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Zhou S, Lin N, Yu L, Su X, Liu Z, Yu X, Gao H, Lin S, Zeng Y. Single-cell multi-omics in the study of digestive system cancers. Comput Struct Biotechnol J 2024; 23:431-445. [PMID: 38223343 PMCID: PMC10787224 DOI: 10.1016/j.csbj.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 01/16/2024] Open
Abstract
Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.
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Affiliation(s)
- Shuang Zhou
- The Second Clinical Medical School of Fujian Medical University, Quanzhou, Fujian Province, China
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Nanfei Lin
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Liying Yu
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Xiaoshan Su
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Respirology Medicine Centre of Fujian Province, Quanzhou, China
| | - Zhenlong Liu
- Lady Davis Institute for Medical Research, Jewish General Hospital, & Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC, Canada
| | - Xiaowan Yu
- Clinical Laboratory, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Hongzhi Gao
- The Clinical Center of Molecular Diagnosis and Therapy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Shu Lin
- Centre of Neurological and Metabolic Research, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- Diabetes and Metabolism Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, NSW 2010, Australia
| | - Yiming Zeng
- Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Respirology Medicine Centre of Fujian Province, Quanzhou, China
- Fujian Provincial Key Laboratory of Lung Stem Cells, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan, Shandong Province, China
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2
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Li M, Zuo J, Yang K, Wang P, Zhou S. Proteomics mining of cancer hallmarks on a single-cell resolution. MASS SPECTROMETRY REVIEWS 2024; 43:1019-1040. [PMID: 37051664 DOI: 10.1002/mas.21842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 11/25/2022] [Accepted: 03/15/2023] [Indexed: 06/19/2023]
Abstract
Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.
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Affiliation(s)
- Maomao Li
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, China
| | - Jing Zuo
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, China
| | - Kailin Yang
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, Ohio, USA
| | - Ping Wang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan, China
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3
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Gomes FA, Souza Junior DR, Massafera MP, Ronsein GE. Robust assessment of sample preparation protocols for proteomics of cells and tissues. BIOCHIMICA ET BIOPHYSICA ACTA. PROTEINS AND PROTEOMICS 2024; 1872:141030. [PMID: 38944097 DOI: 10.1016/j.bbapap.2024.141030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/12/2024] [Accepted: 06/26/2024] [Indexed: 07/01/2024]
Abstract
In proteomic studies, the reliability and reproducibility of results hinge on well-executed protein extraction and digestion protocols. Here, we systematically compared three established digestion methods for macrophages, namely filter-assisted sample preparation (FASP), in-solution, and in-gel digestion protocols. We also compared lyophilization and manual lysis for liver tissue protein extraction, each of them tested using either sodium deoxycholate (SDC)- or RIPA-based lysis buffer. For the macrophage cell line, FASP using passivated filter units outperformed the other tested methods regarding the number of identified peptides and proteins. However, a careful standardization has shown that all three methods can yield robust results across a wide range of starting material (even starting with 1 μg of proteins). Importantly, inter and intra-day coefficients of variance (CVs) were determined for all sample preparation protocols. Thus, the median inter-day CVs for in-solution, in-gel and FASP protocols were respectively 10, 8 and 9%, very similar to the median CVs obtained for the intra-day analysis (9, 8 and 8%, respectively). Moreover, FASP digestion presented 80% of proteins with a CV lower than 25%, followed closely by in-gel digestion (78%) and in-solution sample preparation (72%) protocols. For tissue proteomics, both manual lysis and lyophilization presented similar proteome coverage and reproducibility, but the efficiency of protein extraction depended on the lysis buffer used, with RIPA buffer showing better results. In conclusion, although each sample preparation method has its own particularity, they are all suited for successful proteomic experiments if a careful standardization of the sample preparation workflow is carried out.
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Affiliation(s)
- Francielle Aguiar Gomes
- Department of Biochemistry, Institute of Chemistry, University of São Paulo, São Paulo, Brazil
| | | | | | - Graziella Eliza Ronsein
- Department of Biochemistry, Institute of Chemistry, University of São Paulo, São Paulo, Brazil.
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4
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Leduc A, Khoury L, Cantlon J, Khan S, Slavov N. Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. Nat Protoc 2024:10.1038/s41596-024-01033-8. [PMID: 39117766 DOI: 10.1038/s41596-024-01033-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/27/2024] [Indexed: 08/10/2024]
Abstract
Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with high specificity and sensitivity. To increase its throughput, we developed nano-proteomic sample preparation (nPOP), a method for parallel preparation of thousands of single cells in nanoliter-volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation using the plexDIA MS multiplexing method, which uses non-isobaric mass tags to barcode peptides from different samples for data-independent acquisition, demonstrates accurate quantification of ~3,000-3,700 proteins per human cell. A separate implementation with isobaric mass tags and prioritized data acquisition demonstrates analysis of 1,827 single cells at a rate of >1,000 single cells per day at a depth of 800-1,200 proteins per human cell. The protocol is implemented by using a cell-dispensing and liquid-handling robot-the CellenONE instrument-and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 d to prepare >3,000 single cells. We provide metrics and software (the QuantQC R package) for quality control and data exploration. QuantQC supports the robust scaling of nPOP to higher plex reagents for achieving reliable and scalable single-cell proteomics.
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Affiliation(s)
- Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | | | - Saad Khan
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
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5
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Rogers ZJ, Colombani T, Khan S, Bhatt K, Nukovic A, Zhou G, Woolston BM, Taylor CT, Gilkes DM, Slavov N, Bencherif SA. Controlling Pericellular Oxygen Tension in Cell Culture Reveals Distinct Breast Cancer Responses to Low Oxygen Tensions. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2402557. [PMID: 38874400 PMCID: PMC11321643 DOI: 10.1002/advs.202402557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/11/2024] [Indexed: 06/15/2024]
Abstract
In oxygen (O2)-controlled cell culture, an indispensable tool in biological research, it is presumed that the incubator setpoint equals the O2 tension experienced by cells (i.e., pericellular O2). However, it is discovered that physioxic (5% O2) and hypoxic (1% O2) setpoints regularly induce anoxic (0% O2) pericellular tensions in both adherent and suspension cell cultures. Electron transport chain inhibition ablates this effect, indicating that cellular O2 consumption is the driving factor. RNA-seq analysis revealed that primary human hepatocytes cultured in physioxia experience ischemia-reperfusion injury due to cellular O2 consumption. A reaction-diffusion model is developed to predict pericellular O2 tension a priori, demonstrating that the effect of cellular O2 consumption has the greatest impact in smaller volume culture vessels. By controlling pericellular O2 tension in cell culture, it is found that hypoxia vs. anoxia induce distinct breast cancer transcriptomic and translational responses, including modulation of the hypoxia-inducible factor (HIF) pathway and metabolic reprogramming. Collectively, these findings indicate that breast cancer cells respond non-monotonically to low O2, suggesting that anoxic cell culture is not suitable for modeling hypoxia. Furthermore, it is shown that controlling atmospheric O2 tension in cell culture incubators is insufficient to regulate O2 in cell culture, thus introducing the concept of pericellular O2-controlled cell culture.
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Affiliation(s)
- Zachary J. Rogers
- Department of Chemical EngineeringNortheastern UniversityBostonMA02115USA
| | - Thibault Colombani
- Department of Chemical EngineeringNortheastern UniversityBostonMA02115USA
| | - Saad Khan
- Department of BioengineeringNortheastern UniversityBostonMA02115USA
| | - Khushbu Bhatt
- Department of Pharmaceutical SciencesNortheastern UniversityBostonMA02115USA
| | - Alexandra Nukovic
- Department of Chemical EngineeringNortheastern UniversityBostonMA02115USA
| | - Guanyu Zhou
- Department of Chemical EngineeringNortheastern UniversityBostonMA02115USA
| | | | - Cormac T. Taylor
- Conway Institute of Biomolecular and Biomedical Research and School of MedicineUniversity College DublinBelfieldDublinD04 V1W8Ireland
| | - Daniele M. Gilkes
- Department of OncologyThe Sidney Kimmel Comprehensive Cancer CenterThe Johns Hopkins University School of MedicineBaltimoreMD21321USA
- Cellular and Molecular Medicine ProgramThe Johns Hopkins University School of MedicineBaltimoreMD21321USA
- Department of Chemical and Biomolecular EngineeringThe Johns Hopkins UniversityBaltimoreMD21218USA
- Johns Hopkins Institute for NanoBioTechnologyThe Johns Hopkins UniversityBaltimoreMD21218USA
| | - Nikolai Slavov
- Department of BioengineeringNortheastern UniversityBostonMA02115USA
- Departments of BioengineeringBiologyChemistry and Chemical BiologySingle Cell Center and Barnett InstituteNortheastern UniversityBostonMA02115USA
- Parallel Squared Technology InstituteWatertownMA02472USA
| | - Sidi A. Bencherif
- Department of Chemical EngineeringNortheastern UniversityBostonMA02115USA
- Department of BioengineeringNortheastern UniversityBostonMA02115USA
- Harvard John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMA02138USA
- Biomechanics and Bioengineering (BMBI)UTC CNRS UMR 7338University of Technology of CompiègneSorbonne UniversityCompiègne60203France
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6
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Dutta T, Vlassakis J. Microscale measurements of protein complexes from single cells. Curr Opin Struct Biol 2024; 87:102860. [PMID: 38848654 DOI: 10.1016/j.sbi.2024.102860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024]
Abstract
Proteins execute numerous cell functions in concert with one another in protein-protein interactions (PPI). While essential in each cell, such interactions are not identical from cell to cell. Instead, PPI heterogeneity contributes to cellular phenotypic heterogeneity in health and diseases such as cancer. Understanding cellular phenotypic heterogeneity thus requires measurements of properties of PPIs such as abundance, stoichiometry, and kinetics at the single-cell level. Here, we review recent, exciting progress in single-cell PPI measurements. Novel technology in this area is enabled by microscale and microfluidic approaches that control analyte concentration in timescales needed to outpace PPI disassembly kinetics. We describe microscale innovations, needed technical capabilities, and methods poised to be adapted for single-cell analysis in the near future.
