Automated method for analysis of tryptophan and tyrosine metabolites using capillary electrophoresis...

ORIGINAL PAPER

Automated method for analysis of tryptophanand tyrosine metabolites using capillary electrophoresiswith native fluorescence detection

Christopher A. Dailey & Nicolas Garnier &

Stanislav S. Rubakhin & Jonathan V. Sweedler

Received: 18 July 2012 /Revised: 28 November 2012 /Accepted: 20 December 2012 /Published online: 11 January 2013# Springer-Verlag Berlin Heidelberg 2013

Abstract Capillary electrophoresis (CE) with laser-inducednative fluorescence (LINF) detection offers the ability tocharacterize low levels of selected analyte classes, depend-ing on the excitation and emission wavelengths used. Here anew automated CE-LINF system that provides deep ultravi-olet (DUV) excitation (224 nm) and variable emissionwavelength detection was evaluated for the analysis of smallmolecule tryptophan- and tyrosine-related metabolites. Theoptimized instrument design includes several features thatincrease throughput, lower instrument cost and mainte-nance, and decrease complexity when compared with earliersystems using DUV excitation. Sensitivity is enhanced byusing an ellipsoid detection cell to increase the fluorescencecollection efficiency. The limits of detection ranged from 4to 30 nmol/L for serotonin and tyrosine, respectively. Thesystem demonstrated excellent linearity over several ordersof magnitude of concentration and intraday precision from1–11 % relative standard deviation (RSD). The instrument’sperformance was validated via tryptophan and serotonincharacterization using tissue extracts from the mammalianbrain stem, with RSDs of less than 10 % for both metabo-lites. The flexibility and sensitivity offered by DUV laserexcitation and tunable emission enables a broad range ofsmall-volume measurements.

Keywords Capillary electrophoresis . Laser-induced nativefluorescence . Serotonin . Automation . High throughput

Introduction

The latest human metabolome database [1] reports more than6,800 identified metabolites, demonstrating the demand forselective and information-rich analytical techniques capableof examining chemically complex environments that consistof lipids, proteins, peptides, salts, and the focus of this work,small molecule metabolites. Techniques for measuringthe abundance and distribution of these metabolites withinthe brain are made challenging by its complex morphologyand the often vanishingly small quantities of analytes andvolumes available for analysis. While universal character-ization approaches such as mass spectrometry (MS) [2] andnuclear magnetic resonance (NMR) [3] offer great flexibility,the selectivity provided by separations hyphenated to elec-trochemical (EC) and laser-induced fluorescence (LIF)detection offers advantages for investigating defined analyteclasses in applications ranging from clinical analysis tobasic research.

Capillary electrophoresis (CE) is a separation methodwell-suited for the analysis of trace-level signaling mole-cules from neural systems due to its small-sample volumerequirements (nanoliter to femtoliter), high separation effi-ciencies (more than 106 theoretical plates), and online sam-ple concentration techniques, as reviewed by Lapainis et al.[4]. A variety of detection modalities are available for usewith CE (e.g., EC [5], ultraviolet (UV) absorbance [6], LIF[7, 8], NMR [9], and MS [10]). Oftentimes, the universalnature of these approaches and the high chemical informa-tion content they yield complement many studies. However,the selective detection provided by EC and LIF allows tracelevel characterization, even from incompletely separatedcomplex samples, as only a subset of the molecules withinthe sample are detected. Both labeled and label-free meth-ods can be used for LIF detection schemes. Labeling targetmolecules by derivatization with highly fluorescent dyes

C. A. Dailey : S. S. Rubakhin : J. V. Sweedler (*)Department of Chemistry and the Beckman Institute, Universityof Illinois, Urbana-Champaign, 600 South Mathews Avenue 63-5,Urbana 61801( IL, USAe-mail: [email protected]

N. GarnierFlowgene, Rond-Point du Biopôle,63360 Saint-Beauzire, France

Anal Bioanal Chem (2013) 405:2451–2459DOI 10.1007/s00216-012-6685-0

can achieve high sensitivities, but it is difficult to achieveefficient labeling of small-volume samples without causinganalyte dilution. In addition, non-specific and incompletereactions can make analysis challenging, particularly withinchemically complex sample environments and with limitedsample volumes, such as those examined in the present work.

