Urinary pellet sample preparation for shotgun proteomic analysis of microbial infection and host-pathogen interactions.

Urine is one of the most important biofluids in clinical proteomics, and in the past decades many potential disease biomarkers have been identified using mass spectrometry-based proteomics. Current studies mainly perform analyses of the urine supernatant devoid of cells and cell debris, and the pellet (or sediment) fraction is discarded. However, the pellet fraction is biologically of interest. It may contain whole human cells shed into the urine from anatomically proximal tissues and organs (e.g., kidney, prostate, bladder, urothelium, and genitals), disintegrated cells and cell aggregates derived from such tissues, viruses and microbial organisms which colonize or infect the urogenital tract. Knowledge of the function, abundance, and tissue of origin of such proteins can explain a pathological process, identify a microbe as the cause of urinary tract infection, and measure the human immune response to the infection-associated pathogen(s). Successful detection of microbial species in the urinary pellet via proteomics can serve as a clinical diagnostic alternative to traditional cell culture-based laboratory tests. Filter-aided sample preparation (FASP) has been widely used in shotgun proteomics. The methodology presented here implements an effective lysis of cells present in urinary pellets, solubilizes the majority of the proteins derived from microbial and human cells, and generates enzymatic digestion-compatible protein mixtures using FASP followed by optimized desalting procedures to provide a peptide fraction for sensitive and comprehensive LC-MS/MS analysis. A highly parallel sample preparation method in 96-well plates to allow scaling up such experiments is discussed as well. Separating peptides by nano-LC in one dimension followed by online MS/MS analysis on a Q-Exactive mass spectrometer, we have shown that more than 1,000 distinct microbial proteins and 1,000 distinct human proteins can be identified from a single experiment.