Plasmon-Enhanced Autofluorescence Imaging of Organelles in Label-Free Cells by Deep-Ultraviolet Excitation

2016 ◽  
Vol 88 (2) ◽  
pp. 1407-1411 ◽  
Author(s):  
Masakazu Kikawada ◽  
Atushi Ono ◽  
Wataru Inami ◽  
Yoshimasa Kawata
2016 ◽  
Vol 88 (8) ◽  
pp. 4580-4580
Author(s):  
Masakazu Kikawada ◽  
Atsushi Ono ◽  
Wataru Inami ◽  
Yoshimasa Kawata

2012 ◽  
Vol 591-593 ◽  
pp. 1800-1804
Author(s):  
Lu Zhang ◽  
Zong Yao Li ◽  
Hong Zhao ◽  
Wei Chen ◽  
Li Yuan ◽  
...  

Cell health situation relates to its inter structures closely. Cell scattering measurement can be a non-invasive measurement method to obtain cells structure information. But normal scattering detection by original scattering spectrum can not identify cells inner structure changing, such as nucleus radii difference. Traditional scattering spectrum analysis method for identifying cells is to plot the forward scattering (FS) light intensity against side scattering (SS) light intensity. Overlapping phenomenon always occurs which leads to serious error or even mistakes in cells identification results. The Novel even scattering angle superposition algorithm and even incident angle superposition algorithm are put forward herein. In this way, the same kind of cells with different inner structures can be effectively distinguished. The rapid, convenient and label-free cells assorting and detecting can be therefore well accomplished, and these novel methods could be a kind of important diagnostic tool in cancer or other malignant cells diagnosis.


2020 ◽  
Vol 27 (3) ◽  
pp. 772-778 ◽  
Author(s):  
Frédéric Jamme ◽  
Bertrand Cinquin ◽  
Yann Gohon ◽  
Eva Pereiro ◽  
Matthieu Réfrégiers ◽  
...  

A lipid droplet (LD) core of a cell consists mainly of neutral lipids, triacylglycerols and/or steryl esters (SEs). The structuration of these lipids inside the core is still under debate. Lipid segregation inside LDs has been observed but is sometimes suggested to be an artefact of LD isolation and chemical fixation. LD imaging in their native state and in unaltered cellular environments appears essential to overcome these possible technical pitfalls. Here, imaging techniques for ultrastructural study of native LDs in cellulo are provided and it is shown that LDs are organized structures. Cryo soft X-ray tomography and deep-ultraviolet (DUV) transmittance imaging are showing a partitioning of SEs at the periphery of the LD core. Furthermore, DUV transmittance and tryptophan/tyrosine auto-fluorescence imaging on living cells are combined to obtain complementary information on cell chemical contents. This multimodal approach paves the way for a new label-free organelle imaging technique in living cells.


2020 ◽  
Vol 2020 (14) ◽  
pp. 341-1-341-10
Author(s):  
Han Hu ◽  
Yang Lei ◽  
Daisy Xin ◽  
Viktor Shkolnikov ◽  
Steven Barcelo ◽  
...  

Separation and isolation of living cells plays an important role in the fields of medicine and biology with label-free imaging often used for isolating cells. The analysis of label-free cell images has many challenges when examining the behavior of cells. This paper presents methods to analyze label-free cells. Many of the tools we describe are based on machine learning approaches. We also investigate ways of augmenting limited availability of training data. Our results demonstrate that our proposed methods are capable of successfully segmenting and classifying label-free cells.


2021 ◽  
Author(s):  
Tongcheng Qian ◽  
Tiffany M. Heaster ◽  
Melissa C. Skala

Abstract Human pluripotent stem cell (hPSC)-derived cardiomyocytes provide a promising regenerative cell therapy for cardiovascular patients and an important model system to accelerate drug discovery. However, cost-effective and time-efficient platforms must be developed to evaluate the quality of hPSC-derived cardiomyocytes during biomanufacturing. Here, we develop a non-invasive label-free live cell imaging platform to predict the efficiency of hPSC differentiation into cardiomyocytes. Autofluorescence imaging of metabolic co-enzymes is performed under varying differentiation conditions (cell density, concentration of Wnt signaling activator) across three hPSC lines.


Author(s):  
Zetian Yang ◽  
Jieqi Hu ◽  
Lisa I D J Martin ◽  
David Van der Heggen ◽  
Dirk Poelman

Photochromic materials exhibiting luminescence modulation behavior are regarded as promising for high-density optical information storage media. During the luminescence readout process however, many of these materials are subject to coloration...


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tongcheng Qian ◽  
Tiffany M. Heaster ◽  
Angela R. Houghtaling ◽  
Kexin Sun ◽  
Kayvan Samimi ◽  
...  

AbstractHuman pluripotent stem cell (hPSC)-derived cardiomyocytes provide a promising regenerative cell therapy for cardiovascular patients and an important model system to accelerate drug discovery. However, cost-effective and time-efficient platforms must be developed to evaluate the quality of hPSC-derived cardiomyocytes during biomanufacturing. Here, we develop a non-invasive label-free live cell imaging platform to predict the efficiency of hPSC differentiation into cardiomyocytes. Autofluorescence imaging of metabolic co-enzymes is performed under varying differentiation conditions (cell density, concentration of Wnt signaling activator) across five hPSC lines. Live cell autofluorescence imaging and multivariate classification models provide high accuracy to separate low (< 50%) and high (≥ 50%) differentiation efficiency groups (quantified by cTnT expression on day 12) within 1 day after initiating differentiation (area under the receiver operating characteristic curve, 0.91). This non-invasive and label-free method could be used to avoid batch-to-batch and line-to-line variability in cell manufacturing from hPSCs.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Irene Costantini ◽  
Enrico Baria ◽  
Michele Sorelli ◽  
Felix Matuschke ◽  
Francesco Giardini ◽  
...  

AbstractAnalyzing the structure of neuronal fibers with single axon resolution in large volumes is a challenge in connectomics. Different technologies try to address this goal; however, they are limited either by the ineffective labeling of the fibers or in the achievable resolution. The possibility of discriminating between different adjacent myelinated axons gives the opportunity of providing more information about the fiber composition and architecture within a specific area. Here, we propose MAGIC (Myelin Autofluorescence imaging by Glycerol Induced Contrast enhancement), a tissue preparation method to perform label-free fluorescence imaging of myelinated fibers that is user friendly and easy to handle. We exploit the high axial and radial resolution of two-photon fluorescence microscopy (TPFM) optical sectioning to decipher the mixture of various fiber orientations within the sample of interest. We demonstrate its broad applicability by performing mesoscopic reconstruction at a sub-micron resolution of mouse, rat, monkey, and human brain samples and by quantifying the different fiber organization in control and Reeler mouse's hippocampal sections. Our study provides a novel method for 3D label-free imaging of nerve fibers in fixed samples at high resolution, below micrometer level, that overcomes the limitation related to the myelinated axons exogenous labeling, improving the possibility of analyzing brain connectivity.


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