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Zhang C, Wang L, Shi Q. Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics. Comput Struct Biotechnol J 2024; 23:2109-2115. [PMID: 38800634 PMCID: PMC11126885 DOI: 10.1016/j.csbj.2024.05.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024] Open
Abstract
Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.
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Affiliation(s)
- Chuanchao Zhang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, Hangzhou 310024; University of Chinese Academy of Sciences, China
| | - Lequn Wang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qianqian Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Engineering Technology Research Center of Agricultural Big Data, Huazhong Agricultural University, Wuhan 430070, Hubei, China
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Li Q, Wang C, Gou J, Kitanovski S, Tang X, Cai Y, Zhang C, Zhang X, Zhang Z, Qiu Y, Zhao F, Lu M, He Y, Wang J, Lu H. Deciphering lung granulomas in HIV & TB co-infection: unveiling macrophages aggregation with IL6R/STAT3 activation. Emerg Microbes Infect 2024; 13:2366359. [PMID: 38855910 PMCID: PMC11188963 DOI: 10.1080/22221751.2024.2366359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 06/05/2024] [Indexed: 06/11/2024]
Abstract
Tuberculosis (TB) remains a leading cause of mortality among individuals coinfected with HIV, characterized by progressive pulmonary inflammation. Despite TB's hallmark being focal granulomatous lung lesions, our understanding of the histopathological features and regulation of inflammation in HIV & TB coinfection remains incomplete. In this study, we aimed to elucidate these histopathological features through an immunohistochemistry analysis of HIV & TB co-infected and TB patients, revealing marked differences. Notably, HIV & TB granulomas exhibited aggregation of CD68 + macrophage (Mφ), while TB lesions predominantly featured aggregation of CD20+ B cells, highlighting distinct immune responses in coinfection. Spatial transcriptome profiling further elucidated CD68+ Mφ aggregation in HIV & TB, accompanied by activation of IL6 pathway, potentially exacerbating inflammation. Through multiplex immunostaining, we validated two granuloma types in HIV & TB versus three in TB, distinguished by cell architecture. Remarkably, in the two types of HIV & TB granulomas, CD68 + Mφ highly co-expressed IL6R/pSTAT3, contrasting TB granulomas' high IFNGRA/SOCS3 expression, indicating different signaling pathways at play. Thus, activation of IL6 pathway may intensify inflammation in HIV & TB-lungs, while SOCS3-enriched immune microenvironment suppresses IL6-induced over-inflammation in TB. These findings provide crucial insights into HIV & TB granuloma formation, shedding light on potential therapeutic targets, particularly for granulomatous pulmonary under HIV & TB co-infection. Our study emphasizes the importance of a comprehensive understanding of the immunopathogenesis of HIV & TB coinfection and suggests potential avenues for targeting IL6 signaling with SOCS3 activators or anti-IL6R agents to mitigate lung inflammation in HIV & TB coinfected individuals.
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MESH Headings
- Humans
- Coinfection/virology
- Coinfection/immunology
- Coinfection/microbiology
- HIV Infections/complications
- HIV Infections/immunology
- Macrophages/immunology
- STAT3 Transcription Factor/metabolism
- STAT3 Transcription Factor/genetics
- Granuloma/immunology
- Lung/pathology
- Lung/immunology
- Receptors, Interleukin-6/metabolism
- Receptors, Interleukin-6/genetics
- Suppressor of Cytokine Signaling 3 Protein/metabolism
- Suppressor of Cytokine Signaling 3 Protein/genetics
- Antigens, Differentiation, Myelomonocytic/metabolism
- Antigens, Differentiation, Myelomonocytic/genetics
- Antigens, CD/metabolism
- Antigens, CD/genetics
- Signal Transduction
- Tuberculosis, Pulmonary/immunology
- Tuberculosis, Pulmonary/complications
- Male
- Tuberculosis/immunology
- Tuberculosis/microbiology
- Tuberculosis/complications
- Female
- Adult
- Interleukin-6/metabolism
- Interleukin-6/genetics
- CD68 Molecule
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Affiliation(s)
- Qian Li
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Cheng Wang
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Jizhou Gou
- Department of Pathology, National Clinical Research Center for Infectious Diseases, Shenzhen Third People’s Hospital, Shenzhen, People’s Republic of China
| | - Simo Kitanovski
- Bioinformatics and Computational Biophysics, University of Duisburg-Essen, Essen, Germany
| | - XiangYi Tang
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Yixuan Cai
- Clinical Research Center, The Fifth People’s Hospital of Wuxi, Jiangnan University, Wuxi, People’s Republic of China
| | - Chenxia Zhang
- Clinical Research Center, The Fifth People’s Hospital of Wuxi, Jiangnan University, Wuxi, People’s Republic of China
| | - Xiling Zhang
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Zhenfeng Zhang
- School of Public Health and Emergency Management, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Yuanwang Qiu
- Clinical Research Center, The Fifth People’s Hospital of Wuxi, Jiangnan University, Wuxi, People’s Republic of China
| | - Fang Zhao
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Mengji Lu
- Institute of Virology, Essen University Hospital, University of Duisburg-Essen, Essen, German
| | - Yun He
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Jun Wang
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
- Bioinformatics and Computational Biophysics, University of Duisburg-Essen, Essen, Germany
- Clinical Research Center, The Fifth People’s Hospital of Wuxi, Jiangnan University, Wuxi, People’s Republic of China
| | - Hongzhou Lu
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen and The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, People’s Republic of China
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3
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Brennan PG, Mota L, Aridi T, Patel N, Liang P, Ferran C. Advancements in Omics and Breakthrough Gene Therapies: A Glimpse into the Future of Peripheral Artery Disease. Ann Vasc Surg 2024; 107:229-246. [PMID: 38582204 DOI: 10.1016/j.avsg.2024.01.031] [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: 12/31/2023] [Accepted: 01/01/2024] [Indexed: 04/08/2024]
Abstract
Peripheral artery disease (PAD), a highly prevalent global disease, associates with significant morbidity and mortality in affected patients. Despite progress in endovascular and open revascularization techniques for advanced PAD, these interventions grapple with elevated rates of arterial restenosis and vein graft failure attributed to intimal hyperplasia (IH). Novel multiomics technologies, coupled with sophisticated analyses tools recently powered by advances in artificial intelligence, have enabled the study of atherosclerosis and IH with unprecedented single-cell and spatial precision. Numerous studies have pinpointed gene hubs regulating pivotal atherogenic and atheroprotective signaling pathways as potential therapeutic candidates. Leveraging advancements in viral and nonviral gene therapy (GT) platforms, gene editing technologies, and cutting-edge biomaterial reservoirs for delivery uniquely positions us to develop safe, efficient, and targeted GTs for PAD-related diseases. Gene therapies appear particularly fitting for ex vivo genetic engineering of IH-resistant vein grafts. This manuscript highlights currently available state-of-the-art multiomics approaches, explores promising GT-based candidates, and details GT delivery modalities employed by our laboratory and others to thwart mid-term vein graft failure caused by IH, as well as other PAD-related conditions. The potential clinical translation of these targeted GTs holds the promise to revolutionize PAD treatment, thereby enhancing patients' quality of life and life expectancy.
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Affiliation(s)
- Phillip G Brennan
- Division of Vascular and Endovascular Surgery, and Center for Vascular Biology Research, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Lucas Mota
- Division of Vascular and Endovascular Surgery, and Center for Vascular Biology Research, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Tarek Aridi
- Division of Vascular and Endovascular Surgery, and Center for Vascular Biology Research, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Nyah Patel
- Division of Vascular and Endovascular Surgery, and Center for Vascular Biology Research, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Patric Liang
- Division of Vascular and Endovascular Surgery, and Center for Vascular Biology Research, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Christiane Ferran
- Division of Vascular and Endovascular Surgery, and Center for Vascular Biology Research, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Division of Nephrology and the Transplant Institute, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
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Li R, Sone N, Gotoh S, Sun X, Hagood JS. Contemporary and emerging technologies for research in children's rare and interstitial lung disease. Pediatr Pulmonol 2024; 59:2349-2359. [PMID: 37204232 DOI: 10.1002/ppul.26490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/04/2023] [Accepted: 05/09/2023] [Indexed: 05/20/2023]
Abstract
Although recent decades have seen the identification, classification and discovery of the genetic basis of many children's interstitial and rare lung disease (chILD) disorders, detailed understanding of pathogenesis and specific therapies are still lacking for most of them. Fortunately, a revolution of technological advancements has created new opportunities to address these critical knowledge gaps. High-throughput sequencing has facilitated analysis of transcription of thousands of genes in thousands of single cells, creating tremendous breakthroughs in understanding normal and diseased cellular biology. Spatial techniques allow analysis of transcriptomes and proteomes at the subcellular level in the context of tissue architecture, in many cases even in formalin-fixed, paraffin-embedded specimens. Gene editing techniques allow creation of "humanized" animal models in a shorter time frame, for improved knowledge and preclinical therapeutic testing. Regenerative medicine approaches and bioengineering advancements facilitate the creation of patient-derived induced pluripotent stem cells and their differentiation into tissue-specific cell types which can be studied in multicellular "organoids" or "organ-on-a-chip" approaches. These technologies, singly and in combination, are already being applied to gain new biological insights into chILD disorders. The time is ripe to systematically apply these technologies to chILD, together with sophisticated data science approaches, to improve both biological understanding and disease-specific therapy.
