Stain-free nucleus identification in holographic learning flow cyto-tomography

Quantitative Phase Imaging (QPI) has gained popularity because it can avoid the staining step, which in some cases is difficult or impossible. However, QPI does not provide the well-known specificity to various parts of the cell (e.g., organelles, membrane). Here we show a novel computational segmentation method based on statistical inference that bridges the gap between the specificity of Fluorescence Microscopy (FM) and the label-free property of QPI techniques to identify the cell nucleus. We demonstrate application to stain-free cells reconstructed through the holographic learning and in flow cyto-tomography modality. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus-like regions can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal FM data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting the three-dimensional (3D) intracellular specificity directly from the phase-contrast data in a typical flow cytometry configuration.

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Journal of Visualized Experiments ◽

10.3791/58305 ◽

2018 ◽

Cited By ~ 4

Author(s):

Jonghee Yoon ◽

YoungJu Jo ◽

Young Seo Kim ◽

Yeongjin Yu ◽

Jiyeon Park ◽

...

Keyword(s):

Machine Learning ◽

Three Dimensional ◽

Phase Imaging ◽

Label Free ◽

Quantitative Phase Imaging ◽

Quantitative Phase

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Measuring three-dimensional dynamics of platelet activation using 3-D quantitative phase imaging

10.1101/827436 ◽

2019 ◽

Cited By ~ 2

Author(s):

SangYun Lee ◽

Seongsoo Jang ◽

YongKeun Park

Keyword(s):

Platelet Activation ◽

Three Dimensional ◽

Dry Mass ◽

Phase Imaging ◽

Label Free ◽

Quantitative Phase Imaging ◽

Biochemical Alterations ◽

Quantitative Phase ◽

Before And After ◽

Quantitative Manner

AbstractPlatelets, or thrombocytes, are anucleated tiny blood cells with an indispensable contribution to the hemostatic properties of whole blood, detecting injured sites at the surface of blood vessels and forming blood clots. Here, we quantitatively and non-invasively investigated the morphological and biochemical alterations of individual platelets during activation in the absence of exogenous agents by employing 3-D quantitative phase imaging (QPI). By reconstructing 3-D refractive index (RI) tomograms of individual platelets, we investigated alterations in platelet activation before and after the administration of various platelet agonists. Our results showed that while the integrity of collagen-stimulated platelets was preserved despite the existence of a few degranulated platelets with developed pseudopods, platelets stimulated by thrombin or thrombin receptor-activating peptide (TRAP) exhibited significantly lower cellular concentration and dry mass than did resting platelets. Our work provides a means to systematically investigate drug-respondents of individual platelets in a label-free and quantitative manner, and open a new avenue to the study of the activation of platelets.Abstract Figure

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Rapid and label-free identification of individual bacterial pathogens exploiting three-dimensional quantitative phase imaging and deep learning

10.1101/596486 ◽

2019 ◽

Cited By ~ 2

Author(s):

Geon Kim ◽

Daewoong Ahn ◽

Minhee Kang ◽

YoungJu Jo ◽

Donghun Ryu ◽

...

Keyword(s):

Refractive Index ◽

Deep Learning ◽

Infectious Diseases ◽

Bacterial Pathogens ◽

Bacterial Species ◽

Three Dimensional ◽

Phase Imaging ◽

Label Free ◽

Quantitative Phase Imaging ◽

Quantitative Phase

ABSTRACTFor appropriate treatments of infectious diseases, rapid identification of the pathogens is crucial. Here, we developed a rapid and label-free method for identifying common bacterial pathogens as individual bacteria by using three-dimensional quantitative phase imaging and deep learning. We achieved 95% accuracy in classifying 19 bacterial species by exploiting the rich information in three-dimensional refractive index tomograms with a convolutional neural network classifier. Extensive analysis of the features extracted by the trained classifier was carried out, which supported that our classifier is capable of learning species-dependent characteristics. We also confirmed that utilizing three-dimensional refractive index tomograms was crucial for identification ability compared to two-dimensional imaging. This method, which does not require time-consuming culture, shows high feasibility for diagnosing patients with infectious diseases who would benefit from immediate and adequate antibiotic treatment.

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Lensless Three-Dimensional Quantitative Phase Imaging Using Phase Retrieval Algorithm

Journal of Imaging ◽

10.3390/jimaging6090099 ◽

2020 ◽

Vol 6 (9) ◽

pp. 99 ◽

Cited By ~ 2

Author(s):

Vijayakumar Anand ◽

Tomas Katkus ◽

Denver P. Linklater ◽

Elena P. Ivanova ◽

Saulius Juodkazis

Keyword(s):

Phase Retrieval ◽

Three Dimensional ◽

Retrieval Algorithm ◽

Phase Imaging ◽

Label Free ◽

Quantitative Phase Imaging ◽

Single Plane ◽

Quantitative Phase ◽

Web Camera ◽

Phase Retrieval Algorithm

Quantitative phase imaging (QPI) techniques are widely used for the label-free examining of transparent biological samples. QPI techniques can be broadly classified into interference-based and interferenceless methods. The interferometric methods which record the complex amplitude are usually bulky with many optical components and use coherent illumination. The interferenceless approaches which need only the intensity distribution and works using phase retrieval algorithms have gained attention as they require lesser resources, cost, space and can work with incoherent illumination. With rapid developments in computational optical techniques and deep learning, QPI has reached new levels of applications. In this tutorial, we discuss one of the basic optical configurations of a lensless QPI technique based on the phase-retrieval algorithm. Simulative studies on QPI of thin, thick, and greyscale phase objects with assistive pseudo-codes and computational codes in Octave is provided. Binary phase samples with positive and negative resist profiles were fabricated using lithography, and a single plane and two plane phase objects were constructed. Light diffracted from a point object is modulated by phase samples and the corresponding intensity patterns are recorded. The phase retrieval approach is applied for 2D and 3D phase reconstructions. Commented codes in Octave for image acquisition and automation using a web camera in an open source operating system are provided.

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