1
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Zhang Y, Chang K, Ogunlade B, Herndon L, Tadesse LF, Kirane AR, Dionne JA. From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis. ACS NANO 2024; 18:18101-18117. [PMID: 38950145 DOI: 10.1021/acsnano.4c04282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.
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Affiliation(s)
- Yirui Zhang
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Kai Chang
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States
| | - Babatunde Ogunlade
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Liam Herndon
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Loza F Tadesse
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Jameel Clinic for AI & Healthcare, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Amanda R Kirane
- Department of Surgery, Stanford University, Stanford, California 94305, United States
| | - Jennifer A Dionne
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California 94305, United States
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2
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Man QW. HIF-1α signaling pathway is related with glycolysis in odontogenic keratocysts at single-cell level. Oral Dis 2024; 30:3501-3503. [PMID: 37279062 DOI: 10.1111/odi.14636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/25/2023] [Accepted: 05/17/2023] [Indexed: 06/07/2023]
Affiliation(s)
- Qi-Wen Man
- Department of Oral and Maxillofacial - Head and Neck Oncology, School of Stomatology Wuhan University, Wuhan, China
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3
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Wang J, Ding HK, Xu HJ, Hu DK, Hankey W, Chen L, Xiao J, Liang CZ, Zhao B, Xu LF. Single-cell analysis revealing the metabolic landscape of prostate cancer. Asian J Androl 2024:00129336-990000000-00179. [PMID: 38657119 DOI: 10.4103/aja20243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/29/2024] [Indexed: 04/26/2024] Open
Abstract
Tumor metabolic reprogramming is a hallmark of cancer development, and targeting metabolic vulnerabilities has been proven to be an effective approach for castration-resistant prostate cancer (CRPC) treatment. Nevertheless, treatment failure inevitably occurs, largely due to cellular heterogeneity, which cannot be deciphered by traditional bulk sequencing techniques. By employing computational pipelines for single-cell RNA sequencing, we demonstrated that epithelial cells within the prostate are more metabolically active and plastic than stromal cells. Moreover, we identified that neuroendocrine (NE) cells tend to have high metabolic rates, which might explain the high demand for nutrients and energy exhibited by neuroendocrine prostate cancer (NEPC), one of the most lethal variants of prostate cancer (PCa). Additionally, we demonstrated through computational and experimental approaches that variation in mitochondrial activity is the greatest contributor to metabolic heterogeneity among both tumor cells and nontumor cells. These results establish a detailed metabolic landscape of PCa, highlight a potential mechanism of disease progression, and emphasize the importance of future studies on tumor heterogeneity and the tumor microenvironment from a metabolic perspective.
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Affiliation(s)
- Jing Wang
- Department of Urologic Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230031, China
| | - He-Kang Ding
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China
- Institute of Urology, Anhui Medical University, Hefei 230001, China
- Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei 230001, China
| | - Han-Jiang Xu
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China
- Institute of Urology, Anhui Medical University, Hefei 230001, China
- Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei 230001, China
| | - De-Kai Hu
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China
- Institute of Urology, Anhui Medical University, Hefei 230001, China
- Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei 230001, China
| | - William Hankey
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Li Chen
- Department of Geriatrics, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Jun Xiao
- Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Chao-Zhao Liang
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China
- Institute of Urology, Anhui Medical University, Hefei 230001, China
- Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei 230001, China
| | - Bing Zhao
- Department of Geriatrics, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Ling-Fan Xu
- Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei 230001, China
- Institute of Urology, Anhui Medical University, Hefei 230001, China
- Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei 230001, China
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4
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Che YF, Li RF, Man QW. Metabolic profile of osteoclasts from a recurrent adenoid ameloblastoma at single-cell level. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101873. [PMID: 38621581 DOI: 10.1016/j.jormas.2024.101873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 04/10/2024] [Indexed: 04/17/2024]
Affiliation(s)
- Yin-Fu Che
- Department of Stomatology, Lanzhou University First Affiliated Hospital, Lanzhou University, Lanzhou, China
| | - Rui-Fang Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, China
| | - Qi-Wen Man
- Department of Oral and Maxillofacial-Head and Neck Oncology, School of Stomatology Wuhan University, Wuhan, China.
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5
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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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6
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Demicco M, Liu XZ, Leithner K, Fendt SM. Metabolic heterogeneity in cancer. Nat Metab 2024; 6:18-38. [PMID: 38267631 DOI: 10.1038/s42255-023-00963-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/06/2023] [Indexed: 01/26/2024]
Abstract
Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.
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Affiliation(s)
- Margherita Demicco
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Xiao-Zheng Liu
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Katharina Leithner
- Division of Pulmonology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium.
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
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7
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He Y, Yuan H, Liang Y, Liu X, Zhang X, Ji Y, Zhao B, Yang K, Zhang J, Zhang S, Zhang Y, Zhang L. On-capillary alkylation micro-reactor: a facile strategy for proteo-metabolome profiling in the same single cells. Chem Sci 2023; 14:13495-13502. [PMID: 38033888 PMCID: PMC10686037 DOI: 10.1039/d3sc05047e] [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: 09/26/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023] Open
Abstract
Single-cell multi-omics analysis can provide comprehensive insights to study cell-to-cell heterogeneity in normal and disease physiology. However, due to the lack of amplification technique, the measurement of proteome and metabolome in the same cell is challenging. Herein, a novel on-capillary alkylation micro-reactor (OCAM) was developed to achieve proteo-metabolome profiling in the same single cells, by which proteins were first covalently bound to an iodoacetic acid functionalized open-tubular capillary micro-reactor via sulfhydryl alkylation reaction, and metabolites were rapidly eluted, followed by on-column digestion of captured proteins. Compared with existing methods for low-input proteome sample preparation, OCAM exhibited improved efficiency, anti-interference ability and recovery, enabling the identification of an average of 1509 protein groups in single HeLa cells. This strategy was applied to single-cell proteo-metabolome analysis of mouse oocytes at different stages, 3457 protein groups and 171 metabolites were identified in single oocytes, which is the deepest coverage of proteome and metabolome from single mouse oocytes to date, achieving complementary characterization of metabolic patterns during oocyte maturation.
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Affiliation(s)
- Yingyun He
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
- University of Chinese Academy of Sciences Beijing 100049 China
| | - Huiming Yuan
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Yu Liang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Xinxin Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Xiaozhe Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Yahui Ji
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Baofeng Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Kaiguang Yang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Jue Zhang
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA Changsha 410013 China
| | - Shen Zhang
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA Changsha 410013 China
| | - Yukui Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
| | - Lihua Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian 116023 China
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8
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Lu H, Zhang H, Li L. Chemical tagging mass spectrometry: an approach for single-cell omics. Anal Bioanal Chem 2023; 415:6901-6913. [PMID: 37466681 PMCID: PMC10729908 DOI: 10.1007/s00216-023-04850-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/20/2023]
Abstract
Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.
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Affiliation(s)
- Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Hua Zhang
- 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.
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9
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Alam S, Doherty E, Ortega-Prieto P, Arizanova J, Fets L. Membrane transporters in cell physiology, cancer metabolism and drug response. Dis Model Mech 2023; 16:dmm050404. [PMID: 38037877 PMCID: PMC10695176 DOI: 10.1242/dmm.050404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023] Open
Abstract
By controlling the passage of small molecules across lipid bilayers, membrane transporters influence not only the uptake and efflux of nutrients, but also the metabolic state of the cell. With more than 450 members, the Solute Carriers (SLCs) are the largest transporter super-family, clustering into families with different substrate specificities and regulatory properties. Cells of different types are, therefore, able to tailor their transporter expression signatures depending on their metabolic requirements, and the physiological importance of these proteins is illustrated by their mis-regulation in a number of disease states. In cancer, transporter expression is heterogeneous, and the SLC family has been shown to facilitate the accumulation of biomass, influence redox homeostasis, and also mediate metabolic crosstalk with other cell types within the tumour microenvironment. This Review explores the roles of membrane transporters in physiological and malignant settings, and how these roles can affect drug response, through either indirect modulation of sensitivity or the direct transport of small-molecule therapeutic compounds into cells.
