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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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2
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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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3
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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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4
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Zhu Z, Jiang L, Ding X. Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels. Cancers (Basel) 2023; 15:4164. [PMID: 37627192 PMCID: PMC10452610 DOI: 10.3390/cancers15164164] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/23/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.
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Affiliation(s)
- Zijian Zhu
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
| | - Xianting Ding
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
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5
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Matzinger M, Mayer RL, Mechtler K. Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing. Proteomics 2023; 23:e2200162. [PMID: 36806919 PMCID: PMC10909491 DOI: 10.1002/pmic.202200162] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/21/2023]
Abstract
The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.
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Affiliation(s)
- Manuel Matzinger
- Research Institute of Molecular Pathology (IMP)Vienna BioCenterViennaAustria
| | - Rupert L. Mayer
- Research Institute of Molecular Pathology (IMP)Vienna BioCenterViennaAustria
| | - Karl Mechtler
- Research Institute of Molecular Pathology (IMP)Vienna BioCenterViennaAustria
- Gregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of SciencesVienna BioCenter (VBC)ViennaAustria
- Institute of Molecular Biotechnology (IMBA), Austrian Academy of SciencesVienna BioCenter (VBC)ViennaAustria
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Sandoval L, Mohammed Ismail W, Mazzone A, Dumbrava M, Fernandez J, Munankarmy A, Lasho T, Binder M, Simon V, Kim KH, Chia N, Lee JH, Weroha SJ, Patnaik M, Gaspar-Maia A. Characterization and Optimization of Multiomic Single-Cell Epigenomic Profiling. Genes (Basel) 2023; 14:1245. [PMID: 37372428 PMCID: PMC10297939 DOI: 10.3390/genes14061245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n = 18) and a solid tumor type, ovarian cancer (OC, n = 18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell type identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n = 6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.
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Affiliation(s)
- Leticia Sandoval
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Wazim Mohammed Ismail
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Amelia Mazzone
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Mihai Dumbrava
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
- Mayo Clinic Medical Scientist Training Program, Mayo Clinic Alix School of Medicine and Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Jenna Fernandez
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.F.); (T.L.)
| | - Amik Munankarmy
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Terra Lasho
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.F.); (T.L.)
| | - Moritz Binder
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.F.); (T.L.)
| | - Vernadette Simon
- Medical Genome Facility, Genome Analysis Core, Mayo Clinic, Rochester, MN 55905, USA;
| | - Kwan Hyun Kim
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Jeong-Heon Lee
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - S. John Weroha
- Department of Medical Oncology, Mayo Clinic, Rochester, MN 55905, USA;
| | - Mrinal Patnaik
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.F.); (T.L.)
| | - Alexandre Gaspar-Maia
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
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The Role of Proteomics and Phosphoproteomics in the Discovery of Therapeutic Targets and Biomarkers in Acquired EGFR-TKI-Resistant Non-Small Cell Lung Cancer. Int J Mol Sci 2023; 24:ijms24054827. [PMID: 36902280 PMCID: PMC10003401 DOI: 10.3390/ijms24054827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 02/25/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023] Open
Abstract
The discovery of potent EGFR-tyrosine kinase inhibitors (EGFR-TKIs) has revolutionized the treatment of EGFR-mutated lung cancer. Despite the fact that EGFR-TKIs have yielded several significant benefits for lung cancer patients, the emergence of resistance to EGFR-TKIs has been a substantial impediment to improving treatment outcomes. Understanding the molecular mechanisms underlying resistance is crucial for the development of new treatments and biomarkers for disease progression. Together with the advancement in proteome and phosphoproteome analysis, a diverse set of key signaling pathways have been successfully identified that provide insight for the discovery of possible therapeutically targeted proteins. In this review, we highlight the proteome and phosphoproteomic analyses of non-small cell lung cancer (NSCLC) as well as the proteome analysis of biofluid specimens that associate with acquired resistance in response to different generations of EGFR-TKI. Furthermore, we present an overview of the targeted proteins and potential drugs that have been tested in clinical studies and discuss the challenges of implementing this discovery in future NSCLC treatment.
