Noninvasive detection of macrophage activation with single-cell resolution through machine learning.

ABSTRACT: We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphol

SUBMITTER: Pavillon N 

PROVIDER: S-EPMC5866539 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature