scholarly journals Prediction of a cell-type specific mouse mesoconnectome using gene expression data

2019 ◽  
Author(s):  
Nestor Timonidis ◽  
Rembrandt Bakker ◽  
Paul Tiesinga
Keyword(s):  
Gene Expression ◽  
Computational Models ◽  
Missing Values ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Major Step ◽  
Gene Modules

AbstractReconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-type specificity would be a major step forward. We analysed the ability of gene expression patterns to predict cell-type and laminar specific projection patterns and analyzed the biological context of the most predictive groups of genes. To achieve our goal, we used publicly available volumetric gene expression and connectivity data and pre-processed it for prediction by averaging across brain regions, imputing missing values and rescaling. Afterwards, we predicted the strength of axonal projections and their binary form using expression patterns of individual genes and co-expression patterns of spatial gene modules.For predicting projection strength, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using the dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function.Taken together, we have demonstrated a prediction pipeline that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

scholarly journals Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

2020 ◽  
Vol 18 (4) ◽  
pp. 611-626
Author(s):  
Nestor Timonidis ◽  
Rembrandt Bakker ◽  
Paul Tiesinga
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Computational Models ◽  
Brain Connectivity ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Expression Data ◽  
Binary Forms ◽  
Major Step ◽  
Cell Class

Abstract Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

scholarly journals Nanopore Assay Reveals Cell-type-dependent Gene Expression of Indiana Vesiculovirus and Differential Host Cell Response

2021 ◽  
Author(s):  
Balázs Kakuk ◽  
András Attila Kiss ◽  
Gábor Torma ◽  
Zsolt Csabai ◽  
István Prazsák ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Patterns ◽  
Viral Gene Expression ◽  
Fibroblast Cell ◽  
Economic Loss ◽  
Viral Gene ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Vesicular Stomatitis

Indiana Vesiculovirus (IVV; formerly as Vesicular stomatitis virus and Vesicular stomatitis Indiana virus) causes a disease in livestock that is very similar to the foot and mouth disease thereby an outbreak may lead to significant economic loss. Long-read sequencing (LRS) -based approaches revealed a hidden complexity of the transcriptomes in several viruses already. This technique was utilized already for the sequencing of the IVV genome, but our study is the first for the application of this technique for the profiling of IVV transcriptome. Since LRS is able to sequence full-length RNA molecules, and thereby providing more accurate annotation of the transcriptomes than the traditional short-read sequencing methods. The objectives of this study were to assemble the complete transcriptome of using nanopore sequencing, to ascertain cell-type specificity and dynamics of viral gene expression and to evaluate host gene expression changes induced by the viral infection. We carried out a time-course analysis of IVV gene expression in human glioblastoma and primate fibroblast cell lines using a nanopore-based LRS approach and applied both amplified and direct cDNA sequencing, as well as cap-selection for a fraction of samples. Our investigations revealed that, although the IVV genome is simple, it generates a relative complex transcriptomic architecture. In this study, we also demonstrated that IVV transcripts vary in structure and exhibit differential gene expression patterns in the two examined cell types.

scholarly journals Nanopore Assay Reveals Cell-Type-Dependent Gene Expression of Vesicular Stomatitis Indiana Virus and Differential Host Cell Response

Pathogens ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1196
Author(s):  
Balázs Kakuk ◽  
András Attila Kiss ◽  
Gábor Torma ◽  
Zsolt Csabai ◽  
István Prazsák ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Patterns ◽  
Viral Gene Expression ◽  
Fibroblast Cell ◽  
Economic Loss ◽  
Viral Gene ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Vesicular Stomatitis

Vesicular stomatitis Indiana virus (VSIV) of genus Vesiculovirus, species IndianaVesiculovirus (formerly as Vesicular stomatitis virus, VSV) causes a disease in livestock that is very similar to the foot and mouth disease, thereby an outbreak may lead to significant economic loss. Long-read sequencing (LRS) -based approaches already reveal a hidden complexity of the transcriptomes in several viruses. This technique has been utilized for the sequencing of the VSIV genome, but our study is the first for the application of this technique for the profiling of the VSIV transcriptome. Since LRS is able to sequence full-length RNA molecules, it thereby provides more accurate annotation of the transcriptomes than the traditional short-read sequencing methods. The objectives of this study were to assemble the complete transcriptome of using nanopore sequencing, to ascertain cell-type specificity and dynamics of viral gene expression, and to evaluate host gene expression changes induced by the viral infection. We carried out a time-course analysis of VSIV gene expression in human glioblastoma and primate fibroblast cell lines using a nanopore-based LRS approach and applied both amplified and direct cDNA sequencing (as well as cap-selection) for a fraction of samples. Our investigations revealed that, although the VSIV genome is simple, it generates a relatively complex transcriptomic architecture. In this study, we also demonstrated that VSIV transcripts vary in structure and exhibit differential gene expression patterns in the two examined cell types.

