Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomicists. There are now a wide variety of robust and inexpensive enrichment strategies to generate phosphoproteomes, while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities (ABRF) umbrella, the Proteomics Standards Research Group (sPRG) has worked to develop a multipathway phosphopeptide prototype mixture based on a pool of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This prototype mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/IGF-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. We describe a characterization of the standard spiked into a HeLa tryptic digest stimulated with both EGF and IGF1 to activate the MAPK and PI3K/AKT/mTOR pathways. We demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently by the co-authors with this prototype mixture with data independent acquisition.

This data publication contains the code and demonstration data from a study using non-negative matrix factorization to learn, characterize, and chemically map crystal polymorphs at the single particle scale from high spatial resolution time-of-flight secondary ion mass spectrometry (ToF-SIMS) images. The data from this study includes the ToF-SIMS chemical imaging of three inkjet printed arrays of acetaminophen deposits, corresponding THz Raman spectra, and ToF-SIMS chemical images of a pure acetaminophen powder and a migraine medicine. Also included are the data analysis code (MATLAB 2022a*) used for non-negative matrix factorization and other processes. The code is used to learn the dataset's latent dimensionality and decompose the data into constituent phases representative of acetaminophen polymorphs. The process is also demonstrated by unmixing a multi-component particle migraine medicine sample.Associated publication: https://doi.org/10.1021/acs.analchem.2c03913*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

This data publication contains the code and demonstration data from a study using non-negative matrix factorization to learn, characterize, and chemically map crystal polymorphs at the single particle scale from high spatial resolution time-of-flight secondary ion mass spectrometry (ToF-SIMS) images. The data from this study includes the ToF-SIMS chemical imaging of three inkjet printed arrays of acetaminophen deposits, corresponding THz Raman spectra, and ToF-SIMS chemical images of a pure acetaminophen powder and a migraine medicine. Also included are the data analysis code (MATLAB 2022a*) used for non-negative matrix factorization and other processes. The code is used to learn the dataset's latent dimensionality and decompose the data into constituent phases representative of acetaminophen polymorphs. The process is also demonstrated by unmixing a multi-component particle migraine medicine sample.Associated publication: https://doi.org/10.1021/acs.analchem.2c03913*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.