Selective detection can target a defined subset of mole-cules with a specific molecular property such as nativefluorescence or electrochemical activity. The compoundstargeted here are metabolites of tryptophan (Trp) and tyro-sine (Try), two important analyte classes that include avariety of signaling molecules, including neurotransmitters,neuromodulators and trophic factors. The distribution andtissue-specific levels of these metabolites are correlated withfunctional, behavioral, and pathologic states. For example,serotonin, a neurotransmitter derived from Trp, is a knownmodulator of mood. It is also involved in physiologicalfunctions such as circadian rhythm, neurogenesis, memory,learning, and body temperature regulation, and has beenimplicated in pathologies including depression, schizophre-nia, and bipolar disorder [11]. One quantitative method forthe analysis of these metabolites, along with Trp- and Tyr-containing peptides and proteins, is native fluorescencedetection. These species contain aromatic rings that undergoS0 → S1 or S0 → S2 transitions upon deep ultraviolet (DUV)excitation (200–300 nm) and have quantum yields rangingfrom 0.13–0.28, which are sufficient to take advantage oftheir native fluorescence as a means of detection at tracelevels [12–14]. Thus, laser-induced native fluorescence(LINF) detection is an attractive choice for both traditionaland microchip CE systems [15].

Until recently, several technical challenges have limitedthe use of CE separations coupled to LINF detection. Manyinstruments have been large, costly, and/or high mainte-nance, thereby limiting the wide-spread application of CEsystems using native fluorescence [16–23]. DUV excitationsources were typically full-frame ion lasers such as argonion (257, 305, and 275 nm) [24–26] and frequency quadru-pled Nd:YAG (266 nm) [27] lasers. In an effort to producemore accessible instrumentation, stable DUV sources suchas light emitting diodes [28, 29] and hollow-cathode metalvapor lasers [19], which are smaller, lower cost, and requirereduced maintenance, have replaced older excitation sourcesin CE-LINF applications. CE-LINF using DUVexcitation iscomplicated by the high amount of scatter and backgroundfluorescence with on-column excitation. Sheath-flow cellsachieve off-column excitation and detection with impressiveperformance [8, 30], but they require more complex liquidhandling compared to more traditional CE-LIF systems. Ashas been the case with most analytical techniques, ongoingimprovements can contribute to making a system morerobust, versatile, and higher throughput, which should ex-pand its use.

Here, we describe a prototype of an automated CE-LINFinstrument used for the analysis of mammalian central ner-vous system (CNS) tissues, and evaluate the analytical figuresof merit for several neuroactive metabolites of tryptophan andtyrosine. When designing the new system, the criteria includ-ed automated sampling, DUV excitation, and flexible emis-sion wavelength detection, packaged into a format compatiblewith existing CE systems. While having features in commonwith our previous systems [19, 24, 31], this instrument offersseveral new features: a small DUV excitation source, on-column excitation, an elliptical fluorescence collection cellto increase the collection efficiency, and automated sampleand injection systems for higher throughput analyses. Thesystem uses a pulsed He-Ag metal vapor laser with a 224-nm output for excitation, and tunable emission wavelengthselection via a spectrometer, followed by detection with aphotomultiplier tube. The components of the instrument arerelatively low-cost and can be installed on an existing auto-mated CE instrument, allowing increased reliability and re-producibility. This new instrument demonstrates competitiveanalytical figures of merit compared with significantly larger,more complex native fluorescence-based systems.

In order to demonstrate the applicability and performanceof the automated CE-LINF system compared to our earlierCE-wavelength-resolved (WR)-LINF system, we examinedrat brain stem tissue using both instruments. In addition toits ability to target selected metabolites, the prototype in-strument can be used to investigate a wide range of otheranalyte classes via native fluorescence detection, includingproteins and peptides containing Tyr and Trp residues.

