Comparative Study on Methods of Analyzing Proteome in Blood Samples

doi:10.13560/j.cnki.biotech.bull.1985.2021-0183

Abstract

Abstract:

The objective is to compare and optimize the technologies of blood sample preparation and mass spectrometry analysis,for further studying and mining proteomic information in blood samples. A Q-Exactive Plus mass-spectrometer was used to compare the proteome composition of R. norvegicus blood samples prepared from plasma,serum and serum with high-abundance protein removed. The efficiency of trypsin digestion of serum proteins was also investigated by using conventional digestion,45℃ digestion,heat assisted digestion,repeated heat assisted digestion,urea assisted digestion and variable temperature digestion. Then the qualitative and quantitative characteristics of three mass spectrometry analysis methods,Data-Dependent Acquisition（DDA）,Data Independent Acquisition（DIA）and Parallel Reaction Monitoring（PRM）were compared. Finally,the blood sample of R. norvegicus was analyzed based on optimized proteomic technologies. After removing high-abundance protein from serum samples,the number of identified proteins was higher and the quantitative repeatability was better. The number of identified proteins and peptides,and the rate of matched mass spectrum were relatively high when serum samples digested by heat assisted digestion and variable temperature digestion. The efficiency of these two digestion methods and the repeatability of their qualitative and quantitative data were acceptable. Compared the three mass spectrometry methods,DDA was more convenient,DIA was highly reproducible and PRM was more accurate. The 490,490 and 504 proteins were identified in 3 repeated experiments respectively when high-abundance protein was removed from serum samples and digested proteins with heat assisted-variable temperature digestion and collected data by DDA method. While a total of 590 proteins were detected,and repetition rate of identified protein was 69.8%. In conclusion,the optimized method has advantages of simple operation,high protein identification rate and good repeatability,and is suitable for proteomic analysis of blood samples.

Key words: sample preparation, enzymatic digestion, data acquisition, blood sample, proteomics

WANG Zhi-bo, WANG Dao-ping, MIAO Lan, LI Ying, PAN Ying-hong, LIU Jian-xun. Comparative Study on Methods of Analyzing Proteome in Blood Samples[J]. Biotechnology Bulletin, 2021, 37(8): 307-318.

Figures/Tables 7

Fig.1 Experimental flow

Fig. 2 Comparison of mass spectrometry results of blood samples prepared by 3 pre-processing methods A：Protein identification numbers and duplication rates of three repeated sample preparations for plasma（P）,serum（SK）with high-abundance proteins remained,and serum（SR）with high-abundance proteins removed. B：The average number of proteins,peptides,PSMs and MS/MS spectrum for SK and SR. **：P<0.01. C：Principal component analysis of protein quantification

Fig. 3 Comparison of mass spectrometry results of serum proteins with different enzymatic digestion methods A：Average number of identified proteins,peptides and PSMs. *：P<0.05,and **：P<0.01. B：Comparison of percentage of missed enzymatic cleavage sites. C：Protein identification number and duplication rate of 3 repeated sample preparations. D：Sequence coverage of protein. E：Principal component analysis of protein quantification

Fig. 4 Comparison of DDA and DIA data indices A：Average number of identified proteins in 3 technical repeats,*：P<0.05. B：Missing value matrix of protein quantification in 3 technical repeats,the red area represents missing values. C：Comparison of protein qualitative repeatability by 3 technical repeats. D：The statistics in coefficient of variation of the protein（peptides）peak intensity

Table 1 Quantitative analysis on the coefficient of variation of protein from DIA and corresponding peptides from PRM in 3 repeats

