https://doi.org/10.4081/ejtm.2024.12564
Proteomic reference map for sarcopenia research: mass spectrometric identification of key muscle proteins located in the sarcomere, cytoskeleton and the extracellular matrix
Published: 24 May 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Sarcopenia of old age is characterized by the progressive loss of skeletal muscle mass and concomitant decrease in contractile strength. Age-related skeletal muscle dysfunctions play a key pathophysiological role in the frailty syndrome and can result in a drastically diminished quality of life in the elderly. Here we have used mass spectrometric analysis of the mouse hindlimb musculature to establish the muscle protein constellation at advanced age of a widely used sarcopenic animal model. Proteomic results were further analyzed by systems bioinformatics of voluntary muscles. In this report, the proteomic survey of aged muscles has focused on the expression patterns of proteins involved in the contraction-relaxation cycle, membrane cytoskeletal maintenance and the formation of the extracellular matrix. This includes proteomic markers of the fast versus slow phenotypes of myosin-containing thick filaments and actin-containing thin filaments, as well as proteins that are associated with the non-sarcomeric cytoskeleton and various matrisomal layers. The bioanalytical usefulness of the newly established reference map was demonstrated by the comparative screening of normal versus dystrophic muscles of old age, and findings were verified by immunoblot analysis.
Hatton IA, Galbraith ED, Merleau NSC, et al. The human cell count and size distribution. Proc Natl Acad Sci U S A 2023;120:e2303077120.
DOI: https://doi.org/10.1073/pnas.2303077120
Frontera WR, Ochala J. Skeletal muscle: a brief review of structure and function. Calcif Tissue Int 2015;96:183-95.
DOI: https://doi.org/10.1007/s00223-014-9915-y
Mukund K, Subramaniam S. Skeletal muscle: A review of molecular structure and function, in health and disease. Wiley Interdiscip Rev Syst Biol Med 2020;12:e1462.
DOI: https://doi.org/10.1002/wsbm.1462
Brooks SV, Guzman SD, Ruiz LP. Skeletal muscle structure, physiology, and function. Handb Clin Neurol 2023;195:3-16
DOI: https://doi.org/10.1016/B978-0-323-98818-6.00013-3
Pette D, Vrbov G. The Contribution of Neuromuscular Stimulation in Elucidating Muscle Plasticity Revisited. Eur J Transl Myol 2017;27:6368.
DOI: https://doi.org/10.4081/ejtm.2017.6368
Ravara B, Giuriati W, Maccarone MC, et al. Optimized progression of Full-Body In-Bed Gym workout: an educational case report. Eur J Transl Myol 2023;33:11525.
DOI: https://doi.org/10.4081/ejtm.2023.11525
Seaborne RAE, Ochala J. The dawn of the functional genomics era in muscle physiology. J Physiol 2023;601:1343-52.
DOI: https://doi.org/10.1113/JP284206
Robinson NB, Krieger K, Khan FM, et al. The current state of animal models in research: A review. Int J Surg 2019;72:9-13.
DOI: https://doi.org/10.1016/j.ijsu.2019.10.015
Navabpour S, Kwapis JL, Jarome TJ. A neuroscientist's guide to transgenic mice and other genetic tools. Neurosci Biobehav Rev 2020;108:732-748.
DOI: https://doi.org/10.1016/j.neubiorev.2019.12.013
Serano M, Paolini C, Michelucci A, et al. High-fat diet impairs muscle function and increases the risk of environmental heatstroke in mice. Int J Mol Sci 2022;23:5286.
DOI: https://doi.org/10.3390/ijms23095286
Girolami B, Serano M, Michelucci A, et al. Store-operated Ca2+ entry in skeletal muscle contributes to the increase in body temperature during exertional stress. Int J Mol Sci 2022;23:3772.
DOI: https://doi.org/10.3390/ijms23073772
Moreno-Jiménez L, Benito-Martín MS, Sanclemente-Alamán I, et al. Murine experimental models of amyotrophic lateral sclerosis: an update. Neurologia (Engl Ed) 2024;39:282-91.
