scholarly journals Transcriptomic and Cellular Decoding of Regional Brain Vulnerability to Neurodevelopmental Disorders

2019 ◽  
Author(s):  
Jakob Seidlitz ◽  
Ajay Nadig ◽  
Siyuan Liu ◽  
Richard A.I. Bethlehem ◽  
Petra E. Vértes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Patterns ◽  
Neurodevelopmental Disorders ◽  
Copy Number Variations ◽  
Brain Anatomy ◽  
Specific Cell ◽  
Expression Data ◽  
Regional Brain ◽  
Genetic Risks ◽  
Neuroimaging Data

AbstractNeurodevelopmental disorders are highly heritable and associated with spatially-selective disruptions of brain anatomy. The logic that translates genetic risks into spatially patterned brain vulnerabilities remains unclear but is a fundamental question in disease pathogenesis. Here, we approach this question by integrating (i) in vivo neuroimaging data from patient subgroups with known causal genomic copy number variations (CNVs), and (ii) bulk and single-cell gene expression data from healthy cortex. First, for each of six different CNV disorders, we show that spatial patterns of cortical anatomy change in youth are correlated with spatial patterns of expression for CNV region genes in bulk cortical tissue from typically-developing adults. Next, by transforming normative bulk-tissue cortical expression data into cell-type expression maps, we further link each disorder’s anatomical change map to specific cell classes and specific CNV-region genes that these cells express. Finally, we establish convergent validity of this “transcriptional vulnerability model” by inter-relating patient neuroimaging data with measures of altered gene expression in both brain and blood-derived patient tissue. Our work clarifies general biological principles that govern the mapping of genetic risks onto regional brain disruption in neurodevelopmental disorders. We present new methods that can harness these principles to screen for potential cellular and molecular determinants of disease from readily available patient neuroimaging data.

scholarly journals Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakob Seidlitz ◽  
Ajay Nadig ◽  
Siyuan Liu ◽  
Richard A. I. Bethlehem ◽  
Petra E. Vértes ◽  
...  
Keyword(s):  
Neurodevelopmental Disorders ◽  
Molecular Mechanisms ◽  
Copy Number Variations ◽  
Brain Anatomy ◽  
Specific Cell ◽  
Expression Data ◽  
Regional Brain ◽  
Genetic Risks ◽  
Neuroimaging Data ◽  
Neurogenetic Disorders

AbstractNeurodevelopmental disorders have a heritable component and are associated with region specific alterations in brain anatomy. However, it is unclear how genetic risks for neurodevelopmental disorders are translated into spatially patterned brain vulnerabilities. Here, we integrated cortical neuroimaging data from patients with neurodevelopmental disorders caused by genomic copy number variations (CNVs) and gene expression data from healthy subjects. For each of the six investigated disorders, we show that spatial patterns of cortical anatomy changes in youth are correlated with cortical spatial expression of CNV genes in neurotypical adults. By transforming normative bulk-tissue cortical expression data into cell-type expression maps, we link anatomical change maps in each analysed disorder to specific cell classes as well as the CNV-region genes they express. Our findings reveal organizing principles that regulate the mapping of genetic risks onto regional brain changes in neurogenetic disorders. Our findings will enable screening for candidate molecular mechanisms from readily available neuroimaging data.

scholarly journals CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kai Kang ◽  
Caizhi Huang ◽  
Yuanyuan Li ◽  
David M. Umbach ◽  
Leping Li
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Biological Tissues ◽  
Specific Gene ◽  
Specific Cell ◽  
Specific Information ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific

Abstract Background Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community. Result We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Conclusions The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell–cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.

scholarly journals A practical guide to linking brain-wide gene expression and neuroimaging data

2018 ◽  
Author(s):  
Aurina Arnatkevičiūtė ◽  
Ben D. Fulcher ◽  
Alex Fornito
Keyword(s):  
Gene Expression ◽  
Brain Atlas ◽  
Expression Data ◽  
Analysis Pipeline ◽  
Atlas Data ◽  
Processing Pipeline ◽  
Neuroimaging Data ◽  
Brain Structure And Function ◽  
Expression Atlas ◽  
And Function

AbstractThe recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on the molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

scholarly journals Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

2020 ◽  
Author(s):  
Jiebiao Wang ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Cell ◽  
Expression Data ◽  
Cell Type ◽  
Disease Etiology ◽  
Cell Type Specific

AbstractWhen assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

scholarly journals Topological gene-expression networks recapitulate brain anatomy and function

2018 ◽  
Author(s):  
Alice Patania ◽  
Pierluigi Selvaggi ◽  
Mattia Veronese ◽  
Ottavia Dipasquale ◽  
Paul Expert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Expression Data ◽  
Pharmacological Challenge ◽  
Topological Network ◽  
Dopaminergic Pathway ◽  
And Function

