1
|
Li K, Ling H, Huang W, Luo W, Gu C, Tao B, Xie Q, Qiu P. Single-cell RNA-sequencing analysis reveals α-syn induced astrocyte-neuron crosstalk-mediated neurotoxicity. Int Immunopharmacol 2024; 139:112676. [PMID: 39053230 DOI: 10.1016/j.intimp.2024.112676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 07/05/2024] [Accepted: 07/10/2024] [Indexed: 07/27/2024]
Abstract
Accumulation of alpha-synuclein (α-syn) is a key pathological hallmark of synucleinopathies and has been shown to negatively impact neuronal function and activity. α-syn is an important factor contributing to astrocyte overactivation, though the effect of astrocyte overactivation on neurons remains unclear. Single-cell RNA sequencing data of mouse brain frontal cortex and midbrain from Hua-Syn (A53T) and wild type mice were utilized from the GEO database. Enrichment analysis, protein-protein interaction networks, and cell-cell interaction networks all indicated enhanced communication between astrocytes and neurons, along with the involvement of TNF and inflammation-related signaling pathways. In vitro experiments were performed to further explore the mechanism of neurotoxicity in astrocyte-neuron crosstalk. Astrocytes were treated by α-syn, neuronal TNFR1 receptors were antagonized by R-7050, and the cells were co-cultured after 24 h treatment. ELISA results revealed that cytokines such as TNF-α and IL-6 were significantly upregulated in astrocytes following the endocytosis of α-syn. Immunofluorescence (IF) showed neuronal dendritic reduction, axon elongation and increased co-localisation of TNFR1 receptor expression. Western blot showed up-regulation of PKR, P-eIF2α and ATF4 protein expression. Conversely, after antagonizing neuronal TNFR1 receptors with the R-7050 chemical inhibitor, neuronal synaptic structure was significantly restored and the expression of PKR, P-eIF2α and ATF4 was down-regulated. In summary, TNF-α acts as a signaling molecule mediating the up-regulated astrocyte-neuron crosstalk, providing new insights into the pathogenesis of α-syn-related neurological disorders.
Collapse
Affiliation(s)
- Kuan Li
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Haosen Ling
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Wei Huang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Wenyu Luo
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Cihang Gu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Bowen Tao
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Qiqian Xie
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Pingming Qiu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, China; Department of Thyroid Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
| |
Collapse
|
2
|
Udugampolage NS, Frolova S, Taurino J, Pini A, Martelli F, Voellenkle C. Coding and Non-Coding Transcriptomic Landscape of Aortic Complications in Marfan Syndrome. Int J Mol Sci 2024; 25:7367. [PMID: 39000474 PMCID: PMC11242319 DOI: 10.3390/ijms25137367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
Marfan syndrome (MFS) is a rare congenital disorder of the connective tissue, leading to thoracic aortic aneurysms (TAA) and dissection, among other complications. Currently, the most efficient strategy to prevent life-threatening dissection is preventive surgery. Periodic imaging applying complex techniques is required to monitor TAA progression and to guide the timing of surgical intervention. Thus, there is an acute demand for non-invasive biomarkers for diagnosis and prognosis, as well as for innovative therapeutic targets of MFS. Unraveling the intricate pathomolecular mechanisms underlying the syndrome is vital to address these needs. High-throughput platforms are particularly well-suited for this purpose, as they enable the integration of different datasets, such as transcriptomic and epigenetic profiles. In this narrative review, we summarize relevant studies investigating changes in both the coding and non-coding transcriptome and epigenome in MFS-induced TAA. The collective findings highlight the implicated pathways, such as TGF-β signaling, extracellular matrix structure, inflammation, and mitochondrial dysfunction. Potential candidates as biomarkers, such as miR-200c, as well as therapeutic targets emerged, like Tfam, associated with mitochondrial respiration, or miR-632, stimulating endothelial-to-mesenchymal transition. While these discoveries are promising, rigorous and extensive validation in large patient cohorts is indispensable to confirm their clinical relevance and therapeutic potential.
Collapse
Affiliation(s)
| | - Svetlana Frolova
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, 20097 Milan, Italy; (S.F.); (C.V.)
- Department of Biosciences, University of Milan, 20122 Milan, Italy
| | - Jacopo Taurino
- Cardiovascular-Genetic Center, IRCCS Policlinico San Donato, 20097 Milan, Italy; (N.S.U.); (J.T.); (A.P.)
| | - Alessandro Pini
- Cardiovascular-Genetic Center, IRCCS Policlinico San Donato, 20097 Milan, Italy; (N.S.U.); (J.T.); (A.P.)
