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Palmer JA, Rosenthal N, Teichmann SA, Litvinukova M. Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics. Circ Res 2024; 134:1681-1702. [PMID: 38843288 PMCID: PMC11149945 DOI: 10.1161/circresaha.124.323672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/16/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Abstract
Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.
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Affiliation(s)
- Jack A. Palmer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom (J.A.P., S.A.T.)
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus (J.A.P., S.A.T.), University of Cambridge, United Kingdom
| | - Nadia Rosenthal
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME (N.R.)
- National Heart and Lung Institute, Imperial College London, United Kingdom (N.R.)
| | - Sarah A. Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom (J.A.P., S.A.T.)
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus (J.A.P., S.A.T.), University of Cambridge, United Kingdom
- Theory of Condensed Matter Group, Department of Physics, Cavendish Laboratory (S.A.T.), University of Cambridge, United Kingdom
| | - Monika Litvinukova
- University Hospital Würzburg, Germany (M.L.)
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Germany (M.L.)
- Helmholtz Pioneer Campus, Helmholtz Munich, Germany (M.L.)
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2
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Patel AS, Yanai I. A developmental constraint model of cancer cell states and tumor heterogeneity. Cell 2024; 187:2907-2918. [PMID: 38848676 DOI: 10.1016/j.cell.2024.04.032] [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: 12/29/2023] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Abstract
Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.
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Affiliation(s)
- Ayushi S Patel
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA; Department of Biochemistry & Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA; Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Itai Yanai
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA; Department of Biochemistry & Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA; Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA.
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3
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Wang W, Liu D, Yao J, Yuan Z, Yan L, Cao B. ANXA5: A Key Regulator of Immune Cell Infiltration in Hepatocellular Carcinoma. Med Sci Monit 2024; 30:e943523. [PMID: 38824386 PMCID: PMC11155417 DOI: 10.12659/msm.943523] [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: 12/16/2023] [Accepted: 04/10/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) poses a significant threat to human life and is the most prevalent form of liver cancer. The intricate interplay between apoptosis, a common form of programmed cell death, and its role in immune regulation stands as a crucial mechanism influencing tumor metastasis. MATERIAL AND METHODS Utilizing HCC samples from the TCGA database and 61 anoikis-related genes (ARGs) sourced from GeneCards, we analyzed the relationship between ARGs and immune cell infiltration in HCC. Subsequently, we identified long non-coding RNAs (lncRNAs) associated with ARGs, using the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a robust prognostic model. The predictive capabilities of the model were then validated through examination in a single-cell dataset. RESULTS Our constructed prognostic model, derived from lncRNAs linked to ARGs, comprised 11 significant lncRNAs: NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18. The risk score assigned to HCC samples demonstrated associations with immune indicators and the infiltration of immune cells. Further, we identified Annexin A5 (ANXA5) as the pivotal gene among ARGs, with it exerting a prominent role in regulating the lncRNA gene signature. Our validation in a single-cell database elucidated the involvement of ANXA5 in immune cell infiltration, specifically in the regulation of mononuclear cells. CONCLUSIONS This study delves into the intricate correlation between ARGs and immune cell infiltration in HCC, culminating in the development of a novel prognostic model reliant on 11 ARGs-associated lncRNAs. Furthermore, our findings highlight ANXA5 as a promising target for immune regulation in HCC, offering new perspectives for immune therapy in the context of HCC.
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4
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Cadavid JL, Li NT, McGuigan AP. Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease. BIOPHYSICS REVIEWS 2024; 5:021301. [PMID: 38617201 PMCID: PMC11008916 DOI: 10.1063/5.0179125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.
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5
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Filipovic D, Kana O, Marri D, Bhattacharya S. Unique challenges and best practices for single cell transcriptomic analysis in toxicology. CURRENT OPINION IN TOXICOLOGY 2024; 38:100475. [PMID: 38645720 PMCID: PMC11027889 DOI: 10.1016/j.cotox.2024.100475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose-response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.
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Affiliation(s)
- David Filipovic
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
| | - Omar Kana
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
| | - Daniel Marri
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Sudin Bhattacharya
- Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA
- Department of Pharmacology & Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA
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6
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Ferriera Neres D, Wright RC. Pleiotropy, a feature or a bug? Toward co-ordinating plant growth, development, and environmental responses through engineering plant hormone signaling. Curr Opin Biotechnol 2024; 88:103151. [PMID: 38823314 DOI: 10.1016/j.copbio.2024.103151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 06/03/2024]
Abstract
The advent of gene editing technologies such as CRISPR has simplified co-ordinating trait development. However, identifying candidate genes remains a challenge due to complex gene networks and pathways. These networks exhibit pleiotropy, complicating the determination of specific gene and pathway functions. In this review, we explore how systems biology and single-cell sequencing technologies can aid in identifying candidate genes for co-ordinating specifics of plant growth and development within specific temporal and tissue contexts. Exploring sequence-function space of these candidate genes and pathway modules with synthetic biology allows us to test hypotheses and define genotype-phenotype relationships through reductionist approaches. Collectively, these techniques hold the potential to advance breeding and genetic engineering strategies while also addressing genetic diversity issues critical for adaptation and trait development.
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Affiliation(s)
- Deisiany Ferriera Neres
- Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blackburg, Virginia, United States; Translational Plant Science Center, Virginia Polytechnic Institute and State University, Blackburg, Virginia, United States
| | - R Clay Wright
- Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blackburg, Virginia, United States; Translational Plant Science Center, Virginia Polytechnic Institute and State University, Blackburg, Virginia, United States.
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7
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Su EY, Fread K, Goggin S, Zunder ER, Cahan P. Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells. Sci Data 2024; 11:559. [PMID: 38816402 PMCID: PMC11139855 DOI: 10.1038/s41597-024-03399-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/21/2024] [Indexed: 06/01/2024] Open
Abstract
Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.
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Affiliation(s)
- Emily Y Su
- Institute for Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kristen Fread
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Sarah Goggin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Eli R Zunder
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Patrick Cahan
- Institute for Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Molecular Biology and Genetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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8
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024:10.1038/s44320-024-00045-6. [PMID: 38811801 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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9
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Ma C, Hao Y, Shi B, Wu Z, Jin D, Yu X, Jin B. Unveiling mitochondrial and ribosomal gene deregulation and tumor microenvironment dynamics in acute myeloid leukemia. Cancer Gene Ther 2024:10.1038/s41417-024-00788-2. [PMID: 38806621 DOI: 10.1038/s41417-024-00788-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 05/30/2024]
Abstract
Acute myeloid leukemia (AML) is a malignant clonal hematopoietic disease with a poor prognosis. Understanding the interaction between leukemic cells and the tumor microenvironment (TME) can help predict the prognosis of leukemia and guide its treatment. Re-analyzing the scRNA-seq data from the CSC and G20 cohorts, using a Python-based pipeline including machine-learning-based scVI-tools, recapitulated the distinct hierarchical structure within the samples of AML patients. Weighted correlation network analysis (WGCNA) was conducted to construct a weighted gene co-expression network and to identify gene modules primarily focusing on hematopoietic stem cells (HSCs), multipotent progenitors (MPPs), and natural killer (NK) cells. The analysis revealed significant deregulation in gene modules associated with aerobic respiration and ribosomal/cytoplasmic translation. Cell-cell communications were elucidated by the CellChat package, revealing an imbalance of activating and inhibitory immune signaling pathways. Interception of genes upregulated in leukemic HSCs & MPPs as well as in NKG2A-high NK cells was used to construct prognostic models. Normal Cox and artificial neural network models based on 10 genes were developed. The study reveals the deregulation of mitochondrial and ribosomal genes in AML patients and suggests the co-occurrence of stimulatory and inhibitory factors in the AML TME.
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Affiliation(s)
- Chao Ma
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China
| | - Yuchao Hao
- Department of Hematology, The Second Hospital of Dalian Medical University, West Section Lvshun South Road, Dalian, 116027, Liaoning, China
| | - Bo Shi
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China
| | - Zheng Wu
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China
| | - Di Jin
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China
| | - Xiao Yu
- NHC Key Laboratory of Pneumoconiosis, The First Hospital of Shanxi Medical University, South Jiefang Road, Taiyuan, 030001, Shanxi, China.
| | - Bilian Jin
- Institute of Cancer Stem Cell, Dalian Medical University, West Section Lvshun South Road, Dalian, 116044, Liaoning, China.
