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Liu Y, Yang C. Computational methods for alignment and integration of spatially resolved transcriptomics data. Comput Struct Biotechnol J 2024; 23:1094-1105. [PMID: 38495555 PMCID: PMC10940867 DOI: 10.1016/j.csbj.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/19/2024] Open
Abstract
Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms. To fully leverage spatial information, the focus extends beyond individual two-dimensional (2D) slices. Two tasks known as slices alignment and data integration have been introduced to establish correlations between multiple slices, enhancing the effectiveness of downstream tasks. Currently, numerous related methods have been developed. In this review, we first elucidate the details and principles behind several representative methods. Then we report the testing results of these methods on various SRT datasets, and assess their performance in representative downstream tasks. Insights into the strengths and weaknesses of each method and the reasons behind their performance are discussed. Finally, we provide an outlook on future developments. The codes and details of experiments are now publicly available at https://github.com/YangLabHKUST/SRT_alignment_and_integration.
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Affiliation(s)
- Yuyao Liu
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
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2
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Zhang H, Lu KH, Ebbini M, Huang P, Lu H, Li L. Mass spectrometry imaging for spatially resolved multi-omics molecular mapping. NPJ IMAGING 2024; 2:20. [PMID: 39036554 PMCID: PMC11254763 DOI: 10.1038/s44303-024-00025-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024]
Abstract
The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.
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Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Kelly H. Lu
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Malik Ebbini
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Penghsuan Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706 USA
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705 USA
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3
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Lane AN, Higashi RM, Fan TWM. Challenges of Spatially Resolved Metabolism in Cancer Research. Metabolites 2024; 14:383. [PMID: 39057706 PMCID: PMC11278851 DOI: 10.3390/metabo14070383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/28/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
Stable isotope-resolved metabolomics comprises a critical set of technologies that can be applied to a wide variety of systems, from isolated cells to whole organisms, to define metabolic pathway usage and responses to perturbations such as drugs or mutations, as well as providing the basis for flux analysis. As the diversity of stable isotope-enriched compounds is very high, and with newer approaches to multiplexing, the coverage of metabolism is now very extensive. However, as the complexity of the model increases, including more kinds of interacting cell types and interorgan communication, the analytical complexity also increases. Further, as studies move further into spatially resolved biology, new technical problems have to be overcome owing to the small number of analytes present in the confines of a single cell or cell compartment. Here, we review the overall goals and solutions made possible by stable isotope tracing and their applications to models of increasing complexity. Finally, we discuss progress and outstanding difficulties in high-resolution spatially resolved tracer-based metabolic studies.
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Affiliation(s)
- Andrew N. Lane
- Department of Toxicology and Cancer Biology and Markey Cancer Center, University of Kentucky, 789 S. Limestone St., Lexington, KY 40536, USA; (R.M.H.); (T.W.-M.F.)
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4
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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5
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Cheng S, Cao C, Qian Y, Yao H, Gong X, Dai X, Ouyang Z, Ma X. High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers. Chem Sci 2024; 15:6314-6320. [PMID: 38699276 PMCID: PMC11062128 DOI: 10.1039/d3sc06573a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/02/2024] [Indexed: 05/05/2024] Open
Abstract
Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.
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Affiliation(s)
- Simin Cheng
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology Bejing 100029 China
| | - Chenxi Cao
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University Beijing 100084 China
| | - Yao Qian
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University Beijing 100084 China
| | - Huan Yao
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology Beijing 100029 China
| | - Xiaoyun Gong
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology Bejing 100029 China
| | - Xinhua Dai
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology Bejing 100029 China
| | - Zheng Ouyang
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University Beijing 100084 China
| | - Xiaoxiao Ma
- State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University Beijing 100084 China
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6
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Santos AA, Delgado TC, Marques V, Ramirez-Moncayo C, Alonso C, Vidal-Puig A, Hall Z, Martínez-Chantar ML, Rodrigues CM. Spatial metabolomics and its application in the liver. Hepatology 2024; 79:1158-1179. [PMID: 36811413 PMCID: PMC11020039 DOI: 10.1097/hep.0000000000000341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/05/2023] [Indexed: 02/24/2023]
Abstract
Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.
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Affiliation(s)
- André A. Santos
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal
| | - Teresa C. Delgado
- Liver Disease Lab, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance, Derio, Bizkaia, Spain
- Congenital Metabolic Disorders, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Vanda Marques
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal
| | - Carmen Ramirez-Moncayo
- Institute of Clinical Sciences, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, London, UK
| | | | - Antonio Vidal-Puig
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Centro Investigation Principe Felipe, Valencia, Spain
| | - Zoe Hall
- Division of Systems Medicine, Imperial College London, London, UK
| | - María Luz Martínez-Chantar
- Liver Disease Lab, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance, Derio, Bizkaia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Carlos III National Health Institute, Madrid, Spain
| | - Cecilia M.P. Rodrigues
- Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal
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Sun X, Yu Y, Qian K, Wang J, Huang L. Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis. SMALL METHODS 2024; 8:e2301317. [PMID: 38032130 DOI: 10.1002/smtd.202301317] [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: 09/27/2023] [Revised: 11/10/2023] [Indexed: 12/01/2023]
Abstract
Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.
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Affiliation(s)
- Xuming Sun
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, P. R. China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang Medical University, Xinxiang, 453003, P. R. China
- Xinxiang Key Laboratory of Neurobiosensor, Xinxiang Medical University, Xinxiang, 453003, P. R. China
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, 453003, P. R. China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang Medical University, Xinxiang, 453003, P. R. China
- Xinxiang Key Laboratory of Neurobiosensor, Xinxiang Medical University, Xinxiang, 453003, P. R. China
| | - Kun Qian
- School of Biomedical Engineering, Institute of Medical Robotics and Med X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jiayi Wang
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
| | - Lin Huang
- Department of Clinical Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
- Shanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
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8
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Lin S, Zhao F, Wu Z, Yao J, Zhao Y, Yuan Z. Streamlining spatial omics data analysis with Pysodb. Nat Protoc 2024; 19:831-895. [PMID: 38135744 DOI: 10.1038/s41596-023-00925-5] [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: 04/04/2023] [Accepted: 10/02/2023] [Indexed: 12/24/2023]
Abstract
Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.
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Affiliation(s)
- Senlin Lin
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | | | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
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9
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Porat-Shliom N. Compartmentalization, cooperation, and communication: The 3Cs of Hepatocyte zonation. Curr Opin Cell Biol 2024; 86:102292. [PMID: 38064779 PMCID: PMC10922296 DOI: 10.1016/j.ceb.2023.102292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 02/15/2024]
Abstract
The unique architecture of the liver allows for spatial compartmentalization of its functions, also known as liver zonation. In contrast to organelles and cells, this compartment is devoid of a surrounding membrane, rendering traditional biochemical tools ineffective for studying liver zonation. Recent advancements in tissue imaging and single-cell technologies have provided new insights into the complexity of tissue organization, rich cellular composition, and the gradients that shape zonation. Hepatocyte gene expression profiles and metabolic programs differ based on their location. Non-parenchymal cells further support hepatocytes from different zones through local secretion of factors that instruct hepatocyte activities. Collectively, these elements form a cohesive and dynamic network of cell-cell interactions that vary across space, time, and disease states. This review will examine the cell biology of hepatocytes in vivo, presenting the latest discoveries and emerging principles that govern tissue-level and sub-cellular compartmentalization.
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Affiliation(s)
- Natalie Porat-Shliom
- Cell Biology and Imaging Section, Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA.
