scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

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Single-cell transcriptomic analysis elucidates APOE genotype specific changes across cell types in two brain regions in Alzheimer’s disease

10.21203/rs.3.rs-291648/v1 ◽

2021 ◽

Author(s):

Stella Belonwu ◽

Yaqiao Li ◽

Daniel Bunis ◽

Arjun Arkal Rao ◽

Caroline Warly Solsberg ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Single Cell ◽

Molecular Mechanisms ◽

Apoe Genotype ◽

Cell Types ◽

Brain Regions ◽

Single Cell Level ◽

Cell Level ◽

Single Nucleus

Abstract Alzheimer’s Disease (AD) is a complex neurodegenerative disease that gravely affects patients and imposes an immense burden on caregivers. Apolipoprotein E4 (APOE4) has been identified as the most common genetic risk factor for AD, yet the molecular mechanisms connecting APOE4 to AD are not well understood. Past transcriptomic analyses in AD have revealed APOE genotype-specific transcriptomic differences; however, these differences have not been explored at a single-cell level. Here, we leverage the first two single-nucleus RNA sequencing AD datasets from human brain samples, including nearly 55,000 cells from the prefrontal and entorhinal cortices. We observed more global transcriptomic changes in APOE4 positive AD cells and identified differences across APOE genotypes primarily in glial cell types. Our findings highlight the differential transcriptomic perturbations of APOE isoforms at a single-cell level in AD pathogenesis and have implications for precision medicine development in the diagnosis and treatment of AD.

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Molecular phenotypic profiling of a Saccharomyces cerevisiae strain at the single-cell level

The Analyst ◽

10.1039/c4an01119h ◽

2014 ◽

Vol 139 (22) ◽

pp. 5709-5717 ◽

Cited By ~ 12

Author(s):

A. Mareike Schmidt ◽

Stephan R. Fagerer ◽

Konstantins Jefimovs ◽

Florian Buettner ◽

Christian Marro ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

Single Cell ◽

Single Cell Level ◽

Cell Heterogeneity ◽

Cell Level ◽

Cell Sensitivity

Studying cell-to-cell heterogeneity requires techniques which robustly deliver reproducible results with single-cell sensitivity.

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scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btz701 ◽

2019 ◽

Cited By ~ 2

Author(s):

Congting Ye ◽

Qian Zhou ◽

Xiaohui Wu ◽

Chen Yu ◽

Guoli Ji ◽

...

Keyword(s):

Single Cell ◽

Alternative Polyadenylation ◽

Cellular Heterogeneity ◽

Supplementary Information ◽

Rna Seq ◽

Computational Tool ◽

Cell Level ◽

Wilcoxon Rank Sum Test ◽

Transcriptional Regulatory ◽

Cell Groups

Abstract Motivation Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Cell-type-specific analysis of alternative polyadenylation using single-cell transcriptomics data

Nucleic Acids Research ◽

10.1093/nar/gkz781 ◽

2019 ◽

Vol 47 (19) ◽

pp. 10027-10039 ◽

Cited By ~ 11

Author(s):

Eldad David Shulman ◽

Ran Elkon

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Alternative Polyadenylation ◽

Cell Types ◽

Protein Coding ◽

Multiple Datasets ◽

Transcriptomics Data ◽

Cell Type Specific ◽

Transcriptional Gene Regulation ◽

Different Cell Types

AbstractAlternative polyadenylation (APA) is emerging as an important layer of gene regulation because the majority of mammalian protein-coding genes contain multiple polyadenylation (pA) sites in their 3′ UTR. By alteration of 3′ UTR length, APA can considerably affect post-transcriptional gene regulation. Yet, our understanding of APA remains rudimentary. Novel single-cell RNA sequencing (scRNA-seq) techniques allow molecular characterization of different cell types to an unprecedented degree. Notably, the most popular scRNA-seq protocols specifically sequence the 3′ end of transcripts. Building on this property, we implemented a method for analysing patterns of APA regulation from such data. Analyzing multiple datasets from diverse tissues, we identified widespread modulation of APA in different cell types resulting in global 3′ UTR shortening/lengthening and enhanced cleavage at intronic pA sites. Our results provide a proof-of-concept demonstration that the huge volume of scRNA-seq data that accumulates in the public domain offers a unique resource for the exploration of APA based on a very broad collection of cell types and biological conditions.

