151
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Phaneuf CG, Aizikov K, Grinfeld D, Kreutzmann A, Mourad D, Lange O, Dai D, Zhang B, Belenky A, Makarov AA, Ivanov AR. Experimental strategies to improve drug-target identification in mass spectrometry-based thermal stability assays. Commun Chem 2023; 6:64. [PMID: 37024568 PMCID: PMC10079678 DOI: 10.1038/s42004-023-00861-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 03/23/2023] [Indexed: 04/08/2023] Open
Abstract
Mass spectrometry (MS)-based thermal stability assays have recently emerged as one of the most promising solutions for the identification of protein-ligand interactions. Here, we have investigated eight combinations of several recently introduced MS-based advancements, including the Phase-Constrained Spectral Deconvolution Method, Field Asymmetric Ion Mobility Spectrometry, and the implementation of a carrier sample as improved MS-based acquisition approaches for thermal stability assays (iMAATSA). We used intact Jurkat cells treated with a commercially available MEK inhibitor, followed by heat treatment, to prepare a set of unfractionated isobarically-labeled proof-of-concept samples to compare the performance of eight different iMAATSAs. Finally, the best-performing iMAATSA was compared to a conventional approach and evaluated in a fractionation experiment. Improvements of up to 82% and 86% were demonstrated in protein identifications and high-quality melting curves, respectively, over the conventional approach in the proof-of-concept study, while an approximately 12% improvement in melting curve comparisons was achieved in the fractionation experiment.
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Affiliation(s)
- Clifford G Phaneuf
- Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
- Sanofi, Disease Profiling and Functional Genomics, Cambridge, MA, USA
| | | | | | | | | | | | - Daniel Dai
- Sanofi, Disease Profiling and Functional Genomics, Cambridge, MA, USA
| | - Bailin Zhang
- Sanofi, Disease Profiling and Functional Genomics, Cambridge, MA, USA
| | | | | | - Alexander R Ivanov
- Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA.
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152
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Franciosa G, Locard-Paulet M, Jensen LJ, Olsen JV. Recent advances in kinase signaling network profiling by mass spectrometry. Curr Opin Chem Biol 2023; 73:102260. [PMID: 36657259 DOI: 10.1016/j.cbpa.2022.102260] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 01/19/2023]
Abstract
Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.
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Affiliation(s)
- Giulia Franciosa
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marie Locard-Paulet
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars J Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jesper V Olsen
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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153
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Helm EY, Zhou L. Transcriptional regulation of innate lymphoid cells and T cells by aryl hydrocarbon receptor. Front Immunol 2023; 14:1056267. [PMID: 37056785 PMCID: PMC10089284 DOI: 10.3389/fimmu.2023.1056267] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/10/2023] [Indexed: 03/30/2023] Open
Abstract
The aryl hydrocarbon receptor (Ahr) is a ligand-dependent transcription factor and facilitates immune cell environmental sensing through its activation by cellular, dietary, and microbial metabolites, as well as environmental toxins. Although expressed in various cell types, Ahr in innate lymphoid cells (ILCs) and their adaptive T cell counterparts regulates essential aspects of their development and function. As opposed to T cells, ILCs exclusively rely on germ-line encoded receptors for activation, but often share expression of core transcription factors and produce shared effector molecules with their T cell counterparts. As such, core modules of transcriptional regulation are both shared and diverge between ILCs and T cells. In this review, we highlight the most recent findings regarding Ahr’s transcriptional regulation of both ILCs and T cells. Furthermore, we focus on insights elucidating the shared and distinct mechanisms by which Ahr regulates both innate and adaptive lymphocytes.
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154
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Gebreyesus ST, Muneer G, Huang CC, Siyal AA, Anand M, Chen YJ, Tu HL. Recent advances in microfluidics for single-cell functional proteomics. LAB ON A CHIP 2023; 23:1726-1751. [PMID: 36811978 DOI: 10.1039/d2lc01096h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.
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Affiliation(s)
- Sofani Tafesse Gebreyesus
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Gul Muneer
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | | | - Asad Ali Siyal
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
| | - Mihir Anand
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan
| | - Hsiung-Lin Tu
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan
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155
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Black HH, Hanson JL, Roberts JE, Leslie SN, Campodonico W, Ebmeier CC, Holling GA, Tay JW, Matthews AM, Ung E, Lau CI, Whiteley AM. UBQLN2 restrains the domesticated retrotransposon PEG10 to maintain neuronal health in ALS. eLife 2023; 12:e79452. [PMID: 36951542 PMCID: PMC10076021 DOI: 10.7554/elife.79452] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 03/15/2023] [Indexed: 03/24/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive motor neuron dysfunction and loss. A portion of ALS cases are caused by mutation of the proteasome shuttle factor Ubiquilin 2 (UBQLN2), but the molecular pathway leading from UBQLN2 dysfunction to disease remains unclear. Here, we demonstrate that UBQLN2 regulates the domesticated gag-pol retrotransposon 'paternally expressed gene 10 (PEG10)' in human cells and tissues. In cells, the PEG10 gag-pol protein cleaves itself in a mechanism reminiscent of retrotransposon self-processing to generate a liberated 'nucleocapsid' fragment, which uniquely localizes to the nucleus and changes the expression of genes involved in axon remodeling. In spinal cord tissue from ALS patients, PEG10 gag-pol is elevated compared to healthy controls. These findings implicate the retrotransposon-like activity of PEG10 as a contributing mechanism in ALS through the regulation of gene expression, and restraint of PEG10 as a primary function of UBQLN2.
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Affiliation(s)
- Holly H Black
- Department of Biochemistry, University of Colorado BoulderBoulderUnited States
| | - Jessica L Hanson
- Institute for Behavioral Genetics, University of Colorado BoulderBoulderUnited States
| | - Julia E Roberts
- Department of Biochemistry, University of Colorado BoulderBoulderUnited States
| | - Shannon N Leslie
- Department of Biochemistry, University of Colorado BoulderBoulderUnited States
| | - Will Campodonico
- Department of Biochemistry, University of Colorado BoulderBoulderUnited States
| | | | - G Aaron Holling
- Department of Biochemistry, University of Colorado BoulderBoulderUnited States
| | - Jian Wei Tay
- Biofrontiers Institute, University of Colorado BoulderBoulderUnited States
| | - Autumn M Matthews
- Department of Biochemistry, University of Colorado BoulderBoulderUnited States
| | - Elizabeth Ung
- Department of Biochemistry, University of Colorado BoulderBoulderUnited States
| | - Cristina I Lau
- Department of Biochemistry, University of Colorado BoulderBoulderUnited States
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156
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Schäfer JA, Sutandy FXR, Münch C. Omics-based approaches for the systematic profiling of mitochondrial biology. Mol Cell 2023; 83:911-926. [PMID: 36931258 DOI: 10.1016/j.molcel.2023.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/06/2023] [Accepted: 02/14/2023] [Indexed: 03/18/2023]
Abstract
Mitochondria are essential for cellular functions such as metabolism and apoptosis. They dynamically adapt to the changing environmental demands by adjusting their protein, nucleic acid, metabolite, and lipid contents. In addition, the mitochondrial components are modulated on different levels in response to changes, including abundance, activity, and interaction. A wide range of omics-based approaches has been developed to be able to explore mitochondrial adaptation and how mitochondrial function is compromised in disease contexts. Here, we provide an overview of the omics methods that allow us to systematically investigate the different aspects of mitochondrial biology. In addition, we show examples of how these methods have provided new biological insights. The emerging use of these toolboxes provides a more comprehensive understanding of the processes underlying mitochondrial function.
