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Chen L, Guo Z, Deng T, Wu H. scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq. Genome Biol 2024; 25:269. [PMID: 39402623 PMCID: PMC11472465 DOI: 10.1186/s13059-024-03410-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 09/30/2024] [Indexed: 10/19/2024] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.
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Affiliation(s)
- Luxiao Chen
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Zhenxing Guo
- School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), Shenzhen, 518172, Guangdong, China
| | - Tao Deng
- School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), Shenzhen, 518172, Guangdong, China
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
| | - Hao Wu
- Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, 518055, Guangdong, China.
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China.
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Chen H, Lu Y, Rao Y. A self-training interpretable cell type annotation framework using specific marker gene. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae569. [PMID: 39312689 DOI: 10.1093/bioinformatics/btae569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 09/03/2024] [Accepted: 09/19/2024] [Indexed: 09/25/2024]
Abstract
MOTIVATION Recent advances in sequencing technology provide opportunities to study biological processes at a higher resolution. Cell type annotation is an important step in scRNA-seq analysis, which often relies on established marker genes. However, most of the previous methods divide the identification of cell types into two stages, clustering and assignment, whose performances are susceptible to the clustering algorithm, and the marker information cannot effectively guide the clustering process. Furthermore, their linear heuristic-based cell assignment process is often insufficient to capture potential dependencies between cells and types. RESULTS Here, we present Interpretable Cell Type Annotation based on self-training (sICTA), a marker-based cell type annotation method that combines the self-training strategy with pseudo-labeling and the nonlinear association capturing capability of Transformer. In addition, we incorporate biological priori knowledge of genes and pathways into the classifier through an attention mechanism to enhance the transparency of the model. A benchmark analysis on 11 publicly available single-cell datasets demonstrates the superiority of sICTA compared to state-of-the-art methods. The robustness of our method is further validated by evaluating the prediction accuracy of the model on different cell types for each single-cell data. Moreover, ablation studies show that self-training and the ability to capture potential dependencies between cells and cell types, both of which are mutually reinforcing, work together to improve model performance. Finally, we apply sICTA to the pancreatic dataset, exemplifying the interpretable attention matrix captured by sICTA. AVAILABILITY AND IMPLEMENTATION The source code of sICTA is available in public at https://github.com/nbnbhwyy/sICTA. The processed datasets can be found at https://drive.google.com/drive/folders/1jbqSxacL_IDIZ4uPjq220C9Kv024m9eL. The final version of the model will be permanently available at https://doi.org/10.5281/zenodo.13474010.
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Affiliation(s)
- Hegang Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Yuyin Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Yanghui Rao
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
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3
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Qin X, Strand SH, Lee MR, Saraswathibhatla A, van IJzendoorn DGP, Zhu C, Vennam S, Varma S, Hall A, Factor RE, King L, Simpson L, Luo X, Colditz GA, Jiang S, Chaudhuri O, Hwang ES, Marks JR, Owzar K, West RB. Single Cell Expression Analysis of Ductal Carcinoma in Situ Identifies Complex Genotypic-Phenotypic Relationships Altering Epithelial Composition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.10.561724. [PMID: 39386437 PMCID: PMC11463646 DOI: 10.1101/2023.10.10.561724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
To identify mechanisms underlying the growth of ductal carcinoma in situ (DCIS) and properties that lead to progression to invasive cancer, we performed single-cell RNA-sequencing (scRNA-seq) on DCIS lesions and matched synchronous normal breast tissue. Using inferred copy number variations (CNV), we identified neoplastic epithelial cells from the clinical specimens which contained a mixture of DCIS and normal ducts. Phylogenetic analysis based on the CNVs demonstrated intratumoral clonal heterogeneity was associated with significant gene expression differences. We also classified epithelial cells into mammary cell states and found that individual genetic clones contained a mixture of cell states suggesting an ongoing pattern of differentiation after neoplastic transformation. Cell state proportions were significantly different based on estrogen receptor (ER) expression with ER-DCIS more closely resembling the distribution in the normal breast, particularly with respect to cells with basal characteristics. Using deconvolution from bulk RNA-seq in archival DCIS specimens, we show that specific alterations in cell state proportions are associated with progression to invasive cancer. Loss of an intact basement membrane (BM) is the functional definition of invasive breast cancer (IBC) and scRNA-seq data demonstrated that ongoing transcription of key BM genes occurs in specific subsets of epithelial cell states. Examining BM in archival microinvasive breast cancers and an in vitro model of invasion, we found that passive loss of BM gene expression due to cell state proportion alterations is associated with loss of the structural integrity of the duct leading to an invasive phenotype. Our analyses provide detailed insight into DCIS biology. SIGNIFICANCE Single cell analysis reveals that preinvasive breast cancer is comprised of multiple genetic clones and there is substantial phenotypic diversity both within and between these clones. Ductal carcinoma in situ (DCIS) of the breast is a non-invasive condition commonly identified through mammographic screening. A primary diagnosis of DCIS carries little mortality risk on its own, but its presence is a risk factor for subsequent clonally related invasive breast cancer (IBC) (1-5).
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Asami S, Yin C, Garza LA, Kalhor R. Deconvolving organogenesis in space and time via spatial transcriptomics in thick tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.24.614640. [PMID: 39386671 PMCID: PMC11463617 DOI: 10.1101/2024.09.24.614640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Organ development is guided by a space-time landscape that constraints cell behavior. This landscape is challenging to characterize for the hair follicle - the most abundant mini organ - due to its complex microscopic structure and asynchronous development. We developed 3DEEP, a tissue clearing and spatial transcriptomic strategy for characterizing tissue blocks up to 400 µm in thickness. We captured 371 hair follicles at different stages of organogenesis in 1 mm 3 of skin of a 12-hour-old mouse with 6 million transcripts from 81 genes. From this single time point, we deconvoluted follicles by age based on whole-organ molecular pseudotimes to animate a stop-motion 3D atlas of follicle development along its trajectory. We defined molecular stages for hair follicle organogenesis and characterized the order of emergence for its structures, differential signaling dynamics at its top and bottom, morphogen shifts preceding and accompanying structural changes, and series of structural changes leading to the formation of its canal and opening. We further found that hair follicle stem cells and their niche are established and stratified early in organogenesis, before the formation of the hair bulb. Overall, this work demonstrates the power of increased depth of spatial transcriptomics to provide a four-dimensional analysis of organogenesis.
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Affiliation(s)
- Soichiro Asami
- Department of Biomedical Engineering, Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chenshuo Yin
- Department of Biomedical Engineering, Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Luis A. Garza
- Department of Dermatology, Department of Cell Biology, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Reza Kalhor
- Department of Biomedical Engineering, Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Molecular Biology and Genetics, Department of Medicine, Department of Neuroscience, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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5
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Lee SS, Sweren E, Dare E, Derr P, Derr K, Wang CC, Hardesty B, Willis AA, Chen J, Vuillier JK, Du J, Wool J, Ruci A, Wang VY, Lee C, Iyengar S, Asami S, Daskam M, Lee C, Lee JC, Cho D, Kim J, Martinez-Peña EG, Lee SM, He X, Wakeman M, Sicilia I, Dobbs DT, van Ee A, Li A, Xue Y, Williams KL, Kirby CS, Kim D, Kim S, Xu L, Wang R, Ferrer M, Chen Y, Kang JU, Kalhor R, Kang S, Garza LA. The use of ectopic volar fibroblasts to modify skin identity. Science 2024; 385:eadi1650. [PMID: 39236183 PMCID: PMC11457755 DOI: 10.1126/science.adi1650] [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: 04/12/2023] [Revised: 03/20/2024] [Accepted: 07/11/2024] [Indexed: 09/07/2024]
Abstract
Skin identity is controlled by intrinsic features of the epidermis and dermis and their interactions. Modifying skin identity has clinical potential, such as the conversion of residual limb and stump (nonvolar) skin of amputees to pressure-responsive palmoplantar (volar) skin to enhance prosthesis use and minimize skin breakdown. Greater keratin 9 (KRT9) expression, higher epidermal thickness, keratinocyte cytoplasmic size, collagen length, and elastin are markers of volar skin and likely contribute to volar skin resiliency. Given fibroblasts' capacity to modify keratinocyte differentiation, we hypothesized that volar fibroblasts influence these features. Bioprinted skin constructs confirmed the capacity of volar fibroblasts to induce volar keratinocyte features. A clinical trial of healthy volunteers demonstrated that injecting volar fibroblasts into nonvolar skin increased volar features that lasted up to 5 months, highlighting a potential cellular therapy.
