
UPLC-ESI-MS分析中棉花次生代谢物标准品推定和加合物形成
李社增,牛露欣,李博超,陈秀叶,马平,马峙英
UPLC-ESI-MS分析中棉花次生代谢物标准品推定和加合物形成
Putative Identification and Adduct Formation of Reference Materials for Cotton Secondary Metabolite using UPLC-ESI-MS Analysis
【目的】 建立超高效液相色谱电喷雾质谱(Ultra-performance liquid chromatography-electrospray ionization-mass spectrometry,UPLC-ESI-MS)分析中棉花代谢产物的高通量鉴定方法;探讨特定UPLC和ESI模式条件下棉花次生代谢产物的加合物种类、主导加合物及适宜的ESI模式。【方法】 利用高效液相色谱电喷雾质谱对18个棉花代谢产物标准品进行分析,采用在线XCMS软件进行无靶标质谱数据提取,利用MATLAB软件编程计算程序建立标准品鉴定方法。【结果】 针对电喷雾正、负离子模式,建立了基于计算准确相对分子质量的棉花次生代谢产物标准品的快速鉴定方法POSid和NEGid;特定超高效液相色谱和电喷雾正、负离子模式下14个标准品得到正确鉴定。正离子模式下出现[M+H]+、[M+Na]+、[M+NH4]+、[2M+NH4]+、[2M+Na]+和[2M+H]+ 6种加合物,负离子模式下出现[M-H]-、[2M-H]-、[M+Cl]-、[M+FA-H]-、[3M-H]-、[M+Na-2H]-、[M-H2O-H]-和[M+TFA-H]- 8种加合物,单个标准品的质谱可观察到1~6种加合物,每个标准品均有主导加合物并具有电喷雾离子模式的偏好性。蜜二糖适合电喷雾正离子模式检测,棉酚适合2种离子模式检测;12种化合物均是负离子模式信号强于正离子模式,适合采用负离子模式检测。【结论】 建立的基于计算准确相对分子质量的代谢产物鉴定方法,能实现对18个棉花次生代谢产物标准品的无靶标质谱数据的鉴定。特定超高效液相色谱和电喷雾条件下,棉花次生代谢产物的主导加合物具有电喷雾离子模式的偏好性。这些结果为开展棉花代谢组研究提供了技术和理论数据支撑。
[Objective] The aim of this study is to establish an method for cotton metabolites identification by ultra-performance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) analysis, and to investigate the adduct types, dominant adducts and appropriate ESI ion modes of cotton secondary metabolites under the determined UPLC and ESI conditions. [Method] UPLC-ESI-MS was employed to analyze 18 cotton metabolite standards, Online XCMS software was used to extract the nontargeted mass spectrum data, and MATLAB software was used to prepare calculation programs for the identification method of cotton metabolite standards. [Result] A high-throughput identification method of cotton secondary metabolite, named POSid and NEGid separately for the positive and negative ESI modes, was established based on the calculated accurate molecular weight. In the determined UPLC condition and positive and negative ESI mode, 14 cotton metabolite standards were correctly identified. There found 6 adducts including [M+H]+, [M+Na]+, [M+NH4]+, [2M+NH4]+, [2M+Na]+ and [2M+H]+ in positive ion mode, and 8 adducts including [M-H]-, [2M-H]-, [M+Cl]-, [M+FA-H]-, [3M-H]-, [M+Na-2H]-, [M-H2O-H]- and [M+TFA-H]- in negative ion mode, while 1 to 6 adducts were observed in the mass spectrum of a single standard, and each metabolite standard had a dominant adduct of a preference for ESI mode. Melibose was suitable for ESI positive ion mode detection, gossypol was suitable for both ion modes detection; and 12 compounds were suitable for negative ion mode detection due to mass spectrum signals of their dominant adduct stronger in negative ion mode than positive ion mode. [Conclusion] Based on the accurate molecular weight, the established identifying method can identify the 18 cotton metabolite standards with their nontarget mass spectrometry data. Under the determined UPLC and ESI condition, the dominant adducts of cotton secondary metabolites have the preference of ESI mode. These results provide technical and theoretical data support for further study of cotton metabolomics.
