1
|
Priyadarshani P, Van Grouw A, Liversage AR, Rui K, Nikitina A, Tehrani KF, Aggarwal B, Stice SL, Sinha S, Kemp ML, Fernández FM, Mortensen LJ. Investigation of MSC potency metrics via integration of imaging modalities with lipidomic characterization. Cell Rep 2024; 43:114579. [PMID: 39153198 DOI: 10.1016/j.celrep.2024.114579] [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: 11/15/2023] [Revised: 06/17/2024] [Accepted: 07/18/2024] [Indexed: 08/19/2024] Open
Abstract
Mesenchymal stem/stromal cell (MSC) therapies have had limited success so far in clinical trials due in part to heterogeneity in immune-responsive phenotypes. Therefore, techniques to characterize these properties of MSCs are needed during biomanufacturing. Imaging cell shape, or morphology, has been found to be associated with MSC immune responsivity-but a direct relationship between single-cell morphology and function has not been established. We used label-free differential phase contrast imaging and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to evaluate single-cell morphology and explore relationships with lipid metabolic immune response. In interferon gamma (IFN-γ)-stimulated MSCs, we found higher lipid abundances from the ceramide-1-phosphate (C1P), phosphatidylcholine (PC), LysoPC, and triglyceride (TAG) families that are involved in cell immune function. Furthermore, we identified differences in lipid signatures in morphologically defined MSC subpopulations. The use of single-cell optical imaging coupled with single-cell spatial lipidomics could assist in optimizing the MSC production process and improve mechanistic understanding of manufacturing process effects on MSC immune activity and heterogeneity.
Collapse
Affiliation(s)
- Priyanka Priyadarshani
- School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, GA 30602, USA; Regenerative Bioscience Center, Rhodes Center for ADS, University of Georgia, Athens, GA 30602, USA
| | - Alexandria Van Grouw
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Adrian Ross Liversage
- School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, GA 30602, USA; Regenerative Bioscience Center, Rhodes Center for ADS, University of Georgia, Athens, GA 30602, USA
| | - Kejie Rui
- School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, GA 30602, USA; Regenerative Bioscience Center, Rhodes Center for ADS, University of Georgia, Athens, GA 30602, USA
| | - Arina Nikitina
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Kayvan Forouhesh Tehrani
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA
| | - Bhavay Aggarwal
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA 30332, USA
| | - Steven L Stice
- Regenerative Bioscience Center, Rhodes Center for ADS, University of Georgia, Athens, GA 30602, USA
| | - Saurabh Sinha
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA 30332, USA
| | - Melissa L Kemp
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA 30332, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Luke J Mortensen
- School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, GA 30602, USA; Regenerative Bioscience Center, Rhodes Center for ADS, University of Georgia, Athens, GA 30602, USA.
| |
Collapse
|
2
|
Yang X, Mann KK, Wu H, Ding J. scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration. Genome Biol 2024; 25:198. [PMID: 39075536 PMCID: PMC11285326 DOI: 10.1186/s13059-024-03338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.
Collapse
Affiliation(s)
- Xiuhui Yang
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Koren K Mann
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Hao Wu
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China.
| | - Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada.
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada.
- Mila-Quebec AI Institute, Montreal, QC, H2S 3H1, Canada.
| |
Collapse
|
3
|
Lin H, Hu H, Feng Z, Xu F, Lyu J, Li X, Liu L, Yang G, Shuai J. SCTC: inference of developmental potential from single-cell transcriptional complexity. Nucleic Acids Res 2024; 52:6114-6128. [PMID: 38709881 PMCID: PMC11194082 DOI: 10.1093/nar/gkae340] [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: 11/22/2022] [Revised: 03/09/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.
Collapse
Affiliation(s)
- Hai Lin
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325001, China
| | - Huan Hu
- Institute of Applied Genomics, Fuzhou University, Fuzhou 350108, China
| | - Zhen Feng
- First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou 325000, China
| | - Fei Xu
- Department of Physics, Anhui Normal University, Wuhu, Anhui 241002, China
| | - Jie Lyu
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325001, China
| | - Xiang Li
- Department of Physics, College of Physical Science and Technology, Xiamen University, Xiamen 361005, China
| | - Liyu Liu
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China
| | - Gen Yang
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing 100871, China
| | - Jianwei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325001, China
| |
Collapse
|
4
|
Lee H, Kim SY, Kwon NJ, Jo SJ, Kwon O, Kim JI. Single-Cell and Spatial Transcriptome Analysis of Dermal Fibroblast Development in Perinatal Mouse Skin: Dynamic Lineage Differentiation and Key Driver Genes. J Invest Dermatol 2024; 144:1238-1250.e11. [PMID: 38072389 DOI: 10.1016/j.jid.2023.11.008] [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: 02/20/2023] [Revised: 11/13/2023] [Accepted: 11/21/2023] [Indexed: 01/21/2024]
Abstract
Several single-cell RNA studies of developing mouse skin have elucidated the molecular and cellular processes involved in skin development. However, they have primarily focused on either the fetal or early postnatal period, leaving a gap in our understanding of skin development. In this study, we conducted a comprehensive time-series analysis by combining single-cell RNA-sequencing datasets collected at different stages of development (embryonic days 13.5, 14.5, and 16.5 and postnatal days 0, 2, and 4) and validated our findings through multipanel in situ spatial transcriptomics. Our analysis indicated that embryonic fibroblasts exhibit heterogeneity from a very early stage and that the rapid determination of each lineage occurs within days after birth. The expression of putative key driver genes, including Hey1, Ebf1, Runx3, and Sox11 for the dermal papilla trajectory; Lrrc15 for the dermal sheath trajectory; Zfp536 and Nrn1 for the papillary fibroblast trajectory; and Lrrn4cl and Mfap5 for the fascia fibroblast trajectory, was detected in the corresponding, spatially identified cell types. Finally, cell-to-cell interaction analysis indicated that the dermal papilla lineage is the primary source of the noncanonical Wnt pathway during skin development. Together, our study provides a transcriptomic reference for future research in the field of skin development and regeneration.
Collapse
Affiliation(s)
- Hanjae Lee
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea; Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea
| | - So Young Kim
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea; Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | | | - Seong Jin Jo
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea; Laboratory of Cutaneous Aging and Hair Research, Clinical Research Institute, Seoul National University Hospital, Seoul, Korea; Institute of Human-Environment Interface Biology, Seoul National University College of Medicine, Seoul, Korea
| | - Ohsang Kwon
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea; Laboratory of Cutaneous Aging and Hair Research, Clinical Research Institute, Seoul National University Hospital, Seoul, Korea; Institute of Human-Environment Interface Biology, Seoul National University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea.
| | - Jong-Il Kim
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea; Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea; Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Korea; Cancer Research Institute, Seoul National University, Seoul, Korea
| |
Collapse
|
5
|
Yang X, Chen Y, Yang Y, Li S, Mi P, Jing N. The molecular and cellular choreography of early mammalian lung development. MEDICAL REVIEW (2021) 2024; 4:192-206. [PMID: 38919401 PMCID: PMC11195428 DOI: 10.1515/mr-2023-0064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/08/2024] [Indexed: 06/27/2024]
Abstract
Mammalian lung development starts from a specific cluster of endodermal cells situated within the ventral foregut region. With the orchestrating of delicate choreography of transcription factors, signaling pathways, and cell-cell communications, the endodermal diverticulum extends into the surrounding mesenchyme, and builds the cellular and structural basis of the complex respiratory system. This review provides a comprehensive overview of the current molecular insights of mammalian lung development, with a particular focus on the early stage of lung cell fate differentiation and spatial patterning. Furthermore, we explore the implications of several congenital respiratory diseases and the relevance to early organogenesis. Finally, we summarize the unprecedented knowledge concerning lung cell compositions, regulatory networks as well as the promising prospect for gaining an unbiased understanding of lung development and lung malformations through state-of-the-art single-cell omics.
Collapse
Affiliation(s)
- Xianfa Yang
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
| | - Yingying Chen
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
| | - Yun Yang
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
- Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Shiting Li
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
- Institute of Biomedical Research, Yunnan University, Kunming, Yunnan Province, China
| | - Panpan Mi
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
- Department of Histology and Embryology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Naihe Jing
- Guangzhou National Laboratory, Guangzhou, Guangdong Province, China
| |
Collapse
|
6
|
Mok GF, Turner S, Smith EL, Mincarelli L, Lister A, Lipscombe J, Uzun V, Haerty W, Macaulay IC, Münsterberg AE. Single cell RNA-sequencing and RNA-tomography of the avian embryo extending body axis. Front Cell Dev Biol 2024; 12:1382960. [PMID: 38863942 PMCID: PMC11165230 DOI: 10.3389/fcell.2024.1382960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/29/2024] [Indexed: 06/13/2024] Open
Abstract
Introduction: Vertebrate body axis formation initiates during gastrulation and continues within the tail bud at the posterior end of the embryo. Major structures in the trunk are paired somites, which generate the musculoskeletal system, the spinal cord-forming part of the central nervous system, and the notochord, with important patterning functions. The specification of these different cell lineages by key signalling pathways and transcription factors is essential, however, a global map of cell types and expressed genes in the avian trunk is missing. Methods: Here we use high-throughput sequencing approaches to generate a molecular map of the emerging trunk and tailbud in the chick embryo. Results and Discussion: Single cell RNA-sequencing (scRNA-seq) identifies discrete cell lineages including somites, neural tube, neural crest, lateral plate mesoderm, ectoderm, endothelial and blood progenitors. In addition, RNA-seq of sequential tissue sections (RNA-tomography) provides a spatially resolved, genome-wide expression dataset for the avian tailbud and emerging body, comparable to other model systems. Combining the single cell and RNA-tomography datasets, we identify spatially restricted genes, focusing on somites and early myoblasts. Thus, this high-resolution transcriptome map incorporating cell types in the embryonic trunk can expose molecular pathways involved in body axis development.
