BrainTACO: an explorable multi-scale multi-modal brain transcriptomic and connectivity data resource.

Exploring the relationships between genes and brain circuitry can be accelerated by joint analysis of heterogeneous datasets from 3D imaging data, anatomical data, as well as brain networks at varying scales, resolutions, and modalities. Generating an integrated view, beyond the individual resources' original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few platforms for integration and exploration of this heterogeneous data exist.

Stress deficits in reward behaviour are associated with and replicated by dysregulated amygdala-nucleus accumbens pathway function in mice.

Reduced reward interest/learning and reward-to-effort valuation are distinct, common symptoms in neuropsychiatric disorders for which chronic stress is a major aetiological factor. Glutamate neurons in basal amygdala (BA) project to various regions including nucleus accumbens (NAc). The BA-NAc neural pathway is activated by reward and aversion, with many neurons being monovalent.

Transcriptomic and proteomic profiling of young and old mice in the bleomycin model reveals high similarity.

The most common preclinical, in vivo model to study lung fibrosis is the bleomycin-induced lung fibrosis model in 2- to 3-mo-old mice. Although this model resembles key aspects of idiopathic pulmonary fibrosis (IPF), there are limitations in its predictability for the human disease. One of the main differences is the juvenile age of animals that are commonly used in experiments, resembling humans of around 20 yr.

Transcriptome analysis of AAV-induced retinopathy models expressing human VEGF, TNF-α, and IL-6 in murine eyes.

Retinopathies are multifactorial diseases with complex pathologies that eventually lead to vision loss. Animal models facilitate the understanding of the pathophysiology and identification of novel treatment options. However, each animal model reflects only specific disease aspects and understanding of the specific molecular changes in most disease models is limited.

Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method.

Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest.

Mapping the metabolomic and lipidomic changes in the bleomycin model of pulmonary fibrosis in young and aged mice.

Alterations in metabolic pathways were recently recognized as potential underlying drivers of idiopathic pulmonary fibrosis (IPF), translating into novel therapeutic targets. However, knowledge of metabolic and lipid regulation in fibrotic lungs is limited. To comprehensively characterize metabolic perturbations in the bleomycin mouse model of IPF, we analyzed the metabolome and lipidome by mass spectrometry.

Molecular characterization and cell type composition deconvolution of fibrosis in NAFLD.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease worldwide. In adults with NAFLD, fibrosis can develop and progress to liver cirrhosis and liver failure. However, the underlying molecular mechanisms of fibrosis progression are not fully understood.

HieRFIT: a hierarchical cell type classification tool for projections from complex single-cell atlas datasets.

Motivation: The emergence of single-cell RNA sequencing (scRNA-seq) has led to an explosion in novel methods to study biological variation among individual cells, and to classify cells into functional and biologically meaningful categories.

Results: Here, we present a new cell type projection tool, Hierarchical Random Forest for Information Transfer (HieRFIT), based on hierarchical random forests. HieRFIT uses a priori information about cell type relationships to improve classification accuracy, taking as input a hierarchical tree structure representing the class relationships, along with the reference data.

In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy.

Diabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited.

A dual druggable genome-wide siRNA and compound library screening approach identifies modulators of parkin recruitment to mitochondria.

Genetic and biochemical evidence points to an association between mitochondrial dysfunction and Parkinson's disease (PD). PD-associated mutations in several genes have been identified and include those encoding PTEN-induced putative kinase 1 (PINK1) and parkin. To identify genes, pathways, and pharmacological targets that modulate the clearance of damaged or old mitochondria (mitophagy), here we developed a high-content imaging-based assay of parkin recruitment to mitochondria and screened both a druggable genome-wide siRNA library and a small neuroactive compound library.

DMSO cryopreservation is the method of choice to preserve cells for droplet-based single-cell RNA sequencing.

Combining single-cell RNA sequencing (scRNA-seq) with upstream cell preservation procedures such as cryopreservation or methanol fixation has recently become more common. By separating cell handling and preparation, from downstream library generation, scRNA-seq workflows are more flexible and manageable. However, the inherent transcriptomic changes associated with cell preservation and how they may bias further downstream analysis remain unknown.

FELLA: an R package to enrich metabolomics data.

Background: Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.

BATLAS: Deconvoluting Brown Adipose Tissue.

Recruitment and activation of thermogenic adipocytes have received increasing attention as a strategy to improve systemic metabolic control. The analysis of brown and brite adipocytes is complicated by the complexity of adipose tissue biopsies. Here, we provide an in-depth analysis of pure brown, brite, and white adipocyte transcriptomes.

Null diffusion-based enrichment for metabolomics data.

Metabolomics experiments identify metabolites whose abundance varies as the conditions under study change. Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, metabolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels.

Intensity drift removal in LC/MS metabolomics by common variance compensation.

Unlabelled: Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects.

An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit).

Unlabelled: Current tools for liquid chromatography and mass spectrometry for metabolomic data cover a limited number of processing steps, whereas online tools are hard to use in a programmable fashion. This article introduces the Metabolite Automatic Identification Toolkit (MAIT) package, which makes it possible for users to perform metabolomic end-to-end liquid chromatography and mass spectrometry data analysis. MAIT is focused on improving the peak annotation stage and provides essential tools to validate statistical analysis results.

Peak aggregation as an innovative strategy for improving the predictive power of LC-MS metabolomic profiles.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomic datasets consist of different features including (de)protonated molecules, fragments, adducts, and isotopes that may show high correlation values related to a high level of collinearity. There have been described several sources of these high correlation patterns regarding metabolomic datasets. Among these sources, it should be highlighted the high level of correlation computed between features coming from the same metabolite.

Network-based enrichment analysis of gene expression through protein-protein interaction data.

High-throughput analysis of gene expression data is subject to technological and statistical issues that confuse the underlying expression-condition associations. In this contribution a network-based candidate gene prioritization strategy was applied to the enrichment of a publicly available gene expression dataset, focused on the study of the mechanosensitivity of genes exposed to altered pulmonary matrix stiffness. Results suggested that some genes which had not been taken into account in the original study could have an important role in the processes causing, or affected by, pulmonary fibrosis.