Accelerated proteomic sample preparation for accurate ultrafast mass spectrometry-based quantitative analysis of cell and tissue proteomes

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

Advances in liquid chromatography/mass spectrometry (LC-MS) have enabled proteome-wide quantitation in minutes, achieving rate of 1000 analyses per day. This necessitates revisiting the rapid sample preparation approaches to match this data acquisition speed. Despite the fact that these approaches have been developed decades ago, their application in quantitative ultrafast proteomics and comprehensive comparison of their performance under different conditions have not been explored. In this study, the ultrasound, microwave irradiation, and elevated temperature-assisted approaches for accelerated protein reduction, alkylation, and trypsin digestion were compared. Validation was carried out with label-free quantitative LC-MS/MS and fragmentation-free DirectMS1 methods of shotgun proteome analyses of Saccharomyces cerevisiae, human cell lines, and winter wheat shoots. These data acquisition methods were applied in ultrafast implementations employing 5 to 16 min LC gradients. Human–yeast proteome mixtures were used as standards to evaluate quantitation accuracy of the sample preparation workflows. Our findings indicate that the reduced time of sample preparation insignificantly decreased efficiency of reduction, alkylation, and digestion, yet, preserved reproducible peptide and protein identification. We also found that the 30-min microwave-assisted and overnight trypsin digestion yielded comparable quantitation accuracy in ultrafast analyses using DirectMS1 method.

Толық мәтін

Рұқсат жабық

Авторлар туралы

D. Emekeeva

V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: iatarasova@yandex.ru
Ресей, 119334 Moscow

T. Kusainova

V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: iatarasova@yandex.ru
Ресей, 119334 Moscow

L. Garibova

V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: iatarasova@yandex.ru
Ресей, 119334 Moscow

A. Shelepchikov

The Russian State Center for Animal Feed and Drug Standardization and Quality

Email: iatarasova@yandex.ru
Ресей, 123022 Moscow

A. Kononikhin

V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: iatarasova@yandex.ru
Ресей, 119334 Moscow

V. Tretyakov

The Russian State Center for Animal Feed and Drug Standardization and Quality

Email: iatarasova@yandex.ru
Ресей, 123022 Moscow

O. Lavrukhina

The Russian State Center for Animal Feed and Drug Standardization and Quality

Email: iatarasova@yandex.ru
Ресей, 123022 Moscow

E. Nikolaev

V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: iatarasova@yandex.ru
Ресей, 119334 Moscow

M. Gorshkov

V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Email: iatarasova@yandex.ru
Ресей, 119334 Moscow

