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Tian and colleagues\u2019 method allows for a more holistic picture of a single cell without the use of any harsh chemicals or gene editing. By combining new microscopy techniques with machine learning, scientists can quickly and efficiently examine hundreds of individual cells at an incredibly fine level of detail.<\/p>\n
The researchers used reflectance light microscopy to look at the microscopic details of a cell\u2019s intricate structures. Reflected light bounces off the top of specimens, while transmitted light illuminates specimens from below. Though both methods can be used to look at individual cells, reflectance light microscopy helps researchers detect structural changes on the \u201ctop\u201d, or surface, of a cell that can be hard to see with transmitted light.<\/p>\n
To capture a single cell\u2019s elaborate interior, Tian and colleagues used multiple light-emitting diodes (LEDs) in their microscope. The LEDs were arranged in a grid so that the researchers could change the number and direction of lights used to illuminate the cells. Changing the brightness and angle of the LEDs affects how the light reflects off the surface of a cell and in turn, how the cell looks through the microscope. Researchers can better capture the rich details of a cell\u2019s microscopic components, like its nucleus or membrane, by viewing the cell from different perspectives. And the more detailed the images are, the better the deep learning algorithm can be at \u201cdigitally labeling\u201d tiny structures within an individual cell.<\/p>\n
The researchers used their microscope to look for four different cellular structures–deoxyribonucleic acid (DNA), Golgi apparatus, endosomes, and actin. The team also looked for signs that a cell could be replicating or dying. They then trained their deep learning model on images of cells stained with fluorescent markers for each of these six features.<\/p>\n
After training, the team tested their deep learning framework on images of cells without the fluorescent markers. They found that their model can accurately predict and label where the four different structures should be in individual cells. And, their model can combine multiple predictions into a single image. So rather than having separate images of a cell\u2019s DNA, Golgi apparatus, endosomes, or actin, the team can look at all these different cellular components together.<\/p>\n This \u201cmultiplexing\u201d, or combining of different labels into a single image, is so accurate that scientists can even use the labeled images to determine a cell\u2019s phase in the cell cycle.\u00a0 Tian and colleagues\u2019 deep learning model can detect slight changes to the Golgi apparatus that happen when a cell transitions into mitosis, or cellular division.<\/p>\n \u201cMultiplexing can be hard. This technique could be useful in high-throughput applications to digitally fuse many different fluorescent labels together and give you a holistic picture of large populations of cells,\u201d said Tian, \u201cThe Hariri Institute\u2019s award gave our team a nice boost to get this project off the ground.\u201d<\/p>\n![\"\"](\"\/hic\/files\/2021\/02\/Tian-Figure-4-cells-636x361.png\")
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