Tissue factor expression in monocyte subsets during human immunothrombosis, endotoxemia and sepsis.

INTRODUCTION: Tissue factor expression on monocytes is implicated in the pathophysiology of sepsis-induced coagulopathy. How tissue factor is expressed by monocyte subsets (classical, intermediate and non-classical) is unknown. METHODS: Monocytic tissue factor surface expression was investigated during three conditions. Primary human monocytes and microvascular endothelial cell co-cultures were used for in vitro studies. Volunteers received a bolus of lipopolysaccharide (2 ng/kg) to induce endotoxemia. Patients with sepsis, or controls with critical illness unrelated to sepsis, were recruited from four intensive care units. RESULTS: Contact with endothelium and stimulation with lipopolysaccharide reduced the proportion of intermediate monocytes. Lipopolysaccharide increased tissue factor surface expression on classical and non-classical monocytes. Endotoxemia induced profound, transient monocytopenia, along with activation of coagulation pathways. In the remaining circulating monocytes, tissue factor was up-regulated in intermediate monocytes, though approximately 60 % of individuals (responders) up-regulated tissue factor across all monocyte subsets. In critically ill patients, tissue factor expression on intermediate and non-classical monocytes was significantly higher in patients with established sepsis than among non-septic patients. Upon recovery of sepsis, expression of tissue factor increased significantly in classical monocytes. CONCLUSION: Tissue factor expression in monocyte subsets varies significantly during health, endotoxemia and sepsis.

Classification of Human White Blood Cells Using Machine Learning for Stain-Free Imaging Flow Cytometry.

Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information-rich images of single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks, hence it would be advantageous to replace manual gating with an automated process. Ideally, this automated process would be based on stain-free measurements, as the currently used staining techniques are expensive and potentially confounding. These stain-free measurements originate from the brightfield and darkfield image channels, which capture transmitted and scattered light, respectively. To realize this automated, stain-free approach, advanced machine learning (ML) methods are required. Previous works have successfully tested this approach on cell cycle phase classification with both a classical ML approach based on manually engineered features, and a deep learning (DL) approach. In this work, we compare both approaches extensively on the problem of white blood cell classification. Four human whole blood samples were assayed on an ImageStream-X MK II imaging flow cytometer. Two samples were stained for the identification of eight white blood cell types, while two other sample sets were stained for the identification of resting and active eosinophils. For both data sets, four ML classifiers were evaluated on stain-free imagery with stratified 5-fold cross-validation. On the white blood cell data set, the best obtained results were 0.778 and 0.703 balanced accuracy for classical ML and DL, respectively. On the eosinophil data set, this was 0.871 and 0.856 balanced accuracy. We conclude that classifying cell types based on only stain-free images is possible with all four classifiers. Noteworthy, we also find that the DL approaches tested in this work do not outperform the approaches based on manually engineered features. © 2019 International Society for Advancement of Cytometry.