Optical diffraction tomography techniques for the study of cell pathophysiology

K, Kim; Yoon, Jonghee; Shin, Seungwoo; Lee, Sang-Yun; Yang, Su-A; Park, YongKeun

doi:10.18287/jbpe16.02.020201

Cited by 151 publications

(172 citation statements)

References 110 publications

(187 reference statements)

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“…Then, a 3-D RI tomogram of a lymphocyte is reconstructed using the retrieved multiple optical amplitude and phase information via an optical diffraction tomography algorithm 14,45 .…”

Section: Resultsmentioning

confidence: 99%

“…tomogram of a sample which quantitatively provides morphological and biochemical information 13,14 . Previously, ODT has been widely used to study various biological samples including red blood cells [15][16][17][18][19][20][21][22] however, we were unable to identify lymphocyte cell types due to their indistinguishable cellular morphology and biochemical characteristics.…”

Section: Identification Of Lymphocyte Cell Types Is Crucial For Undermentioning

confidence: 99%

“…The angle of the beam impinging onto the sample is controlled by a dual-axis galvanomirror. From the measured holograms, complex optical fields consisting of both the amplitude and quantitative phase images are retrieved using a field retrieval algorithm (See Methods).Then, a 3-D RI tomogram of a lymphocyte is reconstructed using the retrieved multiple optical amplitude and phase information via an optical diffraction tomography algorithm 14,45 .To test whether 3-D RI tomograms of lymphocytes enable the identification of their cell types, three types of lymphocytes 35.14 fL) and CD8+ (152.77  26.52 fL) lymphocytes (Fig. 3b).…”

mentioning

confidence: 99%

“…tomogram of a sample which quantitatively provides morphological and biochemical information 13,14 . Previously, ODT has been widely used to study various biological samples including red blood cells [15][16][17][18][19][20][21][22] , white blood cells (WBC) 23,24 , hepatocytes 25 , cancer cells 16,[26][27][28][29][30][31][32] , neurons 32,33 , bacteria 34,35 , phytoplankton 36 , and hair 37 .…”

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confidence: 99%

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Label-free identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Yoon

Kim

et al. 2017

Preprint

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Identification of lymphocyte cell types is crucial for understanding their pathophysiologic roles in human diseases.Current methods for discriminating lymphocyte cell types primarily relies on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present label-free identification of non-activated lymphocyte subtypes using refractive index tomography. From the measurements of three-dimensional refractive index maps of individual lymphocytes, the morphological and biochemical properties of the lymphocytes are quantitatively retrieved. Machine learning methods establish an optimized classification model using the retrieved quantitative characteristics of the lymphocytes to identify lymphocyte subtypes at the individual cell level. We show that our approach enables label-free identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T lymphocytes) with high specificity and sensitivity. The present method will be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.Lymphocytes consisting of various cell types including B, helper (CD4+) T, cytotoxic (CD8+) T, and regulatory T lymphocytes, and play crucial roles in the adaptive immune system 1 . Each lymphocyte cell type has different functions: B lymphocytes produce antibodies, and T lymphocytes recognize a specific antigen and execute effector functions. The lymphocyte population and function are tightly regulated to defend the host against harmful invaders or abnormal conditions 1,2 . Disturbances in lymphocyte function and regulation are related to various diseases including cancers 3-5 , autoimmune diseases 6,7 , and virus infections 8,9 .. CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/107805 doi: bioRxiv preprint first posted online Feb. 11, 2017; To understand the roles of different types of lymphocytes, several methods based on labelling techniques have been developed to identify and discriminate lymphocyte cell types. Because different kinds of lymphocytes have very similar cellular morphology such as a large nucleus with small cytosolic regions and round shapes, conventional optical methods such as bright-field microscopy or phase contrast microscopy have limited ability in classifying lymphocyte cell types 10 .To overcome this, a specific surface membrane proteins, known as surface markers, are recognized and tagged with magnetic beads or fluorescence molecules via antigen-antibody binding. And then, specific types of lymphocytes are identified and separated by magnetic forces or fluorescence signals 11 . Targeting surface markers is a precise and efficient manner to determine the cell types; however, labelling methods have potential risks of altering cellular functions b...