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Integrated profiling of single cell epigenomic and transcriptomic landscape of Parkinson’s disease mouse brain

10.1101/2020.02.04.933259 ◽

2020 ◽

Author(s):

Jixing Zhong ◽

Gen Tang ◽

Jiacheng Zhu ◽

Xin Qiu ◽

Weiying Wu ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Single Cell ◽

Early Stage ◽

Cell Types ◽

Cellular Heterogeneity ◽

Rna Seq ◽

Cell Level ◽

Distinct Cell ◽

Single Nucleus

AbstractParkinson’s disease (PD) is a neurodegenerative disease leading to the impairment of execution of movement. PD pathogenesis has been largely investigated, but either restricted in bulk level or at certain cell types, which failed to capture cellular heterogeneity and intrinsic interplays among distinct cell types. To overcome this, we applied single-nucleus RNA-seq and single cell ATAC-seq on cerebellum, midbrain and striatum of PD mouse and matched control. With 74,493 cells in total, we comprehensively depicted the dysfunctions under PD pathology covering proteostasis, neuroinflammation, calcium homeostasis and extracellular neurotransmitter homeostasis. Besides, by multi-omics approach, we identified putative biomarkers for early stage of PD, based on the relationships between transcriptomic and epigenetic profiles. We located certain cell types that primarily contribute to PD early pathology, narrowing the gap between genotypes and phenotypes. Taken together, our study provides a valuable resource to dissect the molecular mechanism of PD pathogenesis at single cell level, which could facilitate the development of novel methods regarding diagnosis, monitoring and practical therapies against PD at early stage.

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In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity

10.1101/2020.03.30.016576 ◽

2020 ◽

Author(s):

Maria Anna Rapsomaniki ◽

Stella Maxouri ◽

Manuel Ramirez Garrastacho ◽

Patroula Nathanailidou ◽

Nickolaos Nikiforos Giakoumakis ◽

...

Keyword(s):

Single Cell ◽

In Silico ◽

Complete Genome ◽

Population Level ◽

Yeast Genome ◽

Control Mechanisms ◽

Single Cell Level ◽

Genome Plasticity ◽

Cell Heterogeneity ◽

Cell Level

AbstractDNA replication is a complex and remarkably robust process: despite its inherent uncertainty, manifested through stochastic replication timing at a single-cell level, multiple control mechanisms ensure its accurate and timely completion across a population. Disruptions in these mechanisms lead to DNA re-replication, closely connected to genomic instability and oncogenesis. We present a stochastic hybrid model of DNA re-replication that accurately portrays the interplay between discrete dynamics, continuous dynamics, and uncertainty. Using experimental data on the fission yeast genome, model simulations show how different regions respond to re-replication, and permit insight into the key mechanisms affecting re-replication dynamics. Simulated and experimental population-level profiles exhibit good correlation along the genome, which is robust to model parameters, validating our approach. At a single-cell level, copy numbers of individual loci are affected by intrinsic properties of each locus, in cis effects from adjoining loci and in trans effects from distant loci. In silico analysis and single-cell imaging reveal that cell-to-cell heterogeneity is inherent in re-replication and can lead to a plethora of genotypic variations. Our thorough in silico analysis of DNA re-replication across a complete genome reveals that heterogeneity at the single cell level and robustness at the population level are emerging and co-existing principles of DNA re-replication. Our results indicate that re-replication can promote genome plasticity by generating many diverse genotypes within a population, potentially offering an evolutionary advantage in cells with aberrations in replication control mechanisms.

