Deep learning enables high-throughput analysis of particle-aggregation-based bio-sensors imaged using holography

TL;DR

This paper introduces a deep learning-enhanced holographic microscopy method for rapid, high-throughput analysis of particle aggregation in bio-sensors, enabling sensitive detection of viruses like HSV with minimal computation time.

Contribution

The study presents a novel deep learning-based holographic image reconstruction technique that maintains constant processing time regardless of particle count, significantly increasing assay throughput.

Findings

Achieved rapid detection of HSV with ~5 viral copies per microliter.

Demonstrated constant computation time for hologram reconstruction regardless of particle number.

Enabled automated 3D analysis of particle aggregation in bio-sensing applications.

Abstract

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).