scholarly journals ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux
Keyword(s):  
Big Data ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Omics Data ◽  
Cell Analysis ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Data Analyses ◽  
The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

scholarly journals Single-cell analysis identifies a key role for Hhip in murine coronal suture development

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Greg Holmes ◽  
Ana S. Gonzalez-Reiche ◽  
Madrikha Saturne ◽  
Susan M. Motch Perrine ◽  
Xianxiao Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Hedgehog Signaling ◽  
Bone Growth ◽  
Single Cell Analysis ◽  
Coronal Suture ◽  
Cell Analysis ◽  
Rna Seq ◽  
Wild Type ◽  
Calvarial Bone ◽  
Osteogenic Cells

AbstractCraniofacial development depends on formation and maintenance of sutures between bones of the skull. In sutures, growth occurs at osteogenic fronts along the edge of each bone, and suture mesenchyme separates adjacent bones. Here, we perform single-cell RNA-seq analysis of the embryonic, wild type murine coronal suture to define its population structure. Seven populations at E16.5 and nine at E18.5 comprise the suture mesenchyme, osteogenic cells, and associated populations. Expression of Hhip, an inhibitor of hedgehog signaling, marks a mesenchymal population distinct from those of other neurocranial sutures. Tracing of the neonatal Hhip-expressing population shows that descendant cells persist in the coronal suture and contribute to calvarial bone growth. In Hhip−/− coronal sutures at E18.5, the osteogenic fronts are closely apposed and the suture mesenchyme is depleted with increased hedgehog signaling compared to those of the wild type. Collectively, these data demonstrate that Hhip is required for normal coronal suture development.

scholarly journals Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Xinbing Liu ◽  
Wei Gao ◽  
Wei Liu
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Regulatory Networks ◽  
Single Cell Analysis ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulator ◽  
Cell Analysis ◽  
Rna Seq ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

Background. To further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out. Methods. The mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset. Results. A total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset. Conclusions. This study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

scholarly journals Single Cell Sequencing Reveals Early PGC-like Intermediates During Mouse Primed to Naïve Transition

2021 ◽  
Author(s):  
Jing Liu ◽  
Shengyong Yu ◽  
Chunhua Zhou ◽  
Jiangping He ◽  
Xingnan Huang ◽  
...  
Keyword(s):  
Germ Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cell Analysis ◽  
Primordial Germ Cell ◽  
Model System ◽  
Cell Analysis ◽  
Rna Seq ◽  
Single Cell Sequencing

Abstract Single cell analysis provides clarity unattainable with bulk approaches. Here we apply single cell RNA-seq to a newly established BMP4 induced mouse primed to naive transition (Bi-PNT) system and show that the reset is not a direct reversal of cell fate but through developmental intermediates. We first show that mEpiSCs bifurcate into c-Kit+ naïve and c-Kit- placenta-like cells, among which, the naive branch undergoes further transition through a primordial germ cell-like cells (PGCLCs) intermediate capable of spermatogenesis in vivo. Indeed, deficiency of Prdm1/Blimp1, the key regulator for PGC specification, blocks the induction of PGCLCs and naïve cells. Instead, Gata2 knockout arrests placenta-like fate, but facilitates the generation of PGCLCs. Our results not only reveal a newly cell fate dynamics between primed and naive states at single-cell resolution, but also provide a model system to explore mechanisms involved in regaining germline competence from primed pluripotency.

scholarly journals SIMBA: SIngle-cell eMBedding Along with features

2021 ◽  
Author(s):  
Huidong Chen ◽  
Jayoung Ryu ◽  
Michael Edward Vinyard ◽  
Adam Lerer ◽  
Luca Pinello
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Dna Sequences ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Omics Data ◽  
Cell Analysis ◽  
Genomic Features ◽  
The Individual

Recent advances in single cell omics technologies enable the individual or joint profiling of cellular measurements including gene expression, epigenetic features, chromatin structure and DNA sequences. Currently, most single-cell analysis pipelines are cluster-centric, i.e., they first cluster cells into non-overlapping cellular states and then extract their defining genomic features. These approaches assume that discrete clusters correspond to biologically relevant subpopulations and do not explicitly model the interactions between different feature types. However, cellular processes are defined in individual cells and inherently involve multiple genomic features that interact with each other and together provide complementary views on principles of gene regulation. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a single-cell embedding method that embeds single cells along with their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for cellular heterogeneity study, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a common way and thus simplifies the development of new analyses and integration of other single-cell modalities.