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“…Then, a 3-D RI tomogram of a lymphocyte is reconstructed using the retrieved multiple optical amplitude and phase information via an optical diffraction tomography algorithm 14,45 .…”

Section: Resultsmentioning

confidence: 99%

Section: Identification Of Lymphocyte Cell Types Is Crucial For Undermentioning

confidence: 99%

mentioning

confidence: 99%

“…tomogram of a sample which quantitatively provides morphological and biochemical information 13,14 . Previously, ODT has been widely used to study various biological samples including red blood cells [15][16][17][18][19][20][21][22] , white blood cells (WBC) 23,24 , hepatocytes 25 , cancer cells 16,[26][27][28][29][30][31][32] , neurons 32,33 , bacteria 34,35 , phytoplankton 36 , and hair 37 .…”

mentioning

confidence: 99%

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Label-free identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Yoon

Kim

et al. 2017

Preprint

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“…2,3 By measuring the refractive index distribution of transparent cells using the principle of interferometry or holography, quantitative phase imaging techniques achieved clear visualization of weakly scattering samples in three dimensions. 4,5 However, the issue of multiple light scattering from surrounding a Author to whom correspondence should be addressed: yk.park@kaist.ac.kr Light scatters when it meets interfaces having different refractive indices. In biological tissues, for instance, scattering occurs due to the intrinsic complex composition of cells and subcellular structures, exhibiting highly inhomogeneous refractive index distributions.…”

Section: Introductionmentioning

confidence: 99%

Perspective: Wavefront shaping techniques for controlling multiple light scattering in biological tissues: Toward in vivo applications

Park

Lee

et al. 2018

APL Photonics

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Multiple light scattering has been regarded as a barrier in imaging through complex media such as biological tissues. Owing to recent advances in wavefront shaping techniques, optical imaging through intact biological tissues without invasive procedures can now be used for direct experimental studies, presenting promising application opportunities in in vivo imaging and diagnosis. Although most of the recent proof of principle breakthroughs have been achieved in the laboratory setting with specialties in physics and engineering, we anticipate that these technologies can be translated to biological laboratories and clinical settings, which will revolutionize how we diagnose and treat a disease. To provide insight into the physical principle that enables the control of multiple light scattering in biological tissues and how recently developed techniques can improve bioimaging through thick tissues, we summarize recent progress on wavefront shaping techniques for controlling multiple light scattering in biological tissues.

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Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning

Cohen,

Dudaie,

Barnea

et al. 2023

Advanced Intelligent Systems

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A new label‐free imaging flow cytometry method for noninvasive and automated biological cell classification is presented. Each cell is rolled during flow, and its off‐axis holograms from multiple viewpoints are acquired. Using the reconstructed quantitative phase profiles of the cell projections, highly discriminating features, enabling cell detection, classification, and differentiation, are extracted via a modified ResNet‐18 deep convolutional neural network architecture. The model is first validated by classifying metastatic breast carcinoma cells (MCF‐7) and normal human mammary epithelial cells (MCF‐10A). An increase in classification accuracy by 1% is achieved when processing five interferometric projections versus processing a single interferometric projection. This model is further tested on four types of white blood cells and exhibits an accuracy increase of 5% when processing 12 interferometric projections versus processing a single interferometric projection. This approach is shown to be superior to that of using conventional 2D‐rotation augmentation, and can be used to decrease substantially the number of cell examples needed for training the classification model without impairing the results. This novel concept has great potential to be incorporated into label‐free imaging flow cytometry and improve cell classification, and be used to detect various types of medical conditions and diseases.

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Optical diffraction tomography techniques for the study of cell pathophysiology

Cited by 151 publications

References 110 publications

Label-free identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Label-free identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Perspective: Wavefront shaping techniques for controlling multiple light scattering in biological tissues: Toward in vivo applications

Label‐Free Imaging Flow Cytometry for Cell Classification Based on Multiple Interferometric Projections Using Deep Learning

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