Dataset Information

MmSTORM: Multimodal localization based super-resolution microscopy.

ABSTRACT: Super-resolution localization microscopy provides a powerful tool to study biochemical mechanisms at single molecule level. Although the lateral position of the fluorescent dye molecules can be determined routinely with high precision, measurement of other modalities such as 3D and multicolor without the degradation of the original super-resolved image is still in the focus. In this paper a dual-objective multimodal single molecule localization microscopy (SMLM) technique has been developed, optimized and tested. The proposed optical arrangement can be implemented onto a conventional inverted microscope without serious system modification. The performance of the method was tested using fluorescence beads, F-actin filaments and sarcomere structures. It was shown that the proposed imaging method does not degrade the image quality of the original SMLM 2D image but could provide information on the axial position or emission spectra of the dye molecules.

SUBMITTER: Gajdos T

PROVIDER: S-EPMC6349879 | biostudies-other | 2019 Jan

REPOSITORIES: biostudies-other

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mmSTORM: Multimodal localization based super-resolution microscopy.

Gajdos Tamás T Cserteg Zsófia Z Szikora Szilárd S Novák Tibor T H Kovács Bálint Barna BB Szabó Gábor G Mihály József J Erdélyi Miklós M

Scientific reports 20190128 1

Super-resolution localization microscopy provides a powerful tool to study biochemical mechanisms at single molecule level. Although the lateral position of the fluorescent dye molecules can be determined routinely with high precision, measurement of other modalities such as 3D and multicolor without the degradation of the original super-resolved image is still in the focus. In this paper a dual-objective multimodal single molecule localization microscopy (SMLM) technique has been developed, opt ...[more]

PMID: 30692575

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Project description:BackgroundAutomated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Previous works have attempted to create broadly trained networks for universal nuclei segmentation; however, such networks do not work on all imaging modalities, and best results are still commonly found when the network is retrained on user specific data. Stochastic optical reconstruction microscopy (STORM) based super-resolution fluorescence microscopy has opened a new avenue to image nuclear architecture at nanoscale resolutions. Due to the large size and discontinuous features typical of super-resolution images, automatic nuclei segmentation can be difficult. In this study, we apply commonly used networks (Mask R-CNN and UNet architectures) towards the task of segmenting super-resolution images of nuclei. First, we assess whether networks broadly trained on conventional fluorescence microscopy datasets can accurately segment super-resolution images. Then, we compare the resultant segmentations with results obtained using networks trained directly on our super-resolution data. We next attempt to optimize and compare segmentation accuracy using three different neural network architectures.ResultsResults indicate that super-resolution images are not broadly compatible with neural networks trained on conventional bright-field or fluorescence microscopy images. When the networks were trained on super-resolution data, however, we attained nuclei segmentation accuracies (F1-Score) in excess of 0.8, comparable to past results found when conducting nuclei segmentation on conventional fluorescence microscopy images. Overall, we achieved the best results utilizing the Mask R-CNN architecture.ConclusionsWe found that convolutional neural networks are powerful tools capable of accurately and quickly segmenting localization-based super-resolution microscopy images of nuclei. While broadly trained and widely applicable segmentation algorithms are desirable for quick use with minimal input, optimal results are still found when the network is both trained and tested on visually similar images. We provide a set of Colab notebooks to disseminate the software into the broad scientific community ( https://github.com/YangLiuLab/Super-Resolution-Nuclei-Segmentation ).

Dataset Information

MmSTORM: Multimodal localization based super-resolution microscopy.

Publications

mmSTORM: Multimodal localization based super-resolution microscopy.

Similar Datasets

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