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

摘要

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 largescale and continuous monitoring of the ocean microbiome,including its plankton composition.