Browsing by Subject "Single-Cell Analysis"

Now showing 1 - 4 of 4
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    Early dynamic fate changes in haemogenic endothelium characterized at the single-cell level
    (Nature Publishing Group, 2013) Swiers, Gemma; Baumann, Claudia; O'Rourke, John; Giannoulatou, Eleni; Taylor, Stephen; Joshi, Anagha; Moignard, Victoria; Pina, Cristina; Bee, Thomas; Kokkaliaris, Konstantinos D.; Yoshimoto, Momoko; Yoder, Mervin C.; Frampton, Jon; Schroeder, Timm; Enver, Tariq; Göttgens, Berthold; de Bruijn, Marella F. T. R.; Department of Pediatrics, IU School of Medicine
    Haematopoietic stem cells (HSCs) are the founding cells of the adult haematopoietic system, born during ontogeny from a specialized subset of endothelium, the haemogenic endothelium (HE) via an endothelial-to-haematopoietic transition (EHT). Although recently imaged in real time, the underlying mechanism of EHT is still poorly understood. We have generated a Runx1 + 23 enhancer-reporter transgenic mouse (23GFP) for the prospective isolation of HE throughout embryonic development. Here we perform functional analysis of over 1,800 and transcriptional analysis of 268 single 23GFP+ HE cells to explore the onset of EHT at the single-cell level. We show that initiation of the haematopoietic programme occurs in cells still embedded in the endothelial layer, and is accompanied by a previously unrecognized early loss of endothelial potential before HSCs emerge. Our data therefore provide important insights on the timeline of early haematopoietic commitment.
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    IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq
    (Oxford, 2020-05-18) Ma, Anjun; Wang, Cankun; Chang, Yuzhou; Brennan, Faith H; McDermaid, Adam; Liu, Bingqiang; Zhang, Chi; Popovich, Phillip G; Ma, Qin; Medical and Molecular Genetics, School of Medicine
    group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development.
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    LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
    (Oxford University Press, 2019-10-10) Wan, Changlin; Chang, Wennan; Zhang, Yu; Shah, Fenil; Lu, Xiaoyu; Zang, Yong; Zhang, Anru; Cao, Sha; Fishel, Melissa L.; Ma, Qin; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.
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    Monitoring focal adhesion kinase phosphorylation dynamics in live cells
    (Royal Society of Chemistry, 2017-07-24) Damayanti, Nur P.; Buno, Kevin; Narayanan, Nagarajan; Harbin, Sherry L Voytik; Deng, Meng; Irudayaraj, Joseph M.K.; Medicine, School of Medicine
    Focal adhesion kinase (FAK) is a cytoplasmic non-receptor tyrosine kinase essential for a diverse set of cellular functions. Current methods for monitoring FAK activity in response to an extracellular stimulus lack spatiotemporal resolution and/or the ability to perform multiplex detection. Here we report on a novel approach to monitor the real-time kinase phosphorylation activity of FAK in live single cells by fluorescence lifetime imaging.