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Affiliation(s)
- Rongbo Li
- Department of Pediatrics, Division of Respiratory Medicine, UC-San Diego, La Jolla, California, USA
| | - Naoyuki Sone
- Center for iPS Cell Research and Application, Kyoto University, Kyoto, Japan
| | - Shimpei Gotoh
- Center for iPS Cell Research and Application, Kyoto University, Kyoto, Japan
| | - Xin Sun
- Department of Pediatrics, Division of Respiratory Medicine, UC-San Diego, La Jolla, California, USA
| | - James S Hagood
- Department of Pediatrics, Pulmonology Division, Program for Rare and Interstitial Lung Disease, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Wang M, Hong Y, Fu X, Sun X. Advances and applications of biomimetic biomaterials for endogenous skin regeneration. Bioact Mater 2024; 39:492-520. [PMID: 38883311 PMCID: PMC11179177 DOI: 10.1016/j.bioactmat.2024.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 06/18/2024] Open
Abstract
Endogenous regeneration is becoming an increasingly important strategy for wound healing as it facilitates skin's own regenerative potential for self-healing, thereby avoiding the risks of immune rejection and exogenous infection. However, currently applied biomaterials for inducing endogenous skin regeneration are simplistic in their structure and function, lacking the ability to accurately mimic the intricate tissue structure and regulate the disordered microenvironment. Novel biomimetic biomaterials with precise structure, chemical composition, and biophysical properties offer a promising avenue for achieving perfect endogenous skin regeneration. Here, we outline the recent advances in biomimetic materials induced endogenous skin regeneration from the aspects of structural and functional mimicry, physiological process regulation, and biophysical property design. Furthermore, novel techniques including in situ reprograming, flexible electronic skin, artificial intelligence, single-cell sequencing, and spatial transcriptomics, which have potential to contribute to the development of biomimetic biomaterials are highlighted. Finally, the prospects and challenges of further research and application of biomimetic biomaterials are discussed. This review provides reference to address the clinical problems of rapid and high-quality skin regeneration.
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Affiliation(s)
- Mengyang Wang
- Research Center for Tissue Repair and Regeneration Affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, 100853, PR China
- PLA Key Laboratory of Tissue Repair and Regenerative Medicine and Beijing Key Research Laboratory of Skin Injury, Repair and Regeneration, Beijing, 100089, PR China
| | - Yiyue Hong
- Research Center for Tissue Repair and Regeneration Affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, 100853, PR China
- PLA Key Laboratory of Tissue Repair and Regenerative Medicine and Beijing Key Research Laboratory of Skin Injury, Repair and Regeneration, Beijing, 100089, PR China
| | - Xiaobing Fu
- Research Center for Tissue Repair and Regeneration Affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, 100853, PR China
- PLA Key Laboratory of Tissue Repair and Regenerative Medicine and Beijing Key Research Laboratory of Skin Injury, Repair and Regeneration, Beijing, 100089, PR China
- Research Unit of Trauma Care, Tissue Repair and Regeneration, Chinese Academy of Medical Sciences, 2019RU051, Beijing, 100048, PR China
| | - Xiaoyan Sun
- Research Center for Tissue Repair and Regeneration Affiliated to the Medical Innovation Research Department, PLA General Hospital and PLA Medical College, Beijing, 100853, PR China
- PLA Key Laboratory of Tissue Repair and Regenerative Medicine and Beijing Key Research Laboratory of Skin Injury, Repair and Regeneration, Beijing, 100089, PR China
- Research Unit of Trauma Care, Tissue Repair and Regeneration, Chinese Academy of Medical Sciences, 2019RU051, Beijing, 100048, PR China
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6
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Guo Y, Ren C, He Y, Wu Y, Yang X. Deciphering the spatiotemporal transcriptional landscape of intestinal diseases (Review). Mol Med Rep 2024; 30:157. [PMID: 38994768 PMCID: PMC11258600 DOI: 10.3892/mmr.2024.13281] [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: 11/21/2023] [Accepted: 04/19/2024] [Indexed: 07/13/2024] Open
Abstract
The intestines are the largest barrier organ in the human body. The intestinal barrier plays a crucial role in maintaining the balance of the intestinal environment and protecting the intestines from harmful bacterial invasion. Single‑cell RNA sequencing technology allows the detection of the different cell types in the intestine in two dimensions and the exploration of cell types that have not been fully characterized. The intestinal mucosa is highly complex in structure, and its proper functioning is linked to multiple structures in the proximal‑distal intestinal and luminal‑mucosal axes. Spatial localization is at the core of the efforts to explore the interactions between the complex structures. Spatial transcriptomics (ST) is a method that allows for comprehensive tissue analysis and the acquisition of spatially separated genetic information from individual cells, while preserving their spatial location and interactions. This approach also prevents the loss of fragile cells during tissue disaggregation. The emergence of ST technology allows us to spatially dissect enzymatic processes and interactions between multiple cells, genes, proteins and signals in the intestine. This includes the exchange of oxygen and nutrients in the intestine, different gradients of microbial populations and the role of extracellular matrix proteins. This regionally precise approach to tissue studies is gaining more acceptance and is increasingly applied in the investigation of disease mechanisms related to the gastrointestinal tract. Therefore, this review summarized the application of ST in gastrointestinal diseases.
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Affiliation(s)
- Yajing Guo
- School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, P.R. China
| | - Chao Ren
- Graduate School, Hunan University of Traditional Chinese Medicine, Changsha, Hunan 410208, P.R. China
| | - Yuxi He
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| | - Yue Wu
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
| | - Xiaojun Yang
- Department of Digestive Medicine, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing 400021, P.R. China
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Yin Z, Huang W, Li K, Fernie AR, Yan S. Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 119:2168-2180. [PMID: 38990529 DOI: 10.1111/tpj.16924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/24/2024] [Accepted: 07/01/2024] [Indexed: 07/12/2024]
Abstract
Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.
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Affiliation(s)
- Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
- Institute of Advanced Science Facilities, Shenzhen, 518107, Guangdong, China
| | - Wenjie Huang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| | - Kun Li
- Guangdong Key Laboratory of Crop Genetic Improvement, Crop Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
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He C, Shou H. PHO1: linking phosphate nutrition translocation and floral signalling in plants. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:4693-4696. [PMID: 39192696 DOI: 10.1093/jxb/erae302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 08/29/2024]
Abstract
This article comments on:
Dai S, Chen H, Shi Y, Xiao X, Xu L, Qin C, Zhu Y, Yi K, Lei M, Zeng H. 2024. PHOSPHATE1-mediated phosphate translocation from roots to shoots regulates floral transition in plants. Journal of Experimental Botany 75, 5054–5075. https://doi.org/10.1093/jxb/erae222
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Affiliation(s)
- Cunman He
- The Provincial International Science and Technology Cooperation Base on Engineering Biology, International Campus of Zhejiang University, Haining, Zhejiang 314400, China
| | - Huixia Shou
- The Provincial International Science and Technology Cooperation Base on Engineering Biology, International Campus of Zhejiang University, Haining, Zhejiang 314400, China
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Liu X, Peng T, Xu M, Lin S, Hu B, Chu T, Liu B, Xu Y, Ding W, Li L, Cao C, Wu P. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. J Hematol Oncol 2024; 17:72. [PMID: 39182134 PMCID: PMC11344930 DOI: 10.1186/s13045-024-01596-9] [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: 05/22/2024] [Accepted: 08/09/2024] [Indexed: 08/27/2024] Open
Abstract
The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.
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Affiliation(s)
- Xiaojie Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ting Peng
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Miaochun Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shitong Lin
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bai Hu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tian Chu
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Binghan Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yashi Xu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wencheng Ding
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Li Li
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Canhui Cao
- Department of Gynecologic Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Peng Wu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- National Clinical Research Center for Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Cancer Biology Research Center (Key Laboratory of the Ministry of Education), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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10
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Yasumizu Y, Kinoshita M, Zhang MJ, Motooka D, Suzuki K, Nojima S, Koizumi N, Okuzaki D, Funaki S, Shintani Y, Ohkura N, Morii E, Okuno T, Mochizuki H. Spatial transcriptomics elucidates medulla niche supporting germinal center response in myasthenia gravis-associated thymoma. Cell Rep 2024; 43:114677. [PMID: 39180749 DOI: 10.1016/j.celrep.2024.114677] [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: 02/05/2024] [Revised: 07/30/2024] [Accepted: 08/08/2024] [Indexed: 08/26/2024] Open
Abstract
Myasthenia gravis (MG) is etiologically associated with thymus abnormalities, but its pathology in the thymus remains unclear. In this study, we attempt to narrow down the features associated with MG using spatial transcriptome analysis of thymoma and thymic hyperplasia samples. We find that the majority of thymomas are constituted by the cortical region. However, the small medullary region is enlarged in seropositive thymomas and contains polygenic enrichment and MG-specific germinal center structures. Neuromuscular medullary thymic epithelial cells, previously identified as MG-specific autoantigen-producing cells, are enriched in the cortico-medullary junction. The medulla is characterized by a specific chemokine pattern and immune cell composition, including migratory dendritic cells and effector regulatory T cells. Similar germinal center structures and immune microenvironments are also observed in the thymic hyperplasia medulla. This study shows that the medulla and junction areas are linked to MG pathology and provides insights into future MG research.
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Affiliation(s)
- Yoshiaki Yasumizu
- Department of Neurology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan; Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan; Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
| | - Makoto Kinoshita
- Department of Neurology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Martin Jinye Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Daisuke Motooka
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan; Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
| | - Koichiro Suzuki
- BIKEN-RIMD NGS Laboratory, Research Institute for Microbial Diseases, Osaka University, Suita, Japan; Biomedical Science Center, The Research Foundation for Microbial Diseases of Osaka University (BIKEN), Suita, Japan
| | - Satoshi Nojima
- Department of Pathology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Naoshi Koizumi
- Department of Neurology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Daisuke Okuzaki
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan; Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
| | - Soichiro Funaki
- Department of General Thoracic Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Yasushi Shintani
- Department of General Thoracic Surgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Naganari Ohkura
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan; Department of Frontier Research in Tumor Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Eiichi Morii
- Department of Pathology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Tatsusada Okuno
- Department of Neurology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
| | - Hideki Mochizuki
- Department of Neurology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan
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11
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Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [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: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
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Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
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12
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Poovathingal S, Davie K, Borm LE, Vandepoel R, Poulvellarie N, Verfaillie A, Corthout N, Aerts S. Nova-ST: Nano-patterned ultra-dense platform for spatial transcriptomics. CELL REPORTS METHODS 2024; 4:100831. [PMID: 39111312 DOI: 10.1016/j.crmeth.2024.100831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/07/2024] [Accepted: 07/11/2024] [Indexed: 08/22/2024]
Abstract
Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.
