An Optimized Data Structure for High Throughput 3D Proteomics Data: mzRTree

As an emerging field, MS-based proteomics still requires software tools for efficiently storing and accessing experimental data. In this work, we focus on the management of LC-MS data, which are typically made available in standard XML-based portable formats. The structures that are currently employed to manage these data can be highly inefficient, especially when dealing with high-throughput profile data. LC-MS datasets are usually accessed through 2D range queries. Optimizing this type of operation could dramatically reduce the complexity of data analysis. We propose a novel data structure for LC-MS datasets, called mzRTree, which embodies a scalable index based on the R-tree data structure. mzRTree can be efficiently created from the XML-based data formats and it is suitable for handling very large datasets. We experimentally show that, on all range queries, mzRTree outperforms other known structures used for LC-MS data, even on those queries these structures are optimized for. Besides, mzRTree is also more space efficient. As a result, mzRTree reduces data analysis computational costs for very large profile datasets.Comment: Paper details: 10 pages, 7 figures, 2 tables. To be published in Journal of Proteomics. Source code available at http://www.dei.unipd.it/mzrtre

Recurrent Sweet's syndrome in a patient with multiple myeloma

Key Clinical MessageWe report on a case of Sweet's syndrome associated with multiple myeloma, as harbinger for disease relapse

Beta-Blocker Use in Older Hospitalized Patients Affected by Heart Failure and Chronic Obstructive Pulmonary Disease: An Italian Survey From the REPOSI Register

Beta (β)-blockers (BB) are useful in reducing morbidity and mortality in patients with heart failure (HF) and concomitant chronic obstructive pulmonary disease (COPD). Nevertheless, the use of BBs could induce bronchoconstriction due to β2-blockade. For this reason, both the ESC and GOLD guidelines strongly suggest the use of selective β1-BB in patients with HF and COPD. However, low adherence to guidelines was observed in multiple clinical settings. The aim of the study was to investigate the BBs use in older patients affected by HF and COPD, recorded in the REPOSI register. Of 942 patients affected by HF, 47.1% were treated with BBs. The use of BBs was significantly lower in patients with HF and COPD than in patients affected by HF alone, both at admission and at discharge (admission, 36.9% vs. 51.3%; discharge, 38.0% vs. 51.7%). In addition, no further BB users were found at discharge. The probability to being treated with a BB was significantly lower in patients with HF also affected by COPD (adj. OR, 95% CI: 0.50, 0.37-0.67), while the diagnosis of COPD was not associated with the choice of selective β1-BB (adj. OR, 95% CI: 1.33, 0.76-2.34). Despite clear recommendations by clinical guidelines, a significant underuse of BBs was also observed after hospital discharge. In COPD affected patients, physicians unreasonably reject BBs use, rather than choosing a β1-BB. The expected improvement of the BB prescriptions after hospitalization was not observed. A multidisciplinary approach among hospital physicians, general practitioners, and pharmacologists should be carried out for better drug management and adherence to guideline recommendations

ECMO for COVID-19 patients in Europe and Israel

Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients. The mean annual number of cases treated with ECMO in the participating centres before the pandemic (2019) was 55. The number of COVID-19 patients has increased rapidly each week reaching 1531 treated patients as of September 14th. The greatest number of cases has been reported from France (n = 385), UK (n = 193), Germany (n = 176), Spain (n = 166), and Italy (n = 136) .The mean age of treated patients was 52.6 years (range 16–80), 79% were male. The ECMO configuration used was VV in 91% of cases, VA in 5% and other in 4%. The mean PaO2 before ECMO implantation was 65 mmHg. The mean duration of ECMO support thus far has been 18 days and the mean ICU length of stay of these patients was 33 days. As of the 14th September, overall 841 patients have been weaned from ECMO support, 601 died during ECMO support, 71 died after withdrawal of ECMO, 79 are still receiving ECMO support and for 10 patients status n.a. . Our preliminary data suggest that patients placed on ECMO with severe refractory respiratory or cardiac failure secondary to COVID-19 have a reasonable (55%) chance of survival. Further extensive data analysis is expected to provide invaluable information on the demographics, severity of illness, indications and different ECMO management strategies in these patients

qcML: an exchange format for quality control metrics from mass spectrometry experiments.