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10
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Jackson BT, Finley LWS. Metabolic regulation of the hallmarks of stem cell biology. Cell Stem Cell 2024; 31:161-180. [PMID: 38306993 PMCID: PMC10842269 DOI: 10.1016/j.stem.2024.01.003] [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: 12/03/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Stem cells perform many different functions, each of which requires specific metabolic adaptations. Over the past decades, studies of pluripotent and tissue stem cells have uncovered a range of metabolic preferences and strategies that correlate with or exert control over specific cell states. This review aims to describe the common themes that emerge from the study of stem cell metabolism: (1) metabolic pathways supporting stem cell proliferation, (2) metabolic pathways maintaining stem cell quiescence, (3) metabolic control of cellular stress responses and cell death, (4) metabolic regulation of stem cell identity, and (5) metabolic requirements of the stem cell niche.
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Affiliation(s)
- Benjamin T Jackson
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, New York, NY, USA
| | - Lydia W S Finley
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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11
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Liu Y, Tan Y, Zhang Z, Yi M, Zhu L, Peng W. The interaction between ageing and Alzheimer's disease: insights from the hallmarks of ageing. Transl Neurodegener 2024; 13:7. [PMID: 38254235 PMCID: PMC10804662 DOI: 10.1186/s40035-024-00397-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: 09/13/2023] [Revised: 12/31/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
Ageing is a crucial risk factor for Alzheimer's disease (AD) and is characterised by systemic changes in both intracellular and extracellular microenvironments that affect the entire body instead of a single organ. Understanding the specific mechanisms underlying the role of ageing in disease development can facilitate the treatment of ageing-related diseases, such as AD. Signs of brain ageing have been observed in both AD patients and animal models. Alleviating the pathological changes caused by brain ageing can dramatically ameliorate the amyloid beta- and tau-induced neuropathological and memory impairments, indicating that ageing plays a crucial role in the pathophysiological process of AD. In this review, we summarize the impact of several age-related factors on AD and propose that preventing pathological changes caused by brain ageing is a promising strategy for improving cognitive health.
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Affiliation(s)
- Yuqing Liu
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Yejun Tan
- School of Mathematics, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Zheyu Zhang
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Min Yi
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China
| | - Lemei Zhu
- Academician Workstation, Changsha Medical University, Changsha, 410219, People's Republic of China
| | - Weijun Peng
- Department of Integrated Traditional Chinese and Western Medicine, The Second Xiangya Hospital, Central South University, No.139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- National Clinical Research Center for Metabolic Diseases, Changsha, 410011, People's Republic of China.
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12
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Liu T, Fang Z, Li X, Zhang L, Cao DS, Li M, Yin M. Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep. Bioinformatics 2024; 40:btae023. [PMID: 38243703 PMCID: PMC10832355 DOI: 10.1093/bioinformatics/btae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/24/2023] [Accepted: 01/13/2024] [Indexed: 01/21/2024] Open
Abstract
MOTIVATION Spatial clustering is essential and challenging for spatial transcriptomics' data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation. RESULTS In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering method is compared with state-of-the-art algorithms to demonstrate its effectiveness and superiority. The significant new findings include: (i) the number of genes or proteins of spatial omics data is quite crucial in spatial clustering algorithms; (ii) the variational graph autoencoder is more suitable for spatial clustering tasks than deep graph infomax module; (iii) UniMP, SAGE, SuperGAT, GATv2, GCN, and TAG are the recommended graph neural networks for spatial clustering tasks; and (iv) the used graph neural network in the existent spatial clustering frameworks is not the best candidate. This study could be regarded as desirable guidance for choosing an appropriate graph neural network for spatial clustering. AVAILABILITY AND IMPLEMENTATION The source code of GRAPHDeep is available at https://github.com/narutoten520/GRAPHDeep. The studied spatial omics data are available at https://zenodo.org/record/8141084.
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Affiliation(s)
- Teng Liu
- Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, 404000, China
- Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, 400044, China
| | - Zhaoyu Fang
- Department of Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, 410083, China
| | - Xin Li
- Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, 404000, China
- Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, 400044, China
| | - Lining Zhang
- Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, 404000, China
- Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, 400044, China
| | - Dong-Sheng Cao
- Department of Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, P.R. China
| | - Min Li
- Department of Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, 410083, China
| | - Mingzhu Yin
- Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, 404000, China
- Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, 400044, China
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13
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Cao J, Yao QJ, Wu J, Chen X, Huang L, Liu W, Qian K, Wan JJ, Zhou BO. Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution. Cell Metab 2024; 36:209-221.e6. [PMID: 38171334 DOI: 10.1016/j.cmet.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/14/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024]
Abstract
Metabolic status is crucial for stem cell functions; however, the metabolic heterogeneity of endogenous stem cells has never been directly assessed. Here, we develop a platform for high-throughput single-cell metabolomics (hi-scMet) of hematopoietic stem cells (HSCs). By combining flow cytometric isolation and nanoparticle-enhanced laser desorption/ionization mass spectrometry, we routinely detected >100 features from single cells. We mapped the single-cell metabolomes of all hematopoietic cell populations and HSC subpopulations with different division times, detecting 33 features whose levels exhibited trending changes during HSC proliferation. We found progressive activation of the oxidative pentose phosphate pathway (OxiPPP) from dormant to active HSCs. Genetic or pharmacological interference with OxiPPP increased reactive oxygen species level in HSCs, reducing HSC self-renewal upon oxidative stress. Together, our work uncovers the metabolic dynamics during HSC proliferation, reveals a role of OxiPPP for HSC activation, and illustrates the utility of hi-scMet in dissecting metabolic heterogeneity of immunophenotypically defined cell populations.
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Affiliation(s)
- Jing Cao
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai 200030, PRC; Shanghai Key Laboratory of Gynecologic Oncology, Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC
| | - Qi Jason Yao
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiao Wu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai 200030, PRC; Shanghai Key Laboratory of Gynecologic Oncology, Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC
| | - Xiaonan Chen
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200241, China
| | - Lin Huang
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai 200030, PRC; Shanghai Key Laboratory of Gynecologic Oncology, Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC
| | - Wanshan Liu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai 200030, PRC; Shanghai Key Laboratory of Gynecologic Oncology, Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC
| | - Kun Qian
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai 200030, PRC; Shanghai Key Laboratory of Gynecologic Oncology, Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PRC.
| | - Jing-Jing Wan
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200241, China.
| | - Bo O Zhou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences, Tianjin 300020, China.
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14
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Chen Y, Yang S, Yu K, Zhang J, Wu M, Zheng Y, Zhu Y, Dai J, Wang C, Zhu X, Dai Y, Sun Y, Wu T, Wang S. Spatial omics: An innovative frontier in aging research. Ageing Res Rev 2024; 93:102158. [PMID: 38056503 DOI: 10.1016/j.arr.2023.102158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/25/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
Disentangling the impact of aging on health and disease has become critical as population aging progresses rapidly. Studying aging at the molecular level is complicated by the diverse aging profiles and dynamics. However, the examination of cellular states within aging tissues in situ is hampered by the lack of high-resolution spatial data. Emerging spatial omics technologies facilitate molecular and spatial analysis of tissues, providing direct access to precise information on various functional regions and serving as a favorable tool for unraveling the heterogeneity of aging. In this review, we summarize the recent advances in spatial omics application in multi-organ aging research, which has enhanced the understanding of aging mechanisms from multiple standpoints. We also discuss the main challenges in spatial omics research to date, the opportunities for further developing the technology, and the potential applications of spatial omics in aging and aging-related diseases.
