MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search.

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.

Purification Analysis, Intracellular Tracking, and Colocalization of Extracellular Vesicles Using Atomic Force and 3D Single-Molecule Localization Microscopy.

Extracellular vesicles (EVs) play a key role in cell-cell communication and thus have great potential to be utilized as therapeutic agents and diagnostic tools. In this study, we implemented single-molecule microscopy techniques as a toolbox for a comprehensive characterization as well as measurement of the cellular uptake of HEK293T cell-derived EVs (eGFP-labeled) in HeLa cells. A combination of fluorescence and atomic force microscopy revealed a fraction of 68% fluorescently labeled EVs with an average size of ∼45 nm. Two-color single-molecule fluorescence microscopy analysis elucidated the 3D dynamics of EVs entering HeLa cells. 3D colocalization analysis of two-color direct stochastic optical reconstruction microscopy (dSTORM) images revealed that 25% of EVs that experienced uptake colocalized with transferrin, which has been linked to early recycling of endosomes and clathrin-mediated endocytosis. The localization analysis was combined with stepwise photobleaching, providing a comparison of protein aggregation outside and inside the cells.

ImmunoDataAnalyzer: a bioinformatics pipeline for processing barcoded and UMI tagged immunological NGS data.

BACKGROUND: Next-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior. RESULTS: ImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples. CONCLUSIONS: IMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/ .

MS Annika: A New Cross-Linking Search Engine.

Cross-linking mass spectrometry (XL-MS) has become a powerful technique that enables insights into protein structures and protein interactions. The development of cleavable cross-linkers has further promoted XL-MS through search space reduction, thereby allowing for proteome-wide studies. These new analysis possibilities foster the development of new cross-linkers, which not every search engine can deal with out of the box. In addition, some search engines for XL-MS data also struggle with the validation of identified cross-linked peptides, that is, false discovery rate (FDR) estimation, as FDR calculation is hampered by the fact that not only one but two peptides in a single spectrum have to be correct. We here present our new search engine, MS Annika, which can identify cross-linked peptides in MS2 spectra from a wide variety of cleavable cross-linkers. We show that MS Annika provides realistic estimates of FDRs without the need of arbitrary score cutoffs, being able to provide on average 44% more identifications at a similar or better true FDR than comparable tools. In addition, MS Annika can be used on proteome-wide studies due to fast, parallelized processing and provides a way to visualize the identified cross-links in protein 3D structures.

Laser-facilitated epicutaneous immunotherapy with hypoallergenic beta-glucan neoglycoconjugates suppresses lung inflammation and avoids local side effects in a mouse model of allergic asthma.

BACKGROUND: Allergen-specific immunotherapy via the skin targets a tissue rich in antigen-presenting cells, but can be associated with local and systemic side effects. Allergen-polysaccharide neoglycogonjugates increase immunization efficacy by targeting and activating dendritic cells via C-type lectin receptors and reduce side effects. OBJECTIVE: We investigated the immunogenicity, allergenicity, and therapeutic efficacy of laminarin-ovalbumin neoglycoconjugates (LamOVA). METHODS: The biological activity of LamOVA was characterized in vitro using bone marrow-derived dendritic cells. Immunogenicity and therapeutic efficacy were analyzed in BALB/c mice. Epicutaneous immunotherapy (EPIT) was performed using fractional infrared laser ablation to generate micropores in the skin, and the effects of LamOVA on blocking IgG, IgE, cellular composition of BAL, lung, and spleen, lung function, and T-cell polarization were assessed. RESULTS: Conjugation of laminarin to ovalbumin reduced its IgE binding capacity fivefold and increased its immunogenicity threefold in terms of IgG generation. EPIT with LamOVA induced significantly higher IgG levels than OVA, matching the levels induced by s.c. injection of OVA/alum (SCIT). EPIT was equally effective as SCIT in terms of blocking IgG induction and suppression of lung inflammation and airway hyperresponsiveness, but SCIT was associated with higher levels of therapy-induced IgE and TH2 cytokines. EPIT with LamOVA induced significantly lower local skin reactions during therapy compared to unconjugated OVA. CONCLUSION: Conjugation of ovalbumin to laminarin increased its immunogenicity while at the same time reducing local side effects. LamOVA EPIT via laser-generated micropores is safe and equally effective compared to SCIT with alum, without the need for adjuvant.

MS Amanda 2.0: Advancements in the standalone implementation.

