A novel direct spray-from-tissue ionization method for mass spectrometric analysis of human brain tumors

Modern machine-learning applications in ambient ionization mass spectrometry

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.

Mass spectrometry for neurosurgery: Intraoperative support in decision-making

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.

Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

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.

Discovery of Potential Lipid Biomarkers for Human Colorectal Cancer by In-Capillary Extraction Nanoelectrospray Ionization Mass Spectrometry

Discovering cancer biomarkers is of significance for clinical medicine and disease diagnosis. In this article, we develop an in-capillary extraction nanoelectrospray ionization mass spectrometry (ICE-nanoESI-MS) method to rapidly and in situ investigate human colorectal cancer for discovering lipid biomarkers. The ICE-nanoESI-MS method is performed using a tungsten microdissecting probe for in situ microsampling of surgical human colorectal cancer tumors and their paired distal noncancerous tissues during/after surgery. After sampling, the tungsten probe and the adhered tissues are inserted into a nanospray tip prefilled with some solvent for simultaneous in-capillary extraction and nanoESI-MS detection under ambient and open-air conditions. Online coupling of the Paternò-Büchi reaction and radical-direct fragmentation with ICE-nanoESI-MS is easily realized, which provides the opportunity to precisely determine carbon-carbon double bond (C═C) locations and stereospecific numbering (sn) positions of lipid biomarkers. Subsequently, a total of 12 pairs of colorectal cancer tumors and distal noncancerous tissues from different patients are investigated by our proposed ICE-nanoESI-MS method. A significant increase in lysophospholipids and fatty acids as well as a significant decrease in ceramides are discovered, and lysophospholipids are found as the potential biomarkers related to the formation and pathogenesis of human colorectal cancer.

Alterations in lipid profile upon uterine fibroids and its recurrence

Uterine fibroids (UF) is the most common (about 70% cases) type of gynecological disease, with the recurrence rate varying from 11 to 40%. Because UF has no distinct symptomatology and is often asymptomatic, the specific and sensitive diagnosis of UF as well as the assessment for the probability of UF recurrence pose considerable challenge. The aim of this study was to characterize alterations in the lipid profile of tissues associated with the first-time diagnosed UF and recurrent uterine fibroids (RUF) and to explore the potential of mass spectrometry (MS) lipidomics analysis of blood plasma samples for the sensitive and specific determination of UF and RUF with low invasiveness of analysis. MS analysis of lipid levels in the myometrium tissues, fibroids tissues and blood plasma samples was carried out on 66 patients, including 35 patients with first-time diagnosed UF and 31 patients with RUF. The control group consisted of 15 patients who underwent surgical treatment for the intrauterine septum. Fibroids and myometrium tissue samples were analyzed using direct MS approach. Blood plasma samples were analyzed using high performance liquid chromatography hyphened with mass spectrometry (HPLC/MS). MS data were processed by discriminant analysis with projection into latent structures (OPLS-DA). Significant differences were found between the first-time UF, RUF and control group in the levels of lipids involved in the metabolism of glycerophospholipids, sphingolipids, lipids with an ether bond, triglycerides and fatty acids. Significant differences between the control group and the groups with UF and RUF were found in the blood plasma levels of cholesterol esters, triacylglycerols, (lyso) phosphatidylcholines and sphingomyelins. Significant differences between the UF and RUF groups were found in the blood plasma levels of cholesterol esters, phosphotidylcholines, sphingomyelins and triacylglycerols. Diagnostic models based on the selected differential lipids using logistic regression showed sensitivity and specificity of 88% and 86% for the diagnosis of first-time UF and 95% and 79% for RUF, accordingly. This study confirms the involvement of lipids in the pathogenesis of uterine fibroids. A diagnostically significant panel of differential lipid species has been identified for the diagnosis of UF and RUF by low-invasive blood plasma analysis. The developed diagnostic models demonstrated high potential for clinical use and further research in this direction.

