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A universal analysis tool for the detection of asymmetric signal distribution in microscopic images.

ABSTRACT: Polarization of tissue is achieved by asymmetric distribution of proteins and organelles within individual cells. However, existing quantitative assays to measure this asymmetry in an automated and unbiased manner suffer from significant limitations.Here, we report a new way to assess protein and organelle localization in tissue based on correlative fluorescence analysis. As a proof of principle, we successfully characterized planar cell polarity dependent asymmetry in developing Drosophila melanogaster tissues on the single cell level using fluorescence cross-correlation.Systematic modulation of signal strength and distribution show that fluorescence cross-correlation reliably detects asymmetry over a broad parameter space. The novel method described here produces robust, rapid, and unbiased measurement of biometrical properties of cell components in live tissue that is readily applicable in other model systems.

SUBMITTER: Matis M

PROVIDER: S-EPMC3469164 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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A universal analysis tool for the detection of asymmetric signal distribution in microscopic images.

Matis Maja M Axelrod Jeffrey D JD Galic Milos M

Developmental dynamics : an official publication of the American Association of Anatomists 20120623 8

<h4>Background</h4>Polarization of tissue is achieved by asymmetric distribution of proteins and organelles within individual cells. However, existing quantitative assays to measure this asymmetry in an automated and unbiased manner suffer from significant limitations.<h4>Results</h4>Here, we report a new way to assess protein and organelle localization in tissue based on correlative fluorescence analysis. As a proof of principle, we successfully characterized planar cell polarity dependent asym ...[more]

PMID: 22689329

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Project description:BackgroundMalaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria.MethodA method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives.ResultsThe image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites.ConclusionImage based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method provides a consistent and robust way of generating the parasite clearance curves.

Dataset Information

A universal analysis tool for the detection of asymmetric signal distribution in microscopic images.

Publications

A universal analysis tool for the detection of asymmetric signal distribution in microscopic images.

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