系统生物学

Protein synthesis and degradation (i.e., turnover) forms an important part of protein homeostasis and has been implicated in many age-associated diseases. Different cellular locations, such as organelles and membraneless compartments, often contain individual protein quality control and degradation machineries. Conventional methods to assess protein turnover across subcellular compartments require targeted genetic manipulation or isolation of specific organelles. Here we describe a protocol for simultaneous proteome localization and turnover (SPLAT) analysis, which combines protein turnover measurements with unbiased subcellular spatial proteomics to measure compartment-specific protein turnover rates on a proteome-wide scale. This protocol utilizes dynamic stable isotope labeling of amino acids in cell culture (dynamic SILAC) to resolve the temporal information of protein turnover and multi-step differential ultracentrifugation to assign proteins to multiple subcellular localizations. We further incorporate 2D liquid chromatography fractionation to greatly increase analytical depth while multiplexing with tandem mass tags (TMT) to reduce acquisition time 10-fold. This protocol resolves the spatial and temporal distributions of proteins and can also reveal temporally distinct spatial localizations within a protein pool.

Cellular communication relies on the intricate interplay of signaling molecules, which come together to form the cell–cell interaction (CCI) network that orchestrates tissue behavior. Researchers have shown that shallow neural networks can effectively reconstruct the CCI from the abundant molecular data captured in spatial transcriptomics (ST). However, in scenarios characterized by sparse connections and excessive noise within the CCI, shallow networks are often susceptible to inaccuracies, leading to suboptimal reconstruction outcomes. To achieve a more comprehensive and precise CCI reconstruction, we propose a novel method called triple-enhancement-based graph neural network (TENET). The TENET framework has been implemented and evaluated on both real and synthetic ST datasets. This protocol primarily introduces our network architecture and its implementation.