Glioblastoma Metabolomics-In Vitro Studies

In 2016, the WHO introduced new guidelines for the diagnosis of brain gliomas based on new genomic markers. The addition of these new markers to the pre-existing diagnostic methods provided a new level of precision for the diagnosis of glioma and the prediction of treatment effectiveness. Yet, despite this new classification tool, glioblastoma (GBM), a grade IV glioma, continues to have one of the highest mortality rates among central nervous system tumors. Metabolomics is a particularly promising tool for the analysis of GBM tumors and potential methods of treating them, as it is the only "omics" approach that is capable of providing a metabolic signature of a tumor's phenotype. With careful experimental design, cell cultures can be a useful matrix in GBM metabolomics, as they ensure stable conditions and, under proper conditions, are capable of capturing different tumor phenotypes. This paper reviews in vitro metabolomic profiling studies of high-grade gliomas, with a particular focus on sample-preparation techniques, crucial metabolites identified, cell culture conditions, in vitro-in vivo extrapolation, and pharmacometabolomics. Ultimately, this review aims to elucidate potential future directions for in vitro GBM metabolomics.

Assessment of variation of inline cartridge extraction mass spectra

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.

Rapid estimation of tumor cell percentage in brain tissue biopsy samples using inline cartridge extraction mass spectrometry

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.

The software for interactive evaluation of mass spectra stability and reproducibility

SUMMARY

Mass spectrometry methods are widely used for the analysis of biological and medical samples. Recently developed methods such as DESI, REIMS, 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 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

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.

Amyotrophic Lateral Sclerosis: Proteins, Proteostasis, Prions, and Promises

Amyotrophic lateral sclerosis (ALS) is characterized by the progressive degeneration of the motor neurons that innervate muscle, resulting in gradual paralysis and culminating in the inability to breathe or swallow. This neuronal degeneration occurs in a spatiotemporal manner from a point of onset in the central nervous system (CNS), suggesting that there is a molecule that spreads from cell-to-cell. There is strong evidence that the onset and progression of ALS pathology is a consequence of protein misfolding and aggregation. In line with this, a hallmark pathology of ALS is protein deposition and inclusion formation within motor neurons and surrounding glia of the proteins TAR DNA-binding protein 43, superoxide dismutase-1, or fused in sarcoma. Collectively, the observed protein aggregation, in conjunction with the spatiotemporal spread of symptoms, strongly suggests a prion-like propagation of protein aggregation occurs in ALS. In this review, we discuss the role of protein aggregation in ALS concerning protein homeostasis (proteostasis) mechanisms and prion-like propagation. Furthermore, we examine the experimental models used to investigate these processes, including in vitro assays, cultured cells, invertebrate models, and murine models. Finally, we evaluate the therapeutics that may best prevent the onset or spread of pathology in ALS and discuss what lies on the horizon for treating this currently incurable disease.

[The role of lipids in the classification of astrocytoma and glioblastoma using MS tumor profiling]

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.

Ambient mass spectrometry for the molecular diagnosis of lung cancer

Lung cancer is one of the most common malignancies and the leading cause of cancer-related death worldwide. Among the technologies suitable for the rapid and accurate molecular diagnosis of lung cancer, ambient mass spectrometry (AMS) has gained increasing interest as it allows the direct profiling of molecular information from various biological samples (e.g., tissue, serum, urine and sputum) in real-time and with minimal or no sample pretreatment. This minireview summarizes the applications of AMS in lung cancer studies (including tissue molecular identification, the discovery of potential biomarkers, and surgical margin assessment), and discusses the challenges and perspectives of AMS in the clinical precision molecular diagnosis of lung cancer.

Paper Spray Ionization Mass Spectrometry as a Potential Tool for Early Diagnosis of Cervical Cancer

Squamous intraepithelial lesion is an abnormal growth of epithelial cells on the surface of the cervix that may lead to cervical cancer. Analytical protocols for the determination of squamous intraepithelial lesions are in high demand, since cervical cancer is the fourth most diagnosed cancer among women in the world. Here, paper spray ionization mass spectrometry (PSI-MS) is used to distinguish between healthy (negative for intraepithelial lesion or malignancy) and diseased (high-grade squamous intraepithelial lesion) blood plasmas. A total of 86 blood samples of different women (49 healthy samples, 37 diseased samples) were collected, and the plasmas were prepared. Then, 10 μL of each plasma sample was deposited onto triangular papers for PSI-MS analysis. No additional step of sample preparation was necessary. The interval-successive projection algorithm linear discriminant analysis (iSPA-LDA) was applied to the PSI mass spectra, showing six ions (mostly phospholipids) that were predictive of healthy and diseased plasmas. Values of 77% accuracy, 86% sensitivity, 80% positive predictive value (PPV), and 75% negative predictive value (NPV) were achieved. This study provides evidence that PSI-MS may potentially be used as a fast and simple analytical technique for the early diagnosis of cervical cancer.