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Affiliation(s)
- Sam S. Lee
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Evan Sweren
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Erika Dare
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Paige Derr
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Kristy Derr
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Chen Chia Wang
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Brooke Hardesty
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Aiden A. Willis
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Junjie Chen
- Department of Mechanical Engineering, Johns Hopkins University, MD 21210, USA
| | - Jonathan K. Vuillier
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Joseph Du
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Julia Wool
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Amanda Ruci
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Vicky Y. Wang
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Chaewon Lee
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Sampada Iyengar
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Soichiro Asami
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Maria Daskam
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Claudia Lee
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Jeremy C. Lee
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Darren Cho
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Joshua Kim
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | | | - So Min Lee
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Xu He
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Michael Wakeman
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Iralde Sicilia
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Dalhart T. Dobbs
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Amy van Ee
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Ang Li
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Yingchao Xue
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Kaitlin L. Williams
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Charles S. Kirby
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Dongwon Kim
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Sooah Kim
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Lillian Xu
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Ruizhi Wang
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Marc Ferrer
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Yun Chen
- Department of Mechanical Engineering, Johns Hopkins University, MD 21210, USA
| | - Jin U. Kang
- Department of Electrical and Computer Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Reza Kalhor
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Sewon Kang
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Luis A. Garza
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
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De Rijk P, Watzeels T, Küçükali F, Van Dongen J, Faura J, Willems P, De Deyn L, Duchateau L, Grones C, Eekhout T, De Pooter T, Joris G, Rombauts S, De Rybel B, Rademakers R, Van Breusegem F, Strazisar M, Sleegers K, De Coster W. Scywalker: scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing. Bioinformatics 2024; 40:btae549. [PMID: 39254601 PMCID: PMC11419950 DOI: 10.1093/bioinformatics/btae549] [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: 04/23/2024] [Revised: 08/29/2024] [Accepted: 09/06/2024] [Indexed: 09/11/2024] Open
Abstract
MOTIVATION Existing nanopore single-cell data analysis tools showed severe limitations in handling current data sizes. RESULTS We introduce scywalker, an innovative and scalable package developed to comprehensively analyze long-read sequencing data of full-length single-cell or single-nuclei cDNA. We developed novel scalable methods for cell barcode demultiplexing and single-cell isoform calling and quantification and incorporated these in an easily deployable package. Scywalker streamlines the entire analysis process, from sequenced fragments in FASTQ format to demultiplexed pseudobulk isoform counts, into a single command suitable for execution on either server or cluster. Scywalker includes data quality control, cell type identification, and an interactive report. Assessment of datasets from the human brain, Arabidopsis leaves, and previously benchmarked data from mixed cell lines demonstrate excellent correlation with short-read analyses at both the cell-barcoding and gene quantification levels. At the isoform level, we show that scywalker facilitates the direct identification of cell-type-specific expression of novel isoforms. AVAILABILITY AND IMPLEMENTATION Scywalker is available on github.com/derijkp/scywalker under the GNU General Public License (GPL) and at https://zenodo.org/records/13359438/files/scywalker-0.108.0-Linux-x86_64.tar.gz.
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Affiliation(s)
- Peter De Rijk
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Universiteitsplein 1, Antwerp, 2610, Belgium
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Tijs Watzeels
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Complex Genetics of Alzheimer’s Disease Group, VIB Center for Molecular Neurology, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Fahri Küçükali
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Complex Genetics of Alzheimer’s Disease Group, VIB Center for Molecular Neurology, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Jasper Van Dongen
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Complex Genetics of Alzheimer’s Disease Group, VIB Center for Molecular Neurology, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Júlia Faura
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Patrick Willems
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Zwijnaarde, 9052, Belgium
- VIB Center for Plant Systems Biology, VIB, Technologiepark 71, Zwijnaarde, 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Corneel Heymanslaan 10, Ghent, 9000, Belgium
- VIB Center for Medical Biotechnology, VIB, Technologiepark-Zwijnaarde 75, Ghent, 9052, Belgium
| | - Lara De Deyn
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Complex Genetics of Alzheimer’s Disease Group, VIB Center for Molecular Neurology, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Lena Duchateau
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Complex Genetics of Alzheimer’s Disease Group, VIB Center for Molecular Neurology, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Carolin Grones
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Zwijnaarde, 9052, Belgium
- VIB Center for Plant Systems Biology, VIB, Technologiepark 71, Zwijnaarde, 9052, Belgium
| | - Thomas Eekhout
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Zwijnaarde, 9052, Belgium
- VIB Center for Plant Systems Biology, VIB, Technologiepark 71, Zwijnaarde, 9052, Belgium
- VIB Single Cell Core, VIB, Technologiepark-Zwijnaarde 71, Ghent, 9052, Belgium
| | - Tim De Pooter
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Universiteitsplein 1, Antwerp, 2610, Belgium
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Geert Joris
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Universiteitsplein 1, Antwerp, 2610, Belgium
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Stephane Rombauts
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Zwijnaarde, 9052, Belgium
- VIB Center for Plant Systems Biology, VIB, Technologiepark 71, Zwijnaarde, 9052, Belgium
| | - Bert De Rybel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Zwijnaarde, 9052, Belgium
- VIB Center for Plant Systems Biology, VIB, Technologiepark 71, Zwijnaarde, 9052, Belgium
| | - Rosa Rademakers
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Frank Van Breusegem
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Zwijnaarde, 9052, Belgium
- VIB Center for Plant Systems Biology, VIB, Technologiepark 71, Zwijnaarde, 9052, Belgium
| | - Mojca Strazisar
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Universiteitsplein 1, Antwerp, 2610, Belgium
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Kristel Sleegers
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Complex Genetics of Alzheimer’s Disease Group, VIB Center for Molecular Neurology, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Wouter De Coster
- Department of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, Universiteitsplein 1, Antwerp, 2610, Belgium
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7
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Liu J, Ma J, Wen J, Zhou X. A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401815. [PMID: 38887194 PMCID: PMC11336957 DOI: 10.1002/advs.202401815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/22/2024] [Indexed: 06/20/2024]
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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Affiliation(s)
- Jiajia Liu
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Jian Ma
- Department of Electronic Information and Computer EngineeringThe Engineering & Technical College of Chengdu University of TechnologyLeshanSichuan614000China
| | - Jianguo Wen
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- McGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- School of DentistryThe University of Texas Health Science Center at HoustonHoustonTX77030USA
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8
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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9
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Muralidharan C, Huang F, Enriquez JR, Wang JE, Nelson JB, Nargis T, May SC, Chakraborty A, Figatner KT, Navitskaya S, Anderson CM, Calvo V, Surguladze D, Mulvihill MJ, Yi X, Sarkar S, Oakes SA, Webb-Robertson BJM, Sims EK, Staschke KA, Eizirik DL, Nakayasu ES, Stokes ME, Tersey SA, Mirmira RG. Inhibition of the eukaryotic initiation factor-2α kinase PERK decreases risk of autoimmune diabetes in mice. J Clin Invest 2024; 134:e176136. [PMID: 38889047 PMCID: PMC11324307 DOI: 10.1172/jci176136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
Preventing the onset of autoimmune type 1 diabetes (T1D) is feasible through pharmacological interventions that target molecular stress-responsive mechanisms. Cellular stresses, such as nutrient deficiency, viral infection, or unfolded proteins, trigger the integrated stress response (ISR), which curtails protein synthesis by phosphorylating eukaryotic translation initiation factor-2α (eIF2α). In T1D, maladaptive unfolded protein response (UPR) in insulin-producing β cells renders these cells susceptible to autoimmunity. We found that inhibition of the eIF2α kinase PKR-like ER kinase (PERK), a common component of the UPR and ISR, reversed the mRNA translation block in stressed human islets and delayed the onset of diabetes, reduced islet inflammation, and preserved β cell mass in T1D-susceptible mice. Single-cell RNA-Seq of islets from PERK-inhibited mice showed reductions in the UPR and PERK signaling pathways and alterations in antigen-processing and presentation pathways in β cells. Spatial proteomics of islets from these mice showed an increase in the immune checkpoint protein programmed death-ligand 1 (PD-L1) in β cells. Golgi membrane protein 1, whose levels increased following PERK inhibition in human islets and EndoC-βH1 human β cells, interacted with and stabilized PD-L1. Collectively, our studies show that PERK activity enhances β cell immunogenicity and that inhibition of PERK may offer a strategy for preventing or delaying the development of T1D.