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Wu Y, Zhang W, Zhao Y, Wang X, Guo G. Technology development trend of electrospray ionization mass spectrometry for single-cell proteomics. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Valencia-Ortiz M, Sankaran S. Development of a semi-automated volatile organic compounds (VOCs) sampling system for field asymmetric ion mobility spectrometry (FAIMS) analysis. HARDWAREX 2022; 12:e00344. [PMID: 36033547 PMCID: PMC9403554 DOI: 10.1016/j.ohx.2022.e00344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/24/2022] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
In recent years, applications of volatile organic compounds (VOCs) sensing technologies such as field asymmetric-waveform ion-mobility spectrometry (FAIMS) system in agriculture have accelerated. FAIMS system for VOCs sensing is attractive as it offers high sensitivity, selectivity, real-time monitoring, and portability. However, the development of a robust instrumentation system is needed for precise sampling, high accumulation of VOCs, and careful handling of samples. In this study, we developed a simple semi-automated VOC sampling (SAVS) system using a Raspberry Pi microcontroller, flowmeters, electromechanical solenoid, and cellphone-based app to control cleaning and sampling loops. The system was compared with customized headspace sampling apparatus (CHSA) and validated with a biomarker (acetone) identified to be associated with potato rot development during postharvest storage. The standard error within ion current data across different compensation voltage was lower using the SAVS system than the CHSA. In addition, the maximum peak values across scans displayed a high coefficient of variation using the CHSA (16.23%) than the SAVS system (4.51%). Future work will involve improving system efficiency by adapting multiple sample units, system miniaturization, and automating the flowmeter operation. Such automation is critical to characterize VOCs precisely and automatically across several samples for multiple applications such as pathogen detection, evaluation of crop responses, etc.
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Sirikaew N, Pruksakorn D, Chaiyawat P, Chutipongtanate S. Mass Spectrometric-Based Proteomics for Biomarker Discovery in Osteosarcoma: Current Status and Future Direction. Int J Mol Sci 2022; 23:ijms23179741. [PMID: 36077137 PMCID: PMC9456544 DOI: 10.3390/ijms23179741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/23/2022] [Accepted: 08/26/2022] [Indexed: 11/16/2022] Open
Abstract
Due to a lack of novel therapies and biomarkers, the clinical outcomes of osteosarcoma patients have not significantly improved for decades. The advancement of mass spectrometry (MS), peptide quantification, and downstream pathway analysis enables the investigation of protein profiles across a wide range of input materials, from cell culture to long-term archived clinical specimens. This can provide insight into osteosarcoma biology and identify candidate biomarkers for diagnosis, prognosis, and stratification of chemotherapy response. In this review, we provide an overview of proteomics studies of osteosarcoma, indicate potential biomarkers that might be promising therapeutic targets, and discuss the challenges and opportunities of mass spectrometric-based proteomics in future osteosarcoma research.
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Affiliation(s)
- Nutnicha Sirikaew
- Musculoskeletal Science and Translational Research (MSTR) Center, Department of Orthopedics, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
- Department of Biochemistry, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Dumnoensun Pruksakorn
- Musculoskeletal Science and Translational Research (MSTR) Center, Department of Orthopedics, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Parunya Chaiyawat
- Musculoskeletal Science and Translational Research (MSTR) Center, Department of Orthopedics, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
- Center of Multidisciplinary Technology for Advanced Medicine (CMUTEAM), Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
- Correspondence: (P.C.); (S.C.)
| | - Somchai Chutipongtanate
- Division of Epidemiology, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
- Correspondence: (P.C.); (S.C.)
