Quantitative Workflow Research Articles

Role of the Power Function Value in Linearity and Universality for Charged Aerosol Detectors: Theoretical Elucidations from a Validated Model.

High-throughput drug discovery on the microgram scale is now common, making analyte quantitation without molecule-specific calibration imperative. The charged aerosol detector (CAD) was invented to be a next-generation universal liquid chromatography (LC) detector with excellent response universality for nonvolatile analytes as well as sensitivity for nonchromophoric compounds. Although the CAD is a mass flow-sensitive detector, its response to mass is inherently nonlinear, which challenges traditional quantitation. In CAD software, there is a "power function value" (p) setting that can be used to linearize the signal through digital signal processing. The exact workings of this power function value algorithm remain unknown; however, its optimization is a crucial aspect of analytical method development for LC-CAD. Herein, we developed a theoretical relationship that can be used to predict the chromatogram (plus peak area, width, and height) at any p if the data are collected at p = 1. This model was validated using a diverse dataset comprising 1440 measurements including peak heights, areas, and widths. Predicted areas had an average error of less than 2% showing excellent agreement between calculated and experimental results. An open-access automated code is tested and provided, which predicts the power function value that produces the most linear response. It is vital to note that optimizing the power function value affects peaks of different heights disproportionately. Low-level impurities were shown to be minimized and eventually eliminated by increasing the power function value. This model provides an easy-to-implement tool (MATLAB or Excel) that assists in choosing the optimal p for each LC-CAD method, increasing the speed of method development and improving the accuracy of quantitative workflows.

Video-Based Performance Analysis in Pituitary Surgery—Part 1: Surgical Outcomes

Endoscopic pituitary adenoma surgery has a steep learning curve, with varying surgical techniques and outcomes across centers. In other surgeries, superior performance is linked with superior surgical outcomes. This study aimed to explore the prediction of patient-specific outcomes using surgical video analysis in pituitary surgery. Endoscopic pituitary adenoma surgery videos from a single center were annotated by experts for operative workflow (3 surgical phases and 15 surgical steps) and operative skill (using modified Objective Structured Assessment of Technical Skills [mOSATS]). Quantitative workflow metrics were calculated, including phase duration and step transitions. Poisson or logistic regression was used to assess the association of workflow metrics and mOSATS with common inpatient surgical outcomes. 100 videos from 100 patients were included. Nasal phase mean duration was 24minutes and mean mOSATS was 21.2/30. Mean duration was 34minutes and mean mOSATS was 20.9/30 for the sellar phase, and 11minutes and 21.7/30, respectively, for the closure phase. The most common adverse outcomes were new anterior pituitary hormone deficiency (n= 26), dysnatremia (n= 24), and cerebrospinal fluid leak (n= 5). Higher mOSATS for all 3 phases and shorter operation duration were associated with decreased length of stay (P= 0.003 &P < 0.001). Superior closure phase mOSATS were associated with reduced postoperative cerebrospinal fluid leak (P < 0.001), and superior sellar phase mOSATS were associated with reduced postoperative visual deterioration (P= 0.041). Superior surgical skill and shorter surgical time were associated with superior surgical outcomes, at a generic and phase-specific level. Such video-based analysis has promise for integration into data-driven training and service improvement initiatives.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction

Glioblastoma presents characteristically with an exuberant, poorly functional vasculature that causes malperfusion, hypoxia and necrosis. Despite limited clinical efficacy, anti-angiogenesis resulting in vascular normalization remains a promising therapeutic approach. Yet, fundamental questions concerning anti-angiogenic therapy remain unanswered, partly due to the scale and resolution gap between microscopy and clinical imaging and a lack of quantitative data readouts. To what extend does treatment lead to vessel regression or vessel normalization and does it ameliorate or aggravate hypoxia? Clearly, a better understanding of the underlying mechanisms would greatly benefit the development of desperately needed improved treatment regimens. Here, using orthotopic transplantation of Gli36 cells, a widely used murine glioma model, we present a mesoscopic approach based on light sheet fluorescence microscopic imaging of wholemount stained tumors. Deep learning-based segmentation followed by automated feature extraction allowed quantitative analyses of the entire tumor vasculature and oxygenation statuses. Unexpectedly in this model, the response to both cytotoxic and anti-angiogenic therapy was dominated by vessel normalization with little evidence for vessel regression. Equally surprising, only cytotoxic therapy resulted in a significant alleviation of hypoxia. Taken together, we provide and evaluate a quantitative workflow that addresses some of the most urgent mechanistic questions in anti-angiogenic therapy.

