Analytical Chemistry Chemical Cytometry Quantitates Superoxide

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a series of different samples, the general process is iterated thousands, if not millions, of times. Today, the modern proteomics researcher can choose from several computational tools to translate MS/MS spectra into PSMs, including SEQUEST, 9 Mascot, 10 X!Tandem, 11,12 and OMSSA, 13 among others. A number of studies have been performed that evaluate the analytical performance of these algorithms, with regards to precision and sensitivity of the PSM collections they generate. 14-16 While it appears that certain algorithms perform slightly better or worse than others based on the nature of the sample (e.g., phosphorylation, enzyme digest), the type of mass spectrometer used to generate these MS/MS data, and the mechanism of peptide fragmentation, etc., they are in general more similar than they are different, at least, in terms of the nature of the PSM collections they produce. However, given the recent trend toward larger and larger numbers of MS/MS spectra per experiment, the relative processing speed of these algorithms has become an important practical consideration, as some of these algorithms perform significantly slower than others. In particular, SEQUEST has lagged significantly behind, although limited efforts to improve performance by parallelization have been reported. 17 In general, the most basic solution to any large discrepancies in performance (or “productivity”, defined as the number of MS/MS spectra processed per unit computational time) has been to index the protein database by predigesting the protein sequences to peptides, then indexing each candidate peptide by precursor mass. While these peptide indices do result in significant performance gains that in general level the playing field among algorithms, there are drawbacks to using indexed databases. For example, the use of an indexed database as opposed to the use of a FASTA database results in a significant expansion of disk memory, requires separate indices for each enzyme used for digestion, and is problematic when considering post-translational modifications, all of which is repeated and exacerbated for each organism and/or release version of the organism-specific genome sequence. A recent report attempted to reconcile the issues of both performance and indexed database files by reading the target FASTA database once for the first MS/MS spectrum to be analyzed, indexing “on-the-fly” and storing this peptide index in fast CPU memory for use in finding candidates for subsequent MS/MS spectra. 18 An alternative approach that addresses the performance and convenience issues associated with both indexed database files (9) Eng, J. K.; Mccormack, A. L.; Yates, J. R. J. Am. Soc. Mass Spectrom. 1994, 5, 976–989. (10) Perkins, D. N.; Pappin, D. J.; Creasy, D. M.; Cottrell, J. S. Electrophoresis 1999, 20, 3551–3567. (11) Craig, R.; Beavis, R. C. Bioinformatics 2004, 20, 1466–1467. (12) Craig, R.; Beavis, R. C. Rapid Commun. Mass Spectrom. 2003, 17, 2310– 2316. (13) Geer, L. Y.; Markey, S. P.; Kowalak, J. A.; Wagner, L.; Xu, M.; Maynard, D. M.; Yang, X.; Shi, W.; Bryant, S. H. J. Proteome Res. 2004, 3, 958–964. (14) Bakalarski, C. E.; Haas, W.; Dephoure, N. E.; Gygi, S. P. Anal. Bioanal. Chem. 2007, 389, 1409–1419. (15) Elias, J. E.; Haas, W.; Faherty, B. K.; Gygi, S. P. Nat. Methods 2005, 2, 667–675. (16) Kapp, E. A.; Schutz, F.; Connolly, L. M.; Chakel, J. A.; Meza, J. E.; Miller, C. A.; Fenyo, D.; Eng, J. K.; Adkins, J. N.; Omenn, G. S.; Simpson, R. J. Proteomics 2005, 5, 3475–3490. (17) Sadygov, R. G.; Eng, J.; Durr, E.; Saraf, A.; McDonald, H.; MacCoss, M. J.; Yates, J. R. J. Proteome Res. 2002, 1, 211–215. (18) Park, C. Y.; Klammer, A. A.; Kall, L.; MacCoss, M. J.; Noble, W. S. J. Proteome Res. 2008, 7, 3022–3027. 6822 Analytical Chemistry, Vol. 82, No. 16, August 15, 2010 as well as indexing the protein database in CPU memory is to consider an entire set of MS/MS spectra to be searched (e.g., a complete LC-MS/MS run) as an array of precursor masses and spectra and to transiently index these MS/MS spectra by precursor mass at the beginning of a search. This allows for efficient matching of candidate peptides during a single in silico digestion pass through a FASTA database. Because the number of MS/ MS spectra in an LC-MS/MS run is small relative to the number of candidate peptides in a typical organism database, this indexing step is extremely fast and can easily be implemented at the launch of a search. Furthermore, additional performance gains can be realized through elimination of redundant candidate peptide spectral processing steps that occur when single MS/MS spectra are considered in the absence of other MS/MS spectra that have overlapping candidate peptide search spaces. Indeed, the benefits of this “candidate-centric” (as opposed to “spectrum-centric”) searching approach have been described explicitly, first in principle by Edwards and Lippert 19 and later in practice by Tabb and co-workers. 