I. Tarasova

V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences

Хат алмасуға жауапты Автор.
Email: iatarasova@yandex.ru
Ресей, 119334 Moscow

Әдебиет тізімі

  1. Rayaprolu, S., Higginbotham, L., Bagchi, P., Watson, C. M., Zhang, T., Levey, A. I., Rangaraju, S., and Seyfried, N. T. (2021) Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau, Neuropsychopharmacology, 46, 98-115, https://doi.org/10.1038/s41386-020-00840-3.
  2. Johnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., Thambisetty, M., Montine, T. J., Lee, E. B., Trojanowski, J. Q., Beach, T. G., Reiman, E. M., Haroutunian, V., Wang, M., Schadt, E., Zhang, B., Dickson, D. W., Ertekin-Taner, N., Golde, T. E., Petyuk, V. A., De Jager, P. L., Bennett, D. A., Wingo, T. S., Rangaraju, S., Hajjar, I., Shulman, J. M., Lah, J. J., Levey, A. I., and Seyfried, N. T. (2020) Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation, Nat. Med., 26, 769-780, https://doi.org/10.1038/s41591-020-0815-6.
  3. Amiri-Dashatan, N., Koushki, M., Abbaszadeh, H.-A., Rostami-Nejad, M., and Rezaei-Tavirani, M. (2018) Proteomics applications in health: biomarker and drug discovery and food industry, Iran. J. Pharm. Res., 17, 1523-1536.
  4. Purvine, S., Eppel, J.-T., Yi, E. C., and Goodlett, D. R. (2003) Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer, Proteomics, 3, 847-850, https://doi.org/10.1002/pmic.200300362.
  5. Michalski, A., Cox, J., and Mann, M. (2011) More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS, J. Proteome Res., 10, 1785-1793, https://doi.org/10.1021/pr101060v.
  6. Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., Johnstone, R., Mohammed, A. K. A., and Hamon, C. (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS, Anal. Chem., 75, 1895-1904, https://doi.org/10.1021/ac0262560.
  7. Fedorov, I. I., Protasov, S. A., Tarasova, I. A., and Gorshkov, M. V. (2024) Ultrafast proteomics, Biochemistry (Moscow), 89, 1349-1361, https://doi.org/10.1134/S0006297924080017.
  8. Nakayasu, E. S., Gritsenko, M., Piehowski, P. D., Gao, Y., Orton, D. J., Schepmoes, A. A., Fillmore, T. L., Frohnert, B. I., Rewers, M., Krischer, J. P., Ansong, C., Suchy-Dicey, A. M., Evans-Molina, C., Qian, W.-J., Webb-Robertson, B.-J. M., and Metz, T. O. (2021) Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation, Nat. Protoc., 16, 3737-3760, https://doi.org/10.1038/s41596-021-00566-6.
  9. Wang, M., Beckmann, N. D., Roussos, P., Wang, E., Zhou, X., Wang, Q., Ming, C., Neff, R., Ma, W., Fullard, J. F., Hauberg, M. E., Bendl, J., Peters, M. A., Logsdon, B., Wang, P., Mahajan, M., Mangravite, L. M., Dammer, E. B., Duong, D. M., Lah, J. J., Seyfried, N. T., Levey, A. I., Buxbaum, J. D., Ehrlich, M., Gandy, S., Katsel, P., Haroutunian, V., Schadt, E., and Zhang, B. (2018) The mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer’s disease, Sci. Data, 5, 180-185, https://doi.org/10.1038/sdata.2018.185.
  10. Ivanov, M. V., Bubis, J. A., Gorshkov, V., Tarasova, I. A., Levitsky, L. I., Lobas, A. A., Solovyeva, E. M., Pridatchenko, M. L., Kjeldsen, F., and Gorshkov, M. V. (2020) DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes, Anal. Chem., 92, 4326-4333, https://doi.org/10.1021/acs.analchem.9b05095.
  11. Ivanov, M. V., Bubis, J. A., Gorshkov, V., Tarasova, I. A., Levitsky, L. I., Solovyeva, E. M., Lipatova, A. V., Kjeldsen, F., and Gorshkov, M. V. (2022) DirectMS1Quant: ultrafast quantitative proteomics with MS/MS-Free mass spectrometry, Anal. Chem., 94, 13068-13075, https://doi.org/10.1021/acs.analchem.2c02255.
  12. Kusainova, T. T., Emekeeva, D. D., Kazakova, E. M., Gorshkov, V. A., Kjeldsen, F., Kuskov, M. L., Zhigach, A. N., Olkhovskaya, I. P., Bogoslovskaya, O. A., Glushchenko, N. N., and Tarasova, I. A. (2023) Ultra-fast mass spectrometry in plant biochemistry: response of winter wheat proteomics to pre-sowing treatment with iron compounds, Biochemistry (Moscow), 88, 1390-1403, https://doi.org/10.1134/S0006297923090183.
  13. Kazakova, E., Ivanov, M., Kusainova, T., Bubis, J., Polivtseva, V., Petrikov, K., Gorshkov, V., Kjeldsen, F., Gorshkov, M., Delegan, Y., Solyanikova, I., and Tarasova, I. (2024) Ultrafast metaproteomics for quantitative assessment of strain isolates and microbiomes, Microchem. J., 207, 111823, https://doi.org/10.1016/j.microc.2024.111823.