Detection of Stress-Induced Changes in Subcellular Protein Distribution.

Proteins often show alterations in their subcellular localization with changing environmental conditions; transcription factors enter the nucleus or are actively removed from the nucleus; some even bind to endo-membranes by conditional membrane anchors; and other proteins and mRNA arrange in RNA granules. These are some examples of the complex regulation of subcellular localization, which often depends on posttranslational modifications and is triggered by environmental stressors. The challenge is the precise identification of the compartments, the quantitative analysis of proteins, which reside in multiple compartments, and their transport dynamics. Therefore, appropriate compartment markers and routines for a reproducible quantitative workflow are required.

Age- and Sex-Specific Nomographic CT Quantitative Plaque Data From a Large International Cohort

BackgroundWith growing adoption of coronary computed tomographic angiography (CTA), there is increasing evidence for and interest in the prognostic importance of atherosclerotic plaque volume. Manual tools for plaque segmentation are cumbersome, and their routine implementation in clinical practice is limited. ObjectivesThe aim of this study was to develop nomographic quantitative plaque values from a large consecutive multicenter cohort using coronary CTA. MethodsQuantitative assessment of total atherosclerotic plaque and plaque subtype volumes was performed in patients undergoing clinically indicated coronary CTA, using an Artificial Intelligence–Enabled Quantitative Coronary Plaque Analysis tool. ResultsA total of 11,808 patients were included in the analysis; their mean age was 62.7 ± 12.2 years, and 5,423 (45.9%) were women. The median total plaque volume was 223 mm3 (IQR: 29-614 mm3) and was significantly higher in male participants (360 mm3; IQR: 78-805 mm3) compared with female participants (108 mm3; IQR: 10-388 mm3) (P < 0.0001). Total plaque increased with age in both male and female patients. Younger patients exhibited a higher prevalence of noncalcified plaque. The distribution of total plaque volume and its components was reported in every decile by age group and sex. ConclusionsThe authors developed pragmatic age- and sex-stratified percentile nomograms for atherosclerotic plaque measures using findings from coronary CTA. The impact of age and sex on total plaque and its components should be considered in the risk-benefit analysis when treating patients. Artificial Intelligence–Enabled Quantitative Coronary Plaque Analysis work flows could provide context to better interpret coronary computed tomographic angiographic measures and could be integrated into clinical decision making.

Quantitative evaluation of 3D land acquisition geometries with arrays and single sensors: Closing the loop between acquisition and processing

The growing popularity of land nodes demands careful survey design practices to smoothly supersede cabled seismic acquisition with geophone arrays. Unfortunately, trace density is often used as a catchall proxy to describe survey quality, which is a gross oversimplification. We describe comprehensive and quantitative workflows focusing on final image quality for evaluating existing or new 3D land acquisition geometries with arrays and single sensors. They streamline the design process, remove human bias, and close the loop between acquisition and processing. A central element is a data-driven approach for deriving absolute signal-to-noise ratio (S/N) directly from the data. The resulting S/N volumes can be analyzed as cubes or slices or distilled to statistical quantities. We apply new workflows to three typical use cases from 3D land seismic data. First, we quantitatively contrast different 3D data sets acquired with various field acquisition geometries and understand which acquisition parameters are likely responsible for S/N differences. Second, we perform a realistic numerical feasibility study evaluating multiple 3D acquisition geometries with arrays and single sensors and assess their expected performance on a complex SEG Advanced Modeling Arid data set representative of the desert environment. For feasibility studies, complete automation can be achieved by applying migration in lieu of processing and data-driven S/N as evaluation steps. Finally, we show how to predict absolute S/N outcomes of new 3D acquisitions based on the existing legacy data with different acquisition geometry. We demonstrate the excellent predictive power of the analytical signal-strength estimate formula for both field and synthetic elastic data sets. Translating survey design into commonly spoken “image S/N language” improves communication between geoscientists and enables more effective decision-making.

