Biology subjects

Gatto, L.

Publications and source records attributed to Gatto, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Mitochondrial dysfunction as a hallmark of brain senescence in telomerase-deficient mice

Neurodegenerative diseases, including Alzheimer's disease (AD), are strongly associated with aging. However, the molecular mechanisms underlying pathological brain aging remain incompletely understood. In this study, we used a mouse model of telomere attrition, a major driver of cellular senescence, to perform an unbiased analysis of how telomere-driven senescence affects cellular physiology and contributes to processes relevant to neurodegenerative conditions. After validating the presence of senescence hallmarks in telomerase-deficient brains, we characterized their transcriptomic and proteomic profiles. Mitochondrial function and associated energy metabolism emerged as the major dysregulated pathways, driven predominantly by proteomic rather than transcriptomic changes. Functional biochemical analyses on isolated brain mitochondria demonstrated impaired electron transport chain (ETC) complex activity and reduced energetic status, despite preserved ETC complex integrity and mitochondrial content. Further analyses in senescent primary neurons indicated an accumulation of dysfunctional mitochondria, characterized by increased reactive oxygen species (ROS) production and reduced ATP levels, although basal cellular respiration was maintained. At the tissue level, these alterations were associated with moderate reductions in neuronal density in the subiculum and cortical layer V, indicating region-specific vulnerability rather than widespread neurodegeneration. We propose that a major consequence of telomere dysfunction associated with pathological brain aging is the downregulation of mitochondrial activity, which contributes to the selective vulnerability of specific brain regions. These findings highlight mitochondrial pathways as attractive targets for interventions aimed at preserving brain health during aging.

neuroscience↗

bioRxiv2026

Beige/brown fat-mediated cardiac protection from high-fat diet is dependent on adipocyte beta3-adrenergic receptor

Cardiometabolic diseases associated with obesity are continuously increasing worldwide. Yet, current therapeutic strategies remain insufficient to improve patient outcomes. The beta3-adrenergic receptor ({beta}3AR) promotes lipolysis in adipose tissue (AT) and thermogenesis specifically in brown adipose tissue (BAT). In mice, BAT activation also improves systemic metabolism and limits cardiometabolic damage. While BAT is limited in humans (e.g., with ageing and obesity), {beta}3AR activation induces beige adipocytes within white adipose depots with similar thermogenic properties. To study the role of adipocyte {beta}3AR in the regulation of cardiac metabolism and remodeling, mice with/without adipocyte-specific {beta}3AR genetic deletion were fed a high-fat-sucrose (HF-S) diet and treated with the selective {beta}3AR agonist CL316,243 (CL). The metabolic and cardiac protection following {beta}3AR activation was abrogated upon {beta}3AR deletion in adipocytes, together with the beiging of the epididymal (visceral) AT, highlighting a critical role of adipose {beta}3AR signalling in mediating these benefits. Multi-omic analysis of AT and cardiac samples identified CL-induced secreted mediators of the crosstalk between AT and the heart. Therefore, adipocyte {beta}3AR is critical for the adipose-cardiac communication and supports the therapeutic potential of targeting {beta}3AR for the management of obesity-related cardiometabolic diseases.

systems biology↗

bioRxiv2025

Enabling reproducible and reusable genetic demultiplexing benchmarking with Nextflow and Apptainer

Reproducibility, reusability, and portability were identified as key requirements for analysis pipelines in genomics. A previous study identified that less than 4% of notebooks used for biomedical research were fully reproducible. Reusable and portable software are required to ensure compatibility with federated and cloud computing. The field requires computational workflows that meet high standards for reproducibility, reusability, and portability to allow benchmarking to be repeated and updated periodically. This is of particular importance in areas where new methods are frequently released, such as demultiplexing scRNA-seq, a critical early step in most scRNA-seq analysis pipelines. To address this, we developed demux_bench for benchmarking genetic demultiplexing methods in single-cell RNA sequencing, which meets the gold standard for reproducibility of computational workflows, and incorporates best practices for reusability and portability. We used workflow manager Nextflow to enable the simulation and testing of various benchmarking scenarios and methods in parallel from a single pipeline execution. Different experimental configurations can be simulated, including % doublets and class size imbalance. The pipeline includes genotype-free methods, Vireo and souporcell, and additional existing or novel methods can be added modularly. Demux_bench is configurable to reproduce specific analyses and generalisable to address new research questions. Software dependencies are handled through containers via Apptainer, allowing portability to different compute environments and avoiding the need for manual installation of software. Demux_bench is available on WorkflowHub (https://workflowhub.eu/workflows/1769), and can also be run on Galaxy and other platforms through RO-Crates. Demux_bench facilitates gold standard benchmarking for genetic demultiplexing through reproducibility, reusability, scalability, and portability.

