A machine learning and chemometrics assisted interpretation of spectroscopic data: a NMR-based metabolomics platform for the assessment of Brazilian propolis

In this work, a metabolomics dataset from 1H nuclear magnetic resonance spectroscopy of Brazilian propolis was analyzed using machine learning algorithms, including feature selection and classification methods. Partial least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper methods combining decision trees and rules with evolutionary algorithms (EA) showed to be complementary approaches, allowing to obtain relevant information as to the importance of a given set of features, mostly related to the structural fingerprint of aliphatic and aromatic compounds typically found in propolis, e.g., fatty acids and phenolic compounds. The feature selection and decision tree-based algorithms used appear to be suitable tools for building classification models for the Brazilian propolis metabolomics regarding its geographic origin, with consistency, high accuracy, and avoiding redundant information as to the metabolic signature of relevant compounds.The work is partially funded by ERDF -European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEstOE/ EEI/UI0752/2011. RC's work is funded by a PhD grant from the Portuguese FCT ( ref. SFRH/BD/66201/2009)

Development of an integrated computational platform for metabolomics data analysis and knowledge extraction

Dissertação de mestrado em Computing EngineeringIn the last few years, biological and biomedical research has been generating a large amount of quantitative data, given the surge of high-throughput techniques that are able to quantify different types of molecules in the cell. While transcriptomics and proteomics, which measure gene expression and amounts of proteins respectively, are the most mature, metabolomics, the quantification of small compounds, has been emerging in the last years as an advantageous alternative in many applications. As it happens with other omics data, metabolomics brings important challenges regarding the capability of extracting relevant knowledge from typically large amounts of data. To respond to these challenges, an integrated computational platform for metabolomics data analysis and knowledge extraction was created to facilitate the use of several methods of visualization, data analysis and data mining. In the first stage of the project, a state of the art analysis was conducted to assess the existing methods and computational tools in the field and what was missing or was difficult to use for a common user without computational expertise. This step helped to figure out which strategies to adopt and the main functionalities which were important to develop in the software. As a supporting framework, R was chosen given the easiness of creating and documenting data analysis scripts and the possibility of developing new packages adding new functions, while taking advantage of the numerous resources created by the vibrant R community. So, the next step was to develop an R package with an integrated set of functions that would allow to conduct a metabolomics data analysis pipeline, with reduced effort, allowing to explore the data, apply different data analysis methods and visualize their results, in this way supporting the extraction of relevant knowledge from metabolomics data. Regarding data analysis, the package includes functions for data loading from different formats and pre-processing, as well as different methods for univariate and multivariate data analysis, including t-tests, analysis of variance, correlations, principal component analysis and clustering. Also, it includes a large set of methods for machine learning with distinct models for classification and regression, as well as feature selection methods. The package supports the analysis of metabolomics data from infrared, ultra violet visible and nuclear magnetic resonance spectroscopies. The package has been validated on real examples, considering three case studies, including the analysis of data from natural products including bees propolis and cassava, as well as metabolomics data from cancer patients. Each of these data were analyzed using the developed package with different pipelines of analysis and HTML reports that include both analysis scripts and their results, were generated using the documentation features provided by the package.Nos últimos anos, a investigação biológica e biomédica tem gerado um grande número de dados quantitativos, devido ao aparecimento de técnicas de alta capacidade que permitem quantificar diferentes tipos de moléculas na célula. Enquanto a transcriptómica e a proteómica, que medem a expressão genética e quantidade de proteínas respectivamente, estão mais desenvolvidas, a metabolómica, que tem por definição a quantificação de pequenos compostos, tem emergido nestes últimos anos como uma alternativa vantajosa em muitas aplicações. Como acontece com outros dados ómicos, a metabolómica traz importantes desafios em relação à capacidade de extracção de conhecimento relevante de uma grande quantidade de dados tipicamente. Para responder a esses desafios, uma plataforma computacional integrada para a análise de dados de metabolómica e extracção de informação foi criada para facilitar o uso de diversos métodos de visualização, análise de dados e mineração de dados. Na primeira fase do projecto, foi efectuado um levantamento do estado da arte para avaliar os métodos e ferramentas computacionais existentes na área e o que estava em falta ou difícil de usar para um utilizador comum sem conhecimentos de informática. Esta fase ajudou a esclarecer que estratégias adoptar e as principais funcionalidades que fossem importantes para desenvolver no software. Como uma plataforma de apoio, o R foi escolhido pela sua facilidade de criação e documentar scripts de análise de dados e a possibilidade de novos pacotes adicionarem novas funcionalidades, enquanto se tira vantagem dos inúmeros recursos criados pela vibrante comunidade do R. Assim, o próximo passo foi o desenvolvimento do pacote do R com um conjunto integrado de funções que permitem conduzir um pipeline de análise de dados, com reduzido esforço, permitindo explorar os dados, aplicar diferentes métodos de análise de dados e visualizar os seus resultados, desta maneira suportando a extracção de conhecimento relevante de dados de metabolómica. Em relação à análise de dados, o pacote inclui funções para o carregamento dos dados de diversos formatos e para pré-processamento, assim como diferentes métodos para a análise univariada e multivariada dos dados, incluindo t-tests, análise de variância, correlações, análise de componentes principais e agrupamentos. Também inclui um grande conjunto de métodos para aprendizagem automática com modelos distintos para classificação ou regressão, assim como métodos de selecção de atributos. Este pacote suporta a análise de dados de metabolómica de espectroscopia de infravermelhos, ultra violeta visível e ressonância nuclear magnética. O pacote foi validado com exemplos reais, considerando três casos de estudo, incluindo a análise dos dados de produtos naturais como a própolis e a mandioca, assim como dados de metabolómica de pacientes com cancro. Cada um desses dados foi analisado usando o pacote desenvolvido com diferentes pipelines de análise e relatórios HTML que incluem ambos scripts de análise e os seus resultados, foram gerados usando as funcionalidades documentadas fornecidas pelo pacote