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Affiliation(s)
- Ying Chen
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Shuhao Yang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Kaixu Yu
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinjin Zhang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Meng Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yongqiang Zheng
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Centre, Sun Yat-sen University, Guangzhou, China
| | - Yun Zhu
- Department of Internal Medicine, Southern Illinois University School of Medicine, 801 N. Rutledge, P.O. Box 19628, Springfield, IL 62702, USA
| | - Jun Dai
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Chunyan Wang
- College of Science & Engineering Jinan University, Guangzhou, China
| | - Xiaoran Zhu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yun Dai
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yunhong Sun
- Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tong Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China.
| | - Shixuan Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China.
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15
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Pade LR, Stepler KE, Portero EP, DeLaney K, Nemes P. Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles. MASS SPECTROMETRY REVIEWS 2024; 43:106-138. [PMID: 36647247 PMCID: PMC10668589 DOI: 10.1002/mas.21824] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/14/2022] [Accepted: 09/17/2022] [Indexed: 06/17/2023]
Abstract
Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.
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Affiliation(s)
- Leena R. Pade
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Kaitlyn E. Stepler
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Erika P. Portero
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Kellen DeLaney
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
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16
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Kumar BS. Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 16:8-32. [PMID: 38088775 DOI: 10.1039/d3ay01267k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.
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Affiliation(s)
- Bharath Sampath Kumar
- Independent researcher, 21, B2, 27th Street, Lakshmi Flats, Nanganallur, Chennai 600061, India.
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17
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Hu T, Allam M, Cai S, Henderson W, Yueh B, Garipcan A, Ievlev AV, Afkarian M, Beyaz S, Coskun AF. Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology. Nat Commun 2023; 14:8260. [PMID: 38086839 PMCID: PMC10716522 DOI: 10.1038/s41467-023-43917-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet's ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.
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Affiliation(s)
- Thomas Hu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Mayar Allam
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Shuangyi Cai
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Walter Henderson
- Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Brian Yueh
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Anton V Ievlev
- Oak Ridge National Laboratory, Center for Nanophase Materials Sciences, Oak Ridge, TN, USA
| | - Maryam Afkarian
- Division of Nephrology, Department of Internal Medicine, University of California, Davis, CA, USA
| | - Semir Beyaz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ahmet F Coskun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Interdisciplinary Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA.
- Winship Cancer Institute, Emory University, Atlanta, GA, USA.
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
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18
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Yang P, Jin L, Liao J, Jin K, Shao X, Li C, Qian J, Cheng J, Yu D, Guo R, Xu X, Lu X, Fan X. Revealing spatial multimodal heterogeneity in tissues with SpaTrio. CELL GENOMICS 2023; 3:100446. [PMID: 38116121 PMCID: PMC10726534 DOI: 10.1016/j.xgen.2023.100446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/28/2023] [Accepted: 10/26/2023] [Indexed: 12/21/2023]
Abstract
Capturing and depicting the multimodal tissue information of tissues at the spatial scale remains a significant challenge owing to technical limitations in single-cell multi-omics and spatial transcriptomics sequencing. Here, we developed a computational method called SpaTrio that can build spatial multi-omics data by integrating these two datasets through probabilistic alignment and enabling further analysis of gene regulation and cellular interactions. We benchmarked SpaTrio using simulation datasets and demonstrated its accuracy and robustness. Next, we evaluated SpaTrio on biological datasets and showed that it could detect topological patterns of cells and modalities. SpaTrio has also been applied to multiple sets of actual data to uncover spatially multimodal heterogeneity, understand the spatiotemporal regulation of gene expression, and resolve multimodal communication among cells. Our data demonstrated that SpaTrio could accurately map single cells and reconstruct the spatial distribution of various biomolecules, providing valuable multimodal insights into spatial biology.
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Affiliation(s)
- Penghui Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Lijun Jin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Kaiyu Jin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Chengyu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Junyun Cheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Dingyi Yu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Rongfang Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiao Xu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
| | - Xiaoyan Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Jinhua Institute of Zhejiang University, Jinhua 321016 China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
- Jinhua Institute of Zhejiang University, Jinhua 321016 China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China
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19
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Zhang Q, Lin L, Yi X, Xie T, Xing G, Li Y, Wang X, Lin JM. Microfluidic Sampling of Undissolved Components from Subcellular Regions of Living Single Cells for Mass Spectrometry Analysis. Anal Chem 2023; 95:18082-18090. [PMID: 38032315 DOI: 10.1021/acs.analchem.3c03086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
Precise sampling of undissolved chemical components from subcellular regions of living single cells is a prerequisite for their in-depth analysis, which could promote understanding of subtle early stage physiological or pathological processes. Here we report a microfluidic method to extract undissolved components from subcellular regions for MS analysis. The target single cell was isolated by the microchamber beneath the microfluidic probe and washed by the injected biocompatible isotonic glucose aqueous solution (IGAS). Then, the sampling solvent was injected to extract undissolved components from the expected subcellular region of the living single cell, where the position and size of the sampling region could be controlled. The components immobilized by undissolved cellular structures were proven to be successfully extracted. Since unextracted subcellular regions were protected by IGAS, the single cell could survive after a tiny part was extracted, providing the possibility of repetitive sampling of the same living cell. Phospholipids extracted from the subcellular regions were successfully identified. The results demonstrated the feasibility of our method for subcellular sampling and analysis.
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Affiliation(s)
- Qiang Zhang
- Department of Chemistry, Beijing Key Laboratory of Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology (Ministry of Education), Tsinghua University, Beijing 100084, China
| | - Ling Lin
- Department of Bioengineering, Beijing Technology and Business University, Beijing 100048, China
| | - Xizhen Yi
- Department of Chemistry, Tshinghua University, Beijing 100084, China
| | - Tianze Xie
- Department of Chemistry, Tshinghua University, Beijing 100084, China
| | - Gaowa Xing
- Department of Chemistry, Tshinghua University, Beijing 100084, China
| | - Yuxuan Li
- Department of Chemistry, Tshinghua University, Beijing 100084, China
| | - Xiaorui Wang
- Department of Bioengineering, Beijing Technology and Business University, Beijing 100048, China
| | - Jin-Ming Lin
- Department of Chemistry, Beijing Key Laboratory of Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology (Ministry of Education), Tsinghua University, Beijing 100084, China
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20
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Zeng Q, Xia MC, Yin X, Cheng S, Xue Z, Tan S, Gong X, Ye Z. Recent developments in ionization techniques for single-cell mass spectrometry. Front Chem 2023; 11:1293533. [PMID: 38130875 PMCID: PMC10733462 DOI: 10.3389/fchem.2023.1293533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry.
