Analysis Workflow Research Articles

A multiresolution approach with method-informed statistical analysis for quantifying lymphatic pumping dynamics

Despite significant strides in lymphatic system imaging, the timely diagnosis of lymphatic disorders remains elusive. This is driven by the absence of standardized, non-invasive, reliable, quantitative methods for real-time functional analysis of lymphatic contractility with adequate spatial and temporal resolution. Here, we address this unmet need by integrating near-infrared fluorescence lymphangiography imaging with an innovative analytical workflow that combines data acquisition, signal processing, and statistical analysis to integrate traditional peak-and-valley analysis with advanced wavelet time-frequency analyses. Variance component analysis was used to evaluate the drivers of variance attributable to each experimental variable for each lymphangiography measurement type. Generalizability studies were used to assess the reliability of measured parameters and how reliability improves as the number of repeat measurements per subject increases. This allowed us to determine the minimum number of repeat measurements needed per subject for acceptable measurement reliability. This approach not only offers detailed insights into lymphatic pumping behaviors across species, sex and age, but also significantly boosts the reliability of these measurements by incorporating multiple regions of interest and evaluating the lymphatic system under various gravitational loads. For example, the reliability of the peak-and-valley analysis of human lymphatic vessels was increased 3-fold using the described approach. By addressing the critical need for improved imaging and quantification methods, our study offers a new standard approach for the imaging and analysis of lymphatic function that can improve our understanding, diagnosis, and treatment of lymphatic diseases. The results highlight the importance of comprehensive data acquisition strategies to fully capture the dynamic behavior of the lymphatic system.

Three-Dimensional Morphological Characterisation of Human Cortical Organoids Using a Customised Image Analysis Workflow

Summary Statement: A tailored image analysis workflow was applied to quantify cortical organoid health, development, morphology and cellular composition over time. The assessment of cellular composition and viability of stem cell-derived organoid models is a complex but essential approach to understanding the mechanisms of human development and disease. Aim: Our study was motivated by the need for an image-analysis workflow, including high-cell content, high-throughput methods, to measure the architectural features of developing organoids. We assessed stem cell-derived cortical organoids at 4 and 6 months post-induction using immunohistochemistry-labelled sections as the analysis testbed. The workflow leveraged fluorescence imaging tailored to classify cells as viable and dying or non-viable and assign neuronal and astrocytic perinuclear markers to count cells. Results/Outcomes: Image acquisition was accelerated by capturing the organoid slice in 3D using widefield-fluorescence microscopy. This method used computational clearing to resolve nuclear and perinuclear markers and retain their spatial information within the organoid’s heterogeneous structure. The customised workflow analysed over 1.5 million cells using DAPI-stained nuclei, filtering and quantifying viable and non-viable cells and the necrotic-core regions. Temporal analyses of neuronal cell number derived from perinuclear labelling were consistent with organoid maturation from 4 to 6 months of in vitro differentiation. Overall: We have provided a comprehensive and enhanced image analysis workflow for organoid structural evaluation, creating the ability to gather cellular-level statistics in control and disease models.

Three-dimensional linkage analysis with digital PCR for genome integrity and identity of recombinant adeno-associated virus

