scholarly journals Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data

2020 ◽  
Author(s):  
Yasin Uzun ◽  
Hao Wu ◽  
Kai Tan
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Single Cell ◽  
Single Cells ◽  
Gene Activity ◽  
Transcriptome Data ◽  
Cell Type ◽  
Computational Framework ◽  
Multiple Datasets ◽  
Experimental Protocols

AbstractDespite rapid advances in single-cell DNA methylation profiling methods, computational tools for data analysis are lagging far behind. A number of tasks, including cell type calling and integration with transcriptome data, requires the construction of a robust gene activity matrix as the prerequisite but challenging task. The advent of multi-omics data enables measurement of both DNA methylation and gene expression for the same single cells. Although such data is rather sparse, they are sufficient to train supervised models that capture the complex relationship between DNA methylation and gene expression and predict gene activities at single-cell level. Here, we present MAPLE (Methylome Association by Predictive Linkage to Expression), a computational framework that learns the association between DNA methylation and expression using both gene- and cell-dependent statistical features. Using multiple datasets generated with different experimental protocols, we show that using predicted gene activity values significantly improves several analysis tasks, including clustering, cell type identification and integration with transcriptome data. With the rapid accumulation of single-cell epigenomics data, MAPLE provides a general framework for integrating such data with transcriptome data.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

scholarly journals RNA splicing programs define tissue compartments and cell types at single cell resolution

2021 ◽  
Author(s):  
Julia Eve Olivieri ◽  
Roozbeh Dehghannasiri ◽  
Peter Wang ◽  
SoRi Jang ◽  
Antoine de Morree ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
High Throughput ◽  
Rna Splicing ◽  
Single Cells ◽  
Cell Types ◽  
Mouse Lemur ◽  
Cell Type ◽  
Multiple Organs ◽  
Single Cell Pcr

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach that is agnostic to transcript annotation, to detect cell-type-specific regulated splicing in > 110K carefully annotated single cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type specifically spliced. These results are validated with RNA FISH, single cell PCR, and in high throughput with Smart-seq2. Regulated splicing is found in ubiquitously expressed genes such as actin light chain subunit MYL6 and ribosomal protein RPS24, which has an epithelial-specific microexon. 13% of the statistically most variable splice sites in cell-type specifically regulated genes are also most variable in mouse lemur or mouse. SpliZ analysis further reveals 170 genes with regulated splicing during sperm development using, 10 of which are conserved in mouse and mouse lemur. The statistical properties of the SpliZ allow model-based identification of subpopulations within otherwise indistinguishable cells based on gene expression, illustrated by subpopulations of classical monocytes with stereotyped splicing, including an un-annotated exon, in SAT1, a Diamine acetyltransferase. Together, this unsupervised and annotation-free analysis of differential splicing in ultra high throughput droplet-based sequencing of human cells across multiple organs establishes splicing is regulated cell-type-specifically independent of gene expression.

scholarly journals ReadZS detects developmentally regulated RNA processing programs in single cell RNA-seq and defines subpopulations independent of gene expression

2021 ◽  
Author(s):  
Elisabeth Meyer ◽  
Roozbeh Dehghannasiri ◽  
Kaitlin Chaung ◽  
Julia Salzman
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Processing ◽  
Single Cells ◽  
Alternative Polyadenylation ◽  
Cell Type ◽  
Computationally Efficient ◽  
Developmentally Regulated ◽  
Eukaryotic Gene ◽  
Human Spermatogenesis

Post-transcriptional regulation of RNA processing (RNAP), including splicing and alternative polyadenylation (APA), controls eukaryotic gene function. Conservative estimates based on bulk tissue studies conclude that at least 50% of mammalian genes undergo APA. Single-cell RNA sequencing (scRNA-seq) could enable a near complete estimate of the extent, function, and regulation of these and other forms of RNA processing. Yet, statistical methods to detect regulated RNAP are limited in their detection power because they suffer from reliance on (a) incomplete annotations of 3' untranslated regions (3' UTRs), (b) peak calling heuristics, (c) analysis based on measurements collapsed over all cells in a cell type (pseudobulking), or (d) APA-specific detection. Here, we introduce ReadZS, a computationally-efficient, and annotation-free statistical approach to identify regulated RNAP, including but not limited to APA, in single cells. ReadZS rediscovers and substantially extends the scope of known cell type-specific RNAP in the human lung and during human spermatogenesis. The unique single-cell resolution and statistical properties of ReadZS enable discovery of new evolutionarily conserved, developmentally regulated RNAP and subpopulations of lung-resident macrophages, homogenous by gene expression alone.