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Affiliation(s)
- Tanushree Dutta
- Department of Bioengineering, Rice University, Houston, TX 77005, USA. https://twitter.com/duttatanu1717
| | - Julea Vlassakis
- Department of Bioengineering, Rice University, Houston, TX 77005, USA.
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7
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Ghatak S, Diedrich JK, Talantova M, Bhadra N, Scott H, Sharma M, Albertolle M, Schork NJ, Yates JR, Lipton SA. Single-Cell Patch-Clamp/Proteomics of Human Alzheimer's Disease iPSC-Derived Excitatory Neurons Versus Isogenic Wild-Type Controls Suggests Novel Causation and Therapeutic Targets. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400545. [PMID: 38773714 PMCID: PMC11304297 DOI: 10.1002/advs.202400545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 04/03/2024] [Indexed: 05/24/2024]
Abstract
Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC-neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch-proteomics technique creates a new proteogenetic-therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single-cells.
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Affiliation(s)
- Swagata Ghatak
- Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCA92037USA
- Department of Molecular MedicineThe Scripps Research InstituteLa JollaCA92037USA
- Present address:
School of Biological SciencesNational Institute of Science Education and Research (NISER)‐Bhubaneswar, an OCC of Homi Bhabha National InstituteJataniOdisha752050India
| | - Jolene K. Diedrich
- Department of Molecular MedicineThe Scripps Research InstituteLa JollaCA92037USA
| | - Maria Talantova
- Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCA92037USA
- Department of Molecular MedicineThe Scripps Research InstituteLa JollaCA92037USA
| | - Nivedita Bhadra
- Quantitative Medicine and Systems BiologyThe Translational Genomics Research InstitutePhoenixAZ85004USA
| | - Henry Scott
- Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCA92037USA
- Department of Molecular MedicineThe Scripps Research InstituteLa JollaCA92037USA
| | - Meetal Sharma
- Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCA92037USA
- Department of Molecular MedicineThe Scripps Research InstituteLa JollaCA92037USA
| | - Matthew Albertolle
- Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCA92037USA
- Department of Molecular MedicineThe Scripps Research InstituteLa JollaCA92037USA
- Present address:
Drug Metabolism and Pharmacokinetics DepartmentTakeda Development Center AmericasSan DiegoCA92121USA
| | - Nicholas J. Schork
- Quantitative Medicine and Systems BiologyThe Translational Genomics Research InstitutePhoenixAZ85004USA
| | - John R. Yates
- Department of Molecular MedicineThe Scripps Research InstituteLa JollaCA92037USA
| | - Stuart A. Lipton
- Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCA92037USA
- Department of Molecular MedicineThe Scripps Research InstituteLa JollaCA92037USA
- Department of NeurosciencesSchool of MedicineUniversity of California, San DiegoLa JollaCA92093USA
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8
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Ghosh G, Shannon AE, Searle BC. Data acquisition approaches for single cell proteomics. Proteomics 2024:e2400022. [PMID: 39088833 DOI: 10.1002/pmic.202400022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/03/2024]
Abstract
Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.
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Affiliation(s)
- Gautam Ghosh
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Ariana E Shannon
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
| | - Brian C Searle
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
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9
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Hutton A, Ai L, Meyer JG. Distributed Collaboration for Data, Analysis Pipelines, and Results in Single-Cell Omics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.30.605714. [PMID: 39131282 PMCID: PMC11312633 DOI: 10.1101/2024.07.30.605714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Single-cell omics data analysis pipelines are complicated to design and difficult to share or reproduce. We describe a web platform that enables no-code analysis pipeline design, simple computing via the Open Science Grid, and sharing of entire data analysis pipelines, their input data, and interactive results. We expect this platform to increase the accessibility and reproducibility of single-cell omics.
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Affiliation(s)
- Alexandre Hutton
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
| | - Lizhuo Ai
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
| | - Jesse G. Meyer
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles CA, 90048, USA
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10
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Leduc A, Xu Y, Shipkovenska G, Dou Z, Slavov N. Limiting the impact of protein leakage in single-cell proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.26.605378. [PMID: 39091738 PMCID: PMC11291177 DOI: 10.1101/2024.07.26.605378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Limiting artifacts during sample preparation can significantly increase data quality in single-cell proteomics experiments. Towards this goal, we characterize the impact of protein leakage by analyzing thousands of primary single cells that were prepared either fresh immediately after dissociation or cryopreserved and prepared at a later date. We directly identify permeabilized cells and use the data to define a signature for protein leakage. We use this signature to build a classifier for identifying damaged cells that performs accurately across cell types and species.
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Affiliation(s)
- Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA 02115, USA
| | - Yanxin Xu
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gergana Shipkovenska
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Zhixun Dou
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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11
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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12
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Mun DG, Bhat FA, Joshi N, Sandoval L, Ding H, Jain A, Peterson JA, Kang T, Pujari GP, Tomlinson JL, Budhraja R, Zenka RM, Kannan N, Kipp BR, Dasari S, Gaspar-Maia A, Smoot RL, Kandasamy RK, Pandey A. Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry. Commun Biol 2024; 7:884. [PMID: 39030393 PMCID: PMC11271535 DOI: 10.1038/s42003-024-06579-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024] Open
Abstract
The rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling.
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Affiliation(s)
- Dong-Gi Mun
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Firdous A Bhat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Neha Joshi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
- Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
| | - Leticia Sandoval
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Husheng Ding
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Anu Jain
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | - Taewook Kang
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ganesh P Pujari
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | - Rohit Budhraja
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Roman M Zenka
- Proteomics Core, Mayo Clinic, Rochester, MN, 55905, USA
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Benjamin R Kipp
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Surendra Dasari
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA
| | - Alexandre Gaspar-Maia
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Rory L Smoot
- Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - Richard K Kandasamy
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA.
- Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
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13
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Kanuri B, Sreejit G, Biswas P, Murphy AJ, Nagareddy PR. Macrophage heterogeneity in myocardial infarction: Evolution and implications for diverse therapeutic approaches. iScience 2024; 27:110274. [PMID: 39040061 PMCID: PMC11261154 DOI: 10.1016/j.isci.2024.110274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024] Open
Abstract
Given the extensive participation of myeloid cells (especially monocytes and macrophages) in both inflammation and resolution phases post-myocardial infarction (MI) owing to their biphasic role, these cells are considered as crucial players in the disease pathogenesis. Multiple studies have agreed on the significant contribution of macrophage polarization theory (M2 vs. M1) while determining the underlying reasons behind the observed biphasic effects; nevertheless, this simplistic classification attracts severe drawbacks. The advent of multiple advanced technologies based on OMICS platforms facilitated a successful path to explore comprehensive cellular signatures that could expedite our understanding of macrophage heterogeneity and plasticity. While providing an overall basis behind the MI disease pathogenesis, this review delves into the literature to discuss the current knowledge on multiple macrophage clusters, including the future directions in this research arena. In the end, our focus will be on outlining the possible therapeutic implications based on the emerging observations.
