1
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Holman DR, Rubin SJS, Ferenc M, Holman EA, Koron AN, Daniel R, Boland BS, Nolan GP, Chang JT, Rogalla S. Automated spatial omics landscape analysis approach reveals novel tissue architectures in ulcerative colitis. Sci Rep 2024; 14:18934. [PMID: 39147769 PMCID: PMC11327370 DOI: 10.1038/s41598-024-68397-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 07/23/2024] [Indexed: 08/17/2024] Open
Abstract
The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.
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Affiliation(s)
- Derek R Holman
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University, Stanford, CA, USA.
| | - Samuel J S Rubin
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Mariusz Ferenc
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | - Elizabeth A Holman
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Alexander N Koron
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Robel Daniel
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Brigid S Boland
- Division of Gastroenterology, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - John T Chang
- Division of Gastroenterology, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Stephan Rogalla
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University, Stanford, CA, USA.
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2
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Maulding ND, Seninge L, Stuart JM. Associating transcription factors to single-cell trajectories with DREAMIT. Genome Biol 2024; 25:220. [PMID: 39143494 PMCID: PMC11323358 DOI: 10.1186/s13059-024-03368-7] [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: 05/26/2023] [Accepted: 08/06/2024] [Indexed: 08/16/2024] Open
Abstract
Inferring gene regulatory networks from single-cell RNA-sequencing trajectories has been an active area of research yet methods are still needed to identify regulators governing cell transitions. We developed DREAMIT (Dynamic Regulation of Expression Across Modules in Inferred Trajectories) to annotate transcription-factor activity along single-cell trajectory branches, using ensembles of relations to target genes. Using a benchmark representing several different tissues, as well as external validation with ATAC-Seq and Perturb-Seq data on hematopoietic cells, the method was found to have higher tissue-specific sensitivity and specificity over competing approaches.
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Affiliation(s)
- Nathan D Maulding
- UCSC Genomics Institute, Biomolecular Engineering, University of California, Santa Cruz, USA
| | - Lucas Seninge
- UCSC Genomics Institute, Biomolecular Engineering, University of California, Santa Cruz, USA
| | - Joshua M Stuart
- UCSC Genomics Institute, Biomolecular Engineering, University of California, Santa Cruz, USA.
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3
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Xia Y, Liu Y, Li T, He S, Chang H, Wang Y, Zhang Y, Ge W. Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data. Methods 2024; 228:12-21. [PMID: 38759908 DOI: 10.1016/j.ymeth.2024.05.007] [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: 04/12/2024] [Revised: 04/28/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024] Open
Abstract
Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.
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Affiliation(s)
- Yucheng Xia
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, China
| | - Yuhang Liu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Tianhao Li
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Sihan He
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Hong Chang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yaqing Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Wenyi Ge
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
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4
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Kim GD, Lim C, Park J. A practical handbook on single-cell RNA sequencing data quality control and downstream analysis. Mol Cells 2024; 47:100103. [PMID: 39094968 DOI: 10.1016/j.mocell.2024.100103] [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: 05/01/2024] [Revised: 07/19/2024] [Accepted: 07/29/2024] [Indexed: 08/04/2024] Open
Abstract
Advancements in single-cell analysis have facilitated high-resolution observation of the transcriptome in individual cells. However, standards for obtaining high-quality cells and data analysis pipelines remain variable. Here, we provide the groundwork for improving the quality of single-cell analysis by delineating guidelines for selecting high-quality cells and considerations throughout the analysis. This review will streamline researchers' access to single-cell analysis and serve as a valuable guide for analysis.
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Affiliation(s)
- Gyeong Dae Kim
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
| | - Chaemin Lim
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
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5
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Fu Q, Dong C, Liu Y, Xia X, Liu G, Zhong F, Liu L. A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset. Brief Bioinform 2024; 25:bbae392. [PMID: 39120646 PMCID: PMC11312369 DOI: 10.1093/bib/bbae392] [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: 04/29/2024] [Revised: 07/04/2024] [Accepted: 07/30/2024] [Indexed: 08/10/2024] Open
Abstract
Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.
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Affiliation(s)
- Qiqing Fu
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Chenyu Dong
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Yunhe Liu
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Xiaoqiong Xia
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Gang Liu
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Fan Zhong
- Intelligent Medicine Institute, Fudan University, 200032 Shanghai, P.R. China
| | - Lei Liu
- Intelligent Medicine Institute, Fudan University, 200032 Shanghai, P.R. China
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6
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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7
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Qi Q, Wang Y, Huang Y, Fan Y, Li X. PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae421. [PMID: 38924517 PMCID: PMC11236098 DOI: 10.1093/bioinformatics/btae421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/27/2024] [Accepted: 06/25/2024] [Indexed: 06/28/2024]
Abstract
MOTIVATION The annotation of cell types from single-cell transcriptomics is essential for understanding the biological identity and functionality of cellular populations. Although manual annotation remains the gold standard, the advent of automatic pipelines has become crucial for scalable, unbiased, and cost-effective annotations. Nonetheless, the effectiveness of these automatic methods, particularly those employing deep learning, significantly depends on the architecture of the classifier and the quality and diversity of the training datasets. RESULTS To address these limitations, we present a Pruning-enabled Gene-Cell Net (PredGCN) incorporating a Coupled Gene-Cell Net (CGCN) to enable representation learning and information storage. PredGCN integrates a Gene Splicing Net (GSN) and a Cell Stratification Net (CSN), employing a pruning operation (PrO) to dynamically tackle the complexity of heterogeneous cell identification. Among them, GSN leverages multiple statistical and hypothesis-driven feature extraction methods to selectively assemble genes with specificity for scRNA-seq data while CSN unifies elements based on diverse region demarcation principles, exploiting the representations from GSN and precise identification from different regional homogeneity perspectives. Furthermore, we develop a multi-objective Pareto pruning operation (Pareto PrO) to expand the dynamic capabilities of CGCN, optimizing the sub-network structure for accurate cell type annotation. Multiple comparison experiments on real scRNA-seq datasets from various species have demonstrated that PredGCN surpasses existing state-of-the-art methods, including its scalability to cross-species datasets. Moreover, PredGCN can uncover unknown cell types and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into cell type identification and characterizing scRNA-seq data from different perspectives. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/IrisQi7/PredGCN and test data is available at https://figshare.com/articles/dataset/PredGCN/25251163.
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Affiliation(s)
- Qi Qi
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Yunhe Wang
- School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
| | - Yujian Huang
- College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
| | - Yi Fan
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
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8
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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.
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Affiliation(s)
| | | | | | - Hao Wu
- Shenzhen University of Advanced Technology
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9
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Gonzalez-Ferrer J, Lehrer J, O'Farrell A, Paten B, Teodorescu M, Haussler D, Jonsson VD, Mostajo-Radji MA. SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis. CELL GENOMICS 2024; 4:100581. [PMID: 38823397 PMCID: PMC11228957 DOI: 10.1016/j.xgen.2024.100581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 04/02/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.