Abstract 4293: Strategies and resources for applying a quantitative multiplex IHC imaging workflow to characterize immune contexture in solid tumors

Abstract Conventional immunohistochemistry (IHC) is a standardized diagnostic technique used in tissue pathology. However, the capacity to label only one marker per tissue section is a critical limitation. Multiplex immunohistochemistry (mIHC) is a powerful imaging technique used in basic, translational research and clinical settings to simultaneously detect the expression of multiple epitopes in a single formalin-fixed paraffin-embedded (FFPE) tissue section, allowing for characterization and quantification of cells while maintaining their spatial location. This technique allows for a comprehensive view of the immune milieu, expression patterns, and interactions between cell types that can help elucidate the complexity of the tumor-immune microenvironment (TiME). Multiplex imaging has emerged as a powerful tool to better understand tumor progression, response or resistance to therapy, and potentially identify predictive biomarkers. However, this technique requires careful tissue considerations and generates enormous amounts of hierarchical data, including single cell marker expressions, locations, and shape features, resulting in complex and challenging analyses. We previously published a validated platform using nine matched primary and recurrent head and neck squamous cell carcinoma (HNSCC) sections stained sequentially with a panel of 29 antibodies identifying malignant tumor cells, and 17 distinct leukocyte lineages and their functional states1. Here, we have optimized and consolidated important considerations, including tissue quality and QC steps necessary for generating high quality data from multiplex imaging studies. We demonstrate strategies for a streamline analytic pipeline using our quantitative workflow developed to characterize immune cells and infiltration patterns within spatial compartments of solid tumors in FFPE tissues, and herein, provide strategies and resources to practically adapt and utilize our analytic mIHC pipeline. 1. Banik, G., et al., Methods Enzymol., 2020 Citation Format: Shamilene Sivagnanam, Courtney M. Betts, Nell Kirchberger, Konjit Betre, Lisa M. Coussens. Strategies and resources for applying a quantitative multiplex IHC imaging workflow to characterize immune contexture in solid tumors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4293.

From geodiversity assessment to geosite analysis – a GIS-aided workflow from the Bakony–Balaton UNESCO Global Geopark, Hungary

Abstract Geodiversity and geosite assessments precede geoheritage and geotourism utilization. The process first determines the geodiversity value of an area (based on geoscientific attributions) and then the geotourism potential of the available sites. As a result, significant geosites can be identified, which are the bases for protection and tourism. During geosite assessment, scientific and infrastructural aspects are essential because spectacular sites and landscapes carrying intrinsic or visible values generate interest among tourists and professionals. In this study, a quantitative workflow to determine the geodiversity index over an area, evaluate geosites and monitor significant ones is presented. The study area is the Bakony–Balaton UNESCO Global Geopark, where no quantitative assessment was conducted previously. A GIS-based geodiversity analysis identified the most diverse areas which gave the basis for the geosite assessment done in a ‘geodiverse’ subregion of the geopark. The most important nine of the 75 identified potential geosites were chosen to examine the spatial variance of the assessment. By continuous monitoring, we got an image of what the visitors liked or did not like there. In this way, we were able to monitor the various opinions of geotourists to present unique development strategies for each of them. A connection between the location of geosites and the spatial distribution of geodiversity values was also determined by analysing and visualizing the connection between geodiversity and geosite assessment results.

Improving history matching performance in a 3D field-scale case study by combining tracer and production data

Reservoir models are often subject to uncertainties, which, if not properly taken into account, may introduce biases to the subsequent reservoir management process. To improve reliability and reduce uncertainties, it is crucial to condition reservoir models on available field datasets through history matching. There are different types of field data. Among others, production data are the most common choice, but they are subject to a major limitation of carrying relatively low value of information. On the other hand, inter-well tracer data have been shown to provide additional information about well-to-well connectivity and reservoir dynamics. However, jointly history matching production and inter-well tracer data still remains challenging due to the lack of a coherent quantitative workflow to fully integrate them. This work can be considered a step towards tackling this noticed problem. To this end, we propose a non-intrusive and derivative-free ensemble history matching workflow, in which reservoir models are more coherently conditioned on both production and inter-well tracer data with the help of a recently developed technique (correlation-based adaptive localization). The workflow is successfully implemented in the Brugge benchmark case. Our study indicates that the history matching algorithm matches the production data well, regardless of the presence or absence of the tracer data. Nevertheless, by including tracer data as an additional source of information, we are able to improve the quality of the estimated reservoir models, in terms of both improved data match and reduced model discrepancies. As such, the finding of this study can help to achieve a better understanding of the impacts of tracer data on history matching performance, and the proposed workflow could serve as a useful too for more proper uncertainty quantification, and more coherent utilization of different types of field data in real case studies in general.

