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CD4+ versus CD8+ T-lymphocytes identification in integrated microfluidic chip using light scattering and machine learning

Abstract

T-Lymphocytes are a group of cells representing the main effectors of the human adaptive immunity. Characterization of the most representative T-lymphocytes subclasses - CD4+ and CD8+ - is challenging, but has significant impact on clinical decisions. Up to now T-lymphocytes are identified by quite complex cytometric assays, which are based on antibody labeling. However, a label-free approach based on pure biophysical evaluation at single cell level could enable the capability to distinguish those subclasses. Here we report a light scattering approach -supported by accurate data mining- to evaluate cell biophysical properties on an integrated microfluidic chip. In order to perform single cell optical analysis in viscoelastic fluids such chip is composed of mixing, alignment, read-out and collection sections. In particular, we measured: cell dimension, nucleus refractive index, cytosol refractive index as well as nucleus-to-cytosol ratio. The combination of biophysical properties and machine learning allows both distinguishing and counting human CD4+ and CD8+ with an accuracy of 79%. An enhanced identification accuracy of 88% can be achieved by stimulating cells with a selective anti-apoptotic protein as result of increased biophysical differences between CD4+ and CD8+. Such approach has been successfully validated by analysis of samples recapitulating physiologic and pathologic scenarios (CD4+/CD8+ ratios). Results encourage the possibility of application of our approach in hematologic clinical routine as well as into diagnosis and follow-up of pathologies, such as HIV progression.

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Supplementary files

Publication details

The article was received on 18 Jul 2019, accepted on 05 Oct 2019 and first published on 09 Oct 2019


Article type: Paper
DOI: 10.1039/C9LC00695H
Lab Chip, 2019, Accepted Manuscript

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    CD4+ versus CD8+ T-lymphocytes identification in integrated microfluidic chip using light scattering and machine learning

    D. Rossi, D. Dannhauser, M. Telesco, P. A. Netti and F. Causa, Lab Chip, 2019, Accepted Manuscript , DOI: 10.1039/C9LC00695H

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