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11
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diaPASEF Proteomics and Feature Selection for the Description of Sputum Proteome Profiles in a Cohort of Different Subtypes of Lung Cancer Patients and Controls. Int J Mol Sci 2022; 23:ijms23158737. [PMID: 35955870 PMCID: PMC9369298 DOI: 10.3390/ijms23158737] [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: 07/12/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 11/21/2022] Open
Abstract
The high mortality, the presence of an initial asymptomatic stage and the fact that diagnosis in early stages reduces mortality justify the implementation of screening programs in the populations at risk of lung cancer. It is imperative to develop less aggressive methods that can complement existing diagnosis technologies. In this study, we aimed to identify lung cancer protein biomarkers and pathways affected in sputum samples, using the recently developed diaPASEF mass spectrometry (MS) acquisition mode. The sputum proteome of lung cancer cases and controls was analyzed through nano-HPLC–MS using the diaPASEF mode. For functional analysis, the results from differential expression analysis were further analyzed in the STRING platform, and feature selection was performed using sparse partial least squares discriminant analysis (sPLS-DA). Our results showed an activation of inflammation, with an alteration of pathways and processes related to acute-phase, complement, and immune responses. The resulting sPLS-DA model separated between case and control groups with high levels of sensitivity and specificity. In conclusion, we showed how new-generation proteomics can be used to detect potential biomarkers in sputum samples, and ultimately to discriminate patients from controls and even to help to differentiate between different cancer subtypes.
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Suzuki Y, Hayasaka R, Hasebe M, Ikeda S, Soga T, Tomita M, Hirayama A, Kuroda H. Comparative Metabolomics of Small Molecules Specifically Expressed in the Dorsal or Ventral Marginal Zones in Vertebrate Gastrula. Metabolites 2022; 12:metabo12060566. [PMID: 35736498 PMCID: PMC9229639 DOI: 10.3390/metabo12060566] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/14/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
Many previous studies have reported the various proteins specifically secreted as inducers in the dorsal or ventral regions in vertebrate gastrula. However, little is known about the effect on cell fate of small molecules below 1000 Da. We therefore tried to identify small molecules specifically expressed in the dorsal marginal zone (DMZ) or ventral marginal zone (VMZ) in vertebrate gastrula. Small intracellular and secreted molecules were detected using explants and supernatant samples. Hydrophilic metabolites were analyzed by capillary ion chromatography-mass spectrometry and liquid chromatography-mass spectrometry, and lipids were analyzed by supercritical fluid chromatography-tandem mass spectrometry. In total, 190 hydrophilic metabolites and 396 lipids were identified. The DMZ was found to have high amounts of glycolysis- and glutathione metabolism-related metabolites in explants, and the VMZ was richer in purine metabolism-related metabolites. We also discovered some hydrophilic metabolites and lipids differentially contained in the DMZ or VMZ. Our research would contribute to a deeper understanding of the cellular physiology that regulates early embryogenesis.
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Affiliation(s)
- Yukako Suzuki
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan; (Y.S.); (R.H.); (M.H.); (S.I.); (T.S.); (M.T.); (A.H.)
| | - Ryosuke Hayasaka
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan; (Y.S.); (R.H.); (M.H.); (S.I.); (T.S.); (M.T.); (A.H.)
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Kanagawa, Japan
| | - Masako Hasebe
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan; (Y.S.); (R.H.); (M.H.); (S.I.); (T.S.); (M.T.); (A.H.)
| | - Satsuki Ikeda
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan; (Y.S.); (R.H.); (M.H.); (S.I.); (T.S.); (M.T.); (A.H.)
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan; (Y.S.); (R.H.); (M.H.); (S.I.); (T.S.); (M.T.); (A.H.)
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Kanagawa, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan; (Y.S.); (R.H.); (M.H.); (S.I.); (T.S.); (M.T.); (A.H.)
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Kanagawa, Japan
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan; (Y.S.); (R.H.); (M.H.); (S.I.); (T.S.); (M.T.); (A.H.)
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Kanagawa, Japan
| | - Hiroki Kuroda
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan; (Y.S.); (R.H.); (M.H.); (S.I.); (T.S.); (M.T.); (A.H.)