蛋白 Protein	DIA*	PRM**
A0A0G2JSK1	0.163	IFSQQADLSR,0.045;KIFSQQADLSR,0.064;DTLPHEDQGKGR,0.123
A0A0G2JVP4	0.030	ESATVTCLVK,0.058;TFPTLR,0.0732;DLPSPQK,0.128
A0A0G2JV1	0.083	HMEASLQEFKASPR,0.077;AVYLPNCDR,0.090;ISELKAEAVK,0.520
A0A0G2K4K2	0.867	LWIYDTSK,0.502
A0A0G2K531	0.209	QAALGAR,0.029;LFWEPMKIHDIR,0.460;NSCPPTAELLGSPGR,0.480
A0A0G2K7X7	0.283	CVGSAFETQSCNPER,0.019;ILPLTICK,0.019;ACGACPIWSK,0.032
A0A0G2K896	0.217	SSTFQLFGSPHGK,0.013;WCIVSDHEATK,0.059;VKWCAVGQQER,0.063
B0BNN4	0.157	SISCDEIPGQQSR,0.023;LCTPLLPK,0.031;HCYDIHNCVLK,0.129
B2RYM3	0.052	ELAAQTIK,0.024;ANLSSQILK,0.031;IADHKLSTFKADVR,0.049
B5DEH7	0.114	ANPGNFPWQAFTNIHGR,0.053;LPIADR,0.122;GLTVHLK,0.248
D3ZAB3	0.711	LMIYGATNLEDGVPSR,0.140
D3ZAE6	1.732	YLQGNTVQLR,0.085;QLDEGLFGR,0.450
F1LWD0	0.867	ANSYTTEYNPSVK,0.033
F1LWS4	1.292	NGYLYHENIRR,0.227;SYFPVPIGK,0.333;QCVFHYVENGESSYWQR,0.375
F1LWW1	0.902	VTISCR,1.485
F1LXY6	0.040	APEWLGFIR,0.037;ANGYTTEYNPSVK,0.040;AEDTATYYCAR,0.062
F1LYU4	1.732	ASNLASGIPAR,0.020
G3V7L3	0.193	TNVIQLR,0.019;CGTYGIYTK,0.066;LPITSLEK,0.080
G3V7N9	0.055	VITNVNDNYEPR,0.022;TVNSALRPNQAIR,0.057;TVNSALRPNQAIRFEK,0.146
G3V7P2	0.250	TLQEAVDSLKK,0.296
G3V7P5	1.076	WQSLPR,0.011;SCDVPVFENAK,0.022;IDHGSIKLPR,0.026

Fig.5 Qualitative and quantitative results of blood sample proteome based on optimized workflow A：Venn diagram of DDA qualitative results. B：CV distribution diagram of DDA protein quantitative results

