1
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Liao C, Walters BW, DiStasio M, Lesch BJ. Human-specific epigenomic states in spermatogenesis. Comput Struct Biotechnol J 2024; 23:577-588. [PMID: 38274996 PMCID: PMC10809009 DOI: 10.1016/j.csbj.2023.12.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/23/2023] [Accepted: 12/23/2023] [Indexed: 01/27/2024] Open
Abstract
Infertility is becoming increasingly common, affecting one in six people globally. Half of these cases can be attributed to male factors, many driven by abnormalities in the process of sperm development. Emerging evidence from genome-wide association studies, genetic screening of patient cohorts, and animal models highlights an important genetic contribution to spermatogenic defects, but comprehensive identification and characterization of the genes critical for male fertility remain lacking. High divergence of gene regulation in spermatogenic cells across species poses challenges for delineating the genetic pathways required for human spermatogenesis using common model organisms. In this study, we leveraged post-translational histone modification and gene transcription data for 15,491 genes in four mammalian species (human, rhesus macaque, mouse, and opossum), to identify human-specific patterns of gene regulation during spermatogenesis. We combined H3K27me3 ChIP-seq, H3K4me3 ChIP-seq, and RNA-seq data to define epigenetic states for each gene at two stages of spermatogenesis, pachytene spermatocytes and round spermatids, in each species. We identified 239 genes that are uniquely active, poised, or dynamically regulated in human spermatogenic cells distinct from the other three species. While some of these genes have been implicated in reproductive functions, many more have not yet been associated with human infertility and may be candidates for further molecular and epidemiologic studies. Our analysis offers an example of the opportunities provided by evolutionary and epigenomic data for broadly screening candidate genes implicated in reproduction, which might lead to discoveries of novel genetic targets for diagnosis and management of male infertility and male contraception.
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Affiliation(s)
- Caiyun Liao
- Department of Obstetrics, Gynecology & Reproductive Sciences, Yale School of Medicine, 333 Cedar St., New Haven, CT 06510, USA
| | | | - Marcello DiStasio
- Department of Pathology, Yale School of Medicine, 333 Cedar St., New Haven, CT 06510, USA
- Department of Opthamology & Visual Sciences, Yale School of Medicine, 333 Cedar St., New Haven, CT 06510, USA
| | - Bluma J. Lesch
- Department of Obstetrics, Gynecology & Reproductive Sciences, Yale School of Medicine, 333 Cedar St., New Haven, CT 06510, USA
- Department of Genetics, Yale School of Medicine, 333 Cedar St., New Haven, CT 06510, USA
- Yale Cancer Center, Yale School of Medicine, 333 Cedar St., New Haven, CT 06510, USA
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2
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Ma Y, Zhou X. Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics. Nat Methods 2024:10.1038/s41592-024-02284-9. [PMID: 38844627 DOI: 10.1038/s41592-024-02284-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 04/18/2024] [Indexed: 06/23/2024]
Abstract
Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities in mapping complex tissue structures and functions. Here we present integrative and reference-informed tissue segmentation (IRIS), a computational method designed to characterize tissue spatial organization in SRT studies through accurately and efficiently detecting spatial domains. IRIS uniquely leverages single-cell RNA sequencing data for reference-informed detection of biologically interpretable spatial domains, integrating multiple SRT slices while explicitly considering correlations both within and across slices. We demonstrate the advantages of IRIS through in-depth analysis of six SRT datasets encompassing diverse technologies, tissues, species and resolutions. In these applications, IRIS achieves substantial accuracy gains (39-1,083%) and speed improvements (4.6-666.0) in moderate-sized datasets, while representing the only method applicable for large datasets including Stereo-seq and 10x Xenium. As a result, IRIS reveals intricate brain structures, uncovers tumor microenvironment heterogeneity and detects structural changes in diabetes-affected testis, all with exceptional speed and accuracy.
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Affiliation(s)
- Ying Ma
- Department of Biostatistics, Brown University, Providence, RI, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
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3
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Mecca R, Tang S, Jones C, Coward K. The limitations of testicular organoids: are they truly as promising as we believe? Reprod Fertil Dev 2024; 36:RD23216. [PMID: 38935835 DOI: 10.1071/rd23216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
Organoid systems have revolutionised various facets of biological research by offering a three-dimensional (3D), physiologically relevant in vitro model to study complex organ systems. Over recent years, testicular organoids have been publicised as promising platforms for reproductive studies, disease modelling, drug screening, and fertility preservation. However, the full potential of these systems has yet to be realised due to inherent limitations. This paper offers a comprehensive analysis of the current challenges associated with testicular organoid models. Firstly, we address the inability of current organoid systems to fully replicate the intricate spatial organisation and cellular diversity of the in vivo testis. Secondly, we scrutinise the fidelity of germ cell maturation within the organoids, highlighting incomplete spermatogenesis and epigenetic inconsistencies. Thirdly, we consider the technical challenges faced during organoid culture, including nutrient diffusion limits, lack of vasculature, and the need for specialised growth factors. Finally, we discuss the ethical considerations surrounding the use of organoids for human reproduction research. Addressing these limitations in combination with integrating complementary approaches, will be essential if we are to advance our understanding of testicular biology and develop novel strategies for addressing reproductive health issues in males.
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Affiliation(s)
- R Mecca
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - S Tang
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - C Jones
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - K Coward
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Level 3, Women's Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK
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4
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Chen WB, Zhang MF, Yang F, Hua JL. Applications of single-cell RNA sequencing in spermatogenesis and molecular evolution. Zool Res 2024; 45:575-585. [PMID: 38766742 PMCID: PMC11188606 DOI: 10.24272/j.issn.2095-8137.2024.010] [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: 01/13/2023] [Accepted: 03/08/2024] [Indexed: 05/22/2024] Open
Abstract
Spermatogenic cell heterogeneity is determined by the complex process of spermatogenesis differentiation. However, effectively revealing the regulatory mechanisms underlying mammalian spermatogenic cell development and differentiation via traditional methods is difficult. Advances in technology have led to the emergence of many single-cell transcriptome sequencing protocols, which have partially addressed these challenges. In this review, we detail the principles of 10x Genomics technology and summarize the methods for downstream analysis of single-cell transcriptome sequencing data. Furthermore, we explore the role of single-cell transcriptome sequencing in revealing the heterogeneity of testicular ecological niche cells, delineating the establishment and disruption of testicular immune homeostasis during human spermatogenesis, investigating abnormal spermatogenesis in humans, and, ultimately, elucidating the molecular evolution of mammalian spermatogenesis.
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Affiliation(s)
- Wen-Bo Chen
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A & F University, Yangling, Shaanxi 712100, China
| | - Meng-Fei Zhang
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A & F University, Yangling, Shaanxi 712100, China
| | - Fan Yang
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A & F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Livestock Biology, Northwest A & F University, Yangling, Shaanxi 712100, China
| | - Jin-Lian Hua
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A & F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Livestock Biology, Northwest A & F University, Yangling, Shaanxi 712100, China. E-mail:
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5
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Ospina OE, Soupir AC, Manjarres-Betancur R, Gonzalez-Calderon G, Yu X, Fridley BL. Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models. Sci Rep 2024; 14:10967. [PMID: 38744956 PMCID: PMC11094014 DOI: 10.1038/s41598-024-61758-0] [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: 02/01/2024] [Accepted: 05/09/2024] [Indexed: 05/16/2024] Open
Abstract
Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).
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Affiliation(s)
- Oscar E Ospina
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alex C Soupir
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Xiaoqing Yu
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
- Biostatistics and Epidemiology Core, Division of Health Services & Outcomes Research, Children's Mercy, Kansas City, MO, USA.
