Evaluation of Acquisition Modes for Semi-Quantitative Analysis by Targeted and Untargeted Mass Spectrometry

RATIONALE: Analyte quantitation by mass spectrometry underpins a diverse range of scientific endeavors. The fast growing field of mass spectrometer development has resulted in several targeted and untargeted acquisition modes suitable for these applications. By characterizing the acquisition methods available on an ion mobility (IM) enabled orthogonal acceleration time-of-flight (oa-ToF) instrument, the optimum modes for analyte semi-quantitation can be deduced. METHODS: Serial dilutions of commercial metabolite, peptide, or crosslinked peptide analytes were prepared in matrices of human urine or E. coli digest. Each analyte dilution was introduced into an IM separation enabled oa-ToF mass spectrometer by reversed phase liquid chromatography and electrospray ionization. Data were acquired for each sample in duplicate using nine different acquisition modes, including four IM enabled acquisitions modes, available on the mass spectrometer. RESULTS: Five (metabolite) or seven (peptide/crosslinked peptide) point calibration curves were prepared for analytes across each of the acquisition modes. A non-linear response was observed at high concentrations for some modes, attributed to saturation effects. Two correction methods, one MS1 isotope-correction and one MS2 ion intensity-correction, were applied to address this observation, resulting in an up to two-fold increase in dynamic range. By averaging the semi-quantitative results across analyte classes, two parameters, linear dynamic range (LDR) and lower limit of quantitation (LLOQ), were determined to evaluate each mode. CONCLUSION: Comparison of the acquisition modes revealed that data independent acquisition and parallel reaction monitoring methods are most robust for semi-quantitation when considering achievable LDR and LLOQ. IM enabled modes exhibited sensitivity increases, but a simultaneous reduction in dynamic range which required correction methods to recover. These findings will assist users in identifying the optimum acquisition mode for their analyte quantitation needs, supporting a diverse range of applications and providing guidance for future acquisition mode developments

Exponents, pre-exponential factors, and relative gain by location and crime categories for local scale Nottinghamshire and Derbyshire data.

<p>The multiple categories with <i>A</i> = 1 reflects the reference condition selected for the model and the finding that the constant in the regression was not significant and subsequently eliminated from the model.</p><p>Exponents, pre-exponential factors, and relative gain by location and crime categories for local scale Nottinghamshire and Derbyshire data.</p

Fluctuation scaling for reported mortality in England and Wales.

<p>Left panel: The mean variance plot shows reasonable correspondence to TL (dashed line) with log(<i>A</i>) = −1.186±0.041 and <i>α</i> = 1.798±0.019. The solid line represents Poisson distributed data with no gain. Multiplication by 10 had no effect on <i>α</i>, but changed <i>A</i> to 0.1042. Right Panel: The relationship between pre-exponential factor and TL exponent for different data segments in panel on the left. Plot was created by sorting the data by mean value, computing <i>A</i> and <i>α</i> by linear regression on a 30 point moving segment, and including all values in which <i>α</i> was significant with 95% confidence. The arrow represents the approximate direction of the spatial scale. A perfect Poisson process with no gain is a point at (1, 1). The solid line is to guide the eye.</p

TL plots for the Derbyshire and Nottinghamshire policing neighborhoods.

<p>Plots include total crime reports, anti-social behavior incidents, burglaries, and violence. Each point represents the average and variance computed over a 12 month period as represented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109004#pone-0109004-g002" target="_blank">Figure 2</a>. Each panel includes the best fit line to the data (dotted line) and a Poisson system having a gain of 1 (solid line). These plots indicate that within a policing region, different crime categories exhibit specific exponents and pre-exponential factors. The data represent 205,857 reported crimes, 84,165 incidents of anti-social behavior, 16,369 burglaries, and 24,759 reports of violence.</p

TL plots of the Derbyshire data following data manipulation.