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10
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Finlay JB, Ireland AS, Hawgood SB, Reyes T, Ko T, Olsen RR, Abi Hachem R, Jang DW, Bell D, Chan JM, Goldstein BJ, Oliver TG. Olfactory neuroblastoma mimics molecular heterogeneity and lineage trajectories of small-cell lung cancer. Cancer Cell 2024:S1535-6108(24)00164-8. [PMID: 38788720 DOI: 10.1016/j.ccell.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/13/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024]
Abstract
The olfactory epithelium undergoes neuronal regeneration from basal stem cells and is susceptible to olfactory neuroblastoma (ONB), a rare tumor of unclear origins. Employing alterations in Rb1/Trp53/Myc (RPM), we establish a genetically engineered mouse model of high-grade metastatic ONB exhibiting a NEUROD1+ immature neuronal phenotype. We demonstrate that globose basal cells (GBCs) are a permissive cell of origin for ONB and that ONBs exhibit cell fate heterogeneity that mimics normal GBC developmental trajectories. ASCL1 loss in RPM ONB leads to emergence of non-neuronal histopathologies, including a POU2F3+ microvillar-like state. Similar to small-cell lung cancer (SCLC), mouse and human ONBs exhibit mutually exclusive NEUROD1 and POU2F3-like states, an immune-cold tumor microenvironment, intratumoral cell fate heterogeneity comprising neuronal and non-neuronal lineages, and cell fate plasticity-evidenced by barcode-based lineage tracing and single-cell transcriptomics. Collectively, our findings highlight conserved similarities between ONB and neuroendocrine tumors with significant implications for ONB classification and treatment.
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Affiliation(s)
- John B Finlay
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Abbie S Ireland
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA
| | - Sarah B Hawgood
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA
| | - Tony Reyes
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA; Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA
| | - Tiffany Ko
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Rachelle R Olsen
- Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA
| | - Ralph Abi Hachem
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - David W Jang
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA
| | - Diana Bell
- Division of Anatomic Pathology, City of Hope Comprehensive Cancer Center, Duarte 91010, CA, USA
| | - Joseph M Chan
- Human Oncology and Pathogenesis Program, Memorial-Sloan Kettering Cancer Center, New York City 10065, NY, USA
| | - Bradley J Goldstein
- Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham 27710, NC, USA; Department of Neurobiology, Duke University, Durham 27710, NC, USA.
| | - Trudy G Oliver
- Department of Pharmacology and Cancer Biology, Duke University, Durham 27710, NC, USA; Department of Oncological Sciences, University of Utah, Salt Lake City 84112, UT, USA.
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11
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Chen EZ, Kannan S, Murphy S, Farid M, Kwon C. Protocol for quantifying stem-cell-derived cardiomyocyte maturity using transcriptomic entropy score. STAR Protoc 2024; 5:103083. [PMID: 38781077 PMCID: PMC11145390 DOI: 10.1016/j.xpro.2024.103083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/15/2024] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
The inability to quantify cardiomyocyte (CM) maturation remains a significant barrier to evaluating the effects of ongoing efforts to produce adult-like CMs from pluripotent stem cells (PSCs). Here, we present a protocol to quantify stem-cell-derived CM maturity using a single-cell RNA sequencing-based metric "entropy score." We describe steps for generating an entropy score using customized R code. This tool can be used to quantify maturation levels of PSC-CMs and potentially other cell types. For complete details on the use and execution of this protocol, please refer to Kannan et al.1.
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Affiliation(s)
- Elaine Zhelan Chen
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA
| | - Suraj Kannan
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA
| | - Sean Murphy
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA
| | - Michael Farid
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA
| | - Chulan Kwon
- Division of Cardiology, Department of Medicine, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA; Department of Cell Biology, Johns Hopkins School of Medicine; Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins School of Medicine; Baltimore, MD, USA.
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12
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Kotliar M, Kartashov A, Barski A. Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.28.582604. [PMID: 38464095 PMCID: PMC10925325 DOI: 10.1101/2024.02.28.582604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Single-cell (sc) RNA, ATAC and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform (https://SciDAP.com) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.
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Affiliation(s)
- Michael Kotliar
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | | | - Artem Barski
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
- Datirium, LLC, Cincinnati, OH, USA
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13
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Barry T, Mason K, Roeder K, Katsevich E. Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection. Genome Biol 2024; 25:124. [PMID: 38760839 PMCID: PMC11100084 DOI: 10.1186/s13059-024-03254-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 04/19/2024] [Indexed: 05/19/2024] Open
Abstract
Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method - SCEPTRE low-MOI - that resolves these analysis challenges and demonstrates improved calibration and power.
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Affiliation(s)
- Timothy Barry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, USA.
| | - Kaishu Mason
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, USA
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, USA
| | - Eugene Katsevich
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, USA.
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14
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Hu Z, Przytycki PF, Pollard KS. CellWalker2: multi-omic discovery of hierarchical cell type relationships and their associations with genomic annotations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594770. [PMID: 38798605 PMCID: PMC11118555 DOI: 10.1101/2024.05.17.594770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
CellWalker2 is a graph diffusion-based method for single-cell genomics data integration. It extends the CellWalker model by incorporating hierarchical relationships between cell types, providing estimates of statistical significance, and adding data structures for analyzing multi-omics data so that gene expression and open chromatin can be jointly modeled. Our open-source software enables users to annotate cells using existing ontologies and to probabilistically match cell types between two or more contexts, including across species. CellWalker2 can also map genomic regions to cell ontologies, enabling precise annotation of elements derived from bulk data, such as enhancers, genetic variants, and sequence motifs. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell type annotation and mapping. We then use data from the brain and immune system to demonstrate CellWalker2's ability to discover cell type-specific regulatory programs and both conserved and divergent cell type relationships in complex tissues.
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Affiliation(s)
- Zhirui Hu
- Gladstone Institute of Data Science & Biotechnology, 1650 Owens Street, San Francisco, 94158, CA, USA
| | - Pawel F Przytycki
- Gladstone Institute of Data Science & Biotechnology, 1650 Owens Street, San Francisco, 94158, CA, USA
- Faculty of Computing & Data Sciences, Boston University, 665 Commonwealth Avenue, Boston, 02215, MA, USA
| | - Katherine S Pollard
- Gladstone Institute of Data Science & Biotechnology, 1650 Owens Street, San Francisco, 94158, CA, USA
- Department of Epidemiology & Biostatistics, University of California, 1650 Owens Street, San Francisco, 94158, CA, USA
- Chan Zuckerberg Biohub SF, 499 Illinois Street, San Francisco, 94158, CA, USA
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15
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Francis L, Capon F, Smith CH, Haniffa M, Mahil SK. Inflammatory memory in psoriasis: From remission to recurrence. J Allergy Clin Immunol 2024:S0091-6749(24)00506-2. [PMID: 38761994 DOI: 10.1016/j.jaci.2024.05.008] [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: 02/09/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Abstract
The routine use of targeted systemic immunomodulatory therapies has transformed outcomes for people with severe psoriasis, with skin clearance (clinical remission) rates up to 60% at 1 year of biologic treatment. However, psoriasis may recur following drug withdrawal, and as a result, patients tend to continue receiving costly treatment indefinitely. Here, we review research into the "inflammatory memory" in resolved psoriasis skin and the potential mechanisms leading to psoriasis recurrence following drug withdrawal. Research has implicated immune cells such as tissue resident memory T cells, Langerhans cells, and dermal dendritic cells, and there is growing interest in keratinocytes and fibroblasts. A better understanding of the interactions between these cell populations, enabled by single cell technologies, will help to elucidate the events underpinning the shift from remission to recurrence. This may inform the development of personalized strategies for sustaining remission while reducing long-term drug burden.