Experimental

Chemicals, solutions, and materials

The following standards were used: epinephrine, norepineph-rine, dopamine, tyramine, tryptamine, 5-hydroxy indole aceticacid, melatonin, tryptophan, tyrosine, N-acetyl serotonin (Sig-ma Aldrich, St. Louis, MO, USA), and serotonin (Alfa Aeser,Ward Hill, MA, USA). Glacial acetic acid, citric acid mono-hydrate, and methanol were purchased from Sigma Aldrich.Chemicals for standards were purchased at the highest puritypossible. Ultrapure water for stock solutions and standardswas obtained from an Elga PureLab Prima water filtrationsystem (Elga LLC, Woodridge, IL, USA) with a purity of18.2 MΩ. Fused silica capillaries were obtained from Poly-micro (Phoenix, AZ, USA).

The extraction media consisted of 49.5/49.5/1, methanol(LC-MS grade)/water/glacial acetic acid (99 %) by volume.The background electrolyte (BGE) for separations was madeby dissolving 5.25 g of citric acid monohydrate (25 mM,pH 2.75) in 1.0 L of ultrapure water and sonicated to

2452 C.A. Dailey et al.

dissolve and de-gas. Sodium hydroxide (0.1 N), used forcapillary conditioning, was obtained from Beckman CoulterInc. (Brea, CA, USA).

Preparation of standards

Individual, high concentration stocks for standards wereprepared by weighing 1–2 mg on a microbalance (Met-tler Toledo; Columbus, OH, USA) and dissolving theanalyte into the extraction media. Stocks were stored at−80 °C until dilution to the working concentrations asindicated herein.

Tissue extraction and quantitation

Brain stem tissue was dissected from a Sprague Dawley rat(Harlan, Inc., Indianapolis, IN, USA). Euthanasia was per-formed in accordance with animal use protocols approved bythe University of Illinois Institutional Animal Care and UseCommittee, and local and federal regulations. The brainstemwas surgically removed immediately after animal decapitationby sharp guillotine. The excised brain structure was flashfrozen in dry-ice cooled isopentane, transferred into individualcapped plastic tubes, and stored at −80 °C until extraction.Extraction media was added to the weighed tissue at a con-centration of 5 μL/mg of wet weight. The tissue was manuallyhomogenized with the extraction media and allowed to extractfor 90 min at 4 °C [32], centrifuged at 16,000×g for 15 min,and the supernatant filtered using an Amicon 10 kDa molec-ular weight cut-off filter (Millipore; Billerica, MA, USA) at4 °C. Samples were stored −80 °C until analysis.

Working curves were established using dilutions of highstock standards at concentrations of 1.00 μM and 500, 250,125, and 62.5 nM. Each concentration was analyzed in dupli-cate on the same day as the tissue analysis; linear regressionanalysis was used to determine the working equation.

CE separation conditions

The CE separations for both the automated and laboratory-built systems were performed using a BGE of 25 mM citricacid (pH 2.5) and an applied voltage of +30 kV (18–20 μAmp). The BGE was filtered using a 0.2-μmsurfactant-free, cellulose acetate syringe filter (Nalgene;Rochester, NY, USA) immediately prior to use. Betweeneach separation, the capillary was conditioned for 1 minwith 0.1 N of NaOH at 15 psi and then rinsed with BGEfor 2 min at 30 psi. Total analysis time, including capillaryconditioning, was approximately 10 min for each analysis.

Automated CE-LINF system

A prototype CE-LINF detection system was provided byBeckman Coulter, Inc.; the fundamental design of the LINFdetector has been previously described in application with ahigh performance liquid chromatography (HPLC) system andwas installed on a modified automated CE system (BeckmanCoulter, Inc.) [33]. Briefly, a hollow-cathode He-Ag laseroperating at 224 nm is used as the excitation source. Theincident angle for excitation is set at 30 to reduce scatter andpower loss. Fluorescence is collected using a patented ellipti-cal cell with the capillary and the spectrometer entrance slitpositioned at the two foci [34] (Fig. 1).

