Acquisition Workflow Research Articles

High sensitivity and high-confidence compound identification with a flexible BoxCar acquisition method

Liquid chromatography-mass spectrometry (LC-MS) is in wide use for compound identification and quantification in complex matrices. While advances in mass spectrometry and the incorporation of new acquisition methods have resulted in greatly improved detection, there is an ongoing need to expand the limits of highly sensitive and confident identification of low abundance species in complex samples. The data acquisition method known as “BoxCar” was originally designed to achieve in-depth proteome profiling on an Orbitrap mass analyzer by decomposing ions into segments with narrow m/z windows. Using this method, selected segments are packaged in C-trap and all ions are then sent to Orbitrap for detection. In this study, we developed a flexible BoxCar acquisition method by placing more segments in the low m/z range for small molecule profiling. This new MS1 acquisition method was successfully integrated with iterative data dependent MS/MS acquisition by generating an inclusion list of ions detected in the flexible BoxCar to trigger the fragmentation of parent ions. The developed acquisition method was applied to the analysis of cell culture media, which plays a key role in antibody production. This challenging goal is of critical importance, as none of the currently available methods provide a comprehensive understanding of how individual components, metabolites, and impurities associated with the cell culture process might influence recombinant antibody production. Even when present at relatively low abundance, some components or impurities in the cell culture medium could have a profound impact on the quality and titer of the antibodies produced. The complex soy hydrolysate cell culture medium used in antibody generation has not been fully characterized. Using the developed flexible BoxCar acquisition method, we achieved 90 % higher sensitivity in experiments designed to detect spiked chemical substances at low abundance at the MS1 level compared to the full scan method. Iterative data-dependent acquisition (DDA) based on the targeted inclusion list generated much higher quality MS2 spectra and facilitated confident identification of low-abundance compounds. Our method achieved a 50 % increase in MS2 coverage of compounds present at low concentrations compared to conventional DDA methods. The results of our study demonstrate that this data acquisition workflow can be easily operated on Orbitrap mass spectrometers and used as a highly effective approach to improve sensitivity and high-confidence small molecule profiling in soy hydrolysate-based cell culture medium and thus provides significant support for therapeutic monoclonal antibody production.

Optimizing data-independent acquisition (DIA) spectral library workflows for plasma proteomics studies.

Traditional data-independent acquisition (DIA) workflows employ off-column fractionation with data-dependent acquisition (DDA) to generate spectral libraries for data extraction. Recent advances have led to the establishment of library-independent approaches for DIA analyses. The selection of a DIA workflow may affect the outcome of plasma proteomics studies. Here, we establish a gas-phase fractionation (GPF) workflow to create DIA libraries for DIA with parallel accumulation and serial fragmentation (diaPASEF). This workflow along with three other workflows, fractionated DDA libraries, fractionated DIA libraries, and predicted spectra libraries, were evaluated on 20 plasma samples from nonsmall cell lung cancer patients with low or high levels of IL-6. We sought to optimize protein identification and total experiment time. The novel GPF workflow for diaPASEF outperformed the traditional ddaPASEF workflow in the number of identified and quantified proteins. A library-independent workflow based on predicted spectra identified and quantified the most proteins in our experiment at the cost of computational power. Overall, the choice of DIA library workflow seemed to have a limited effect on the overall outcome of a plasma proteomics experiment, but it can affect the number of proteins identified and the total experiment time.

Interleaved fluid-attenuated inversion recovery (FLAIR) MRI and deuterium metabolic imaging (DMI) on human brain in vivo.