Collapse
Affiliation(s)
- G. F. Mok
- School of Biological Sciences, University of East Anglia, Norwich, United Kingdom
| | - S. Turner
- Earlham Institute, Norwich, United Kingdom
| | - E. L. Smith
- School of Biological Sciences, University of East Anglia, Norwich, United Kingdom
| | | | - A. Lister
- Earlham Institute, Norwich, United Kingdom
| | | | - V. Uzun
- Earlham Institute, Norwich, United Kingdom
| | - W. Haerty
- Earlham Institute, Norwich, United Kingdom
| | | | - A. E. Münsterberg
- School of Biological Sciences, University of East Anglia, Norwich, United Kingdom
| |
Collapse
|
7
|
Shu C, Street K, Breton CV, Bastain TM, Wilson ML. A review of single-cell transcriptomics and epigenomics studies in maternal and child health. Epigenomics 2024; 16:775-793. [PMID: 38709139 PMCID: PMC11318716 DOI: 10.1080/17501911.2024.2343276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.
Collapse
Affiliation(s)
- Chang Shu
- Center for Genetic Epidemiology, Division of Epidemiology & Genetics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Kelly Street
- Division of Biostatistics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Carrie V Breton
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Theresa M Bastain
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Melissa L Wilson
- Division of Disease Prevention, Policy, & Global Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles,CA USA
| |
Collapse
|
8
|
Massri AJ, Berrio A, Afanassiev A, Greenstreet L, Pipho K, Byrne M, Schiebinger G, McClay DR, Wray GA. Single-cell transcriptomics reveals evolutionary reconfiguration of embryonic cell fate specification in the sea urchin Heliocidaris erythrogramma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.30.591752. [PMID: 38746376 PMCID: PMC11092583 DOI: 10.1101/2024.04.30.591752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Altered regulatory interactions during development likely underlie a large fraction of phenotypic diversity within and between species, yet identifying specific evolutionary changes remains challenging. Analysis of single-cell developmental transcriptomes from multiple species provides a powerful framework for unbiased identification of evolutionary changes in developmental mechanisms. Here, we leverage a "natural experiment" in developmental evolution in sea urchins, where a major life history switch recently evolved in the lineage leading to Heliocidaris erythrogramma, precipitating extensive changes in early development. Comparative analyses of scRNA-seq developmental time courses from H. erythrogramma and Lytechinus variegatus (representing the derived and ancestral states respectively) reveals numerous evolutionary changes in embryonic patterning. The earliest cell fate specification events, and the primary signaling center are co-localized in the ancestral dGRN but remarkably, in H. erythrogramma they are spatially and temporally separate. Fate specification and differentiation are delayed in most embryonic cell lineages, although in some cases, these processes are conserved or even accelerated. Comparative analysis of regulator-target gene co-expression is consistent with many specific interactions being preserved but delayed in H. erythrogramma, while some otherwise widely conserved interactions have likely been lost. Finally, specific patterning events are directly correlated with evolutionary changes in larval morphology, suggesting that they are directly tied to the life history shift. Together, these findings demonstrate that comparative scRNA-seq developmental time courses can reveal a diverse set of evolutionary changes in embryonic patterning and provide an efficient way to identify likely candidate regulatory interactions for subsequent experimental validation.
Collapse
Affiliation(s)
- Abdull J Massri
- Department of Biology, Duke University, Durham, NC 27701 USA
| | | | - Anton Afanassiev
- Department of Mathematics, University of British Colombia, Vancouver, BC V6T 1Z4 Canada
| | - Laura Greenstreet
- Department of Mathematics, University of British Colombia, Vancouver, BC V6T 1Z4 Canada
| | - Krista Pipho
- Department of Biology, Duke University, Durham, NC 27701 USA
| | - Maria Byrne
- School of Life and Environmental Sciences, Sydney University, Sydney, NSW Australia
| | - Geoffrey Schiebinger
- Department of Mathematics, University of British Colombia, Vancouver, BC V6T 1Z4 Canada
| | - David R McClay
- Department of Biology, Duke University, Durham, NC 27701 USA
| | - Gregory A Wray
- Department of Biology, Duke University, Durham, NC 27701 USA
| |
Collapse
|
9
|
He Y, Qi W, Xie X, Jiang H. Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing. BMC Med Genomics 2024; 17:103. [PMID: 38654290 DOI: 10.1186/s12920-024-01871-1] [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: 09/12/2023] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles. METHODS The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530. RESULTS Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease. CONCLUSION Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.
Collapse
Affiliation(s)
- Yujian He
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Hebei Key Laboratory of Gastroenterology, Hebei Institute of Gastroenterology, Hebei Clinical Research Center for Digestive Diseases, Shijiazhuang, Hebei, China
| | - Wei Qi
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Hebei Key Laboratory of Gastroenterology, Hebei Institute of Gastroenterology, Hebei Clinical Research Center for Digestive Diseases, Shijiazhuang, Hebei, China
| | - Xiaoli Xie
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Hebei Key Laboratory of Gastroenterology, Hebei Institute of Gastroenterology, Hebei Clinical Research Center for Digestive Diseases, Shijiazhuang, Hebei, China
| | - Huiqing Jiang
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Hebei Key Laboratory of Gastroenterology, Hebei Institute of Gastroenterology, Hebei Clinical Research Center for Digestive Diseases, Shijiazhuang, Hebei, China.
| |
Collapse
|
10
|
Huang F, Welner RS, Chen JY, Yue Z. PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis. FRONTIERS IN BIOINFORMATICS 2024; 4:1336135. [PMID: 38690527 PMCID: PMC11058213 DOI: 10.3389/fbinf.2024.1336135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/01/2024] [Indexed: 05/02/2024] Open
Abstract
Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.
Collapse
Affiliation(s)
- Fengyuan Huang
- Department of Biomedical Informatics and Data Science, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Robert S. Welner
- Hematology & Oncology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jake Y. Chen
- Department of Biomedical Informatics and Data Science, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Zongliang Yue
- Health Outcome Research and Policy Department, Harrison College of Pharmacy, Auburn University, Auburn, AL, United States
| |
Collapse
|
11
|
Fan D, Cong Y, Liu J, Zhang H, Du Z. Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference. Cell Rep 2024; 43:113928. [PMID: 38461413 DOI: 10.1016/j.celrep.2024.113928] [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: 08/08/2023] [Revised: 01/31/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.
Collapse
Affiliation(s)
- Duchangjiang Fan
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Yulin Cong
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Jinyi Liu
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Haoye Zhang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuo Du
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
12
|
Liu Y, Lyons CJ, Ayu C, O'Brien T. Recent advances in endothelial colony-forming cells: from the transcriptomic perspective. J Transl Med 2024; 22:313. [PMID: 38532420 PMCID: PMC10967123 DOI: 10.1186/s12967-024-05108-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/18/2024] [Indexed: 03/28/2024] Open
Abstract
Endothelial colony-forming cells (ECFCs) are progenitors of endothelial cells with significant proliferative and angiogenic ability. ECFCs are a promising treatment option for various diseases, such as ischemic heart disease and peripheral artery disease. However, some barriers hinder the clinical application of ECFC therapeutics. One of the current obstacles is that ECFCs are dysfunctional due to the underlying disease states. ECFCs exhibit dysfunctional phenotypes in pathologic states, which include but are not limited to the following: premature neonates and pregnancy-related diseases, diabetes mellitus, cancers, haematological system diseases, hypoxia, pulmonary arterial hypertension, coronary artery diseases, and other vascular diseases. Besides, ECFCs are heterogeneous among donors, tissue sources, and within cell subpopulations. Therefore, it is important to elucidate the underlying mechanisms of ECFC dysfunction and characterize their heterogeneity to enable clinical application. In this review, we summarize the current and potential application of transcriptomic analysis in the field of ECFC biology. Transcriptomic analysis is a powerful tool for exploring the key molecules and pathways involved in health and disease and can be used to characterize ECFC heterogeneity.
