1
|
Xu H, Lotfy P, Gelb S, Pragana A, Hehnly C, Byer LIJ, Shipley FB, Zawadzki ME, Cui J, Deng L, Taylor M, Webb M, Lidov HGW, Andermann ML, Chiu IM, Ordovas-Montanes J, Lehtinen MK. The choroid plexus synergizes with immune cells during neuroinflammation. Cell 2024:S0092-8674(24)00717-7. [PMID: 39089253 DOI: 10.1016/j.cell.2024.07.002] [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: 08/09/2023] [Revised: 04/19/2024] [Accepted: 07/01/2024] [Indexed: 08/03/2024]
Abstract
The choroid plexus (ChP) is a vital brain barrier and source of cerebrospinal fluid (CSF). Here, we use longitudinal two-photon imaging in awake mice and single-cell transcriptomics to elucidate the mechanisms of ChP regulation of brain inflammation. We used intracerebroventricular injections of lipopolysaccharides (LPS) to model meningitis in mice and observed that neutrophils and monocytes accumulated in the ChP stroma and surged across the epithelial barrier into the CSF. Bi-directional recruitment of monocytes from the periphery and, unexpectedly, macrophages from the CSF to the ChP helped eliminate neutrophils and repair the barrier. Transcriptomic analyses detailed the molecular steps accompanying this process and revealed that ChP epithelial cells transiently specialize to nurture immune cells, coordinating their recruitment, survival, and differentiation as well as regulation of the tight junctions that control the permeability of the ChP brain barrier. Collectively, we provide a mechanistic understanding and a comprehensive roadmap of neuroinflammation at the ChP brain barrier.
Collapse
Affiliation(s)
- Huixin Xu
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Peter Lotfy
- Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Graduate Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sivan Gelb
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Aja Pragana
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Christine Hehnly
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lillian I J Byer
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Frederick B Shipley
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA; Graduate Program in Biophysics, Harvard University, Cambridge, MA 02138, USA
| | - Miriam E Zawadzki
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA; Graduate Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA; Harvard MD-PhD Program, Harvard Medical School, Boston, MA 02115, USA
| | - Jin Cui
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Liwen Deng
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Milo Taylor
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA; Harvard College, Harvard University, Cambridge, MA 02138, USA
| | - Mya Webb
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Hart G W Lidov
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Mark L Andermann
- Harvard MD-PhD Program, Harvard Medical School, Boston, MA 02115, USA; Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Isaac M Chiu
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Jose Ordovas-Montanes
- Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Graduate Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA; Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Maria K Lehtinen
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA; Graduate Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA; Graduate Program in Biophysics, Harvard University, Cambridge, MA 02138, USA; Harvard MD-PhD Program, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA.
| |
Collapse
|
2
|
Zhang X, Luo Z, Marand AP, Yan H, Jang H, Bang S, Mendieta JP, Minow MA, Schmitz RJ. A spatially resolved multiomic single-cell atlas of soybean development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.03.601616. [PMID: 39005400 PMCID: PMC11244997 DOI: 10.1101/2024.07.03.601616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Cis-regulatory elements (CREs) precisely control spatiotemporal gene expression in cells. Using a spatially resolved single-cell atlas of gene expression with chromatin accessibility across ten soybean tissues, we identified 103 distinct cell types and 303,199 accessible chromatin regions (ACRs). Nearly 40% of the ACRs showed cell-type-specific patterns and were enriched for transcription factor (TF) motifs defining diverse cell identities. We identified de novo enriched TF motifs and explored conservation of gene regulatory networks underpinning legume symbiotic nitrogen fixation. With comprehensive developmental trajectories for endosperm and embryo, we uncovered the functional transition of the three sub-cell types of endosperm, identified 13 sucrose transporters sharing the DOF11 motif that were co-up-regulated in late peripheral endosperm and identified key embryo cell-type specification regulators during embryogenesis, including a homeobox TF that promotes cotyledon parenchyma identity. This resource provides a valuable foundation for analyzing gene regulatory programs in soybean cell types across tissues and life stages.
Collapse
Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Ziliang Luo
- Department of Genetics, University of Georgia, Athens, GA, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Alexandre P. Marand
- Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: College of Grassland Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Hosung Jang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Sohyun Bang
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | | | - Mark A.A. Minow
- Department of Genetics, University of Georgia, Athens, GA, USA
| | | |
Collapse
|
3
|
Singh R, Wu AP, Mudide A, Berger B. Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors. Cell Syst 2024; 15:462-474.e5. [PMID: 38754366 DOI: 10.1016/j.cels.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/25/2023] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.
Collapse
Affiliation(s)
- Rohit Singh
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
| | - Alexander P Wu
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Anish Mudide
- Phillips Exeter Academy, Exeter, NH 03883, USA; Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, MIT, Cambridge, MA 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, MIT, Cambridge, MA 02139, USA.
| |
Collapse
|
4
|
Grunert M, Dorn C, Dopazo A, Sánchez-Cabo F, Vázquez J, Rickert-Sperling S, Lara-Pezzi E. Technologies to Study Genetics and Molecular Pathways. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1441:435-458. [PMID: 38884724 DOI: 10.1007/978-3-031-44087-8_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Over the last few decades, the study of congenital heart disease (CHD) has benefited from various model systems and the development of molecular biological techniques enabling the analysis of single gene as well as global effects. In this chapter, we first describe different models including CHD patients and their families, animal models ranging from invertebrates to mammals, and various cell culture systems. Moreover, techniques to experimentally manipulate these models are discussed. Second, we introduce cardiac phenotyping technologies comprising the analysis of mouse and cell culture models, live imaging of cardiogenesis, and histological methods for fixed hearts. Finally, the most important and latest molecular biotechniques are described. These include genotyping technologies, different applications of next-generation sequencing, and the analysis of transcriptome, epigenome, proteome, and metabolome. In summary, the models and technologies presented in this chapter are essential to study the function and development of the heart and to understand the molecular pathways underlying CHD.
Collapse
Affiliation(s)
- Marcel Grunert
- Cardiovascular Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- DiNAQOR AG, Schlieren, Switzerland
| | - Cornelia Dorn
- Cardiovascular Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ana Dopazo
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Fátima Sánchez-Cabo
- Bioinformatics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Jésus Vázquez
- Proteomics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | | | - Enrique Lara-Pezzi
- Myocardial Homeostasis and Cardiac Injury Programme, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain.
| |
Collapse
|
5
|
Clausen BE, Amon L, Backer RA, Berod L, Bopp T, Brand A, Burgdorf S, Chen L, Da M, Distler U, Dress RJ, Dudziak D, Dutertre CA, Eich C, Gabele A, Geiger M, Ginhoux F, Giusiano L, Godoy GJ, Hamouda AEI, Hatscher L, Heger L, Heidkamp GF, Hernandez LC, Jacobi L, Kaszubowski T, Kong WT, Lehmann CHK, López-López T, Mahnke K, Nitsche D, Renkawitz J, Reza RA, Sáez PJ, Schlautmann L, Schmitt MT, Seichter A, Sielaff M, Sparwasser T, Stoitzner P, Tchitashvili G, Tenzer S, Tochoedo NR, Vurnek D, Zink F, Hieronymus T. Guidelines for mouse and human DC functional assays. Eur J Immunol 2023; 53:e2249925. [PMID: 36563126 DOI: 10.1002/eji.202249925] [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: 03/28/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 12/24/2022]
Abstract
This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. Recent studies have provided evidence for an increasing number of phenotypically distinct conventional DC (cDC) subsets that on one hand exhibit a certain functional plasticity, but on the other hand are characterized by their tissue- and context-dependent functional specialization. Here, we describe a selection of assays for the functional characterization of mouse and human cDC. The first two protocols illustrate analysis of cDC endocytosis and metabolism, followed by guidelines for transcriptomic and proteomic characterization of cDC populations. Then, a larger group of assays describes the characterization of cDC migration in vitro, ex vivo, and in vivo. The final guidelines measure cDC inflammasome and antigen (cross)-presentation activity. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.