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Kanagawa, Japan
- Correspondence: ; Tel.: +81-466-49-3404
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Alexovič M, Lindner JR, Bober P, Longuespée R, Sabo J, Davalieva K. Human peripheral blood mononuclear cells: A review of recent proteomic applications. Proteomics 2022; 22:e2200026. [PMID: 35348286 DOI: 10.1002/pmic.202200026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/17/2022] [Accepted: 03/24/2022] [Indexed: 11/07/2022]
Abstract
Human peripheral blood mononuclear cells (PBMCs) represent a sentinel blood sample which reacts to different pathophysiological stimuli in the form of immunological responses/immunophenotypic changes. The study of molecular content of PBMCs can provide better understanding of immune processes giving the possibility of monitoring the health conditions of the host organism. Proteomic analysis of PBMCs can achieve mentioned goal as important immune-related biomarkers are easily accessible for analysis. PBMCs have been gaining attention in different research areas including preclinical or clinical investigations. In this review, recent applications of proteomic analysis of PBMCs are described and discussed. Approaches are divided based on different proteomic workflows such as in-gel, in-solution and on-filter modes. The effect of various diseases such as autoimmune, cancer, neurodegenerative, viral, metabolic, and various immune stimulations such as radiation, vaccine, corticosteroids over PBMCs proteome, are described with emphasis on promising protein biomarker candidates. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Michal Alexovič
- Department of Medical and Clinical Biophysics, Faculty of Medicine, University of P.J. Šafárik in Košice, Košice, Slovakia
| | - Joshua Raoul Lindner
- Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Peter Bober
- Department of Medical and Clinical Biophysics, Faculty of Medicine, University of P.J. Šafárik in Košice, Košice, Slovakia
| | - Rémi Longuespée
- Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Ján Sabo
- Department of Medical and Clinical Biophysics, Faculty of Medicine, University of P.J. Šafárik in Košice, Košice, Slovakia
| | - Katarina Davalieva
- Research Centre for Genetic Engineering and Biotechnology, "Georgi D Efremov", Macedonian Academy of Sciences and Arts, USA
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MicroRNAs as Predictors of Future Uterine Malignancy in Endometrial Hyperplasia without Atypia. J Pers Med 2022; 12:jpm12020311. [PMID: 35207799 PMCID: PMC8879120 DOI: 10.3390/jpm12020311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/29/2022] [Accepted: 02/17/2022] [Indexed: 11/29/2022] Open
Abstract
The histological criteria for classifying endometrial hyperplasia (EH) are based on architectural crowding and nuclear atypia; however, diagnostic agreement among pathologists is poor. We investigated molecular biomarkers of endometrial cancer (EC) risk in women with simple hyperplasia or complex hyperplasia without atypia (SH/CH-nonA). Forty-nine patients with EC preceded by SH/CH-nonA were identified, of which 23 were excluded (15 with complex atypical hyperplasia (CAH), six not consenting, one with a diagnosis < 6 months prior, and one lost to follow-up). The EH tissues of these patients were compared with those of patients with SH/CH-nonA that did not progress to EC (control) through microRNA (miRNA) array analysis, and the results were verified in an expanded cohort through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). MiRNA arrays analyses revealed 20 miRNAs that differed significantly (p < 0.05, fold change > 4) between the control (n = 12) and case (n = 6) patients. Multiplex RT-qPCR for the 20 miRNAs in the expanded cohort (94 control and 25 case patients) led to the validation of miR-30a-3p (p = 0.0009), miR-141 (p < 0.0001), miR-200a (p < 0.0001), and miR-200b (p < 0.0001) as relevant biomarkers, among which miR-141, miR-200a, and miR-200b regulate the expression of phosphatase and tensin homolog (PTEN). For the prediction of EC, the area under the curve for miR-30a-3p, miR-141, miR-200a, and miR-200b was 0.623, 0.754, 0.783, and 0.704, respectively. The percentage of complete PTEN loss was significantly higher in the case group than in the control group (24% vs. 0%, p < 0.001, Fisher’s exact test). A combination of complete PTEN loss and miR-200a provided optimal prediction performance (sensitivity = 0.760; specificity = 1.000; positive predictive value = 1.000; negative predictive value = 0.937; accuracy = 0.947). MiR-30a-3p, miR-141, miR-200a, miR-200b, and complete PTEN loss may be useful tissue biomarkers for predicting EC risk among patients with SH/CH-nonA.
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