Kangaroo Island Propolis: Improved Characterisation and Assessment of Chemistry and Botanical Origins through Metabolomics

Introduction: Propolis, a sticky substance produced by bees from plant resins, has a long history of safe use medicinally. Kangaroo Island, SA (KI) lacks many introduced European plants bees preferentially collect resin from; consequentially, propolis from KI is produced from resinous native plants. Several identifiably reproducible pure-source KI propolis types exist. Research into medical use of compounds from KI native plants is limited. Metabolomics is a growing field of interest in natural products chemistry, including beehive products. Metabolomic and similarity-scoring assessment of KI propolis, through statistical evaluation of 1D 1H-NMR fingerprints, provides an entry point for research into medical use of KI native plant compounds. Many avenues to product discovery in pharmaceutical chemistry are suffering diminishing returns: metabolomics-guided natural products assessment has the potential for further identification of novel therapeutic compounds from resinous plants. Aim: To assess and identify, via metabolomic investigation of NMR fingerprints, major propolis types on KI, and to produce, from this, similarity-scoring tools for assessment of propolis samples. Method: KI propolis samples, identified as pure-source by TLC, and resinous KI plants were analysed by 1H-NMR and HPLC. Data points of interest were normalised and binned to form individual sample ‘fingerprints’. Data from these fingerprints were analysed by hierarchical clustering and principal component analysis (PCA) to confirm provisionally-identified pure-source propolis types and identify subtypes within propolis and resinous plant species. From this, calculator tools were created to score similarity (out of 1000) of 1H-NMR fingerprints to the average spectrum of pure-source propolis types, as well as to calculated mixtures of these average spectra. Assessment of the chemistry of two major KI propolis types identified (CP- and F-type) was made by fractionation and NMR, with one compound, 6,8-diprenyleriodictyol, isolated from CP-type propolis in quantity, submitted for epigenetic and other biological assays. Results: Source resinous plants were demonstrated, through hierarchical clustering and PCA, to cluster with propolis types arising from these sources, with closely related plants and sub-chemotypes clustering separately, confirming specificity. A number of previously-identified pure-source propolis types and known botanical sources were shown to have very high similarity (> 800/1000) to the expected propolis type. Calculator tools were observed to accurately predict the content of mixed propolis samples to within ± 10%. A number of methylflavanones, and two novel terminally-hydroxylated prenyldihydrochalcones were isolated from F-type propolis. 6,8-diprenyleriodictyol demonstrated a range of promising activity in biological assays. Conclusion: Metabolomic evaluation of 1H-NMR fingerprints can reliably identify and assess pure-source KI propolis and identify botanical origin of source resins. Similarity scoring calculators can accurately identify mixed-source propolis samples. KI propolis types are a rich source of pharmaceutically-interesting flavanones and related compounds, many of which are prenylated. 6,8-diprenyleriodictyol displays strong anti-inflammatory and anticancer activity, especially against Burkitt’s lymphoma. A number of possible epigenetic pathways for this activity were observed