Fig. 6 Sample preparation and analysis workflow for blood proteome

References 29

[1]	Aslam B, Basit M, Nisar MA, et al. Proteomics:technologies and their applications[J]. J Chromatogr Sci, 2017, 55(2):182-196. doi: 10.1093/chromsci/bmw167 URL
[2]	Geyer PE, Holdt LM, Teupser D, et al. Revisiting biomarker discovery by plasma proteomics[J]. Mol Syst Biol, 2017, 13(9):942. doi: 10.15252/msb.20156297 URL
[3]	Geyer PE, Kulak NA, Pichler G, et al. Plasma proteome profiling to assess human health and disease[J]. Cell Syst, 2016, 2(3):185-195.
[4]	Peng L, Cantor DI, Huang C, et al. Tissue and plasma proteomics for early stage cancer detection[J]. Mol Omics, 2018, 14(6):405-423. doi: 10.1039/C8MO00126J URL
[5]	牟永莹, 王道平, 陈明, 等. 大豆种子蛋白质组样品制备与数据分析方法[J]. 生物技术通报, 2020, 36(12):247-255.
	Mu YY, Wang DP, Chen M, et al. Sample preparation and data analysis method for soybean seed proteome[J]. Biotechnol Bull, 2020, 36(12):247-255.
[6]	Wiśniewski JR. Filter-aided sample preparation:the versatile and efficient method for proteomic analysis[J]. Methods Enzymol, 2017, 585:15-27.
[7]	Zolotarjova N, Martosella J, Nicol G, et al. Differences among techniques for high-abundant protein depletion[J]. Proteomics, 2005, 5(13):3304-3313. pmid: 16052628
[8]	Polaskova V, Kapur A, Khan A, et al. High-abundance protein depletion:comparison of methods for human plasma biomarker discovery[J]. Electrophoresis, 2010, 31(3):471-482. doi: 10.1002/elps.200900286 pmid: 20119956
[9]	Lima-Oliveira G, Monneret D, Guerber F, et al. Sample management for clinical biochemistry assays:Are serum and plasma interchangeable specimens?[J]. Crit Rev Clin Lab Sci, 2018, 55(7):480-500. doi: 10.1080/10408363.2018.1499708 URL
[10]	Park ZY, Russell DH. Thermal denaturation:a useful technique in peptide mass mapping[J]. Anal Chem, 2000, 72(11):2667-2670. pmid: 10857653
[11]	Schniers A, Pasing Y, Hansen T. Lys-C/trypsin tandem-digestion protocol for gel-free proteomic analysis of colon biopsies[J]. Methods Mol Biol, 2019, 1959:113-122. doi: 10.1007/978-1-4939-9164-8_7 pmid: 30852818
[12]	Betancourt LH, Sanchez A, Pla I, et al. Quantitative assessment of urea in-solution Lys-C/trypsin digestions reveals superior performance at room temperature over traditional proteolysis at 37℃[J]. J Proteome Res, 2018, 17(7):2556-2561. doi: 10.1021/acs.jproteome.8b00228 pmid: 29812944
[13]	Budnik B, Levy E, Harmange G, et al. SCoPE-MS:mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation[J]. Genome Biol, 2018, 19(1):161. doi: 10.1186/s13059-018-1547-5 URL
[14]	Hu A, Noble WS, Wolf-Yadlin A. Technical advances in proteomics:new developments in data-independent acquisition[J]. F1000 Research, 2016, 5:419. doi: 10.12688/f1000research URL
[15]	Zhang Y, Fonslow BR, Shan B, et al. Protein analysis by shotgun/bottom-up proteomics[J]. Chem Rev, 2013, 113(4):2343-2394. doi: 10.1021/cr3003533 URL
[16]	Barkovits K, Pacharra S, Pfeiffer K, et al. Reproducibility, specificity and accuracy of relative quantification using spectral library-based data-independent acquisition[J]. Mol Cell Proteomics, 2020, 19(1):181-197. doi: 10.1074/mcp.RA119.001714 pmid: 31699904
[17]	Venable JD, Dong MQ, Wohlschlegel J, et al. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra[J]. Nat Methods, 2004, 1(1):39-45. pmid: 15782151
[18]	Bilbao A, Varesio E, Luban J, et al. Processing strategies and software solutions for data-independent acquisition in mass spectrometry[J]. Proteomics, 2015, 15(5/6):964-980. doi: 10.1002/pmic.201400323 URL
[19]	Shi T, Song E, Nie S, et al. Advances in targeted proteomics and applications to biomedical research[J]. Proteomics, 2016, 16(15/16):2160-2182. doi: 10.1002/pmic.v16.15-16 URL
[20]	Uzozie AC, Aebersold R. Advancing translational research and precision medicine with targeted proteomics[J]. J Proteomics, 2018, 189:1-10. doi: 10.1016/j.jprot.2018.02.021 URL
[21]	Silva ARM, Toyoshima MTK, Passarelli M, et al. Comparing data-independent acquisition and parallel reaction monitoring in their abilities to differentiate high-density lipoprotein subclasses[J]. J Proteome Res, 2020, 19(1):248-259. doi: 10.1021/acs.jproteome.9b00511 URL
[22]	Chiva C, Ortega M, Sabidó E. Influence of the digestion technique, protease, and missed cleavage peptides in protein quantitation[J]. J Proteome Res, 2014, 13(9):3979-3986. doi: 10.1021/pr500294d URL
[23]	Bruderer R, Muntel J, Müller S, et al. Analysis of 1508 plasma samples by capillary-flow data-independent acquisition profiles proteomics of weight loss and maintenance[J]. Mol Cell Proteomics, 2019, 18(6):1242-1254. doi: 10.1074/mcp.RA118.001288 pmid: 30948622
[24]	Manes NP, Nita-Lazar A. Application of targeted mass spectrometry in bottom-up proteomics for systems biology research[J]. J Proteom, 2018, 189:75-90. doi: 10.1016/j.jprot.2018.02.008 URL
[25]	Woo CM, Lund PJ, Huang AC, et al. Mapping and quantification of over 2000 O-linked glycopeptides in activated human T cells with isotope-targeted glycoproteomics(isotag)[J]. Mol Cell Proteom, 2018, 17(4):764-775. doi: 10.1074/mcp.RA117.000261 URL
[26]	Qiu FH, Hou TY, Huang DH, et al. Evaluation of two high-abundance protein depletion kits and optimization of downstream isoelectric focusing[J]. Mol Med Rep, 2015, 12(5):7749-7755. doi: 10.3892/mmr.2015.4417 URL
[27]	Liu B, Qiu FH, Voss C, et al. Evaluation of three high abundance protein depletion kits for umbilical cord serum proteomics[J]. Proteome Sci, 2011, 9(1):24. doi: 10.1186/1477-5956-9-24 URL
[28]	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]. J Proteome Res, 2012, 11(11):5145-5156. doi: 10.1021/pr300273g pmid: 23017020
[29]	Kailasa S, Wu HF. Advances in nanomaterial-based microwaves and infrared wave-assisted tryptic digestion for ultrafast proteolysis and rapid detection by MALDI-MS[J]. Comb Chem High Throughput Screen, 2014, 17(1):68-79. doi: 10.2174/1386207316666131110211353 URL