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Affiliation(s)
- Suresh Poovathingal
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, CBD Technologies, Single Cell & Microfluidics Expertise Unit, 3000 Leuven, Belgium; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA.
| | - Kristofer Davie
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, CBD Technologies, Single Cell Bioinformatics Expertise Unit, 3000 Leuven, Belgium
| | - Lars E Borm
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Roel Vandepoel
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Nicolas Poulvellarie
- VIB-KU Leuven Center for Brain & Disease Research, CBD Technologies, Neuroengineering Expertise Unit, 3000 Leuven, Belgium
| | | | - Nikky Corthout
- Bio Imaging Core, VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium
| | - Stein Aerts
- VIB Center for AI & Computational Biology (VIB.AI), 3000 Leuven, Belgium; VIB-KU Leuven Center for Brain & Disease Research, 3000 Leuven, Belgium; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium.
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13
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Wu Z, Kondo A, McGrady M, Baker EAG, Chidester B, Wu E, Rahim MK, Bracey NA, Charu V, Cho RJ, Cheng JB, Afkarian M, Zou J, Mayer AT, Trevino AE. Discovery and generalization of tissue structures from spatial omics data. CELL REPORTS METHODS 2024; 4:100838. [PMID: 39127044 DOI: 10.1016/j.crmeth.2024.100838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/15/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024]
Abstract
Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.
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Affiliation(s)
- Zhenqin Wu
- Enable Medicine, Menlo Park, CA 94025, USA.
| | | | | | | | | | - Eric Wu
- Enable Medicine, Menlo Park, CA 94025, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | | | - Nathan A Bracey
- Institute of Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA
| | - Vivek Charu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Raymond J Cho
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey B Cheng
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Department of Dermatology, Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Maryam Afkarian
- Division of Nephrology, Department of Medicine, University of California, Davis, Davis, CA 95618, USA
| | - James Zou
- Enable Medicine, Menlo Park, CA 94025, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
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14
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Ruan Z, Zhou W, Liu H, Wei J, Pan Y, Yan C, Wei X, Xiang W, Yan C, Chen S, Liu J. Precise detection of cell-type-specific domains in spatial transcriptomics. CELL REPORTS METHODS 2024; 4:100841. [PMID: 39127046 DOI: 10.1016/j.crmeth.2024.100841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/17/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024]
Abstract
Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.
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Affiliation(s)
- Zhihan Ruan
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Weijun Zhou
- Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
| | - Hong Liu
- The Second Surgical Department of Breast Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| | - Jinmao Wei
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Yichen Pan
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Chaoyang Yan
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Xiaoyi Wei
- Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China
| | - Wenting Xiang
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Chengwei Yan
- Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin 300350, China
| | - Shengquan Chen
- School of Mathematical Sciences, Nankai University, Tianjin 300350, China
| | - Jian Liu
- State Key Laboratory of Medicinal Chemical Biology, College of Computer Science, Nankai University, Tianjin 300350, China.
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15
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Yang W, Wang P, Xu S, Wang T, Luo M, Cai Y, Xu C, Xue G, Que J, Ding Q, Jin X, Yang Y, Pang F, Pang B, Lin Y, Nie H, Xu Z, Ji Y, Jiang Q. Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network. Nat Commun 2024; 15:7101. [PMID: 39155292 PMCID: PMC11330978 DOI: 10.1038/s41467-024-51329-2] [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: 01/10/2024] [Accepted: 08/01/2024] [Indexed: 08/20/2024] Open
Abstract
The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.
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Affiliation(s)
- Wenyi Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Pingping Wang
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Shouping Xu
- Department of Breast Cancer, Harbin Medical University Cancer Hospital, Harbin, China
| | - Tao Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Meng Luo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yideng Cai
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Chang Xu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Guangfu Xue
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jinhao Que
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qian Ding
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiyun Jin
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Yuexin Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Fenglan Pang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Boran Pang
- Center for Difficult and Complicated Abdominal Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yi Lin
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Huan Nie
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Zhaochun Xu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China.
| | - Yong Ji
- State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Harbin Medical University, Harbin, China.
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China.
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16
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Aung TN, Warrell J, Martinez-Morilla S, Gavrielatou N, Vathiotis I, Yaghoobi V, Kluger HM, Gerstein M, Rimm DL. Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma. Clin Cancer Res 2024; 30:3520-3532. [PMID: 38837895 PMCID: PMC11326985 DOI: 10.1158/1078-0432.ccr-23-3932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/15/2024] [Accepted: 06/03/2024] [Indexed: 06/07/2024]
Abstract
PURPOSE We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment. EXPERIMENTAL DESIGN We collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. RESULTS We achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment. CONCLUSIONS We conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.
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Affiliation(s)
- Thazin N Aung
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Jonathan Warrell
- NEC Laboratories America, Princeton, New Jersey
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut
| | | | - Niki Gavrielatou
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Ioannis Vathiotis
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Vesal Yaghoobi
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Harriet M Kluger
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut
- Department of Computer Science, Yale University, New Haven, Connecticut
- Department of Statistics and Data Science, Yale University, New Haven, Connecticut
- Department of Biomedical Informatics and Data Science, Yale University, New Haven, Connecticut
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
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17
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Zaman F, Smith ML, Balagopal A, Durand CM, Redd AD, Tobian AAR. Spatial technologies to evaluate the HIV-1 reservoir and its microenvironment in the lymph node. mBio 2024; 15:e0190924. [PMID: 39058091 PMCID: PMC11324018 DOI: 10.1128/mbio.01909-24] [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: 07/28/2024] Open
Abstract
The presence of the HIV-1 reservoir, a group of immune cells that contain intact, integrated, and replication-competent proviruses, is a major challenge to cure HIV-1. HIV-1 reservoir cells are largely unaffected by the cytopathic effects of viruses, antiviral immune responses, or antiretroviral therapy (ART). The HIV-1 reservoir is seeded early during HIV-1 infection and augmented during active viral replication. CD4+ T cells are the primary target for HIV-1 infection, and recent studies suggest that memory T follicular helper cells within the lymph node, more precisely in the B cell follicle, harbor integrated provirus, which contribute to viral rebound upon ART discontinuation. The B cell follicle, more specifically the germinal center, possesses a unique environment because of its distinct property of being partly immune privileged, potentially allowing HIV-1-infected cells within the lymph nodes to be protected from CD8+ T cells. This modified immune response in the germinal center of the follicle is potentially explained by the exclusion of CD8+ T cells and the presence of T regulatory cells at the junction of the follicle and extrafollicular region. The proviral makeup of HIV-1-infected cells is similar in lymph nodes and blood, suggesting trafficking between these compartments. Little is known about the cell-to-cell interactions, microenvironment of HIV-1-infected cells in the follicle, and trafficking between the lymph node follicle and other body compartments. Applying a spatiotemporal approach that integrates genomics, transcriptomics, and proteomics to investigate the HIV-1 reservoir and its neighboring cells in the lymph node has promising potential for informing HIV-1 cure efforts.
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Affiliation(s)
- Fatima Zaman
- Department of
Pathology, Johns Hopkins University School of
Medicine, Baltimore,
Maryland, USA
| | - Melissa L. Smith
- Department of
Biochemistry and Molecular Genetics, University of Louisville School of
Medicine, Louisville,
Kentucky, USA
| | - Ashwin Balagopal
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
| | - Christine M. Durand
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
| | - Andrew D. Redd
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
- Laboratory of
Immunoregulation, National Institute of Allergy and Infectious Diseases,
National Institutes of Health,
Bethesda, Maryland, USA
- Institute of
Infectious Disease and Molecular Medicine, University of Cape
Town, Cape Town,
South Africa
| | - Aaron A. R. Tobian
- Department of
Pathology, Johns Hopkins University School of
Medicine, Baltimore,
Maryland, USA
- Division of Infectious
Diseases, Department of Medicine, Johns Hopkins
University, Baltimore,
Maryland, USA
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18
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Jin W, Pei J, Roy JR, Jayaraman S, Ahalliya RM, Kanniappan GV, Mironescu M, Palanisamy CP. Comprehensive review on single-cell RNA sequencing: A new frontier in Alzheimer's disease research. Ageing Res Rev 2024; 100:102454. [PMID: 39142391 DOI: 10.1016/j.arr.2024.102454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/07/2024] [Accepted: 08/09/2024] [Indexed: 08/16/2024]
Abstract
Alzheimer's disease (AD) is a multifaceted neurodegenerative condition marked by gradual cognitive deterioration and the loss of neurons. While conventional bulk RNA sequencing techniques have shed light on AD pathology, they frequently obscure the cellular diversity within brain tissues. The advent of single-cell RNA sequencing (scRNA-seq) has transformed our capability to analyze the cellular composition of AD, allowing for the detection of unique cell populations, rare cell types, and gene expression alterations at an individual cell level. This review examines the use of scRNA-seq in AD research, focusing on its contributions to understanding cellular diversity, disease progression, and potential therapeutic targets. We discuss key technological innovations, data analysis techniques, and challenges associated with scRNA-seq in studying AD. Furthermore, we highlight recent studies that have utilized scRNA-seq to identify novel biomarkers, uncover disease-associated pathways, and elucidate the role of non-neuronal cells, such as microglia and astrocytes, in AD pathogenesis. By providing a comprehensive overview of advancements in scRNA-seq for unraveling cellular heterogeneity in AD, this review highlights the transformative impact of scRNA-seq on our comprehension of disease mechanisms and the creation of targeted treatments.