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Affiliation(s)
- Babunageswararao Kanuri
- Department of Internal Medicine, Section of Cardiovascular Diseases, University of Oklahoma Health Sciences Center (OUHSC), Oklahoma City, OK, USA
| | - Gopalkrishna Sreejit
- Department of Pathology, New York University Grossman School of Medicine, New York City, NY, USA
| | - Priosmita Biswas
- Department of Molecular and Cell Biology, University of California Merced, Merced, CA, USA
| | - Andrew J. Murphy
- Baker Heart and Diabetes Institute, Division of Immunometabolism, Melbourne, VIC, Australia
| | - Prabhakara R. Nagareddy
- Department of Internal Medicine, Section of Cardiovascular Diseases, University of Oklahoma Health Sciences Center (OUHSC), Oklahoma City, OK, USA
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14
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Coulombe B, Chapleau A, Macintosh J, Durcan TM, Poitras C, Moursli YA, Faubert D, Pinard M, Bernard G. Towards a Treatment for Leukodystrophy Using Cell-Based Interception and Precision Medicine. Biomolecules 2024; 14:857. [PMID: 39062571 PMCID: PMC11274857 DOI: 10.3390/biom14070857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/12/2024] [Accepted: 07/14/2024] [Indexed: 07/28/2024] Open
Abstract
Cell-based interception and precision medicine is a novel approach aimed at improving healthcare through the early detection and treatment of diseased cells. Here, we describe our recent progress towards developing cell-based interception and precision medicine to detect, understand, and advance the development of novel therapeutic approaches through a single-cell omics and drug screening platform, as part of a multi-laboratory collaborative effort, for a group of neurodegenerative disorders named leukodystrophies. Our strategy aims at the identification of diseased cells as early as possible to intercept progression of the disease prior to severe clinical impairment and irreversible tissue damage.
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Affiliation(s)
- Benoit Coulombe
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC H3T 1A8, Canada
| | - Alexandra Chapleau
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, Montréal, QC H9X 3V9, Canada;
| | - Julia Macintosh
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
| | - Thomas M. Durcan
- The Neuro’s Early Drug Discovery Unit (EDDU), McGill University, Montréal, QC H9X 3V9, Canada;
| | - Christian Poitras
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Yena A. Moursli
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Denis Faubert
- Mass Spectrometry and Proteomics Platform, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada;
| | - Maxime Pinard
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; (C.P.); (Y.A.M.); (M.P.)
| | - Geneviève Bernard
- Department of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montréal, QC H9X 3V9, Canada; (A.C.); (J.M.); (G.B.)
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montréal, QC H4A 3J1, Canada
- Department Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, QC H4A 3J1, Canada
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15
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Cumin C, Gee L, Litfin T, Muchabaiwa R, Martin G, Cooper O, Heinzelmann-Schwarz V, Lange T, von Itzstein M, Jacob F, Everest-Dass A. Highly Sensitive Spatial Glycomics at Near-Cellular Resolution by On-Slide Derivatization and Mass Spectrometry Imaging. Anal Chem 2024; 96:11163-11171. [PMID: 38953530 PMCID: PMC11256013 DOI: 10.1021/acs.analchem.3c05984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/04/2024]
Abstract
Glycans on proteins and lipids play important roles in maturation and cellular interactions, contributing to a variety of biological processes. Aberrant glycosylation has been associated with various human diseases including cancer; however, elucidating the distribution and heterogeneity of glycans in complex tissue samples remains a major challenge. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is routinely used to analyze the spatial distribution of a variety of molecules including N-glycans directly from tissue surfaces. Sialic acids are nine carbon acidic sugars that often exist as the terminal sugars of glycans and are inherently difficult to analyze using MALDI-MSI due to their instability prone to in- and postsource decay. Here, we report on a rapid and robust method for stabilizing sialic acid on N-glycans in FFPE tissue sections. The established method derivatizes and identifies the spatial distribution of α2,3- and α2,6-linked sialic acids through complete methylamidation using methylamine and PyAOP ((7-azabenzotriazol-1-yloxy)tripyrrolidinophosphonium hexafluorophosphate). Our in situ approach increases the glycans detected and enhances the coverage of sialylated species. Using this streamlined, sensitive, and robust workflow, we rapidly characterize and spatially localize N-glycans in human tumor tissue sections. Additionally, we demonstrate this method's applicability in imaging mammalian cell suspensions directly on slides, achieving cellular resolution with minimal sample processing and cell numbers. This workflow reveals the cellular locations of distinct N-glycan species, shedding light on the biological and clinical significance of these biomolecules in human diseases.
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Affiliation(s)
- Cécile Cumin
- Institute
for Glycomics, Griffith University, Gold Coast, Queensland 4222, Australia
- Ovarian
Cancer Research, University Hospital Basel,
University of Basel, Basel 4001, Switzerland
| | - Lindsay Gee
- Institute
for Glycomics, Griffith University, Gold Coast, Queensland 4222, Australia
| | - Thomas Litfin
- Institute
for Glycomics, Griffith University, Gold Coast, Queensland 4222, Australia
| | - Ropafadzo Muchabaiwa
- Institute
for Glycomics, Griffith University, Gold Coast, Queensland 4222, Australia
| | - Gael Martin
- Institute
for Glycomics, Griffith University, Gold Coast, Queensland 4222, Australia
| | - Oren Cooper
- Institute
for Glycomics, Griffith University, Gold Coast, Queensland 4222, Australia
| | - Viola Heinzelmann-Schwarz
- Ovarian
Cancer Research, University Hospital Basel,
University of Basel, Basel 4001, Switzerland
- Hospital
for Women, Department of Gynaecology and Gynaecological Oncology, University Hospital Basel and University of Basel, Basel 4001, Switzerland
| | - Tobias Lange
- Institute
of Anatomy and Experimental Morphology, University Cancer Center Hamburg
(UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany
- Institute
of Anatomy I, Comprehensive Cancer Center Central Germany (CCCG), Jena University Hospital, Jena 07740, Germany
| | - Mark von Itzstein
- Institute
for Glycomics, Griffith University, Gold Coast, Queensland 4222, Australia
| | - Francis Jacob
- Ovarian
Cancer Research, University Hospital Basel,
University of Basel, Basel 4001, Switzerland
| | - Arun Everest-Dass
- Institute
for Glycomics, Griffith University, Gold Coast, Queensland 4222, Australia
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16
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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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17
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He Y, Li Y, Zhao L, Ying G, Lu G, Zhang L, Zhang Z. An Optimized Miniaturized Filter-Aided Sample Preparation Method for Sensitive Cross-Linking Mass Spectrometry Analysis of Microscale Samples. Anal Chem 2024. [PMID: 39007547 DOI: 10.1021/acs.analchem.4c01600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Cross-linking mass spectrometry (XL-MS) is a powerful tool for elucidating protein structures and protein-protein interactions (PPIs) at the global scale. However, sensitive XL-MS analysis of mass-limited samples remains challenging, due to serious sample loss during sample preparation of the low-abundance cross-linked peptides. Herein, an optimized miniaturized filter-aided sample preparation (O-MICROFASP) method was presented for sensitive XL-MS analysis of microscale samples. By systematically investigating and optimizing crucial experimental factors, this approach dramatically improves the XL identification of low and submicrogram samples. Compared with the conventional FASP method, more than 7.4 times cross-linked peptides were identified from single-shot analysis of 1 μg DSS cross-linked HeLa cell lysates (440 vs 59). The number of cross-linked peptides identified from 0.5 μg HeLa cell lysates was increased by 58% when further reducing the surface area of the filter to 0.058 mm2 in the microreactor. To deepen the identification coverage of XL-proteome, five different types of cross-linkers were used and each μg of cross-linked HeLa cell lysates was processed by O-MICROFASP integrated with tip-based strong cation exchange (SCX) fractionation. Up to 2741 unique cross-linked peptides were identified from the 5 μg HeLa cell lysates, representing 2579 unique K-K linkages on 1092 proteins. About 96% of intraprotein cross-links were within the maximal distance restraints of 26 Å, and 75% of the identified PPIs reported by the STRING database were with high confidence (scores ≥0.9), confirming the high validity of the identified cross-links for protein structural mapping and PPI analysis. This study demonstrates that O-MICROFASP is a universal and efficient method for proteome-wide XL-MS analysis of microscale samples with high sensitivity and reliability.
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Affiliation(s)
- Yu He
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Yang Li
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Lili Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian, Liaoning 116023, China
| | - Guojin Ying
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Gang Lu
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Lihua Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Science, Dalian, Liaoning 116023, China
| | - Zhenbin Zhang
- Institute of Drug Discovery Technology, Ningbo University, Ningbo, Zhejiang 315211, China
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18
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Campbell AJ, Cakar S, Palstrøm NB, Riber LP, Rasmussen LM, Beck HC. A carrier-based quantitative proteomics method applied to biomarker discovery in pericardial fluid. Mol Cell Proteomics 2024:100812. [PMID: 39004188 DOI: 10.1016/j.mcpro.2024.100812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 07/03/2024] [Accepted: 07/10/2024] [Indexed: 07/16/2024] Open
Abstract
Data-dependent liquid chromatography tandem mass spectrometry (LC-MS/MS) is challenged by the large concentration range of proteins in plasma and related fluids. We adapted the SCoPE method from single-cell proteomics to pericardial fluid, where a myocardial tissue carrier was used to aid protein quantification. The carrier proteome and patient samples were labeled with distinct isobaric labels, which allowed separate quantification. Undepleted pericardial fluid from patients with type 2 diabetes mellitus and/or heart failure undergoing heart surgery was analyzed with either a traditional LC-MS/MS method or with the carrier proteome. In total, 1398 proteins were quantified with a carrier, compared to 265 without, and a higher proportion of these proteins were of myocardial origin. The number of differentially expressed proteins also increased nearly four-fold. For patients with both heart failure and type 2 diabetes mellitus, pathway analysis of upregulated proteins demonstrated enrichment of immune activation, blood coagulation, and stress pathways. Overall, our work demonstrates the applicability of a carrier for enhanced protein quantification in challenging biological matrices such as pericardial fluid, with potential applications for biomarker discovery. Mass spectrometry data are available via ProteomeXchange with identifier PXD053450.
