A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples

We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h. The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel. These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling. Motion blur is eliminated by simultaneously illuminating the sample with red, green, and blue light-emitting diodes that are pulsed. Operated by a laptop computer, this portable device measures 15.5 cm × 15 cm × 12.5 cm, weighs 1 kg, and compared to standard imaging flow cytometers, it provides extreme reductions of cost, size and weight while also providing a high volumetric throughput over a large object size range. We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro- and nanoplankton composition. Furthermore, we measured the concentration of a potentially toxic alga (Pseudo-nitzschia) in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health. The cost-effectiveness, compactness, and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for large-scale and continuous monitoring of the ocean microbiome, including its plankton composition.Bio-analysis: Rapidly spotting toxicity in a drop of the oceanA portable device that combines holographic imaging with artificial intelligence can rapidly detect potentially harmful algae in ocean water. Aydogan Ozcan, Zoltan Gorocs and colleagues from the University of California Los Angeles in the United States developed an inexpensive flow cytometer that pumps water samples containing tiny marine organisms, past an LED chip pulsing red, blue, and green light simultaneously. Deep learning algorithms trained to recognize background signals automatically analyze the holographic interference patterns created by the marine organisms and rapidly generate color images with microscale resolution. Sample throughput is boosted 10-fold over conventional imaging flow cytometry by avoiding the use of lenses. Using a lightweight and inexpensive prototype, the team monitored plankton levels at six public beaches and detected a likely toxic organism, the algae Pseudo-nitzschia, at levels matching those from public health laboratories.