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Affiliation(s)
- Wengang Jin
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 2011 QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C, Shaanxi Province Key Laboratory of Bio-Resources, College of Bioscience and Bioengineering, Shaanxi University of Technology, Hanzhong 723001, China
| | - JinJin Pei
- Qinba State Key Laboratory of Biological Resources and Ecological Environment, 2011 QinLing-Bashan Mountains Bioresources Comprehensive Development C. I. C, Shaanxi Province Key Laboratory of Bio-Resources, College of Bioscience and Bioengineering, Shaanxi University of Technology, Hanzhong 723001, China
| | - Jeane Rebecca Roy
- Department of Anatomy, Bhaarath Medical College and hospital, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu 600073, India
| | - Selvaraj Jayaraman
- Centre of Molecular Medicine and Diagnostics (COMManD), Department of Biochemistry, Saveetha Dental College & Hospital, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai 600077, India
| | - Rathi Muthaiyan Ahalliya
- Department of Biochemistry and Cancer Research Centre, FASCM, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu 641021, India
| | - Gopalakrishnan Velliyur Kanniappan
- Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, Tamil Nadu 602105, India.
| | - Monica Mironescu
- Faculty of Agricultural Sciences Food Industry and Environmental Protection, Lucian Blaga University of Sibiu, Bv. Victoriei 10, Sibiu 550024, Romania.
| | - Chella Perumal Palanisamy
- Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
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19
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Eagles NJ, Bach SV, Tippani M, Ravichandran P, Du Y, Miller RA, Hyde TM, Page SC, Martinowich K, Collado-Torres L. Integrating gene expression and imaging data across Visium capture areas with visiumStitched. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.08.607222. [PMID: 39149358 PMCID: PMC11326277 DOI: 10.1101/2024.08.08.607222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Background Visium is a widely-used spatially-resolved transcriptomics assay available from 10x Genomics. Standard Visium capture areas (6.5mm by 6.5mm) limit the survey of larger tissue structures, but combining overlapping images and associated gene expression data allow for more complex study designs. Current software can handle nested or partial image overlaps, but is designed for merging up to two capture areas, and cannot account for some technical scenarios related to capture area alignment. Results We generated Visium data from a postmortem human tissue sample such that two capture areas were partially overlapping and a third one was adjacent. We developed the R/Bioconductor package visiumStitched, which facilitates stitching the images together with Fiji (ImageJ), and constructing SpatialExperiment R objects with the stitched images and gene expression data. visiumStitched constructs an artificial hexagonal array grid which allows seamless downstream analyses such as spatially-aware clustering without discarding data from overlapping spots. Data stitched with visiumStitched can then be interactively visualized with spatialLIBD. Conclusions visiumStitched provides a simple, but flexible framework to handle various multi-capture area study design scenarios. Specifically, it resolves a data processing step without disrupting analysis workflows and without discarding data from overlapping spots. visiumStiched relies on affine transformations by Fiji, which have limitations and are less accurate when aligning against an atlas or other situations. visiumStiched provides an easy-to-use solution which expands possibilities for designing multi-capture area study designs.
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Affiliation(s)
- Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Svitlana V. Bach
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Prashanthi Ravichandran
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, 21218, Baltimore, USA
| | - Yufeng Du
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Ryan A. Miller
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, USA
- Department of Neurology, Johns Hopkins School of Medicine, 21205, Baltimore, USA
| | - Stephanie C. Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, USA
- Department of Neurology, Johns Hopkins School of Medicine, 21205, Baltimore, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, 21205, Baltimore, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, 21218, Baltimore, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 21205, Baltimore, USA
- Center for Computational Biology, Johns Hopkins University, 21205, Baltimore, USA
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20
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Ding J, Li L, Lu Q, Venegas J, Wang Y, Wu L, Jin W, Wen H, Liu R, Tang W, Dai X, Li Z, Zuo W, Chang Y, Lei YL, Shang L, Danaher P, Xie Y, Tang J. SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology. J Comput Biol 2024. [PMID: 39117342 DOI: 10.1089/cmb.2024.0532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024] Open
Abstract
Recent technological advancements have enabled spatially resolved transcriptomic profiling but at a multicellular resolution that is more cost-effective. The task of cell type deconvolution has been introduced to disentangle discrete cell types from such multicellular spots. However, existing benchmark datasets for cell type deconvolution are either generated from simulation or limited in scale, predominantly encompassing data on mice and are not designed for human immuno-oncology. To overcome these limitations and promote comprehensive investigation of cell type deconvolution for human immuno-oncology, we introduce a large-scale spatial transcriptomic deconvolution benchmark dataset named SpatialCTD, encompassing 1.8 million cells and 12,900 pseudo spots from the human tumor microenvironment across the lung, kidney, and liver. In addition, SpatialCTD provides more realistic reference than those generated from single-cell RNA sequencing (scRNA-seq) data for most reference-based deconvolution methods. To utilize the location-aware SpatialCTD reference, we propose a graph neural network-based deconvolution method (i.e., GNNDeconvolver). Extensive experiments show that GNNDeconvolver often outperforms existing state-of-the-art methods by a substantial margin, without requiring scRNA-seq data. To enable comprehensive evaluations of spatial transcriptomics data from flexible protocols, we provide an online tool capable of converting spatial transcriptomic data from various platforms (e.g., 10× Visium, MERFISH, and sci-Space) into pseudo spots, featuring adjustable spot size. The SpatialCTD dataset and GNNDeconvolver implementation are available at https://github.com/OmicsML/SpatialCTD, and the online converter tool can be accessed at https://omicsml.github.io/SpatialCTD/.
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Affiliation(s)
- Jiayuan Ding
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Lingxiao Li
- Boston University, Boston, Massachusetts, USA
| | - Qiaolin Lu
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Julian Venegas
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, California, USA
| | - Lidan Wu
- NanoString Technologies, Seattle, USA
| | - Wei Jin
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Hongzhi Wen
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Wenzhuo Tang
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
| | - Xinnan Dai
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Zhaoheng Li
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Wangyang Zuo
- Department of Computer Science, Zhejiang University of Technology, Hangzhou, China
| | - Yi Chang
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Yu Leo Lei
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, USA
- University of Michigan Rogel Cancer Center, Ann Arbor, USA
| | - Lulu Shang
- Department of Biostatistics, MD Anderson Cancer Center, Houston, USA
| | | | - Yuying Xie
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
| | - Jiliang Tang
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
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21
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Masatti L, Marchetti M, Pirrotta S, Spagnol G, Corrà A, Ferrari J, Noventa M, Saccardi C, Calura E, Tozzi R. The unveiled mosaic of intra-tumor heterogeneity in ovarian cancer through spatial transcriptomic technologies: A systematic review. Transl Res 2024; 273:104-114. [PMID: 39111726 DOI: 10.1016/j.trsl.2024.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 07/16/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024]
Abstract
Epithelial ovarian cancer is a significant global health issue among women. Diagnosis and treatment pose challenges due to difficulties in predicting patient responses to therapy, primarily stemming from gaps in understanding tumor chemoresistance mechanisms. Recent advancements in transcriptomic technologies like single-cell RNA sequencing and spatial transcriptomics have greatly improved our understanding of ovarian cancer intratumor heterogeneity and tumor microenvironment composition. Spatial transcriptomics, in particular, comprises a plethora of technologies that enable the detection of hundreds of transcriptomes and their spatial distribution within a histological section, facilitating the study of cell types, states, and interactions within the tumor and its microenvironment. Studies investigating the spatial distribution of gene expression in ovarian cancer masses have identified specific features that impact prognosis and therapy outcomes. Emerging evidence suggests that specific spatial patterns of tumor cells and their immune and non-immune microenvironment significantly influence therapy response, as well as the behavior and progression of primary tumors and metastatic sites. The importance of spatially contextualizing ovarian cancer transcriptomes is underscored by these findings, which will advance our understanding and therapeutic approaches for this complex disease.
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Affiliation(s)
- Laura Masatti
- Department of Biology, University of Padova, Padova, Italy
| | - Matteo Marchetti
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | | | - Giulia Spagnol
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | - Anna Corrà
- Department of Biology, University of Padova, Padova, Italy; Fondazione Istituto di Ricerca Pediatrica Città della Speranza, Padova, Italy
| | - Jacopo Ferrari
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | - Marco Noventa
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | - Carlo Saccardi
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
| | - Enrica Calura
- Department of Biology, University of Padova, Padova, Italy.
| | - Roberto Tozzi
- Department of Gynecology and Obstetrics, Division of Women and Children, Padova University Hospital, Padova, Italy
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22
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Isnard P, Li D, Xuanyuan Q, Wu H, Humphreys BD. Histopathological-Based Analysis of Human Kidney Spatial Transcriptomics Data: Toward Precision Pathology. THE AMERICAN JOURNAL OF PATHOLOGY 2024:S0002-9440(24)00274-8. [PMID: 39097165 DOI: 10.1016/j.ajpath.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/04/2024] [Accepted: 06/26/2024] [Indexed: 08/05/2024]
Abstract
The application of spatial transcriptomics (ST) technologies is booming and has already yielded important insights across many different tissues and disease models. In nephrology, ST technologies have helped to decipher the cellular and molecular mechanisms at work in kidney diseases and have allowed the recent creation of spatially anchored human kidney atlases in healthy and diseased kidney tissues. During ST data analysis, the obtained computationally annotated clusters are often superimposed on a histologic image without their initial identification being based on the morphologic and spatial analyses of the tissues and lesions. In this study, we conduct a histopathologic-based analysis of ST data on a human kidney sample corresponding as closely as possible to the reality of the interpretation of a kidney biopsy sample in a health care or research context. This study shows the feasibility of a morphology-based approach to interpreting ST data, helping to improve our understanding of the lesion phenomena at work in chronic kidney disease at both the cellular and the molecular level. Finally, we show that our newly identified pathology-based clusters can be accurately projected onto other slides from nephrectomy or needle biopsy samples. They thus serve as a reference for analyzing other kidney tissues, paving the way for the future of molecular microscopy and precision pathology.
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Affiliation(s)
- Pierre Isnard
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Dian Li
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Qiao Xuanyuan
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Haojia Wu
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, Missouri; Department of Developmental Biology, Washington University in St. Louis, St. Louis, Missouri.
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23
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Li J, Shyr Y, Liu Q. aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph. Genome Biol 2024; 25:203. [PMID: 39090647 PMCID: PMC11293182 DOI: 10.1186/s13059-024-03339-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 07/16/2024] [Indexed: 08/04/2024] Open
Abstract
Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones. Here, we develop aKNNO to simultaneously identify abundant and rare cell types based on an adaptive k-nearest neighbor graph with optimization. Benchmarking on 38 simulated and 20 single-cell and spatial transcriptomics datasets demonstrates that aKNNO identifies both abundant and rare cell types more accurately than general and specialized methods. Using only gene expression aKNNO maps abundant and rare cells more precisely compared to integrative approaches.
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Affiliation(s)
- Jia Li
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, 37203, USA
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
| | - Qi Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.
