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

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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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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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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Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

Nature Communications ◽

10.1038/s41467-020-20062-x ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Mikhail E. Kandel ◽

Yuchen R. He ◽

Young Jae Lee ◽

Taylor Hsuan-Yu Chen ◽

Kathryn Michele Sullivan ◽

...

Keyword(s):

Cell Biology ◽

Imaging System ◽

High Specificity ◽

Dry Mass ◽

Live Cells ◽

Phase Imaging ◽

Label Free ◽

Quantitative Phase Imaging ◽

Cell Nuclei ◽

Quantitative Phase

AbstractDue to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

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