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Affiliation(s)
- Amanda J Campbell
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark; Center for Clinical Proteomics (CCP), Odense University Hospital, Odense, Denmark
| | - Samir Cakar
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark; Center for Clinical Proteomics (CCP), Odense University Hospital, Odense, Denmark
| | - Nicolai B Palstrøm
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark; Center for Clinical Proteomics (CCP), Odense University Hospital, Odense, Denmark; Department of Clinical Research, Faculty of Health Science, University of Southern Denmark, Odense, Denmark
| | - Lars P Riber
- Department of Cardiac, Thoracic and Vascular Surgery, Odense University Hospital, Odense, Denmark; Department of Clinical Research, Faculty of Health Science, University of Southern Denmark, Odense, Denmark
| | - Lars M Rasmussen
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark; Center for Clinical Proteomics (CCP), Odense University Hospital, Odense, Denmark; Center for Individualized Medicine in Arterial Diseases (CIMA), Odense University Hospital, Odense, Denmark; Department of Clinical Research, Faculty of Health Science, University of Southern Denmark, Odense, Denmark
| | - Hans C Beck
- Department of Clinical Biochemistry, Odense University Hospital, Odense, Denmark; Center for Clinical Proteomics (CCP), Odense University Hospital, Odense, Denmark; Center for Individualized Medicine in Arterial Diseases (CIMA), Odense University Hospital, Odense, Denmark; Department of Clinical Research, Faculty of Health Science, University of Southern Denmark, Odense, Denmark
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19
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Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qian WJ, Zhu Y. Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583367. [PMID: 38496682 PMCID: PMC10942300 DOI: 10.1101/2024.03.04.583367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.
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Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Sarah M. Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J. Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Alexey I. Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Department of Proteomic and Genomic Technologies, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, United States
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20
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Ctortecka C, Clark NM, Boyle BW, Seth A, Mani DR, Udeshi ND, Carr SA. Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications. Nat Commun 2024; 15:5707. [PMID: 38977691 PMCID: PMC11231172 DOI: 10.1038/s41467-024-49651-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 06/14/2024] [Indexed: 07/10/2024] Open
Abstract
The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.
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Affiliation(s)
| | | | - Brian W Boyle
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - D R Mani
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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21
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Weng L, Yan G, Liu W, Tai Q, Gao M, Zhang X. Picoliter Single-Cell Reactor for Proteome Profiling by In Situ Cell Lysis, Protein Immobilization, Digestion, and Droplet Transfer. J Proteome Res 2024; 23:2441-2451. [PMID: 38833655 DOI: 10.1021/acs.jproteome.4c00117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Global profiling of single-cell proteomes can reveal cellular heterogeneity, thus benefiting precision medicine. However, current mass spectrometry (MS)-based single-cell proteomic sample processing still faces technical challenges associated with processing efficiency and protein recovery. Herein, we present an innovative sample processing platform based on a picoliter single-cell reactor (picoSCR) for single-cell proteome profiling, which involves in situ protein immobilization and sample transfer. PicoSCR helped minimize surface adsorptive losses by downscaling the processing volume to 400 pL with a contact area of less than 0.4 mm2. Besides, picoSCR reached highly efficient cell lysis and digestion within 30 min, benefiting from optimal reagent and high reactant concentrations. Using the picoSCR-nanoLC-MS system, over 1400 proteins were identified from an individual HeLa cell using data-dependent acquisition mode. Proteins with copy number below 1000 were identified, demonstrating this system with a detection limit of 1.7 zmol. Furthermore, we profiled the proteome of circulating tumor cells (CTCs). Data are available via ProteomeXchange with the identifier PXD051468. Proteins associated with epithelial-mesenchymal transition and neutrophil extracellular traps formation (which are both related to tumor metastasis) were observed in all CTCs. The cellular heterogeneity was revealed by differences in signaling pathways within individual cells. These results highlighted the potential of the picoSCR platform to help discover new biomarkers and explore differences in biological processes between cells.
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Affiliation(s)
- Lingxiao Weng
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
| | - Guoquan Yan
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
| | - Wei Liu
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
| | - Qunfei Tai
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
| | - Mingxia Gao
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
- Pharmacy Department, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China
| | - Xiangmin Zhang
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
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22
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Rhaman MS, Ali M, Ye W, Li B. Opportunities and Challenges in Advancing Plant Research with Single-cell Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae026. [PMID: 38996445 DOI: 10.1093/gpbjnl/qzae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 07/14/2024]
Abstract
Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.
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Affiliation(s)
- Mohammad Saidur Rhaman
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Muhammad Ali
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Wenxiu Ye
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Bosheng Li
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
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23
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Peeters F, Cappuyns S, Piqué-Gili M, Phillips G, Verslype C, Lambrechts D, Dekervel J. Applications of single-cell multi-omics in liver cancer. JHEP Rep 2024; 6:101094. [PMID: 39022385 PMCID: PMC11252522 DOI: 10.1016/j.jhepr.2024.101094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/18/2024] [Accepted: 03/27/2024] [Indexed: 07/20/2024] Open
Abstract
Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.
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Affiliation(s)
- Frederik Peeters
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Sarah Cappuyns
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Marta Piqué-Gili
- Liver Cancer Translational Research Laboratory, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Gino Phillips
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Chris Verslype
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Jeroen Dekervel
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
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24
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Long Q, Zhang P, Ou Y, Li W, Yan Q, Yuan X. Single-cell sequencing advances in research on mesenchymal stem/stromal cells. Hum Cell 2024; 37:904-916. [PMID: 38743204 DOI: 10.1007/s13577-024-01076-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/04/2024] [Indexed: 05/16/2024]
Abstract
Mesenchymal stem/stromal cells (MSCs), originating from the mesoderm, represent a multifunctional stem cell population capable of differentiating into diverse cell types and exhibiting a wide range of biological functions. Despite more than half a century of research, MSCs continue to be among the most extensively studied cell types in clinical research projects globally. However, their significant heterogeneity and phenotypic instability have significantly hindered their exploration and application. Single-cell sequencing technology emerges as a powerful tool to address these challenges, offering precise dissection of complex cellular samples. It uncovers the genetic structure and gene expression status of individual contained cells on a massive scale and reveals the heterogeneity among these cells. It links the molecular characteristics of MSCs with their clinical applications, contributing to the advancement of regenerative medicine. With the development and cost reduction of single-cell analysis techniques, sequencing technology is now widely applied in fundamental research and clinical trials. This study aimed to review the application of single-cell sequencing in MSC research and assess its prospects.
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Affiliation(s)
- Qingxi Long
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Pingshu Zhang
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China
| | - Ya Ou
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China
| | - Wen Li
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Qi Yan
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China
| | - Xiaodong Yuan
- Department of Neurology, Kailuan General Hospital, Affiliated North China University of Science and Technology, Tangshan, 063000, China.
- Hebei Provincial Key Laboratory of Neurobiological Function, Tangshan, 063000, China.
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25
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Montes C, Zhang J, Nolan TM, Walley JW. Single-cell proteomics differentiates Arabidopsis root cell types. THE NEW PHYTOLOGIST 2024. [PMID: 38923440 DOI: 10.1111/nph.19923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024]
Abstract
Single-cell proteomics (SCP) is an emerging approach to resolve cellular heterogeneity within complex tissues of multi-cellular organisms. Here, we demonstrate the feasibility of SCP on plant samples using the model plant Arabidopsis thaliana. Specifically, we focused on examining isolated single cells from the cortex and endodermis, which are two adjacent root cell types derived from a common stem cell lineage. From 756 root cells, we identified 3763 proteins and 1118 proteins/cell. Ultimately, we focus on 3217 proteins quantified following stringent filtering. Of these, we identified 596 proteins whose expression is enriched in either the cortex or endodermis and are able to differentiate these closely related plant cell types. Collectivity, this study demonstrates that SCP can resolve neighboring cell types with distinct functions, thereby facilitating the identification of biomarkers and candidate proteins to enable functional genomics.