Validation of Breast Cancer Margins by Tissue Spray Mass Spectrometry

Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 × 1 × 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts; peaks with a statistically significant difference of intensity determined by the Mann–Whitney U-test (p < 0.05); peaks identified as lipids; both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the “gold” standard for tissue classification. “All peaks” and “significantly different peaks” datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention.

Feature selection for OPLS discriminant analysis of cancer tissue lipidomics data

The mass spectrometry-based molecular profiling can be used for better differentiation between normal and cancer tissues and for the detection of neoplastic transformation, which is of great importance for diagnostics of a pathology, prognosis of its evolution trend, and development of a treatment strategy. The aim of the present study is the evaluation of tissue classification approaches based on various data sets derived from the molecular profile of the organic solvent extracts of a tissue. A set of possibilities are considered for the orthogonal projections to latent structures discriminant analysis: all mass spectrometric peaks over 300 counts threshold, subset of peaks selected by ranking with support vector machine algorithm, peaks selected by random forest algorithm, peaks with the statistically significant difference of the intensity determined by the Mann-Whitney U test, peaks identified as lipids, and both identified and significantly different peaks. The best predictive potential is obtained for OPLS-DA model built on nonpolar glycerolipids (Q² = 0.64, area under curve [AUC] = 0.95); the second one is OPLS-DA model with lipid peaks selected by random forest algorithm (Q² = 0.58, AUC = 0.87). Moreover, models based on particular molecular classes are more preferable from biological point of view, resulting in new explanatory mechanisms of pathophysiology and providing a pathway analysis. Another promising features for OPLS-DA modeling are phosphatidylethanolamines (Q² = 0.48, AUC = 0.86).

Inline cartridge extraction for rapid brain tumor tissue identification by molecular profiling

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.

Relative quantitation of phosphatidylcholines with interfered masses of protonated and sodiated molecules by tandem and Fourier-transform ion cyclotron resonance mass spectrometry

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.

Metrics for evaluating the stability and reproducibility of mass spectra

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.

Utility of neurological smears for intrasurgical brain cancer diagnostics and tumour cell percentage by DESI-MS

Analysis of neurological smears by desorption electrospray ionization mass spectrometry (DESI-MS) is an emerging diagnostic strategy for intraoperative consultation in brain tumor resection. DESI-MS allows rapid sampling while providing accurate diagnostic information. We assess the chemical homogeneity of neurological smears using DESI-MS imaging and the quality of rapid DESI-MS diagnosis.

[The role of lipid metabolism disorders, atypical isoforms of protein kinase C, and mutational status of cytosolic and mitochondrial forms of isocitrate dehydrogenase in carcinogenesis of glial tumors]

The relationship between molecular genetic and metabolic disorders is one of the challenges of modern oncology. In this review, we consider lipid metabolism and its changes as one of the factors of oncogenesis of glial tumors. Also, we demonstrate that the genome and the metabolome are interconnected by a large number of links, and the metabolic pathways, during their reorganization, are able to drastically affect the genetic structure of the cell and, in particular, cause its tumor transformation. Our own observations and analysis of the literature data allow us to conclude that mass spectrometry is a highly accurate current method for assessing metabolic disorders at the cellular level. The use of mass spectrometry during surgery allows the neurosurgeon to obtain real-time data on the level of specific molecular markers in the resected tissue, thereby bringing intraoperative navigation techniques to the molecular level. The generation of molecular fingerprints for each tumor significantly complements the available neuroimaging, molecular genetic, and immunohistochemical data.

