Literature DB >> 30353667

Detection of the T cell activation state using nonlinear optical microscopy.

Evangelia Gavgiotaki1,2, George Filippidis1, Ioanna Zerva3, George Kenanakis1, Emmanuel Archontakis1,4, Sofia Agelaki2, Vasilios Georgoulias2, Irene Athanassakis3.   

Abstract

The ability to monitor the activation state of T-cells during immunotherapy is of great importance. Although specific activation markers do exist, their abundance and complicated regulation cannot definitely define the activation state of the cells. Previous studies have shown that Third Harmonic Generation (THG) imaging could distinguish between activated versus resting microglia and healthy versus cancerous cells, mainly based on their lipid-body profiles. In the present study, mitogen or antigen-stimulated T-cells were subjected to THG imaging microscopy. Qualitative and quantitative analysis showed statistically significant increase of THG mean area and intensity in activated versus resting T-cells. The connection of THG imaging to chemical information was achieved using Raman spectroscopy, which showed significant differences between the activation processes and controls, correlating of THG signal area with cholesterol and lipid compounds, but not with triglycerides. The obtained results suggested a potential employment of nonlinear microscopy in evaluating of T-cell activation, which is expected to be largely appreciated in the clinical practice.
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Raman spectroscopy; T lymphocytes; concanavalin A; human serum albumin; third harmonic generation imaging

Mesh:

Year:  2018        PMID: 30353667     DOI: 10.1002/jbio.201800277

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  2 in total

1.  Classifying T cell activity in autofluorescence intensity images with convolutional neural networks.

Authors:  Zijie J Wang; Alex J Walsh; Melissa C Skala; Anthony Gitter
Journal:  J Biophotonics       Date:  2019-12-15       Impact factor: 3.207

2.  Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition.

Authors:  Lenka Strbkova; Brittany B Carson; Theresa Vincent; Pavel Vesely; Radim Chmelik
Journal:  J Biomed Opt       Date:  2020-08       Impact factor: 3.170

  2 in total

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