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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

GSE171553 GSE · via ncbi_elink
GEO ↗

Mapping cell structure across scales by fusing protein images and interactions

Homo sapiens

8 data files
FileTypeSize
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.

Accession Source Title
GSE171553 GSE · GEO GEO ↗ Mapping cell structure across scales by fusing protein images and interactions

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)