Biology subjects

Belgard, T. G.

Publications and source records attributed to Belgard, T. G..

6 recordsLinked to original sources

bioRxiv2023

Unravelling cell type specific response to Parkinson's Disease at single cell resolution

Parkinsons Disease (PD) is the second most common neurodegenerative disorder and is generally characterized by impaired motor functions. It currently affects 6.3 million people aged 60 years and more, worldwide. The pathological hallmarks of PD are Lewy bodies (abnormal aggregation of -synuclein inside cells), which are observed primarily in the substantia nigra (SN) region of the midbrain. It is yet not known how different cell types in SN respond during PD and what are the molecular mechanisms underlying neurodegeneration. To address this question, we generated a large-scale single cell transcriptomics dataset from human post-mortem SN tissue of 29 donors including 15 sporadic cases and 14 controls. We obtained data for a total of [~]80K nuclei, representing major cell types of the brain (including neurons, astrocytes, microglia and oligodendrocytes). Pathway and differential gene expression analysis revealed multicellular character of PD pathology involving major cellular response from neuronal and glial cells.

bioinformatics↗

bioRxiv2022

Single-nucleus co-expression networks of dopaminergic neurons support iron accumulation as a plausible explanation to their vulnerability in Parkinson's disease

Motivationgene co-expression networks have been widely applied to identify critical genes and pathways for neurodegenerative diseases such as Parkinsons and Alzheimers disease. Now, with the advent of single-cell RNA-sequencing, we have the opportunity to create cell-type specific gene co-expression networks. However, single-cell RNA-sequencing data is characterized by its sparsity, amongst some other issues raised by this new type of data. ResultsWe present scCoExpNets, a framework for the discovery and analysis of cell-type specific gene coexpression networks (GCNs) from single-cell RNA-seq data. We propose a new strategy to address the problem of sparsity, named iterative pseudo-cell identification. It consists of adding the gene expression of pairs of cells that belong to the same individual and the same cell-type while the number of cells is over 200, thus creating multiple matrices and multiple scGCNs for the same cell-type, all of them seen as alternative and complementary views of the same phenomena. We applied this new tool on a snRNA-seq dataset human post-mortem substantia nigra pars compacta tissue of 13 controls and 14 Parkinsons disease (PD) cases (18 males and 9 females) with 30-99 years. We show that one of the hypotheses that support the selective vulnerability of dopaminergic neurons in PD, the iron accumulation, is sustained in our dopaminergic neurons network models. Moreover, after successive pseudo-celluling iterations, the gene groups sustaining this hypothesis remain intact. At the same time, this pseudo-celulling strategy also allows us to discover genes whose grouping changes considerably throughout the iterations and provides new insights. Finally, since some of our models were correlated with diagnosis and age at the same time, we also developed our own framework to create covariate-specific GCNs, called CovCoExpNets. We applied this new software to our snRNA-seq dataset and we identified 11 age-specific genes and 5 diagnosis-specific genes which do not overlap. Availability and implementationThe CoExpNets implementations are available as R packages: scCoExpNets for creating single-cell GCNs and CovCoExpNets for creating covariate-specific GCNs. Users can either download the development version via github https://github.com/aliciagp/scCoExpNets and https://github.com/aliciagp/CovCoExpNets Contactalicia.gomez1@um.es Supplementary informationsupplementary data is available online.

bioinformatics↗

bioRxiv2022

Bone marrow haematopoietic stem cells influence liver homeostatic networks and cancer development after dietary intervention

A holistic understanding of anti-tumor immunity requires examination of systemic immunity beyond the tumor microenvironment. This link between systemic and tumor immune activity is underexplored. We demonstrate that a stronger Type I interferon response in human liver tumors predicts better survival and correlates with the immune response in the adjacent non-tumor liver. Further, in patients with liver cancer, clonal hematopoiesis (CH) (a marker of systemic immune activation) is associated with a trend towards improved survival. In a mouse model of liver cancer, basal liver immune responses correlate with the degree of bone marrow hematopoietic stem and progenitor cell responses. A higher systemic and liver immune response, marked by innate myeloid cell infiltration reduces tumor burden, while sub-optimal systemic and liver immunity corresponded with a higher tumor burden. Our findings indicate that the state of bone marrow hematopoiesis impacts liver tumor outcomes, offering both therapeutic and prognostic opportunities.

systems biology↗

bioRxiv2020

Broad transcriptomic dysregulation across the cerebral cortex in ASD

Classically, psychiatric disorders have been considered to lack defining pathology, but recent work has demonstrated consistent disruption at the molecular level, characterized by transcriptomic and epigenetic alterations.1-3 In ASD, upregulation of microglial, astrocyte, and immune signaling genes, downregulation of specific synaptic genes, and attenuation of regional gene expression differences are observed.1,2,4-6 However, whether these changes are limited to the cortical association areas profiled is unknown. Here, we perform RNA-sequencing (RNA-seq) on 725 brain samples spanning 11 distinct cortical areas in 112 ASD cases and neurotypical controls. We identify substantially more genes and isoforms that differentiate ASD from controls than previously observed. These alterations are pervasive and cortex-wide, but vary in magnitude across regions, roughly showing an anterior to posterior gradient, with the strongest signal in visual cortex, followed by parietal cortex and the temporal lobe. We find a notable enrichment of ASD genetic risk variants among cortex-wide downregulated synaptic plasticity genes and upregulated protein folding gene isoforms. Finally, using snRNA-seq, we determine that regional variation in the magnitude of transcriptomic dysregulation reflects changes in cellular proportion and cell-type-specific gene expression, particularly impacting L3/4 excitatory neurons. These results highlight widespread, genetically-driven neuronal dysfunction as a major component of ASD pathology in the cerebral cortex, extending beyond association cortices to involve primary sensory regions.

neuroscience↗

bioRxiv2019

A Framework for Integrating Directed and Undirected Annotations to Build Explanatory Models of cis-eQTL Data.

1A longstanding goal of regulatory genetics is to understand how variants in genome sequences lead to changes in gene expression. Here we present a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations. In a use case, we integrated directed genomic annotations with eQTL summary statistics from tissues of various origins. This analysis revealed epigenetic marks that are relevant for gene expression in different tissues and cell types. We estimated the predictive power of the models that were fitted based on directed genomic annotations. This analysis showed that, depending on the underlying eQTL data used, the directed genomic annotations could predict up to 1.5% of the variance observed in the expression of genes with top nominal eQTL association p-values < 10-7. For genes with estimated effect sizes in the top 25% quantile, up to 5% of the expression variance could be predicted. Based on our results, we recommend the use of BAGEA for the analysis of cis-eQTL data to reveal annotations relevant to expression biology.

genetics↗

bioRxiv2018

Molecularly distinct astrocyte subpopulations spatially pattern the adult mouse brain

Astrocytes are a numerous cell type of the central nervous system. They perform many important functions in synapse formation and maintenance, control of local homeostasis and modulation of synaptic transmission. However, the degree to which specialist astrocyte subtypes fulfil these specific tasks is currently unclear. Here we use single cell transcriptomics to demonstrate that astrocytes, even those lying within the same region of adult mouse brain, show distinct molecular and spatial profiles, with profound implications for local CNS functions, including axon guidance, synaptogenesis and synaptic transmission.

neuroscience↗