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Affiliation(s)
- Luc Francis
- St John's Institute of Dermatology, King's College London and Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Francesca Capon
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - Catherine H Smith
- St John's Institute of Dermatology, King's College London and Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, United Kingdom; Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Satveer K Mahil
- St John's Institute of Dermatology, King's College London and Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom.
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16
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Jia S, Zhao F. Single-cell transcriptomic profiling of the neonatal oviduct and uterus reveals new insights into upper Müllerian duct regionalization. FASEB J 2024; 38:e23632. [PMID: 38686936 DOI: 10.1096/fj.202400303r] [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: 02/07/2024] [Revised: 03/20/2024] [Accepted: 04/12/2024] [Indexed: 05/02/2024]
Abstract
The upper Müllerian duct (MD) is patterned and specified into two morphologically and functionally distinct organs, the oviduct and uterus. It is known that this regionalization process is instructed by inductive signals from the adjacent mesenchyme. However, the interaction landscape between epithelium and mesenchyme during upper MD development remains largely unknown. Here, we performed single-cell transcriptomic profiling of mouse neonatal oviducts and uteri at the initiation of MD epithelial differentiation (postnatal day 3). We identified major cell types including epithelium, mesenchyme, pericytes, mesothelium, endothelium, and immune cells in both organs with established markers. Moreover, we uncovered region-specific epithelial and mesenchymal subpopulations and then deduced region-specific ligand-receptor pairs mediating mesenchymal-epithelial interactions along the craniocaudal axis. Unexpectedly, we discovered a mesenchymal subpopulation marked by neurofilaments with specific localizations at the mesometrial pole of both the neonatal oviduct and uterus. Lastly, we analyzed and revealed organ-specific signature genes of pericytes and mesothelial cells. Taken together, our study enriches our knowledge of upper MD development, and provides a manageable list of potential genes, pathways, and region-specific cell subtypes for future functional studies.
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Affiliation(s)
- Shuai Jia
- Department of Comparative Biosciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Fei Zhao
- Department of Comparative Biosciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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17
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Diamanti E, López-Gallego F. Single-Particle and Single-Molecule Characterization of Immobilized Enzymes: A Multiscale Path toward Optimizing Heterogeneous Biocatalysts. Angew Chem Int Ed Engl 2024; 63:e202319248. [PMID: 38476019 DOI: 10.1002/anie.202319248] [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: 12/13/2023] [Revised: 03/04/2024] [Accepted: 03/07/2024] [Indexed: 03/14/2024]
Abstract
Heterogeneous biocatalysis is highly relevant in biotechnology as it offers several benefits and practical uses. To leverage the full potential of heterogeneous biocatalysts, the establishment of well-crafted protocols, and a deeper comprehension of enzyme immobilization on solid substrates are essential. These endeavors seek to optimize immobilized biocatalysts, ensuring maximal enzyme performance within confined spaces. For this aim, multidimensional characterization of heterogeneous biocatalysts is required. In this context, spectroscopic and microscopic methodologies conducted at different space and temporal scales can inform about the intraparticle enzyme kinetics, the enzyme spatial distribution, and the mass transport issues. In this Minireview, we identify enzyme immobilization, enzyme catalysis, and enzyme inactivation as the three main processes for which advanced characterization tools unveil fundamental information. Recent advances in operando characterization of immobilized enzymes at the single-particle (SP) and single-molecule (SM) levels inform about their functional properties, unlocking the full potential of heterogeneous biocatalysis toward biotechnological applications.
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Affiliation(s)
- Eleftheria Diamanti
- Center for Cooperative Research in Biomaterials (CIC biomaGUNE)-, Basque Research and Technology Alliance (BRTA), Paseo Miramón, 194, 20014, Donostia-San Sebastián, Spain
| | - Fernando López-Gallego
- Center for Cooperative Research in Biomaterials (CIC biomaGUNE)-, Basque Research and Technology Alliance (BRTA), Paseo Miramón, 194, 20014, Donostia-San Sebastián, Spain
- IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013, Bilbao, Spain
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18
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Lotfollahi M, Yuhan Hao, Theis FJ, Satija R. The future of rapid and automated single-cell data analysis using reference mapping. Cell 2024; 187:2343-2358. [PMID: 38729109 DOI: 10.1016/j.cell.2024.03.009] [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: 01/05/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 05/12/2024]
Abstract
As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.
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Affiliation(s)
- Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Yuhan Hao
- Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York Genome Center, New York, NY, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK; Department of Mathematics, Technical University of Munich, Garching, Germany.
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York Genome Center, New York, NY, USA.
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19
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Ferrena A, Zheng XY, Jackson K, Hoang B, Morrow B, Zheng D. scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592708. [PMID: 38766089 PMCID: PMC11100619 DOI: 10.1101/2024.05.06.592708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or "pseudobulking" samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. Availability and Implementation: scDAPP is freely available for non-commercial usage as an R package under the MIT license. Source code, documentation and sample data are available at the GitHub (https://github.com/bioinfoDZ/scDAPP).
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Affiliation(s)
- Alexander Ferrena
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Institute for Clinical and Translational Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Xiang Yu Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kevyn Jackson
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bang Hoang
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bernice Morrow
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Departments of Obstetrics and Gynecology, and Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Deyou Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
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20
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Tomimatsu K, Fujii T, Bise R, Hosoda K, Taniguchi Y, Ochiai H, Ohishi H, Ando K, Minami R, Tanaka K, Tachibana T, Mori S, Harada A, Maehara K, Nagasaki M, Uchida S, Kimura H, Narita M, Ohkawa Y. Precise immunofluorescence canceling for highly multiplexed imaging to capture specific cell states. Nat Commun 2024; 15:3657. [PMID: 38719795 PMCID: PMC11078938 DOI: 10.1038/s41467-024-47989-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 04/17/2024] [Indexed: 05/12/2024] Open
Abstract
Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imaging has profiled cell states by achieving a comprehensive acquisition of spatial protein information from cells. However, the specificity of antibodies is still compromised when visualizing activated signals. Here, we develop Precise Emission Canceling Antibodies (PECAbs) that have cleavable fluorescent labeling. PECAbs enable high-specificity sequential imaging using hundreds of antibodies, allowing for reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system can serve as a comprehensive platform for analyzing complex cell processes.
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Affiliation(s)
- Kosuke Tomimatsu
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Takeru Fujii
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Ryoma Bise
- Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | | | - Yosuke Taniguchi
- Department of Medicinal Sciences, Faculty of Pharmaceutical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Hiroshi Ochiai
- Division of Gene Expression Dynamics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Hiroaki Ohishi
- Division of Gene Expression Dynamics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Kanta Ando
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Ryoma Minami
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Kaori Tanaka
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Taro Tachibana
- Department of Chemistry and Bioengineering, Osaka Metropolitan University, Osaka, 558-8585, Japan
| | - Seiichi Mori
- Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Akihito Harada
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Kazumitsu Maehara
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Masao Nagasaki
- Division of Biomedical Information Analysis, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan
| | - Seiichi Uchida
- Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
| | - Hiroshi Kimura
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, 226-8503, Japan
| | - Masashi Narita
- Cancer Research UK Cambridge Institute, Li Ka Shing Center, University of Cambridge, Cambridge, CB2 0RE, UK
- World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, 226-8503, Japan
| | - Yasuyuki Ohkawa
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-0054, Japan.
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21
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Tang B, Vadgama A, Redmann B, Hong J. Charting the cellular landscape of pulmonary arterial hypertension through single-cell omics. Respir Res 2024; 25:192. [PMID: 38702687 PMCID: PMC11067161 DOI: 10.1186/s12931-024-02823-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024] Open
Abstract
This review examines how single-cell omics technologies, particularly single-cell RNA sequencing (scRNAseq), enhance our understanding of pulmonary arterial hypertension (PAH). PAH is a multifaceted disorder marked by pulmonary vascular remodeling, leading to high morbidity and mortality. The cellular pathobiology of this heterogeneous disease, involving various vascular and non-vascular cell types, is not fully understood. Traditional PAH studies have struggled to resolve the complexity of pathogenic cell populations. scRNAseq offers a refined perspective by detailing cellular diversity within PAH, identifying unique cell subsets, gene networks, and molecular pathways that drive the disease. We discuss significant findings from recent literature, summarizing how scRNAseq has shifted our understanding of PAH in human, rat, and mouse models. This review highlights the insights gained into cellular phenotypes, gene expression patterns, and novel molecular targets, and contemplates the challenges and prospective paths for research. We propose ways in which single-cell omics could inform future research and translational efforts to combat PAH.