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, 20097 Milan, Italy; (S.F.); (C.V.)
| | - Christine Voellenkle
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, 20097 Milan, Italy; (S.F.); (C.V.)
| |
Collapse
|
3
|
Wei X, Ma W, Wu Z, Wu H. Target-Oriented Reference Construction for supervised cell-type identification in scRNA-seq. RESEARCH SQUARE 2024:rs.3.rs-4559348. [PMID: 38978578 PMCID: PMC11230472 DOI: 10.21203/rs.3.rs-4559348/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Cell-type identification is the most crucial step in single cell RNA-seq (scRNA-seq) data analysis, for which the supervised cell-type identification method is a desired solution due to the accuracy and efficiency. The performance of such methods is highly dependent on the quality of the reference data. Even though there are many supervised cell-type identification tools, there is no method for selecting and constructing reference data. Here we develop Target-Oriented Reference Construction (TORC), a widely applicable strategy for constructing reference given target dataset in scRNA-seq supervised cell-type identification. TORC alleviates the differences in data distribution and cell-type composition between reference and target. Extensive benchmarks on simulated and real data analyses demonstrate consistent improvements in cell-type identification from TORC. TORC is freely available at https://github.com/weix21/TORC.
Collapse
Affiliation(s)
| | | | | | - Hao Wu
- Shenzhen University of Advanced Technology
| |
Collapse
|
4
|
Schiebout C, Frost HR. CAraCAl: CAMML with the integration of chromatin accessibility. BMC Bioinformatics 2024; 25:212. [PMID: 38872103 PMCID: PMC11170880 DOI: 10.1186/s12859-024-05833-3] [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] [Received: 03/07/2024] [Accepted: 06/10/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression. RESULTS To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity. CONCLUSIONS We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.
Collapse
Affiliation(s)
- Courtney Schiebout
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, 03766, USA.
| | - H Robert Frost
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, 03766, USA
| |
Collapse
|
5
|
McLean AK, Reynolds G, Pratt AG. Leveraging Multi-Tissue, Single-Cell Atlases as Tools to Elucidate Shared Mechanisms of Immune-Mediated Inflammatory Diseases. Biomedicines 2024; 12:1297. [PMID: 38927506 PMCID: PMC11201400 DOI: 10.3390/biomedicines12061297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/05/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
The observation that certain therapeutic strategies for targeting inflammation benefit patients with distinct immune-mediated inflammatory diseases (IMIDs) is exemplified by the success of TNF blockade in conditions including rheumatoid arthritis, ulcerative colitis, and skin psoriasis, albeit only for subsets of individuals with each condition. This suggests intersecting "nodes" in inflammatory networks at a molecular and cellular level may drive and/or maintain IMIDs, being "shared" between traditionally distinct diagnoses without mapping neatly to a single clinical phenotype. In line with this proposition, integrative tumour tissue analyses in oncology have highlighted novel cell states acting across diverse cancers, with important implications for precision medicine. Drawing upon advances in the oncology field, this narrative review will first summarise learnings from the Human Cell Atlas in health as a platform for interrogating IMID tissues. It will then review cross-disease studies to date that inform this endeavour before considering future directions in the field.
Collapse
Affiliation(s)
- Anthony K. McLean
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Gary Reynolds
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Arthur G. Pratt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
| |
Collapse
|
6
|
Benjamin K, Bhandari A, Kepple JD, Qi R, Shang Z, Xing Y, An Y, Zhang N, Hou Y, Crockford TL, McCallion O, Issa F, Hester J, Tillmann U, Harrington HA, Bull KR. Multiscale topology classifies cells in subcellular spatial transcriptomics. Nature 2024; 630:943-949. [PMID: 38898271 PMCID: PMC11208150 DOI: 10.1038/s41586-024-07563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/14/2024] [Indexed: 06/21/2024]
Abstract
Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.
Collapse
Affiliation(s)
| | - Aneesha Bhandari
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jessica D Kepple
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rui Qi
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | - Zhouchun Shang
- BGI Research, Riga, Latvia
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yanan Xing
- BGI Research, Riga, Latvia
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | | | | | | | - Tanya L Crockford
- Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Oliver McCallion
- Translational Research Immunology Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Fadi Issa
- Translational Research Immunology Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Joanna Hester
- Translational Research Immunology Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Ulrike Tillmann
- Mathematical Institute, University of Oxford, Oxford, UK
- Isaac Newton Institute for Mathematical Sciences, University of Cambridge, Cambridge, UK
| | - Heather A Harrington
- Mathematical Institute, University of Oxford, Oxford, UK.
- Centre for Human Genetics, University of Oxford, Oxford, UK.
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
- Centre for Systems Biology, Dresden, Dresden, Germany.
- Faculty of Mathematics, Technische Universität Dresden, Dresden, Germany.
| | - Katherine R Bull
- Centre for Human Genetics, University of Oxford, Oxford, UK.