  14. Kazakova, E. M., Solovyeva, E. M., Levitsky, L. I., Bubis, J. A., Emekeeva, D. D., Antonets, A. A., Nazarov, A. A., Gorshkov, M. V., and Tarasova, I. A. (2023) Proteomics-based scoring of cellular response to stimuli for improved characterization of signaling pathway activity, Proteomics, 23, e2200275, https://doi.org/10.1002/pmic.202200275.
  15. Solovyeva, E. M., Bubis, J. A., Tarasova, I. A., Lobas, A. A., Ivanov, M. V., Nazarov, A. A., Shutkov, I. A., and Gorshkov, M. V. (2022) On the feasibility of using an ultra-fast DirectMS1 method of proteome-wide analysis for searching drug targets in chemical proteomics, Biochemistry (Moscow), 87, 1342-1353, https://doi.org/10.1134/S000629792211013X.
  16. Fedorov, I. I., Bubis, J. A., Kazakova, E. M., Lobas, A. A., Ivanov, M. V., Emekeeva, D. D., Tarasova, I. A., Nazarov, A. A., and Gorshkov, M. V. (2024) On the utility of ultrafast MS1-only proteomics in drug target discovery studies based on thermal proteome profiling method, Anal. Bioanal. Chem., 416, 4083-4089, https://doi.org/10.1007/s00216-024-05330-9.
  17. Fedorov, I. I., Ivanov, M. V., and Gorshkov, M. V. (2025) Effect of drug-to-protein reaction kinetics on the results of thermal proteome profiling, Anal. Chem., 97, 22-26, https://doi.org/10.1021/acs.analchem.4c04313.
  18. Müller, T., and Winter, D. (2017) Systematic evaluation of protein reduction and alkylation reveals massive unspecific side effects by iodine-containing reagents, Mol. Cell. Proteomics, 16, 1173-1187, https://doi.org/10.1074/mcp.M116.064048.
  19. Varnavides, G., Madern, M., Anrather, D., Hartl, N., Reiter, W., and Hartl, M. (2022) In search of a universal method: a comparative survey of bottom-up proteomics sample preparation methods, J. Proteome Res., 21, 2397-2411, https://doi.org/10.1021/acs.jproteome.2c00265.
  20. Poulsen, J. W., Madsen, C. T., Young, C., Poulsen, F. M., and Nielsen, M. L. (2013) Using guanidine-hydrochloride for fast and efficient protein digestion and single-step affinity-purification mass spectrometry, J. Proteome Res., 12, 1020-1030, https://doi.org/10.1021/pr300883y.
  21. Loraine, J., Alhumaidan, O., Bottrill, A. R., Mistry, S. C., Andrew, P., Mukamolova, G. V., and Turapov, O. (2019) Efficient protein digestion at elevated temperature in the presence of sodium dodecyl sulfate and calcium ions for membrane proteomics, Anal. Chem., 91, 9516-9521, https://doi.org/10.1021/acs.analchem.9b00484.
  22. Wang, Y., Zhang, W., and Ouyang, Z. (2020) Fast protein analysis enabled by high-temperature hydrolysis, Chem. Sci., 11, 10506-10516, https://doi.org/10.1039/D0SC03237A.
  23. Santos, H. M., Kouvonen, P., Capelo, J.-L., and Corthals, G. L. (2013) On-target ultrasonic digestion of proteins, Proteomics, 13, 1423-1427, https://doi.org/10.1002/pmic.201200241.
  24. López-Ferrer, D., Capelo, J. L., and Vázquez, J. (2005) Ultra fast trypsin digestion of proteins by high intensity focused ultrasound, J. Proteome Res., 4, 1569-1574, https://doi.org/10.1021/pr050112v.
  25. Wang, S., Bao, H., Zhang, L., Yang, P., and Chen, G. (2008) Infrared-assisted on-plate proteolysis for MALDI-TOF-MS peptide mapping, Anal. Chem., 80, 5640-5647, https://doi.org/10.1021/ac800349u.
  26. Chen, Z., Li, Y., Lin, S., Wei, M., Du, F., and Ruan, G. (2014) Development of continuous microwave-assisted protein digestion with immobilized enzyme, Biochem. Biophys. Res. Commun., 445, 491-496, https://doi.org/ 10.1016/j.bbrc.2014.02.025.
  27. Lill, J. R., and Nesatyy, V. J. (2018) Microwave-assisted protein staining, destaining, and in-gel/in-solution digestion of proteins, Methods Mol. Biol., 1853, 75-86, https://doi.org/10.1007/978-1-4939-8745-0_10.
  28. Hustoft, H. K., Malerod, H., Wilson, S. R., Reubsaet, L., Lundanes, E., Greibrokk, T., Hustoft, H. K., Malerod, H., Wilson, S. R., Reubsaet, L., Lundanes, E., and Greibrokk, T. (2012) A Critical review of trypsin digestion for LC-MS based proteomics, Integr. Proteom., https://doi.org/10.5772/29326, doi: 10.5772/29326.