scholarly journals Joint Inference of Clonal Structure using Single-cell Genome and Transcriptome Sequencing Data

2020 ◽  
Author(s):  
Xiangqi Bai ◽  
Zhana Duren ◽  
Lin Wan ◽  
Li C. Xia
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cancer Cell ◽  
Transcriptome Sequencing ◽  
Single Cells ◽  
Clonal Structure ◽  
Computational Framework ◽  
Joint Inference ◽  
Cell Genome ◽  
Single Cell Genome

AbstractLatest developments in high-throughput single-cell genome (scDNA-) and transcriptome sequencing (scRNA-seq) technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster single cells of the same tissue origin across scRNA- and scDNA-seq platforms. In this work, we present a computational framework – CCNMF, which uses a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for paired scDNA- and scRNA-seq data of the same specimen. CCNMF clusters single cells through statistically modeling their shared clonal structure and coupling copy number and gene expression profiles by their global correlation. We validated CCNMF using both simulated and real cell mixture benchmarks and fully demonstrated its robustness and accuracy. As real world applications of CCNMF, we analyzed data from a gastric cancer cell line, an ovarian cancer cell mixture, and a triple-negative breast cancer xenograft. We resolved the underlying clonal structures and identified dosage-sensitive genes between co-existing clones. In summary, CCNMF is a coherent computational framework that simul-taneously resolves genome and transcriptome clonal structures, facilitating understanding of how cellular gene expression changes along with clonal genome alternations.AvailabilityThe R package of CCNMF is available at https://github.com/XQBai/CCNMF.

scholarly journals Bfimpute: A Bayesian factorization method to recover single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zi-Hang Wen ◽  
Jeremy L. Langsam ◽  
Lu Zhang ◽  
Wenjun Shen ◽  
Xin Zhou
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Group ◽  
Specific Gene ◽  
Cell Type ◽  
Sequencing Data ◽  
Gene Expression Matrix ◽  
Study Gene Expression ◽  
Expression Matrix

AbstractSingle-cell RNA-seq (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. We introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than other six publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene or cell related information that users provide to increase the performance. Availability: Bfimpute is implemented in R and is freely available at https://github.com/maiziezhoulab/Bfimpute.

scholarly journals Single-cell multi-omic profiling of chromatin conformation and DNA methylation

2019 ◽  
Author(s):  
Dong-Sung Lee ◽  
Chongyuan Luo ◽  
Jingtian Zhou ◽  
Sahaana Chandran ◽  
Angeline Rivkin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Cell Types ◽  
Cell Type ◽  
Cell Level ◽  
3D Genome ◽  
Heterogeneous Samples ◽  
Single Data

Abstract The ability to profile epigenomic features in single cells is facilitating the study of the variation in transcription regulation at the single cell level. Single cell methods have also facilitated the generation of cell-type resolved transcriptomic and epigenetic profiles of lineages derived from complex heterogeneous samples. However, integrating different epigenetic features remain challenging, as many current methods profile a single data type at at time. Furthermore, some epigenetic features, such as 3D genome organization, are intrinsically variable between single cells of the same lineage, so it remains unclear how well these methods may resolve cell-types from complex mixtures. Here we describe a method for profiling 3D genome organization and DNA methylation in single cells. This protocol accompanies Lee et al. (Nature Methods 2019) after peer review to aid potential users in applying the method to their own samples.

scholarly journals Single-cell regulatory landscape and disease vulnerability map of adult Macaque cortex

2020 ◽  
Author(s):  
Ying Lei ◽  
Mengnan Cheng ◽  
Zihao Li ◽  
Zhenkun Zhuang ◽  
Liang Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Primary Motor Cortex ◽  
Neurological Diseases ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Nucleotide Polymorphisms ◽  
Cell Type