Collapse
Affiliation(s)
- Yaqiong Liu
- Regenerative Medicine Institute (REMEDI), Biomedical Sciences Building, University of Galway, Galway, Ireland
| | - Caomhán J Lyons
- Regenerative Medicine Institute (REMEDI), Biomedical Sciences Building, University of Galway, Galway, Ireland
| | - Christine Ayu
- Regenerative Medicine Institute (REMEDI), Biomedical Sciences Building, University of Galway, Galway, Ireland
| | - Timothy O'Brien
- Regenerative Medicine Institute (REMEDI), Biomedical Sciences Building, University of Galway, Galway, Ireland.
| |
Collapse
|
13
|
Brash JT, Diez-Pinel G, Colletto C, Castellan RF, Fantin A, Ruhrberg C. The BulkECexplorer compiles endothelial bulk transcriptomes to predict functional versus leaky transcription. NATURE CARDIOVASCULAR RESEARCH 2024; 3:460-473. [PMID: 38708406 PMCID: PMC7615926 DOI: 10.1038/s44161-024-00436-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/26/2024] [Indexed: 05/07/2024]
Abstract
Transcriptomic data can be mined to understand the molecular activity of cell types. Yet, functional genes may remain undetected in RNA sequencing (RNA-seq) experiments for technical reasons, such as insufficient read depth or gene dropout. Conversely, RNA-seq experiments may detect lowly expressed mRNAs thought to be biologically irrelevant products of leaky transcription. To represent a cell type's functional transcriptome more accurately, we propose compiling many bulk RNA-seq datasets into a compendium and applying established classification models to predict whether detected transcripts are likely products of active or leaky transcription. Here, we present the BulkECexplorer (bulk RNA-seq endothelial cell explorer) compendium of 240 bulk RNA-seq datasets from five vascular endothelial cell subtypes. This resource reports transcript counts for genes of interest and predicts whether detected transcripts are likely the products of active or leaky gene expression. Beyond its usefulness for vascular biology research, this resource provides a blueprint for developing analogous tools for other cell types.
Collapse
Affiliation(s)
- James T. Brash
- UCL Institute of Ophthalmology, University College London, London, UK
| | | | - Chiara Colletto
- Department of Biosciences, University of Milan, Milan, Italy
| | | | - Alessandro Fantin
- UCL Institute of Ophthalmology, University College London, London, UK
- Department of Biosciences, University of Milan, Milan, Italy
| | | |
Collapse
|
14
|
Kotarska K, Gąsior Ł, Rudnicka J, Polański Z. Long-run real-time PCR analysis of repetitive nuclear elements as a novel tool for DNA damage quantification in single cells: an approach validated on mouse oocytes and fibroblasts. J Appl Genet 2024; 65:181-190. [PMID: 38110826 PMCID: PMC10789673 DOI: 10.1007/s13353-023-00817-0] [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: 06/22/2023] [Revised: 11/17/2023] [Accepted: 12/05/2023] [Indexed: 12/20/2023]
Abstract
Since DNA damage is of great importance in various biological processes, its rate is frequently assessed both in research studies and in medical diagnostics. The most precise methods of quantifying DNA damage are based on real-time PCR. However, in the conventional version, they require a large amount of genetic material and therefore their usefulness is limited to multicellular samples. Here, we present a novel approach to long-run real-time PCR-based DNA-damage quantification (L1-LORD-Q), which consists in amplification of long interspersed nuclear elements (L1) and allows for analysis of single-cell genomes. The L1-LORD-Q was compared with alternative methods of measuring DNA breaks (Bioanalyzer system, γ-H2AX foci staining), which confirmed its accuracy. Furthermore, it was demonstrated that the L1-LORD-Q is sensitive enough to distinguish between different levels of UV-induced DNA damage. The method was validated on mouse oocytes and fibroblasts, but the general idea is universal and can be applied to various types of cells and species.
Collapse
Affiliation(s)
- Katarzyna Kotarska
- Laboratory of Genetics and Evolution, Institute of Zoology and Biomedical Research, Department of Biology, Jagiellonian University, Kraków, Poland.
| | - Łukasz Gąsior
- Laboratory of Neurobiology of Trace Elements, Department of Neurobiology, Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
| | - Joanna Rudnicka
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
| | - Zbigniew Polański
- Laboratory of Genetics and Evolution, Institute of Zoology and Biomedical Research, Department of Biology, Jagiellonian University, Kraków, Poland
| |
Collapse
|
15
|
Sun SN, Zhou Y, Fu X, Zheng YZ, Xie C, Qin GY, Liu F, Chu C, Wang F, Liu CL, Zhou QT, Yang DH, Zhu D, Wang MW, Gui YH. A pilot study of the differentiated landscape of peripheral blood mononuclear cells from children with incomplete versus complete Kawasaki disease. World J Pediatr 2024; 20:189-200. [PMID: 37688719 DOI: 10.1007/s12519-023-00752-4] [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] [Received: 01/12/2023] [Accepted: 07/14/2023] [Indexed: 09/11/2023]
Affiliation(s)
- Shu-Na Sun
- Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Yan Zhou
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Xing Fu
- Accuramed Technology (Shanghai) Ltd., Shanghai, 200233, China
| | - Yuan-Zheng Zheng
- Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Cao Xie
- School of Pharmacy, Fudan University, Shanghai, 201203, China
| | - Guo-You Qin
- School of Public Health, Fudan University, Shanghai, 200032, China
| | - Fang Liu
- Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Chen Chu
- Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Feng Wang
- Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China
| | - Cheng-Long Liu
- School of Pharmacy, Fudan University, Shanghai, 201203, China
| | - Qing-Tong Zhou
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
- Research Center for Deepsea Bioresources, Sanya, 572025, China
| | - De-Hua Yang
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
- Research Center for Deepsea Bioresources, Sanya, 572025, China
| | - Di Zhu
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
| | - Ming-Wei Wang
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
- Research Center for Deepsea Bioresources, Sanya, 572025, China.
- Department of Chemistry, School of Science, The University of Tokyo, Tokyo, 113-0033, Japan.
- School of Pharmacy, Hainan Medical University, Haikou, 570288, China.
| | - Yong-Hao Gui
- Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, China.
| |
Collapse
|
16
|
Xu F, Hu H, Lin H, Lu J, Cheng F, Zhang J, Li X, Shuai J. scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks. Brief Bioinform 2024; 25:bbae091. [PMID: 38487851 PMCID: PMC10940817 DOI: 10.1093/bib/bbae091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/08/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.
Collapse
Affiliation(s)
- Fei Xu
- Department of Physics, Anhui Normal University, Wuhu 241002, China
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Huan Hu
- Institute of Applied Genomics, Fuzhou University, Fuzhou 350108, China
| | - Hai Lin
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Jun Lu
- Department of Physics, Anhui Normal University, Wuhu 241002, China
- School of Medical Imageology, Wannan Medical College, Wuhu 241002, China
| | - Feng Cheng
- Department of Physics, and Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jiqian Zhang
- Department of Physics, Anhui Normal University, Wuhu 241002, China
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jianwei Shuai
- Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou 325001, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325001, China
| |
Collapse
|
17
|
Zhou S, Li Y, Wu W, Li L. scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data. Brief Bioinform 2024; 25:bbad523. [PMID: 38300515 PMCID: PMC10833085 DOI: 10.1093/bib/bbad523] [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: 09/04/2023] [Revised: 11/27/2023] [Accepted: 12/19/2023] [Indexed: 02/02/2024] Open
Abstract
Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation.