Progress in Analytical Methods for the Characterization, Quality and Safety of the Beehive Products

This reprint presents some recent results from applying original analytical methods to the most renowned hive matrices. Particular consideration was given to methods devoted to the attribution of the origin of honey and propolis, but also studies dealing with the chemical characterization of honey and other hive matrices are here reported. Attention has also been paid to the use of optimized methods of elemental analysis in several hive products for quality and safety purposes, but also for environmental biomonitoring. The treatment of the data was often achieved through multivariate analysis methods, which made it possible to obtain reliable classifications of honeys and propolis according to their botanic or geographical origin

Applications of multivariate statistics in honey bee research, analysis of metabolomics data from samples of honey bee propolis

This thesis was previously held under moratorium from 20/04/2020 to 20/04/2022Honey bees play a significant role both ecologically and economically, through the pollination of flowering plants and crops. Additionally, honey is an ancient food source that is highly valued by different religions and cultures and has been shown to possess a wide range of beneficial uses, including cosmetic treatment, eye disease, bronchial asthma and hiccups. In addition to honey, honey bees also produce beeswax, pollen, royal jelly and propolis. In this thesis, data is studied which comes from samples of propolis from various geographical locations. Propolis is a resinous product, which consists of a combination of beeswax, saliva and resins that have been gathered by honey bees from the exudates of various surrounding plants. It is used by the bees to seal small gaps and maintain the hives, but is also an anti-microbial substance that may protect them against disease. The appearance and consistency of propolis changes depending on the temperature; it becomes elastic and sticky when warm, but hard and brittle when cold. Furthermore, its composition and colour varies from yellowish-green to dark brown, depending on its age and the sources of resin from the environment. Propolis is a highly biochemically active substance with many potential benefits in health care, which have attracted much attention. Biochemical analysis of propolis leads to highly multivariate metabolomics data. The main benefit of metabolomics is to generate a spectrum, in which peaks correspond to different chemical components, making possible the detection of multiple substances simultaneously. Relevant spectral features may be used for pattern recognition. The purpose of this research is to study methods used for statistical analysis of biochemical data arising from propolis samples. We investigate the use of different statistical methods for metabolomics data from chemical analysis of propolis samples using Mass Spectrometry (MS). Methods studied will include pre-treatment methods and multivariate analysis techniques including principal component analysis (PCA), multidimensional scaling (MDS), and clustering methods including hierarchical cluster analysis (HCA), k-means clustering and self organising maps (SOMs). Background material and results of data analysis will be presented from samples of propolis from beehives in Scotland, Libya and Europe. Conclusions are drawn in terms of the data sets themselves as well as the properties of the different methods studied for analysing such metabolomics data.Honey bees play a significant role both ecologically and economically, through the pollination of flowering plants and crops. Additionally, honey is an ancient food source that is highly valued by different religions and cultures and has been shown to possess a wide range of beneficial uses, including cosmetic treatment, eye disease, bronchial asthma and hiccups. In addition to honey, honey bees also produce beeswax, pollen, royal jelly and propolis. In this thesis, data is studied which comes from samples of propolis from various geographical locations. Propolis is a resinous product, which consists of a combination of beeswax, saliva and resins that have been gathered by honey bees from the exudates of various surrounding plants. It is used by the bees to seal small gaps and maintain the hives, but is also an anti-microbial substance that may protect them against disease. The appearance and consistency of propolis changes depending on the temperature; it becomes elastic and sticky when warm, but hard and brittle when cold. Furthermore, its composition and colour varies from yellowish-green to dark brown, depending on its age and the sources of resin from the environment. Propolis is a highly biochemically active substance with many potential benefits in health care, which have attracted much attention. Biochemical analysis of propolis leads to highly multivariate metabolomics data. The main benefit of metabolomics is to generate a spectrum, in which peaks correspond to different chemical components, making possible the detection of multiple substances simultaneously. Relevant spectral features may be used for pattern recognition. The purpose of this research is to study methods used for statistical analysis of biochemical data arising from propolis samples. We investigate the use of different statistical methods for metabolomics data from chemical analysis of propolis samples using Mass Spectrometry (MS). Methods studied will include pre-treatment methods and multivariate analysis techniques including principal component analysis (PCA), multidimensional scaling (MDS), and clustering methods including hierarchical cluster analysis (HCA), k-means clustering and self organising maps (SOMs). Background material and results of data analysis will be presented from samples of propolis from beehives in Scotland, Libya and Europe. Conclusions are drawn in terms of the data sets themselves as well as the properties of the different methods studied for analysing such metabolomics data