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Computational approaches towards reducing contamination in single-cell RNA-seq data

10.1101/2020.07.15.205062 ◽

2020 ◽

Author(s):

Siamak Yousefi ◽

Hao Chen ◽

Jesse F. Ingels ◽

Melinda S. McCarty ◽

Arthur G. Centeno ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Real Life ◽

Cell Types ◽

Cell Capture ◽

Rna Seq ◽

Sequence Analyses ◽

Cell Functions ◽

Biological Interpretation ◽

Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

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Synovial single-cell heterogeneity, zonation, and interactions: a patchwork of effectors in arthritis

Rheumatology ◽

10.1093/rheumatology/keab721 ◽

2021 ◽

Author(s):

Barbora Schonfeldova ◽

Kristina Zec ◽

Irina A Udalova

Keyword(s):

Rheumatoid Arthritis ◽

Single Cell ◽

Cell Signalling ◽

Synovial Cell ◽

Cellular Heterogeneity ◽

Cell Populations ◽

Single Cell Level ◽

Cell Heterogeneity ◽

Cell Level ◽

Peripheral Neurons

Abstract Despite extensive research, there is still no treatment that would lead to remission in all patients with rheumatoid arthritis as our understanding of the affected site, the synovium, is still incomplete. Recently, single-cell technologies helped to decipher the cellular heterogeneity of the synovium; however, certain synovial cell populations, such as endothelial cells or peripheral neurons, remain to be profiled on a single-cell level. Furthermore, associations between certain cellular states and inflammation were found; whether these cells cause the inflammation remains to be answered. Similarly, cellular zonation and interactions between individual effectors in the synovium are yet to be fully determined. A deeper understanding of cell signalling and interactions in the synovium is crucial for a better design of therapeutics with the goal of complete remission in all patients.

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Unfolding and identification of membrane proteins from native cell membranes

10.1101/732933 ◽

2019 ◽

Author(s):

Nicola Galvanetto ◽

Sourav Maity ◽

Nina Ilieva ◽

Zhongjie Ye ◽

Alessandro Laio ◽

...

Keyword(s):

Membrane Proteins ◽

Single Molecule ◽

Single Cells ◽

Total Content ◽

Cell Types ◽

Single Cell Level ◽

Cell Level ◽

Single Molecule Force Spectroscopy ◽

Native Cell ◽

Different Cell Types

AbstractIs the mechanical unfolding of proteins just a technological feat applicable only to synthetic preparations or can it provide useful information even for real biological samples? Here, we describe a pipeline for analyzing native membranes based on high throughput single-molecule force spectroscopy. The protocol includes a technique for the isolation of the plasma membrane of single cells. Afterwards, one harvests hundreds of thousands SMSF traces from the sample. Finally, one characterizes and identifies the embedded membrane proteins. This latter step is the cornerstone of our approach and involves combining, within a Bayesian framework, the information of the shape of the SMFS Force-distance which are observed more frequently, with the information from Mass Spectrometry and from proteomic databases (Uniprot, PDB). We applied this method to four cell types where we classified the unfolding of 5-10% of their total content of membrane proteins. The ability to mechanically probe membrane proteins directly in their native membrane enables the phenotyping of different cell types with almost single-cell level of resolution.

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Cross-Tissue Characterization of Heterogeneities of Mesenchymal Stem Cells and Their Differentiation Potentials

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.781021 ◽

2021 ◽

Vol 9 ◽

Author(s):

Wenhong Hou ◽

Li Duan ◽

Changyuan Huang ◽

Xingfu Li ◽

Xiao Xu ◽

...

Keyword(s):

Single Cell ◽

Umbilical Cord ◽

Stromal Cells ◽

Tissue Characterization ◽

Single Cell Level ◽

Rna Seq ◽

Cell Level ◽

Cell Sources ◽

Cell Subpopulations

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.

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