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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 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.

SUBMITTER: Saed B

PROVIDER: S-EPMC8322097 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Several studies have shown that the power spectrum of x-ray breast images is well described by a power-law at lower frequencies where anatomical variability dominates. However, an image generated from a Gaussian process with this spectrum is easily distinguished from an image of actual breast tissue by eye. This demonstrates that higher order non-Gaussian statistical properties of mammograms are readily accessible to the visual system. The authors' purpose is to quantify and characterize non-Gaussian statistical properties of breast images as influenced by processing of a digital mammogram, different imaging modalities, and breast density.To quantify non-Gaussian statistical properties, the authors consider histograms of filter responses from the interior of a breast image that have similar properties to receptive fields in the early visual system. They quantify departure from a Gaussian distribution by the relative entropy of the histogram compared to a best-fit Gaussian distribution. This entropy is normalized by the relative entropy of a best-fit Laplacian distribution into a measure they refer to as Laplacian fractional entropy (LFE). They test the LFE on a set of 26 patients recalled at screening for which they have available full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and dedicated breast CT (bCT) images as well as breast density scores and biopsy results.A study of LFE in FFDM comparing the raw "for-processing" transmission data from the device to log-converted density estimates and the processed "for-display" data shows that processing mammographic image data enhances the non-Gaussian content of the image. A check of the methodology using a Gaussian process with a power-law power spectrum shows relatively little bias from the finite extent of the region of interests used. A second study comparing LFE across FFDM, DBT, and bCT modalities shows that each maximized the non-Gaussian content of the image for different ranges of spatial frequency. FFDM is optimal at high spatial frequencies (>0.7 mm(-1)), DBT is optimal at mid-range frequencies (0.3-0.7 mm(-1)), and bCT is optimal at low spatial frequency (<0.3 mm(-1)). A third study of breast density in FFDM and bCT shows that LFE generally rises slightly going from the low-to moderate density, and then falls considerably at higher densities.Non-Gaussian statistical structure in breast images that is manifest in the responses of Gabor filters similar to receptive fields of the early visual system is dependent on how the image data are processed, the modality used to acquire the image, and the density of the breast tissue being imaged. Higher LFE corresponds with expected improvements from image processing and 3D imaging.

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

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