Uncovering distinct protein-network topologies in heterogeneous cell populations

• Autores: Wieczorek, Jakob; Malik Sheriff, Rahuman S.; Fermin, Yessica; Grecco, Hernan Edgardo; Zamir, Eli; Ickstadt, Katja
• Año de publicación: 2015
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: BAYESIAN ANALYSIS; CLUSTER ANALYSIS; INTERCELLULAR VARIABILITY; NETWORK ANALYSIS; PROTEIN NETWORKS; REVERSE ENGINEERING; UNMIXING
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Background: Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation. Results: Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins. Conclusions: Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.
Fil: Wieczorek, Jakob. Universitat Dortmund; Alemania
Fil: Malik Sheriff, Rahuman S.. Institut Max Planck fur Molekulare Physiologie; Alemania. Imperial College London; Reino Unido. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
Fil: Fermin, Yessica. Universitat Dortmund; Alemania
Fil: Grecco, Hernan Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Institut Max Planck fur Molekulare Physiologie; Alemania
Fil: Zamir, Eli. Institut Max Planck fur Molekulare Physiologie; Alemania
Fil: Ickstadt, Katja. Universitat Dortmund; Alemania