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Affiliation(s)
- Charanya Muralidharan
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Fei Huang
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Jacob R. Enriquez
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Jiayi E. Wang
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Jennifer B. Nelson
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Titli Nargis
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Sarah C. May
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Advaita Chakraborty
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Kayla T. Figatner
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Svetlana Navitskaya
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Cara M. Anderson
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | | | | | | | - Xiaoyan Yi
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Soumyadeep Sarkar
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Scott A. Oakes
- Department of Pathology, The University of Chicago, Chicago, Illinois, USA
| | | | - Emily K. Sims
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Wells Center for Pediatric Research, and
| | - Kirk A. Staschke
- Department of Biochemistry and Molecular Biology and the Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Decio L. Eizirik
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | | | - Sarah A. Tersey
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
| | - Raghavendra G. Mirmira
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, Illinois, USA
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10
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Koelsch N, Mirshahi F, Aqbi HF, Saneshaw M, Idowu MO, Olex AL, Sanyal AJ, Manjili MH. Effective anti-tumor immune response against HCC is orchestrated by immune cell partnership network that functions through hepatic homeostatic pathways, not direct cytotoxicity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598563. [PMID: 38903113 PMCID: PMC11188117 DOI: 10.1101/2024.06.12.598563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
The liver harbors a diverse array of immune cells during both health and disease. The specific roles of these cells in nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) remain unclear. Using a systems immunology approach, we demonstrate that reciprocal cell-cell communications function through dominant-subdominant pattern of ligand-receptor homeostatic pathways. In the healthy control, hepatocyte-dominated homeostatic pathways induce local immune responses to maintain liver homeostasis. Chronic intake of a Western diet (WD) alters hepatocytes and induces hepatic stellate cell (HSC), cancer cell and NKT cell-dominated interactions during NAFLD. During HCC, monocytes, hepatocytes, and myofibroblasts join the dominant cellular interactions network to restore liver homeostasis. Dietary correction during NAFLD results in nonlinear outcomes with various cellular rearrangements. When cancer cells and stromal cells dominate hepatic interactions network without inducing homeostatic immune responses, HCC progression occurs. Conversely, myofibroblast and fibroblast-dominated network orchestrates monocyte-dominated HCC-preventive immune responses. Tumor immune surveillance by 75% of immune cells successfully promoting liver homeostasis can create a tumor-inhibitory microenvironment, while only 5% of immune cells manifest apoptosis-inducing functions, primarily for facilitating homeostatic liver cell turnover rather than direct tumor killing. These data suggest that an effective immunotherapy should promote liver homeostasis rather than direct tumor killing.
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Affiliation(s)
- Nicholas Koelsch
- Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
| | - Faridoddin Mirshahi
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA 23298, USA
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, Richmond, VA 23298
| | - Hussein F. Aqbi
- College of Science, Mustansiriyah University, Baghdad, P.O. Box 14022, Iraq
| | - Mulugeta Saneshaw
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA 23298, USA
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, Richmond, VA 23298
| | - Michael O. Idowu
- Department of Pathology, VCU School of Medicine, Richmond, VA 23298, USA
- VCU Massey Comprehensive Cancer Center, Richmond, VA 23298, USA
| | - Amy L. Olex
- VCU Massey Comprehensive Cancer Center, Richmond, VA 23298, USA
- C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University School of Medicine
| | - Arun J. Sanyal
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA 23298, USA
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, Richmond, VA 23298
- VCU Massey Comprehensive Cancer Center, Richmond, VA 23298, USA
| | - Masoud H. Manjili
- Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
- VCU Massey Comprehensive Cancer Center, Richmond, VA 23298, USA
- VCU Institute of Molecular Medicine, Richmond VA 23298
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11
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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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12
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Muralidharan C, Huang F, Enriquez JR, Wang JE, Nelson JB, Nargis T, May SC, Chakraborty A, Figatner KT, Navitskaya S, Anderson CM, Calvo V, Surguladze D, Mulvihill MJ, Yi X, Sarkar S, Oakes SA, Webb-Robertson BJM, Sims EK, Staschke KA, Eizirik DL, Nakayasu ES, Stokes ME, Tersey SA, Mirmira RG. Inhibition of the Eukaryotic Initiation Factor-2-α Kinase PERK Decreases Risk of Autoimmune Diabetes in Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.06.561126. [PMID: 38895427 PMCID: PMC11185543 DOI: 10.1101/2023.10.06.561126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Preventing the onset of autoimmune type 1 diabetes (T1D) is feasible through pharmacological interventions that target molecular stress-responsive mechanisms. Cellular stresses, such as nutrient deficiency, viral infection, or unfolded proteins, trigger the integrated stress response (ISR), which curtails protein synthesis by phosphorylating eIF2α. In T1D, maladaptive unfolded protein response (UPR) in insulin-producing β cells renders these cells susceptible to autoimmunity. We show that inhibition of the eIF2α kinase PERK, a common component of the UPR and ISR, reverses the mRNA translation block in stressed human islets and delays the onset of diabetes, reduces islet inflammation, and preserves β cell mass in T1D-susceptible mice. Single-cell RNA sequencing of islets from PERK-inhibited mice shows reductions in the UPR and PERK signaling pathways and alterations in antigen processing and presentation pathways in β cells. Spatial proteomics of islets from these mice shows an increase in the immune checkpoint protein PD-L1 in β cells. Golgi membrane protein 1, whose levels increase following PERK inhibition in human islets and EndoC-βH1 human β cells, interacts with and stabilizes PD-L1. Collectively, our studies show that PERK activity enhances β cell immunogenicity, and inhibition of PERK may offer a strategy to prevent or delay the development of T1D.
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Affiliation(s)
- Charanya Muralidharan
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Fei Huang
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Jacob R. Enriquez
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Jiayi E. Wang
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Jennifer B. Nelson
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Titli Nargis
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Sarah C. May
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Advaita Chakraborty
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Kayla T. Figatner
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Svetlana Navitskaya
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Cara M. Anderson
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | | | | | | | - Xiaoyan Yi
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Soumyadeep Sarkar
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Scott A. Oakes
- Department of Pathology, The University of Chicago, Chicago, IL, USA
| | | | - Emily K. Sims
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, and the Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
| | - Kirk A Staschke
- Department of Biochemistry and Molecular Biology and the Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Decio L. Eizirik
- ULB Center for Diabetes Research, Université Libre de Bruxelles, Brussels, Belgium
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Sarah A. Tersey
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
| | - Raghavendra G. Mirmira
- Department of Medicine and the Kovler Diabetes Center, The University of Chicago, Chicago, IL, USA
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13
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Douglas C, Lomeli N, Vu T, Pham J, Bota DA. WITHDRAWN: LonP1 Drives Proneural Mesenchymal Transition in IDH1-R132H Diffuse Glioma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.13.536817. [PMID: 37131765 PMCID: PMC10153221 DOI: 10.1101/2023.04.13.536817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The authors have withdrawn their manuscript owing to massive revision and data validation. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
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14
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Patil AR, Schug J, Liu C, Lahori D, Descamps HC, Naji A, Kaestner KH, Faryabi RB, Vahedi G. Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets. Cell Rep Med 2024; 5:101535. [PMID: 38677282 PMCID: PMC11148720 DOI: 10.1016/j.xcrm.2024.101535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/22/2024] [Accepted: 04/07/2024] [Indexed: 04/29/2024]
Abstract
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.
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Affiliation(s)
- Abhijeet R Patil
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jonathan Schug
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Chengyang Liu
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Deeksha Lahori
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Hélène C Descamps
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ali Naji
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Klaus H Kaestner
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Robert B Faryabi
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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15
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Gan D, Zhu Y, Lu X, Li J. SCIPAC: quantitative estimation of cell-phenotype associations. Genome Biol 2024; 25:119. [PMID: 38741183 PMCID: PMC11089691 DOI: 10.1186/s13059-024-03263-1] [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: 01/30/2023] [Accepted: 04/30/2024] [Indexed: 05/16/2024] Open
Abstract
Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.
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Affiliation(s)
- Dailin Gan
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556, IN, USA
| | - Yini Zhu
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, 46556, IN, USA
| | - Xin Lu
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, 46556, IN, USA
- Tumor Microenvironment and Metastasis Program, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, 46202, IN, USA
| | - Jun Li
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556, IN, USA.
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16
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Callebaut A, Guyer P, Derua R, Buitinga M, Manganaro A, Yi X, Sodré FMC, Vig S, Suleiman M, Marchetti P, Eizirik DL, Kent SC, Mathieu C, James EA, Overbergh L. CD4+ T Cells From Individuals With Type 1 Diabetes Respond to a Novel Class of Deamidated Peptides Formed in Pancreatic Islets. Diabetes 2024; 73:728-742. [PMID: 38387030 PMCID: PMC11043062 DOI: 10.2337/db23-0588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/05/2024] [Indexed: 02/24/2024]
Abstract
The β-cell plays a crucial role in the pathogenesis of type 1 diabetes, in part through the posttranslational modification of self-proteins by biochemical processes such as deamidation. These neoantigens are potential triggers for breaking immune tolerance. We report the detection by LC-MS/MS of 16 novel Gln and 27 novel Asn deamidations in 14 disease-related proteins within inflammatory cytokine-stressed human islets of Langerhans. T-cell clones responsive against one Gln- and three Asn-deamidated peptides could be isolated from peripheral blood of individuals with type 1 diabetes. Ex vivo HLA class II tetramer staining detected higher T-cell frequencies in individuals with the disease compared with control individuals. Furthermore, there was a positive correlation between the frequencies of T cells specific for deamidated peptides, insulin antibody levels at diagnosis, and duration of disease. These results highlight that stressed human islets are prone to enzymatic and biochemical deamidation and suggest that both Gln- and Asn-deamidated peptides can promote the activation and expansion of autoreactive CD4+ T cells. These findings add to the growing evidence that posttranslational modifications undermine tolerance and may open the road for the development of new diagnostic and therapeutic applications for individuals living with type 1 diabetes. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Aïsha Callebaut
- Laboratory of Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA
| | - Perrin Guyer
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA
| | - Rita Derua
- Laboratory of Protein Phosphorylation and Proteomics, KU Leuven, Leuven, Belgium
| | - Mijke Buitinga
- Laboratory of Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium
| | - Anthony Manganaro
- Diabetes Center of Excellence, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA
| | - Xiaoyan Yi
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Saurabh Vig
- Laboratory of Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium
| | - Mara Suleiman
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Piero Marchetti
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Decio L. Eizirik
- ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium
| | - Sally C. Kent
- Diabetes Center of Excellence, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA
| | - Chantal Mathieu
- Laboratory of Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium
| | - Eddie A. James
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA
| | - Lut Overbergh
- Laboratory of Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium
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17
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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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18
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Pullin JM, McCarthy DJ. A comparison of marker gene selection methods for single-cell RNA sequencing data. Genome Biol 2024; 25:56. [PMID: 38409056 PMCID: PMC10895860 DOI: 10.1186/s13059-024-03183-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/07/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalog and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper, we benchmark 59 computational methods for selecting marker genes in scRNA-seq data. RESULTS We compare the performance of the methods using 14 real scRNA-seq datasets and over 170 additional simulated datasets. Methods are compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed, and their implementation quality. In addition, various case studies are used to scrutinize the most commonly used methods, highlighting issues and inconsistencies. CONCLUSIONS Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student's t-test, and logistic regression.