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Affiliation(s)
- Sara Alam
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Emily Doherty
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Paula Ortega-Prieto
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Julia Arizanova
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
| | - Louise Fets
- Drug Transport and Tumour Metabolism Lab, MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, Du Cane Road, London, W12 0NN, UK
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10
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Saunders KD, von Gerichten J, Lewis HM, Gupta P, Spick M, Costa C, Velliou E, Bailey MJ. Single-Cell Lipidomics Using Analytical Flow LC-MS Characterizes the Response to Chemotherapy in Cultured Pancreatic Cancer Cells. Anal Chem 2023; 95:14727-14735. [PMID: 37725657 PMCID: PMC10551860 DOI: 10.1021/acs.analchem.3c02854] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/07/2023] [Indexed: 09/21/2023]
Abstract
In this work, we demonstrate the development and first application of nanocapillary sampling followed by analytical flow liquid chromatography-mass spectrometry for single-cell lipidomics. Around 260 lipids were tentatively identified in a single cell, demonstrating remarkable sensitivity. Human pancreatic ductal adenocarcinoma cells (PANC-1) treated with the chemotherapeutic drug gemcitabine can be distinguished from controls solely on the basis of their single-cell lipid profiles. Notably, the relative abundance of LPC(0:0/16:0) was significantly affected in gemcitabine-treated cells, in agreement with previous work in bulk. This work serves as a proof of concept that live cells can be sampled selectively and then characterized using automated and widely available analytical workflows, providing biologically relevant outputs.
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Affiliation(s)
| | | | - Holly-May Lewis
- Faculty
of Health & Medical Sciences, University
of Surrey, Guildford GU2 7XH, U.K.
| | - Priyanka Gupta
- Centre
for 3D Models of Health and Disease, University
College London—Division of Surgery and Interventional Science, London W1W 7TY, U.K.
| | - Matt Spick
- Faculty
of Health & Medical Sciences, University
of Surrey, Guildford GU2 7XH, U.K.
| | - Catia Costa
- Ion
Beam Centre, University of Surrey, Guildford GU2 7XH, U.K.
| | - Eirini Velliou
- Centre
for 3D Models of Health and Disease, University
College London—Division of Surgery and Interventional Science, London W1W 7TY, U.K.
| | - Melanie J. Bailey
- Department
of Chemistry, University of Surrey, Guildford GU2 7XH, U.K.
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11
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Zhang Z, Zhu H, Dang P, Wang J, Chang W, Wang X, Alghamdi N, Lu A, Zang Y, Wu W, Wang Y, Zhang Y, Cao S, Zhang C. FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data. Nucleic Acids Res 2023; 51:W180-W190. [PMID: 37216602 PMCID: PMC10320190 DOI: 10.1093/nar/gkad444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/29/2023] [Accepted: 05/11/2023] [Indexed: 05/24/2023] Open
Abstract
Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.
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Affiliation(s)
- Zixuan Zhang
- College of Software, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Haiqi Zhu
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Pengtao Dang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Electric Computer Engineering, Purdue University, Indianapolis, IN 46202, USA
| | - Jia Wang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Wennan Chang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Xiao Wang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Norah Alghamdi
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Alex Lu
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yong Zang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Wenzhuo Wu
- Department of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Yijie Wang
- Department of Computer Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Yu Zhang
- College of Software, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sha Cao
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Chi Zhang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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12
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Yang G, Huang S, Hu K, Lu A, Yang J, Meroueh N, Dang P, Wang Y, Zhu H, Cao S, Zhang C. Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer. Front Oncol 2023; 13:1117810. [PMID: 37377905 PMCID: PMC10291142 DOI: 10.3389/fonc.2023.1117810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 05/02/2023] [Indexed: 06/29/2023] Open
Abstract
Introduction Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue. Method In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data. Result Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. Discussion This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.
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Affiliation(s)
- Grace Yang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Shaoyang Huang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Kevin Hu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Alex Lu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Park Tudor School, Indianapolis, IN, United States
| | - Jonathan Yang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Noah Meroueh
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Pengtao Dang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, United States
| | - Yijie Wang
- Department of Computer Science, Indiana University, Bloomington, IN, United States
| | - Haiqi Zhu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Computer Science, Indiana University, Bloomington, IN, United States
| | - Sha Cao
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Chi Zhang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
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13
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Danzi F, Pacchiana R, Mafficini A, Scupoli MT, Scarpa A, Donadelli M, Fiore A. To metabolomics and beyond: a technological portfolio to investigate cancer metabolism. Signal Transduct Target Ther 2023; 8:137. [PMID: 36949046 PMCID: PMC10033890 DOI: 10.1038/s41392-023-01380-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 03/24/2023] Open
Abstract
Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.
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Affiliation(s)
- Federica Danzi
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Raffaella Pacchiana
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Andrea Mafficini
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Maria T Scupoli
- Department of Neurosciences, Biomedicine and Movement Sciences, Biology and Genetics Section, University of Verona, Verona, Italy
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
- ARC-NET Research Centre, University and Hospital Trust of Verona, Verona, Italy
| | - Massimo Donadelli
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
| | - Alessandra Fiore
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
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14
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Fu J, Zhu F, Xu CJ, Li Y. Metabolomics meets systems immunology. EMBO Rep 2023; 24:e55747. [PMID: 36916532 PMCID: PMC10074123 DOI: 10.15252/embr.202255747] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/24/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.
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Affiliation(s)
- Jianbo Fu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Cheng-Jian Xu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yang Li
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
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15
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Wheeler AM, Eberhard CD, Mosher EP, Yuan Y, Wilkins HN, Seneviratne HK, Orsburn BC, Bumpus NN. Achieving a Deeper Understanding of Drug Metabolism and Responses Using Single-Cell Technologies. Drug Metab Dispos 2023; 51:350-359. [PMID: 36627162 PMCID: PMC10029823 DOI: 10.1124/dmd.122.001043] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 01/12/2023] Open
Abstract
Recent advancements in single-cell technologies have enabled detection of RNA, proteins, metabolites, and xenobiotics in individual cells, and the application of these technologies has the potential to transform pharmacological research. Single-cell data has already resulted in the development of human and model species cell atlases, identifying different cell types within a tissue, further facilitating the characterization of tumor heterogeneity, and providing insight into treatment resistance. Research discussed in this review demonstrates that distinct cell populations express drug metabolizing enzymes to different extents, indicating there may be variability in drug metabolism not only between organs, but within tissue types. Additionally, we put forth the concept that single-cell analyses can be used to expose underlying variability in cellular response to drugs, providing a unique examination of drug efficacy, toxicity, and metabolism. We will outline several of these techniques: single-cell RNA-sequencing and mass cytometry to characterize and distinguish different cell types, single-cell proteomics to quantify drug metabolizing enzymes and characterize cellular responses to drug, capillary electrophoresis-ultrasensitive laser-induced fluorescence detection and single-probe single-cell mass spectrometry for detection of drugs, and others. Emerging single-cell technologies such as these can comprehensively characterize heterogeneity in both cell-type-specific drug metabolism and response to treatment, enhancing progress toward personalized and precision medicine. SIGNIFICANCE STATEMENT: Recent technological advances have enabled the analysis of gene expression and protein levels in single cells. These types of analyses are important to investigating mechanisms that cannot be elucidated on a bulk level, primarily due to the variability of cell populations within biological systems. Here, we summarize cell-type-specific drug metabolism and how pharmacologists can utilize single-cell approaches to obtain a comprehensive understanding of drug metabolism and cellular heterogeneity in response to drugs.
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Affiliation(s)
- Abigail M Wheeler
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Colten D Eberhard
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Eric P Mosher
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Yuting Yuan
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Hannah N Wilkins
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Herana Kamal Seneviratne
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Benjamin C Orsburn
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Namandjé N Bumpus
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
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16
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Palermo A. Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets. Drug Discov Today 2023; 28:103460. [PMID: 36427778 DOI: 10.1016/j.drudis.2022.103460] [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: 08/31/2022] [Revised: 11/16/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.
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Affiliation(s)
- Amelia Palermo
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA.
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17
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Bai Y, Guo Z, Pereira FC, Wagner M, Cheng JX. Mid-Infrared Photothermal-Fluorescence In Situ Hybridization for Functional Analysis and Genetic Identification of Single Cells. Anal Chem 2023; 95:2398-2405. [PMID: 36652555 PMCID: PMC9893215 DOI: 10.1021/acs.analchem.2c04474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Simultaneous identification and metabolic analysis of microbes with single-cell resolution and high throughput are necessary to answer the question of "who eats what, when, and where" in complex microbial communities. Here, we present a mid-infrared photothermal-fluorescence in situ hybridization (MIP-FISH) platform that enables direct bridging of genotype and phenotype. Through multiple improvements of MIP imaging, the sensitive detection of isotopically labeled compounds incorporated into proteins of individual bacterial cells became possible, while simultaneous detection of FISH labeling with rRNA-targeted probes enabled the identification of the analyzed cells. In proof-of-concept experiments, we showed that the clear spectral red shift in the protein amide I region due to incorporation of 13C atoms originating from 13C-labeled glucose can be exploited by MIP-FISH to discriminate and identify 13C-labeled bacterial cells within a complex human gut microbiome sample. The presented methods open new opportunities for single-cell structure-function analyses for microbiology.