Collapse
Affiliation(s)
- Björn E Clausen
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute for Molecular Medicine, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Lukas Amon
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Ronald A Backer
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute for Molecular Medicine, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Luciana Berod
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute of Molecular Medicine, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
| | - Tobias Bopp
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute of Immunology, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
| | - Anna Brand
- Institute for Molecular Medicine, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sven Burgdorf
- Laboratory of Cellular Immunology, LIMES Institute, University of Bonn, Bonn, Germany
| | - Luxia Chen
- Department of Dermatology, University Hospital Heidelberg, Heidelberg, Germany
| | - Meihong Da
- Department of Dermatology, University Hospital Heidelberg, Heidelberg, Germany
| | - Ute Distler
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute of Immunology, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
| | - Regine J Dress
- Institute of Systems Immunology, Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Diana Dudziak
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
- Medical Immunology Campus Erlangen (MICE), Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Germany
| | - Charles-Antoine Dutertre
- Gustave Roussy Cancer Campus, Villejuif, France
- Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Christina Eich
- Institute for Molecular Medicine, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Anna Gabele
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute of Immunology, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
| | - Melanie Geiger
- Institute for Biomedical Engineering, Department of Cell Biology, RWTH Aachen University, Medical Faculty, Aachen, Germany
- Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Florent Ginhoux
- Gustave Roussy Cancer Campus, Villejuif, France
- Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research, Singapore, Singapore
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Lucila Giusiano
- Institute of Medical Microbiology and Hygiene, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
| | - Gloria J Godoy
- Institute of Medical Microbiology and Hygiene, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
| | - Ahmed E I Hamouda
- Institute for Biomedical Engineering, Department of Cell Biology, RWTH Aachen University, Medical Faculty, Aachen, Germany
- Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Lukas Hatscher
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Lukas Heger
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Gordon F Heidkamp
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Lola C Hernandez
- Cell Communication and Migration Laboratory, Institute of Biochemistry and Molecular Cell Biology, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lukas Jacobi
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Tomasz Kaszubowski
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Wan Ting Kong
- Gustave Roussy Cancer Campus, Villejuif, France
- Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Christian H K Lehmann
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
- Medical Immunology Campus Erlangen (MICE), Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), Germany
| | - Tamara López-López
- Cell Communication and Migration Laboratory, Institute of Biochemistry and Molecular Cell Biology, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karsten Mahnke
- Department of Dermatology, University Hospital Heidelberg, Heidelberg, Germany
| | - Dominik Nitsche
- Laboratory of Cellular Immunology, LIMES Institute, University of Bonn, Bonn, Germany
| | - Jörg Renkawitz
- Biomedical Center (BMC), Walter Brendel Center of Experimental Medicine, Institute of Cardiovascular Physiology and Pathophysiology, Klinikum der Universität, LMU Munich, Munich, Germany
| | - Rifat A Reza
- Biomedical Center (BMC), Walter Brendel Center of Experimental Medicine, Institute of Cardiovascular Physiology and Pathophysiology, Klinikum der Universität, LMU Munich, Munich, Germany
| | - Pablo J Sáez
- Cell Communication and Migration Laboratory, Institute of Biochemistry and Molecular Cell Biology, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Laura Schlautmann
- Laboratory of Cellular Immunology, LIMES Institute, University of Bonn, Bonn, Germany
| | - Madeleine T Schmitt
- Biomedical Center (BMC), Walter Brendel Center of Experimental Medicine, Institute of Cardiovascular Physiology and Pathophysiology, Klinikum der Universität, LMU Munich, Munich, Germany
| | - Anna Seichter
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Malte Sielaff
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute of Immunology, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
| | - Tim Sparwasser
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute of Medical Microbiology and Hygiene, University Medical Center of the Johannes Gutenberg-University Mainz, Germany
| | - Patrizia Stoitzner
- Department of Dermatology, Venerology & Allergology, Medical University Innsbruck, Innsbruck, Austria
| | - Giorgi Tchitashvili
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Stefan Tenzer
- Research Center for Immunotherapy (FZI), University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Institute of Immunology, Paul Klein Center for Immune Intervention, University Medical Center of the Johannes-Gutenberg University Mainz, Mainz, Germany
- Helmholtz Institute for Translational Oncology Mainz (HI-TRON Mainz), Mainz, Germany
| | - Nounagnon R Tochoedo
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Damir Vurnek
- Laboratory of Dendritic Cell Biology, Department of Dermatology, University Hospital Erlangen, Germany
| | - Fabian Zink
- Laboratory of Cellular Immunology, LIMES Institute, University of Bonn, Bonn, Germany
| | - Thomas Hieronymus
- Institute for Biomedical Engineering, Department of Cell Biology, RWTH Aachen University, Medical Faculty, Aachen, Germany
- Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
- Institute of Cell and Tumor Biology, RWTH Aachen University, Medical Faculty, Germany
| |
Collapse
|
6
|
Tisi A, Palaniappan S, Maccarrone M. Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease. Biomolecules 2023; 13:1534. [PMID: 37892216 PMCID: PMC10605747 DOI: 10.3390/biom13101534] [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: 08/24/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.
Collapse
Affiliation(s)
- Annamaria Tisi
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Sakthimala Palaniappan
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Mauro Maccarrone
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
- Laboratory of Lipid Neurochemistry, European Center for Brain Research (CERC), Santa Lucia Foundation IRCCS, 00143 Rome, Italy
| |
Collapse
|
7
|
Gunawan I, Vafaee F, Meijering E, Lock JG. An introduction to representation learning for single-cell data analysis. CELL REPORTS METHODS 2023; 3:100547. [PMID: 37671013 PMCID: PMC10475795 DOI: 10.1016/j.crmeth.2023.100547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.
Collapse
Affiliation(s)
- Ihuan Gunawan
- School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, NSW, Australia
- UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales, Sydney, NSW, Australia
| | - John George Lock
- School of Biomedical Sciences, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- UNSW Data Science Hub, University of New South Wales, Sydney, NSW, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
| |
Collapse
|
8
|
DeMeo B, Berger B. SCA: recovering single-cell heterogeneity through information-based dimensionality reduction. Genome Biol 2023; 24:195. [PMID: 37626411 PMCID: PMC10464206 DOI: 10.1186/s13059-023-02998-7] [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: 08/07/2022] [Accepted: 06/28/2023] [Indexed: 08/27/2023] Open
Abstract
Dimensionality reduction summarizes the complex transcriptomic landscape of single-cell datasets for downstream analyses. Current approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly defined populations. Here, we present surprisal component analysis (SCA), a technique that newly leverages the information-theoretic notion of surprisal for dimensionality reduction to promote more meaningful signal extraction. For example, SCA uncovers clinically important cytotoxic T-cell subpopulations that are indistinguishable using existing pipelines. We also demonstrate that SCA substantially improves downstream imputation. SCA's efficient information-theoretic paradigm has broad applications to the study of complex biological tissues in health and disease.