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Affiliation(s)
- Jeffrey M Pullin
- Bioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, 9 Princes St, Fitzroy, 3065, VIC, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, 3010, VIC, Australia
- Melbourne Integrative Genomics, University of Melbourne, Parkville, 3010, VIC, Australia
| | - Davis J McCarthy
- Bioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, 9 Princes St, Fitzroy, 3065, VIC, Australia.
- School of Mathematics and Statistics, University of Melbourne, Parkville, 3010, VIC, Australia.
- Melbourne Integrative Genomics, University of Melbourne, Parkville, 3010, VIC, Australia.
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19
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Liu J, Ma J, Wen J, Zhou X. A Cell Cycle-aware Network for Data Integration and Label Transferring of Single-cell RNA-seq and ATAC-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578213. [PMID: 38352302 PMCID: PMC10862874 DOI: 10.1101/2024.01.31.578213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity and confounding factors. As we know, cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it's not clear how it will work on the integrated single-cell multi-omics data. Here, we developed a Cell Cycle-Aware Network (CCAN) to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the out-standing performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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20
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Yang S, Zhou X. SRT-Server: powering the analysis of spatial transcriptomic data. Genome Med 2024; 16:18. [PMID: 38279156 PMCID: PMC10811909 DOI: 10.1186/s13073-024-01288-6] [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: 05/23/2023] [Accepted: 01/15/2024] [Indexed: 01/28/2024] Open
Abstract
BACKGROUND Spatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds. RESULTS Here, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results-all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise. CONCLUSIONS Overall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .
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Affiliation(s)
- Sheng Yang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
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21
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Salem NA, Manzano L, Keist MW, Ponomareva O, Roberts AJ, Roberto M, Mayfield RD. Cell-type brain-region specific changes in prefrontal cortex of a mouse model of alcohol dependence. Neurobiol Dis 2024; 190:106361. [PMID: 37992784 PMCID: PMC10874299 DOI: 10.1016/j.nbd.2023.106361] [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: 06/29/2023] [Revised: 10/31/2023] [Accepted: 11/18/2023] [Indexed: 11/24/2023] Open
Abstract
The prefrontal cortex is a crucial regulator of alcohol drinking, and dependence, and other behavioral phenotypes associated with AUD. Comprehensive identification of cell-type specific transcriptomic changes in alcohol dependence will improve our understanding of mechanisms underlying the excessive alcohol use associated with alcohol dependence and will refine targets for therapeutic development. We performed single nucleus RNA sequencing (snRNA-seq) and Visium spatial gene expression profiling on the medial prefrontal cortex (mPFC) obtained from C57BL/6 J mice exposed to the two-bottle choice-chronic intermittent ethanol (CIE) vapor exposure (2BC-CIE, defined as dependent group) paradigm which models phenotypes of alcohol dependence including escalation of alcohol drinking. Gene co-expression network analysis and differential expression analysis identified highly dysregulated co-expression networks in multiple cell types. Dysregulated modules and their hub genes suggest novel understudied targets for studying molecular mechanisms contributing to the alcohol dependence state. A subtype of inhibitory neurons was the most alcohol-sensitive cell type and contained a downregulated gene co-expression module; the hub gene for this module is Cpa6, a gene previously identified by GWAS to be associated with excessive alcohol consumption. We identified an astrocytic Gpc5 module significantly upregulated in the alcohol-dependent group. To our knowledge, there are no studies linking Cpa6 and Gpc5 to the alcohol-dependent phenotype. We also identified neuroinflammation related gene expression changes in multiple cell types, specifically enriched in microglia, further implicating neuroinflammation in the escalation of alcohol drinking. Here, we present a comprehensive atlas of cell-type specific alcohol dependence mediated gene expression changes in the mPFC and identify novel cell type-specific targets implicated in alcohol dependence.
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Affiliation(s)
- Nihal A Salem
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Lawrence Manzano
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA
| | - Michael W Keist
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA
| | - Olga Ponomareva
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA
| | - Amanda J Roberts
- Animal Models Core Facility, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
| | - Marisa Roberto
- Departments of Molecular Medicine and Neuroscience, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - R Dayne Mayfield
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712, USA; Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712, USA
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22
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Liang Q, Huang Y, He S, Chen K. Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity. Nat Commun 2023; 14:8416. [PMID: 38110427 PMCID: PMC10728201 DOI: 10.1038/s41467-023-44206-x] [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: 06/26/2023] [Accepted: 12/04/2023] [Indexed: 12/20/2023] Open
Abstract
Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.
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Affiliation(s)
- Qingnan Liang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Yuefan Huang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Shan He
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA.
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23
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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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24
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Shi X, Yang Y, Ma X, Zhou Y, Guo Z, Wang C, Liu J. Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno. Nucleic Acids Res 2023; 51:e115. [PMID: 37941153 PMCID: PMC10711557 DOI: 10.1093/nar/gkad1023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 10/03/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023] Open
Abstract
In the analysis of both single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data, classifying cells/spots into cell/domain types is an essential analytic step for many secondary analyses. Most of the existing annotation methods have been developed for scRNA-seq datasets without any consideration of spatial information. Here, we present SpatialAnno, an efficient and accurate annotation method for spatial transcriptomics datasets, with the capability to effectively leverage a large number of non-marker genes as well as 'qualitative' information about marker genes without using a reference dataset. Uniquely, SpatialAnno estimates low-dimensional embeddings for a large number of non-marker genes via a factor model while promoting spatial smoothness among neighboring spots via a Potts model. Using both simulated and four real spatial transcriptomics datasets from the 10x Visium, ST, Slide-seqV1/2, and seqFISH platforms, we showcase the method's improved spatial annotation accuracy, including its robustness to the inclusion of marker genes for irrelevant cell/domain types and to various degrees of marker gene misspecification. SpatialAnno is computationally scalable and applicable to SRT datasets from different platforms. Furthermore, the estimated embeddings for cellular biological effects facilitate many downstream analyses.
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Affiliation(s)
- Xingjie Shi
- KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, School of Statistics, East China Normal University, Shanghai 200062, China
| | - Yi Yang
- The Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210018, China
| | - Xiaohui Ma
- College of Life Sciences, Nanjing University, Nanjing 210033, China
| | - Yong Zhou
- KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, School of Statistics, East China Normal University, Shanghai 200062, China
| | - Zhenxing Guo
- School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, China
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430070, China
| | - Jin Liu
- School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, China
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25
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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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26
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Sarkar S, Syed F, Webb-Robertson BJ, Melchior JT, Chang G, Gritsenko M, Wang YT, Tsai CF, Liu J, Yi X, Cui Y, Eizirik DL, Metz TO, Rewers M, Evans-Molina C, Mirmira RG, Nakayasu ES. Protection of β cells against pro-inflammatory cytokine stress by the GDF15-ERBB2 signaling. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.27.23298904. [PMID: 38076918 PMCID: PMC10705646 DOI: 10.1101/2023.11.27.23298904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Aim/hypothesis Growth/differentiation factor 15 (GDF15) is a therapeutic target for a variety of metabolic diseases, including type 1 diabetes (T1D). However, the nausea caused by GDF15 is a challenging point for therapeutic development. In addition, it is unknown why the endogenous GDF15 fails to protect from T1D development. Here, we investigate the GDF15 signaling in pancreatic islets towards opening possibilities for therapeutic targeting in β cells and to understand why this protection fails to occur naturally. Methods GDF15 signaling in islets was determined by proximity-ligation assay, untargeted proteomics, pathway analysis, and treatment of cells with specific inhibitors. To determine if GDF15 levels would increase prior to disease onset, plasma levels of GDF15 were measured in a longitudinal prospective study of children during T1D development (n=132 cases vs. n=40 controls) and in children with islet autoimmunity but normoglycemia (n=47 cases vs. n=40 controls) using targeted mass spectrometry. We also investigated the regulation of GDF15 production in islets by fluorescence microscopy and western blot analysis. Results The proximity-ligation assay identified ERBB2 as the GDF15 receptor in islets, which was confirmed using its specific antagonist, tucatinib. The untargeted proteomics analysis and caspase assay showed that ERBB2 activation by GDF15 reduces β cell apoptosis by downregulating caspase 8. In plasma, GDF15 levels were higher (p=0.0024) during T1D development compared to controls, but not in islet autoimmunity with normoglycemia. However, in the pancreatic islets GDF15 was depleted via sequestration of its mRNA into stress granules, resulting in translation halting. Conclusions/interpretation GDF15 protects against T1D via ERBB2-mediated decrease of caspase 8 expression in pancreatic islets. Circulating levels of GDF15 increases pre-T1D onset, which is insufficient to promote protection due to its localized depletion in the islets. These findings open opportunities for targeting GDF15 downstream signaling for pancreatic β cell protection in T1D and help to explain the lack of natural protection by the endogenous protein.
