaster University 1280 Main Street West Hamilton Ontario L8S 4K1 Canada
| | - Reuven Dukas
- Animal Behaviour Group Department of Psychology, Neuroscience & Behaviour McMaster University 1280 Main Street West Hamilton Ontario L8S 4K1 Canada
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40
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Arend L, Bernett J, Manz Q, Klug M, Lazareva O, Baumbach J, Bongiovanni D, List M. A systematic comparison of novel and existing differential analysis methods for CyTOF data. Brief Bioinform 2021; 23:6446270. [PMID: 34850807 DOI: 10.1093/bib/bbab471] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/30/2021] [Accepted: 10/13/2021] [Indexed: 01/29/2023] Open
Abstract
Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist. These tools either reduce the data distribution to medians, discarding valuable information, or have underlying assumptions that may not hold for all expression patterns. Here, we systematically evaluated existing and novel approaches for differential expression analysis on real and simulated CyTOF data. We found that methods using median marker expressions compute fast and reliable results when the data are not strongly zero-inflated. Methods using all data detect changes in strongly zero-inflated markers, but partially suffer from overprediction or cannot handle big datasets. We present a new method, CyEMD, based on calculating the earth mover's distance between expression distributions that can handle strong zero-inflation without being too sensitive. Additionally, we developed CYANUS - CYtometry ANalysis Using Shiny - a user-friendly R Shiny App allowing the user to analyze cytometry data with state-of-the-art tools, including well-performing methods from our comparison. A public web interface is available at https://exbio.wzw.tum.de/cyanus/.
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Affiliation(s)
- Lis Arend
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Judith Bernett
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Quirin Manz
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Melissa Klug
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.,Department of Internal Medicine I, School of Medicine, University Hospital rechts der Isar, Technical University of Munich, Munich, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Olga Lazareva
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Dario Bongiovanni
- Department of Internal Medicine I, School of Medicine, University Hospital rechts der Isar, Technical University of Munich, Munich, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.,Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS and Humanitas University, Rozzano, Milan, Italy
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
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