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Affiliation(s)
- Qingli Zeng
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou, China
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
| | - Meng-Chan Xia
- National Anti-Drug Laboratory Beijing Regional Center, Beijing, China
| | - Xinchi Yin
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
| | - Simin Cheng
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
| | - Zhichao Xue
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
| | - Siyuan Tan
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
| | - Xiaoyun Gong
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
| | - Zihong Ye
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou, China
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21
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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22
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Chen Y, Liu Y, Li X, He Y, Li W, Peng Y, Zheng J. Recent Advances in Mass Spectrometry-Based Spatially Resolved Molecular Imaging of Drug Disposition and Metabolomics. Drug Metab Dispos 2023; 51:1273-1283. [PMID: 37295949 DOI: 10.1124/dmd.122.001069] [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: 08/14/2022] [Revised: 05/04/2023] [Accepted: 05/17/2023] [Indexed: 06/12/2023] Open
Abstract
Mass spectrometric imaging is a nontargeted, tag-free, high-throughput, and highly responsive analytical approach. The highly accurate molecular visualization detection technology enables qualitative and quantitative analyses of biologic tissues or cells scanned by mass spectrometry in situ, extracting known and unknown multiple compounds, and simultaneously assessing relative contents of targeting molecules by monitoring their molecular ions and pinpointing the spatial locations of those molecules distributed. Five mass spectrometric imaging techniques and their characteristics are introduced in the review, including matrix-assisted laser desorption ionization mass spectrometry, secondary ion mass spectrometry, desorption electrospray ionization mass spectrometry, laser ablation electrospray ionization mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry. The mass spectrometry-based techniques provide the possibility for spatial metabolomics with the capability of high throughput and precision detection. The approaches have been widely employed to spatially image not only metabolome of endogenous amino acids, peptides, proteins, neurotransmitters, and lipids but also the disposition of exogenous chemicals, such as pharmaceutical agents, environmental pollutants, toxicants, natural products, and heavy metals. The techniques also provide us with spatial distribution imaging of analytes in single cells, tissue microregions, organs, and whole animals. SIGNIFICANCE STATEMENT: The review article includes an overview of five commonly used mass spectrometers for spatial imaging and describes the advantages and disadvantages of each. Examples of the technology applications cover drug disposition, diseases, and omics. Technical aspects of relative and absolute quantification by mass spectrometric imaging and challenges for future new applications are discussed as well. The reviewed knowledge may benefit the development of new drugs and provide a better understanding of biochemical processes related to physiology and diseases.
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Affiliation(s)
- Yu Chen
- State Key Laboratory of Functions and Applications of Medicinal Plants, Key Laboratory of Pharmaceutics of Guizhou Province, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); School of Basic Medicine, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); Division of Pain Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C.); and Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning, P.R. China (Y.P., J.Z.)
| | - Ying Liu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Key Laboratory of Pharmaceutics of Guizhou Province, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); School of Basic Medicine, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); Division of Pain Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C.); and Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning, P.R. China (Y.P., J.Z.)
| | - Ximei Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Key Laboratory of Pharmaceutics of Guizhou Province, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); School of Basic Medicine, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); Division of Pain Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C.); and Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning, P.R. China (Y.P., J.Z.)
| | - Yan He
- State Key Laboratory of Functions and Applications of Medicinal Plants, Key Laboratory of Pharmaceutics of Guizhou Province, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); School of Basic Medicine, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); Division of Pain Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C.); and Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning, P.R. China (Y.P., J.Z.)
| | - Weiwei Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Key Laboratory of Pharmaceutics of Guizhou Province, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); School of Basic Medicine, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); Division of Pain Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C.); and Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning, P.R. China (Y.P., J.Z.)
| | - Ying Peng
- State Key Laboratory of Functions and Applications of Medicinal Plants, Key Laboratory of Pharmaceutics of Guizhou Province, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); School of Basic Medicine, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); Division of Pain Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C.); and Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning, P.R. China (Y.P., J.Z.)
| | - Jiang Zheng
- State Key Laboratory of Functions and Applications of Medicinal Plants, Key Laboratory of Pharmaceutics of Guizhou Province, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); School of Basic Medicine, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C., Y.L., X.L., Y.H., W.L.); Division of Pain Management, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, P.R. China (Y.C.); and Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, Liaoning, P.R. China (Y.P., J.Z.)
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23
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Zhang H, Liu Y, Fields L, Shi X, Huang P, Lu H, Schneider AJ, Tang X, Puglielli L, Welham NV, Li L. Single-cell lipidomics enabled by dual-polarity ionization and ion mobility-mass spectrometry imaging. Nat Commun 2023; 14:5185. [PMID: 37626051 PMCID: PMC10457347 DOI: 10.1038/s41467-023-40512-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Single-cell (SC) analysis provides unique insight into individual cell dynamics and cell-to-cell heterogeneity. Here, we utilize trapped ion mobility separation coupled with dual-polarity ionization mass spectrometry imaging (MSI) to enable high-throughput in situ profiling of the SC lipidome. Multimodal SC imaging, in which dual-polarity-mode MSI is used to perform serial data acquisition runs on individual cells, significantly enhanced SC lipidome coverage. High-spatial resolution SC-MSI identifies both inter- and intracellular lipid heterogeneity; this heterogeneity is further explicated by Uniform Manifold Approximation and Projection and machine learning-driven classifications. We characterize SC lipidome alteration in response to stearoyl-CoA desaturase 1 inhibition and, additionally, identify cell-layer specific lipid distribution patterns in mouse cerebellar cortex. This integrated multimodal SC-MSI technology enables high-resolution spatial mapping of intercellular and cell-to-cell lipidome heterogeneity, SC lipidome remodeling induced by pharmacological intervention, and region-specific lipid diversity within tissue.
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Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Yuan Liu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Lauren Fields
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Xudong Shi
- Division of Otolaryngology, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53792, USA
| | - Penghsuan Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Andrew J Schneider
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Xindi Tang
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Luigi Puglielli
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Nathan V Welham
- Division of Otolaryngology, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53792, USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA.
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA.
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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24
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Jia F, Zhao X, Zhao Y. Advancements in ToF-SIMS imaging for life sciences. Front Chem 2023; 11:1237408. [PMID: 37693171 PMCID: PMC10483116 DOI: 10.3389/fchem.2023.1237408] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023] Open
Abstract
In the last 2 decades, Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) has gained significant prominence as a powerful imaging technique in the field of life sciences. This comprehensive review provides an in-depth overview of recent advancements in ToF-SIMS instrument technology and its applications in metabolomics, lipidomics, and single-cell analysis. We highlight the use of ToF-SIMS imaging for studying lipid distribution, composition, and interactions in cells and tissues, and discuss its application in metabolomics, including the analysis of metabolic pathways. Furthermore, we review recent progress in single-cell analysis using ToF-SIMS, focusing on sample preparation techniques, in situ investigation for subcellular distribution of drugs, and interactions between drug molecules and biological targets. The high spatial resolution and potential for multimodal analysis of ToF-SIMS make it a promising tool for unraveling the complex molecular landscape of biological systems. We also discuss future prospects and potential advancements of ToF-SIMS in the research of life sciences, with the expectation of a significant impact in the field.
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Affiliation(s)
- Feifei Jia
- National Institutes for Food and Drug Control, Beijing, China
| | - Xia Zhao
- National Institutes for Food and Drug Control, Beijing, China
| | - Yao Zhao
- Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, CAS Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China
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25
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Meng Y, Hang W, Zare RN. Microlensed fiber allows subcellular imaging by laser-based mass spectrometry. Nat Protoc 2023; 18:2558-2578. [PMID: 37479826 DOI: 10.1038/s41596-023-00848-1] [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: 10/20/2022] [Accepted: 05/02/2023] [Indexed: 07/23/2023]
Abstract
Mass spectrometry imaging (MSI) enables the chemical mapping of molecules and elements in a label-free, high-throughput manner. Because this approach can be accomplished rapidly, it also enables chemical changes to be monitored. Here, we describe a protocol for MSI with subcellular spatial resolution. This is achieved by using a microlensed fiber, which is made by grinding an optical fiber. It is a universal and economic technique that can be adapted to most laser-based mass spectrometry methods. In this protocol, the output of laser radiation from the microlensed fiber causes laser ablation of the sample, and the resulting plume is mass spectrometrically analyzed. The microlensed fiber can be used with matrix-assisted laser desorption ionization, laser desorption ionization, laser ablation electrospray desorption ionization and laser ablation inductively coupled plasma, in each case to achieve submicroscale imaging of single cells and biological tissues. This report provides a detailed introduction of the microlensed fiber design and working principles, sample preparation, microlensed fiber ion source setup and multiple MSI platforms with different kinds of mass spectrometers. A researcher with a little background (such as a trained graduate student) is able to complete all the steps for the experimental setup in ~2 h, including fiber test, laser coupling and ion source modification. The imaging time spent mainly depends on the size of the imaging area. It is suggested that most existing laser-based MSI platforms, especially atmospheric pressure applications, can achieve breakthroughs in spatial resolution by introducing a microlensed fiber module.