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24
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Dezem FS, Arjumand W, DuBose H, Morosini NS, Plummer J. Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives. Annu Rev Biomed Data Sci 2024; 7:131-153. [PMID: 38768396 DOI: 10.1146/annurev-biodatasci-102523-103640] [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: 05/22/2024]
Abstract
Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.
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Affiliation(s)
- Felipe Segato Dezem
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Wani Arjumand
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Hannah DuBose
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Natalia Silva Morosini
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Jasmine Plummer
- Department of Cellular and Molecular Biology and Comprehensive Cancer Center, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
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25
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Tian T, Zhang J, Lin X, Wei Z, Hakonarson H. Dependency-aware deep generative models for multitasking analysis of spatial omics data. Nat Methods 2024; 21:1501-1513. [PMID: 38783067 DOI: 10.1038/s41592-024-02257-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 03/25/2024] [Indexed: 05/25/2024]
Abstract
Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively.
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Affiliation(s)
- Tian Tian
- School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, Hubei, China
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jie Zhang
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
| | - Xiang Lin
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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26
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Yu Z, Coorens THH, Uddin MM, Ardlie KG, Lennon N, Natarajan P. Genetic variation across and within individuals. Nat Rev Genet 2024; 25:548-562. [PMID: 38548833 DOI: 10.1038/s41576-024-00709-x] [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] [Accepted: 02/09/2024] [Indexed: 04/12/2024]
Abstract
Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.
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Affiliation(s)
- Zhi Yu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Md Mesbah Uddin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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27
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Lan YP, Ding CZ, Xia JX, Yang YZ, Zhao YB. Analysis of the functional role and mRNA expression of GABA B R in the nucleus accumbens of cocaine-addicted rats. J Chin Med Assoc 2024; 87:754-764. [PMID: 38860774 DOI: 10.1097/jcma.0000000000001119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND Drug addiction is a social and medical problem that must be urgently addressed. The nucleus accumbens (NAc) is closely related to addiction-related learning memory, and γ-aminobutyric acid type B receptor (GABA B R) is a potential target for the treatment of drug addiction. However, the role of GABA B R activity levels in the NAc in cocaine addiction is unclear. METHODS In this study, we established an animal model of cocaine dependence, modulated the level of GABA B R activity, applied a conditioned place preference assay (CPP) to assess the role of the NAc in reconsolidation of addiction memory, evaluated learning and memory functions by behavioral experiments, examined the expression of GB1, GB2, cyclic adenosine monophosphate response element binding protein (CREB), p-CREB, protein kinase A (PKA), protein kinase (ERK), and Brain-derived neurotrophic factor (BDNF) in the NAc by molecular biology experiments, and screened differentially significantly expressed genes by transcriptome sequencing. RESULTS Our study showed that the GABA B receptor agonist baclofen (BLF) had a significant effect on locomotor distance in rats, promoted an increase in GABA levels and significantly inhibited the PKA and ERK1/2/CREB/BDNF signaling pathways. Moreover, transcriptome sequencing showed that GABA B R antagonist intervention identified a total of 21 upregulated mRNAs and 21 downregulated mRNAs. The differentially expressed (DE) mRNA genes were mainly enriched in tyrosine metabolism; however, further study is needed. CONCLUSION GABA B R activity in the NAc is involved in the regulation of cocaine addiction and may play an important role through key mRNA pathways.
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Affiliation(s)
- Yan-Ping Lan
- Department of Neurosurgery, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, China
- Key Laboratory of Craniocerebral Diseases, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Chen-Zhe Ding
- Department of Neurosurgery, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, China
| | - Jian-Xue Xia
- Department of Neurosurgery, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, China
| | - Yun-Zhen Yang
- Department of Neurosurgery, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, China
| | - Yan-Bin Zhao
- Department of Neurosurgery, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia, China
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28
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Guo B, Ling W, Kwon SH, Panwar P, Ghazanfar S, Martinowich K, Hicks SC. Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities. ARXIV 2024:arXiv:2408.00367v1. [PMID: 39130195 PMCID: PMC11312629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future.
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Affiliation(s)
- Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Wodan Ling
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, NY, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Biochemistry, Cellular, and Molecular Biology Graduate Program, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Pratibha Panwar
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
| | - Shila Ghazanfar
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
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29
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Zhang J, Huang Y, Tan X, Wang Z, Cheng R, Zhang S, Chen Y, Jiang F, Tan W, Deng X, Li F. Integrated analysis of multiple transcriptomic approaches and machine learning integration algorithms reveals high endothelial venules as a prognostic immune-related biomarker in bladder cancer. Int Immunopharmacol 2024; 136:112184. [PMID: 38824904 DOI: 10.1016/j.intimp.2024.112184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/19/2024] [Accepted: 04/28/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Despite the availability of established surgical and chemotherapy options, the treatment of bladder cancer (BCa) patients remains challenging. While immunotherapy has emerged as a promising approach, its benefits are limited to a subset of patients. The exploration of additional targets to enhance the efficacy of immunotherapy is a valuable research direction. METHOD High endothelial venules (HEV) ssGSEA analysis was conducted using BEST. Through the utilization of R packages Limma, Seurat, SingleR, and Harmony, analyses were performed on spatial transcriptomics, bulk RNA-sequencing (bulk RNA-seq), and single-cell RNA sequencing (scRNA-seq) data, yielding HEV-related genes (HEV.RGs). Molecular subtyping analysis based on HEV.RGs was conducted using R package MOVICS, and various machine learning-integrated algorithm was employed to construct prognostic model. LDLRAD3 was validated through subcutaneous tumor formation in mice, HEV induction, Western blot, and qPCR. RESULTS A correlation between higher HEV levels and improved immune response and prognosis was revealed by HEV ssGSEA analysis in BCa patients receiving immunotherapy. HEV.RGs were identified in subsequent transcriptomic analyses. Based on these genes, BCa patients were stratified into two molecular clusters with distinct survival and immune infiltration patterns using various clustering-integrated algorithm. Prognostic model was developed using multiple machine learning-integrated algorithm. Low LDLRAD3 expression may promote HEV generation, leading to enhanced immunotherapy efficacy, as suggested by bulk RNA-seq, scRNA-seq analyses, and experimental validation of LDLRAD3. CONCLUSIONS HEV served as a predictive factor for immune response and prognosis in BCa patients receiving immunotherapy. LDLRAD3 represented a potential target for HEV induction and enhancing the efficacy of immunotherapy.
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Affiliation(s)
- Jinge Zhang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Yuan Huang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Xing Tan
- Department of Nanfang Hospital Administration Office, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Zihuan Wang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Ranyang Cheng
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Shenlan Zhang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Yuwen Chen
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Feifan Jiang
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Wanlong Tan
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China.
| | - Xiaolin Deng
- Department of Urology, Ganzhou People's Hospital, Ganzhou, PR China.
| | - Fei Li
- Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, PR China.
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Qiu ZX, Huang LX, Wang XX, Wang ZL, Li XH, Feng B. Exploring the Pathogenesis of Autoimmune Liver Diseases from the Heterogeneity of Target Cells. J Clin Transl Hepatol 2024; 12:659-666. [PMID: 38993508 PMCID: PMC11233981 DOI: 10.14218/jcth.2023.00531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 04/28/2024] [Accepted: 04/30/2024] [Indexed: 07/13/2024] Open
Abstract
The incidence of autoimmune liver diseases (ALDs) and research on their pathogenesis are increasing annually. However, except for autoimmune hepatitis, which responds well to immunosuppression, primary biliary cholangitis and primary sclerosing cholangitis are insensitive to immunosuppressive therapy. Besides the known effects of the environment, genetics, and immunity on ALDs, the heterogeneity of target cells provides new insights into their pathogenesis. This review started by exploring the heterogeneity in the development, structures, and functions of hepatocytes and epithelial cells of the small and large bile ducts. For example, cytokeratin (CK) 8 and CK18 are primarily expressed in hepatocytes, while CK7 and CK19 are primarily expressed in intrahepatic cholangiocytes. Additionally, emerging technologies of single-cell RNA sequencing and spatial transcriptomic are being applied to study ALDs. This review offered a new perspective on understanding the pathogenic mechanisms and potential treatment strategies for ALDs.
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Affiliation(s)
| | | | - Xiao-Xiao Wang
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, China
| | - Zi-Long Wang
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, China
| | - Xiao-He Li
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, China
| | - Bo Feng
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, China
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Ariotta V, Azzalini E, Canzonieri V, Hautaniemi S, Bonin S. Comparative Analysis of Gene Expression Analysis Methods for RNA in Situ Hybridization Images. J Mol Diagn 2024:S1525-1578(24)00163-6. [PMID: 39068989 DOI: 10.1016/j.jmoldx.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/27/2024] [Accepted: 06/26/2024] [Indexed: 07/30/2024] Open
Abstract
Gene expression analysis is pivotal in cancer research and clinical practice. Although traditional methods lack spatial context, RNA in situ hybridization (RNA-ISH) is a powerful technique that retains spatial tissue information. Here, RNAscope score, RT-droplet digital PCR, and automated QuantISH and QuPath were used for quantifying RNA-ISH expression values from formalin-fixed, paraffin-embedded samples. The methods were compared using high-grade serous ovarian carcinoma samples, focusing on CCNE1, WFDC2, and PPIB genes. The findings demonstrate good concordance between automated methods and RNAscope, with RT-droplet digital PCR showing less concordance. Additionally, QuantISH exhibits robust performance, even for low-expressed genes like CCNE1, showcasing its modular design and enhancing accessibility as a viable alternative for gene expression analysis.
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Affiliation(s)
- Valeria Ariotta
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Eros Azzalini
- Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Vincenzo Canzonieri
- Department of Medical Sciences, University of Trieste, Trieste, Italy; Pathology Unit, Centro di Riferimento Oncologico IRCCS, Aviano-National Cancer Institute, Pordenone, Italy
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Serena Bonin
- Department of Medical Sciences, University of Trieste, Trieste, Italy.