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6
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Ma Y, Chen Y, Li Y, Chen S, Zhu C, Liu Q, Li L, Cao H, Wu Z, Dong W. Seasonal modulation of the testis transcriptome reveals insights into hibernation and reproductive adaptation in Onychostoma macrolepis. FISH PHYSIOLOGY AND BIOCHEMISTRY 2024:10.1007/s10695-024-01335-4. [PMID: 38649597 DOI: 10.1007/s10695-024-01335-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 03/22/2024] [Indexed: 04/25/2024]
Abstract
The Onychostoma macrolepis have a unique survival strategy, overwintering in caves and returning to the river for reproduction in summer. The current knowledge on the developmental status of its testes during winter and summer is still undiscovered. We performed RNA-seq analysis on O. macrolepis testes between January and June, using the published genome (NCBI, ASM1243209v1). Through KEGG and GO enrichment analysis, we were able to identify 2111 differentially expressed genes (DEGs) and demonstrate their functions in signaling networks associated with the development of organism. At the genomic level, we found that during the overwintering phase, genes associated with cell proliferation (ccnb1, spag5, hdac7) were downregulated while genes linked to testicular fat metabolism (slc27a2, scd, pltp) were upregulated. This indicates suppression of both mitosis and meiosis, thereby inhibiting energy expenditure through genetic regulation of testicular degeneration. Furthermore, in January, we observed the regulation of autophagy and apoptosis (becn1, casp13), which may have the function of protecting reproductive organs and ensuring their maturity for the breeding season. The results provide a basis for the development of specialized feed formulations to regulate the expression of specific genes, or editing of genes during the fish egg stage, to ensure that the testes of O. macrolepis can mature more efficiently after overwintering, thereby enhancing reproductive performance.
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Affiliation(s)
- Yuxuan Ma
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Yining Chen
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Yan Li
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Shaoxian Chen
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Chao Zhu
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Qimin Liu
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Long Li
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Heran Cao
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Zifang Wu
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China
| | - Wuzi Dong
- College of Animal Science and Technology, Northwest A&F University, No. 22 Xinong Road, Yangling, Shaanxi, 712100, People's Republic of China.
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7
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Yu S, Li WV. spVC for the detection and interpretation of spatial gene expression variation. Genome Biol 2024; 25:103. [PMID: 38641849 PMCID: PMC11027374 DOI: 10.1186/s13059-024-03245-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 04/10/2024] [Indexed: 04/21/2024] Open
Abstract
Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC's accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.
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Affiliation(s)
- Shan Yu
- Department of Statistics, Unversity of Virginia, Charlottesville, 22903, VA, USA.
| | - Wei Vivian Li
- Department of Statistics, University of California, Riverside, 92521, CA, USA.
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8
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Mahyari E, Vigh-Conrad KA, Daube C, Lima AC, Guo J, Carrell DT, Hotaling JM, Aston KI, Conrad DF. The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis. Andrology 2024. [PMID: 38577799 DOI: 10.1111/andr.13637] [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: 07/15/2023] [Revised: 02/03/2024] [Accepted: 03/08/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines. An integrated atlas of scRNA-Seq expression constructed from multiple donors, developmental ages, and fertility states would be widely useful for the testis research community. OBJECTIVE To describe the generation and use of the human infertility single-cell testis atlas (HISTA), an interactive web tool for understanding human spermatogenesis through scRNA-Seq analysis. METHODS We obtained scRNA-Seq datasets derived from 12 donors, including healthy adult controls, juveniles, and several infertility cases, and reprocessed these data using methods to remove batch effects. Using Shiny, an open-source environment for data visualization, we created numerous interactive tools for exploring the data, some of which support simple statistical hypothesis testing. We used the resulting HISTA browser and its underlying data to demonstrate HISTA's value for testis researchers. RESULTS A primary application of HISTA is to search by a single gene or a set of genes; thus, we present various analyses that quantify and visualize gene expression across the testis cells and pathology. HISTA also contains machine-learning-derived gene modules ("components") that capture the entire transcriptional landscape of the testis tissue. We show how the use of these components can simplify the highly complex data in HISTA and assist with the interpretation of genes with unknown functions. Finally, we demonstrate the diverse ways HISTA can be used for new data analysis, including hypothesis testing. DISCUSSION AND CONCLUSIONS HISTA is a research environment that can help scientists organize and understand the high-dimensional transcriptional landscape of the human testis. HISTA has already contributed to published testis research and can be updated as needed with input from the research community or downloaded and modified for individual needs.
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Affiliation(s)
- Eisa Mahyari
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Katinka A Vigh-Conrad
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Clément Daube
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Ana C Lima
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Jingtao Guo
- Andrology and IVF Laboratory, Division of Urology, Department of Surgery, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Douglas T Carrell
- Andrology and IVF Laboratory, Division of Urology, Department of Surgery, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - James M Hotaling
- Andrology and IVF Laboratory, Division of Urology, Department of Surgery, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Kenneth I Aston
- Andrology and IVF Laboratory, Division of Urology, Department of Surgery, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Donald F Conrad
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Portland, Oregon, USA
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9
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Zhu J, Dai H, Chen L. Revealing cell-cell communication pathways with their spatially coupled gene programs. Brief Bioinform 2024; 25:bbae202. [PMID: 38706319 PMCID: PMC11070651 DOI: 10.1093/bib/bbae202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/14/2024] [Accepted: 04/05/2024] [Indexed: 05/07/2024] Open
Abstract
Inference of cell-cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand-receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.
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Affiliation(s)
- Junchao Zhu
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Cell building, No. 320 Yueyang Road, Xuhui District, Shanghai 200031, China
| | - Hao Dai
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Cell building, No. 320 Yueyang Road, Xuhui District, Shanghai 200031, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Cell building, No. 320 Yueyang Road, Xuhui District, Shanghai 200031, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, No. 1, Xiangshan Zhinong, Xihu District, Hangzhou 310024, China
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10
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Huang R, Chen J, Guo B, Jiang C, Sun W. Diabetes-induced male infertility: potential mechanisms and treatment options. Mol Med 2024; 30:11. [PMID: 38225568 PMCID: PMC10790413 DOI: 10.1186/s10020-023-00771-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 12/14/2023] [Indexed: 01/17/2024] Open
Abstract
Male infertility is a physiological phenomenon in which a man is unable to impregnate a fertile woman during a 12-month period of continuous, unprotected sexual intercourse. A growing body of clinical and epidemiological evidence indicates that the increasing incidence of male reproductive problems, especially infertility, shows a very similar trend to the incidence of diabetes within the same age range. In addition, a large number of previous in vivo and in vitro experiments have also suggested that the complex pathophysiological changes caused by diabetes may induce male infertility in multiple aspects, including hypothalamic-pituitary-gonadal axis dysfunction, spermatogenesis and maturation disorders, testicular interstitial cell damage erectile dysfunction. Based on the above related mechanisms, a large number of studies have focused on the potential therapeutic association between diabetes progression and infertility in patients with diabetes and infertility, providing important clues for the treatment of this population. In this paper, we summarized the research results of the effects of diabetes on male reproductive function in recent 5 years, elaborated the potential pathophysiological mechanisms of male infertility induced by diabetes, and reviewed and prospected the therapeutic measures.
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Affiliation(s)
- Runchun Huang
- The First Clinical Medical College, Lanzhou University, Lanzhou, People's Republic of China, 730000
| | - Jiawang Chen
- The First Clinical Medical College, Lanzhou University, Lanzhou, People's Republic of China, 730000
| | - Buyu Guo
- The First Clinical Medical College, Lanzhou University, Lanzhou, People's Republic of China, 730000
| | - Chenjun Jiang
- The First Clinical Medical College, Lanzhou University, Lanzhou, People's Republic of China, 730000
| | - Weiming Sun
- The First Clinical Medical College, Lanzhou University, Lanzhou, People's Republic of China, 730000.
- Department of Endocrinology, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu, People's Republic of China.
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11
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Yuan Z. MENDER: fast and scalable tissue structure identification in spatial omics data. Nat Commun 2024; 15:207. [PMID: 38182575 PMCID: PMC10770058 DOI: 10.1038/s41467-023-44367-9] [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: 06/26/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024] Open
Abstract
Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER's identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER's scalability makes it freely appliable on a million-level brain spatial atlas. MENDER's discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, 200433, China.
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12
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Matsushita Y, Noguchi A, Ono W, Ono N. Multi-omics analysis in developmental bone biology. JAPANESE DENTAL SCIENCE REVIEW 2023; 59:412-420. [PMID: 38022387 PMCID: PMC10665596 DOI: 10.1016/j.jdsr.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/23/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Single-cell omics and multi-omics have revolutionized our understanding of molecular and cellular biological processes at a single-cell level. In bone biology, the combination of single-cell RNA-sequencing analyses and in vivo lineage-tracing approaches has successfully identified multi-cellular diversity and dynamics of skeletal cells. This established a new concept that bone growth and regeneration are regulated by concerted actions of multiple types of skeletal stem cells, which reside in spatiotemporally distinct niches. One important subtype is endosteal stem cells that are particularly abundant in young bone marrow. The discovery of this new skeletal stem cell type has been facilitated by single-cell multi-omics, which simultaneously measures gene expression and chromatin accessibility. Using single-cell omics, it is now possible to computationally predict the immediate future state of individual cells and their differentiation potential. In vivo validation using histological approaches is the key to interpret the computational prediction. The emerging spatial omics, such as spatial transcriptomics and epigenomics, have major advantage in retaining the location of individual cells within highly complex tissue architecture. Spatial omics can be integrated with other omics to further obtain in-depth insights. Single-cell multi-omics are now becoming an essential tool to unravel intricate multicellular dynamics and intercellular interactions of skeletal cells.