20 In the present work, we demonstrate the general utility of this “candidate-centric” approach by modifying the commercial version of SEQUEST to perform searches in a candidate-centric fashion, resulting in a program we call MacroSEQUEST. MacroSEQUEST reads FASTA-formatted protein databases and returns PSMs for a collection of MS/MS spectra in a fraction of the time it takes legacy SEQUEST. We compare the performance of MacroSEQUEST to a suite of other commonly used database search engines. Finally, we demonstrate a useful application for the performance gains associated with MacroSEQUEST by leveraging this increase in speed to perform high-resolution MS/MS correlation analysis and describe the benefits to classical SEQUEST scores associated with high-mass precision fragment ion searching. MATERIALS AND METHODS Sample Preparation. Peptide synthesis was performed by New England Peptide (Gardner, MA). Yeast cells were harvested during logarithmic growth, pelleted, and lysed by addition of SDS- PAGE pH 8.1 sample buffer (3× volume buffer/pellet weight) and bead beating at 4 °C. HeLa cell lysate was prepared by harvesting a confluent 15 cm dish of HeLa cells by trypsinization, washing 2× in PBS, and addition of 4 mL of lysis buffer (0.5% Triton X-100, 50 mM Tris pH 8.1, 150 mM NaCl, 1 mM MgCl2, Roche Minicomplete protease inhibitors), followed by sonication and clarification of the lysate in a centrifuge (14 000g) for 10 min at 4 °C. Both protein preps were reduced by addition of DTT to 5 mM and incubation in a water bath at 55 °C for 20 min, followed by alkylation of cysteines in 12.5 mM iodoacetamide at room temperature for 45 min. After the alkylation reaction was quenched (addition of 2.5 mM DTT), the lysates were aliquotted, snap frozen in liquid nitrogen, and stored at -80 °C until use. To prepare the protein digests, an aliquot of cell lysate was warmed rapidly under warm water and mixed with SDS-PAGE sample buffer to a protein concentration of ∼0.25 (19) Edwards, N.; Lippert, R. In Algorithms in Bioinformatics: Second International Workshop, WABI 2002, Rome, Italy, September 17-21, 2002 Proceedings; Guigo, R., Gusfield, D., Eds.; Springer: Berlin, Germany, 2002; Vol. 2452, pp 68-81. (20) Tabb, D. L.; Narasimhan, C.; Strader, M. B.; Hettich, R. L. Anal. Chem. 2005, 77, 2464–2474.
mg/mL protein, followed by separation on 2-well, 4-12% NOVEX minigels (for 2D separations) with a protein marker in the narrow lane. Proteins were visualized with Coomassie blue, and the region between 80 and 125 kDa was excised, destained, and digested with trypsin. Peptide samples were analyzed on an LTQ Orbitrap (ThermoFisher Scientific, Bremen, Germany) per established procedures. 5 LTQ-Orbitrap .RAW files were converted to .mzXML files using ReAdW.exe (version 4.0, http://sourceforge.net/projects/sashimi/files/). Peptide precursor mass assignments were adjusted postacquisition with in-house software that (i) updates MS2 scan headers with high-mass accuracy precursor information from MS1 scans and (ii) averages multiple observations of these precursor values across each peptide chromatographic elution profile. MacroSEQUEST. MacroSEQUEST was written from scratch in ANSI C using standard libraries and compiled with GCC version 4.1.2 on a Unix platform using the SEQUEST2.8 source code as a guide. Input is provided as command line arguments. Experimental data is read in .DTA file format, and candidate peptides are read from FASTA-formatted protein databases. Macro outputs SEQUEST-like .out files for each input spectrum.. To measure the efficiency of the “candidate-centric” method, we created a version of Macro (MacroParser) in which the Xcorr scoring function was replaced with a random number generator. Database Searching. Searches were performed using the latest database builds from the yeast proteome (Saccharomyces Genome Database, http://www.yeastgenome.org/) or the human proteome (UniProtKB, http://www.uniprot.org/). Target-decoy databases were generated using in-house scripts that reverse each protein sequence, label decoys proteins with a specific identifier, and append the decoy sequences to the end of a forward database. 