Drying biofluid droplets used to diagnose disease

A research team led by The University of Tokyo has developed an automated, high-throughput system that relies on imaging droplets of biofluids (such as blood, saliva and urine) for disease diagnosis in an attempt to reduce the number of consumables and equipment needed for biomedical testing. Biofluid droplet images are analysed by machine-learning algorithms to diagnose disease, with the technology relying on the drying process of the droplets to distinguish between normal and abnormal samples.

Current medical diagnostic tests typically require 5–10 mL of blood, necessitating a trip to the clinic or other phlebotomy service to draw blood with needles and tubes. Besides being painful, inconvenient and inefficient, blood draws are often a luxury of developed nations with modern healthcare infrastructure. By eliminating the need for phlebotomy services and other consumables, diagnostic tests could be implemented worldwide to improve disease diagnosis and cost-efficiency.

“We set out to develop a simple, rapid and reliable approach to analyse what happens when a droplet of blood dries on a surface,” said Associate Professor Miho Yanagisawa from The University of Tokyo. “Traditionally, researchers have focused only on the final pattern left after drying. In our study, we looked beyond that, observing the entire drying process in real time. By tracking how the droplet’s shape and internal structures evolve over time, we were able to uncover rich information about the fluid’s composition.”

By using machine learning, the team could ‘decode’ the evolving patterns in drying blood droplets, allowing them to clearly distinguish between healthy blood and samples with abnormalities based solely on their drying behaviour. This technique, described in the journal Advanced Intelligent Systems, doesn’t require specialised equipment to make an accurate diagnosis.

Images of drying blood samples are acquired using brightfield microscopy and a common 4x objective lens, which magnifies samples four times. Images are acquired over time with a digital camera mounted on the microscope. The same workflow can also be used to analyse bodily fluids including saliva and urine, expanding its diagnostic capacity without the need for additional equipment.

“The key takeaway is that every moment of the drying process holds valuable clues, not just the final pattern left behind,” said Anusuya Pal, a postdoctoral research fellow in the Yanagisawa Lab and first author of the study. “Each stage reveals how proteins, cells and other components move and reorganise within the droplet, capturing a dynamic ‘story’ of the sample’s internal state.”

By combining this time-evolving information with machine learning, the team can accurately identify subtle abnormalities in blood samples. According to Pal, “This approach opens up a new way of thinking about medical diagnostics: one that is simple, fast and low cost, yet remarkably informative.”

The current research establishes proof of concept for the team, demonstrating an effective workflow for detecting diseases such as diabetes, influenza, malaria and others, that has potential in the field. The researchers hope to translate their methodology into a mobile and practical health-screening tool for use in developing countries.

“Such a tool could make health monitoring faster, more affordable and more accessible, especially in communities with limited access to laboratory testing,” said co-author Assistant Professor Amalesh Gope, from Tezpur University. “Ultimately, our goal is to bring laboratory-level insights to the point of care, enabling early detection and preventive health care for everyone.”

Image credit: iStock.com/robybret