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Affiliation(s)
- Yuki Matsushita
- Department of Cell Biology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8588, Japan
| | - Azumi Noguchi
- Department of Cell Biology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8588, Japan
| | - Wanida Ono
- University of Texas Health Science Center at Houston School of Dentistry, Houston, TX 77054, USA
| | - Noriaki Ono
- University of Texas Health Science Center at Houston School of Dentistry, Houston, TX 77054, USA
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13
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Zhou Z, Zhong Y, Zhang Z, Ren X. Spatial transcriptomics deconvolution at single-cell resolution using Redeconve. Nat Commun 2023; 14:7930. [PMID: 38040768 PMCID: PMC10692090 DOI: 10.1038/s41467-023-43600-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 11/14/2023] [Indexed: 12/03/2023] Open
Abstract
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.
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Affiliation(s)
- Zixiang Zhou
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, 100871, Beijing, China
| | - Yunshan Zhong
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
| | - Zemin Zhang
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, 100871, Beijing, China
| | - Xianwen Ren
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China.
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14
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Ji YH, Wang LM, Zhang FX, Hou HZ, Luo ZR, Xue Q, Shi MM, Jiao Y, Cui D, He DL, Xue W, Wen YQ, Tang QS, Zhang B. Cascading effects of hypobaric hypoxia on the testis: insights from a single-cell RNA sequencing analysis. Front Cell Dev Biol 2023; 11:1282119. [PMID: 38033870 PMCID: PMC10684926 DOI: 10.3389/fcell.2023.1282119] [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: 08/23/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Most mammals tolerate exposure to hypobaric hypoxia poorly as it may affect multiple regulatory mechanisms and inhibit cell proliferation, promote apoptosis, limit tissue vascularization, and disrupt the acid-base equilibrium. Here, we quantified the functional state of germ cell development and demonstrated the interaction between the germ and somatic cells via single-cell RNA sequencing (scRNA-seq). The present study elucidated the regulatory effects of hypobaric hypoxia exposure on germ cell formation and sperm differentiation by applying enrichment analysis to genomic regions. Hypobaric hypoxia downregulates the genes controlling granule secretion and organic matter biosynthesis, upregulates tektin 1 (TEKT1) and kinesin family member 2C (KIF2C), and downregulates 60S ribosomal protein 11 (RPL11) and cilia- and flagella-associated protein 206 (CFAP206). Our research indicated that prosaposin-G protein-coupled receptor 37 (PSAP-GPR37) ligands mediate the damage to supporting cells caused by hypobaric hypoxic exposure. The present work revealed that hypoxia injures peritubular myoid (PTM) cells and spermatocytes in the S phase. It also showed that elongating spermatids promote maturation toward the G2 phase and increase their functional reserve for sperm-egg binding. The results of this study provide a theoretical basis for future investigations on prophylactic and therapeutic approaches toward protecting the reproductive system against the harmful effects of hypobaric hypoxic exposure.
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Affiliation(s)
- Yun-Hua Ji
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Lin-Meng Wang
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Fu-Xun Zhang
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Hao-Zhong Hou
- Department of Urology, Xijing Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Zhi-Rong Luo
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Qi Xue
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Man-Man Shi
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Yong Jiao
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Dong Cui
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Da-Li He
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Wei Xue
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Yu-qi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Qi-Sheng Tang
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
| | - Bo Zhang
- Department of Urology, Tangdu Hospital, Air Force Military Medical University, Xi’an, Shanxi, China
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15
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Zhang X, Cao Q, Rajachandran S, Grow EJ, Evans M, Chen H. Dissecting mammalian reproduction with spatial transcriptomics. Hum Reprod Update 2023; 29:794-810. [PMID: 37353907 PMCID: PMC10628492 DOI: 10.1093/humupd/dmad017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/15/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction. OBJECTIVE AND RATIONALE We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come. SEARCH METHODS The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed. OUTCOMES First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations. WIDER IMPLICATIONS Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.
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Affiliation(s)
- Xin Zhang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiqi Cao
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shreya Rajachandran
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward J Grow
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Melanie Evans
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Haiqi Chen
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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16
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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17
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Fu ZD, Wang Y, Yan HL. Male infertility risk and gut microbiota: a Mendelian randomization study. Front Microbiol 2023; 14:1228693. [PMID: 37822739 PMCID: PMC10562550 DOI: 10.3389/fmicb.2023.1228693] [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: 05/31/2023] [Accepted: 08/31/2023] [Indexed: 10/13/2023] Open
Abstract
Background In recent decades, the decline of male sperm quality has become a worldwide phenomenon, with sperm quality of critical importance for the ability to conceive naturally. Recent studies suggest that male fertility function is closely linked to the gut microbiota, however, the cause-and-effect association between the gut microbiota and male infertility risk is currently unclear. Methods We performed one two-sample Mendelian randomization (MR) study, which uses summary data on human gut microbiota from the MiBioGen consortium as factors of exposure. FinnGen Consortium R8 data was used to obtain GWAS data for male infertility. To evaluate cause-and-effect associations linking gut microbiota and male infertility risk with multiple Mendelian randomization methods, we included inverse variance weighted (IVW), MR-Egger, and Maximum Likelihood (ML) Ratio. The heterogeneity of instrumental variables was evaluated through Cochran's Q, Rucker's Q, and leave-one-out analysis methods. Results We found a positive association between Allisonella, Anaerotruncus, Barnesiella, Intestinibacter, and Lactococcus with male infertility risk according to the MR analysis results. Bacteroides Romboutsia, Ruminococcaceae (NK4A2140group), and Ruminococcaceae (UCG011) play a protective function in male infertility pathogenesis. Conclusion It was found that gut microbiota and infertility are causally related in this study. In subsequent studies, there is a need to build a larger and more comprehensive GWAS database on male infertility, which will reveal the underlying mechanisms for gut microbiota and male infertility. There is a need for randomized controlled trials for validating the protective effect of the associated gut microbiota against male infertility risk, and for exploring the associated mechanisms.
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Affiliation(s)
| | | | - Hong-li Yan
- Center for Reproductive Medicine, Changhai Hospital, Naval Medical University, Shanghai, China
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18
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Jia H, Wang W, Zhou Z, Chen Z, Lan Z, Bo H, Fan L. Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives. Mol Cell Biochem 2023:10.1007/s11010-023-04840-x. [PMID: 37659974 DOI: 10.1007/s11010-023-04840-x] [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: 06/09/2023] [Accepted: 08/14/2023] [Indexed: 09/04/2023]
Abstract
Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.
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Affiliation(s)
- Hanbo Jia
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Wei Wang
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Zhaowen Zhou
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Zhiyi Chen
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Zijun Lan
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Hao Bo
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China.
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, Hunan, China.
| | - Liqing Fan
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China.
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, Hunan, China.
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19
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Chen TY, You L, Hardillo JAU, Chien MP. Spatial Transcriptomic Technologies. Cells 2023; 12:2042. [PMID: 37626852 PMCID: PMC10453065 DOI: 10.3390/cells12162042] [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: 06/24/2023] [Revised: 08/02/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applications as well as the perspective of the techniques and new emerging technologies that are developed to address current limitations of spatial methodologies. In addition, we describe how spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization.
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Affiliation(s)
- Tsai-Ying Chen
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (T.-Y.C.); (L.Y.)
- Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands;
- Oncode Institute, 3521 AL Utrecht, The Netherlands
| | - Li You
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (T.-Y.C.); (L.Y.)
- Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands;
- Oncode Institute, 3521 AL Utrecht, The Netherlands
| | - Jose Angelito U. Hardillo
- Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands;
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Miao-Ping Chien
- Department of Molecular Genetics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands; (T.-Y.C.); (L.Y.)
- Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands;
- Oncode Institute, 3521 AL Utrecht, The Netherlands
- Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
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20
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He M, Liu K, Cao J, Chen Q. An update on the role and potential mechanisms of clock genes regulating spermatogenesis: A systematic review of human and animal experimental studies. Rev Endocr Metab Disord 2023; 24:585-610. [PMID: 36792803 DOI: 10.1007/s11154-022-09783-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/25/2022] [Indexed: 02/17/2023]
Abstract
Circadian clocks can be traced in nearly all life kingdoms, with the male reproductive system no exception. However, our understanding of the circadian clock in spermatogenesis seems to fall behind other scenarios. The present review aims to summarize the current knowledge about the role and especially the potential mechanisms of clock genes in spermatogenesis regulation. Accumulating studies have revealed rhythmic oscillation in semen parameters and some physiological events of spermatogenesis. Disturbing the clock gene expression by genetic mutations or environmental changes will also notably damage spermatogenesis. On the other hand, the mechanisms of spermatogenetic regulation by clock genes remain largely unclear. Some recent studies, although not revealing the entire mechanisms, indeed attempted to shed light on this issue. Emerging clues hinted that gonadal hormones, retinoic acid signaling, homologous recombination, and the chromatoid body might be involved in the regulation of spermatogenesis by clock genes. Then we highlight the challenges and the promising directions for future studies so as to stimulate attention to this critical field which has not gained adequate concern.
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Affiliation(s)
- Mengchao He
- Key Lab of Medical Protection for Electromagnetic Radiation, Ministry of Education of China, Institute of Toxicology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, 400038, China
| | - Kun Liu
- Center for Disease Control and Prevention of Southern Theatre Command, Guangzhou, 510630, China
| | - Jia Cao
- Key Lab of Medical Protection for Electromagnetic Radiation, Ministry of Education of China, Institute of Toxicology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
| | - Qing Chen
- Key Lab of Medical Protection for Electromagnetic Radiation, Ministry of Education of China, Institute of Toxicology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
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21
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Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, Wang L, Yang X, Yang M, Liu G. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023; 4:e315. [PMID: 37533767 PMCID: PMC10390758 DOI: 10.1002/mco2.315] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 08/04/2023] Open
Abstract
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.
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Affiliation(s)
- Chongyang Chen
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
| | - Jing Wang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Donghui Pan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xinyu Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Yuping Xu
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Junjie Yan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Lizhen Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xifei Yang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Min Yang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Gong‐Ping Liu
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
- Department of PathophysiologySchool of Basic MedicineKey Laboratory of Ministry of Education of China and Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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22
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Rajachandran S, Zhang X, Cao Q, Caldeira-Brant AL, Zhang X, Song Y, Evans M, Bukulmez O, Grow EJ, Nagano M, Orwig KE, Chen H. Dissecting the spermatogonial stem cell niche using spatial transcriptomics. Cell Rep 2023; 42:112737. [PMID: 37393620 PMCID: PMC10530051 DOI: 10.1016/j.celrep.2023.112737] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/07/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023] Open
Abstract
Spermatogonial stem cells (SSCs) in the testis support the lifelong production of sperm. SSCs reside within specialized microenvironments called "niches," which are essential for SSC self-renewal and differentiation. However, our understanding of the molecular and cellular interactions between SSCs and niches remains incomplete. Here, we combine spatial transcriptomics, computational analyses, and functional assays to systematically dissect the molecular, cellular, and spatial composition of SSC niches. This allows us to spatially map the ligand-receptor (LR) interaction landscape in both mouse and human testes. Our data demonstrate that pleiotrophin regulates mouse SSC functions through syndecan receptors. We also identify ephrin-A1 as a potential niche factor that influences human SSC functions. Furthermore, we show that the spatial re-distribution of inflammation-related LR interactions underlies diabetes-induced testicular injury. Together, our study demonstrates a systems approach to dissect the complex organization of the stem cell microenvironment in health and disease.
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Affiliation(s)
- Shreya Rajachandran
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xin Zhang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiqi Cao
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Andre L Caldeira-Brant
- Department of Obstetrics, Gynecology and Reproductive Sciences, Magee-Womens Research Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Xiangfan Zhang
- Department of Obstetrics and Gynecology, McGill University, Montreal, QC, Canada; Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Youngmin Song
- Department of Obstetrics and Gynecology, McGill University, Montreal, QC, Canada; Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Melanie Evans
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Orhan Bukulmez
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward J Grow
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Makoto Nagano
- Department of Obstetrics and Gynecology, McGill University, Montreal, QC, Canada; Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Kyle E Orwig
- Department of Obstetrics, Gynecology and Reproductive Sciences, Magee-Womens Research Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Haiqi Chen
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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23
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Zhang X, Liu Y, Sosa F, Gunewardena S, Crawford PA, Zielen AC, Orwig KE, Wang N. Transcriptional metabolic reprogramming implements meiotic fate decision in mouse testicular germ cells. Cell Rep 2023; 42:112749. [PMID: 37405912 PMCID: PMC10529640 DOI: 10.1016/j.celrep.2023.112749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 05/24/2023] [Accepted: 06/19/2023] [Indexed: 07/07/2023] Open
Abstract
Nutrient starvation drives yeast meiosis, whereas retinoic acid (RA) is required for mammalian meiosis through its germline target Stra8. Here, by using single-cell transcriptomic analysis of wild-type and Stra8-deficient juvenile mouse germ cells, our data show that the expression of nutrient transporter genes, including Slc7a5, Slc38a2, and Slc2a1, is downregulated in germ cells during meiotic initiation, and this process requires Stra8, which binds to these genes and induces their H3K27 deacetylation. Consequently, Stra8-deficient germ cells sustain glutamine and glucose uptake in response to RA and exhibit hyperactive mTORC1/protein kinase A (PKA) activities. Importantly, expression of Slc38a2, a glutamine importer, is negatively correlated with meiotic genes in the GTEx dataset, and Slc38a2 knockdown downregulates mTORC1/PKA activities and induces meiotic gene expression. Thus, our study indicates that RA via Stra8, a chordate morphogen pathway, induces meiosis partially by generating a conserved nutrient restriction signal in mammalian germ cells by downregulating their nutrient transporter expression.
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Affiliation(s)
- Xiaoyu Zhang
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA; Center for Reproductive Sciences, Institute for Reproductive and Developmental Sciences (IRDS), University of Kansas Medical Center, Kansas City, KS 66160, USA.
| | - Yan Liu
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA; Center for Reproductive Sciences, Institute for Reproductive and Developmental Sciences (IRDS), University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Froylan Sosa
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA; Center for Reproductive Sciences, Institute for Reproductive and Developmental Sciences (IRDS), University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Sumedha Gunewardena
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Peter A Crawford
- Department of Medicine, Division of Molecular Medicine, University of Minnesota, Minneapolis, MN 55455, USA; Department of Molecular Biology, Biochemistry, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Amanda C Zielen
- Department of Obstetrics, Gynecology and Reproductive Sciences and Magee-Womens Research Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Kyle E Orwig
- Department of Obstetrics, Gynecology and Reproductive Sciences and Magee-Womens Research Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ning Wang
- Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS 66160, USA; Center for Reproductive Sciences, Institute for Reproductive and Developmental Sciences (IRDS), University of Kansas Medical Center, Kansas City, KS 66160, USA.
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24
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Li Y, Luo Y. Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.532112. [PMID: 37333198 PMCID: PMC10274700 DOI: 10.1101/2023.03.10.532112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Spatially resolved transcriptomics performs high-throughput measurement of transcriptomes while preserving spatial information about the cellular organizations. However, many spatially resolved transcriptomic technologies can only distinguish spots consisting of a mixture of cells instead of working at single-cell resolution. Here, we present STdGCN, a graph neural network model designed for cell type deconvolution of spatial transcriptomic (ST) data that can leverage abundant single-cell RNA sequencing (scRNA-seq) data as reference. STdGCN is the first model incorporating the expression profiles from single cell data as well as the spatial localization information from the ST data for cell type deconvolution. Extensive benchmarking experiments on multiple ST datasets showed that STdGCN outperformed 14 published state-of-the-art models. Applied to a human breast cancer Visium dataset, STdGCN discerned spatial distributions between stroma, lymphocytes and cancer cells for tumor microenvironment dissection. In a human heart ST dataset, STdGCN detected the changes of potential endothelial-cardiomyocyte communications during tissue development.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
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25
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Tian L, Chen F, Macosko EZ. The expanding vistas of spatial transcriptomics. Nat Biotechnol 2023; 41:773-782. [PMID: 36192637 PMCID: PMC10091579 DOI: 10.1038/s41587-022-01448-2] [Citation(s) in RCA: 85] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/26/2022] [Indexed: 11/09/2022]
Abstract
The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput. As such, they have enabled the determination of the cell-type architecture of tissues, the querying of cell-cell interactions and the monitoring of molecular interactions between tissue components. The rapidly evolving spatial genomics landscape will enable generalized high-throughput genomic measurements and perturbations to be performed in the context of tissues. These advances will empower hypothesis generation and biological discovery and bridge the worlds of tissue biology and genomics.