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27
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Quan F, Liang X, Cheng M, Yang H, Liu K, He S, Sun S, Deng M, He Y, Liu W, Wang S, Zhao S, Deng L, Hou X, Zhang X, Xiao Y. Annotation of cell types (ACT): a convenient web server for cell type annotation. Genome Med 2023; 15:91. [PMID: 37924118 PMCID: PMC10623726 DOI: 10.1186/s13073-023-01249-5] [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: 03/14/2023] [Accepted: 10/18/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND The advancement of single-cell sequencing has progressed our ability to solve biological questions. Cell type annotation is of vital importance to this process, allowing for the analysis and interpretation of enormous single-cell datasets. At present, however, manual cell annotation which is the predominant approach remains limited by both speed and the requirement of expert knowledge. METHODS To address these challenges, we constructed a hierarchically organized marker map through manually curating over 26,000 cell marker entries from about 7000 publications. We then developed WISE, a weighted and integrated gene set enrichment method, to integrate the prevalence of canonical markers and ordered differentially expressed genes of specific cell types in the marker map. Benchmarking analysis suggested that our method outperformed state-of-the-art methods. RESULTS By integrating the marker map and WISE, we developed a user-friendly and convenient web server, ACT ( http://xteam.xbio.top/ACT/ or http://biocc.hrbmu.edu.cn/ACT/ ), which only takes a simple list of upregulated genes as input and provides interactive hierarchy maps, together with well-designed charts and statistical information, to accelerate the assignment of cell identities and made the results comparable to expert manual annotation. Besides, a pan-tissue marker map was constructed to assist in cell assignments in less-studied tissues. Applying ACT to three case studies showed that all cell clusters were quickly and accurately annotated, and multi-level and more refined cell types were identified. CONCLUSIONS We developed a knowledge-based resource and a corresponding method, together with an intuitive graphical web interface, for cell type annotation. We believe that ACT, emerging as a powerful tool for cell type annotation, would be widely used in single-cell research and considerably accelerate the process of cell type identification.
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Affiliation(s)
- Fei Quan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Xin Liang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Mingjiang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Huan Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Kun Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Shengyuan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Shangqin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Menglan Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Yanzhen He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Wei Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Shuai Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Shuxiang Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Lantian Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Xiaobo Hou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
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Teefy BB, Lemus AJJ, Adler A, Xu A, Bhala R, Hsu K, Benayoun BA. Widespread sex dimorphism across single-cell transcriptomes of adult African turquoise killifish tissues. Cell Rep 2023; 42:113237. [PMID: 37837621 PMCID: PMC10842523 DOI: 10.1016/j.celrep.2023.113237] [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: 05/05/2023] [Revised: 08/18/2023] [Accepted: 09/25/2023] [Indexed: 10/16/2023] Open
Abstract
The African turquoise killifish (Nothobranchius furzeri), the shortest-lived vertebrate that can be bred in captivity, is an emerging model organism for aging research. Here, we describe a multitissue, single-cell gene expression atlas of female and male blood, kidney, liver, and spleen. We annotate 22 cell types, define marker genes, and infer differentiation trajectories. We find pervasive sex-dimorphic gene expression across cell types. Sex-dimorphic genes tend to be linked to lipid metabolism, consistent with clear differences in lipid storage in female vs. male turquoise killifish livers. We use machine learning to predict sex using single-cell gene expression and identify potential markers for molecular sex identity. As a proof of principle, we show that our atlas can be used to deconvolute existing bulk RNA sequencing (RNA-seq) data to obtain accurate estimates of cell type proportions. This atlas can be a resource to the community that could be leveraged to develop cell-type-specific expression in transgenic animals.
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Affiliation(s)
- Bryan B Teefy
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Aaron J J Lemus
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA; Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts, and Sciences, Los Angeles, CA 90089, USA
| | - Ari Adler
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Alan Xu
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA; Quantitative & Computational Biology Department, USC Dornsife College of Letters, Arts, and Sciences, Los Angeles, CA 90089, USA
| | - Rajyk Bhala
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Katelyn Hsu
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA; Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts, and Sciences, Los Angeles, CA 90089, USA
| | - Bérénice A Benayoun
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA; Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts, and Sciences, Los Angeles, CA 90089, USA; Biochemistry and Molecular Medicine Department, USC Keck School of Medicine, Los Angeles, CA 90089, USA; Epigenetics and Gene Regulation, USC Norris Comprehensive Cancer Center, Los Angeles, CA 90089, USA; USC Stem Cell Initiative, Los Angeles, CA 90089, USA.
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29
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [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: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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30
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Lyu P, Zhai Y, Li T, Qian J. CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server. Bioinformatics 2023; 39:btad521. [PMID: 37610325 PMCID: PMC10477937 DOI: 10.1093/bioinformatics/btad521] [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: 03/21/2023] [Revised: 07/17/2023] [Accepted: 08/22/2023] [Indexed: 08/24/2023] Open
Abstract
MOTIVATION Single-cell sequencing technology has become a routine in studying many biological problems. A core step of analyzing single-cell data is the assignment of cell clusters to specific cell types. Reference-based methods are proposed for predicting cell types for single-cell clusters. However, the scalability and lack of preprocessed reference datasets prevent them from being practical and easy to use. RESULTS Here, we introduce a reference-based cell annotation web server, CellAnn, which is super-fast and easy to use. CellAnn contains a comprehensive reference database with 204 human and 191 mouse single-cell datasets. These reference datasets cover 32 organs. Furthermore, we developed a cluster-to-cluster alignment method to transfer cell labels from the reference to the query datasets, which is superior to the existing methods with higher accuracy and higher scalability. Finally, CellAnn is an online tool that integrates all the procedures in cell annotation, including reference searching, transferring cell labels, visualizing results, and harmonizing cell annotation labels. Through the user-friendly interface, users can identify the best annotation by cross-validating with multiple reference datasets. We believe that CellAnn can greatly facilitate single-cell sequencing data analysis. AVAILABILITY AND IMPLEMENTATION The web server is available at www.cellann.io, and the source code is available at https://github.com/Pinlyu3/CellAnn_shinyapp.
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Affiliation(s)
- Pin Lyu
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
| | - Yijie Zhai
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
| | - Taibo Li
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21218, United States
| | - Jiang Qian
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
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31
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Koelsch N, Mirshahi F, Aqbi HF, Saneshaw M, Idowu MO, Olex AL, Sanyal AJ, Manjili MH. The crosstalking immune cells network creates a collective function beyond the function of each cellular constituent during the progression of hepatocellular carcinoma. Sci Rep 2023; 13:12630. [PMID: 37537225 PMCID: PMC10400568 DOI: 10.1038/s41598-023-39020-w] [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: 02/22/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
Abundance of data on the role of inflammatory immune responses in the progression or inhibition of hepatocellular carcinoma (HCC) has failed to offer a curative immunotherapy for HCC. This is largely because of focusing on detailed specific cell types and missing the collective function of the hepatic immune system. To discover the collective immune function, we take systems immunology approach by performing high-throughput analysis of snRNAseq data collected from the liver of DIAMOND mice during the progression of nonalcoholic fatty liver disease (NAFLD) to HCC. We report that mutual signaling interactions of the hepatic immune cells in a dominant-subdominant manner, as well as their interaction with structural cells shape the immunological pattern manifesting a collective function beyond the function of the cellular constituents. Such pattern discovery approach recognized direct role of the innate immune cells in the progression of NASH and HCC. These data suggest that discovery of the immune pattern not only detects the immunological mechanism of HCC in spite of dynamic changes in immune cells during the course of disease but also offers immune modulatory interventions for the treatment of NAFLD and HCC.