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml

MASS SPECTROMETRY-BASED PROTEOMICS: A 3D APPROACH TO DATA HANDLING AND QUANTIFICATION

This thesis describes the Ph.D. research project in Bioengineering for Computational Proteomics carried out during the last three years (January 2008 - January 2011). Activities focused on design and development of methods for the analysis of Quantitative Mass Spectrometry-based Proteomics data. The Introduction briefly elucidates the main themes developed in the thesis and how the work was schemed. It reviews the computational issues associated to both data handling and quantification, and introduces the solutions proposed in the following. The first two chapters are introductory to the Proteomics and Mass Spectrometry field. The objective is to provide the reader with the information needed to understand Quantitative Mass Spectrometry-based Proteomics. In particular, Chapter 1 explains how proteomics was born, as the –omics science of proteins. Then proteomics main applications and goals are illustrated, which are ranging from clinics and pharmaceutics to systems biology. Chapter 2 shows the main technologies and instrumentation exploited in Mass Spectrometry-based proteomics. The most common experimental setups are reported: among them, the Liquid Chromatography-Mass Spectrometry (LC-MS) technique is thoroughly explained since it is the principal technique for Quantitative Mass Spectrometry-based Proteomics. The third Chapter presents the main concepts necessary to introduce the reader to the main topic of the PhD research Project, that is the development of bioinformatics tools for the handling and quantification of Mass Spectrometry-based Quantitative Proteomics data, focusing on LC-MS quantitative data and their analysis. Indeed, LC-MS data are highly informative for quantification aims, but challenging to parse. Data features that were pivotal for the design of the proposed solutions (i.e., the 3D structure of LC-MS data and the high quality profile acquisition) are highlighted. In the fourth Chapter, the state of art both for data handling and quantification is described and available standard data formats and software are illustrated as well as related open challenges. In Chapter 5, the dataset used to carry out the analyses is technically described. It consists of LC-MS data from a labeled controlled mixture of proteins with known quantification ratios, acquired in profile acquisition mode and in triplicates. In particular, this thesis presents 2 software solutions to address the handling and quantification of Quantitative Mass Spectrometry-based Proteomics data: mzRTree and 3DSpectra, respectively. Chapter 6 presents the solution proposed for the data handling issue. The proposal is a scalable 2D indexing approach implemented through an R-tree-based data structure, called mzRTree, that relies on a sparse matrix representation of the dataset, which is appropriate for LC-MS data, and more in generally for MS-based proteomics data. mzRTree allows efficient data access, storage and enables a computationally sustainable analysis of profile MS data. Regarding the quantification, which is one of the most relevant problem in mass spectrometry-based proteomics, Chapter 7 illustrates the solution proposed for the quantification problem: 3DSpectra. It is an innovative quantification algorithm for LC-MS labeled profile data exploiting both the 3-dimensionality of data and the profile acquisition. 3DSpectra fits on peptide data the 3D isotopic distribution model shaped by a Gaussian Mixture Model including a noise component, using the Expectation-Maximization approach. This model enables the software to both recognize the borders of the 3D isotopic distribution and reject noise. 3DSpectra is a reliable and accurate quantification strategy for labeled LC-MS data, providing significantly wide and reproducible proteome coverage. In the conclusion section of this thesis future and ongoing research work, regarding further development of both the mzRTree data structure and 3DSpectra quantification software, are discussed.La presente tesi descrive il progetto di ricerca in Bioingegneria per la Proteomica Computazionale svolto durante i tre anni di dottorato (Gennaio 2008 - Gennaio 2011). L’attività di ricerca è stata incentrata sulla progettazione e lo sviluppo di metodi per l’analisi di dati di Proteomica basata su Spettrometria di Massa. Nell’introduzione si illustrano brevemente i temi principali trattati nella tesi, fornendo così lo schema del lavoro svolto. Si considerano quindi i 2 problemi principali associati all’analisi dati, cioè la gestione e quantificazione dei dati, e vengono presentate le soluzioni descritte nel prosieguo. I primi due capitoli sono introduttivi al settore della Proteomica e della Spettrometria di Massa. L’obiettivo è fornire al lettore tutte le informazioni necessarie per meglio comprendere la Proteomica Quantitativa basata su Spettrometria di Massa. Il Capitolo 1 spiega in che modo sia nata la Proteomica, ossia come il complemento proteico del genoma. Dopodiché, si espongono le principali applicazioni legate alla Proteomica e i suoi obiettivi, spaziando dagli aspetti clinici, alla farmaceutica, fino alla biologia dei sistemi. Il secondo Capitolo invece è legato agli aspetti tecnici e mostra le principali tecnologie e strumentazioni usate in Proteomica basata su Spettrometria di Massa. I setup sperimentali più comuni sono quindi illustrati e, tra tutti, ci si focalizza in particolare sulla Spettrometria di Massa abbinata a Cromatografia Liquida (LC-MS), che è la principale tecnica per esperimenti di Proteomica Quantitativa basata su Spettrometria di Massa. Il terzo Capitolo presenta i concetti fondamentali necessari per introdurre il lettore al tema principale del progetto di ricerca di Dottorato, ossia lo sviluppo di metodi bioinformatici per la gestione e la quantificazione di dati di Proteomica Quantitativa basata su Spettrometria di Massa, in particolare per l’analisi di dati quantitativi di LC-MS. Infatti, i dati di LC-MS hanno un alto contenuto informativo per scopi quantitativi, però sono estremamente problematici da analizzare. Sono quindi riassunti i setup sperimentali per la Proteomica Quantitativa basata su LC-MS così come le caratteristiche dei dati che sono state determinanti per lo sviluppo delle soluzioni proposte (ossia la struttura 3D dei dati LC-MS e l’alto contenuto informativo dei dati profile). Nel quarto Capitolo vengono descritti lo stato dell’arte, sia per la gestione che la quantificazione dei dati, e i relativi problemi aperti, che verranno trattati nei capitoli seguenti dove si propongono possibili soluzioni. Il Capitolo 5 è interamente dedicato alla descrizione tecnica dei dati utilizzati per validare le metodologie proposte. Si tratta di dati LC-MS generati da una mistura di proteine tracciate ed a rapporti di quantificazione note. Di ogni esperimento sono disponibili tre repliche. In particolare, questa tesi presenta 2 software per la gestione e la quantificazione di dati di Proteomica Quantitativa basata su Spettrometria di Massa. Il Capitolo 6 presenta la soluzione proposta per risolvere i problemi di gestione dati. Si tratta di un approccio di indicizzazione 2D scalabile che è stato implementato tramite una struttura dati basata sull’R-tree, chiamata mzRTree, e si basa sulla rappresentazione del dataset come matrice sparsa, che ben si adatta a dati di LC-MS e più in generale di Spettrometria di Massa. Nello specifico, mzRTree consente di accedere e memorizzare efficientemente i dati, rendendo così possibile un’analisi computazionalmente sostenibile di dati profile. Per quel che concerne la quantificazione, il Capitolo 7 illustra la soluzione proposta per il problema della quantificazione, 3DSpectra, un innovativo metodo di quantificazione che sfrutta sia la 3-dimensionalità dei dati LC-MS, sia l’alto contenuto informativo dei dati profile. 3DSpectra applica infatti un approccio 3D al riconoscimento della distribuzione isotopica del peptide da quantificare basato sul fit tramite l’algoritmo Expectation-Maximization di un Modello 3D a Mistura di Gaussiane. Tale modello consente di identificare i bordi del segnale da quantificare e di rigettare il rumore presente. 3DSpectra incorpora un’affidabile ed accurata strategia di quantificazione per dati LC-MS tracciati e acquisiti in modalità profile. Soprattutto, 3DSpectra offre, a livello di quantificazione, un’estesa e riproducibile copertura del proteoma. Nella sezione conclusiva della tesi si discute il lavoro futuro e in corso, che riguarda essenzialmente ulteriori sviluppi sia della struttura dati, mzRTree, che del software di quantificazione, 3DSpectra

