LIFE SCIENCES
‘We are providing widefield clinical imaging that is so much faster than classical staining’
g
as Gibson said, work well in an event-based sensing set-up. ‘We can see the cells flowing past the
microscope and are only working with the data that we care about,’ he said. ‘[Tis means] the machine learning algorithms can more easily recognise if those cells are, say, T cells or something more unusual as we don’t have any other changes in the scene.’ So far, Cambridge Consultants researchers
have used PureSentry to distinguish between E. coli and T cells in real-time, but the system – with its resolution of around half a micron – could be applied to other contaminants including Staphylococcus aureus and Candida albicans. ‘We’ve distinguished between E. coli and T cells based on size, but we’ve also seen these bacteria rotate and cartwheel as they flow through the system and should be able to distinguish contaminants based on how they move as well as shape,’ said Gibson. ‘We can also monitor the population of different-sized particles over time.’ Gibson is also adamant that PureSentry,
with the Prophesee sensor, is set to make a big impact on contamination detection for cell and gene therapy monitoring, as well as other sectors. ‘We could look at yeast cells and contaminants flowing through beer or lactose, and contaminants flowing through milk,’ he said. ‘Te prototype is in our lab and we’re looking for a commercial partner to take this forward with us.’ Researchers at France-based CEA-Leti
have also developed a novel method that so far targets two key applications. In a similar vein to the PureSentry system, the setup can identify and discriminate between different species of bacteria. However, it can also bring down the time taken to detect cancer in tissue samples from days to minutes. As scientific director, Dr Laurent
Duraffourg, and research engineer, Dr Mathieu Dupoy, noted, for tumour detection, their label-free mid-infrared multispectral method can image a 1cm² tissue sample in minutes using four wavelengths. In contrast, the same sample would take several hours to image using FTIR spectroscopy, and around two days using today’s tumour-biopsy staining and immuno-histochemistry labelling procedures, which demand human assessment to confirm disease.
Roche’s Ventana 200 promises reliable, high-speed scanning of histology slides for digital pathology ‘Tis is a new paradigm for laboratories,’
said Dupoy. ‘We are removing the sample prep and shortening the analysis time – we are providing widefield clinical imaging that is so much faster than classical staining and labelling.’ Teir multispectral method comprises
an array of mid-infrared quantum cascade lasers, in the 5µm to 11µm wavelength range, and lensless imaging, with an uncooled bolometer matrix, comprising an array of heat-detecting sensors that are sensitive to infrared radiation wavelengths. Te setup delivers biochemical mapping over a 2.73 by 2.73mm²-wide field of view in a 20ms measurement time per wavelength. Te laser beams are directed at the tissue,
with the infrared radiation transmitted from the tissue then detected by the bolometer matrix. As the infrared radiation is absorbed by a bolometer element, that element increases in temperature, changing its electrical resistance – this change is measured and processed into temperature values that are used to create a mid-infrared absorption image. Such images correspond to the vibrations of targeted chemical bonds, giving a biochemical fingerprint. According to Dupoy, many of these images
can then be combined to create a false- colour image, ready for classification and
cancer detection. ‘From these images, you can see if the tissue is healthy or not and you can also get information on the spread of the cancer, if that tissue is cancerous,’ he said. Perhaps not surprisingly, the experimental
setup is coupled with machine learning algorithms to assist biological cell or bacteria classification in a fast and reproducible way. Indeed, the CEA-Leti researchers have developed machine learning algorithms to work with head and neck cancers, breast cancer, as well as microbial detection. And in recent tumour analyses on mouse tissue, which used 6µm and 10µm infrared wavelengths that are absorbed by endogenous markers (proteins and DNA), the method detected 94 per cent of cancer cells. Likewise, trials on 1,050 colonies of different Staphylococcus strains correctly identified 93 to 96 per cent of the species. Given the results so far, both Dupoy and
Duraffourg are convinced their method, in time, will be used in the digital pathology workflows of tomorrow. Neither CEA-Leti researcher knows of a lab that routinely uses infrared-based imaging in cancer tissue assessment, and are about to complete the development of a portable multispectral lensless imaging setup that can be easily transported to hospitals. Duraffourg is also heading up the launch of a start-up to commercialise the technology. ‘For now, tissues are analysed in our
laboratory, but before translating this into a commercial product, we are building a robust, portable demonstrator that we will take to several hospital labs across Europe that we are partnering with,’ said Duraffourg. Perhaps predictably, the researchers
Prophesee’s Metavision event-based sensor 20 IMAGING AND MACHINE VISION EUROPE DECEMBER 2021/JANUARY 2022
say widespread use of their technology in clinical settings is several years away. Still, their thoughts on industry demand surely echo those across so many life science sectors. As Duraffourg put it: ‘If you talk to clinicians that are also researchers, they really want to have new methods as they are good for science.’ O
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Prophesee
Roche
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