Qin Y, Huttlin EL, Winsnes CF, Gosztyla ML, Wacheul L, Kelly MR, Blue SM, Zheng F, Chen M, Schaffer LV, Licon K, Bäckström A, Vaites LP, Lee JJ, Ouyang W, Liu SN, Zhang T, Silva E, Park J, Pitea A, Kreisberg JF, Gygi SP, Ma J, Harper JW, Yeo GW, Lafontaine DLJ, Lundberg E, Ideker T
Nature - (-) - [2021-11-24; online 2021-11-24]
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.