  29. Switzar, L., Giera, M., and Niessen, W. M. A. (2013) Protein digestion: an overview of the available techniques and recent developments, J. Proteome Res., 12, 1067-1077, https://doi.org/10.1021/pr301201x.
  30. Sun, W., Gao, S., Wang, L., Chen, Y., Wu, S., Wang, X., Zheng, D., and Gao, Y. (2006) Microwave-assisted protein preparation and enzymatic digestion in proteomics, Mol. Cell. Proteomics, 5, 769-776, https://doi.org/10.1074/mcp.T500022-MCP200.
  31. Shen, L., Zhang, Z., Wu, P., Yang, J., Cai, Y., Chen, K., Chai, S., Zhao, J., Chen, H., Dai, X., Yang, B., Wei, W., Dong, L., Chen, J., Jiang, P., Cao, C., Ma, C., Xu, C., Zou, Y., Zhang, J., Xiong, W., Li, Z., Xu, S., Shu, B., Wang, M., Li, Z., Wan, Q., Xiong, N., and Chen, S. (2024) Mechanistic insight into glioma through spatially multidimensional proteomics, Sci. Adv., 10, eadk1721, https://doi.org/10.1126/sciadv.adk1721.
  32. Sivakova, B., Wagner, A., Kretova, M., Jakubikova, J., Gregan, J., Kratochwill, K., Barath, P., and Cipak, L. (2024) Quantitative proteomics and phosphoproteomics profiling of meiotic divisions in the fission yeast Schizosaccharomyces pombe, Sci. Rep., 14, 23105, https://doi.org/10.1038/s41598-024-74523-0.
  33. Tang, Y., and Gu, Y. (2025) Unraveling plant nuclear envelope composition using proximity labeling proteomics, Methods Mol. Biol., 2873, 145-165, https://doi.org/10.1007/978-1-0716-4228-3_9.
  34. Zeng, X., Wei, T., Wang, X., Liu, Y., Tan, Z., Zhang, Y., Feng, T., Cheng, Y., Wang, F., Ma, B., Qin, W., Gao, C., Xiao, J., and Wang, C. (2024) Discovery of metal-binding proteins by thermal proteome profiling, Nat. Chem. Biol., 20, 770-778, https://doi.org/10.1038/s41589-024-01563-y.
  35. Sang, T., Xu, Y., Qin, G., Zhao, S., Hsu, C.-C., and Wang, P. (2024) Highly sensitive site-specific SUMOylation proteomics in Arabidopsis, Nat. Plants, 10, 1330-1342, https://doi.org/10.1038/s41477-024-01783-z.
  36. Pirmoradian, M., Budamgunta, H., Chingin, K., Zhang, B., Astorga-Wells, J., and Zubarev, R. A. (2013) Rapid and deep human proteome analysis by single-dimension shotgun proteomics, Mol. Cell. Proteomics, 12, 3330-3338, https://doi.org/10.1074/mcp.O113.028787.
  37. Wang, H., Yang, Y., Li, Y., Bai, B., Wang, X., Tan, H., Liu, T., Beach, T. G., Peng, J., and Wu, Z. (2015) Systematic optimization of long gradient chromatography mass spectrometry for deep analysis of brain proteome, J. Proteome Res., 14, 829-838, https://doi.org/10.1021/pr500882h.
  38. Richards, A. L., Hebert, A. S., Ulbrich, A., Bailey, D. J., Coughlin, E. E., Westphall, M. S., and Coon, J. J. (2015) One-hour proteome analysis in yeast, Nat. Protoc., 10, 701-714, https://doi.org/10.1038/nprot.2015.040.
  39. Nie, L., Zhu, M., Sun, S., Zhai, L., Wu, Z., Qian, L., and Tan, M. (2015) An optimization of the LC-MS/MS workflow for deep proteome profiling on an Orbitrap Fusion, Anal. Methods, 8, 425-434, https://doi.org/10.1039/C5AY01900A.
  40. Moshkovskii, S. A., Lobas, A. A., and Gorshkov, M. V. (2020) Single cell proteogenomics – immediate prospects, Biochemistry (Moscow), 85, 140-146, https://doi.org/10.1134/S0006297920020029.
  41. Ahmad, R., and Budnik, B. (2023) A review of the current state of single-cell proteomics and future perspective, Anal. Bioanal. Chem., 415, 6889-6899, https://doi.org/10.1007/s00216-023-04759-8.
  42. Sinn, L. R., and Demichev, V. (2025) Entering the era of deep single-cell proteomics, Nat. Methods, 22, 459-460, https://doi.org/10.1038/s41592-025-02620-7.
  43. Bennett, H. M., Stephenson, W., Rose, C. M., and Darmanis, S. (2023) Single-cell proteomics enabled by next-generation sequencing or mass spectrometry, Nat. Methods, 20, 363-374, https://doi.org/10.1038/s41592-023-01791-5.
  44. Bubis, J. A., Arrey, T. N., Damoc, E., Delanghe, B., Slovakova, J., Sommer, T. M., Kagawa, H., Pichler, P., Rivron, N., Mechtler, K., and Matzinger, M. (2025) Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity, Nat. Methods, 22, 510-519, https://doi.org/10.1038/s41592-024-02559-1.
  45. Savitski, M. M., Reinhard, F. B. M., Franken, H., Werner, T., Savitski, M. F., Eberhard, D., Martinez Molina, D., Jafari, R., Dovega, R. B., Klaeger, S., Kuster, B., Nordlund, P., Bantscheff, M., and Drewes, G. (2014) Tracking cancer drugs in living cells by thermal profiling of the proteome, Science, 346, 1255784, https://doi.org/10.1126/science.1255784.