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Affiliation(s)
- Jesus Gonzalez-Ferrer
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Live Cell Biotechnology Discovery Lab, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95060, USA
| | - Julian Lehrer
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Live Cell Biotechnology Discovery Lab, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Applied Mathematics, University of California, Santa Cruz, Santa Cruz, CA 95060, USA
| | - Ash O'Farrell
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA
| | - Benedict Paten
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95060, USA
| | - Mircea Teodorescu
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, CA 95060, USA
| | - David Haussler
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95060, USA
| | - Vanessa D Jonsson
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Applied Mathematics, University of California, Santa Cruz, Santa Cruz, CA 95060, USA.
| | - Mohammed A Mostajo-Radji
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Live Cell Biotechnology Discovery Lab, University of California, Santa Cruz, Santa Cruz, CA 95060, USA.
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10
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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.
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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
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11
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Caron DP, Specht WL, Chen D, Wells SB, Szabo PA, Jensen IJ, Farber DL, Sims PA. Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.06.547944. [PMID: 37461466 PMCID: PMC10350048 DOI: 10.1101/2023.07.06.547944] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and dissecting transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins, as in cells of the immune system. Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell type annotation requires a classifier that integrates multimodal data. Here, we describe MultiModal Classifier Hierarchy (MMoCHi), a marker-based approach for classification, reconciling gene and protein expression without reliance on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal novel subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.
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Affiliation(s)
- Daniel P. Caron
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - William L. Specht
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - David Chen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Steven B. Wells
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Peter A. Szabo
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Isaac J. Jensen
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Donna L. Farber
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Peter A. Sims
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
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12
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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.
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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
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13
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Xie Y, Chen H, Chellamuthu VR, Lajam ABM, Albani S, Low AHL, Petretto E, Behmoaras J. Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing. Int J Mol Sci 2024; 25:3828. [PMID: 38612639 PMCID: PMC11011421 DOI: 10.3390/ijms25073828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10x Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10x data where samples were not multiplexed. Despite relatively lower cell capture affecting library preparation, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, a comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.
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Affiliation(s)
- Yi Xie
- Programme in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; (Y.X.)
| | - Huimei Chen
- Programme in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; (Y.X.)
| | - Vasuki Ranjani Chellamuthu
- Translational Immunology Institute, SingHealth/Duke-NUS Academic Medical Centre, Academia, Singapore 169856, Singapore; (V.R.C.)
| | - Ahmad bin Mohamed Lajam
- Translational Immunology Institute, SingHealth/Duke-NUS Academic Medical Centre, Academia, Singapore 169856, Singapore; (V.R.C.)
| | - Salvatore Albani
- Translational Immunology Institute, SingHealth/Duke-NUS Academic Medical Centre, Academia, Singapore 169856, Singapore; (V.R.C.)
| | - Andrea Hsiu Ling Low
- Department of Rheumatology and Immunology, Singapore General Hospital, Academia, Singapore 169856, Singapore;
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Enrico Petretto
- Programme in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; (Y.X.)
- Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University, Nanjing 210009, China
| | - Jacques Behmoaras
- Programme in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; (Y.X.)
- Department of Immunology and Inflammation, Centre for Inflammatory Disease, Imperial College London, Hammersmith Hospital, London W12 0NN, UK
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14
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Huang X, Liu R, Yang S, Chen X, Li H. scAnnoX: an R package integrating multiple public tools for single-cell annotation. PeerJ 2024; 12:e17184. [PMID: 38560451 PMCID: PMC10981883 DOI: 10.7717/peerj.17184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
Background Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. Methods This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. Results The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.
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Affiliation(s)
- Xiaoqian Huang
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Ruiqi Liu
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Shiwei Yang
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Xiaozhou Chen
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Huamei Li
- Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, Jiangsu Province, China
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15
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Zheng C, Wang Y, Cheng Y, Wang X, Wei H, King I, Li Y. scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics. Brief Bioinform 2024; 25:bbae112. [PMID: 38555470 PMCID: PMC10981759 DOI: 10.1093/bib/bbae112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 04/02/2024] Open
Abstract
Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.
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Affiliation(s)
- Chuanyang Zheng
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
| | - Yixuan Wang
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
| | - Yuqi Cheng
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Xuesong Wang
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
| | - Hongxin Wei
- MLR Lab, Southern University of Science and Technology
| | - Irwin King
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
| | - Yu Li
- Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China
- The CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
- Institute for Medical Enginering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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16
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Zhang W, Cui Y, Liu B, Loza M, Park SJ, Nakai K. HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data. Brief Bioinform 2024; 25:bbae152. [PMID: 38581422 PMCID: PMC10998639 DOI: 10.1093/bib/bbae152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/19/2024] [Accepted: 03/10/2024] [Indexed: 04/08/2024] Open
Abstract
Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.
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Affiliation(s)
- Weihang Zhang
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Yang Cui
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Bowen Liu
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Martin Loza
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Sung-Joon Park
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Kenta Nakai
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
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17
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Busarello E, Biancon G, Lauria F, Ibnat Z, Ramirez C, Tomè G, Aass KR, VanOudenhove J, Standal T, Viero G, Halene S, Tebaldi T. Interpreting single-cell messages in normal and aberrant hematopoiesis with the Cell Marker Accordion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584053. [PMID: 38559181 PMCID: PMC10979856 DOI: 10.1101/2024.03.08.584053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell technologies offer a unique opportunity to explore cellular heterogeneity in hematopoiesis, reveal malignant hematopoietic cells with clinically significant features and measure gene signatures linked to pathological pathways. However, reliable identification of cell types is a crucial bottleneck in single-cell analysis. Available databases contain dissimilar nomenclature and non-concurrent marker sets, leading to inconsistent annotations and poor interpretability. Furthermore, current tools focus mostly on physiological cell types, lacking extensive applicability in disease. We developed the Cell Marker Accordion, a user-friendly platform for the automatic annotation and biological interpretation of single-cell populations based on consistency weighted markers. We validated our approach on peripheral blood and bone marrow single-cell datasets, using surface markers and expert-based annotation as the ground truth. In all cases, we significantly improved the accuracy in identifying cell types with respect to any single source database. Moreover, the Cell Marker Accordion can identify disease-critical cells and pathological processes, extracting potential biomarkers in a wide variety of contexts in human and murine single-cell datasets. It characterizes leukemia stem cell subtypes, including therapy-resistant cells in acute myeloid leukemia patients; it identifies malignant plasma cells in multiple myeloma samples; it dissects cell type alterations in splicing factor-mutant cells from myelodysplastic syndrome patients; it discovers activation of innate immunity pathways in bone marrow from mice treated with METTL3 inhibitors. The breadth of these applications elevates the Cell Marker Accordion as a flexible, faithful and standardized tool to annotate and interpret hematopoietic populations in single-cell datasets focused on the study of hematopoietic development and disease.
