scholarly journals Cell-intrinsic and -extrinsic mechanisms promote cell-type-specific cytokinetic diversity

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Tim Davies ◽  
Han X Kim ◽  
Natalia Romano Spica ◽  
Benjamin J Lesea-Pringle ◽  
Julien Dumont ◽  
...  
Keyword(s):  
Cell Fate ◽  
Individual Cell ◽  
Cell Types ◽  
Temperature Sensitive ◽  
Cell Type ◽  
Filamentous Actin ◽  
Animal Cells ◽  
Contractile Ring ◽  
Distinct Cell ◽  
Cell Type Specific

Cytokinesis, the physical division of one cell into two, is powered by constriction of an actomyosin contractile ring. It has long been assumed that all animal cells divide by a similar molecular mechanism, but growing evidence suggests that cytokinetic regulation in individual cell types has more variation than previously realized. In the four-cell Caenorhabditis elegans embryo, each blastomere has a distinct cell fate, specified by conserved pathways. Using fast-acting temperature-sensitive mutants and acute drug treatment, we identified cell-type-specific variation in the cytokinetic requirement for a robust forminCYK-1-dependent filamentous-actin (F-actin) cytoskeleton. In one cell (P2), this cytokinetic variation is cell-intrinsically regulated, whereas in another cell (EMS) this variation is cell-extrinsically regulated, dependent on both SrcSRC-1 signaling and direct contact with its neighbor cell, P2. Thus, both cell-intrinsic and -extrinsic mechanisms control cytokinetic variation in individual cell types and can protect against division failure when the contractile ring is weakened.

scholarly journals Independent glial subtypes delay development and extend healthy lifespan upon reduced insulin-PI3K signalling

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Nathaniel S. Woodling ◽  
Arjunan Rajasingam ◽  
Lucy J. Minkley ◽  
Alberto Rizzo ◽  
Linda Partridge
Keyword(s):  
Glial Cell ◽  
Cell Types ◽  
Therapeutic Interventions ◽  
Developmental Timing ◽  
Cell Type ◽  
Specific Effects ◽  
Trade Offs ◽  
Distinct Cell ◽  
Pi3k Signalling ◽  
Cell Type Specific

Abstract Background The increasing age of global populations highlights the urgent need to understand the biological underpinnings of ageing. To this end, inhibition of the insulin/insulin-like signalling (IIS) pathway can extend healthy lifespan in diverse animal species, but with trade-offs including delayed development. It is possible that distinct cell types underlie effects on development and ageing; cell-type-specific strategies could therefore potentially avoid negative trade-offs when targeting diseases of ageing, including prevalent neurodegenerative diseases. The highly conserved diversity of neuronal and non-neuronal (glial) cell types in the Drosophila nervous system makes it an attractive system to address this possibility. We have thus investigated whether IIS in distinct glial cell populations differentially modulates development and lifespan in Drosophila. Results We report here that glia-specific IIS inhibition, using several genetic means, delays development while extending healthy lifespan. The effects on lifespan can be recapitulated by adult-onset IIS inhibition, whereas developmental IIS inhibition is dispensable for modulation of lifespan. Notably, the effects we observe on both lifespan and development act through the PI3K branch of the IIS pathway and are dependent on the transcription factor FOXO. Finally, IIS inhibition in several glial subtypes can delay development without extending lifespan, whereas the same manipulations in astrocyte-like glia alone are sufficient to extend lifespan without altering developmental timing. Conclusions These findings reveal a role for distinct glial subpopulations in the organism-wide modulation of development and lifespan, with IIS in astrocyte-like glia contributing to lifespan modulation but not to developmental timing. Our results enable a more complete picture of the cell-type-specific effects of the IIS network, a pathway whose evolutionary conservation in humans make it tractable for therapeutic interventions. Our findings therefore underscore the necessity for cell-type-specific strategies to optimise interventions for the diseases of ageing.