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Affiliation(s)
- Brian Tang
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, University of California, 200 UCLA Medical Plaza, Suite 365-B, Box 951693, Los Angeles, CA, 90095, USA
| | - Arjun Vadgama
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, University of California, 200 UCLA Medical Plaza, Suite 365-B, Box 951693, Los Angeles, CA, 90095, USA
| | - Bryce Redmann
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, University of California, 200 UCLA Medical Plaza, Suite 365-B, Box 951693, Los Angeles, CA, 90095, USA
| | - Jason Hong
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, University of California, 200 UCLA Medical Plaza, Suite 365-B, Box 951693, Los Angeles, CA, 90095, USA.
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22
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Marx V. scRNA-seq: oh, the joys. Nat Methods 2024; 21:750-753. [PMID: 38654084 DOI: 10.1038/s41592-024-02263-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
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23
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Nathans JF, Ayers JL, Shendure J, Simpson CL. Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin. J Invest Dermatol 2024; 144:936-949. [PMID: 38643988 PMCID: PMC11034889 DOI: 10.1016/j.jid.2024.02.006] [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: 12/19/2023] [Revised: 01/28/2024] [Accepted: 02/08/2024] [Indexed: 04/23/2024]
Abstract
The epidermis is the body's first line of protection against dehydration and pathogens, continually regenerating the outermost protective skin layers throughout life. During both embryonic development and wound healing, epidermal stem and progenitor cells must respond to external stimuli and insults to build, maintain, and repair the cutaneous barrier. Recent advances in CRISPR-based methods for cell lineage tracing have remarkably expanded the potential for experiments that track stem and progenitor cell proliferation and differentiation over the course of tissue and even organismal development. Additional tools for DNA-based recording of cellular signaling cues promise to deepen our understanding of the mechanisms driving normal skin morphogenesis and response to stressors as well as the dysregulation of cell proliferation and differentiation in skin diseases and cancer. In this review, we highlight cutting-edge methods for cell lineage tracing, including in organoids and model organisms, and explore how cutaneous biology researchers might leverage these techniques to elucidate the developmental programs that support the regenerative capacity and plasticity of the skin.
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Affiliation(s)
- Jenny F Nathans
- Medical Scientist Training Program, University of Washington, Seattle, Washington, USA; Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Jessica L Ayers
- Molecular Medicine and Mechanisms of Disease PhD Program, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA; Department of Dermatology, University of Washington, Seattle, Washington, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA; Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, Washington, USA
| | - Cory L Simpson
- Department of Dermatology, University of Washington, Seattle, Washington, USA; Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, Washington, USA.
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24
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Yabo YA, Heiland DH. Understanding glioblastoma at the single-cell level: Recent advances and future challenges. PLoS Biol 2024; 22:e3002640. [PMID: 38814900 PMCID: PMC11139343 DOI: 10.1371/journal.pbio.3002640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Abstract
Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.
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Affiliation(s)
- Yahaya A Yabo
- Translational Neurosurgery, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Microenvironment and Immunology Research Laboratory, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Dieter Henrik Heiland
- Translational Neurosurgery, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Microenvironment and Immunology Research Laboratory, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Neurosurgery, Faculty of Medicine, Medical Center University of Freiburg, Freiburg, Germany
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- German Cancer Consortium (DKTK) partner site, Freiburg, Germany
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25
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Filippi J, Casti P, Antonelli G, Murdocca M, Mencattini A, Corsi F, D'Orazio M, Pecora A, De Luca M, Curci G, Ghibelli L, Sangiuolo F, Neale SL, Martinelli E. Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In-Flow Identification of Single Cells. SMALL METHODS 2024:e2300923. [PMID: 38693090 DOI: 10.1002/smtd.202300923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 04/04/2024] [Indexed: 05/03/2024]
Abstract
A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi-frequency cell trajectories. Like in a "ping-pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so-derived cell motions are characterized via time-lapse microscopy, cell tracking, and state-of-the-art machine learning algorithms, like the wavelet scattering transform (WST). As a cell-electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug-induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine-like oxidized low-density lipoprotein receptor-1 (LOX-1) transcript levels in human colorectal adenocarcinoma (DLD-1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP-based assays addressing cancer heterogeneity for precision medicine and pharmacological research.
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Affiliation(s)
- Joanna Filippi
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Paola Casti
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Gianni Antonelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Michela Murdocca
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, Rome, 00133, Italy
| | - Arianna Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Francesca Corsi
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
- Department of Chemical Science and Technologies, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
| | - Michele D'Orazio
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Alessandro Pecora
- Italian Nation Research Council (CNR), Via del Fosso del Cavaliere 100, Rome, 00133, Italy
| | - Massimiliano De Luca
- Italian Nation Research Council (CNR), Via del Fosso del Cavaliere 100, Rome, 00133, Italy
| | - Giorgia Curci
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Lina Ghibelli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
| | - Federica Sangiuolo
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, Rome, 00133, Italy
| | - Steven L Neale
- James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
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26
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Hong YA, Nangaku M. Endogenous adenine as a key player in diabetic kidney disease progression: an integrated multiomics approach. Kidney Int 2024; 105:918-920. [PMID: 38642987 DOI: 10.1016/j.kint.2023.11.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 11/30/2023] [Indexed: 04/22/2024]
Affiliation(s)
- Yu Ah Hong
- Division of Nephrology, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Jung-gu, Daejeon, Republic of Korea; Division of Nephrology and Endocrinology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan.
| | - Masaomi Nangaku
- Division of Nephrology and Endocrinology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan
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27
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Park Y, Hauschild AC. The effect of data transformation on low-dimensional integration of single-cell RNA-seq. BMC Bioinformatics 2024; 25:171. [PMID: 38689234 PMCID: PMC11059821 DOI: 10.1186/s12859-024-05788-5] [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: 11/02/2023] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Recent developments in single-cell RNA sequencing have opened up a multitude of possibilities to study tissues at the level of cellular populations. However, the heterogeneity in single-cell sequencing data necessitates appropriate procedures to adjust for technological limitations and various sources of noise when integrating datasets from different studies. While many analysis procedures employ various preprocessing steps, they often overlook the importance of selecting and optimizing the employed data transformation methods. RESULTS This work investigates data transformation approaches used in single-cell clustering analysis tools and their effects on batch integration analysis. In particular, we compare 16 transformations and their impact on the low-dimensional representations, aiming to reduce the batch effect and integrate multiple single-cell sequencing data. Our results show that data transformations strongly influence the results of single-cell clustering on low-dimensional data space, such as those generated by UMAP or PCA. Moreover, these changes in low-dimensional space significantly affect trajectory analysis using multiple datasets, as well. However, the performance of the data transformations greatly varies across datasets, and the optimal method was different for each dataset. Additionally, we explored how data transformation impacts the analysis of deep feature encodings using deep neural network-based models, including autoencoder-based models and proto-typical networks. Data transformation also strongly affects the outcome of deep neural network models. CONCLUSIONS Our findings suggest that the batch effect and noise in integrative analysis are highly influenced by data transformation. Low-dimensional features can integrate different batches well when proper data transformation is applied. Furthermore, we found that the batch mixing score on low-dimensional space can guide the selection of the optimal data transformation. In conclusion, data preprocessing is one of the most crucial analysis steps and needs to be cautiously considered in the integrative analysis of multiple scRNA-seq datasets.
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Affiliation(s)
- Youngjun Park
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
- International Max Planck Research Schools for Genome Science, Georg-August-Universität Göttingen, Göttingen, Germany
| | - Anne-Christin Hauschild
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany.
- Campus-Institute Data Science (CIDAS), Georg-August-Universität Göttingen, Göttingen, Germany.