Recombinant adeno-associated virus (rAAV) has emerged as the vector of choice for in vivo gene delivery, with numerous clinical trials underway for the treatment of various human diseases. Utilizing rAAV in gene therapy requires a highly precise quantification method to determine the viral genome titer and further establish the optimal therapeutic dosage for a rAAV product. The conventional single-channel droplet digital PCR (1D ddPCR) method offers only partial information regarding the viral vector genome titer, lacking insights into its integrity. In our pursuit of further advancing rAAV analysis, we have developed a novel 3D ddPCR assay with advanced 3D linkage analysis. We have designed the three amplicon sites targeting both ends of the viral genome, as well as the center of key therapeutic gene of interest (GOI). This study aims to offer a more comprehensive and insightful assessment of rAAV products which includes not only quantity of viral genome titer but also the quality, distinguishing between partial ones and intact full-length viral genomes with the right GOI. Importantly, due to the random partitioning property of a digital PCR system, the 3D linkage analysis of rAAV viral genome requires a proper mathematical model to identify the true linked DNA molecules (full-length/intact DNA) from the population of false/unlinked DNA molecules (fragmented/partial DNA). We therefore have developed an AAV 3D linkage analysis workflow to characterize genomic integrity and intact titer for rAAV gene therapy products. In this study, we focus on evaluating our 3D linkage mathematical model by performing DNA mixing experiments and a case study using multiple rAAV samples. Particularly, we rigorously tested our algorithms by conducting experiments involving the mixing of seven DNA fragments to represent various AAV viral genome populations, including 3 single partials, 3 double partials, and 1 full-length genomes. Across all 37 tested scenarios, we validated the accuracy of our workflow’s output for the percentages of 3D linkage by comparing to the known percentages of input DNA. Consequently, our comprehensive AAV analytical package not only offers insights into viral genome titer but also provides valuable information on its integrity and identity. This cost-effective approach, akin to the setup of traditional 1D or 2D dPCR, holds the potential to advance the application of rAAV in cell and gene therapy for the treatment of human diseases.

Enhanced Discovery of Alternative Proteins (AltProts) in Mouse Cardiac Development Using Data-Independent Acquisition (DIA) Proteomics.

Alternative proteins (AltProts) are a class of proteins encoded by DNA sequences previously classified as noncoding. Despite their historically being overlooked, recent studies have highlighted their widespread presence and distinctive biological roles. So far, direct detection of AltProt has been relying on data-dependent acquisition (DDA) mass spectrometry (MS). However, data-independent acquisition (DIA) MS, a method that is rapidly gaining popularity for the analysis of canonical proteins, has seen limited application in AltProt research, largely due to the complexities involved in constructing DIA libraries. In this study, we present a novel DIA workflow that leverages a fragmentation spectra predictor for the efficient construction of DIA libraries, significantly enhancing the detection of AltProts. Our method achieved a 2-fold increase in the identification of AltProts and a 50% reduction in missing values compared to DDA. We conducted a comprehensive comparison of four AltProt databases, four DIA-library construction strategies, and three analytical software tools to establish an optimal workflow for AltProt analysis. Utilizing this workflow, we investigated the mouse heart development process and identified over 50 AltProts with differential expression between embryonic and adult heart tissues. Over 30 unannotated mouse AltProts were validated, including ASDURF, which played a crucial role in cardiac development. Our findings not only provide a practical workflow for MS-based AltProt analysis but also reveal novel AltProts with potential significance in biological functions.

Multi-omics analysis in inclusion body myositis identifies mir-16 responsible for HLA overexpression

BackgroundInclusion Body Myositis is an acquired muscle disease. Its pathogenesis is unclear due to the co-existence of inflammation, muscle degeneration and mitochondrial dysfunction. We aimed to provide a more advanced understanding of the disease by combining multi-omics analysis with prior knowledge. We applied molecular subnetwork identification to find highly interconnected subnetworks with a high degree of change in Inclusion Body Myositis. These could be used as hypotheses for potential pathomechanisms and biomarkers that are implicated in this disease.ResultsOur multi-omics analysis resulted in five subnetworks that exhibit changes in multiple omics layers. These subnetworks are related to antigen processing and presentation, chemokine-mediated signaling, immune response-signal transduction, rRNA processing, and mRNA splicing. An interesting finding is that the antigen processing and presentation subnetwork links the underexpressed miR-16-5p to overexpressed HLA genes by negative expression correlation. In addition, the rRNA processing subnetwork contains the RPS18 gene, which is not differentially expressed, but has significant variant association. The RPS18 gene could potentially play a role in the underexpression of the genes involved in 18 S ribosomal RNA processing, which it is highly connected to.ConclusionsOur analysis highlights the importance of interrogating multiple omics to enhance knowledge discovery in rare diseases. We report five subnetworks that can provide additional insights into the molecular pathogenesis of Inclusion Body Myositis. Our analytical workflow can be reused as a method to study disease mechanisms involved in other diseases when multiple omics datasets are available.

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease.

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

A Broad Survey and Functional Analysis of Immunoglobulin Loci Variation in Rhesus Macaques.

