A multi-scale map of cell structure fusing protein images and interactions.
Yue Qin, Edward L Huttlin, Casper F Winsnes, Maya L Gosztyla, Ludivine Wacheul, Marcus R Kelly, Steven M Blue, Fan Zheng, Michael Chen, Leah V Schaffer, Katherine Licon, Anna Bäckström, Laura Pontano Vaites, John J Lee, Wei Ouyang, Sophie N Liu, Tian Zhang, Erica Silva, Jisoo Park, Adriana Pitea, Jason F Kreisberg, Steven P Gygi, Jianzhu Ma, J Wade Harper, Gene W Yeo, Denis L J Lafontaine, Emma Lundberg, Trey Ideker
Nature · 2021 · Vol. 600 (7889) · pp. 536-542
Abstract
The cell is a multi-scale structure with modular organization across at least four orders of magnitude<sup>1</sup>. 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<sup>2,3</sup>. Here we integrate immunofluorescence images in the Human Protein Atlas<sup>4</sup> with affinity purifications in BioPlex<sup>5</sup> 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.
Linked datasets 1
Mapping cell structure across scales by fusing protein images and interactions
Homo sapiens
8 data files
| File | Type | Size |
|---|---|---|
| RPS3A_rep1_input_S5_L008_R1_001.fastq.gz | RIP-Seq | 379.6 MB |
| RPS3A_rep1_IP_S15_L008_R1_001.fastq.gz | RIP-Seq | 444.6 MB |
| RPS3A_rep2_input_S6_L008_R1_001.fastq.gz | RIP-Seq | 423.2 MB |
| RPS3A_rep2_IP_S16_L008_R1_001.fastq.gz | RIP-Seq | 357.2 MB |
| SRR14150910_GSM5227081 | RIP-Seq | 379.0 MB |
| SRR14150911_GSM5227082 | RIP-Seq | 444.0 MB |
| SRR14150912_GSM5227083 | RIP-Seq | 423.0 MB |
| SRR14150913_GSM5227084 | RIP-Seq | 357.0 MB |
Potentially related datasets 1
These accessions were text-mined from the PMC full text. They may be referenced for comparison, cited from other studies, or otherwise mentioned without being primary data for this paper.
Analysis pipelines 1
| Pipeline | Assay | Steps | Dataset |
|---|---|---|---|
| GSE171553 eCLIP Data Processing geo_data_processing | eCLIP | 2 | GSE171553 |
Funding 11 grants
| Grant | Funder |
|---|---|
| R01 HG004659 | NHGRI NIH HHS (United States) |
| U54 CA209891 | NCI NIH HHS (United States) |
| T32 CA067754 | NCI NIH HHS (United States) |
| U41 HG009889 | NHGRI NIH HHS (United States) |
| U01 MH115747 | NIMH NIH HHS (United States) |
| P41 GM103504 | NIGMS NIH HHS (United States) |
| R01 HL137223 | NHLBI NIH HHS (United States) |
| R50 CA243885 | NCI NIH HHS (United States) |
| U24 HG006673 | NHGRI NIH HHS (United States) |
| F99 CA264422 | NCI NIH HHS (United States) |
| R01 HG009979 | NHGRI NIH HHS (United States) |