Individual ion mass spectrometry (I2MS) is the Orbitrap-based extension of the niche mass spectrometry technique known as charge detection mass spectrometry (CDMS). While traditional CDMS analysis is performed on in-house-built instruments such as the electrostatic linear ion trap, I2MS extends CDMS analysis to Orbitrap analyzers, allowing charge detection analysis to be available to the scientific community at large. I2MS simultaneously measures the mass-to-charge ratios (m/z) and charges (z) of hundreds to thousands of individual ions within one acquisition event, creating a spectral output directly into the mass domain without the need for further spectral deconvolution. A mass distribution or ‘profile’ can be created for any desired sample regardless of composition or heterogeneity. To assist in reducing I2MS analysis to practice, we developed this workflow for data acquisition and subsequent data analysis, which includes (i) protein sample preparation, (ii) attenuation of ion signals to obtain individual ions, (iii) the creation of a charge-calibration curve from standard proteins with known charge states and finally (iv) producing a meaningful mass spectral output from a complex or unknown sample by using the STORIboard software. This protocol is suitable for users with prior experience in mass spectrometry and bioanalytical chemistry. First, the analysis of protein standards in native and denaturing mode is presented, setting the foundation for the analysis of complex mixtures that are intractable via traditional mass spectrometry techniques. Examples of complex mixtures included here demonstrate the relevant analysis of an intact human monoclonal antibody and its intricate glycosylation patterns. Orbitrap-based individual ion mass spectrometry enables charge detection mass spectrometry application on a broadly accessible mass spectrometry platform, enabling the analysis of complex mixtures that are intractable via traditional mass spectrometry techniques.

The quality of data in charge detection mass spectrometry depends on accurate determination of ion charge. While the method of selective temporal overview of resonant ions (STORI) has proven to be highly enabling for determining the charge of ions that survive for variable amounts of time, it assumes that the ion frequency exactly matches the frequency being used in the calculation. Any mismatches result in low charge estimates. To address this, the misSTORI method was developed to correct these discrepancies. This can significantly reduce the charge measurement errors for samples with unstable masses. As an example, the misSTORI approach can eliminate a 5.7% charge determination error for a VP3-only AAV capsid that shifts 25 ppm in mass.

Data-independent acquisition (DIA) mass spectrometry has grown in popularity in recent years, because of the reproducibility and quantitative rigor of a systematic tandem mass spectrometry (MS/MS) sampling method. However, traditional DIA methods may spend valuable instrument time acquiring MS/MS spectra with no usable information in them, affecting sensitivity and quantitative performance. We developed a DIA strategy that dynamically adjusts the MS/MS windows during the chromatographic separation. The method focuses MS/MS acquisition on the most relevant mass range at each point in time─increasing the quantitative sensitivity by increasing the time spent on each DIA window. We demonstrate an improved lower limit of quantification, on average, without sacrificing the number of peptides detected.

Native mass spectrometry involves transferring large biomolecular complexes into the gas phase, enabling the characterization of their composition and stoichiometry. However, the overlap in distributions created by residual solvation, ionic adducts, and post-translational modifications creates a high degree of complexity that typically goes unresolved at masses above ∼150 kDa. Therefore, native mass spectrometry would greatly benefit from higher resolution approaches for intact proteins and their complexes. By recording mass spectra of individual ions via charge detection mass spectrometry, we report isotopic resolution for pyruvate kinase (232 kDa) and β-galactosidase (466 kDa), extending the limits of isotopic resolution for high mass and high m/z by >2.5-fold and >1.6-fold, respectively.

Targeted mass spectrometry methods produce high-quality quantitative data in terms of limits of detection and dynamic range, at the cost of a substantial compromise in throughput compared to methods such as data independent and data dependent acquisition. The logistical and experimental issues inherent to maintaining assays of even several hundred targets are significant. Prominent among these issues is the drift in analyte retention time as liquid chromatography (LC) columns wear, forcing targeted scheduling windows to be much larger than LC peak widths. If these problems could be solved, proteomics assays would be capable of targeting thousands of peptides in an hour-long experiment, enabling large cohort studies to be performed without sacrificing sensitivity and specificity. We describe a solution in the form of a new method for real-time chromatographic alignment and demonstrate its application to a 56 min LC-gradient HeLa digest assay with 1489 targets. The method is based on the periodic acquisition of untargeted survey scans in a reference experiment and alignment to those scans during subsequent experiments. We describe how the method enables narrower scheduled retention time windows to be used. The narrower scheduling windows enables more targets to be included in the assay or proportionally more time to be allocated to each target, improving the sensitivity. Finally, we point out how the procedure could be improved and how much additional target multiplexing could be gained in the future.