Collapse
Affiliation(s)
- Benjamin DeMeo
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, 02139, MA, USA
- Department of Biomedical Informatics, Harvard University, Cambridge, 02138, MA, USA
- Department of Mathematics, MIT, Cambridge, 02139, MA, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, 02139, MA, USA.
- Department of Mathematics, MIT, Cambridge, 02139, MA, USA.
| |
Collapse
|
9
|
Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S. Methods and Insights from Single-Cell Expression Quantitative Trait Loci. Annu Rev Genomics Hum Genet 2023; 24:277-303. [PMID: 37196361 PMCID: PMC10784788 DOI: 10.1146/annurev-genom-101422-100437] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
Collapse
Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
| |
Collapse
|
10
|
Xu H, Lotfy P, Gelb S, Pragana A, Hehnly C, Shipley FB, Zawadzki ME, Cui J, Deng L, Taylor M, Webb M, Lidov HGW, Andermann ML, Chiu IM, Ordovas-Montanes J, Lehtinen MK. A collaboration between immune cells and the choroid plexus epithelium in brain inflammation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.07.552298. [PMID: 37609192 PMCID: PMC10441321 DOI: 10.1101/2023.08.07.552298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The choroid plexus (ChP) is a vital brain barrier and source of cerebrospinal fluid (CSF). Here, we use chronic two-photon imaging in awake mice and single-cell transcriptomics to demonstrate that in addition to these roles, the ChP is a complex immune organ that regulates brain inflammation. In a mouse meningitis model, neutrophils and monocytes accumulated in ChP stroma and surged across the epithelial barrier into the CSF. Bi-directional recruitment of monocytes from the periphery and, unexpectedly, macrophages from the CSF to the ChP helped eliminate neutrophils and repair the barrier. Transcriptomic analyses detailed the molecular steps accompanying this process, including the discovery of epithelial cells that transiently specialized to nurture immune cells, coordinate their recruitment, survival, and differentiation, and ultimately, control the opening/closing of the ChP brain barrier. Collectively, we provide a new conceptual understanding and comprehensive roadmap of neuroinflammation at the ChP brain barrier.
Collapse
|
11
|
He C, Zhou P, Nie Q. exFINDER: identify external communication signals using single-cell transcriptomics data. Nucleic Acids Res 2023; 51:e58. [PMID: 37026478 PMCID: PMC10250247 DOI: 10.1093/nar/gkad262] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/22/2023] [Accepted: 03/28/2023] [Indexed: 04/08/2023] Open
Abstract
Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.
Collapse
Affiliation(s)
- Changhan He
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
- Department of Cell and Developmental Biology, University of California, Irvine, Irvine, CA 92697, USA
| |
Collapse
|
12
|
Genuth MA, Kojima Y, Jülich D, Kiryu H, Holley SA. Automated time-lapse data segmentation reveals in vivo cell state dynamics. SCIENCE ADVANCES 2023; 9:eadf1814. [PMID: 37267354 PMCID: PMC10413672 DOI: 10.1126/sciadv.adf1814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/27/2023] [Indexed: 06/04/2023]
Abstract
Embryonic development proceeds as a series of orderly cell state transitions built upon noisy molecular processes. We defined gene expression and cell motion states using single-cell RNA sequencing data and in vivo time-lapse cell tracking data of the zebrafish tailbud. We performed a parallel identification of these states using dimensional reduction methods and a change point detection algorithm. Both types of cell states were quantitatively mapped onto embryos, and we used the cell motion states to study the dynamics of biological state transitions over time. The time average pattern of cell motion states is reproducible among embryos. However, individual embryos exhibit transient deviations from the time average forming left-right asymmetries in collective cell motion. Thus, the reproducible pattern of cell states and bilateral symmetry arise from temporal averaging. In addition, collective cell behavior can be a source of asymmetry rather than a buffer against noisy individual cell behavior.
Collapse
Affiliation(s)
- Miriam A. Genuth
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
| | - Yasuhiro Kojima
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
- Division of Systems Biology, Graduate School of Medicine, Nagoya University, Nagoya 4668550, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Dörthe Jülich
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
| | - Hisanori Kiryu
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Scott A. Holley
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
| |
Collapse
|
13
|
Wang Y, Xuan C, Wu H, Zhang B, Ding T, Gao J. P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network. Brief Bioinform 2023; 24:bbad180. [PMID: 37170676 DOI: 10.1093/bib/bbad180] [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: 12/07/2022] [Revised: 03/14/2023] [Accepted: 04/19/2023] [Indexed: 05/13/2023] Open
Abstract
Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.
Collapse
Affiliation(s)
- Yan Wang
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Chenxu Xuan
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Hanwen Wu
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Bai Zhang
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Tao Ding
- School of Mathematics Statistics and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Jie Gao
- School of Science, Jiangnan University, Wuxi 214122, China
| |
Collapse
|
14
|
Froelicher D, Cho H, Edupalli M, Sousa JS, Bossuat JP, Pyrgelis A, Troncoso-Pastoriza JR, Berger B, Hubaux JP. Scalable and Privacy-Preserving Federated Principal Component Analysis. PROCEEDINGS. IEEE SYMPOSIUM ON SECURITY AND PRIVACY 2023; 2023:1908-1925. [PMID: 38665901 PMCID: PMC11044025 DOI: 10.1109/sp46215.2023.10179350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confidentiality. Our solution, SF-PCA, is an end-to-end secure system that preserves the confidentiality of both the original data and all intermediate results in a passive-adversary model with up to all-but-one colluding parties. SF-PCA jointly leverages multiparty homomorphic encryption, interactive protocols, and edge computing to efficiently interleave computations on local cleartext data with operations on collectively encrypted data. SF-PCA obtains results as accurate as non-secure centralized solutions, independently of the data distribution among the parties. It scales linearly or better with the dataset dimensions and with the number of data providers. SF-PCA is more precise than existing approaches that approximate the solution by combining local analysis results, and between 3x and 250x faster than privacy-preserving alternatives based solely on secure multiparty computation or homomorphic encryption. Our work demonstrates the practical applicability of secure and federated PCA on private distributed datasets.
Collapse
|
15
|
Nie X, Qin D, Zhou X, Duo H, Hao Y, Li B, Liang G. Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges. Comput Biol Med 2023; 159:106939. [PMID: 37075602 DOI: 10.1016/j.compbiomed.2023.106939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 04/21/2023]
Abstract
With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.
Collapse
Affiliation(s)
- Xiner Nie
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China; College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Dan Qin
- Department of Biology, College of Science, Northeastern University, Boston, MA, 02115, USA
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China.
| |
Collapse
|
16
|
Berger B, Yu YW. Navigating bottlenecks and trade-offs in genomic data analysis. Nat Rev Genet 2023; 24:235-250. [PMID: 36476810 DOI: 10.1038/s41576-022-00551-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2022] [Indexed: 12/12/2022]
Abstract
Genome sequencing and analysis allow researchers to decode the functional information hidden in DNA sequences as well as to study cell to cell variation within a cell population. Traditionally, the primary bottleneck in genomic analysis pipelines has been the sequencing itself, which has been much more expensive than the computational analyses that follow. However, an important consequence of the continued drive to expand the throughput of sequencing platforms at lower cost is that often the analytical pipelines are struggling to keep up with the sheer amount of raw data produced. Computational cost and efficiency have thus become of ever increasing importance. Recent methodological advances, such as data sketching, accelerators and domain-specific libraries/languages, promise to address these modern computational challenges. However, despite being more efficient, these innovations come with a new set of trade-offs, both expected, such as accuracy versus memory and expense versus time, and more subtle, including the human expertise needed to use non-standard programming interfaces and set up complex infrastructure. In this Review, we discuss how to navigate these new methodological advances and their trade-offs.
