MS1Connect: a mass spectrometry run similarity measure.

MOTIVATION: Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols. RESULTS: We propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though these data are not used by MS1Connect. AVAILABILITY AND IMPLEMENTATION: The MS1Connect software is available at https://github.com/bmx8177/MS1Connect. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Polysaccharide-based liquid storage and transport media for non-refrigerated preservation of bacterial pathogens.

The preservation of biological samples for an extended time period of days to weeks after initial collection is important for the identification, screening, and characterization of bacterial pathogens. Traditionally, preservation relies on cold-chain infrastructure; however, in many situations this is impractical or not possible. Thus, our goal was to develop alternative bacterial sample preservation and transport media that are effective without refrigeration or external instrumentation. The viability, nucleic acid stability, and protein stability of Bacillus anthracis Sterne 34F2, Francisella novicida U112, Staphylococcus aureus ATCC 43300, and Yersinia pestis KIM D27 (pgm-) was assessed for up to 28 days. Xanthan gum (XG) prepared in PBS with L-cysteine maintained more viable F. novicida U112 cells at elevated temperature (40°C) compared to commercial reagents and buffers. Viability was maintained for all four bacteria in XG with 0.9 mM L-cysteine across a temperature range of 22-40°C. Interestingly, increasing the concentration to 9 mM L-cysteine resulted in the rapid death of S. aureus. This could be advantageous when collecting samples in the built environment where there is the potential for Staphylococcus collection and stabilization rather than other organisms of interest. F. novicida and S. aureus DNA were stable for up to 45 days upon storage at 22°C or 40°C, and direct analysis by real-time qPCR, without DNA extraction, was possible in the XG formulations. XG was not compatible with proteomic analysis via LC-MS/MS due to the high amount of residual Xanthomonas campestris proteins present in XG. Our results demonstrate that polysaccharide-based formulations, specifically XG with L-cysteine, maintain bacterial viability and nucleic acid integrity for an array of both Gram-negative and Gram-positive bacteria across ambient and elevated temperatures.

Proteomics Goes to Court: A Statistical Foundation for Forensic Toxin/Organism Identification Using Bottom-Up Proteomics.

Bottom-up proteomics is increasingly being used to characterize unknown environmental, clinical, and forensic samples. Proteomics-based bacterial identification typically proceeds by tabulating peptide "hits" (i.e., confidently identified peptides) associated with the organisms in a database; those organisms with enough hits are declared present in the sample. This approach has proven to be successful in laboratory studies; however, important research gaps remain. First, the common-practice reliance on unique peptides for identification is susceptible to a phenomenon known as signal erosion. Second, no general guidelines are available for determining how many hits are needed to make a confident identification. These gaps inhibit the transition of this approach to real-world forensic samples where conditions vary and large databases may be needed. In this work, we propose statistical criteria that overcome the problem of signal erosion and can be applied regardless of the sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result of an organism or toxin identification. We test the proposed criteria on 919 LC-MS/MS data sets originating from 2 toxins and 32 bacterial strains acquired using multiple data collection platforms. Results reveal a > 95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.