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Ex-vivo molecular profiling has recently emerged as a promising method for intraoperative tissue identification, especially in neurosurgery. The short-term storage of resected samples at room temperature is proposed to have negligible influence on the lipid molecular profiles. However, a detailed investigation of short-term molecular profile stability is required to implement molecular profiling in a clinic. This study evaluates the effect of storage media, temperature, and washing solution to determine conditions that provide stable and reproducible molecular profiles, with the help of ambient ionization mass spectrometry using rat cerebral cortex as model brain tissue samples. Utilizing normal saline for sample storage and washing media shows a positive effect on the reproducibility of the spectra; however, the refrigeration shows a negligible effect on the spectral similarity. Thus, it was demonstrated that up to hour-long storage in normal saline, even at room temperature, ensures the acquisition of representative molecular profiles using ambient ionization mass spectrometry.

Summary: Mass spectrometry (MS) methods are widely used for the analysis of biological and medical samples. Recently developed methods, such as DESI, REIMS and NESI allow fast analyses without sample preparation at the cost of higher variability of spectra. In biology and medicine, MS profiles are often used with machine learning (classification, regression, etc.) algorithms and statistical analysis, which are sensitive to outliers and intraclass variability. Here, we present spectra similarity matrix (SSM) Display software, a tool for fast visual outlier detection and variance estimation in mass spectrometric profiles. The tool speeds up the process of manual spectra inspection, improves accuracy and explainability of outlier detection, and decreases the requirements to the operator experience. It was shown that the batch effect could be revealed through SSM analysis and that the SSM calculation can also be used for tuning novel ion sources concerning the quality of obtained mass spectra., Availability and Implementation: Source code, example datasets, binaries and other information are available at https://github.com/EvgenyZhvansky/R_matrix., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Recently, mass-spectrometry methods show its utility in tumor boundary location. The effect of differences between research and clinical protocols such as low- and high-resolution measurements and sample storage have to be understood and taken into account to transfer methods from bench to bedside. In this study, we demonstrate a simple way to compare mass spectra obtained by different experimental protocols, assess its quality, and check for the presence of outliers and batch effect in the dataset. We compare the mass spectra of both fresh and frozen-thawed astrocytic brain tumor samples obtained with the inline cartridge extraction prior to electrospray ionization. Our results reveal the importance of both positive and negative ion mode mass spectrometry for getting reliable information about sample diversity. We show that positive mode highlights the difference between protocols of mass spectra measurement, such as fresh and frozen-thawed samples, whereas negative mode better characterizes the histological difference between samples. We also show how the use of similarity spectrum matrix helps to identify the proper choice of the measurement parameters, so data collection would be kept reliable, and analysis would be correct and meaningful., (© 2020 John Wiley & Sons, Ltd.)

In this work, we demonstrate a new approach for interactively assessing hyperspectral data spatial structures for heterogeneity using mass spectrometry imaging. This approach is based on the visualization of the cosine distance as the similarity levels between mass spectra of a chosen region and the rest of the image (sample). The applicability of the method is demonstrated on a set of mass spectrometry images of frontal mouse brain slices. Selection of the reference pixel of the mass spectrometric image and a further view of the corresponding cosine distance map helps to prepare supporting vectors for further analysis, select features, and carry out biological interpretation of different tissues in the mass spectrometry context with or without histological annotation. Visual inspection of the similarity maps reveals the spatial distribution of features in tissue samples, which can serve as the molecular histological annotation of a slide.

Express MS identification of biological tissues has become a much more accessible research method due to the application of direct specimen ionization at atmospheric pressure. In contrast to traditional methods of analysis employing GC-MS methods for determining the molecular composition of the analyzed objects it eliminates the influence of mutual ion suppression. Despite significant progress in the field of direct MS of biological tissues, the question of mass spectrometric profile attribution to a certain type of tissue still remains open. The use of modern machine learning methods and protocols (e.g., "random forests") enables us to trace possible relationships between the components of the sample MS profile and the result of brain tumor tissue classification (astrocytoma or glioblastoma). It has been shown that the most pronounced differences in the mass spectrometric profiles of these tumors are due to their lipid composition. Detection of statistically significant differences in lipid profiles of astrocytoma and glioblastoma may be used to perform an express test during surgery and inform the neurosurgeon what type of malignant tissue he is working with. The ability to accurately determine the boundaries of the neoplastic growth significantly improves the quality of both surgical intervention and postoperative rehabilitation, as well as the duration and quality of life of patients.

The development of perspective diagnostic techniques in medicine requires efficient high-throughput biological sample analysis methods. Here, we present an inline cartridge extraction that facilitates the screening rate of mass spectrometry shotgun lipidomic analysis of tissue samples. We illustrate the method by its application to tumor tissue identification in neurosurgery. In perspective, this high-performance method provides new possibilities for the investigation of cancer pathogenesis and metabolic disorders.

The majority of research in the biomedical sciences is carried out with the highest resolution accessible to the scientist, but, in the clinic, cost constraints necessitate the use of low-resolution devices. Here, we compare high- and low-resolution direct mass spectrometry profiling data and propose a simple pre-processing technique that makes high-resolution data suitable for the development of classification and regression techniques applicable to low-resolution data, while retaining high accuracy of analysis. This work demonstrates an approach to de-noising spectra to make the same representation for both high- and low-resolution spectra. This approach uses noise threshold detection based on the Tversky index, which compares spectra with different resolutions, and minimizes the percentage of resolution-specific peaks. The presented method provides an avenue for the development of analytical algorithms using high-resolution mass spectrometry data, while applying these algorithms in the clinic using low-resolution mass spectrometers., (© 2019 Published by Elsevier B.V. on behalf of The Association for Mass Spectrometry: Applications to the Clinical Lab (MSACL).)

In this work, we demonstrate a new approach for assessing the stability and reproducibility of mass spectra obtained via ambient ionization methods. This method is suitable for both comparing experiments during which only one mass spectrum is measured and for evaluating the internal homogeneity of mass spectra collected over a period of time. The approach uses Pearson's r coefficient and the cosine measure to compare the spectra. It is based on the visualization of dissimilarities between measurements, thus leading to the analysis of dissimilarity patterns. The cosine measure and correlations are compared to obtain better metrics for spectra homogeneity. The method filters out unreliable scans to prevent the analyzed sample from being wrongly characterized. The applicability of the method is demonstrated on a set of brain tumor samples. The developed method could be employed in neurosurgical applications, where mass spectrometry is used to monitor the intraoperative tumor border.