Collapse
Affiliation(s)
- Songqi Zhou
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| | - Yang Li
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
- Chongqing Research Institute of Big Data, Peking University, Chongqing, China
| | - Wenyuan Wu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| | - Li Li
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| |
Collapse
|
18
|
Bai X, Bao Y, Bei S, Bu C, Cao R, Cao Y, Cen H, Chao J, Chen F, Chen H, Chen K, Chen M, Chen M, Chen M, Chen Q, Chen R, Chen S, Chen T, Chen X, Chen X, Cheng Y, Chu Y, Cui Q, Dong L, Du Z, Duan G, Fan S, Fan Z, Fang X, Fang Z, Feng Z, Fu S, Gao F, Gao G, Gao H, Gao W, Gao X, Gao X, Gao X, Gong J, Gong J, Gou Y, Gu S, Guo AY, Guo G, Guo X, Han C, Hao D, Hao L, He Q, He S, He S, Hu W, Huang K, Huang T, Huang X, Huang Y, Jia P, Jia Y, Jiang C, Jiang M, Jiang S, Jiang T, Jiang X, Jin E, Jin W, Kang H, Kang H, Kong D, Lan L, Lei W, Li CY, Li C, Li C, Li H, Li J, Li J, Li L, Li P, Li R, Li X, Li Y, Li Y, Li Z, Liao X, Lin S, Lin Y, Ling Y, Liu B, Liu CJ, Liu D, Liu GH, Liu L, Liu S, Liu W, Liu X, Liu X, Liu Y, Liu Y, Lu M, Lu T, Luo H, Luo H, Luo M, Luo S, Luo X, Ma L, Ma Y, Mai J, Meng J, Meng X, Meng Y, Meng Y, Miao W, Miao YR, Ni L, Nie Z, Niu G, Niu X, Niu Y, Pan R, Pan S, Peng D, Peng J, Qi J, Qi Y, Qian Q, Qin Y, Qu H, Ren J, Ren J, Sang Z, Shang K, Shen WK, Shen Y, Shi Y, Song S, Song T, Su T, Sun J, Sun Y, Sun Y, Sun Y, Tang B, Tang D, Tang Q, Tang Z, Tian D, Tian F, Tian W, Tian Z, Wang A, Wang G, Wang G, Wang J, Wang J, Wang P, Wang P, Wang W, Wang Y, Wang Y, Wang Y, Wang Y, Wang Z, Wei H, Wei Y, Wei Z, Wu D, Wu G, Wu S, Wu S, Wu W, Wu W, Wu Z, Xia Z, Xiao J, Xiao L, Xiao Y, Xie G, Xie GY, Xie J, Xie Y, Xiong J, Xiong Z, Xu D, Xu S, Xu T, Xu T, Xue Y, Xue Y, Yan C, Yang D, Yang F, Yang F, Yang H, Yang J, Yang K, Yang N, Yang QY, Yang S, Yang X, Yang X, Yang X, Yang YG, Ye W, Yu C, Yu F, Yu S, Yuan C, Yuan H, Zeng J, Zhai S, Zhang C, Zhang F, Zhang G, Zhang M, Zhang P, Zhang Q, Zhang R, Zhang S, Zhang W, Zhang W, Zhang W, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang YE, Zhang Y, Zhang Z, Zhang Z, Zhao D, Zhao F, Zhao G, Zhao M, Zhao W, Zhao W, Zhao X, Zhao Y, Zhao Y, Zhao Z, Zheng X, Zheng Y, Zhou C, Zhou H, Zhou X, Zhou X, Zhou Y, Zhou Y, Zhu J, Zhu L, Zhu R, Zhu T, Zong W, Zou D, Zuo Z. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024. Nucleic Acids Res 2024; 52:D18-D32. [PMID: 38018256 PMCID: PMC10767964 DOI: 10.1093/nar/gkad1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023] Open
Abstract
The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
Collapse
|
19
|
Kirschenbaum D, Xie K, Ingelfinger F, Katzenelenbogen Y, Abadie K, Look T, Sheban F, Phan TS, Li B, Zwicky P, Yofe I, David E, Mazuz K, Hou J, Chen Y, Shaim H, Shanley M, Becker S, Qian J, Colonna M, Ginhoux F, Rezvani K, Theis FJ, Yosef N, Weiss T, Weiner A, Amit I. Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma. Cell 2024; 187:149-165.e23. [PMID: 38134933 DOI: 10.1016/j.cell.2023.11.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 09/15/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023]
Abstract
Deciphering the cell-state transitions underlying immune adaptation across time is fundamental for advancing biology. Empirical in vivo genomic technologies that capture cellular dynamics are currently lacking. We present Zman-seq, a single-cell technology recording transcriptomic dynamics across time by introducing time stamps into circulating immune cells, tracking them in tissues for days. Applying Zman-seq resolved cell-state and molecular trajectories of the dysfunctional immune microenvironment in glioblastoma. Within 24 hours of tumor infiltration, cytotoxic natural killer cells transitioned to a dysfunctional program regulated by TGFB1 signaling. Infiltrating monocytes differentiated into immunosuppressive macrophages, characterized by the upregulation of suppressive myeloid checkpoints Trem2, Il18bp, and Arg1, over 36 to 48 hours. Treatment with an antagonistic anti-TREM2 antibody reshaped the tumor microenvironment by redirecting the monocyte trajectory toward pro-inflammatory macrophages. Zman-seq is a broadly applicable technology, enabling empirical measurements of differentiation trajectories, which can enhance the development of more efficacious immunotherapies.
Collapse
Affiliation(s)
- Daniel Kirschenbaum
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Ken Xie
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Florian Ingelfinger
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | | | - Kathleen Abadie
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Thomas Look
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Fadi Sheban
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Truong San Phan
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Baoguo Li
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Pascale Zwicky
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Ido Yofe
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Eyal David
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Kfir Mazuz
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Jinchao Hou
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Yun Chen
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Hila Shaim
- Department of Stem Cell Transplantation and Cellular Therapy, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mayra Shanley
- Department of Stem Cell Transplantation and Cellular Therapy, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Soeren Becker
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jiawen Qian
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Marco Colonna
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Florent Ginhoux
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research, Singapore 138648, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research, Singapore 138648, Singapore
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Nir Yosef
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Assaf Weiner
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Ido Amit
- Department of Systems Immunology, Weizmann Institute of Science, 7610001 Rehovot, Israel.
| |
Collapse
|
20
|
Chrysinas P, Venkatesan S, Ang I, Ghosh V, Chen C, Neelamegham S, Gunawan R. Cell and tissue-specific glycosylation pathways informed by single-cell transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.26.559616. [PMID: 38260527 PMCID: PMC10802235 DOI: 10.1101/2023.09.26.559616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
While single cell studies have made significant impacts in various subfields of biology, they lag in the Glycosciences. To address this gap, we analyzed single-cell glycogene expressions in the Tabula Sapiens dataset of human tissues and cell types using a recent glycosylation-specific gene ontology (GlycoEnzOnto). At the median sequencing (count) depth, ~40-50 out of 400 glycogenes were detected in individual cells. Upon increasing the sequencing depth, the number of detectable glycogenes saturates at ~200 glycogenes, suggesting that the average human cell expresses about half of the glycogene repertoire. Hierarchies in glycogene and glycopathway expressions emerged from our analysis: nucleotide-sugar synthesis and transport exhibited the highest gene expressions, followed by genes for core enzymes, glycan modification and extensions, and finally terminal modifications. Interestingly, the same cell types showed variable glycopathway expressions based on their organ or tissue origin, suggesting nuanced cell- and tissue-specific glycosylation patterns. Probing deeper into the transcription factors (TFs) of glycogenes, we identified distinct groupings of TFs controlling different aspects of glycosylation: core biosynthesis, terminal modifications, etc. We present webtools to explore the interconnections across glycogenes, glycopathways, and TFs regulating glycosylation in human cell/tissue types. Overall, the study presents an overview of glycosylation across multiple human organ systems.
Collapse
Affiliation(s)
- Panagiotis Chrysinas
- Department of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo, NY, 14260, USA
| | - Shriramprasad Venkatesan
- Department of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo, NY, 14260, USA
| | - Isaac Ang
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
| | - Vishnu Ghosh
- Department of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo, NY, 14260, USA
| | - Changyou Chen
- Department of Computer Science and Engineering, University at Buffalo-SUNY, Buffalo, NY, 14260, USA
| | - Sriram Neelamegham
- Department of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo, NY, 14260, USA
| | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo, NY, 14260, USA
| |
Collapse
|
21
|
Li Z, Yang W, Wu P, Shan Y, Zhang X, Chen F, Yang J, Yang JR. Reconstructing cell lineage trees with genomic barcoding: approaches and applications. J Genet Genomics 2024; 51:35-47. [PMID: 37269980 DOI: 10.1016/j.jgg.2023.05.011] [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: 02/03/2023] [Revised: 05/19/2023] [Accepted: 05/20/2023] [Indexed: 06/05/2023]
Abstract
In multicellular organisms, developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree (CLT). The reconstruction of the CLT has long been a major goal in developmental biology and other related fields. Recent technological advancements, especially those in editable genomic barcodes and single-cell high-throughput sequencing, have sparked a new wave of experimental methods for reconstructing CLTs. Here we review the existing experimental approaches to the reconstruction of CLT, which are broadly categorized as either image-based or DNA barcode-based methods. In addition, we present a summary of the related literature based on the biological insight provided by the obtained CLTs. Moreover, we discuss the challenges that will arise as more and better CLT data become available in the near future. Genomic barcoding-based CLT reconstructions and analyses, due to their wide applicability and high scalability, offer the potential for novel biological discoveries, especially those related to general and systemic properties of the developmental process.
Collapse
Affiliation(s)
- Zizhang Li
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wenjing Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Peng Wu
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yuyan Shan
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xiaoyu Zhang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Feng Chen
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junnan Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Jian-Rong Yang
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong 510080, China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
| |
Collapse
|
22
|
Croydon-Veleslavov IA, Stumpf MPH. Repeated Decision Stumping Distils Simple Rules from Single-Cell Data. J Comput Biol 2024; 31:21-40. [PMID: 38170180 DOI: 10.1089/cmb.2021.0613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
Single-cell data afford unprecedented insights into molecular processes. But the complexity and size of these data sets have proved challenging and given rise to a large armory of statistical and machine learning approaches. The majority of approaches focuses on either describing features of these data, or making predictions and classifying unlabeled samples. In this study, we introduce repeated decision stumping (ReDX) as a method to distill simple models from single-cell data. We develop decision trees of depth one-hence "stumps"-to identify in an inductive manner, gene products involved in driving cell fate transitions, and in applications to published data we are able to discover the key players involved in these processes in an unbiased manner without prior knowledge. Our algorithm is deliberately targeting the simplest possible candidate hypotheses that can be extracted from complex high-dimensional data. There are three reasons for this: (1) the predictions become straightforwardly testable hypotheses; (2) the identified candidates form the basis for further mechanistic model development, for example, for engineering and synthetic biology interventions; and (3) this approach complements existing descriptive modeling approaches and frameworks. The approach is computationally efficient, has remarkable predictive power, including in simulation studies where the ground truth is known, and yields robust and statistically stable predictors; the same set of candidates is generated by applying the algorithm to different subsamples of experimental data.
Collapse
Affiliation(s)
- Ivan A Croydon-Veleslavov
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
| | - Michael P H Stumpf
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, United Kingdom
- School of BioSciences, University of Melbourne, Parkville, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, Australia
| |
Collapse
|
23
|
Wang P, Wen X, Li H, Lang P, Li S, Lei Y, Shu H, Gao L, Zhao D, Zeng J. Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nat Commun 2023; 14:8459. [PMID: 38123534 PMCID: PMC10733330 DOI: 10.1038/s41467-023-44103-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.