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Affiliation(s)
- Christian Montes
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA, 50011, USA
| | - Jingyuan Zhang
- Department of Biology, Duke University, Durham, NC, 27708, USA
| | - Trevor M Nolan
- Department of Biology, Duke University, Durham, NC, 27708, USA
- Howard Hughes Medical Institute, Duke University, Durham, NC, 27708, USA
| | - Justin W Walley
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA, 50011, USA
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26
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Orsburn BC. Single cell proteomics by mass spectrometry reveals deep epigenetic insight into the actions of an orphan histone deacetylase inhibitor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574437. [PMID: 38260471 PMCID: PMC10802306 DOI: 10.1101/2024.01.05.574437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Epigenetic programming has been shown to play a role in nearly every human system and disease where anyone has thought to look. However, the levels of heterogeneity at which epigenetic or epiproteomic modifications occur at single cell resolution across a population remains elusive. While recent advances in sequencing technology have allowed between 1 and 3 histone post-translational modifications to be analyzed in each single cell, over twenty separate chemical PTMs are known to exist, allowing thousands of possible combinations. Single cell proteomics by mass spectrometry (SCP) is an emerging technology in which hundreds or thousands of proteins can be directly quantified in typical human cells. As the proteins detected and quantified by SCP are heavily biased toward proteins of highest abundance, chromatin proteins are an attractive target for analysis. To this end, I applied SCP to the analysis of cancer cells treated with mocetinostat, a class specific histone deacetylase inhibitor. I find that 16 PTMs can be confidently identified and localized with high site specificity in single cells. In addition, the high abundance of histone proteins allows higher throughput methods to be utilized for SCP than previously described. While quantitative accuracy suffers when analyzing more than 700 cells per day, 9 histone proteins can be measured in single cells analyzed at even 3,500 cells per day, a throughput 10-fold greater than any previous report. In addition, the unbiased global approach utilized herein identifies a previously uncharacterized response to this drug through the S100-A8/S100-A9 protein complex partners. This response is observed in nearly every cell of the over 1,000 analyzed in this study, regardless of the relative throughput of the method utilized. While limitations exist in the methods described herein, current technologies can easily improve upon the results presented here to allow comprehensive analysis of histone PTMs to be performed in any mass spectrometry lab. All raw and processed data described in this study has been made publicly available through the ProteomeXchange/MASSIVE repository system as MSV000093434.
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27
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Derks J, Jonson T, Leduc A, Khan S, Khoury L, Rafiee MR, Slavov N. Single-nucleus proteomics identifies regulators of protein transport. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.17.599449. [PMID: 38948785 PMCID: PMC11212961 DOI: 10.1101/2024.06.17.599449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The physiological response of a cell to stimulation depends on its proteome configuration. Therefore, the abundance variation of regulatory proteins across unstimulated single cells can be associatively linked with their response to stimulation. Here we developed an approach that leverages this association across individual cells and nuclei to systematically identify potential regulators of biological processes, followed by targeted validation. Specifically, we applied this approach to identify regulators of nucleocytoplasmic protein transport in macrophages stimulated with lipopolysaccharide (LPS). To this end, we quantified the proteomes of 3,412 individual nuclei, sampling the dynamic response to LPS treatment, and linking functional variability to proteomic variability. Minutes after the stimulation, the protein transport in individual nuclei correlated strongly with the abundance of known protein transport regulators, thus revealing the impact of natural protein variability on functional cellular response. We found that simple biophysical constraints, such as the quantity of nuclear pores, partially explain the variability in LPS-induced nucleocytoplasmic transport. Among the many proteins newly identified to be associated with the response, we selected 16 for targeted validation by knockdown. The knockdown phenotypes confirmed the inferences derived from natural protein and functional variation of single nuclei, thus demonstrating the potential of (sub-)single-cell proteomics to infer functional regulation. We expect this approach to generalize to broad applications and enhance the functional interpretability of single-cell omics data.
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Affiliation(s)
- Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
| | - Tobias Jonson
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Saad Khan
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Mahmoud-Reza Rafiee
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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28
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Martin KR, Le HT, Abdelgawad A, Yang C, Lu G, Keffer JL, Zhang X, Zhuang Z, Asare-Okai PN, Chan CS, Batish M, Yu Y. Development of an efficient, effective, and economical technology for proteome analysis. CELL REPORTS METHODS 2024; 4:100796. [PMID: 38866007 PMCID: PMC11228373 DOI: 10.1016/j.crmeth.2024.100796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/21/2024] [Accepted: 05/20/2024] [Indexed: 06/14/2024]
Abstract
We present an efficient, effective, and economical approach, named E3technology, for proteomics sample preparation. By immobilizing silica microparticles into the polytetrafluoroethylene matrix, we develop a robust membrane medium, which could serve as a reliable platform to generate proteomics-friendly samples in a rapid and low-cost fashion. We benchmark its performance using different formats and demonstrate them with a variety of sample types of varied complexity, quantity, and volume. Our data suggest that E3technology provides proteome-wide identification and quantitation performance equivalent or superior to many existing methods. We further propose an enhanced single-vessel approach, named E4technology, which performs on-filter in-cell digestion with minimal sample loss and high sensitivity, enabling low-input and low-cell proteomics. Lastly, we utilized the above technologies to investigate RNA-binding proteins and profile the intact bacterial cell proteome.
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Affiliation(s)
- Katherine R Martin
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716, USA
| | - Ha T Le
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716, USA
| | - Ahmed Abdelgawad
- Department of Biological Sciences, University of Delaware, Newark, DE 19716, USA; Department of Medical and Molecular Sciences, University of Delaware, Newark, DE 19716, USA
| | - Canyuan Yang
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716, USA
| | - Guotao Lu
- CDS Analytical, LLC, Oxford, PA 19363, USA
| | - Jessica L Keffer
- Department of Earth Sciences, University of Delaware, Newark, DE 19716, USA
| | | | - Zhihao Zhuang
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716, USA
| | - Papa Nii Asare-Okai
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716, USA
| | - Clara S Chan
- Department of Earth Sciences, University of Delaware, Newark, DE 19716, USA; School of Marine Science and Policy, University of Delaware, Newark, DE 19716, USA
| | - Mona Batish
- Department of Biological Sciences, University of Delaware, Newark, DE 19716, USA; Department of Medical and Molecular Sciences, University of Delaware, Newark, DE 19716, USA.
| | - Yanbao Yu
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716, USA.
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29
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Greguš M, Koller A, Ray S, Ivanov AR. Improved Data Acquisition Settings on Q Exactive HF-X and Fusion Lumos Tribrid Orbitrap-Based Mass Spectrometers for Proteomic Analysis of Limited Samples. J Proteome Res 2024; 23:2230-2240. [PMID: 38690845 PMCID: PMC11165581 DOI: 10.1021/acs.jproteome.4c00181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/03/2024]
Abstract
Deep proteomic profiling of complex biological and medical samples available at low nanogram and subnanogram levels is still challenging. Thorough optimization of settings, parameters, and conditions in nanoflow liquid chromatography-tandem mass spectrometry (MS)-based proteomic profiling is crucial for generating informative data using amount-limited samples. This study demonstrates that by adjusting selected instrument parameters, e.g., ion injection time, automated gain control, and minimally altering the conditions for resuspending or storing the sample in solvents of different compositions, up to 15-fold more thorough proteomic profiling can be achieved compared to conventionally used settings. More specifically, the analysis of 1 ng of the HeLa protein digest standard by Q Exactive HF-X Hybrid Quadrupole-Orbitrap and Orbitrap Fusion Lumos Tribrid mass spectrometers yielded an increase from 1758 to 5477 (3-fold) and 281 to 4276 (15-fold) peptides, respectively, demonstrating that higher protein identification results can be obtained using the optimized methods. While the instruments applied in this study do not belong to the latest generation of mass spectrometers, they are broadly used worldwide, which makes the guidelines for improving performance desirable to a wide range of proteomics practitioners.
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Affiliation(s)
- Michal Greguš
- Barnett Institute of Chemical
and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, 360 Huntington Ave., Boston, Massachusetts 02115, United States
| | - Antonius Koller
- Barnett Institute of Chemical
and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, 360 Huntington Ave., Boston, Massachusetts 02115, United States
| | - Somak Ray
- Barnett Institute of Chemical
and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, 360 Huntington Ave., Boston, Massachusetts 02115, United States
| | - Alexander R. Ivanov
- Barnett Institute of Chemical
and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, 360 Huntington Ave., Boston, Massachusetts 02115, United States
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30
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Colón Rosado J, Sun L. Solid-Phase Microextraction-Aided Capillary Zone Electrophoresis-Mass Spectrometry: Toward Bottom-Up Proteomics of Single Human Cells. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1120-1127. [PMID: 38514245 PMCID: PMC11157658 DOI: 10.1021/jasms.3c00429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/02/2024] [Accepted: 03/13/2024] [Indexed: 03/23/2024]
Abstract
Capillary zone electrophoresis-mass spectrometry (CZE-MS) has been recognized as a valuable technique for the proteomics of mass-limited biological samples (i.e., single cells). However, its broad adoption for single cell proteomics (SCP) of human cells has been impeded by the low sample loading capacity of CZE, only allowing us to use less than 5% of the available peptide material for each measurement. Here we present a reversed-phase-based solid-phase microextraction (RP-SPME)-CZE-MS platform to solve the issue, paving the way for SCP of human cells using CZE-MS. The RP-SPME-CZE system was constructed in one fused silica capillary with zero dead volume for connection via in situ synthesis of a frit, followed by packing C8 beads into the capillary to form a roughly 2 mm long SPME section. Peptides captured by SPME were eluted with a buffer containing 30% (v/v) acetonitrile and 50 mM ammonium acetate (pH 6.5), followed by dynamic pH junction-based CZE-MS. The SPME-CZE-MS enabled the injection of nearly 40% of the available peptide sample for each measurement. The system identified 257 ± 24 proteins and 523 ± 69 peptides (N = 2) using a Q-Exactive HF mass spectrometer when only 0.25 ng of a commercial HeLa cell digest was available in the sample vial and 0.1 ng of the sample was injected. The amount of available peptide is equivalent to the protein mass of one HeLa cell. The data indicate that SPME-CZE-MS is ready for SCP of human cells.
