BackgroundCell-based functional immune-assays may allow for risk stratification of patients with complex, heterogeneous immune disorders such as sepsis. Given the heterogeneity of patient responses and the uncertain immune pathogenesis of sepsis, these assays must first be defined and calibrated in the healthy population.ObjectiveOur objective was to compare the internal consistency and practicality of two immune assays that may provide data on surrogate markers of the innate and adaptive immune response. We hypothesized that a rapid turnaround, microfluidic-based immune assay (ELLA) would be comparable to a dual-color, enzyme-linked immunospot (ELISpot) assay in identifying tumor necrosis factor (TNF) and interferon (IFN)γ production following ex vivo whole blood stimulation.DesignThis was a prospective, observational cohort analysis. Whole blood samples from ten healthy, immune-competent volunteers were stimulated for either 4 hours or 18 hours with lipopolysaccharide, anti-CD3/anti-CD28 antibodies, or phorbol 12-myristate 13-acetate with ionomycin to interrogate innate and adaptive immune responses, respectively.Measurements and Main ResultsELLA analysis produced more precise measurement of TNF and IFNγ concentrations as compared with ELISpot, as well as a four- to five-log10 dynamic range for TNF and IFNγ concentrations, as compared with a two-log10 dynamic range with ELISpot. Unsupervised clustering accurately predicted the ex vivo immune stimulant used for 90% of samples analyzed via ELLA, as compared with 72% of samples analyzed via ELISpot.ConclusionsWe describe, for the first time, a rapid and precise assay for functional interrogation of the innate and adaptive arms of the immune system in healthy volunteers. The advantages of the ELLA microfluidic platform may represent a step forward in generating a point-of-care test with clinical utility, for identifying deranged immune phenotypes in septic patients.

Highly heterogeneous cell populations require multiple flow cytometric markers for appropriate phenotypic characterization. This exponentially increases the complexity of 2D scatter plot analyses and exacerbates human errors due to variations in manual gating of flow data. We describe a semi-automated workflow, based entirely on the Flowjo Graphical User Interface (GUI), that involves the stepwise integration of several, newly available machine learning tools for the analysis of myeloid-derived suppressor cells (MDSCs) in septic and non-septic critical illness. Supervised clustering of flow cytometric data showed correlation with, but significantly different numbers of, MDSCs as compared with the cell numbers obtained by manual gating. Neither quantification method predicted 30-day clinical outcomes in a cohort of 16 critically ill and septic patients and 5 critically ill and non-septic patients. Machine learning identified a significant decrease in the proportion of PMN-MDSC in critically ill and septic patients as compared with healthy controls. There was no difference between the proportion of these MDSCs in septic and non-septic critical illness.

Highly heterogeneous cell populations require multiple flow cytometric markers for appropriate phenotypic characterization. This exponentially increases the complexity of 2D scatter plot analysis and exacerbates human errors due to variations in manual gating of flow data. We describe a workflow involving the stepwise integration of several, newly available machine learning tools for the analysis of myeloid-derived suppressor cells (MDSCs) in septic and non-septic critical illness. Unsupervised clustering of flow cytometric data showed good correlation with, but significantly different numbers of, MDSCs as compared with the cell numbers obtained by manual gating. However, both quantification methods revealed a significant difference between numbers of PMN-MDSC at day 1 in healthy volunteers and critically ill patients having septic or non-septic illness. Numbers of PMN-MDSC obtained by machine learning positively correlated with 30 days hospital readmission following critical illness, whereas manual gating of this cell population distinguished between septic and non-septic critical illness. Neither gating strategy found a correlation between number of MDSCs and 30-day mortality or hospital length of stay.