A new Orbitrap-based single ion analysis procedure is shown to be possible by determining the direct charge on numerous measurements of individual protein ions to generate true mass spectra. The deployment of an Orbitrap system for charge detection enables the characterization of highly complicated mixtures of proteoforms and their complexes in both denatured and native modes of operation, revealing information not obtainable by traditional measurement of an ensemble of ions.

Charge detection mass spectrometry (CDMS) of low-level signals is currently limited to the analysis of individual ions that generate a persistent signal during the entire observation period. Ions that disintegrate during the observation period produce reduced frequency domain signal amplitudes, which lead to an underestimation of the ion charge state, and thus the ion mass. The charge assignment can only be corrected through an accurate determination of the time of ion disintegration. The traditional mechanisms for temporal signal analysis have severe limitations for temporal resolution, spectral resolution, and signal-to-noise ratios. Selective Temporal Overview of Resonant Ions (STORI) plots provide a new framework to accurately analyze low-level time domain signals of individual ions. STORI plots allow for complete correction of intermittent signals, the differentiation of single and multiple ions at the same frequency, and the association of signals that spontaneously change frequency.

FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC=0.933) and CA-125 (AUC=0.907) were the most informative biomarkers, followed by IL-2 receptor α, α1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800). To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912). Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the detection of ovarian cancer.

In previous studies we described the use of a retrospective collection of ovarian cancer and benign disease samples, in combination with a large set of multiplexed immunoassays and a multivariate pattern recognition algorithm, to develop an 11-biomarker classification profile that is predictive for the presence of epithelial ovarian cancer. In this study, customized, Luminex-based multiplexed immunoassay kits were GMP-manufactured and the classification profile was refined from 11 to 8 biomarkers (CA-125, epidermal growth factor receptor, CA 19-9, C-reactive protein, tenascin C, apolipoprotein AI, apolipoprotein CIII, and myoglobin). The customized kits and the 8-biomarker profile were then validated in a double-blinded manner using prospective samples collected from women scheduled for surgery, with a gynecologic oncologist, for suspicion of having ovarian cancer. The performance observed in model development held in validation, demonstrating 81.1% sensitivity (95% CI 72.6 – 87.9%) for invasive epithelial ovarian cancer and 85.4% specificity (95% CI 81.1 – 88.9%) for benign ovarian conditions. The specificity for normal healthy women was 95.6% (95% CI 83.6 – 99.2%). These results have encouraged us to undertake a second validation study arm, currently in progress, to examine the performance of the 8-biomarker profile on the population of women not under the surgical care of a gynecologic oncologist.

Reproducibility in mass spectral data is important in both biomarker discovery and spectral database searching. We report a strategy, employing a series of substituted benzylpyridinium thermometer ions that can be used to monitor changes in performance of multiple aspects of an electrospray ionization source that impact the intensity axis of a spectrum. Performance attributes, which could confound even isotope-based quantification strategies, are readily assessed using a mixture of thermometer ions. Based on the observed behavior of the ions, a procedure is proposed for monitoring instrument performance and compensating for factors that affect reproducibility across both time and instruments.

Direct injection mass spectrometric analysis of biological samples is potentially an attractive approach to the discovery of diagnostic patterns for specific pathophysiological conditions because of its speed and simplicity. Despite the possible benefits offered by such a method, its extensive application has been limited so far by several factors, including the inadequate reproducibility of the analytical results. We describe a method for monitoring and optimizing the performance of mass spectrometers used for biomarker discovery studies, based on the analysis of patterns of standardized spectral features. The method was successfully applied to maintaining spectral reproducibility during a multi-day analysis of hundreds of serum samples despite an ion source failure, which necessitated minor maintenance. The monitoring method allowed the early detection of that failure and the restoration of the spectral profiles after the system was restarted.