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Affiliation(s)
- Jorge
A. Colón Rosado
- Department of Chemistry, Michigan
State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan
State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
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31
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Pang M, Jones JJ, Wang TY, Quan B, Kubat NJ, Qiu Y, Roukes ML, Chou TF. Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples. J Proteome Res 2024. [PMID: 38832920 DOI: 10.1021/acs.jproteome.4c00062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.
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Affiliation(s)
- Marion Pang
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Jeff J Jones
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Ting-Yu Wang
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Baiyi Quan
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Nicole J Kubat
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Yanping Qiu
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
| | - Michael L Roukes
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Division of Engineering and Applied Science, California Institute of Technology, 1200 East California Blvd, Pasadena, California 91125, United States
| | - Tsui-Fen Chou
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, United States
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32
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Yang Z, Jin K, Chen Y, Liu Q, Chen H, Hu S, Wang Y, Pan Z, Feng F, Shi M, Xie H, Ma H, Zhou H. AM-DMF-SCP: Integrated Single-Cell Proteomics Analysis on an Active Matrix Digital Microfluidic Chip. JACS AU 2024; 4:1811-1823. [PMID: 38818059 PMCID: PMC11134390 DOI: 10.1021/jacsau.4c00027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 06/01/2024]
Abstract
Single-cell proteomics offers unparalleled insights into cellular diversity and molecular mechanisms, enabling a deeper understanding of complex biological processes at the individual cell level. Here, we develop an integrated sample processing on an active-matrix digital microfluidic chip for single-cell proteomics (AM-DMF-SCP). Employing the AM-DMF-SCP approach and data-independent acquisition (DIA), we identify an average of 2258 protein groups in single HeLa cells within 15 min of the liquid chromatography gradient. We performed comparative analyses of three tumor cell lines: HeLa, A549, and HepG2, and machine learning was utilized to identify the unique features of these cell lines. Applying the AM-DMF-SCP to characterize the proteomes of a third-generation EGFR inhibitor, ASK120067-resistant cells (67R) and their parental NCI-H1975 cells, we observed a potential correlation between elevated VIM expression and 67R resistance, which is consistent with the findings from bulk sample analyses. These results suggest that AM-DMF-SCP is an automated, robust, and sensitive platform for single-cell proteomics and demonstrate the potential for providing valuable insights into cellular mechanisms.
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Affiliation(s)
- Zhicheng Yang
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- University
of the Chinese Academy of Sciences, Beijing 100049, China
| | - Kai Jin
- CAS
Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical
Engineering and Technology, Chinese Academy
of Sciences, Suzhou 215163, China
| | - Yimin Chen
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- University
of the Chinese Academy of Sciences, Beijing 100049, China
| | - Qian Liu
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
| | - Hongxu Chen
- School
of Chinese Materia Medica, Nanjing University
of Chinese Medicine, 138 Xianlin Avenue, Nanjing, Jiangsu 210023, China
| | - Siyi Hu
- CAS
Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical
Engineering and Technology, Chinese Academy
of Sciences, Suzhou 215163, China
| | - Yuqiu Wang
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
| | - Zilu Pan
- Division
of Antitumor Pharmacology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Fang Feng
- Division
of Antitumor Pharmacology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Mude Shi
- Guangdong
ACXEL Micro & Nano Tech Co. Ltd., Foshan, Guangdong Province 528000, China
| | - Hua Xie
- University
of the Chinese Academy of Sciences, Beijing 100049, China
- Zhongshan
Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
- Division
of Antitumor Pharmacology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Hanbin Ma
- CAS
Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical
Engineering and Technology, Chinese Academy
of Sciences, Suzhou 215163, China
- Guangdong
ACXEL Micro & Nano Tech Co. Ltd., Foshan, Guangdong Province 528000, China
| | - Hu Zhou
- Department
of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai
Institute of Materia Medica, Chinese Academy
of Sciences, Shanghai 201203, China
- University
of the Chinese Academy of Sciences, Beijing 100049, China
- Hangzhou
Institute for Advanced Study, University
of Chinese Academy of Sciences, Hangzhou 310024, China
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33
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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34
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Marie AL, Gao Y, Ivanov AR. Native N-glycome profiling of single cells and ng-level blood isolates using label-free capillary electrophoresis-mass spectrometry. Nat Commun 2024; 15:3847. [PMID: 38719792 PMCID: PMC11079027 DOI: 10.1038/s41467-024-47772-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 04/12/2024] [Indexed: 05/12/2024] Open
Abstract
The development of reliable single-cell dispensers and substantial sensitivity improvement in mass spectrometry made proteomic profiling of individual cells achievable. Yet, there are no established methods for single-cell glycome analysis due to the inability to amplify glycans and sample losses associated with sample processing and glycan labeling. In this work, we present an integrated platform coupling online in-capillary sample processing with high-sensitivity label-free capillary electrophoresis-mass spectrometry for N-glycan profiling of single mammalian cells. Direct and unbiased quantitative characterization of single-cell surface N-glycomes are demonstrated for HeLa and U87 cells, with the detection of up to 100 N-glycans per single cell. Interestingly, N-glycome alterations are unequivocally detected at the single-cell level in HeLa and U87 cells stimulated with lipopolysaccharide. The developed workflow is also applied to the profiling of ng-level amounts (5-500 ng) of blood-derived protein, extracellular vesicle, and total plasma isolates, resulting in over 170, 220, and 370 quantitated N-glycans, respectively.
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Affiliation(s)
- Anne-Lise Marie
- Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, 360 Huntington Ave., Boston, MA, 02115, US
| | - Yunfan Gao
- Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, 360 Huntington Ave., Boston, MA, 02115, US
| | - Alexander R Ivanov
- Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, 360 Huntington Ave., Boston, MA, 02115, US.
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35
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Peters-Clarke TM, Liang Y, Mertz KL, Lee KW, Westphall MS, Hinkle JD, McAlister GC, Syka JEP, Kelly RT, Coon JJ. Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Infrared-Tandem Mass Tags. J Proteome Res 2024. [PMID: 38713017 DOI: 10.1021/acs.jproteome.4c00076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS3 approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.
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Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Yiran Liang
- Department of Chemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Keaton L Mertz
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Kenneth W Lee
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Michael S Westphall
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua D Hinkle
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | | | - John E P Syka
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Ryan T Kelly
- Department of Chemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- National Center for Quantitative Biology of Complex Systems, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53515, United States
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Yu SH, Chen SC, Wu PS, Kuo PI, Chen TA, Lee HY, Lin MH. Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis. Mol Cell Proteomics 2024; 23:100768. [PMID: 38621647 PMCID: PMC11103571 DOI: 10.1016/j.mcpro.2024.100768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/12/2024] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.
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Affiliation(s)
- Sung-Huan Yu
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Shiau-Ching Chen
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Pei-Shan Wu
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Pei-I Kuo
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ting-An Chen
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hsiang-Ying Lee
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Miao-Hsia Lin
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan.
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37
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Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
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38
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Khan S, Conover R, Asthagiri AR, Slavov N. Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition. J Proteome Res 2024. [PMID: 38663020 DOI: 10.1021/acs.jproteome.4c00277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Physiological processes, such as the epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions.