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Affiliation(s)
- Emma Busarello
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Giulia Biancon
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Fabio Lauria
- Institute of Biophysics, CNR Unit at Trento, Italy
| | - Zuhairia Ibnat
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Christian Ramirez
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Gabriele Tomè
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
- Institute of Biophysics, CNR Unit at Trento, Italy
| | - Kristin R Aass
- Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Jennifer VanOudenhove
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Therese Standal
- Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Stephanie Halene
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Toma Tebaldi
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
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18
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Rogalla S, Holman D, Rubin S, Ferenc M, Holman E, Koron A, Daniel R, Boland B, Nolan G, Chang J. Automated Spatial Omics Landscape Analysis Approach Reveals Novel Tissue Architectures in Ulcerative Colitis. RESEARCH SQUARE 2024:rs.3.rs-3965505. [PMID: 38559236 PMCID: PMC10980100 DOI: 10.21203/rs.3.rs-3965505/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: 04/04/2024]
Abstract
The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.
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19
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Theunissen L, Mortier T, Saeys Y, Waegeman W. Uncertainty-aware single-cell annotation with a hierarchical reject option. Bioinformatics 2024; 40:btae128. [PMID: 38441258 PMCID: PMC10957513 DOI: 10.1093/bioinformatics/btae128] [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: 09/18/2023] [Revised: 02/23/2024] [Accepted: 03/01/2024] [Indexed: 03/23/2024] Open
Abstract
MOTIVATION Automatic cell type annotation methods assign cell type labels to new datasets by extracting relationships from a reference RNA-seq dataset. However, due to the limited resolution of gene expression features, there is always uncertainty present in the label assignment. To enhance the reliability and robustness of annotation, most machine learning methods address this uncertainty by providing a full reject option, i.e. when the predicted confidence score of a cell type label falls below a user-defined threshold, no label is assigned and no prediction is made. As a better alternative, some methods deploy hierarchical models and consider a so-called partial rejection by returning internal nodes of the hierarchy as label assignment. However, because a detailed experimental analysis of various rejection approaches is missing in the literature, there is currently no consensus on best practices. RESULTS We evaluate three annotation approaches (i) full rejection, (ii) partial rejection, and (iii) no rejection for both flat and hierarchical probabilistic classifiers. Our findings indicate that hierarchical classifiers are superior when rejection is applied, with partial rejection being the preferred rejection approach, as it preserves a significant amount of label information. For optimal rejection implementation, the rejection threshold should be determined through careful examination of a method's rejection behavior. Without rejection, flat and hierarchical annotation perform equally well, as long as the cell type hierarchy accurately captures transcriptomic relationships. AVAILABILITY AND IMPLEMENTATION Code is freely available at https://github.com/Latheuni/Hierarchical_reject and https://doi.org/10.5281/zenodo.10697468.
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Affiliation(s)
- Lauren Theunissen
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Thomas Mortier
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Willem Waegeman
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
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20
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Nechanitzky R, Ramachandran P, Nechanitzky D, Li WY, Wakeham AC, Haight J, Saunders ME, Epelman S, Mak TW. CaSSiDI: novel single-cell "Cluster Similarity Scoring and Distinction Index" reveals critical functions for PirB and context-dependent Cebpb repression. Cell Death Differ 2024; 31:265-279. [PMID: 38383888 PMCID: PMC10923835 DOI: 10.1038/s41418-024-01268-8] [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: 05/16/2023] [Revised: 01/15/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
PirB is an inhibitory cell surface receptor particularly prominent on myeloid cells. PirB curtails the phenotypes of activated macrophages during inflammation or tumorigenesis, but its functions in macrophage homeostasis are obscure. To elucidate PirB-related functions in macrophages at steady-state, we generated and compared single-cell RNA-sequencing (scRNAseq) datasets obtained from myeloid cell subsets of wild type (WT) and PirB-deficient knockout (PirB KO) mice. To facilitate this analysis, we developed a novel approach to clustering parameter optimization called "Cluster Similarity Scoring and Distinction Index" (CaSSiDI). We demonstrate that CaSSiDI is an adaptable computational framework that facilitates tandem analysis of two scRNAseq datasets by optimizing clustering parameters. We further show that CaSSiDI offers more advantages than a standard Seurat analysis because it allows direct comparison of two or more independently clustered datasets, thereby alleviating the need for batch-correction while identifying the most similar and different clusters. Using CaSSiDI, we found that PirB is a novel regulator of Cebpb expression that controls the generation of Ly6Clo patrolling monocytes and the expansion properties of peritoneal macrophages. PirB's effect on Cebpb is tissue-specific since it was not observed in splenic red pulp macrophages (RPMs). However, CaSSiDI revealed a segregation of the WT RPM population into a CD68loIrf8+ "neuronal-primed" subset and an CD68hiFtl1+ "iron-loaded" subset. Our results establish the utility of CaSSiDI for single-cell assay analyses and the determination of optimal clustering parameters. Our application of CaSSiDI in this study has revealed previously unknown roles for PirB in myeloid cell populations. In particular, we have discovered homeostatic functions for PirB that are related to Cebpb expression in distinct macrophage subsets.
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Affiliation(s)
- Robert Nechanitzky
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.
- Providence Therapeutics Holdings Inc., Calgary, AB, Canada.
| | - Parameswaran Ramachandran
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Duygu Nechanitzky
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Wanda Y Li
- Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
| | - Andrew C Wakeham
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Jillian Haight
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Mary E Saunders
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Slava Epelman
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ted Rogers Centre for Heart Research, Translational Biology and Engineering Program, Toronto, ON, Canada
- Peter Munk Cardiac Centre, UHN, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Tak W Mak
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.
- Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China.
- Department of Pathology Department of Pathology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
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21
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Zhang H, Lu X, Lu B, Gullo G, Chen L. Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future. Future Oncol 2024; 20:1207-1220. [PMID: 38362731 PMCID: PMC11318690 DOI: 10.2217/fon-2023-0658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/24/2024] [Indexed: 02/17/2024] Open
Abstract
Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.
