Single-Cell Transcriptome Analysis Identifies Ligand-Receptor Pairs Associated With BCP-ALL Prognosis.
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ABSTRACT:
SUBMITTER: Wu L
PROVIDER: S-EPMC7987943 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Interactions between different cell types are critical for a plethora of biological processes, such as the immune response. We recently developed a novel technology, called LIPSTIC (labeling of immune partnership by SorTagging intercellular contacts), that allows for identifying cells undergoing specific interactions thanks to an enzymatic labeling reaction. Our work demonstrated the use of this technology to monitor interactions between immune cells, both in vitro and in vivo, by the genetic engineering of CD40 and CD40L, an essential costimulatory axis between antigen-presenting cells and T cells. Here we describe protocols to design novel LIPSTIC-engineered ligand and receptor pairs, clone constructs into retroviral expression vector, perform their initial validation, and use them to measure interactions ex vivo. This information will be useful to investigators interested in exploiting the LIPSTIC technology to track their favorite immune interaction. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Design of LIPSTIC-engineered ligand and receptor pairs Basic Protocol 2: Cloning of LIPSTIC-engineered ligand and receptor pairs Basic Protocol 3: Validation of LIPSTIC-engineered ligand and receptor pairs in 293T cells Basic Protocol 4: Measuring interaction with LIPSTIC in immune cells ex vivo.
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Project description:BackgroundRegulation of cellular events is, often, initiated via extracellular signaling. Extracellular signaling occurs when a circulating ligand interacts with one or more membrane-bound receptors. Identification of receptor-ligand pairs is thus an important and specific form of PPI prediction.ResultsGiven a set of disparate data sources (expression data, domain content, and phylogenetic profile) we seek to predict new receptor-ligand pairs. We create a combined kernel classifier and assess its performance with respect to the Database of Ligand-Receptor Partners (DLRP) 'golden standard' as well as the method proposed by Gertz et al. Among our findings, we discover that our predictions for the tgfβ family accurately reconstruct over 76% of the supported edges (0.76 recall and 0.67 precision) of the receptor-ligand bipartite graph defined by the DLRP "golden standard". In addition, for the tgfβ family, the combined kernel classifier is able to relatively improve upon the Gertz et al. work by a factor of approximately 1.5 when considering that our method has an F-measure of 0.71 while that of Gertz et al. has a value of 0.48.ConclusionsThe prediction of receptor-ligand pairings is a difficult and complex task. We have demonstrated that using kernel learning on multiple data sources provides a stronger alternative to the existing method in solving this task.
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Project description:Cell-to-cell interactions (CCIs) through ligand-receptor (LR) pairs in the tumor microenvironment underlie the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). However, there is scant knowledge of the association of CCIs with PDAC prognosis, which is critical to the identification of potential therapeutic candidates. Here, we sought to identify the LR pairs associated with PDAC patient prognosis by integrating survival analysis and single-cell CCI prediction. Via survival analysis using gene expression from cancer cohorts, we found 199 prognostic LR pairs. CCI prediction based on single-cell RNA-seq data revealed the enriched LR pairs associated with poor prognosis. Notably, the CCIs involved epithelial tumor cells, cancer-associated fibroblasts, and tumor-associated macrophages through integrin-related and ANXA1-FPR pairs. Finally, we determined that CCIs involving 33 poor-prognostic LR pairs were associated with tumor grade. Although the clinical implication of the set of LR pairs must be determined, our results may provide potential therapeutic targets in PDAC.
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2023-06-06 | GSE213245 | GEO
Project description:MotivationIntracellular communication is crucial to many biological processes, such as differentiation, development, homeostasis and inflammation. Single-cell transcriptomics provides an unprecedented opportunity for studying cell-cell communications mediated by ligand-receptor interactions. Although computational methods have been developed to infer cell type-specific ligand-receptor interactions from one single-cell transcriptomics profile, there is lack of approaches considering ligand and receptor simultaneously to identifying dysregulated interactions across conditions from multiple single-cell profiles.ResultsWe developed scLR, a statistical method for examining dysregulated ligand-receptor interactions between two conditions. scLR models the distribution of the product of ligands and receptors expressions and accounts for inter-sample variances and small sample sizes. scLR achieved high sensitivity and specificity in simulation studies. scLR revealed important cytokine signaling between macrophages and proliferating T cells during severe acute COVID-19 infection, and activated TGF-β signaling from alveolar type II cells in the pathogenesis of pulmonary fibrosis.Availability and implementationscLR is freely available at https://github.com/cyhsuTN/scLR.Supplementary informationSupplementary data are available at Bioinformatics online.
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