Collapse
Affiliation(s)
- Bonnie Berger
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Yun William Yu
- Department of Computer and Mathematical Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- Tri-Campus Department of Mathematics, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
17
|
He C, Zhou P, Nie Q. exFINDER: identify external communication signals using single-cell transcriptomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.24.533888. [PMID: 37034624 PMCID: PMC10081188 DOI: 10.1101/2023.03.24.533888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.
Collapse
Affiliation(s)
- Changhan He
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA 92697, USA
- Department of Cell and Developmental Biology, University of California, Irvine, Irvine, CA 92697, USA
| |
Collapse
|
18
|
Xu H, Woicik A, Poon H, Altman RB, Wang S. Multilingual translation for zero-shot biomedical classification using BioTranslator. Nat Commun 2023; 14:738. [PMID: 36759510 PMCID: PMC9911740 DOI: 10.1038/s41467-023-36476-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
Existing annotation paradigms rely on controlled vocabularies, where each data instance is classified into one term from a predefined set of controlled vocabularies. This paradigm restricts the analysis to concepts that are known and well-characterized. Here, we present the novel multilingual translation method BioTranslator to address this problem. BioTranslator takes a user-written textual description of a new concept and then translates this description to a non-text biological data instance. The key idea of BioTranslator is to develop a multilingual translation framework, where multiple modalities of biological data are all translated to text. We demonstrate how BioTranslator enables the identification of novel cell types using only a textual description and how BioTranslator can be further generalized to protein function prediction and drug target identification. Our tool frees scientists from limiting their analyses within predefined controlled vocabularies, enabling them to interact with biological data using free text.
Collapse
Affiliation(s)
- Hanwen Xu
- School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Addie Woicik
- School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | | | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Sheng Wang
- School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
| |
Collapse
|
19
|
Drake RS, Villanueva MA, Vilme M, Russo DD, Navia A, Love JC, Shalek AK. Profiling Transcriptional Heterogeneity with Seq-Well S 3: A Low-Cost, Portable, High-Fidelity Platform for Massively Parallel Single-Cell RNA-Seq. Methods Mol Biol 2023; 2584:57-104. [PMID: 36495445 DOI: 10.1007/978-1-0716-2756-3_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Seq-Well is a high-throughput, picowell-based single-cell RNA-seq technology that can be used to simultaneously profile the transcriptomes of thousands of cells (Gierahn et al. Nat Methods 14(4):395-398, 2017). Relative to its reverse-emulsion-droplet-based counterparts, Seq-Well addresses key cost, portability, and scalability limitations. Recently, we introduced an improved molecular biology for Seq-Well to enhance the information content that can be captured from individual cells using the platform. This update, which we call Seq-Well S3 (S3: Second-Strand Synthesis), incorporates a second-strand-synthesis step after reverse transcription to boost the detection of cellular transcripts normally missed when running the original Seq-Well protocol (Hughes et al. Immunity 53(4):878-894.e7, 2020). This chapter provides details and tips on how to perform Seq-Well S3, along with general pointers on how to subsequently analyze the resultant single-cell RNA-seq data.
Collapse
Affiliation(s)
- Riley S Drake
- Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Martin Arreola Villanueva
- Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Mike Vilme
- Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniela D Russo
- Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew Navia
- Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - J Christopher Love
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Alex K Shalek
- Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
- The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
20
|
Harmanci A, Harmanci AS, Klisch TJ, Patel AJ. XCVATR: detection and characterization of variant impact on the Embeddings of single -cell and bulk RNA-sequencing samples. BMC Genomics 2022; 23:841. [PMID: 36539717 PMCID: PMC9764736 DOI: 10.1186/s12864-022-09004-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/09/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states. RESULTS Here, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local "clumps" of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors. CONCLUSIONS XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR .
Collapse
Affiliation(s)
- Arif Harmanci
- grid.267308.80000 0000 9206 2401University of Texas Health Science Center, School of Biomedical Informatics, Center for Secure Artificial intelligence For hEalthcare (SAFE), Center for Precision Health, Houston, USA
| | - Akdes Serin Harmanci
- grid.39382.330000 0001 2160 926XDepartment of Neurosurgery, Baylor College of Medicine, Houston, TX 77030 USA
| | - Tiemo J. Klisch
- grid.416975.80000 0001 2200 2638Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030 USA ,grid.39382.330000 0001 2160 926XDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Akash J. Patel
- grid.39382.330000 0001 2160 926XDepartment of Neurosurgery, Baylor College of Medicine, Houston, TX 77030 USA ,grid.416975.80000 0001 2200 2638Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030 USA ,grid.39382.330000 0001 2160 926XDepartment of Otolaryngology – Head and Neck Surgery, Baylor College of Medicine, Houston, TX 77030 USA
| |
Collapse
|
21
|
Shalita R, Amit I. The industrial genomic revolution: A new era in neuroimmunology. Neuron 2022; 110:3429-3443. [PMID: 36257321 DOI: 10.1016/j.neuron.2022.09.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/06/2022]
Abstract
Recent discoveries have highlighted the importance of understanding the complex interactions between the brain and immune systems in health and neurodegenerative disease. In this Primer, we outline single-cell multiomics approaches. Applied to patient samples with rich metadata, functional organoids, and animal models, single-cell studies will facilitate the next big leap in translational neuro-immune research. We believe this will pave the way for reshaping our understanding of the neuro-immune interplay: from descriptive to functional, from broad cell types to effective pathways, spatial organization, biomarkers, and targets, toward a comprehensive mechanistic understanding that will be the impetus for drug discovery and therapeutic breakthroughs. We envision that in the near future, single-cell multiomics technologies, along with the advances in immunotherapy development, will become a major driving force and fully integrated resource in the toolset for the development of therapeutic agents in neuroimmunology, which will revolutionize drug development for treating neurodegenerative diseases.
Collapse
Affiliation(s)
- Rotem Shalita
- Department of Systems Immunology, Weizmann Institute, Rehovot, 7610001, Israel.
| | - Ido Amit
- Department of Systems Immunology, Weizmann Institute, Rehovot, 7610001, Israel.
| |
Collapse
|
22
|
Kolmar L, Autour A, Ma X, Vergier B, Eduati F, Merten CA. Technological and computational advances driving high-throughput oncology. Trends Cell Biol 2022; 32:947-961. [PMID: 35577671 DOI: 10.1016/j.tcb.2022.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 01/21/2023]
Abstract
Engineering and computational advances have opened many new avenues in cancer research, particularly when being exploited in interdisciplinary approaches. For example, the combination of microfluidics, novel sequencing technologies, and computational analyses has been crucial to enable single-cell assays, giving a detailed picture of tumor heterogeneity for the very first time. In a similar way, these 'tech' disciplines have been elementary for generating large data sets in multidimensional cancer 'omics' approaches, cell-cell interaction screens, 3D tumor models, and tissue level analyses. In this review we summarize the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, high-throughput, high-content discipline across all biological scales.