scholarly journals A single cell brain atlas in human Alzheimer’s disease

2019 ◽  
Author(s):  
Alexandra Grubman ◽  
Gabriel Chew ◽  
John F. Ouyang ◽  
Guizhi Sun ◽  
Xin Yi Choo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Patterns ◽  
Cell Types ◽  
Gene Expression Patterns ◽  
Cell Type ◽  
Web Resource ◽  
Cell Type Specific

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com

scholarly journals Prediction of condition-specific regulatory maps in Arabidopsis using integrated genomic data

2019 ◽  
Author(s):  
Qi Song ◽  
Jiyoung Lee ◽  
Shamima Akter ◽  
Ruth Grene ◽  
Song Li
Keyword(s):  
Stress Responses ◽  
Abiotic Stresses ◽  
Individual Cell ◽  
Genomic Data ◽  
Cell Types ◽  
Root Cell ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Cell Type Specific

AbstractRecent advances in genomic technologies have generated large-scale protein-DNA interaction data and open chromatic regions for multiple plant species. To predict condition specific gene regulatory networks using these data, we developed the Condition Specific Regulatory network inference engine (ConSReg), which combines heterogeneous genomic data using sparse linear model followed by feature selection and stability selection to select key regulatory genes. Using Arabidopsis as a model system, we constructed maps of gene regulation under more than 50 experimental conditions including abiotic stresses, cell type-specific expression, and stress responses in individual cell types. Our results show that ConSReg accurately predicted gene expressions (average auROC of 0.84) across multiple testing datasets. We found that, (1) including open chromatin information from ATAC-seq data significantly improves the performance of ConSReg across all tested datasets; (2) choice of negative training samples and length of promoter regions are two key factors that affect model performance. We applied ConSReg to Arabidopsis single cell RNA-seq data of two root cell types (endodermis and cortex) and identified five regulators in two root cell types. Four out of the five regulators have additional experimental evidence to support their roles in regulating gene expression in Arabidopsis roots. By comparing regulatory maps in abiotic stress responses and cell type-specific experiments, we revealed that transcription factors that regulate tissue levels abiotic stresses tend to also regulate stress responses in individual cell types in plants.

scholarly journals Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies

2018 ◽  
Author(s):  
Xiangyu Luo ◽  
Can Yang ◽  
Yingying Wei
Keyword(s):  
High Resolution ◽  
Statistical Method ◽  
Individual Cell ◽  
Association Studies ◽  
Cell Types ◽  
Cell Type ◽  
Cpg Sites ◽  
Cpg Site ◽  
Cell Type Specific ◽  
Different Cell Types

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. Here, we propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types.

scholarly journals Structure and mechanism of SARS-CoV-2 Spike N679-V687 deletion variant elucidate cell-type specific evolution of viral fitness

2021 ◽  
Author(s):  
Kapil Gupta ◽  
Christine Toelzer ◽  
Maia Kavanagh Williamson ◽  
Deborah Shoemark ◽  
A. Sofia F. Oliveira ◽  
...  
Keyword(s):  
Cell Types ◽  
Viral Fitness ◽  
Cell Tropism ◽  
Cell Type ◽  
Amino Acid Deletion ◽  
Recognition Motif ◽  
Mechanistic Insight ◽  
Distinct Cell ◽  
Cell Type Specific ◽  
Insight Into

As the global burden of SARS-CoV-2 infections escalates, so does the evolution of viral variants which is of particular concern due to their potential for increased transmissibility and pathology. In addition to this entrenched variant diversity in circulation, RNA viruses can also display genetic diversity within single infected hosts with co-existing viral variants evolving differently in distinct cell types. The BriSΔ variant, originally identified as a viral subpopulation by passaging SARS-CoV-2 isolate hCoV-19/England/02/2020, comprises in the spike glycoprotein an eight amino-acid deletion encompassing the furin recognition motif and S1/S2 cleavage site. Here, we analyzed the structure, function and molecular dynamics of this variant spike, providing mechanistic insight into how the deletion correlates to viral cell tropism, ACE2 receptor binding and infectivity, allowing the virus to probe diverse trajectories in distinct cell types to evolve viral fitness.

