Ambient ionization mass spectrometry was proved to be a powerful tool for oncological surgery. Still, it remains a translational technique on the way from laboratory to clinic. Brain surgery is the most sensitive to resection accuracy field since the balance between completeness of resection and minimization of nerve fiber damage determines patient outcome and quality of life. In this review, we summarize efforts made to develop various intraoperative support techniques for oncological neurosurgery and discuss difficulties arising on the way to clinical implementation of mass spectrometry-guided brain surgery.

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

Rapid and reliable methods for detecting tumor margins are crucial for neuro-oncology. Several mass spectrometry-based methods have been recently proposed to address this problem. Inline Cartridge Extraction (ICE) demonstrates the potential for clinical application, based on ex-vivo analysis of dissected tissues, but requires timeconsuming steps to avoid cross-contamination. In this work, a method of incorporating a disposable electrospray emitter into the ICE cartridge by PEEK sleeves melting is developed. It reduces total analysis time and improves throughput. The proposed setup also improves the robustness of the ICE molecular profiling as demonstrated with human glial tumor samples in that stability and reproducibility of the spectra were increased.

The automatic processing of high-dimensional mass spectrometry data is required for the clinical implementation of ambient ionization molecular profiling methods. However, complex algorithms required for the analysis of peak-rich spectra are sensitive to the quality of the input data. Therefore, an objective and quantitative indicator, insensitive to the conditions of the experiment, is currently in high demand for the automated treatment of mass spectrometric data. In this work, we demonstrate the utility of the Shapley value as an indicator of the quality of the individual mass spectrum in the classification task for human brain tumor tissue discrimination. The Shapley values are calculated on the training set of glioblastoma and nontumor pathological tissues spectra and used as feedback to create a random forest regression model to estimate the contributions for all spectra of each specimen. As a result, it is shown that the implementation of Shapley values significantly accelerates the data analysis of negative mode mass spectrometry data alongside simultaneous improving the regression models' accuracy.

Mass spectrometry fingerprinting combined with multidimensional data analysis has been proposed in surgery to determine if a biopsy sample is a tumor. In the specific case of brain tumors, it is complicated to obtain control samples, leading to model overfitting due to unbalanced sample cohorts. Usually, classifiers are trained using a single measurement regime, most notably single ion polarity, but mass range and spectral resolution could also be varied. It is known that lipid groups differ significantly in their ability to produce positive or negative ions; hence, using only one polarity significantly restricts the chemical space available for sample discrimination purposes. In this work, we have developed an approach employing mass spectrometry data obtained by eight different regimes of measurement simultaneously. Regime-specific classifiers are trained, then a mixture of experts techniques based on voting or mean probability is used to aggregate predictions of all trained classifiers and assign a class to the whole sample. The aggregated classifiers have shown a much better performance than any of the single-regime classifiers and help significantly reduce the effect of an unbalanced dataset without any augmentation.

Background: Ambient ionisation mass spectrometry, in combination with machine learning techniques, provides a promising tool for rapid intraoperative tumour tissue identification. However, deficiency of non-tumour control samples leads to the classifiers overfitting, especially in neurosurgical applications. Ensemble learning approaches based on the analysis of multimodal mass spectrometry data are able to overcome the overfitting problem through the extended time of data acquisition. In this work, the contribution of each regime of the data acquisition and the requirements for the metrics for further mass spectrometry set-up optimisation are evaluated. Methods: Two independent datasets of the multimodal molecular profiles, a total of 81 glial tumour and non-tumour pathological tissues, were analysed in a cross-validation set-up. The XGboost algorithm was used to build classifiers, and their performance was evaluated using different testing and validating sets. The individual classifiers for each mass spectrometry regime were aggregated into joint classifiers. The impact of each regime was evaluated by the exclusion of specific regimes from the aggregation. Results: The aggregated classifiers with excluded regimes show lower accuracy for most, but not all, excluded regimes. False positive rates have been found to be increased in most cases proving the strong effect of the ensemble learning approach on the overcoming of the “small sample size” problem. Conclusions: The impact of each group of regimes – with different ion polarity, resolution or mass range of spectra was found to be non-linear. It might be attributed to biochemical reasons as well as to the physical limitation of mass analysers. The required metrics for the evaluation of each regime contribution to the classification efficiency should be a numerical estimation of how the classifier depends on any given regime and could not be estimated only by excluding any group of regimes at all.