A Comparison of Tissue Spray and Lipid Extract Direct Injection Electrospray Ionization Mass Spectrometry for the Differentiation of Eutopic and Ectopic Endometrial Tissues

Recent research revealed that tissue spray mass spectrometry enables rapid molecular profiling of biological tissues, which is of great importance for the search of disease biomarkers as well as for online surgery control. However, the payback for the high speed of analysis in tissue spray analysis is the generally lower chemical sensitivity compared with the traditional approach based on the offline chemical extraction and electrospray ionization mass spectrometry detection. In this study, high resolution mass spectrometry analysis of endometrium tissues of different localizations obtained using direct tissue spray mass spectrometry in positive ion mode is compared with the results of electrospray ionization analysis of lipid extracts. Identified features in both cases belong to three lipid classes: phosphatidylcholines, phosphoethanolamines, and sphingomyelins. Lipids coverage is validated by hydrophilic interaction liquid chromatography with mass spectrometry of lipid extracts. Multivariate analysis of data from both methods reveals satisfactory differentiation of eutopic and ectopic endometrium tissues. Overall, our results indicate that the chemical information provided by tissue spray ionization is sufficient to allow differentiation of endometrial tissues by localization with similar reliability but higher speed than in the traditional approach relying on offline extraction. Graphical Abstract ᅟ.

Direct Mass Spectrometry Differentiation of Ectopic and Eutopic Endometrium in Patients with Endometriosis

STUDY OBJECTIVE

To introduce a method for the rapid assessment of endometriotic tissues using direct mass spectrometry (MS)-based lipidomics.

DESIGN

A prospective observational cohort study (Canadian Task Force classification II2).

SETTING

Department of Operative Gynecology of the Research Centre for Obstetrics, Gynecology and Perinatology.

PATIENTS

Fifty patients with ovarian cysts and peritoneal endometriosis who underwent laparoscopic surgery between 2014 and 2016.

INTERVENTION

Differences in mass spectrometric profiles of ectopic endometria (endometriosis) and eutopic endometria were analyzed for each patient in combination with morphohistologic evaluation. The lipidomic approach was applied using a direct high-resolution MS method.

MEASUREMENTS AND MAIN RESULTS

Of 148 metabolites, 15 showed significant differences between endometriotic tissue and a healthy endometrium of the same patient, considered as a control in this study. The main lipids prevalent in endometriotic tissues were phosphoethanolamine (PE O-20:0), sphingomyelin (SM 34:1), diglycerides (DG 44:9), phosphatidylcholines (PC 32:1, PC O-36:3, PC 38:7, PC 38:6, PC 40:8, PC 40:7, PC 40:6, PC 40:9, and PC O-42:1), and triglycerides (TG 41:2, TG 49:4, and TG 52:3). Using partial least squares discriminant analysis models, MS showed that the lipidomic profile of endometriotic tissue (peritoneal endometriosis and ovarian endometriomas) was clearly separated from the eutopic endometrium, indicating tissue-type differentiation.

CONCLUSION

Our results suggest that direct MS may play an important role for endometriotic tissue identification. Such an approach has potential usefulness for real-time tissue determination and differentiation during surgical treatment. Lipids of 3 important classes, sphingolipids, phospholipids, and the fatty acids (di- and triglycerides), were identified. Validation is required to determine whether these lipids can be used to discriminate between patients with endometriosis and those with other gynecologic diseases.

Feature selection algorithm for spray-from-tissue mass spectrometry

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.

Static harmonization of dynamically harmonized Fourier transform ion cyclotron resonance cell

Static harmonization in the Fourier transform ion cyclotron resonance cell improves the resolving power of the cell and prevents dephasing of the ion cloud in the case of any trajectory of the charged particle, not necessarily axisymmetric cyclotron (as opposed to dynamic harmonization). We reveal that the Fourier transform ion cyclotron resonance cell with dynamic harmonization (paracell) is proved to be statically harmonized. The volume of the statically harmonized potential distribution increases with an increase in the number of trap segments.

High-resolution mass spectra processing for the identification of different pathological tissue types of brain tumors

The purpose of the work is to demonstrate the possibilities of identifying the different types of pathological tissue identification directly through tissue mass spectrometry. Glioblastoma parts dissected during neurosurgical operation were investigated. Tumor fragments were investigated by the immunohistochemistry method and were identified as necrotic tissue with necrotized vessels, necrotic tissue with tumor stain, tumor with necrosis (tumor tissue as major), tumor, necrotized tumor (necrotic tissues as major), parts of tumor cells, boundary brain tissue, and brain tissue hyperplasia. The technique of classification of tumor tissues based on mass spectrometric profile data processing is suggested in this paper. Classifiers dividing the researched sample to the corresponding tissue type were created as a result of the processing. Classifiers of necrotic and tumor tissues are shown to yield a combined result when the tissue is heterogeneous and consists of both tumor cells and necrotic tissue.