scholarly journals Automated identification of cell-type–specific genes and alternative promoters

2021 ◽  
Author(s):  
Mickaël Mendez ◽  
Jayson Harshbarger ◽  
Michael M. Hoffman
Keyword(s):  
Random Forest ◽  
Individual Cell ◽  
Cell Types ◽  
Differentially Expressed ◽  
Pairwise Comparisons ◽  
Alternative Promoters ◽  
Cell Type ◽  
Bootstrap Approach ◽  
Cell Type Specific ◽  
Different Cell Types

Background: Identifying key transcriptional features, such as genes or transcripts, involved in cellular differentiation remains a challenging problem. Current methods for identifying key transcriptional features predominantly rely on pairwise comparisons among different cell types. These methods also identify long lists of differentially expressed transcriptional features. Combining the results from many such pairwise comparisons to find the transcriptional features specific only to one cell type is not straightforward. Thus, one must have a principled method for amalgamating pairwise cell type comparisons that makes full use of prior knowledge about the developmental relationships between cell types. Method: We developed Cell Lineage Analysis (CLA), a computational method which identifies transcriptional features with expression patterns that discriminate cell types, incorporating Cell Ontology knowledge on the relationship between different cell types. CLA uses random forest classification with a stratified bootstrap to increase the accuracy of binary classifiers when each cell type have a different number of samples. Regularized random forest results in a classifier that selects few but important transcriptional features. For each cell type pair, CLA runs multiple instances of regularized random forest and reports the transcriptional features consistently selected. CLA not only discriminates individual cell types but can also discriminate lineages of cell types related in the developmental hierarchy. Results: We applied CLA to Functional Annotation of the Mammalian Genome 5 (FANTOM5) data and identified discriminative transcription factor and long non-coding RNA (lncRNA) genes for 71 human cell types. With capped analysis of gene expression (CAGE) data, CLA identified individual cell-type–specific alternative promoters for cell surface markers. Compared to random forest with a standard bootstrap approach, CLA's stratified bootstrap approach improved the accuracy of gene expression classification models for more than 95% of 2060 cell type pairs examined. Applied on 10X Genomics single-cell RNA-seq data for CD14+ monocytes and FCGR3A+ monocytes, CLA selected only 13 discriminative genes. These genes included the top 9 out of 370 significantly differentially expressed genes obtained from conventional differential expression analysis methods. Discussion: Our CLA method combines tools to simplify the interpretation of transcriptome datasets from many cell types. It automates the identification of the most differentially expressed genes for each cell type pairs CLA's lineage score allows easy identification of the best transcriptional markers for each cell type and lineage in both bulk and single-cell transcriptomic data. Availability: CLA is available at https://cla.hoffmanlab.org. We deposited the version of the CLA source with which we ran our experiments at https://doi.org/10.5281/zenodo.3630670. We deposited other analysis code and results at https://doi.org/10.5281/zenodo.5735636.

Characterization and subcellular localization of a bacterial flotillin homologue

Microbiology ◽  
2009 ◽  
Vol 155 (6) ◽  
pp. 1786-1799 ◽  
Author(s):  
Catriona Donovan ◽  
Marc Bramkamp
Keyword(s):  
Early Stage ◽  
Model Organism ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Type ◽  
Distinct Cell ◽  
Sporulation Efficiency ◽  
Cell Type Specific ◽  
Detergent Resistant Membranes

The process of endospore formation in Bacillus subtilis is complex, requiring the generation of two distinct cell types, a forespore and larger mother cell. The development of these cell types is controlled and regulated by cell type-specific gene expression, activated by a σ-factor cascade. Activation of these cell type-specific sigma factors is coupled with the completion of polar septation. Here, we describe a novel protein, YuaG, a eukaryotic reggie/flotillin homologue that is involved in the early stages of sporulation of the Gram-positive model organism B. subtilis. YuaG localizes in discrete foci in the membrane and is highly dynamic. Purification of detergent-resistant membranes revealed that YuaG is associated with negatively charged phospholipids, e.g. phosphatidylglycerol (PG) or cardiolipin (CL). However, localization of YuaG is not always dependent on PG/CL in vivo. A yuaG disruption strain shows a delay in the onset of sporulation along with reduced sporulation efficiency, where the spores develop to a certain stage and then appear to be trapped at this stage. Our results indicate that YuaG is involved in the early stage of spore development, probably playing a role in the signalling cascade at the onset of sporulation.