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Affiliation(s)
- Nicholas Koelsch
- Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Richmond, VA, 23298, USA.
| | - Faridoddin Mirshahi
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA, 23298, USA
| | - Hussein F Aqbi
- College of Science, Mustansiriyah University, P.O. Box 14022, Baghdad, Iraq
| | - Mulugeta Saneshaw
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA, 23298, USA
| | - Michael O Idowu
- Department of Pathology, VCU School of Medicine, Richmond, VA, 23298, USA
- Department of Microbiology & Immunology, VCU Massey Cancer Center, 401 College Street, Box 980035, Richmond, VA, 23298, USA
| | - Amy L Olex
- C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University School of Medicine, Richmond, USA
| | - Arun J Sanyal
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA, 23298, USA.
- Department of Microbiology & Immunology, VCU Massey Cancer Center, 401 College Street, Box 980035, Richmond, VA, 23298, USA.
| | - Masoud H Manjili
- Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Richmond, VA, 23298, USA.
- Department of Microbiology & Immunology, VCU Massey Cancer Center, 401 College Street, Box 980035, Richmond, VA, 23298, USA.
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32
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Kim H, Kim HK, Hong D, Kim M, Jang S, Yang CS, Yoon S. Identification of ulcerative colitis-specific immune cell signatures from public single-cell RNA-seq data. Genes Genomics 2023; 45:957-967. [PMID: 37133723 DOI: 10.1007/s13258-023-01390-w] [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: 03/03/2023] [Accepted: 04/13/2023] [Indexed: 05/04/2023]
Abstract
BACKGROUND Single-cell RNA-seq enabled microscopic studies on tissue microenvironment of many diseases. Inflammatory bowel disease, an autoimmune disease, is involved with various dysfunction of immune cells, for which single-cell RNA-seq may provide us a deeper insight into the causes and mechanism of this complex disease. OBJECTIVE In this work, we used public single-cell RNA-seq data to study tissue microenvironment around ulcerative colitis, an inflammatory bowel disease causing chronic inflammation and ulcers in large intestine. METHODS Since not all the datasets provide cell-type annotations, we first identified cell identities to select cell populations of our interest. Differentially expressed genes and gene set enrichment analysis was then performed to infer the polarization/activation state of macrophages and T cells. Cell-to-cell interaction analysis was also performed to discover distinct interactions in ulcerative colitis. RESULTS Differentially expressed genes analysis of the two datasets confirmed the regulation of CTLA4, IL2RA, and CCL5 genes in the T cell subset and regulation of S100A8/A9, CLEC10A genes in macrophages. Cell-to-cell interaction analysis showed CD4+ T cells and macrophages interact actively to each other. We also identified IL-18 pathway activation in inflammatory macrophages, evidence that CD4+ T cells induce Th1 and Th2 differentiation, and also found that macrophages regulate T cell activation through different ligand-receptor pairs, viz. CD86-CTL4, LGALS9-CD47, SIRPA-CD47, and GRN-TNFRSF1B. CONCLUSION Analysis of these immune cell subsets may suggest novel strategies for the treatment of inflammatory bowel disease.
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Affiliation(s)
- Hanbyeol Kim
- Dept of Computer Science, College of SW Convergence, Dankook Univ, Yongin-si, 16890, Korea
| | - Hyo Keun Kim
- Dept of Molecular and Life Science and Center for Bionano Intelligence Education and Research, Hanyang University, Ansan-si, 15588, Korea
| | - Dawon Hong
- Dept of Molecular Biology, Graduate Department of Bioconvergence Engineering, Dankook University, Yongin-si, 16890, Korea
| | - Minsu Kim
- Dept of Computer Science, College of SW Convergence, Dankook Univ, Yongin-si, 16890, Korea
| | - Sein Jang
- Dept of Molecular and Life Science and Center for Bionano Intelligence Education and Research, Hanyang University, Ansan-si, 15588, Korea
| | - Chul-Su Yang
- Dept of Medicinal/Molecular and Life Science and Center for Bionano Intelligence Education and Research, Hanyang University, Ansan-si, 15588, Korea
| | - Seokhyun Yoon
- Dept of Electronics & Electrical Eng, College of Engineering, Dankook Univ, Yongin-si, 16890, Korea.
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33
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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: 1] [Impact Index Per Article: 1.0] [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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34
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Seeker LA, Bestard-Cuche N, Jäkel S, Kazakou NL, Bøstrand SMK, Wagstaff LJ, Cholewa-Waclaw J, Kilpatrick AM, Van Bruggen D, Kabbe M, Baldivia Pohl F, Moslehi Z, Henderson NC, Vallejos CA, La Manno G, Castelo-Branco G, Williams A. Brain matters: unveiling the distinct contributions of region, age, and sex to glia diversity and CNS function. Acta Neuropathol Commun 2023; 11:84. [PMID: 37217978 PMCID: PMC10204264 DOI: 10.1186/s40478-023-01568-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 04/15/2023] [Indexed: 05/24/2023] Open
Abstract
The myelinated white matter tracts of the central nervous system (CNS) are essential for fast transmission of electrical impulses and are often differentially affected in human neurodegenerative diseases across CNS region, age and sex. We hypothesize that this selective vulnerability is underpinned by physiological variation in white matter glia. Using single nucleus RNA sequencing of human post-mortem white matter samples from the brain, cerebellum and spinal cord and subsequent tissue-based validation we found substantial glial heterogeneity with tissue region: we identified region-specific oligodendrocyte precursor cells (OPCs) that retain developmental origin markers into adulthood, distinguishing them from mouse OPCs. Region-specific OPCs give rise to similar oligodendrocyte populations, however spinal cord oligodendrocytes exhibit markers such as SKAP2 which are associated with increased myelin production and we found a spinal cord selective population particularly equipped for producing long and thick myelin sheaths based on the expression of genes/proteins such as HCN2. Spinal cord microglia exhibit a more activated phenotype compared to brain microglia, suggesting that the spinal cord is a more pro-inflammatory environment, a difference that intensifies with age. Astrocyte gene expression correlates strongly with CNS region, however, astrocytes do not show a more activated state with region or age. Across all glia, sex differences are subtle but the consistent increased expression of protein-folding genes in male donors hints at pathways that may contribute to sex differences in disease susceptibility. These findings are essential to consider for understanding selective CNS pathologies and developing tailored therapeutic strategies.
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Affiliation(s)
- Luise A Seeker
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Nadine Bestard-Cuche
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Sarah Jäkel
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK
- Institute for Stroke and Dementia Research, Klinikum Der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Nina-Lydia Kazakou
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Sunniva M K Bøstrand
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Laura J Wagstaff
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Justyna Cholewa-Waclaw
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - Alastair M Kilpatrick
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK
| | - David Van Bruggen
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Mukund Kabbe
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Fabio Baldivia Pohl
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Zahra Moslehi
- Laboratory of Neurodevelopmental Systems Biology, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Neil C Henderson
- Centre for Inflammation Research, The Queen's Medical Research Institute, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, Western General Hospital, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Catalina A Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Cancer, Western General Hospital, University of Edinburgh, Edinburgh, EH4 2XU, UK
- The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK
| | - Gioele La Manno
- Laboratory of Neurodevelopmental Systems Biology, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Goncalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77, Stockholm, Sweden
- Ming Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Stockholm Node, 171 77, Stockholm, Sweden
| | - Anna Williams
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, Edinburgh Bioquarter, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK.
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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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36
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Xu J, Zhang A, Liu F, Chen L, Zhang X. CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data. Brief Bioinform 2023:7169137. [PMID: 37200157 DOI: 10.1093/bib/bbad195] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/03/2023] [Accepted: 04/30/2023] [Indexed: 05/20/2023] Open
Abstract
Single-cell omics technologies have made it possible to analyze the individual cells within a biological sample, providing a more detailed understanding of biological systems. Accurately determining the cell type of each cell is a crucial goal in single-cell RNA-seq (scRNA-seq) analysis. Apart from overcoming the batch effects arising from various factors, single-cell annotation methods also face the challenge of effectively processing large-scale datasets. With the availability of an increase in the scRNA-seq datasets, integrating multiple datasets and addressing batch effects originating from diverse sources are also challenges in cell-type annotation. In this work, to overcome the challenges, we developed a supervised method called CIForm based on the Transformer for cell-type annotation of large-scale scRNA-seq data. To assess the effectiveness and robustness of CIForm, we have compared it with some leading tools on benchmark datasets. Through the systematic comparisons under various cell-type annotation scenarios, we exhibit that the effectiveness of CIForm is particularly pronounced in cell-type annotation. The source code and data are available at https://github.com/zhanglab-wbgcas/CIForm.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Fang Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Liang Chen
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
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37
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Geras A, Darvish Shafighi S, Domżał K, Filipiuk I, Rączkowska A, Szymczak P, Toosi H, Kaczmarek L, Koperski Ł, Lagergren J, Nowis D, Szczurek E. Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data. Genome Biol 2023; 24:120. [PMID: 37198601 PMCID: PMC10190053 DOI: 10.1186/s13059-023-02951-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 04/21/2023] [Indexed: 05/19/2023] Open
Abstract
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.