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Affiliation(s)
- Yifan Meng
- Ministry of Education (MOE) Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Wei Hang
- Ministry of Education (MOE) Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.
| | - Richard N Zare
- Department of Chemistry, Stanford University, Stanford, CA, USA.
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26
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Zhou Y, Jiang X, Wang X, Huang J, Li T, Jin H, He J. Promise of spatially resolved omics for tumor research. J Pharm Anal 2023; 13:851-861. [PMID: 37719191 PMCID: PMC10499658 DOI: 10.1016/j.jpha.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.
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Affiliation(s)
- Yanhe Zhou
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xinyi Jiang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jianpeng Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Tong Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Hongtao Jin
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
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27
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Zhang C, Le Dévédec SE, Ali A, Hankemeier T. Single-cell metabolomics by mass spectrometry: ready for primetime? Curr Opin Biotechnol 2023; 82:102963. [PMID: 37356380 DOI: 10.1016/j.copbio.2023.102963] [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: 02/08/2023] [Revised: 04/17/2023] [Accepted: 05/23/2023] [Indexed: 06/27/2023]
Abstract
Single-cell metabolomics (SCMs) is a powerful tool for studying cellular heterogeneity by providing insight into the differences between individual cells. With the development of a set of promising SCMs pipelines, this maturing technology is expected to be widely used in biomedical research. However, before SCMs is ready for primetime, there are some challenges to overcome. In this review, we summarize the trends and challenges in the development of SCMs. We also highlight the latest methodologies, applications, and sketch the perspective for integration with other omics and imaging approaches.
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Affiliation(s)
- Congrou Zhang
- Metabolomics and Analytics Center, Leiden Academic Centre of Drug Research, Leiden University, Leiden, the Netherlands
| | - Sylvia E Le Dévédec
- Division of Drug Discovery and Safety, Leiden Academic Centre of Drug Research, Leiden University, Leiden, the Netherlands
| | - Ahmed Ali
- Metabolomics and Analytics Center, Leiden Academic Centre of Drug Research, Leiden University, Leiden, the Netherlands.
| | - Thomas Hankemeier
- Metabolomics and Analytics Center, Leiden Academic Centre of Drug Research, Leiden University, Leiden, the Netherlands.
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28
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Li Z, Zhou J, Wang C, Liu R, Hu J, Lv Y. Isotope-encoded tetrahedral DNA for multiple SARS-CoV-2 variant diagnosis. Chem Sci 2023; 14:6654-6662. [PMID: 37350832 PMCID: PMC10283508 DOI: 10.1039/d3sc01960h] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 05/24/2023] [Indexed: 06/24/2023] Open
Abstract
The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed an unprecedented demand for accurate and cost-effective diagnostic assays to discriminate between different variants. Whilst many bioassays have been successfully demonstrated for SARS-CoV-2 detection, diagnosis of its variants remains challenging and mainly relies on time-consuming and costly sequencing techniques. Herein, we proposed a triplevalent tetrahedral DNA nanostructure (tTDN) with three overhang isotope probes capable of multiplex simultaneous analysis. HV69/70 del (alpha-specific), K417N (beta-specific) and T478K (delta-specific) and omicron with common mutations above of the SARS-CoV-2 S gene were detected selectively with the aid of the TDN scaffold and MNAzyme system, and a sensitive strategy enabling the screening of four kinds of variants of concern (VOC) was achieved.
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Affiliation(s)
- Ziyan Li
- Analytical & Testing Center, Sichuan University Chengdu 610064 China
| | - Jing Zhou
- Analytical & Testing Center, Sichuan University Chengdu 610064 China
| | - Chaoqun Wang
- Analytical & Testing Center, Sichuan University Chengdu 610064 China
| | - Rui Liu
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University Chengdu 610064 Sichuan China
| | - Jianyu Hu
- Division of Analytical and Environmental Toxicology, Faculty of Medicine & Dentistry, University of Alberta Edmonton T6G 2G3 Alberta Canada
| | - Yi Lv
- Analytical & Testing Center, Sichuan University Chengdu 610064 China
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University Chengdu 610064 Sichuan China
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29
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Zhang P, Wang B, Li S. Network-based cancer precision prevention with artificial intelligence and multi-omics. Sci Bull (Beijing) 2023:S2095-9273(23)00342-0. [PMID: 37258376 DOI: 10.1016/j.scib.2023.05.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Affiliation(s)
- Peng Zhang
- Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, Beijing 100084, China
| | - Boyang Wang
- Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shao Li
- Institute of TCM-X/MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist/Department of Automation, Tsinghua University, Beijing 100084, China.
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30
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Luo L, Chua YJB, Liu T, Liang K, Chua MWJ, Ma W, Goh JW, Wang Y, Su J, Ho YS, Li CW, Liu KH, Teh BT, Yu K, Shyh-Chang N. Muscle Injuries Induce a Prostacyclin-PPARγ/PGC1a-FAO Spike That Boosts Regeneration. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2301519. [PMID: 37140179 PMCID: PMC10375192 DOI: 10.1002/advs.202301519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 03/14/2023] [Indexed: 05/05/2023]
Abstract
It is well-known that muscle regeneration declines with aging, and aged muscles undergo degenerative atrophy or sarcopenia. While exercise and acute injury are both known to induce muscle regeneration, the molecular signals that help trigger muscle regeneration have remained unclear. Here, mass spectrometry imaging (MSI) is used to show that injured muscles induce a specific subset of prostanoids during regeneration, including PGG1, PGD2, and the prostacyclin PGI2. The spike in prostacyclin promotes skeletal muscle regeneration via myoblasts, and declines with aging. Mechanistically, the prostacyclin spike promotes a spike in PPARγ/PGC1a signaling, which induces a spike in fatty acid oxidation (FAO) to control myogenesis. LC-MS/MS and MSI further confirm that an early FAO spike is associated with normal regeneration, but muscle FAO became dysregulated during aging. Functional experiments demonstrate that the prostacyclin-PPARγ/PGC1a-FAO spike is necessary and sufficient to promote both young and aged muscle regeneration, and that prostacyclin can synergize with PPARγ/PGC1a-FAO signaling to restore aged muscles' regeneration and physical function. Given that the post-injury prostacyclin-PPARγ-FAO spike can be modulated pharmacologically and via post-exercise nutrition, this work has implications for how prostacyclin-PPARγ-FAO might be fine-tuned to promote regeneration and treat muscle diseases of aging.