棉花次生代谢产物 / 超高效液相色谱-电喷雾质谱 / 质谱鉴定 / 加合物 {{custom_keyword}} /
cotton metabolite / ultra-performance liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) / mass spectrum identification / adduct {{custom_keyword}} /
表1 棉花代谢产物标准品的信息Table 1 Information on cotton metabolite standards tested in presented study |
化合物 Compound | 单同位素准确相对分子质量 Monoisotopic molecular weight | 分子式 Molecular Formula | CAS号 CAS number | 分类 Type | 适用溶剂 Suitable solvent | 生产商 Manufacturer | ||||||
赤霉素A4 Gibberellin A4 | 332.162 374 | C19H24O5 | 468-44-0 | 二萜类 Diterpenoids | 甲醇 Methanol | TLC Pharmaceutical Standards | ||||||
赤霉素A3 Gibberellin A3 | 346.141 638 | C19H22O6 | 77-06-5 | 二萜类 Diterpenoids | 丙酮 Acetone | 北京北方伟业计量技术研究院 Beijing North Weiye Measurement Technology Research Institute | ||||||
水杨酸 Salicylic acid | 138.031 694 | C7H6O3 | 69-72-7 | 酚类 Phenols | 二甲基亚砜 Dimethylsulfoxide (DMSO) | 上海源叶生物科技有限公司 Shanghai Yuanye Bio- Technology Co., Ltd (SYBT Co. Ltd) | ||||||
柽柳黄素 Tamarixetin | 316.058 305 | C16H12O7 | 603-61-2 | 类黄酮 Flavonoids | 二甲基亚砜 DMSO | SYBT Co. Ltd | ||||||
N6-异戊烯基腺嘌呤N6-(delta2-isopentenyl) adenine | 203.117 096 | C10H13N5 | 2365-40-4 | 生物碱类 Alkaloids | 二甲基亚砜 DMSO | SYBT Co. Ltd | ||||||
化合物 Compound | 单同位素准确相对分子质量 Monoisotopic molecular weight | 分子式 Molecular Formula | CAS号 CAS number | 分类 Type | 适用溶剂 Suitable solvent | 生产商 Manufacturer | ||||||
α-石竹烯 α-humulene | 204.187 801 | C15H24 | 6753-98-6 | 石竹烷倍半萜类Caryophyllane sesquiterpenoids | 乙醇 Ethanol | SYBT Co. Ltd | ||||||
角鲨烯 Squalene | 410.391 252 | C30H50 | 7683-64-9 | 线性三萜类 Linear triterpenoids; | 乙醇 Ethanol | SYBT Co. Ltd | ||||||
蜜二糖 Melibiose | 342.116 215 | C12H22O11 | 585-99-9 | 糖类 Carbohydrates | 去离子水 Deionized water | SYBT Co. Ltd | ||||||
蔗糖 Sucrose | 342.116 215 | C12H22O11 | 585-99-9 | 糖类 Carbohydrates | 去离子水 Deionized water | SYBT Co. Ltd | ||||||
紫云英苷 Astragalin | 448.100 562 | C21H20O11 | 480-10-4 | 类黄酮类 Flavonoids | 去离子水 Deionized water | SYBT Co. Ltd | ||||||
脱落酸 (s)-(+)-abscisic acid | 264.136 159 | C15H20O4 | 21293-29-8 | 环法尼烷倍半 Cyclofarnesane sesquiterpenoids | 甲醇 Methanol | SYBT Co. Ltd | ||||||
绿原酸3-O-caffeoylquinic acid | 354.095 082 | C16H18O9 | 327-97-9 | 酚类 Phenols | 甲醇 Methanol | SYBT Co. Ltd | ||||||
白麻苷Quercetin 3-sophoroside | 626.148 305 | C27H30O17 | 18609-17-1 | 类黄酮类 Flavonoids | 甲醇 Methanol | SYBT Co. Ltd | ||||||
咖啡酸 Caffeic acid | 180.042 259 | C9H8O4 | 501-16-6 | 酚类 Phenols | 甲醇 Methanol | SYBT Co. Ltd | ||||||
青榆烯C Lacinilene C | 246.125 595 | C15H18O3 | 41653-72-9 | 杜松烷倍半萜 Cadinane sesquiterpenoids | 甲醇 Methanol | SYBT Co. Ltd | ||||||
3-吲哚甲醛1H-indole-3-carboxaldehyde | 145.052 764 | C9H7NO | 487-89-8 | 生物碱类 Alkaloids | 甲醇 Methanol | SYBT Co. Ltd | ||||||
棉酚 Gossypol | 518.194 068 | C30H30O8 | 303-45-7 | 杜松烷倍半萜 Cadinane sesquiterpenoids | 甲醇 Methanol | SYBT Co. Ltd | ||||||