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Budhkar A, Tang Z, Liu X, Zhang X, Su J, Song Q. xSiGra: explainable model for single-cell spatial data elucidation. Brief Bioinform 2024; 25:bbae388. [PMID: 39120644 PMCID: PMC11312371 DOI: 10.1093/bib/bbae388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/22/2024] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
Recent advancements in spatial imaging technologies have revolutionized the acquisition of high-resolution multichannel images, gene expressions, and spatial locations at the single-cell level. Our study introduces xSiGra, an interpretable graph-based AI model, designed to elucidate interpretable features of identified spatial cell types, by harnessing multimodal features from spatial imaging technologies. By constructing a spatial cellular graph with immunohistology images and gene expression as node attributes, xSiGra employs hybrid graph transformer models to delineate spatial cell types. Additionally, xSiGra integrates a novel variant of gradient-weighted class activation mapping component to uncover interpretable features, including pivotal genes and cells for various cell types, thereby facilitating deeper biological insights from spatial data. Through rigorous benchmarking against existing methods, xSiGra demonstrates superior performance across diverse spatial imaging datasets. Application of xSiGra on a lung tumor slice unveils the importance score of cells, illustrating that cellular activity is not solely determined by itself but also impacted by neighboring cells. Moreover, leveraging the identified interpretable genes, xSiGra reveals endothelial cell subset interacting with tumor cells, indicating its heterogeneous underlying mechanisms within complex cellular interactions.
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Affiliation(s)
- Aishwarya Budhkar
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, 107 S Indiana Ave, Bloomington, IN 47405, United States
| | - Ziyang Tang
- Department of Computer and Information Technology, Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, United States
| | - Xiang Liu
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 340 W 10th St, Indianapolis, IN 46202, United States
| | - Xuhong Zhang
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, 107 S Indiana Ave, Bloomington, IN 47405, United States
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 340 W 10th St, Indianapolis, IN 46202, United States
- Gerontology and Geriatric Medicine, Wake Forest School of Medicine, 475 Vine St, Winston-Salem, NC 27101, United States
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32611, United States
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Fortner A, Bucur O. Multiplexed spatial transcriptomics methods and the application of expansion microscopy. Front Cell Dev Biol 2024; 12:1378875. [PMID: 39105173 PMCID: PMC11298486 DOI: 10.3389/fcell.2024.1378875] [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: 01/30/2024] [Accepted: 06/10/2024] [Indexed: 08/07/2024] Open
Abstract
While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.
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Affiliation(s)
- Andra Fortner
- Medical School, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
- Victor Babes National Institute of Pathology, Bucharest, Romania
| | - Octavian Bucur
- Victor Babes National Institute of Pathology, Bucharest, Romania
- Genomics Research and Development Institute, Bucharest, Romania
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Ma H, Chen M, Xu J, Zhao Y, Liu Y. An Omni-Mesoscope for multiscale high-throughput quantitative phase imaging of cellular dynamics and high-content molecular characterization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.18.604137. [PMID: 39071371 PMCID: PMC11275778 DOI: 10.1101/2024.07.18.604137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The mesoscope has emerged as a powerful imaging tool in biomedical research, yet its high cost and low resolution have limited its broader application. Here, we introduce the Omni-Mesoscope, a cost-effective high-spatial-temporal, multimodal, and multiplex mesoscopic imaging platform built from cost-efficient off-the-shelf components. This system uniquely merges the capabilities of quantitative phase microscopy to capture live-cell dynamics over a large cell population with highly multiplexed fluorescence imaging for comprehensive molecular characterization. This integration facilitates simultaneous tracking of live-cell morphodynamics across thousands of cells, alongside high-content molecular analysis at the single-cell level. Furthermore, the Omni-Mesoscope offers a mesoscale field of view of approximately 5 mm 2 with a high spatial resolution down to 700 nm, enabling the capture of information-rich images with detailed sub-cellular features. We demonstrate such capability in delineating molecular characteristics underlying rare dynamic cellular phenomena, such as cancer cell responses to chemotherapy and the emergence of polyploidy in drug-resistant cells. Moreover, the cost-effectiveness and the simplicity of our Omni-Mesoscope democratizes mesoscopic imaging, making it accessible across diverse biomedical research fields. To further demonstrate its versatility, we integrate expansion microscopy to enhance 3D volumetric super-resolution imaging of thicker tissues, opening new avenues for biological exploration at unprecedented scales and resolutions.
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Li H, Lin Y, He W, Han W, Xu X, Xu C, Gao E, Zhao H, Gao X. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nat Commun 2024; 15:6048. [PMID: 39025895 PMCID: PMC11258319 DOI: 10.1038/s41467-024-50308-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: 02/06/2024] [Accepted: 07/03/2024] [Indexed: 07/20/2024] Open
Abstract
With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.
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Affiliation(s)
- Haoyang Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yingxin Lin
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Wenjia He
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xiaopeng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Chencheng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Elva Gao
- The KAUST school, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA.
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
- Center of Excellence on Smart Health, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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Liu K, Wang Y, Wang C, Guo C, Zhang D, Zhong Y, Yin L, Lu Y, Liu F, Zhang Y, Zhang D. Spatial transcriptomics of gastric cancer brain metastasis reveals atypical vasculature strategies with supportive immune profiles. Gastroenterol Rep (Oxf) 2024; 12:goae067. [PMID: 39027914 PMCID: PMC11257699 DOI: 10.1093/gastro/goae067] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 05/26/2024] [Accepted: 05/31/2024] [Indexed: 07/20/2024] Open
Abstract
Background Gastric cancer brain metastasis (GCBM) represents a rare but highly aggressive malignancy. Metastatic cancer cells are highly heterogeneous and differentially remodels brain vasculature and immune microenvironments, which affects the treatment effectiveness and patient outcome. This study aimed to investigate the spatial interactions among different cell components, especially the vasculature system and the brain microenvironment of GCBM patients. Methods We used digital spatial profiling to examine 140 regions composing tumor, immune, and brain tissues from three GCBM patients. Transcriptomic data with spatial information were analyzed for tissue areas related to different blood recruitment strategies. For validation, independent analysis of patient bulk transcriptomic data and in vivo single-cell transcriptomic data were performed. Results Angiogenesis and blood vessel co-option co-existed within the same GCBM lesion. Tumors with high epithelial-mesenchymal transition and an enhanced transcriptomic gene signature composed of CTNNB1, SPARC, VIM, SMAD3, SMAD4, TGFB1, TGFB2, and TGFB3 were more prone to adopt blood vessel co-option than angiogenesis. Enriched macrophage infiltration, angiogenic chemokines, and NAMPT were found in angiogenic areas, while increased T cells, T cell activating cytokines, and reduced NAMPT were found in vessel co-option regions. Spatially, angiogenesis was enriched at the tumor edge, which showed higher DMBT1 expression than the tumor center. Conclusions This study mapped the orchestrated spatial characteristics of tumor and immunological compositions that support the conventional and atypical vascularization strategies in GCBM. Our data provided molecular insights for more effective combinations of anti-vascular and immune therapies.
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Affiliation(s)
- Kaijing Liu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Ying Wang
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
| | - Chunhua Wang
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
| | - Chengcheng Guo
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
| | - Dun Zhang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Yu Zhong
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Lin Yin
- AccuraMed Technology (Guangzhou) Co., Ltd, Guangzhou, Guangdong, P. R. China
| | - Yunxin Lu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Furong Liu
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
| | - Yang Zhang
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
| | - Dongsheng Zhang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Integrated Traditional Chinese and Western Medicine Research Center, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, P. R. China
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Wang Z, Li Z, Luan T, Cui G, Shu S, Liang Y, Zhang K, Xiao J, Yu W, Cui J, Li A, Peng G, Fang Y. A spatiotemporal molecular atlas of mouse spinal cord injury identifies a distinct astrocyte subpopulation and therapeutic potential of IGFBP2. Dev Cell 2024:S1534-5807(24)00394-0. [PMID: 39029468 DOI: 10.1016/j.devcel.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 03/26/2024] [Accepted: 06/20/2024] [Indexed: 07/21/2024]
Abstract
Spinal cord injury (SCI) triggers a cascade of intricate molecular and cellular changes that determine the outcome. In this study, we resolve the spatiotemporal organization of the injured mouse spinal cord and quantitatively assess in situ cell-cell communication following SCI. By analyzing existing single-cell RNA sequencing datasets alongside our spatial data, we delineate a subpopulation of Igfbp2-expressing astrocytes that migrate from the white matter (WM) to gray matter (GM) and become reactive upon SCI, termed Astro-GMii. Further, Igfbp2 upregulation promotes astrocyte migration, proliferation, and reactivity, and the secreted IGFBP2 protein fosters neurite outgrowth. Finally, we show that IGFBP2 significantly reduces neuronal loss and remarkably improves the functional recovery in a mouse model of SCI in vivo. Together, this study not only provides a comprehensive molecular atlas of SCI but also exemplifies how this rich resource can be applied to endow cells and genes with functional insight and therapeutic potential.
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Affiliation(s)
- Zeqing Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuxia Li
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianle Luan
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guizhong Cui
- Guangzhou National Laboratory, Guangzhou 510005, China
| | - Shunpan Shu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiyao Liang
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong Key Laboratory of Non-human Primate Research, GHM Institute of CNS Regeneration, Jinan University, Guangzhou 510632, China
| | - Kai Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingshu Xiao
- Guangzhou National Laboratory, Guangzhou 510005, China
| | - Wei Yu
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong Key Laboratory of Non-human Primate Research, GHM Institute of CNS Regeneration, Jinan University, Guangzhou 510632, China
| | - Jihong Cui
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China
| | - Ang Li
- Key Laboratory of CNS Regeneration (Ministry of Education), Guangdong Key Laboratory of Non-human Primate Research, GHM Institute of CNS Regeneration, Jinan University, Guangzhou 510632, China.
| | - Guangdun Peng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Yanshan Fang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Li Y, Chen Y, Wang D, Wu L, Li T, An N, Yang H. Elucidating the multifaceted role of MGAT1 in hepatocellular carcinoma: integrative single-cell and spatial transcriptomics reveal novel therapeutic insights. Front Immunol 2024; 15:1442722. [PMID: 39081317 PMCID: PMC11286416 DOI: 10.3389/fimmu.2024.1442722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 06/28/2024] [Indexed: 08/02/2024] Open
Abstract
Background Glycosyltransferase-associated genes play a crucial role in hepatocellular carcinoma (HCC) pathogenesis. This study investigates their impact on the tumor microenvironment and molecular mechanisms, offering insights into innovative immunotherapeutic strategies for HCC. Methods We utilized cutting-edge single-cell and spatial transcriptomics to examine HCC heterogeneity. Four single-cell scoring techniques were employed to evaluate glycosyltransferase genes. Spatial transcriptomic findings were validated, and bulk RNA-seq analysis was conducted to identify prognostic glycosyltransferase-related genes and potential immunotherapeutic targets. MGAT1's role was further explored through various functional assays. Results Our analysis revealed diverse cell subpopulations in HCC with distinct glycosyltransferase gene activities, particularly in macrophages. Key glycosyltransferase genes specific to macrophages were identified. Temporal analysis illustrated macrophage evolution during tumor progression, while spatial transcriptomics highlighted reduced expression of these genes in core tumor macrophages. Integrating scRNA-seq, bulk RNA-seq, and spatial transcriptomics, MGAT1 emerged as a promising therapeutic target, showing significant potential in HCC immunotherapy. Conclusion This comprehensive study delves into glycosyltransferase-associated genes in HCC, elucidating their critical roles in cellular dynamics and immune cell interactions. Our findings open new avenues for immunotherapeutic interventions and personalized HCC management, pushing the boundaries of HCC immunotherapy.
