Disease specific cellular deconvolution: Re-analyzing bulk RNA sequencing data to assess treatment response in Ulcerative Colitis

Aktivität: Vortrag ohne Tagungsband / VorlesungPräsentation auf einer wissenschaftlichen Konferenz / Workshop


Cellular deconvolution is the process of analyzing omics data, such as bulk RNA sequencing data (RNAseq), from a mixture of cell populations and determining their relative proportions. It references data from methods such as single cell RNA sequencing (scRNAseq) and mass cytometry to identify individual cell proportions in heterogenous tissue samples, which are classified using machine learning (ML) algorithms. We show how we leverage publicly available scRNAseq data to design disease specific references for the deconvolution of bulk RNAseq data from ulcerative colitis (UC) samples. By applying this approach, we contribute to the understanding of cell type-specific responses to medical treatments, such as vedolizumab, infliximab and adalimumab. Certain medications have different effects on different cell types in a tissue. By deconvolving a tissue sample and analyzing drug-induced changes in cell type proportions, we are able to identify potential therapeutic targets and predict which patients may benefit from a given treatment. Further, this procedure can be applied to a myriad of purposes such as identifying different immune cell types within a tumor and studying the mechanisms of immune evasion, discovering potential targets for immunotherapy, or revealing changes in cell populations to understand disease progression over time. This powerful in-silico approach for dissecting the complexity of previously sequenced tissue samples opens the door for new discoveries and a more nuanced understanding of cellular interactions and functions based on already existing data.
Zeitraum25 Sept. 2023
EreignistitelAustrian Bioinformatics Workshop 2023: Computational Biology, Artificial Intelligence and Data Science
OrtHagenberg, ÖsterreichAuf Karte anzeigen

Research Field

  • Molecular Diagnostics


  • Bioinformatics
  • Bioinformatik
  • Machine Learning
  • Transcriptomic
  • scRNAseq
  • cell deconvolution
  • health
  • Molecular marker
  • cellular data