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Affiliation(s)
- Jasmin Adriana Schäfer
- Institute of Biochemistry II, Goethe University Frankfurt, Theodor-Stern-Kai 7, Haus 75, 60590 Frankfurt am Main, Germany
| | - F X Reymond Sutandy
- Institute of Biochemistry II, Goethe University Frankfurt, Theodor-Stern-Kai 7, Haus 75, 60590 Frankfurt am Main, Germany
| | - Christian Münch
- Institute of Biochemistry II, Goethe University Frankfurt, Theodor-Stern-Kai 7, Haus 75, 60590 Frankfurt am Main, Germany.
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157
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Peng J, Chan C, Zhang S, Sklavounos AA, Olson ME, Scott EY, Hu Y, Rajesh V, Li BB, Chamberlain MD, Zhang S, Peng H, Wheeler AR. All-in-One digital microfluidics pipeline for proteomic sample preparation and analysis. Chem Sci 2023; 14:2887-2900. [PMID: 36937585 PMCID: PMC10016607 DOI: 10.1039/d3sc00560g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
Highly sensitive and reproducible analysis of samples containing low amounts of protein is restricted by sample loss and the introduction of contaminants during processing. Here, we report an All-in-One digital microfluidic (DMF) pipeline for proteomic sample reduction, alkylation, digestion, isotopic labeling and analysis. The system features end-to-end automation, with integrated thermal control for digestion, optimized droplet additives for sample manipulation and analysis, and an automated interface to liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Dimethyl labeling was integrated into the pipeline to allow for relative quantification of the trace samples at the nanogram level, and the new pipeline was applied to evaluating cancer cell lines and cancer tissue samples. Several known proteins (including HSP90AB1, HSPB1, LDHA, ENO1, PGK1, KRT18, and AKR1C2) and pathways were observed between model breast cancer cell lines related to hormone response, cell metabolism, and cell morphology. Furthermore, differentially quantified proteins (such as PGS2, UGDH, ASPN, LUM, COEA1, and PRELP) were found in comparisons of healthy and cancer breast tissues, suggesting potential utility of the All-in-One pipeline for the emerging application of proteomic cancer sub-typing. In sum, the All-in-One pipeline represents a powerful new tool for automated proteome processing and analysis, with the potential to be useful for evaluating mass-limited samples for a wide range of applications.
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Affiliation(s)
- Jiaxi Peng
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto 160 College Street Toronto ON M5S 3E1 Canada
- Institute of Biomedical Engineering, University of Toronto 164 College Street Toronto ON M5S 3G9 Canada
| | - Calvin Chan
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
| | - Shuailong Zhang
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
- School of Mechatronical Engineering, Beijing Institute of Technology Beijing 100081 China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology Beijing 100081 China
| | - Alexandros A Sklavounos
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto 160 College Street Toronto ON M5S 3E1 Canada
| | - Maxwell E Olson
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
| | - Erica Y Scott
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto 160 College Street Toronto ON M5S 3E1 Canada
- Institute of Biomedical Engineering, University of Toronto 164 College Street Toronto ON M5S 3G9 Canada
| | - Yechen Hu
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto 160 College Street Toronto ON M5S 3E1 Canada
- Institute of Biomedical Engineering, University of Toronto 164 College Street Toronto ON M5S 3G9 Canada
| | - Vigneshwar Rajesh
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
| | - Bingyu B Li
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto 160 College Street Toronto ON M5S 3E1 Canada
- Institute of Biomedical Engineering, University of Toronto 164 College Street Toronto ON M5S 3G9 Canada
| | - M Dean Chamberlain
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto 160 College Street Toronto ON M5S 3E1 Canada
- Institute of Biomedical Engineering, University of Toronto 164 College Street Toronto ON M5S 3G9 Canada
- Saskatchewan Cancer Agency, University of Saskatchewan 107 Wiggins Road Saskatoon SK S7N 5E5 Canada
| | - Shen Zhang
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital 600 University Avenue Toronto ON M5G 1X5 Canada
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA Changsha Hunan 410000 China
| | - Hui Peng
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
- School of Environment, University of Toronto 33 Willcocks Street Toronto ON M5S 3E8 Canada
| | - Aaron R Wheeler
- Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada +1-416-946-3865 +1-416-946-3866
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto 160 College Street Toronto ON M5S 3E1 Canada
- Institute of Biomedical Engineering, University of Toronto 164 College Street Toronto ON M5S 3G9 Canada
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158
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Veličković M, Fillmore TL, Attah K, Posso C, Pino JC, Zhao R, Williams SM, Veličković D, Jacobs JM, Burnum-Johnson KE, Zhu Y, Piehowski PD. Coupling microdroplet-based sample preparation, multiplexed isobaric labeling, and nanoflow peptide fractionation for deep proteome profiling of tissue microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.531822. [PMID: 36993277 PMCID: PMC10055005 DOI: 10.1101/2023.03.13.531822] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity at a cell-type-specific level to better understand and predict the function of complex biological systems, such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverages due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (Microdroplet Processing in One pot for Trace Samples), the multiplexed isobaric labelling, and a nanoflow peptide fractionation approach. The integrated workflow allowed to maximize proteome coverage of laser-isolated tissue samples containing nanogram proteins. We demonstrated the deep spatial proteomics can quantify more than 5,000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 µm2) and reveal unique islet microenvironments.
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159
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Peng J, Chan C, Meng F, Hu Y, Chen L, Lin G, Zhang S, Wheeler AR. Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data. J Proteome Res 2023; 22:1298-1308. [PMID: 36892105 DOI: 10.1021/acs.jproteome.2c00821] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.
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Affiliation(s)
- Jiaxi Peng
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Calvin Chan
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Fei Meng
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China
| | - Yechen Hu
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Lingfan Chen
- Fujian Province New Drug Safety Evaluation Centre, Fujian Medical University, Fuzhou Fujian 350108, China
| | - Ge Lin
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China.,Laboratory of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Central South University, Changsha, Hunan 410075, China
| | - Shen Zhang
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China
| | - Aaron R Wheeler
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
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160
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Abstract
Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.
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Affiliation(s)
- Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
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161
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Advances in Mass Spectrometry-Based Single Cell Analysis. BIOLOGY 2023; 12:biology12030395. [PMID: 36979087 PMCID: PMC10045136 DOI: 10.3390/biology12030395] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023]
Abstract
Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.
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162
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MacCoss MJ, Alfaro JA, Faivre DA, Wu CC, Wanunu M, Slavov N. Sampling the proteome by emerging single-molecule and mass spectrometry methods. Nat Methods 2023; 20:339-346. [PMID: 36899164 PMCID: PMC10044470 DOI: 10.1038/s41592-023-01802-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules, which has major implications in the development of proteomics technologies. We review strategies that have been helpful for counting billions of protein molecules by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome.
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Affiliation(s)
- Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Javier Antonio Alfaro
- International Centre for Cancer Vaccine Science, University of Gdańsk, Gdańsk, Poland.
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada.
- School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Danielle A Faivre
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Christine C Wu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Meni Wanunu
- Department of Physics, 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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163
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Sun F, Suttapitugsakul S, Wu R. Systematic characterization of extracellular glycoproteins using mass spectrometry. MASS SPECTROMETRY REVIEWS 2023; 42:519-545. [PMID: 34047389 PMCID: PMC8627532 DOI: 10.1002/mas.21708] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 05/10/2021] [Accepted: 05/13/2021] [Indexed: 05/13/2023]
Abstract
Surface and secreted glycoproteins are essential to cells and regulate many extracellular events. Because of the diversity of glycans, the low abundance of many glycoproteins, and the complexity of biological samples, a system-wide investigation of extracellular glycoproteins is a daunting task. With the development of modern mass spectrometry (MS)-based proteomics, comprehensive analysis of different protein modifications including glycosylation has advanced dramatically. This review focuses on the investigation of extracellular glycoproteins using MS-based proteomics. We first discuss the methods for selectively enriching surface glycoproteins and investigating protein interactions on the cell surface, followed by the application of MS-based proteomics for surface glycoprotein dynamics analysis and biomarker discovery. We then summarize the methods to comprehensively study secreted glycoproteins by integrating various enrichment approaches with MS-based proteomics and their applications for global analysis of secreted glycoproteins in different biological samples. Collectively, MS significantly expands our knowledge of extracellular glycoproteins and enables us to identify extracellular glycoproteins as potential biomarkers for disease detection and drug targets for disease treatment.