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Affiliation(s)
- Yang Li
- Department of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
| | - Yuan Chen
- Department of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danqiong Wang
- Department of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
| | - Ling Wu
- Tumor Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
| | - Tao Li
- Department of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
| | - Na An
- Department of General Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, China
| | - Haikun Yang
- The Gastroenterology Department, Shanxi Provincial People Hospital, Taiyuan, China
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Cao Y, Li PP, Qiao BL, Li QW. Kombo knife combined with sorafenib in liver cancer treatment: Efficacy and safety under immune function influence. World J Gastrointest Oncol 2024; 16:3118-3157. [PMID: 39072171 PMCID: PMC11271779 DOI: 10.4251/wjgo.v16.i7.3118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/02/2024] [Accepted: 03/20/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND In the quest to manage hepatocellular carcinoma (HCC), the focus has shifted to a more holistic approach encompassing both data analytics and innovative treatments. Analyzing rich data resources, such as the cancer genome atlas (TCGA), and examining progressive therapies can potentially reshape the trajectory of HCC treatment. AIM To elucidate the immunological genes and the underlying mechanism of the combined Kombo knife and sorafenib regimen for HCC by analyzing data from TCGA and machine learning data. METHODS Immune attributes were evaluated via TCGA's postablation HCC RNA sequencing data. Using weighted gene coexpression network analysis and machine learning, we identified genes with high prognostic value. The therapeutic landscape and safety metrics of the integrated treatment were critically evaluated across cellular and animal models. RESULTS Immune genes-specifically, peptidylprolyl isomerase A and solute carrier family 29 member 3-emerged as significant prognostic markers. Enhanced therapeutic outcomes, such as prolonged progression-free survival and an elevated overall response rate, characterize the combined approach, with peripheral blood mononuclear cells displaying potent effects on HCC dynamics. CONCLUSION The combination of Kombo knife with sorafenib is an innovative HCC treatment modality anchored in immune-centric strategies.
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Affiliation(s)
- Yang Cao
- Department of Oncology, The Third People's Hospital of Zhengzhou, Zhengzhou 450000, Henan Province, China
| | - Pei-Pei Li
- Department of Oncology, The Third People's Hospital of Zhengzhou, Zhengzhou 450000, Henan Province, China
| | - Bing-Li Qiao
- Department of Oncology, The Third People's Hospital of Zhengzhou, Zhengzhou 450000, Henan Province, China
| | - Quan-Wang Li
- Department of Oncology, The Affiliated Oriental Hospital of Beijing University of Chinese Medicine, Beijing 100078, China
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Love NR, Williams C, Killingbeck EE, Merleev A, Saffari Doost M, Yu L, McPherson JD, Mori H, Borowsky AD, Maverakis E, Kiuru M. Melanoma progression and prognostic models drawn from single-cell, spatial maps of benign and malignant tumors. SCIENCE ADVANCES 2024; 10:eadm8206. [PMID: 38996022 PMCID: PMC11244543 DOI: 10.1126/sciadv.adm8206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 06/06/2024] [Indexed: 07/14/2024]
Abstract
Melanoma clinical outcomes emerge from incompletely understood genetic mechanisms operating within the tumor and its microenvironment. Here, we used single-cell RNA-based spatial molecular imaging (RNA-SMI) in patient-derived archival tumors to reveal clinically relevant markers of malignancy progression and prognosis. We examined spatial gene expression of 203,472 cells inside benign and malignant melanocytic neoplasms, including melanocytic nevi and primary invasive and metastatic melanomas. Algorithmic cell clustering paired with intratumoral comparative two-dimensional analyses visualized synergistic, spatial gene signatures linking cellular proliferation, metabolism, and malignancy, validated by protein expression. Metastatic niches included up-regulation of CDK2 and FABP5, which independently predicted poor clinical outcome in 473 patients with melanoma via Cox regression analysis. More generally, our work demonstrates a framework for applying single-cell RNA-SMI technology toward identifying gene regulatory landscapes pertinent to cancer progression and patient survival.
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Affiliation(s)
- Nick R Love
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
| | - Claire Williams
- NanoString Technologies, a Bruker Company, Seattle, WA 98109, USA
| | | | - Alexander Merleev
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
| | | | - Lan Yu
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
| | - John D McPherson
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Sacramento, CA 95816, USA
| | - Hidetoshi Mori
- Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA 95816, USA
| | - Alexander D Borowsky
- Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA 95816, USA
| | - Emanual Maverakis
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
| | - Maija Kiuru
- Department of Dermatology, University of California, Davis, Sacramento, CA 95816, USA
- Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA 95816, USA
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41
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Rios JJ, Juan C, Shelton JM, Paria N, Oxendine I, Wassell M, Kidane YH, Cornelia R, Jeffery EC, Podeszwa DA, Conway SJ, Wise CA, Tower RJ. Spatial transcriptomics implicates impaired BMP signaling in NF1 fracture pseudarthrosis in murine and patient tissues. JCI Insight 2024; 9:e176802. [PMID: 38990653 PMCID: PMC11343587 DOI: 10.1172/jci.insight.176802] [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: 07/13/2024] Open
Abstract
The neurofibromatosis type 1 (NF1) RASopathy is associated with persistent fibrotic nonunions (pseudarthrosis) in human and mouse skeletal tissue. Here, we performed spatial transcriptomics to define the molecular signatures occurring during normal endochondral healing following fracture in mice. Within the control fracture callus, we observed spatially restricted activation of morphogenetic pathways, such as TGF-β, WNT, and BMP. To investigate the molecular mechanisms contributing to Nf1-deficient delayed fracture healing, we performed spatial transcriptomic analysis on a Postn-cre;Nf1fl/- (Nf1Postn) fracture callus. Transcriptional analyses, subsequently confirmed through phospho-SMAD1/5/8 immunohistochemistry, demonstrated a lack of BMP pathway induction in Nf1Postn mice. To gain further insight into the human condition, we performed spatial transcriptomic analysis of fracture pseudarthrosis tissue from a patient with NF1. Analyses detected increased MAPK signaling at the fibrocartilaginous-osseus junction. Similar to that in the Nf1Postn fracture, BMP pathway activation was absent within the pseudarthrosis tissue. Our results demonstrate the feasibility of delineating the molecular and tissue-specific heterogeneity inherent in complex regenerative processes, such as fracture healing, and reconstructing phase transitions representing endochondral bone formation in vivo. Furthermore, our results provide in situ molecular evidence of impaired BMP signaling underlying NF1 pseudarthrosis, potentially informing the clinical relevance of off-label BMP2 as a therapeutic intervention.
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Affiliation(s)
- Jonathan J. Rios
- Center for Translational Research, Scottish Rite for Children, Dallas, Texas, USA
- McDermott Center for Human Growth and Development
- Department of Pediatrics
- Department of Orthopaedic Surgery
- Simmons Comprehensive Cancer Center
| | | | | | - Nandina Paria
- Center for Translational Research, Scottish Rite for Children, Dallas, Texas, USA
| | - Ila Oxendine
- Center for Translational Research, Scottish Rite for Children, Dallas, Texas, USA
| | - Meghan Wassell
- Center for Translational Research, Scottish Rite for Children, Dallas, Texas, USA
| | - Yared H. Kidane
- Center for Translational Research, Scottish Rite for Children, Dallas, Texas, USA
| | - Reuel Cornelia
- Center for Translational Research, Scottish Rite for Children, Dallas, Texas, USA
| | - Elise C. Jeffery
- Children’s Research Institute, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - David A. Podeszwa
- Department of Orthopaedic Surgery
- Department of Orthopaedics, Scottish Rite for Children, Dallas, Texas, USA
| | - Simon J. Conway
- Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Carol A. Wise
- Center for Translational Research, Scottish Rite for Children, Dallas, Texas, USA
- McDermott Center for Human Growth and Development
- Department of Pediatrics
- Department of Orthopaedic Surgery
| | - Robert J. Tower
- McDermott Center for Human Growth and Development
- Department of Surgery
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42
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Tirumalasetty MB, Bhattacharya I, Mohiuddin MS, Baki VB, Choubey M. Understanding testicular single cell transcriptional atlas: from developmental complications to male infertility. Front Endocrinol (Lausanne) 2024; 15:1394812. [PMID: 39055054 PMCID: PMC11269108 DOI: 10.3389/fendo.2024.1394812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 06/14/2024] [Indexed: 07/27/2024] Open
Abstract
Spermatogenesis is a multi-step biological process where mitotically active diploid (2n) spermatogonia differentiate into haploid (n) spermatozoa via regulated meiotic programming. The alarming rise in male infertility has become a global concern during the past decade thereby demanding an extensive profiling of testicular gene expression. Advancements in Next-Generation Sequencing (NGS) technologies have revolutionized our empathy towards complex biological events including spermatogenesis. However, despite multiple attempts made in the past to reveal the testicular transcriptional signature(s) either with bulk tissues or at the single-cell, level, comprehensive reviews on testicular transcriptomics and associated disorders are limited. Notably, technologies explicating the genome-wide gene expression patterns during various stages of spermatogenic progression provide the dynamic molecular landscape of testicular transcription. Our review discusses the advantages of single-cell RNA-sequencing (Sc-RNA-seq) over bulk RNA-seq concerning testicular tissues. Additionally, we highlight the cellular heterogeneity, spatial transcriptomics, dynamic gene expression and cell-to-cell interactions with distinct cell populations within the testes including germ cells (Gc), Sertoli cells (Sc), Peritubular cells (PTc), Leydig cells (Lc), etc. Furthermore, we provide a summary of key finding of single-cell transcriptomic studies that have shed light on developmental mechanisms implicated in testicular disorders and male infertility. These insights emphasize the pivotal roles of Sc-RNA-seq in advancing our knowledge regarding testicular transcriptional landscape and may serve as a potential resource to formulate future clinical interventions for male reproductive health.
