Literature DB >> 26732872

Cell refractive index for cell biology and disease diagnosis: past, present and future.

P Y Liu1, L K Chin, W Ser, H F Chen, C-M Hsieh, C-H Lee, K-B Sung, T C Ayi, P H Yap, B Liedberg, K Wang, T Bourouina, Y Leprince-Wang.   

Abstract

Cell refractive index is a key biophysical parameter, which has been extensively studied. It is correlated with other cell biophysical properties including mechanical, electrical and optical properties, and not only represents the intracellular mass and concentration of a cell, but also provides important insight for various biological models. Measurement techniques developed earlier only measure the effective refractive index of a cell or a cell suspension, providing only limited information on cell refractive index and hence hindering its in-depth analysis and correlation. Recently, the emergence of microfluidic, photonic and imaging technologies has enabled the manipulation of a single cell and the 3D refractive index of a single cell down to sub-micron resolution, providing powerful tools to study cells based on refractive index. In this review, we provide an overview of cell refractive index models and measurement techniques including microfluidic chip-based techniques for the last 50 years, present the applications and significance of cell refractive index in cell biology, hematology, and pathology, and discuss future research trends in the field, including 3D imaging methods, integration with microfluidics and potential applications in new and breakthrough research areas.

Mesh:

Year:  2016        PMID: 26732872     DOI: 10.1039/c5lc01445j

Source DB:  PubMed          Journal:  Lab Chip        ISSN: 1473-0189            Impact factor:   6.799


  71 in total

1.  Three-Dimensional Nanoscale Nuclear Architecture Mapping of Rectal Biopsies Detects Colorectal Neoplasia in Patients with Inflammatory Bowel Disease.

Authors:  Shikhar Uttam; Jana G Hashash; Justin LaFace; David Binion; Miguel Regueiro; Douglas J Hartman; Randall E Brand; Yang Liu
Journal:  Cancer Prev Res (Phila)       Date:  2019-06-04

2.  Holotomography: Refractive Index as an Intrinsic Imaging Contrast for 3-D Label-Free Live Cell Imaging.

Authors:  Doyeon Kim; Sangyun Lee; Moosung Lee; Juntaek Oh; Su-A Yang; YongKeun Park
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

3.  Changes in optical properties of electroporated cells as revealed by digital holographic microscopy.

Authors:  Violeta L Calin; Mona Mihailescu; Nicolae Mihale; Alexandra V Baluta; Eugenia Kovacs; Tudor Savopol; Mihaela G Moisescu
Journal:  Biomed Opt Express       Date:  2017-03-16       Impact factor: 3.732

4.  Near-Membrane Refractometry Using Supercritical Angle Fluorescence.

Authors:  Maia Brunstein; Lopamudra Roy; Martin Oheim
Journal:  Biophys J       Date:  2017-05-09       Impact factor: 4.033

5.  Finite-difference time-domain analysis of increased penetration depth in optical coherence tomography by wavefront shaping.

Authors:  Jong Uk Kim; Hyun Choi; YongKeun Park; Jonghwa Shin
Journal:  Biomed Opt Express       Date:  2018-07-26       Impact factor: 3.732

6.  Refractive Index Imaging of Cells with Variable-Angle Near-Total Internal Reflection (TIR) Microscopy.

Authors:  Kevin P Bohannon; Ronald W Holz; Daniel Axelrod
Journal:  Microsc Microanal       Date:  2017-09-18       Impact factor: 4.127

7.  Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture.

Authors:  Cedric Allier; Romaric Vincent; Fabrice Navarro; Mathilde Menneteau; Lamya Ghenim; Xavier Gidrol; Thomas Bordy; Lionel Hervé; Olivier Cioni; Sabine Bardin; Michel Bornens; Yves Usson; Sophie Morales
Journal:  J Vis Exp       Date:  2018-02-23       Impact factor: 1.355

8.  Rapid computational cell-rotation around arbitrary axes in 3D with multi-core fiber.

Authors:  Jiawei Sun; Nektarios Koukourakis; Jochen Guck; Jürgen W Czarske
Journal:  Biomed Opt Express       Date:  2021-05-17       Impact factor: 3.732

9.  Automated red blood cells extraction from holographic images using fully convolutional neural networks.

Authors:  Faliu Yi; Inkyu Moon; Bahram Javidi
Journal:  Biomed Opt Express       Date:  2017-09-12       Impact factor: 3.732

10.  Distinguishing between whole cells and cell debris using surface plasmon coupled emission.

Authors:  Muhammad Anisuzzaman Talukder; Curtis R Menyuk; Yordan Kostov
Journal:  Biomed Opt Express       Date:  2018-03-29       Impact factor: 3.732

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