三十烷酸 Triacontanoic acid | 452.459 331 | C30H60O2 | 506-50-3 | 脂肪族天然产物 Aliphatic natural products | 甲醇 Methanol | SYBT Co. Ltd |
图1 正离子模式(A)和负离子模式(B)下棉花代谢物标准品混合液UPLC-ESI-MS 总离子色谱图Fig. 1 UPLC-ESI-MS TICs of cotton metabolite mixture in positive (A) and negative (B) ion mode |
表2 棉花代谢产物标准品的UPLC-ESI-MS检测数据概况Table 2 A survey of UPLC-ESI-MS data of cotton metabolite standards |
化合物 Compound | 负离子模式 Positive ion mode | 负离子模式Negative ion mode | |||||
离子数 Amount of ions | 质荷比 m/z | 保留时间 Retention time/min | 离子数 Amount of ions | 质荷比 m/z | 保留时间 Retention time/min | ||
水杨酸 Salicylic acid | 2 165 | 59.052 0~1 431.294 3 | 0.13~17.47 | 1 523 | 61.993 2~1 405.012 8 | 0.68~17.44 | |
柽柳黄素 Tamarixetin | 2 165 | 59.052 0~1 431.294 3 | 0.13~17.47 | 1 523 | 61.993 2~1 405.012 8 | 0.68~17.44 | |
N6-异戊烯基腺嘌呤 N6-(delta2-isopentenyl) adenine | 1 977 | 59.052 1~1 431.796 0 | 0.22~17.47 | 1 560 | 61.993 4~1 418.991 2 | 0.68~17.45 | |
α-石竹烯 α-humulen | 1 410 | 59.055 6~1 222.341 8 | 0.14~17.47 | 1 225 | 61.991 6~1 405.003 6 | 0.69~17.44 | |
角鲨烯Squalene | 1 480 | 60.083 7~1 223.338 3 | 0.70~17.47 | 1 149 | 61.993 1~1 470.000 9 | 0.16~17.47 | |
蜜二糖 Melibiose | 1 945 | 59.055 4~1223.3365 | 0.14~17.47 | 1 797 | 61.990 3~1 487.099 2 | 0.16~17.48 | |
蔗糖 Sucrose | 1 945 | 59.055 4~1 223.336 5 | 0.14~17.47 | 1 797 | 61.990 3~1 487.099 2 | 0.16~17.48 | |
紫云英苷 Astragalin | 2 071 | 59.055 4~1 431.894 0 | 0.14~17.47 | 1 709 | 61.990 1~1 487.099 2 | 0.16~17.48 | |
脱落酸 (s)-(+)-abscisic acid | 1 920 | 59.055 4~1 431.791 4 | 0.14~17.47 | 1 450 | 61.990 1~1 488.096 8 | 0.16~17.48 | |
绿原酸 3-O-caffeoylquinic acid | 1 920 | 59.055 4~1 431.791 4 | 0.14~17.47 | 1 450 | 61.990 1~1 488.096 8 | 0.16~17.48 | |
白麻苷 Quercetin 3-sophoroside | 1 668 | 60.083 2~1 431.792 1 | 0.23~17.47 | 1 406 | 61.992 7~1 488.096 8 | 0.18~14.48 | |
咖啡酸 Caffeic acid | 1 997 | 59.055 7~1 431.791 4 | 0.14~17.47 | 1 451 | 61.990 1~1 488.096 8 | 0.16~17.48 | |
青榆烯C Lacinilene C | 1 957 | 59.055 5~1 431.791 4 | 0.14~17.47 | 1 522 | 61.993 8~1 488.096 8 | 0.70~17.47 | |
3-吲哚甲醛1H- indole-3-carboxaldehyde | 1 670 | 60.083 2~1 431.791 4 | 0.74~17.47 | 1 421 | 61.992 8~1 488.096 8 | 0.68~17.48 | |
赤霉素A4 Gibberellin A4 | 1 694 | 60.083 3~1 431.791 4 | 0.74~17.47 | 1 612 | 61.990 4~1 488.096 8 | 0.18~14.48 | |
棉酚 Gossypol | 1 936 | 60.083 4~1 431.791 4 | 0.74~17.47 | 1 481 | 61.993 7~1 488.096 8 | 0.68~14.48 | |
赤霉素A3 Gibberellin A3 | 1 911 | 60.082 7~1 431.791 4 | 0.05~17.47 | 1 574 | 61.990 5~1 488.096 8 | 0.18~14.47 | |
三十烷酸 Triacontanoic acid | 1 647 | 59.055 3~1 150.319 2 | 0.14~17.48 | 911 | 61.993 1~1 403.001 7 | 0.61~17.45 |
表3 LC-ESI-MS检测中供试物质的常见加合物Table 3 Common adducts of the tested compound in LC-ESI-MS detection |
正离子模式Positive ion mode | 负离子模式 Negative ion mode | |||
