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Filtration & fluid control


and sorting technology, is among the first to take the combination of AI, ML and microfluidics into the commercial sphere. The California-based company came out of stealth mode a few months ago and is gearing up to launch its platform, which identifies and isolates viable cells based on morphological distinctions for use in translational research, diagnostic testing and therapeutics. The technology enables the delivery of intact and viable single cells with high precision and maintains cell viability for downstream characterisation. It can be used to isolate any type of cell, even those occurring at frequencies as low as one in a billion. “Our focus is on morphology, which has only been used in a qualitative and descriptive way before,” says Masaeli. “There is a lot going on in a single cell. Our goal is to quantify it, to get morphology data at a massive scale so that we can take a big data approach to diagnosis.” Deepcell’s mission is to enable novel biological insights at the single-cell level for improved diagnosis and treatment of disease. Microfluidics provides the single-cell data, which AI and ML then analyse for meaningful patterns.


“Microfluidics provides clean data for our AI brain,” explains Masaeli. “We did a lot of processing and analysing to understand what data we need for the AI to identify different cell types. So, we can use ML to do the job of identifying cells on the basis of their morphology. We can also use ML for the fabrication process, using non-human observation to see if the chip has been manufactured properly. Automating quality control is important because differences in microfluidics chips will affect accuracy.” The platform automates the once-manual process of identifying cells by their morphology. The results can be used for profiling cells within the same tissue to understand variations in the expression of different genes and proteins, enriching them to study tumour microenvironments and even to improve the early detection of cancer by catching rare circulating tumour cells in body fluids.


“AI helps to translate the morphology into numbers,” says Salek. “Usually, that involves a lot of visual checks using a microscope, but the embedded AI now performs that task. Microfluidics is a very challenging field in terms of fabrication, repeatability and quality control. It is not for a non-expert. Part of our innovation is to streamline the process as much as possible to maximise stability and repeatability.” A key feature of Deepcell’s platform is that cell classification is not limited by labels or markers. The AI is continuously learning to differentiate cells without inherent bias. For instance, people often label larger cells as malignant, which is not necessarily accurate. The AI is trained on cells that have already been identified. No one is applying their own preconceptions


Medical Device Developments / www.nsmedicaldevices.com


of what a malignant cell should look like, which greatly improves accuracy. Ultimately, the technology could have a huge impact on any therapeutic or research area in which cells are studied. In the future, it could conceivably enable the consolidation of many invasive tests into a single non-invasive one.


“There is a real hunger for a tool in this area,” says Masaeli. “Morphology is a very important phenotype that has often been left out, but people now want to try it on their hypotheses to achieve their translational and clinical goals. For instance, non-invasive liquid biopsies could ultimately replace more barbaric procedures. It is all about identifying what is unusual and interesting using AI, which looks for patterns in data instead of someone looking at blood cells through a microscope.” Other companies are also bringing AI-enabled microfluidics platforms to market. Nicoya, for example, has combined surface plasmon resonance (SPR) – an optical technique for detecting molecular interactions – with DMF. Its Alto platform, which can direct and influence nanolitre droplets using electricity, is the first digital, high-throughput benchtop SPR system. “Spatial transcriptomics developed by 10x Genomics is another great example of microfluidics and ML making it to the market,” says Lashkaripour. “It works in conjunction with a droplet-based microfluidic platform to enable researchers to measure gene activity and map that activity to a location on the tissue sample. It can quickly lead to new insights for better understanding disease and developing novel diagnostics.”


Data chasers


The convergence of AI and microfluidics is opening new doors every day. A group of electrical engineers, computer scientists and biomedical engineers at the University of California-Irvine recently reported the development of a new lab-on-a-chip using AI, microfluidics and nanoparticle inkjet printing that can improve the study of tumour heterogeneity, potentially enabling new approaches for reducing resistance to cancer therapies. In its paper, the team showed that the platform allows for the precise characterisation of a variety of cancer cells, opening the door to a better understanding of tumour initiation, progression and metastasis, which could shape the development of better drug therapies. “We are at the beginning of a scientific data boom, so ML will play an ever-increasing role in almost every field, including diagnostic tests,” believes Lashkaripour. Where data is involved, AI and ML are rarely far behind. As companies like Deepcell make their platforms commercially available, the potential for microfluidics to become cheaper, more scalable and within the technical capability of non-experts will no doubt create a hotbed for innovation. ●


85 60%


Estimated deaths from breast cancer due to a lack of early detection programmes in countries with inadequate resources.


WHO


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