We used a polyimide-coated, fused silica capillary, 50 μminner diameter (ID)×365 μm outer diameter (OD) and 48 cmin length, with a detection window created at 37 cm. Thecoating at the detection window was removed using a capil-lary window maker (MicroSolv; Eatontown, NJ, USA) in lieuof the more traditional open flame method to prevent exces-sive damage to the capillary surface. The bare fused silica ofthe detection window was cleaned by sonication for 10 min inethanol and any remaining debris removed using optical lenspaper (Thor Labs; Newton, NJ, USA). Care was taken to clean

Fig. 1 Schematic of theellipsoidal collection cell. Theexcitation point and entrance tothe monochromator arepositioned at the two foci of theellipsoid, allowing forcollection of photons notemitted directly towards themonochromator and increasingthe total signal collectedcompared with more traditionaldetector configurations. Therelative sizes of the componentsare not represented for clarity

Automated method for analysis of tryptophan and tyrosine 2453

and protect the surface of the detection window as contami-nates and/or defects both on and in the surface of the capillarycan increase background and scattering significantly, particu-larly when using DUV radiation.

CE-WR-LINF system

The limits of detection (LODs) and analyte identification resultsfrom the automated CE-LINF instrument were compared withthose from a laboratory-built CE-WR-LINF system [23, 35].Briefly, this instrument uses 264 nm excitation (frequencydouble of the 528 nm fundamental) from an Innova FreD300C argon ion laser (Coherent, Inc; Santa Clara, CA, USA).We used a separation capillary with a 50 μm ID×150 μm ODand a length of 50 cm, and post capillary excitation within asheath-flow cell containing 25 mM citric acid. The resultingfluorescence spectra were imaged onto a liquid nitrogen-cooledcharge-coupled device by a flat-field corrected spectrograph.

Automated system optimization

In order to determine the optimal detection wavelength for theanalytes of interest, the fluorescence emission was scannedusing the automated CE system. Each analyte was dissolved ata concentration of 10 μM in ultrapure water and flowedthrough the capillary using a positive pressure of 15 psi.Fluorescence spectra were scanned from approximately 200to 600 nm and the resulting spectra were normalized to thepeak intensity after background subtraction (Fig. 2). Subsetsof these analytes that had peak intensities at similar wave-lengths were separated into two sets of standards for evalua-tion—set A: serotonin, tryptophan, and tryptamine; and set B:tyrosine, dopamine, epinephrine, norepinephrine, and

tyramine. Both sets were evaluated using a grating positionof optimal fluorescence for the individual set of analytes, asindicated by the vertical lines in Fig. 2.

In order to determine the sample volume for optimalreproducibility, varying volumes of standard set A werepressure-injected and the relative standard deviation (RSD)calculated. Solutions were injected using a pressure of0.5 psi and the duration was varied from 3 to 25 s, withtotal injected sample volumes ranging from 4 to 33 nL; eachinjection volume was repeated five times. The RSD for bothpeak height and area was calculated for tryptamine, seroto-nin, and tryptophan. The injected volume with the lowestRSD for the analytes was 20 nL, and this volume was used

Fig. 2 Fluorescence profiles for (a) tyrosine metabolites and (b)tryptophan metabolites were obtained using the automated CE-LINFinstrument by scanning the emission spectra of the analyte beingflowed through the capillary. The differences in fluorescence maxima

for the various metabolites require selection of a wavelength to mon-itor, corresponding as close as possible to the maxima of the analytes ofinterest. Vertical lines denote the grating position for detection for eachset of metabolites

Fig. 3 Stacked electropherograms of standard set A (tryptamine, se-rotonin, and tryptophan) and B (tyramine, dopamine, epinephrine,norepinephrine, and tyrosine) from the automated CE-LINF system.Electropherograms were monitored at the optimal wavelengths deter-mined for each set


for subsequent quantitative studies for both performanceevaluation and tissue analysis.