DOI: https://doi.org/10.1016/j.nrleng.2021.07.004
Ruberte J, Schofield PN, Sundberg JP, et al. Bridging mouse and human anatomies; a knowledge-based approach to comparative anatomy for disease model phenotyping. Mamm Genome 2023;34:389-407.
DOI: https://doi.org/10.1007/s00335-023-10005-4
Vanhooren V, Libert C. The mouse as a model organism in aging research: usefulness, pitfalls and possibilities. Ageing Res Rev 2013;12:8-21.
DOI: https://doi.org/10.1016/j.arr.2012.03.010
Ackert-Bicknell CL, Anderson LC, Sheehan S, et al. Aging research using mouse models. Curr Protoc Mouse Biol 2015;5:95-133.
DOI: https://doi.org/10.1002/9780470942390.mo140195
Cai N, Wu Y, Huang Y. Induction of accelerated aging in a mouse model. Cells 2022;11:1418.
DOI: https://doi.org/10.3390/cells11091418
Christian CJ, Benian GM. Animal models of sarcopenia. Aging Cell 2020;19:e13223.
DOI: https://doi.org/10.1111/acel.13223
Xie WQ, He M, Yu DJ, et al. Mouse models of sarcopenia: classification and evaluation. J Cachexia Sarcopenia Muscle 2021;12:538-54.
DOI: https://doi.org/10.1002/jcsm.12709
Van Long N, Chien PN, Tung TX, et al. Complementary combination of biomarkers for diagnosis of sarcopenia in C57BL/6J mice. Life Sci 2023;312:121213.
DOI: https://doi.org/10.1016/j.lfs.2022.121213
Cruz-Jentoft AJ, Sayer AA. Sarcopenia. Lancet 2019;393:2636-46. Erratum in: Lancet 2019;393:2590.
DOI: https://doi.org/10.1016/S0140-6736(19)31138-9
Larsson L, Degens H, Li M, et al. Sarcopenia: aging-related loss of muscle mass and function. Physiol Rev 2019;99:427-511.
DOI: https://doi.org/10.1152/physrev.00061.2017
Nishikawa H, Fukunishi S, Asai A, et al. Pathophysiology and mechanisms of primary sarcopenia (Review). Int J Mol Med 2021;48:156.
DOI: https://doi.org/10.3892/ijmm.2021.4989
Kim JW, Kim R, Choi H, et al. Understanding of sarcopenia: from definition to therapeutic strategies. Arch Pharm Res 2021;44:876-89.
DOI: https://doi.org/10.1007/s12272-021-01349-z
Zheng Y, Feng J, Yu Y, et al. Advances in sarcopenia: mechanisms, therapeutic targets, and intervention strategies. Arch Pharm Res 2024 Apr 9. doi: 10.1007/s12272-024-01493-2. Epub ahead of print.
DOI: https://doi.org/10.1007/s12272-024-01493-2
Liu JC, Dong SS, Shen H, Yang DY, Chen BB, Ma XY, Peng YR, Xiao HM, Deng HW. Multi-omics research in sarcopenia: Current progress and future prospects. Ageing Res Rev 2022;76:101576.
DOI: https://doi.org/10.1016/j.arr.2022.101576
Rivero-Segura NA, Bello-Chavolla OY, Barrera-Vázquez OS, et al. Promising biomarkers of human aging: In search of a multi-omics panel to understand the aging process from a multidimensional perspective. Ageing Res Rev 2020;64:101164.
DOI: https://doi.org/10.1016/j.arr.2020.101164
Pan Y, Ji T, Li Y, Ma L. Omics biomarkers for frailty in older adults. Clin Chim Acta 2020;510:363-72.
DOI: https://doi.org/10.1016/j.cca.2020.07.057
Danese E, Montagnana M, Lippi G. Proteomics and frailty: a clinical overview. Expert Rev Proteomics 2018;15:657-64.