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK.
| |
Collapse
|
7
|
Renaut S, Saavedra Armero V, Boudreau DK, Gaudreault N, Desmeules P, Thériault S, Mathieu P, Joubert P, Bossé Y. Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provide both common and discordant biological insights. PLoS Genet 2024; 20:e1011301. [PMID: 38814983 PMCID: PMC11166281 DOI: 10.1371/journal.pgen.1011301] [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: 12/13/2023] [Revised: 06/11/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Whether single-cell RNA-sequencing (scRNA-seq) captures the same biological information as single-nucleus RNA-sequencing (snRNA-seq) remains uncertain and likely to be context-dependent. Herein, a head-to-head comparison was performed in matched normal-adenocarcinoma human lung samples to assess biological insights derived from scRNA-seq versus snRNA-seq and better understand the cellular transition that occurs from normal to tumoral tissue. Here, the transcriptome of 160,621 cells/nuclei was obtained. In non-tumor lung, cell type proportions varied widely between scRNA-seq and snRNA-seq with a predominance of immune cells in the former (81.5%) and epithelial cells (69.9%) in the later. Similar results were observed in adenocarcinomas, in addition to an overall increase in cell type heterogeneity and a greater prevalence of copy number variants in cells of epithelial origin, which suggests malignant assignment. The cell type transition that occurs from normal lung tissue to adenocarcinoma was not always concordant whether cells or nuclei were examined. As expected, large differential expression of the whole-cell and nuclear transcriptome was observed, but cell-type specific changes of paired normal and tumor lung samples revealed a set of common genes in the cells and nuclei involved in cancer-related pathways. In addition, we showed that the ligand-receptor interactome landscape of lung adenocarcinoma was largely different whether cells or nuclei were evaluated. Immune cell depletion in fresh specimens partly mitigated the difference in cell type composition observed between cells and nuclei. However, the extra manipulations affected cell viability and amplified the transcriptional signatures associated with stress responses. In conclusion, research applications focussing on mapping the immune landscape of lung adenocarcinoma benefit from scRNA-seq in fresh samples, whereas snRNA-seq of frozen samples provide a low-cost alternative to profile more epithelial and cancer cells, and yield cell type proportions that more closely match tissue content.
Collapse
Affiliation(s)
- Sébastien Renaut
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Victoria Saavedra Armero
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Dominique K. Boudreau
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Nathalie Gaudreault
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Patrice Desmeules
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Sébastien Thériault
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Patrick Mathieu
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Philippe Joubert
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec–Université Laval, Quebec City, Canada
- Department of Molecular Medicine, Université Laval, Quebec City, Canada
| |
Collapse
|
8
|
Senoussi M, Artieres T, Villoutreix P. Partial label learning for automated classification of single-cell transcriptomic profiles. PLoS Comput Biol 2024; 20:e1012006. [PMID: 38578796 PMCID: PMC11023635 DOI: 10.1371/journal.pcbi.1012006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 04/17/2024] [Accepted: 03/18/2024] [Indexed: 04/07/2024] Open
Abstract
Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods.
Collapse
Affiliation(s)
- Malek Senoussi
- Aix Marseille Univ, Université de Toulon, CNRS, LIS, Turing Centre for Living Systems, Marseille, France
| | - Thierry Artieres
- Aix Marseille Univ, Université de Toulon, CNRS, LIS, Turing Centre for Living Systems, Marseille, France
- Ecole Centrale de Marseille, Marseille, France
| | - Paul Villoutreix
- Aix Marseille Univ, Université de Toulon, CNRS, LIS, Turing Centre for Living Systems, Marseille, France
- Aix-Marseille Université, MMG, Inserm U1251, Turing Centre for Living systems, Marseille, France
| |
Collapse
|
9
|
Grones C, Eekhout T, Shi D, Neumann M, Berg LS, Ke Y, Shahan R, Cox KL, Gomez-Cano F, Nelissen H, Lohmann JU, Giacomello S, Martin OC, Cole B, Wang JW, Kaufmann K, Raissig MT, Palfalvi G, Greb T, Libault M, De Rybel B. Best practices for the execution, analysis, and data storage of plant single-cell/nucleus transcriptomics. THE PLANT CELL 2024; 36:812-828. [PMID: 38231860 PMCID: PMC10980355 DOI: 10.1093/plcell/koae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 10/17/2023] [Accepted: 10/24/2023] [Indexed: 01/19/2024]
Abstract
Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.
Collapse
Affiliation(s)
- Carolin Grones
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Thomas Eekhout
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
- VIB Single Cell Core Facility, Ghent 9052, Belgium
| | - Dongbo Shi
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
- Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany
| | - Manuel Neumann
- Institute of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Lea S Berg
- Institute of Plant Sciences, University of Bern, 3012 Bern, Switzerland
| | - Yuji Ke
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Rachel Shahan
- Department of Biology, Duke University, Durham, NC 27708, USA
- Howard Hughes Medical Institute, Duke University, Durham, NC 27708, USA
| | - Kevin L Cox
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Fabio Gomez-Cano
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| | - Jan U Lohmann
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
| | - Stefania Giacomello
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, 17165 Solna, Sweden
| | - Olivier C Martin
- Universities of Paris-Saclay, Paris-Cité and Evry, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay, Gif-sur-Yvette 91192, France
| | - Benjamin Cole
- DOE-Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jia-Wei Wang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences (CEMPS), Institute of Plant Physiology and Ecology (SIPPE), Chinese Academy of Sciences (CAS), Shanghai 200032, China
| | - Kerstin Kaufmann
- Institute of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Michael T Raissig
- Institute of Plant Sciences, University of Bern, 3012 Bern, Switzerland
| | - Gergo Palfalvi
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
| | - Thomas Greb
- Centre for Organismal Studies, Heidelberg University, 69120 Heidelberg, Germany
| | - Marc Libault
- Division of Plant Science and Technology, Interdisciplinary Plant Group, College of Agriculture, Food, and Natural Resources, University of Missouri-Columbia, Columbia, MO 65201, USA
| | - Bert De Rybel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent 9052, Belgium
| |
Collapse
|
10
|
Zhang W, Cui Y, Liu B, Loza M, Park SJ, Nakai K. HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data. Brief Bioinform 2024; 25:bbae152. [PMID: 38581422 PMCID: PMC10998639 DOI: 10.1093/bib/bbae152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/19/2024] [Accepted: 03/10/2024] [Indexed: 04/08/2024] Open
Abstract
Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.