La citta degli studi e la citta della salute: strategie progettuali a supporto del piano di sviluppo urbano dell'Universita di Pisa

Il lavoro svolto si è incentrato su uno dei principali obiettivi della società contemporanea, che mira al miglioramento della salute e del benessere fisico e psicologico della popolazione che vive in contesti urbani. L’architettura assume un ruolo fondamentale per il raggiungimento di tale scopo in termini di progettazione e strategie attraverso l’Healthy Cities, un progetto promosso dall’Organizzazione Mondiale della Sanità. Sulla base di queste tematiche, un ruolo fondamentale per lo sviluppo degli spazi urbani è da attribuire alla crescita delle università nelle città che, a seconda del contesto nel quale si inseriscono non possono prescindere dal disegno urbano di cui fanno parte, ma soprattutto influiscono il modo di vivere la città. Per questo motivo lo studio si concentra nello specifico sulla città di Pisa e lo sviluppo del suo patrimonio edilizio universitario, il quale ha avuto e continuerà ad avere un ruolo fondamentale per lo sviluppo degli spazi urbani.Sempre con lo scopo di migliorare la salute fisica e psicologica degli studenti pisani, non solo a livello urbano, ma anche in scala architettonica, è stata posta l’attenzione sulla problematica riguardante la domanda e l’offerta di alcuni edifici messi a disposizione dall’Università di Pisa, ad esempio le aule studio. A titolo di esempio sono state applicate queste tematiche al caso in esame Aula Studio Pacinotti

Ariadne's thread : a robust software solution leading to automated absolute and relative quantification of SRM data

Selected reaction monitoring (SRM) MS is a highly selective and sensitive technique to quantify protein abundances in complex biological samples. To enhance the pace of SRM large studies, a validated, robust method to fully automate absolute quantification and to substitute for interactive evaluation would be valuable. To address this demand, we present Ariadne, a Matlab software. To quantify monitored targets, Ariadne exploits metadata imported from the transition lists, and targets can be filtered according to mProphet output. Signal processing Sand statistical learning approaches are combined to compute peptide quantifications. To robustly estimate absolute abundances, the external calibration curve method is applied, ensuring linearity over the measured dynamic range. Ariadne was benchmarked against mProphet and Skyline by comparing its quantification performance on three different dilution series, featuring either noisy/smooth traces without background or smooth traces with complex background. Results, evaluated as efficiency, linearity, accuracy, and precision of quantification, showed that Ariadne's performance is independent of data smoothness and complex background presence and that Ariadne outperforms mProphet on the noisier data set and improved 2-fold Skyline's accuracy and precision for the lowest abundant dilution with complex background. Remarkably, Ariadne could statistically distinguish from each other all different abundances, discriminating dilutions as low as 0.1 and 0.2 fmol. These results suggest that Ariadne offers reliable and automated analysis of large-scale SRM differential expression studies