Rhesus macaques (RMs) are vital models for studying human disease, and are invaluable to pre-clinical pipelines for vaccine discovery and testing. Particularly in this regard, they are often used to study infection and vaccine-associated broadly neutralizing antibody responses. This has resulted in an increasing demand for improved genetic resources for the immunoglobulin (IG) loci, which harbor antibody-encoding genes. However, the highly polymorphic and structurally variable nature of these loci have them historically challenging to sequence and characterize at the level of both the genome and expressed repertoire. To address these challenges, we have developed a novel integrated analysis workflow for conducting the combined processing of B cell receptor repertoire sequencing data with matched whole-genome and targeted long-read genomic sequencing data. Using this novel approach, we have assembled the largest collection of IG germline alleles reported to date, amassed from 106 Indian origin RMs. Using a conservative annotation approach, requiring sample-level internal validation from both genomic and expressed datasets, we created a comprehensive resource that captures extensive diversity of IG heavy and light chain variable (V), diversity (D), and joining (J) alleles, as well as leader, intronic, and recombination signal sequences (RSSs). This publicly available, continually updated database will advance vaccine research for infectious disease, and provide a robust foundation for immunogenomics and future translational research.

Advancements in Microextraction by Packed Sorbent: Insights into Sorbent Phases and Automation Strategies

Miniaturized solid-based approaches have added an eco-friendly dimension to analytical procedures, establishing themselves as promising strategies for a wide range of applications. Among these, microextraction by packed sorbent (MEPS) stands out due to its ability to facilitate efficient sample interaction with a densely packed sorb ent phase within the microextraction system. MEPS offers several advantages, including preconcentration capabilities and the use of minimal sample and solvent volumes, making it an appealing choice for modern analytical workflows. Since the extraction efficiency is largely dictated by the sorbent phase, recent advancements in sorbent design have garnered considerable attention in the field of sample preparation. Innovations in sorbent phases have not only enhanced the MEPS efficiency but also enabled the development of semi- and fully automated systems, paving the way for high-throughput methodologies. These advancements have elevated MEPS beyond traditional offline miniaturized sample preparation methods, offering new opportunities for streamlined and scalable analyses. Therefore, this study provides a comprehensive overview of novel sorbent phases used in MEPS, with a particular focus on both bio-based and synthetic materials. Furthermore, it explores the semi- and fully automated aspects of MEPS, highlighting current trends, technological advancements, and future directions in this rapidly evolving field.

Standardized workflow for multiplexed charge detection mass spectrometry on orbitrap analyzers.

Individual ion mass spectrometry (I2MS) is the Orbitrap-based extension of the niche mass spectrometry technique known as charge detection mass spectrometry (CDMS). While traditional CDMS analysis is performed on in-house-built instruments such as the electrostatic linear ion trap, I2MS extends CDMS analysis to Orbitrap analyzers, allowing charge detection analysis to be available to the scientific community at large. I2MS simultaneously measures the mass-to-charge ratios (m/z) and charges (z) of hundreds to thousands of individual ions within one acquisition event, creating a spectral output directly into the mass domain without the need for further spectral deconvolution. A mass distribution or 'profile' can be created for any desired sample regardless of composition or heterogeneity. To assist in reducing I2MS analysis to practice, we developed this workflow for data acquisition and subsequent data analysis, which includes (i) protein sample preparation, (ii) attenuation of ion signals to obtain individual ions, (iii) the creation of a charge-calibration curve from standard proteins with known charge states and finally (iv) producing a meaningful mass spectral output from a complex or unknown sample by using the STORIboard software. This protocol is suitable for users with prior experience in mass spectrometry and bioanalytical chemistry. First, the analysis of protein standards in native and denaturing mode is presented, setting the foundation for the analysis of complex mixtures that are intractable via traditional mass spectrometry techniques. Examples of complex mixtures included here demonstrate the relevant analysis of an intact human monoclonal antibody and its intricate glycosylation patterns.

Efficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework

AbstractThe expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency.

LLM Comparator: Interactive Analysis of Side-by-Side Evaluation of Large Language Models.