bioinformatics↗

bioRxiv2025

MDSvis, a Shiny application for interactive visualisation of Multi Dimensional Scaling projections

MotivationVisual exploratory data analysis is key for making sense of high dimensional data. In particular, Multi Dimensional Scaling (MDS), a low dimensional representation of data points, is frequently used in early stages of data analysis pipelines, for Quality Control (QC), batch effect detection and hypothesis generation. However, interpreting representations in more than two dimensions necessitates the mental integration of multiple two-dimensional (2D) plots - a process that greatly benefits from an interactive Graphical User Interface (GUI), where such plots can be easily and iteratively customised without the need for programming. ResultsHere, we present MDSvis, a Shiny application for user-interactive customised visualisation of MDS projections. MDSvis is based on the Bioconductor package CytoMDS for calculating MDS coordinates. We describe the functional workflow of the application, and illustrate two different use cases, which are detailed in the package vignettes. The first use case pertains to cytometry data analysis, which was the original motivation for building the application. The second use case demonstrates how to extend the use of MDSvis to MDS visualisation of any sample distance matrix. Availability and implementationMDSvis is implemented in the R programming language and is available in a GitHub repository: github.com/UCLouvain-CBIO/MDSvis, under a GPL-3 license.

bioinformatics↗

bioRxiv2025

A survey of human cancer-germline genes: linking X chromosome localization, DNA methylation and sex-biased expression in early embryos

Human cancer-germline (CG) genes are a group of testis-specific genes that become aberrantly activated in various tumors. Ongoing studies aim to understand their functions in order to evaluate their potential as anti-cancer therapeutic targets. Evidence suggests the existence of subcategories of CG genes, depending on location on autosomal or sex chromosomes, reliance on DNA methylation for transcriptional regulation, and profile of expression during gametogenesis and early embryogenesis. To clarify this issue, we developed CTexploreR, a R/Bioconductor package that integrates an up-to-date reference list of human CG genes (n=146) with multiple bulk and single-cell methylomic and transcriptomic datasets. Based on promoter methylation profiles and responsiveness to a DNA methylation inhibitor, 74% of the CG genes were classified as DNA methylation dependent (Methdep). Intriguingly, most X-linked CG genes (69/70) fell into this category, thereby implicating DNA methylation dependency in the well-documented over-representation of testis-specific genes on the X chromosome. We further observed that, whereas X-linked Methdep CG genes become demethylated and activated in pre-spermatogonia in the fetal testis, most of them resist DNA demethylation in female germ cells and remain therefore silent in fetal and adult oocytes. Importantly, a number of X-linked Methdep CG genes (e.g. FMR1NB, GAGE2A, MAGEB2/C2, PAGE2, VCX3A/B) maintained this maternal-specific imprinting after fertilization, and were expressed exclusively in female preimplantation embryos, which inherit a paternal X chromosome. Together, our study using the CTexploreR package has allowed us to show that X-linked CG genes undergo transient maternal imprinting and contribute therefore to transcriptional sexual dimorphism in early embryos. Author SummaryCancer-germline (CG) genes include a set of genes that are normally active only in the testis but become aberrantly switched on in different types of tumor, making them potential targets for new anti-cancer treatments. We developed a new analytical tool called CTexploreR, to study these genes, and observed that a large subset of CG genes reside on the X chromosome and use DNA methylation as a mechanism of repression in non-testicular tissues. In fetal testis, these genes lose methylation and become activated in early spermatogenic cells, while in female ovaries they stay methylated and remain silent. Notably, we demonstrate that several CG genes keep this methylation pattern after fertilization, and are therefore expressed in female but not male embryos, which inherit only one X chromosome of maternal origin. CG genes carrying this transient maternal imprinting appear therefore as main contributors of sex-biased mRNA expression in preimplantation embryos. Our Jindings therefore open up new Jields of investigation into the functions of CG genes, the sexual dimorphism of early embryos, and the intergenerational transmission of epigenetic imprints.

