Screening Molecular Recognition Element-Based SWCNT Optical Sensors for the Inflammatory Cytokine TNF-α.

TNF-α (Tumor Necrosis Factor) is a proinflammatory cytokine that amplifies inflammatory response and promotes leukocyte recruitment. TNF-α is primarily produced by activated macrophages, among others, in response to infection, inflammation, or tissue damage. Given its central role in normal and abnormal immune responses, it is the target of several therapeutics, such as adalimumab and etanercept. TNF-α is also a prognostic and diagnostic biomarker associated with Rheumatoid Arthritis, Alzheimer's disease, Multiple Sclerosis, several kidney diseases, several cancers, Type 2 diabetes, sepsis, and others. Spatial quantification of TNF-α in disease models can also be a powerful tool to understand the contributions of inflammatory processes to disease progression. Single-walled carbon nanotubes (SWCNT) are cylindrical carbon lattices that emit distinct near-infrared bandgap photoluminescence. In this work, we evaluated three aptamer-based sensor constructs, plus an additional two iterations of one aptamer sequence, and two antibody-based sensor constructs for TNF-α that use SWCNT near-infrared photoluminescence signal transduction. Several, but not all, of these aptamer and antibody-based sensors sensitively and selectively detected TNF-α in serum in a physiologically relevant range, and we found that their sensing was improved by both passivation and incorporating an exogenous quencher onto the aptamer sequence. Specifically, we found that modification of one aptamer sequence with a Black Hole Quencher induced selective detection in serum when passivated with poly-L-Lysine. This study highlights the importance, and challenges, of translating previously-validated molecular recognition elements to new detection conditions, in this case on the surface of SWCNT and in challenging serum conditions. It also validated a lead sensor construct that builds upon constructs that failed in serum. We anticipate that the sensors evaluated here will have utility in both the diagnosis and study of inflammation-driven chronic disease, while the sensor assessment framework will help drive the broader field of molecularly specific diagnostics.