A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation

Abstract Alzheimer’s disease (AD) is a pervasive neurodegenerative disorder that disproportionately affects women. Since neural anatomy and disease pathophysiology differ by sex, investigating sex-specific mechanisms in AD pathophysiology can inform new therapeutic approaches for both sexes. Here, we utilized nearly 74,000 cells from human prefrontal and entorhinal cortex samples from the first two publicly available single-cell RNA sequencing AD datasets to study cell type-specific sex-stratified transcriptomic perturbations in AD. Our examination at the single-cell level revealed that sex-specific gene and pathway differences in AD were most prominently observed in glial cells of the prefrontal cortex. In the entorhinal cortex, we observed the same genes and pathways to be perturbed in opposing directions between sexes in AD relative to healthy state. Our findings contribute to growing evidence of sex differences in AD-related transcriptomic changes, which can fuel the development of therapies that may prove more effective at reversing AD pathophysiology.

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Sources of Variation in Cell-Type RNA-Seq Profiles

10.21203/rs.2.23415/v1 ◽

2020 ◽

Cited By ~ 1

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.

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Sources of Variation in Cell-Type RNA-Seq Profiles

10.21203/rs.2.23415/v2 ◽

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.

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Deep autoencoder enables interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

10.1101/2021.10.26.465846 ◽

2021 ◽

Author(s):

Yanshuo Chen ◽

Yixuan Wang ◽

Yuelong Chen ◽

Yumeng Wei ◽

Yunxiang Li ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Clinical Data ◽

Gene Set Enrichment Analysis ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Specific Gene Expression ◽

Wide Range ◽

Cell Type Specific

AbstractSingle-cell RNA-seq has become a powerful tool for researchers to study biologically significant characteristics at explicitly high resolution, but its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce TAPE, a deep learning method that connects bulk RNA-seq and single-cell RNA-seq to balance the demands of big data and precision. By taking advantage of constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with existing methods on several benchmarking datasets, TAPE is more accurate (up to 40% performnace improvement on the real bulk data) and faster than the previous methods. It is sensitive enough to provide biologically meaningful predictions. For example, only TAPE can predict the tendency of increasing monocytes-to-lymphocytes (MLR) ratio in COVID-19 patients from mild to serious symptoms, whose estimated indices are consistent with laboratory data. More importantly, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. Combining with single-sample gene set enrichment analysis (ssGSEA), TAPE also provides valuable clues for people to investigate the immune response in different virus-infected patients. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.

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