Collapse
Affiliation(s)
- Weihang Zhang
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Yang Cui
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Bowen Liu
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Martin Loza
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Sung-Joon Park
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Kenta Nakai
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| |
Collapse
|
11
|
Goggin SM, Zunder ER. A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.571953. [PMID: 38187667 PMCID: PMC10769222 DOI: 10.1101/2023.12.18.571953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Clustering analysis is widely used to group objects by similarity, but for complex datasets such as those produced by single-cell analysis, the currently available clustering methods are limited by accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method with hyperparameter randomization that outperforms other methods across a broad range of single-cell and synthetic datasets, without the need for manual hyperparameter selection. In addition to hard cluster labels, it also outputs soft cluster memberships to characterize continuum-like regions and per cell overlap scores to quantify the uncertainty in cluster assignment. We demonstrate the improved clustering interpretability from these features by tracing the intermediate stages between handwritten digits in the MNIST dataset, and between tanycyte subpopulations in the hypothalamus. This approach improves the quality of clustering and subsequent downstream analyses for single-cell datasets, and may also prove useful in other fields of data analysis.
Collapse
Affiliation(s)
- Sarah M. Goggin
- Neuroscience Graduate Program, School of Medicine, University of Virginia, Charlottesville, VA 22902
| | - Eli R. Zunder
- Neuroscience Graduate Program, School of Medicine, University of Virginia, Charlottesville, VA 22902
- Department of Biomedical Engineering, School of Engineering, University of Virginia, Charlottesville, VA 22902
| |
Collapse
|
12
|
Liu D, Fu Y, Wang X, Wang X, Fang X, Zhou Y, Wang R, Zhang P, Jiang M, Jia D, Wang J, Chen H, Guo G, Han X. Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses. Stem Cell Reports 2023; 18:2464-2481. [PMID: 37995704 PMCID: PMC10724075 DOI: 10.1016/j.stemcr.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023] Open
Abstract
In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.
Collapse
Affiliation(s)
- Daiyuan Liu
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Yuting Fu
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Xinru Wang
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Xueyi Wang
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Xing Fang
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang 310058, China
| | - Yincong Zhou
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Renying Wang
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Peijing Zhang
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang 310058, China
| | - Mengmeng Jiang
- Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Danmei Jia
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Jingjing Wang
- Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Haide Chen
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China; Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China; M20 Genomics, Hangzhou, China
| | - Guoji Guo
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang 310058, China; Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
| | - Xiaoping Han
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang 310058, China.
| |
Collapse
|
13
|
Ghaddar B, De S. Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census. Bioinformatics 2023; 39:btad714. [PMID: 38011649 PMCID: PMC10713118 DOI: 10.1093/bioinformatics/btad714] [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: 02/21/2023] [Revised: 10/26/2023] [Accepted: 11/25/2023] [Indexed: 11/29/2023] Open
Abstract
MOTIVATION Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. RESULTS We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. AVAILABILITY AND IMPLEMENTATION Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.
Collapse
Affiliation(s)
- Bassel Ghaddar
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 08901, United States
| | - Subhajyoti De
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 08901, United States
| |
Collapse
|
14
|
Silkwood K, Dollinger E, Gervin J, Atwood S, Nie Q, Lander AD. Leveraging gene correlations in single cell transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532643. [PMID: 36993765 PMCID: PMC10055147 DOI: 10.1101/2023.03.14.532643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
BACKGROUND Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data when ground truth about biological variation is unknown (i.e., usually). RESULTS We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p-values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. CONCLUSIONS New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.
Collapse
Affiliation(s)
- Kai Silkwood
- Center for Complex Biological Systems, University of California, Irvine, Irvine CA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine CA
| | - Emmanuel Dollinger
- Center for Complex Biological Systems, University of California, Irvine, Irvine CA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine CA
- Department of Mathematics, University of California, Irvine, Irvine CA
| | - Josh Gervin
- Center for Complex Biological Systems, University of California, Irvine, Irvine CA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine CA
| | - Scott Atwood
- Center for Complex Biological Systems, University of California, Irvine, Irvine CA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine CA
| | - Qing Nie
- Center for Complex Biological Systems, University of California, Irvine, Irvine CA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine CA
- Department of Mathematics, University of California, Irvine, Irvine CA
| | - Arthur D. Lander
- Center for Complex Biological Systems, University of California, Irvine, Irvine CA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine CA
| |
Collapse
|
15
|
Lazaros K, Vlamos P, Vrahatis AG. Methods for cell-type annotation on scRNA-seq data: A recent overview. J Bioinform Comput Biol 2023; 21:2340002. [PMID: 37743364 DOI: 10.1142/s0219720023400024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in single-cell gene expressions pose a substantial challenge, despite the myriad of tools at our disposal. The rapid growth in data, resources, and tools has consequently brought about significant alterations in this area over the years. In our study, we spotlight all note-worthy cell type annotation techniques developed over the past four years. We provide an overview of the latest trends in this field, showcasing the most advanced methods in taxonomy. Our research underscores the demand for additional tools that incorporate a biological context and also predicts that the rising trend of graph neural network approaches will likely lead this research field in the coming years.