DOI: https://doi.org/10.1080/14789450.2018.1505511
Fernández-Lázaro D, Garrosa E, Seco-Calvo J, Garrosa M. Potential satellite cell-linked biomarkers in aging skeletal muscle tissue: proteomics and proteogenomics to monitor sarcopenia. Proteomes 2022;10:29.
DOI: https://doi.org/10.3390/proteomes10030029
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;24:2415.
DOI: https://doi.org/10.3390/ijms24032415
Moaddel R, Ubaida-Mohien C, Tanaka T, et al. Proteomics in aging research: A roadmap to clinical, translational research. Aging Cell 2021;20:e13325.
DOI: https://doi.org/10.1111/acel.13325
Dowling P, Swandulla D, Ohlendieck K. Mass spectrometry-based proteomic technology and its application to study skeletal muscle cell biology. Cells 2023;12:2560.
DOI: https://doi.org/10.3390/cells12212560
Murphy S, Dowling P, Ohlendieck K. Comparative Skeletal Muscle Proteomics Using Two-Dimensional Gel Electrophoresis. Proteomes 2016;4:27.
DOI: https://doi.org/10.3390/proteomes4030027
Burniston JG, Connolly J, Kainulainen H, et al. Label-free profiling of skeletal muscle using high-definition mass spectrometry. Proteomics 2014;14:2339-44.
DOI: https://doi.org/10.1002/pmic.201400118
Ohlendieck K. Skeletal muscle proteomics: current approaches, technical challenges and emerging techniques. Skelet Muscle 2011;1:6.
DOI: https://doi.org/10.1186/2044-5040-1-6
Deshmukh AS, Murgia M, Nagaraj N, et al. Deep proteomics of mouse skeletal muscle enables quantitation of protein isoforms, metabolic pathways, and transcription factors. Mol Cell Proteomics 2015;14:841-53.
DOI: https://doi.org/10.1074/mcp.M114.044222
Højlund K, Yi Z, Hwang H, et al. Characterization of the human skeletal muscle proteome by one-dimensional gel electrophoresis and HPLC-ESI-MS/MS. Mol Cell Proteomics 2008;7:257-67.
DOI: https://doi.org/10.1074/mcp.M700304-MCP200
Parker KC, Walsh RJ, Salajegheh M, et al. Characterization of human skeletal muscle biopsy samples using shotgun proteomics. J Proteome Res 2009;8:3265-77.
DOI: https://doi.org/10.1021/pr800873q
Murphy S, Zweyer M, Raucamp M, et al. Proteomic profiling of the mouse diaphragm and refined mass spectrometric analysis of the dystrophic phenotype. J Muscle Res Cell Motil 2019;40:9-28.
DOI: https://doi.org/10.1007/s10974-019-09507-z
Adhikari S, Nice EC, Deutsch EW, et al. A high-stringency blueprint of the human proteome. Nat Commun 2020;11:5301.
DOI: https://doi.org/10.1038/s41467-020-19045-9
Capitanio D, Moriggi M, Gelfi C. Mapping the human skeletal muscle proteome: progress and potential. Expert Rev Proteomics 2017;14:825-39.
DOI: https://doi.org/10.1080/14789450.2017.1364996
Gonzalez-Freire M, Semba RD, Ubaida-Mohien C, et al. The Human Skeletal Muscle Proteome Project: a reappraisal of the current literature. J Cachexia Sarcopenia Muscle 2017;8:5-18.
DOI: https://doi.org/10.1002/jcsm.12121
Hadrévi J, Hellström F, Kieselbach T, et al. Protein differences between human trapezius and vastus lateralis muscles determined with a proteomic approach. BMC Musculoskelet Disord 2011;12:181.
DOI: https://doi.org/10.1186/1471-2474-12-181
Eggers B, Schork K, Turewicz M, et al. Advanced fiber type-specific protein profiles derived from adult murine skeletal muscle. Proteomes 2021;9:28.