  46. Fedorov, I. I., Lineva, V. I., Tarasova, I. A., and Gorshkov, M. V. (2022) Mass spectrometry-based chemical proteomics for drug target discoveries, Biochemistry (Moscow), 87, 983-994, https://doi.org/10.1134/ S0006297922090103.
  47. Bubis, J. A., Spasskaya, D. S., Gorshkov, V. A., Kjeldsen, F., Kofanova, A. M., Lekanov, D. S., Gorshkov, M. V., Karpov, V. L., Tarasova, I. A., and Karpov, D. S. (2020) Rpn4 and proteasome-mediated yeast resistance to ethanol includes regulation of autophagy, Appl. Microbiol. Biotechnol., 104, 4027-4041, https://doi.org/10.1007/ s00253-020-10518-x.
  48. Wessel, D., and Flügge, U. I. (1984) A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids, Anal. Biochem., 138, 141-143, https://doi.org/10.1016/0003-2697(84)90782-6.
  49. Emekeeva, D., Kusainova, T., Garibova, L., and Tarasova, I. A. (2024) DirectMS1 Ultrafast proteomics: protein extraction & digestion, Protocols.io, https://doi.org/10.17504/protocols.io.261gernowl47/v1.
  50. Hulstaert, N., Shofstahl, J., Sachsenberg, T., Walzer, M., Barsnes, H., Martens, L., and Perez-Riverol, Y. (2020) ThermoRawFileParser: modular, scalable, and cross-platform RAW File conversion, J. Proteome Res., 19, 537-542, https://doi.org/10.1021/acs.jproteome.9b00328.
  51. Abdrakhimov, D. A., Bubis, J. A., Gorshkov, V., Kjeldsen, F., Gorshkov, M. V., and Ivanov, M. V. (2021) Biosaur: An open-source Python software for liquid chromatography-mass spectrometry peptide feature detection with ion mobility support, Rapid Commun. Mass Spectrom., e9045, https://doi.org/10.1002/rcm.9045.
  52. Ivanov, M. V., Bubis, J. A., Gorshkov, V., Abdrakhimov, D. A., Kjeldsen, F., and Gorshkov, M. V. (2021) Boosting MS1-only proteomics with machine learning allows 2000 protein identifications in single-shot human proteome analysis using 5 min HPLC gradient, J. Proteome Res., 20, 1864-1873, https://doi.org/10.1021/acs.jproteome.0c00863.
  53. Elias, J. E., and Gygi, S. P. (2010) Target-decoy search strategy for mass spectrometry-based proteomics, Methods Mol. Biol., 604, 55-71, https://doi.org/10.1007/978-1-60761-444-9_5.
  54. Levitsky, L. I., Ivanov, M. V., Lobas, A. A., Bubis, J. A., Tarasova, I. A., Solovyeva, E. M., Pridatchenko, M. L., and Gorshkov, M. V. (2018) IdentiPy: an extensible search engine for protein identification in shotgun proteomics, J. Proteome Res., 17, 2249-2255, https://doi.org/10.1021/acs.jproteome.7b00640.
  55. Ivanov, M. V., Levitsky, L. I., Bubis, J. A., and Gorshkov, M. V. (2019) Scavager: a versatile postsearch validation algorithm for shotgun proteomics based on gradient boosting, Proteomics, 19, e1800280, https://doi.org/10.1002/pmic.201800280.
  56. Zybailov, B., Mosley, A. L., Sardiu, M. E., Coleman, M. K., Florens, L., and Washburn, M. P. (2006) Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae, J. Proteome Res., 5, 2339-2347, https://doi.org/10.1021/pr060161n.
  57. Ivanov, M. V., Kopeykina, A. S., and Gorshkov, M. V. (2024) Reanalysis of DIA data demonstrates the capabilities of MS/MS-free proteomics to reveal new biological insights in disease-related samples, J. Am. Soc. Mass Spectrom., 35, 1775-1785, https://doi.org/10.1021/jasms.4c00134.
  58. Bubis, J. A., Gorshkov, V., Gorshkov, M. V., and Kjeldsen, F. (2020) PhosphoShield: improving trypsin digestion of phosphoproteins by shielding the negatively charged phosphate moiety, J. Am. Soc. Mass Spectrom., 31, 2053-2060, https://doi.org/10.1021/jasms.0c00171.
  59. Tuli, L., and Ressom, H. W. (2009) LC-MS based detection of differential protein expression, J. Proteomics Bioinform., 2, 416-438, https://doi.org/10.4172/jpb.1000102.
  60. Hahn, H. W., Rainer, M., Ringer, T., Huck, C. W., and Bonn, G. K. (2009) Ultrafast microwave-assisted in-tip digestion of proteins, J. Proteome Res., 8, 4225-4230, https://doi.org/10.1021/pr900188x.
  61. Devi, S., Wu, B.-H., Chu, P.-Y., Liu, Y.-P., Wu, H.-L., and Ho, Y.-P. (2017) Studying the effect of microwave heating on the digestion process and identification of proteins, Electrophoresis, 38, 429-440, https://doi.org/ 10.1002/elps.201600392.