Endometriosis foci differentiation by rapid lipid profiling using tissue spray ionization and high resolution mass spectrometry

Obtaining fast screening information on molecular composition of a tissue sample is of great importance for a disease biomarkers search and for online surgery control. In this study, high resolution mass spectrometry analysis of eutopic and ectopic endometrium tissues (90 samples) is done using direct tissue spray mass spectrometry in both positive and negative ion modes. The most abundant peaks in the both ion modes are those corresponding to lipids. Species of three lipid classes are observed, phosphatidylcholines (PC), sphingomyelins (SM) and phosphoethanolamines (PE). Direct tissue analysis gives mainly information on PC and SM lipids (29 species) in positive ion mode and PC, SM and PE lipids (50 species) in negative ion mode which gives complementary data for endometriosis foci differentiation. The biggest differences were found for phospholipids with polyunsaturated acyls and alkils. Although, tissue spray shows itself as appropriate tool for tissue investigation, caution should be paid to the interpretation of mass spectra because of their higher complexity with more possible adducts formation and multiple interferences must be taken into account. The present work extends the application of direct tissue analysis for the rapid differentiation between endometriotic tissues of different foci.

Lipid and metabolite profiles of human brain tumors by desorption electrospray ionization-MS

Examination of tissue sections using desorption electrospray ionization (DESI)-MS revealed phospholipid-derived signals that differ between gray matter, white matter, gliomas, meningiomas, and pituitary tumors, allowing their ready discrimination by multivariate statistics. A set of lower mass signals, some corresponding to oncometabolites, including 2-hydroxyglutaric acid and N-acetyl-aspartic acid, was also observed in the DESI mass spectra, and these data further assisted in discrimination between brain parenchyma and gliomas. The combined information from the lipid and metabolite MS profiles recorded by DESI-MS and explored using multivariate statistics allowed successful differentiation of gray matter (n = 223), white matter (n = 66), gliomas (n = 158), meningiomas (n = 111), and pituitary tumors (n = 154) from 58 patients. A linear discriminant model used to distinguish brain parenchyma and gliomas yielded an overall sensitivity of 97.4% and a specificity of 98.5%. Furthermore, a discriminant model was created for tumor types (i.e., glioma, meningioma, and pituitary), which were discriminated with an overall sensitivity of 99.4% and a specificity of 99.7%. Unsupervised multivariate statistics were used to explore the chemical differences between anatomical regions of brain parenchyma and secondary infiltration. Infiltration of gliomas into normal tissue can be detected by DESI-MS. One hurdle to implementation of DESI-MS intraoperatively is the need for tissue freezing and sectioning, which we address by analyzing smeared biopsy tissue. Tissue smears are shown to give the same chemical information as tissue sections, eliminating the need for sectioning before MS analysis. These results lay the foundation for implementation of intraoperative DESI-MS evaluation of tissue smears for rapid diagnosis.

Peculiarities of data interpretation upon direct tissue analysis by Fourier transform ion cyclotron resonance mass spectrometry

The importance of high-resolution mass spectrometry for the correct data interpretation of a direct tissue analysis is demonstrated with an example of its clinical application for an endometriosis study. Multivariate analysis of the data discovers lipid species differentially expressed in different tissues under investigation. High-resolution mass spectrometry allows unambiguous separation of peaks with close masses that correspond to proton and sodium adducts of phosphatidylcholines and to phosphatidylcholines differing in double bond number.

Atmospheric pressure thermal ionization ion source for peptide analysis

A novel atmospheric pressure thermal ionization (APTI) ion source was developed for the analysis of liquid samples. The feasibility of the ion source was demonstrated on peptides using two configurations-assisted by hot wire or hot surface. Microalloyed molybdenum, used as a thermal ion- emitter, notably facilitated the formation of multiply-charged ions, but fragmentation products were still observed. Peptide fragmentation accompanying thermal ionization can be used for peptide identification. The described method is promising for studies of biological samples with minimal pre-treatment.