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

2017 ◽  
Author(s):  
Jonghee Yoon ◽  
YoungJu Jo ◽  
Min-hyeok Kim ◽  
Kyoohyun Kim ◽  
SangYun Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Refractive Index ◽  
Magnetic Beads ◽  
Three Dimensional ◽  
Cell Types ◽  
Biochemical Properties ◽  
Classification Model ◽  
Label Free ◽  
Specificity And Sensitivity ◽  
Lymphocyte Cell

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.

scholarly journals Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcelo Saito Nogueira ◽  
Leonardo Barbosa Leal ◽  
Wena Macarini ◽  
Raquel Lemos Pimentel ◽  
Matheus Muller ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Cost Effective ◽  
Attenuated Total Reflection ◽  
Classification Model ◽  
Label Free ◽  
Nearest Neighbours ◽  
Reduction Strategies ◽  
Oropharyngeal Swab ◽  
Atr Spectroscopy

AbstractEarly diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.

scholarly journals Label-free non-invasive quantitative measurement of lipid contents in individual microalgal cells using refractive index tomography

2017 ◽  
Author(s):  
JaeHwang Jung ◽  
Seong-Joo Hong ◽  
Han-Byeol Kim ◽  
Geon Kim ◽  
Moosung Lee ◽  
...  
Keyword(s):  
Refractive Index ◽  
Lipid Content ◽  
Three Dimensional ◽  
Biofuel Production ◽  
Chemical Extraction ◽  
Label Free ◽  
Non Invasive ◽  
Lipid Contents ◽  
Dry Cell Weight ◽  
Rapid Quantification

Microalgae are promising candidates for biofuel production due to their high lipid content. To facilitate utilization of the microalgae for biofuel, rapid quantification of the lipid contents in microalgae is necessary. However, conventional methods based on the chemical extraction of lipids require a time-consuming destructive extraction process. Here, we demonstrate label-free, non-invasive, rapid quantification of the lipid contents in individual micro-algal cells measuring the three-dimensional refractive index tomograms. We measure three-dimensional refractive index distributions within Nannochloropsis oculata cells and find that lipid droplets are identifiable in tomograms by their high refractive index. In addition, we alter N. oculata under nitrogen deficiency by measuring the volume, lipid weight, and dry cell weight of individual cells. Characterization of individual cells allows correlative analysis between the lipid content and size of individual cells.

scholarly journals Correlative three-dimensional fluorescence and refractive index tomography: bridging the gap between molecular specificity and quantitative bioimaging

2017 ◽  
Author(s):  
Kyoohyun Kim ◽  
Wei Sun Park ◽  
Sangchan Na ◽  
Sangbum Kim ◽  
Taehong Kim ◽  
...  
Keyword(s):  
Refractive Index ◽  
Three Dimensional ◽  
Optical Diffraction ◽  
Live Cells ◽  
Specific Information ◽  
Label Free ◽  
Optical Diffraction Tomography ◽  
Molecular Specificity ◽  
3D Fluorescence Microscopy ◽  
Nih 3T3

AbstractOptical diffraction tomography (ODT) provides label-free three-dimensional (3D) refractive index (RI) measurement of biological samples. However, due to the nature of the RI values of biological specimens, ODT has limited access to molecular specific information. Here, we present an optical setup combining ODT with three-channel 3D fluorescence microscopy, to enhance the molecular specificity of the 3D RI measurement. The 3D RI distribution and 3D deconvoluted fluorescence images of HeLa cells and NIH-3T3 cells are measured, and the cross-correlative analysis between RI and fluorescence of live cells are presented.

scholarly journals Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jonghee Yoon ◽  
YoungJu Jo ◽  
Min-hyeok Kim ◽  
Kyoohyun Kim ◽  
SangYun Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Refractive Index ◽  
Three Dimensional ◽  
Activated Lymphocytes

scholarly journals 3D label-free imaging and analysis of Pinus pollen grains using optical diffraction tomography

2017 ◽  
Author(s):  
Geon Kim ◽  
SangYun Lee ◽  
Seungwoo Shin ◽  
YongKeun Park
Keyword(s):  
Refractive Index ◽  
Three Dimensional ◽  
Pollen Grains ◽  
Reproductive Function ◽  
Optical Diffraction ◽  
List Type ◽  
Diffraction Tomography ◽  
Label Free ◽  
Optical Diffraction Tomography ◽  
Index Maps

SummaryThe structure of pollen grains is related to the reproductive function of the plants. Here, three-dimensional (3D) refractive index maps were obtained for individual conifer pollen grains using optical diffraction tomography (ODT).The 3D morphological features of pollen grains from pine trees were investigated using measured refractive index maps, in which distinct substructures were clearly distinguished and analyzed.Morphological and physiochemical parameters of the pollen grains were quantified from the obtained refractive index (RI) maps and used to quantitatively study the interspecific differences of pollen grains from different strains.Our results demonstrate that ODT can assess the structure of pollen grains. This label-free and rapid 3D imaging approach may provide a new platform for understanding the physiology of pollen grains.

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

2019 ◽  
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.

scholarly journals Fourier spotting: a novel setup for single-color reflectometry

2022 ◽  
Author(s):  
Johannes Siegel ◽  
Marcel Berner ◽  
Juergen H. Werner ◽  
Guenther Proll ◽  
Peter Fechner ◽  
...  
Keyword(s):  
Refractive Index ◽  
Performance Test ◽  
Reference Signal ◽  
Three Dimensional ◽  
Optical Biosensor ◽  
Affinity Constant ◽  
Label Free ◽  
Binding Interaction ◽  
Robust Detection ◽  
Efficient Reduction

AbstractSingle-color reflectrometry is a sensitive and robust detection method in optical biosensor applications, for example for bioanalysis. It is based on the interference of reflected monochromatic radiation and is label free. We present a novel setup for single-color reflectometry based on the patented technology of Berner et al. from 2016. Tilting areas of micro-mirrors allow us to encode the optical reflection signal of an analyte and reference channel into a particular carrier frequency with the amplitude being proportional to the local reflection. Therefore, a single photodiode is sufficient to collect the signals from both channels simultaneously. A 180∘ phase shift in the tilt frequency of two calibrated micro-mirror areas leads to a superposition of the analyte and reference signal which enables an efficient reduction of the baseline offset and potential baseline offset drift. A performance test reveals that we are able to detect changes of the refractive index n down to Δn < 0.01 of saline solutions as regents. A further test validates the detection of heterogeneous binding interaction. This test compromises immobilized testosterone-bovine serum albumin on a three-dimensional layer of biopolymer as ligand and monoclonal anti-testosterone antibodies as analyte. Antibody/antigen binding induces a local growth of the biolayer and change in the refractive index, which is measured via the local change of the reflection. Reproducible measurements enable for the analysis of the binding kinetics by determining the affinity constant KA = 1.59 × 10− 7 M− 1. In summary, this work shows that the concept of differential Fourier spotting as novel setup for single-color reflectometry is suitable for reliable bioanalysis.

scholarly journals STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

2021 ◽  
Author(s):  
Dongqing Sun ◽  
Yihan Xiao ◽  
Zhaoyang Liu ◽  
Taiwen Li ◽  
Qiu Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Computational Method ◽  
Cellular Heterogeneity ◽  
Integrated Analysis ◽  
Cell Type ◽  
Specificity And Sensitivity ◽  
Cell Type Specific ◽  
Spatial Locations

AbstractThe recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell-types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrate STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell-types to spatial locations but also improved the identification of spatial localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions, and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/DongqingSun96/STRIDE.

scholarly journals Measurements of three-dimensional refractive index tomography and membrane deformability of live erythrocytes from Pelophylax nigromaculatus

2017 ◽  
Author(s):  
Geon Kim ◽  
Moosung Lee ◽  
SeongYeon Youn ◽  
EuiTae Lee ◽  
Daeheon Kwon ◽  
...  
Keyword(s):  
Refractive Index ◽  
Three Dimensional ◽  
Large Cell ◽  
Optical Diffraction ◽  
Label Free ◽  
Amphibian Species ◽  
Optical Diffraction Tomography ◽  
Interspecific Differences ◽  
Membrane Fluctuations ◽  
Pelophylax Nigromaculatus

Unlike mammalian erythrocytes, amphibian erythrocytes have distinct morphological features including large cell sizes and the presence of nuclei. The sizes of the cytoplasm and nuclei of erythrocytes vary significantly over different species, their environments, or pathophysiology, which makes hematological studies important for investigating amphibian species. Here, we present a label-free three-dimensional optical quantification of individual amphibian erythrocytes from frogs Pelophylax nigromaculatus (Rana nigromaculata). Using optical diffraction tomography, we measured three-dimensional refractive index (RI) tomograms of the cells, which clearly distinguished the cytoplasm and nuclei inside the erythrocytes. From the measured RI tomograms, we extracted the relevant biochemical parameters of the cells, including hemoglobin contents and hemoglobin concentrations. Furthermore, we measured dynamic membrane fluctuations and investigated the mechanical properties of the cell membrane. From the statistical and correlative analysis of these retrieved parameters, we investigated interspecific differences between frogs and previously studied mammals.

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