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Affiliation(s)
- Saad Khan
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Rachel Conover
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Anand R Asthagiri
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
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39
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Mansuri MS, Bathla S, Lam TT, Nairn AC, Williams KR. Optimal conditions for carrying out trypsin digestions on complex proteomes: From bulk samples to single cells. J Proteomics 2024; 297:105109. [PMID: 38325732 PMCID: PMC10939724 DOI: 10.1016/j.jprot.2024.105109] [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: 11/13/2023] [Revised: 01/10/2024] [Accepted: 01/31/2024] [Indexed: 02/09/2024]
Abstract
To identify proteins by the bottom-up mass spectrometry workflow, enzymatic digestion is essential to break down proteins into smaller peptides amenable to both chromatographic separation and mass spectrometric analysis. Trypsin is the most extensively used protease due to its high cleavage specificity and generation of peptides with desirable positively charged N- and C-terminal amino acid residues that are amenable to reverse phase HPLC separation and MS/MS analyses. However, trypsin can yield variable digestion profiles and its protein cleavage activity is interdependent on trypsin source and quality, digestion time and temperature, pH, denaturant, trypsin and substrate concentrations, composition/complexity of the sample matrix, and other factors. There is therefore a need for a more standardized, general-purpose trypsin digestion protocol. Based on a review of the literature we delineate optimal conditions for carrying out trypsin digestions of complex proteomes from bulk samples to limiting amounts of protein extracts. Furthermore, we highlight recent developments and technological advances used in digestion protocols to quantify complex proteomes from single cells. SIGNIFICANCE: Currently, bottom-up MS-based proteomics is the method of choice for global proteome analysis. Since trypsin is the most utilized protease in bottom-up MS proteomics, delineating optimal conditions for carrying out trypsin digestions of complex proteomes in samples ranging from tissues to single cells should positively impact a broad range of biomedical research.
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Affiliation(s)
- M Shahid Mansuri
- Yale/NIDA Neuroproteomics Center, New Haven, CT 06511, USA; Molecular Biophysics and Biochemistry, Yale University School of Medicine, New Haven, CT 06511, USA.
| | - Shveta Bathla
- Yale/NIDA Neuroproteomics Center, New Haven, CT 06511, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - TuKiet T Lam
- Yale/NIDA Neuroproteomics Center, New Haven, CT 06511, USA; Molecular Biophysics and Biochemistry, Yale University School of Medicine, New Haven, CT 06511, USA; Keck MS & Proteomics Resource, Yale School of Medicine, New Haven, CT 06511, USA
| | - Angus C Nairn
- Yale/NIDA Neuroproteomics Center, New Haven, CT 06511, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA
| | - Kenneth R Williams
- Yale/NIDA Neuroproteomics Center, New Haven, CT 06511, USA; Molecular Biophysics and Biochemistry, Yale University School of Medicine, New Haven, CT 06511, USA; Keck MS & Proteomics Resource, Yale School of Medicine, New Haven, CT 06511, USA.
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40
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Coulombe B, Durcan TM, Bernard G, Moursli A, Poitras C, Faubert D, Pinard M. The 37TrillionCells initiative for improving global healthcare via cell-based interception and precision medicine: focus on neurodegenerative diseases. Mol Brain 2024; 17:18. [PMID: 38605409 PMCID: PMC11007934 DOI: 10.1186/s13041-024-01088-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Abstract
One of the main burdens in the treatment of diseases is imputable to the delay between the appearance of molecular dysfunctions in the first affected disease cells and their presence in sufficient number for detection in specific tissues or organs. This delay obviously plays in favor of disease progression to an extent that makes efficient treatments difficult, as they arrive too late. The development of a novel medical strategy, termed cell-based interception and precision medicine, seeks to identify dysfunctional cells early, when tissue damages are not apparent and symptoms not yet present, and develop therapies to treat diseases early. Central to this strategy is the use of single-cell technologies that allow detection of molecular changes in cells at the time of phenotypical bifurcation from health to disease. In this article we describe a general procedure to support such an approach applied to neurodegenerative disorders. This procedure combines four components directed towards highly complementary objectives: 1) a high-performance single-cell proteomics (SCP) method (Detect), 2) the development of disease experimental cell models and predictive computational models of cell trajectories (Understand), 3) the discovery of specific targets and personalized therapies (Cure), and 4) the creation of a community of collaborating laboratories to accelerate the development of this novel medical paradigm (Collaborate). A global initiative named 37TrillionCells (37TC) was launched to advance the development of cell-based interception and precision medicine.
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Affiliation(s)
- Benoit Coulombe
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W 1R7, Canada.
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada.
| | - Thomas M Durcan
- The Neuro's Early Drug Discovery Unit (EDDU), McGill University, Montreal, Canada
| | - Geneviève Bernard
- Departments of Neurology and Neurosurgery, Pediatrics and Human Genetics, McGill University, Montreal, Canada
- Department Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montreal, Canada
- Child Health and Human Development Program, Research Institute of the McGill University Health Centre, Montreal, Canada
| | - Asmae Moursli
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W 1R7, Canada
| | - Christian Poitras
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W 1R7, Canada
| | - Denis Faubert
- Mass Spectrometry and Proteomics Platform, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W1R7, Canada
| | - Maxime Pinard
- Translational Proteomics Laboratory, Institut de Recherches Cliniques de Montréal, Montreal, QC, H2W 1R7, Canada
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41
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Khan S, Conover R, Asthagiri AR, Slavov N. Dynamics of single-cell protein covariation during epithelial-mesenchymal transition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.21.572913. [PMID: 38187715 PMCID: PMC10769332 DOI: 10.1101/2023.12.21.572913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Physiological processes, such as epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and thus reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offer a window into protein regulation during physiological transitions.
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Affiliation(s)
- Saad Khan
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
| | - Rachel Conover
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Anand R. Asthagiri
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Department of Chemical Engineering, Northeastern University, Boston, MA, USA
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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42
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Fowowe M, Yu A, Wang J, Onigbinde S, Nwaiwu J, Bennett AI, Mechref Y. Suppressing the background of LC-ESI-MS analysis of permethylated glycans using the active background ion reduction device. Electrophoresis 2024. [PMID: 38573014 DOI: 10.1002/elps.202300301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
Mass spectrometry (MS) has revolutionized analytical chemistry, enabling precise identification and quantification of chemical species, which is pivotal for biomarker discovery and understanding complex biological systems. Despite its versatility, the presence of background ions in MS analysis hinders the sensitive detection of low-abundance analytes. Therefore, studies aimed at lowering background ion levels have become increasingly important. Here, we utilized the commercially available Active Background Ion Reduction Device (ABIRD) to suppress background ions and assess its effect on the liquid chromatography-electrospray ionization (LC-ESI)-MS analyses of N-glycans on the Q Exactive HF mass spectrometer. We also investigated the effect of different solvent vapors in the ESI source on N-glycan analysis by MS. ABIRD generally had no effect on high-mannose and neutral structures but reduced the intensity of some structures that contained sialic acid, fucose, or both when methanol vapor filled the ESI source. Based on our findings on the highest number of identified N-glycans from human serum, methanol vapor in the ion source compartment may enhance N-glycan LC-ESI-MS analyses by improving the desolvation of droplets formed during the ESI process due to its high volatility. This protocol may be further validated and extended to advanced bottom-up proteomic/glycoproteomic studies for the analysis of peptide/glycopeptide ions by MS.
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Affiliation(s)
- Mojibola Fowowe
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Aiying Yu
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Junyao Wang
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Sherifdeen Onigbinde
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Judith Nwaiwu
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Andrew I Bennett
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
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43
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Li W, Yang F, Wang F, Rong Y, Liu L, Wu B, Zhang H, Yao J. scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding. Nat Methods 2024; 21:623-634. [PMID: 38504113 DOI: 10.1038/s41592-024-02214-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/16/2024] [Indexed: 03/21/2024]
Abstract
Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.
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Affiliation(s)
- Wei Li
- College of Artificial Intelligence, Nankai University, Tianjin, China
- AI Lab, Tencent, Shenzhen, China
| | - Fan Yang
- AI Lab, Tencent, Shenzhen, China
| | | | - Yu Rong
- AI Lab, Tencent, Shenzhen, China
| | | | | | - Han Zhang
- College of Artificial Intelligence, Nankai University, Tianjin, China.
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44
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Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
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Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
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45
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Nalehua MR, Zaia J. A critical evaluation of ultrasensitive single-cell proteomics strategies. Anal Bioanal Chem 2024; 416:2359-2369. [PMID: 38358530 DOI: 10.1007/s00216-024-05171-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024]
Abstract
Success of mass spectrometry characterization of the proteome of single cells allows us to gain a greater understanding than afforded by transcriptomics alone but requires clear understanding of the tradeoffs between analytical throughput and precision. Recent advances in mass spectrometry acquisition techniques, including updated instrumentation and sample preparation, have improved the quality of peptide signals obtained from single cell data. However, much of the proteome remains uncharacterized, and higher throughput techniques often come at the expense of reduced sensitivity and coverage, which diminish the ability to measure proteoform heterogeneity, including splice variants and post-translational modifications, in single cell data analysis. Here, we assess the growing body of ultrasensitive single-cell approaches and their tradeoffs as researchers try to balance throughput and precision in their experiments.
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Affiliation(s)
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biochemistry and Cell Biology, Boston University, Boston, MA, USA.
