Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

Profiling population-wide exposure to environmental chemicals: A case study of naphthalene.

Long-term exposure to environmental chemicals can detrimentally impact human health, and understanding the relationship between age distribution and levels of external and internal exposure is crucial. Nonetheless, existing methods for assessing population-wide exposure across age groups are limited. To bridge this research gap, we introduced a modeling approach designed to assess both chronic external and internal exposure to chemicals at the population level. The external and internal exposure assessments were quantified in terms of the average daily dose (ADD) and steady-state blood concentration of the environmental chemical, respectively, which were categorized by age and gender groups. The modeling process was presented within a spreadsheet framework, affording users the capability to execute population-wide exposure analyses across a spectrum of chemicals. Our simulation outcomes underscored a salient trend: younger age groups, particularly infants and children, exhibited markedly higher ADD values and blood concentrations of environmental chemicals compared to their older counterparts. This observation is due to the elevated basal metabolic rate per unit of body weight characteristic of younger individuals, coupled with their diminished biotransformation kinetics of xenobiotics within their livers. These factors collectively contribute to increased intake rates of environmental chemicals per unit of body weight through air and food consumption, along with heightened bioaccumulation of these chemicals within their bodies (e.g., blood). Furthermore, we augmented the precision of the external and internal exposure assessment by incorporating the age distribution across the population. The simulation outcomes unveiled that, to estimate the central tendency of the population's exposure levels, employing the baseline value group (age group 21-30) or the surrogate age of 25 serves as a simple yet dependable approach. However, for comprehensive population protection, our recommendation aligns with conducting exposure assessments for the younger age groups (age group 0-11). Future studies should integrate individual-level exposure assessment, analyze vulnerable population groups, and refine population structures within our developed model.

Neoantigen identification: Technological advances and challenges.

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators.

Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

SWIGH-SCORE: A translational light-weight approach in computational detection of rearranged immunoglobulin heavy chain to be used in monoclonal lymphoproliferative disorders.

We present a lightweight tool for clonotyping and measurable residual disease (MRD) assessment in monoclonal lymphoproliferative disorders. It is a translational method that enables computational detection of rearranged immunoglobulin heavy chain gene sequences.•The swigh-score clonotyping tool emphasizes parallelization and applicability across sequencing platforms.•The algorithm is based on an adaptation of the Smith-Waterman algorithm for local alignment of reads generated by 2nd and 3rd generation of sequencers.For method validation, we demonstrate the targeted sequences of immunoglobulin heavy chain genes from diagnostic bone marrow using serial dilutions of CD138+ plasma cells from a patient with multiple myeloma. Sequencing libraries from diagnostic samples were prepared for the three sequencing platforms, Ion S5 (Thermo Fisher Scientific), MiSeq (Illumina), and MinION (Oxford Nanopore), using the LymphoTrack assay. Basic quality filtering was performed, and a Smith-Waterman-based swigh-score algorithm was developed in shell and C for clonotyping and MRD assessment using FASTQ data files. Performance is demonstrated across the three different sequencing platforms.

Desarrollo de una herramienta computacional para el análisis de conductancia galvánica de la piel, ECG y frecuencia respiratoria por arritmia sinusal respiratoria (SCRATER) / Development of a computational tool for the analysis of galvanic skin conductance, EKG and respiratory rate due to respiratory sinus arrhythmia (SCRATER)

Resumen: La existencia de una correlación entre la frecuencia cardíaca (FC), la frecuencia respiratoria (FR) y las respuestas electrodérmicas de la piel (Skin Conductance Response, SCR) ha sido reportada en la literatura, así como también el uso de estos parámetros como medida del nivel de activación del sistema nervioso autónomo. Objetivo: Este trabajo presenta una herramienta (SCRATER) para el análisis conjunto de SCR, FC y FR, las dos últimas, calculadas a partir del análisis del registro de electrocardiograma (ECG). Metodología: En esta investigación, se realizó una descripción detallada de cada algoritmo desarrollado, asi como una una descripción de la interfaz para utilizarlos. En la validación de los algoritmos empleados, se analizaron 192 registros de ECG y 231 registros de actividad electrodérmica (Electro-Dermal Activity, EDA) de 40 participantes masculinos sanos, de los cuales se calculó el número de complejos QRS y FC en cada registro de ECG y el número de SCRs de cada registro de EDA. Resultados: Los datos obtenidos fueron comparados con otras herramientas que analizan SCR y FC pero de manera independiente, obteniendo resultados equiparables mediante coeficientes de correlación. Limitaciones: El ruido y los artefactos presentes en los registros no permiten una correcta estimación de los parámetros y afectan los resultados de todas las herramientas empleadas en el desarrollo de este trabajo. Valor: SCRATER ofrece tres ventajas principales sobre las otras herramientas: 1) libre acceso, 2) código abierto y no utiliza formatos codificados o exclusivos. Conclusión: Este trabajo proporciona una herramienta computacional gratuita que permite analizar simultáneamente SCRs, FC y FR.

Abstract: The existence of a correlation between heart rate (HR), respiratory rate (RR) and skin conductance response (SCR) has been reported in the literature, as well as the use of these parameters as a measure of the activation level of the autonomous nervous system. Objective: This paper introduces a computational tool (SCRATER) developed with the aim to analyze simultaneous recordings of SCR, and heart and respiratory rates, which were calculated from the electrocardiogram recording (ECG) analysis. Methodology: In this research, a detailed description of each developed algorithm was made, as well as a description of the interface to be used. In the validation of the algorithms used, 192 ECG records and 231 Electro-Dermal Activity (EDA) registers of 40 healthy male participants were analyzed, from which the number of QRS complexes and HR in each ECG record and the number of SCRs of each EDA record are calculated. Results: The data obtained were compared with other tools that analyze SCR and HR separately, obtaining comparable results using correlation coefficients. Limitations: The noise and artifacts present in the records do not allow a correct estimation of the parameters and affect the results of all the tools used in the development of this work. Value: SCRATER offers three main advantages over other tools: 1) free access, 2) open source and 3) does not use coded or exclusive formats. Conclusion: This work provides a free computational tool that allows simultaneous analysis of SCRs, FC and FR.