scholarly journals Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with signatures from post mortem adult brains

2017 ◽  
Author(s):  
Gabriel E. Hoffman ◽  
Brigham J. Hartley ◽  
Erin Flaherty ◽  
Ian Ladran ◽  
Peter Gochman ◽  
...  
Keyword(s):  
Neural Progenitor Cells ◽  
Genetic Disorders ◽  
Induced Pluripotent Stem Cell ◽  
Post Mortem ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Sources Of Variation ◽  
Induced Pluripotent ◽  
Number Of Individuals

ABSTRACTWhereas highly penetrant variants have proven well-suited to human induced pluripotent stem cell (hiPSC)-based models, the power of hiPSC-based studies to resolve the much smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. In developing a large case/control schizophrenia (SZ) hiPSC-derived cohort of neural progenitor cells and neurons, we identified and accounted for a variety of technical and biological sources of variation. Reducing the stochastic effects of the differentiation process by correcting for cell type composition boosted the SZ signal in hiPSC-based models and increased the concordance with post mortem datasets. Because this concordance was strongest in hiPSC-neurons, it suggests that this cell type may better model genetic risk for SZ. We predict a growing convergence between hiPSC and post mortem studies as both approaches expand to larger cohort sizes. For studies of complex genetic disorders, to maximize the power of hiPSC cohorts currently feasible, in most cases and whenever possible, we recommend expanding the number of individuals even at the expense of the number of replicate hiPSC clones.

scholarly journals Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition

2019 ◽  
Author(s):  
Gonzalo S. Nido ◽  
Fiona Dick ◽  
Lilah Toker ◽  
Kjell Petersen ◽  
Guido Alves ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Ribosomal Rna ◽  
Post Mortem ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Gene Expression Signatures ◽  
Differential Gene ◽  
Rna Depletion

AbstractBackgroundThe etiology of Parkinson’s disease (PD) is largely unknown. Genome-wide transcriptomic studies in bulk brain tissue have identified several molecular signatures associated with the disease. While these studies have the potential to shed light into the pathogenesis of PD, they are also limited by two major confounders: RNA post mortem degradation and heterogeneous cell type composition of bulk tissue samples. We performed RNA sequencing following ribosomal RNA depletion in the prefrontal cortex of 49 individuals from two independent case-control cohorts. Using cell-type specific markers, we estimated the cell-type composition for each sample and included this in our analysis models to compensate for the variation in cell-type proportions.ResultsRibosomal RNA depletion results in substantially more even transcript coverage, compared to poly(A) capture, in post mortem tissue. Moreover, we show that cell-type composition is a major confounder of differential gene expression analysis in the PD brain. Correcting for cell-type proportions attenuates numerous transcriptomic signatures that have been previously associated with PD, including vesicle trafficking, synaptic transmission, immune and mitochondrial function. Conversely, pathways related to endoplasmic reticulum, lipid oxidation and unfolded protein response are strengthened and surface as the top differential gene expression signatures in the PD prefrontal cortex.ConclusionsDifferential gene expression signatures in PD bulk brain tissue are significantly confounded by underlying differences in cell-type composition. Modeling cell-type heterogeneity is crucial in order to unveil transcriptomic signatures that represent regulatory changes in the PD brain and are, therefore, more likely to be associated with underlying disease mechanisms.

scholarly journals Distinguishing biological from technical sources of variation by leveraging multiple methylation datasets

2019 ◽  
Author(s):  
Mike Thompson ◽  
Zeyuan Johnson Chen ◽  
Elior Rahmani ◽  
Eran Halperin
Keyword(s):  
Biological Effects ◽  
Association Studies ◽  
Real Data ◽  
Relevant Information ◽  
R Package ◽  
Cell Type ◽  
Global Methylation ◽  
Cell Type Composition ◽  
Type Composition ◽  
Sources Of Variation

AbstractDNA methylation remains one of the most widely studied epigenetic markers. One of the major challenges in population studies of methylation is the presence of global methylation effects that may mask local signals. Such global effects may be due to either technical effects (e.g., batch effects) or biological effects (e.g., cell-type composition, genetics). Many methods have been developed for the detection of such global effects, typically in the context of epigenome-wide association studies. However, current unsupervised methods do not distinguish between biological and technical effects, resulting in a loss of highly relevant information. Though supervised methods can be used to estimate known biological effects, it remains difficult to identify and estimate unknown biological effects that globally affect the methylome. Here, we proposeCONFINED,a reference-free method based on sparse canonical correlation analysis that captures replicable sources of variation—such as age, sex, and cell-type composition—across multiple methylation datasets and distinguishes them from dataset-specific sources of variability (e.g., technical effects). Consequently, we demonstrate through simulated and real data that by leveraging multiple datasets simultaneously, our approach captures several replicable sources of biological variation better than previous reference-free methods and is considerably more robust to technical noise than previous reference-free methods.CONFINEDis available as an R package as detailed athttps://github.com/cozygene/CONFINED.

scholarly journals Inference of Cell Type Composition from Human Brain Transcriptomic Datasets Illuminates the Effects of Age, Manner of Death, Dissection, and Psychiatric Diagnosis