genomics↗

bioRxiv2024

Visual quality control with CytoMDS, a Bioconductor package for low dimensional representation of cytometry sample distances

Quality Control (QC) of samples is an essential preliminary step in cytometry data analysis. Notably, identification of potential batch effects and outlying samples is paramount, to avoid mistaking these effects for true biological signal in downstream analyses. However, this task can prove to be delicate and tedious, especially for datasets with dozens of samples. Here, we present CytoMDS, a Bioconductor package implementing a dedicated method for low dimensional representation of cytometry samples composed of marker expressions for up to millions of single cells. This method allows a global representation of all samples of a study, with one single point per sample, in such a way that projected distances can be visually interpreted. CytoMDS uses Earth Movers Distance for assessing dissimilarities between multi-dimensional distributions of marker expression, and Multi Dimensional Scaling for low dimensional projection of distances. Some additional visualization tools, both for projection quality diagnosis and for user interpretation of the projection coordinates, are also provided in the package. We demonstrate the strengths and advantages of CytoMDS for QC of cytometry data on three real biological datasets, revealing the presence of low quality samples, batch effects and biological signal between sample groups.

bioinformatics↗

bioRxiv2024

demuxSNP: supervised demultiplexing scRNAseq using cell hashing and SNPs

BackgroundMultiplexing single-cell RNA sequencing experiments reduces sequencing cost and facilitates larger scale studies. However, factors such as cell hashing quality and class size imbalance impact demultiplexing algorithm performance, reducing cost effectiveness FindingsWe propose a supervised algorithm, demuxSNP, leveraging both cell hashing and genetic variation between individuals (SNPs). The supervised algorithm addresses fundamental limitations in demultiplexing with only one data modality. The genetic variants (SNPs) of the subset of cells assigned with high confidence using a probabilistic hashing algorithm are used to train a KNN classifier that predicts the demultiplexing classes of unassigned or uncertain cells. We benchmark demuxSNP against hashing (HTODemux, cellhashR, GMM-demux, demuxmix) and genotype-free SNP (souporcell) methods on simulated and real data from renal cell cancer. Our results demonstrate that demuxSNP outperformed standalone hashing methods on low quality hashing data, improving overall classification accuracy and allowing more high RNA quality cells to be recovered. Through varying simulated doublet rates, we show genotype-free SNP methods are unable to identify biological samples with low cell counts at high doublet rates. When compared to unsupervised SNP demultiplexing methods, demuxSNPs supervised approach was more robust to doublet rate in experiments with class size imbalance. ConclusionsdemuxSNP is a performant demultiplexing approach that uses hashing and SNP data to demultiplex datasets with low hashing quality where biological samples are genetically distinct. Unassigned cells (negatives) with high RNA quality can be recovered, making more cells available for analysis, especially when applied to data with low hashing quality or suspected misassigned cells. Pipelines for simulated data and processed benchmarking data for 5-50% doublets are publicly available. demuxSNP is available as an R/Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.demuxSNP).

bioinformatics↗

bioRxiv2024

Spatial transcriptomics profiling of gallbladder adenocarcinoma: a detailed two-case study of progression from precursor lesions to cancer