Data processing and analysis

Data were processed using Origin 8.0 (OriginLab Corpora-tion; Northhampton, MA) for smoothing, background sub-traction, and peak quantitation. The Savitzky–Golaysmoothing method was used to reduce the high-frequencynoise from the electropherograms using a window of fivedata points and a 2nd order polynomial fitting. The peakintegration analysis function of the Origin software wasused to subtract the background and quantify several peakparameters: migration time, peak height, full width at halfmaximum, and peak area.

Evaluation of analytical performance

The LODs were defined as the concentration of analyte witha signal-to-noise ratio of 3, with the signal as the peak

fluorescence intensity of the particular analyte peak, andthe noise as the standard deviation of the background im-mediately preceding the peak. Each analyte was analyzedindividually using a standard at a concentration of 500 nMand the data processed as described in the “Data processingand analysis” section above. Concentrations of 30, 20, 15,10, and 5 nM for both standard sets were prepared fromhigh-concentration stocks for the determination of the limitsof quantitation (LOQs). Each concentration was analyzed atn=5. Measurements were considered quantitative at a par-ticular concentration of analytes if the RSD was less than20 %. Within-day, day-to-day, and total precision wereevaluated over a period of five consecutive days using threeconcentrations of both standard sets: 1.25 μM and 312 and78 nM. The same standards at each concentration wereanalyzed in triplicate each day. Linearity was evaluated from29 nM to 20 μM using mixed standards. Both 1st and 2ndorder regression was used to determine the linearity of thepeak area.

Results and discussion

Design and application

CE-LINF as a detection method has proven to be a usefulapproach that is applicable to a variety of fields of research.It has been used for discovering new serotonin metabolites[36], characterizing single cells [17, 23], elucidating phar-maceutical metabolism [37], enabling environmental moni-toring [38], and allowing multicolor sequencing [39–41].The success of these previous systems and methods provid-ed the motivation to adapt the key concepts into the designof a less expensive and automated instrument, thereby en-abling routine and economical applications. Obstacles to thegeneral application of CE-LINF have been the cost andcomplexity of DUV excitation sources, which have beenprimarily limited to large-frame ion and quadrupled Nd:YAG lasers. The hollow-cathode He-Ag laser (emitting at224 nm) used here costs 5 to 10 times less than more

Table 1 Comparison of the detection limits (S/N=3) of the automatedCE-LINF platform with the CE-WR-LINF system

Analyte Automated system(nmol/L)/(Attomoles)(224 nm)

Wavelength-resolvedsystem (nmol/L)/(Attomoles)(264 nm)

Tryptophan 18/342 5/50

Serotonin 4/76 1/10

5-HIAA 13/247 50/500

Melatonin 6/114 NA

NAS 9/171 NA

Tyrosine 15/285 88/880

Dopamine 16/304 36/360

Epinephrine 16/304 94/940

Norepinephrine 30/570 91/910

The LODs for the tryptophan metabolites with the automated systemare comparable to the wavelength-resolved system and show a 5-foldimprovement for tyrosine metabolites

Table 2 Evaluation of linearityfor selected standards

Typical linearity descriptors areincluded, along with the coeffi-cient for the 2nd order polyno-mial, which indicates anegligible 2nd order componentto the fit

Analyte Linear range(nM)

R2 (1st orderregression)

Intercept Slope 2nd ordercoefficient

Serotonin 39–1,250 0.9997 −0.12 0.0083 1.7e-05

Tryptophan 39–2,500 0.9951 0.16 0.0045 −4.5e-07

Tryptamine 39–1,250 0.9998 −0.09 0.0104 3.7e-07

Dopamine 39–20,000 0.9993 −0.02 0.0006 2.6e-09

Epinephrine 39–20,000 0.9995 0.10 0.0008 −2.7e-09

Norepinephrine 39–10,000 0.9991 0.10 0.0014 −1.2e-08

Tyramine 39–5,000 0.9959 0.07 0.0016 −7.2e-08

Tyrosine 39–10,000 0.9979 0.16 0.0016 −2.3e-08


traditional full-frame ion laser DUV sources and iseconomically competitive with other fluorescence-basedexcitation sources used in commercial CE detectionsystems using longer wavelengths. Excitation at 224 nmincreases the sensitivity of catecholamines by accessing theS0 → S2 transition, which has a higher absorbance crosssection than more traditional DUV excitation wavelengths(e.g., 257, 264, 266, and 287 nm) that use the S0 → S1transition [12].

