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.
Highlights
The spatial organization of T cell receptors (TCRs) correlates with membrane-associated signal amplification, dispersion, and regulation during T cell activation
The advent of single-molecule and superresolution microscopy has enabled the investigation of nanoscale and microscale spatial organization and rearrangement of T cell receptors (TCRs) on the plasma membrane of T cells during T cell activation[1,2,3,4,5,6]
We developed an analytical image analysis technique based on partial differential equation (PDE) image models followed by linear class discrimination of the estimated model parameters
Summary
The spatial organization of T cell receptors (TCRs) correlates with membrane-associated signal amplification, dispersion, and regulation during T cell activation. The spatial redistribution of TCRs and the formation of TCR clusters on the plasma membrane begs the question whether the organization of TCRs, e.g., obtained through standard fluorescence imaging, contain diagnostic or prognostic values Such information could potentially augment the overall expression level registered by flow cytometry to improve the segmentation and diagnostic accuracy. We set out to develop a rapid, quantitative technique for analyzing standard fluorescence images of TCRs. Our premise is that pixel-based image information contains statistical information useful for discrimination analysis of TCRs from T cell images on different surface conditions. The development of thinning out clusters while conserving stoichiometry of labeling (TOCCSL)[36] and label-density-variation single-molecule localization microscopy (SMLM)[37] indicates that the interpretation of the TCR organization and distribution can be affected by the labeling and detection artifacts associated with SMLM To this end, fluorescence TIRF imaging is immune to the clustering artifact of SMLM. SCAMPI developed towards this imaging modality may circumvent the artifacts that arose from previous imaging studies
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