Smartphone-Based Paper Microfluidic Immunoassay of Salmonella and E. coli.

Previous studies from our lab have created a simple procedure for single-cell count of bacteria on a paper chip platform using optical detection from a smartphone. The procedure and steps employed are outlined along with the lessons learned and details of certain steps and how the design was optimized. Smartphone optical detection is easy to use, low cost, and potentially field deployable, which can be useful for early and rapid detection of pathogens. Smartphone imaging of a paper microfluidic chip preloaded with antibody-conjugated particles provides an adaptable platform for detection of different bacterial targets. The paper microfluidic chip was fabricated with a multichannel design. Each channel was preloaded with either a negative control of bovine serum albumin (BSA) conjugated particles, anti-Salmonella Typhimurium-conjugated particles with varying amounts (to cover different ranges of assay), or anti-Escherichia coli-conjugated particles. Samples were introduced to the paper microfluidic chip using pipetting. Antigens of Salmonella Typhimurium traveled through the channel by capillary action confined within the paper fibers surrounded by the hydrophobic barrier. The paper channel was observed to act as a filter for unwanted particles and contaminants found in field samples. Serial dilutions of known concentrations of bacterial targets were also tested using this procedure to construct a standard curve prior to the assays. The antibody-conjugated particles were able to immunoagglutinate which was quantified through evaluation of Mie scatter intensity. This Mie scattering was quantified in images taken with a smartphone at an optimized angle and distance. Mie scatter simulation provided a method of optimizing the experimental setup and could translate easily to other types of target sample matrices. A smartphone application was developed to help the user position the smartphone optimally in relation to the paper microfluidic chip. The application integrated both image capturing capability and a simple image processing algorithm that calculated bacteria concentrations. The detection limit was at a single-cell level with a total assay time ranging from 90 to less than 60 s depending on the target.