Sheath-flow detection cells are typically used for off-column excitation due to the high level of background andpower losses associated with Raleigh scattering in DUV [30,42]. The system described here is enhanced by using on-column excitation at an incident angle of 30° to reduce thescatter and a patented ellipsoid excitation cell (Fig. 1) tocollect a larger fraction of emitted photons compared withtraditional collection optics, making on-column excitationcompetitive with the more complicated, off-column sheath-flow setups. An added benefit of this excitation and detectionsetup is greater ease in optical alignment.While the ellipsoidal

cell has been used previously with HPLC [33], we expand itsuse here by integrating the flow cell with CE instrumentation.This new system for CE handles a 1,000-fold smaller samplevolume than used in LC, and so offers improved compatibilitywith the volume-limited sample characteristic of most neuro-nal analyses. This configuration allows for the application ofLINF detection on existing automated CE instrumentation,poising the technique for ready implementation by laborato-ries currently using automated CE systems.

When one tags an analyte with a fluorophore, the fluores-cence properties of the tagged analyte tend to be dictated by thecharacteristics of the particular fluorescence tag. Native fluo-rescence, with its inherent information-rich nature, allows in-dividual species to be identified based on the differing maximaof their fluorescence spectra. Small changes in the functionalgroups near the aromatic rings result in subtle changes in thefluorescence spectra, creating clusters of fluorescence maxima,as shown in Fig. 2. Reconfiguration of the ring structure duringmetabolism can shift the fluorescence maxima substantially, asis the case for kynurenine and kynurenic acid. As Fig. 2illustrates, even though the fluorescence spectra for the metab-olites of interest are broad and have maxima centered aroundsimilar wavelengths, ∼300 nm for tyrosine metabolites and∼350 nm for tryptophan metabolites, analytes can be differen-tiated based on their spectral properties. For optimal perfor-mance, this requires that either the entire spectra be acquired ora predefined, optimal wavelength be determined and moni-tored for the analytes of interest.

We divided the metabolites of interest into two setsaccording to their fluorescence maxima: set A (tyrosinemetabolites) and set B (tryptophan metabolites). The twodetection wavelengths for each set were chosen to corre-spond as close to the maximum fluorescence for as many ofthe analytes as possible from each cluster. The selectedgrating position for each is indicated by vertical lines inFig. 2. Some of the analytes for which fluorescence spectrawere included in Fig. 2 were insufficiently fluorescent at thechosen detection wavelengths and thus, were excluded from

Fig. 4 Optimization of injection volumes. Varying volumes of stan-dard set A were injected by applying pressure at 0.5 psi for varyingdurations. While there was minimal variation in the LODs between thedifferent injection volumes, the intra-day precision was substantiallyimproved at a volume of ∼20 nL

Table 3 Summary of within-day, day-to-day, and total precision for the seven metabolite standards used to evaluate the analytical merits of themethods used in this study

Serotonin Tryptamine Tryptophan Tyramine Dopamine Epinephrine Norepinephrine

Level 1 (78 nM) Within-day 1 % 2 % 8 % 6 % 11 % 11 % 10 %

Day-to-day 14 % 12 % 13 % 20 % 36 % 24 % 14 %

Total 14 % 13 % 15 % 21 % 38 % 26 % 18 %

Level 2 (312 nM) Within-day 2 % 2 % 4 % 2 % 6 % 4 % 3 %

Day-to-day 9 % 9 % 10 % 9 % 10 % 9 % 9 %

Total 9 % 9 % 10 % 9 % 11 % 10 % 10 %

Level 3 (1.25 μM) Within-Day 1 % 1 % 2 % 2 % 1 % 1 % 1 %

Day-to-Day 4 % 3 % 3 % 6 % 8 % 7 % 7 %

Total 4 % 3 % 3 % 7 % 8 % 8 % 8 %


the performance studies, specifically, kynurenine, kynurenicacid, phenylalanine, and L-DOPA. The electropherogramsfor each set are shown in Fig. 3.