<p>Panels a and b illustrate the effects of incentives to reach targets. Panels c and d model the effects of applying a threshold to the reports. Panel a shows data subjected to an incentive to reach 10 reports a month operating above 5 reports. This model consists of reports being “snapped” to 10 once 5 reports are exceeded. Note the gap in the results beginning near 5, the level variance near 10 and subsequent drop in variance at 10. TL fit (long dashed line) is similar to that obtained from the un-manipulated data. Panel b illustrates the impact of an incentive to reach 75 reports with a width of 35 reports. TL fits show how the manipulation results in an increase in exponent and a decrease in pre-exponential factor. Note the gaps in the results beginning near the lower end of the incentive zone, the flattening of variance between the bottom of the incentive zone and the target value, and subsequent drop in variance at 75. These features give clues about the “strength” of the incentive and the target value. Panels c and d are TL plots for data to which a threshold of 10 crimes must be exceeded before a crime is reported (solid squares and long dashed line) in comparison to the original TL fit (dotted line). The solid lines are the behavior of a Poisson distribution with no gain.</p

Fluctuation scaling relationships for total crime, anti-social behavior, burglary, and violence spanning local (filled squares), regional and country scales (filled diamonds).

<p>Intersection points between the TL relationships for the two scales occurred at 2.44±0.25 (275), 2.30±0.27 (200), 2.72±0.24 (526), and 1.79±0.27 (61) events in log units with number units in parentheses for total crime, anti-social behavior, burglary, and violence, respectively. The highest point in each panel is country wide aggregation of 43 constabularies.</p

ANOVA Table for the general regression model.

<p>Overall the model explained 98.46% of total variance. The dependent variable is log(variance). In this model, log(mean) is the log₁₀ of the average over 12 monthly values for reported crime of a particular type, location*crime indicates how <i>A</i> varies for particular crimes between the regions served by the Derbyshire and Nottinghamshire constabularies, and crime*log(mean) indicates the variation in <i>α</i> for particular crime types. Note, the significance levels depend on which condition is used as a reference value, but the basic conclusions are unchanged.</p><p>ANOVA Table for the general regression model.</p

Example data showing reported crimes from high and low crime policing neighborhoods in Nottinghamshire and Derbyshire.

<p>Each point represents the number of crimes reported in a one month period. Data is shown for total crime (▪), anti-social behavior (♦), burglary (▴), and violence (•).</p

Evaluation of Acquisition Modes for the Quantitative Analysis of Cross-Linked Peptides by Targeted and Untargeted Mass Spectrometry

Cross-linking mass spectrometry (XL-MS) is a structural biology technique that can provide insights into the structure and interactions of proteins and their complexes, especially those that cannot be easily assessed by other methods. Quantitative XL-MS has the potential to probe the structural and temporal dynamics of protein complexes; however, it requires further development. Until recently, quantitative XL-MS has largely relied upon isotopic labeling and data dependent acquisition (DDA) methods, limiting the number of biological samples that can be studied in a single experiment. Here, the acquisition modes available on an ion mobility (IM) enabled QToF mass spectrometer are evaluated for the quantitation of cross-linked peptides, eliminating the need for isotopic labels and thus expanding the number of comparable studies that can be conducted in parallel. Workflows were optimized using metabolite and peptide standards analyzed in biological matrices, facilitating modelling of the data and addressing linearity issues, which allow for significant increases in dynamic range. Evaluation of the DDA acquisition method commonly used in XL-MS studies indicated consistency issues between technical replicates and reduced performance in quantitative metrics. On the contrary, data independent acquisition (DIA) and parallel reaction monitoring (PRM) modes proved more robust for analyte quantitation. Mobility enabled modes exhibited an improvement in sensitivity due to the added dimension of separation, and a simultaneous reduction in dynamic range, which was largely recovered by correction methods. Hi[3] and probabilistic quantitation methods were successfully applied to the DIA data, determining the molar amounts of cross-linked peptides relative to their linear counterparts.</div