Dataset Information

Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry.

ABSTRACT: Modern microscopic imaging devices are able to extract more information regarding the subcellular organization of different molecules and proteins than can be obtained by visual inspection. Predetermined numerical features (descriptors) often used to quantify cells extracted from these images have long been shown useful for discriminating cell populations (e.g., normal vs. diseased). Direct visual or biological interpretation of results obtained, however, is often not a trivial task. We describe an approach for detecting and visualizing phenotypic differences between classes of cells based on the theory of optimal mass transport. The method is completely automated, does not require the use of predefined numerical features, and at the same time allows for easily interpretable visualizations of the most significant differences. Using this method, we demonstrate that the distribution pattern of peripheral chromatin in the nuclei of cells extracted from liver and thyroid specimens is associated with malignancy. We also show the method can correctly recover biologically interpretable and statistically significant differences in translocation imaging assays in a completely automated fashion.

SUBMITTER: Basu S

PROVIDER: S-EPMC3948221 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Publications

Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry.

Basu Saurav S Kolouri Soheil S Rohde Gustavo K GK

Proceedings of the National Academy of Sciences of the United States of America 20140218 9

Modern microscopic imaging devices are able to extract more information regarding the subcellular organization of different molecules and proteins than can be obtained by visual inspection. Predetermined numerical features (descriptors) often used to quantify cells extracted from these images have long been shown useful for discriminating cell populations (e.g., normal vs. diseased). Direct visual or biological interpretation of results obtained, however, is often not a trivial task. We describe ...[more]

PMID: 24550445

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Project description:Motivation:Fluorescence localization microscopy is extensively used to study the details of spatial architecture of subcellular compartments. This modality relies on determination of spatial positions of fluorophores, labeling an extended biological structure, with precision exceeding the diffraction limit. Several established models describe influence of pixel size, signal-to-noise ratio and optical resolution on the localization precision. The labeling density has been also recognized as important factor affecting reconstruction fidelity of the imaged biological structure. However, quantitative data on combined influence of sampling and localization errors on the fidelity of reconstruction are scarce. It should be noted that processing localization microscopy data is similar to reconstruction of a continuous (extended) non-periodic signal from a non-uniform, noisy point samples. In two dimensions the problem may be formulated within the framework of matrix completion. However, no systematic approach has been adopted in microscopy, where images are typically rendered by representing localized molecules with Gaussian distributions (widths determined by localization precision). Results:We analyze the process of two-dimensional reconstruction of extended biological structures as a function of the density of registered emitters, localization precision and the area occupied by the rendered localized molecule. We quantify overall reconstruction fidelity with different established image similarity measures. Furthermore, we analyze the recovered similarity measure in the frequency space for different reconstruction protocols. We compare the cut-off frequency to the limiting sampling frequency, as determined by labeling density. Availability and implementation:The source code used in the simulations along with test images is available at https://github.com/blazi13/qbioimages. Contact:bruszczy@nencki.gov.pl or t.bernas@nencki.gov.pl. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry.

Publications

Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry.

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