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Affiliation(s)
- Luyi Tian
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Harvard Stem Cell and Regenerative Biology, Cambridge, MA, USA.
| | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
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26
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Cao R, Ling Y, Meng J, Jiang A, Luo R, He Q, Li A, Chen Y, Zhang Z, Liu F, Li Y, Zhang G. SMDB: a Spatial Multimodal Data Browser. Nucleic Acids Res 2023:7175352. [PMID: 37216588 DOI: 10.1093/nar/gkad413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/01/2023] [Accepted: 05/05/2023] [Indexed: 05/24/2023] Open
Abstract
Understanding the relationship between fine-scale spatial organization and biological function necessitates a tool that effectively combines spatial positions, morphological information, and spatial transcriptomics (ST) data. We introduce the Spatial Multimodal Data Browser (SMDB, https://www.biosino.org/smdb), a robust visualization web service for interactively exploring ST data. By integrating multimodal data, such as hematoxylin and eosin (H&E) images, gene expression-based molecular clusters, and more, SMDB facilitates the analysis of tissue composition through the dissociation of two-dimensional (2D) sections and the identification of gene expression-profiled boundaries. In a digital three-dimensional (3D) space, SMDB allows researchers to reconstruct morphology visualizations based on manually filtered spots or expand anatomical structures using high-resolution molecular subtypes. To enhance user experience, it offers customizable workspaces for interactive exploration of ST spots in tissues, providing features like smooth zooming, panning, 360-degree rotation in 3D and adjustable spot scaling. SMDB is particularly valuable in neuroscience and spatial histology studies, as it incorporates Allen's mouse brain anatomy atlas for reference in morphological research. This powerful tool provides a comprehensive and efficient solution for examining the intricate relationships between spatial morphology, and biological function in various tissues.
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Affiliation(s)
- Ruifang Cao
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Yunchao Ling
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Jiayue Meng
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Ao Jiang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Ruijin Luo
- Shanghai Southgene Technology Co., Ltd., Shanghai 201203, China
| | - Qinwen He
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215123, China
| | - Yujie Chen
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Zoutao Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Feng Liu
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Yixue Li
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
- Guangzhou Laboratory, Guangzhou 510005, China
| | - Guoqing Zhang
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
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27
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Du J, Yang YC, An ZJ, Zhang MH, Fu XH, Huang ZF, Yuan Y, Hou J. Advances in spatial transcriptomics and related data analysis strategies. J Transl Med 2023; 21:330. [PMID: 37202762 PMCID: PMC10193345 DOI: 10.1186/s12967-023-04150-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Abstract
Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.
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Affiliation(s)
- Jun Du
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
| | - Yu-Chen Yang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Zhi-Jie An
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Ming-Hui Zhang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Xue-Hang Fu
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
| | - Zou-Fang Huang
- Ganzhou Key Laboratory of Hematology, Department of Hematology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000 Jiangxi China
| | - Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240 China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240 China
| | - Jian Hou
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
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28
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Singh A, Hermann BP. Conserved Transcriptome Features Define Prepubertal Primate Spermatogonial Stem Cells as A dark Spermatogonia and Identify Unique Regulators. Int J Mol Sci 2023; 24:4755. [PMID: 36902187 PMCID: PMC10002546 DOI: 10.3390/ijms24054755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
Antineoplastic treatments for cancer and other non-malignant disorders can result in long-term or permanent male infertility by ablating spermatogonial stem cells (SSCs). SSC transplantation using testicular tissue harvested before a sterilizing treatment is a promising approach for restoring male fertility in these cases, but a lack of exclusive biomarkers to unequivocally identify prepubertal SSCs limits their therapeutic potential. To address this, we performed single-cell RNA-seq on testis cells from immature baboons and macaques and compared these cells with published data from prepubertal human testis cells and functionally-defined mouse SSCs. While we found discrete groups of human spermatogonia, baboon and rhesus spermatogonia appeared less heterogenous. A cross-species analysis revealed cell types analogous to human SSCs in baboon and rhesus germ cells, but a comparison with mouse SSCs revealed significant differences with primate SSCs. Primate-specific SSC genes were enriched for components and regulators of the actin cytoskeleton and participate in cell-adhesion, which may explain why the culture conditions for rodent SSCs are not appropriate for primate SSCs. Furthermore, correlating the molecular definitions of human SSC, progenitor and differentiating spermatogonia with the histological definitions of Adark/Apale spermatogonia indicates that both SSCs and progenitor spermatogonia are Adark, while Apale spermatogonia appear biased towards differentiation. These results resolve the molecular identity of prepubertal human SSCs, define novel pathways that could be leveraged for advancing their selection and propagation in vitro, and confirm that the human SSC pool resides entirely within Adark spermatogonia.
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Affiliation(s)
| | - Brian P. Hermann
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX 78249, USA
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29
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Yuan Z, Pan W, Zhao X, Zhao F, Xu Z, Li X, Zhao Y, Zhang MQ, Yao J. SODB facilitates comprehensive exploration of spatial omics data. Nat Methods 2023; 20:387-399. [PMID: 36797409 DOI: 10.1038/s41592-023-01773-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 01/06/2023] [Indexed: 02/18/2023]
Abstract
Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/ . The command-line package is available at https://pysodb.readthedocs.io/en/latest/ .
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Tencent AI Lab, Shenzhen, China.
| | - Wentao Pan
- Tencent AI Lab, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | | | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xiu Li
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA.
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30
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Shi J, Gao S, Chen Z, Chen Z, Yun D, Wu X, Sun F. Absence of MerTK disrupts spermatogenesis in an age-dependent manner. Mol Cell Endocrinol 2023; 560:111815. [PMID: 36379275 DOI: 10.1016/j.mce.2022.111815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 11/15/2022]
Abstract
Spermatogenesis is a highly specialized cell differentiation process regulated by the testicular microenvironment. During the process of spermatogenesis, phagocytosis performs an essential role in male germ cell development, and its dysfunction in the testis can cause reproduction defects. MerTK, as a critical protein of phagocytosis, facilitates the removal of apoptotic substrates from the retina and ovaries through cooperation with several phagocytosis receptors. However, its role in mammalian spermatogenesis remains undefined. Here, we found that 30-week-old MerTK-/- male mice developed oligoasthenospermia due to abnormal spermatogenesis. These mice showed damaged seminiferous tubule structure, as well as altered spermatogonia proliferation and differentiation. We also found that Sertoli cells from MerTK-/- mice had decreased phagocytic activity on apoptotic germ cells in vitro. Moreover, a transcriptomic analysis demonstrated that the pivotal genes involved in spermatid differentiation and development changed expression. These results indicate that MerTK is crucial for spermatogenesis, as it regulates the crosstalk between germ cells and Sertoli cells. This provides us insight into the molecular mechanism of MerTK on spermatogenesis and its implications for the diagnosis and treatment of human male infertility.
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Affiliation(s)
- Jie Shi
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, 226001, China
| | - Sheng Gao
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, 226001, China
| | - Zhengru Chen
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, 226001, China
| | - Zifeng Chen
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, 226001, China
| | - Damin Yun
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, 226001, China
| | - Xiaolong Wu
- Department of Urology & Andrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310016, China
| | - Fei Sun
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, 226001, China.
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31
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Fan Z, Luo Y, Lu H, Wang T, Feng Y, Zhao W, Kim P, Zhou X. SPASCER: spatial transcriptomics annotation at single-cell resolution. Nucleic Acids Res 2023; 51:D1138-D1149. [PMID: 36243975 PMCID: PMC9825565 DOI: 10.1093/nar/gkac889] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/21/2022] [Accepted: 10/13/2022] [Indexed: 01/30/2023] Open
Abstract
In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.
