MS-DIAL FAQ
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- ゆたか おおかわち
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1 GC-MS データの詳細 Cnidium officinale Makino 川芎 ( センキュウ )
2 メタボロミクスの行程概要 (GC/MS の場合 )
3 メタボロミクスの抽出工程
4 メタボロミクスの誘導体化行程 E-isomer Z-isomer Glucose + 50 ml methoxyamine (20 mg/ml pyridine) + 50 ml MSTFA Vortex 10 s Shaking (30 C, 90 min) E-isomer Z-isomer Vortex 10 s Shaking (37 C, 30 min) Transfer 60 ml Centrifugation (3 min; 16,000 rcf)
5 GC/MS の分析条件 Injection order 1QC + 5samples Alkane mix Procedure brank QC QC QC QC QC injection 30 min / sample min injection Dunn W.B. et al., Nature Protocols 6 (2011) Column CPSil-8CB MS 30 m 0.25 mm i.d.; 0.25 μm film Inlet temp. 230 C Gas flow 1.12 ml/min Transfer line temp. 250 C Ion source templ. 200 C Scan rate 20 Hz Mass range m/z
6 Alkane mix は保持指標計算に必要 n-alkane standart mixture 1 (C8-C20) 50 ml (Sigma 04070) n-alkane standard mixture 2 (C21-C40) 50 ml (Sigma 04070) pyridine (GC-MS 誘導体化用 ) 50 ml 計 150 ml
7 netcdf (AIA) は各ベンダーソフトで出力可
8 Overview of common data formats 8
9 netcdf を直接読み込むための準備 9
10 保持指標の互換性
11 LC-MS リピドミクスデータの詳細 9 algal species Euglena gracilis (UTEX B367) Cricosphaera carterae (UTEX LB1014) Nannochloropsis oculata (UTEX LB2164) Dunaliella salina (UTEX LB200) Pavlova lutheri (UTEX 1293) Chlamydomonas reinhardtii(cc-125) Originally Chlorella minutissima (UTEX 2341) Chlorella sorokiniana (UTEX 2805) Chlorella variabilis (ATCC NC64A)
12 Mobile phase B(%) LC-MS 分析条件 (reverse phase LC) 100 TAG, CE min / sample LysoPL PL, SM, DG, MGDG, DGDG min Separation of lipids Column: Mobile phase A: Mobile phase B: Oven temp.: 65 C Flow rate: Injection volume: ACQUITY UPLC CSH C18 ( mm; 1.7 mm) 60:40 ACN:H 2 O + 10 mm AmF + 0.1% FA 90:10 IPA:ACN + 10 mm AmF + 0.1% FA 0.6 ml/min 3 ml Sample temp.: 4 C * ACN: Acetonitrile IPA: 2-Propanol AmF: Ammonium formate FA: Formic acid
13 Overview of common data formats 13
14 脂質構造を定義するフラグメントイオン (Negative) 14
15 脂質構造を定義するフラグメントイオン (Positive) 15
16 リピドミクスにおける脂質の命名法 16
17 LC-MS ワインデータの詳細
18 アノテーションとは?
19 アノテーションの第一選択 : スペクトル DB MS/MS database ID Name Number of MS/MS spectra Number of compounds Online search Freely Instrument available diversity 1 NIST14 MS/MS 234,284 9,344 - $ MassBank 26,296 3, Metlin 67,428 13, /$ + 4 MONA 194,000 68, mzcloud 209,000 2, GNPS 9,000 8, Respect 9,000 4, MSforID 20,000 1,200 - $ + 9 HMDB 5,773 3, MetaMS Sumner Plant Lib 1, ChemicalSoft 1,619 6, /$ + 13 Designer Drugs 10, $ + 14 Drugs/Poisons 10,000 6,816 - $ + 15 DTU Microbials WEISMS 3,540 3, /$ + Biomolecule database ID Name Species Number of compounds 1 BMDB Bovine ChEBI Small biomolecules DrugBank Drug ECMDB E. coli FooDB Food HMDB Human KNApSAcK Natural product PlantCyc Plant PubChem Small biomolecules SMPDB Small biomolecules T3DB Toxic UNPD Natural product YMDB Yeast STOFF Environment 8515
20 アノテーションの第一選択 : スペクトル DB MoNA: MassBank of North America
21 アノテーションの第一選択 : スペクトル DB MS-DIAL start up kit
22 How to annotate metabolites 22 Nature methods, 15, 53-56, 2018; Current opinion in biotechnology 54, 10-17, 2018