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Affiliation(s)
- Lanfang Luo
- Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine, Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yan-Jiang Benjamin Chua
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore City, 119077, Singapore
- Genome Institute of Singapore, Institute of Molecular and Cell Biology, Agency for Science Technology and Research, Singapore City, 138672, Singapore
| | - Taoyan Liu
- Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine, Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, P. R. China
| | - Kun Liang
- Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine, Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Min-Wen Jason Chua
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore City, 119077, Singapore
- Genome Institute of Singapore, Institute of Molecular and Cell Biology, Agency for Science Technology and Research, Singapore City, 138672, Singapore
| | - Wenwu Ma
- Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine, Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Jun-Wei Goh
- Genome Institute of Singapore, Institute of Molecular and Cell Biology, Agency for Science Technology and Research, Singapore City, 138672, Singapore
| | - Yuefan Wang
- Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine, Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Jiali Su
- Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine, Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Ying Swan Ho
- Bioprocessing Technology Institute, Agency for Science Technology and Research, Singapore City, 138668, Singapore
| | - Chun-Wei Li
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China
| | - Ke Hui Liu
- Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine, Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, P. R. China
| | - Bin Tean Teh
- Laboratory of Cancer Therapeutics, Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore City, 169857, Singapore
- Division of Medical Science, Laboratory of Cancer Epigenome, National Cancer Centre Singapore, Singapore City, 119074, Singapore
| | - Kang Yu
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China
| | - Ng Shyh-Chang
- Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- Beijing Institute for Stem Cell and Regenerative Medicine, Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
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31
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Mogilenko DA, Sergushichev A, Artyomov MN. Systems Immunology Approaches to Metabolism. Annu Rev Immunol 2023; 41:317-342. [PMID: 37126419 DOI: 10.1146/annurev-immunol-101220-031513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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32
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Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 91] [Impact Index Per Article: 91.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
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33
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Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development. Molecules 2023; 28:molecules28062712. [PMID: 36985684 PMCID: PMC10057629 DOI: 10.3390/molecules28062712] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/14/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Mass Spectrometry Imaging (MSI) has emerged as a powerful imaging technique for the analysis of biological samples, providing valuable insights into the spatial distribution and structural characterization of lipids. The advancements in high-resolution MSI have made it an indispensable tool for single-cell or subcellular lipidomics. By preserving both intracellular and intercellular information, MSI enables a comprehensive analysis of lipidomics in individual cells and organelles. This enables researchers to delve deeper into the diversity of lipids within cells and to understand the role of lipids in shaping cell behavior. In this review, we aim to provide a comprehensive overview of recent advancements and future prospects of MSI for cellular/subcellular lipidomics. By keeping abreast of the cutting-edge studies in this field, we will continue to push the boundaries of the understanding of lipid metabolism and the impact of lipids on cellular behavior.
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34
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Yan K, Li T, Marques JAL, Gao J, Fong SJ. A review on multimodal machine learning in medical diagnostics. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:8708-8726. [PMID: 37161218 DOI: 10.3934/mbe.2023382] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.
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Affiliation(s)
- Keyue Yan
- Department of Computer and Information Science, University of Macau, Macau SAR, China
| | - Tengyue Li
- Department of Computer and Information Science, University of Macau, Macau SAR, China
| | | | - Juntao Gao
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
| | - Simon James Fong
- Department of Computer and Information Science, University of Macau, Macau SAR, China
- Institute of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, China
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35
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Zhang H, Delafield DG, Li L. Mass spectrometry imaging: the rise of spatially resolved single-cell omics. Nat Methods 2023; 20:327-330. [PMID: 36899158 DOI: 10.1038/s41592-023-01774-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Daniel G Delafield
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA.
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA.
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36
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Yuan Z, Pan W, Zhao X, Zhao F, Xu Z, Li X, Zhao Y, Zhang MQ, Yao J. SODB facilitates comprehensive exploration of spatial omics data. Nat Methods 2023; 20:387-399. [PMID: 36797409 DOI: 10.1038/s41592-023-01773-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 01/06/2023] [Indexed: 02/18/2023]
Abstract
Spatial omics technologies generate wealthy but highly complex datasets. Here we present Spatial Omics DataBase (SODB), a web-based platform providing both rich data resources and a suite of interactive data analytical modules. SODB currently maintains >2,400 experiments from >25 spatial omics technologies, which are freely accessible as a unified data format compatible with various computational packages. SODB also provides multiple interactive data analytical modules, especially a unique module, Spatial Omics View (SOView). We conduct comprehensive statistical analyses and illustrate the utility of both basic and advanced analytical modules using multiple spatial omics datasets. We demonstrate SOView utility with brain spatial transcriptomics data and recover known anatomical structures. We further delineate functional tissue domains with associated marker genes that were obscured when analyzed using previous methods. We finally show how SODB may efficiently facilitate computational method development. The SODB website is https://gene.ai.tencent.com/SpatialOmics/ . The command-line package is available at https://pysodb.readthedocs.io/en/latest/ .
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Tencent AI Lab, Shenzhen, China.
| | - Wentao Pan
- Tencent AI Lab, Shenzhen, China
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | | | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xiu Li
- Shenzhen International Graduate School, Tsinghua University, Shenzen, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, USA.
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37
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Xie YR, Chari VK, Castro DC, Grant R, Rubakhin SS, Sweedler JV. Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry. J Proteome Res 2023; 22:491-500. [PMID: 36695570 PMCID: PMC9901547 DOI: 10.1021/acs.jproteome.2c00714] [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: 01/26/2023]
Abstract
Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Varsha K. Chari
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Romans Grant
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Stanislav S. Rubakhin
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Mailing Address: Department of Chemistry, University of Illinois, 71 RAL, Box 63-5, 600 South Mathews Avenue, Urbana, Illinois 61801, United States; Phone: (217) 244-7359;
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38
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Choe K, Pak U, Pang Y, Hao W, Yang X. Advances and Challenges in Spatial Transcriptomics for Developmental Biology. Biomolecules 2023; 13:biom13010156. [PMID: 36671541 PMCID: PMC9855858 DOI: 10.3390/biom13010156] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/15/2022] [Accepted: 12/17/2022] [Indexed: 01/15/2023] Open
Abstract
Development from single cells to multicellular tissues and organs involves more than just the exact replication of cells, which is known as differentiation. The primary focus of research into the mechanism of differentiation has been differences in gene expression profiles between individual cells. However, it has predominantly been conducted at low throughput and bulk levels, challenging the efforts to understand molecular mechanisms of differentiation during the developmental process in animals and humans. During the last decades, rapid methodological advancements in genomics facilitated the ability to study developmental processes at a genome-wide level and finer resolution. Particularly, sequencing transcriptomes at single-cell resolution, enabled by single-cell RNA-sequencing (scRNA-seq), was a breath-taking innovation, allowing scientists to gain a better understanding of differentiation and cell lineage during the developmental process. However, single-cell isolation during scRNA-seq results in the loss of the spatial information of individual cells and consequently limits our understanding of the specific functions of the cells performed by different spatial regions of tissues or organs. This greatly encourages the emergence of the spatial transcriptomic discipline and tools. Here, we summarize the recent application of scRNA-seq and spatial transcriptomic tools for developmental biology. We also discuss the limitations of current spatial transcriptomic tools and approaches, as well as possible solutions and future prospects.
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Affiliation(s)
- Kyongho Choe
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Unil Pak
- College of Landscape Architecture, Northeast Forestry University, Harbin 150040, China
| | - Yu Pang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Wanjun Hao
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
| | - Xiuqin Yang
- College of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, China
- Correspondence: ; Tel.: +86-451-55191738
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39
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Guo L, Dong J, Xu X, Wu Z, Zhang Y, Wang Y, Li P, Tang Z, Zhao C, Cai Z. Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data. Anal Chem 2023; 95:1924-1932. [PMID: 36633187 PMCID: PMC9878502 DOI: 10.1021/acs.analchem.2c04045] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.