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Affiliation(s)
- Zhiwei Fan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yangyang Luo
- West China Hospital, Sichuan University, Chengdu 610041, China
| | - Huifen Lu
- West China Hospital, Sichuan University, Chengdu 610041, China
| | - Tiangang Wang
- School of Life Science and Technology, Xidian University, Xi’an 710126, China
| | - YuZhou Feng
- West China Hospital, Sichuan University, Chengdu 610041, China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Pora Kim
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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32
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Di Persio S, Neuhaus N. Human spermatogonial stem cells and their niche in male (in)fertility: novel concepts from single-cell RNA-sequencing. Hum Reprod 2023; 38:1-13. [PMID: 36409992 PMCID: PMC9825264 DOI: 10.1093/humrep/deac245] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/12/2022] [Indexed: 11/23/2022] Open
Abstract
The amount of single-cell RNA-sequencing (scRNA-seq) data produced in the field of human male reproduction has steadily increased. Transcriptional profiles of thousands of testicular cells have been generated covering the human neonatal, prepubertal, pubertal and adult period as well as different types of male infertility; the latter include non-obstructive azoospermia, cryptozoospermia, Klinefelter syndrome and azoospermia factor deletions. In this review, we provide an overview of transcriptional changes in different testicular subpopulations during postnatal development and in cases of male infertility. Moreover, we review novel concepts regarding the existence of spermatogonial and somatic cell subtypes as well as their crosstalk and provide corresponding marker genes to facilitate their identification. We discuss the potential clinical implications of scRNA-seq findings, the need for spatial information and the necessity to corroborate findings by exploring other levels of regulation, including at the epigenetic or protein level.
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Affiliation(s)
- Sara Di Persio
- Centre of Reproductive Medicine and Andrology, University Hospital of Münster, Münster, Germany
| | - Nina Neuhaus
- Centre of Reproductive Medicine and Andrology, University Hospital of Münster, Münster, Germany
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33
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Ospina O, Soupir A, Fridley BL. A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data. Methods Mol Biol 2023; 2629:115-140. [PMID: 36929076 DOI: 10.1007/978-1-0716-2986-4_7] [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: 03/18/2023]
Abstract
Recent developments in spatially resolved transcriptomics (ST) have resulted in a large number of studies characterizing the architecture of tissues, the spatial distribution of cell types, and their interactions. Furthermore, ST promises to enable the discovery of more accurate drug targets while also providing a better understanding of the etiology and evolution of complex diseases. The analysis of ST brings similar challenges as seen in other gene expression assays such as scRNA-seq; however, there is the additional spatial information that warrants the development of suitable algorithms for the quality control, preprocessing, visualization, and other discovery-enabling approaches (e.g., clustering, cell phenotyping). In this chapter, we review some of the existing algorithms to perform these analytical tasks and highlight some of the unmet analytical challenges in the analysis of ST data. Given the diversity of available ST technologies, we focus this chapter on the analysis of barcode-based RNA quantitation techniques.
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Affiliation(s)
- Oscar Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Alex Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
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34
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Dong F, Ping P, Ma Y, Chen XF. Application of single-cell RNA sequencing on human testicular samples: a comprehensive review. Int J Biol Sci 2023; 19:2167-2197. [PMID: 37151874 PMCID: PMC10158017 DOI: 10.7150/ijbs.82191] [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: 12/27/2022] [Accepted: 03/25/2023] [Indexed: 05/09/2023] Open
Abstract
So far there has been no comprehensive review using systematic literature search strategies to show the application of single-cell RNA sequencing (scRNA-seq) in the human testis of the whole life cycle (from embryos to aging males). Here, we summarized the application of scRNA-seq analyses on various human testicular biological samples. A systematic search was conducted in PubMed and Gene Expression Omnibus (GEO), focusing on English researches published after 2009. Articles related to GEO data-series were also retrieved in PubMed or BioRxiv. 81 full-length studies were finally included in the review. ScRNA-seq has been widely used on different human testicular samples with various library strategies, and new cell subtypes such as State 0 spermatogonial stem cells (SSC) and stage_a/b/c Sertoli cells (SC) were identified. For the development of normal testes, scRNA-seq-based evidence showed dynamic transcriptional changes of both germ cells and somatic cells from embryos to adults. And dysregulated metabolic signaling or hedgehog signaling were revealed by scRNA-seq in aged SC or Leydig cells (LC), respectively. For infertile males, scRNA-seq studies revealed profound changes of testes, such as the increased proportion of immature SC/LC of Klinefelter syndrome, the somatic immaturity and altered germline autophagy of patients with non-obstructive azoospermia, and the repressed differentiation of SSC in trans-females receiving testosterone inhibition therapy. Besides, the re-analyzing of public scRNA-seq data made further discoveries such as the potential vulnerability of testicular SARS-CoV-2 infection, and both evolutionary conservatism and divergence among species. ScRNA-seq analyses would unveil mechanisms of testes' development and changes so as to help developing novel treatments for male infertility.
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Affiliation(s)
- Fan Dong
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
| | - Ping Ping
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
| | - Yi Ma
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
- ✉ Corresponding author: Dr. Xiang-Feng Chen. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address: . Dr. Yi Ma. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address:
| | - Xiang-Feng Chen
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
- Shanghai Human Sperm Bank, Shanghai, China
- ✉ Corresponding author: Dr. Xiang-Feng Chen. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address: . Dr. Yi Ma. Address: 845 Lingshan Road, Shanghai, P. R. China, 200135. Telephone: +86-21-20284500; Fax: +86-21-58394262; Email address:
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Suzuki T. Overview of single-cell RNA sequencing analysis and its application to spermatogenesis research. Reprod Med Biol 2023; 22:e12502. [PMID: 36726594 PMCID: PMC9884325 DOI: 10.1002/rmb2.12502] [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: 05/18/2022] [Revised: 12/18/2022] [Accepted: 01/10/2023] [Indexed: 01/30/2023] Open
Abstract
Background Single-cell transcriptomics allows parallel analysis of multiple cell types in tissues. Because testes comprise somatic cells and germ cells at various stages of spermatogenesis, single-cell RNA sequencing is a powerful tool for investigating the complex process of spermatogenesis. However, single-cell RNA sequencing analysis needs extensive knowledge of experimental technologies and bioinformatics, making it difficult for many, particularly experimental biologists and clinicians, to use it. Methods Aiming to make single-cell RNA sequencing analysis familiar, this review article presents an overview of experimental and computational methods for single-cell RNA sequencing analysis with a history of transcriptomics. In addition, combining the PubMed search and manual curation, this review also provides a summary of recent novel insights into human and mouse spermatogenesis obtained using single-cell RNA sequencing analyses. Main Findings Single-cell RNA sequencing identified mesenchymal cells and type II innate lymphoid cells as novel testicular cell types in the adult mouse testes, as well as detailed subtypes of germ cells. This review outlines recent discoveries into germ cell development and subtypes, somatic cell development, and cell-cell interactions. Conclusion The findings on spermatogenesis obtained using single-cell RNA sequencing may contribute to a deeper understanding of spermatogenesis and provide new directions for male fertility therapy.
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Affiliation(s)
- Takahiro Suzuki
- RIKEN Center for Integrated Medical Science (IMS)Yokohama CityKanagawaJapan
- Graduate School of Medical Life ScienceYokohama City UniversityYokohama CityKanagawaJapan
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36
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Ya D, Zhang Y, Cui Q, Jiang Y, Yang J, Tian N, Xiang W, Lin X, Li Q, Liao R. Application of spatial transcriptome technologies to neurological diseases. Front Cell Dev Biol 2023; 11:1142923. [PMID: 36936681 PMCID: PMC10020196 DOI: 10.3389/fcell.2023.1142923] [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: 01/12/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.
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Affiliation(s)
- Dongshan Ya
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yingmei Zhang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qi Cui
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yanlin Jiang
- Department of Pharmacology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Jiaxin Yang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Ning Tian
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Wenjing Xiang
- Department of Neurology ward 2, Guilin People’s Hospital, Guilin, China
| | - Xiaohui Lin
- Department of Geriatrics, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qinghua Li
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Rujia Liao
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- *Correspondence: Rujia Liao,
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Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine. Int J Mol Sci 2022; 24:ijms24010004. [PMID: 36613446 PMCID: PMC9819745 DOI: 10.3390/ijms24010004] [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: 10/18/2022] [Revised: 12/16/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.