To integrate deuterium metabolic imaging (DMI) with clinical MRI through an interleaved MRI and DMI acquisition workflow. Interleaved MRI-DMI was enabled with hardware and pulse sequence modifications, and the performance was demonstrated using fluid-attenuated inversion recovery (FLAIR) MRI as an example. Interleaved FLAIR-DMI was developed by interleaving the 2 H excitation and acquisition time windows into the intrinsic delay periods presented in the FLAIR method. All 2 H MR signals were up-converted to the 1 H Larmor frequency using a custom-built hardware unit, which also achieved frequency and phase locking of the output signal in real-time. The interleaved measurements were compared with direct measurements both in phantom and in the human brain in vivo. The interleaved MRI-DMI acquisition strategy allowed simultaneous detection of FLAIR MRI and DMI in the same scan time as a FLAIR-only MRI acquisition. Both phantom and in vivo data showed that the MR image quality, DMI sensitivity as well as information content were preserved using interleaved MRI-DMI. The interleaved MRI-DMI technology can be used to extend clinical MRI protocols with DMI, thereby offering a metabolic component to the MR imaging contrasts without a penalty on patient comfort or scan time.

Reproducible untargeted metabolomics workflow for exhaustive MS2 data acquisition of MS1 features

Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the post-hoc validation of ions selected following a secondary analysis impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for reproducible untargeted mass spectrometry MS2 fragment ion collection of unknown compounds found in MS1 full scan. Our workflow first removes redundant peaks from MS1 data and then exports a list of precursor ions for pseudo-targeted MS/MS analysis on independent peaks. This workflow provides comprehensive coverage of MS2 collection on unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We compared pseudo-spectra formation and the number of MS2 spectra linked to MS1 data using the PMDDA workflow to that obtained using CAMERA and RAMclustR algorithms. More annotated compounds, molecular networks, and unique MS/MS spectra were found using PMDDA compared with CAMERA and RAMClustR. In addition, PMDDA can generate a preferred ion list for iterative DDA to enhance coverage of compounds when instruments support such functions. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template.Graphical

Adequacy and Accuracy of Biopsy Specimen Acquisition During Longitudinal Sampling: Importance and Improvement

Adequacy and Accuracy of Biopsy Specimen Acquisition During Longitudinal Sampling: Importance and Improvement

Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry-Based Proteomics.

In bottom-up mass spectrometry-based proteomics, deep proteome coverage is limited by high cofragmentation rates. Cofragmentation occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry, which separates peptides by their collisional cross section. Here, we use a computational model to investigate the capability of the trapped IM spectrometry (TIMS) device at effectively separating peptide ions and quantify the separation power of the TIMS device in the context of a parallel accumulation-serial fragmentation (PASEF) workflow. We found that TIMS separation increases the number of interference-free MS1 peptide features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5-fold. In a data-dependent acquisition PASEF workflow, IM separation increases the number of spectra without cofragmentation by a factor of 4.1 and the number of high-quality spectra 17-fold. Using a categorical model, we estimate that this observed decrease in spectral complexity results in an increased likelihood for peptide spectral matches, which may improve peptide identification rates. In the context of a data-independent acquisition workflow, the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in the isolation window width (from 25 to 6.5 Da). Our study demonstrates that TIMS separation decreases spectral complexity by reducing cofragmentation rates, suggesting that TIMS separation may contribute toward the high identification rates observed in PASEF workflows.

Integration of Remote Sensing Data into a Composite Voxel Model for Environmental Performance Analysis of Terraced Vineyards in Tuscany, Italy

Understanding socio-ecological systems and the discovery, recovery and adaptation of land knowledge are key challenges for sustainable land use. The analysis of sustainable agricultural systems and practices, for instance, requires interdisciplinary and transdisciplinary research and coordinated data acquisition, data integration and analysis. However, datasets, which are acquired using remote sensing, geospatial analysis and simulation techniques, are often limited by narrow disciplinary boundaries and therefore fall short in enabling a holistic approach across multiple domains and scales. In this work, we demonstrate a new workflow for interdisciplinary data acquisition and integration, focusing on terraced vineyards in Tuscany, Italy. We used multi-modal data acquisition and performed data integration via a voxelised point cloud that we term a composite voxel model. The latter facilitates a multi-domain and multi-scale data-integrated approach for advancing the discovery and recovery of land knowledge. This approach enables integration, correlation and analysis of data pertaining to different domains and scales in a single data structure.

