Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer
This enables rapid, automated screening of drinking water for parasites in resource-limited settings, addressing a public health issue.
The researchers tackled the problem of detecting Giardia lamblia cysts in water samples by developing a portable imaging flow cytometer using deep learning, achieving real-time detection at a throughput of 100 mL/h and sensitivity to low contamination levels (e.g., <10 cysts per 50 mL).
We report a field-portable and cost-effective imaging flow cytometer that uses deep learning to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL/h. This flow cytometer uses lensfree color holographic imaging to capture and reconstruct phase and intensity images of microscopic objects in a continuously flowing sample, and automatically identifies Giardia Lamblia cysts in real-time without the use of any labels or fluorophores. The imaging flow cytometer is housed in an environmentally-sealed enclosure with dimensions of 19 cm x 19 cm x 16 cm and weighs 1.6 kg. We demonstrate that this portable imaging flow cytometer coupled to a laptop computer can detect and quantify, in real-time, low levels of Giardia contamination (e.g., <10 cysts per 50 mL) in both freshwater and seawater samples. The field-portable and label-free nature of this method has the potential to allow rapid and automated screening of drinking water supplies in resource limited settings in order to detect waterborne parasites and monitor the integrity of the filters used for water treatment.