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Affiliation(s)
- Agnieszka Geras
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Shadi Darvish Shafighi
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR, Paris, France
| | - Kacper Domżał
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Igor Filipiuk
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Alicja Rączkowska
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Paulina Szymczak
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Hosein Toosi
- KTH Royal Institute of Technology, Stockholm, Sweden
| | - Leszek Kaczmarek
- BRAINCITY, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
| | - Łukasz Koperski
- Department of Pathology, Medical University of Warsaw, Warsaw, Poland
| | | | - Dominika Nowis
- Laboratory of Experimental Medicine, Medical University of Warsaw, Warsaw, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland.
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Teefy BB, Lemus AJ, Adler A, Xu A, Bhala R, Hsu K, Benayoun BA. Widespread sex-dimorphism across single-cell transcriptomes of adult African turquoise killifish tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.05.539616. [PMID: 37214847 PMCID: PMC10197525 DOI: 10.1101/2023.05.05.539616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The African turquoise killifish (Nothobranchius furzeri), the shortest-lived vertebrate that can be bred in captivity, is an emerging model organism to study vertebrate aging. Here we describe the first multi-tissue, single-cell gene expression atlas of female and male turquoise killifish tissues comprising immune and metabolic cells from the blood, kidney, liver, and spleen. We were able to annotate 22 distinct cell types, define associated marker genes, and infer differentiation trajectories. Using this dataset, we found pervasive sex-dimorphic gene expression across cell types, especially in the liver. Sex-dimorphic genes tended to be involved in processes related to lipid metabolism, and indeed, we observed clear differences in lipid storage in female vs. male turquoise killifish livers. Importantly, we use machine-learning to predict sex using single-cell gene expression in our atlas and identify potential transcriptional markers for molecular sex identity in this species. As proof-of-principle, we show that our atlas can be used to deconvolute existing liver bulk RNA-seq data in this species to obtain accurate estimates of cell type proportions across biological conditions. We believe that this single-cell atlas can be a resource to the community that could notably be leveraged to identify cell type-specific genes for cell type-specific expression in transgenic animals.
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Affiliation(s)
- Bryan B. Teefy
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Aaron J.J. Lemus
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
- Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, CA 90089, USA
| | - Ari Adler
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Alan Xu
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
- Quantitative & Computational Biology Department, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, CA 90089, USA
| | - Rajyk Bhala
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Katelyn Hsu
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
- Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, CA 90089, USA
| | - Bérénice A. Benayoun
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
- Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, CA 90089, USA
- Biochemistry and Molecular Medicine Department, USC Keck School of Medicine, Los Angeles, CA 90089, USA
- USC Norris Comprehensive Cancer Center, Epigenetics and Gene Regulation, Los Angeles, CA 90089, USA
- USC Stem Cell Initiative, Los Angeles, CA 90089, USA
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Patil AR, Schug J, Naji A, Kaestner KH, Faryabi RB, Vahedi G. Single-cell expression profiling of islets generated by the Human Pancreas Analysis Program. Nat Metab 2023; 5:713-715. [PMID: 37188822 PMCID: PMC10731597 DOI: 10.1038/s42255-023-00806-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Affiliation(s)
- Abhijeet R Patil
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jonathan Schug
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ali Naji
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Klaus H Kaestner
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Robert B Faryabi
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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Pei G, Yan F, Simon LM, Dai Y, Jia P, Zhao Z. deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:370-384. [PMID: 35470070 PMCID: PMC10626171 DOI: 10.1016/j.gpb.2022.04.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 03/25/2022] [Accepted: 04/07/2022] [Indexed: 02/02/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and subjective. The increasing number of scRNA-seq datasets, as well as numerous published genetic studies, has motivated us to build a comprehensive human cell type reference atlas.Here, we present decoding Cell type Specificity (deCS), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth, and feature selection strategies. Our results demonstrate that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deep insights into the cellular mechanisms underlying disease pathogenesis. All documents for deCS, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.
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Affiliation(s)
- Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.
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Lee J, Kim M, Kang K, Yang CS, Yoon S. Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing. Brief Bioinform 2023; 24:bbad006. [PMID: 36681937 PMCID: PMC10025442 DOI: 10.1093/bib/bbad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/22/2022] [Accepted: 01/02/2023] [Indexed: 01/23/2023] Open
Abstract
Single-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously.
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Affiliation(s)
- Joongho Lee
- Dept. of Computer Science, College of SW Convergence, Dankook University, Yongin-si, Korea, 16890
| | - Minsoo Kim
- Dept. of Computer Science, College of SW Convergence, Dankook University, Yongin-si, Korea, 16890
| | - Keunsoo Kang
- Dept. of Microbiology, College of Natural Sciences, Dankook University, Cheonan-si, Korea, 31116
| | - Chul-Su Yang
- Dept. of Molecular and Life Science, Center for Bionano Intelligence Education and Research, Hanyang University, Ansan, Korea, 15588
| | - Seokhyun Yoon
- Dept. of Electronics & Electrical Eng., College of Engineering, Dankook University, Yongin-si Korea, 16890
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Lee J, Kim H, Kim M, Yoon S, Lee S. Role of lymphoid lineage cells aberrantly expressing alarmins S100A8/A9 in determining the severity of COVID-19. Genes Genomics 2023; 45:337-346. [PMID: 36107397 PMCID: PMC9476394 DOI: 10.1007/s13258-022-01285-2] [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: 04/27/2022] [Accepted: 07/08/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Alarmins S100A8 and S100A9 are recognized as hallmarks of severe COVID-19 and are primarily produced in myeloid cells, such as monocytes and neutrophils. As single-cell RNA-sequencing (scRNA-seq) data from patients with COVID-19 revealed the expression of S100A8/A9 in lymphoid cells in patients with severe COVID-19. OBJECTIVE We investigated the characteristics of lymphoid cells expressing S100A8/A9 in COVID-19 patients. METHODS Publicly available scRNA-seq data from patients with mild (N = 12) or severe (N = 7) COVID-19 were reanalyzed. The data were further divided into the following two groups based on the time of sample collection (from infection-onset): within 6 days (early phase) and after 6 days (late phase). Differential expression and gene set enrichment analyses were performed between S100A8/A9High and S100A8/A9Low lymphoid cells. Finally, cell-cell interaction analysis was performed to investigate the role of lymphoid cells expressing high levels of S100A8/A9 in COVID-19. RESULTS S100A8/A9 overexpression was observed in lymphoid cells, including B cells, T cells, and NK cells, in patients with severe COVID-19 (compared to patients with mild COVID-19). Cells exhibiting strong interferon/cytokine responses were found to be associated with the severity of COVID-19. Furthermore, differences in S100A8/A9-TLR4/RAGE interactions were confirmed between patients with severe and mild disease. CONCLUSIONS Lymphoid cells overexpressing S100A8/A9 contribute to the dysregulation of the innate immune response in patients with severe COVID-19, specifically during the early phase of infection. This study fosters a better understanding of the hyper-induction of pro-inflammatory cytokine expression and the generation of a cytokine storm in response to COVID-19 infection.
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Affiliation(s)
- Joongho Lee
- Department of Computer Science and Engineering, Graduate School, Dankook University, Yongin-si, Republic of Korea
| | - Hanbyeol Kim
- Department of Computer Science and Engineering, Graduate School, Dankook University, Yongin-si, Republic of Korea
| | - Minsoo Kim
- Department of Computer Science and Engineering, Graduate School, Dankook University, Yongin-si, Republic of Korea
| | - Seokhyun Yoon
- Department of Computer Science and Engineering, Graduate School, Dankook University, Yongin-si, Republic of Korea. .,Department of Electronics and Electrical Engineering, College of Engineering, Dankook University, Yongin-si, Republic of Korea.
| | - Sanghun Lee
- Department of Bioconvergence Engineering, Graduate School, Dankook University, Yongin-si, Republic of Korea.
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Khozyainova AA, Valyaeva AA, Arbatsky MS, Isaev SV, Iamshchikov PS, Volchkov EV, Sabirov MS, Zainullina VR, Chechekhin VI, Vorobev RS, Menyailo ME, Tyurin-Kuzmin PA, Denisov EV. Complex Analysis of Single-Cell RNA Sequencing Data. BIOCHEMISTRY. BIOKHIMIIA 2023; 88:231-252. [PMID: 37072324 PMCID: PMC10000364 DOI: 10.1134/s0006297923020074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/13/2022] [Accepted: 12/13/2022] [Indexed: 03/12/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.
