Analytical Chemistry Chemical Cytometry Quantitates Superoxide

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Figure 1. Comparison of legacy SEQUEST and MacroSEQUEST (Macro) workflows. (a) Scheme of the serial nature of SEQUEST. To analyze n number of spectra, SEQUEST must be executed n number of times, parsing the database with each instance and using both preliminary and XCorr scores. (b) Scheme of the Macro workflow. Macro considers all spectra in a single analysis and therefore requires only one pass through a target database during a search. (c) Analysis of the time distribution for SEQUEST and Macro searches of 10 000 spectra with a target-decoy human (UniProt) database. SEQUEST spends approximately 1% of the search time on scoring functions while Macro spends only 13% of the search time parsing the database. The inset shows an expanded view of the Macro time distribution for the first 20 min of the search time. database. Macro then concludes by calculating final score differences, etc. for the top-ranked candidate PSMs and writing this information to result files. Because Macro was developed from the original SEQUEST source code, the primary scoring metric Xcorr in Macro is identical to those created by legacy SEQUEST, a major difference between the two is that Macro no longer performs Sp scoring as a preliminary scoring step but instead calculates Xcorr for all candidate PSMs, owing to the computationally fast, non-FFT correlation algorithm. Macro spends significantly less time parsing the target-decoy human database than SEQUEST during a search of the same 10 000 spectra (Figure 1c, inset), and almost 87% of the total run time performing scoring functions. Because such a small fraction of the actual SEQUEST process is spent scoring candidate PSMs and because this cycle is repeated in its entirety for each spectrum to be searched, SEQUEST’s productivity (number of spectra searched/unit time spent searching) is relatively flat as a function of the number of spectra searched (Figure 2). Macro, however, benefits from having more spectra to search by distributing the fixed time cost associated with a single digestion and parsing pass through the database across many spectra until the time spent parsing the database is small relative to the time spent scoring candidate PSMs, at which point Macro’s search productivity flattens out. This improvement in productivity as a function of number of spectra searched is clearly depicted in Figure 2, where searches of a target-decoy yeast (Figure 2a), human (Figure 2b), and human databases with dynamic phosphorylation on serine, threonine, and tyrosine (Figure 2c) using Macro all outperform legacy SEQUEST by 102×, 107×, and 42×, respectively, when searching 10 000 MS/MS 6824 Analytical Chemistry, Vol. 82, No. 16, August 15, 2010 spectra. Note that the increase in parsing logic associated with determining dynamic protein post-translational modifications, such as phosphorylation, substantially compresses and flattens the Macro productivity curve and extends the point in numbers of spectra at which Macro achieves maximum productivity to well beyond the 10 000 mark. In order to assess the level of spectral array processing overhead in Macro, we generated a “MacroParser” version of the program that replaces the XCorr scoring functions with a simple random number generator. This allowed us to subtract the time it takes Macro to parse the database and scan the array of spectra from the total search time, which generated an ideal power fit as a function of the number of spectra searched (Figure 2a-c). Comparison of MacroSEQUEST to Other Contemporary Tools. Although it was among the earliest algorithms to be adopted into widespread use, SEQUEST is now not the only program available to retrieve candidate PSMs from FASTA databases. Other commercial (Mascot) and noncommercial (OMSSA, X!Tandem) programs are also designed to execute database searches, although the core components and matching algorithms differ substantially. To establish Macro’s performance rank among programs that are commonly encountered in proteomics research laboratories and protein identification core facilities, we generated productivity curves for searches against our target-decoy human database for Macro, SEQUEST, Mascot, OMSSA, and X!Tandem. We also noted that all three of these programs have been written to take advantage of multiple physical and logical cores now increasingly common in modern CPU architectures, including the Intel Conroe chip used in this comparison. In order to cleanly reconcile differences in perfor-
Figure 2. Productivity of SEQUST and Macro under typical experimental conditions. We created three biological samples to use in testing the performance of Macro (green circles) versus legacy SEQUEST (orange circles) in common applications: (a) yeast 80-130 kDa protein digest, (b) human 80-130 kDa protein digest, and (c) human phosphorylation samples, all analyzed by a 90 min gradient LC-Orbitrap-MS/MS. Variable, random portions of each full collection of MS/MS spectra were searched to define the productivity (number of MS/MS spectra searched/minute of search time) of each search type as shown in the left panel. A version of Macro that does not produce scores and is useful as a measurement of database parsing work, MacroParser, was used to calculate the time spent on scoring by difference. In the right panel, the Macro, MacroParser, and calculated Macro Scoring productivity distributions are plotted as the log for the variable, random portion search sizes versus search time. A power function was fitted to the Macro Scoring distribution using a nonlinear least-squares method in R. Note that Macro is 102×, 107×, and 42× faster than SEQUEST under each search condition, respectively. mance due to multithreading, we disabled one of the two cores on our Conroe prior to performing the comparison, the results of which are depicted in Figure 3. At peak productivity, Macro performs equivalent to OMSSA, about 20% faster than Mascot, and twice as fast X!Tandem, with legacy SEQUEST lagging far behind the others. High-Resolution MS/MS Correlation Analysis. In light of the significant performance improvement of Macro over legacy SEQUEST, we reasoned that this additional speed could be leveraged to also produce gains in the quality of spectral matches by creating sparse arrays of MS/MS fragment ions from high-resolution, highmass accuracy instruments such as the LTQ Orbitrap 2 and, in particular, the LTQ Orbitrap Velos 3 and by performing full correlation analyses on them to produce Xcorr scores, a feature not currently available in SEQUEST. Given the novelty of Xcorr and dCn values derived from high-resolution MS/MS spectra, we were also interested in evaluating the qualitative impact that variable search “resolution” might have on these scores. To do this, we created a parameter for Macro that defines the bin width of the arrays (in m/z) used for correlation analysis and used this factor to scale the number of bins that are generated within the range of observed fragment ions during MS/MS spectrum preprocessing steps. Macro then populates these bins with normalized ion intensity values using logic consistent with Analytical Chemistry, Vol. 82, No. 16, August 15, 2010 6825
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Anal. Chem. 2010, 82, 6745-6750 Let
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purchased from Invitrogen-Molecular
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Figure 3. Electropherograms of TPP-
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Anal. Chem. 2010, 82, 6751-6755 Res
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(pH 8.0) with cysteine and cystamin
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Figure 4. Ion mobility mass spectra
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GOx glucose + O298 gluconic acid +
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coater at 2000 rpm for 20 s, and th
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Figure 5. Comparison of cyclic volt
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in channels with either no grooves
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indicators of atmospheric processin