加合物名称a Adduct name | 计算的准确相对分子质量b Calculated molecular weight (CM) | 加合物名称a Adduct name | 计算的准确相对分子质量b Calculated molecular weight (CM) | |
[M+3H]3+ | 3×(m/z-1.007 276) | [M-3H]3- | 3×(m/z+1.007 276) | |
[M+2H+Na]3+ | 3×(m/z-8.334 590) | [M-2H]2- | 2×(m/z+1.007 276) | |
[M+H+2Na]3+ | 3×(m/z-15.7 661 904) | [M-H2O-H]- | m/z+19.01 839 | |
[M+3Na]3+ | 3×(m/z-22.989 218) | [M-H]- | m/z+1.007 276 | |
[M+2H]2+ | 2×(m/z-1.007 276) | [M+Na-2H]- | m/z-20.974 666 | |
[M+H+NH4]2+ | 2×(m/z-9.520 550) | [M+Cl]- | m/z-34.969 402 | |
[M+H+Na]2+ | 2×(m/z-11.998 247) | [M+K-2H]- | m/z-36.948 606 | |
[M+H+K]2+ | 2×(m/z-19.985 217) | [M+FA-H]- | m/z-44.998 201 | |
[M+ACN+2H]2+ | 2×(m/z-21.520 550) | [M+HAc-H]- | m/z-59.013 851 | |
[M+2Na]2+ | 2×(m/z-22.989 218) | [M+Br]- | m/z-78.918 885 | |
[M+2ACN+2H]2+ | 2×(m/z-42.033 823) | [M+TFA-H]- | m/z-112.985 586 | |
[M+3ACN+2H]2+ | 2×(m/z-62.547 097) | [2M-H]- | (m/z+1.007 276)÷2 | |
[M+H]+ | m/z-1.007 276 | [2M+FA-H]- | (m/z-44.998 201)÷2 | |
[M+NH4]+ | m/z-18.033 823 | [2M+HAc-H]- | (m/z-59.013 851)÷2 | |
[M+Na]+ | m/z-22.989 218 | [3M-H]- | (m/z+1.007 276)÷3 | |
[M+CH3OH+H]+ | m/z-33.033 489 | |||
[M+K]+ | m/z-38.963 158 | |||
[M+ACN+H]+ | m/z-42.033 823 | |||
[M+2Na-H]+ | m/z-44.971 160 | |||
[M+IsoProp+H]+ | m/z-61.06 534 | |||
[M+ACN+Na]+ | m/z-64.015 765 | |||
[M+2K-H]+ | m/z-76.919 040 | |||
[M+DMSO+H]+ | m/z-79.02 122 | |||
[M+2ACN+H]+ | m/z-83.060 370 | |||
[M+IsoProp+Na+H]+ | m/z-84.05 511 | |||
[2M+H]+ | (m/z-1.007 276)÷2 | |||
[2M+NH4]+ | (m/z-18.033 823)÷2 | |||
[2M+Na]+ | (m/z-22.989 218)÷2 | |||
[2M+3H2O+2H]+ | (m/z-28.02 312)÷2 | |||
[2M+K]+ | (m/z-38.963 158)÷2 | |||
[2M+ACN+H]+ | (m/z-42.033 823)÷2 | |||
[2M+ACN+Na]+ | (m/z-64.015 765)÷2 |
注:aM,物质分子;ACN,乙腈;DMSO,二甲基亚砜;FA,甲酸;HAc,乙酸;TFA,三氟乙酸;IsoProp,异丙醇;CH3OH,甲醇;b 由准确相对分子质量推导相应质荷比的计算公式。 | |
Note: aM, compound molecular; ACN, acetonitrile; DMSO, dimethylsulfoxide; FA, formic acid; HAc, acetic acid; TFA, trifluoroacetic acid; IsoProp, isopropanol; CH3OH, methanol. b The formula for calculated molecular weight derived from m/z in this column. |
表4 正离子模式下棉花代谢产物标准品鉴定Table 4 Putative identification of cotton metabolite standards in positive ion mode |
化合物Compound | 质荷比 m/z | 保留时间 Retention time/min | 加合物 Adduct | 计算的相对分子质量 Calculated molecular weight (CM) | 误差 Error (ΔM)/ 10-6 | 峰强度(均值±标准差) Peak intensity (mean±standard deviation) |
水杨酸 Salicylic acid | 139.039 372 | 9.40 | [M+H]+ | 138.032 096 | -2.9 | 7 978.8±695.4 |
柽柳黄素 Tamarixetin | 317.064 865 | 10.99 | [M+H]+ | 316.057 589 | 2.3 | 79 834.0±2 961.4 |
N6-异戊烯基腺嘌呤 N6-(delta2-isopentenyl) adenine | 204.124 352 | 7.52 | [M+H]+ | 203.117 076 | 0.1 | 122 130.8±3 291.5 |
蜜二糖Melibiose | 365.104 807 | 0.82 | [M+Na]+ | 342.115 589 | 1.8 | 109 513.7±5 559.8 |
360.149 727 | [M+NH4]+ | 342.115 904 | 0.9 | 209 629.9±6 917.8 | ||
343.123 083 | [M+H]+ | 342.115 807 | 1.2 | 12 543.4±919.6 | ||