Collapse
Affiliation(s)
- Leonie Kolmar
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alexis Autour
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Xiaoli Ma
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Blandine Vergier
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
| | - Christoph A Merten
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| |
Collapse
|
23
|
Morgan DM, Shreffler WG, Love JC. Revealing the heterogeneity of CD4+ T cells through single-cell transcriptomics. J Allergy Clin Immunol 2022; 150:748-755. [DOI: 10.1016/j.jaci.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/15/2022] [Accepted: 08/19/2022] [Indexed: 11/07/2022]
|
24
|
Zhang C, Chen R, Zhang Y. Accurate inference of genome-wide spatial expression with iSpatial. SCIENCE ADVANCES 2022; 8:eabq0990. [PMID: 36026447 PMCID: PMC9417177 DOI: 10.1126/sciadv.abq0990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Because of the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here, we developed a new algorithm named iSpatial to derive the spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other existing methods, iSpatial has higher accuracy in predicting gene expression and spatial distribution. Furthermore, it reduces false-positive and false-negative signals in the original datasets. By testing iSpatial with multiple spatial transcriptomic datasets, we demonstrate its wide applicability to datasets from different tissues and by different techniques. Thus, we provide a computational approach to reveal spatial organization of the entire transcriptome at single-cell resolution. With numerous high-quality datasets available in the public domain, iSpatial provides a unique way to understand the structure and function of complex tissues and disease processes.
Collapse
Affiliation(s)
- Chao Zhang
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Renchao Chen
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Yi Zhang
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Harvard Stem Cell Institute, WAB-149G, 200 Longwood Avenue, Boston, MA 02115, USA
| |
Collapse
|
25
|
Mallick H, Chatterjee S, Chowdhury S, Chatterjee S, Rahnavard A, Hicks SC. Differential expression of single-cell RNA-seq data using Tweedie models. Stat Med 2022; 41:3492-3510. [PMID: 35656596 PMCID: PMC9288986 DOI: 10.1002/sim.9430] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 12/13/2022]
Abstract
The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.
Collapse
Affiliation(s)
- Himel Mallick
- Biostatistics and Research Decision Sciences, Merck &
Co., Inc., Rahway, NJ 07065, USA
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population
Health Research, Eunice Kennedy Shriver National Institute of Child
Health and Human Development, National Institutes of Health, Bethesda, MD 20892,
USA
| | - Shrabanti Chowdhury
- Department of Genetics and Genomic Sciences and Icahn
Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount
Sinai, New York, NY 10029, USA
| | - Saptarshi Chatterjee
- Department of Statistics, Data and Analytics, Eli Lilly
& Company, Indianapolis, IN 46225, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of
Biostatistics and Bioinformatics, Milken Institute School of Public Health, The
George Washington University, Washington, DC 20052, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School
of Public Health, Baltimore, MD 21205, USA
| |
Collapse
|
26
|
Wang R, Peng G, Tam PPL, Jing N. Integration of computational analysis and spatial transcriptomics in single-cell study. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00084-5. [PMID: 35901961 PMCID: PMC10372908 DOI: 10.1016/j.gpb.2022.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 06/08/2022] [Accepted: 06/19/2022] [Indexed: 04/08/2023]
Abstract
Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology. Meanwhile, pioneering explorations in spatial transcriptomics have opened avenues to address fundamental biological questions in health and diseases. Here, we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics, and the core concepts of computational data analysis. We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.
Collapse
Affiliation(s)
- Ran Wang
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Guangdun Peng
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Patrick P L Tam
- Embryology Research Unit, Children's Medical Research Institute, University of Sydney, Sydney, NSW 2145, Australia; School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2145, Australia
| | - Naihe Jing
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; Guangzhou Laboratory, Guangzhou 510005, China; CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China.
| |
Collapse
|
27
|
Mo Y, Jiao Y. Advances and applications of single-cell omics technologies in plant research. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 110:1551-1563. [PMID: 35426954 DOI: 10.1111/tpj.15772] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
Single-cell sequencing approaches reveal the intracellular dynamics of individual cells and answer biological questions with high-dimensional catalogs of millions of cells, including genomics, transcriptomics, chromatin accessibility, epigenomics, and proteomics data across species. These emerging yet thriving technologies have been fully embraced by the field of plant biology, with a constantly expanding portfolio of applications. Here, we introduce the current technical advances used for single-cell omics, especially single-cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well-executed benchmarking studies to highlight advances made through the application of single-cell omics techniques. Looking forward, we offer a glimpse of additional hurdles and future opportunities that will introduce broad adoption of single-cell sequencing with revolutionary perspectives in plant biology.
Collapse
Affiliation(s)
- Yajin Mo
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, School of Life Sciences, Peking University, Beijing, 100871, China
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yuling Jiao
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, School of Life Sciences, Peking University, Beijing, 100871, China
- State Key Laboratory of Plant Genomics and National Center for Plant Gene Research (Beijing), Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
| |
Collapse
|
28
|
Deng M, Wang Y, Yan Y. Mining cell–cell signaling in single-cell transcriptomics atlases. Curr Opin Cell Biol 2022; 76:102101. [DOI: 10.1016/j.ceb.2022.102101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 03/16/2022] [Accepted: 04/24/2022] [Indexed: 12/22/2022]
|
29
|
Nyquist SK, Gao P, Haining TKJ, Retchin MR, Golan Y, Drake RS, Kolb K, Mead BE, Ahituv N, Martinez ME, Shalek AK, Berger B, Goods BA. Cellular and transcriptional diversity over the course of human lactation. Proc Natl Acad Sci U S A 2022; 119:e2121720119. [PMID: 35377806 PMCID: PMC9169737 DOI: 10.1073/pnas.2121720119] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/14/2022] [Indexed: 12/04/2022] Open
Abstract
Human breast milk (hBM) is a dynamic fluid that contains millions of cells, but their identities and phenotypic properties are poorly understood. We generated and analyzed single-cell RNA-sequencing (scRNA-seq) data to characterize the transcriptomes of cells from hBM across lactational time from 3 to 632 d postpartum in 15 donors. We found that the majority of cells in hBM are lactocytes, a specialized epithelial subset, and that cell-type frequencies shift over the course of lactation, yielding greater epithelial diversity at later points. Analysis of lactocytes reveals a continuum of cell states characterized by transcriptional changes in hormone-, growth factor-, and milk production-related pathways. Generalized additive models suggest that one subcluster, LC1 epithelial cells, increases as a function of time postpartum, daycare attendance, and the use of hormonal birth control. We identify several subclusters of macrophages in hBM that are enriched for tolerogenic functions, possibly playing a role in protecting the mammary gland during lactation. Our description of the cellular components of breast milk, their association with maternal–infant dyad metadata, and our quantification of alterations at the gene and pathway levels provide a detailed longitudinal picture of hBM cells across lactational time. This work paves the way for future investigations of how a potential division of cellular labor and differential hormone regulation might be leveraged therapeutically to support healthy lactation and potentially aid in milk production.
Collapse
Affiliation(s)
- Sarah K. Nyquist
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Program in Computational and Systems Biology, Massachusetts Institute of Technology; Cambridge, MA 02139
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
- Computer Science and Artificial Intelligence Laboratory, Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Patricia Gao
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Tessa K. J. Haining
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Michael R. Retchin
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Yarden Golan
- Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, University of California, San Francisco, CA 94143
| | - Riley S. Drake
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139
| | - Kellie Kolb
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Benjamin E. Mead
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, University of California, San Francisco, CA 94143
| | | | - Alex K. Shalek
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Program in Computational and Systems Biology, Massachusetts Institute of Technology; Cambridge, MA 02139
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139
- Division of Health Science & Technology, Harvard Medical School, Boston, MA 02115
- Department of Immunology, Massachusetts General Hospital, Boston, MA 02114
| | - Bonnie Berger
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Computer Science and Artificial Intelligence Laboratory, Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Brittany A. Goods
- Thayer School of Engineering, Program in Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755
| |
Collapse
|
30
|
Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets. Int J Mol Sci 2022; 23:ijms23073811. [PMID: 35409169 PMCID: PMC8998543 DOI: 10.3390/ijms23073811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/26/2022] [Accepted: 03/29/2022] [Indexed: 12/10/2022] Open
Abstract
Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.