ChemiTEM – Transmission Electron Microscopy Optimized for Chemistry and Material Science

AbstractInvited for this month's cover is the group of Walid Hetaba and Thomas Lunkenbein at the Max‐Planck‐Institutes in Berlin and Mülheim (Germany). The cover picture shows the ChemiTEM acquisition workflow in a tablet app in front of a transmission electron microscope (TEM). Additionally, the results obtained by a TEM‐expert and by non‐expert TEM users are shown left and right of the microscope, respectively. It symbolises that using the ChemiTEM workflows, non‐expert TEM users can proficiently use a TEM and generate data which is of comparable quality as the data obtained by a TEM‐expert. Furthermore, the training time of the users is reduced and electron microscopical investigations can be performed with much higher efficiency. ChemiTEM extends the TEM user base and makes this instrument a versatile standard lab tool. Read the full text of their Full Paper at 10.1002/cmtd.202100001.

Cover Picture: ChemiTEM – Transmission Electron Microscopy Optimized for Chemistry and Material Science (Chemistry ‐ Methods 9/2021)

The Front Cover shows the ChemiTEM acquisition workflow in a tablet app in front of a transmission electron microscope (TEM). Additionally, the results obtained by a TEM-expert and by non-expert TEM users are shown left and right of the microscope, respectively. By using the ChemiTEM workflows, non-expert TEM users can proficiently use a TEM and generate data which is of comparable quality as the data obtained by a TEM-expert. Furthermore, the training time of the users is reduced and electron microscopical investigations can be performed with much higher efficiency. ChemiTEM extends the TEM user base and makes this instrument a versatile standard lab tool. More information can be found in the Full Paper by Walid Hetaba et al.

Abstract LB010: SMMART Program: A multi-omics tumor board with a focus on breast cancer

Abstract Many of the current approaches to personalized medicine rely on sequencing DNA to identify actionable mutations. However, growing evidence suggests that a multi-omic approach to more broadly assess biology is needed to improve patient outcomes. We have implemented a workflow for tissue acquisition, multi-omic clinical testing, and correlated computational biology analysis, within the Serial Measurements of Molecular and Architectural Responses to Therapy (SMMART) Program (Mitri et al, J Transl Med 2018). We report biopsy metrics, CLIA analytics utilized, the operation of a multi-omics tumor board, and clinical outcomes in metastatic breast cancer patients. A detailed clinical history of each patient was obtained, including demographics, tumor type, treatment and response to prior therapies, imaging, and blood tumor biomarkers. A comprehensive set of clinical assays was performed on newly obtained tumor biopsies, including immunohistochemistry (ER, PR, HER2, AR, BCL-2, and PD-L1), a targeted next-generation sequencing panel covering 225 genes (GeneTrails® Comprehensive Solid Tumor Panel), whole exome sequencing (Tempus xE), whole transcriptomic sequencing (Illumina TruSeq RNA exome), and a multiplex protein analysis of 22 key cancer proteins and phosphoproteins on the Nanostring platform (NanoString Vantage 3D™ Solid Tumor Panel). The integrated clinical and analytical information was made available to the multidisciplinary SMMART Clinical Tumor Board that provided treatment recommendations; the final treatment plan was at the discretion of the treating physician. Between 1/1/2017-1/1/2020, 53 breast cancer patients were consented. Seven screen-failed due to a lack of sites amenable to biopsy and 8 were actively co-consented to other clinical trials. The remaining 38 patients are included in this preliminary report. A total of 63 biopsies were collected from lymph node, liver, bone, soft tissue, lung, skin, breast, and brain. Serial biopsies (≥2) were obtained for 15 patients. Analytics were generated in 93.7% of biopsies. Tumor boards were held for 15 patients (17 total sessions). The experience and information gathered thus far have yielded the following unique cases: (1) single patient analysis of omics, imaging, and response over 42 months, (2) identification of an ERBB3 mutation with downstream pathway activation that responded to HER2-targeted therapy, and (3) clinically significant variation in hormonal and HER2 receptor status over time. We will provide an analysis across the 38 patients of treatment outcomes, analytics information content, biological changes observed through serial biopsies, and tumor board interventions. We demonstrate the feasibility of implementing a deep, real-time analytics platform for metastatic breast cancer patients that can provide new insight into therapeutic opportunities. The observed clinical responses support further use and investigation of this approach. Citation Format: Ben L. Kong, Brett E. Johnson, Jamie M. Keck, Souraya Mitri, Patrick Leyshock, Jayne M. Stommel, Kiara Siex, Marlana Klinger, Christina L. Zheng, Rochelle Williams-Belizaire, Shannon McWeeney, Jeremy Goecks, Annette Kolodzie, Alexander R. Guimaraes, George V. Thomas, Christopher L. Corless, Zahi I. Mitri, Joe W. Gray, Gordon B. Mills, Raymond C. Bergan. SMMART Program: A multi-omics tumor board with a focus on breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB010.