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Affiliation(s)
| | | | - Ronghu Wu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
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164
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Gatto L, Aebersold R, Cox J, Demichev V, Derks J, Emmott E, Franks AM, Ivanov AR, Kelly RT, Khoury L, Leduc A, MacCoss MJ, Nemes P, Perlman DH, Petelski AA, Rose CM, Schoof EM, Van Eyk J, Vanderaa C, Yates JR, Slavov N. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nat Methods 2023; 20:375-386. [PMID: 36864200 PMCID: PMC10130941 DOI: 10.1038/s41592-023-01785-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 01/24/2023] [Indexed: 03/04/2023]
Abstract
Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .
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Affiliation(s)
- Laurent Gatto
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Juergen Cox
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | - Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Edward Emmott
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK
| | - Alexander M Franks
- Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Alexander R Ivanov
- Department of Chemistry and Chemical Biology, Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, USA
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | | | - Peter Nemes
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA
| | - David H Perlman
- Merck Exploratory Science Center, Merck Sharp & Dohme Corp., Cambridge, MA, USA
| | - Aleksandra A Petelski
- 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
| | - Christopher M Rose
- Department of Microchemistry, Proteomics and Lipidomics, Genentech Inc., South San Francisco, CA, USA
| | - Erwin M Schoof
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | | | - Christophe Vanderaa
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - John R Yates
- Departments of Molecular Medicine and Neurobiology, the Scripps Research Institute, La Jolla, CA, 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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165
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Geiger T. Tackling tumor complexity with single-cell proteomics. Nat Methods 2023; 20:324-326. [PMID: 36899159 DOI: 10.1038/s41592-023-01784-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Affiliation(s)
- Tamar Geiger
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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166
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Rosenberger FA, Thielert M, Mann M. Making single-cell proteomics biologically relevant. Nat Methods 2023; 20:320-323. [PMID: 36899157 DOI: 10.1038/s41592-023-01771-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Affiliation(s)
- Florian A Rosenberger
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Marvin Thielert
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Matthias Mann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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167
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Single-cell proteomics enabled by next-generation sequencing or mass spectrometry. Nat Methods 2023; 20:363-374. [PMID: 36864196 DOI: 10.1038/s41592-023-01791-5] [Citation(s) in RCA: 66] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/24/2023] [Indexed: 03/04/2023]
Abstract
In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.
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168
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Wheeler AM, Eberhard CD, Mosher EP, Yuan Y, Wilkins HN, Seneviratne HK, Orsburn BC, Bumpus NN. Achieving a Deeper Understanding of Drug Metabolism and Responses Using Single-Cell Technologies. Drug Metab Dispos 2023; 51:350-359. [PMID: 36627162 PMCID: PMC10029823 DOI: 10.1124/dmd.122.001043] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 01/12/2023] Open
Abstract
Recent advancements in single-cell technologies have enabled detection of RNA, proteins, metabolites, and xenobiotics in individual cells, and the application of these technologies has the potential to transform pharmacological research. Single-cell data has already resulted in the development of human and model species cell atlases, identifying different cell types within a tissue, further facilitating the characterization of tumor heterogeneity, and providing insight into treatment resistance. Research discussed in this review demonstrates that distinct cell populations express drug metabolizing enzymes to different extents, indicating there may be variability in drug metabolism not only between organs, but within tissue types. Additionally, we put forth the concept that single-cell analyses can be used to expose underlying variability in cellular response to drugs, providing a unique examination of drug efficacy, toxicity, and metabolism. We will outline several of these techniques: single-cell RNA-sequencing and mass cytometry to characterize and distinguish different cell types, single-cell proteomics to quantify drug metabolizing enzymes and characterize cellular responses to drug, capillary electrophoresis-ultrasensitive laser-induced fluorescence detection and single-probe single-cell mass spectrometry for detection of drugs, and others. Emerging single-cell technologies such as these can comprehensively characterize heterogeneity in both cell-type-specific drug metabolism and response to treatment, enhancing progress toward personalized and precision medicine. SIGNIFICANCE STATEMENT: Recent technological advances have enabled the analysis of gene expression and protein levels in single cells. These types of analyses are important to investigating mechanisms that cannot be elucidated on a bulk level, primarily due to the variability of cell populations within biological systems. Here, we summarize cell-type-specific drug metabolism and how pharmacologists can utilize single-cell approaches to obtain a comprehensive understanding of drug metabolism and cellular heterogeneity in response to drugs.
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Affiliation(s)
- Abigail M Wheeler
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Colten D Eberhard
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Eric P Mosher
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Yuting Yuan
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Hannah N Wilkins
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Herana Kamal Seneviratne
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Benjamin C Orsburn
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
| | - Namandjé N Bumpus
- Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland (A.M.W., C.D.E., E.P.M., Y.Y., H.N.W., H.K.S., B.C.O., N.N.B.) and Department of Chemistry and Biochemistry, University of Maryland, Baltimore County, Baltimore, Maryland (H.K.S.)
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169
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Boekweg H, Payne SH. Challenges and opportunities for single cell computational proteomics. Mol Cell Proteomics 2023; 22:100518. [PMID: 36828128 PMCID: PMC10060113 DOI: 10.1016/j.mcpro.2023.100518] [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: 12/07/2022] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 02/25/2023] Open
Abstract
Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it's necessary to realize that current algorithms being employed on single-cell data were designed for bulk data, and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data, and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.
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Affiliation(s)
- Hannah Boekweg
- Biology Department, Brigham Young University, Provo, Utah, USA
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah, USA.
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170
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Phlairaharn T, Ye Z, Krismer E, Pedersen AK, Pietzner M, Olsen JV, Schoof EM, Searle BC. Optimizing linear ion trap data independent acquisition towards single cell proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529444. [PMID: 36865114 PMCID: PMC9980145 DOI: 10.1101/2023.02.21.529444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
A linear ion trap (LIT) is an affordable, robust mass spectrometer that proves fast scanning speed and high sensitivity, where its primary disadvantage is inferior mass accuracy compared to more commonly used time-of-flight (TOF) or orbitrap (OT) mass analyzers. Previous efforts to utilize the LIT for low-input proteomics analysis still rely on either built-in OTs for collecting precursor data or OT-based library generation. Here, we demonstrate the potential versatility of the LIT for low-input proteomics as a stand-alone mass analyzer for all mass spectrometry measurements, including library generation. To test this approach, we first optimized LIT data acquisition methods and performed library-free searches with and without entrapment peptides to evaluate both the detection and quantification accuracy. We then generated matrix-matched calibration curves to estimate the lower limit of quantification using only 10 ng of starting material. While LIT-MS1 measurements provided poor quantitative accuracy, LIT-MS2 measurements were quantitatively accurate down to 0.5 ng on column. Finally, we optimized a suitable strategy for spectral library generation from low-input material, which we used to analyze single-cell samples by LIT-DIA using LIT-based libraries generated from as few as 40 cells.