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Affiliation(s)
- Yeran Bai
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States,Photonics
Center, Boston University, Boston, Massachusetts 02215, United States
| | - Zhongyue Guo
- Department
of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States,Photonics
Center, Boston University, Boston, Massachusetts 02215, United States
| | - Fátima C. Pereira
- Centre
for Microbiology and Environmental Systems Science, Department of
Microbiology and Ecosystem Science, University
of Vienna, Vienna 1030, Austria
| | - Michael Wagner
- Centre
for Microbiology and Environmental Systems Science, Department of
Microbiology and Ecosystem Science, University
of Vienna, Vienna 1030, Austria,Department
of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark,
| | - Ji-Xin Cheng
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States,Department
of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States,Photonics
Center, Boston University, Boston, Massachusetts 02215, United States,
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18
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Liu Q, Martínez-Jarquín S, Ge W, Zenobi R. Development of a 3D-Printed Ionization Source for Single-Cell Analysis. Anal Chem 2023; 95:1823-1828. [PMID: 36622658 DOI: 10.1021/acs.analchem.2c04279] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Understanding the physiologies and pathologies of diseases requires a thorough understanding of metabolic heterogeneity in cells. This technical note presents a 3D printing technology for manufacturing an ionization source that is specially adapted for mass spectrometry-based single-cell analysis. This all-in-one 3D-printed electrospray ionization source integrates the sample introduction, metabolite extraction, and ionization into one device, simplifying the process of single-cell analysis and improving the reproducibility of the measurement. We successfully used it for high-throughput analysis of three types of cancer cells (around 17 cells/min) and used the t-distributed stochastic neighbor embedding algorithm to distinguish different cell types based on detected metabolites. By simply adjusting the printing parameters of the 3D-printed ionization source, it can be applied to cells with different sizes. The proposed 3D-printed ionization source promises to open new possibilities for single-cell analysis.
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Affiliation(s)
- Qinlei Liu
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich CH-8093, Switzerland
| | | | - Wenjie Ge
- Department of Biology, ETH Zurich, CH-8093 Zurich, Switzerland
| | - Renato Zenobi
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich CH-8093, Switzerland
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19
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Wang Y, Tong N, Li F, Zhao K, Wang D, Niu Y, Xu F, Cheng J, Wang J. Trapping of a Single Microparticle Using AC Dielectrophoresis Forces in a Microfluidic Chip. MICROMACHINES 2023; 14:159. [PMID: 36677221 PMCID: PMC9863554 DOI: 10.3390/mi14010159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/21/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
Precise trap and manipulation of individual cells is a prerequisite for single-cell analysis, which has a wide range of applications in biology, chemistry, medicine, and materials. Herein, a microfluidic trapping system with a 3D electrode based on AC dielectrophoresis (DEP) technology is proposed, which can achieve the precise trapping and release of specific microparticles. The 3D electrode consists of four rectangular stereoscopic electrodes with an acute angle near the trapping chamber. It is made of Ag-PDMS material, and is the same height as the channel, which ensures the uniform DEP force will be received in the whole channel space, ensuring a better trapping effect can be achieved. The numerical simulation was conducted in terms of electrode height, angle, and channel width. Based on the simulation results, an optimal chip structure was obtained. Then, the polystyrene particles with different diameters were used as the samples to verify the effectiveness of the designed trapping system. The findings of this research will contribute to the application of cell trapping and manipulation, as well as single-cell analysis.
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Affiliation(s)
- Yanjuan Wang
- Software Institute, Dalian Jiaotong University, Dalian 116028, China
- Liaoning Key Laboratory of Marine Sensing and Intelligent Detection, College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Ning Tong
- Software Institute, Dalian Jiaotong University, Dalian 116028, China
| | - Fengqi Li
- Software Institute, Dalian Jiaotong University, Dalian 116028, China
| | - Kai Zhao
- Liaoning Key Laboratory of Marine Sensing and Intelligent Detection, College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Deguang Wang
- Software Institute, Dalian Jiaotong University, Dalian 116028, China
| | - Yijie Niu
- Software Institute, Dalian Jiaotong University, Dalian 116028, China
| | - Fengqiang Xu
- Software Institute, Dalian Jiaotong University, Dalian 116028, China
| | - Jiale Cheng
- Liaoning Key Laboratory of Marine Sensing and Intelligent Detection, College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Junsheng Wang
- Liaoning Key Laboratory of Marine Sensing and Intelligent Detection, College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
- College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
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20
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Hu R, Li Y, Yang Y, Liu M. Mass spectrometry-based strategies for single-cell metabolomics. MASS SPECTROMETRY REVIEWS 2023; 42:67-94. [PMID: 34028064 DOI: 10.1002/mas.21704] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/05/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.
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Affiliation(s)
- Rui Hu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ying Li
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yunhuang Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Maili Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
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21
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Li H, Dixon EE, Wu H, Humphreys BD. Comprehensive single-cell transcriptional profiling defines shared and unique epithelial injury responses during kidney fibrosis. Cell Metab 2022; 34:1977-1998.e9. [PMID: 36265491 PMCID: PMC9742301 DOI: 10.1016/j.cmet.2022.09.026] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 07/19/2022] [Accepted: 09/28/2022] [Indexed: 01/12/2023]
Abstract
The underlying cellular events driving kidney fibrogenesis and metabolic dysfunction are incompletely understood. Here, we employed single-cell combinatorial indexing RNA sequencing to analyze 24 mouse kidneys from two fibrosis models. We profiled 309,666 cells in one experiment, representing 50 cell types/states encompassing epithelial, endothelial, immune, and stromal populations. Single-cell analysis identified diverse injury states of the proximal tubule, including two distinct early-phase populations with dysregulated lipid and amino acid metabolism, respectively. Lipid metabolism was defective in the chronic phase but was transiently activated in the very early stages of ischemia-induced injury, where we discovered increased lipid deposition and increased fatty acid β-oxidation. Perilipin 2 was identified as a surface marker of intracellular lipid droplets, and its knockdown in vitro disrupted cell energy state maintenance during lipid accumulation. Surveying epithelial cells across nephron segments identified shared and unique injury responses. Stromal cells exhibited high heterogeneity and contributed to fibrogenesis by epithelial-stromal crosstalk.
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Affiliation(s)
- Haikuo Li
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Eryn E Dixon
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Haojia Wu
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA; Department of Developmental Biology, Washington University in St. Louis, St. Louis, MO, USA.
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22
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Nascentes Melo LM, Lesner NP, Sabatier M, Ubellacker JM, Tasdogan A. Emerging metabolomic tools to study cancer metastasis. Trends Cancer 2022; 8:988-1001. [PMID: 35909026 DOI: 10.1016/j.trecan.2022.07.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/24/2022] [Accepted: 07/06/2022] [Indexed: 12/24/2022]
Abstract
Metastasis is responsible for 90% of deaths in patients with cancer. Understanding the role of metabolism during metastasis has been limited by the development of robust and sensitive technologies that capture metabolic processes in metastasizing cancer cells. We discuss the current technologies available to study (i) metabolism in primary and metastatic cancer cells and (ii) metabolic interactions between cancer cells and the tumor microenvironment (TME) at different stages of the metastatic cascade. We identify advantages and disadvantages of each method and discuss how these tools and technologies will further improve our understanding of metastasis. Studies investigating the complex metabolic rewiring of different cells using state-of-the-art metabolomic technologies have the potential to reveal novel biological processes and therapeutic interventions for human cancers.
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Affiliation(s)
| | - Nicholas P Lesner
- Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marie Sabatier
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jessalyn M Ubellacker
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Alpaslan Tasdogan
- Department of Dermatology, University Hospital Essen and German Cancer Consortium, Partner Site, Essen, Germany.
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23
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Metabolic flux between organs measured by arteriovenous metabolite gradients. EXPERIMENTAL & MOLECULAR MEDICINE 2022; 54:1354-1366. [PMID: 36075951 PMCID: PMC9534916 DOI: 10.1038/s12276-022-00803-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/15/2022] [Accepted: 03/04/2022] [Indexed: 12/15/2022]
Abstract
Mammalian organs convert dietary nutrients into circulating metabolites and share them to maintain whole-body metabolic homeostasis. While the concentrations of circulating metabolites have been frequently measured in a variety of pathophysiological conditions, the exchange flux of circulating metabolites between organs is not easily measurable due to technical difficulties. Isotope tracing is useful for measuring such fluxes for a metabolite of interest, but the shuffling of isotopic atoms between metabolites requires mathematical modeling. Arteriovenous metabolite gradient measurements can complement isotope tracing to infer organ-specific net fluxes of many metabolites simultaneously. Here, we review the historical development of arteriovenous measurements and discuss their advantages and limitations with key example studies that have revealed metabolite exchange flux between organs in diverse pathophysiological contexts.