DOI: https://doi.org/10.3390/proteomes9020028
Murgia M, Nagaraj N, Deshmukh AS, et al. Single muscle fiber proteomics reveals unexpected mitochondrial specialization. EMBO Rep 2015;16:387-95.
DOI: https://doi.org/10.15252/embr.201439757
Fomchenko KM, Walsh EM, Yang X, et al. Spatial proteomic approach to characterize skeletal muscle myofibers. J Proteome Res 2021;20:888-94.
DOI: https://doi.org/10.1021/acs.jproteome.0c00673
Donoghue P, Doran P, Wynne K, et al. Proteomic profiling of chronic low-frequency stimulated fast muscle. Proteomics 2007;7:3417-30.
DOI: https://doi.org/10.1002/pmic.200700262
Dowling P, Murphy S, Ohlendieck K. Proteomic profiling of muscle fibre type shifting in neuromuscular diseases. Expert Rev Proteomics 2016;13:783-99.
DOI: https://doi.org/10.1080/14789450.2016.1209416
Hunt LC, Graca FA, Pagala V, et al. Integrated genomic and proteomic analyses identify stimulus-dependent molecular changes associated with distinct modes of skeletal muscle atrophy. Cell Rep 2021;37:109971.
DOI: https://doi.org/10.1016/j.celrep.2021.109971
Deshmukh AS, Steenberg DE, Hostrup M, et al. Deep muscle-proteomic analysis of freeze-dried human muscle biopsies reveals fiber type-specific adaptations to exercise training. Nat Commun 2021;12:304. Erratum in: Nat Commun 2021;12:1600.
DOI: https://doi.org/10.1038/s41467-020-20556-8
Li FH, Sun L, Wu DS, et al. Proteomics-based identification of different training adaptations of aged skeletal muscle following long-term high-intensity interval and moderate-intensity continuous training in aged rats. Aging (Albany NY) 2019;11:4159-82. Erratum in: Aging (Albany NY) 2019;11:10781-2.
DOI: https://doi.org/10.18632/aging.102596
de Sousa Neto IV, Carvalho MM, Marqueti RC, et al. Proteomic changes in skeletal muscle of aged rats in response to resistance training. Cell Biochem Funct 2020;38:500-9.
DOI: https://doi.org/10.1002/cbf.3497
Hesketh SJ, Stansfield BN, Stead CA, Burniston JG. The application of proteomics in muscle exercise physiology. Expert Rev Proteomics 2020;17:813-25.
DOI: https://doi.org/10.1080/14789450.2020.1879647
Gelfi C, Vasso M, Cerretelli P. Diversity of human skeletal muscle in health and disease: contribution of proteomics. J Proteomics 2011;74:774-95.
DOI: https://doi.org/10.1016/j.jprot.2011.02.028
Choi YC, Hong JM, Park KD, et al. Proteomic analysis of the skeletal muscles from dysferlinopathy patients. J Clin Neurosci 2020;71:186-90.
DOI: https://doi.org/10.1016/j.jocn.2019.08.068
Gargan S, Dowling P, Zweyer M, et al. Proteomic identification of markers of membrane repair, regeneration and fibrosis in the aged and dystrophic diaphragm. Life (Basel) 2022;12:1679.
DOI: https://doi.org/10.3390/life12111679
Giebelstein J, Poschmann G, Højlund K, et al. The proteomic signature of insulin-resistant human skeletal muscle reveals increased glycolytic and decreased mitochondrial enzymes. Diabetologia 2012;55:1114-27.
DOI: https://doi.org/10.1007/s00125-012-2456-x
Kruse R, Højlund K. Proteomic study of skeletal muscle in obesity and type 2 diabetes: progress and potential. Expert Rev Proteomics 2018;15:817-828.
DOI: https://doi.org/10.1080/14789450.2018.1528147
Shum AMY, Poljak A, Bentley NL, et al. Proteomic profiling of skeletal and cardiac muscle in cancer cachexia: alterations in sarcomeric and mitochondrial protein expression. Oncotarget 2018;9:22001-22.