Collapse
Affiliation(s)
- Konstantinos Lazaros
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| |
Collapse
|
16
|
Madadi Y, Monavarfeshani A, Chen H, Stamer WD, Williams RW, Yousefi S. Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2837-2852. [PMID: 37294649 PMCID: PMC10631573 DOI: 10.1109/tcbb.2023.3284795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However, scRNA-seq based identification of discrete cell-types is still labor intensive and depends on prior molecular knowledge. Artificial intelligence has provided faster, more accurate, and user-friendly approaches for cell-type identification. In this review, we discuss recent advances in cell-type identification methods using artificial intelligence techniques based on single-cell and single-nucleus RNA sequencing data in vision science. The main purpose of this review paper is to assist vision scientists not only to select suitable datasets for their problems, but also to be aware of the appropriate computational tools to perform their analysis. Developing novel methods for scRNA-seq data analysis remains to be addressed in future studies.
Collapse
|
17
|
Xie Z, Li X, Mora A. A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data. Biomolecules 2023; 13:1211. [PMID: 37627276 PMCID: PMC10452151 DOI: 10.3390/biom13081211] [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: 06/18/2023] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023] Open
Abstract
Computational prediction of cell-cell interactions (CCIs) is becoming increasingly important for understanding disease development and progression. We present a benchmark study of available CCI prediction tools based on single-cell RNA sequencing (scRNA-seq) data. By comparing prediction outputs with a manually curated gold standard for idiopathic pulmonary fibrosis (IPF), we evaluated prediction performance and processing time of several CCI prediction tools, including CCInx, CellChat, CellPhoneDB, iTALK, NATMI, scMLnet, SingleCellSignalR, and an ensemble of tools. According to our results, CellPhoneDB and NATMI are the best performer CCI prediction tools, among the ones analyzed, when we define a CCI as a source-target-ligand-receptor tetrad. In addition, we recommend specific tools according to different types of research projects and discuss the possible future paths in the field.
Collapse
Affiliation(s)
- Zihong Xie
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Guangzhou 511436, China;
| | - Xuri Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Antonio Mora
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Guangzhou 511436, China;
| |
Collapse
|
18
|
Jiang Y, Chen Z, Han N, Shang J, Wu A. sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq. Front Immunol 2023; 14:1223471. [PMID: 37545533 PMCID: PMC10399579 DOI: 10.3389/fimmu.2023.1223471] [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: 05/16/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions.
Collapse
|
19
|
Tosoni G, Ayyildiz D, Bryois J, Macnair W, Fitzsimons CP, Lucassen PJ, Salta E. Mapping human adult hippocampal neurogenesis with single-cell transcriptomics: Reconciling controversy or fueling the debate? Neuron 2023; 111:1714-1731.e3. [PMID: 37015226 DOI: 10.1016/j.neuron.2023.03.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 02/06/2023] [Accepted: 03/08/2023] [Indexed: 04/05/2023]
Abstract
The notion of exploiting the regenerative potential of the human brain in physiological aging or neurological diseases represents a particularly attractive alternative to conventional strategies for enhancing or restoring brain function. However, a major first question to address is whether the human brain does possess the ability to regenerate. The existence of human adult hippocampal neurogenesis (AHN) has been at the center of a fierce scientific debate for many years. The advent of single-cell transcriptomic technologies was initially viewed as a panacea to resolving this controversy. However, recent single-cell RNA sequencing studies in the human hippocampus yielded conflicting results. Here, we critically discuss and re-analyze previously published AHN-related single-cell transcriptomic datasets. We argue that, although promising, the single-cell transcriptomic profiling of AHN in the human brain can be confounded by methodological, conceptual, and biological factors that need to be consistently addressed across studies and openly discussed within the scientific community.