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24
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Stopka SA, van der Reest J, Abdelmoula WM, Ruiz DF, Joshi S, Ringel AE, Haigis MC, Agar NYR. Spatially resolved characterization of tissue metabolic compartments in fasted and high-fat diet livers. PLoS One 2022; 17:e0261803. [PMID: 36067168 PMCID: PMC9447892 DOI: 10.1371/journal.pone.0261803] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 08/12/2022] [Indexed: 11/18/2022] Open
Abstract
Cells adapt their metabolism to physiological stimuli, and metabolic heterogeneity exists between cell types, within tissues, and subcellular compartments. The liver plays an essential role in maintaining whole-body metabolic homeostasis and is structurally defined by metabolic zones. These zones are well-understood on the transcriptomic level, but have not been comprehensively characterized on the metabolomic level. Mass spectrometry imaging (MSI) can be used to map hundreds of metabolites directly from a tissue section, offering an important advance to investigate metabolic heterogeneity in tissues compared to extraction-based metabolomics methods that analyze tissue metabolite profiles in bulk. We established a workflow for the preparation of tissue specimens for matrix-assisted laser desorption/ionization (MALDI) MSI that can be implemented to achieve broad coverage of central carbon, nucleotide, and lipid metabolism pathways. Herein, we used this approach to visualize the effect of nutrient stress and excess on liver metabolism. Our data revealed a highly organized metabolic tissue compartmentalization in livers, which becomes disrupted under high fat diet. Fasting caused changes in the abundance of several metabolites, including increased levels of fatty acids and TCA intermediates while fatty livers had higher levels of purine and pentose phosphate-related metabolites, which generate reducing equivalents to counteract oxidative stress. This spatially conserved approach allowed the visualization of liver metabolic compartmentalization at 30 μm pixel resolution and can be applied more broadly to yield new insights into metabolic heterogeneity in vivo.
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Affiliation(s)
- Sylwia A. Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
| | - Jiska van der Reest
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Department of Cell Biology, Blavatnik Institute, Ludwig Center, Harvard Medical School, Boston, MA, United Statees of America
| | - Walid M. Abdelmoula
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
| | - Daniela F. Ruiz
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, United Statees of America
| | - Shakchhi Joshi
- Department of Cell Biology, Blavatnik Institute, Ludwig Center, Harvard Medical School, Boston, MA, United Statees of America
| | - Alison E. Ringel
- Department of Cell Biology, Blavatnik Institute, Ludwig Center, Harvard Medical School, Boston, MA, United Statees of America
| | - Marcia C. Haigis
- Department of Cell Biology, Blavatnik Institute, Ludwig Center, Harvard Medical School, Boston, MA, United Statees of America
- * E-mail: (MCH); (NYRA)
| | - Nathalie Y. R. Agar
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United Statees of America
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, United Statees of America
- * E-mail: (MCH); (NYRA)
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25
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Wang X, Wang Z, Yu C, Ge Z, Yang W. Advances in precise single-cell capture for analysis and biological applications. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:3047-3063. [PMID: 35946358 DOI: 10.1039/d2ay00625a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Cells are the basic structural and functional units of living organisms. However, conventional cell analysis only averages millions of cell populations, and some important information is lost. It is essential to quantitatively characterize the physiology and pathology of single-cell activities. Precise single-cell capture is an extremely challenging task during cell sample preparation. In this review, we summarize the category of technologies to capture single cells precisely with a focus on the latest development in the last five years. Each technology has its own set of benefits and specific challenges, which provide opportunities for researchers in different fields. Accordingly, we introduce the applications of captured single cells in cancer diagnosis, analysis of metabolism and secretion, and disease treatment. Finally, some perspectives are provided on the current development trends, future research directions, and challenges of single-cell capture.
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Affiliation(s)
- Xiaowen Wang
- School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, China.
| | - Zhen Wang
- School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, China.
| | - Chang Yu
- College of Computer Science, Chongqing University, Chongqing 400000, China
| | - Zhixing Ge
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
| | - Wenguang Yang
- School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, China.
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26
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Portero EP, Pade L, Li J, Choi SB, Nemes P. Single-Cell Mass Spectrometry of Metabolites and Proteins for Systems and Functional Biology. NEUROMETHODS 2022; 184:87-114. [PMID: 36699808 PMCID: PMC9872963 DOI: 10.1007/978-1-0716-2525-5_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Molecular composition is intricately intertwined with cellular function, and elucidation of this relationship is essential for understanding life processes and developing next-generational therapeutics. Technological innovations in capillary electrophoresis (CE) and liquid chromatography (LC) mass spectrometry (MS) provide previously unavailable insights into cellular biochemistry by allowing for the unbiased detection and quantification of molecules with high specificity. This chapter presents our validated protocols integrating ultrasensitive MS with classical tools of cell, developmental, and neurobiology to assess the biological function of important biomolecules. We use CE and LC MS to measure hundreds of metabolites and thousands of proteins in single cells or limited populations of tissues in chordate embryos and mammalian neurons, revealing molecular heterogeneity between identified cells. By pairing microinjection and optical microscopy, we demonstrate cell lineage tracing and testing the roles the dysregulated molecules play in the formation and maintenance of cell heterogeneity and tissue specification in frog embryos (Xenopus laevis). Electrophysiology extends our workflows to characterizing neuronal activity in sections of mammalian brain tissues. The information obtained from these studies mutually strengthen chemistry and biology and highlight the importance of interdisciplinary research to advance basic knowledge and translational applications forward.
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Affiliation(s)
| | | | - Jie Li
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, MD 20742
| | - Sam B. Choi
- 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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27
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Wink K, van der Loh M, Hartner N, Polack M, Dusny C, Schmid A, Belder D. Quantification of Biocatalytic Transformations by Single Microbial Cells Enabled by Tailored Integration of Droplet Microfluidics and Mass Spectrometry. Angew Chem Int Ed Engl 2022; 61:e202204098. [PMID: 35511505 PMCID: PMC9401594 DOI: 10.1002/anie.202204098] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Indexed: 12/23/2022]
Abstract
Improving the performance of chemical transformations catalysed by microbial biocatalysts requires a deep understanding of cellular processes. While the cellular heterogeneity of cellular characteristics, such as the concentration of high abundant cellular content, is well studied, little is known about the reactivity of individual cells and its impact on the chemical identity, quantity, and purity of excreted products. Biocatalytic transformations were monitored chemically specific and quantifiable at the single-cell level by integrating droplet microfluidics, cell imaging, and mass spectrometry. Product formation rates for individual Saccharomyces cerevisiae cells were obtained by i) incubating nanolitre-sized droplets for product accumulation in microfluidic devices, ii) an imaging setup to determine the number of cells in the droplets, and iii) electrospray ionisation mass spectrometry for reading the chemical contents of individual droplets. These findings now enable the study of whole-cell biocatalysis at single-cell resolution.
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Affiliation(s)
- Konstantin Wink
- University of LeipzigInstitute of Analytical Chemistry04107LeipzigGermany
| | - Marie van der Loh
- University of LeipzigInstitute of Analytical Chemistry04107LeipzigGermany
| | - Nora Hartner
- University of LeipzigInstitute of Analytical Chemistry04107LeipzigGermany
| | - Matthias Polack
- University of LeipzigInstitute of Analytical Chemistry04107LeipzigGermany
| | - Christian Dusny
- Department Solar MaterialsHelmholtz Centre for Environmental Research (UFZ)04318LeipzigGermany
| | - Andreas Schmid
- Department Solar MaterialsHelmholtz Centre for Environmental Research (UFZ)04318LeipzigGermany
| | - Detlev Belder
- University of LeipzigInstitute of Analytical Chemistry04107LeipzigGermany
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28
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Baquer G, Sementé L, Mahamdi T, Correig X, Ràfols P, García-Altares M. What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2022:e21794. [PMID: 35822576 DOI: 10.1002/mas.21794] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.