Non-human primates (NHP) provide a unique opportunity to study human neurological diseases, yet detailed characterization of the cell types and transcriptional regulatory features in the NHP brain is lacking. We applied a combinatorial indexing assay, sci-ATAC-seq, as well as single-nuclei RNA-seq, to profile chromatin accessibility in 43,793 single cells and transcriptomics in 11,477 cells, respectively, from prefrontal cortex, primary motor cortex and the primary visual cortex of adult cynomolgus monkey Macaca fascularis. Integrative analysis of these two datasets, resolved regulatory elements and transcription factors that specify cell type distinctions, and discovered area-specific diversity in chromatin accessibility and gene expression within excitatory neurons. We also constructed the dynamic landscape of chromatin accessibility and gene expression of oligodendrocyte maturation to characterize adult remyelination. Furthermore, we identified cell type-specific enrichment of differentially spliced gene isoforms and disease-associated single nucleotide polymorphisms. Our datasets permit integrative exploration of complex regulatory dynamics in macaque brain tissue at single-cell resolution.

scholarly journals SpiceMix: Integrative single-cell spatial modeling for inferring cell identity

2020 ◽  
Author(s):  
Benjamin Chidester ◽  
Tianming Zhou ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Spatial Information ◽  
Single Cells ◽  
Joint Analysis ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Identity ◽  
Cell Type Composition ◽  
Type Composition

AbstractSpatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that capture the unique properties of single-cell spatial transcriptome data to unveil cell identities remains a challenge. Here, we report SpiceMix, a new probabilistic model that enables effective joint analysis of spatial information and gene expression of single cells based on spatial transcriptome data. Both simulation and real data evaluations demonstrate that SpiceMix consistently improves upon the inference of the intrinsic cell types compared with existing approaches. As a proof-of-principle, we use SpiceMix to analyze single-cell spatial transcriptome data of the mouse primary visual cortex acquired by seqFISH+ and STARmap. We find that SpiceMix can improve cell identity assignments and uncover potentially new cell subtypes. SpiceMix is a generalizable framework for analyzing spatial transcriptome data that may provide critical insights into the cell-type composition and spatial organization of cells in complex tissues.

scholarly journals In search of a core cellular network in single cell transcriptome data

2021 ◽  
Author(s):  
Ming Yang ◽  
Benjamin R. Harrison ◽  
Daniel E.L. Promislow
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cellular Network ◽  
Cell Types ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractBackgroundAlong with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, remains a central question in biology. Single-cell RNA-sequencing (scRNA-seq) measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current analyses focus primarily on identifying differentially expressed genes across cells. However, patterns of co-expression between genes are probably more indicative of biological processes than are the expression of individual genes. Using single cell transcriptome data from the fly brain, here we focus on gene co-expression to search for a core cellular network.ResultsIn this study, we constructed cell type-specific gene co-expression networks using single cell transcriptome data of brains from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types in fly brains and defined this set as the core cellular network. This core is very small compared with cell type-specific gene co-expression networks and shows dense connectivity. Modules within this core are enriched for basic cellular functions, such as translation and ATP metabolic processes, and gene members of these modules have distinct evolutionary signatures.ConclusionsOverall, we demonstrated that a core cellular network exists in diverse cell types of fly brains and this core exhibits unique topological, structural, functional and evolutionary properties.

scholarly journals alona: a web server for single-cell RNA-seq analysis

2020 ◽  
Vol 36 (12) ◽  
pp. 3910-3912 ◽  
Author(s):  
Oscar Franzén ◽  
Johan L M Björkegren
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cluster Structure ◽  
Web Server ◽  
Cell Types ◽  
Supplementary Information ◽  
Marker Genes ◽  
Cell Type

Abstract Summary Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis. We have developed a web server, alona that incorporates several of the most popular single-cell analysis algorithms into a flexible pipeline. alona can perform quality filtering, normalization, batch correction, clustering, cell type annotation and differential gene expression analysis. Data are visualized in the web browser using an interface based on JavaScript, allowing the user to query genes of interest and visualize the cluster structure. alona accepts a compressed gene expression matrix and identifies cell clusters with a graph-based clustering strategy. Cell types are identified from a comprehensive collection of marker genes or by specifying a custom set of marker genes. Availability and implementation The service runs at https://alona.panglaodb.se and the Python package can be downloaded from https://oscar-franzen.github.io/adobo/. Supplementary information Supplementary data are available at Bioinformatics online.

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