scholarly journals A human single-cell atlas of the Substantia nigra reveals novel cell-specific pathways associated with the genetic risk of Parkinson’s disease and neuropsychiatric disorders

2020 ◽  
Author(s):  
Devika Agarwal ◽  
Cynthia Sandor ◽  
Viola Volpato ◽  
Tara Caffrey ◽  
Jimena Monzon-Sandoval ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Genetic Risk ◽  
Expression Profiles ◽  
Neuropsychiatric Disorders ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression

AbstractWe describe a human single-nuclei transcriptomic atlas for the Substantia nigra (SN), generated by sequencing ~ 17,000 nuclei from matched cortical and SN samples. We show that the common genetic risk for Parkinson’s disease (PD) is associated with dopaminergic neuron (DaN)-specific gene expression, including mitochondrial functioning, protein folding and ubiquitination pathways. We identify a distinct cell type association between PD risk and oligodendrocyte-specific gene expression. Unlike Alzheimer’s disease (AD), we find no association between PD risk and microglia or astrocytes, suggesting that neuroinflammation plays a less causal role in PD than AD. Beyond PD, we find associations between SN DaNs and GABAergic neuron gene expression patterns with multiple neuropsychiatric disorders. Nevertheless, we find that each neuropsychiatric disorder is associated with a distinct set of genes within that neuron type. This atlas guides our aetiological understanding by associating SN cell type expression profiles with specific disease risk.

scholarly journals Correcting differential gene expression analysis for cyto-architectural alterations in substantia nigra of Parkinson's disease patients reveals known and potential novel disease-associated genes and pathways

2021 ◽  
Author(s):  
Federico Ferraro ◽  
Christina Fevga ◽  
Vincenzo Bonifati ◽  
Wim Mandemakers ◽  
Ahmed Mahfouz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Substantia Nigra Pars Compacta ◽  
Ammonium Transport ◽  
Protein Interaction Data

Several studies have analyzed gene expression profiles in the substantia nigra to better understand the pathological mechanisms causing Parkinson's disease (PD). However, the concordance between the identified gene signatures in these individual studies was generally low. This might be caused by a change in cell type composition as loss of dopaminergic neurons in the substantia nigra pars compacta is a hallmark of PD. Through an extensive meta-analysis of nine previously published microarray studies, we demonstrated that a big proportion of the detected differentially expressed genes was indeed caused by cyto-architectural alterations due to the heterogeneity in the neurodegenerative stage and/or technical artifacts. After correcting for cell composition, we identified a common signature that deregulated the previously unreported ammonium transport, as well as known biological processes including bioenergetic pathways, response to proteotoxic stress, and immune response. By integrating with protein-interaction data, we shortlisted a set of key genes, such as LRRK2, PINK1, and PRKN known to be related to PD; others with compelling evidence for their role in neurodegeneration, such as GSK3β, WWOX, and VPC; as well as novel potential players in the PD pathogenesis, including NTRK1, TRIM25, ELAVL1. Together, these data showed the importance of accounting for cyto-architecture in these analyses and highlight the contribution of multiple cell types and novel processes to PD pathology providing potential new targets for drug development.

scholarly journals Digital sorting of complex tissues for cell type-specific gene expression profiles

2013 ◽  
Vol 14 (1) ◽  
pp. 89 ◽  
Author(s):  
Yi Zhong ◽  
Ying-Wooi Wan ◽  
Kaifang Pang ◽  
Lionel ML Chow ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific

scholarly journals Correcting Differential Gene Expression Analysis for Cyto—Architectural Alterations in Substantia Nigra of Parkinson’s Disease Patients Reveals Known and Potential Novel Disease—Associated Genes and Pathways

Cells ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 198
Author(s):  
Federico Ferraro ◽  
Christina Fevga ◽  
Vincenzo Bonifati ◽  
Wim Mandemakers ◽  
Ahmed Mahfouz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Substantia Nigra Pars Compacta ◽  
Ammonium Transport ◽  
Protein Interaction Data

Several studies have analyzed gene expression profiles in the substantia nigra to better understand the pathological mechanisms causing Parkinson’s disease (PD). However, the concordance between the identified gene signatures in these individual studies was generally low. This might have been caused by a change in cell type composition as loss of dopaminergic neurons in the substantia nigra pars compacta is a hallmark of PD. Through an extensive meta-analysis of nine previously published microarray studies, we demonstrated that a big proportion of the detected differentially expressed genes was indeed caused by cyto-architectural alterations due to the heterogeneity in the neurodegenerative stage and/or technical artefacts. After correcting for cell composition, we identified a common signature that deregulated the previously unreported ammonium transport, as well as known biological processes such as bioenergetic pathways, response to proteotoxic stress, and immune response. By integrating with protein interaction data, we shortlisted a set of key genes, such as LRRK2, PINK1, PRKN, and FBXO7, known to be related to PD, others with compelling evidence for their role in neurodegeneration, such as GSK3β, WWOX, and VPC, and novel potential players in the PD pathogenesis. Together, these data show the importance of accounting for cyto-architecture in these analyses and highlight the contribution of multiple cell types and novel processes to PD pathology, providing potential new targets for drug development.

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

2020 ◽  
Author(s):  
Johan Gustafsson ◽  
Felix Held ◽  
Jonathan Robinson ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific ◽  
Technical Factors

Abstract Background Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. Results We evaluated different normalization methods, quantified the magnitude of variation introduced by different sources, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We applied methods such as random forest regression to a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is of the same magnitude as the biological variation across cell types. Tissue of origin and cell subtype are less important but still substantial factors, while the difference between individuals is relatively small. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample.Conclusions Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

scholarly journals Sources of Variation in Cell-Type RNA-Seq Profiles

2020 ◽  
Author(s):  
Johan Gustafsson ◽  
Felix Held ◽  
Jonathan Robinson ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Specific ◽  
Technical Factors

Abstract Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of applying cell type profiles derived from blood on mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

scholarly journals A novel computational complete deconvolution method using RNA-seq data

2018 ◽  
Author(s):  
Kai Kang ◽  
Qian Meng ◽  
Igor Shats ◽  
David M. Umbach ◽  
Melissa Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Type Specific

AbstractThe cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell type composition and compared its performance to the performance of two state-of-the art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-typespecificgene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue (MATLAB code is available at https://github.com/kkang7/CDSeq_011).

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