Development, Validation, and Implementation Challenges of Decision Point and Quantitative Workflows for Marijuana Testing After the Passage of the 2018 Farm Bill

Development, Validation, and Implementation Challenges of Decision Point and Quantitative Workflows for Marijuana Testing After the Passage of the 2018 Farm Bill

Uncovering and Visualizing Work Process Interruptions through Quantitative Workflow Analysis

Question: Continuous improvement requires visualizing process constraints which interrupt workflows. Production control from a management perspective often operates at lower levels of information granularity than required at operational levels to perform work without interruptions. How can workflow interruptions in plumbing work be analysed and explained? How can an analysis of workflow interruptions help to close the information granularity gap between operational and management levels? Purpose: The purpose of this paper is to evaluate methods for their fit in revealing and closing the information granularity gap between between operational and management levels. Research Method: The paper examines workflows of plumbing work from video footage. This video data is classified and analyzed for frequency, causes, and effects of work interruptions. Findings: Results indicate that value-supporting activities caused the largest proportion of interruptions. Moreover, the proportion of non-value-adding activities increases when durations of interruptions rise. Limitations: The analyzed and tested data includes one working day of one worker in one certain construction project, which limits the meaningfulness of these results and explanations. Implications: The conducted time-motion-study and its classified data set made it possible to develop an agent-based simulation model of construction workers behavior. Value for authors: This paper provides a framework to examine workflow interruptions in craft work so that the information gap between operational and management levels is closed. Keywords: continuous improvement, production control, time-motion study, workflow. Paper type: Full paper

Novel cryo-tomography workflow reveals nanometer-scale responses of epithelial cells to matrix stiffness and dimensionality.

Matrix stiffness and dimensionality have been shown to be major determinants of cell behavior. However, a workflow for examining nanometer-scale responses of the associated molecular machinery is not available. Here, we describe a comprehensive, quantitative workflow that permits the analysis of cells responding to mechanical and dimensionality cues in their native state at nanometer scale by cryogenic electron tomography. Using this approach, we quantified distinct cytoskeletal nanoarchitectures and vesicle phenotypes induced in human mammary epithelial cells in response to stiffness and dimensionality of reconstituted basement membrane. Our workflow closely recapitulates the microenvironment associated with acinar morphogenesis and identified distinct differences in situ at nanometer scale. Using drug treatment, we showed that molecular events and nanometer-scale rearrangements triggered by engagement of apical cell receptors with reconstituted basement membrane correspond to changes induced by reduction of cortical tension. Our approach is fully adaptable to any kind of stiffness regime, extracellular matrix composition, and drug treatment.

Reaching new sensitivity levels for the detection of fentanyl analogs and highly potent novel synthetic opioids in blood