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Affiliation(s)
- Gerard Baquer
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Lluc Sementé
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Toufik Mahamdi
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Xavier Correig
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - María García-Altares
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
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29
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Ge X, Pereira FC, Mitteregger M, Berry D, Zhang M, Hausmann B, Zhang J, Schintlmeister A, Wagner M, Cheng JX. SRS-FISH: A high-throughput platform linking microbiome metabolism to identity at the single-cell level. Proc Natl Acad Sci U S A 2022; 119:e2203519119. [PMID: 35727976 PMCID: PMC9245642 DOI: 10.1073/pnas.2203519119] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/08/2022] [Indexed: 12/26/2022] Open
Abstract
One of the biggest challenges in microbiome research in environmental and medical samples is to better understand functional properties of microbial community members at a single-cell level. Single-cell isotope probing has become a key tool for this purpose, but the current detection methods for determination of isotope incorporation into single cells do not allow high-throughput analyses. Here, we report on the development of an imaging-based approach termed stimulated Raman scattering-two-photon fluorescence in situ hybridization (SRS-FISH) for high-throughput metabolism and identity analyses of microbial communities with single-cell resolution. SRS-FISH offers an imaging speed of 10 to 100 ms per cell, which is two to three orders of magnitude faster than achievable by state-of-the-art methods. Using this technique, we delineated metabolic responses of 30,000 individual cells to various mucosal sugars in the human gut microbiome via incorporation of deuterium from heavy water as an activity marker. Application of SRS-FISH to investigate the utilization of host-derived nutrients by two major human gut microbiome taxa revealed that response to mucosal sugars tends to be dominated by Bacteroidales, with an unexpected finding that Clostridia can outperform Bacteroidales at foraging fucose. With high sensitivity and speed, SRS-FISH will enable researchers to probe the fine-scale temporal, spatial, and individual activity patterns of microbial cells in complex communities with unprecedented detail.
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Affiliation(s)
- Xiaowei Ge
- Department of Electrical & Computer Engineering, Boston University, Boston, MA 02215
| | - Fátima C. Pereira
- Centre for Microbiology and Environmental Systems Science, Department of Microbiology and Ecosystem Science, University of Vienna, 1030 Vienna, Austria
| | - Matthias Mitteregger
- Centre for Microbiology and Environmental Systems Science, Department of Microbiology and Ecosystem Science, University of Vienna, 1030 Vienna, Austria
| | - David Berry
- Centre for Microbiology and Environmental Systems Science, Department of Microbiology and Ecosystem Science, University of Vienna, 1030 Vienna, Austria
| | - Meng Zhang
- Department of Electrical & Computer Engineering, Boston University, Boston, MA 02215
| | - Bela Hausmann
- Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna, 1030 Vienna, Austria
- Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Jing Zhang
- Department of Biomedical Engineering, Photonics Center, Boston University, Boston, MA 02215
| | - Arno Schintlmeister
- Centre for Microbiology and Environmental Systems Science, Department of Microbiology and Ecosystem Science, University of Vienna, 1030 Vienna, Austria
| | - Michael Wagner
- Centre for Microbiology and Environmental Systems Science, Department of Microbiology and Ecosystem Science, University of Vienna, 1030 Vienna, Austria
- Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark
| | - Ji-Xin Cheng
- Department of Electrical & Computer Engineering, Boston University, Boston, MA 02215
- Department of Biomedical Engineering, Photonics Center, Boston University, Boston, MA 02215
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30
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Wink K, Loh M, Hartner N, Polack M, Dusny C, Schmid A, Belder D. Quantifizierung biokatalytischer Umwandlungen durch einzelne mikrobielle Zellen mittels maßgeschneiderter Integration von Tröpfchenmikrofluidik und Massenspektrometrie. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202204098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Konstantin Wink
- Universität Leipzig Institut für Analytische Chemie 04107 Leipzig Deutschland
| | - Marie Loh
- Universität Leipzig Institut für Analytische Chemie 04107 Leipzig Deutschland
| | - Nora Hartner
- Universität Leipzig Institut für Analytische Chemie 04107 Leipzig Deutschland
| | - Matthias Polack
- Universität Leipzig Institut für Analytische Chemie 04107 Leipzig Deutschland
| | - Christian Dusny
- Department Solare Materialien Helmholtz-Zentrum für Umweltforschung (UFZ) 04318 Leipzig Deutschland
| | - Andreas Schmid
- Department Solare Materialien Helmholtz-Zentrum für Umweltforschung (UFZ) 04318 Leipzig Deutschland
| | - Detlev Belder
- Universität Leipzig Institut für Analytische Chemie 04107 Leipzig Deutschland
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31
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Abouleila Y, Ali A, Masuda K, Mashaghi A, Shimizu Y. Capillary microsampling-based single-cell metabolomics by mass spectrometry and its applications in medicine and drug discovery. Cancer Biomark 2022; 33:437-447. [DOI: 10.3233/cbm-210184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Characterization of cellular metabolic states is a technical challenge in biomedicine. Cellular heterogeneity caused by inherent diversity in expression of metabolic enzymes or due to sensitivity of metabolic reactions to perturbations, necessitates single cell analysis of metabolism. Heterogeneity is typically seen in cancer and thus, single-cell metabolomics is expectedly useful in studying cancer progression, metastasis, and variations in cancer drug response. However, low sample volumes and analyte concentrations limit detection of critically important metabolites. Capillary microsampling-based mass spectrometry approaches are emerging as a promising solution for achieving single-cell omics. Herein, we focus on the recent advances in capillary microsampling-based mass spectrometry techniques for single-cell metabolomics. We discuss recent technical developments and applications to cancer medicine and drug discovery.
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Affiliation(s)
- Yasmine Abouleila
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Research Center, Misr International University, Cairo, Egypt
| | - Ahmed Ali
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Research Center, Misr International University, Cairo, Egypt
| | - Keiko Masuda
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
| | - Alireza Mashaghi
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
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32
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Nyquist SK, Gao P, Haining TKJ, Retchin MR, Golan Y, Drake RS, Kolb K, Mead BE, Ahituv N, Martinez ME, Shalek AK, Berger B, Goods BA. Cellular and transcriptional diversity over the course of human lactation. Proc Natl Acad Sci U S A 2022; 119:e2121720119. [PMID: 35377806 PMCID: PMC9169737 DOI: 10.1073/pnas.2121720119] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/14/2022] [Indexed: 12/04/2022] Open
Abstract
Human breast milk (hBM) is a dynamic fluid that contains millions of cells, but their identities and phenotypic properties are poorly understood. We generated and analyzed single-cell RNA-sequencing (scRNA-seq) data to characterize the transcriptomes of cells from hBM across lactational time from 3 to 632 d postpartum in 15 donors. We found that the majority of cells in hBM are lactocytes, a specialized epithelial subset, and that cell-type frequencies shift over the course of lactation, yielding greater epithelial diversity at later points. Analysis of lactocytes reveals a continuum of cell states characterized by transcriptional changes in hormone-, growth factor-, and milk production-related pathways. Generalized additive models suggest that one subcluster, LC1 epithelial cells, increases as a function of time postpartum, daycare attendance, and the use of hormonal birth control. We identify several subclusters of macrophages in hBM that are enriched for tolerogenic functions, possibly playing a role in protecting the mammary gland during lactation. Our description of the cellular components of breast milk, their association with maternal–infant dyad metadata, and our quantification of alterations at the gene and pathway levels provide a detailed longitudinal picture of hBM cells across lactational time. This work paves the way for future investigations of how a potential division of cellular labor and differential hormone regulation might be leveraged therapeutically to support healthy lactation and potentially aid in milk production.
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Affiliation(s)
- Sarah K. Nyquist
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Program in Computational and Systems Biology, Massachusetts Institute of Technology; Cambridge, MA 02139
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
- Computer Science and Artificial Intelligence Laboratory, Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Patricia Gao
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Tessa K. J. Haining
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Michael R. Retchin
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Yarden Golan
- Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, University of California, San Francisco, CA 94143
| | - Riley S. Drake
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139
| | - Kellie Kolb
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Benjamin E. Mead
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, University of California, San Francisco, CA 94143
| | | | - Alex K. Shalek
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Program in Computational and Systems Biology, Massachusetts Institute of Technology; Cambridge, MA 02139
- Department of Chemistry and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139
- Division of Health Science & Technology, Harvard Medical School, Boston, MA 02115
- Department of Immunology, Massachusetts General Hospital, Boston, MA 02114
| | - Bonnie Berger
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Computer Science and Artificial Intelligence Laboratory, Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Brittany A. Goods
- Thayer School of Engineering, Program in Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755
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33
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Lopez JG, Wingreen NS. Noisy metabolism can promote microbial cross-feeding. eLife 2022; 11:70694. [PMID: 35380535 PMCID: PMC8983042 DOI: 10.7554/elife.70694] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 02/21/2022] [Indexed: 12/21/2022] Open
Abstract
Cross-feeding, the exchange of nutrients between organisms, is ubiquitous in microbial communities. Despite its importance in natural and engineered microbial systems, our understanding of how inter-species cross-feeding arises is incomplete, with existing theories limited to specific scenarios. Here, we introduce a novel theory for the emergence of such cross-feeding, which we term noise-averaging cooperation (NAC). NAC is based on the idea that, due to their small size, bacteria are prone to noisy regulation of metabolism which limits their growth rate. To compensate, related bacteria can share metabolites with each other to ‘average out’ noise and improve their collective growth. According to the Black Queen Hypothesis, this metabolite sharing among kin, a form of ‘leakage’, then allows for the evolution of metabolic interdependencies among species including de novo speciation via gene deletions. We first characterize NAC in a simple ecological model of cell metabolism, showing that metabolite leakage can in principle substantially increase growth rate in a community context. Next, we develop a generalized framework for estimating the potential benefits of NAC among real bacteria. Using single-cell protein abundance data, we predict that bacteria suffer from substantial noise-driven growth inefficiencies, and may therefore benefit from NAC. We then discuss potential evolutionary pathways for the emergence of NAC. Finally, we review existing evidence for NAC and outline potential experimental approaches to detect NAC in microbial communities.
