Dataset Information

RAMTaB: robust alignment of multi-tag bioimages.

ABSTRACT:

Background

In recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images.

Methodology/principal findings

We employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies.

Conclusions

For the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.

SUBMITTER: Raza SE

PROVIDER: S-EPMC3280195 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

RAMTaB: robust alignment of multi-tag bioimages.

Raza Shan-e-Ahmed SE Humayun Ahmad A Abouna Sylvie S Nattkemper Tim W TW Epstein David B A DB Khan Michael M Rajpoot Nasir M NM

PloS one 20120208 2

<h4>Background</h4>In recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images ...[more]

PMID: 22363510

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Dataset Information

RAMTaB: robust alignment of multi-tag bioimages.

Background

Methodology/principal findings

Conclusions

Publications

RAMTaB: robust alignment of multi-tag bioimages.

Similar Datasets

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