DOI: https://doi.org/10.18632/oncotarget.25146
Gelfi C, Vigano A, Ripamonti M, et al. The human muscle proteome in aging. J Proteome Res 2006;5:1344-53.
DOI: https://doi.org/10.1021/pr050414x
Staunton L, Zweyer M, Swandulla D, Ohlendieck K. Mass spectrometry-based proteomic analysis of middle-aged vs. aged vastus lateralis reveals increased levels of carbonic anhydrase isoform 3 in senescent human skeletal muscle. Int J Mol Med 2012;30:723-33.
DOI: https://doi.org/10.3892/ijmm.2012.1056
Ohlendieck K. Two-cydye-based 2D-DIGE analysis of aged human muscle biopsy specimens. Methods Mol Biol 2023;2596:265-89.
DOI: https://doi.org/10.1007/978-1-0716-2831-7_19
Gueugneau M, Coudy-Gandilhon C, Gourbeyre O, et al. Proteomics of muscle chronological ageing in post-menopausal women. BMC Genomics 2014;15:1165.
DOI: https://doi.org/10.1186/1471-2164-15-1165
Baraibar MA, Gueugneau M, Duguez S, et al. Expression and modification proteomics during skeletal muscle ageing. Biogerontology 2013;14:339-52.
DOI: https://doi.org/10.1007/s10522-013-9426-7
Théron L, Gueugneau M, Coudy C, et al. Label-free quantitative protein profiling of vastus lateralis muscle during human aging. Mol Cell Proteomics 2014;13:283-94.
DOI: https://doi.org/10.1074/mcp.M113.032698
Liao CY, Kennedy BK. Mouse models and aging: longevity and progeria. Curr Top Dev Biol 2014;109:249-85.
DOI: https://doi.org/10.1016/B978-0-12-397920-9.00003-2
Ersoy U, Kanakis I, Alameddine M, et al. Lifelong dietary protein restriction accelerates skeletal muscle loss and reduces muscle fibre size by impairing proteostasis and mitochondrial homeostasis. Redox Biol 2024;69:102980.
DOI: https://doi.org/10.1016/j.redox.2023.102980
Murphy S, Zweyer M, Henry M, et al. Proteomic analysis of the sarcolemma-enriched fraction from dystrophic mdx-4cv skeletal muscle. J Proteomics 2019;191:212-27.
DOI: https://doi.org/10.1016/j.jprot.2018.01.015
Gargan S, Dowling P, Zweyer M, et al. Mass spectrometric profiling of extraocular muscle and proteomic adaptations in the mdx-4cv model of duchenne muscular dystrophy. Life (Basel) 2021;11:595.
DOI: https://doi.org/10.3390/life11070595
Dowling P, Gargan S, Zweyer M, et al. Proteome-wide Changes in the mdx-4cv Spleen due to Pathophysiological Cross Talk with Dystrophin-Deficient Skeletal Muscle. iScience 2020;23:101500.
DOI: https://doi.org/10.1016/j.isci.2020.101500
Dowling P, Gargan S, Zweyer M, et al. Proteomic profiling of the interface between the stomach wall and the pancreas in dystrophinopathy. Eur J Transl Myol 2021;31:9627.
DOI: https://doi.org/10.4081/ejtm.2020.9627
Gargan S, Ohlendieck K. Sample Preparation and Protein Determination for 2D-DIGE Proteomics. Methods Mol Biol 2023;2596:325-37.
DOI: https://doi.org/10.1007/978-1-0716-2831-7_22
Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods 2009;6:359-62.
DOI: https://doi.org/10.1038/nmeth.1322
Wiśniewski JR. Filter aided sample preparation - a tutorial. Anal Chim Acta 2019;1090:23-30.
DOI: https://doi.org/10.1016/j.aca.2019.08.032
Dowling P, Gargan S, Zweyer M, et al. Protocol for the bottom-up proteomic analysis of mouse spleen. STAR Protoc 2020;1:100196.