Digital media archive for gross pathology images based on open-source tools and Fast Healthcare Interoperability Resources (FHIR).

Macroscopic examination of surgical pathology and autopsy cases is a fundamental component of anatomic pathology. The photographic documentation of such clinical specimens is essential, and it may be required in certain instances. Our department began using consumer-grade digital cameras in 2005 to improve the practice of gross photography. However, the lack of an application to correctly catalog the photographs resulted in thousands of digital image files scattered across shared network drives, with limited case and patient metadata, making image retrieval a cumbersome and sometimes impossible task. Thirteen years later, we examined the legacy method of acquiring and accessing gross photographs in our department and determined the need for a web-based digital media archive to capture images with structured metadata. Using several open-source tools, including MediaWiki, we developed a flexible platform for building our digital media archive with a data schema based on the Fast Healthcare Interoperability Resources standard. Following a short pilot, we replaced the legacy method of handling gross pathology images with a new acquisition workflow and digital media archive. Through March 2021, 233 distinct users have accessed the system, 58 of which have uploaded 21,024 images. Of those images, 13,684 (65.1%) correspond to surgical pathology images, 4045 (19.2%) belong to neuropathology cases, and 3295 (15.7%) originate from autopsies. We demonstrate the design and implementation of a customizable anatomic pathology digital media archive solution in an academic pathology department setting using a modern standard for exchanging healthcare information electronically. The system’s efficiency and scalability for our current operation will enable us to integrate with other applications and pathology informatics initiatives in the future.