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Affiliation(s)
- Han Zhang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Binfeng Lu
- Center for Discovery & Innovation, Hackensack Meridian Health, Nutley, NJ 07110, USA
| | - Giuseppe Gullo
- Department of Obstetrics & Gynecology, Villa Sofia Cervello Hospital, University of Palermo, 90146, Palermo, Italy
| | - Lujia Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
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22
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Majd H, Cesiulis A, Samuel RM, Richter MN, Elder N, Guyer RA, Hao MM, Stamp LA, Goldstein AM, Fattahi F. A call for a unified and multimodal definition of cellular identity in the enteric nervous system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575794. [PMID: 38293133 PMCID: PMC10827084 DOI: 10.1101/2024.01.15.575794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The enteric nervous system (ENS) is a tantalizing frontier in neuroscience. With the recent emergence of single cell transcriptomic technologies, this rare and poorly understood tissue has begun to be better characterized in recent years. A precise functional mapping of enteric neuron diversity is critical for understanding ENS biology and enteric neuropathies. Nonetheless, this pursuit has faced considerable technical challenges. By leveraging different methods to compare available primary mouse and human ENS datasets, we underscore the urgent need for careful identity annotation, achieved through the harmonization and advancements of wet lab and computational techniques. We took different approaches including differential gene expression, module scoring, co-expression and correlation analysis, unbiased biological function hierarchical clustering, data integration and label transfer to compare and contrast functional annotations of several independently reported ENS datasets. These analyses highlight substantial discrepancies stemming from an overreliance on transcriptomics data without adequate validation in tissues. To achieve a comprehensive understanding of enteric neuron identity and their functional context, it is imperative to expand tissue sources and incorporate innovative technologies such as multiplexed imaging, electrophysiology, spatial transcriptomics, as well as comprehensive profiling of epigenome, proteome, and metabolome. Harnessing human pluripotent stem cell (hPSC) models provides unique opportunities for delineating lineage trees of the human ENS, and offers unparalleled advantages, including their scalability and compatibility with genetic manipulation and unbiased screens. We encourage a paradigm shift in our comprehension of cellular complexity and function in the ENS by calling for large-scale collaborative efforts and research investments.
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Affiliation(s)
- Homa Majd
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Andrius Cesiulis
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Ryan M Samuel
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Mikayla N Richter
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Nicholas Elder
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Richard A Guyer
- Department of Pediatric Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Marlene M. Hao
- Department of Anatomy and Physiology, the University of Melbourne, Parkville, VIC, Australia
| | - Lincon A. Stamp
- Department of Anatomy and Physiology, the University of Melbourne, Parkville, VIC, Australia
| | - Allan M Goldstein
- Department of Pediatric Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Faranak Fattahi
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, 94143, USA
- Program in Craniofacial Biology, University of California, San Francisco, California, USA
- Lead contact
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23
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Li W, Bazaz SR, Mayoh C, Salomon R. Analytical Workflows for Single-Cell Multiomic Data Using the BD Rhapsody Platform. Curr Protoc 2024; 4:e963. [PMID: 38353375 DOI: 10.1002/cpz1.963] [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: 02/16/2024]
Abstract
The conversion of raw sequencing reads to biologically relevant data in high-throughput single-cell RNA sequencing experiments is a complex and involved process. Drawing meaning from thousands of individual cells to provide biological insight requires ensuring not only that the data are of the highest quality but also that the signal can be separated from noise. In this article, we describe a detailed analytical workflow, including six pipelines, that allows high-quality data analysis in single-cell multiomics. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Image analysis Basic Protocol 2: Sequencing quality control and generation of a gene expression matrix Basic Protocol 3: Gene expression matrix data pre-processing and analysis Basic Protocol 4: Advanced analysis Basic Protocol 5: Conversion to flow cytometry standard (FCS) format Basic Protocol 6: Visualization using graphical interfaces.
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Affiliation(s)
- Wenyan Li
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Sajad Razavi Bazaz
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Chelsea Mayoh
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Robert Salomon
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia
- School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
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24
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Qu HQ, Kao C, Hakonarson H. Single-Cell RNA Sequencing Technology Landscape in 2023. Stem Cells 2024; 42:1-12. [PMID: 37934608 DOI: 10.1093/stmcls/sxad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 09/29/2023] [Indexed: 11/09/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and the dynamics of gene expression, bearing profound significance in stem cell research. Depending on the starting materials used for analysis, scRNA-seq encompasses scRNA-seq and single-nucleus RNA sequencing (snRNA-seq). scRNA-seq excels in capturing cellular heterogeneity and characterizing rare cell populations within complex tissues, while snRNA-seq is advantageous in situations where intact cell dissociation is challenging or undesirable (eg, epigenomic studies). A number of scRNA-seq technologies have been developed as of late, including but not limited to droplet-based, plate-based, hydrogel-based, and spatial transcriptomics. The number of cells, sequencing depth, and sequencing length in scRNA-seq can vary across different studies. Addressing current technical challenges will drive the future of scRNA-seq, leading to more comprehensive and precise insights into cellular biology and disease mechanisms informing therapeutic interventions.
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Affiliation(s)
- Hui-Qi Qu
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PAUSA
| | - Charlly Kao
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PAUSA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PAUSA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
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25
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Zhang Y, Sun H, Zhang W, Fu T, Huang S, Mou M, Zhang J, Gao J, Ge Y, Yang Q, Zhu F. CellSTAR: a comprehensive resource for single-cell transcriptomic annotation. Nucleic Acids Res 2024; 52:D859-D870. [PMID: 37855686 PMCID: PMC10767908 DOI: 10.1093/nar/gkad874] [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: 08/15/2023] [Revised: 09/12/2023] [Accepted: 09/27/2023] [Indexed: 10/20/2023] Open
Abstract
Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first time and (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.
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Affiliation(s)
- Ying Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yichao Ge
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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26
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Du ZH, Hu WL, Li JQ, Shang X, You ZH, Chen ZZ, Huang YA. scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data. Commun Biol 2023; 6:1268. [PMID: 38097699 PMCID: PMC10721875 DOI: 10.1038/s42003-023-05634-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.
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Affiliation(s)
- Zhi-Hua Du
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Wei-Lin Hu
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Zhuang-Zhuang Chen
- College of Computer Science and Software Engineering, ShenZhen University, 3688 Nanhai Avenue, Shenzhen, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
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27
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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28
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Molstad AJ, Motwani K. Multiresolution categorical regression for interpretable cell-type annotation. Biometrics 2023; 79:3485-3496. [PMID: 37798600 DOI: 10.1111/biom.13926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 08/07/2023] [Indexed: 10/07/2023]
Abstract
In many categorical response regression applications, the response categories admit a multiresolution structure. That is, subsets of the response categories may naturally be combined into coarser response categories. In such applications, practitioners are often interested in estimating the resolution at which a predictor affects the response category probabilities. In this paper, we propose a method for fitting the multinomial logistic regression model in high dimensions that addresses this problem in a unified and data-driven way. Our method allows practitioners to identify which predictors distinguish between coarse categories but not fine categories, which predictors distinguish between fine categories, and which predictors are irrelevant. For model fitting, we propose a scalable algorithm that can be applied when the coarse categories are defined by either overlapping or nonoverlapping sets of fine categories. Statistical properties of our method reveal that it can take advantage of this multiresolution structure in a way existing estimators cannot. We use our method to model cell-type probabilities as a function of a cell's gene expression profile (i.e., cell-type annotation). Our fitted model provides novel biological insights which may be useful for future automated and manual cell-type annotation methodology.