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Curion F, Wu X, Heumos L, André MMG, Halle L, Ozols M, Grant-Peters M, Rich-Griffin C, Yeung HY, Dendrou CA, Schiller HB, Theis FJ. hadge: a comprehensive pipeline for donor deconvolution in single-cell studies. Genome Biol 2024; 25:109. [PMID: 38671451 PMCID: PMC11055383 DOI: 10.1186/s13059-024-03249-z] [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: 08/08/2023] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at known genomic coordinates. We develop hadge (hashing deconvolution combined with genotype information), a Nextflow pipeline that combines 12 methods to perform both hashing- and genotype-based deconvolution. We propose a joint deconvolution strategy combining best-performing methods and demonstrate how this approach leads to the recovery of previously discarded cells in a nuclei hashing of fresh-frozen brain tissue.
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Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Xichen Wu
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Lukas Heumos
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
- Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Mylene Mariana Gonzales André
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Lennard Halle
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
| | - Matiss Ozols
- Wellcome Sanger Institute, Hinxton, UK
- School of Cell Matrix and Regenerative Medicine, The University of Manchester, Manchester, UK
| | - Melissa Grant-Peters
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Charlotte Rich-Griffin
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Hing-Yuen Yeung
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Calliope A Dendrou
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, The Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Herbert B Schiller
- Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
- Research Unit Precision Regenerative Medicine, Helmholtz Munich, Neuherberg, Germany
- Institute of Experimental Pneumology, LMU University Hospital, Ludwig-Maximilians University, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Neuherberg, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
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Baghdassarian HM, Dimitrov D, Armingol E, Saez-Rodriguez J, Lewis NE. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. CELL REPORTS METHODS 2024; 4:100758. [PMID: 38631346 PMCID: PMC11046036 DOI: 10.1016/j.crmeth.2024.100758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/22/2023] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.
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Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany
| | - Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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30
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Pina C. Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230052. [PMID: 38432321 PMCID: PMC10909511 DOI: 10.1098/rstb.2023.0052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 12/04/2023] [Indexed: 03/05/2024] Open
Abstract
Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Cristina Pina
- College of Health, Medicine and Life Sciences, Brunel University London, Kingston Lane, Uxbridge, London, UB8 3PH, United Kingdom
- CenGEM – Centre for Genome Engineering and Maintenance, Brunel University London, Kingston Lane, Uxbridge, London, UB8 3PH, United Kingdom
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31
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Tian J, Bai X, Quek C. Single-Cell Informatics for Tumor Microenvironment and Immunotherapy. Int J Mol Sci 2024; 25:4485. [PMID: 38674070 PMCID: PMC11050520 DOI: 10.3390/ijms25084485] [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: 03/08/2024] [Revised: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.
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Affiliation(s)
| | | | - Camelia Quek
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (J.T.); (X.B.)
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32
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Ouwendijk WJD, Roychoudhury P, Cunningham AL, Jerome KR, Koelle DM, Kinchington PR, Mohr I, Wilson AC, Verjans GGMGM, Depledge DP. Reanalysis of single-cell RNA sequencing data does not support herpes simplex virus 1 latency in non-neuronal ganglionic cells in mice. J Virol 2024; 98:e0185823. [PMID: 38445887 PMCID: PMC11019907 DOI: 10.1128/jvi.01858-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/14/2024] [Indexed: 03/07/2024] Open
Abstract
Most individuals are latently infected with herpes simplex virus type 1 (HSV-1), and it is well-established that HSV-1 establishes latency in sensory neurons of peripheral ganglia. However, it was recently proposed that latent HSV-1 is also present in immune cells recovered from the ganglia of experimentally infected mice. Here, we reanalyzed the single-cell RNA sequencing (scRNA-Seq) data that formed the basis for that conclusion. Unexpectedly, off-target priming in 3' scRNA-Seq experiments enabled the detection of non-polyadenylated HSV-1 latency-associated transcript (LAT) intronic RNAs. However, LAT reads were near-exclusively detected in mixed populations of cells undergoing cell death. Specific loss of HSV-1 LAT and neuronal transcripts during quality control filtering indicated widespread destruction of neurons, supporting the presence of contaminating cell-free RNA in other cells following tissue processing. In conclusion, the reported detection of latent HSV-1 in non-neuronal cells is best explained using compromised scRNA-Seq datasets.IMPORTANCEMost people are infected with herpes simplex virus type 1 (HSV-1) during their life. Once infected, the virus generally remains in a latent (silent) state, hiding within the neurons of peripheral ganglia. Periodic reactivation (reawakening) of the virus may cause fresh diseases such as cold sores. A recent study using single-cell RNA sequencing (scRNA-Seq) proposed that HSV-1 can also establish latency in the immune cells of mice, challenging existing dogma. We reanalyzed the data from that study and identified several flaws in the methodologies and analyses performed that invalidate the published conclusions. Specifically, we showed that the methodologies used resulted in widespread destruction of neurons which resulted in the presence of contaminants that confound the data analysis. We thus conclude that there remains little to no evidence for HSV-1 latency in immune cells.
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Affiliation(s)
- Werner J. D. Ouwendijk
- HerpesLabNL, Department of Viroscience, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Pavitra Roychoudhury
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Anthony L. Cunningham
- Centre for Virus Research, The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Keith R. Jerome
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - David M. Koelle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
- Department of Translational Research, Benaroya Research Institute, Seattle, Washington, USA
| | - Paul R. Kinchington
- Department of Ophthalmology and of Molecular Microbiology and Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ian Mohr
- Department of Microbiology, New York University School of Medicine, New York, New York, USA
| | - Angus C. Wilson
- Department of Microbiology, New York University School of Medicine, New York, New York, USA
| | | | - Daniel P. Depledge
- Department of Microbiology, New York University School of Medicine, New York, New York, USA
- Institute of Virology, Hannover Medical School, Hannover, Germany
- German Center for Infection Research (DZIF) partner site Hannover-Braunschweig, Hannover, Germany
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany
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33
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O'Leary K, Zheng D. Metacell-based differential expression analysis identifies cell type specific temporal gene response programs in COVID-19 patient PBMCs. NPJ Syst Biol Appl 2024; 10:36. [PMID: 38580667 PMCID: PMC10997786 DOI: 10.1038/s41540-024-00364-2] [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: 12/07/2023] [Accepted: 03/27/2024] [Indexed: 04/07/2024] Open
Abstract
By profiling gene expression in individual cells, single-cell RNA-sequencing (scRNA-seq) can resolve cellular heterogeneity and cell-type gene expression dynamics. Its application to time-series samples can identify temporal gene programs active in different cell types, for example, immune cells' responses to viral infection. However, current scRNA-seq analysis has limitations. One is the low number of genes detected per cell. The second is insufficient replicates (often 1-2) due to high experimental cost. The third lies in the data analysis-treating individual cells as independent measurements leads to inflated statistics. To address these, we explore a new computational framework, specifically whether "metacells" constructed to maintain cellular heterogeneity within individual cell types (or clusters) can be used as "replicates" for increasing statistical rigor. Toward this, we applied SEACells to a time-series scRNA-seq dataset from peripheral blood mononuclear cells (PBMCs) after SARS-CoV-2 infection to construct metacells, and used them in maSigPro for quadratic regression to find significantly differentially expressed genes (DEGs) over time, followed by clustering expression velocity trends. We showed that such metacells retained greater expression variances and produced more biologically meaningful DEGs compared to either metacells generated randomly or from simple pseudobulk methods. More specifically, this approach correctly identified the known ISG15 interferon response program in almost all PBMC cell types and many DEGs enriched in the previously defined SARS-CoV-2 infection response pathway. It also uncovered additional and more cell type-specific temporal gene expression programs. Overall, our results demonstrate that the metacell-pseudoreplicate strategy could potentially overcome the limitation of 1-2 replicates.
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Affiliation(s)
- Kevin O'Leary
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Deyou Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA.
