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We might not know half of what's in our cells, new AI technique reveals (phys.org)
4 points by panabee 2 days ago | hide | past | favorite | 1 comment





Key Points:

* Proteins and cells are typically studied with one of two techniques: microscope imaging or biophysical association. With imaging, researchers add florescent tags of various colors to proteins of interest and track their movements and associations across the microscope's field of view. To look at biophysical associations, researchers might use an antibody specific to a protein to pull it out of the cell and see what else is attached to it.

* Microscopes allow scientists to see down to the level of a single micron, about the size of some organelles, such as mitochondria. Smaller elements, such as individual proteins and protein complexes, can't be seen through a microscope. Biochemistry techniques, which start with a single protein, allow scientists to get down to the nanometer scale.

* The team trained an AI 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. With this system, the team discovered new components inside a human kidney cell line, including a new complex of proteins that bind to RNA.

Paper:

https://www.nature.com/articles/s41586-021-04115-9

Paper Abstract:

The cell is a multi-scale structure with modular organization across at least four orders of magnitude1. 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 separately2,3. Here we integrate immunofluorescence images in the Human Protein Atlas4 with affinity purifications in BioPlex5 to create a unified hierarchical map of human cell architecture. Integration is achieved by configuring each approach as a general measure of protein distance, then calibrating the two measures using machine learning. The map, known as the multi-scale integrated cell (MuSIC 1.0), resolves 69 subcellular systems, of which approximately half are to our knowledge undocumented. Accordingly, we perform 134 additional affinity purifications and validate subunit associations for the majority of systems. The map reveals a pre-ribosomal RNA processing assembly and accessory factors, which we show govern rRNA maturation, and functional roles for SRRM1 and FAM120C in chromatin and RPS3A in splicing. By integration across scales, MuSIC increases the resolution of imaging while giving protein interactions a spatial dimension, paving the way to incorporate diverse types of data in proteome-wide cell maps.




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