Collapse
Affiliation(s)
- Peizhuo Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Xiao Wen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Peng Lang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Yipin Lei
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, Shaanxi Province, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China.
| |
Collapse
|
24
|
Mundy C, Yao L, Shaughnessy KA, Saunders C, Shore EM, Koyama E, Pacifici M. Palovarotene Action Against Heterotopic Ossification Includes a Reduction of Local Participating Activin A-Expressing Cell Populations. JBMR Plus 2023; 7:e10821. [PMID: 38130748 PMCID: PMC10731142 DOI: 10.1002/jbm4.10821] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 09/09/2023] [Indexed: 12/23/2023] Open
Abstract
Heterotopic ossification (HO) consists of extraskeletal bone formation. One form of HO is acquired and instigated by traumas or surgery, and another form is genetic and characterizes fibrodysplasia ossificans progressiva (FOP). Recently, we and others showed that activin A promotes both acquired and genetic HO, and in previous studies we found that the retinoid agonist palovarotene inhibits both HO forms in mice. Here, we asked whether palovarotene's action against HO may include an interference with endogenous activin A expression and/or function. Using a standard mouse model of acquired HO, we found that activin A and its encoding RNA (Inhba) were prominent in chondrogenic cells within developing HO masses in untreated mice. Single-cell RNAseq (scRNAseq) assays verified that Inhba expression characterized chondroprogenitors and chondrocytes in untreated HO, in addition to its expected expression in inflammatory cells and macrophages. Palovarotene administration (4 mg/kg/d/gavage) caused a sharp inhibition of both HO and amounts of activin A and Inhba transcripts. Bioinformatic analyses of scRNAseq data sets indicated that the drug had reduced interactions and cross-talk among local cell populations. To determine if palovarotene inhibited Inhba expression directly, we assayed primary chondrocyte cultures. Drug treatment inhibited their cartilaginous phenotype but not Inhba expression. Our data reveal that palovarotene markedly reduces the number of local Inhba-expressing HO-forming cell populations. The data broaden the spectrum of HO culprits against which palovarotene acts, accounting for its therapeutic effectiveness. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Collapse
Affiliation(s)
- Christina Mundy
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic SurgeryThe Children's Hospital of PhiladelphiaPhiladelphiaPAUSA
| | - Lutian Yao
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic SurgeryThe Children's Hospital of PhiladelphiaPhiladelphiaPAUSA
- Department of OrthopaedicsThe First Hospital of China Medical UniversityShenyangChina
| | - Kelly A. Shaughnessy
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic SurgeryThe Children's Hospital of PhiladelphiaPhiladelphiaPAUSA
| | - Cheri Saunders
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic SurgeryThe Children's Hospital of PhiladelphiaPhiladelphiaPAUSA
| | - Eileen M. Shore
- Departments of Orthopaedic Surgery and Genetics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
| | - Eiki Koyama
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic SurgeryThe Children's Hospital of PhiladelphiaPhiladelphiaPAUSA
| | - Maurizio Pacifici
- Translational Research Program in Pediatric Orthopaedics, Division of Orthopaedic SurgeryThe Children's Hospital of PhiladelphiaPhiladelphiaPAUSA
| |
Collapse
|
25
|
Biswas A, Kumari A, Gaikwad DS, Pandey DK. Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:550-569. [PMID: 38100404 DOI: 10.1089/omi.2023.0197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.
Collapse
Affiliation(s)
- Aakanksha Biswas
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - Aditi Kumari
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - D S Gaikwad
- Amity Institute of Organic Agriculture, Amity University, Noida, India
| | - Dhananjay K Pandey
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| |
Collapse
|
26
|
Ori C, Ansari M, Angelidis I, Olmer R, Martin U, Theis FJ, Schiller HB, Drukker M. Human pluripotent stem cell fate trajectories toward lung and hepatocyte progenitors. iScience 2023; 26:108205. [PMID: 38026193 PMCID: PMC10663741 DOI: 10.1016/j.isci.2023.108205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 07/13/2023] [Accepted: 10/11/2023] [Indexed: 12/01/2023] Open
Abstract
In this study, we interrogate molecular mechanisms underlying the specification of lung progenitors from human pluripotent stem cells (hPSCs). We employ single-cell RNA-sequencing with high temporal precision, alongside an optimized differentiation protocol, to elucidate the transcriptional hierarchy of lung specification to chart the associated single-cell trajectories. Our findings indicate that Sonic hedgehog, TGF-β, and Notch activation are essential within an ISL1/NKX2-1 trajectory, leading to the emergence of lung progenitors during the foregut endoderm phase. Additionally, the induction of HHEX delineates an alternate trajectory at the early definitive endoderm stage, preceding the lung pathway and giving rise to a significant hepatoblast population. Intriguingly, neither KDR+ nor mesendoderm progenitors manifest as intermediate stages in the lung and hepatic lineage development. Our multistep model offers insights into lung organogenesis and provides a foundation for in-depth study of early human lung development and modeling using hPSCs.
Collapse
Affiliation(s)
- Chaido Ori
- Institute of Stem Cell Research, Helmholtz Munich, Neuherberg, Munich, Germany
| | - Meshal Ansari
- Comprehensive Pneumology Center Munich (CPC-M), Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Munich, Germany
| | - Ilias Angelidis
- Comprehensive Pneumology Center Munich (CPC-M), Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Ruth Olmer
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Department of Cardiothoracic, Transplantation and Vascular Surgery (HTTG), Hannover Medical School, 30625 Hannover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease (BREATH), Member of the German Center for Lung Research (DZL), Hannover Medical School, 30625 Hannover, Germany
- REBIRTH-Research Center for Translational and Regenerative Medicine, Hannover Medical School, 30625 Hannover, Germany
| | - Ulrich Martin
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Department of Cardiothoracic, Transplantation and Vascular Surgery (HTTG), Hannover Medical School, 30625 Hannover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease (BREATH), Member of the German Center for Lung Research (DZL), Hannover Medical School, 30625 Hannover, Germany
- REBIRTH-Research Center for Translational and Regenerative Medicine, Hannover Medical School, 30625 Hannover, Germany
| | - Fabian J. Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Herbert B. Schiller
- Comprehensive Pneumology Center Munich (CPC-M), Institute of Lung Health and Immunity (LHI), Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- Institute of Experimental Pneumology, LMU University Hospital, Ludwig-Maximilians University, Munich, Germany
| | - Micha Drukker
- Institute of Stem Cell Research, Helmholtz Munich, Neuherberg, Munich, Germany
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, the Netherlands
| |
Collapse
|
27
|
Sajid RS, van Winden LJ, Diamandis P. Towards deciphering glioblastoma intra-tumoral heterogeneity: The importance of integrating multidimensional models. Proteomics 2023; 23:e2200401. [PMID: 37488996 DOI: 10.1002/pmic.202200401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/26/2023]
Abstract
Glioblastoma (GBM) is the most common and severe form of brain cancer among adults. Its aggressiveness is largely attributed to its complex and heterogeneous biology that despite maximal surgery and multimodal chemoradiation treatment, inevitably recurs. Traditional large-scale profiling approaches have contributed substantially to the understanding of patient-to-patient inter-tumoral differences in GBM. However, it is now clear that biological differences within an individual (intra-tumoral heterogeneity) are also a prominent factor in treatment resistance and recurrence of GBM and will likely require integration of data from multiple recently developed omics platforms to fully unravel. Here we dissect the growing geospatial model of GBM, which layers intra-tumoral heterogeneity on a GBM stem cell (GSC) precursor, single cell, and spatial level. We discuss potential unique and inter-dependant aspects of the model including potential discordances between observed genotypes and phenotypes in GBM.
Collapse
Affiliation(s)
- Rifat Shahriar Sajid
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Lennart J van Winden
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Canada
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| |
Collapse
|
28
|
Xu L, Zhang J, He Y, Yang Q, Mu T, Guo Q, Li Y, Tong T, Chen S, Ye RD. ScRNAPip: A systematic and dynamic pipeline for single-cell RNA sequencing analysis. IMETA 2023; 2:e132. [PMID: 38868218 PMCID: PMC10989915 DOI: 10.1002/imt2.132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/21/2023] [Accepted: 08/02/2023] [Indexed: 06/14/2024]
Abstract
With the advancement of sequencing technology, cell separation, and whole-genome amplification techniques, single cell technology for genome sequencing is emerging gradually. In comparison to traditional genome sequencing at the multi-cellular level, single-cell sequencing can not only measure the gene expression level more accurately but also can detect a small amount of gene expression or rare noncoding RNA. This technology has garnered increasing interest among researchers engaged in single-cell studies in recent years. Here, we developed a reproducible computational workflow for scRNA-seq data analysis which including tasks like quality control, normalization, data correction, pseudotime analysis, copy number analysis, etc. We illustrate the application of these steps using publicly available datasets and provide practical recommendations for their implementation. This study serves as a comprehensive tutorial for researchers keen on single-cell data analysis, aiding users in constructing and refining their own analysis pipelines.