Collapse
Affiliation(s)
- Giorgia Tosoni
- Laboratory of Neurogenesis and Neurodegeneration, Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, the Netherlands
| | - Dilara Ayyildiz
- Laboratory of Neurogenesis and Neurodegeneration, Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, the Netherlands
| | - Julien Bryois
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, CH-4070, Basel, Switzerland
| | - Will Macnair
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, CH-4070, Basel, Switzerland
| | - Carlos P Fitzsimons
- Brain Plasticity group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, 1098 XH, Amsterdam, the Netherlands
| | - Paul J Lucassen
- Brain Plasticity group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, 1098 XH, Amsterdam, the Netherlands; Center for Urban Mental Health, University of Amsterdam, 1098 SM, Amsterdam, the Netherlands
| | - Evgenia Salta
- Laboratory of Neurogenesis and Neurodegeneration, Netherlands Institute for Neuroscience, 1105 BA, Amsterdam, the Netherlands.
| |
Collapse
|
20
|
Xiong YX, Wang MG, Chen L, Zhang XF. Cell-type annotation with accurate unseen cell-type identification using multiple references. PLoS Comput Biol 2023; 19:e1011261. [PMID: 37379341 PMCID: PMC10335708 DOI: 10.1371/journal.pcbi.1011261] [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: 01/20/2023] [Revised: 07/11/2023] [Accepted: 06/11/2023] [Indexed: 06/30/2023] Open
Abstract
The recent advances in single-cell RNA sequencing (scRNA-seq) techniques have stimulated efforts to identify and characterize the cellular composition of complex tissues. With the advent of various sequencing techniques, automated cell-type annotation using a well-annotated scRNA-seq reference becomes popular. But it relies on the diversity of cell types in the reference, which may not capture all the cell types present in the query data of interest. There are generally unseen cell types in the query data of interest because most data atlases are obtained for different purposes and techniques. Identifying previously unseen cell types is essential for improving annotation accuracy and uncovering novel biological discoveries. To address this challenge, we propose mtANN (multiple-reference-based scRNA-seq data annotation), a new method to automatically annotate query data while accurately identifying unseen cell types with the aid of multiple references. Key innovations of mtANN include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric that considers three complementary aspects to distinguish between unseen cell types and shared cell types. Additionally, we provide a data-driven method to adaptively select a threshold for identifying previously unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for unseen cell-type identification and cell-type annotation on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets. The source code and tutorial are available at https://github.com/Zhangxf-ccnu/mtANN.
Collapse
Affiliation(s)
- Yi-Xuan Xiong
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
| | - Meng-Guo Wang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
- Key Laboratory of Nonlinear Analysis & Applications (Ministry of Education), Central China Normal University, Wuhan, China
| |
Collapse
|
21
|
Cheng Y, Fan X, Zhang J, Li Y. A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data. Commun Biol 2023; 6:545. [PMID: 37210444 DOI: 10.1038/s42003-023-04928-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/11/2023] [Indexed: 05/22/2023] Open
Abstract
Automatic cell type annotation methods are increasingly used in single-cell RNA sequencing (scRNA-seq) analysis due to their fast and precise advantages. However, current methods often fail to account for the imbalance of scRNA-seq datasets and ignore information from smaller populations, leading to significant biological analysis errors. Here, we introduce scBalance, an integrated sparse neural network framework that incorporates adaptive weight sampling and dropout techniques for auto-annotation tasks. Using 20 scRNA-seq datasets with varying scales and degrees of imbalance, we demonstrate that scBalance outperforms current methods in both intra- and inter-dataset annotation tasks. Additionally, scBalance displays impressive scalability in identifying rare cell types in million-level datasets, as shown in the bronchoalveolar cell landscape. scBalance is also significantly faster than commonly used tools and comes in a user-friendly format, making it a superior tool for scRNA-seq analysis on the Python-based platform.
Collapse
Affiliation(s)
- Yuqi Cheng
- Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Xingyu Fan
- School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Jianing Zhang
- Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China
| | - Yu Li
- Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China.
- The CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, 518057, Shenzhen, China.
| |
Collapse
|
22
|
Nofech-Mozes I, Soave D, Awadalla P, Abelson S. Pan-cancer classification of single cells in the tumour microenvironment. Nat Commun 2023; 14:1615. [PMID: 36959212 PMCID: PMC10036554 DOI: 10.1038/s41467-023-37353-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 03/10/2023] [Indexed: 03/25/2023] Open
Abstract
Single-cell RNA sequencing can reveal valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells' identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >300,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancers and employ a hierarchical approach, outperforming current classification methods. We extensively confirm scATOMIC's accuracy on 225 tumour biopsies encompassing >350,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC's practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours' primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyse multicellular cancer TMEs.
Collapse
Affiliation(s)
- Ido Nofech-Mozes
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - David Soave
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Mathematics, Wilfrid Laurier University, Waterloo, ON, Canada
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
| | - Sagi Abelson
- Ontario Institute for Cancer Research, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
23
|
Lee J, Kim M, Kang K, Yang CS, Yoon S. Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing. Brief Bioinform 2023; 24:6995373. [PMID: 36681937 PMCID: PMC10025442 DOI: 10.1093/bib/bbad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/22/2022] [Accepted: 01/02/2023] [Indexed: 01/23/2023] Open
Abstract
Single-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously.