scholarly journals SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

2017 ◽  
Author(s):  
Bo Wang ◽  
Daniele Ramazzotti ◽  
Luca De Sano ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Cell Analysis ◽  
Rna Seq ◽  
A Cell ◽  
Supplementary Material ◽  
Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals ASAP: A web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

2016 ◽  
Author(s):  
Vincent Gardeux ◽  
Fabrice David ◽  
Adrian Shajkofci ◽  
Petra C Schwalie ◽  
Bart Deplancke
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Complete Analysis ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Web Based ◽  
Wide Range

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet, these groups often lack the expertise to handle complex scRNA-seq data sets.ResultsWe developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering, and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types.AvailabilityThe tool is freely available at http://[email protected]

scholarly journals Computational cell cycle analysis of single cell RNA-Seq data

2020 ◽  
Author(s):  
Marmar Moussa ◽  
Ion I. Măndoiu
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Analysis Tool ◽  
Rna Seq ◽  
Cell Type ◽  
Computational Cell ◽  
Analysis Pipeline ◽  
Web Based

AbstractThe variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, Gene Smoothness Score (GSS) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.

scholarly journals An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq

2016 ◽  
Author(s):  
Jinzhou Yuan ◽  
Peter A. Sims
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Cell Capture ◽  
Rna Seq ◽  
Fluorescent Cell ◽  
Recent Developments ◽  
Improved Performance

Recent developments have enabled rapid, inexpensive RNA sequencing of thousands of individual cells from a single specimen, raising the possibility of unbiased and comprehensive expression profiling from complex tissues. Microwell arrays are a particularly attractive microfluidic platform for single cell analysis due to their scalability, cell capture efficiency, and compatibility with imaging. We report an automated microwell array platform for single cell RNA-Seq with significantly improved performance over previous implementations. We demonstrate cell capture efficiencies of >50%, compatibility with commercially available barcoded mRNA capture beads, and parallel expression profiling from thousands of individual cells. We evaluate the level of cross-contamination in our platform by both tracking fluorescent cell lysate in sealed microwells and with a human-mouse mixed species RNA-Seq experiment. Finally, we apply our system to comprehensively assess heterogeneity in gene expression of patient-derived glioma neurospheres and uncover subpopulations similar to those observed in human glioma tissue.

scholarly journals Single-cell analysis of early chick hypothalamic development reveals that hypothalamic cells are induced from prethalamic-like progenitors.

2021 ◽  
Author(s):  
Dong Won Kim ◽  
Elsie Place ◽  
Kavitha Chinnaiya ◽  
Elizabeth Manning ◽  
Changyu Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Developmental Stages ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Proof Of Concept ◽  
Rna Seq ◽  
Neuroepithelial Cells ◽  
Hypothalamic Cells ◽  
Innate Behaviors

The hypothalamus is an evolutionarily ancient brain region that regulates many innate behaviors, but its development is still poorly understood. To identify molecular mechanisms controlling hypothalamic specification and patterning, we used single-cell RNA-Seq to profile multiple stages of early hypothalamic development in the chick. We observe that hypothalamic neuroepithelial cells are initially induced from prethalamic-like cells. Two distinct hypothalamic progenitor populations emerge later, which give rise to paraventricular/mammillary and tuberal hypothalamus, respectively. At later developmental stages, the regional organization of the chick and mouse hypothalamus closely resembles one another. This study identifies selective markers for major subdivisions of the developing chick hypothalamus and many uncharacterized candidate regulators of hypothalamic patterning and neurogenesis. As proof of concept for the utility of the dataset, we demonstrate that prethalamic progenitor-derived follistatin inhibits hypothalamic induction. This study both clarifies the organization of the early developing hypothalamus and identifies novel molecular mechanisms controlling hypothalamic induction, regionalization, and neurogenesis.

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