基于多维液相色谱分离的蛋白质组深度覆盖鉴定方法研究

引用本文: 施若晗, 晏国全, 高明霞, 张祥民. 基于多维液相色谱分离的蛋白质组深度覆盖鉴定方法研究[J]. 分析化学, 2022, 50(8): 1179-1187,1242. doi: 10.19756/j.issn.0253-3820.221117 shu

Citation: SHI Ruo-Han, YAN Guo-Quan, GAO Ming-Xia, ZHANG Xiang-Min. Deep Coverage in Identification of Proteome Based on Separation by Multi-dimensional Liquid Chromatography[J]. Chinese Journal of Analytical Chemistry, 2022, 50(8): 1179-1187,1242. doi: 10.19756/j.issn.0253-3820.221117 shu

基于多维液相色谱分离的蛋白质组深度覆盖鉴定方法研究

施若晗 ^1, ,
晏国全 ^{2,
,} ,
高明霞 ^1, ,
张祥民 ^{1,2,
,}

1.
复旦大学化学系, 上海 200438;
2.
复旦大学生物医学研究院, 上海 200030

通讯作者: 晏国全,E-mail:xmzhang@fudan.edu.cn; 张祥民,E-mail:yanguoquan@fudan.edu.cn

基金项目:
国家重点研发计划项目(No.2017YFA0505003)和国家自然科学基金项目(No.21775027)资助。

摘要: 基于多维液相色谱技术对人尿源性蛋白质组的高效分离大幅提升了蛋白质鉴定的序列覆盖度。采用强阴离子交换色谱(1^stD-SAX)为第一维色谱、反相液相色谱(2^ndD-RPLC)为第二维色谱,组成两维色谱高效分离系统。经过分离条件的选择和优化,建立了理论峰容量超过40000的两维色谱系统。根据实际鉴定能力和技术需求,在实际样品分离时,第一维收集50个馏分,第二维收集64个馏分,系统有效峰容量取值3200。在此基础上,对人尿蛋白质组进行了蛋白质水平分离和深度覆盖鉴定。收集一组健康志愿者的尿液样品,通过离心、超滤等前处理纯化制备样品。为了提高目标的针对性,所制备的样品先通过第一维的强阴离子交换色谱分离,选取了1^stD-SAX分离的14号馏分(39~42 min)作为主要研究目标,将收集的14号馏分均分成两份,其中一份对其直接酶解鉴定,获得色谱质谱鉴定数据;另一份经过2^ndD-RPLC分离并收集64个馏分,再逐个酶解,分别鉴定,获得色谱质谱数据。结果表明,以第14号馏分为对象,前者共鉴定出628种蛋白,后者64个馏分共鉴定出2440种蛋白。在两者共同鉴定出的588种蛋白中,增加第二维分离后的序列覆盖度显著提高。进一步的分析统计表明,经第二维分离后,长度不超过500个氨基酸的蛋白的序列覆盖度达到31.30%,相较仅采用第一维分离鉴定的覆盖度值提升了2.02倍;对于长度500以上且未超过1000个氨基酸的蛋白,第一维分离的序列覆盖度为7.79%,经第二维分离后,覆盖度提升至21.35%,覆盖度提高了2.74倍。对于由1000个以上氨基酸组成的较大分子量的蛋白质,第二维分离可将序列覆盖度进一步提升3.87倍。本研究结果验证了蛋白质经过多维色谱充分分离后,使用常见的Shotgun蛋白鉴定技术显著提高了鉴定覆盖度和可靠性。同时,质谱鉴定得到的肽段数量显著增加,减小了错配或漏配的概率,有助于获得蛋白质更完整、更精准的序列信息,对蛋白质组学深度覆盖鉴定、建立更加精准的蛋白质组数据库具有重要意义。

关键词:

English

Deep Coverage in Identification of Proteome Based on Separation by Multi-dimensional Liquid Chromatography

1.
Department of Chemistry, Fudan University, Shanghai 200438, China;
2.
Institutes of Biomedical Sciences, Fudan University, Shanghai 200030, China

Corresponding author: YAN Guo-Quan, ; ZHANG Xiang-Min,

Received Date: 09 March 2022
Revised Date: 14 May 2022
Fund Project:
Supported by the National Key Research and Development Program of China (No.2017YFA0505003) and the National Natural Science Foundation of China (No.21775027).

