Extended Data Fig. 1: Endosomal proteome scoring method and optimization of large-scale Endo-IP.

a, Overview of datasets used for endosomal scoring, including the number of proteins identified in each and across datasets, and the number of well-known endosomal proteins identified in the indicated studies. b, Multiple correspondence analysis (MCA) showing an overview of the relationship among datasets from a. Each node represents a dataset color-coded by isolation method and proportional size to the total number of proteins identified. c, Bar plot depicting the number of proteins identified across multiple datasets for several subcellular compartments. d, Line graph showing the percentage of proteins identified across all 16 datasets in a and their protein abundance in our Endo-IP experiments from HEK293 cells (Supplementary Table 1) represented as loess (Locally Estimated Scatterplot Smoothing) regression line and 95% confidence level interval band. f,g, Number of bait proteins, protein-protein interactions (PPIs), and PPIs per protein in Bioplex²⁸ (panel f) and Open Cell¹¹⁹ (panel g) according to organelle assignment (n number of proteins in each category is indicated on top). For box plots, the middle line corresponds to the median, the lower and upper end of the box correspond respectively to the first and third quartiles, and the whiskers extend from the box to 1.5 times the inter-quartile range. h, Schematic of the purification steps from Endo-IP to endosomal pellet used for complexomics. i,j, Number of proteins identified (panel i) and abundance per compartment (panel j) in endosomal pellet compared to the input (PNS), supernatant, or NP40 eluate from the Endo-IP as depicted in panel h. k, Violin plot showing the fold-change enrichment of proteins from individual organelle compartments in endosomal pellets compared to input (PNS). l, Volcano plot showing fold-changes and FDR adjusted p-value for proteins in endosomal pellets compared to input (PNS) (n = 3 biologically independent replicates). DEqMS algorithm was used for statistical analysis with multiple testing correction as implemented in¹⁴⁶. h, Images modified from ref. ⁴⁴ (Copyright (2024) National Academy of Sciences).

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