RATIONALE: Database search engines are the preferred method to identify peptides in mass spectrometry data. However, valuable software is in this context not only defined by a powerful algorithm to separate correct from false identifications, but also by constant maintenance and continuous improvements. METHODS: In 2014, we presented our peptide identification algorithm MS Amanda, showing its suitability for identifying peptides in high-resolution tandem mass spectrometry data and its ability to outperform widely used tools to identify peptides. Since then, we have continuously worked on improvements to enhance its usability and to support new trends and developments in this fast-growing field, while keeping the original scoring algorithm to assess the quality of a peptide spectrum match unchanged. RESULTS: We present the outcome of these efforts, MS Amanda 2.0, a faster and more flexible standalone version with the original scoring algorithm. The new implementation has led to a 3-5× speedup, is able to handle new ion types and supports standard data formats. We also show that MS Amanda 2.0 works best when using only the most common ion types in a particular search instead of all possible ion types. CONCLUSIONS: MS Amanda is available free of charge from https://ms.imp.ac.at/index.php?action=msamanda.

Next generation sequencing based assessment of the alloreactive T cell receptor repertoire in kidney transplant patients during rejection: a prospective cohort study.

BACKGROUND: Kidney transplantation is the optimal treatment in end stage renal disease but the allograft survival is still hampered by immune reactions against the allograft. This process is driven by the recognition of allogenic antigens presented to T-cells and their unique T-cell receptor (TCR) via the major histocompatibility complex (MHC), which triggers a complex immune response potentially leading to graft injury. Although the immune system and kidney transplantation have been studied extensively, the subtlety of alloreactive immune responses has impeded sensitive detection at an early stage. Next generation sequencing of the TCR enables us to monitor alloreactive T-cell populations and might thus allow the detection of early rejection events. METHODS/DESIGN: This is a prospective cohort study designed to sequentially evaluate the alloreactive T cell repertoire after kidney transplantation. The TCR repertoire of patients who developed biopsy confirmed acute T cell mediated rejection (TCMR) will be compared to patients without rejection. To track the alloreactive subsets we will perform a mixed lymphocyte reaction between kidney donor and recipient before transplantation and define the alloreactive TCR repertoire by next generation sequencing of the complementary determining region 3 (CDR3) of the T cell receptor beta chain. After initial clonotype assembly from sequencing reads, TCR repertoire diversity and clonal expansion of T cells of kidney transplant recipients in periphery and kidney biopsy will be analyzed for changes after transplantation, during, prior or after a rejection. The goal of this study is to describe changes of overall T cell repertoire diversity, clonality in kidney transplant recipients, define and track alloreactive T cells in the posttransplant course and decipher patterns of expanded alloreactive T cells in acute cellular rejection to find an alternative monitoring to invasive and delayed diagnostic procedures. DISCUSSION: Changes of the T cell repertoire and tracking of alloreactive T cell clones after combined bone marrow and kidney transplant has proven to be of potential use to monitor the donor directed alloresponse. The dynamics of the donor specific T cells in regular kidney transplant recipients in rejection still rests elusive and can give further insights in human alloresponse. TRIAL REGISTRATION: Clinicaltrials.gov: NCT03422224 , registered February 5th 2018.

PhoStar: Identifying Tandem Mass Spectra of Phosphorylated Peptides before Database Search.

Standard proteomics workflows use tandem mass spectrometry followed by sequence database search to analyze complex biological samples. The identification of proteins carrying post-translational modifications, for example, phosphorylation, is typically addressed by allowing variable modifications in the searched sequences. Accounting for these variations exponentially increases the combinatorial space in the database, which leads to increased processing times and more false positive identifications. The here-presented tool PhoStar identifies spectra that originate from phosphorylated peptides before database search using a supervised machine learning approach. The model for the prediction of phosphorylation was trained and validated with an accuracy of 97.6% on a large set of high-confidence spectra collected from publicly available experimental data. Its power was further validated by predicting phosphorylation in the complete NIST human and mouse high collision-dissociation spectral libraries, achieving an accuracy of 98.2 and 97.9%, respectively. We demonstrate the application of PhoStar by using it for spectra filtering before database search. In database search of HeLa samples the peptide search space was reduced by 27-66% while finding at least 97% of total peptide identifications (at 1% FDR) compared with a standard workflow.

CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction.