Collapse
Affiliation(s)
- Joongho Lee
- Dept. of Computer Science, College of SW Convergence, Dankook University, Yongin-si, Korea, 16890
| | - Minsoo Kim
- Dept. of Computer Science, College of SW Convergence, Dankook University, Yongin-si, Korea, 16890
| | - Keunsoo Kang
- Dept. of Microbiology, College of Natural Sciences, Dankook University, Cheonan-si, Korea, 31116
| | - Chul-Su Yang
- Dept. of Molecular and Life Science, Center for Bionano Intelligence Education and Research, Hanyang University, Ansan, Korea, 15588
| | - Seokhyun Yoon
- Dept. of Electronics & Electrical Eng., College of Engineering, Dankook University, Yongin-si Korea, 16890
| |
Collapse
|
24
|
Chen J, Xu H, Tao W, Chen Z, Zhao Y, Han JDJ. Transformer for one stop interpretable cell type annotation. Nat Commun 2023; 14:223. [PMID: 36641532 PMCID: PMC9840170 DOI: 10.1038/s41467-023-35923-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 01/09/2023] [Indexed: 01/15/2023] Open
Abstract
Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and batch-insensitive integration while providing biologically interpretable insights for understanding cellular behavior during development and disease progressions. We demonstrate TOSICA's advantages by applying it to scRNA-seq data of tumor-infiltrating immune cells, and CD14+ monocytes in COVID-19 to reveal rare cell types, heterogeneity and dynamic trajectories associated with disease progression and severity.
Collapse
Affiliation(s)
- Jiawei Chen
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Hao Xu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Wanyu Tao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Zhaoxiong Chen
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Yuxuan Zhao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| |
Collapse
|
25
|
Ikonomou L, Yampolskaya M, Mehta P. Multipotent Embryonic Lung Progenitors: Foundational Units of In Vitro and In Vivo Lung Organogenesis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1413:49-70. [PMID: 37195526 PMCID: PMC10351616 DOI: 10.1007/978-3-031-26625-6_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Transient, tissue-specific, embryonic progenitors are important cell populations in vertebrate development. In the course of respiratory system development, multipotent mesenchymal and epithelial progenitors drive the diversification of fates that results to the plethora of cell types that compose the airways and alveolar space of the adult lungs. Use of mouse genetic models, including lineage tracing and loss-of-function studies, has elucidated signaling pathways that guide proliferation and differentiation of embryonic lung progenitors as well as transcription factors that underlie lung progenitor identity. Furthermore, pluripotent stem cell-derived and ex vivo expanded respiratory progenitors offer novel, tractable, high-fidelity systems that allow for mechanistic studies of cell fate decisions and developmental processes. As our understanding of embryonic progenitor biology deepens, we move closer to the goal of in vitro lung organogenesis and resulting applications in developmental biology and medicine.
Collapse
Affiliation(s)
- Laertis Ikonomou
- Department of Oral Biology, University at Buffalo, The State University of New York, Buffalo, NY, USA.
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University at Buffalo, The State University of New York, Buffalo, NY, USA.
- Cell, Gene and Tissue Engineering Center, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | | | - Pankaj Mehta
- Department of Physics, Boston University, Boston, MA, USA
- Faculty of Computing and Data Science, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| |
Collapse
|
26
|
Ren T, Huang S, Liu Q, Wang G. scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome. Comput Biol Med 2023; 152:106409. [PMID: 36512878 DOI: 10.1016/j.compbiomed.2022.106409] [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: 10/01/2022] [Revised: 11/16/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
Rapid advances in single-cell transcriptome analysis provide deeper insights into the study of tissue heterogeneity at the cellular level. Unsupervised clustering can identify potential cell populations in single-cell RNA-sequencing (scRNA-seq) data, but fail to further determine the identity of each cell. Existing automatic annotation methods using scRNA-seq data based on machine learning mainly use single feature set and single classifier. In view of this, we propose a Weighted Ensemble classification framework for Cell Type Annotation, named scWECTA, which improves the accuracy of cell type identification. scWECTA uses five informative gene sets and integrates five classifiers based on soft weighted ensemble framework. And the ensemble weights are inferred through the constrained non-negative least squares. Validated on multiple pairs of scRNA-seq datasets, scWECTA is able to accurately annotate scRNA-seq data across platforms and across tissues, especially for imbalanced data containing rare cell types. Moreover, scWECTA outperforms other comparable methods in balancing the prediction accuracy of common cell types and the unassigned rate of non-common cell types at the same time. The source code of scWECTA is freely available at https://github.com/ttren-sc/scWECTA.
Collapse
Affiliation(s)
- Tongtong Ren
- School of Computer Science and Technology, Harbin Institute of Technology, No.92 West Dazhi Street, Nangang District, Harbin, Heilongjiang, 150001, PR China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, No. 246, Xuefu Street, Nangang District, Harbin, Heilongjiang, 150081, PR China
| | - Qiaoming Liu
- School of Computer Science and Technology, Harbin Institute of Technology, No.92 West Dazhi Street, Nangang District, Harbin, Heilongjiang, 150001, PR China
| | - Guohua Wang
- School of Computer Science and Technology, Harbin Institute of Technology, No.92 West Dazhi Street, Nangang District, Harbin, Heilongjiang, 150001, PR China.