The aim of this presentation is to demonstrate a sensitive quantitative workflow for the analysis of 32 of potent substances in poly-drug, discarded postmortem case samples at low (pg/mL) levels. The presentation will demonstrate that the unparalleled quantification performance of the method enabled accurate detection of potent substances in poly-drug, discarded postmortem case samples at trace levels that were not previously achievable, providing toxicologists a valuable insight into the causation of accidental overdoses. A total of 32 NSO including 17 fentanyl analogs and 15 newly emerging non-fentanyl opioids were selected for this panel. NSO were extracted from the 200 μL of spiked whole blood mixtures using a liquid-liquid extraction (LLE) procedure. Analytes were chromatographically separated using a Phenomenex Kinetex C18 column (50 × 3.0 mm, 2.6 μm, 00B-4462-Y0). Mobile phase A (MPA) and mobile phase B (MPB) were ammonium formate with formic acid and formic acid in methanol and acetonitrile, respectively. The injection volume was 10 μL and the total LC runtime was 15.5 minutes. A single acquisition method consisting of 68 MRM transitions (64 for the NSO and 4 for the internal standards) was created using the Scheduled MRM Algorithm. Control human whole blood samples spiked with the 32 target analytes were prepared at concentrations ranging from 1 pg/mL to 100 ng/mL. Excellent linearity was observed across the concentration ranges analyzed with R2 values greater than 0.99 for all of the NPS in the panel. Reported LLOQ values ranged between 10 to 50 pg/mL for the 32 analytes in the panel. The accuracy and precision of measurements ranged from 80.50–116.64% and from 0.42–17.80%, respectively. The excellent accuracy and precision were observed over the entire concentration range, including at the LLOQ. Overall, the developed method showed excellent reproducibility and linearity, proving the robustness of the developed method and quantitative performance of the SCIEX 7500 System even at low concentration levels for each of the targeted NSO in this study. The applicability of the method to analyze discarded postmortem case samples from subjects suspected of NPS ingestion resulting in accidental overdoses was investigated. For example, the results from the analysis of one of these case samples showed the successful detection of one NSO and its metabolite: isotonitazene and 5-aminoisotonitazene, as well as fentanyl and four of its analogs/metabolites: beta-hydroxy-fentanyl, norfentanyl, 4-ANPP and acetyl fentanyl at concentrations of 1434.33, 7.93, 599.10, 9756.67, 147.69 and 1465.00 pg/mL, respectively. The results demonstrate that the developed method enabled accurate detection of potent substances in poly-drug, authentic case samples at trace levels that were not previously achievable. A robust and sensitive drug screening workflow for the analysis of 32 potent NSO was successfully developed using the SCIEX 7500 System. Overall, the remarkable quantification performance of the SCIEX 7500 System enabled accurate detection of potent NSO at concentrations that were not previously achievable, providing a means to monitor ultra-potent NSO in overdose scenarios. The sensitivity levels afforded by the SCIEX 7500 system provided the ability to detect low levels of NSO in postmortem case samples that would normally go undetected, providing a clearer picture for help in determining the cause of death.

Early Detection of Non-Conformance in Monitoring of CO2 Storage Reservoirs Using Auto-Encoder Neural Networks

Summary Planning and managing operations of subsurface reservoir assets in terms of conformance is crucial for responsible CO2 storage. One important component of conformance management is the monitoring of the reservoir dynamics in response to the implemented operational strategies, particularly for early detection of deviations from intended behaviour (i.e., non-conformance). In recent work, we have introduced a model-based quantitative workflow to objectively assess the usefulness of monitoring for conformance verification in CO2 storage and shown how to use state-of-the-art supervised learning techniques to achieve a more practical workflow. In the present work, we investigate the use of a semi-supervised anomaly detection approach based on auto-encoder neural networks as an alternative to circumvent limitations of the supervised classification approaches explored so far. The results of our case study of a real storage aquifer show that auto-encoders trained on simulated time-lapse seismic data from (only) conformance scenarios can be used to accurately detect scenarios where the migration of CO2 deviates from the desired range of behaviours. These promising results confirm that the proposed approach can be used to derive efficient conformance classification workflows without an explicit finite dataset representing non-conformance to be defined in advance.

Quantifying molecular imaging patterns of treatment response or progression using a novel traffic light workflow within a prospective phase I/II trial of 177LuPSMA-617 and NOX66 (LuPIN).

166 Background: 177Lutetium PSMA-617 (LuPSMA) is an effective therapy for metastatic castrate-resistant prostate cancer (mCRPC). However, treatment resistance may occur. We developed a quantitative workflow for serial PSMA PET/CT to optimise predictive and prognostic imaging biomarker capability for progression free (PFS) and overall survival (OS). Methods: 56 men with mCRPC previously treated with taxane chemotherapy and androgen signaling inhibitor were enrolled, receiving up to 6 doses of LuPSMA and a radiation sensitizer idronoxil (NOX66). 68Ga-PSMA-11 PET/CT was performed at study entry and exit. Traffic Light (TL) quantification workflow was developed to track changes in both tumour volume and intensity at a total body and lesional level. Lesions were classified as responding in green (&gt;30% decline in volume), stable in yellow (&lt;30% change in volume/intensity), progressive in red (&gt;30% increase in volume/intensity, or new). Overall response pattern was categorised as responding ( green/yellow), low volume red (&lt;50% progressive disease) or high volume red (&gt;50% progressive disease). TL workflow results were correlated with PFS and OS. Results: 37/56 men underwent both entry and exit imaging. The median PSA decline was 77% (IQR 34-92%), and 70% (26/37) achieved PSA response &gt;50%. PSA progression occurred in 54% (20/37) at exit imaging. Median PFS was 8.6 months (95%CI 5.6-11.6) and median OS 22 months (95% CI 18.6-25.6). 95% (35/37) had reduction in PSMA SUVmax (-26.1 (IQR +11.7 to -89.4)) and SUVmean (-3.3 (+2.9 to -14.2)). PSMA total tumor volume reduced in 68% (25/37) (median -0.64 liters (range +1.44 to -1.1)). On TL workflow, 24% (9/37) had responding/stable disease ( green/ yellow), 76% (28/37) had progressive disease ( red) of whom 41% (15/37) had low volume progression and 35% (13/37) high volume progression. Men with high volume progression had worse OS compared to responders (HR 0.18 (0.05-0.59), p 0.005), and low volume progression (HR 0.30 (0.11-0.80), p 0.02). 68% (19/28) had progression on both TL workflow and PSA, while 32% (9/28) had progression on TL workflow without PSA progression. In multivariable analysis, TL workflow and PSA progression at time of exit scans were independent predictors of OS (Table). Conclusions: This study demonstrates the feasibility of characterizing lesional response on molecular imaging with a quantification TL workflow. TL workflow response independently correlated with survival outcomes, indicating serial PSMA PET has prognostic biomarker potential. Clinical trial information: ACTRN12618001073291.[Table: see text]