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46
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Youssef A, Paul I, Crovella M, Emili A. DESP demixes cell-state profiles from dynamic bulk molecular measurements. CELL REPORTS METHODS 2024; 4:100729. [PMID: 38490205 PMCID: PMC10985230 DOI: 10.1016/j.crmeth.2024.100729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 12/22/2023] [Accepted: 02/16/2024] [Indexed: 03/17/2024]
Abstract
Understanding the dynamic expression of proteins and other key molecules driving phenotypic remodeling in development and pathobiology has garnered widespread interest, yet the exploration of these systems at the foundational resolution of the underlying cell states has been significantly limited by technical constraints. Here, we present DESP, an algorithm designed to leverage independent estimates of cell-state proportions, such as from single-cell RNA sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell-state signatures from bulk-level measurements of both the proteome and transcriptome, providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell-state level using established bulk-level workflows.
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Affiliation(s)
- Ahmed Youssef
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Indranil Paul
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Mark Crovella
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Computer Science Department, Boston University, Boston, MA, USA; Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.
| | - Andrew Emili
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA; Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
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47
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Chen J, Chu Z, Zhang Q, Wang C, Luo P, Zhang Y, Xia F, Gu L, Wong YK, Shi Q, Xu C, Tang H, Wang J. STEP: profiling cellular-specific targets and pathways of bioactive small molecules in tissues via integrating single-cell transcriptomics and chemoproteomics. Chem Sci 2024; 15:4313-4321. [PMID: 38516082 PMCID: PMC10952072 DOI: 10.1039/d3sc04826h] [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: 09/12/2023] [Accepted: 02/06/2024] [Indexed: 03/23/2024] Open
Abstract
Identifying the cellular targets of bioactive small molecules within tissues has been a major concern in drug discovery and chemical biology research. Compared to cell line models, tissues consist of multiple cell types and complicated microenvironments. Therefore, elucidating the distribution and heterogeneity of targets across various cells in tissues would enhance the mechanistic understanding of drug or toxin action in real-life scenarios. Here, we present a novel multi-omics integration pipeline called Single-cell TargEt Profiling (STEP) that enables the global profiling of protein targets in mammalian tissues with single-cell resolution. This pipeline integrates single-cell transcriptome datasets with tissue-level protein target profiling using chemoproteomics. Taking well-established classic drugs such as aspirin, aristolochic acid, and cisplatin as examples, we confirmed the specificity and precision of cellular drug-target profiles and their associated molecular pathways in tissues using the STEP analysis. Our findings provide more informative insights into the action modes of bioactive molecules compared to in vitro models. Collectively, STEP represents a novel strategy for profiling cellular-specific targets and functional processes with unprecedented resolution.
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Affiliation(s)
- Jiayun Chen
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Zheng Chu
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Qian Zhang
- School of Traditional Chinese Medicine and School of Pharmaceutical Sciences, Southern Medical University Guangzhou 510515 China
| | - Chen Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Piao Luo
- School of Traditional Chinese Medicine and School of Pharmaceutical Sciences, Southern Medical University Guangzhou 510515 China
| | - Ying Zhang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Fei Xia
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Liwei Gu
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yin Kwan Wong
- Department of Nephrology, Shenzhen Key Laboratory of Kidney Diseases, Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital, The First Affiliated Hospital, Southern University of Science and Technology Shenzhen 518020 China
| | - Qiaoli Shi
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Chengchao Xu
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Huan Tang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Jigang Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
- School of Traditional Chinese Medicine and School of Pharmaceutical Sciences, Southern Medical University Guangzhou 510515 China
- Department of Nephrology, Shenzhen Key Laboratory of Kidney Diseases, Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital, The First Affiliated Hospital, Southern University of Science and Technology Shenzhen 518020 China
- State Key Laboratory of Antiviral Drugs, School of Pharmacy, Henan University Kaifeng 475004 China
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48
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Gong L, Cui X, Liu Y, Lin C, Gao Z. SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares. Comput Biol Med 2024; 171:108225. [PMID: 38442556 DOI: 10.1016/j.compbiomed.2024.108225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 01/28/2024] [Accepted: 02/25/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND AND OBJECTIVES Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is equivalent to introducing noise into the data of cell gene expression entries. The data is contaminated, which affects the performance of downstream analyses, including clustering, cell annotation, differential gene expression analysis, and so on. Therefore, it is a crucial work to accurately determine which zeros are due to dropout events and perform imputation operations on them. METHODS Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations. RESULTS A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization. CONCLUSIONS SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in clustering, visualization and other aspects. It is applicable to various single-cell sequencing datasets.
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Affiliation(s)
- Lejun Gong
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China.
| | - Xiong Cui
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yang Liu
- Jiangsu Key Lab of Big Data Security & Intelligent Processing, School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Cai Lin
- Department of Burn, Wound Repair and Regenerative Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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49
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Kashima Y, Reteng P, Haga Y, Yamagishi J, Suzuki Y. Single-cell analytical technologies: uncovering the mechanisms behind variations in immune responses. FEBS J 2024; 291:819-831. [PMID: 36082537 DOI: 10.1111/febs.16622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/25/2022] [Accepted: 09/08/2022] [Indexed: 11/30/2022]
Abstract
The immune landscape varies among individuals. It determines the immune response and results in surprisingly diverse symptoms, even in response to similar external stimuli. However, the detailed mechanisms underlying such diverse immune responses have remained mostly elusive. The utilization of recently developed single-cell multimodal analysis platforms has started to answer this question. Emerging studies have elucidated several molecular networks that may explain diversity with respect to age or other factors. An elaborate interplay between inherent physical conditions and environmental conditions has been demonstrated. Furthermore, the importance of modifications by the epigenome resulting in transcriptome variation among individuals is gradually being revealed. Accordingly, epigenomes and transcriptomes are direct indicators of the medical history and dynamic interactions with environmental factors. Coronavirus disease 2019 (COVID-19) has recently become one of the most remarkable examples of the necessity of in-depth analyses of diverse responses with respect to various factors to improve treatment in severe cases and to prevent viral transmission from asymptomatic carriers. In fact, determining why some patients develop serious symptoms is still a pressing issue. Here, we review the current "state of the art" in single-cell analytical technologies and their broad applications to healthy individuals and representative diseases, including COVID-19.
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Affiliation(s)
- Yukie Kashima
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Patrick Reteng
- Division of Collaboration and Education, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Yasuhiko Haga
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Junya Yamagishi
- Division of Collaboration and Education, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
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50
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Bahiraii S, Brenner M, Weckwerth W, Heiss EH. Sulforaphane impedes mitochondrial reprogramming and histone acetylation in polarizing M1 (LPS) macrophages. Free Radic Biol Med 2024; 213:443-456. [PMID: 38301976 DOI: 10.1016/j.freeradbiomed.2024.01.029] [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: 01/11/2024] [Accepted: 01/18/2024] [Indexed: 02/03/2024]
Abstract
M1 (LPS) macrophages are characterized by a high expression of pro-inflammatory mediators, and distinct metabolic features that comprise increased glycolysis, a broken TCA cycle, or impaired OXPHOS with augmented mitochondrial ROS production. This study investigated whether the phytochemical sulforaphane (Sfn) influences mitochondrial reprogramming during M1 polarization, as well as to what extent this can contribute to Sfn-mediated inhibition of M1 marker expression in murine macrophages. The use of extracellular flux-, metabolite-, and immunoblot analyses as well as fluorescent dyes indicative for mitochondrial morphology, membrane potential or superoxide production, demonstrated that M1 (LPS/Sfn) macrophages maintain an unbroken TCA cycle, higher OXPHOS rate, boosted fusion dynamics, lower membrane potential, and less superoxide production in their mitochondria when compared to control M1 (LPS) cells. Sustained OXPHOS and TCA activity but not the concomitantly observed high dependency on fatty acids as fuel appeared necessary for M1 (LPS/Sfn) macrophages to reduce expression of nos2, il1β, il6 and tnfα. M1 (LPS/Sfn) macrophages also displayed lower nucleo/cytosolic acetyl-CoA levels in association with lower global and site-specific histone acetylation at selected pro-inflammatory gene promoters than M1 (LPS), evident in colorimetric coupled enzyme assays, immunoblot and ChIP-qPCR analyses, respectively. Supplementation with acetate or citrate was able to rescue both histone acetylation and mRNA expression of the investigated M1 marker genes in Sfn-treated cells. Overall, Sfn preserves mitochondrial functionality and restricts indispensable nuclear acetyl-CoA for histone acetylation and M1 marker expression in LPS-stimulated macrophages.
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Affiliation(s)
- Sheyda Bahiraii
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria; ViennaDoctoral School of Pharmaceutical, Nutritional and Sport Sciences (VDS PhaNuSpo), University of Vienna, Vienna, Austria
| | - Martin Brenner
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria; ViennaDoctoral School of Pharmaceutical, Nutritional and Sport Sciences (VDS PhaNuSpo), University of Vienna, Vienna, Austria; Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria
| | - Wolfram Weckwerth
- Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria; Molecular Systems Biology (MOSYS), Department of Functional and Evolutionary Ecology (FEE), University of Vienna, Vienna, Austria
| | - Elke H Heiss
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria.
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