Evaluating large language models (LLMs) presents unique challenges. While automatic side-by-side evaluation, also known as LLM-as-a-judge, has become a promising solution, model developers and researchers face difficulties with scalability and interpretability when analyzing these evaluation outcomes. To address these challenges, we introduce LLM Comparator, a new visual analytics tool designed for side-by-side evaluations of LLMs. This tool provides analytical workflows that help users understand when and why one LLM outperforms or underperforms another, and how their responses differ. Through close collaboration with practitioners developing LLMs at Google, we have iteratively designed, developed, and refined the tool. Qualitative feedback from these users highlights that the tool facilitates in-depth analysis of individual examples while enabling users to visually overview and flexibly slice data. This empowers users to identify undesirable patterns, formulate hypotheses about model behavior, and gain insights for model improvement. LLM Comparator has been integrated into Google's LLM evaluation platforms and open-sourced.

2D human pose tracking in the cardiac catheterisation laboratory with BYTE.

Workflow insights can enable safety- and efficiency improvements in the Cardiac Catheterisation Laboratory (Cath Lab). Human pose tracklets from video footage can provide a source of workflow information. However, occlusions and visual similarity between personnel make the Cath Lab a challenging environment for the re-identification of individuals. We propose a human pose tracker that addresses these problems specifically, and test it on recordings of real coronary angiograms. This tracker uses no visual information for re-identification, and instead employs object keypoint similarity between detections and predictions from a third-order motion model. Algorithm performance is measured on Cath Lab footage using Higher-Order Tracking Accuracy (HOTA). To evaluate its stability during procedures, this is done separately for five different surgical steps of the procedure. We achieve up to 0.71 HOTA where tested state-of-the-art pose trackers score up to 0.65 on the used dataset. We observe that the pose tracker HOTA performance varies with up to 10 percentage point (pp) between workflow phases, where tested state-of-the-art trackers show differences of up to 23 pp. In addition, the tracker achieves up to 22.5 frames per second, which is 9 frames per second faster than the current state-of-the-art on our setup in the Cath Lab. The fast and consistent short-term performance of the provided algorithm makes it suitable for use in workflow analysis in the Cath Lab and opens the door to real-time use-cases. Our code is publicly available at https://github.com/RM-8vt13r/PoseBYTE.

SpAi: A Machine-learning Supported Experimental Workflow for High-throughput Spheroid Production and Analysis

SpAi: A Machine-learning Supported Experimental Workflow for High-throughput Spheroid Production and Analysis

UHPLC-TIMS-PASEF®-MS for Lipidomics: From Theory to Practice.

Trapped ion mobility spectrometry (TIMS) using parallel accumulation serial fragmentation (PASEF®) is an advanced analytical technique that offers several advantages in mass spectrometry (MS)-based lipidomics. TIMS provides an additional dimension of separation to mass spectrometry and accurate collision cross-section (CCS) measurements for ions, aiding in the structural characterization of molecules. This is especially valuable in lipidomics for identifying and distinguishing isomeric or structurally similar compounds. On the other hand, PASEF technology allows for fast and efficient data acquisition by accumulating ions in parallel and then serially fragmenting them. This accelerates the analysis process and improves throughput, making it suitable for high-throughput applications. Moreover, the combination of TIMS and PASEF reduces co-elution and ion coalescence issues, leading to cleaner and more interpretable mass spectra. This results in higher data quality and more confident identifications. In this chapter, a data-dependent TIMS-PASEF® workflow for lipidomics analysis is presented.

Computational Prediction of Gene Regulation by lncRNAs.

High-throughput sequencing technologies and innovative bioinformatics tools discovered that most of the genome is transcribed into RNA. However, only a fraction of the RNAs in cell translates into proteins, while the majority of them are categorized as noncoding RNAs (ncRNAs). The ncRNAs with more than 200nt without protein-coding ability are termed long noncoding RNAs (lncRNAs). Hundreds of studies established that lncRNAs are a crucial RNA family regulating gene expression. Regulatory RNAs, including lncRNAs, modulate gene expression by interacting with RNA, DNA, and proteins. Several databases and computational tools have been developed to explore the functions of lncRNAs in cellular physiology. This chapter discusses the tools available for lncRNA functional analysis and provides a detailed workflow for the computational analysis of lncRNAs.

Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalatedegradation.

As a global environmental challenge, plastic pollution raises serious ecological and health concerns owing to the excessive accumulation of plastic waste, which disrupts ecosystems, harms wildlife, and threatens human health. Polyethylene terephthalate (PET), one of the most commonly used plastics, has contributed significantly to this growing crisis. This study offers a solution for plastic pollution by identifying novel PET-degrading enzymes. Using a combined approach of computational analysis and metagenomic workflow, we identified a diverse array of genes and enzymes linked to plastic degradation. Our study identified 1305,282 unmapped genes, 36,000 CAZymes, and 317 plastizymes in the soil samples were heavily contaminated with plastic. We extended our approach by training machine learning models to discover candidate PET-degrading enzymes. To overcome the scarcity of known PET-degrading enzymes, we used a Generative Adversarial Network (GAN) model for dataset augmentation and a pretrained deep Evolutionary Scale Language Model (ESM) to generate sequence embeddings for classification. Finally, 21 novel PET-degrading enzymes were identified. These enzymes were further validated through active site analysis, amino acid composition analysis, and 3D structure comparison. Additionally, we isolated bacterial strains from contaminated soils and extracted plastizymes to demonstrate their potential for environmental remediation. This study highlights the importance of biotechnological solutions for plastic pollution, emphasizing scalable, cost-effective processes and the integration of computational and metagenomic methods.

Evaluating the impact of rock damage and induced fracture evolution on heat extraction using supercritical carbon dioxide as the working fluid: Semi-analytical model, numerical model, workflow, economic analysis, and field case

Geothermal energy, a clean and renewable source, has garnered significant attention. The excellent physical properties of supercritical carbon dioxide make it an ideal fluid for geothermal development. Fractures act as channels for the efficient seepage and heat transfer of supercritical carbon dioxide. Consequently, the macroscopic fracture damage and evolution induced by the flow of supercritical carbon dioxide impact heat extraction efficiency. Here, pseudo-pressure (multi-stage induced fracture and non-Darcy multi-stage fractured) and temperature well-testing (multi-stage induced fracture thermal and non-Darcy multi-stage fractured temperature) models are developed to monitor the fractures evolution during heat transfer. The finite element method, superposition principle, and Duhamel principle are utilized to establish the proposed models. Numerical models that either consider or disregard rock damage and fracture evolution are developed to simulate and compare the efficiency of heat extraction. The COMSOL Multiphysics 6.1 software is used to solve a heat extraction transient evaluation model. A workflow for utilizing the developed models is proposed, followed by an economic sensitivity analysis. Finally, the models are applied to field cases in the Babbage Oilfield to guide the extraction of geothermal energy. Core displacement experiments are conducted to validate its accuracy, and a differential economic analysis method demonstrates its economic benefits. Results show that the well-testing models invert parameters related to reservoir and fracture evolution. Rock damage and fracture evolution during a heat extraction process are primarily influenced by thermal stress, thermal shock, and pressure-driven effects. As production progresses, fracture permeability and porosity increase, rock elastic modulus decreases, and production temperature drops. Comparing the results with versus without considering rock damage and fracture evolution reveals that their maximum pressure difference is 14% and their maximum temperature difference is 5%. The production pseudo-pressure has the greatest impact on fracture evolution. The injection temperature has the most significant effect on heat extraction efficiency, followed by injection pressure, fracture permeability, and a mass flow rate, with elastic modulus having the least impact. The maximum differential economic benefit for a single horizontal well injection-production system reaches $293,000. Field case studies from the Babbage Oilfield demonstrate the practicality of the developed models and workflow. In summary, the models, workflow, and economic analysis methods proposed in this work provide guidance for engineers in formulating more optimized heat extraction schemes.