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Affiliation(s)
| | - Indrashis Bhattacharya
- Department of Zoology, School of Biological Sciences, Central University of Kerala, Kasargod, Kerala, India
| | - Mohammad Sarif Mohiuddin
- Department of Foundations of Medicine, NYU Grossman Long Island School of Medicine, Mineola, NY, United States
| | - Vijaya Bhaskar Baki
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, Riverside, CA, United States
| | - Mayank Choubey
- Department of Foundations of Medicine, NYU Grossman Long Island School of Medicine, Mineola, NY, United States
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Nitz AA, Giraldez Chavez JH, Eliason ZG, Payne SH. Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses. J Proteome Res 2024. [PMID: 38981598 DOI: 10.1021/acs.jproteome.4c00091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.
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Affiliation(s)
- Alyssa A Nitz
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | | | - Zachary G Eliason
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
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Wen Y, Yang H, Hong Y. Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review. ACS Biomater Sci Eng 2024; 10:4175-4194. [PMID: 38934720 DOI: 10.1021/acsbiomaterials.4c00303] [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: 06/28/2024]
Abstract
Biomaterials, essential for supporting, enhancing, and repairing damaged tissues, play a critical role in various medical applications. This Review focuses on the interaction of biomaterials and cardiomyocytes, emphasizing the unique significance of transcriptomic approaches in understanding their interactions, which are pivotal in cardiac bioengineering and regenerative medicine. Transcriptomic approaches serve as powerful tools to investigate how cardiomyocytes respond to biomaterials, shedding light on the gene expression patterns, regulatory pathways, and cellular processes involved in these interactions. Emerging technologies such as bulk RNA-seq, single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics offer promising avenues for more precise and in-depth investigations. Longitudinal studies, pathway analyses, and machine learning techniques further improve the ability to explore the complex regulatory mechanisms involved. This review also discusses the challenges and opportunities of utilizing transcriptomic techniques in cardiomyocyte-biomaterial research. Although there are ongoing challenges such as costs, cell size limitation, sample differences, and complex analytical process, there exist exciting prospects in comprehensive gene expression analyses, biomaterial design, cardiac disease treatment, and drug testing. These multimodal methodologies have the capacity to deepen our understanding of the intricate interaction network between cardiomyocytes and biomaterials, potentially revolutionizing cardiac research with the aim of promoting heart health, and they are also promising for studying interactions between biomaterials and other cell types.
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Affiliation(s)
- Yufeng Wen
- Department of Bioengineering, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Huaxiao Yang
- Department of Biomedical Engineering, University of North Texas, Denton, Texas 76207, United States
| | - Yi Hong
- Department of Bioengineering, University of Texas at Arlington, Arlington, Texas 76019, United States
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45
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Yuan X, Ma Y, Gao R, Cui S, Wang Y, Fa B, Ma S, Wei T, Ma S, Yu Z. HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics. Nat Commun 2024; 15:5700. [PMID: 38972896 PMCID: PMC11228050 DOI: 10.1038/s41467-024-49846-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higherF 1 scores (averageF 1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.
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Affiliation(s)
- Xin Yuan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China
| | - Yanran Ma
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitian Gao
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Shuya Cui
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China
| | - Yifan Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Botao Fa
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Shiyang Ma
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Shuangge Ma
- SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.
- Department of Biostatistics, Yale University, New Haven, USA.
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Center for Biomedical Data Science, Translational Science Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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46
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Defard T, Laporte H, Ayan M, Soulier J, Curras-Alonso S, Weber C, Massip F, Londoño-Vallejo JA, Fouillade C, Mueller F, Walter T. A point cloud segmentation framework for image-based spatial transcriptomics. Commun Biol 2024; 7:823. [PMID: 38971915 PMCID: PMC11227573 DOI: 10.1038/s42003-024-06480-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 06/20/2024] [Indexed: 07/08/2024] Open
Abstract
Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated and experimental datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and in-situ cell type calling. ComSeg is available as a documented and open source pip package at https://github.com/fish-quant/ComSeg .
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Affiliation(s)
- Thomas Defard
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, 75006, Paris, France
- Institut Curie, PSL University, 75005, Paris, France
- INSERM, U900, 75005, Paris, France
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015, Paris, France
| | - Hugo Laporte
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
- Institute of Cell Biology (Cancer Research), University Hospital Essen, Essen, Germany
| | - Mallick Ayan
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Juliette Soulier
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Sandra Curras-Alonso
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Christian Weber
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015, Paris, France
| | - Florian Massip
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, 75006, Paris, France
- Institut Curie, PSL University, 75005, Paris, France
- INSERM, U900, 75005, Paris, France
| | - José-Arturo Londoño-Vallejo
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Charles Fouillade
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Florian Mueller
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015, Paris, France.
- Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015, Paris, France.
| | - Thomas Walter
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, 75006, Paris, France.
- Institut Curie, PSL University, 75005, Paris, France.
- INSERM, U900, 75005, Paris, France.
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47
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Casoni F, Croci L, Marroni F, Demenego G, Marullo C, Cremona O, Codazzi F, Consalez GG. A spatial-temporal map of glutamatergic neurogenesis in the murine embryonic cerebellar nuclei uncovers a high degree of cellular heterogeneity. J Anat 2024. [PMID: 38970393 DOI: 10.1111/joa.14107] [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: 12/18/2023] [Revised: 05/23/2024] [Accepted: 06/20/2024] [Indexed: 07/08/2024] Open
Abstract
The nuclei are the main output structures of the cerebellum. Each and every cerebellar cortical computation reaches several areas of the brain by means of cerebellar nuclei processing and integration. Nevertheless, our knowledge of these structures is still limited compared to the cerebellar cortex. Here, we present a mouse genetic inducible fate-mapping study characterizing rhombic lip-derived glutamatergic neurons of the nuclei, the most conspicuous family of long-range cerebellar efferent neurons. Glutamatergic neurons mainly occupy dorsal and lateral territories of the lateral and interposed nuclei, as well as the entire medial nucleus. In mice, they are born starting from about embryonic day 9.5, with a peak between 10.5 and 12.5, and invade the nuclei with a lateral-to-medial progression. While some markers label a heterogeneous population of neurons sharing a common location (BRN2), others appear to be lineage specific (TBR1, LMX1a, and MEIS2). A comparative analysis of TBR1 and LMX1a distributions reveals an incomplete overlap in their expression domains, in keeping with the existence of separate efferent subpopulations. Finally, some tagged glutamatergic progenitors are not labeled by any of the markers used in this study, disclosing further complexity. Taken together, our results obtained in late embryonic nuclei shed light on the heterogeneity of the excitatory neuron pool, underlying the diversity in connectivity and functions of this largely unexplored cerebellar territory. Our findings contribute to laying the groundwork for a comprehensive functional analysis of nuclear neuron subpopulations.
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Affiliation(s)
- Filippo Casoni
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Laura Croci
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesca Marroni
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Giulia Demenego
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Chiara Marullo
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ottavio Cremona
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Franca Codazzi
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - G Giacomo Consalez
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
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48
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Sethi R, Ang KS, Li M, Long Y, Ling J, Chen J. ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists. Nat Commun 2024; 15:5600. [PMID: 38961061 PMCID: PMC11222513 DOI: 10.1038/s41467-024-48188-2] [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: 03/22/2023] [Accepted: 04/22/2024] [Indexed: 07/05/2024] Open
Abstract
ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell's streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application ( https://immunesinglecell.org/ezsc/ ) or a software package with a shinyApp interface ( https://github.com/JinmiaoChenLab/ezSingleCell2 ) for offline analysis.
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Affiliation(s)
- Raman Sethi
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, Matrix, Singapore, 138671, Singapore
| | - Kok Siong Ang
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore, 138673, Singapore
| | - Mengwei Li
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore, 138673, Singapore
| | - Yahui Long
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore, 138673, Singapore
| | - Jingjing Ling
- Singapore Immunology Network (SIgN), Agency of Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos, Singapore, 138648, Singapore
| | - Jinmiao Chen
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, Matrix, Singapore, 138671, Singapore.
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore, 138673, Singapore.
- Immunology Translational Research Program, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 5 Science Drive 2, Blk MD4, Level 3, Singapore, 117545, Singapore.
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49
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Rhaman MS, Ali M, Ye W, Li B. Opportunities and Challenges in Advancing Plant Research with Single-cell Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae026. [PMID: 38996445 DOI: 10.1093/gpbjnl/qzae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 07/14/2024]
Abstract
Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.
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Affiliation(s)
- Mohammad Saidur Rhaman
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Muhammad Ali
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Wenxiu Ye
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Bosheng Li
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
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Ospina OE, Manjarres-Betancur R, Gonzalez-Calderon G, Soupir AC, Smalley I, Tsai K, Markowitz J, Vallebuona E, Berglund A, Eschrich S, Yu X, Fridley BL. spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.601050. [PMID: 39005315 PMCID: PMC11244876 DOI: 10.1101/2024.06.27.601050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.
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Affiliation(s)
- Oscar E. Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | - Alex C. Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Inna Smalley
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kenneth Tsai
- Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA
| | - Joseph Markowitz
- Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Ethan Vallebuona
- Department of Metabolism and Physiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Steven Eschrich
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Xiaoqing Yu
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Brooke L. Fridley
- Division of Health Services and Outcomes Research, Children’s Mercy, Kansas City, MO, USA
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