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34
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Engel A, Rishik S, Hirsch P, Keller V, Fehlmann T, Kern F, Keller A. SingmiR: a single-cell miRNA alignment and analysis tool. Nucleic Acids Res 2024:gkae225. [PMID: 38572750 DOI: 10.1093/nar/gkae225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/08/2024] [Accepted: 04/02/2024] [Indexed: 04/05/2024] Open
Abstract
Single-cell RNA sequencing (RNA-seq) has revolutionized our understanding of cell biology, developmental and pathophysiological molecular processes, paving the way toward novel diagnostic and therapeutic approaches. However, most of the gene regulatory processes on the single-cell level are still unknown, including post-transcriptional control conferred by microRNAs (miRNAs). Like the established single-cell gene expression analysis, advanced computational expertise is required to comprehensively process newly emerging single-cell miRNA-seq datasets. A web server providing a workflow tailored for single-cell miRNA-seq data with a self-explanatory interface is currently not available. Here, we present SingmiR, enabling the rapid (pre-)processing and quantification of human miRNAs from noncoding single-cell samples. It performs read trimming for different library preparation protocols, generates automated quality control reports and provides feature-normalized count files. Numerous standard and advanced analyses such as dimension reduction, clustered feature heatmaps, sample correlation heatmaps and differential expression statistics are implemented. We aim to speed up the prototyping pipeline for biologists developing single-cell miRNA-seq protocols on small to medium-sized datasets. SingmiR is freely available to all users without the need for a login at https://www.ccb.uni-saarland.de/singmir.
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Affiliation(s)
- Annika Engel
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Shusruto Rishik
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Pascal Hirsch
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Verena Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Fabian Kern
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
- Department of Clinical Bioinformatics (CLIB), Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123 Saarbrücken, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
- Department of Clinical Bioinformatics (CLIB), Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123 Saarbrücken, Germany
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35
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Duong P, Rodriguez-Parks A, Kang J, Murphy PJ. CUT&Tag Applied to Zebrafish Adult Tail Fins Reveals a Return of Embryonic H3K4me3 Patterns During Regeneration. RESEARCH SQUARE 2024:rs.3.rs-4189493. [PMID: 38645155 PMCID: PMC11030498 DOI: 10.21203/rs.3.rs-4189493/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Regenerative potential is governed by a complex process of transcriptional reprogramming, involving chromatin reorganization and dynamics in transcription factor binding patterns throughout the genome. The degree to which chromatin and epigenetic changes contribute to this process remains partially understood. Here we provide a modified CUT&Tag protocol suitable for improved characterization and interrogation of epigenetic changes during adult fin regeneration in zebrafish. Our protocol generates data that recapitulates results from previously published ChIP-Seq methods, requires far fewer cells as input, and significantly improves signal to noise ratios. We deliver high-resolution enrichment maps for H3K4me3 of uninjured and regenerating fin tissues. During regeneration, we find that H3K4me3 levels increase over gene promoters which become transcriptionally active and genes which lose H3K4me3 become silenced. Interestingly, these epigenetic reprogramming events recapitulate the H3K4me3 patterns observed in developing fin folds of 24-hour old zebrafish embryos. Our results indicate that changes in genomic H3K4me3 patterns during fin regeneration occur in a manner consistent with reactivation of developmental programs, demonstrating CUT&Tag to be an effective tool for profiling chromatin landscapes in regenerating tissues.
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36
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Gondal MN, Cieslik M, Chinnaiyan AM. Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576110. [PMID: 38328153 PMCID: PMC10849474 DOI: 10.1101/2024.01.17.576110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 174 patients, and 90,270 cancer cells. This compilation forms a unique resource tailored for investigating how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.
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Affiliation(s)
- Mahnoor N. Gondal
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
| | - Marcin Cieslik
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
| | - Arul M. Chinnaiyan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Urology, University of Michigan, Ann Arbor, MI USA
- Howard Hughes Medical Institute, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
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37
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Boakye Serebour T, Cribbs AP, Baldwin MJ, Masimirembwa C, Chikwambi Z, Kerasidou A, Snelling SJB. Overcoming barriers to single-cell RNA sequencing adoption in low- and middle-income countries. Eur J Hum Genet 2024:10.1038/s41431-024-01564-4. [PMID: 38565638 DOI: 10.1038/s41431-024-01564-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 04/04/2024] Open
Abstract
The advent of single-cell resolution sequencing and spatial transcriptomics has enabled the delivery of cellular and molecular atlases of tissues and organs, providing new insights into tissue health and disease. However, if the full potential of these technologies is to be equitably realised, ancestrally inclusivity is paramount. Such a goal requires greater inclusion of both researchers and donors in low- and middle-income countries (LMICs). In this perspective, we describe the current landscape of ancestral inclusivity in genomic and single-cell transcriptomic studies. We discuss the collaborative efforts needed to scale the barriers to establishing, expanding, and adopting single-cell sequencing research in LMICs and to enable globally impactful outcomes of these technologies.
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Affiliation(s)
- Tracy Boakye Serebour
- The Botnar Institute for Musculoskeletal Science, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Adam P Cribbs
- The Botnar Institute for Musculoskeletal Science, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Mathew J Baldwin
- The Botnar Institute for Musculoskeletal Science, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Collen Masimirembwa
- The African Institute of Biomedical Science and Technology, Harare, Zimbabwe
| | - Zedias Chikwambi
- The African Institute of Biomedical Science and Technology, Harare, Zimbabwe
| | - Angeliki Kerasidou
- The Ethox Centre and the Wellcome Centre for Ethics and Humanities, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sarah J B Snelling
- The Botnar Institute for Musculoskeletal Science, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
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38
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Chen Y, Gustafsson J, Yang J, Nielsen J, Kerkhoven EJ. Single-cell omics analysis with genome-scale metabolic modeling. Curr Opin Biotechnol 2024; 86:103078. [PMID: 38359604 DOI: 10.1016/j.copbio.2024.103078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
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Affiliation(s)
- Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Johan Gustafsson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Jingyu Yang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; BioInnovation Institute, DK-2200 Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
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39
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Andersson D, Kebede FT, Escobar M, Österlund T, Ståhlberg A. Principles of digital sequencing using unique molecular identifiers. Mol Aspects Med 2024; 96:101253. [PMID: 38367531 DOI: 10.1016/j.mam.2024.101253] [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: 11/16/2023] [Revised: 01/26/2024] [Accepted: 02/03/2024] [Indexed: 02/19/2024]
Abstract
Massively parallel sequencing technologies have long been used in both basic research and clinical routine. The recent introduction of digital sequencing has made previously challenging applications possible by significantly improving sensitivity and specificity to now allow detection of rare sequence variants, even at single molecule level. Digital sequencing utilizes unique molecular identifiers (UMIs) to minimize sequencing-induced errors and quantification biases. Here, we discuss the principles of UMIs and how they are used in digital sequencing. We outline the properties of different UMI types and the consequences of various UMI approaches in relation to experimental protocols and bioinformatics. Finally, we describe how digital sequencing can be applied in specific research fields, focusing on cancer management where it can be used in screening of asymptomatic individuals, diagnosis, treatment prediction, prognostication, monitoring treatment efficacy and early detection of treatment resistance as well as relapse.
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Affiliation(s)
- Daniel Andersson
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Firaol Tamiru Kebede
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Mandy Escobar
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden
| | - Tobias Österlund
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 413 90, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
| | - Anders Ståhlberg
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 413 90, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden.