蔗糖 Sucrose | 365.104 807 | 0.82 | [M+Na]+ | 342.115 589 | 1.8 | 29 171.7±676.7 |
360.149 727 | [M+NH4]+ | 342.115 904 | 0.9 | 63 866.2±4 974.6 | ||
343.123 083 | [M+H]+ | 342.115 807 | 1.2 | 11 979.9±519.5 | ||
紫云英苷 Astragalin | 471.088 494 | 9.14 | [M+Na]+ | 448.099 276 | 2.9 | 11 338.0±362.1 |
449.106 679 | [M+H]+ | 448.099 403 | 2.6 | 70 894.7±2 835.8 | ||
脱落酸 (s)-(+)-abscisic acid | 287.124 907 | 10.05 | [M+Na]+ | 264.135 689 | 1.8 | 10 936.9±945.2 |
265.143 275 | [M+H]+ | 264.135 999 | 0.6 | 26 010.4±499.2 | ||
546.305 596 | 10.03 | [2M+NH4]+ | 264.135 887 | 1.0 | 14 020.0±420.6 | |
551.260 231 | [2M+Na]+ | 264.135 506 | 2.5 | 25 030.7±1 001.2 | ||
529.278 663 | [2M+H]+ | 264.135 694 | 1.8 | 17 756.3±754.8 | ||
282.169 968 | [M+NH4]+ | 264.136 145 | 0.1 | 6 672.3±333.6 | ||
绿原酸 3-O-caffeoylquinic acid | 355.102 131 | 6.22 | [M+H]+ | 354.094 855 | 0.6 | 85 454.1±4272.7 |
377.083 865 | [M+Na]+ | 354.094 537 | 1.2 | 19 178.5±392.8 | ||
377.083 755 | 6.94 | [M+Na]+ | 354.094 725 | 1.5 | 12 596.9±894.4 | |
355.102 001 | [M+H]+ | 354.094 647 | 1.0 | 35 417.9±137.6 | ||
372.128 286 | [M+NH4]+ | 354.094 463 | 1.7 | 20 344.4±1 831.0 | ||
白麻苷 Quercetin 3-sophoroside | 649.136 524 | 7.98 | [M+Na]+ | 626.147 306 | 1.6 | 7 629.1±177.2 |
627.154 853 | [M+H]+ | 626.147 577 | 1.2 | 113 288.4±6 797.3 | ||
咖啡酸Caffeic acid | 181.049 082 | 6.79 | [M+H]+ | 180.041 806 | 2.5 | 14 140.3±289.6 |
青榆烯C Lacinilene C | 247.132 676 | 12.50 | [M+H]+ | 246.125 400 | 0.8 | 514 004.6±20 560.2 |
510.284 037 | [2M+NH4]+ | 246.125 107 | 2.0 | 22 745.4±88.4 | ||
515.239 456 | [2M+Na]+ | 246.125 119 | 1.9 | 146 961.4±4 408.8 | ||
赤霉素A4 Gibberellin A4 | 333.169 188 | 11.19 | [M+H]+ | 332.161 912 | 1.4 | 7 749.0±180.0 |
棉酚 Gossypol | 519.199 742 | 14.63 | [M+H]+ | 518.192 466 | 3.1 | 334 371.6±20 062.3 |
赤霉素A3 Gibberellin A3 | 347.148 415 | 8.56 | [M+H]+ | 346.141 139 | 1.4 | 6 354.0±24.7 |
364.175 788 | 8.57 | [M+NH4]+ | 346.141 638 | -0.9 | 10 071.32±906.4 |
表5 负离子模式下棉花代谢产物标准品鉴定Table 5 Putative identification of cotton metabolite standards in negative ion mode |
化合物Compound | 质荷比 m/z | 保留时间 Retention time/min | 加合物 Adduct | 计算的相对分子质量 Calculated molecular weight (CM) | 误差 Error (ΔM)/ 10-6 | 峰强度(均值±标准差) Peak intensity (mean±standard deviation) |
水杨酸Salicylic acid | 137.025 536 | 9.43 | [M-H]- | 138.032 812 | -8.1 | 198 088.9±5 942.7 |
柽柳黄素Tamarixetin | 631.108 934 | 10.95 | [2M-H]- | 316.058 105 | 0.6 | 109 455.2±4 060.1 |
315.051 258 | 10.95 | [M-H]- | 316.058 534 | -0.7 | 816 319.8±22 000.2 | |
N6-异戊烯基腺嘌呤 N6-(delta2-isopentenyl) adenine | 202.110 473 | 7.54 | [M-H]- | 203.117 749 | -3.2 | 189 001.7±9 595.2 |
341.109 546 | 0.84 | [M-H]- | 342.116 822 | -1.8 | 24 146.5±796.8 | |
蜜二糖Melibiose | ||||||
387.114 900 | 0.84 | [M+FA-H]- | 342.116 699 | -1.4 | 32 762.2±2 401.8 | |