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Affiliation(s)
- Lei Guo
- Department
of Electronic Science, National Institute
for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361005, China
| | - Jiyang Dong
- Department
of Electronic Science, National Institute
for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361005, China
| | - Xiangnan Xu
- School
of Mathematics and Statistics, The University
of Sydney, Sydney, NSW 2006, Australia
| | - Zhichao Wu
- School
of Artificial Intelligence, Beijing Normal
University, Beijing 100875, China
| | - Yinbin Zhang
- Department
of Oncology, The Second Affiliated Hospital
of Medical College, Xi’an Jiaotong University, Xi’an, Shaanxi 710004, China
| | - Yongwei Wang
- Bruker
Scientific Technology Co., Ltd., Beijing 100086, China
| | - Pengfei Li
- Bruker
Scientific Technology Co., Ltd., Beijing 100086, China
| | - Zhi Tang
- School
of Public Health, Dongguan Key Laboratory of Environmental Medicine, Institute of Environmental Health, Guangdong Medical
University, Dongguan, Guangdong 523808, China
| | - Chao Zhao
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- State
Key Laboratory of Environmental and Biological Analysis, Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Zongwei Cai
- State
Key Laboratory of Environmental and Biological Analysis, Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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40
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Zhang Z, Bao C, Jiang L, Wang S, Wang K, Lu C, Fang H. When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective. Front Oncol 2023; 12:1054233. [PMID: 36686803 PMCID: PMC9854130 DOI: 10.3389/fonc.2022.1054233] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.
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Affiliation(s)
- Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Jiang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kankan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Hai Fang,
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Duan H, Cheng T, Cheng H. Spatially resolved transcriptomics: advances and applications. BLOOD SCIENCE 2023; 5:1-14. [PMID: 36742187 PMCID: PMC9891446 DOI: 10.1097/bs9.0000000000000141] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
Spatial transcriptomics, which is capable of both measuring all gene activity in a tissue sample and mapping where this activity occurs, is vastly improving our understanding of biological processes and disease. The field has expanded rapidly in recent years, and the development of several new technologies has resulted in spatially resolved transcriptomics (SRT) becoming highly multiplexed, high-resolution, and high-throughput. Here, we summarize and compare the major methods of SRT, including imaging-based methods, sequencing-based methods, and in situ sequencing methods. We also highlight some typical applications of SRT in neuroscience, cancer biology, developmental biology, and hematology. Finally, we discuss future possibilities for improving spatially resolved transcriptomic methods and the expected applications of such methods, especially in the adult bone marrow, anticipating that new developments will unlock the full potential of spatially resolved multi-omics in both biological research and the clinic.
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Affiliation(s)
- Honglin Duan
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Tao Cheng
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Center for Stem Cell Medicine, Chinese Academy of Medical Sciences, Tianjin, China
- Department of Stem Cell & Regenerative Medicine, Peking Union Medical College, Tianjin, China
| | - Hui Cheng
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Center for Stem Cell Medicine, Chinese Academy of Medical Sciences, Tianjin, China
- Department of Stem Cell & Regenerative Medicine, Peking Union Medical College, Tianjin, China
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Ya D, Zhang Y, Cui Q, Jiang Y, Yang J, Tian N, Xiang W, Lin X, Li Q, Liao R. Application of spatial transcriptome technologies to neurological diseases. Front Cell Dev Biol 2023; 11:1142923. [PMID: 36936681 PMCID: PMC10020196 DOI: 10.3389/fcell.2023.1142923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.
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Affiliation(s)
- Dongshan Ya
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yingmei Zhang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qi Cui
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yanlin Jiang
- Department of Pharmacology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Jiaxin Yang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Ning Tian
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Wenjing Xiang
- Department of Neurology ward 2, Guilin People’s Hospital, Guilin, China
| | - Xiaohui Lin
- Department of Geriatrics, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qinghua Li
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Rujia Liao
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- *Correspondence: Rujia Liao,
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43
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Pan X, Yao H, Zhang S, Zhang X. Recent progress in mass spectrometry for single-cell metabolomics. Curr Opin Chem Biol 2022; 71:102226. [PMID: 36347197 DOI: 10.1016/j.cbpa.2022.102226] [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: 08/13/2022] [Revised: 10/05/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
Abstract
Metabolites are the end products of cellular vital activities and can reflect the state of cellular to a certain extent. Rapid change of metabolites and the low abundance of signature metabolites cause difficulties in single-cell detection, which is a great challenge in single-cell metabolomics analysis. Mass spectrometry (MS) is a powerful tool that uniquely suited to detect intracellular small-molecule metabolites and has shown good application in single-cell metabolite analysis. In this mini-review, we describe three types of emerging technologies for MS-based single-cell metabolic analysis in recent years, including nano-ESI-MS based single-cell metabolomics analysis, high-throughput analysis via flow cytometry, and cellular metabolic imaging analysis. These techniques provide a large amount of single-cell metabolic data, allowing the potential of MS in single-cell metabolic analysis is gradually being explored and is of great importance in disease and life science research.
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Affiliation(s)
- Xingyu Pan
- Department of Chemistry, Tsinghua University, Beijing, China
| | - Huan Yao
- Division of Chemistry and Analytical Science; National Institute of Metrology, Beijing, China
| | - Sichun Zhang
- Department of Chemistry, Tsinghua University, Beijing, China
| | - Xinrong Zhang
- Department of Chemistry, Tsinghua University, Beijing, China.
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44
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Zhou L, Lin Q, Sonnenberg GF. Metabolic control of innate lymphoid cells in health and disease. Nat Metab 2022; 4:1650-1659. [PMID: 36424470 PMCID: PMC9789197 DOI: 10.1038/s42255-022-00685-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/13/2022] [Indexed: 11/27/2022]
Abstract
Innate lymphoid cells (ILCs) are a family of predominantly tissue-resident lymphocytes that critically orchestrate immunity, inflammation, tolerance and repair at barrier surfaces of the mammalian body. Heterogeneity among ILC subsets is comparable to that of adaptive CD4+ T helper cell counterparts, and emerging studies demonstrate that ILC biology is also dictated by cellular metabolism that adapts bioenergetic requirements during activation, proliferation or cytokine production. Accumulating evidence in mouse models and human samples indicates that ILCs exhibit profound roles in shaping states of metabolic health and disease. Here we summarize and discuss our current knowledge of the cell-intrinsic and cell-extrinsic metabolic factors controlling ILC responses, as well as highlight contributions of ILCs to organismal metabolism. It is expected that continued research in this area will advance our understanding of how to manipulate ILCs or their metabolism for therapeutic strategies that benefit human health.
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Affiliation(s)
- Lei Zhou
- Shanghai Immune Therapy Institute, Shanghai Jiaotong University School of Medicine-affiliated Renji Hospital, Shanghai, China.
| | - Qingxia Lin
- Shanghai Immune Therapy Institute, Shanghai Jiaotong University School of Medicine-affiliated Renji Hospital, Shanghai, China
| | - Gregory F Sonnenberg
- Joan and Sanford I. Weill Department of Medicine, Division of Gastroenterology & Hepatology, Department of Microbiology and Immunology, and the Jill Roberts Institute for Research in Inflammatory Bowel Disease, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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45
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Ma S, Leng Y, Li X, Meng Y, Yin Z, Hang W. High spatial resolution mass spectrometry imaging for spatial metabolomics: Advances, challenges, and future perspectives. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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46
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Yuan Z, Li Y, Shi M, Yang F, Gao J, Yao J, Zhang MQ. SOTIP is a versatile method for microenvironment modeling with spatial omics data. Nat Commun 2022; 13:7330. [PMID: 36443314 PMCID: PMC9705407 DOI: 10.1038/s41467-022-34867-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/10/2022] [Indexed: 11/29/2022] Open
Abstract
The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module's accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- Tencent AI Lab, Shenzhen, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yisi Li
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China
| | | | - Juntao Gao
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | | | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China.