21 Unless otherwise stated, all searches were performed with a 1.1 Da precursor mass tolerance and filtered to ±1.5 ppm precursor mass measurement accuracy (Figure S-1 in the Supporting Information); Xcorr and dCn (delta-correlation) cutoff values were adjusted to achieve a false discovery rate (FDR) of less than 1% (Table S-1 in the Supporting Information). Only the yeast searches were conducted with semitryptic enzyme specificity; all other searches were performed with full trypsin specificity. The maximum number of missed cleavages for all searches was set to three. All searches were also performed with acetamidemodified Cys as a static modification (+57.021 461 Da) and with oxidized Met as a variable modification (±15.991 915 Da); for phosphorylation searches, Ser, Thr, and Tyr were allowed to vary by ±79.966 331 Da. Variable modifications were limited to a maximum of 3 per peptide, where applicable. Refinement or iterative searches were not permitted when using X!Tandem and OMSSA. No multithreading was used for any search algorithm. RESULTS Candidate-Centric Database Spectral Matching. Historically, database spectral matching algorithms such as SEQUEST dealt with very small numbers of MS/MS spectra acquired per experiment, from as few as tens to at most one hundred spectra in a typical analysis. 9 Individual MS/MS spectra were then matched with candidate peptides by searching through a target database. In general, the focus for algorithm development has (21) Elias, J. E.; Gygi, S. P. Nat. Methods 2007, 4, 207–214. been on the steps involved in this single iteration and on improving the sensitivity, accuracy, and productivity of a single analysis, with the expectation that this “optimized” core process is iterated until PSMs have been generated for all MS/MS spectra in an analysis which, while computationally inefficient, was adequate given the historical context of the problem space. A generalized scheme describing this classical workflow for SEQUEST is depicted in Figure 1a. However, this workflow fails to recognize potential elements of relatedness between MS/MS spectra that are collected together. Clearly, all of these spectra require matching to candidate peptides in the same database; thus, presenting the entire collection of spectra to the database (or vice versa) as a “single analysis” has the potential to reduce areas of overhead associated with repeating a single process, such as digesting a database, thousands of times (Figure 1b). Our analysis of the work distribution for SEQUEST when searching a target-decoy human UniProtKB protein database (∼150 000 proteins) against 10 000 spectra reveals that only a very small portion of the actual search time is spent on scoring candidate PSMs (∼1%), while the remainder is spent parsing the database (Figure 1c). Although the database must be read once in order to generate a PSM for a single spectrum, it does not need to be reread to search other MS/MS spectra under the same set of parameters if those additional spectra are considered simultaneously. In this way, legacy SEQUEST wastes a significant amount of search time when large numbers of MS/MS spectra are searched against large databases. We sought to address these issues through a wholesale rearchitecting of the SEQUEST scoring components in order to conduct them in a candidate-centric fashion and named the resulting program MacroSEQUEST or “Macro” for short. The Macro workflow is described in Figure 1b. Similar to the candidate-centric algorithm DBDigger, 20 a single Macro process at launch is fed a parameter set that includes a path to a collection of MS/MS spectra. This collection of spectra is first read into memory and indexed by precursor ion mass to create a “spectral data array” that includes preprocessing for each experimental spectrum and memory mapping for candidate peptide sequences, protein references, search scores, etc. After this data structure is created, Macro begins parsing the FASTA database of interest by digesting it based on a desired enzyme cleavage specificity. For each newly digested peptide, Macro calculates its mass and checks it against the spectral array, including a given mass tolerance; if a peptide falls outside these boundaries, Macro discards it and proceeds to the next logical peptide in the database. If, however, a peptide falls into one or more spectral “bins” in the spectral array, Macro enters a scoring loop in which a reimplementation of a fast SEQUEST cross correlation algorithm 22,23 is performed on the candidate peptide for each MS/MS spectrum that falls within the peptide’s desired precursor mass tolerance, and plugs these scores and associated peptide/protein information into the spectral array. The scoring loop then closes by returning to database parsing and to the next logical peptide in the database; Macro continues this cycle of peptide generation, spectral array scanning, and scoring until it reaches the end of the FASTA (22) Eng, J. K.; Fischer, B.; Grossmann, J.; Maccoss, M. J. J. Proteome Res. 2008, 7, 4598–4602. (23) Venable, J. D.; Xu, T.; Cociorva, D.; Yates, J. R. Anal. Chem. 2006, 78, 1921–1929. Analytical Chemistry, Vol. 82, No. 16, August 15, 2010 6823
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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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Figure 2. Influence of a surfactant
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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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