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171
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Xie YR, Chari VK, Castro DC, Grant R, Rubakhin SS, Sweedler JV. Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry. J Proteome Res 2023; 22:491-500. [PMID: 36695570 PMCID: PMC9901547 DOI: 10.1021/acs.jproteome.2c00714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Varsha K. Chari
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Romans Grant
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Stanislav S. Rubakhin
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Mailing Address: Department of Chemistry, University of Illinois, 71 RAL, Box 63-5, 600 South Mathews Avenue, Urbana, Illinois 61801, United States; Phone: (217) 244-7359;
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172
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Schumacher D, Kramann R. Multiomic Spatial Mapping of Myocardial Infarction and Implications for Personalized Therapy. Arterioscler Thromb Vasc Biol 2023; 43:192-202. [PMID: 36579644 DOI: 10.1161/atvbaha.122.318333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Ischemic heart disease including myocardial infarction is still the leading cause of death worldwide. Although the survival early after myocardial infarction has been significantly improved by the introduction of percutaneous coronary intervention, long-term morbidity and mortality remain high. The elevated long-term mortality is mainly driven by cardiac remodeling processes triggering ischemic heart failure and electric instability. Despite the new developments in pharmaco-therapy of heart failure, we still lack targeted therapies for cardiac remodeling and fibrosis. Single-cell and genomic technologies allow us to map the human heart at unprecedented resolution and allow to gain insights into cellular and molecular heterogeneity. However, these technologies rely on digested tissue and isolated cells or nuclei and thus lack spatial information. Spatial information is critical to understand tissue homeostasis and disease and can be utilized to identify disease-driving cell populations and mechanisms including cellular cross-talk. Here, we discuss recent advances in single-cell and spatial genomic technologies that give insights into cellular and molecular mechanisms of cardiac remodeling after injury and can be utilized to identify novel therapeutic targets and pave the way toward new therapies in heart failure.
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Affiliation(s)
- David Schumacher
- Institute of Experimental Medicine and Systems Biology (D.S., R.K.), RWTH Aachen University, Germany.,Department of Anesthesiology, University Hospital (D.S.), RWTH Aachen University, Germany
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology (D.S., R.K.), RWTH Aachen University, Germany.,Department of Nephrology and Clinical Immunology (R.K.), RWTH Aachen University, Germany.,Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, the Netherlands (R.K.)
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173
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Dowling P, Gargan S, Swandulla D, Ohlendieck K. Fiber-Type Shifting in Sarcopenia of Old Age: Proteomic Profiling of the Contractile Apparatus of Skeletal Muscles. Int J Mol Sci 2023; 24:ijms24032415. [PMID: 36768735 PMCID: PMC9916839 DOI: 10.3390/ijms24032415] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 01/28/2023] Open
Abstract
The progressive loss of skeletal muscle mass and concomitant reduction in contractile strength plays a central role in frailty syndrome. Age-related neuronal impairments are closely associated with sarcopenia in the elderly, which is characterized by severe muscular atrophy that can considerably lessen the overall quality of life at old age. Mass-spectrometry-based proteomic surveys of senescent human skeletal muscles, as well as animal models of sarcopenia, have decisively improved our understanding of the molecular and cellular consequences of muscular atrophy and associated fiber-type shifting during aging. This review outlines the mass spectrometric identification of proteome-wide changes in atrophying skeletal muscles, with a focus on contractile proteins as potential markers of changes in fiber-type distribution patterns. The observed trend of fast-to-slow transitions in individual human skeletal muscles during the aging process is most likely linked to a preferential susceptibility of fast-twitching muscle fibers to muscular atrophy. Studies with senescent animal models, including mostly aged rodent skeletal muscles, have confirmed fiber-type shifting. The proteomic analysis of fast versus slow isoforms of key contractile proteins, such as myosin heavy chains, myosin light chains, actins, troponins and tropomyosins, suggests them as suitable bioanalytical tools of fiber-type transitions during aging.
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Affiliation(s)
- Paul Dowling
- Department of Biology, Maynooth University, National University of Ireland, W23 F2H6 Maynooth, Co. Kildare, Ireland
- Kathleen Lonsdale Institute for Human Health Research, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland
| | - Stephen Gargan
- Department of Biology, Maynooth University, National University of Ireland, W23 F2H6 Maynooth, Co. Kildare, Ireland
- Kathleen Lonsdale Institute for Human Health Research, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland
| | - Dieter Swandulla
- Institute of Physiology, University of Bonn, D53115 Bonn, Germany
| | - Kay Ohlendieck
- Department of Biology, Maynooth University, National University of Ireland, W23 F2H6 Maynooth, Co. Kildare, Ireland
- Kathleen Lonsdale Institute for Human Health Research, Maynooth University, W23 F2H6 Maynooth, Co. Kildare, Ireland
- Correspondence: ; Tel.: +353-1-7083842
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174
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Nazli S, Zimmerman KD, Riojas AM, Cox LA, Olivier M. An Isobaric Labeling Approach to Enhance Detection and Quantification of Tissue-Derived Plasma Proteins as Potential Early Disease Biomarkers. Biomolecules 2023; 13:215. [PMID: 36830584 PMCID: PMC9952993 DOI: 10.3390/biom13020215] [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: 12/10/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
The proteomic analysis of plasma holds great promise to advance precision medicine and identify biomarkers of disease. However, it is likely that many potential biomarkers circulating in plasma originate from other tissues and are only present in low abundances in the plasma. Accurate detection and quantification of low abundance proteins by standard mass spectrometry approaches remain challenging. In addition, it is difficult to link low abundance plasma proteins back to their specific tissues or organs of origin with confidence. To address these challenges, we developed a mass spectrometry approach based on the use of tandem mass tags (TMT) and a tissue reference sample. By applying this approach to nonhuman primate plasma samples, we were able to identify and quantify 820 proteins by using a kidney tissue homogenate as reference. On average, 643 ± 16 proteins were identified per plasma sample. About 58% of proteins identified in replicate experiments were identified both times. A ratio of 50 μg kidney protein to 10 μg plasma protein, and the use of the TMT label with the highest molecular weight (131) for the kidney reference yielded the largest number of proteins in the analysis, and identified low abundance proteins in plasma that are prominently found in the kidney. Overall, this methodology promises efficient quantification of plasma proteins potentially released from specific tissues, thereby increasing the number of putative disease biomarkers for future study.
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Affiliation(s)
- Sumaiya Nazli
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Kip D. Zimmerman
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Angelica M. Riojas
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Laura A. Cox
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
| | - Michael Olivier
- Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA
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175
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Tsai CF, Wang YT, Hsu CC, Kitata RB, Chu RK, Velickovic M, Zhao R, Williams SM, Chrisler WB, Jorgensen ML, Moore RJ, Zhu Y, Rodland KD, Smith RD, Wasserfall CH, Shi T, Liu T. A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics. Commun Biol 2023; 6:70. [PMID: 36653408 PMCID: PMC9849344 DOI: 10.1038/s42003-022-04400-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 12/23/2022] [Indexed: 01/19/2023] Open
Abstract
Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We develop a tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3000 (>9500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enables precise quantification of ~600 phosphopeptides from 100 sorted cells (single-cell level input for the enriched phosphopeptides) and ~700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ~100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.
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Affiliation(s)
- Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
| | - Yi-Ting Wang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Chuan-Chih Hsu
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan
| | - Reta Birhanu Kitata
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Rosalie K Chu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Marija Velickovic
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Rui Zhao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - William B Chrisler
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Marda L Jorgensen
- Department of Pathology, Immunology, and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Ying Zhu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Clive H Wasserfall
- Department of Pathology, Immunology, and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Tujin Shi
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
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176
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Carrillo-Rodriguez P, Selheim F, Hernandez-Valladares M. Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps. Cancers (Basel) 2023; 15:cancers15020555. [PMID: 36672506 PMCID: PMC9856946 DOI: 10.3390/cancers15020555] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.