Incorporating standardised drift-tube ion mobility to enhance non-targeted assessment of the wine metabolome (LC×IM-MS)

Liquid chromatography with drift-tube ion mobility spectrometry-mass spectrometry (LCxIM-MS) is emerging as a powerful addition to existing LC-MS workflows for addressing a diverse range of metabolomics-related questions [1,2]. Importantly, excellent precision under repeatability and reproducibility conditions of drift-tube IM separations [3] supports the development of non-targeted approaches for complex metabolome assessment such as wine characterisation [4]. In this work, fundamentals of this new analytical metabolomics approach are introduced and application to the analysis of 90 authentic red and white wine samples originating from Macedonia is presented. Following measurements, intersample alignment of metabolites using non-targeted extraction and three-dimensional alignment of molecular features (retention time, collision cross section, and high-resolution mass spectra) provides confidence for metabolite identity confirmation. Applying a fingerprinting metabolomics workflow allows statistical assessment of the influence of geographic region, variety, and age. This approach is a state-of-the-art tool to assess wine chemodiversity and is particularly beneficial for the discovery of wine biomarkers and establishing product authenticity based on development of fingerprint libraries

Própolis catarinense: influência da sazonalidade e da origem geográfica no perfil de metabólitos secundários

Tese (doutorado) - Universidade Federal de Santa Catarina, Programa de Pós-Graduação em Biotecnologia, Florianópolis, 2016O presente estudo objetivou determinar a influência da origem geográfica (OG), sazonalidade e variáveis ambientais (temperatura e altitude) na composição química da própolis produzida em Santa Catarina (SC). Adicionalmente, identificou-se entre as amostras com maior concentração de compostos fenólicos, os metabólitos candidatos a marcadores e as eventuais espécies botânicas fontes de resina. Para tal, os teores de fenólicos totais (FT), flavononas e flavonóis (FF), flavanonas e dihidroflavonois (FD), extrato seco e a atividade antioxidante (ensaio do radical DPPH) de extratos hidroalcoólicos de própolis (EHP), coletadas ao final de cada estação nos anos de 2010 e 2011, em vinte apiários de SC (n=133), foram determinados. Os resultados revelaram teores superiores de FT, FF e FD, porém similar atividade antioxidante, em amostras coletadas em maiores altitudes (=900m). A influência das variáveis ambientais foi determinada via análises de predição à classificação da própolis quanto a região de coleta ou estação, utilizando-se dados de espectrofotometria de varredura UVVis (200-700 nm) dos EHP. Os modelos de classificação quanto a região e sazonalidade alcançaram acurácias de até 75% e 46%, respectivamente. Para o detalhamento da caracterização química de própolis coletadas em altitude (n=27), duas abordagens analíticas foram adotadas: i) injeção direta em espectrômetro de massa (EM), utilizando como fonte de ionização electrospray em modo negativo [IES(-)] e, ii) CLAE acoplada à EM em tandem (EM/EM). Quarenta e cinco compostos foram identificados e a seleção dos analitos majoritários nas amostras permitiu identificar dois tipos de perfis químicos: i) contendo ácidos diterpênicos (ácidos isocupréssico, comúnico, 15-acetoxiisocupréssico, agático e agatálico), identificados na própolis de Água Doce, Bom Retiro, Urupena, Porto União e São Joaquim (SJ), e ii) rico em substâncias comuns à própolis