Label-free identification of individual bacteria using Fourier transform light scattering

Jo, YoungJu; Jung, Jin‐Woo; Kim, Min-Hyeok; Park, HyunJoo; Kang, Suk‐Jo; Park, YongKeun

doi:10.1364/oe.23.015792

Cited by 65 publications

(61 citation statements)

References 93 publications

(98 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the principle of holography, QPI techniques exploits the refractive index (RI) distribution of optical contrasts to provide quantitative and label-free images of live cells and tissues. QPI techniques have been applied to study the pathophysiology of various biological samples, including hematology (10), malaria infection (11,12), sickle cell diseases (13)(14)(15)(16), and microbiology (17,18). In particular, 2D QPI techniques or digital holographic microscopy techniques had been utilized for the study of biophysics of neuron cells (19,20) and for imaging alterations in brain tissue slices caused by Alzheimer's disease (21).…”

Section: Introductionmentioning

confidence: 99%

Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson's disease

Yang

Yoon

et al. 2017

Cytometry Pt A

Self Cite

View full text Add to dashboard Cite

Parkinson's disease (PD) is a common neurodegenerative disease. However, therapeutic methods of PD are still limited due to complex pathophysiology in PD. Here, optical measurements of individual neurons from in vitro PD model using optical diffraction tomography (ODT) are presented. By measuring 3D refractive index distribution of neurons, morphological and biophysical alterations in in-vitro PD model are quantitatively investigated. It was found that neurons show apoptotic features in early PD progression. The present approach will open up new opportunities for quantitative investigation of the pathophysiology of various neurodegenerative diseases. © 2017 International Society for Advancement of Cytometry.

show abstract

Section: Introductionmentioning

confidence: 99%

Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson's disease

Yang

Yoon

et al. 2017

Cytometry Pt A

Self Cite

View full text Add to dashboard Cite

show abstract

“…The lensless imaging system only needs the 2D imaging photosensor and a light source ( e . g ., light emitting diode), and thus it is simpler than other optical systems that have been employed for bacterial identification such as BARDOT, holographic microscopy [18, 19], angle resolved dark-field imaging [20]. A striking feature of this system is a wide imaging field as large as 2D sensors (mm 2 scale).…”

Section: Introductionmentioning

confidence: 99%

Colony fingerprint for discrimination of microbial species based on lensless imaging of microcolonies

et al. 2017

View full text Add to dashboard Cite

Detection and identification of microbial species are crucial in a wide range of industries, including production of beverages, foods, cosmetics, and pharmaceuticals. Traditionally, colony formation and its morphological analysis (e.g., size, shape, and color) with a naked eye have been employed for this purpose. However, such a conventional method is time consuming, labor intensive, and not very reproducible. To overcome these problems, we propose a novel method that detects microcolonies (diameter 10–500 μm) using a lensless imaging system. When comparing colony images of five microorganisms from different genera (Escherichia coli, Salmonella enterica, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans), the images showed obvious different features. Being closely related species, St. aureus and St. epidermidis resembled each other, but the imaging analysis could extract substantial information (colony fingerprints) including the morphological and physiological features, and linear discriminant analysis of the colony fingerprints distinguished these two species with 100% of accuracy. Because this system may offer many advantages such as high-throughput testing, lower costs, more compact equipment, and ease of automation, it holds promise for microbial detection and identification in various academic and industrial areas.

show abstract

“…However, exogenous labeling agents were used in the diagnosis. Furthermore, quantitative phase‐contrast imaging (QPI) techniques combined with machine learning algorithms have been utilized to recognize types of cells or classify the states of biosamples, including bacteria , cancer cells , sperm cells , lymphocytes , macrophage activation , microorganisms , microobjects and RBCs . Since QPI techniques provide valuable phase information related with 3D morphology and biophysical properties of samples, iRBCs could be distinguished from healthy RBCs (hRBCs) with a relatively high accuracy (>91%) .…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based in‐line holographic sensing of unstained malaria‐infected red blood cells

Kim

Byeon

et al. 2018

Journal of Biophotonics

View full text Add to dashboard Cite

Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (iRBCs). To identify the RBC characteristics, 13 descriptors were extracted from segmented holograms of individual RBCs. Among the 13 descriptors, 10 features were highly statistically different between healthy RBCs (hRBCs) and iRBCs. Six machine learning algorithms were applied to effectively combine the dominant features and to greatly improve the diagnostic capacity of the present method. Among the classification models trained by the 6 tested algorithms, the model trained by the support vector machine (SVM) showed the best accuracy in separating hRBCs and iRBCs for training (n = 280, 96.78%) and testing sets (n = 120, 97.50%). This DIHM-based artificial intelligence methodology is simple and does not require blood staining. Thus, it will be beneficial and valuable in the diagnosis of malaria.

show abstract

Label-free identification of individual bacteria using Fourier transform light scattering

Cited by 65 publications

References 93 publications

Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson's disease

Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson's disease

Colony fingerprint for discrimination of microbial species based on lensless imaging of microcolonies

Machine learning‐based in‐line holographic sensing of unstained malaria‐infected red blood cells

Contact Info

Product

Resources

About