Combined CD25, CD64, and CD69 Biomarker in 3D-Printed Multizone Millifluidic Device for Sepsis Detection in Clinical Samples.

Abstract

Sepsis is a serious medical condition that arises from a runaway response to an infection, which triggers the immune system to release chemicals into the bloodstream. This immune response can result in widespread inflammation throughout the body, which may cause harm to vital organs and, in more severe cases, lead to organ failure and death. Timely and accurate diagnosis of sepsis remains a challenge in analytical diagnostics. In this work, we have developed and validated a sepsis detection device, utilizing 3D printing technology, which incorporates multiple affinity separation zones. Our device requires minimal operator intervention and utilizes CD64, CD69, and CD25 as the biomarker targets for detecting sepsis in liquid biopsies. We assessed the effectiveness of our 3D-printed multizone cell separation device by testing it on clinical samples obtained from both septic patients (n = 35) and healthy volunteers (n = 8) and validated its performance accordingly. Unlike previous devices using poly(dimethyl siloxane), the 3D-printed device had reduced nonspecific binding for anti-CD25 capture, allowing this biomarker to be assayed for the first time in cell separations. Our results showed a statistically significant difference in cell capture between septic and healthy samples (with p values of 0.0001 for CD64, CD69, and CD25), suggesting that 3D-printed multizone cell capture is a reliable method for distinguishing sepsis. A receiver operator characteristic (ROC) analysis was performed to determine the accuracy of the captured cell counts for each antigen in detecting sepsis. The ROC area under the curve (AUC) values for on-chip detection of CD64+, CD69+, and CD25+ leukocytes were 0.96, 0.92, and 0.88, respectively, indicating our diagnostic test matches clinical outcomes. When combined for sepsis diagnosis, the AUC value for CD64, CD69, and CD25 was 0.99, indicating an improved diagnostic performance due to the use of multiple biomarkers.