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Affiliation(s)
- Aaron J Molstad
- School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Keshav Motwani
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
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29
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Scipione CA, Hyduk SJ, Polenz CK, Cybulsky MI. Unveiling the Hidden Landscape of Arterial Diseases at Single-Cell Resolution. Can J Cardiol 2023; 39:1781-1794. [PMID: 37716639 DOI: 10.1016/j.cjca.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/25/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023] Open
Abstract
High-resolution single-cell technologies have shed light on the pathogenesis of cardiovascular diseases by enabling the discovery of novel cellular and transcriptomic signatures associated with various conditions, and uncovering new contributions of inflammatory processes, immunity, metabolic stress, and risk factors. We review the information obtained from studies using single-cell technologies in tissues with atherosclerosis and aortic aneurysms. Insights are provided on the biology of endothelial, smooth muscle, and immune cells in the arterial intima and media. In addition to cellular diversity, numerous examples of plasticity and phenotype switching are highlighted and presented in the context of normal cell functions.
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Affiliation(s)
- Corey A Scipione
- Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada; Departments of Laboratory Medicine and Pathobiology and Immunology, University of Toronto, Toronto, Ontario, Canada.
| | - Sharon J Hyduk
- Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Chanele K Polenz
- Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada; Departments of Laboratory Medicine and Pathobiology and Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Myron I Cybulsky
- Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada; Departments of Laboratory Medicine and Pathobiology and Immunology, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
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30
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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.
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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
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31
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Yin Q, Chen L. CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data. Brief Bioinform 2023; 25:bbad449. [PMID: 38061196 PMCID: PMC10703497 DOI: 10.1093/bib/bbad449] [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: 06/27/2023] [Revised: 10/30/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023] Open
Abstract
Identifying cell types is crucial for understanding the functional units of an organism. Machine learning has shown promising performance in identifying cell types, but many existing methods lack biological significance due to poor interpretability. However, it is of the utmost importance to understand what makes cells share the same function and form a specific cell type, motivating us to propose a biologically interpretable method. CellTICS prioritizes marker genes with cell-type-specific expression, using a hierarchy of biological pathways for neural network construction, and applying a multi-predictive-layer strategy to predict cell and sub-cell types. CellTICS usually outperforms existing methods in prediction accuracy. Moreover, CellTICS can reveal pathways that define a cell type or a cell type under specific physiological conditions, such as disease or aging. The nonlinear nature of neural networks enables us to identify many novel pathways. Interestingly, some of the pathways identified by CellTICS exhibit differential expression "variability" rather than differential expression across cell types, indicating that expression stochasticity within a pathway could be an important feature characteristic of a cell type. Overall, CellTICS provides a biologically interpretable method for identifying and characterizing cell types, shedding light on the underlying pathways that define cellular heterogeneity and its role in organismal function. CellTICS is available at https://github.com/qyyin0516/CellTICS.
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Affiliation(s)
- Qingyang Yin
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
| | - Liang Chen
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
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32
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Gonzalez-Ferrer J, Lehrer J, O’Farrell A, Paten B, Teodorescu M, Haussler D, Jonsson VD, Mostajo-Radji MA. Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.529615. [PMID: 36909548 PMCID: PMC10002667 DOI: 10.1101/2023.02.28.529615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Machine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.
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Affiliation(s)
- Jesus Gonzalez-Ferrer
- These authors contributed equally to this work
- Genomics Institute, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
| | - Julian Lehrer
- These authors contributed equally to this work
- Genomics Institute, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Department of Applied Mathematics, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
| | - Ash O’Farrell
- Genomics Institute, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
| | - Benedict Paten
- Genomics Institute, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
| | - Mircea Teodorescu
- Genomics Institute, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
| | - David Haussler
- Genomics Institute, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
| | - Vanessa D. Jonsson
- Department of Applied Mathematics, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Co-senior authors
| | - Mohammed A. Mostajo-Radji
- Genomics Institute, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, 95060, CA, USA
- Co-senior authors
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33
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Kwon J, Kang J, Jo A, Seo K, An D, Baykan MY, Lee JH, Kim N, Eum HH, Hwang S, Lee JM, Park WY, An HJ, Lee HO, Park JE, Choi JK. Single-cell mapping of combinatorial target antigens for CAR switches using logic gates. Nat Biotechnol 2023; 41:1593-1605. [PMID: 36797491 DOI: 10.1038/s41587-023-01686-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/20/2023] [Indexed: 02/18/2023]
Abstract
Identification of optimal target antigens that distinguish cancer cells from normal surrounding tissue cells remains a key challenge in chimeric antigen receptor (CAR) cell therapy for tumors with intratumoral heterogeneity. In this study, we dissected tissue complexity to the level of individual cells through the construction of a single-cell expression atlas that integrates ~1.4 million tumor, tumor-infiltrating normal and reference normal cells from 412 tumors and 12 normal organs. We used a two-step screening method using random forest and convolutional neural networks to select gene pairs that contribute most to discrimination between individual malignant and normal cells. Tumor coverage and specificity are evaluated for the AND, OR and NOT logic gates based on the combinatorial expression pattern of the pairing genes across individual single cells. Single-cell transcriptome-coupled epitope profiling validates the AND, OR and NOT switch targets identified in ovarian cancer and colorectal cancer.
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Affiliation(s)
- Joonha Kwon
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Junho Kang
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Areum Jo
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine and Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kayoung Seo
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Dohyeon An
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Mert Yakup Baykan
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Jun Hyeong Lee
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Nayoung Kim
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine and Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hye Hyeon Eum
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Department of Biomedicine and Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sohyun Hwang
- Department of Pathology, CHA Bundang Medical Center, CHA University, Seongnam-si, Republic of Korea
- Department of Biomedical Science, CHA University, Pocheon-si, Republic of Korea
| | - Ji Min Lee
- CHA Advanced Research Institute, CHA Bundang Medical Center, Seongnam-si, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jung An
- Department of Pathology, CHA Bundang Medical Center, CHA University, Seongnam-si, Republic of Korea.
| | - Hae-Ock Lee
- Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- Department of Biomedicine and Health Sciences, Graduate School, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Jong-Eun Park
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea.
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
- Penta Medix Co., Ltd., Seongnam-si, Republic of Korea.