蔗糖Sucrose | 341.109 546 | 0.84 | [M-H]- | 342.116 822 | -1.8 | 279 325.5±6 480.0 |
387.114 900 | 0.84 | [M+FA-H]- | 342.116 699 | -1.4 | 315 114.6±24 544.5 | |
紫云英苷Astragalin | 447.092 879 | 9.19 | [M-H]- | 448.100 155 | 0.9 | 706 162.4±30 621.1 |
895.194 237 | 9.19 | [2M-H]- | 448.100 757 | -0.4 | 61 671.9±1 969.9 | |
脱落酸 (s)-(+)-abscisic acid | 527.262 734 | 10.04 | [2M-H]- | 264.135 005 | 4.4 | 820 955.1±32 838.2 |
263.129 076 | 10.04 | [M-H]- | 264.136 352 | -0.7 | 566 054.7±5 660.5 | |
299.104 968 | 10.06 | [M+Cl]- | 264.135 566 | 2.2 | 11 299.5±216.9 | |
绿原酸3-O-caffeoylquinic acid | 353.086 885 | 6.10 | [M-H]- | 354.094 161 | 2.6 | 160 270.3±4 808.1 |
707.181 308 | 6.18 | [2M-H]- | 354.094 292 | 2.2 | 8 307.3±332.3 | |
375.067 942 | 6.18 | [M+Na-2H]- | 354.093 276 | 5.1 | 6 738.5±286.4 | |
353.087 136 | 6.90 | [M-H]- | 354.094 412 | 1.9 | 214 170.1±4 283.4 | |
707.181 680 | 6.90 | [2M-H]- | 354.094 478 | 1.7 | 12 790.6±639.5 | |
375.068 452 | 6.90 | [M+Na-2H]- | 354.093 786 | 3.7 | 17 940.0±367.4 | |
白麻苷 Quercetin 3-sophoroside | 625.140 555 | 7.98 | [M-H]- | 626.147 831 | 0.8 | 1 433 724.8±37 276.8 |
661.117 307 | 7.98 | [M+Cl]- | 626.147 905 | 0.6 | 171 675.8±667.0 | |
1251.287805 | 7.98 | [2M-H]- | 626.147 541 | 1.2 | 20 615.0±1 855.4 | |
739.123 388 | 7.99 | [M+TFA-H]- | 626.137 802 | 16.8 | 7 925.1±184.1 | |
咖啡酸Caffeic acid | 179.035 573 | 6.71 | [M-H]- | 180.042 849 | -3.3 | 247 971.9±14 878.3 |
179.035 500 | 6.95 | [M-H]- | 180.042 776 | -2.9 | 96 644.4±1 979.3 | |
3-吲哚甲醛 1H-indole-3-carboxaldehyde | 144.047 447 | 9.23 | [M-H]- | 145.054 723 | -13.5 | 781 825.7±31 273.0 |
赤霉素A4 Gibberellin A4 | 663.315 967 | 11.20 | [2M-H]- | 332.161 622 | 2.3 | 1 681 786.4±6 534.3 |
331.154 800 | 11.20 | [M-H]- | 332.162 076 | 0.9 | 872 433.9±26 173.0 | |
995.475 686 | 11.20 | [3M-H]- | 332.160 987 | 4.2 | 5 614.1±130.4 | |
377.159 456 | 11.20 | [M+FA-H]- | 332.161 255 | 3.4 | 66 434.1±3 986.0 | |
367.130 460 | 11.20 | [M+Cl]- | 332.161 058 | 4.0 | 80 332.5±312.1 | |
棉酚Gossypol | 499.173 942 | 14.39 | [M-H2O-H]- | 518.192 332 | 3.4 | 34 406.0±3 096.5 |
517.184 256 | 14.69 | [M-H]- | 518.191 532 | 4.9 | 322 041.7±7 471.0 | |
赤霉素A3 Gibberellin A3 | 691.273 483 | 8.52 | [2M-H]- | 346.140 380 | 3.6 | 1 338 600.7±56 221.2 |
345.133 419 | 8.52 | [M-H]- | 346.140 695 | 2.7 | 936 088.3±40 591.3 | |
1 037.412 910 | 8.52 | [3M-H]- | 346.140 062 | 4.6 | 12 650.3±404.1 | |
381.109 261 | 8.52 | [M+Cl]- | 346.139 859 | 5.1 | 85 687.1±3 427.5 | |
391.138 037 | 8.52 | [M+FA-H]- | 346.139 836 | 5.2 | 41 285.3±1 416.3 |
[1] |
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To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of >20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between- and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.