DOI: https://doi.org/10.1016/j.xpro.2020.100196
Mi H, Ebert D, Muruganujan A, et al. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res 2021;49:D394-D403.
DOI: https://doi.org/10.1093/nar/gkaa1106
Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021;49:D605-12.
DOI: https://doi.org/10.1093/nar/gkaa1074
Gargan S, Dowling P, Ohlendieck K. Sample preparation for proteomics and MS from clinical tissue. In Proteomics Mass Spectrometry Methods: Sample Preparation, Protein Digestion, and Research Protocols, 1st ed.; Meleady, P., Ed.; Academic Press: London, United Kingdom, 2024; Chapter 4, pp. 55-77.
DOI: https://doi.org/10.1016/B978-0-323-90395-0.00011-5
Doran P, O'Connell K, Gannon J, Kavanagh M, Ohlendieck K. Opposite pathobiochemical fate of pyruvate kinase and adenylate kinase in aged rat skeletal muscle as revealed by proteomic DIGE analysis. Proteomics 2008;8:364-77.
DOI: https://doi.org/10.1002/pmic.200700475
Capitanio D, Vasso M, Fania C, et al. Comparative proteomic profile of rat sciatic nerve and gastrocnemius muscle tissues in ageing by 2-D DIGE. Proteomics 2009;9:2004-20.
DOI: https://doi.org/10.1002/pmic.200701162
Lombardi A, Silvestri E, Cioffi F, et al. Defining the transcriptomic and proteomic profiles of rat ageing skeletal muscle by the use of a cDNA array, 2D- and Blue native-PAGE approach. J Proteomics 2009;72:708-21.
DOI: https://doi.org/10.1016/j.jprot.2009.02.007
Gannon J, Doran P, Kirwan A, Ohlendieck K. Drastic increase of myosin light chain MLC-2 in senescent skeletal muscle indicates fast-to-slow fibre transition in sarcopenia of old age. Eur J Cell Biol 2009;88:685-700.
DOI: https://doi.org/10.1016/j.ejcb.2009.06.004
Lourenço Dos Santos S, Baraibar MA, Lundberg S, et al. Oxidative proteome alterations during skeletal muscle ageing. Redox Biol 2015;5:267-74.
DOI: https://doi.org/10.1016/j.redox.2015.05.006
Gregorich ZR, Peng Y, Cai W, et al. Top-down targeted proteomics reveals decrease in myosin regulatory light-chain phosphorylation that contributes to sarcopenic muscle dysfunction. J Proteome Res 2016;15:2706-16.
DOI: https://doi.org/10.1021/acs.jproteome.6b00244
Capitanio D, Vasso M, De Palma S, et al. Specific protein changes contribute to the differential muscle mass loss during ageing. Proteomics 2016;16:645-56.
DOI: https://doi.org/10.1002/pmic.201500395
Doran P, Gannon J, O'Connell K, Ohlendieck K. Aging skeletal muscle shows a drastic increase in the small heat shock proteins alphaB-crystallin/HspB5 and cvHsp/HspB7. Eur J Cell Biol 2007;86:629-40.
DOI: https://doi.org/10.1016/j.ejcb.2007.07.003
Dowling P, Trollet C, Negroni E, et al. How can proteomics help to elucidate the pathophysiological crosstalk in muscular dystrophy and associated multi-system dysfunction? Proteomes 2024;12:4.
DOI: https://doi.org/10.3390/proteomes12010004
Aslam B, Basit M, Nisar MA, et al. Proteomics: technologies and their applications. J Chromatogr Sci 2017;55:182-96.
DOI: https://doi.org/10.1093/chromsci/bmw167
Duong VA, Lee H. Bottom-up proteomics: advancements in sample preparation. Int J Mol Sci 2023;24:5350.
DOI: https://doi.org/10.3390/ijms24065350
Miller RM, Smith LM. Overview and considerations in bottom-up proteomics. Analyst 2023;148:475-86.