Abstract: Sequence coverage of identified proteins could be significantly improved when human urine proteins were effectively separated based on multi-dimensional liquid chromatography (MDLC). The high-efficient two-dimensional (2D) separating system involved strong anion exchange chromatography as the first dimension (1^stD-SAX) and reversed phase liquid chromatography as the second dimension (2^ndD-RPLC). After optimization of separate conditions, the peak capacity of 2D chromatographic system exceeded 40000. With regard to technique demands and ability to identify, 50 fractions were collected in the first dimension and 64 fractions were collected in the second dimension respectively, with valid peak capacity reaching 3200. On the basis of this system, the separation of intact proteins as well as the deep coverage of identification could be realized in human urine proteome. Urine samples from a group of healthy volunteers were collected and pretreatment including centrifugation and ultrafiltration was conducted subsequently to purify samples. To further improve pertinence, all samples were initially separated by 1^stD-SAX, and the fraction 14 collected from 39 to 42 min was regarded as the main target. Fraction 14 was divided equally into two parts. One was digested and identified directly to obtain mass spectrometry (MS) data, while the other was further separated by 2^ndD-RPLC and 64 fractions were collected. Then the 64 fractions were digested and identified individually to obtain aggregate data. Statistics indicated that 628 proteins were identified without 2^ndD-RPLC separation while 2440 proteins were identified from 64 fractions. Among 588 proteins identified in common, the sequence coverage was significantly improved when a 2D separation was involved. Further analysis revealed that the sequence coverage of proteins composed of no more than 500 amino acids (AAs) reached 31.30%, a 2.02 fold-change compared with merely one-dimensional (1D) separation. For proteins composed of 501-1000 AAs, the sequence coverage increased from 7.79% in 1D separation to 21.35% in 2D separation. For larger proteins composed of over 1000 AAs, the sequence coverage could be enhanced by 3.87 times. These results verified the extraordinary improvement in coverage and reliability of identification when proteins were adequately separated by MDLC compared with the commonly used shotgun method. Meanwhile, the peptide counts from MS analysis could be increased to reduce the probability of mismatch or omission, thus contributing to acquiring more complete and detailed information on protein sequence, which was of great significance to realize deep coverage identification of proteome and establish more accurate proteomic databases.

Key words:

Urine
/ Proteomics
/ Multi-dimensional liquid chromatography
/ Strong anion exchange chromatography
/ Reversed phase liquid chromatography
/ Peak capacity
/ Deep coverage