Collapse
Affiliation(s)
- Limin Xu
- The Chinese University of Hong KongShenzhen Futian Biomedical Innovation R&D CenterShenzhenChina
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | | | - Yiqian He
- The Chinese University of Hong KongShenzhen Futian Biomedical Innovation R&D CenterShenzhenChina
| | - Qianqian Yang
- The Chinese University of Hong KongShenzhen Futian Biomedical Innovation R&D CenterShenzhenChina
| | | | - Qiushi Guo
- The Chinese University of Hong KongShenzhen Futian Biomedical Innovation R&D CenterShenzhenChina
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | | | | | | | - Richard D. Ye
- The Chinese University of Hong KongShenzhen Futian Biomedical Innovation R&D CenterShenzhenChina
- Kobilka Institute of Innovative Drug Discovery, School of MedicineThe Chinese University of Hong KongShenzhenChina
| |
Collapse
|
29
|
Gorin G, Vastola JJ, Pachter L. Studying stochastic systems biology of the cell with single-cell genomics data. Cell Syst 2023; 14:822-843.e22. [PMID: 37751736 PMCID: PMC10725240 DOI: 10.1016/j.cels.2023.08.004] [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: 05/29/2023] [Revised: 08/16/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023]
Abstract
Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.
Collapse
Affiliation(s)
- Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - John J Vastola
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA.
| |
Collapse
|
30
|
Groves SM, Quaranta V. Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1225736. [PMID: 37731743 PMCID: PMC10507267 DOI: 10.3389/fnetp.2023.1225736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/25/2023] [Indexed: 09/22/2023]
Abstract
Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.
Collapse
Affiliation(s)
- Sarah M. Groves
- Department of Pharmacology, Vanderbilt University, Nashville, TN, United States
| | - Vito Quaranta
- Department of Pharmacology, Vanderbilt University, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
| |
Collapse
|
31
|
Erfanian N, Heydari AA, Feriz AM, Iañez P, Derakhshani A, Ghasemigol M, Farahpour M, Razavi SM, Nasseri S, Safarpour H, Sahebkar A. Deep learning applications in single-cell genomics and transcriptomics data analysis. Biomed Pharmacother 2023; 165:115077. [PMID: 37393865 DOI: 10.1016/j.biopha.2023.115077] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/22/2023] [Accepted: 06/23/2023] [Indexed: 07/04/2023] Open
Abstract
Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.
Collapse
Affiliation(s)
- Nafiseh Erfanian
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - A Ali Heydari
- Department of Applied Mathematics, University of California, Merced, CA, USA; Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Adib Miraki Feriz
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Pablo Iañez
- Cellular Systems Genomics Group, Josep Carreras Research Institute, Barcelona, Spain
| | - Afshin Derakhshani
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
| | | | - Mohsen Farahpour
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Seyyed Mohammad Razavi
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Saeed Nasseri
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Hossein Safarpour
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran.
| | - Amirhossein Sahebkar
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
| |
Collapse
|
32
|
Daniels RR, Taylor RS, Robledo D, Macqueen DJ. Single cell genomics as a transformative approach for aquaculture research and innovation. REVIEWS IN AQUACULTURE 2023; 15:1618-1637. [PMID: 38505116 PMCID: PMC10946576 DOI: 10.1111/raq.12806] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/16/2023] [Accepted: 02/16/2023] [Indexed: 03/21/2024]
Abstract
Single cell genomics encompasses a suite of rapidly maturing technologies that measure the molecular profiles of individual cells within target samples. These approaches provide a large up-step in biological information compared to long-established 'bulk' methods that profile the average molecular profiles of all cells in a sample, and have led to transformative advances in understanding of cellular biology, particularly in humans and model organisms. The application of single cell genomics is fast expanding to non-model taxa, including aquaculture species, where numerous research applications are underway with many more envisaged. In this review, we highlight the potential transformative applications of single cell genomics in aquaculture research, considering barriers and potential solutions to the broad uptake of these technologies. Focusing on single cell transcriptomics, we outline considerations for experimental design, including the essential requirement to obtain high quality cells/nuclei for sequencing in ectothermic aquatic species. We further outline data analysis and bioinformatics considerations, tailored to studies with the under-characterized genomes of aquaculture species, where our knowledge of cellular heterogeneity and cell marker genes is immature. Overall, this review offers a useful source of knowledge for researchers aiming to apply single cell genomics to address biological challenges faced by the global aquaculture sector though an improved understanding of cell biology.
Collapse
Affiliation(s)
- Rose Ruiz Daniels
- The Roslin Institute and Royal (Dick) School of Veterinary StudiesThe University of EdinburghMidlothianUK
| | - Richard S. Taylor
- The Roslin Institute and Royal (Dick) School of Veterinary StudiesThe University of EdinburghMidlothianUK
| | - Diego Robledo
- The Roslin Institute and Royal (Dick) School of Veterinary StudiesThe University of EdinburghMidlothianUK
| | - Daniel J. Macqueen
- The Roslin Institute and Royal (Dick) School of Veterinary StudiesThe University of EdinburghMidlothianUK
| |
Collapse
|
33
|
Zhai Y, Zhang J, Huang Z, Shi R, Guo F, Zhang F, Chen M, Gao Y, Tao X, Jin Z, Guo S, Lin Y, Ye P, Wu J. Single-cell RNA sequencing integrated with bulk RNA sequencing analysis reveals diagnostic and prognostic signatures and immunoinfiltration in gastric cancer. Comput Biol Med 2023; 163:107239. [PMID: 37450965 DOI: 10.1016/j.compbiomed.2023.107239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 06/19/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Early diagnosis and prognostic predication of gastric cancer (GC) pose significant challenges in current clinical practice of GC treatments. Therefore, our aim was to explore relevant gene signatures that can predict the prognosis of GC patients. METHODS Here, we established a single-cell transcriptional atlas of GC, focusing on the expression of T-cell-related genes for cell-cell communication analysis, trajectory analysis, and transcription factor regulatory network analysis. Additionally, we conducted validation and prediction of immune-related prognostic gene signatures in GC patients using TCGA and GEO data. Based on these prognostic gene signatures, we predicted the immune infiltration status of GC patients by grouping the patient samples into high or low-risk groups. RESULTS Based on 10 tumor samples and corresponding normal samples from GC patients, we selected 18,416 cells for subsequent analysis using single-cell sequencing. From these, we identified 3,284 T-cells and obtained 641 differentially expressed genes related to T-cells from 5 different T-cell subtypes. By integrating bulk RNA sequencing data, we identified prognostic signatures associated with T-cells. Stratifying patients based on these prognostic signatures into high-risk or low-risk groups allowed us to effectively predict their survival rates and the immunoinfiltration status of the tumor microenvironment. CONCLUSION This study explored prognostic gene signatures associated with T-cells in GC patients, providing insights into predicting patients' survival rates and immunoinfiltration levels.
Collapse
Affiliation(s)
- Yiyan Zhai
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Jingyuan Zhang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Zhihong Huang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Rui Shi
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Fengying Guo
- School of Management, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Fanqin Zhang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Meilin Chen
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Yifei Gao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Xiaoyu Tao
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Zhengsen Jin
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Siyu Guo
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Yifan Lin
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Peizhi Ye
- National Cancer Center, National Clinical Research Center for Cancer, Chinese Medicine Department of the Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Jiarui Wu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
| |
Collapse
|
34
|
Logotheti S, Papadaki E, Zolota V, Logothetis C, Vrahatis AG, Soundararajan R, Tzelepi V. Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics. Cancers (Basel) 2023; 15:4357. [PMID: 37686633 PMCID: PMC10486655 DOI: 10.3390/cancers15174357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.
Collapse
Affiliation(s)
- Souzana Logotheti
- Department of Pathology, University of Patras, 26504 Patras, Greece; (S.L.); (E.P.); (V.Z.)
| | - Eugenia Papadaki
- Department of Pathology, University of Patras, 26504 Patras, Greece; (S.L.); (E.P.); (V.Z.)
- Department of Informatics, Ionian University, 49100 Corfu, Greece;
| | - Vasiliki Zolota
- Department of Pathology, University of Patras, 26504 Patras, Greece; (S.L.); (E.P.); (V.Z.)
| | - Christopher Logothetis
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | | | - Rama Soundararajan
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Vasiliki Tzelepi
- Department of Pathology, University of Patras, 26504 Patras, Greece; (S.L.); (E.P.); (V.Z.)
| |
Collapse
|
35
|
Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
Collapse
Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
| |
Collapse
|
36
|
Lubatti G, Stock M, Iturbide A, Ruiz Tejada Segura ML, Riepl M, Tyser RCV, Danese A, Colomé-Tatché M, Theis FJ, Srinivas S, Torres-Padilla ME, Scialdone A. CIARA: a cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data. Development 2023; 150:dev201264. [PMID: 37294170 DOI: 10.1242/dev.201264] [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: 09/05/2022] [Accepted: 04/25/2023] [Indexed: 05/18/2023]
Abstract
A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.