Collapse
|
31
|
Ginhoux F, Yalin A, Dutertre CA, Amit I. Single-cell immunology: Past, present, and future. Immunity 2022; 55:393-404. [PMID: 35263567 DOI: 10.1016/j.immuni.2022.02.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/30/2021] [Accepted: 02/09/2022] [Indexed: 02/08/2023]
Abstract
The immune system is a complex, dynamic, and plastic ecosystem composed of multiple cell types that constantly sense and interact with their local microenvironment to protect from infection and maintain homeostasis. For over a century, great efforts and ingenuity have been applied to the characterization of immune cells and their microenvironments, but traditional marker-based and bulk technologies left key questions unanswered. In the past decade, the advent of single-cell genomic approaches has revolutionized our knowledge of the cellular and molecular makeup of the immune system. In this perspective, we outline the past, present, and future applications of single-cell genomics in immunology and discuss how the integration of multiomics at the single-cell level will pave the way for future advances in immunology research and clinical translation.
Collapse
Affiliation(s)
- Florent Ginhoux
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A(∗)STAR), Singapore 138648, Singapore; Gustave Roussy Cancer Campus, Villejuif 94800, France; Inserm U1015, Gustave Roussy, Villejuif 94800, France; Shanghai Institute of Immunology, Shanghai JiaoTong University School of Medicine, Shanghai 200025, China; Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore 169856, Singapore.
| | - Adam Yalin
- Department of Immunology, Weizmann Institute, Rehovot 7610001, Israel.
| | - Charles Antoine Dutertre
- Gustave Roussy Cancer Campus, Villejuif 94800, France; Inserm U1015, Gustave Roussy, Villejuif 94800, France.
| | - Ido Amit
- Department of Immunology, Weizmann Institute, Rehovot 7610001, Israel.
| |
Collapse
|
32
|
Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins. Cell Syst 2022; 13:274-285.e6. [PMID: 35120643 DOI: 10.1016/j.cels.2022.01.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/15/2021] [Accepted: 01/12/2022] [Indexed: 11/23/2022]
Abstract
The degree to which evolution is predictable is a fundamental question in biology. Previous attempts to predict the evolution of protein sequences have been limited to specific proteins and to small changes, such as single-residue mutations. Here, we demonstrate that by using a protein language model to predict the local evolution within protein families, we recover a dynamic "vector field" of protein evolution that we call evolutionary velocity (evo-velocity). Evo-velocity generalizes to evolution over vastly different timescales, from viral proteins evolving over years to eukaryotic proteins evolving over geologic eons, and can predict the evolutionary dynamics of proteins that were not used to develop the original model. Evo-velocity also yields new evolutionary insights by predicting strategies of viral-host immune escape, resolving conflicting theories on the evolution of serpins, and revealing a key role of horizontal gene transfer in the evolution of eukaryotic glycolysis.
Collapse
|
33
|
Ao C, Jiao S, Wang Y, Yu L, Zou Q. Biological Sequence Classification: A Review on Data and General Methods. RESEARCH 2022. [DOI: 10.34133/research.0011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (
http://lab.malab.cn/~acy/BioseqData/home.html
), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.
Collapse
Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi’an, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shihu Jiao
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yansu Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
34
|
Mädler SC, Julien-Laferriere A, Wyss L, Phan M, Sonrel A, Kang ASW, Ulrich E, Schmucki R, Zhang JD, Ebeling M, Badi L, Kam-Thong T, Schwalie PC, Hatje K. Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research. NAR Genom Bioinform 2021; 3:lqab102. [PMID: 34761219 PMCID: PMC8573822 DOI: 10.1093/nargab/lqab102] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 02/07/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.
Collapse
Affiliation(s)
- Sophia Clara Mädler
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Alice Julien-Laferriere
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Luis Wyss
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Miroslav Phan
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Anthony Sonrel
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Albert S W Kang
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Eric Ulrich
- Roche Pharma Research and Early Development, I2O Disease Translational Area, Roche Innovation Center Basel, Basel, Switzerland
| | - Roland Schmucki
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Jitao David Zhang
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Martin Ebeling
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Laura Badi
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Tony Kam-Thong
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Petra C Schwalie
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Klas Hatje
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| |
Collapse
|
35
|
Sheng J, Li WV. Selecting gene features for unsupervised analysis of single-cell gene expression data. Brief Bioinform 2021; 22:bbab295. [PMID: 34351383 PMCID: PMC8574996 DOI: 10.1093/bib/bbab295] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 06/17/2021] [Accepted: 07/12/2021] [Indexed: 11/15/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) technologies facilitate the characterization of transcriptomic landscapes in diverse species, tissues, and cell types with unprecedented molecular resolution. In order to evaluate various biological hypotheses using high-dimensional single-cell gene expression data, most computational and statistical methods depend on a gene feature selection step to identify genes with high biological variability and reduce computational complexity. Even though many gene selection methods have been developed for scRNA-seq analysis, there lacks a systematic comparison of the assumptions, statistical models, and selection criteria used by these methods. In this article, we summarize and discuss 17 computational methods for selecting gene features in unsupervised analysis of single-cell gene expression data, with unified notations and statistical frameworks. Our discussion provides a useful summary to help practitioners select appropriate methods based on their assumptions and applicability, and to assist method developers in designing new computational tools for unsupervised learning of scRNA-seq data.
Collapse
Affiliation(s)
- Jie Sheng
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Wei Vivian Li
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ 08854, USA
| |
Collapse
|
36
|
Ranjan B, Sun W, Park J, Mishra K, Schmidt F, Xie R, Alipour F, Singhal V, Joanito I, Honardoost MA, Yong JMY, Koh ET, Leong KP, Rayan NA, Lim MGL, Prabhakar S. DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data. Nat Commun 2021; 12:5849. [PMID: 34615861 PMCID: PMC8494900 DOI: 10.1038/s41467-021-26085-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 09/15/2021] [Indexed: 11/09/2022] Open
Abstract
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even resulting in poorer clustering accuracy than without feature selection. Moreover, existing methods ignore information contained in gene-gene correlations. Here, we introduce DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. Additionally, DUBStepR was the only method to robustly deconvolve T and NK heterogeneity by identifying disease-associated common and rare cell types and subtypes in PBMCs from rheumatoid arthritis patients. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.
Collapse
Affiliation(s)
- Bobby Ranjan
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Wenjie Sun
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jinyu Park
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Kunal Mishra
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Florian Schmidt
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Ronald Xie
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Fatemeh Alipour
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Vipul Singhal
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Ignasius Joanito
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Mohammad Amin Honardoost
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
- Department of Medicine, School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore
| | - Jacy Mei Yun Yong
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore, 308433, Singapore
| | - Ee Tzun Koh
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore, 308433, Singapore
| | - Khai Pang Leong
- Department of Rheumatology, Allergy and Immunology, Tan Tock Seng Hospital, Singapore, 308433, Singapore
| | - Nirmala Arul Rayan
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Michelle Gek Liang Lim
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Shyam Prabhakar
- Laboratory of Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore, 138672, Singapore.
| |
Collapse
|
37
|
Abstract
Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.
