Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions

Abstract The spatial organization of T cell receptors (TCRs) correlates with membrane-associated signal amplification, dispersion, and regulation during T cell activation. Despite its potential clinical importance, quantitative analysis of the spatial arrangement of TCRs from standard fluorescence i...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Badeia Saed, Rangika Munaweera, Jesse Anderson, William D. O’Neill, Ying S. Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c4254c84a17c45ca8a001e6dec68bfff
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c4254c84a17c45ca8a001e6dec68bfff
record_format	dspace
spelling	oai:doaj.org-article:c4254c84a17c45ca8a001e6dec68bfff2021-12-02T16:31:51ZRapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions10.1038/s41598-021-94730-32045-2322https://doaj.org/article/c4254c84a17c45ca8a001e6dec68bfff2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94730-3https://doaj.org/toc/2045-2322Abstract The spatial organization of T cell receptors (TCRs) correlates with membrane-associated signal amplification, dispersion, and regulation during T cell activation. Despite its potential clinical importance, quantitative analysis of the spatial arrangement of TCRs from standard fluorescence images remains difficult. Here, we report Statistical Classification Analyses of Membrane Protein Images or SCAMPI as a technique capable of analyzing the spatial arrangement of TCRs on the plasma membrane of T cells. We leveraged medical image analysis techniques that utilize pixel-based values. We transformed grayscale pixel values from fluorescence images of TCRs into estimated model parameters of partial differential equations. The estimated model parameters enabled an accurate classification using linear discrimination techniques, including Fisher Linear Discriminant (FLD) and Logistic Regression (LR). In a proof-of-principle study, we modeled and discriminated images of fluorescently tagged TCRs from Jurkat T cells on uncoated cover glass surfaces (Null) or coated cover glass surfaces with either positively charged poly-L-lysine (PLL) or TCR cross-linking anti-CD3 antibodies (OKT3). Using 80 training images and 20 test images per class, our statistical technique achieved 85% discrimination accuracy for both OKT3 versus PLL and OKT3 versus Null conditions. The run time of image data download, model construction, and image discrimination was 21.89 s on a laptop computer, comprised of 20.43 s for image data download, 1.30 s on the FLD-SCAMPI analysis, and 0.16 s on the LR-SCAMPI analysis. SCAMPI represents an alternative approach to morphology-based qualifications for discriminating complex patterns of membrane proteins conditioned on a small sample size and fast runtime. The technique paves pathways to characterize various physiological and pathological conditions using the spatial organization of TCRs from patient T cells.Badeia SaedRangika MunaweeraJesse AndersonWilliam D. O’NeillYing S. HuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Badeia Saed Rangika Munaweera Jesse Anderson William D. O’Neill Ying S. Hu Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions
description	Abstract The spatial organization of T cell receptors (TCRs) correlates with membrane-associated signal amplification, dispersion, and regulation during T cell activation. Despite its potential clinical importance, quantitative analysis of the spatial arrangement of TCRs from standard fluorescence images remains difficult. Here, we report Statistical Classification Analyses of Membrane Protein Images or SCAMPI as a technique capable of analyzing the spatial arrangement of TCRs on the plasma membrane of T cells. We leveraged medical image analysis techniques that utilize pixel-based values. We transformed grayscale pixel values from fluorescence images of TCRs into estimated model parameters of partial differential equations. The estimated model parameters enabled an accurate classification using linear discrimination techniques, including Fisher Linear Discriminant (FLD) and Logistic Regression (LR). In a proof-of-principle study, we modeled and discriminated images of fluorescently tagged TCRs from Jurkat T cells on uncoated cover glass surfaces (Null) or coated cover glass surfaces with either positively charged poly-L-lysine (PLL) or TCR cross-linking anti-CD3 antibodies (OKT3). Using 80 training images and 20 test images per class, our statistical technique achieved 85% discrimination accuracy for both OKT3 versus PLL and OKT3 versus Null conditions. The run time of image data download, model construction, and image discrimination was 21.89 s on a laptop computer, comprised of 20.43 s for image data download, 1.30 s on the FLD-SCAMPI analysis, and 0.16 s on the LR-SCAMPI analysis. SCAMPI represents an alternative approach to morphology-based qualifications for discriminating complex patterns of membrane proteins conditioned on a small sample size and fast runtime. The technique paves pathways to characterize various physiological and pathological conditions using the spatial organization of TCRs from patient T cells.
format	article
author	Badeia Saed Rangika Munaweera Jesse Anderson William D. O’Neill Ying S. Hu
author_facet	Badeia Saed Rangika Munaweera Jesse Anderson William D. O’Neill Ying S. Hu
author_sort	Badeia Saed
title	Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions
title_short	Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions
title_full	Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions
title_fullStr	Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions
title_full_unstemmed	Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions
title_sort	rapid statistical discrimination of fluorescence images of t cell receptors on immobilizing surfaces with different coating conditions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c4254c84a17c45ca8a001e6dec68bfff
work_keys_str_mv	AT badeiasaed rapidstatisticaldiscriminationoffluorescenceimagesoftcellreceptorsonimmobilizingsurfaceswithdifferentcoatingconditions AT rangikamunaweera rapidstatisticaldiscriminationoffluorescenceimagesoftcellreceptorsonimmobilizingsurfaceswithdifferentcoatingconditions AT jesseanderson rapidstatisticaldiscriminationoffluorescenceimagesoftcellreceptorsonimmobilizingsurfaceswithdifferentcoatingconditions AT williamdoneill rapidstatisticaldiscriminationoffluorescenceimagesoftcellreceptorsonimmobilizingsurfaceswithdifferentcoatingconditions AT yingshu rapidstatisticaldiscriminationoffluorescenceimagesoftcellreceptorsonimmobilizingsurfaceswithdifferentcoatingconditions
_version_	1718383834169344000

Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions

Ejemplares similares