Aggregation-based assays, using micro- and nano-particles have been widely
accepted as an efficient and cost-effective bio-sensing tool, particularly in
microbiology, where particle clustering events are used as a metric to infer
the presence of a specific target analyte and quantify its concentration. Here,
we present a sensitive and automated readout method for aggregation-based
assays using a wide-field lens-free on-chip microscope, with the ability to
rapidly analyze and quantify microscopic particle aggregation events in 3D,
using deep learning-based holographic image reconstruction. In this method, the
computation time for hologram reconstruction and particle autofocusing steps
remains constant, regardless of the number of particles/clusters within the 3D
sample volume, which provides a major throughput advantage, brought by deep
learning-based image reconstruction. As a proof of concept, we demonstrate
rapid detection of herpes simplex virus (HSV) by monitoring the clustering of
antibody-coated micro-particles, achieving a detection limit of ~5 viral copies
per micro-liter (i.e., ~25 copies per test).
[Journal_ref: ACS Photonics (2018)]
