Supplementary data are available at Bioinformatics online.

Applying Hadamard transform multiplexing to ion mobility separations (IMS) can significantly improve the signal-to-noise ratio and throughput for IMS coupled mass spectrometry (MS) measurements by increasing the ion utilization efficiency. However, it has been determined that fluctuations in ion intensity as well as spatial shifts in the multiplexed data lower the signal-to-noise ratios and appear as noise in downstream processing of the data. To address this problem, we have developed a novel algorithm that discovers and eliminates data artifacts. The algorithm employs an analytical approach to identify and remove artifacts from the data, decreasing the likelihood of false identifications in subsequent data processing. Following application of the algorithm, IMS-MS measurement sensitivity is greatly increased and artifacts that previously limited the utility of applying the Hadamard transform to IMS are avoided.

Rapid diagnosis of disease states using less invasive, safer, and more clinically acceptable approaches than presently employed is a crucial direction for the field of medicine. While MS-based proteomics approaches have attempted to meet these objectives, challenges such as the enormous dynamic range of protein concentrations in clinically relevant biofluid samples coupled with the need to address human biodiversity have slowed their employment. Herein, we report on the use of a new instrumental platform that addresses these challenges by coupling technical advances in rapid gas phase multiplexed ion mobility spectrometry separations with liquid chromatography and MS to dramatically increase measurement sensitivity and throughput, further enabling future high throughput To date pre-clinical and clinical applications of MS-based proteomic techniques analyzing complex biofluids have fallen short of expectations, largely due to deficiencies in both analytical sensitivity and throughput. These deficiencies result in measurements typically failing to confidently detect and quantify proteins at moderate to low concentrations, or not providing sufficient sample analysis throughput for statistical relevance. Targeted MS analyses with higher sensitivity are currently utilized to address these shortcomings (1, 2); however, these studies often only analyze a small list of proteins identified as biologically significant. While targeted MS measurements are increasingly common in clinical applications (3, 4), the limited number of proteins they examine does not necessarily reflect the biodiversity across a population, making broad untargeted measurements essential in developing individual disease metrics for diagnosis (5). As the future of medicine proceeds toward a personal profiling approach (6, 7), the potential for robust high throughput clinical measurements based upon MS is highly attractive, though only if its deficiencies can be addressed. MSAn initial step in attaining broad untargeted measurements that increasingly retain the benefits of targeted analyses exploits technological advances such as faster separations, more effective ion sources, detectors with greater dynamic range, and MS measurements with both higher resolution and accuracy. Advanced liquid-phase separations have already

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1–2 orders of magnitude for peptides in a blood serum sample.