System-Wide Quantitative N-Glycoproteomic Analysis from K562 Cells and Mouse Liver Tissues.

As a key regulator of many biological processes, glycosylation is an essential post-translational modification (PTM) in the living system. Over 50% of human proteins are known to be glycosylated. Alterations in glycoproteins are directly linked to many diseases, making it crucial to understand system-wide glycosylation changes. The majority of known glycoproteins are from plasma membrane; however, glycosylation is a dynamic process that occurs throughout multiple subcellular organelles and involves sets of enzymes, chaperones, transporters, and sugar donor molecules. Many glycoproteins are expressed not only in plasma membranes but also in subcellular organelles. Here, we developed a mass-spectrometry-based quantitative workflow for the system-wide N-glycoproteomic analysis of membrane and cytosolic proteins extracted using a MEM-PER kit. The kit facilitates the extraction and solubilization of both membrane and cytosolic proteins in a simple, efficient, and reproducible manner. We analyzed the K562 cell line and mouse liver tissue to evaluate this approach. A total of 934 glycosites, 5154 glycopeptides, and 536 glycoproteins from the K562 cell line and a total of 1449 glycosites, 7549 glycopeptides, and 660 glycoproteins from mouse liver tissue were identified. This simple and reproducible approach provides a unique way to understand system-wide glycosylation in biological processes and enables the identification and quantitation of glycan profiles at glycosylation sites in proteins.

LipidQuant 1.0: automated data processing in lipid class separation-mass spectrometry quantitative workflows.

We present the LipidQuant 1.0 tool for automated data processing workflows in lipidomic quantitation based on lipid class separation coupled with high-resolution mass spectrometry. Lipid class separation workflows, such as hydrophilic interaction liquid chromatography or supercritical fluid chromatography, should be preferred in lipidomic quantitation due to the coionization of lipid class internal standards with analytes from the same class. The individual steps in the LipidQuant workflow are explained, including lipid identification, quantitation, isotopic correction and reporting results. We show the application of LipidQuant data processing to a small cohort of human serum samples. The LipidQuant 1.0 is freely available at Zenodo https://doi.org/10.5281/zenodo.5151201 and https://holcapek.upce.cz/lipidquant.php. Supplementary data are available at Bioinformatics online.

IceR improves proteome coverage and data completeness in global and single-cell proteomics

Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

Quantitative assessment of monitoring strategies for conformance verification of CO2 storage projects

We propose a quantitative model-based workflow for conformance verification of CO2 storage projects. Bayesian inference is applied to update an ensemble of simulation models that capture prior uncertainty based on mismatches with measured data. Conformance assessments are derived by comparison of updated model predictions with storage permit requirements and confidence criteria. Two examples, one conceptual and one based on a real candidate storage site, are provided in which the quantitative workflow is applied to the a priori assessment of candidate monitoring strategies. The examples illustrate the limitations of pressure monitoring in the presence of realistic subsurface uncertainties, and the potential for cost saving by informed design of geophysical monitoring surveys. Approximate methods are discussed that could make the workflow also applicable for (quasi) real-time conformance monitoring.