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Large-scale metabolic profiling is expected to develop into an integral part of functional genomics and systems biology. The metabolome of a cell or an organism is chemically highly complex. Therefore, comprehensive biochemical phenotyping requires a multitude of analytical techniques. Here, we describe a profiling approach that combines separation by capillary liquid chromatography with the high resolution, high sensitivity, and high mass accuracy of quadrupole time-of-flight mass spectrometry. About 2000 different mass signals can be detected in extracts of Arabidopsis roots and leaves. Many of these originate from Arabidopsis secondary metabolites. Detection based on retention times and exact masses is robust and reproducible. The dynamic range is sufficient for the quantification of metabolites. Assessment of the reproducibility of the analysis showed that biological variability exceeds technical variability. Tools were optimized or established for the automatic data deconvolution and data processing. Subtle differences between samples can be detected as tested with the chalcone synthase deficient tt4 mutant. The accuracy of time-of-flight mass analysis allows to calculate elemental compositions and to tentatively identify metabolites. In-source fragmentation and tandem mass spectrometry can be used to gain structural information. This approach has the potential to significantly contribute to establishing the metabolome of Arabidopsis and other model systems. The principles of separation and mass analysis of this technique, together with its sensitivity and resolving power, greatly expand the range of metabolic profiling.
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Quantitative analysis of target compounds with liquid chromatography atmospheric pressure ionization mass spectrometry is sometimes hampered by adduct formation. In these situations, cationization with alkali metal ions instead of proton addition is often observed in the positive ion mode. This work studies the process of adduct formation and investigates potential strategies to control this phenomenon. Paclitaxel, a pharmaceutical chemotherapeutic agent, was used as a model compound. Electrospray (ESI), atmospheric pressure chemical ionization (APCI) and sonic spray ionization (SSI) are evaluated and compared. The work was performed on two different instruments, allowing the evaluation of different ionization behavior for different source design for electrospray, if any. Different mobile phase additives were compared, including acetic acid, formic acid, ammonium formate, and a range of primary amines. Continuous infusion was used for a fast screening, to detect optimal conditions. These were then further investigated in detail by LC-MS. The results indicate that electrospray is the more sensitive interface for this compound on the investigated apparatus. Unacceptable quantitative data were acquired without additives in the mobile phase. Generally, additives increased the reproducibility significantly. A response of mainly one ion was achieved with dodecylamine/acetic acid and acetic acid/sodium acetate. The data also point out the importance of evaluating adduct formation for compounds prone to this phenomenon during method development, especially in view of accurate quantitation.
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The negative ion electrospray ionization mass spectrometric (ESI-MS) detection of adducts of high explosives with chloride, formate, acetate, and nitrate was used to demonstrate the gas-phase interaction of neutral explosives with these anions. The relative intensities of the adduct species were determined to compare the competitive formation of the selected high explosives and anions. The relative stability of the adduct species varies, yielding preferential formation of certain anionic adducts with different high explosives. To exploit this effect, an isocratic high-performance liquid chromatography (HPLC)/ESI-MS method was developed and used for the simultaneous analysis of high explosives using two different techniques for the addition of the anionic additives; pre- and post-column. The results show that the pre-column approach provides similar results with improved selectivity for specific explosives. By detecting characteristic adduct species for each explosive, this method provides a qualitative and quantitative approach for the analysis and identification of high explosives.