scholarly journals Cell type-specific modulation of healthspan by Forkhead family transcription factors in the nervous system

2021 ◽  
Vol 118 (8) ◽  
pp. e2011491118 ◽  
Author(s):  
Ekin Bolukbasi ◽  
Nathaniel S. Woodling ◽  
Dobril K. Ivanov ◽  
Jennifer Adcott ◽  
Andrea Foley ◽  
...  
Keyword(s):  
Nervous System ◽  
Transcription Factors ◽  
Cell Types ◽  
Growth Factor Signaling ◽  
Cell Type ◽  
Model Of Alzheimer’S Disease ◽  
Distinct Cell ◽  
Evolutionarily Conserved ◽  
Cell Type Specific ◽  
Specific Mapping

Reduced activity of insulin/insulin-like growth factor signaling (IIS) increases healthy lifespan among diverse animal species. Downstream of IIS, multiple evolutionarily conserved transcription factors (TFs) are required; however, distinct TFs are likely responsible for these effects in different tissues. Here we have asked which TFs can extend healthy lifespan within distinct cell types of the adult nervous system in Drosophila. Starting from published single-cell transcriptomic data, we report that forkhead (FKH) is endogenously expressed in neurons, whereas forkhead-box-O (FOXO) is expressed in glial cells. Accordingly, we find that neuronal FKH and glial FOXO exert independent prolongevity effects. We have further explored the role of neuronal FKH in a model of Alzheimer’s disease-associated neuronal dysfunction, where we find that increased neuronal FKH preserves behavioral function and reduces ubiquitinated protein aggregation. Finally, using transcriptomic profiling, we identify Atg17, a member of the Atg1 autophagy initiation family, as one FKH-dependent target whose neuronal overexpression is sufficient to extend healthy lifespan. Taken together, our results underscore the importance of cell type-specific mapping of TF activity to preserve healthy function with age.

scholarly journals ANANSE: An enhancer network-based computational approach for predicting key transcription factors in cell fate determination

2020 ◽  
Author(s):  
Quan Xu ◽  
Georgios Georgiou ◽  
Gert Jan C. Veenstra ◽  
Huiqing Zhou ◽  
Simon J. van Heeringen
Keyword(s):  
Transcription Factors ◽  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Cell Fate Determination ◽  
Cell Type ◽  
Fate Determination ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractProper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate conversions using differential gene networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

scholarly journals Cell-type specific epigenetic links to schizophrenia risk in brain

2019 ◽  
Author(s):  
Isabel Mendizabal ◽  
Stefano Berto ◽  
Noriyoshi Usui ◽  
Kazuya Toriumi ◽  
Paramita Chatterjee ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cell Types ◽  
Epigenetic Variation ◽  
Transcriptome Data ◽  
Cell Type ◽  
Coding Regions ◽  
Methylation Difference ◽  
Distinct Cell ◽  
Cell Type Specific ◽  
Targeted Deep Sequencing

AbstractThe importance of cell-type specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generated cell-type specific whole-genome methylomes (N=95) and transcriptomes (N=89) from neurons and oligodendrocytes from brains of schizophrenia and matched controls. The methylomes of these two cell-types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation difference between control and schizophrenia brains is subtle compared to cell-type difference, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in cell-type differentially methylated sites, highlighting the significance of cell-type specific epigenetic dysregulation in a complex neuropsychiatric disorder. Our resource provides novel and comprehensive methylome and transcriptome data from distinct cell populations from schizophrenia brains, further revealing reduced cell-type epigenetic distinction in schizophrenia.

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