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Affiliation(s)
- Jaime G Lopez
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
| | - Ned S Wingreen
- Department of Molecular Biology, Princeton University, Princeton, United States
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34
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Chou WC, Rampanelli E, Li X, Ting JPY. Impact of intracellular innate immune receptors on immunometabolism. Cell Mol Immunol 2022; 19:337-351. [PMID: 34697412 PMCID: PMC8891342 DOI: 10.1038/s41423-021-00780-y] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/17/2021] [Indexed: 12/21/2022] Open
Abstract
Immunometabolism, which is the metabolic reprogramming of anaerobic glycolysis, oxidative phosphorylation, and metabolite synthesis upon immune cell activation, has gained importance as a regulator of the homeostasis, activation, proliferation, and differentiation of innate and adaptive immune cell subsets that function as key factors in immunity. Metabolic changes in epithelial and other stromal cells in response to different stimulatory signals are also crucial in infection, inflammation, cancer, autoimmune diseases, and metabolic disorders. The crosstalk between the PI3K-AKT-mTOR and LKB1-AMPK signaling pathways is critical for modulating both immune and nonimmune cell metabolism. The bidirectional interaction between immune cells and metabolism is a topic of intense study. Toll-like receptors (TLRs), cytokine receptors, and T and B cell receptors have been shown to activate multiple downstream metabolic pathways. However, how intracellular innate immune sensors/receptors intersect with metabolic pathways is less well understood. The goal of this review is to examine the link between immunometabolism and the functions of several intracellular innate immune sensors or receptors, such as nucleotide-binding and leucine-rich repeat-containing receptors (NLRs, or NOD-like receptors), absent in melanoma 2 (AIM2)-like receptors (ALRs), and the cyclic dinucleotide receptor stimulator of interferon genes (STING). We will focus on recent advances and describe the impact of these intracellular innate immune receptors on multiple metabolic pathways. Whenever appropriate, this review will provide a brief contextual connection to pathogenic infections, autoimmune diseases, cancers, metabolic disorders, and/or inflammatory bowel diseases.
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Affiliation(s)
- Wei-Chun Chou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Elena Rampanelli
- Amsterdam UMC (University Medical Center, location AMC), Department of Experimental Vascular Medicine, AGEM (Amsterdam Gastroenterology Endocrinology Metabolism) Institute, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Xin Li
- Comparative Immunology Research Center, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China.
| | - Jenny P-Y Ting
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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35
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Odenkirk M, Horman BM, Dodds JN, Patisaul HB, Baker ES. Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping. ACS MEASUREMENT SCIENCE AU 2022; 2:67-75. [PMID: 35647605 PMCID: PMC9139744 DOI: 10.1021/acsmeasuresciau.1c00035] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
While decades of technical and analytical advancements have been utilized to discover novel lipid species, increase speciation, and evaluate localized lipid dysregulation at subtissue, cellular, and subcellular levels, many challenges still exist. One limitation is that the acquisition of both in-depth spatial information and comprehensive lipid speciation is extremely difficult, especially when biological material is limited or lipids are at low abundance. In neuroscience, for example, it is often desired to focus on only one brain region or subregion, which can be well under a square millimeter for rodents. Herein, we evaluate a micropunch histology method where cortical brain tissue at 2.0, 1.25, 1.0, 0.75, 0.5, and 0.25 mm diameter sizes and 1 mm depth was collected and analyzed with multidimensional liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC-IMS-CID-MS) measurements. Lipid extraction was optimized for the small sample sizes, and assessment of lipidome coverage for the 2.0 to 0.25 mm diameter sizes showed a decline from 304 to 198 lipid identifications as validated by all 4 analysis dimensions (~35% loss in coverage for ~88% less tissue). While losses were observed, the ~200 lipids and estimated 4630 neurons contained within the 0.25 punch still provided in-depth characterization of the small tissue region. Furthermore, while localization routinely achieved by mass spectrometry imaging (MSI) and single cell analyses is greater, this diameter is sufficiently small to isolate subcortical, hypothalamic, and other brain regions in adult rats, thereby increasing the coverage of lipids within relevant spatial windows without sacrificing speciation. Therefore, micropunch histology enables in-depth, region-specific lipid evaluations, an approach that will prove beneficial to a variety of lipidomic applications.
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Affiliation(s)
- Melanie
T. Odenkirk
- Department
of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Brian M. Horman
- Department
of Biological Sciences, North Carolina State
University, Raleigh, North Carolina 27695, United States
| | - James N. Dodds
- Department
of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Heather B. Patisaul
- Department
of Biological Sciences, North Carolina State
University, Raleigh, North Carolina 27695, United States
- Center
for Human Health and the Environment, North
Carolina State University, Raleigh, North Carolina 27695, United States
| | - Erin S. Baker
- Department
of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
- Comparative
Medicine Institute, North Carolina State
University, Raleigh, North Carolina 27695, United States
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36
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Chen WW, Liu W, Li Y, Wang J, Ren Y, Wang G, Chen C, Li H. Deciphering the Immune-Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology. Front Oncol 2022; 11:716042. [PMID: 35047383 PMCID: PMC8761635 DOI: 10.3389/fonc.2021.716042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 11/08/2021] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Cancer immunotherapy has shown great success in treating advanced-stage lung cancer but has yet been used to treat early-stage lung cancer, mostly due to lack of understanding of the tumor immune microenvironment in early-stage lung cancer. The immune system could both constrain and promote tumorigenesis in a process termed immune editing that can be divided into three phases, namely, elimination, equilibrium, and escape. Current understanding of the immune response toward tumor is mainly on the "escape" phase when the tumor is clinically detectable. The detailed mechanism by which tumor progenitor lesions was modulated by the immune system during early stage of lung cancer development remains elusive. The advent of single-cell sequencing technology enables tumor immunologists to address those fundamental questions. In this perspective, we will summarize our current understanding and big gaps about the immune response during early lung tumorigenesis. We will then present the state of the art of single-cell technology and then envision how single-cell technology could be used to address those questions. Advances in the understanding of the immune response and its dynamics during malignant transformation of pre-malignant lesion will shed light on how malignant cells interact with the immune system and evolve under immune selection. Such knowledge could then contribute to the development of precision and early intervention strategies toward lung malignancy.
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Affiliation(s)
- Wei-Wei Chen
- Department of Clinical Oncology, University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Wei Liu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yingze Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jun Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guangsuo Wang
- Department of Thoracic Surgery, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Hanjie Li
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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37
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Tourigny DS, Goldberg AP, Karr JR. Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm. Biophys J 2021; 120:5231-5242. [PMID: 34757076 DOI: 10.1016/j.bpj.2021.10.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 10/01/2021] [Accepted: 10/26/2021] [Indexed: 10/19/2022] Open
Abstract
Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterize their metabolism at the genome scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modeling formalism of metabolic network modeling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded flux-balance analysis models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.
