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Issue 32, 2018
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Automated 4-sample protein immunoassays using 3D-printed microfluidics

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Abstract

Low cost, miniaturized assay platforms that work with small sample volumes, high sensitivity and rapid detection will have high value in future biomolecular diagnostics. Herein we report an automated, 3D printed electrochemiluminescent (ECL) immunoarray integrated with a nanostructured pyrolytic graphite sheet (PGS) microwell chip configured to detect 2 proteins simultaneously from complex liquid samples with high sensitivity and selectivity. Assays are done in 18 min at cost of <$1.00 using 1–2 microliters of sample. 3D printed microfluidic array design integrates reagent and sample chambers with rapid ECL detection. A commercial programmable syringe pump used with a preset program allows pump to pause and resume reagent delivery as required for completion of the sandwich immunoassays. Nanostructured surfaces feature antibody-decorated single wall carbon nanotube forests on PGS chip microwells, and sensitivity is amplified via massively labeled RuBPY-silica nanoparticles for detection. Prostate specific antigen (PSA) and prostate specific membrane antigen (PSMA) were measured simultaneously from human serum on the immunoarray with detection limits 150 fg mL−1 for PSA and 230 fg mL−1 for PSMA, with dynamic ranges up to 5 ng mL−1. Validation of the immunoarray by measuring these proteins in human serum showed good correlation with single protein ELISA. These 3D printed platforms can be easily adapted to multiple applications and configurable CAD files for the immunoarray can be downloaded from our lab's website.

Graphical abstract: Automated 4-sample protein immunoassays using 3D-printed microfluidics

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Supplementary files

Article information


Submitted
04 Jun 2018
Accepted
27 Jul 2018
First published
30 Jul 2018

Anal. Methods, 2018,10, 4000-4006
Article type
Technical Note

Automated 4-sample protein immunoassays using 3D-printed microfluidics

K. Kadimisetty, A. P. Spak, K. S. Bhalerao, M. Sharafeldin, Islam M. Mosa, N. H. Lee and J. F. Rusling, Anal. Methods, 2018, 10, 4000
DOI: 10.1039/C8AY01271G

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