Cell-type-specific signaling networks in heterocellular organoids.

Despite the widespread adoption of organoids as biomimetic tissue models, methods to comprehensively analyze cell-type-specific post-translational modification (PTM) signaling networks in organoids are absent. Here, we report multivariate single-cell analysis of such networks in organoids and organoid cocultures. Simultaneous analysis by mass cytometry of 28 PTMs in >1 million single cells derived from small intestinal organoids reveals cell-type- and cell-state-specific signaling networks in stem, Paneth, enteroendocrine, tuft and goblet cells, as well as enterocytes. Integrating single-cell PTM analysis with thiol-reactive organoid barcoding in situ (TOBis) enables high-throughput comparison of signaling networks between organoid cultures. Cell-type-specific PTM analysis of colorectal cancer organoid cocultures reveals that shApc, KrasG12D and Trp53R172H cell-autonomously mimic signaling states normally induced by stromal fibroblasts and macrophages. These results demonstrate how standard mass cytometry workflows can be modified to perform high-throughput multivariate cell-type-specific signaling analysis of healthy and cancerous organoids.

Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification.

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between liquid chromatography-mass spectrometry runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without the common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at http://maxquant.org.

Estimating the total number of phosphoproteins and phosphorylation sites in eukaryotic proteomes.

BACKGROUND: Phosphorylation is the most frequent post-translational modification made to proteins and may regulate protein activity as either a molecular digital switch or a rheostat. Despite the cornucopia of high-throughput (HTP) phosphoproteomic data in the last decade, it remains unclear how many proteins are phosphorylated and how many phosphorylation sites (p-sites) can exist in total within a eukaryotic proteome. We present the first reliable estimates of the total number of phosphoproteins and p-sites for four eukaryotes (human, mouse, Arabidopsis, and yeast). RESULTS: In all, 187 HTP phosphoproteomic datasets were filtered, compiled, and studied along with two low-throughput (LTP) compendia. Estimates of the number of phosphoproteins and p-sites were inferred by two methods: Capture-Recapture, and fitting the saturation curve of cumulative redundant vs. cumulative non-redundant phosphoproteins/p-sites. Estimates were also adjusted for different levels of noise within the individual datasets and other confounding factors. We estimate that in total, 13 000, 11 000, and 3000 phosphoproteins and 230 000, 156 000, and 40 000 p-sites exist in human, mouse, and yeast, respectively, whereas estimates for Arabidopsis were not as reliable. CONCLUSIONS: Most of the phosphoproteins have been discovered for human, mouse, and yeast, while the dataset for Arabidopsis is still far from complete. The datasets for p-sites are not as close to saturation as those for phosphoproteins. Integration of the LTP data suggests that current HTP phosphoproteomics appears to be capable of capturing 70 % to 95 % of total phosphoproteins, but only 40 % to 60 % of total p-sites.