DOI: https://doi.org/10.1039/D2AN01246D
Ercan H, Resch U, Hsu F, et al. A practical and analytical comparative study of gel-based top-down and gel-free bottom-up proteomics including unbiased proteoform detection. Cells 2023;12:747.
DOI: https://doi.org/10.3390/cells12050747
Zhang Y, Fonslow BR, Shan B, et al. Protein analysis by shotgun/bottom-up proteomics. Chem Rev 2013;113:2343-94.
DOI: https://doi.org/10.1021/cr3003533
Manes NP, Nita-Lazar A. Application of targeted mass spectrometry in bottom-up proteomics for systems biology research. J Proteomics 2018;189:75-90.
DOI: https://doi.org/10.1016/j.jprot.2018.02.008
Murphy S, Ohlendieck K. Protein Digestion for 2D-DIGE Analysis. Methods Mol Biol 2023;2596:339-49.
DOI: https://doi.org/10.1007/978-1-0716-2831-7_23
Glatter T, Ludwig C, Ahrné E, et al. Large-scale quantitative assessment of different in-solution protein digestion protocols reveals superior cleavage efficiency of tandem Lys-C/trypsin proteolysis over trypsin digestion. J Proteome Res 2012;11:5145-56.
DOI: https://doi.org/10.1021/pr300273g
Giansanti P, Tsiatsiani L, Low TY, Heck AJ. Six alternative proteases for mass spectrometry-based proteomics beyond trypsin. Nat Protoc 2016;11:993-1006.
DOI: https://doi.org/10.1038/nprot.2016.057
Dau T, Bartolomucci G, Rappsilber J. Proteomics using protease alternatives to trypsin benefits from sequential digestion with trypsin. Anal Chem 2020;92:9523-7.
DOI: https://doi.org/10.1021/acs.analchem.0c00478
Duong VA, Park JM, Lee H. Review of three-dimensional liquid chromatography platforms for bottom-up proteomics. Int J Mol Sci 2020;21:1524.
DOI: https://doi.org/10.3390/ijms21041524
Shah AD, Goode RJA, Huang C, et al. LFQ-analyst: an easy-to-use interactive web platform to analyze and visualize label-free proteomics data preprocessed with MaxQuant. J Proteome Res 2020;19:204-211.
DOI: https://doi.org/10.1021/acs.jproteome.9b00496
Distler U, Sielaff M, Tenzer S. Label-free proteomics of quantity-limited samples using ion mobility-assisted data-independent acquisition mass spectrometry. Methods Mol Biol 2021;2228:327-339.
DOI: https://doi.org/10.1007/978-1-0716-1024-4_23
Matzinger M, Mayer RL, Mechtler K. Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing. Proteomics 2023;23:e2200162.
DOI: https://doi.org/10.1002/pmic.202200162
Raddatz K, Albrecht D, Hochgrfe F, et al. A proteome map of murine heart and skeletal muscle. Proteomics 2008;8:1885-97.
DOI: https://doi.org/10.1002/pmic.200700902
Drexler HC, Ruhs A, Konzer A, et al. On marathons and Sprints: an integrated quantitative proteomics and transcriptomics analysis of differences between slow and fast muscle fibers. Mol Cell Proteomics 2012;11:M111.010801.
DOI: https://doi.org/10.1074/mcp.M111.010801
Dowling P, Zweyer M, Swandulla D, Ohlendieck K. Characterization of Contractile Proteins from Skeletal Muscle Using Gel-Based Top-Down Proteomics. Proteomes 2019;7:25. Erratum in: Proteomes 2019;7.
DOI: https://doi.org/10.3390/proteomes7030028
Reed PW, Densmore A, Bloch RJ. Optimization of large gel 2D electrophoresis for proteomic studies of skeletal muscle. Electrophoresis 2012;33:1263-70.
DOI: https://doi.org/10.1002/elps.201100642
Drissi R, Dubois ML, Boisvert FM. Proteomics methods for subcellular proteome analysis. FEBS J 2013;280:5626-34.