Collapse
Affiliation(s)
- Ruben Dries
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Jiaji Chen
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Natalie Del Rossi
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Mohammed Muzamil Khan
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| |
Collapse
|
38
|
Zhao J, Wang N, Wang H, Zheng C, Su Y. SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder. Front Genet 2021; 12:733906. [PMID: 34512734 PMCID: PMC8429846 DOI: 10.3389/fgene.2021.733906] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 07/26/2021] [Indexed: 01/04/2023] Open
Abstract
Dimensionality reduction of high-dimensional data is crucial for single-cell RNA sequencing (scRNA-seq) visualization and clustering. One prominent challenge in scRNA-seq studies comes from the dropout events, which lead to zero-inflated data. To address this issue, in this paper, we propose a scRNA-seq data dimensionality reduction algorithm based on a hierarchical autoencoder, termed SCDRHA. The proposed SCDRHA consists of two core modules, where the first module is a deep count autoencoder (DCA) that is used to denoise data, and the second module is a graph autoencoder that projects the data into a low-dimensional space. Experimental results demonstrate that SCDRHA has better performance than existing state-of-the-art algorithms on dimension reduction and noise reduction in five real scRNA-seq datasets. Besides, SCDRHA can also dramatically improve the performance of data visualization and cell clustering.
Collapse
Affiliation(s)
- Jianping Zhao
- College of Mathematics and System Sciences, Xinjiang University, Ürümqi, China
| | - Na Wang
- College of Mathematics and System Sciences, Xinjiang University, Ürümqi, China
| | - Haiyun Wang
- College of Mathematics and System Sciences, Xinjiang University, Ürümqi, China
| | - Chunhou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
| | - Yansen Su
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
| |
Collapse
|
39
|
Simon F, Konstantinides N. Single-cell transcriptomics in the Drosophila visual system: Advances and perspectives on cell identity regulation, connectivity, and neuronal diversity evolution. Dev Biol 2021; 479:107-122. [PMID: 34375653 DOI: 10.1016/j.ydbio.2021.08.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/10/2021] [Accepted: 08/03/2021] [Indexed: 11/17/2022]
Abstract
The Drosophila visual system supports complex behaviors and shares many of its anatomical and molecular features with the vertebrate brain. Yet, it contains a much more manageable number of neurons and neuronal types. In addition to the extensive Drosophila genetic toolbox, this relative simplicity has allowed decades of work to yield a detailed account of its neuronal type diversity, morphology, connectivity and specification mechanisms. In the past three years, numerous studies have applied large scale single-cell transcriptomic approaches to the Drosophila visual system and have provided access to the complete gene expression profile of most neuronal types throughout development. This makes the fly visual system particularly well suited to perform detailed studies of the genetic mechanisms underlying the evolution and development of neuronal systems. Here, we highlight how these transcriptomic resources allow exploring long-standing biological questions under a new light. We first present the efforts made to characterize neuronal diversity in the Drosophila visual system and suggest ways to further improve this description. We then discuss current advances allowed by the single-cell datasets, and envisage how these datasets can be further leveraged to address fundamental questions regarding the regulation of neuronal identity, neuronal circuit development and the evolution of neuronal diversity.
Collapse
Affiliation(s)
- Félix Simon
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Nikolaos Konstantinides
- Department of Biology, New York University, New York, NY, 10003, USA; Institut Jacques Monod, Centre National de la Recherche Scientifique-UMR 7592, Université Paris Diderot, Paris, France.
| |
Collapse
|
40
|
Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor. Nat Protoc 2021; 16:4031-4067. [PMID: 34234317 PMCID: PMC8826496 DOI: 10.1038/s41596-021-00575-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 05/25/2021] [Indexed: 02/06/2023]
Abstract
Single-cell RNA-sequencing data have significantly advanced the characterization of cell-type diversity and composition. However, cell-type definitions vary across data and analysis pipelines, raising concerns about cell-type validity and generalizability. With MetaNeighbor, we proposed an efficient and robust quantification of cell-type replicability that preserves dataset independence and is highly scalable compared to dataset integration. In this protocol, we show how MetaNeighbor can be used to characterize cell-type replicability by following a simple three-step procedure: gene filtering, neighbor voting and visualization. We show how these steps can be tailored to quantify cell-type replicability, determine gene sets that contribute to cell-type identity and pretrain a model on a reference taxonomy to rapidly assess newly generated data. The protocol is based on an open-source R package available from Bioconductor and GitHub, requires basic familiarity with Rstudio or the R command line and can typically be run in <5 min for millions of cells.
Collapse
|
41
|
Wu AP, Peng J, Berger B, Cho H. Bayesian information sharing enhances detection of regulatory associations in rare cell types. Bioinformatics 2021; 37:i349-i357. [PMID: 34252956 PMCID: PMC8275330 DOI: 10.1093/bioinformatics/btab269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective. RESULTS We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory networks by propagating information across related cell types via an information sharing structure that is adaptively optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell type-specific gene regulation in the rapidly growing compendium of scRNA-seq datasets. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. AVAILABILITY AND IMPLEMENTATION The code for ShareNet is available at http://sharenet.csail.mit.edu and https://github.com/alexw16/sharenet.
Collapse
Affiliation(s)
- Alexander P Wu
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Jian Peng
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.,Department of Mathematics, MIT, Cambridge, MA 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hyunghoon Cho
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| |
Collapse
|
42
|
Abstract
It has been known for over a century that the basic organization of the retina is conserved across vertebrates. It has been equally clear that retinal cells can be classified into numerous types, but only recently have methods been devised to explore this diversity in unbiased, scalable, and comprehensive ways. Advances in high-throughput single-cell RNA-sequencing (scRNA-seq) have played a pivotal role in this effort. In this article, we outline the experimental and computational components of scRNA-seq and review studies that have used them to generate retinal atlases of cell types in several vertebrate species. These atlases have enabled studies of retinal development, responses of retinal cells to injury, expression patterns of genes implicated in retinal disease, and the evolution of cell types. Recently, the inquiry has expanded to include the entire eye and visual centers in the brain. These studies have enhanced our understanding of retinal function and dysfunction and provided tools and insights for exploring neural diversity throughout the brain. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Collapse
Affiliation(s)
- Karthik Shekhar
- Department of Chemical and Biomolecular Engineering; Helen Wills Neuroscience Institute; and California Institute for Quantitative Biosciences, QB3, University of California, Berkeley, California 94720, USA;
| | - Joshua R Sanes
- Center for Brain Science and Department of Molecular and Cell Biology, Harvard University, Cambridge, Massachusetts 02138, USA;
| |
Collapse
|
43
|
Liu J, Qu S, Zhang T, Gao Y, Shi H, Song K, Chen W, Yin W. Applications of Single-Cell Omics in Tumor Immunology. Front Immunol 2021; 12:697412. [PMID: 34177965 PMCID: PMC8221107 DOI: 10.3389/fimmu.2021.697412] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 05/17/2021] [Indexed: 12/20/2022] Open
Abstract
The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.