Ambient ionization mass spectrometry has become one of the most promising approaches for rapid and high-throughput screening of small molecules in complex biological matrices for emergency medicine, forensics, and food and agriculture applications. The simple procedures for sample collection and ionization without additional pretreatment are vital in these fields. Many efforts have been devoted to modifying various ambient ionization techniques to simplify the procedures and improve the robustness and sensitivity of the methods. Here, we demonstrate the implementation of rigid spherical sampler probes to improve the robustness of touch spray ionization mass spectrometry. The sphericity of the probes increases the stability of the cone-jet mode of electrospray, reduces the requirements for fine positioning of a sampler in the ion source, and decreases the possibility of corona discharge occurrence. The utilization of spherical sampler probes allows fast, non-invasive sampling, followed by rapid analysis for various drugs of different chemical classes in complex biological matrices, such as the whole blood or sebum collected from the skin surface. The linearity of the analytical signal response from drug concentration confirms the possibility of creating a simple semiquantitative method for small molecules monitoring using spherical sampler probes.

The continuous monitoring of health conditions before, during, and after spaceflight is an important task in gravitational physiology. Thus a simple, non-invasive method for biological samples collection is required, but the specific limitations, such as weight and volume restrictions, limit the variety of suitable samplers. The physiological changes exist at microgravity (μG) conditions and could influence drug pharmacokinetics. Here we present the application of lightweight spherical sampler probes for the simplified sampling of human saliva with further detection of exogenous small molecules by the ambient mass spectrometry. The rigidity and spherical form of the sampler probes allow collecting saliva smears or biological fluids without additional instruments and improve the stability of the ionization process leading to the increased sensitivity of the analysis. The detection and quantitation of acetaminophen, which is used as a model substance, was proved to demonstrate the analytical capabilities of the proposed method to the saliva smears or whole blood samples even after long-term storage at room temperature. The further investigation of drug pharmacokinetic at μG conditions might be provided by means of proposed samplers.

Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a dataset of 124 high-resolution negative ionization mode lipid profiles of human brain tumors resected during neurosurgery. The dataset is supplemented with 38 non-tumor pathological brain tissue samples resected during elective surgery. The change in lipid composition alterations of brain tumors enables the possibility of discriminating between malignant and healthy tissues with the implementation of ambient mass spectrometry. On the other hand, the collection of clinical samples allows the comparison of the metabolism alteration patterns in animal models or in vitro models with natural tumor samples ex vivo. The presented dataset is intended to be a data sample for bioinformaticians to test various data analysis techniques with ambient mass spectrometry profiles, or to be a source of clinically relevant data for lipidomic research in oncology.

Background: Recently developed methods of ambient ionization allow rapid obtaining of large mass spectrometric datasets, which have a great application in biological and medical analysis. One of the areas that could employ such analysis is neurosurgery. The fast in situ identification of dissected tissues could assist the neurosurgery procedure. The additional information about tumor could help the tumor border monitoring. In this paper, tumor tissues of astrocytoma and glioblastoma are compared, as their identifications during surgery could influence the extent of resection and, hence, the median and overall survival. Methods: Mass spectrometric profiles of brain tumor tissues contain molecular information, which is rather hard to interpret in terms of identifications of individual molecules. The machine learning algorithms are employed for the fast automated mass spectra classification. Different algorithms of dimensionality reduction are considered to process the mass spectra before the classification task, as the initial dimensionality of mass spectra is too high compared with the number of mass spectra. Results: Different classifiers are compared for both just preprocessed data and after dimensionality reduction. The Non-Negative Matrix Factorization appears to be the most effective dimensionality reduction algorithm. The random forest algorithm demonstrated the most robust appearance on the tested data. Also, the comparison of the accuracy of the trained classifier on the mass spectra of tissues measured with different instruments and different resolution is provided in the paper. Conclusions: Machine learning classifiers overfit the raw mass spectrometric data. The dimensionality reduction allows the classification of both train and test data with 88% accuracy. Positive mode data provides better accuracy. A combination of principal component analysis and AdaBoost algorithms appears to be most robust to changing the instrument and conditions.

Tumor cell percentage (TCP) is an essential characteristic of biopsy samples that directly affects the sensitivity of molecular testing in clinical practice. Apart from clarifying diagnoses, rapid evaluation of TCP combined with various neuronavigation systems can be used to support decision making in neurosurgery. It is known that ambient mass spectrometry makes it possible to rapidly distinguish healthy from malignant tissues. In connection with this, here we demonstrate the possibility of using non-imaging ambient mass spectrometry to evaluate TCP in glial tumor tissues with a high degree of confidence. Molecular profiles of histologically annotated human glioblastoma tissue samples were obtained using the inline cartridge extraction ambient mass spectrometry approach. XGBoost regressors were trained to evaluate tumor cell percentage. Using cross-validation, it was estimated that the TCP was determined by the regressors with a precision of approximately 90% using only low-resolution data. This result demonstrates that ambient mass spectrometry provides an accurate method todetermine TCP in dissected tissues even without implementing mass spectrometry imaging. The application of such techniques offers the possibility to automate routine tissue screening and TCP evaluation to boost the throughput of pathology laboratories.