BackgroundMost studies on tumour progression from precursor lesion toward gallbladder adenocarcinoma investigate lesions sampled from distinct patients, providing an overarching view of pathogenic cascades. Whether this reflects the tumourigenic process in individual patients remains insufficiently explored. Genomic and epigenomic studies suggest that a subset of gallbladder cancers originate from biliary intraepithelial neoplasia (BilIN) precursor lesions, whereas others form independently from BilINs. Spatial transcriptomic data supporting these conclusions are missing. Moreover, multiple areas with precursor or adenocarcinoma lesions can be detected within the same pathological sample. Yet, knowledge about intra-patient variability of such lesions is lacking. MethodsTo characterise the spatial transcriptomics of gallbladder cancer tumourigenesis in individual patients, we selected two patients with distinct cancer aetiology and whose samples simultaneously displayed multiple areas of normal epithelium, BilINs and adenocarcinoma. Using GeoMx digital spatial profiling, we characterised the whole transcriptome of a high number of regions of interest (ROIs) per sample in the two patients (24 and 32 ROIs respectively), with each ROI covering approximately 200 cells of normal epithelium, low-grade BilIN, high-grade BilIN or adenocarcinoma. Human gallbladder organoids and cell-ine derived tumours were used to investigate the tumour-promoting role of genes. ResultsSpatial transcriptomics revealed that each type of lesion displayed limited intra-patient transcriptomic variability. Our data further suggest that adenocarcinoma derived from high-grade BilIN in one patient and from low-grade BilIN in the other patient, with co-existing high-grade BilIN evolving via a distinct process in the latter case. The two patients displayed distinct sequences of signalling pathway activation during tumour progression, but Semaphorin 4A (SEMA4A) expression was repressed in both patients. Using human gallbladder-derived organoids and cell line-derived tumours, we provide evidence that repression of SEMA4A promotes pseudostratification of the epithelium and enhances cell migration and survival. ConclusionGallbladder adenocarcinoma can develop according to patient-specific processes, and limited intra-patient variability of precursor and cancer lesions was noticed. Our data suggest that repression of SEMA4A can promote tumour progression. They also highlight the need to gain gene expression data in addition to histological information to avoid understimating the risk of low-grade preneoplastic lesions.

cancer biology↗

bioRxiv2024

Deep plasma proteomics with data-independent acquisition: A fastlane towards biomarkers identification.

Plasma proteomic is a precious tool in human disease research, but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional Data-Dependent Acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and Data-Independent Acquisition (DIA) to significantly improve proteome coverage and depth, while remaining cost- and time-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilises commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 LC-MS/MS injections for a 360-minutes DIA run time. DIA-NN software was then used for precursor identification, and the QFeatures R package was used for protein aggregation. We detect 1,321 proteins on average per patient, and 2,031 unique proteins across the cohort. Filtering precursors present in under 25% of patients, we still detect 1,230 average proteins and 1,590 unique proteins, indicating robust protein identification. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification. In summary, this study introduces a streamlined, cost- and time-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multi-omics investigations in clinical settings.

systems biology↗

bioRxiv2023

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data

Analysing mass spectrometry (MS)-based single-cell proteomics (SCP) data is challenging. The data analysis must address numerous problems that are inherent to both MS-based proteomics technologies and single-cell experiments. This has led to the development of complex and divergent data processing workflows within the field. In this work, we present scplainer, a principled and standardised approach for extracting meaningful insights from SCP data. The approach relies on minimal data processing combined with linear modelling. The approach is a simple yet powerful approach for exploring and interpreting various types of SCP data. scplainer performs variance analysis, differential abundance analysis and component analysis while streamlining the visualization of the results. This thorough exploration enhances our capacity to gain a deeper understanding of the biological processes hidden in the data. Finally, we demonstrate that scplainer corrects for technical variability, and even enables the integration of data sets from different SCP experiments. The approach effectively generates high-quality data that are amenable to perform downstream analyses. In conclusion, this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.

bioinformatics↗

bioRxiv2023

CytoPipeline & CytoPipelineGUI: An R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data

BackgroundWith the increase of the dimensionality in flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. A crucial part of these pipelines consists of pre-processing and applying quality control filtering to the raw data, in order to use high quality events in the downstream analyses. This part can in turn be split into a number of elementary steps : signal compensation or unmixing, scale transformation, debris, doublets and dead cells removal, batch effect correction, etc. However, assembling and assessing the pre-processing part can be challenging for a number of reasons. First, each of the involved elementary steps can be implemented using various methods and R packages. Second, the order of the steps can have an impact on the downstream analysis results. Finally, each method typically comes with its specific, non standardized diagnostic and visualizations, making objective comparison difficult for the end user. ResultsHere, we present CytoPipeline and CytoPipelineGUI, two R packages to build, compare and assess pre-processing pipelines for flow cytometry data. To exemplify these new tools, we present the steps involved in designing a pre-processing pipeline on a real life dataset and demonstrate different visual assessment use cases. We also set up a benchmarking comparing two pre-processing pipelines differing by their quality control methods, and show how the package visualization utilities can provide crucial user insight into the obtained benchmark metrics. ConclusionCytoPipeline and CytoPipelineGUI are two Bioconductor R packages that help building, visualizing and assessing pre-processing pipelines for flow cytometry data. They increase productivity during pipeline development and testing, and complement benchmarking tools, by providing user intuitive insight into benchmarking results.