Harnessing the Power of Proteomics to Advance Precision Medicine

Clinical OMICsVol. 8, No. 3 Sponsored ContentFree AccessHarnessing the Power of Proteomics to Advance Precision MedicinePublished Online:18 Jun 2021https://doi.org/10.1089/clinomi.08.03.18AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Jennifer Van Eyk, PhD, Director, Precision Biomarker LabsWhat is the mission of Precision Biomarker Laboratories (PBL)?Precision Biomarker Laboratories is focused on releasing the power of precise proteomics by utilizing our growing understanding of the proteome to transform how medicine is practiced. Our goal is to reduce the barriers in translating therapies and biomarkers to the clinic for the benefit of precision health. PBL builds on the Cedars-Sinai Medical Center health network patient population to validate assays to meet clinical rigor as laboratory developed tests into our CAP/CLIA lab, and to implement these new protein markers within the context of thousands of individuals in a time- and cost-effective manner.How can a better understanding of the human proteome aid in advancing precision medicine?Our proteome, continuously changing from the time we are born until we reach old age, contributes more than genetics to disease risk over a lifetime. It can aid precision medicine since the proteome represents a personalized signature or timestamp for each patient of their current physiological status based on their response to multiple perturbations including stress, environment, smoking, and obesity, among other things.But the proteome can, and does, change rapidly in response to these perturbations, which makes it an active signature of a person's current physiological status and provides insights to how you will respond to a specific therapy. Your proteomic timestamp can thus help predict your future health and provide a responsive health readout.How is PBL well-positioned to deliver on this promise?For our clients, we can accelerate development, allowing faster go/ no-go decisions by providing precise and accurate protein data with full analytical support. We aid our partners to more quickly discover and develop drug targets by ensuring that the drug is targeting the protein or network of choice. We provide rapid, targeted protein panel development, which allows focusing on the key proteins or pathways with maximum biological insight. Finally, our affiliation with Cedars-Sinai provides access to a clinical infrastructure that is vital to establishing the validity of protein biomarkers.How do you accomplish all of this?We have three synergistic laboratories. Each works independently, but our clients can use any aspect of each or all of their capabilities.The proteomics contract research lab leverages our automated sample preparation, mass spectrometry, and integrated bioinformatic pipelines and expertise. It is focused on discovery and early research support to provide discovery services, or clients can use one of their own targeted assays. We have biomarkers of both biological and clinical relevance.Our commercial development lab provides clients with a bioinformatic and data analysis framework for clinical investigations with advanced analytical methods built to support clinical and commercial deployment. It also provides access to our clinical expertise and clinical cohorts, as needed.Finally, the clinical specialty lab completes the translation of research to clinical applications, to support a product launch either in the clinic or for clinical trials. It develops assays that define clinical use, supports validated assays from external sources, and supports clinical results interpretation.PBL uses mass spec for its assays and assay development. In what ways are you leveraging mass spec?PBL takes emerging and cutting-edge mass spec technologies and develops them into robust quantitative workflows. Using our plasma or dried blood assays (remote sampling) we can do discovery or use one of our in-house multiplexed protein targeted assays. For example, the Health Surveillance protein panel consists of 59 plasma proteins that represent nine pathological signatures such as the heart, kidneys, the lungs, inflammation, and immunity. It can be used for risk stratification in a clinical trial or to measure response to a therapy. We also have disease-specific assays such as our COVID-19 Acute Phase Protein assay. Upstream of our targeted and customizable assays we provide mass spec discovery services using data-independent acquisition workflows. Leveraging mass spec services on discovery and targeted workflows increases the likelihood of clinical translation for precision health applications.For more information: cedars-sinai.org/pblFiguresReferencesRelatedDetails Volume 8Issue 3May 2021 InformationCopyright © GEN PublishingTo cite this article:Clinical OMICs.May 2021.15-15.http://doi.org/10.1089/clinomi.08.03.18Published in Volume: 8 Issue 3: June 18, 2021PDF download

Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): a robust imaging workflow to monitor antibiotic stress response in bacterial cells in vivo

The search for novel drugs that efficiently eliminate prokaryotic pathogens is one of the most urgent health topics of our time. Robust evaluation methods for monitoring the antibiotic stress response in prokaryotes are therefore necessary for developing respective screening strategies. Besides advantages of common in vitrotechniques, there is a growing demand for in vivoinformation based on imaging techniques that allow to screen antibiotic candidates in a dynamic manner. Gathering information from imaging data in a reproducible manner, robust data processing and analysis workflows demand advanced (semi-)automation and data management to increase reproducibility. Here we demonstrate a versatile and robust semi-automated image acquisition, processing and analysis workflow to investigate bacterial cell morphology in a quantitative manner. The presented workflow, A.D.I.C.T, covers aspects of experimental setup deployment, data acquisition and handling, image processing (e.g. ROI management, data transformation into binary images, background subtraction, filtering, projections) as well as statistical evaluation of the cellular stress response (e.g. shape measurement distributions, cell shape modeling, probability density evaluation of fluorescence imaging micrographs) towards antibiotic-induced stress, obtained from time-course experiments. The imaging workflow is based on regular brightfield images combined with live-cell imaging data gathered from bacteria, in our case from recombinant Shewanellacells, which are processed as binary images. The model organism expresses target proteins relevant for membrane-biogenesis that are functionally fused to respective fluorescent proteins. Data processing and analysis are based on customized scripts using ImageJ2/FIJI, Celltool and R packages that can be easily reproduced and adapted by users. Summing up, our approach aims at supporting life-scientists to establish their own imaging-pipeline in order to exploit their data as versatile as possible and in a reproducible manner.

