Deep Learning of Morphologic Correlations To Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow Cytometry.

Autor:	Zhao L; Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China.; Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States.; School of Information Science & Technology, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China., Tang L; Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China.; School of Information Science & Technology, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China., Greene MS; Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States., Sa Y; Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China., Wang W; Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China.; School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China., Jin J; Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China.; Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States.; School of Physics & Electronic Science, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China., Hong H; Department of Pathology and Comparative Medicine, Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina 27109, United States., Lu JQ; Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China.; Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States., Hu XH; Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China.; Department of Physics, East Carolina University, Greenville, North Carolina 27858, United States.
Jazyk:	angličtina
Zdroj:	Analytical chemistry [Anal Chem] 2022 Jan 25; Vol. 94 (3), pp. 1567-1574. Date of Electronic Publication: 2022 Jan 10.
DOI:	10.1021/acs.analchem.1c03337
Abstrakt:	The two major subtypes of human T cells, CD4+ and CD8+, play important roles in adaptive immune response by their diverse functions. To understand the structure-function relation at the single cell level, we isolated 2483 CD4+ and 2450 CD8+ T cells from fresh human splenocytes by immunofluorescent sorting and investigated their morphologic relations to the surface CD markers by acquisition and analysis of cross-polarized diffraction image (p-DI) pairs. A deep neural network of DINet-R has been built to extract 2560 features across multiple pixel scales of a p-DI pair per imaged cell. We have developed a novel algorithm to form a matrix of Pearson correlation coefficients by these features for selection of a support cell set with strong morphologic correlation in each subtype. The p-DI pairs of support cells exhibit significant pattern differences between the two subtypes defined by CD markers. To explore the relation between p-DI features and CD markers, we divided each subtype into two groups of A and B using the two support cell sets. The A groups comprise 90.2% of the imaged T cells and classification of them by DINet-R yields an accuracy of 97.3 ± 0.40% between the two subtypes. Analysis of depolarization ratios further reveals the significant differences in molecular polarizability between the two subtypes. These results prove the existence of a strong structure-function relation for the two major T cell subtypes and demonstrate the potential of diffraction imaging flow cytometry for accurate and label-free classification of T cell subtypes.
Databáze:	MEDLINE
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