Collapse
Affiliation(s)
- Gabriele Lubatti
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
| | - Marco Stock
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, D-85354 Freising, Germany
| | - Ane Iturbide
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
| | - Mayra L Ruiz Tejada Segura
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
| | - Melina Riepl
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
| | - Richard C V Tyser
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Anna Danese
- Biomedical Center Munich (BMC), Physiological Genomics, Faculty of Medicine, Ludwig Maximilians University, D-82152 Munich, Germany
| | - Maria Colomé-Tatché
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
- Biomedical Center (BMC), Physiological Chemistry, Faculty of Medicine, Ludwig Maximilians University, D-82152 Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, D-85748 Munich, Germany
| | - Shankar Srinivas
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK
| | - Maria-Elena Torres-Padilla
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Faculty of Biology, Ludwig-Maximilians University, D-82152 Munich, Germany
| | - Antonio Scialdone
- Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany
- Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany
- Institute of Computational Biology, Helmholtz Munich, D-85764 Neuherberg, Germany
| |
Collapse
|
37
|
Gorin G, Vastola JJ, Pachter L. Studying stochastic systems biology of the cell with single-cell genomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.17.541250. [PMID: 37292934 PMCID: PMC10245677 DOI: 10.1101/2023.05.17.541250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.
Collapse
Affiliation(s)
- Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125
| | - John J. Vastola
- Department of Neurobiology, Harvard Medical School, Boston, MA, 02115
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125
| |
Collapse
|
38
|
Maitra C, Seal DB, Das V, De RK. Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease. Front Mol Biosci 2023; 10:1184748. [PMID: 37293552 PMCID: PMC10244650 DOI: 10.3389/fmolb.2023.1184748] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 05/04/2023] [Indexed: 06/10/2023] Open
Abstract
Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well.
Collapse
Affiliation(s)
- Chayan Maitra
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
| | | | | | - Rajat K. De
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
| |
Collapse
|
39
|
Arif S, Domingo-Vila C, Pollock E, Christakou E, Williams E, Tree TIM. Monitoring islet specific immune responses in type 1 diabetes clinical immunotherapy trials. Front Immunol 2023; 14:1183909. [PMID: 37283770 PMCID: PMC10240960 DOI: 10.3389/fimmu.2023.1183909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/02/2023] [Indexed: 06/08/2023] Open
Abstract
The number of immunotherapeutic clinical trials in type 1 diabetes currently being conducted is expanding, and thus there is a need for robust immune-monitoring assays which are capable of detecting and characterizing islet specific immune responses in peripheral blood. Islet- specific T cells can serve as biomarkers and as such can guide drug selection, dosing regimens and immunological efficacy. Furthermore, these biomarkers can be utilized in patient stratification which can then benchmark suitability for participation in future clinical trials. This review focusses on the commonly used immune-monitoring techniques including multimer and antigen induced marker assays and the potential to combine these with single cell transcriptional profiling which may provide a greater understanding of the mechanisms underlying immuno-intervention. Although challenges remain around some key areas such as the need for harmonizing assays, technological advances mean that multiparametric information derived from a single sample can be used in coordinated efforts to harmonize biomarker discovery and validation. Moreover, the technologies discussed here have the potential to provide a unique insight on the effect of therapies on key players in the pathogenesis of T1D that cannot be obtained using antigen agnostic approaches.
Collapse
|
40
|
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.
Collapse
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
| |
Collapse
|
41
|
Wei Y, Xu Z, Hu M, Wu Z, Liu A, Czajkowsky DM, Guo Y, Shao Z. Time-resolved transcriptomics of mouse gastric pit cells during postnatal development reveals features distinct from whole stomach development. FEBS Lett 2023; 597:418-426. [PMID: 36285639 DOI: 10.1002/1873-3468.14525] [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: 07/27/2022] [Revised: 09/27/2022] [Accepted: 10/10/2022] [Indexed: 11/05/2022]
Abstract
Whole-organ transcriptomic analyses have emerged as a common method for characterizing developmental transitions in mammalian organs. However, it is unclear if all cell types in an organ follow the whole-organ defined developmental trajectory. Recently, a postnatal two-stage developmental process was described for the mouse stomach. Here, using laser capture microdissection to obtain in situ transcriptomic data, we show that mouse gastric pit cells exhibit four postnatal developmental stages. Interestingly, early stages are characterized by the up-regulation of genes associated with metabolism, a functionality not typically associated with pit cells. Hence, beyond revealing that not all constituent cells develop according to the whole-organ determined pathway, these results broaden our understanding of the pit cell phenotypic landscape during stomach development.
Collapse
Affiliation(s)
- Ying Wei
- School of Biomedical Engineering, State Key Laboratory for Oncogenes and Bio-ID Center, Shanghai Jiao Tong University, China
| | - Zeqian Xu
- School of Biomedical Engineering, State Key Laboratory for Oncogenes and Bio-ID Center, Shanghai Jiao Tong University, China
| | - Miaomiao Hu
- School of Biomedical Engineering, State Key Laboratory for Oncogenes and Bio-ID Center, Shanghai Jiao Tong University, China
| | - Zhongqin Wu
- School of Biomedical Engineering, State Key Laboratory for Oncogenes and Bio-ID Center, Shanghai Jiao Tong University, China
| | - Axian Liu
- School of Biomedical Engineering, State Key Laboratory for Oncogenes and Bio-ID Center, Shanghai Jiao Tong University, China
| | - Daniel M Czajkowsky
- School of Biomedical Engineering, State Key Laboratory for Oncogenes and Bio-ID Center, Shanghai Jiao Tong University, China
| | - Yan Guo
- School of Biomedical Engineering, State Key Laboratory for Oncogenes and Bio-ID Center, Shanghai Jiao Tong University, China
| | - Zhifeng Shao
- School of Biomedical Engineering, State Key Laboratory for Oncogenes and Bio-ID Center, Shanghai Jiao Tong University, China
| |
Collapse
|
42
|
Cordes M, Pike-Overzet K, Van Den Akker EB, Staal FJT, Canté-Barrett K. Multi-omic analyses in immune cell development with lessons learned from T cell development. Front Cell Dev Biol 2023; 11:1163529. [PMID: 37091971 PMCID: PMC10118026 DOI: 10.3389/fcell.2023.1163529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/29/2023] [Indexed: 04/25/2023] Open
Abstract
Traditionally, flow cytometry has been the preferred method to characterize immune cells at the single-cell level. Flow cytometry is used in immunology mostly to measure the expression of identifying markers on the cell surface, but-with good antibodies-can also be used to assess the expression of intracellular proteins. The advent of single-cell RNA-sequencing has paved the road to study immune development at an unprecedented resolution. Single-cell RNA-sequencing studies have not only allowed us to efficiently chart the make-up of heterogeneous tissues, including their most rare cell populations, it also increasingly contributes to our understanding how different omics modalities interplay at a single cell resolution. Particularly for investigating the immune system, this means that these single-cell techniques can be integrated to combine and correlate RNA and protein data at the single-cell level. While RNA data usually reveals a large heterogeneity of a given population identified solely by a combination of surface protein markers, the integration of different omics modalities at a single cell resolution is expected to greatly contribute to our understanding of the immune system.
Collapse
Affiliation(s)
- Martijn Cordes
- Department of Immunology, Leiden University Medical Center, Leiden, Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Karin Pike-Overzet
- Department of Immunology, Leiden University Medical Center, Leiden, Netherlands
| | - Erik B. Van Den Akker
- Department of Immunology, Leiden University Medical Center, Leiden, Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
- Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, Netherlands
| | - Frank J. T. Staal
- Department of Immunology, Leiden University Medical Center, Leiden, Netherlands
- Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, Netherlands
- Department of Pediatrics, Leiden University Medical Center, Leiden, Netherlands
- *Correspondence: Frank J. T. Staal,
| | - Kirsten Canté-Barrett
- Department of Immunology, Leiden University Medical Center, Leiden, Netherlands
- Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, Netherlands
| |
Collapse
|
43
|
Dong G, Xu X, Li Y, Ouyang W, Zhao W, Gu Y, Li J, Liu T, Zeng X, Zou H, Wang S, Chen Y, Liu S, Sun H, Liu C. Stemness-related genes revealed by single-cell profiling of naïve and stimulated human CD34 + cells from CB and mPB. Clin Transl Med 2023; 13:e1175. [PMID: 36683248 PMCID: PMC9868212 DOI: 10.1002/ctm2.1175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Hematopoietic stem cells (HSCs) from different sources show varied repopulating capacity, and HSCs lose their stemness after long-time ex vivo culture. A deep understanding of these phenomena may provide helpful insights for HSCs. METHODS Here, we applied single-cell RNA-seq (scRNA-seq) to analyse the naïve and stimulated human CD34+ cells from cord blood (CB) and mobilised peripheral blood (mPB). RESULTS We collected over 16 000 high-quality single-cell data to construct a comprehensive inference map and characterised the HSCs under a quiescent state on the hierarchy top. Then, we compared HSCs in CB with those in mPB and HSCs of naïve samples to those of cultured samples, and identified stemness-related genes (SRGs) associated with cell source (CS-SRGs) and culture time (CT-SRGs), respectively. Interestingly, CS-SRGs and CT-SRGs share genes enriched in the signalling pathways such as mRNA catabolic process, translational initiation, ribonucleoprotein complex biogenesis and cotranslational protein targeting to membrane, suggesting dynamic protein translation and processing may be a common requirement for stemness maintenance. Meanwhile, CT-SRGs are enriched in pathways involved in glucocorticoid and corticosteroid response that affect HSCs homing and engraftment. In contrast, CS-SRGs specifically contain genes related to purine and ATP metabolic process, which is crucial for HSC homeostasis in the stress settings. Particularly, when CT-SRGs are used as reference genes for the construction of the development trajectory of CD34+ cells, lymphoid and myeloid lineages are clearly separated after HSCs/MPPs. Finally, we presented an application through a small-scale drug screening using Connectivity Map (CMap) against CT-SRGs. A small molecule, cucurbitacin I, was found to efficiently expand HSCs ex vivo while maintaining its stemness. CONCLUSIONS Our findings provide new perspectives for understanding HSCs, and the strategy to identify candidate molecules through SRGs may be applicable to study other stem cells.