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Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding. Nat Commun 2022; 13:7640. [PMID: 36496406 PMCID: PMC9741613 DOI: 10.1038/s41467-022-35288-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.
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Rabbani M, Zheng X, Manske GL, Vargo A, Shami AN, Li JZ, Hammoud SS. Decoding the Spermatogenesis Program: New Insights from Transcriptomic Analyses. Annu Rev Genet 2022; 56:339-368. [PMID: 36070560 PMCID: PMC10722372 DOI: 10.1146/annurev-genet-080320-040045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Spermatogenesis is a complex differentiation process coordinated spatiotemporally across and along seminiferous tubules. Cellular heterogeneity has made it challenging to obtain stage-specific molecular profiles of germ and somatic cells using bulk transcriptomic analyses. This has limited our ability to understand regulation of spermatogenesis and to integrate knowledge from model organisms to humans. The recent advancement of single-cell RNA-sequencing (scRNA-seq) technologies provides insights into the cell type diversity and molecular signatures in the testis. Fine-grained cell atlases of the testis contain both known and novel cell types and define the functional states along the germ cell developmental trajectory in many species. These atlases provide a reference system for integrated interspecies comparisons to discover mechanistic parallels and to enable future studies. Despite recent advances, we currently lack high-resolution data to probe germ cell-somatic cell interactions in the tissue environment, but the use of highly multiplexed spatial analysis technologies has begun to resolve this problem. Taken together, recent single-cell studies provide an improvedunderstanding of gametogenesis to examine underlying causes of infertility and enable the development of new therapeutic interventions.
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Affiliation(s)
- Mashiat Rabbani
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
| | - Xianing Zheng
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
| | - Gabe L Manske
- Cellular and Molecular Biology Graduate Program, University of Michigan, Ann Arbor, Michigan, USA
| | - Alexander Vargo
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
| | - Adrienne N Shami
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
| | - Jun Z Li
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Saher Sue Hammoud
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Urology, University of Michigan, Ann Arbor, Michigan, USA
- Cellular and Molecular Biology Graduate Program, University of Michigan, Ann Arbor, Michigan, USA
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40
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Yuan Z, Li Y, Shi M, Yang F, Gao J, Yao J, Zhang MQ. SOTIP is a versatile method for microenvironment modeling with spatial omics data. Nat Commun 2022; 13:7330. [PMID: 36443314 PMCID: PMC9705407 DOI: 10.1038/s41467-022-34867-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/10/2022] [Indexed: 11/29/2022] Open
Abstract
The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module's accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- Tencent AI Lab, Shenzhen, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yisi Li
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China
| | | | - Juntao Gao
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | | | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China.
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
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41
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Yang D, Lu Q, Peng S, Hua J. Ubiquitin C-terminal hydrolase L1 (UCHL1), a double-edged sword in mammalian oocyte maturation and spermatogenesis. Cell Prolif 2022; 56:e13347. [PMID: 36218038 PMCID: PMC9890544 DOI: 10.1111/cpr.13347] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/14/2022] [Accepted: 09/29/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Recent studies have shown that ubiquitin-mediated cell apoptosis can modulate protein interaction and involve in the progress of oocyte maturation and spermatogenesis. As one of the key regulators involved in ubiquitin signal, ubiquitin C-terminal hydrolase L1 (UCHL1) is considered a molecular marker associated with spermatogonia stem cells. However, the function of UCHL1 was wildly reported to regulate various bioecological processes, such as Parkinson's disease, lung cancer, breast cancer and colon cancer, how UCHL1 affects the mammalian reproductive system remains an open question. METHODS We identified papers through electronic searches of PubMed database from inception to July 2022. RESULTS Here, we summarize the important function of UCHL1 in controlling mammalian oocyte development, regulating spermatogenesis and inhibiting polyspermy, and we posit the balance of UCHL1 was essential to maintaining reproductive cellular and tissue homeostasis. CONCLUSION This study considers the 'double-edged sword' role of UCHL1 during gametogenesis and presents new insights into UCHL1 in germ cells.
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Affiliation(s)
- Donghui Yang
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & TechnologyNorthwest A&F UniversityYanglingShaanxiChina
| | - Qizhong Lu
- State Key Laboratory of Biotherapy and Cancer Center, Research Unit of Gene and Immunotherapy, Collaborative Innovation Center of Biotherapy, West China HospitalSichuan UniversityChengduChina
| | - Sha Peng
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & TechnologyNorthwest A&F UniversityYanglingShaanxiChina
| | - Jinlian Hua
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & TechnologyNorthwest A&F UniversityYanglingShaanxiChina
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Cheng H, Shang D, Zhou R. Germline stem cells in human. Signal Transduct Target Ther 2022; 7:345. [PMID: 36184610 PMCID: PMC9527259 DOI: 10.1038/s41392-022-01197-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 12/02/2022] Open
Abstract
The germline cells are essential for the propagation of human beings, thus essential for the survival of mankind. The germline stem cells, as a unique cell type, generate various states of germ stem cells and then differentiate into specialized cells, spermatozoa and ova, for producing offspring, while self-renew to generate more stem cells. Abnormal development of germline stem cells often causes severe diseases in humans, including infertility and cancer. Primordial germ cells (PGCs) first emerge during early embryonic development, migrate into the gentile ridge, and then join in the formation of gonads. In males, they differentiate into spermatogonial stem cells, which give rise to spermatozoa via meiosis from the onset of puberty, while in females, the female germline stem cells (FGSCs) retain stemness in the ovary and initiate meiosis to generate oocytes. Primordial germ cell-like cells (PGCLCs) can be induced in vitro from embryonic stem cells or induced pluripotent stem cells. In this review, we focus on current advances in these embryonic and adult germline stem cells, and the induced PGCLCs in humans, provide an overview of molecular mechanisms underlying the development and differentiation of the germline stem cells and outline their physiological functions, pathological implications, and clinical applications.
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Affiliation(s)
- Hanhua Cheng
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Renmin Hospital of Wuhan University, Wuhan University, 430072, Wuhan, China.
| | - Dantong Shang
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Renmin Hospital of Wuhan University, Wuhan University, 430072, Wuhan, China
| | - Rongjia Zhou
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Renmin Hospital of Wuhan University, Wuhan University, 430072, Wuhan, China.
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Cable DM, Murray E, Shanmugam V, Zhang S, Zou LS, Diao M, Chen H, Macosko EZ, Irizarry RA, Chen F. Cell type-specific inference of differential expression in spatial transcriptomics. Nat Methods 2022; 19:1076-1087. [PMID: 36050488 PMCID: PMC10463137 DOI: 10.1038/s41592-022-01575-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 07/15/2022] [Indexed: 12/13/2022]
Abstract
A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE's framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer's disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr .
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Affiliation(s)
- Dylan M Cable
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Evan Murray
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vignesh Shanmugam
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Simon Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luli S Zou
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard University, Boston, MA, USA
| | - Michael Diao
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Haiqi Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Evan Z Macosko
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Rafael A Irizarry
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Biostatistics, Harvard University, Boston, MA, USA.
| | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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Kleino I, Frolovaitė P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J 2022; 20:4870-4884. [PMID: 36147664 PMCID: PMC9464853 DOI: 10.1016/j.csbj.2022.08.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/18/2022] Open
Abstract
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.
