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ScATAC-seq and scRNA-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Mus musculus

PROVIDER: GSE198567 | GEO | 2022/07/26

REPOSITORIES: GEO

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scATAC-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

Project description:We performed scATAC-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO). We dissected primarily the cardiac region but also the regions dorsal to the heart and the pharyngeal arches to capture the progenitor cells that migrate into the heart and the neural crest cells. For time points E10.5 and E11.5, we primarily dissected the regions behind the heart and included less of the overall cardiac region. We included pharyngeal arches for all time points and, at E11.5, we excluded the first arch. For Tbx1 null embryos, we also generated scRNA-seq data for WT and mutant embryos from the same pool of dissociated cells used for scATAC-seq.

2022-07-27 | GSE198561 | GEO

scRNA-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

Project description:We performed scRNA-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO). We dissected primarily the cardiac region but also the regions dorsal to the heart and the pharyngeal arches to capture the progenitor cells that migrate into the heart and the neural crest cells. For time points E10.5 and E11.5, we primarily dissected the regions behind the heart and included less of the overall cardiac region. We included pharyngeal arches for all time points and, at E11.5, we excluded the first arch. For Tbx1 null embryos, we also generated scRNA-seq data for WT and mutant embryos from the same pool of dissociated cells used for scATAC-seq.

2022-07-27 | GSE198562 | GEO

scATAC-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

Project description:scATAC-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

| PRJNA815971 | ENA

scATAC-seq and scRNA-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

Project description:scATAC-seq and scRNA-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

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scRNA-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

Project description:scRNA-seq over multiple time points of heart development in WT C57Bl6/J embryos and in Tbx1 mutant mice (Tbx1 KO)

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scNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization.

Project description:MotivationscATAC-seq has enabled chromatin accessibility landscape profiling at the single-cell level, providing opportunities for determining cell-type-specific regulation codes. However, high dimension, extreme sparsity, and large scale of scATAC-seq data have posed great challenges to cell-type identification. Thus, there has been a growing interest in leveraging the well-annotated scRNA-seq data to help annotate scATAC-seq data. However, substantial computational obstacles remain to transfer information from scRNA-seq to scATAC-seq, especially for their heterogeneous features.ResultsWe propose a new transfer learning method, scNCL, which utilizes prior knowledge and contrastive learning to tackle the problem of heterogeneous features. Briefly, scNCL transforms scATAC-seq features into gene activity matrix based on prior knowledge. Since feature transformation can cause information loss, scNCL introduces neighborhood contrastive learning to preserve the neighborhood structure of scATAC-seq cells in raw feature space. To learn transferable latent features, scNCL uses a feature projection loss and an alignment loss to harmonize embeddings between scRNA-seq and scATAC-seq. Experiments on various datasets demonstrated that scNCL not only realizes accurate and robust label transfer for common types, but also achieves reliable detection of novel types. scNCL is also computationally efficient and scalable to million-scale datasets. Moreover, we prove scNCL can help refine cell-type annotations in existing scATAC-seq atlases.Availability and implementationThe source code and data used in this paper can be found in https://github.com/CSUBioGroup/scNCL-release.

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scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously.

Project description:It is a challenging task to integrate scRNA-seq and scATAC-seq data obtained from different batches. Existing methods tend to use a pre-defined gene activity matrix to convert the scATAC-seq data into scRNA-seq data. The pre-defined gene activity matrix is often of low quality and does not reflect the dataset-specific relationship between the two data modalities. We propose scDART, a deep learning framework that integrates scRNA-seq and scATAC-seq data and learns cross-modalities relationships simultaneously. Specifically, the design of scDART allows it to preserve cell trajectories in continuous cell populations and can be applied to trajectory inference on integrated data.

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility using scRNA-Seq and scATAC-Seq.

Project description:Powerful next generation sequencing techniques offer robust and comprehensive analysis to investigate how retinal gene regulatory networks function during development and in disease states. Single-cell RNA sequencing allows us to comprehensively profile gene expression changes observed in retinal development and disease at a cellular level, while single-cell ATAC-Seq allows analysis of chromatin accessibility and transcription factor binding to be profiled at similar resolution. Here the use of these techniques in the developing retina is described, and MULTI-Seq is demonstrated, where individual samples are labeled with a modified oligonucleotide-lipid complex, enabling researchers to both increase the scope of individual experiments and substantially reduce costs.

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Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps.

Project description:Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.

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Integrative scATAC-seq and scRNA-seq analyses map thymic iNKT cell development and identify Cbfβ for its commitment.

Project description:Unlike conventional αβT cells, invariant natural killer T (iNKT) cells complete their terminal differentiation to functional iNKT1/2/17 cells in the thymus. However, underlying molecular programs that guide iNKT subset differentiation remain unclear. Here, we profiled the transcriptomes of over 17,000 iNKT cells and the chromatin accessibility states of over 39,000 iNKT cells across four thymic iNKT developmental stages using single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to define their developmental trajectories. Our study discovered novel features for iNKT precursors and different iNKT subsets and indicated that iNKT2 and iNKT17 lineage commitment may occur as early as stage 0 (ST0) by two distinct programs, while iNKT1 commitments may occur post ST0. Both iNKT1 and iNKT2 cells exhibit extensive phenotypic and functional heterogeneity, while iNKT17 cells are relatively homogenous. Furthermore, we identified that a novel transcription factor, Cbfβ, was highly expressed in iNKT progenitor commitment checkpoint, which showed a similar expression trajectory with other known transcription factors for iNKT cells development, Zbtb16 and Egr2, and could direct iNKT cells fate and drive their effector phenotype differentiation. Conditional deletion of Cbfβ blocked early iNKT cell development and led to severe impairment of iNKT1/2/17 cell differentiation. Overall, our findings uncovered distinct iNKT developmental programs as well as their cellular heterogeneity, and identified a novel transcription factor Cbfβ as a key regulator for early iNKT cell commitment.

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