Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets.
Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets.
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DOI：
10.1021/acs.analchem.1c04898
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发表时间：
2022-03-22
影响因子：
7.4
通讯作者：
Scurr DJ
中科院分区：
文献类型：
--
作者：
Edney MK;Kotowska AM;Spanu M;Trindade GF;Wilmot E;Reid J;Barker J;Aylott JW;Shard AG;Alexander MR;Snape CE;Scurr DJ
现代质谱技术产生了丰富的光谱数据，虽然这在可用信息的丰富性方面是一个优势，但数据的数量和复杂性可能会阻碍彻底的解释，从而得出有用的结论。应用分子式预测（MFP）来产生已通过其元素组成过滤并考虑结构双键等效性的离子的注释列表被广泛用于高分辨率质谱数据集。然而，这还没有被应用到二次离子质谱数据。在这里，我们将这种数据解释方法应用于3D OrbiSIMS数据集，并针对一系列日益复杂的样本进行测试。在无机样品上的有机物中，我们成功地将有机污染物覆盖层与基底分开注释。在更具挑战性的纯有机人血清样品中，我们根据元素组成过滤出蛋白质和脂质，使用现有数据库鉴定和验证了226种不同的脂质，并分配了丰富的血清蛋白质（包括白蛋白、纤连蛋白和转铁蛋白）的氨基酸序列。最后，我们测试的方法从层状碳质发动机存款的深度剖面数据和注释以前未识别的润滑油种类。在从该样品执行MFP之后，将无监督机器学习方法应用于过滤的离子，独特地分离了物种的深度分布，这在对整个数据集执行该方法时没有观察到。总的来说，使用MFP的化学过滤方法在从大量材料类型中全面解释复杂的3D OrbiSIMS数据集方面具有巨大的潜力。
Modern mass spectrometry techniques produce a wealth of spectral data, and although this is an advantage in terms of the richness of the information available, the volume and complexity of data can prevent a thorough interpretation to reach useful conclusions. Application of molecular formula prediction (MFP) to produce annotated lists of ions that have been filtered by their elemental composition and considering structural double bond equivalence are widely used on high resolving power mass spectrometry datasets. However, this has not been applied to secondary ion mass spectrometry data. Here, we apply this data interpretation approach to 3D OrbiSIMS datasets, testing it for a series of increasingly complex samples. In an organic on inorganic sample, we successfully annotated the organic contaminant overlayer separately from the substrate. In a more challenging purely organic human serum sample we filtered out both proteins and lipids based on elemental compositions, 226 different lipids were identified and validated using existing databases, and we assigned amino acid sequences of abundant serum proteins including albumin, fibronectin, and transferrin. Finally, we tested the approach on depth profile data from layered carbonaceous engine deposits and annotated previously unidentified lubricating oil species. Application of an unsupervised machine learning method on filtered ions after performing MFP from this sample uniquely separated depth profiles of species, which were not observed when performing the method on the entire dataset. Overall, the chemical filtering approach using MFP has great potential in enabling full interpretation of complex 3D OrbiSIMS datasets from a plethora of material types.
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影响因子：
48
作者：
Passarelli, Melissa K.;Pirkl, Alexander;Gilmore, Ian S.
通讯作者：
Gilmore, Ian S.
DOI：
10.1073/pnas.0805069105
发表时间：
2008-11-25
影响因子：
11.1
作者：
Marshall, Alan G.;Rodgers, Ryan P.
通讯作者：
Rodgers, Ryan P.
影响因子：
9.5
作者：
Edney, Max K.;Lamb, Joseph S.;Scurr, David J.
通讯作者：
Scurr, David J.
影响因子：
2.1
作者：
Graham, Daniel J.;Castner, David G.
通讯作者：
Castner, David G.
影响因子：
16.6
作者：
Kotowska AM;Trindade GF;Mendes PM;Williams PM;Aylott JW;Shard AG;Alexander MR;Scurr DJ
通讯作者：
Scurr DJ