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In accomplishing successful electrospray ionization analyses, it is imperative to have an understanding of the effects of variables such as analyte structure, instrumental parameters, and solution composition. Here, we review some fundamental studies of the ESI process that are relevant to these issues. We discuss how analyte chargeability and surface activity are related to ESI response, and how accessible parameters such as nonpolar surface area and reversed phase HPLC retention time can be used to predict relative ESI response. Also presented is a description of how derivitizing agents can be used to maximize or enable ESI response by improving the chargeability or hydrophobicity of ESI analytes. Limiting factors in the ESI calibration curve are discussed. At high concentrations, these factors include droplet surface area and excess charge concentration, whereas at low concentrations ion transmission becomes an issue, and chemical interference can also be limiting. Stable and reproducible non-pneumatic ESI operation depends on the ability to balance a number of parameters, including applied voltage and solution surface tension, flow rate, and conductivity. We discuss how changing these parameters can shift the mode of ESI operation from stable to unstable, and how current-voltage curves can be used to characterize the mode of ESI operation. Finally, the characteristics of the ideal ESI solvent, including surface tension and conductivity requirements, are discussed. Analysis in the positive ion mode can be accomplished with acidified methanol/water solutions, but negative ion mode analysis necessitates special constituents that suppress corona discharge and facilitate the production of stable negative ions.Copyright 2002 Wiley Periodicals, Inc.
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Hydrophilic interaction liquid chromatography (HILIC)/ electrospray ionisation-mass spectrometry (ESI-MS) has gained interest for the analysis of polar analytes in bioanalytical applications in recent years. However, ESI-MS is prone to adduct formation of analytes. In contrast to reversed phase chromatography, small inorganic ions have retention in HILIC, i.e. analytes and inorganic ions may co-elute, which could influence the adduct formation. In the present paper, it was demonstrated that the co-elution of sodium ions or potassium ions and analytes in HILIC/ESI-MS affect the adduct formation and that different concentrations of sodium ions and potassium ions in biological samples could have an impact on the quantitative response of the respective adducts as well as the quantitative response of the protonated adduct. The co-elution also lead to cluster formation of analytes and sodium formate or potassium formate, causing extremely complicated spectra. In analytical applications using HILIC/ESI-MS where internal standards are rarely used or not properly matched, great care needs to be taken to ensure minimal variation of inorganic ion concentration between samples. Moreover, the use of alkali metal ion adducts as quantitative target ions in relative quantitative applications should be made with caution if proper internal standards are not used.Copyright © 2019 Elsevier B.V. All rights reserved.
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Recently, interest in untargeted metabolomics has become prevalent in the general scientific community among an increasing number of investigators. The majority of these investigators, however, do not have the bioinformatic expertise that has been required to process metabolomic data by using command-line driven software programs. Here we introduce a novel platform to process untargeted metabolomic data that uses an intuitive graphical interface and does not require installation or technical expertise. This platform, called XCMS Online, is a web-based version of the widely used XCMS software that allows users to easily upload and process liquid chromatography/mass spectrometry data with only a few mouse clicks. XCMS Online provides a solution for the complete untargeted metabolomic workflow including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Results can be browsed online in an interactive, customizable table showing statistics, chromatograms, and putative METLIN identities for each metabolite. Additionally, all results and images can be downloaded as zip files for offline analysis and publication. XCMS Online is available at https://xcmsonline.scripps.edu.
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An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.
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KNApSAcK. KNApSAcK metabolite information-Gossypium [DB/OL]. (2008-07-01)[2020-06-07]. http://www.knapsackfamily.com/knapsack_core/result.php?sname=organism&word=gossypium.2020/6/7
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CRC Press. Dictionary of natural products 29.1 chemical search[DB/OL].[2020-06-07] http://dnp.chemnetbase.com/faces/chemical/ChemicalSearch.xhtml
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Nucleoside-adduct analysis by liquid chromatography mass spectrometry is a powerful tool in genotoxicity studies. Efforts to date have quantified an impressive array of DNA damage products, although methodological diversity suggests quantification is still a challenging task. For example, inadequate co-examination of normal nucleosides, cumbersome sample preparation and large DNA requirements were identified to be recurring issues. A six-minute ultra-performance liquid chromatography method is presented which adequately separates seven candidate nucleoside-adducts from the four unmodified nucleosides. The method was sensitive to 1 adduct per 10 normal bases with 20 µg DNA input for most targets. The method was shown to be accurate (81-119% across quintuplets of six tissue types) and precise (relative standard deviation 4-13%). The fast method time facilitated a second quantitation for normal nucleosides at an appropriate dilution, allowing DNA damage concentrations to be contextualised accurately sample-to-sample. From DNA samples, the analytical processing time was < 8 h, and 96 samples can easily be prepared in a day. The method was used to quantify carbonyl, chloro- and oxo- adducts in murine tissue samples.Copyright © 2018 Elsevier B.V. All rights reserved.
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