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34
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Fu M, Shu S, Peng Z, Liu X, Chen X, Zeng Z, Yang Y, Cui H, Zhao R, Wang X, Du L, Wu M, Feng W, Song J. Single-Cell RNA Sequencing of Coronary Perivascular Adipose Tissue From End-Stage Heart Failure Patients Identifies SPP1+ Macrophage Subpopulation as a Target for Alleviating Fibrosis. Arterioscler Thromb Vasc Biol 2023; 43:2143-2164. [PMID: 37706320 PMCID: PMC10597444 DOI: 10.1161/atvbaha.123.319828] [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: 07/06/2023] [Accepted: 08/29/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND Perivascular adipose tissue (PVAT) is vital for vascular homeostasis, and PVAT dysfunction is associated with increased atherosclerotic plaque burden. But the mechanisms underlining coronary PVAT dysfunction in coronary atherosclerosis remain elusive. METHODS We performed single-cell RNA sequencing of the stromal vascular fraction of coronary PVAT from 3 groups of heart transplant recipients with end-stage heart failure, including 3 patients with nonobstructive coronary atherosclerosis, 3 patients with obstructive coronary artery atherosclerosis, and 4 nonatherosclerosis control subjects. Bioinformatics was used to annotate the cellular populations, depict the cellular developmental trajectories and interactions, and explore the differences among 3 groups of coronary PVAT at the cellular and molecular levels. Pathological staining, quantitative real-time polymerase chain reaction, and in vitro studies were performed to validate the key findings. RESULTS Ten cell types were identified among 67 936 cells from human coronary PVAT. Several cellular subpopulations, including SPP1+ (secreted phosphoprotein 1) macrophages and profibrotic fibroadipogenic progenitor cells, were accumulated in PVAT surrounding atherosclerotic coronary arteries compared with nonatherosclerosis coronary arteries. The fibrosis percentage was increased in PVAT surrounding atherosclerotic coronary arteries, and it was positively associated with the grade of coronary artery stenosis. Cellular interaction analysis suggested OPN (osteopontin) secreted by SPP1+ macrophages interacted with CD44 (cluster of differentiation 44)/integrin on fibroadipogenic progenitor cells. Strikingly, correlation analyses uncovered that higher level of SPP1 in PVAT correlates with a more severe fibrosis degree and a higher coronary stenosis grade. In vitro studies showed that conditioned medium from atherosclerotic coronary PVAT promoted the migration and proliferation of fibroadipogenic progenitor cells, while such effect was prevented by blocking CD44 or integrin. CONCLUSIONS SPP1+ macrophages accumulated in the PVAT surrounding atherosclerotic coronary arteries, and they promoted the migration and proliferation of fibroadipogenic progenitor cells via OPN-CD44/integrin interaction and thus aggravated the fibrosis of coronary PVAT, which was positively correlated to the coronary stenosis burden. Therefore, SPP1+ macrophages in coronary PVAT may participate in the progression of coronary atherosclerosis.
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Affiliation(s)
- Mengxia Fu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Galactophore Department, Galactophore Center, Beijing Shijitan Hospital (M.F., M.W.), Capital Medical University, China
| | - Songren Shu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- The Cardiomyopathy Research Group at Fuwai Hospital, China (S.S., X.L., X.C., H.C., R.Z., X.W., J.S.)
| | - Zhiming Peng
- Department of Orthopedics, Peking Union Medical College Hospital (Z.P.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaorui Liu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- The Cardiomyopathy Research Group at Fuwai Hospital, China (S.S., X.L., X.C., H.C., R.Z., X.W., J.S.)
| | - Xiao Chen
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- The Cardiomyopathy Research Group at Fuwai Hospital, China (S.S., X.L., X.C., H.C., R.Z., X.W., J.S.)
| | - Zhiwei Zeng
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yicheng Yang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hao Cui
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- The Cardiomyopathy Research Group at Fuwai Hospital, China (S.S., X.L., X.C., H.C., R.Z., X.W., J.S.)
| | - Ruojin Zhao
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- The Cardiomyopathy Research Group at Fuwai Hospital, China (S.S., X.L., X.C., H.C., R.Z., X.W., J.S.)
| | - Xiaohu Wang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- The Cardiomyopathy Research Group at Fuwai Hospital, China (S.S., X.L., X.C., H.C., R.Z., X.W., J.S.)
| | - Leilei Du
- Laboratory of Cardiovascular Science, Beijing Clinical Research Institute, Beijing Friendship Hospital (L.D.), Capital Medical University, China
| | - Min Wu
- Galactophore Department, Galactophore Center, Beijing Shijitan Hospital (M.F., M.W.), Capital Medical University, China
| | - Wei Feng
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases (W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangping Song
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases (M.F., S.S., X.L., X.C., Z.Z., Y.Y., H.C., R.Z., X.W., W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases (W.F., J.S.), Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- The Cardiomyopathy Research Group at Fuwai Hospital, China (S.S., X.L., X.C., H.C., R.Z., X.W., J.S.)
- Shenzhen Key Laboratory of Cardiovascular Disease, Fuwai Hospital Chinese Academy of Medical Sciences, China (J.S.)
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Du J, Gu XR, Yu XX, Cao YJ, Hou J. Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value. BLOOD SCIENCE 2023; 5:221-236. [PMID: 37941914 PMCID: PMC10629747 DOI: 10.1097/bs9.0000000000000172] [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: 05/17/2023] [Accepted: 09/18/2023] [Indexed: 11/10/2023] Open
Abstract
Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.
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Affiliation(s)
- Jun Du
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xiao-Ran Gu
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Xiao-Xiao Yu
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Yang-Jia Cao
- Department of Hematology, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shanxi 710000, China
| | - Jian Hou
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
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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.
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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
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Aybey B, Zhao S, Brors B, Staub E. Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets. Front Immunol 2023; 14:1194745. [PMID: 37609075 PMCID: PMC10441575 DOI: 10.3389/fimmu.2023.1194745] [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: 03/27/2023] [Accepted: 07/14/2023] [Indexed: 08/24/2023] Open
Abstract
Background Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches. Results We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment. Discussion and conclusion We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.
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Affiliation(s)
- Bogac Aybey
- Oncology Data Science, Merck Healthcare KGaA, Darmstadt, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Sheng Zhao
- Oncology Data Science, Merck Healthcare KGaA, Darmstadt, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center, Heidelberg, Germany
- German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Eike Staub
- Oncology Data Science, Merck Healthcare KGaA, Darmstadt, Germany
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 168] [Impact Index Per Article: 168.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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Nassiri I, Fairfax B, Lee A, Wu Y, Buck D, Piazza P. scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data. BMC Genomics 2023; 24:381. [PMID: 37415108 DOI: 10.1186/s12864-023-09447-6] [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: 10/25/2022] [Accepted: 06/13/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for scRNA-seq data, they do not include expression-based QC to discriminate between true variation and background noise. RESULTS We present scQCEA (acronym of the single-cell RNA sequencing Quality Control and Enrichment Analysis), an R package to generate reports of process optimisation metrics for comparing sets of samples and visual evaluation of quality scores. scQCEA can import data from 10X or other single-cell platforms and includes functions for generating an interactive report of QC metrics for multi-omics data. In addition, scQCEA provides automated cell type annotation on scRNA-seq data using differential gene expression patterns for expression-based quality control. We provide a repository of reference gene sets, including 2348 marker genes, which are exclusively expressed in 95 human and mouse cell types. Using scRNA-seq data from 56 gene expressions and V(D)J T cell replicates, we show how scQCEA can be applied for the visual evaluation of quality scores for sets of samples. In addition, we use the summary of QC measures from 342 human and mouse shallow-sequenced gene expression profiles to specify optimal sequencing requirements to run a cell-type enrichment analysis function. CONCLUSIONS The open-source R tool will allow examining biases and outliers over biological and technical measures, and objective selection of optimal cluster numbers before downstream analysis. scQCEA is available at https://isarnassiri.github.io/scQCEA/ as an R package. Full documentation, including an example, is provided on the package website.