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40
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Splendiani E, Besharat ZM, Covre A, Maio M, Di Giacomo AM, Ferretti E. Immunotherapy in melanoma: Can we predict response to treatment with circulating biomarkers? Pharmacol Ther 2024; 256:108613. [PMID: 38367867 DOI: 10.1016/j.pharmthera.2024.108613] [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: 10/16/2023] [Revised: 01/08/2024] [Accepted: 02/09/2024] [Indexed: 02/19/2024]
Abstract
Melanoma is the most aggressive form of skin cancer, representing approximately 4% of all cutaneous neoplasms and accounting for up to 80% of deaths. Advanced stages of melanoma involve metastatic processes and are associated with high mortality and morbidity, mainly due to the rapid dissemination and heterogeneous responses to current therapies, including immunotherapy. Immune checkpoint inhibitors (ICIs) are currently used in the treatment of metastatic melanoma (MM) and despite being linked to an increase in patient survival, a high percentage of them still do not benefit from it. Accordingly, the number of therapeutic regimens for MM patients using ICIs either alone or in combination with other therapies has increased, together with the need for reliable biomarkers that can both predict and monitor response to ICIs. In this context, circulating biomarkers, such as DNA, RNA, proteins, and cells, have emerged due to their ability to reflect disease status. Moreover, blood tests are minimally invasive and provide an attractive option to detect biomarkers, avoiding stressful medical procedures. This systematic review aims to evaluate the possibility of a non-invasive biomarker signature that can guide therapeutic decisions. The studies reported here offer valuable insight into how circulating biomarkers can have a role in personalized treatments for melanoma patients receiving ICIs therapy, emphasizing the need for rigorous clinical trials to confirm findings and establish standardized procedures.
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Affiliation(s)
- Elena Splendiani
- Department of Experimental Medicine, Sapienza University, Rome, Italy
| | | | - Alessia Covre
- Center for Immuno-Oncology, Medical Oncology and Immunotherapy, Department of Oncology, University Hospital of Siena, 53100 Siena, Italy; Medical Oncology, Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy
| | - Michele Maio
- Center for Immuno-Oncology, Medical Oncology and Immunotherapy, Department of Oncology, University Hospital of Siena, 53100 Siena, Italy; Medical Oncology, Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy
| | - Anna Maria Di Giacomo
- Center for Immuno-Oncology, Medical Oncology and Immunotherapy, Department of Oncology, University Hospital of Siena, 53100 Siena, Italy; Medical Oncology, Department of Molecular and Developmental Medicine, University of Siena, 53100 Siena, Italy
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41
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Bourn JJ, Dorrity MW. Degrees of freedom: temperature's influence on developmental rate. Curr Opin Genet Dev 2024; 85:102155. [PMID: 38335718 DOI: 10.1016/j.gde.2024.102155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 02/12/2024]
Abstract
Temperature exerts a fundamental influence across scales of biology, from the biophysical nature of molecules, to the sensitivity of cells, and the coordinated progression of development in embryos. Species-specific developmental rates and temperature-induced acceleration of development indicate that these sensing mechanisms are harnessed to influence developmental dynamics. Tracing how temperature sensitivity propagates through biological scales to influence the pace of development can therefore reveal how embryogenesis remains robust to environmental influences. Cellular protein homeostasis (proteostasis), and cellular metabolic rate are linked to both temperature-induced and species-specific developmental tempos in specific cell types, hinting toward generalized mechanisms of timing control. New methods to extract timing information from single-cell profiling experiments are driving further progress in understanding how mechanisms of temperature sensitivity can direct cell-autonomous responses, coordination across cell types, and evolutionary modifications of developmental timing.
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Affiliation(s)
- Jess J Bourn
- Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany. https://twitter.com/@bournsupremacy
| | - Michael W Dorrity
- Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany.
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42
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Tian T, Lin S, Yang C. Beyond single cells: microfluidics empowering multiomics analysis. Anal Bioanal Chem 2024; 416:2203-2220. [PMID: 38008783 DOI: 10.1007/s00216-023-05028-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 11/28/2023]
Abstract
Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.
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Affiliation(s)
- Tian Tian
- Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Shichao Lin
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Xiamen, 361005, China
| | - Chaoyong Yang
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Xiamen, 361005, China.
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
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43
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Chen P, Wang Y, Zhou B. Insights into targeting cellular senescence with senolytic therapy: The journey from preclinical trials to clinical practice. Mech Ageing Dev 2024; 218:111918. [PMID: 38401690 DOI: 10.1016/j.mad.2024.111918] [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: 12/26/2023] [Revised: 02/07/2024] [Accepted: 02/21/2024] [Indexed: 02/26/2024]
Abstract
Interconnected, fundamental aging processes are central to many illnesses and diseases. Cellular senescence is a mechanism that halts the cell cycle in response to harmful stimuli. Senescent cells (SnCs) can emerge at any point in life, and their persistence, along with the numerous proteins they secrete, can negatively affect tissue function. Interventions aimed at combating persistent SnCs, which can destroy tissues, have been used in preclinical models to delay, halt, or even reverse various diseases. Consequently, the development of small-molecule senolytic medicines designed to specifically eliminate SnCs has opened potential avenues for the prevention or treatment of multiple diseases and age-related issues in humans. In this review, we explore the most promising approaches for translating small-molecule senolytics and other interventions targeting senescence in clinical practice. This discussion highlights the rationale for considering SnCs as therapeutic targets for diseases affecting individuals of all ages.
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Affiliation(s)
- Peng Chen
- Department of Pharmacy, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China.
| | - Yulai Wang
- Department of Pharmacy, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, Hubei, P.R. China
| | - Benhong Zhou
- Department of Pharmacy, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, PR China
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44
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Jiménez-Gracia L, Marchese D, Nieto JC, Caratù G, Melón-Ardanaz E, Gudiño V, Roth S, Wise K, Ryan NK, Jensen KB, Hernando-Momblona X, Bernardes JP, Tran F, Sievers LK, Schreiber S, van den Berge M, Kole T, van der Velde PL, Nawijn MC, Rosenstiel P, Batlle E, Butler LM, Parish IA, Plummer J, Gut I, Salas A, Heyn H, Martelotto LG. FixNCut: single-cell genomics through reversible tissue fixation and dissociation. Genome Biol 2024; 25:81. [PMID: 38553769 PMCID: PMC10979608 DOI: 10.1186/s13059-024-03219-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 03/18/2024] [Indexed: 04/02/2024] Open
Abstract
The use of single-cell technologies for clinical applications requires disconnecting sampling from downstream processing steps. Early sample preservation can further increase robustness and reproducibility by avoiding artifacts introduced during specimen handling. We present FixNCut, a methodology for the reversible fixation of tissue followed by dissociation that overcomes current limitations. We applied FixNCut to human and mouse tissues to demonstrate the preservation of RNA integrity, sequencing library complexity, and cellular composition, while diminishing stress-related artifacts. Besides single-cell RNA sequencing, FixNCut is compatible with multiple single-cell and spatial technologies, making it a versatile tool for robust and flexible study designs.
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Affiliation(s)
- Laura Jiménez-Gracia
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Domenica Marchese
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Juan C Nieto
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Ginevra Caratù
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Elisa Melón-Ardanaz
- Inflammatory Bowel Disease Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Victoria Gudiño
- Inflammatory Bowel Disease Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Sara Roth
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
- Monash University Department of Surgery, Alfred Hospital, Melbourne, VIC, Australia
| | - Kellie Wise
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, Adelaide, South Australia, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia
- Australian Genomics Research Facility, Adelaide, South Australia, Australia
| | - Natalie K Ryan
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia
- Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Kirk B Jensen
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, Adelaide, South Australia, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia
- Australian Genomics Research Facility, Adelaide, South Australia, Australia
| | - Xavier Hernando-Momblona
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Barcelona, Spain
| | - Joana P Bernardes
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Florian Tran
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
- Department of Internal Medicine I, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Germany
| | - Laura Katharina Sievers
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
- Department of Internal Medicine I, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Germany
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
- Department of Internal Medicine I, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Germany
| | - Maarten van den Berge
- Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Tessa Kole
- Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Petra L van der Velde
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Pathology & Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Pathology & Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Eduard Batlle
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Lisa M Butler
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia
- Freemasons Foundation Centre for Men's Health, University of Adelaide, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Ian A Parish
- Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Jasmine Plummer
- St Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Ivo Gut
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
| | - Azucena Salas
- Inflammatory Bowel Disease Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), 08028, Barcelona, Spain.
- Universitat de Barcelona (UB), Barcelona, Spain.
- Omniscope, Barcelona, Spain.
| | - Luciano G Martelotto
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, Adelaide, South Australia, Australia.
- South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, Adelaide, South Australia, Australia.
- Omniscope, Barcelona, Spain.