bioinformatics↗

bioRxiv2022

Cardiovirus leader proteins retarget RSK kinases toward alternative substrates to perturb nucleocytoplasmic traffic

Proteins from some unrelated pathogens, including viruses and bacteria can recruit and activate cellular p90-ribosomal protein S6 kinases (RSKs) through a common linear motif. Our data suggested that such pathogens proteins might act as adapters to dock the kinase toward specific substrates. We explored this hypothesis using the Cardiovirus leader protein (L) as a paradigm. L is known to trigger phenylalanine-glycine nucleoporins (FG-NUPs) hyperphosphorylation and nucleocytoplasmic trafficking perturbation. Using a biotin ligase fused to either RSK or to L, we identified FG-NUPs as primary partners of the L-RSK complex in infected cells. Mutant analysis shows that L uses distinct motifs to recruit RSK and to dock the L-RSK complex toward the FG-NUPs. Using an analog-sensitive RSK2 mutant kinase, we show that, in infected cells, L can trigger RSK to use NUP98 and NUP214 as direct substrates. Our data illustrate a novel virulence mechanism where pathogens proteins retarget cellular protein kinases toward specific substrates.

microbiology↗

bioRxiv2022

Identification and implication of tissue-enriched ligands in epithelial-endothelial crosstalk during pancreas development

Development of the pancreas is driven by an intrinsic program coordinated with signals from other cell types in the epithelial environment. These intercellular communications have been so far challenging to study because of the low concentration, localized production and diversity of the signals released. Here, we combined scRNAseq data with a computational interactomic approach to identify signals involved in the reciprocal interactions between the various cell types of the developing pancreas. This in silico approach yielded 40,607 potential ligand-target interactions between the different main pancreatic cell types. Among this vast network of interactions, we focused on three ligands potentially involved in communications between epithelial and endothelial cells. Bmp7 and Wnt7b, expressed by pancreatic epithelial cells and predicted to target endothelial cells, and Sema6d, involved in the reverse interaction. In situ hybridization confirmed the localized expression of Bmp7 in the pancreatic epithelial tip cells and of Wnt7b in the trunk cells. On the contrary, Sema6d was enriched in endothelial cells. Functional experiments on ex vivo cultured pancreatic explants indicated that tip cell-produced Bmp7 restrained development of endothelial cells. This work identified ligands with a restricted tissular and cellular distribution and highlighted the role of Bmp7 in the intercellular communications shaping vessel development during pancreas organogenesis.

developmental biology↗

bioRxiv2022

ProtFinder: finding subcellular locations of proteins using protein interaction networks

AO_SCPLOWBSTRACTC_SCPLOWProtein subcellular localization prediction plays a crucial role in improving our understanding of different diseases and consequently assists in building drug targeting and drug development pipelines. Proteins are known to co-exist at multiple subcellular locations which make the task of prediction extremely challenging. A protein interaction network is a graph that captures interactions between different proteins. It is safe to assume that if two proteins are interacting, they must share some subcellular locations. With this regard, we propose ProtFinder - the first deep learning-based model that exclusively relies on protein interaction networks to predict the multiple subcellular locations of proteins. We also integrate biological priors like the cellular component of Gene Ontology to make ProtFinder a more biology-aware intelligent system. ProtFinder is trained and tested using the STRING and BioPlex databases whereas the annotations of proteins are obtained from the Human Protein Atlas. Our model obtained an AUC-ROC score of 90.00% and an MCC score of 83.42% on a held-out set of proteins. We also apply ProtFinder to annotate proteins that currently do not have confident location annotations. We observe that ProtFinder is able to confirm some of these unreliable location annotations, while in some cases complementing the existing databases with novel location annotations. The source code for ProtFinder is available at https://github.com/UCLouvain-CBIO/ProtFinder.

