Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns

Many proteins or other biological macromolecules are localized to more than one subcellular structure. The fraction of a protein in different cellular compartments is often measured by colocalization with organelle-specific fluorescent markers, requiring availability of fluorescent probes for each c...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2010-02, Vol.107 (7), p.2944-2949
Main Authors: Peng, Tao, Bonamy, Ghislain M.C, Glory-Afshar, Estelle, Rines, Daniel R, Chanda, Sumit K, Murphy, Robert F
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biological Sciences
Cell cycle
Cell lines
Cellular biology
Datasets
Fluorescence
Fluorescent Dyes - metabolism
Geodetic position
HeLa cells
Image Processing, Computer-Assisted - methods
Intracellular Space - metabolism
Macromolecules
Microscopy
Outliers
Protein Transport - physiology
Proteins
Proteomics
Proteomics - methods
Studies
Training
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Summary:Many proteins or other biological macromolecules are localized to more than one subcellular structure. The fraction of a protein in different cellular compartments is often measured by colocalization with organelle-specific fluorescent markers, requiring availability of fluorescent probes for each compartment and acquisition of images for each in conjunction with the macromolecule of interest. Alternatively, tailored algorithms allow finding particular regions in images and quantifying the amount of fluorescence they contain. Unfortunately, this approach requires extensive hand-tuning of algorithms and is often cell type-dependent. Here we describe a machine-learning approach for estimating the amount of fluorescent signal in different subcellular compartments without hand tuning, requiring only the acquisition of separate training images of markers for each compartment. In testing on images of cells stained with mixtures of probes for different organelles, we achieved a 93% correlation between estimated and expected amounts of probes in each compartment. We also demonstrated that the method can be used to quantify drug-dependent protein translocations. The method enables automated and unbiased determination of the distributions of protein across cellular compartments, and will significantly improve imaging-based high-throughput assays and facilitate proteome-scale localization efforts.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0912090107