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Figure 1. GC/MS total ion chromatog
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Table 2. Concentrations and Stable
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on a substrate are preferred. 20-24
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Figure 5. Device to eject and mix s
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Anal. Chem. 2010, 82, 6877-6886 Imm
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NaCl, phosphate buffer saline (PBS)
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Figure 2. Product ion mass spectra
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-70 °C resolved the problem, givin
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Table 1. Intraday Precision and Acc
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Anal. Chem. 2010, 82, 6887-6894 Fer
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Figure 1. Infrared spectra of (A) u
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Figure 3. Cyclic voltammograms obta
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Figure 6. Calculated charge from ch
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Anal. Chem. 2010, 82, 6895-6903 Ele
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Table 1. Chemical Structure, pKa Va
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pH with a tilted baseline (Figure 1
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Table 2. Linearity and Detection Li
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ascorbic acid (AA), uric acid (UA),
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the multielement capabilities, the
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RESULTS AND DISCUSSION Sulfur Detec
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Table 2. Molecular Properties and C
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Anal. Chem. 2010, 82, 6911-6918 Dir
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dilution and hybridization buffer.
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solution under appropriate incubati
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Figure 4. Standardization curve for
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Anal. Chem. 2010, 82, 6919-6925 Ele
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the ×10 objective, to have a large
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Figure 2. With a suitable removal o
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the NB signal in a much better foot
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Scheme 1. Reactions of Selenium Rea
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Figure 2. (a) ESI-MS spectrum showi
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Figure 4. (a) ESI-MS spectrum showi
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Anal. Chem. 2010, 82, 6933-6939 Dif
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trode 28 by a finite element using
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Figure 4. Comparison between simula
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Figure 6. Comparison between simula
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educed in the vicinity of double bo
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Figure 2. Normalized product ion ab
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Figure 4. EID (a) and IRMPD (b) of
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Anal. Chem. 2010, 82, 6947-6957 Ide
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Figure 1. Schematic flowchart showi
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difference, ppm compound Table 1. I
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isoforms, its successful use, in th
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Figure 5. Extracted ion current ESI
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m/z 1172.935 was observed for Ser14
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linked products via affinity tags.
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Scheme 2. Fragmentation Mechanism o
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Figure 1. (A) ESI-LTQ-CID-MS 2 prod
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Figure 3. (A) ESI-LTQ-CID-MS 2 prod
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Figure 5. (A) MALDI-TOF/TOF product
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Anal. Chem. 2010, 82, 6969-6975 Ana
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Figure 3. Equilibrium response as a
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Figure 6. The average measured resp
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for the fill time, and we find that
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nucleotide tails. 3-5 Thus, the amo
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allow the use of higher aptamer con
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quent ligation of the aptamers afte
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Anal. Chem. 2010, 82, 6983-6990 Imp
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Figure 2. Configuration editor wind
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Figure 4. Dependencies between volu
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Since the time for liquid expulsion
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Anal. Chem. 2010, 82, 6991-6999 Int
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Figure 1. Representation of the Car
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Table 2. Capillary Electrophoresis
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Figure 4. (A) Typical raw electroph
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field. DNA profiles were delivered
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Another approach to handle this lim
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(principal components, PCs) in whic
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Figure 5. Relevant score plots and
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CONCLUSIONS In this paper we have i
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electroactive-species loaded liposo
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Figure 2. Qdot-based FLFTS response
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Figure 6. Fluorescence imaging of Q
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Anal. Chem. 2010, 82, 7015-7020 Sel
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Table 1. Effect of 1 D-LC Condition
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Figure 3. Peak-production rate vers
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Anal. Chem. 2010, 82, 7021-7026 Cel
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luciferase (T7 control vector) was
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Figure 3. Inhibitory effects of luc
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Anal. Chem. 2010, 82, 7027-7034 Pat
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Electrochemistry. Electrochemical m
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Figure 2. SEM images of Au substrat
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Table 3. Film Thickness Measurement
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Anal. Chem. 2010, 82, 7035-7043 Qua
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Figure 1. (A) FL spectra of BSPOTPE
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Figure 4. (A) Variation in the FL i
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Figure 6. (A) FL spectra of BSPOTPE
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Scheme 1. Proposed Mechanism for Fl
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one or more drawbacks including poo
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Figure 2. Free fluorophores do not
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Anal. Chem. 2010, 82, 7049-7052 Dev
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Figure 2. Comparative analysis of d
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Analytical Chemistry Chemical Cytometry Quantitates Superoxide

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