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
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47
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Hsieh WC, Budiarto BR, Wang YF, Lin CY, Gwo MC, So DK, Tzeng YS, Chen SY. Spatial multi-omics analyses of the tumor immune microenvironment. J Biomed Sci 2022; 29:96. [DOI: 10.1186/s12929-022-00879-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 11/02/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractIn the past decade, single-cell technologies have revealed the heterogeneity of the tumor-immune microenvironment at the genomic, transcriptomic, and proteomic levels and have furthered our understanding of the mechanisms of tumor development. Single-cell technologies have also been used to identify potential biomarkers. However, spatial information about the tumor-immune microenvironment such as cell locations and cell–cell interactomes is lost in these approaches. Recently, spatial multi-omics technologies have been used to study transcriptomes, proteomes, and metabolomes of tumor-immune microenvironments in several types of cancer, and the data obtained from these methods has been combined with immunohistochemistry and multiparameter analysis to yield markers of cancer progression. Here, we review numerous cutting-edge spatial ‘omics techniques, their application to study of the tumor-immune microenvironment, and remaining technical challenges.
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48
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Luo J, Huang Z, Li S, Zheng F, Liu F, Huang Q, Huang X, Xie H. Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation. Anal Chem 2022; 94:14801-14809. [PMID: 36239120 DOI: 10.1021/acs.analchem.2c04014] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Via the photodegradation of dissolved iron (dFe) complexes in the euphotic zone, released free Fe(III) is the most important source of bioavailable iron for eukaryotic phytoplankton. There is an urgent need to establish bioavailability-based dissolved iron speciation (BDIS) methods. Herein, an intelligent system with dFe pretreatment and a colorimetric sensor is developed for real-time monitoring of newly generated Fe(III) ions. According to the photodegradation kinetics of dFe, including kinetic constant and photogenerated time of free Fe(III) ions, 3 sources, 6 kinds, and 12 species of dFe are determined by our photocatalytic-assisted colorimetric sensor and deep learning model within 20.0 min. The algal dFe-uptake for 4 days can be predicted by BDIS with correlation coefficient 0.85, which could be explained by the hard and soft acids and bases theory (HSAB) and density functional theory (DFT). These results successfully demonstrate the proof-of-concept for photodegradation kinetics-based speciation and bioavailability assessments of dissolved metals.
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Affiliation(s)
- Jiayi Luo
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Zhaojing Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Shunxing Li
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China.,Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Fengying Zheng
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China.,Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Fengjiao Liu
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China.,Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Qianyan Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China
| | - Xuguang Huang
- College of Chemistry, Chemical Engineering & Environmental Science, Minnan Normal University, Zhangzhou 363000, China.,Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology, Fujian Province University Key Laboratory of Pollution Monitoring and Control, Minnan Normal University, Zhangzhou 3630003, China
| | - Haijiao Xie
- Hangzhou Yanqu Information Technology Company of Limited Liability, Hangzhou 310003, China
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49
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Ali A, Davidson S, Fraenkel E, Gilmore I, Hankemeier T, Kirwan JA, Lane AN, Lanekoff I, Larion M, McCall LI, Murphy M, Sweedler JV, Zhu C. Single cell metabolism: current and future trends. Metabolomics 2022; 18:77. [PMID: 36181583 PMCID: PMC10063251 DOI: 10.1007/s11306-022-01934-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
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Affiliation(s)
- Ahmed Ali
- Leiden Academic Centre for Drug Research, University of Leiden, Gorlaeus Building Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Shawn Davidson
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Ernest Fraenkel
- Department of Biological Engineering and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ian Gilmore
- National Physical Laboratory, Teddington, TW11 0LW, Middlesex, UK
| | - Thomas Hankemeier
- Leiden Academic Centre for Drug Research, University of Leiden, Room number GW4.07, Gorlaeus Building, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Jennifer A Kirwan
- Berlin Institute of Health, Metabolomics Platform, Translational Research Unit of the Charite-Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str 2, 10178, Berlin, Germany
| | - Andrew N Lane
- Department of Toxicology and Cancer Biology, and Center for Environmental and Systems Biochemistry, University of Kentucky, 789 S. Limestone St, Lexington, KY, 40536, USA.
| | - Ingela Lanekoff
- Department of Chemistry-BMC, Uppsala University, Husargatan 3 (576), 751 23, Uppsala, Sweden
| | - Mioara Larion
- Center for Cancer Research, National Cancer Institute, Building 37, Room 1136A, Bethesda, MD, 20892, USA
| | - Laura-Isobel McCall
- Department of Chemistry & Biochemistry, Department of Microbiology and Plant Biology, Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, 101 Stephenson Parkway, room 3750, Norman, OK, 73019-5251, USA
| | - Michael Murphy
- Departments of Biological Engineering, Department of Electrical Engineering, and Computer Science and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, USA
| | - Jonathan V Sweedler
- Department of Chemistry, and the Beckman Institute, University of Illinois Urbana-Champaign, 505 South Mathews Avenue, Urbana, IL, 61801, USA
| | - Caigang Zhu
- Department of Biomedical Engineering, University of Kentucky, Lexington, KY, 40536, USA
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50
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Chen Y, Galea I, Macdonald RL, Wong GKC, Zhang JH. Rethinking the initial changes in subarachnoid haemorrhage: Focusing on real-time metabolism during early brain injury. EBioMedicine 2022; 83:104223. [PMID: 35973388 PMCID: PMC9396538 DOI: 10.1016/j.ebiom.2022.104223] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/17/2022] [Accepted: 07/30/2022] [Indexed: 11/16/2022] Open
Abstract
Over the last two decades, neurological researchers have uncovered many pathophysiological mechanisms associated with subarachnoid haemorrhage (SAH), with early brain injury and delayed cerebral ischaemia both contributing to morbidity and mortality. The current dilemma in SAH management inspired us to rethink the nature of the insult in SAH: sudden bleeding into the subarachnoid space and hypoxia due to disturbed cerebral circulation and increased intracranial pressure, generating exogenous stimuli and subsequent pathophysiological processes. Exogenous stimuli are defined as factors which the brain tissue is not normally exposed to when in the healthy state. Intersections of these initial pathogenic factors lead to secondary brain injury with related metabolic changes after SAH. Herein, we summarized the current understanding of efforts to monitor and analyse SAH-related metabolic changes to identify those precise pathophysiological processes and potential therapeutic strategies; in particular, we highlight the restoration of normal cerebrospinal fluid circulation and the normalization of brain-blood interface physiology to alleviate early brain injury and delayed neurological deterioration after SAH.
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Affiliation(s)
- Yujie Chen
- Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Ian Galea
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, United Kingdom
| | - R Loch Macdonald
- Community Neurosciences Institutes, Community Regional Medical Center, Fresno, CA 93701, USA
| | - George Kwok Chu Wong
- Division of Neurosurgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - John H Zhang
- Neuroscience Research Center, Loma Linda University School of Medicine, Loma Linda, CA 92350, USA; Department of Physiology and Pharmacology, Loma Linda University School of Medicine, Loma Linda, CA 92354, USA.
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