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Affiliation(s)
- Isar Nassiri
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
| | - Benjamin Fairfax
- MRC-Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Department of Oncology, University of Oxford & Oxford Cancer Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Angela Lee
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Yanxia Wu
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - David Buck
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Paolo Piazza
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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Lee E, Chern K, Nissen M, Wang X, Huang C, Gandhi AK, Bouchard-Côté A, Weng AP, Roth A. SpatialSort: a Bayesian model for clustering and cell population annotation of spatial proteomics data. Bioinformatics 2023; 39:i131-i139. [PMID: 37387130 DOI: 10.1093/bioinformatics/btad242] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Recent advances in spatial proteomics technologies have enabled the profiling of dozens of proteins in thousands of single cells in situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead probe the spatial relationships between cells. However, most current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample. RESULTS To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. Our method is able to account for the affinities of cells of different types to neighbour in space, and by incorporating prior information about expected cell populations, it is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data, we show that by using spatial and prior information SpatialSort improves clustering accuracy. We also demonstrate how SpatialSort can perform label transfer between spatial and nonspatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset. AVAILABILITY AND IMPLEMENTATION Source code is available on Github at: https://github.com/Roth-Lab/SpatialSort.
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Affiliation(s)
- Eric Lee
- Department of Molecular Oncology, BC Cancer Agency, 675 West 10th Avenue, Vancouver, BC V5Z1L3, Canada
- Graduate Bioinformatics Training Program, University of British Columbia, 100-570 West 7th Avenue, Vancouver, BC V5T4S6, Canada
| | - Kevin Chern
- Department of Statistics, University of British Columbia, 2207 Main Mall, Vancouver, BC V6T1Z4, Canada
| | - Michael Nissen
- Terry Fox Laboratory, British Columbia Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z1L3, Canada
| | - Xuehai Wang
- Terry Fox Laboratory, British Columbia Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z1L3, Canada
| | - Chris Huang
- Translational Medicine Hematology, Bristol Myers Squibb, 86 Morris Ave, Summit, NJ 07901, United States
| | - Anita K Gandhi
- Translational Medicine Hematology, Bristol Myers Squibb, 86 Morris Ave, Summit, NJ 07901, United States
| | - Alexandre Bouchard-Côté
- Department of Statistics, University of British Columbia, 2207 Main Mall, Vancouver, BC V6T1Z4, Canada
| | - Andrew P Weng
- Terry Fox Laboratory, British Columbia Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z1L3, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T1Z7, Canada
| | - Andrew Roth
- Department of Molecular Oncology, BC Cancer Agency, 675 West 10th Avenue, Vancouver, BC V5Z1L3, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T1Z7, Canada
- Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada
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Lin X, Chau C, Ma K, Huang Y, Ho JWK. DCATS: differential composition analysis for flexible single-cell experimental designs. Genome Biol 2023; 24:151. [PMID: 37365636 PMCID: PMC10294334 DOI: 10.1186/s13059-023-02980-3] [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: 03/21/2022] [Accepted: 06/07/2023] [Indexed: 06/28/2023] Open
Abstract
Differential composition analysis - the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions - is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods.
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Affiliation(s)
- Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Chuen Chau
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Kun Ma
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Yuanhua Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
- Department of Statistics and Actuarial Science, Faculty of Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China.
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Caliskan A, Caliskan D, Rasbach L, Yu W, Dandekar T, Breitenbach T. Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning. Comput Struct Biotechnol J 2023; 21:3293-3314. [PMID: 37333862 PMCID: PMC10276237 DOI: 10.1016/j.csbj.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 06/20/2023] Open
Abstract
Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.
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Lewinsohn DP, Vigh-Conrad KA, Conrad DF, Scott CB. Consensus label propagation with graph convolutional networks for single-cell RNA sequencing cell type annotation. Bioinformatics 2023; 39:btad360. [PMID: 37267208 PMCID: PMC10272704 DOI: 10.1093/bioinformatics/btad360] [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: 12/05/2022] [Revised: 05/02/2023] [Accepted: 06/01/2023] [Indexed: 06/04/2023] Open
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective. RESULTS We present a Graph Convolutional Network (GCN)-based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq datasets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a nonparametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data. AVAILABILITY AND IMPLEMENTATION Our code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP.
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Affiliation(s)
- Daniel P Lewinsohn
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, United States
- Department of Mathematics and Computer Science, Colorado College, Colorado Springs, CO 80903, United States
| | - Katinka A Vigh-Conrad
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, United States
| | - Donald F Conrad
- Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, United States
| | - Cory B Scott
- Department of Mathematics and Computer Science, Colorado College, Colorado Springs, CO 80903, United States
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44
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Davalos OA, Heydari AA, Fertig EJ, Sindi SS, Hoyer KK. Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.29.542760. [PMID: 37398136 PMCID: PMC10312486 DOI: 10.1101/2023.05.29.542760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scRNAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNA's performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.
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Affiliation(s)
- Oscar A. Davalos
- Quantitative and Systems Biology Graduate Program, University of California, Merced, CA, USA
| | - A. Ali Heydari
- Department of Applied Mathematics, University of California, Merced, CA, USA
- Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Elana J. Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California, Merced, CA, USA
- Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Katrina K. Hoyer
- Health Sciences Research Institute, University of California, Merced, CA, USA
- Department of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, CA, USA
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Liu H, Li H, Sharma A, Huang W, Pan D, Gu Y, Lin L, Sun X, Liu H. scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets. Brief Bioinform 2023; 24:bbad179. [PMID: 37183449 DOI: 10.1093/bib/bbad179] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/29/2023] [Accepted: 04/19/2023] [Indexed: 05/16/2023] Open
Abstract
Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.
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Affiliation(s)
- Hongjia Liu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Huamei Li
- Department of General Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, PR China
| | - Amit Sharma
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | | | - Duo Pan
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yu Gu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Lu Lin
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xiao Sun
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Hongde Liu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
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46
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Liu Y, Wei G, Li C, Shen LC, Gasser RB, Song J, Chen D, Yu DJ. TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level. Brief Bioinform 2023; 24:bbad132. [PMID: 37080771 PMCID: PMC10199768 DOI: 10.1093/bib/bbad132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/02/2023] [Accepted: 03/14/2023] [Indexed: 04/22/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.