Towards Standardized Acquisition with a Dual-probe Ultrasound Robot for Fetal Imaging.

Standardized acquisitions and diagnoses using robots and AI would potentially increase the general usability and reliability of medical ultrasound. Working towards this prospect, this paper presents the recent developments of a standardized acquisition workflow using a novel dual-probe ultrasound robot, for a project known as intelligent Fetal Imaging and Diagnosis (iFIND). The workflow includes an abdominal surface mapping step to obtain a non-parametric spline surface, a rule-based end-point calculation method to position each individual joint, and a motor synchronization method to achieve a smooth motion towards a target point. The design and implementation of the robot are first presented in this paper and the proposed workflow is then explained in detail with simulation and volunteer experiments performed and analyzed. The closed-form analytical solution to the specific motion planning problem has demonstrated a reliable performance controlling the robot to move towards the expected scanning areas and the calculated proximity of the robot to the surface shows that the robot maintains a safe distance while moving around the abdomen. The volunteer study has successfully demonstrated the reliable working and controllability of the robot in terms of acquiring desired ultrasound views. Our future work will focus on improving the motion planning, and on integrating the proposed standardized acquisition workflow with newly- developed ultrasound image processing methods to obtain diagnostic results in an accurate and consistent way.

Accurate and Precise External Calibration Enhances the Versatility of Quantitative NMR (qNMR)

Quantitative 1H nuclear magnetic resonance (qHNMR) is a highly regarded analytical methodology for purity determination as it balances metrological rigor, practicality, and versatility well. While ideal for intrinsically mass-limited samples, external calibration (EC) qHNMR is overshadowed by the prevalence of internal calibration and perceived rather than real practical limitations. To overcome this hurdle, this study applied the principle of reciprocity, certified reference materials (caffeine as analyte, dimethyl sulfone as calibrant), and a systematic evaluation of data acquisition workflows to extract key factors for the achievement of accuracy and precision in EC-qHNMR. Automatic calibration of the 90° pulse width (90 PW) formed the foundation for the principle of reciprocity and used optimized nutation experiments, showing good agreement with values derived from manual high-precision measurement of 360 PW. Employing the automatic 90 PW calibration, EC-qHNMR with automatic vs manual tuning and matching (T&M) yielded the certified purity value within 1% error. The timing of T&M (before vs after shimming) turned out to be critically important: sufficient time is required to achieve full-temperature equilibrium relative to thermal gradients in the air inside the probe and the sample. Achievable accuracy across different NMR solvents varies with differences in thermal conductivity and leads to 2% or greater errors. With matching solvents, the demonstrated accuracy of ∼1.0% underscores the feasibility of EC-qHNMR as a highly practical research tool.

Use of Hybrid Data-Dependent and -Independent Acquisition Spectral Libraries Empowers Dual-Proteome Profiling.

In the context of bacterial infections, it is imperative that physiological responses can be studied in an integrated manner, meaning a simultaneous analysis of both the host and the pathogen responses. To improve the sensitivity of detection, data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows in identifying and quantifying low-abundant proteins. Here, by making use of representative bacterial pathogen/host proteome samples, we report an optimized hybrid library generation workflow for DIA mass spectrometry relying on the use of data-dependent and in silico-predicted spectral libraries. When compared to searching DDA experiment-specific libraries only, the use of hybrid libraries significantly improved peptide detection to an extent suggesting that infection-relevant host-pathogen conditions could be profiled in sufficient depth without the need of a priori bacterial pathogen enrichment when studying the bacterial proteome. Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD017904 and PXD017945.

DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data

Existing data acquisition modes such as full-scan, data-dependent (DDA), and data-independent acquisition (DIA) often present limited capabilities in capturing metabolic information in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. In this work, we proposed a novel metabolomic data acquisition workflow that combines DDA and DIA analyses to achieve better metabolomic data quality, including enhanced metabolome coverage, tandem mass spectrometry (MS2) coverage, and MS2 quality. This workflow, named data-dependent-assisted data-independent acquisition (DaDIA), performs untargeted metabolomic analysis of individual biological samples using DIA mode and the pooled quality control (QC) samples using DDA mode. This combination takes advantage of the high-feature number and MS2 spectral coverage of the DIA data and the high MS2 spectral quality of the DDA data. To analyze the heterogeneous DDA and DIA data, we further developed a computational program, DaDIA.R, to automatically extract metabolic features and perform streamlined metabolite annotation of DaDIA data set. Using human urine samples, we demonstrated that the DaDIA workflow delivers remarkably improved data quality when compared to conventional DDA or DIA metabolomics. In particular, both the number of detected features and annotated metabolites were greatly increased. Further biological demonstration using a leukemia metabolomics study also proved that the DaDIA workflow can efficiently detect and annotate around 4 times more significant metabolites than DDA workflow with broad MS2 coverage and high MS2 spectral quality for downstream statistical analysis and biological interpretation. Overall, this work represents a critical development of data acquisition mode in untargeted metabolomics, which can greatly benefit untargeted metabolomics for a wide range of biological applications.

Quantitative Data-Independent Acquisition Glycoproteomics of Sparkling Wine

AbstractSparkling wine is an alcoholic beverage enjoyed around the world. The sensory properties of sparkling wine depend on a complex interplay between the chemical and biochemical components in the final product. Glycoproteins have been linked to positive and negative qualities in sparkling wine, but the glycosylation profiles of sparkling wine have not been previously investigated in detail. We analyzed the glycoproteome of sparkling wines using protein- and glycopeptide-centric approaches. We developed an automated workflow that created ion libraries to analyze sequential window acquisition of all theoretical mass spectra data-independent acquisition mass spectrometry data based on glycopeptides identified by Byonic (Protein Metrics; version 2.13.17). We applied our workflow to three pairs of experimental sparkling wines to assess the effects of aging on lees and of different yeast strains used in the liqueur de tirage for secondary fermentation. We found that aging a cuvée on lees for 24 months compared with 8 months led to a dramatic decrease in overall protein abundance and an enrichment in large glycans at specific sites in some proteins. Secondary fermentation of a Riesling wine with Saccharomyces cerevisiae yeast strain Siha4 produced more yeast proteins and glycoproteins than with S. cerevisiae yeast strain DV10. The abundance and glycosylation profiles of grape glycoproteins were also different between grape varieties. To our knowledge, this work represents the first in-depth study into protein- and peptide-specific glycosylation in sparkling wines and describes a quantitative glycoproteomic sequential window acquisition of all theoretical mass spectra/data-independent acquisition workflow that is broadly applicable to other sample types.

The Effect of Camera Calibration on Multichannel Texture Classification

Abstract The efficiency of a texture classification procedure depends on the color space in which it is performed. Classification in a perceptually meaningful space requires chromatic coordinates obtained from a calibrated acquisition setup. The authors assess the impact of camera calibration, within a generic color picture acquisition workflow, on the performance of a number of texture classification techniques. An image calibration pipeline is established and applied to a texture database, and the accuracy of the classification algorithms is evaluated for each step. The results show that the most significant step of the workflow is color rendering although the effect is relatively small. Hence precise scene-referred characterization of the raw data from an acquisition camera is not essential for most texture classification tasks. In addition, working with output-referred RGB data is likely to be adequate for the majority of classification tasks.

Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.