Identification of Nogo as a novel indicator of heart failure

2008 ◽  
Vol 32 (2) ◽  
pp. 182-189 ◽  
Author(s):  
Tara A. Bullard ◽  
Tricia L. Protack ◽  
Frédérick Aguilar ◽  
Suveer Bagwe ◽  
H. Todd Massey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Animal Models ◽  
Expression Patterns ◽  
Left Ventricular ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Novel Genes ◽  
Specific Expression ◽  
Adrenergic Signaling

Numerous genetically engineered animal models of heart failure (HF) exhibit multiple characteristics of human HF, including aberrant β-adrenergic signaling. Several of these HF models can be rescued by cardiac-targeted expression of the Gβγ inhibitory carboxy-terminus of the β-adrenergic receptor kinase (βARKct). We recently reported microarray analysis of gene expression in multiple animal models of HF and their βARKct rescue, where we identified gene expression patterns distinct and predictive of HF and rescue. We have further investigated the muscle LIM protein knockout model of HF (MLP−/−), which closely parallels human dilated cardiomyopathy disease progression and aberrant β-adrenergic signaling, and their βARKct rescue. A group of known and novel genes was identified and validated by quantitative real-time PCR whose expression levels predicted phenotype in both the larger HF group and in the MLP−/− subset. One of these novel genes is herein identified as Nogo, a protein widely studied in the nervous system, where it plays a role in regeneration. Nogo expression is altered in HF and normalized with rescue, in an isoform-specific manner, using left ventricular tissue harvested from both animal and human subjects. To investigate cell type-specific expression of Nogo in the heart, immunofluorescence and confocal microscopy were utilized. Nogo expression appears to be most clearly associated with cardiac fibroblasts. To our knowledge, this is the first report to demonstrate the relationship between Nogo expression and HF, including cell-type specificity, in both mouse and human HF and phenotypic rescue.

scholarly journals Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiao Li ◽  
Jakob Seidlitz ◽  
John Suckling ◽  
Feiyang Fan ◽  
Gong-Jun Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Structural Changes ◽  
Brain Regions ◽  
Cell Type ◽  
Major Depressive ◽  
Structural Differences ◽  
Neuroimaging Data ◽  
Cell Type Specific

AbstractMajor depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

scholarly journals Analysis of Transcriptional Changes in Different Brassica napus Synthetic Allopolyploids

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 82
Author(s):  
Yunxiao Wei ◽  
Guoliang Li ◽  
Shujiang Zhang ◽  
Shifan Zhang ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Napus ◽  
Differentially Expressed Genes ◽  
Expression Patterns ◽  
Female Parent ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Transcriptional Changes ◽  
Aa Genome ◽  
High Degree

Allopolyploidy is an evolutionary and mechanistically intriguing process involving the reconciliation of two or more sets of diverged genomes and regulatory interactions, resulting in new phenotypes. In this study, we explored the gene expression patterns of eight F2 synthetic Brassica napus using RNA sequencing. We found that B. napus allopolyploid formation was accompanied by extensive changes in gene expression. A comparison between F2 and the parent shows a certain proportion of differentially expressed genes (DEG) and activation\silent gene, and the two genomes (female parent (AA)\male parent (CC) genomes) showed significant differences in response to whole-genome duplication (WGD); non-additively expressed genes represented a small portion, while Gene Ontology (GO) enrichment analysis showed that it played an important role in responding to WGD. Besides, genome-wide expression level dominance (ELD) was biased toward the AA genome, and the parental expression pattern of most genes showed a high degree of conservation. Moreover, gene expression showed differences among eight individuals and was consistent with the results of a cluster analysis of traits. Furthermore, the differential expression of waxy synthetic pathways and flowering pathway genes could explain the performance of traits. Collectively, gene expression of the newly formed allopolyploid changed dramatically, and this was different among the selfing offspring, which could be a prominent cause of the trait separation. Our data provide novel insights into the relationship between the expression of differentially expressed genes and trait segregation and provide clues into the evolution of allopolyploids.

Specificity of gene expression in adipocytes

1985 ◽  
Vol 5 (2) ◽  
pp. 419-421
Author(s):  
K M Zezulak ◽  
H Green
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Cell Type ◽  
3T3 Cells ◽  
Cell Type Specificity ◽  
Number Of Genes ◽  
Enhanced Expression ◽  
Adipose Cells ◽  
Distinctive Phenotype

During the differentiation of preadipose 3T3 cells into adipose cells, the mRNAs for three proteins increase strikingly in abundance. To determine the degree of cell-type specificity in the expression of these mRNAs, we estimated their abundances in several nonadipose tissues of the mouse. None of these mRNAs was strictly confined to adipocytes, but the ensemble of three mRNAs was rather specific to adipocytes. Insofar as is revealed by these three markers, the distinctive phenotype of adipocytes is the result of the enhanced expression of a number of genes, none of which is completely silent in all other cell types.

scholarly journals A scalable platform for the development of cell-type-specific viral drivers

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Sinisa Hrvatin ◽  
Christopher P Tzeng ◽  
M Aurel Nagy ◽  
Hume Stroud ◽  
Charalampia Koutsioumpa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heterologous Gene Expression ◽  
High Specificity ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cell Type Specific ◽  
The Many ◽  
Dna Regulatory Elements

Enhancers are the primary DNA regulatory elements that confer cell type specificity of gene expression. Recent studies characterizing individual enhancers have revealed their potential to direct heterologous gene expression in a highly cell-type-specific manner. However, it has not yet been possible to systematically identify and test the function of enhancers for each of the many cell types in an organism. We have developed PESCA, a scalable and generalizable method that leverages ATAC- and single-cell RNA-sequencing protocols, to characterize cell-type-specific enhancers that should enable genetic access and perturbation of gene function across mammalian cell types. Focusing on the highly heterogeneous mammalian cerebral cortex, we apply PESCA to find enhancers and generate viral reagents capable of accessing and manipulating a subset of somatostatin-expressing cortical interneurons with high specificity. This study demonstrates the utility of this platform for developing new cell-type-specific viral reagents, with significant implications for both basic and translational research.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

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