Transcriptional heterogeneity between primary adult grey and white matter astrocytes underlie differences in modulation of in vitro myelination

BACKGROUND: Multiple sclerosis (MS) is an inflammation-mediated demyelinating disease of the central nervous system that eventually results in secondary axonal degeneration due to remyelination failure. Successful remyelination is orchestrated by astrocytes (ASTRs) and requires sequential activation, recruitment, and maturation of oligodendrocyte progenitor cells (OPCs). In both MS and experimental models, remyelination is more robust in grey matter (GM) than white matter (WM), which is likely related to local differences between GM and WM lesions. Here, we investigated whether adult gmASTRs and wmASTRs per se and in response to MS relevant Toll-like receptor (TLR) activation differently modulate myelination. METHODS: Differences in modulation of myelination between adult gmASTRs and wmASTRs were examined using an in vitro myelinating system that relies on a feeding layer of ASTRs. Transcriptional profiling and weighted gene co-expression network analysis were used to analyze differentially expressed genes and gene networks. Potential differential modulation of OPC proliferation and maturation by untreated adult gmASTRs and wmASTRs and in response to TLR3 and TLR4 agonists were assessed. RESULTS: Our data reveal that adult wmASTRs are less supportive to in vitro myelination than gmASTRs. WmASTRs more abundantly express reactive ASTR genes and genes of a neurotoxic subtype of ASTRs, while gmASTRs have more neuro-reparative transcripts. We identified a gene network module containing cholesterol biosynthesis enzyme genes that positively correlated with gmASTRs, and a network module containing extracellular matrix-related genes that positively correlated with wmASTRs. Adult wmASTRs and gmASTRs responding to TLR3 agonist Poly(I:C) distinctly modulate OPC behavior, while exposure to TLR4 agonist LPS of both gmASTRs and wmASTRs results in a prominent decrease in myelin membrane formation. CONCLUSIONS: Primary adult gmASTRs and wmASTRs are heterogeneous at the transcriptional level, differed in their support of in vitro myelination, and their pre-existing phenotype determined TLR3 agonist responses. These findings point to a role of ASTR heterogeneity in regional differences in remyelination efficiency between GM and WM lesions

Deconvolution of bulk blood eQTL effects into immune cell subpopulations

BACKGROUND: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). RESULTS: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. CONCLUSIONS: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution)

An economical and highly adaptable optogenetics system for individual and population-level manipulation of Caenorhabditis elegans

This dataset contains the raw imaging files of the following study. Background: Optogenetics allows the experimental manipulation of excitable cells by a light stimulus without the need for technically challenging and invasive procedures. The high degree of spatial, temporal and intensity control that can be achieved with a light stimulus, combined with cell type-specific expression of light-sensitive ion channels, enables highly specific and precise stimulation of excitable cells. Optogenetic tools have therefore revolutionized the study of neuronal circuits in a number of models, including Caenorhabditis elegans. Despite the existence of several optogenetic systems that allow spatial and temporal photoactivation of light-sensitive actuators in C. elegans, their high costs and low flexibility have limited wide access to optogenetics. Here, we developed an inexpensive, easy-to-build, modular and adjustable optogenetics device for use on different microscopes and worm trackers, which we called the OptoArm. Results: The OptoArm allows for single- and multiple-worm illumination and is adaptable in terms of light intensity, lighting profiles and light-color. We demonstrate the OptoArm′s power in a population-based multi-parameter study on the contributions of motor circuit cells to age-related motility decline. We found that individual components of the neuromuscular system display different rates of age-dependent deterioration. The functional decline of cholinergic neurons mirrors motor decline, while GABAergic neurons and muscle cells are relatively age-resilient, suggesting that rate-limiting cells exist and determine neuronal circuit aging. Conclusion: We have assembled an economical, reliable and highly adaptable optogenetics system which can be deployed to address diverse biological questions. We provide a detailed description of the construction as well as technical and biological validation of our set-up. Importantly, use of the OptoArm is not limited to C. elegans and may benefit studies in multiple model organisms, making optogenetics more accessible to the broader research communit

Additional file 3 of An economical and highly adaptable optogenetics system for individual and population-level manipulation of Caenorhabditis elegans

Additional file 3

Netherlands Institute for Neuroscience - Netherlands Brain Bank - Netherlands Neurogenetics Database Project

Additional affiliation with the NiN and NBB for our collaboration on the Netherlands Neurogenetics Database Project. The datasets will consists of three main types of data: clinical data, neuropathological data, and genetics (common genetic variants). The clinical and neuropathological data is based on text summaries from the NBB, and we're currenlty using google-Bert Natural Language Processing methods to process this data.This is work ongoing. When published we will share are relevant data and meta-data types in agreement with the NBB

Netherlands Institute for Neuroscience - Netherlands Brain Bank - Netherlands Neurogenetics Database Project

Additional affiliation with the NiN and NBB for our collaboration on the Netherlands Neurogenetics Database Project. The datasets will consists of three main types of data: clinical data, neuropathological data, and genetics (common genetic variants). The clinical and neuropathological data is based on text summaries from the NBB, and we're currenlty using google-Bert Natural Language Processing methods to process this data.This is work ongoing. When published we will share are relevant data and meta-data types in agreement with the NBB

Netherlands Institute for Neuroscience - Netherlands Brain Bank - Netherlands Neurogenetics Database Project

Additional affiliation with the NiN and NBB for our collaboration on the Netherlands Neurogenetics Database Project. The datasets will consists of three main types of data: clinical data, neuropathological data, and genetics (common genetic variants). The clinical and neuropathological data is based on text summaries from the NBB, and we're currenlty using google-Bert Natural Language Processing methods to process this data.This is work ongoing. When published we will share are relevant data and meta-data types in agreement with the NBB

Netherlands Institute for Neuroscience - Netherlands Brain Bank - Netherlands Neurogenetics Database Project

Additional affiliation with the NiN and NBB for our collaboration on the Netherlands Neurogenetics Database Project. The datasets will consists of three main types of data: clinical data, neuropathological data, and genetics (common genetic variants). The clinical and neuropathological data is based on text summaries from the NBB, and we're currenlty using google-Bert Natural Language Processing methods to process this data.This is work ongoing. When published we will share are relevant data and meta-data types in agreement with the NBB

Netherlands Institute for Neuroscience - Netherlands Brain Bank - Netherlands Neurogenetics Database Project

Additional affiliation with the NiN and NBB for our collaboration on the Netherlands Neurogenetics Database Project. The datasets will consists of three main types of data: clinical data, neuropathological data, and genetics (common genetic variants). The clinical and neuropathological data is based on text summaries from the NBB, and we're currenlty using google-Bert Natural Language Processing methods to process this data.This is work ongoing. When published we will share are relevant data and meta-data types in agreement with the NBB