verde como a artepillin C, bacarina e drupanina, restrito à própolis de SJ. A análise comparativa dos espectros de massas de amostras destes quimiotipos com os de resinas de Baccharis dracunculifolia e Araucaria angustifolia, revelou ser B. dracunculifolia a principal fonte de resina à produção de própolis em SJ, enquanto a própolis contendo ácidos diterpênicos teve seu perfil químico associado à resina incolor de A. angustifolia. Estes resultados indicam que a própolis coletada na regiões de altitude em SC destaca-se em relação as demais regiões do estado, apresentando dois quimiotipos principais, um contendo metabólitos típicos de própolis verde e outro ácidos diterpênicos, provenientes da singular cobertura vegetal do estado. Abstract : This study aimed to determine the influence of geographical origin (G.O.), seasoning, and environmental factors (temperature and altitude) on the chemical composition of propolis produced in Santa Catarina (SC). Besides, propolis samples with superior content of phenolic compounds and flavonoids were further investigated regarding their eventual biochemical markers and the botanical species sources of resins for its production. Firstly, the total content of phenolic compounds (TP), flavones and flavonols (FF), flavanones and dihydroflavonols (FD), dry extract and antioxidant activity (DPPH radical assay) were determined in propolis samples collected in SC, over the seasons in 2010 and 2011, from twenty apiaries (n=133). The results shown that samples from regions with altitude = 900m presented superior amounts of TP, FF, and FD, while the antioxidant activity was similar regardless the G.O. and season of harvest. In a second experimental approach, attempts to classify propolis according to their G.O. and season of collection were performed by applying supervised prediction models on the UV-Vis scanning (200-700 nm) data set. The classification models built reached accuracies of 75% and 46%, in the best cases, to predict the region and season of harvest of propolis samples, respectively. Further, to better characterize the chemical composition of propolis with G.O. in high altitudes regions (n=27), two analytical approaches were adopted: i) direct injection of the extracts into mass spectrometer (MS) equipped with an electrospray ion source, operating in negative mode [IES(-)] and ii) HPLC-MS/MS, allowing to identify forty-five compounds. Besides, two main chemotypes of propolis were detected: i) the first one rich in diterpene acids, such as isocupresic acid, communic acid, agathic acid, agathalic acid, and 15- acetoxy-cupressic acid and, ii) the second group, restricted to SJ propolis, characterized by containing compounds usually found in green propolis, e.g., artepillin C, baccharin, and drupanin. Finally, mass spectra of the EHs of propolis were compared to those of resins from Baccharis dracunculifolia and Araucaria angustifolia species. It was possible to identify B. dracunculifolia as the main source of resin in SJ propolis, while A. angustifolia seemed to be a relevant source of diterpenic acids altitude propolis. These results indicate that catarinense propolis with G.O. in regions above 900m show a peculiar chemical profile as two main chemotypes of propolis could be found: one characterized by the presence of biochemical marker typical of green propolis, and a second group with diterpenic acids, both resulting from the particular flora of SC

Prospecção de fontes botânicas e avaliação do efeito da sazonalidade no perfil químico da própolis de São Joaquim (Santa Catarina)

