Surface interactions largely control how biomaterials interact with biology and how many other types of materials function in industrial applications. ToF-SIMS analysis is extremely useful for interrogating the surfaces of complex materials and shows great promise in analyzing biological samples. Previously, the authors demonstrated that segmentation (between 1 and 0.005 m/z mass bins) of the mass spectral axis can be used to differentiate between polymeric materials with both very similar and dissimilar molecular compositions. Here, the same approach is applied for the analysis of proteins on surfaces, focusing on the effect of binding and orientation of an antibody on the resulting ToF-SIMS spectrum. Due to the complex nature of the samples that contain combinations of only 20 amino acids differing in sequence, it is enormously challenging and prohibitively time-consuming to distinguish the minute variances presented in each dataset through manual analysis alone. Herein, the authors describe how to apply the newly developed rapid data analysis workflow to previously published ToF-SIMS data for complex biological materials, immobilized antibodies. This automated method reduced the analysis time by two orders of magnitudes while enhancing data quality and allows the removal of any user bias. The authors used mass segmentation at 0.005 m/z over a 1-300 mass range to generate 60 000 variables. In contrast to the previous manual binning approach, this method captures the entire mass range of the spectrum resulting in an information-rich dataset rather than specifically selected mass spectral peaks. This work constitutes an additional proof of concept that rapid and automated data analyses involving mass-segmented ToF-SIMS spectra can efficiently and robustly analyze a broader range of complex materials, ranging from generic polymers to complicated biological samples. This automated analysis method is also ideally positioned to provide data to train machine learning models of surface-property relationships that can greatly enhance the understanding of how the surface interacts with biology and provides more accurate and robust quantitative predictions of the biological properties of new materials.

中文翻译：

---

使用自动质量细分的ToF-SIMS快速评估固定的免疫球蛋白。

表面相互作用很大程度上控制了生物材料如何与生物学相互作用以及在工业应用中有多少其他类型的材料起作用。ToF-SIMS分析对于询问复杂材料的表面非常有用，并且在分析生物样品中显示出巨大的希望。以前，作者证明了质谱轴的分段（在1和0.005 m / z质量区间之间）可用于区分具有非常相似和不相似分子组成的聚合物材料。在此，将相同的方法应用于表面蛋白质的分析，重点是抗体的结合和取向对所得ToF-SIMS光谱的影响。由于样品的复杂性质，其中仅包含序列不同的20个氨基酸的组合，仅通过手动分析来区分每个数据集中呈现的微小差异是巨大的挑战，并且非常耗时。在此，作者描述了如何将新开发的快速数据分析工作流程应用于先前发布的复杂生物材料固定化抗体的ToF-SIMS数据。这种自动化方法将分析时间减少了两个数量级，同时提高了数据质量，并消除了任何用户偏见。作者在1-300质量范围内使用0.005 m / z的质量分割生成了60 000个变量。与以前的手动分箱方法相比，此方法捕获质谱图的整个质量范围，从而生成信息丰富的数据集，而不是专门选择的质谱峰。这项工作构成了另外一个概念证明，即涉及质量分段ToF-SIMS光谱的快速和自动化数据分析可以有效，稳健地分析范围更广的复杂材料，从通用聚合物到复杂的生物样品。这种自动分析方法的位置也非常理想，可以提供数据来训练表面属性关系的机器学习模型，从而可以极大地增进对表面与生物学相互作用的理解，并提供对新材料生物学特性的更准确和更可靠的定量预测。