Synthetic molecular recognition nanosensor paint for microalbuminuria

Januka Budhathoki-Uprety, Janki Shah, Joshua A. Korsen, Alysandria E. Wayne, Thomas V. Galassi, Joseph R. Cohen, Jackson D. Harvey, Prakrit V. Jena, Lakshmi V. Ramanathan, Edgar A. Jaimes and Daniel A. Heller ()
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Januka Budhathoki-Uprety: Memorial Sloan Kettering Cancer Center
Janki Shah: Memorial Sloan Kettering Cancer Center
Joshua A. Korsen: Memorial Sloan Kettering Cancer Center
Alysandria E. Wayne: Memorial Sloan Kettering Cancer Center
Thomas V. Galassi: Memorial Sloan Kettering Cancer Center
Joseph R. Cohen: Memorial Sloan Kettering Cancer Center
Jackson D. Harvey: Memorial Sloan Kettering Cancer Center
Prakrit V. Jena: Memorial Sloan Kettering Cancer Center
Lakshmi V. Ramanathan: Memorial Sloan Kettering Cancer Center
Edgar A. Jaimes: Memorial Sloan Kettering Cancer Center
Daniel A. Heller: Memorial Sloan Kettering Cancer Center

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract Microalbuminuria is an important clinical marker of several cardiovascular, metabolic, and other diseases such as diabetes, hypertension, atherosclerosis, and cancer. The accurate detection of microalbuminuria relies on albumin quantification in the urine, usually via an immunoturbidity assay; however, like many antibody-based assessments, this method may not be robust enough to function in global health applications, point-of-care assays, or wearable devices. Here, we develop an antibody-free approach using synthetic molecular recognition by constructing a polymer to mimic fatty acid binding to the albumin, informed by the albumin crystal structure. A single-walled carbon nanotube, encapsulated by the polymer, as the transduction element produces a hypsochromic (blue) shift in photoluminescence upon the binding of albumin in clinical urine samples. This complex, incorporated into an acrylic material, results in a nanosensor paint that enables the detection of microalbuminuria in patient samples and comprises a rapid point-of-care sensor robust enough to be deployed in resource-limited settings.

Date: 2019
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DOI: 10.1038/s41467-019-11583-1

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