Coeluting peptides are still a major challenge for the identification and validation of MS/MS spectra, but carry great potential. To tackle these problems, we have developed the here presented CharmeRT workflow, combining a chimeric spectra identification strategy implemented as part of the MS Amanda algorithm with the validation system Elutator, which incorporates a highly accurate retention time prediction algorithm. For high-resolution data sets this workflow identifies 38-64% chimeric spectra, which results in up to 63% more unique peptides compared to a conventional single search strategy.

Limits on Supernova-Associated

We searched for the presence of ^{26}Al in deep-sea sediments as a signature of supernova influx. Our data show an exponential dependence of ^{26}Al with the sample age that is fully compatible with radioactive decay of terrigenic ^{26}Al. The same set of samples demonstrated a clear supernova ^{60}Fe signal between 1.7 and 3.2 Myr ago. Combining our ^{26}Al data with the recently reported ^{60}Fe data results in a lower limit of 0.18_{-0.08}^{+0.15} for the local interstellar ^{60}Fe/^{26}Al isotope ratio. It compares to most of the ratios deduced from nucleosynthesis models and is within the range of the observed average galactic ^{60}Fe/^{26}Al flux ratio of (0.15±0.05).

Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.

ImmunExplorer (IMEX): a software framework for diversity and clonality analyses of immunoglobulins and T cell receptors on the basis of IMGT/HighV-QUEST preprocessed NGS data.

BACKGROUND: Today's modern research of B and T cell antigen receptors (the immunoglobulins (IG) or antibodies and T cell receptors (TR)) forms the basis for detailed analyses of the human adaptive immune system. For instance, insights in the state of the adaptive immune system provide information that is essentially important in monitoring transplantation processes and the regulation of immune suppressiva. In this context, algorithms and tools are necessary for analyzing the IG and TR diversity on nucleotide as well as on amino acid sequence level, identifying highly proliferated clonotypes, determining the diversity of the cell repertoire found in a sample, comparing different states of the human immune system, and visualizing all relevant information. RESULTS: We here present IMEX, a software framework for the detailed characterization and visualization of the state of human IG and TR repertoires. IMEX offers a broad range of algorithms for statistical analysis of IG and TR data, CDR and V-(D)-J analysis, diversity analysis by calculating the distribution of IG and TR, calculating primer efficiency, and comparing multiple data sets. We use a mathematical model that is able to describe the number of unique clonotypes in a sample taking into account the true number of unique sequences and read errors; we heuristically optimize the parameters of this model. IMEX uses IMGT/HighV-QUEST analysis outputs and includes methods for splitting and merging to enable the submission to this portal and to combine the outputs results, respectively. All calculation results can be visualized and exported. CONCLUSION: IMEX is an user-friendly and flexible framework for performing clonality experiments based on CDR and V-(D)-J rearranged regions, diversity analysis, primer efficiency, and various different visualization experiments. Using IMEX, various immunological reactions and alterations can be investigated in detail. IMEX is freely available for Windows and Unix platforms at http://bioinformatics.fh-hagenberg.at/immunexplorer/.

Characterization of the Distance Relationship Between Localized Serotonin Receptors and Glia Cells on Fluorescence Microscopy Images of Brain Tissue.

We here present two new methods for the characterization of fluorescent localization microscopy images obtained from immunostained brain tissue sections. Direct stochastic optical reconstruction microscopy images of 5-HT1A serotonin receptors and glial fibrillary acidic proteins in healthy cryopreserved brain tissues are analyzed. In detail, we here present two image processing methods for characterizing differences in receptor distribution on glial cells and their distribution on neural cells: One variant relies on skeleton extraction and adaptive thresholding, the other on k-means based discrete layer segmentation. Experimental results show that both methods can be applied for distinguishing classes of images with respect to serotonin receptor distribution. Quantification of nanoscopic changes in relative protein expression on particular cell types can be used to analyze degeneration in tissues caused by diseases or medical treatment.

MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra.

Today's highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring algorithm particularly suitable for high mass accuracy. Our algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at http://ms.imp.ac.at/?goto=msamanda , is provided free of charge both as standalone version for integration into custom workflows and as a plugin for the Proteome Discoverer platform.

Automated Clinical Trial Cohort Definition and Evaluation with CQL and CDS-Hooks.

