Amazing stuff! Artificial intelligence, machine learning are being used more and more to reveal the mysteries of nature! This could be a game changer/breakthrough!
"... “The combination of these technologies is unique and powerful because it’s the first time measurements at vastly different scales [micrometer & nanometer] have been brought together,” ...
“But how do you bridge that gap from nanometer to micron scale? That has long been a big hurdle in the biological sciences,” ... “Turns out you can do it with artificial intelligence — looking at data from multiple sources and asking the system to assemble it into a model of a cell.”
The team trained the MuSIC artificial intelligence platform to look at all the data and construct a model of the cell. The system doesn’t yet map the cell contents to specific locations, like a textbook diagram, in part because their locations aren’t necessarily fixed. Instead, component locations are fluid and change depending on cell type and situation.
... noted this was a pilot study to test MuSIC. They’ve only looked at 661 proteins and one cell type. ..."From the abstract:
"The eukaryotic cell is a multi-scale structure with modular organization across at least four orders of magnitude. Two central approaches for mapping this structure – protein fluorescent imaging and protein biophysical association – each generate extensive datasets but of distinct qualities and resolutions that are typically treated separately. Here, we integrate immunofluorescent images in the Human Protein Atlas with ongoing affinity purification experiments from the BioPlex resource6 to create a unified hierarchical map of eukaryotic cell architecture. Integration involves configuring each approach to produce a general measure of protein distance, then calibrating the two measures using machine learning. The evolving map, called the Multi-Scale Integrated Cell (MuSIC 1.0), currently resolves 69 subcellular systems of which approximately half are undocumented. Based on these findings we perform 134 additional affinity purifications, validating close subunit associations for the majority of systems. The map elucidates roles for poorly characterized proteins, such as the appearance of FAM120C in chromatin; identifies new protein assemblies in ribosomal biogenesis, RNA splicing, nuclear speckles, and ion transport; and reveals crosstalk between cytoplasmic and mitochondrial ribosomal proteins. By integration across scales, MuSIC substantially increases the mapping resolution obtained from imaging while giving protein interactions a spatial dimension, paving the way to incorporate many molecular data types in proteome-wide maps of cells."
Mapping cell structure across scales by fusing protein images and interactions (open access, 2020 preprint)
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