Collapse
Affiliation(s)
- Guoyi Dong
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049China
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐ShenzhenShenzhen518083China
| | - Xiaojing Xu
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049China
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐ShenzhenShenzhen518083China
| | - Yue Li
- Department of Hematology and OncologyShenzhen Children's HospitalShenzhenChina
| | - Wenjie Ouyang
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐ShenzhenShenzhen518083China
| | - Weihua Zhao
- Shenzhen Second People's HospitalFirst Affiliated Hospital of Shenzhen UniversityShenzhenChina
| | - Ying Gu
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐ShenzhenShenzhen518083China
| | - Jie Li
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049China
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐ShenzhenShenzhen518083China
| | - Tianbin Liu
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049China
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐ShenzhenShenzhen518083China
| | - Xinru Zeng
- China National GeneBankBGI‐ShenzhenShenzhen518120China
| | - Huilin Zou
- China National GeneBankBGI‐ShenzhenShenzhen518120China
| | - Shuguang Wang
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Yue Chen
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐ShenzhenShenzhen518083China
| | - Sixi Liu
- Department of Hematology and OncologyShenzhen Children's HospitalShenzhenChina
| | - Hai‐Xi Sun
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049China
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐BeijingBeijing102601China
| | - Chao Liu
- China National GeneBankBGI‐ShenzhenShenzhen518120China
- BGI‐ShenzhenShenzhen518083China
| |
Collapse
|
44
|
Wu S, Schmitz U. Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination. Comput Struct Biotechnol J 2023; 21:2373-2380. [PMID: 37066125 PMCID: PMC10091034 DOI: 10.1016/j.csbj.2023.03.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level). In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.
Collapse
|
45
|
Downs KM. The mouse allantois: new insights at the embryonic-extraembryonic interface. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210251. [PMID: 36252214 PMCID: PMC9574631 DOI: 10.1098/rstb.2021.0251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 01/20/2022] [Indexed: 12/23/2022] Open
Abstract
During the early development of Placentalia, a distinctive projection emerges at the posterior embryonic-extraembryonic interface of the conceptus; its fingerlike shape presages maturation into the placental umbilical cord, whose major role is to shuttle fetal blood to and from the chorion for exchange with the mother during pregnancy. Until recently, the biology of the cord's vital vascular anlage, called the body stalk/allantois in humans and simply the allantois in rodents, has been largely unknown. Here, new insights into the development of the mouse allantois are featured, from its origin and mechanism of arterial patterning through its union with the chorion. Key to generating the allantois and its critical functions are the primitive streak and visceral endoderm, which together are sufficient to create the entire fetal-placental connection. Their newly discovered roles at the embryonic-extraembryonic interface challenge conventional wisdom, including the physical limits of the primitive streak, its function as sole purveyor of mesoderm in the mouse, potency of visceral endoderm, and the putative role of the allantois in the germ line. With this working model of allantois development, understanding a plethora of hitherto poorly understood orphan diseases in humans is now within reach. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.
Collapse
Affiliation(s)
- Karen M. Downs
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Avenue, Madison, WI 53705, USA
| |
Collapse
|
46
|
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: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [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.
Collapse
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.
| |
Collapse
|
47
|
Costa-Silva J, Domingues DS, Menotti D, Hungria M, Lopes FM. Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods. Comput Struct Biotechnol J 2022; 21:86-98. [PMID: 36514333 PMCID: PMC9730150 DOI: 10.1016/j.csbj.2022.11.051] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.
Collapse
Affiliation(s)
- Juliana Costa-Silva
- Department of Informatics – Federal University of Paraná, Rua Coronel Francisco Heráclito dos Santos, 100, 81531-990 Curitiba, Paraná, Brazil
| | - Douglas S. Domingues
- Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, 13418-900 Piracicaba, São Paulo, Brazil
| | - David Menotti
- Department of Informatics – Federal University of Paraná, Rua Coronel Francisco Heráclito dos Santos, 100, 81531-990 Curitiba, Paraná, Brazil
| | - Mariangela Hungria
- Department of Soil Biotecnology - Embrapa Soybean, Cx. Postal 231, 86000-970 Londrina, Paraná, Brazil
| | - Fabrício Martins Lopes
- Department of Computer Science, Universidade Tecnológica Federal do Paraná – UTFPR, Av. Alberto Carazzai, 1640, 86300-000, Cornélio Procópio, Paraná, Brazil
| |
Collapse
|
48
|
Hao Y, Zhang S, Shao C, Li J, Zhao G, Zhang DE, Fu XD. ZetaSuite: computational analysis of two-dimensional high-throughput data from multi-target screens and single-cell transcriptomics. Genome Biol 2022; 23:162. [PMID: 35879727 PMCID: PMC9310463 DOI: 10.1186/s13059-022-02729-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 07/07/2022] [Indexed: 11/10/2022] Open
Abstract
AbstractTwo-dimensional high-throughput data have become increasingly common in functional genomics studies, which raises new challenges in data analysis. Here, we introduce a new statistic called Zeta, initially developed to identify global splicing regulators from a two-dimensional RNAi screen, a high-throughput screen coupled with high-throughput functional readouts, and ZetaSuite, a software package to facilitate general application of the Zeta statistics. We compare our approach with existing methods using multiple benchmarked datasets and then demonstrate the broad utility of ZetaSuite in processing public data from large-scale cancer dependency screens and single-cell transcriptomics studies to elucidate novel biological insights.
Collapse
|
49
|
Zeng L, Yang K, Zhang T, Zhu X, Hao W, Chen H, Ge J. Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review. J Autoimmun 2022; 133:102919. [PMID: 36242821 DOI: 10.1016/j.jaut.2022.102919] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 12/07/2022]
Abstract
Autoimmunity refers to the phenomenon that the body's immune system produces antibodies or sensitized lymphocytes to its own tissues to cause an immune response. Immune disorders caused by autoimmunity can mediate autoimmune diseases. Autoimmune diseases have complicated pathogenesis due to the many types of cells involved, and the mechanism is still unclear. The emergence of single-cell research technology can solve the problem that ordinary transcriptome technology cannot be accurate to cell type. It provides unbiased results through independent analysis of cells in tissues and provides more mRNA information for identifying cell subpopulations, which provides a novel approach to study disruption of immune tolerance and disturbance of pro-inflammatory pathways on a cellular basis. It may fundamentally change the understanding of molecular pathways in the pathogenesis of autoimmune diseases and develop targeted drugs. Single-cell transcriptome sequencing (scRNA-seq) has been widely applied in autoimmune diseases, which provides a powerful tool for demonstrating the cellular heterogeneity of tissues involved in various immune inflammations, identifying pathogenic cell populations, and revealing the mechanism of disease occurrence and development. This review describes the principles of scRNA-seq, introduces common sequencing platforms and practical procedures, and focuses on the progress of scRNA-seq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system diseases, 2 Circulatory system diseases, 6 Liver, Gallbladder and Pancreas diseases, 2 Gastrointestinal system diseases, 3 Muscle, Bones and joint diseases, 3 Urinary system diseases, 2 Reproductive system diseases). This review also prospects the molecular mechanism targets of autoimmune diseases from the multi-molecular level and multi-dimensional analysis combined with single-cell multi-omics sequencing technology (such as scRNA-seq, Single cell ATAC-seq and single cell immune group library sequencing), which provides a reference for further exploring the pathogenesis and marker screening of autoimmune diseases and autoimmune inflammatory diseases in the future.
Collapse
Affiliation(s)
- Liuting Zeng
- Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China.
| | - Kailin Yang
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China.
| | - Tianqing Zhang
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China
| | - Xiaofei Zhu
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China.
| | - Wensa Hao
- Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hua Chen
- Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China.
| | - Jinwen Ge
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China; Hunan Academy of Chinese Medicine, Changsha, China.
| |
Collapse
|
50
|
Single-cell RNA-sequencing data analysis reveals a highly correlated triphasic transcriptional response to SARS-CoV-2 infection. Commun Biol 2022; 5:1302. [PMID: 36435849 PMCID: PMC9701238 DOI: 10.1038/s42003-022-04253-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/11/2022] [Indexed: 11/28/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is currently one of the most powerful techniques available to study the transcriptional response of thousands of cells to an external perturbation. Here, we perform a pseudotime analysis of SARS-CoV-2 infection using publicly available scRNA-seq data from human bronchial epithelial cells and colon and ileum organoids. Our results reveal that, for most genes, the transcriptional response to SARS-CoV-2 infection follows a non-linear pattern characterized by an initial and a final down-regulatory phase separated by an intermediate up-regulatory stage. A correlation analysis of transcriptional profiles suggests a common mechanism regulating the mRNA levels of most genes. Interestingly, genes encoded in the mitochondria or involved in translation exhibited distinct pseudotime profiles. To explain our results, we propose a simple model where nuclear export inhibition of nsp1-sensitive transcripts will be sufficient to explain the transcriptional shutdown of SARS-CoV-2 infected cells.
Collapse
|