Identification and quantification of CD3+ and HLA‐DR+ cells in pellets from low volume samples of human tears through an automated image‐based cell counter

Aims/Purpose: Tears are a novel fluid source for biomarker analysis in several human diseases, becoming attractive for the rapid and less invasive collection method. The low sampling volume and the lack of standardized anlytical procedures are significant challenges. Here, we tested the performance of an automated image‐based cell counter for analysis and counting of CD3+ T lymphocytes and HLA‐DR+ cells in tear pellets.Methods: Tears aspirated from 10 healthy volunteers were centrifuged and pellets were fixed in 4% paraformaldehyde. Pellets were then resuspended in Bovine Serum Albumin (BSA) buffer and divided in two samples for parallel evaluation by Countess FL Automated Cell Counter and the flow cytometer Attune Cytpick (Thermo Fisher Scientific). Pellets were stained with Human Leukocyte Antigen‐ DR as marker of inflammation and CD3 as marker of T cells.Results: The total cell counting between flow cytometry and automated cell counter showed a significant correlation (R2 = 0.9587, p value = 0.0209). HLA‐DR and CD3 fluorescence percentages were comparable between the two methods, without significant differences (HLA‐DR positive cells: Countess 49%, Attune 48.6%; CD3 positive cells <5% in both estimations).Conclusions: The Automated Cell Counter is a promising approach to strengthen the application of tear fluids in the diagnosis of ocular surface diseases. The combination of cell counting with cell imaging into a bench instrument may overcome the limitations of flow cytometry due to the scant number of events in tear volumes. Further studies are aimed at optimizing the whole analytical workflow to bring the method in the daily practice.

EHR-ML: A data-driven framework for designing machine learning applications with electronic health records.

The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, its integration faces challenges resulting in a crisis of generalisability. Key obstacles include; 1) Insufficient consideration of local contextual factors, such as institution-specific data formats, practices, and protocols, which can lead to variability in clinical practices across different institutions. 2) ad-hoc data preparation and design of machine learning strategies. 3) manual subjective adjustment of design parameters resulting in sub-optimal performance. 4) EHR specific challenges regarding data biases affecting the model outcomes and unique intermittent temporal nature of the data necessitating specialised handling 5) lack of cross-institutional data validations. To address these challenges, EHR-ML, provides an easy to use structured framework for designing optimum machine learning applications in a data-driven manner. The framework supports ingestion of local institutional electronic health records (EHRs) and process standardisation. The study design and parameter optimisation is done in a fully data-driven evidence-based approach. It seamlessly integrating with existing quality control tools. To handle the unique characteristics of the EHR data, it offers customisable ensemble models. It enables the acquisition of EHR data from diverse systems and harmonise them into common formats following international standards. The effectiveness of the EHR-ML is demonstrated through a series of case studies. These studies highlight its capability to develop high-performance models in a fully automated manner, consistently surpassing the performance of traditional methodologies. Furthermore, they exhibited strong generalisability across diverse healthcare settings. EHR-ML enhances the clinical relevance and accuracy of predictive models by incorporating local context into machine learning applications. Additionally, by providing an user-friendly fully-automated framework, it facilitates rapid hypothesis testing aimed to generate localised biomedical knowledge.

Non-targeted analysis using gas chromatography mass spectrometry to assess the free and bound aroma fingerprints of the emblematic Greek white winegrape varieties and guarantee varietal authenticity using multivariate chemometrics.

A non-targeted analytical approach using gas chromatography-mass spectrometry (GC-MS) is proposed for the analysis of the free and bound volatile fractions of three emblematic indigenous Greek white winegrape varieties belonging to Vidiano, Malagousia, and Savvatiano and establish volatile varietal markers using multivariate chemometrics. A total of 89 free and 103 bound volatile compounds were identified, categorized into alcohols, aldehydes, esters, acids, terpenes, norisoprenoids, C6 compounds, phenols, and ketones. A robust Partial Least Squares Discriminant Analysis (PLS-DA) prediction model was developed and validated, and successfully classified the grape samples according to the variety with 100% accuracy, demonstrating the potential of volatile profiling as a non-targeted fingerprinting approach for varietal discrimination. Malagousia variety exhibited the highest concentration of terpenes, particularly in the bound fraction, highlighting its intense floral character. The proposed analytical workflow was successfully applied for the investigation of the unique aroma profiles of Greek white winegrape varieties. The findings contribute to the understanding of unique volatile fingerprints of the winegrape varieties and support the adaptation of vineyard and winery practices to enhance their varietal expression.