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Affiliation(s)
- David S Tourigny
- Irving Institute for Cancer Dynamics, Columbia University, New York, New York; School of Mathematics, University of Birmingham, Birmingham, United Kingdom.
| | - Arthur P Goldberg
- Icahn Institute for Data Science and Genomic Technology, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jonathan R Karr
- Icahn Institute for Data Science and Genomic Technology, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
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38
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Choi SB, Muñoz-LLancao P, Manzini MC, Nemes P. Data-Dependent Acquisition Ladder for Capillary Electrophoresis Mass Spectrometry-Based Ultrasensitive (Neuro)Proteomics. Anal Chem 2021; 93:15964-15972. [PMID: 34812615 DOI: 10.1021/acs.analchem.1c03327] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Measurement of broad types of proteins from a small number of cells to single cells would help to better understand the nervous system but requires significant leaps in sensitivity in high-resolution mass spectrometry (HRMS). Microanalytical capillary electrophoresis electrospray ionization (CE-ESI) offers a path to ultrasensitive proteomics by integrating scalability with sensitivity. Here, we systematically evaluate performance limitations in this technology to develop a data acquisition strategy with deeper coverage of the neuroproteome from trace amounts of starting materials than traditional dynamic exclusion. During standard data-dependent acquisition (DDA), compact migration challenged the duty cycle of second-stage transitions and redundant targeting of abundant peptide signals lowered their identification success rate. DDA was programmed to progressively exclude a static set of high-intensity peptide signals throughout replicate measurements, essentially forming rungs of a "DDA ladder." The method was tested for ∼500 pg portions of a protein digest from cultured hippocampal (primary) neurons (mouse), which estimated the total amount of protein from a single neuron. The analysis of ∼5 ng of protein digest over all replicates, approximating ∼10 neurons, identified 428 nonredundant proteins (415 quantified), an ∼35% increase over traditional DDA. The identified proteins were enriched in neuronal marker genes and molecular pathways of neurobiological importance. The DDA ladder enhances CE-HRMS sensitivity to single-neuron equivalent amounts of proteins, thus expanding the analytical toolbox of neuroscience.
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Affiliation(s)
- Sam B Choi
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, Maryland 20742, United States
| | - Pablo Muñoz-LLancao
- Department of Neuroscience & Cell Biology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey 08901, United States.,Department of Cell Biology, Yale University School of Medicine, New Haven, Connecticut 06510, United States
| | - M Chiara Manzini
- Department of Neuroscience & Cell Biology, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey 08901, United States
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, 8051 Regents Drive, College Park, Maryland 20742, United States
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39
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He B, Zhang W, Guled F, Harms A, Ramautar R, Hankemeier T. Analytical techniques for biomass-restricted metabolomics: An overview of the state-of-the-art. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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40
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Toward modeling metabolic state from single-cell transcriptomics. Mol Metab 2021; 57:101396. [PMID: 34785394 PMCID: PMC8829761 DOI: 10.1016/j.molmet.2021.101396] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/21/2021] [Accepted: 11/09/2021] [Indexed: 12/31/2022] Open
Abstract
Background Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge. Scope of Review We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals. Major Conclusions Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models. Single-cell RNA sequencing and prior metabolic knowledge enable metabolic predictions. When compared to bulk, single-cell modeling is linked to unique insights and challenges. Computational modelling approaches differ in applicability and newly provided insights. The use of prior metabolic knowledge paves the way for mechanistic machine-learning.
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41
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Kwon YW, Jo HS, Bae S, Seo Y, Song P, Song M, Yoon JH. Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery. Front Med (Lausanne) 2021; 8:747333. [PMID: 34631760 PMCID: PMC8492935 DOI: 10.3389/fmed.2021.747333] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 08/26/2021] [Indexed: 12/12/2022] Open
Abstract
Proteomics has become an important field in molecular sciences, as it provides valuable information on the identity, expression levels, and modification of proteins. For example, cancer proteomics unraveled key information in mechanistic studies on tumor growth and metastasis, which has contributed to the identification of clinically applicable biomarkers as well as therapeutic targets. Several cancer proteome databases have been established and are being shared worldwide. Importantly, the integration of proteomics studies with other omics is providing extensive data related to molecular mechanisms and target modulators. These data may be analyzed and processed through bioinformatic pipelines to obtain useful information. The purpose of this review is to provide an overview of cancer proteomics and recent advances in proteomic techniques. In particular, we aim to offer insights into current proteomics studies of brain cancer, in which proteomic applications are in a relatively early stage. This review covers applications of proteomics from the discovery of biomarkers to the characterization of molecular mechanisms through advances in technology. Moreover, it addresses global trends in proteomics approaches for translational research. As a core method in translational research, the continued development of this field is expected to provide valuable information at a scale beyond that previously seen.
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Affiliation(s)
- Yang Woo Kwon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Han-Seul Jo
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Sungwon Bae
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Youngsuk Seo
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Parkyong Song
- Department of Convergence Medicine, Pusan National University School of Medicine, Yangsan, South Korea
| | - Minseok Song
- Department of Life Sciences, Yeungnam University, Gyeongsan, South Korea
| | - Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, South Korea
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42
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Yu Z, Jin J, Shui L, Chen H, Zhu Y. Recent advances in microdroplet techniques for single-cell protein analysis. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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43
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Alghamdi N, Chang W, Dang P, Lu X, Wan C, Gampala S, Huang Z, Wang J, Ma Q, Zang Y, Fishel M, Cao S, Zhang C. A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data. Genome Res 2021; 31:1867-1884. [PMID: 34301623 PMCID: PMC8494226 DOI: 10.1101/gr.271205.120] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 07/01/2021] [Indexed: 11/24/2022]
Abstract
The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.
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Affiliation(s)
- Norah Alghamdi
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Wennan Chang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
- Department of Electrical and Computer Engineering, Purdue University, Indianapolis, Indiana 46202, USA
| | - Pengtao Dang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
- Department of Electrical and Computer Engineering, Purdue University, Indianapolis, Indiana 46202, USA
| | - Xiaoyu Lu
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Changlin Wan
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
- Department of Electrical and Computer Engineering, Purdue University, Indianapolis, Indiana 46202, USA
| | - Silpa Gampala
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Zhi Huang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
- Department of Electrical and Computer Engineering, Purdue University, Indianapolis, Indiana 46202, USA
| | - Jiashi Wang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Qin Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, Ohio 43210, USA
| | - Yong Zang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Melissa Fishel
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Sha Cao
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Chi Zhang
- Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
- Department of Electrical and Computer Engineering, Purdue University, Indianapolis, Indiana 46202, USA
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Voss K, Hong HS, Bader JE, Sugiura A, Lyssiotis CA, Rathmell JC. A guide to interrogating immunometabolism. Nat Rev Immunol 2021; 21:637-652. [PMID: 33859379 PMCID: PMC8478710 DOI: 10.1038/s41577-021-00529-8] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2021] [Indexed: 02/07/2023]
Abstract
The metabolic charts memorized in early biochemistry courses, and then later forgotten, have come back to haunt many immunologists with new recognition of the importance of these pathways. Metabolites and the activity of metabolic pathways drive energy production, macromolecule synthesis, intracellular signalling, post-translational modifications and cell survival. Immunologists who identify a metabolic phenotype in their system are often left wondering where to begin and what does it mean? Here, we provide a framework for navigating and selecting the appropriate biochemical techniques to explore immunometabolism. We offer recommendations for initial approaches to develop and test metabolic hypotheses and how to avoid common mistakes. We then discuss how to take things to the next level with metabolomic approaches, such as isotope tracing and genetic approaches. By proposing strategies and evaluating the strengths and weaknesses of different methodologies, we aim to provide insight, note important considerations and discuss ways to avoid common misconceptions. Furthermore, we highlight recent studies demonstrating the power of these metabolic approaches to uncover the role of metabolism in immunology. By following the framework in this Review, neophytes and seasoned investigators alike can venture into the emerging realm of cellular metabolism and immunity with confidence and rigour.
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Affiliation(s)
- Kelsey Voss
- Vanderbilt Center for Immunobiology, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hanna S Hong
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Jackie E Bader
- Vanderbilt Center for Immunobiology, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ayaka Sugiura
- Vanderbilt Center for Immunobiology, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Costas A Lyssiotis
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI, USA
| | - Jeffrey C Rathmell
- Vanderbilt Center for Immunobiology, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
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Wei Y, Ding J, Li J, Cai S, Liu S, Hong L, Yin T, Zhang Y, Diao L. Metabolic Reprogramming of Immune Cells at the Maternal-Fetal Interface and the Development of Techniques for Immunometabolism. Front Immunol 2021; 12:717014. [PMID: 34566973 PMCID: PMC8458575 DOI: 10.3389/fimmu.2021.717014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 08/24/2021] [Indexed: 12/14/2022] Open
Abstract
Immunity and metabolism are interdependent and coordinated, which are the core mechanisms for the body to maintain homeostasis. In tumor immunology research, immunometabolism has been a research hotspot and has achieved groundbreaking changes in recent years. However, in the field of maternal-fetal medicine, research on immunometabolism is still lagging. Reports directly investigating the roles of immunometabolism in the endometrial microenvironment and regulation of maternal-fetal immune tolerance are relatively few. This review highlights the leading techniques used to study immunometabolism and their development, the immune cells at the maternal-fetal interface and their metabolic features required for the implementation of their functions, explores the interaction between immunometabolism and pregnancy regulation based on little evidence and clues, and attempts to propose some new research directions and perspectives.