AbstractUnderstanding how gene expression translates to and affects human behaviour is one of the ultimate aims of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to produce and analyze genes co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas, and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene-set and find that co-expression networks produced by Mapper returned a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that topological network descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenge.

scholarly journals Inferring copy number variation from gene expression data: methods, comparisons, and applications to oncology

2021 ◽  
Author(s):  
Joseph Boen ◽  
Joel P. Wagner ◽  
Noemi Di Nanni
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Copy Number ◽  
Single Cells ◽  
Rapid Development ◽  
Cell Cancer ◽  
Copy Number Variations ◽  
Expression Data ◽  
Cell Gene Expression

Copy number variations (CNVs) are genomic events where the number of copies of a particular gene varies from cell to cell. Cancer cells are associated with somatic CNV changes resulting in gene amplifications and gene deletions. However, short of single-cell whole-genome sequencing, it is difficult to detect and quantify CNV events in single cells. In contrast, the rapid development of single-cell RNA sequencing (scRNA-seq) technologies has enabled easy acquisition of single-cell gene expression data. In this work, we employ three methods to infer CNV events from scRNA-seq data and provide a statistical comparison of the methods' results. In addition, we combine the analysis of scRNA-seq and inferred CNV data to visualize and determine subpopulations and heterogeneity in tumor cell populations.

scholarly journals SNP Variants at 16p13.11 Clarify the Role of the NDE1/miR-484 Locus in Major Mental Illness in Finland

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Vishal Sinha ◽  
Alfredo Ortega-Alonso ◽  
Liisa Ukkola-Vuoti ◽  
Outi Linnaranta ◽  
Amanda B Zheutlin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mental Illness ◽  
Gene Expression Data ◽  
Copy Number ◽  
Copy Number Variations ◽  
Dependent Manner ◽  
Expression Data ◽  
Factor Binding Site ◽  
Association Pattern

Abstract Through copy number variations, the 16p13.11 locus has been consistently linked to mental disorders. This locus contains the NDE1 gene, which also encodes microRNA-484. Both of them have been highlighted to play a role in the etiology of mental illness. A 4-SNP haplotype spanning this locus has been shown to associate with schizophrenia in Finnish females. Here we set out to identify any functional variations implicated by this haplotype. We used a sequencing and genotyping study design to identify variations of interest in a Finnish familial cohort ascertained for schizophrenia. We identified 295 variants through sequencing, none of which were located directly within microRNA-484. Two variants were observed to associate with schizophrenia in a sex-dependent manner (females only) in the whole schizophrenia familial cohort (rs2242549 P = .00044; OR = 1.20, 95% CI 1.03–1.40; rs881803 P = .00021; OR = 1.20, 95% CI 1.02–1.40). Both variants were followed up in additional psychiatric cohorts, with neuropsychological traits, and gene expression data, in order to further examine their role. Gene expression data from the familial schizophrenia cohort demonstrated a significant association between rs881803 and 1504 probes (FDR q < 0.05). These were significantly enriched for genes that are predicted miR-484 targets (n = 54; P = .000193), and with probes differentially expressed between the sexes (n = 48; P = .000187). While both SNPs are eQTLs for NDE1, rs881803 is located in a predicted transcription factor binding site. Based on its location and association pattern, we conclude that rs881803 is the prime functional candidate under this locus, affecting the roles of both NDE1 and miR-484 in psychiatric disorders.

scholarly journals Characterization of Mice Bearing Humanized Androgen Receptor Genes (h/mAr) Varying in Polymorphism Length

2020 ◽  
Author(s):  
Zsuzsa Lindenmaier ◽  
Yohan Yee ◽  
Adrienne Kinman ◽  
Darren Fernandes ◽  
Jacob Ellegood ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Androgen Receptor ◽  
Spatial Patterns ◽  
Disease Risk ◽  
Anatomical Variation ◽  
Brain Anatomy ◽  
Tract Length ◽  
Biological Studies ◽  
And Behavior

AbstractThe androgen receptor (AR) is known for masculinization of behavior and brain. To better understand the role that AR plays, mice bearing humanized Ar genes with varying lengths of a polymorphic N-terminal glutamine (Q) tract were created (Albertelli et al 2006). The length of the Q tract is inversely proporitional to AR activity. Biological studies of the Q tract length may also provide a window into potential AR contributions to sex-biases in disease risk.Here we take a multi-pronged approach to characterizing AR signaling effects on brain and behavior in mice using the humanized Ar Q tract model. We first map effects of Q tract length on regional brain anatomy, and consider if these are modified by gonadal sex. We then test the notion that spatial patterns of anatomical variation related to Q tract length could be organized by intrinsic spatiotemporal patterning of AR gene expression in the mouse brain. Finally, we test influences of Q tract length on four behavioral tests.Altering Q tract length led to neuroanatomical differences in a non-linear dosage-dependent fashion. Gene expression analyses indicated that adult neuroanatomical changes due to Q tract length are only associated with neurodevelopment (as opposed to adulthood). No significant effect of Q tract length was found on the behavior of the three mouse models. These results indicate that AR activity differentially mediates neuroanatomy and behavior, that AR activity alone does not mediate sex differences, and that neurodevelopmental processes are associated with spatial patterns of volume changes due to Q tract length in adulthood. They also indicate that androgen sensitivity in adulthood does not directly lead to autism-related behaviors or neuroanatomy, although neurodevelopmental processes may play a role earlier. Further study into sex differences, development, other behaviors, and other sex-specific mechanisms are needed to better understand AR sensitivity, neurodevelopmental disorders, and the sex difference in their prevalence.