BACKGROUND: Patient recruitment for clinical trials faces major challenges with current methods being costly and often requiring time-consuming acquisition of medical histories and manual matching of potential subjects. OBJECTIVES: Designing and implementing an Electronic Health Record (EHR) and domain-independent automation architecture using Clinical Decision Support (CDS) standards that allows researchers to effortlessly enter standardized trial criteria to retrieve eligibility statistics and integration into a clinician workflow to automatically trigger evaluation without added clinician workload. METHODS: Cohort criteria are translated into the Clinical Quality Language (CQL) and integrated into Measures and CDS-Hooks for patient- and population-level evaluation. RESULTS: Successful application of simplified real-world trial criteria to Fast Healthcare Interoperability Resources (FHIR®) test data shows the feasibility of obtaining individual patient eligibility and trial details as well as population eligibility statistics and a list of qualifying patients. CONCLUSION: Employing CDS standards for automating cohort definition and evaluation shows promise in streamlining patient selection, aligning with increasing legislative demands for standardized healthcare data.

Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.

The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists' NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists' estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.

MetExtract: a new software tool for the automated comprehensive extraction of metabolite-derived LC/MS signals in metabolomics research.

MOTIVATION: Liquid chromatography-mass spectrometry (LC/MS) is a key technique in metabolomics. Since the efficient assignment of MS signals to true biological metabolites becomes feasible in combination with in vivo stable isotopic labelling, our aim was to provide a new software tool for this purpose. RESULTS: An algorithm and a program (MetExtract) have been developed to search for metabolites in in vivo labelled biological samples. The algorithm makes use of the chromatographic characteristics of the LC/MS data and detects MS peaks fulfilling the criteria of stable isotopic labelling. As a result of all calculations, the algorithm specifies a list of m/z values, the corresponding number of atoms of the labelling element (e.g. carbon) together with retention time and extracted adduct-, fragment- and polymer ions. Its function was evaluated using native (12)C- and uniformly (13)C-labelled standard substances. AVAILABILITY: MetExtract is available free of charge and warranty at http://code.google.com/p/metextract/. Precompiled executables are available for Windows operating systems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Measurements of ²³6U in ancient and modern peat samples and implications for postdepositional migration of fallout radionuclides.

(236)U was analyzed in an ombrotrophic peat core representing the last 80 years of atmospheric deposition and a minerotrophic peat sample from the last interglacial period. The determination of (236)U at levels of 10(7) atoms/g was possible by using ultraclean laboratory procedures and accelerator mass spectrometry. The vertical profile of the (236)U/(238)U isotopic ratio along the ombrotrophic peat core represents the first observation of the (236)U bomb peak in a terrestrial environment. A constant level of anthropogenic (236)U with an average (236)U/(238)U isotopic ratio of (1.24 ± 0.08) × 10(-6) in the top layers of the core was observed. Comparing the abundances of the global fallout derived (236)U and (239)Pu along the peat core, the post depositional migration of plutonium clearly exceeds that of uranium. However, the cumulative (236)U/(239)Pu ratio of 0.62 ± 0.31 is in agreement with previous studies on the global fallout uranium and plutonium. In the interglacial peat samples a (236)U/(238)U isotopic ratio of (3.3 ± 0.7) × 10(-12) was detected; although this measurement is an upper limit, it constitutes a significant step forward in the experimental determination of the natural (236)U abundance and represents a true background sample for the ombrotrophic peat core.

From Authoring to Evaluating an Electronic Health Quality Measure - Applying Logic to FHIR® with CQL for Calculating Immunization Coverage.

BACKGROUND: Current monitoring and evaluation methods challenge the healthcare system. Specifically for the use case of immunization coverage calculation, person-level data retrieval is required instead of inaccurate aggregation methods. The Clinical Quality Language (CQL) by HL7®, has the potential to overcome current challenges by offering an automated generation of quality reports on top of an HL7® FHIR® repository. OBJECTIVES: This paper provides a method to author and evaluate an electronic health quality measure as demonstrated by a proof-of-concept on immunization coverage calculation. METHODS: Five artifact types were identified to transform unstructured input into CQL, to define the terminology, to create test data, and to evaluate the new quality measures. RESULTS: CQL logic and FHIR® test data were created and evaluated by using the different approaches of manual evaluation, unit testing in the HAPI FHIR project, as well as showcasing the functionality with a developed user interface for immunization coverage analysis. CONCLUSION: Simple, powerful, and transparent evaluations on a small population can be achieved with existing open-source tools, by applying CQL logic to FHIR®.