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Affiliation(s)
- Paula Carrillo-Rodriguez
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
- Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Frode Selheim
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
| | - Maria Hernandez-Valladares
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
- Department of Physical Chemistry, University of Granada, Avenida de la Fuente Nueva S/N, 18071 Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
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177
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Park J, Yu F, Fulcher JM, Williams SM, Engbrecht K, Moore RJ, Clair GC, Petyuk V, Nesvizhskii AI, Zhu Y. Evaluating Linear Ion Trap for MS3-Based Multiplexed Single-Cell Proteomics. Anal Chem 2023; 95:1888-1898. [PMID: 36637389 DOI: 10.1021/acs.analchem.2c03739] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
There is a growing demand to develop high-throughput and high-sensitivity mass spectrometry methods for single-cell proteomics. The commonly used isobaric labeling-based multiplexed single-cell proteomics approach suffers from distorted protein quantification due to co-isolated interfering ions during MS/MS fragmentation, also known as ratio compression. We reasoned that the use of MS3-based quantification could mitigate ratio compression and provide better quantification. However, previous studies indicated reduced proteome coverages in the MS3 method, likely due to long duty cycle time and ion losses during multilevel ion selection and fragmentation. Herein, we described an improved MS acquisition method for MS3-based single-cell proteomics by employing a linear ion trap to measure reporter ions. We demonstrated that linear ion trap can increase the proteome coverages for single-cell-level peptides with even higher gain obtained via the MS3 method. The optimized real-time search MS3 method was further applied to study the immune activation of single macrophages. Among a total of 126 single cells studied, over 1200 and 1000 proteins were quantifiable when at least 50 and 75% nonmissing data were required, respectively. Our evaluation also revealed several limitations of the low-resolution ion trap detector for multiplexed single-cell proteomics and suggested experimental solutions to minimize their impacts on single-cell analysis.
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Affiliation(s)
- Junho Park
- Department of Pharmacology, School of Medicine, CHA University, Seongnam-si, Gyeonggi-do, Seongnam 13488, Republic of Korea
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
| | - James M Fulcher
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kristin Engbrecht
- Nuclear, Chemistry, and Biology Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Geremy C Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Vladislav Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
| | - Ying Zhu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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178
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Punetha A, Kotiya D. Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice. Proteomes 2023; 11:2. [PMID: 36648960 PMCID: PMC9844371 DOI: 10.3390/proteomes11010002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/12/2023] Open
Abstract
Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.
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Affiliation(s)
- Ankita Punetha
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, Rutgers University, 225 Warren St., Newark, NJ 07103, USA
| | - Deepak Kotiya
- Department of Pharmacology and Nutritional Sciences, University of Kentucky, 900 South Limestone St., Lexington, KY 40536, USA
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179
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Kong S, Li R, Tian Y, Zhang Y, Lu Y, Ou Q, Gao P, Li K, Zhang Y. Single-cell omics: A new direction for functional genetic research in human diseases and animal models. Front Genet 2023; 13:1100016. [PMID: 36685871 PMCID: PMC9846559 DOI: 10.3389/fgene.2022.1100016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023] Open
Abstract
Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.
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Affiliation(s)
- Siyuan Kong
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,*Correspondence: Siyuan Kong, ; Yubo Zhang,
| | - Rongrong Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yunhan Tian
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| | - Yaqiu Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yuhui Lu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Qiaoer Ou
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Peiwen Gao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yubo Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,College of Life Science and Engineering, Foshan University, Foshan, China,*Correspondence: Siyuan Kong, ; Yubo Zhang,
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180
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Franciosa G, Kverneland AH, Jensen AWP, Donia M, Olsen JV. Proteomics to study cancer immunity and improve treatment. Semin Immunopathol 2023; 45:241-251. [PMID: 36598558 PMCID: PMC10121539 DOI: 10.1007/s00281-022-00980-2] [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: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 01/05/2023]
Abstract
Cancer survival and progression depend on the ability of tumor cells to avoid immune recognition. Advances in the understanding of cancer immunity and tumor immune escape mechanisms enabled the development of immunotherapeutic approaches. In patients with otherwise incurable metastatic cancers, immunotherapy resulted in unprecedented response rates with the potential for durable complete responses. However, primary and acquired resistance mechanisms limit the efficacy of immunotherapy. Further therapeutic advances require a deeper understanding of the interplay between immune cells and tumors. Most high-throughput studies within the past decade focused on an omics characterization at DNA and RNA level. However, proteins are the molecular effectors of genomic information; therefore, the study of proteins provides deeper understanding of cellular functions. Recent advances in mass spectrometry (MS)-based proteomics at a system-wide scale may allow translational and clinical discoveries by enabling the analysis of understudied post-translational modifications, subcellular protein localization, cell signaling, and protein-protein interactions. In this review, we discuss the potential contribution of MS-based proteomics to preclinical and clinical research findings in the context of tumor immunity and cancer immunotherapies.
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Affiliation(s)
- Giulia Franciosa
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
| | - Anders H Kverneland
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.,National Center of Cancer Immune Therapy, Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | - Agnete W P Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Marco Donia
- National Center of Cancer Immune Therapy, Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | - Jesper V Olsen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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181
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Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N. Increasing the throughput of sensitive proteomics by plexDIA. Nat Biotechnol 2023; 41:50-59. [PMID: 35835881 PMCID: PMC9839897 DOI: 10.1038/s41587-022-01389-w] [Citation(s) in RCA: 86] [Impact Index Per Article: 86.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 06/13/2022] [Indexed: 01/22/2023]
Abstract
Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.
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Affiliation(s)
- Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Georg Wallmann
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - R Gray Huffman
- 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
| | - Harrison Specht
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Markus Ralser
- Charité - Universitätsmedizin Berlin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | | | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
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182
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Zhi Y, Li M, Lv G. Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation. Front Immunol 2023; 14:1058296. [PMID: 36798139 PMCID: PMC9927650 DOI: 10.3389/fimmu.2023.1058296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.
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Affiliation(s)
- Yao Zhi
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Mingqian Li
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Guoyue Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
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183
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Ogata K, Takagi S, Sugiyama N, Ishihama Y. Motif-Targeting Phosphoproteome Analysis of Cancer Cells for Profiling Kinase Inhibitors. Cancers (Basel) 2022; 15:cancers15010078. [PMID: 36612075 PMCID: PMC9817674 DOI: 10.3390/cancers15010078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
We present a motif-targeting phosphoproteome analysis workflow utilizing in vitro kinase reaction to enrich a subset of peptides with specific primary sequence motifs. Phosphopeptides are enriched and dephosphorylated with alkaline phosphatase, followed by in vitro kinase reaction to phosphorylate substrate peptides with specific primary-sequence motifs. These phosphopeptides are enriched again, TMT-labeled, dephosphorylated to enhance MS-detectability, and analyzed by LC/MS/MS. We applied this approach to inhibitor-treated cancer cells, and successfully profiled the inhibitory spectra of multiple kinase inhibitors. We anticipate this approach will be applicable to target specific subsets of the phosphoproteome using the wide variety of available recombinant protein kinases.
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184
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Bhatia HS, Brunner AD, Öztürk F, Kapoor S, Rong Z, Mai H, Thielert M, Ali M, Al-Maskari R, Paetzold JC, Kofler F, Todorov MI, Molbay M, Kolabas ZI, Negwer M, Hoeher L, Steinke H, Dima A, Gupta B, Kaltenecker D, Caliskan ÖS, Brandt D, Krahmer N, Müller S, Lichtenthaler SF, Hellal F, Bechmann I, Menze B, Theis F, Mann M, Ertürk A. Spatial proteomics in three-dimensional intact specimens. Cell 2022; 185:5040-5058.e19. [PMID: 36563667 DOI: 10.1016/j.cell.2022.11.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/13/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022]
Abstract
Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.
