Vendor-independent software tools for quantification of small molecules and metabolites are lacking, especially for targeted analysis workflows. Skyline is a freely available, open-source software tool for targeted quantitative mass spectrometry method development and data processing with a ten-year history supporting 6 major instrument vendors. Designed initially for proteomic analysis, we describe the expansion of Skyline to data for small molecule analysis, including selected reaction monitoring (SRM), high-resolution mass spectrometry (HRMS), and calibrated quantification. This fundamental expansion of Skyline from a peptide-sequence centric tool to a molecule-centric tool makes it agnostic to the source of the molecule while retaining Skyline features critical for workflows in both peptide and more general biomolecular research. The data visualization and interrogation features already available in Skyline -such as peak picking, chromatographic alignment, and transition selection -have been adapted to support small molecule data, including metabolomics. Herein, we explain the conceptual workflow for small molecule analysis using Skyline, demonstrate Skyline performance benchmarked against a comparable *

A total of 136 spot urine samples from 98 individuals, with and without kidney stone disease, were processed in a predefined assay to measure six DNA and protein markers in order to generate a risk score for the non-invasive detection of nephrolithiasis. From this cohort, 56 individuals had spot, non-timed urine samples collected at the time of radiographically confirmed kidney stones, and 54 demographically matched, healthy controls without kidney stone disease also provided spot, non-timed urine samples. Sixteen individuals with persistent stone disease had more than one urine sample. Using a proprietary microwell-based KIT assay, we measured cell-free DNA (cfDNA), methylated cfDNA, clusterin, creatinine, protein and CXCL10. A KIT stone score was computed across all markers using the prior locked KIT algorithm. The KIT stone score, with a scale of 0 to 100, was then correlated with demographic variables, kidney stone burden, obstructive kidney stone disease, and urine solutes in 24-h urine collections. ResultsThe scaled KIT stone score, a composite of all six biomarkers, readily discriminated individuals with current or prior radiographically confirmed kidney stones from healthy controls without kidney stone disease (P < 0.001). In individuals with nephrolithiasis, KIT stone score also correlated with radiologically measured stone size (P = 0.017) and differentiated patients with a clinical radiological diagnosis of obstructive nephrolithiasis associated with upper renal tract dilatation (P = 0.001). Stone burden as assessed by KIT stone score, however, did not correlate with the any of the traditional measures of 24-h urine solutes or the 24-h urine supersaturation levels. In patients with persistent stone disease, where multiple urine samples were collected over time and after different interventions, the use of KIT stone score could non-invasively track stone burden over time through a spot urine, non-timed urine sample. ConclusionsA random, spot urine-based assay, KIT stone score, can noninvasively detect, quantify and monitor current stone burden, and may thus minimize radiographic exposure for kidney stone detection. The KIT stone score assay may also help monitor stone recurrence risk for patients with nephrolithiasis, without the requirement for 24-h urine collections.

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