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

Proceedings of the National Academy of Sciences - PNAS, 2018-03, Vol.115 (12), p.E2676-E2685 [Peer Reviewed Journal]

Volumes 1–89 and 106–114, copyright as a collective work only; author(s) retains copyright to individual articles ;Copyright National Academy of Sciences Mar 20, 2018 ;2018 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1711872115 ;PMID: 29511099

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Title:
Noninvasive detection of macrophage activation with single-cell resolution through machine learning
Author: Pavillon, Nicolas ; Hobro, Alison J. ; Akira, Shizuo ; Smith, Nicholas I.
Subjects: Biological Sciences ; Cell activation ; Cells ; Dependent variables ; Downstream effects ; Genotype & phenotype ; Heterogeneity ; Immune response ; Immune system ; Indicators ; Inhibition ; Intracellular ; Learning algorithms ; Lipopolysaccharides ; Machine learning ; Macrophages ; Mathematical models ; Microscopy ; Molecular modelling ; Morphology ; Parameters ; Phenotypes ; Physical Sciences ; PNAS Plus ; Progesterone ; Raman spectroscopy ; Regression analysis ; Regression models ; Statistical analysis ; Statistical models ; Subpopulations
Is Part Of: Proceedings of the National Academy of Sciences - PNAS, 2018-03, Vol.115 (12), p.E2676-E2685
Description: 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 morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.
Publisher: United States: National Academy of Sciences
Language: English
Identifier: ISSN: 0027-8424
EISSN: 1091-6490
DOI: 10.1073/pnas.1711872115
PMID: 29511099
Source: GFMER Free Medical Journals
PubMed Central

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

Volumes 1–89 and 106–114, copyright as a collective work only; author(s) retains copyright to individual articles ;Copyright National Academy of Sciences Mar 20, 2018 ;2018 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1711872115 ;PMID: 29511099

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