Collapse
Affiliation(s)
- Junwei Liu
- Department of Cardiology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Saisi Qu
- Department of Cardiology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Tongtong Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The Center for Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yufei Gao
- School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Hongyu Shi
- Department of Biological Testing, Zhejiang Puluoting Health Technology Co., Ltd., Hangzhou, China
| | - Kaichen Song
- Department of Cardiology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Wei Chen
- Department of Cardiology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- School of Mechanical Engineering, Zhejiang University, Hangzhou, China
| | - Weiwei Yin
- Department of Cardiology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China
| |
Collapse
|
44
|
Assessing single-cell transcriptomic variability through density-preserving data visualization. Nat Biotechnol 2021; 39:765-774. [PMID: 33462509 PMCID: PMC8195812 DOI: 10.1038/s41587-020-00801-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/14/2020] [Indexed: 01/29/2023]
Abstract
Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.
Collapse
|
45
|
Almet AA, Cang Z, Jin S, Nie Q. The landscape of cell-cell communication through single-cell transcriptomics. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 26:12-23. [PMID: 33969247 PMCID: PMC8104132 DOI: 10.1016/j.coisb.2021.03.007] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Cell-cell communication is a fundamental process that shapes biological tissue. Historically, studies of cell-cell communication have been feasible for one or two cell types and a few genes. With the emergence of single-cell transcriptomics, we are now able to examine the genetic profiles of individual cells at unprecedented scale and depth. The availability of such data presents an exciting opportunity to construct a more comprehensive description of cell-cell communication. This review discusses the recent explosion of methods that have been developed to infer cell-cell communication from non-spatial and spatial single-cell transcriptomics, two promising technologies which have complementary strengths and limitations. We propose several avenues to propel this rapidly expanding field forward in meaningful ways.
Collapse
Affiliation(s)
- Axel A. Almet
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA 92627, USA
- Department of Mathematics, University of California Irvine, Irvine, CA 92627, USA
| | - Zixuan Cang
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA 92627, USA
- Department of Mathematics, University of California Irvine, Irvine, CA 92627, USA
| | - Suoqin Jin
- Department of Mathematics, University of California Irvine, Irvine, CA 92627, USA
| | - Qing Nie
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA 92627, USA
- Department of Mathematics, University of California Irvine, Irvine, CA 92627, USA
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA 92627, USA
| |
Collapse
|
46
|
Singh R, Hie BL, Narayan A, Berger B. Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities. Genome Biol 2021; 22:131. [PMID: 33941239 PMCID: PMC8091541 DOI: 10.1186/s13059-021-02313-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/12/2021] [Indexed: 02/08/2023] Open
Abstract
A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay multiple modalities simultaneously. We present Schema, which uses a principled metric learning strategy that identifies informative features in a modality to synthesize disparate modalities into a single coherent interpretation. We use Schema to infer cell types by integrating gene expression and chromatin accessibility data; demonstrate informative data visualizations that synthesize multiple modalities; perform differential gene expression analysis in the context of spatial variability; and estimate evolutionary pressure on peptide sequences.
Collapse
Affiliation(s)
- Rohit Singh
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Brian L Hie
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Ashwin Narayan
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| |
Collapse
|
47
|
|
48
|
Zhang X, Man Y, Zhuang X, Shen J, Zhang Y, Cui Y, Yu M, Xing J, Wang G, Lian N, Hu Z, Ma L, Shen W, Yang S, Xu H, Bian J, Jing Y, Li X, Li R, Mao T, Jiao Y, Sodmergen, Ren H, Lin J. Plant multiscale networks: charting plant connectivity by multi-level analysis and imaging techniques. SCIENCE CHINA-LIFE SCIENCES 2021; 64:1392-1422. [PMID: 33974222 DOI: 10.1007/s11427-020-1910-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/04/2021] [Indexed: 12/21/2022]
Abstract
In multicellular and even single-celled organisms, individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation. Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes. Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project. In plant science, network analysis has similarly been applied to study the connectivity of plant components at the molecular, subcellular, cellular, organic, and organism levels. Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype. In this review, we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities. We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants. Finally, we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.
Collapse
Affiliation(s)
- Xi Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Yi Man
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Xiaohong Zhuang
- School of Life Sciences, Centre for Cell & Developmental Biology and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, 999077, China
| | - Jinbo Shen
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300, China
| | - Yi Zhang
- Key Laboratory of Cell Proliferation and Regulation Biology, Ministry of Education, College of Life Science, Beijing Normal University, Beijing, 100875, China
| | - Yaning Cui
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Meng Yu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Jingjing Xing
- Key Laboratory of Plant Stress Biology, School of Life Sciences, Henan University, Kaifeng, 457004, China
| | - Guangchao Wang
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Na Lian
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Zijian Hu
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Lingyu Ma
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Weiwei Shen
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Shunyao Yang
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Huimin Xu
- College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Jiahui Bian
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Yanping Jing
- College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Xiaojuan Li
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Ruili Li
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China.,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China
| | - Tonglin Mao
- State Key Laboratory of Plant Physiology and Biochemistry, Department of Plant Sciences, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Yuling Jiao
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and National Center for Plant Gene Research, Beijing, 100101, China
| | - Sodmergen
- Key Laboratory of Ministry of Education for Cell Proliferation and Differentiation, College of Life Sciences, Peking University, Beijing, 100871, China
| | - Haiyun Ren
- Key Laboratory of Cell Proliferation and Regulation Biology, Ministry of Education, College of Life Science, Beijing Normal University, Beijing, 100875, China
| | - Jinxing Lin
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083, China. .,College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing, 100083, China.
| |
Collapse
|
49
|
Acosta J, Ssozi D, van Galen P. Single-Cell RNA Sequencing to Disentangle the Blood System. Arterioscler Thromb Vasc Biol 2021; 41:1012-1018. [PMID: 33441024 PMCID: PMC7901535 DOI: 10.1161/atvbaha.120.314654] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The blood system is often represented as a tree-like structure with stem cells that give rise to mature blood cell types through a series of demarcated steps. Although this representation has served as a model of hierarchical tissue organization for decades, single-cell technologies are shedding new light on the abundance of cell type intermediates and the molecular mechanisms that ensure balanced replenishment of differentiated cells. In this Brief Review, we exemplify new insights into blood cell differentiation generated by single-cell RNA sequencing, summarize considerations for the application of this technology, and highlight innovations that are leading the way to understand hematopoiesis at the resolution of single cells. Graphic Abstract: A graphic abstract is available for this article.
Collapse
Affiliation(s)
- Jean Acosta
- Division of Hematology, Brigham and Women's Hospital, Boston, MA. Department of Medicine, Harvard Medical School, Boston, MA. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Daniel Ssozi
- Division of Hematology, Brigham and Women's Hospital, Boston, MA. Department of Medicine, Harvard Medical School, Boston, MA. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Peter van Galen
- Division of Hematology, Brigham and Women's Hospital, Boston, MA. Department of Medicine, Harvard Medical School, Boston, MA. Broad Institute of MIT and Harvard, Cambridge, MA
| |
Collapse
|
50
|
Abstract
The community of cells lining our airways plays a collaborative role in the preservation of immune homeostasis in the lung and provides protection from the pathogens and pollutants in the air we breathe. In addition to its structural attributes that provide effective mucociliary clearance of the lower airspace, the airway epithelium is an immunologically active barrier surface that senses changes in the airway environment and interacts with resident and recruited immune cells. Single-cell RNA-sequencing is illuminating the cellular heterogeneity that exists in the airway wall and has identified novel cell populations with unique molecular signatures, trajectories of differentiation and diverse functions in health and disease. In this Review, we discuss how our view of the airway epithelial landscape has evolved with the advent of transcriptomic approaches to cellular phenotyping, with a focus on epithelial interactions with the local neuronal and immune systems.
Collapse
|