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Affiliation(s)
- Harsharan Singh Bhatia
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany
| | - Andreas-David Brunner
- Department for Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany; Boehringer Ingelheim Pharma GmbH & Co. KG, Drug Discovery Sciences, Birkendorfer Str. 65, D-88400 Biberach Riss, Germany
| | - Furkan Öztürk
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Saketh Kapoor
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Zhouyi Rong
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Hongcheng Mai
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Marvin Thielert
- Department for Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Mayar Ali
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Graduate School of Neuroscience (GSN), 82152 Munich, Germany
| | - Rami Al-Maskari
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany
| | - Johannes Christian Paetzold
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Florian Kofler
- Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany; Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Department of Neuroradiology, Klinikum rechts der Isar, 81675 Munich, Germany
| | - Mihail Ivilinov Todorov
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany
| | - Muge Molbay
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Zeynep Ilgin Kolabas
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Graduate School of Neuroscience (GSN), 82152 Munich, Germany
| | - Moritz Negwer
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Luciano Hoeher
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Hanno Steinke
- Institute of Anatomy, University of Leipzig, 04109 Leipzig, Germany
| | - Alina Dima
- Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany
| | - Basavdatta Gupta
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Doris Kaltenecker
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Institute for Diabetes and Cancer, Helmholz Zentrum München, 85764 Neuherberg, Germany
| | - Özüm Sehnaz Caliskan
- Institute for Diabetes and Obesity, Helmholz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research, Helmholz Zentrum München, 85764 Neuherberg, Germany
| | - Daniel Brandt
- Institute for Diabetes and Obesity, Helmholz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research, Helmholz Zentrum München, 85764 Neuherberg, Germany
| | - Natalie Krahmer
- Institute for Diabetes and Obesity, Helmholz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research, Helmholz Zentrum München, 85764 Neuherberg, Germany
| | - Stephan Müller
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany; Neuroproteomics, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Stefan Frieder Lichtenthaler
- Graduate School of Neuroscience (GSN), 82152 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany; Neuroproteomics, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Farida Hellal
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany
| | - Ingo Bechmann
- Institute of Anatomy, University of Leipzig, 04109 Leipzig, Germany
| | - Bjoern Menze
- Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany; Department for Quantitative Biomedicine, University of Zurich, 8006 Zurich, Switzerland
| | - Fabian Theis
- Institute of Computational Biology, Helmholz Zentrum München, 85764 Neuherberg, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany; Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
| | - Matthias Mann
- Department for Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany; NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
| | - Ali Ertürk
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Graduate School of Neuroscience (GSN), 82152 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany.
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185
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Vegliante R, Pastushenko I, Blanpain C. Deciphering functional tumor states at single-cell resolution. EMBO J 2022; 41:e109221. [PMID: 34918370 PMCID: PMC8762559 DOI: 10.15252/embj.2021109221] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/07/2021] [Accepted: 11/10/2021] [Indexed: 01/19/2023] Open
Abstract
Within a tumor, cancer cells exist in different states that are associated with distinct tumor functions, including proliferation, differentiation, invasion, metastasis, and resistance to anti-cancer therapy. The identification of the gene regulatory networks underpinning each state is essential for better understanding functional tumor heterogeneity and revealing tumor vulnerabilities. Here, we review the different studies identifying tumor states by single-cell sequencing approaches and the mechanisms that promote and sustain these functional states and regulate their transitions. We also describe how different tumor states are spatially distributed and interact with the specific stromal cells that compose the tumor microenvironment. Finally, we discuss how the understanding of tumor plasticity and transition states can be used to develop new strategies to improve cancer therapy.
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Affiliation(s)
- Rolando Vegliante
- Laboratory of Stem Cells and CancerUniversité Libre de BruxellesBrusselsBelgium
| | | | - Cédric Blanpain
- Laboratory of Stem Cells and CancerUniversité Libre de BruxellesBrusselsBelgium
- WELBIOUniversité Libre de BruxellesBrusselsBelgium
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186
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Leduc A, Huffman RG, Cantlon J, Khan S, Slavov N. Exploring functional protein covariation across single cells using nPOP. Genome Biol 2022; 23:261. [PMID: 36527135 PMCID: PMC9756690 DOI: 10.1186/s13059-022-02817-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Many biological processes, such as cell division cycle and drug resistance, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell mass spectrometry with sufficiently high throughput and accuracy. RESULTS Here, we describe nPOP, a method that enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes of 8-20 nl. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 pl volumes and performs all subsequent sample preparation steps in small droplets on a fluorocarbon-coated glass slide. Protein covariation analysis identifies cell cycle dynamics that are similar and dynamics that differ between cell types, even within subpopulations of melanoma cells delineated by markers for drug resistance priming. Melanoma cells expressing these markers accumulate in the G1 phase of the cell cycle, display distinct protein covariation across the cell cycle, accumulate glycogen, and have lower abundance of glycolytic enzymes. The non-primed melanoma cells exhibit gradients of protein abundance, suggesting transition states. Within this subpopulation, proteins functioning in oxidative phosphorylation covary with each other and inversely with proteins functioning in glycolysis. This protein covariation suggests divergent reliance on energy sources and its association with other biological functions. These results are validated by different mass spectrometry methods. CONCLUSIONS nPOP enables flexible, automated, and highly parallelized sample preparation for single-cell proteomics. This allows for quantifying protein covariation across thousands of single cells and revealing functionally concerted biological differences between closely related cell states. Support for nPOP is available at https://scp.slavovlab.net/nPOP .
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Affiliation(s)
- Andrew Leduc
- grid.261112.70000 0001 2173 3359Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115 USA
| | - R. Gray Huffman
- grid.261112.70000 0001 2173 3359Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115 USA
| | | | - Saad Khan
- grid.261112.70000 0001 2173 3359Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115 USA
| | - Nikolai Slavov
- grid.261112.70000 0001 2173 3359Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115 USA
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187
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Coulier A, Singh P, Sturrock M, Hellander A. Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation. PLoS Comput Biol 2022; 18:e1010683. [PMID: 36520957 PMCID: PMC9799300 DOI: 10.1371/journal.pcbi.1010683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 12/29/2022] [Accepted: 10/25/2022] [Indexed: 12/23/2022] Open
Abstract
Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results. The choice is important, because models of successively higher fidelity come at a rapidly increasing computational cost. In some situations, the level of detail is clearly motivated by the question under study. In many situations however, many model options could qualitatively agree with available data, depending on the amount of data and the nature of the observations. Here, an important distinction is whether we are interested in inferring the true (but unknown) physical parameters of the model or if it is sufficient to be able to capture and explain available data. The situation becomes complicated from a computational perspective because inference needs to be approximate. Most often it is based on likelihood-free Approximate Bayesian Computation (ABC) and here determining which summary statistics to use, as well as how much data is needed to reach the desired level of accuracy, are difficult tasks. Ultimately, all of these aspects-the model fidelity, the available data, and the numerical choices for inference-interplay in a complex manner. In this paper we develop a computational pipeline designed to systematically evaluate inference accuracy for a wide range of true known parameters. We then use it to explore inference settings for negative feedback gene regulation. In particular, we compare a detailed spatial stochastic model, a coarse-grained compartment-based multiscale model, and the standard well-mixed model, across several data-scenarios and for multiple numerical options for parameter inference. Practically speaking, this pipeline can be used as a preliminary step to guide modelers prior to gathering experimental data. By training Gaussian processes to approximate the distance function values, we are able to substantially reduce the computational cost of running the pipeline.
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Affiliation(s)
- Adrien Coulier
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Prashant Singh
- Science for Life Laboratory, Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Marc Sturrock
- Department of Physiology, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Andreas Hellander
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- * E-mail:
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188
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Ma M, Huo S, Zhang M, Qian S, Zhu X, Pu J, Rasam S, Xue C, Shen S, An B, Wang J, Qu J. In-depth mapping of protein localizations in whole tissue by micro-scaffold assisted spatial proteomics (MASP). Nat Commun 2022; 13:7736. [PMID: 36517484 PMCID: PMC9751300 DOI: 10.1038/s41467-022-35367-2] [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: 10/03/2022] [Accepted: 11/29/2022] [Indexed: 12/16/2022] Open
Abstract
Accurate, in-depth mapping of proteins on whole-tissue levels provides comprehensive insights into the spatially-organized regulatory processes/networks in tissues, but is challenging. Here we describe a micro-scaffold assisted spatial proteomics (MASP) strategy, based on spatially-resolved micro-compartmentalization of tissue using a 3D-printed micro-scaffold, capable of mapping thousands of proteins across a whole-tissue slice with excellent quantitative accuracy/precision. The pipeline includes robust tissue micro-compartmentalization with precisely-preserved spatial information, reproducible procurement and preparation of the micro-specimens, followed by sensitive LC-MS analysis and map generation by a MAsP app. The mapping accuracy was validated by comparing the MASP-generated maps of spiked-in peptides and brain-region-specific markers with known patterns, and by correlating the maps of the two protein components of the same heterodimer. The MASP was applied in mapping >5000 cerebral proteins in the mouse brain, encompassing numerous important brain markers, regulators, and transporters, where many of these proteins had not previously been mapped on the whole-tissue level.