1. [1]
  SMITH G, BARRATT D, ROWLINSON R, NICKSON J, TONGE R. Proteomics, 2005, 5(9):2315-2318.SMITH G, BARRATT D, ROWLINSON R, NICKSON J, TONGE R. Proteomics, 2005, 5(9):2315-2318.
2. [2]
  OH J, PYO J H, JO E H, HWANG S I, KANG S C, JUNG J H, PARKE K, KIM S Y, CHOI J Y, LIM J. Proteomics, 2004, 4(11):3485-3497.OH J, PYO J H, JO E H, HWANG S I, KANG S C, JUNG J H, PARKE K, KIM S Y, CHOI J Y, LIM J. Proteomics, 2004, 4(11):3485-3497.
3. [3]
  KHAN A, PACKER N H. J. Proteome Res., 2006, 5(10):2824-2838.KHAN A, PACKER N H. J. Proteome Res., 2006, 5(10):2824-2838.
4. [4]
  YUAN H, JIANG B, ZHAO B, ZHANG L, ZHANG Y. Anal. Chem., 2019, 91(1):264-276.YUAN H, JIANG B, ZHAO B, ZHANG L, ZHANG Y. Anal. Chem., 2019, 91(1):264-276.
5. [5]
  WANG Shu-Yue, JIA Sheng-Nan, LI Xiao-Ying, ZHANG Hai-Yang, FENG Xin, WANG Shi, TAO Wei-Guo, HU Liang-Hai. Chin. J. Anal.Chem., 2020, 48(10):1334-1342.王舒越,贾胜男,李潇影,张海洋,冯馨,王诗,陶纬国,胡良海.分析化学, 2020, 48(10):1334-1342.
6. [6]
  THONGBOONKERD V, MCLEISH K R, ARTHUR J M, KLEIN J B. Kidney Int., 2002, 62(4):1461-1469.THONGBOONKERD V, MCLEISH K R, ARTHUR J M, KLEIN J B. Kidney Int., 2002, 62(4):1461-1469.
7. [7]
  SPAHR C S, DAVIS M T, MCGINLEY M D, ROBINSON J H, BURES E J, BEIERLE J, MORT J, COURCHESNE P L, CHEN K, WAHL R C, YU W, LUETHY R, PATTERSON S D. Proteomics, 2001, 1(1):93-107.SPAHR C S, DAVIS M T, MCGINLEY M D, ROBINSON J H, BURES E J, BEIERLE J, MORT J, COURCHESNE P L, CHEN K, WAHL R C, YU W, LUETHY R, PATTERSON S D. Proteomics, 2001, 1(1):93-107.
8. [8]
  DAVIS M T, SPAHR C S, MCGINLEY M D, ROBINSON J H, BURES E J, BEIERLE J, MORT J, YU W, LUETHY R, PATTERSON S D. Proteomics, 2001, 1(1):108-117.DAVIS M T, SPAHR C S, MCGINLEY M D, ROBINSON J H, BURES E J, BEIERLE J, MORT J, YU W, LUETHY R, PATTERSON S D. Proteomics, 2001, 1(1):108-117.
9. [9]
  MARIMUTHU A, O'MEALLY R N, CHAERKADY R, SUBBANNAYYA Y, NANJAPPA V, KUMAR P, KELKAR D S, PINTO S M, SHARMA R, RENUSE S, GOEL R, CHRISTOPHER R, DELANGHE B, COLE R N, HARSHA H C, PANDEY A. J. Proteome Res., 2011, 10(6):2734-2743.MARIMUTHU A, O'MEALLY R N, CHAERKADY R, SUBBANNAYYA Y, NANJAPPA V, KUMAR P, KELKAR D S, PINTO S M, SHARMA R, RENUSE S, GOEL R, CHRISTOPHER R, DELANGHE B, COLE R N, HARSHA H C, PANDEY A. J. Proteome Res., 2011, 10(6):2734-2743.
10. [10]
  ZHAO M, LI M, YANG Y, GUO Z, SUN Y, SHAO C, LI M, SUN W, GAO Y. Sci. Rep., 2017, 7:3024.ZHAO M, LI M, YANG Y, GUO Z, SUN Y, SHAO C, LI M, SUN W, GAO Y. Sci. Rep., 2017, 7:3024.
11. [11]
  LIN L, YU Q, ZHENG J, CAI Z, TIAN R. Clin. Proteomics, 2018, 15:42.LIN L, YU Q, ZHENG J, CAI Z, TIAN R. Clin. Proteomics, 2018, 15:42.
12. [12]
  SWENSEN A C, HE J, FANG A C, YE Y, NICORA C D, SHI T, LIU A Y, SIGDEL T K, SARWAL M M, QIAN W. J. Front. Med.(Lausanne), 2021, 8:548212.SWENSEN A C, HE J, FANG A C, YE Y, NICORA C D, SHI T, LIU A Y, SIGDEL T K, SARWAL M M, QIAN W. J. Front. Med.(Lausanne), 2021, 8:548212.
13. [13]
