Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer.

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.

Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification.

Plant samples are potential sources of physiologically active secondary metabolites and their classification is an extremely important task in traditional medicine and other fields of research. In the production of herbal drugs, different plant parts of the same or related species can serve as adulterants for primary plant material. The use of highly informative and relatively easily accessible tools, such as liquid chromatography and low-resolution mass spectrometry, helps to solve these tasks by means of fingerprint analysis. In this study, to reveal specific plant part features for 20 species from one family (Apiaceae), and to preserve the maximum information content, two approaches are suggested. In both cases, minimal raw data pretreatment, including rescaling of time and m/z axes and cutting off some uninformative regions, was applied. For the support vector machine (SVM) method, tensor unfolding was required, while neural networks (NNs) were able to work directly with squared heatmaps as input data. Moreover, five data augmentation variants are proposed, to overcome the typical problem of a lack of data. As a result, a comparable F1-score close to 0.75 was achieved by SVM and two employed NN architectures. Eight marker compounds belonging to chlorophylls, lipids, and coumarin apio-glucosides were tentatively identified as characteristic of their corresponding sample groups: roots, stems, leaves, and fruits. The proposed approaches are simple, information-saving and can be applied to a broad type of tasks in metabolomics.

Unsupervised methods in LC-MS data treatment: Application for potential chemotaxonomic markers search.

The combination of Liquid Chromatography and Mass Spectrometry (LC-MS) is commonly used to determine and characterize biologically active compounds because of its high resolution and sensitivity. In this work we explore the interpretation of LC-MS data using multivariate statistical analysis algorithms to extract useful chemical information and identify clusters of similar samples. Samples of leaves from 19 plants belonging to the Apiaceae family were analyzed in unified LC conditions by high- and low-resolution mass spectrometry in a wide range scan mode. LC-MS data preprocessing was performed followed by statistical analysis using tensor decomposition in the form of Parallel Factor Analysis (PARAFAC); matrix factorization following tensor unfolding with principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF); or unsupervised feature selection (UFS). The optimal number of components for each of these methods were found and results were compared using four different metrics: silhouette score, Davies-Bouldin index, computational time, number of noisy components. It was found that PCA, ICA and UFS give the best results across the majority of the criteria for both low- and high-resolution data. An algorithm for biomarker signal selection is suggested and 23 potential chemotaxonomic markers were tentatively identified using MS2 data. Dendrograms constructed by the methods were compared to the molecular phylogenic tree by calculating pixel-wise mean square error (MSE). Therefore, the suggested approach can support chemotaxonomic studies and yield valuable chemical information for biomarker discovery.

A new PARAFAC-based algorithm for HPLC-MS data treatment: herbal extracts identification.

INTRODUCTION: Role of highly informative high-performance liquid chromatography mass spectrometry (HPLC-MS) methods in quality control is increasing. Complex herbal products and formulations can simultaneously contain extracts from different plants. Therefore, due to the leads to lack of commercial standards it is important to develop novel approaches for comprehensive treatment of big datasets. OBJECTIVE: The aim of this study is to create a straightforward and information-saving algorithm for the identification of plants extracts in commercial products. MATERIAL AND METHODS: In total, 34 samples, including Glycyrrhiza glabra and Panax ginseng dried roots; and Abrus precatorius dried leaves, their double and triple mixtures and flavoured oolong tea samples were analysed by HPLC-MS and combined in a three-dimensional dataset (retention time-mass-to-charge ratio (m/z)-samples). This dataset was subjected to smoothing and denoising techniques and further decomposed using parallel factor analysis (PARAFAC). RESULTS: Samples were divided into eight clusters; loading matrices were interpreted and the presence of the most characteristic triterpene glycoside groups was demonstrated and supported by the characteristic chromatogram approach. The occurrence of Abrus precatorius and G. glabra additives in flavoured tea was confirmed. CONCLUSION: Developed HPLC-MS-PARAFAC method is potentially reliable and an efficient tool for handling untreated experimental data and its future development may lead to more comprehensive evaluation of chemical composition and quality control of food additives and other complex mixtures.

Application of quantitative analysis of multi-component system approach for determination of ginsenosides in different mass-spectrometric conditions.

The lack of standard substances is a bottleneck for quality control in traditional and alternative medicine. Development of the HPLC-UV approaches combined with single standard for quantitative analysis of multi-component system (QAMS) allowed determination of several plant biomarkers by implementation of the relative response factors (RRFs). Robustness and ruggedness of such methods are commonly demonstrated by performing the analysis in changing analytical conditions on the different HPLC equipment and columns. The nature of MS detection is much more complicated and dependent on the instrumentation. Therefore, this study was conducted to justify the use of RRFs for HPLC-MS determination of bioactive compounds from plants. Protopanaxatriol (PPT), protopanaxadiol (PPD) and ocotillol (OT) ginseng saponins (ginsenosides) were successfully separated on a reversed-phase PFP-column with high group selectivity. Fragmentation patterns for these groups of compounds were established on different HPLC-ESI-MS systems and at varied declustering potentials (DPs). The use of sapogenin fragmentation ions in positive detection mode along with group reference standards was shown to be an optimal way to perform quantification. The performance of the developed group targeted HPLC-MS-QAMS approach was tested in the course of measurements conducted on the different instrumentation. The differences between QAMS and external standard method (ESM) quantification results were below 15% for all determined saponins.

Unified strategy for HPLC-MS evaluation of bioactive compounds for quality control of herbal products.

Evaluation of safety, quality and composition of herbal products and food supplements based on botanical ingredients is a matter of serious concern. For screening of botanicals in herbal products multitargeted and group-targeted approaches may be applied. In the group-targeted approach botanicals are characterized by means of an appropriate group of structurally related biomarkers compared with the multitargeted approach where a number of selected analytes are monitored based on a multiple reaction monitoring survey. In this study a unified strategy for quality control of herbal products was developed on the basis of fast ultrasound-assisted extraction, chromatographic separation and mass spectrometric quantification of bioactive compounds. A large list of unique biomarkers were monitored under almost identical chromatographic conditions, while an efficient strategy for HPLC-MS group-targeted analysis was also developed for comprehensive evaluation of chemical composition of botanicals intensively used for herbal product manufacturing. In the latter case, structurally close compounds were determined in a single ion monitoring mode for the characteristic group of fragment ions, allowing fast profiling and quality assessment of the plant material or complex food supplement. The sensitivity of the developed approaches was on the level of 1-50 ng/mL, which is higher than that of existing HPLC-UV quality control methods.