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Affiliation(s)
- Yiqiu Wei
- Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jinli Ding
- Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jianan Li
- Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Songchen Cai
- Shenzhen Key Laboratory for Reproductive Immunology of Peri-Implantation, Shenzhen Zhongshan Institute for Reproduction and Genetics, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | - Su Liu
- Shenzhen Key Laboratory for Reproductive Immunology of Peri-Implantation, Shenzhen Zhongshan Institute for Reproduction and Genetics, Shenzhen Zhongshan Urology Hospital, Shenzhen, China.,Shenzhen Jinxin Medical Technology Innovation Center, Co., Ltd., Shenzhen, China
| | - Ling Hong
- Shenzhen Key Laboratory for Reproductive Immunology of Peri-Implantation, Shenzhen Zhongshan Institute for Reproduction and Genetics, Shenzhen Zhongshan Urology Hospital, Shenzhen, China.,Shenzhen Jinxin Medical Technology Innovation Center, Co., Ltd., Shenzhen, China
| | - Tailang Yin
- Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yan Zhang
- Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianghui Diao
- Shenzhen Key Laboratory for Reproductive Immunology of Peri-Implantation, Shenzhen Zhongshan Institute for Reproduction and Genetics, Shenzhen Zhongshan Urology Hospital, Shenzhen, China.,Shenzhen Jinxin Medical Technology Innovation Center, Co., Ltd., Shenzhen, China
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46
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Xu S, Yang C, Yan X, Liu H. Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques. Anal Bioanal Chem 2021; 414:219-233. [PMID: 34435209 DOI: 10.1007/s00216-021-03624-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/13/2021] [Accepted: 08/17/2021] [Indexed: 12/23/2022]
Abstract
Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.
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Affiliation(s)
- Shuting Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Cheng Yang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Xiuping Yan
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China. .,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.
| | - Huwei Liu
- Institute of Analytical Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.
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47
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Leo L, Colonna Romano N. Emerging Single-Cell Technological Approaches to Investigate Chromatin Dynamics and Centromere Regulation in Human Health and Disease. Int J Mol Sci 2021; 22:ijms22168809. [PMID: 34445507 PMCID: PMC8395756 DOI: 10.3390/ijms22168809] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 12/12/2022] Open
Abstract
Epigenetic regulators play a crucial role in establishing and maintaining gene expression states. To date, the main efforts to study cellular heterogeneity have focused on elucidating the variable nature of the chromatin landscape. Specific chromatin organisation is fundamental for normal organogenesis and developmental homeostasis and can be affected by different environmental factors. The latter can lead to detrimental alterations in gene transcription, as well as pathological conditions such as cancer. Epigenetic marks regulate the transcriptional output of cells. Centromeres are chromosome structures that are epigenetically regulated and are crucial for accurate segregation. The advent of single-cell epigenetic profiling has provided finer analytical resolution, exposing the intrinsic peculiarities of different cells within an apparently homogenous population. In this review, we discuss recent advances in methodologies applied to epigenetics, such as CUT&RUN and CUT&TAG. Then, we compare standard and emerging single-cell techniques and their relevance for investigating human diseases. Finally, we describe emerging methodologies that investigate centromeric chromatin specification and neocentromere formation.
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48
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Islam MT, Holland WL, Lesniewski LA. Multicolor fluorescence biosensors reveal a burning need for diversity in the single-cell metabolic landscape. Trends Endocrinol Metab 2021; 32:537-539. [PMID: 33972177 PMCID: PMC8381684 DOI: 10.1016/j.tem.2021.04.002] [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: 03/15/2021] [Accepted: 04/12/2021] [Indexed: 11/23/2022]
Abstract
Although cellular heterogeneity has been described for metabolic pathways, the upstream mechanisms, the downstream consequences, and the flexibility and transmission of these preferences to daughter cells remains largely unknown. Using live-cell imaging, Kosaisawe et al. demonstrate that cellular metabolism, determined by glycolysis and ATP, is spontaneously heterogeneous, plastic, and regulatory.
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Affiliation(s)
- Md Torikul Islam
- Department of Nutrition and Integrative Physiology, University of Utah College of Health, Salt Lake City, UT 84112, USA.
| | - William L Holland
- Department of Nutrition and Integrative Physiology, University of Utah College of Health, Salt Lake City, UT 84112, USA; Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Lisa A Lesniewski
- Department of Nutrition and Integrative Physiology, University of Utah College of Health, Salt Lake City, UT 84112, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT 84112, USA; Geriatric Research Education and Clinical Centers, Salt Lake City Veterans Affairs Medical Center, Salt Lake City, UT 84112, USA
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49
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Yao H, Zhao H, Pan X, Zhao X, Feng J, Yang C, Zhang S, Zhang X. Discriminating Leukemia Cellular Heterogeneity and Screening Metabolite Biomarker Candidates using Label-Free Mass Cytometry. Anal Chem 2021; 93:10282-10291. [PMID: 34259005 DOI: 10.1021/acs.analchem.1c01746] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Discriminating various leukocyte subsets with specific functions is critical due to their important roles in the development of many diseases. Here, we proposed a general strategy to unravel leukocytes heterogeneity and screen differentiated metabolites as biomarker candidates for leukocyte subtypes using the label-free mass cytometry (CyESI-MS) combined with a homemade data processing workflow. Taking leukemia cells as an example, metabolic fingerprints of single leukemia cells were obtained from 472 HL-60, 416 THP-1, 313 U937, 356 Jurkat, and 366 Ramos cells, with throughput up to 40 cells/min. Five leukemia subtypes were clearly distinguished by unsupervised learning t-SNE analysis of the single-cell metabolic fingerprints. Cell discrimination in the mixed leukemia cell samples was also realized by supervised learning of the single-cell metabolic fingerprints with high recovery and good repetition (98.31 ± 0.24%, -102.35 ± 4.82%). Statistical analysis and metabolite assignment were carried out to screen characteristic metabolites for discrimination and 36 metabolites with significant differences were annotated. Then, differentiated metabolites for pairwise discrimination of five leukemia subtypes were further selected as biomarker candidates. Furthermore, discriminating cultured leukemia cells from human normal leukocytes, separated from fresh human peripheral blood, was performed based on single-cell metabolic fingerprints as well as the proposed biomarker candidates, unveiling the potential of this strategy in clinical research. This work makes efforts to realize high-throughput single-leukocyte metabolic analysis and metabolite-based discrimination of leukocytes. It is expected to be a powerful means for the clinical molecular diagnosis of hematological diseases.
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Affiliation(s)
- Huan Yao
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Hansen Zhao
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Xingyu Pan
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Xu Zhao
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Jiaxin Feng
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Chengdui Yang
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Sichun Zhang
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
| | - Xinrong Zhang
- Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China
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50
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Luzak V, López-Escobar L, Siegel TN, Figueiredo LM. Cell-to-Cell Heterogeneity in Trypanosomes. Annu Rev Microbiol 2021; 75:107-128. [PMID: 34228491 DOI: 10.1146/annurev-micro-040821-012953] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recent developments in single-cell and single-molecule techniques have revealed surprising levels of heterogeneity among isogenic cells. These advances have transformed the study of cell-to-cell heterogeneity into a major area of biomedical research, revealing that it can confer essential advantages, such as priming populations of unicellular organisms for future environmental stresses. Protozoan parasites, such as trypanosomes, face multiple and often hostile environments, and to survive, they undergo multiple changes, including changes in morphology, gene expression, and metabolism. But why does only a subset of proliferative cells differentiate to the next life cycle stage? Why do only some bloodstream parasites undergo antigenic switching while others stably express one variant surface glycoprotein? And why do some parasites invade an organ while others remain in the bloodstream? Building on extensive research performed in bacteria, here we suggest that biological noise can contribute to the fitness of eukaryotic pathogens and discuss the importance of cell-to-cell heterogeneity in trypanosome infections. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Vanessa Luzak
- Division of Experimental Parasitology, Faculty of Veterinary Medicine, Ludwig-Maximilians-Universität München, Munich 82152, Germany.,Biomedical Center, Division of Physiological Chemistry, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich 82152, Germany
| | - Lara López-Escobar
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal;
| | - T Nicolai Siegel
- Division of Experimental Parasitology, Faculty of Veterinary Medicine, Ludwig-Maximilians-Universität München, Munich 82152, Germany.,Biomedical Center, Division of Physiological Chemistry, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich 82152, Germany
| | - Luisa M Figueiredo
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisboa, Portugal;
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