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Affiliation(s)
- Min Ma
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14203, USA
| | - Shihan Huo
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA
| | - Ming Zhang
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA
- New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, 14203, USA
| | - Shuo Qian
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14203, USA
| | - Xiaoyu Zhu
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA
| | - Jie Pu
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA
| | - Sailee Rasam
- Department of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, SUNY at Buffalo, Buffalo, NY, 14203, USA
| | - Chao Xue
- Department of Chemical and Biological Engineering, SUNY at Buffalo, Buffalo, NY, 14214, USA
| | - Shichen Shen
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA
- New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, 14203, USA
| | - Bo An
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA
- Department of DMPK, Huiyu (Seacross) Pharmaceuticals Ltd, Chengdu, 610219, China
| | - Jianmin Wang
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14203, USA
| | - Jun Qu
- Department of Pharmaceutical Sciences, SUNY at Buffalo, Buffalo, NY, 14214, USA.
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14203, USA.
- New York State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY, 14203, USA.
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189
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Petelski AA, Slavov N, Specht H. Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier. J Vis Exp 2022:10.3791/63802. [PMID: 36571403 PMCID: PMC10027359 DOI: 10.3791/63802] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Single-cell proteomics analysis requires sensitive, quantitatively accurate, widely accessible, and robust methods. To meet these requirements, the Single-Cell ProtEomics (SCoPE2) protocol was developed as a second-generation method for quantifying hundreds to thousands of proteins from limited samples, down to the level of a single cell. Experiments using this method have achieved quantifying over 3,000 proteins across 1,500 single mammalian cells (500-1,000 proteins per cell) in 10 days of mass spectrometer instrument time. SCoPE2 leverages a freeze-heat cycle for cell lysis, obviating the need for clean-up of single cells and consequently reducing sample losses, while expediting sample preparation and simplifying its automation. Additionally, the method uses an isobaric carrier, which aids protein identification and reduces sample losses. This video protocol provides detailed guidance to enable the adoption of automated single-cell protein analysis using only equipment and reagents that are widely accessible. We demonstrate critical steps in the procedure of preparing single cells for proteomic analysis, from harvesting up to injection to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Additionally, viewers are guided through the principles of experimental design with the isobaric carrier, quality control for both isobaric carrier and single-cell preparations, and representative results with a discussion of limitations of the approach.
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Affiliation(s)
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University; Barnett Institute, Northeastern University; Department of Biology, Northeastern University
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190
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Proximity sequencing for single-cell measurement of protein complexes. Nat Methods 2022; 19:1528-1529. [DOI: 10.1038/s41592-022-01686-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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191
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Patil N, Howe O, Cahill P, Byrne HJ. Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives. Mol Metab 2022; 66:101635. [PMID: 36379354 PMCID: PMC9703637 DOI: 10.1016/j.molmet.2022.101635] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/28/2022] [Accepted: 11/06/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The dynamics of the cellular glycolysis pathway underpin cellular function and dysfunction, and therefore ultimately health, disease, diagnostic and therapeutic strategies. Evolving our understanding of this fundamental process and its dynamics remains critical. SCOPE OF REVIEW This paper reviews the medical relevance of glycolytic pathway in depth and explores the current state of the art for monitoring and modelling the dynamics of the process. The future perspectives of label free, vibrational microspectroscopic techniques to overcome the limitations of the current approaches are considered. MAJOR CONCLUSIONS Vibrational microspectroscopic techniques can potentially operate in the niche area of limitations of other omics technologies for non-destructive, real-time, in vivo label-free monitoring of glycolysis dynamics at a cellular and subcellular level.
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Affiliation(s)
- Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland; School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological and Health Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
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192
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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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193
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Rabbani M, Zheng X, Manske GL, Vargo A, Shami AN, Li JZ, Hammoud SS. Decoding the Spermatogenesis Program: New Insights from Transcriptomic Analyses. Annu Rev Genet 2022; 56:339-368. [PMID: 36070560 PMCID: PMC10722372 DOI: 10.1146/annurev-genet-080320-040045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Spermatogenesis is a complex differentiation process coordinated spatiotemporally across and along seminiferous tubules. Cellular heterogeneity has made it challenging to obtain stage-specific molecular profiles of germ and somatic cells using bulk transcriptomic analyses. This has limited our ability to understand regulation of spermatogenesis and to integrate knowledge from model organisms to humans. The recent advancement of single-cell RNA-sequencing (scRNA-seq) technologies provides insights into the cell type diversity and molecular signatures in the testis. Fine-grained cell atlases of the testis contain both known and novel cell types and define the functional states along the germ cell developmental trajectory in many species. These atlases provide a reference system for integrated interspecies comparisons to discover mechanistic parallels and to enable future studies. Despite recent advances, we currently lack high-resolution data to probe germ cell-somatic cell interactions in the tissue environment, but the use of highly multiplexed spatial analysis technologies has begun to resolve this problem. Taken together, recent single-cell studies provide an improvedunderstanding of gametogenesis to examine underlying causes of infertility and enable the development of new therapeutic interventions.
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Affiliation(s)
- Mashiat Rabbani
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
| | - Xianing Zheng
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
| | - Gabe L Manske
- Cellular and Molecular Biology Graduate Program, University of Michigan, Ann Arbor, Michigan, USA
| | - Alexander Vargo
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
| | - Adrienne N Shami
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
| | - Jun Z Li
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Saher Sue Hammoud
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA;
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Urology, University of Michigan, Ann Arbor, Michigan, USA
- Cellular and Molecular Biology Graduate Program, University of Michigan, Ann Arbor, Michigan, USA
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194
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Solt LA. Emerging insights and challenges for understanding T cell function through the proteome. Front Immunol 2022; 13:1028366. [DOI: 10.3389/fimmu.2022.1028366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/31/2022] [Indexed: 11/17/2022] Open
Abstract
T cells rapidly transition from a quiescent state into active proliferation and effector function upon exposure to cognate antigen. These processes are tightly controlled by signal transduction pathways that influence changes in chromatin remodeling, gene transcription, and metabolism, all of which collectively drive specific T cell memory or effector cell development. Dysregulation of any of these events can mediate disease and the past several years has shown unprecedented novel approaches to understand these events, down to the single-cell level. The massive explosion of sequencing approaches to assess the genome and transcriptome at the single cell level has transformed our understanding of T cell activation, developmental potential, and effector function under normal and various disease states. Despite these advances, there remains a significant dearth of information regarding how these events are translated to the protein level. For example, resolution of protein isoforms and/or specific post-translational modifications mediating T cell function remains obscure. The application of proteomics can change that, enabling significant insights into molecular mechanisms that regulate T cell function. However, unlike genomic approaches that have enabled exquisite visualization of T cell dynamics at the mRNA and chromatin level, proteomic approaches, including those at the single-cell level, has significantly lagged. In this review, we describe recent studies that have enabled a better understanding of how protein synthesis and degradation change during T cell activation and acquisition of effector function. We also highlight technical advances and how these could be applied to T cell biology. Finally, we discuss future needs to expand upon our current knowledge of T cell proteomes and disease.