2016 ◽  
Author(s):  
Megan Hastings Hagenauer ◽  
Anton Schulmann ◽  
Jun Z. Li ◽  
Marquis P. Vawter ◽  
David M. Walsh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Blood Cell ◽  
Cell Types ◽  
Post Mortem ◽  
Cell Type ◽  
Cell Content ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPsychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type composition for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-packageBrainInABlender(validated and publicly-released:https://github.com/hagenaue/BrainInABlender). Using this method, we found that the principal components of variation in the datasets strongly correlated with the neuron to glia ratio of the samples.This variability was not simply due to dissection – the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.

scholarly journals Hereditary Optic Neuropathies: Induced Pluripotent Stem Cell-Based 2D/3D Approaches

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 112
Author(s):  
Marta García-López ◽  
Joaquín Arenas ◽  
M. Esther Gallardo
Keyword(s):  
Stem Cell ◽  
Retinal Ganglion Cells ◽  
Pluripotent Stem Cell ◽  
Ganglion Cells ◽  
Genetic Disorders ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Underlying Mechanism ◽  
Retinal Ganglion ◽  
Induced Pluripotent

Inherited optic neuropathies share visual impairment due to the degeneration of retinal ganglion cells (RGCs) as the hallmark of the disease. This group of genetic disorders are caused by mutations in nuclear genes or in the mitochondrial DNA (mtDNA). An impaired mitochondrial function is the underlying mechanism of these diseases. Currently, optic neuropathies lack an effective treatment, and the implementation of induced pluripotent stem cell (iPSC) technology would entail a huge step forward. The generation of iPSC-derived RGCs would allow faithfully modeling these disorders, and these RGCs would represent an appealing platform for drug screening as well, paving the way for a proper therapy. Here, we review the ongoing two-dimensional (2D) and three-dimensional (3D) approaches based on iPSCs and their applications, taking into account the more innovative technologies, which include tissue engineering or microfluidics.

scholarly journals Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0147519 ◽  
Author(s):  
Yuh Shiwa ◽  
Tsuyoshi Hachiya ◽  
Ryohei Furukawa ◽  
Hideki Ohmomo ◽  
Kanako Ono ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Systematic Bias ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Dna Collection

scholarly journals Seasonal changes in gene expression represent cell-type composition in whole blood

2014 ◽  
Vol 23 (10) ◽  
pp. 2721-2728 ◽  
Author(s):  
S. De Jong ◽  
M. Neeleman ◽  
J. J. Luykx ◽  
M. J. Ten Berg ◽  
E. Strengman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Whole Blood ◽  
Seasonal Changes ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition

scholarly journals Assessing the Contribution of Relative Macrophage Frequencies to Subcutaneous Adipose Tissue

2021 ◽  
Vol 8 ◽  
Author(s):  
Marianthi Kalafati ◽  
Michael Lenz ◽  
Gökhan Ertaylan ◽  
Ilja C. W. Arts ◽  
Chris T. Evelo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Subcutaneous Adipose Tissue ◽  
Tissue Cell ◽  
Cell Type ◽  
Expression Of Genes ◽  
Cell Type Composition ◽  
Type Composition ◽  
Adipose Tissue Cell ◽  
Subcutaneous Adipose

Background: Macrophages play an important role in regulating adipose tissue function, while their frequencies in adipose tissue vary between individuals. Adipose tissue infiltration by high frequencies of macrophages has been linked to changes in adipokine levels and low-grade inflammation, frequently associated with the progression of obesity. The objective of this project was to assess the contribution of relative macrophage frequencies to the overall subcutaneous adipose tissue gene expression using publicly available datasets.Methods: Seven publicly available microarray gene expression datasets from human subcutaneous adipose tissue biopsies (n = 519) were used together with TissueDecoder to determine the adipose tissue cell-type composition of each sample. We divided the subjects in four groups based on their relative macrophage frequencies. Differential gene expression analysis between the high and low relative macrophage frequencies groups was performed, adjusting for sex and study. Finally, biological processes were identified using pathway enrichment and network analysis.Results: We observed lower frequencies of adipocytes and higher frequencies of adipose stem cells in individuals characterized by high macrophage frequencies. We additionally studied whether, within subcutaneous adipose tissue, interindividual differences in the relative frequencies of macrophages were reflected in transcriptional differences in metabolic and inflammatory pathways. Adipose tissue of individuals with high macrophage frequencies had a higher expression of genes involved in complement activation, chemotaxis, focal adhesion, and oxidative stress. Similarly, we observed a lower expression of genes involved in lipid metabolism, fatty acid synthesis, and oxidation and mitochondrial respiration.Conclusion: We present an approach that combines publicly available subcutaneous adipose tissue gene expression datasets with a deconvolution algorithm to calculate subcutaneous adipose tissue cell-type composition. The results showed the expected increased inflammation gene expression profile accompanied by decreased gene expression in pathways related to lipid metabolism and mitochondrial respiration in subcutaneous adipose tissue in individuals characterized by high macrophage frequencies. This approach demonstrates the hidden strength of reusing publicly available data to gain cell-type-specific insights into adipose tissue function.

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