scholarly journals Topological gene expression networks recapitulate brain anatomy and function

2019 ◽  
Vol 3 (3) ◽  
pp. 744-762 ◽  
Author(s):  
Alice Patania ◽  
Pierluigi Selvaggi ◽  
Mattia Veronese ◽  
Ottavia Dipasquale ◽  
Paul Expert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Expression Data ◽  
Related Gene ◽  
Genetic Pathways ◽  
Dopaminergic Pathway ◽  
And Function

Understanding how gene expression translates to and affects human behavior is one of the ultimate goals of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to analyze gene co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene set and find that co-expression networks produced by Mapper return a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that network based descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenges.

Leukemic B Cells from CD38 Positive but Not CD38 Negative B-CLL Patients Express Heightened Levels of Cell Cycle Related Genes.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 4809-4809
Author(s):  
Brian T. Pittner ◽  
Renee C. Tschumper ◽  
Bruce W. Morlan ◽  
Karla V. Ballman ◽  
Neil E. Kay ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
B Cells ◽  
Differentially Expressed ◽  
Leukemic Cells ◽  
Specific Cell ◽  
Expression Data ◽  
Data Set ◽  
Checkpoint Protein ◽  
Gene And Protein Expression

Abstract Within the past five years studies published from this laboratory and others have identified or confirmed CD38 as a marker for aggressive disease in B cell chronic lymphocytic leukemia (B-CLL). To learn more about the possible role of CD38 in this disease, we performed gene expression profiling (GEP; Affymetrix U133A platform) on primary leukemic B cells isolated from CD38 positive and negative patients. We and others have previously shown that the leukemic compartment within some patients consists of both CD38neg and CD38pos CLL B cells. To optimize identification of differentially expressed genes associated with CD38 expression or lack thereof, we restricted our gene profiling to patient cells that were unimodally negative for CD38 and those that expressed unimodally high levels of CD38. In both groups of patients, CD19 positive cells were purified using magnetic bead cell sorting on a Miltenyi AutoMacs prior to isolation of total RNA. To determine genes that appear to be differentially expressed in this set of experiments, we used a recently developed model-based algorithm that uses the normalized probe-level data as well as information about the underlying experimental design. Reassuringly, the gene expression profiles within the CD38pos versus the CD38neg cohort maintain congruency within each cohort when analyzing the gene expression data set as a whole, as well as within categories such as adhesion, signaling, and transcriptional molecules. Using this analytical approach, several genes emerged as interesting candidates for exploration into the nature of the CD38pos association with disease aggressiveness. Interestingly, although peripheral blood CLL B cells are overwhelmingly in the resting, G0, stage of the cell cycle, several cell cycle related genes were expressed at higher levels in CD38pos patient leukemic cells but not in CD38neg leukemic patients. Moreover, these genes encompass protein families that function at different stages throughout the cell cycle. These genes include: anaphase promoting complex subunit 5 (APC5); cell division cycle (cdc) family members; a checkpoint protein CHFR; the microtubule and actin associated protein, MACF1; the mitotic spindle checkpoint protein, MAD1L1; the DNA replication regulator, MCM7; STAG3, a meiotic regulator of chromosome segregation; and several activators of the ubiquitin pathway, including UBE2N, a known regulator of DNA repair enzymes (p-values of <0.01, <0.01, <0.01, 0.01, 0.02, <0.01, 0.01, and 0.01, respectively). We have begun to confirm these findings at the protein translation level. Through immunoblotting, preliminary data show that 3 out of 5 CD38pos and 0 out of 5 CD38neg patients’ leukemic cells express detectable levels of APC5, suggesting that CD38pos cells, or at least a subpopulation of cells, express APC5. Taken together, these gene and protein expression data suggest that at least a subpopulation of the leukemic cells from CD38pos CLL patients have more recently completed the cell cycle and maintain expression of specific cell cycle related genes. Alternatively, it is possible that this subpopulation is better prepared to re-enter the cell cycle if it encounters proliferative stimuli in other microenvironments.

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