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Data normalization is an essential part of a large-scale untargeted mass spectrometry metabolomics analysis. Autoscaling, Pareto scaling, range scaling, and level scaling methods for liquid chromatography-mass spectrometry data processing were compared with the most common normalization methods, including quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization. These methods were tested on eight datasets from various clinical studies. The efficiency of the data normalization was assessed by the distance between clusters corresponding to batches and the distance between clusters corresponding to clinical groups in the space of principal components, as well as by the number of features with a pairwise statistically significant difference between the batches and the number of features with a pairwise statistically significant difference between clinical groups. Autoscaling demonstrated the most effective reduction in interbatch variation and can be preferable to probabilistic quotient or quantile normalization in liquid chromatography-mass spectrometry data.

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.

Retention time is an important parameter for identification in untargeted LC-MS screening. Precise retention time prediction facilitates the annotation process and is well known for proteomics. However, the lack of available experimental information for a long time has limited the prediction accuracy for small molecules. Recently introduced large databases for small-molecule retention times make possible reliable machine learning-based predictions for the whole diversity of compounds. Applying simple projections may expand these predictions on various LC systems and conditions. In our work, we describe a complex approach to predict retention times for nano-HPLC that includes the consequent deployment of binary and regression gradient boosting models trained on the METLIN small-molecule dataset and simple projection of the results with a small number of easily available compounds onto nano-HPLC separations. The proposed model outperforms previous attempts to use machine learning for predictions with a 46-s mean absolute error. The overall performance after transfer to nano-LC conditions is less than 155 s (10.8%) in terms of the median absolute (relative) error. To illustrate the applicability of the described approach, we successfully managed to eliminate averagely 25 to 42% of false-positives with a filter threshold derived from ROC curves. Thus, the proposed approach should be used in addition to other well-established in silico methods and their integration may broaden the range of correctly identified molecules.

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. Rapid estimation of tumor cell percentage during neurosurgery.

Data normalization is an essential part of a large-scale untargeted mass spectrometry metabolomics analysis. Autoscaling, Pareto scaling, range scaling, and level scaling methods for liquid chromatography-mass spectrometry data processing were compared with the most common normalization methods, including quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization. These methods were tested on eight datasets from various clinical studies. The efficiency of the data normalization was assessed by the distance between clusters corresponding to batches and the distance between clusters corresponding to clinical groups in the space of principal components, as well as by the number of features with a pairwise statistically significant difference between the batches and the number of features with a pairwise statistically significant difference between clinical groups. Autoscaling demonstrated the most effective reduction in interbatch variation and can be preferable to probabilistic quotient or quantile normalization in liquid chromatography-mass spectrometry data.

Evaluation of post-translational modifications of protein molecules is important for both basic and applied biomedical research. Mass spectrometric quantitative studies of modifications, which do not change the mass of the protein, such as isomerization of aspartic acid, do not necessarily require the use of isotope-labelled standards. However, the accurate solution of this problem requires a deep understanding of the relationship between the mole fractions of the isomers and the peak intensities in the mass spectra. In previous studies on the isomerization of aspartic acid in short beta-amyloid fragments, it has been shown that calibration curves used for such quantitative studies often have a non-linear form. The reason for the deviation in the shape of the calibration curves from linearity has not yet been established. Here, we propose an explanation for this phenomenon based on a probabilistic model of the fragmentation process and present a general approach for the selection of fragments that can be used for quantitative studies of the degree of isomerization. Graphical Abstract.

The structures of individual molecules in crude oil remain largely unknown despite the considerable amount of research dedicated to this topic. The extreme complexity of crude oil (recently Marshall reported the observation of more than 400,000 unique compounds in one sample) makes it impossible to separate crude oil into individual compounds and determine their structure by NMR or X-ray spectroscopy. Recently, isotope exchange, performed both in solution and in the gas phase, combined with high-resolution mass spectrometry was used for speciation of certain structural fragments of individual molecules in crude oil and humic substances. 16O/18O exchange allows enumeration of =O groups and speciation of furans, whereas H/D exchange allows enumeration of –OH groups, –NH groups, aromatic hydrogens, alpha hydrogens, etc. Unfortunately, crude oil is insoluble in water (the most available and cleanest source of isotopes), so performance of the exchange in solution requires harsh conditions, such as concentrated acids or bases, which could considerably modify the sample. Here we describe the use of a cheap and simple analytical approach for performing both H/D and 16O/18O exchange in crude oil using only water as the source of the isotopes. Crude oil was incubated in near-critical water and the reaction was monitored by high-resolution Fourier transform mass spectrometry. Although isotope exchange results in complication of the spectrum, the resolving power of modern mass spectrometers is sufficient to determine the number of exchanges for each molecule simultaneously. We determined the number of 16O/18O exchanges in 276 species and the number of H/D exchanges in 150 species. Our results allow deeper investigation of crude oil and other nonpolar samples on the molecular level.

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.