Your browser doesn't support javascript.
loading
Linking Expression of Cell-Surface Receptors with Transcription Factors by Computational Analysis of Paired Single-Cell Proteomes and Transcriptomes.
Sagan, April; Ma, Xiaojun; Ramjattun, Koushul; Osmanbeyoglu, Hatice Ulku.
Affiliation
  • Sagan A; Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
  • Ma X; UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA.
  • Ramjattun K; Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
  • Osmanbeyoglu HU; UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA.
Methods Mol Biol ; 2660: 149-169, 2023.
Article in En | MEDLINE | ID: mdl-37191796
ABSTRACT
Complex signaling and transcriptional programs control the development and physiology of specialized cell types. Genetic perturbations in these programs cause human cancers to arise from a diverse set of specialized cell types and developmental states. Understanding these complex systems and their potential to drive cancer is critical for the development of immunotherapies and druggable targets. Pioneering single-cell multi-omics technologies that analyze transcriptional states have been coupled with the expression of cell-surface receptors. This chapter describes SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network), a computational framework, to link transcription factors with cell-surface protein expression. SPaRTAN uses CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) data and cis-regulatory sites to model the effect of interactions between transcription factors and cell-surface receptors on gene expression. We demonstrate the pipeline for SPaRTAN using CITE-seq data from peripheral blood mononuclear cells.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteome / Transcriptome Type of study: Prognostic_studies Limits: Humans Language: En Journal: Methods Mol Biol Journal subject: BIOLOGIA MOLECULAR Year: 2023 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteome / Transcriptome Type of study: Prognostic_studies Limits: Humans Language: En Journal: Methods Mol Biol Journal subject: BIOLOGIA MOLECULAR Year: 2023 Document type: Article Affiliation country: United States