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195
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Chen S, Zhao J, Xu C, Zhang L, Shi B, Tian J, Zhao S. Intracellular Multicomponent Synchronous DNA-Walking Strategy for the Simultaneous Quantification of Tumor-Associated Proteins in a Single Cell. Anal Chem 2022; 94:15847-15855. [DOI: 10.1021/acs.analchem.2c03771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Shengyu Chen
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
- Key Laboratory of Regional Ecological Environment Analysis and Pollution Control of West Guangxi, College Chemistry & Environment Engineering, Baise University, Baise 533000, China
| | - Jingjin Zhao
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
| | - Chunhuan Xu
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
| | - Liangliang Zhang
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
| | - Bingfang Shi
- Key Laboratory of Regional Ecological Environment Analysis and Pollution Control of West Guangxi, College Chemistry & Environment Engineering, Baise University, Baise 533000, China
| | - Jianniao Tian
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
| | - Shulin Zhao
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
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196
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Lausecker F, Lennon R, Randles MJ. The kidney matrisome in health, aging, and disease. Kidney Int 2022; 102:1000-1012. [PMID: 35870643 DOI: 10.1016/j.kint.2022.06.029] [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: 01/21/2022] [Revised: 06/15/2022] [Accepted: 06/24/2022] [Indexed: 02/06/2023]
Abstract
Dysregulated extracellular matrix is the hallmark of fibrosis, and it has a profound impact on kidney function in disease. Furthermore, perturbation of matrix homeostasis is a feature of aging and is associated with declining kidney function. Understanding these dynamic processes, in the hope of developing therapies to combat matrix dysregulation, requires the integration of data acquired by both well-established and novel technologies. Owing to its complexity, the extracellular proteome, or matrisome, still holds many secrets and has great potential for the identification of clinical biomarkers and drug targets. The molecular resolution of matrix composition during aging and disease has been illuminated by cutting-edge mass spectrometry-based proteomics in recent years, but there remain key questions about the mechanisms that drive altered matrix composition. Basement membrane components are particularly important in the context of kidney function; and data from proteomic studies suggest that switches between basement membrane and interstitial matrix proteins are likely to contribute to organ dysfunction during aging and disease. Understanding the impact of such changes on physical properties of the matrix, and the subsequent cellular response to altered stiffness and viscoelasticity, is of critical importance. Likewise, the comparison of proteomic data sets from multiple organs is required to identify common matrix biomarkers and shared pathways for therapeutic intervention. Coupled with single-cell transcriptomics, there is the potential to identify the cellular origin of matrix changes, which could enable cell-targeted therapy. This review provides a contemporary perspective of the complex kidney matrisome and draws comparison to altered matrix in heart and liver disease.
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Affiliation(s)
- Franziska Lausecker
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals National Health Service (NHS) Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Michael J Randles
- Chester Medical School, Faculty of Medicine and Life Sciences, University of Chester, Chester, UK.
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197
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Zhu L, Zhou T, Iyyappan R, Ming H, Dvoran M, Wang Y, Chen Q, Roberts RM, Susor A, Jiang Z. High-resolution ribosome profiling reveals translational selectivity for transcripts in bovine preimplantation embryo development. Development 2022; 149:280468. [PMID: 36227586 PMCID: PMC9687001 DOI: 10.1242/dev.200819] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
Abstract
High-resolution ribosome fractionation and low-input ribosome profiling of bovine oocytes and preimplantation embryos has enabled us to define the translational landscapes of early embryo development at an unprecedented level. We analyzed the transcriptome and the polysome- and non-polysome-bound RNA profiles of bovine oocytes (germinal vesicle and metaphase II stages) and early embryos at the two-cell, eight-cell, morula and blastocyst stages, and revealed four modes of translational selectivity: (1) selective translation of non-abundant mRNAs; (2) active, but modest translation of a selection of highly expressed mRNAs; (3) translationally suppressed abundant to moderately abundant mRNAs; and (4) mRNAs associated specifically with monosomes. A strong translational selection of low-abundance transcripts involved in metabolic pathways and lysosomes was found throughout bovine embryonic development. Notably, genes involved in mitochondrial function were prioritized for translation. We found that translation largely reflected transcription in oocytes and two-cell embryos, but observed a marked shift in the translational control in eight-cell embryos that was associated with the main phase of embryonic genome activation. Subsequently, transcription and translation become more synchronized in morulae and blastocysts. Taken together, these data reveal a unique spatiotemporal translational regulation that accompanies bovine preimplantation development.
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Affiliation(s)
- Linkai Zhu
- School of Animal Sciences, AgCenter, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Tong Zhou
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, NV 89557-0352, USA
| | - Rajan Iyyappan
- Laboratory of Biochemistry and Molecular Biology of Germ Cells, Institute of Animal Physiology and Genetics, CAS, 277 21 Liběchov, Czech Republic
| | - Hao Ming
- School of Animal Sciences, AgCenter, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Michal Dvoran
- Laboratory of Biochemistry and Molecular Biology of Germ Cells, Institute of Animal Physiology and Genetics, CAS, 277 21 Liběchov, Czech Republic
| | - Yinjuan Wang
- School of Animal Sciences, AgCenter, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Qi Chen
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, CA 92521, USA
| | - R Michael Roberts
- Department of Animal Sciences, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211-7310, USA
| | - Andrej Susor
- Laboratory of Biochemistry and Molecular Biology of Germ Cells, Institute of Animal Physiology and Genetics, CAS, 277 21 Liběchov, Czech Republic
| | - Zongliang Jiang
- School of Animal Sciences, AgCenter, Louisiana State University, Baton Rouge, LA 70803, USA
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Tajik M, Baharfar M, Donald WA. Single-cell mass spectrometry. Trends Biotechnol 2022; 40:1374-1392. [PMID: 35562238 DOI: 10.1016/j.tibtech.2022.04.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/04/2022] [Accepted: 04/09/2022] [Indexed: 01/21/2023]
Abstract
Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening.
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Affiliation(s)
- Mohammad Tajik
- School of Chemistry, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - Mahroo Baharfar
- School of Chemical Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - William A Donald
- School of Chemistry, University of New South Wales, Sydney, New South Wales, 2052, Australia.
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Liu Z, Li H, Dang Q, Weng S, Duo M, Lv J, Han X. Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy. Cell Mol Life Sci 2022; 79:577. [DOI: 10.1007/s00018-022-04608-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/12/2022] [Accepted: 10/20/2022] [Indexed: 11/03/2022]
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Cupp-Sutton KA, Fang M, Wu S. Separation methods in single-cell proteomics: RPLC or CE? INTERNATIONAL JOURNAL OF MASS SPECTROMETRY 2022; 481:116920. [PMID: 36211475 PMCID: PMC9542495 DOI: 10.1016/j.ijms.2022.116920] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Cellular heterogeneity is commonly investigated using single-cell genomics and transcriptomics to investigate biological questions such as disease mechanism, therapeutic screening, and genomic and transcriptomic diversity between cellular populations and subpopulations at the cellular level. Single-cell mass spectrometry (MS)-based proteomics enables the high-throughput examination of protein expression at the single-cell level with wide applicability, and with spatial and temporal resolution, applicable to the study of cellular development, disease, effect of treatment, etc. The study of single-cell proteomics has lagged behind genomics and transcriptomics largely because proteins from single-cell samples cannot be amplified as DNA and RNA can using well established techniques such as PCR. Therefore, analytical methods must be robust, reproducible, and sensitive enough to detect the very small amount of protein within a single cell. To this end, nearly every step of the proteomics process has been extensively altered and improved to facilitate the proteomics analysis of single cells including cell counting and sorting, lysis, protein digestion, sample cleanup, separation, MS data acquisition, and data analysis. Here, we have reviewed recent advances in single-cell protein separation using nano reversed phase liquid chromatography (nRPLC) and capillary electrophoresis (CE) to inform application driven selection of separation techniques in the laboratory setting.
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Affiliation(s)
| | - Mulin Fang
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019
| | - Si Wu
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019
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