| |
Collapse
|
27
|
Wang L, Nie R, Zhang J, Cai J. scCapsNet-mask: an updated version of scCapsNet with extended applicability in functional analysis related to scRNA-seq data. BMC Bioinformatics 2022; 23:539. [PMID: 36510124 PMCID: PMC9743530 DOI: 10.1186/s12859-022-05098-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 12/03/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND With the rapid accumulation of scRNA-seq data, more and more automatic cell type identification methods have been developed, especially those based on deep learning. Although these methods have reached relatively high prediction accuracy, many issues still exist. One is the interpretability. The second is how to deal with the non-standard test samples that are not encountered in the training process. RESULTS Here we introduce scCapsNet-mask, an updated version of scCapsNet. The scCapsNet-mask provides a reasonable solution to the issues of interpretability and non-standard test samples. Firstly, the scCapsNet-mask utilizes a mask to ease the task of model interpretation in the original scCapsNet. The results show that scCapsNet-mask could constrain the coupling coefficients, and make a one-to-one correspondence between the primary capsules and type capsules. Secondly, the scCapsNet-mask can process non-standard samples more reasonably. In one example, the scCapsNet-mask was trained on the committed cells, and then tested on less differentiated cells as the non-standard samples. It could not only estimate the lineage bias of less differentiated cells, but also distinguish the development stages more accurately than traditional machine learning models. Therefore, the pseudo-temporal order of cells for each lineage could be established. Following these pseudo-temporal order, lineage specific genes exhibit a gradual increase expression pattern and stem cell associated genes exhibit a gradual decrease expression pattern. In another example, the scCapsNet-mask was trained on scRNA-seq data, and then used to assign cell type in spatial transcriptomics that may contain non-standard sample of doublets. The results show that the scCapsNet-mask not only restored the spatial map but also identified several non-standard samples of doublet. CONCLUSIONS The scCapsNet-mask offers a suitable solution to the challenge of interpretability and non-standard test samples. By adding a mask, it has the advantages of automatic processing and easy interpretation compared with the original scCapsNet. In addition, the scCapsNet-mask could more accurately reflect the composition of non-standard test samples than traditional machine learning methods. Therefore, it can extend its applicability in functional analysis, such as fate bias prediction in less differentiated cells and cell type assignment in spatial transcriptomics.
Collapse
Affiliation(s)
- Lifei Wang
- grid.413073.20000 0004 1758 9341Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China ,grid.464209.d0000 0004 0644 6935China National Center for Bioinformation, Beijing, 100101 China ,grid.9227.e0000000119573309Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Rui Nie
- grid.464209.d0000 0004 0644 6935China National Center for Bioinformation, Beijing, 100101 China ,grid.9227.e0000000119573309Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Jiang Zhang
- grid.20513.350000 0004 1789 9964School of Systems Science, Beijing Normal University, Beijing, 100875 China
| | - Jun Cai
- grid.464209.d0000 0004 0644 6935China National Center for Bioinformation, Beijing, 100101 China ,grid.9227.e0000000119573309Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| |
Collapse
|
28
|
Jiang T, Zhou W, Sheng Q, Yu J, Xie Y, Ding N, Zhang Y, Xu J, Li Y. ImmCluster: an ensemble resource for immunology cell type clustering and annotations in normal and cancerous tissues. Nucleic Acids Res 2022; 51:D1325-D1332. [PMID: 36271790 PMCID: PMC9825417 DOI: 10.1093/nar/gkac922] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/22/2022] [Accepted: 10/06/2022] [Indexed: 01/30/2023] Open
Abstract
Single-cell transcriptome has enabled the transcriptional profiling of thousands of immune cells in complex tissues and cancers. However, subtle transcriptomic differences in immune cell subpopulations and the high dimensionality of transcriptomic data make the clustering and annotation of immune cells challenging. Herein, we introduce ImmCluster (http://bio-bigdata.hrbmu.edu.cn/ImmCluster) for immunology cell type clustering and annotation. We manually curated 346 well-known marker genes from 1163 studies. ImmCluster integrates over 420 000 immune cells from nine healthy tissues and over 648 000 cells from different tumour samples of 17 cancer types to generate stable marker-gene sets and develop context-specific immunology references. In addition, ImmCluster provides cell clustering using seven reference-based and four marker gene-based computational methods, and the ensemble method was developed to provide consistent cell clustering than individual methods. Five major analytic modules were provided for interactively exploring the annotations of immune cells, including clustering and annotating immune cell clusters, gene expression of markers, functional assignment in cancer hallmarks, cell states and immune pathways, cell-cell communications and the corresponding ligand-receptor interactions, as well as online tools. ImmCluster generates diverse plots and tables, enabling users to identify significant associations in immune cell clusters simultaneously. ImmCluster is a valuable resource for analysing cellular heterogeneity in cancer microenvironments.
Collapse
Affiliation(s)
| | | | | | | | - Yunjin Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Yunpeng Zhang
- Correspondence may also be addressed to Yunpeng Zhang.
| | - Juan Xu
- Correspondence may also be addressed to Juan Xu.
| | - Yongsheng Li
- To whom correspondence should be addressed. Tel: +86 13604805482;
| |
Collapse
|