scholarly journals Rapid bacteria identification using structured illumination microscopy and machine learning

2017 ◽  
Vol 11 (01) ◽  
pp. 1850007 ◽  
Author(s):  
Yingchuan He ◽  
Weize Xu ◽  
Yao Zhi ◽  
Rohit Tyagi ◽  
Zhe Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Microscopy ◽  
Pathogenic Bacteria ◽  
Morphological Features ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Standard Pattern ◽  
Bacteria Identification ◽  
Detection And Identification ◽  
Combined Use

Traditionally, optical microscopy is used to visualize the morphological features of pathogenic bacteria, of which the features are further used for the detection and identification of the bacteria. However, due to the resolution limitation of conventional optical microscopy as well as the lack of standard pattern library for bacteria identification, the effectiveness of this optical microscopy-based method is limited. Here, we reported a pilot study on a combined use of Structured Illumination Microscopy (SIM) with machine learning for rapid bacteria identification. After applying machine learning to the SIM image datasets from three model bacteria (including Escherichia coli, Mycobacterium smegmatis, and Pseudomonas aeruginosa), we obtained a classification accuracy of up to 98%. This study points out a promising possibility for rapid bacterial identification by morphological features.

scholarly journals Quantification of cristae architecture reveals time-dependent characteristics of individual mitochondria

2020 ◽  
Vol 3 (7) ◽  
pp. e201900620
Author(s):  
Mayuko Segawa ◽  
Dane M Wolf ◽  
Nan W Hultgren ◽  
David S Williams ◽  
Alexander M van der Bliek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mitochondrial Fission ◽  
Live Cell ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Fusion Event ◽  
New Strategy ◽  
Function Relationship ◽  
Relationship Of ◽  
Standard Techniques

Recent breakthroughs in live-cell imaging have enabled visualization of cristae, making it feasible to investigate the structure–function relationship of cristae in real time. However, quantifying live-cell images of cristae in an unbiased way remains challenging. Here, we present a novel, semi-automated approach to quantify cristae, using the machine-learning Trainable Weka Segmentation tool. Compared with standard techniques, our approach not only avoids the bias associated with manual thresholding but more efficiently segments cristae from Airyscan and structured illumination microscopy images. Using a cardiolipin-deficient cell line, as well as FCCP, we show that our approach is sufficiently sensitive to detect perturbations in cristae density, size, and shape. This approach, moreover, reveals that cristae are not uniformly distributed within the mitochondrion, and sites of mitochondrial fission are localized to areas of decreased cristae density. After a fusion event, individual cristae from the two mitochondria, at the site of fusion, merge into one object with distinct architectural values. Overall, our study shows that machine learning represents a compelling new strategy for quantifying cristae in living cells.

scholarly journals Improving axial resolution in Structured Illumination Microscopy using deep learning

2021 ◽  
Vol 379 (2199) ◽  
Author(s):  
Miguel A. Boland ◽  
Edward A. K. Cohen ◽  
Seth R. Flaxman ◽  
Mark A. A. Neil
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Super Resolution ◽  
Structured Illumination ◽  
Learning Models ◽  
Axial Resolution ◽  
Structured Illumination Microscopy ◽  
Recent Advances ◽  
Robust To Noise ◽  
Up Scaling

Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)’.

scholarly journals Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure

2018 ◽  
Author(s):  
Romain F. Laine ◽  
Gemma Goodfellow ◽  
Laurence J. Young ◽  
Jon Travers ◽  
Danielle Carroll ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Vaccine Development ◽  
Functional Performance ◽  
Rapid Assessment ◽  
Super Resolution ◽  
Machine Learning Algorithms ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
High Throughput Analysis

AbstractOptical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.

scholarly journals Structured illumination microscopy and its new developments

2016 ◽  
Vol 09 (03) ◽  
pp. 1630010 ◽  
Author(s):  
Jianling Chen ◽  
Caimin Qiu ◽  
Minghai You ◽  
Xiaogang Chen ◽  
Hongqin Yang ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Spatial Resolution ◽  
Imaging Techniques ◽  
Super Resolution ◽  
Living Cells ◽  
The Other ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Recent Developments ◽  
Super Resolution Microscopy

Optical microscopy allows us to observe the biological structures and processes within living cells. However, the spatial resolution of the optical microscopy is limited to about half of the wavelength by the light diffraction. Structured illumination microscopy (SIM), a type of new emerging super-resolution microscopy, doubles the spatial resolution by illuminating the specimen with a patterned light, and the sample and light source requirements of SIM are not as strict as the other super-resolution microscopy. In addition, SIM is easier to combine with the other imaging techniques to improve their imaging resolution, leading to the developments of diverse types of SIM. SIM has great potential to meet the various requirements of living cells imaging. Here, we review the recent developments of SIM and its combination with other imaging techniques.

scholarly journals Quantitative Analysis of Interactive Behavior of Mitochondria and Lysosomes Using Structured Illumination Microscopy

2018 ◽  
Author(s):  
Qixin Chen ◽  
Xintian Shao ◽  
Mingang Hao ◽  
Zhiqi Tian ◽  
Chenran Wang ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Optical Microscopy ◽  
Spatial Resolution ◽  
Cell Biology ◽  
Super Resolution ◽  
Cellular Level ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Interactive Behavior ◽  
Molecular Machinery

ABSTRACTSuper-resolution optical microscopy has extended the spatial resolution of cell biology from the cellular level to the nanoscale, enabling the observation of the interactive behavior of single mitochondria and lysosomes. Quantitative parametrization of interaction between mitochondria and lysosomes under super-resolution optical microscopy, however, is currently unavailable, which has severely limited our understanding of the molecular machinery underlying mitochondrial functionality. Here, we introduce an M-value to quantitatively investigate mitochondria and lysosome contact (MLC) and mitophagy under structured illumination microscopy. We found that the M-value for an MLC is typically less than 0.4, whereas in mitophagy it ranges from 0.5 to 1.0. This system permits further investigation of the detailed molecular mechanism governing the interactive behavior of mitochondria and lysosomes.

scholarly journals Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Romain F Laine ◽  
Gemma Goodfellow ◽  
Laurence J Young ◽  
Jon Travers ◽  
Danielle Carroll ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Vaccine Development ◽  
Functional Performance ◽  
Rapid Assessment ◽  
Super Resolution ◽  
Machine Learning Algorithms ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
High Throughput Analysis

Optical super-resolution microscopy techniques enable high molecular specificity with high spatial resolution and constitute a set of powerful tools in the investigation of the structure of supramolecular assemblies such as viruses. Here, we report on a new methodology which combines Structured Illumination Microscopy (SIM) with machine learning algorithms to image and classify the structure of large populations of biopharmaceutical viruses with high resolution. The method offers information on virus morphology that can ultimately be linked with functional performance. We demonstrate the approach on viruses produced for oncolytic viriotherapy (Newcastle Disease Virus) and vaccine development (Influenza). This unique tool enables the rapid assessment of the quality of viral production with high throughput obviating the need for traditional batch testing methods which are complex and time consuming. We show that our method also works on non-purified samples from pooled harvest fluids directly from the production line.

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