The 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 present a computational framework to build a unified hierarchical map of human cell architecture.

In an article in Nature researchers within HPA and collaborators integrate immunofluorescence images in the Human Protein Atlas with affinity purifications in BioPlex 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. Accordingly, the scientists validated subunit associations for the majority of systems.

The map reveals a pre-ribosomal RNA processing assembly and accessory factors, which are shown to 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.

Finally, the synergy achieved in integrating HPA and BioPlex, two large-scale mapping efforts that might have progressed independently, is noted. Such coordination should continue and encompass collaborative dataset design; for instance, by adopting common cell lines and proteins targeted across projects. Furthermore, new protein systems might arise with the inclusion of additional data modalities, such as proximity-dependent labelling, cross-linking mass spectrometry or cryo-electron microscopy. It will be interesting to explore synergies among these platforms, all of which might be calibrated to measure molecular distances and, in turn, contribute to maps of the multi-scale cell.

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