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Affiliation(s)
- Anna A Khozyainova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia.
| | - Anna A Valyaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mikhail S Arbatsky
- Laboratory of Artificial Intelligence and Bioinformatics, The Russian Clinical Research Center for Gerontology, Pirogov Russian National Medical University, Moscow, 129226, Russia
- School of Public Administration, Lomonosov Moscow State University, Moscow, 119991, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Sergey V Isaev
- Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Pavel S Iamshchikov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
- Laboratory of Complex Analysis of Big Bioimage Data, National Research Tomsk State University, Tomsk, 634050, Russia
| | - Egor V Volchkov
- Department of Oncohematology, Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia
| | - Marat S Sabirov
- Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, Moscow, 119334, Russia
| | - Viktoria R Zainullina
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Vadim I Chechekhin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Rostislav S Vorobev
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Maxim E Menyailo
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Pyotr A Tyurin-Kuzmin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Evgeny V Denisov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
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Jiang T, Zhou W, Sheng Q, Yu J, Xie Y, Ding N, Zhang Y, Xu J, Li Y. ImmCluster: an ensemble resource for immunology cell type clustering and annotations in normal and cancerous tissues. Nucleic Acids Res 2023; 51:D1325-D1332. [PMID: 36271790 PMCID: PMC9825417 DOI: 10.1093/nar/gkac922] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/22/2022] [Accepted: 10/06/2022] [Indexed: 01/30/2023] Open
Abstract
Single-cell transcriptome has enabled the transcriptional profiling of thousands of immune cells in complex tissues and cancers. However, subtle transcriptomic differences in immune cell subpopulations and the high dimensionality of transcriptomic data make the clustering and annotation of immune cells challenging. Herein, we introduce ImmCluster (http://bio-bigdata.hrbmu.edu.cn/ImmCluster) for immunology cell type clustering and annotation. We manually curated 346 well-known marker genes from 1163 studies. ImmCluster integrates over 420 000 immune cells from nine healthy tissues and over 648 000 cells from different tumour samples of 17 cancer types to generate stable marker-gene sets and develop context-specific immunology references. In addition, ImmCluster provides cell clustering using seven reference-based and four marker gene-based computational methods, and the ensemble method was developed to provide consistent cell clustering than individual methods. Five major analytic modules were provided for interactively exploring the annotations of immune cells, including clustering and annotating immune cell clusters, gene expression of markers, functional assignment in cancer hallmarks, cell states and immune pathways, cell-cell communications and the corresponding ligand-receptor interactions, as well as online tools. ImmCluster generates diverse plots and tables, enabling users to identify significant associations in immune cell clusters simultaneously. ImmCluster is a valuable resource for analysing cellular heterogeneity in cancer microenvironments.
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Affiliation(s)
- Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Weiwei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Qi Sheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Jiaxin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Yunjin Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang150081, China
| | - Yongsheng Li
- College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
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Patil AR, Schug J, Naji A, Kaestner KH, Faryabi RB, Vahedi G. Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.03.522578. [PMID: 36711819 PMCID: PMC9881881 DOI: 10.1101/2023.01.03.522578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.
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Ren T, Huang S, Liu Q, Wang G. scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome. Comput Biol Med 2023; 152:106409. [PMID: 36512878 DOI: 10.1016/j.compbiomed.2022.106409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/16/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
Rapid advances in single-cell transcriptome analysis provide deeper insights into the study of tissue heterogeneity at the cellular level. Unsupervised clustering can identify potential cell populations in single-cell RNA-sequencing (scRNA-seq) data, but fail to further determine the identity of each cell. Existing automatic annotation methods using scRNA-seq data based on machine learning mainly use single feature set and single classifier. In view of this, we propose a Weighted Ensemble classification framework for Cell Type Annotation, named scWECTA, which improves the accuracy of cell type identification. scWECTA uses five informative gene sets and integrates five classifiers based on soft weighted ensemble framework. And the ensemble weights are inferred through the constrained non-negative least squares. Validated on multiple pairs of scRNA-seq datasets, scWECTA is able to accurately annotate scRNA-seq data across platforms and across tissues, especially for imbalanced data containing rare cell types. Moreover, scWECTA outperforms other comparable methods in balancing the prediction accuracy of common cell types and the unassigned rate of non-common cell types at the same time. The source code of scWECTA is freely available at https://github.com/ttren-sc/scWECTA.
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Affiliation(s)
- Tongtong Ren
- School of Computer Science and Technology, Harbin Institute of Technology, No.92 West Dazhi Street, Nangang District, Harbin, Heilongjiang, 150001, PR China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, No. 246, Xuefu Street, Nangang District, Harbin, Heilongjiang, 150081, PR China
| | - Qiaoming Liu
- School of Computer Science and Technology, Harbin Institute of Technology, No.92 West Dazhi Street, Nangang District, Harbin, Heilongjiang, 150001, PR China
| | - Guohua Wang
- School of Computer Science and Technology, Harbin Institute of Technology, No.92 West Dazhi Street, Nangang District, Harbin, Heilongjiang, 150001, PR China.
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Su M, Pan T, Chen QZ, Zhou WW, Gong Y, Xu G, Yan HY, Li S, Shi QZ, Zhang Y, He X, Jiang CJ, Fan SC, Li X, Cairns MJ, Wang X, Li YS. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res 2022; 9:68. [PMID: 36461064 PMCID: PMC9716519 DOI: 10.1186/s40779-022-00434-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
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Affiliation(s)
- Min Su
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Tao Pan
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiu-Zhen Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Wei-Wei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Yi Gong
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
- Department of Immunology, Nanjing Medical University, Nanjing, 211166 China
| | - Gang Xu
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Huan-Yu Yan
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Si Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Qiao-Zhen Shi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Ya Zhang
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
| | - Xiao He
- Department of Laboratory Medicine, Women and Children’s Hospital of Chongqing Medical University, Chongqing, 401174 China
| | | | - Shi-Cai Fan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110 Guangdong China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081 Heilongjiang China
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, Callaghan, NSW 2308 Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW 2305 Australia
| | - Xi Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166 China
| | - Yong-Sheng Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199 Hainan China
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Jin JQ, Wu D, Spencer R, Elhage KG, Liu J, Davis M, Hakimi M, Kumar S, Huang ZM, Bhutani T, Liao W. Biologic insights from single-cell studies of psoriasis and psoriatic arthritis. Expert Opin Biol Ther 2022; 22:1449-1461. [PMID: 36317702 DOI: 10.1080/14712598.2022.2142465] [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: 11/05/2022]
Abstract
INTRODUCTION Psoriasis (PSO) and psoriatic arthritis (PSA) represent a large burden of global inflammatory disease, but sustained treatment response and early diagnosis remain challenging. Both conditions arise from complex immune cell dysregulation. Single-cell techniques, including single-cell RNA sequencing (scRNA-seq), have revolutionized our understanding of pathogenesis by illuminating heterogeneous cell populations and their interactions. AREAS COVERED We discuss the transcriptional profiles and cellular interactions unique to PSO/PSA affecting T cells, myeloid cells, keratinocytes, innate lymphoid cells, and stromal cells. We also review advances, limitations, and future challenges associated with single-cell studies. EXPERT OPINION Following analyses of 22 single-cell studies, several themes emerged. A small subpopulation of cells can have a large impact on disease pathogenesis. Multiple cell types identified via scRNA-seq play supporting roles in PSO pathogenesis, contrary to the traditional paradigm focusing on IL-23/IL-17 signaling among dendritic cells and T cells. Immune cell states are dynamic, with psoriatic subpopulations aberrantly re-activating and differentiating into inflammatory phenotypes depending on surrounding signaling cues. Comparison of circulating immune cells with resident skin/joint cells has uncovered specific T cell clonotypes associated with the disease. Finally, machine learning models demonstrate great promise in identifying biomarkers to diagnose clinically ambiguous rashes and PSA at earlier stages.
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Affiliation(s)
- Joy Q Jin
- Department of Medicine, UCSF School of Medicine, San Francisco, CA, USA.,Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - David Wu
- Department of Medicine, UCSF School of Medicine, San Francisco, CA, USA.,Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Riley Spencer
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Kareem G Elhage
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Jared Liu
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Mitchell Davis
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Marwa Hakimi
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Sugandh Kumar
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Zhi-Ming Huang
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Tina Bhutani
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA
| | - Wilson Liao
- Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA.,Institute for Human Genetics, University of California at San Francisco, San Francisco, CA, USA
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49
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Yang F, Wang W, Wang F, Fang Y, Tang D, Huang J, Lu H, Yao J. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00534-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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50
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Ianevski A, Giri AK, Aittokallio T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun 2022; 13:1246. [PMID: 35273156 PMCID: PMC8913782 DOI: 10.1038/s41467-022-28803-w] [Citation(s) in RCA: 185] [Impact Index Per Article: 92.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/03/2022] [Indexed: 12/29/2022] Open
Abstract
Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancer applications. The widely applicable method is deployed both as an interactive web-tool (https://sctype.app), and as an open-source R-package. Cell types are typically identified in single cell transcriptomic data by manual annotation of cell clusters using established marker genes. Here the authors present a fully-automated computational platform that can quickly and accurately distinguish between cell types.
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Affiliation(s)
- Aleksandr Ianevski
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.,Helsinki Institute for Information Technology (HIIT), Aalto University, Helsinki, Finland
| | - Anil K Giri
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland. .,Helsinki Institute for Information Technology (HIIT), Aalto University, Helsinki, Finland. .,Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway. .,Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway.
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