  LI Y, WANG Y, LIU H, SUN W, DING B, ZHAO Y, CHEN P, ZHU L, LI Z, LI N, CHANG L, WANG H, BAI C, XU P. Urine (Amst), 2020, 2:1-8.LI Y, WANG Y, LIU H, SUN W, DING B, ZHAO Y, CHEN P, ZHU L, LI Z, LI N, CHANG L, WANG H, BAI C, XU P. Urine (Amst), 2020, 2:1-8.
14. [14]
  TIAN W, ZHANG N, JIN R, FENG Y, WANG S, GAO S, GAO R, WU G, TIAN D, TAN W, CHEN Y, GAO G F, WONG C L. Nat. Commun., 2020, 11(1):5859.TIAN W, ZHANG N, JIN R, FENG Y, WANG S, GAO S, GAO R, WU G, TIAN D, TAN W, CHEN Y, GAO G F, WONG C L. Nat. Commun., 2020, 11(1):5859.
15. [15]
  BI X, LIU W, DING X, LIANG S, ZHENG Y, ZHU X, QUAN S, YI X, XIANG N, DU J, LYU H, YU D, ZHANG C, XU L, GE W, ZHAN X, HE J, XIONG Z, ZHANG S, LI Y, XU P, ZHU G, WANG D, ZHU H, CHEN S, LI J, ZHAO H, ZHU Y, LIU H, XU J, SHEN B, GUO T. Cell Rep., 2021, 38(3):110271.BI X, LIU W, DING X, LIANG S, ZHENG Y, ZHU X, QUAN S, YI X, XIANG N, DU J, LYU H, YU D, ZHANG C, XU L, GE W, ZHAN X, HE J, XIONG Z, ZHANG S, LI Y, XU P, ZHU G, WANG D, ZHU H, CHEN S, LI J, ZHAO H, ZHU Y, LIU H, XU J, SHEN B, GUO T. Cell Rep., 2021, 38(3):110271.
16. [16]
  WU Qiong, SUI Xin-Tong, TIAN Rui-Jun. Chin. J. Chromatogr., 2021, 39(2):112-117.吴琼,隋欣桐,田瑞军.色谱, 2021, 39(2):112-117.
17. [17]
  KIM K H, MOON M H. J. Proteome Res., 2009, 8(9):4272-4278.KIM K H, MOON M H. J. Proteome Res., 2009, 8(9):4272-4278.
18. [18]
  THOMAS S, HAO L, DELANEY K, MCLEAN D, STEINKE L, MARKER P C, VEZINA C M, LI L, RICKE W A. J. Proteome Res., 2020, 19(4):1375-1382.THOMAS S, HAO L, DELANEY K, MCLEAN D, STEINKE L, MARKER P C, VEZINA C M, LI L, RICKE W A. J. Proteome Res., 2020, 19(4):1375-1382.
19. [19]
  HUANG Z, YAN G, GAO M, ZHANG X. Anal. Chem., 2016, 88(4):2440-2445.HUANG Z, YAN G, GAO M, ZHANG X. Anal. Chem., 2016, 88(4):2440-2445.
20. [20]
  ZHU S, ZHANG X, GAO M, HONG G, YAN G, ZHANG X. Proteomics, 2012, 12(23-24):3451-3463.ZHU S, ZHANG X, GAO M, HONG G, YAN G, ZHANG X. Proteomics, 2012, 12(23-24):3451-3463.
21. [21]
  ZHANG Xiang-Min. Analysis of Modern Chromatography. Shanghai:Fudan University Press, 2004:2.张祥民.现代色谱分析.上海:复旦大学出版社, 2004:2.

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1.
沈阳化工大学材料科学与工程学院沈阳 110142

基于多维液相色谱分离的蛋白质组深度覆盖鉴定方法研究

通讯作者: 晏国全,E-mail:xmzhang@fudan.edu.cn; 张祥民,E-mail:yanguoquan@fudan.edu.cn

English

Deep Coverage in Identification of Proteome Based on Separation by Multi-dimensional Liquid Chromatography

Corresponding author: YAN Guo-Quan, ; ZHANG Xiang-Min,

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通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

基于多维液相色谱分离的蛋白质组深度覆盖鉴定方法研究

通讯作者: 晏国全,E-mail:xmzhang@fudan.edu.cn; 张祥民,E-mail:yanguoquan@fudan.edu.cn

English

Deep Coverage in Identification of Proteome Based on Separation by Multi-dimensional Liquid Chromatography

Corresponding author: YAN Guo-Quan, ; ZHANG Xiang-Min,

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南： 你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

CCS Chemistry：静电组装导向聚合- 聚电解质纳米凝胶量化制备新策略

CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

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