Uncovering distinct protein-network topologies in heterogeneous cell populations

BMC Systems Biology

Katja Ickstadt¹  Eli Zamir²  Hernán E Grecco⁴  Yessica Fermin¹  Rahuman S Malik-Sheriff³  Jakob Wieczorek¹ 

[1]Faculty of Statistics, TU Dortmund University, Dortmund, Germany

[2]Department of Systemic Cell Biology, Max-Planck Institute of Molecular Physiology, Dortmund, Germany

[3]Present address: MRC Clinical Sciences Centre, Imperial College London, London, UK

[4]Present address: Department of Physics, FCEN, University of Buenos Aires and IFIBA, CONICET, Buenos Aires, Argentina

关键词: Unmixing;    Reverse engineering;    Protein networks;    Network analysis;    Intercellular variability;    Cluster analysis;    Bayesian analysis;   

Others  :  1233565
DOI  :  10.1186/s12918-015-0170-2

 received in 2014-11-15, accepted in 2015-05-18,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Wieczorek et al.

【 图 表 】

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