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Affiliation(s)
- Yan Liu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Guo Wei
- School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Chen Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Long-Chen Shen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Dijun Chen
- School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
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47
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Yan D, Sun Z, Fang J, Cao S, Wang W, Chang X, Badirli S, Fu H, Liu Y. scRAA: the development of a robust and automatic annotation procedure for single-cell RNA sequencing data. J Biopharm Stat 2023:1-14. [PMID: 37162278 DOI: 10.1080/10543406.2023.2208671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A critical task in single-cell RNA sequencing (scRNA-Seq) data analysis is to identify cell types from heterogeneous tissues. While the majority of classification methods demonstrated high performance in scRNA-Seq annotation problems, a robust and accurate solution is desired to generate reliable outcomes for downstream analyses, for instance, marker genes identification, differentially expressed genes, and pathway analysis. It is hard to establish a universally good metric. Thus, a universally good classification method for all kinds of scenarios does not exist. In addition, reference and query data in cell classification are usually from different experimental batches, and failure to consider batch effects may result in misleading conclusions. To overcome this bottleneck, we propose a robust ensemble approach to classify cells and utilize a batch correction method between reference and query data. We simulated four scenarios that comprise simple to complex batch effect and account for varying cell-type proportions. We further tested our approach on both lung and pancreas data. We found improved prediction accuracy and robust performance across simulation scenarios and real data. The incorporation of batch effect correction between reference and query, and the ensemble approach improve cell-type prediction accuracy while maintaining robustness. We demonstrated these through simulated and real scRNA-Seq data.
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Affiliation(s)
- Dongyan Yan
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Zhe Sun
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Jiyuan Fang
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Shanshan Cao
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Wenjie Wang
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Xinyue Chang
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Sarkhan Badirli
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Haoda Fu
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Yushi Liu
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
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48
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Miranda AMA, Janbandhu V, Maatz H, Kanemaru K, Cranley J, Teichmann SA, Hübner N, Schneider MD, Harvey RP, Noseda M. Single-cell transcriptomics for the assessment of cardiac disease. Nat Rev Cardiol 2023; 20:289-308. [PMID: 36539452 DOI: 10.1038/s41569-022-00805-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/03/2022] [Indexed: 12/24/2022]
Abstract
Cardiovascular disease is the leading cause of death globally. An advanced understanding of cardiovascular disease mechanisms is required to improve therapeutic strategies and patient risk stratification. State-of-the-art, large-scale, single-cell and single-nucleus transcriptomics facilitate the exploration of the cardiac cellular landscape at an unprecedented level, beyond its descriptive features, and can further our understanding of the mechanisms of disease and guide functional studies. In this Review, we provide an overview of the technical challenges in the experimental design of single-cell and single-nucleus transcriptomics studies, as well as a discussion of the type of inferences that can be made from the data derived from these studies. Furthermore, we describe novel findings derived from transcriptomics studies for each major cardiac cell type in both health and disease, and from development to adulthood. This Review also provides a guide to interpreting the exhaustive list of newly identified cardiac cell types and states, and highlights the consensus and discordances in annotation, indicating an urgent need for standardization. We describe advanced applications such as integration of single-cell data with spatial transcriptomics to map genes and cells on tissue and define cellular microenvironments that regulate homeostasis and disease progression. Finally, we discuss current and future translational and clinical implications of novel transcriptomics approaches, and provide an outlook of how these technologies will change the way we diagnose and treat heart disease.
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Affiliation(s)
| | - Vaibhao Janbandhu
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
- School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Henrike Maatz
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Kazumasa Kanemaru
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - James Cranley
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sarah A Teichmann
- Cellular Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Deptartment of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Norbert Hübner
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | | | - Richard P Harvey
- Victor Chang Cardiac Research Institute, Sydney, NSW, Australia
- School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
- School of Biotechnology and Biomolecular Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Michela Noseda
- National Heart and Lung Institute, Imperial College London, London, UK.
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49
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Wang C, Peng L, Wang Y, Xue Y, Chen T, Ji Y, Li Y, Zhao Y, Yu S. Integrative Analysis of Single-Cell and Bulk Sequencing Data Depicting the Expression and Function of P2ry12 in Microglia Post Ischemia–Reperfusion Injury. Int J Mol Sci 2023; 24:ijms24076772. [PMID: 37047745 PMCID: PMC10095011 DOI: 10.3390/ijms24076772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/26/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023] Open
Abstract
P2ry12 is a microglial marker gene. Recently, increasing evidence has demonstrated that its expression levels can vary in response to different CNS disorders and can affect microglial functions, such as polarization, plasticity, and migration. However, the expression and function of P2ry12 in microglia during ischemia–reperfusion injury (IRI) remain unclear. Here, we developed a computational method to obtain microglia-specific P2ry12 genes (MSPGs) using sequencing data associated with IRI. We evaluated the change in comprehensive expression levels of MSPGs during IRI and compared it to the expression of P2ry12 to determine similarity. Subsequently, the MSPGs were used to explore the P2ry12 functions in microglia through bioinformatics. Moreover, several animal experiments were also conducted to confirm the reliability of the results. The expression of P2ry12 was observed to decrease gradually within 24 h post injury. In response, microglia with reduced P2ry12 expression showed an increase in the expression of one receptor-encoding gene (Flt1) and three ligand-encoding genes (Nampt, Igf1, and Cxcl2). Furthermore, double-labeling immunofluorescence staining revealed that inhibition of P2ry12 blocked microglial migration towards vessels during IRI. Overall, we employ a combined computational and experimental approach to successfully explore P2ry12 expression and function in microglia during IRI.
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Affiliation(s)
- Chenglong Wang
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Li Peng
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yuan Wang
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Ying Xue
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Tianyi Chen
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yanyan Ji
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yishan Li
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yong Zhao
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Shanshan Yu
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, Chongqing 400016, China
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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50
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Jiao L, Ren Y, Wang L, Gao C, Wang S, Song T. MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data. Front Genet 2023; 14:1179859. [PMID: 37082202 PMCID: PMC10110861 DOI: 10.3389/fgene.2023.1179859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 03/27/2023] [Indexed: 04/07/2023] Open
Abstract
Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. However, scRNA-seq data are often high dimensional and sparse, and manual cell type identification can be time-consuming, subjective, and lack reproducibility. Consequently, analyzing scRNA-seq data remains a computational challenge. With the increasing availability of well-annotated scRNA-seq datasets, advanced methods are emerging to aid in cell type identification by leveraging this information. Deep learning neural networks have great potential for analyzing single-cell data. This paper proposes MulCNN, a multi-level convolutional neural network that uses a unique cell type-specific gene expression feature extraction method. This method extracts critical features through multi-scale convolution while filtering noise. Extensive testing using datasets from various species and comparisons with popular classification methods show that MulCNN has outstanding performance and offers a new and scalable direction for scRNA-seq analysis.
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Affiliation(s)
- Linfang Jiao
- College of Computer Science and Technology, China University of Petroleum, Qingdao, China
| | - Yongqi Ren
- College of Computer Science and Technology, China University of Petroleum, Qingdao, China
| | - Lulu Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao, China
| | - Changnan Gao
- College of Computer Science and Technology, China University of Petroleum, Qingdao, China
| | - Shuang Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao, China
| | - Tao Song
- College of Computer Science and Technology, China University of Petroleum, Qingdao, China
- Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Madrid, Spain
- *Correspondence: Tao Song,
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