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2. Fig. 1. SDS-PAGE for verification of accelerated hydrolysis protocols. a – Human cell line A549; b – shoots of 7-day-old wheat T. aestivum seedlings; c – baker's yeast S. cerevisiae. Designations: M – molecular weight markers; B – control protein lysates of each organism; 1 (control), 2, 3, 4_37, 4_50 and 5 – peptide probes obtained using hydrolysis protocols 1–5 (Table 1), respectively.

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3. Fig. 2. Number of protein groups identified by HPLC-MS1 (a) and HPLC-MS/MS (b) for A549 cells (H. sapiens); wheat shoots (T. aestivum) and yeast (S. cerevisiae). Each column represents the mean of five independent technical replicates at the sample preparation level. Columns with whiskers represent the mean ± SD in the 95% confidence interval. Asterisks (*) indicate statistically significant changes (Student's t-test; p-value < 0.05) compared to classical hydrolysis (red shaded columns). Abscissa axis legend: 1 – classical 18-hour hydrolysis; 2 – ultrasound exposure at elevated temperature and reduced time of reduction, alkylation and hydrolysis; 3 – hydrolysis at elevated temperature and reduced time of reduction and alkylation reactions; 4_37 – reduced classical hydrolysis; 4_50 – hydrolysis at elevated temperature; 5 – microwave exposure with reduced time of reduction, alkylation and hydrolysis