23 Database comparison MS/MS database ID Name Number of MS/MS spectra Number of compounds Online search Freely Instrument available diversity 1 NIST14 MS/MS 234,284 9,344 - $ MassBank 26,296 3, Metlin 67,428 13, /$ + 4 LipidBlast TRAP 212, , LipidBlast TOF 135,704 61, MONA 194,000 68, mzcloud 209,000 2, GNPS 9,000 8, Respect 9,000 4, MSforID 20,000 1,200 - $ + 11 HMDB 5,773 3, MetaMS Sumner Plant Lib 1, ChemicalSoft 1,619 6, /$ + 15 Designer Drugs 10, $ + 16 Drugs/Poisons 10,000 6,816 - $ + 17 DTU Microbials WEISMS 3,540 3, /$ + Compound database ID Name Species Number of compounds 1 BMDB Bovine ChEBI Small biomolecules DrugBank Drug ECMDB E. coli FooDB Food HMDB Human KNApSAcK Natural product PlantCyc Plant PubChem Small biomolecules SMPDB Small biomolecules T3DB Toxic UNPD Natural product YMDB Yeast STOFF Environment ,297,635
24 Why are there so many unknowns? 24
25 In silico based annotation (top-down approach) 25 Analytical Chemistry 88, , 2016
26 Relative abundance (%) Relative abundance (%) Relative abundance (%) Relative abundance (%) この MS/MS スペクトルの元構造は何でしょう? 1.Negative, <10ppm, Human 2.Positive, <3ppm, Drug m/z m/z m/z 3.Negative, <10ppm, Human Positive, <3ppm, Plant (Onion) m/z m/z 26
27 Relative abundance (%) 組成式候補の出力 1.Negative, <10ppm, Human m/z m/z ±0.005 as [M-H] - C x H y N z O u S v P w C: 12 H: N: O: S: P: e-:
28 組成式の絞り込み : ルールの活用 原子価測の確認 例 )C 8 H 7 NO 4 S TA: = 21 OV: 7+1 = 8 SV: =56 元素の総数を TA 原子価が奇数の元素の総数を OV 原子価の総数を SV としたとき, OV * SV = even SV >= 2* (TA - 1) ただし,N(3), O(2), P(5), S(6) 経験的閾値 Mass H/C N/C O/C P/C S/C Max Min Mean Stdev Median Percentile 0.15% Percentile 0.005% Percentile 99.85% Percentile %
29 組成式の絞り込み : ルールの活用 原子価測の確認 例外 )C 15 H 11 O 6 TA: = 32 OV: 11 = 11 SV: =83 -> ルールには例外がつきもの! ということを, 理解しておく. 元素の総数を TA 原子価が奇数の元素の総数を OV 原子価の総数を SV としたとき, OV * SV = even SV >= 2* (TA - 1) ただし,N(3), O(2), P(5), S(6) 経験的閾値 Mass H/C N/C O/C P/C S/C Max Min Mean Stdev Median Percentile 0.15% Percentile 0.005% Percentile 99.85% Percentile %
30 3 ヶ月前に受けた質問への回答 C15H11O6+ は原子価則を満たすのではないか ということに関して, ここに整理します. (3ヶ月前の講習会にて) まず,C15H11O6+ と, + を書いてしまっているのが, 混乱の原因だったかと思います. C15H11O6+ と書くと, この組成式は原子価則を満たします. 一方, 前提条件として, 私が作っているMS-FINDERというプログラムは, m/zとアダクトタイプを指定することで, ニュートラル ( 電荷を持たない状態 ) での組成式を出力する というものになります. 出していた構造は, シアニジン Exact mass as C15H11O6+: となっておりますが, アダクトタイプは [M]+ と与えれば, 正解組成式が組成式候補として含まれます. もちろん [M+H]+ としてしまうと, 正解組成式が出力されない, というのはご理解いただけると思います. 一方,[M]+ と考えた場合です. このプログラムが出力する組成式の中で, もちろん, C15H11O6: を候補として考えます. しかし, ここでのポイントは, この組成式は, ニュートラル な構造として解釈されることになります. この場合において, C15H11O6は資料に示すように, 原子価則を満たさないものとして候補から除外されます. 一方, 出力組成式を C15H11O6ではなく,C15H11O6+として考える場合,Hを一つ補完して考えれば, ご指摘のように原子価則を満たすことになります. ですが, プログラムの仕様上, 原子価則を適用する というオプションをつけて組成式を 30 計算させてしまうと, 上記の理由から, この正解組成式は出力されません.
31 Relative abundance (%) Relative abundance (%) Relative abundance (%) Relative abundance (%) 組成式の決定 : コンピューターを使いましょう 1.Negative, <10ppm, Human 2.Positive, <3ppm, Drug C 9 H 15 N 3 O 10 P 2 80 C 33 H 40 N 2 O m/z m/z 3.Negative, <10ppm, Human Positive, <3ppm, Plant (Onion) C 8 H 7 NO 4 S 80 C 13 H 23 N 9 O 4 S m/z m/z 31
32 In silico based annotation (top-down approach) 32 Analytical Chemistry 88, , 2016
33 In silico based annotation (top-down approach) 33 Analytical Chemistry 88, , 2016
34
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