bioinformatics↗

bioRxiv2021

Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor

BackgroundThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes. ResultsWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal package in Bioconductors Cloud-based ExperimentHub. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data. ConclusionsWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease. Author SummaryExperimental data packages that provide landmark datasets have historically played an important role in the development of new statistical methods in Bioconductor by lowering the barrier of access to relevant data, providing a common testing ground for software development and benchmarking, and encouraging interoperability around common data structures. In this manuscript, we review major classes of technologies for collecting multimodal data including genomics, transcriptomics, epigenetics, proteomics, and spatial information at the level of single cells. We present the SingleCellMultiModal R/Bioconductor package that provides single-command access to landmark datasets from seven different technologies, storing datasets using HDF5 and sparse arrays for memory efficiency and integrating data modalities via the MultiAssayExperiment class. We demonstrate two integrative analyses that are greatly simplified by SingleCellMultiModal. The package facilitates development and benchmarking of bioinformatic and statistical methods to integrate molecular layers at the level of single cells with phenotypic outputs including cell differentiation, activity, and disease, within Bioconductors ecosystem of hundreds of packages for single-cell and multimodal data.

bioinformatics↗

bioRxiv2021

Utilizing scp for the analysis and replication of single-cell proteomics data

IntroductionMass spectrometry-based proteomics is actively embracing quantitative, single-cell level analyses. Indeed, recent advances in sample preparation and mass spectrometry (MS) have enabled the emergence of quantitative MS-based single-cell proteomics (SCP). While exciting and promising, SCP still has many rough edges. The current analysis workflows are custom and built from scratch. The field is therefore craving for standardized software that promotes principled and reproducible SCP data analyses. Areas coveredThis special report is the first step toward the formalization and standardization of SCP data analysis. scp, the software that accompanies this work, successfully replicates one of the landmark SCP studies and is applicable to other experiments and designs. We created a repository containing the replicated workflow with comprehensive documentation in order to favor further dissemination and improvements of SCP data analyses. Expert opinionReplicating SCP data analyses uncovers important challenges in SCP data analysis. We describe two such challenges in detail: batch correction and data missingness. We provide the current state-of-the-art and illustrate the associated limitations. We also highlight the intimate dependence that exists between batch effects and data missingness and offer avenues for dealing with these exciting challenges. 1 Article highlightsO_LISingle-cell proteomics (SCP) is emerging thanks to several recent technological advances, but further progress is still lagging due to the lack of principled and systematic data analysis. C_LIO_LIThis work offers a standardized solution for the processing of SCP data demonstrated by the replication of a landmark SCP work. C_LIO_LITwo important challenges remain: batch effects and data missingness. Furthermore, these challenges are not independent and therefore need to be modeled simultaneously. C_LI

bioinformatics↗

bioRxiv2021

On the many advantages of using the VariantExperiment class to store, exchange and analyze SARS-CoV-2 genomic data and associated metadata

On Friday, 19 March 2021, WHO organized a virtual global workshop highlighting the need for a globally coordinated plan to increase SARS-CoV-2 genetic sequencing capacities to detect SARS-CoV-2 mutations and variants, and to monitor virus genomic evolution worldwide. One week later, in another virtual meeting, it focused on sero epidemiology for SARS-CoV-2 variants of concern and variants of interest. Efficient monitoring of the virus relies on the storage, handling and sharing of the genomic data and the associated metadata. In this manuscript, we demonstrate how the Bioconductor VariantExperiment class addresses these needs, offering a robust and efficient solution to the requirements laid out by the WHO.

bioinformatics↗

bioRxiv2021

Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

The steady-state localisation of proteins provides vital insight into their function. These localisations are context specific with proteins translocating between different sub-cellular niches upon perturbation of the subcellular environment. Differential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic insight of subcellular protein dynamics. Aberrant localisation has been implicated in a number of pathologies, thus differential localisation may help characterise disease states and facilitate rational drug discovery by suggesting novel targets. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we propose a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation, as well quantifying the uncertainty in these estimates. Furthermore, BANDLE allows information to be shared across spatial proteomics datasets to improve statistical power. Extensive simulation studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to datasets studying EGF stimulation and AP-4 dependent localisation recovers well studied translocations, using only two-thirds of the provided data. Moreover, we potentially implicate TMEM199 with AP-4 dependent localisation. In an application to cytomegalovirus infection, we obtain novel insights into the rewiring of the host proteome. Integration of high-throughput transcriptomic and proteomic data, along with degradation assays, acetylation experiments and a cytomegalovirus intcractome allows us to provide the functional context of these data.

systems biology↗