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Key Words
- AOI, area of illumination
- BICCN, Brain Initiative Cell Census Network
- BOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses
- Baysor, Bayesian Segmentation of Spatial Transcriptomics Data
- BinSpect, Binary Spatial Extraction
- CCC, cell–cell communication
- CCI, cell–cell interactions
- CNV, copy-number variation
- Computational biology
- DSP, digital spatial profiling
- DbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencing
- FA, factor analysis
- FFPE, formalin-fixed, paraffin-embedded
- FISH, fluorescence in situ hybridization
- FISSEQ, fluorescence in situ sequencing of RNA
- FOV, Field of view
- GRNs, gene regulation networks
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- HDST, high definition spatial transcriptomics
- HMRF, hidden Markov random field
- ICG, interaction changed genes
- ISH, in situ hybridization
- ISS, in situ sequencing
- JSTA, Joint cell segmentation and cell type annotation
- KNN, k-nearest neighbor
- LCM, Laser Capture Microdissection
- LCM-seq, laser capture microdissection coupled with RNA sequencing
- LOH, loss of heterozygosity analysis
- MC, Molecular Cartography
- MERFISH, multiplexed error-robust FISH
- NMF (NNMF), Non-negative matrix factorization
- PCA, Principal Component Analysis
- PIXEL-seq, Polony (or DNA cluster)-indexed library-sequencing
- PL-lig, padlock ligation
- QC, quality control
- RNAseq, RNA sequencing
- ROI, region of interest
- SCENIC, Single-Cell rEgulatory Network Inference and Clustering
- SME, Spatial Morphological gene Expression normalization
- SPATA, SPAtial Transcriptomic Analysis
- ST Pipeline, Spatial Transcriptomics Pipeline
- ST, Spatial transcriptomics
- STARmap, spatially-resolved transcript amplicon readout mapping
- Single-cell analysis
- Spatial data analysis frameworks
- Spatial deconvolution
- Spatial transcriptomics
- TIVA, Transcriptome in Vivo Analysis
- TMA, tissue microarray
- TME, tumor micro environment
- UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction
- UMI, unique molecular identifier
- ZipSeq, zipcoded sequencing.
- scRNA-seq, single-cell RNA sequencing
- scvi-tools, single-cell variational inference tools
- seqFISH, sequential fluorescence in situ hybridization
- sequ-smFISH, sequential single-molecule fluorescent in situ hybridization
- smFISH, single molecule FISH
- t-SNE, t-distributed stochastic neighbor embedding
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Affiliation(s)
- Iivari Kleino
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Paulina Frolovaitė
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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45
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O'Donnell L, Smith LB, Rebourcet D. Sperm-specific proteins: new implications for diagnostic development and cancer immunotherapy. Curr Opin Cell Biol 2022; 77:102104. [PMID: 35671587 DOI: 10.1016/j.ceb.2022.102104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/29/2022] [Accepted: 05/05/2022] [Indexed: 11/03/2022]
Abstract
Spermatozoa are comprised of many unique proteins not expressed elsewhere. Sperm-specific proteins are first expressed at puberty, after the development of immune tolerance to self-antigens, and have been assumed to remain confined inside the seminiferous tubules, protected from immune cell recognition by various mechanisms of testicular immune privilege. However, new data has shown that sperm-specific proteins are released by the tubules into the surrounding interstitial fluid; from here they can contact immune cells, potentially promote immune tolerance, and enter the circulation. These new findings have clinical implications for diagnostics and therapeutics targeted at a specific class of proteins known as cancer-testis antigens (CTA), the opportunity to identify new communication pathways in the testis, and to discover new ways to monitor testis function.
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Affiliation(s)
- Liza O'Donnell
- College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, Australia; Centre for Reproductive Health, Hudson Institute of Medical Research, Clayton, 3168, Victoria, Australia; Monash University, Clayton, 3168, Victoria, Australia.
| | - Lee B Smith
- College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, Australia; MRC Centre for Reproductive Health, University of Edinburgh, The Queen's Medical Research Institute, 47 Little France Crescent, Edinburgh EH16 4TJ, UK; Griffith University, Parklands Drive, Southport, 4222, Queensland, Australia
| | - Diane Rebourcet
- College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, Australia
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46
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Wu X, Zhou L, Shi J, Cheng CY, Sun F. Multiomics analysis of male infertility. Biol Reprod 2022; 107:118-134. [PMID: 35639635 DOI: 10.1093/biolre/ioac109] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/12/2022] [Accepted: 05/17/2022] [Indexed: 11/14/2022] Open
Abstract
Infertility affects 8-12% of couples globally, and the male factor is a primary cause in approximately 50% of couples. Male infertility is a multifactorial reproductive disorder, which can be caused by paracrine and autocrine factors, hormones, genes, and epigenetic changes. Recent studies in rodents and most notably in humans using multiomics approach have yielded important insights into understanding the biology of spermatogenesis. Nonetheless, the etiology and pathogenesis of male infertility are still largely unknown. In this review, we summarized and critically evaluated findings based on the use of advanced technologies to compare normal and obstructive azoospermia (OA) versus non-obstructive azoospermia (NOA) men, including whole-genome bisulfite sequencing (WGBS), single cell RNA-seq (scRNA-seq), whole exome sequencing (WES), and ATAC-seq. It is obvious that the multiomics approach is the method of choice for basic research and clinical studies including clinical diagnosis of male infertility.
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Affiliation(s)
- Xiaolong Wu
- Department of Urology & Andrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China.,Institute of Reproductive Medicine, Nantong University School of Medicine, Nantong, Jiangsu 226001, China
| | - Liwei Zhou
- Institute of Reproductive Medicine, Nantong University School of Medicine, Nantong, Jiangsu 226001, China
| | - Jie Shi
- Institute of Reproductive Medicine, Nantong University School of Medicine, Nantong, Jiangsu 226001, China
| | - C Yan Cheng
- Department of Urology & Andrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China.,Institute of Reproductive Medicine, Nantong University School of Medicine, Nantong, Jiangsu 226001, China
| | - Fei Sun
- Department of Urology & Andrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, China.,Institute of Reproductive Medicine, Nantong University School of Medicine, Nantong, Jiangsu 226001, China
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47
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Marečková M, Massalha H, Lorenzi V, Vento-Tormo R. Mapping Human Reproduction with Single-Cell Genomics. Annu Rev Genomics Hum Genet 2022; 23:523-547. [PMID: 35567278 DOI: 10.1146/annurev-genom-120121-114415] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The trillions of cells in the human body develop as a result of the fusion of two extremely specialized cells: an oocyte and a sperm. This process is essential for the continuation of our species, as it ensures that parental genetic information is mixed and passed on from generation to generation. In addition to producing oocytes, the female reproductive system must provide the environment for the appropriate development of the fetus until birth. New genomic and computational tools offer unique opportunities to study the tight spatiotemporal regulatory mechanisms that are required for the cycle of human reproduction. This review explores how single-cell technologies have been used to build cellular atlases of the human reproductive system across the life span and how these maps have proven useful to better understand reproductive pathologies and dissect the heterogeneity of in vitro model systems. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Magda Marečková
- Wellcome Sanger Institute, Cambridge, United Kingdom; .,Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom;
| | - Hassan Massalha
- Wellcome Sanger Institute, Cambridge, United Kingdom; .,Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
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48
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Marshall JL, Noel T, Wang QS, Chen H, Murray E, Subramanian A, Vernon KA, Bazua-Valenti S, Liguori K, Keller K, Stickels RR, McBean B, Heneghan RM, Weins A, Macosko EZ, Chen F, Greka A. High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways. iScience 2022; 25:104097. [PMID: 35372810 PMCID: PMC8971939 DOI: 10.1016/j.isci.2022.104097] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/15/2022] [Accepted: 03/11/2022] [Indexed: 12/21/2022] Open
Abstract
High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods. A cell neighborhood around LYVE1+ macrophages was discovered in human kidneys The blood pressure regulating apparatus was re-organized in a diabetic mouse model Cell neighborhoods around Trem2+ macrophages were found in a model of proteinopathy A 77 gene signature associated with the UPR was defined in a model of proteinopathy
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49
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Clinical and translational values of spatial transcriptomics. Signal Transduct Target Ther 2022; 7:111. [PMID: 35365599 PMCID: PMC8972902 DOI: 10.1038/s41392-022-00960-w] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 03/04/2022] [Accepted: 03/09/2022] [Indexed: 02/06/2023] Open
Abstract
The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.
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50
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Hofmann MC, McBeath E. Sertoli Cell-Germ Cell Interactions Within the Niche: Paracrine and Juxtacrine Molecular Communications. Front Endocrinol (Lausanne) 2022; 13:897062. [PMID: 35757413 PMCID: PMC9226676 DOI: 10.3389/fendo.2022.897062] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/25/2022] [Indexed: 12/22/2022] Open
Abstract
Male germ cell development depends on multiple biological events that combine epigenetic reprogramming, cell cycle regulation, and cell migration in a spatio-temporal manner. Sertoli cells are a crucial component of the spermatogonial stem cell niche and provide essential growth factors and chemokines to developing germ cells. This review focuses mainly on the activation of master regulators of the niche in Sertoli cells and their targets, as well as on novel molecular mechanisms underlying the regulation of growth and differentiation factors such as GDNF and retinoic acid by NOTCH signaling and other pathways.
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