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Ciências Bioloógicas. Programa de Pós-Graduação em Biotecnologia e Biociências, Florianópolis, 2015A própolis é uma substância de composição complexa, originada de material resinoso de exsudados vegetais coletado por abelhas. Nas regiões tropicais, principalmente, a composição química da própolis é altamente variável, devido à extensa variedade de espécies vegetais doadoras de resina. Além disso, a sua composição química pode variar de acordo com a sazonalidade, altitude, tipo de coletor e disponibilidade de alimentos. A determinação da origem botânica, em conjunto com a origem geográfica é de extrema relevância no controle de qualidade e na padronização das amostras de própolis à aplicação terapêutica. Nesse contexto, este trabalho objetivou identificar a origem botânica e os efeitos da sazonalidade na composição química da própolis originária de São Joaquim (Santa Catarina). A observação a campo do comportamento forrageiro das abelhas Apis mellifera na coleta de resinas vegetais permitiu identificar a predileção desses insetos à espécie Baccharis dracunculifolia, coincidindo com a espécie botânica doadora de resina à produção da própolis verde de Minas Gerais. As análises cromatográficas (CCD e CLAE) e quimiométricas (PCA e HCA) possibilitaram identificar a espécie vegetal B. dracunculifolia como a principal fonte botânica doadora de resina à própolis originária do município de São Joaquim-SC, em especial durante o verão, sendo identificado o Artepillin C como o composto majoritário. Adicionalmente, verificou-se que outras espécies vegetais, não identificadas, também são doadoras de resina à produção de própolis na região em estudo, especialmente durante a primavera e inverno. As análises espectrofotométricas de varredura UV-Visível (? = 280-800 ?m) dos extratos hidroalcoólicos da própolis, aliadas às análises quimiométricas e de bioinformática (Machine Learning), permitiram identificar o efeito da sazonalidade no perfil químico da própolis de São Joaquim, sendo que a região espectral de absorção de compostos fenólicos (? = 280-400 ?m) foi a mais importante à discriminação amostral observada. O melhor algoritmo à análise preditiva de sazonalidade foi a árvore de decisão (rpart), apresentando 81,43% (? = 280-800 ?m) e 78,98% (? = 280-400 ?m) de precisão. Os teores de compostos fenólicos totais, flavonoides e bálsamo, apesar de distintos ao longo das estações, apresentaram-se dentro dos valores mínimos exigidos pelo MAPA. A própolis produzida em São Joaquim, especialmente na estação de verão, mostrou-se relevante à obtenção de matéria-prima com conteúdos superiores de Artepillin C. Adicionalmente, este tipo de própolis apresentou elevada qualidade ao longo todas as estações estudadas, inferindo perspectivas promissoras a sua produção e comercialização na região.Abstract : Propolis is a chemically complex resinous substance collected by bees from plant exudates. In tropics, propolis shows a highly variable chemical composition because of the wide variety of resin donor plant species. Furthermore, propolis? chemical composition may vary due to the influence of the seasonality, altitude, collector type, and availability of food. The determination of the botanical and geographical origins of propolis is important for its quality control and standardization process for further therapeutic application. In this context, this study aimed at to identify the botanical origin and the seasonality effects in the chemical composition of propolis samples originated from the São Joaquim county (Santa Catarina state, southern Brazil). Field observations of Apis mellifera bees' behavior in the collection of plant resins detected a predilection of these insects for the plant species Baccharis dracunculifolia, coinciding with the botanical species donor of resin for the production of green propolis in Minas Gerais state (southeastern Brazil). The chromatographic (i.e., TLC and HPLC) and chemometric (PCA and HCA) analyzes made possible to associate the plant species B. dracunculifolia as the main botanical source of resin for propolis production in São Joaquim, SC, especially during the summer season, being Artepillin C® the major compound identified. Additionally, it was found that other plant species not taxonomically identified also furnish resins for the production of propolis in the studied geographical region, especially during the spring and winter. The UV-Visible scanning spectrophotometry analysis (?= 280-800 ?m) of propolis hydroalcoolic extracts combined with chemometric analysis and bioinformatics tools (Machine Learning) allowed discriminating propolis samples according to their chemical profiles and seasons of harvest. The UV-Vis spectral absorption region of phenolic compounds (? = 280-400 ?m) was the most decisive for the classification obtained. The best algorithm for the predictive analysis of the seasonality effect was the decision tree (rpart), with 81.43% (? = 280-800 ?m dataset) and 78.98% (? = 280-400 ?m dataset) of accuracy in the classification models. The total phenolic contents, flavonoids, and balsam, although distinct in samples as result of the seasoning, met the minimum amounts required by the ongoing legislation determined by MAPA. Propolis produced in São Joaquim, especially in the summer season, proved to be a raw material of superior quality due to their higher content Artepillin C. In addition, the studied propolis showed throughout the seasons to meet the parameters of high quality according to the ongoing legislation inferring promising perspectives for its production and marketing

Sample Preparation in Metabolomics

Metabolomics is increasingly being used to explore the dynamic responses of living systems in biochemical research. The complexity of the metabolome is outstanding, requiring the use of complementary analytical platforms and methods for its quantitative or qualitative profiling. In alignment with the selected analytical approach and the study aim, sample collection and preparation are critical steps that must be carefully selected and optimized to generate high-quality metabolomic data. This book showcases some of the most recent developments in the field of sample preparation for metabolomics studies. Novel technologies presented include electromembrane extraction of polar metabolites from plasma samples and guidelines for the preparation of biospecimens for the analysis with high-resolution μ magic-angle spinning nuclear magnetic resonance (HR-μMAS NMR). In the following chapters, the spotlight is on sample preparation approaches that have been optimized for diverse bioanalytical applications, including the analysis of cell lines, bacteria, single spheroids, extracellular vesicles, human milk, plant natural products and forest trees