Background: The development of direct ambient ionization methods makes way for fast mass-spectrometry profiling of biological samples, which has great potential in medicine. Those methods, unlike traditional mass spectrometric analysis with chromatographic separation, are not able to take into account inter-ion interaction, ion suppression, and matrix effect due to the absence of chromatographic separation of the mixture components. So dynamics of ion current during direct ambient ionization mass-spectra is governed by the component micro-extraction and electrospray ionization influenced by the geometry of the sample, its position, and internal heterogeneity. Despite the progress in mass-spectrometry of biological samples, not much is known about the influence of sample type and structure on its molecular profile peculiarities. Methods: In this work, we propose to use analysis of the correlation between individual ion currents for a better understanding of ion current variability sources and grouping ions of high biological importance. Several fragments of glioblastoma tissue from a single patient are used for these purposes. Results: Ion currents have different dynamics considering different ions in different fragments. The correlation of two selected ion currents could be positive or negative for single fragment measurement. Correlations have persistent or alternating signs in different fragments for two selected ions. The spread of correlations of each pair of ion currents is calculated for evaluation of the signs’ stability. Conclusions: We were able to group ions according to the primary reason for their variabilities such as micro-extraction, mass-spectrometry measurement, or specimens' properties. Such grouping would allow the development of more reliable and reproducible methods of mass-spectrometry data analysis and improve the accuracy of results of its application in medicine.

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.

The present work deals with deconvolution of interfered peaks of protonated and sodiated phosphatidylcholines to solve the problem of relative quantitation of these compounds. The method was developed based on the fact that adducts of phosphatidylcholines with proton and sodium ion give unique characteristic peaks in tandem mass spectrometry. Ultra-high-resolution Fourier-transform ion cyclotron resonance mass spectrometry was used to resolve interfered peaks and thus validate the results of tandem mass spectrometry-based deconvolution. Applicability of the method was tested with synthesized phosphatidylcholines and applied for tissue-spray analysis of the clinical samples and demonstrated good correlation (0.996) between MS/MS and Fourier-transform ion cyclotron resonance mass spectrometry results.

Several years ago the photoinduced reaction of mixed phosphonium‐iodonium ylides (1) with acetylenes (2) to give λ5‐phosphinolines (3) and substituted furans (4) was described. This reaction is one‐pot, metal‐free synthesis of heterocycles 3 and 4 with the yields of 40% to 80%. The reaction proceeds only in dichloromethane (DCM) at the high ylide concentrations (>0.01 mol/L). The product analysis by 31P NMR, electrospray ionization mass spectrometry, UV‐vis spectrophotometry, and the dynamic light scattering study of the self‐aggregation of the ylide in DCM showed a dual role of the solvent in the photoinduced reactions of mixed phosphonium‐iodonium ylide: (i) at the low ylide concentrations (<0.01 mol/L), the conjugated photoinitiation of the chain reaction in DCM results in the formation of chlorine‐containing products and (ii) at the high ylide concentrations (>0.01 mol/L), the photolysis mechanism is determined by self‐organization of the ylide molecules to give large stable aggregates in DCM, in which the target heterocycles are synthesized. Two important issues follow from the study. First, the annulation reaction between mixed phosphonium‐iodonium ylide and acetylenes occurs only when the reactive intermediates are in close proximity to one other, and, second, DCM is not inert reagent in reactions occurring with participation of radicals and in one form or another can participate in photoinduced radical reactions of various solutes.

Detection of the brain tumor margins is one of the most significant problems in neurosurgery. Several mass spectrometry-based approaches have been proposed recently for tumor boundary detection. One of them, spray from tissue does not require sample preparation but needs special algorithms for analysis of its spectra. Here we proposed the feature selection algorithm designed for analysis of spray-from-tissue data.

Real-time feedback about dissected tissue during the neurosurgical procedure is strongly requested. A novel direct ionization mass spectrometric method for identifying pathological differences in tissues is proposed. The method is based on simultaneous extraction of tissue lipids and electrospray ionization which allows mass spectrometric data to be obtained directly from soft tissues. The advantage of this method is the stable flow of solvent, which leads to stable time-dependent spectra. The tissues included necrotized tissues and tumor tissues in different combinations. Capability for direct analysis of samples of dissected tissues during the neurosurgical procedure is demonstrated. Data validation is conducted by compound identification using precise masses from the MS profile, MS/MS, and isotopic distribution structure analysis. The method can be upgraded and applied for real-time identification of tissues during surgery. This paper describes the technique and its application perspective. For these purposes, other methods were compared with the investigated one and the results were shown to be reproducible. Differences in lipid profiles were observed even in tissues from one patient where distinctions between different samples could be poor. The paper presents a proof of concept for the method to be applied in neurosurgery particularly and in tissue analysis generically. The paper also contains preliminary results proving the possibility of observing differences in mass spectra of different tumors.