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4. Fig. 3. Efficiency of enzymatic hydrolysis, reduction and alkylation of cysteine ​​residues for different sample preparation methods based on MS/MS data. a – Number of cysteine-containing peptides relative to the total number of identified peptides for the A549 cell line (Hsapiens), wheat shoots (Taestivum) and yeast (Scerevisiae). b – Relative abundance of peptides with missed hydrolysis sites (0, 1, 2, and 3) calculated from peptide identification in MS/MS data for the A549 cell line (Hsapiens), wheat sprouts (Taestivum), and yeast (Scerevisiae). Data are averaged over five technical replicates for each protocol. Error bars represent the mean ±standard deviation in the 95% confidence interval. Asterisks (*) indicate statistically significant differences (Student's t-test; p-value < 0.05) when compared with the classical protocol (marked with the symbol "0"). Designations on the abscissa axis: 1 - classical 18-hour hydrolysis; 2 - ultrasound exposure at elevated temperature and reduced time of reduction, alkylation and hydrolysis; 3 - hydrolysis at elevated temperature and reduced time of reduction and alkylation reactions; 4_37 - reduced classical hydrolysis; 4_50 - hydrolysis at elevated temperature; 5 - microwave exposure with reduced time of reduction, alkylation and hydrolysis

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5. Fig. 5. Evaluation of the effect of microwave-assisted sample preparation protocol on the results of label-free analysis of quantitative yeast–human protein mixture standards: HPLC-MS1 analysis data. a – Number of identified proteins in human–yeast standards subjected to enzymatic hydrolysis according to the classical (18-hour) protocol 1 (red columns) and the microwave-assisted protocol (purple columns). Designations on the abscissa axis correspond to the microwave-assisted protocol (MW_) or the classical protocol (CL_), as well as to the names of samples with the component ratio (N, N = 1–4) from Table 2. b – Percentage of identified yeast protein groups in human–yeast mixtures compared to the percentage of yeast mass in quantitative standards (shown by dotted lines). Whiskers represent mean ± standard deviation at 95% confidence interval; data are averaged over four replicates (c–h). Scatterplots for pairwise comparison of human–yeast quantitative standards (N2/N1, N3/N1, N4/N1; Table 2) prepared using the classical protocol (c–d) and the microwave-treated protocol (e–h). Dashed brown lines indicate the threshold for selection of differentially regulated proteins. Solid green lines show the actual fold change in protein concentration (FC; log2 scale) calculated using the yeast masses from Table 2. Human and yeast proteins are shown as red and purple markers, respectively

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