Cell type-specific gene expression profiling in brain tissue: comparison between TRAP, LCM and RNA-seq

Cell type-specific gene expression (CSE) brings novel biological insights into both physiological and pathological processes compared with bulk tissue gene expression. Although fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (scRNA-seq) are two widely used techniques to detect gene expression in a cell type-specific manner, the constraints of cost and labor force make it impractical as a routine on large patient cohorts. Here, we present ENIGMA, an algorithm that deconvolutes bulk RNA-seq into cell type-specific expression matrices and cell type fraction matrices without the need of physical sorting or sequencing of single cells. ENIGMA used cell type signature matrix generated from either FACS RNA-seq or scRNA-seq as reference, and applied matrix completion technique to achieve fast and accurate deconvolution. We demonstrated the superior performance of ENIGMA to previously published algorithms (TCA, bMIND and CIBERSORTx) while requiring much less running time on both simulated and realistic datasets. To prove its value in biological discovery, we applied ENIGMA to bulk RNA-seq from arthritis patients and revealed a pseudo-differentiation trajectory that could reflect monocyte to macrophage transition. We also applied ENIGMA to bulk RNA-seq data of pancreatic islet tissue from type 2 diabetes (T2D) patients and discovered a beta cell-specific gene co-expression module related to senescence and apoptosis that possibly contributed to the pathogenesis of T2D. Together, ENIGMA provides a new framework to improve the CSE estimation by integrating FACS RNA-seq and scRNA-seq with tissue bulk RNA-seq data, and will extend our understandings about cell heterogeneity on population level with no need for experimental tissue disaggregation.

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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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A novel computational complete deconvolution method using RNA-seq data

10.1101/496596 ◽

2018 ◽

Cited By ~ 2

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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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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