DOI: https://doi.org/10.1111/febs.12502
Lee YH, Tan HT, Chung MC. Subcellular fractionation methods and strategies for proteomics. Proteomics 2010;10:3935-56.
DOI: https://doi.org/10.1002/pmic.201000289
Ploscher M, Granvogl B, Reisinger V, Masanek A, Eichacker LA. Organelle proteomics. Methods Mol Biol 2009;519:65-82.
DOI: https://doi.org/10.1007/978-1-59745-281-6_5
Dowling P, Gargan S, Swandulla D, Ohlendieck K. Identification of subproteomic markers for skeletal muscle profiling. Methods Mol Biol 2023;2596:291-302.
DOI: https://doi.org/10.1007/978-1-0716-2831-7_20
Holland A, Ohlendieck K. Proteomic profiling of the contractile apparatus from skeletal muscle. Expert Rev Proteomics 2013;10:239-57.
DOI: https://doi.org/10.1586/epr.13.20
Lin BL, Song T, Sadayappan S. Myofilaments: Movers and Rulers of the Sarcomere. Compr Physiol 2017;7:675-92.
DOI: https://doi.org/10.1002/cphy.c160026
Sweeney HL, Hammers DW. Muscle Contraction. Cold Spring Harb Perspect Biol 2018;10:a023200.
DOI: https://doi.org/10.1101/cshperspect.a023200
Schiaffino S, Reggiani C, Murgia M. Fiber type diversity in skeletal muscle explored by mass spectrometry-based single fiber proteomics. Histol Histopathol 2020;35:239-46.
Brunello E, Fusi L. Regulating striated muscle contraction: through thick and thin. Annu Rev Physiol 2024;86:255-75.
DOI: https://doi.org/10.1146/annurev-physiol-042222-022728
Henderson CA, Gomez CG, Novak SM, et al. Overview of the muscle cytoskeleton. Compr Physiol 2017;7:891-944.
DOI: https://doi.org/10.1002/cphy.c160033
Boppart MD, Mahmassani ZS. Integrin signaling: linking mechanical stimulation to skeletal muscle hypertrophy. Am J Physiol Cell Physiol 2019;317:C629-41.
DOI: https://doi.org/10.1152/ajpcell.00009.2019
Dowling P, Gargan S, Murphy S, et al. The dystrophin node as integrator of cytoskeletal organization, lateral force transmission, fiber stability and cellular signaling in skeletal muscle. Proteomes 2021;9:9.
DOI: https://doi.org/10.3390/proteomes9010009
Wilson DGS, Tinker A, Iskratsch T. The role of the dystrophin glycoprotein complex in muscle cell mechanotransduction. Commun Biol 2022;5:1022.
DOI: https://doi.org/10.1038/s42003-022-03980-y
Wohlgemuth RP, Brashear SE, Smith LR. Alignment, cross linking, and beyond: a collagen architect's guide to the skeletal muscle extracellular matrix. Am J Physiol Cell Physiol 2023;325:C1017-30.
DOI: https://doi.org/10.1152/ajpcell.00287.2023
Mahdy MAA. Skeletal muscle fibrosis: an overview. Cell Tissue Res 2019;375:575-88.
DOI: https://doi.org/10.1007/s00441-018-2955-2
Dowling P, Gargan S, Zweyer M, et al. Extracellular matrix proteomics: the mdx-4cv mouse diaphragm as a surrogate for studying myofibrosis in dystrophinopathy. Biomolecules 2023;13:1108.
DOI: https://doi.org/10.3390/biom13071108
Supporting Agencies
Science Foundation Ireland, Kathleen Lonsdale Institute for Human Health ResearchHow to Cite
Dowling, P., Gargan, S., Zweyer, M., Henry, M., Meleady, P., Swandulla, D., & Ohlendieck, K. (2024). Proteomic reference map for sarcopenia research: mass spectrometric identification of key muscle proteins located in the sarcomere, cytoskeleton and the extracellular matrix. European Journal of Translational Myology, 34(2). https://doi.org/10.4081/ejtm.2024.12564
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