Background: Most women with a clinical presentation consistent with ovarian cancer have benign conditions. Therefore methods to distinguish women with ovarian cancer from those with benign conditions would be beneficial. We describe the development and preliminary evaluation of a serum-based multivariate assay for ovarian cancer. This hypothesis-driven study examined whether an informative pattern could be detected in stage I disease that persists through later stages.Methodology/Principal Findings: Sera, collected under uniform protocols from multiple institutions, representing 176 cases and 187 controls from women presenting for surgery were examined using high-throughput, multiplexed immunoassays. All stages and common subtypes of epithelial ovarian cancer, and the most common benign ovarian conditions were represented. A panel of 104 antigens, 44 autoimmune and 56 infectious disease markers were assayed and informative combinations identified. Using a training set of 91 stage I data sets, representing 61 individual samples, and an equivalent number of controls, an 11-analyte profile, composed of CA-125, CA 19-9, EGF-R, C-reactive protein, myoglobin, apolipoprotein A1, apolipoprotein CIII, MIP-1 alpha, IL-6, IL-18 and tenascin C was identified and appears informative for all stages and common subtypes of ovarian cancer. Using a testing set of 245 samples, approximately twice the size of the model building set, the classifier had 91.3% sensitivity and 88.5% specificity. While these preliminary results are promising, further refinement and extensive validation of the classifier in a clinical trial is necessary to determine if the test has clinical value.Conclusions/Significance: We describe a blood-based assay using 11 analytes that can distinguish women with ovarian cancer from those with benign conditions. Preliminary evaluation of the classifier suggests it has the potential to offer approximately 90% sensitivity and 90% specificity. While promising, the performance needs to be assessed in a blinded clinical validation study.

Ovarian cancer is the deadliest gynecologic cancer in the United States. When detected early, the 5-year survival rate is 92%, although most cases remain undetected until the late stages where 5-year survival rates are 30%. Serum biomarkers may hold promise. Although many markers have been proposed and multivariate diagnostic models were built to fit the data on small, disparate sample sets, there has been no systematic evaluation of these markers on a single, large, well-defined sample set. To address this, we evaluated the dysregulation of 204 molecules in a sample set consisting of serum from 294 patients, collected from multiple collection sites, under a well-defined Gynecologic Oncology Group protocol. The population, weighted with early-stage cancers to assess biomarker value for early detection, contained all stages of ovarian cancer and common benign gynecologic conditions. The panel of serum molecules was assayed using rigorously qualified, high-throughput, multiplexed immunoassays and evaluated for their independent ovarian cancer diagnostic potential. Seventy-seven biomarkers were dysregulated in the ovarian cancer samples, although cancer antigen 125, C-reactive protein, epidermal growth factor receptor, interleukin 10, interleukin 8, connective tissue growth factor, haptoglobin, and tissue inhibitor of metalloproteinase 1 stood out as the most informative. When analyzed by cancer subtype and stage, there were differences in the relative value of biomarkers. In this study, using a large sample cohort, we show that some of the reported ovarian cancer biomarkers are more robust than others, and we identify additional informative candidates. These findings may guide the development of multivariate diagnostic models, which should be tested on additional, prospectively collected samples. (Cancer Epidemiol Biomarkers Prev 2008;17(10):2872–81)

There is an urgent need for a small, inexpensive sensor that can rapidly detect bio-warfare agents with high specificity. Bacillus anthracis, the causative agent of anthrax, would be a perilous disease-causing organism in the event of a release. Currently, most anthrax detection research is based on nucleic acid detection, immunoassays and mass spectrometry, with few detection levels reported below 10(5) spores. Here, we show the ability to distinguish Bacillus spores to a level approaching 10(3) spores, below the reported median infectious dose of B. anthracis, using pyrolysis--micromachined differential mobility spectrometry and novel pattern recognition algorithms that combine lead cluster mapping with genetic algorithms.

As bacteria grow and proliferate, they release a variety of volatile compounds that can be profiled and used for speciation, providing an approach amenable to disease diagnosis through quick analysis of clinical cultures as well as patient breath analysis. As a practical alternative to mass spectrometry detection and whole cell pyrolysis approaches, we have developed methodology that involves detection via a sensitive, micromachined differential mobility spectrometer (microDMx), for sampling headspace gases produced by bacteria growing in liquid culture. We have applied pattern discovery/recognition algorithms (ProteomeQuest) to analyze headspace gas spectra generated by microDMx to reliably discern multiple species of bacteria in vitro: Escherichia coli, Bacillus subtilis, Bacillus thuringiensis, and Mycobacterium smegmatis. The overall accuracy for identifying volatile profiles of a species within the 95% confidence interval for the two highest accuracy models evolved was between 70.4 and 89.3% based upon the coordinated expression of between 5 and 11 features. These encouraging in vitro results suggest that the microDMx technology, coupled with bioinformatics data analysis, has potential for diagnosis of bacterial infections.