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45
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Grones C, Eekhout T, Shi D, Neumann M, Berg LS, Ke Y, Shahan R, Cox KL, Gomez-Cano F, Nelissen H, Lohmann JU, Giacomello S, Martin OC, Cole B, Wang JW, Kaufmann K, Raissig MT, Palfalvi G, Greb T, Libault M, De Rybel B. Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics. THE PLANT CELL 2024; 36:812-828. [PMID: 38231860 PMCID: PMC10980355 DOI: 10.1093/plcell/koae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 10/17/2023] [Accepted: 10/24/2023] [Indexed: 01/19/2024]
Abstract
Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.
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Affiliation(s)
- Carolin Grones
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Thomas Eekhout
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
- VIB Single Cell Core Facility, Ghent 9052, Belgium
| | - Dongbo Shi
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
- Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
| | - Manuel Neumann
- Institute of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Lea S Berg
- Institute of Plant Sciences, University of Bern, 3012 Bern, Switzerland
| | - Yuji Ke
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Rachel Shahan
- Department of Biology, Duke University, Durham, NC 27708, USA
- Howard Hughes Medical Institute, Duke University, Durham, NC 27708, USA
| | - Kevin L Cox
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Fabio Gomez-Cano
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Jan U Lohmann
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
| | - Stefania Giacomello
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, 17165 Solna, Sweden
| | - Olivier C Martin
- Universities of Paris-Saclay, Paris-Cité and Evry, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay, Gif-sur-Yvette 91192, France
| | - Benjamin Cole
- DOE-Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jia-Wei Wang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences (CEMPS), Institute of Plant Physiology and Ecology (SIPPE), Chinese Academy of Sciences (CAS), Shanghai 200032, China
| | - Kerstin Kaufmann
- Institute of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Michael T Raissig
- Institute of Plant Sciences, University of Bern, 3012 Bern, Switzerland
| | - Gergo Palfalvi
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Thomas Greb
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
| | - Marc Libault
- Division of Plant Science and Technology, Interdisciplinary Plant Group, College of Agriculture, Food, and Natural Resources, University of Missouri-Columbia, Columbia, MO 65201, USA
| | - Bert De Rybel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
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46
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Zhang W, Cui Y, Liu B, Loza M, Park SJ, Nakai K. HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data. Brief Bioinform 2024; 25:bbae152. [PMID: 38581422 PMCID: PMC10998639 DOI: 10.1093/bib/bbae152] [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: 12/19/2023] [Revised: 02/19/2024] [Accepted: 03/10/2024] [Indexed: 04/08/2024] Open
Abstract
Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.
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Affiliation(s)
- Weihang Zhang
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Yang Cui
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Bowen Liu
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Martin Loza
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Sung-Joon Park
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Kenta Nakai
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
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47
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Chamberlin JT, Lee Y, Marth GT, Quinlan AR. Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments. Genome Res 2024; 34:179-188. [PMID: 38355308 PMCID: PMC10984380 DOI: 10.1101/gr.278253.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024]
Abstract
A mechanistic understanding of the biological and technical factors that impact transcript measurements is essential to designing and analyzing single-cell and single-nucleus RNA sequencing experiments. Nuclei contain the same pre-mRNA population as cells, but they contain a small subset of the mRNAs. Nonetheless, early studies argued that single-nucleus analysis yielded results comparable to cellular samples if pre-mRNA measurements were included. However, typical workflows do not distinguish between pre-mRNA and mRNA when estimating gene expression, and variation in their relative abundances across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA has become commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public data sets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling on gene expression and marker gene selection in single-cell and single-nucleus RNA-seq. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias and limits the generalizability of a recently published normalization method intended to correct for this bias. As an alternative, we repurpose an existing post hoc gene length-based correction method from conventional RNA-seq gene set enrichment analysis. Finally, we show that inclusion of pre-mRNA in bioinformatic processing can impart a larger effect than assay choice itself, which is pivotal to the effective reuse of existing data. These analyses advance our understanding of the sources of variation in single-cell and single-nucleus RNA-seq experiments and provide useful guidance for future studies.
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Affiliation(s)
- John T Chamberlin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA
| | - Younghee Lee
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA
- Seoul National University, College of Veterinary Medicine, Seoul, 08826, South Korea
| | - Gabor T Marth
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah 84112, USA
| | - Aaron R Quinlan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA;
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah 84112, USA
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48
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Korb A, Tajbakhsh S, Comai GE. Functional specialisation and coordination of myonuclei. Biol Rev Camb Philos Soc 2024. [PMID: 38477382 DOI: 10.1111/brv.13063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 01/30/2024] [Accepted: 02/02/2024] [Indexed: 03/14/2024]
Abstract
Myofibres serve as the functional unit for locomotion, with the sarcomere as fundamental subunit. Running the entire length of this structure are hundreds of myonuclei, located at the periphery of the myofibre, juxtaposed to the plasma membrane. Myonuclear specialisation and clustering at the centre and ends of the fibre are known to be essential for muscle contraction, yet the molecular basis of this regionalisation has remained unclear. While the 'myonuclear domain hypothesis' helped explain how myonuclei can independently govern large cytoplasmic territories, novel technologies have provided granularity on the diverse transcriptional programs running simultaneously within the syncytia and added a new perspective on how myonuclei communicate. Building upon this, we explore the critical cellular and molecular sources of transcriptional and functional heterogeneity within myofibres, discussing the impact of intrinsic and extrinsic factors on myonuclear programs. This knowledge provides new insights for understanding muscle development, repair, and disease, but also opens avenues for the development of novel and precise therapeutic approaches.
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Affiliation(s)
- Amaury Korb
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
| | - Shahragim Tajbakhsh
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
| | - Glenda E Comai
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
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49
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Zhang W, Sen A, Pena JK, Reitsma A, Alexander OC, Tajima T, Martinez OM, Krams SM. Application of Mass Cytometry Platforms to Solid Organ Transplantation. Transplantation 2024:00007890-990000000-00687. [PMID: 38467594 DOI: 10.1097/tp.0000000000004925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Transplantation serves as the cornerstone of treatment for patients with end-stage organ disease. The prevalence of complications, such as allograft rejection, infection, and malignancies, underscores the need to dissect the complex interactions of the immune system at the single-cell level. In this review, we discuss studies using mass cytometry or cytometry by time-of-flight, a cutting-edge technology enabling the characterization of immune populations and cell-to-cell interactions in granular detail. We review the application of mass cytometry in human and experimental animal studies in the context of transplantation, uncovering invaluable contributions of the tool to understanding rejection and other transplant-related complications. We discuss recent innovations that have the potential to streamline and standardize mass cytometry workflows for application to multisite clinical trials. Additionally, we introduce imaging mass cytometry, a technique that couples the power of mass cytometry with spatial context, thereby mapping cellular interactions within tissue microenvironments. The synergistic integration of mass cytometry and imaging mass cytometry data with other omics data sets and high-dimensional data platforms to further define immune dynamics is discussed. In conclusion, mass cytometry technologies, when integrated with other tools and data, shed light on the intricate landscape of the immune response in transplantation. This approach holds significant potential for enhancing patient outcomes by advancing our understanding and facilitating the development of new diagnostics and therapeutics.
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Affiliation(s)
- Wenming Zhang
- Department of Surgery, Stanford University, Stanford, CA
| | - Ayantika Sen
- Department of Surgery, Stanford University, Stanford, CA
| | | | - Andrea Reitsma
- Department of Surgery, Stanford University, Stanford, CA
| | - Oliver C Alexander
- Department of Surgery, Stanford University, Stanford, CA
- Meharry Medical College, School of Medicine, Nashville, TN
| | - Tetsuya Tajima
- Department of Surgery, Stanford University, Stanford, CA
| | | | - Sheri M Krams
- Department of Surgery, Stanford University, Stanford, CA
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50
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Morin A, Chu C, Pavlidis P. Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580581. [PMID: 38559016 PMCID: PMC10979919 DOI: 10.1101/2024.02.15.580581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes, rather than cell type compositional effects that emerge from bulk tissue samples. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 millions cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.
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Affiliation(s)
- Alexander Morin
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, Canada
| | - Chingpan Chu
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, Canada
| | - Paul Pavlidis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
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