Séminaire grenoblois de l'IXXI
de 14:00 à 15:00
|Inria Grenoble - Rhône-Alpes, salle F107 (dans la zone badgée, il faut se présenter à l'accueil avant)
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Cell-to-cell heterogeneity unraveled by computational analysis of single-cell mass cytometry data: cell cycle patterns and trajectories
Advanced single-cell approaches like mass cytometry (CyTOF) allow the simultaneous quantification of dozens of proteins at a single-cell level and, with the use of multiplexing techniques, are able to yield high-dimensional data from a variety of different experimental conditions. When used in cancer research, CyTOF data enable us to study the effect of the inhibition or activation of essential pathways, to characterize inter- and intra-tumor heterogeneity, or to identify cells subpopulations associated to different stages of tumor progression.
However, as already demonstrated for the case of single-cell RNAseq data, single-cell technologies are largely influenced by confounding factors, with the most dominant one being the cell cycle-induced variability. Here we present a computational approach to account for this hidden source of variability and unravel subpopulations characterized by different signaling fingerprints. Our method consists of a discrete cell cycle phase classification step based on decision trees and orders single cells on a continuum based on their cell cycle progression, using an embedding trajectory reconstruction technique. Our approach allows thus the deconvolution of cell-cycle effects and perturbation-induced signaling responses, enabling a more accurate classification of cellular populations uniquely based on their signaling signature.
Marianna Rapsomaniki is a computational scientist with a Diploma in Computer Engineering and Informatics and a Master's in Bioinformatics from the University of Patras, Greece. Her PhD was completed in 2014, working jointly in the Cell Cycle Laboratory of the University of Patras and as a visiting scientist at the Automatic Control Lab of ETH Zurich. During her PhD research, she worked on stochastic hybrid modeling of biological systems, with applications in modeling of protein diffusion and binding within the nucleus, modeling of DNA replication and re-replication and parameter inference from experimental microscopy data. Since May 2015 she is a post-doctoral researcher in the Systems Biology team of IBM Research, Zurich, working on the mathematical modeling of tumor heterogeneity during progression to metastases. Her general research interests include Computational Systems Biology, Machine Learning and Data Mining.