System performance

The system was evaluated for several figures of merit foreach mixed standard set: LOD, linearity, LOQ, and preci-sion across concentration ranges relevant to levels that are

typical for biological systems. LODs for the prototype sys-tem and our own laboratory-built, WR instrument [35] aregiven in Table 1 for comparison of their performance. TheCE-WR-LINF system was somewhat more sensitive forindolamines (tryptophan, serotonin, tryptamine), likely dueto a larger absorbance cross section for the S0 → S1 at264 nm for indolamines than catecholamines. There was asubstantial, more than 5-fold, improvement in the detectionlimits for the catecholamines with the automated CE-LINFover the WR system, also likely due to an increased S0 → S2absorbance cross section, in this case at 224 nm.

Typically, fluorescence demonstrates linearity acrossmany orders of magnitude, with the limiting factor beingthe method used for detection. Here, the gain settings wereoptimized for the detection of low concentration analytes,which limits the dynamic range, but is optimal for theanalytes of interest in CNS tissues. Even so, as summarizedin Table 2, a minimum of two orders of magnitude oflinearity was observed for the analytes with higher fluores-cent yields (serotonin, tryptamine, tyramine, tryptophan)and three orders of magnitude for analytes with lower fluo-rescent yields (dopamine, epinephrine, norepinephrine,tyrosine).

One of the many advantages of an automated CE systemis the level of precision that can be achieved by limitingseveral of the more common sources of variation inherent tolaboratory-built systems. The automated system we describeoffers temperature-controlled sample and capillary environ-ments and pressure-controlled sample injections, two varia-bles that can substantially degrade reproducibility. Here, thevariation associated with sample introduction was mini-mized by optimizing the pressure-injected volume. Figure 4clearly shows that the most reproducible injection volume isapproximately 20 nL. Because LODs were not significantlydependent on injection volume, they were not taken intoconsideration when optimizing the injection volume. Theintraday precision ranged from 1–11 % for the lowest concen-trations, and 1–2 % for the highest concentrations (Table 3).As expected, the total precision over 5 days was higher than

Fig. 5 Typical electropherograms for the same brain stem tissue ex-tract from the (a) automated prototype CE-LINF instrument providedby Beckman Coulter, Inc. and the (b) laboratory built, wavelength-resolved instrument. The differences in capillary lengths between thetwo instruments account for the differences in migration times. Theidentified peaks from serotonin and tryptophan were aligned for com-parison. While the electropherograms are similar, the differences canbe attributed to individual analyte responses to the differing excitationwavelengths. Time scales have been aligned using known peaks(numbered) key: 1—unidentified, but quantified peak; 2—serotonin;3—tryptophan; 4—tyrosine

Fig. 6 Analysis of the sametissue extract shown in Fig. 5using wavelength-resolved na-tive fluorescence detection.Several of the peaks can beidentified by comparison ofspectral and migration times tostandards. The unidentifiedpeaks are likely other trypto-phan metabolites or tryptophan-containing peptides/proteins.Key: 1—unidentified, butquantified peak; 2—serotonin;3—tryptophan; 4—tyrosine,asterisk—unidentified peak


intraday, but the data trending (not shown) showed a slightdrift toward lower total signal, indicating a degradation of thestandards or minor changes in alignment. However, even withthe minor change in signal, the precision was still excellentand comparable with many clinical assay performancecharacteristics.

Analysis of mammalian CNS tissue extracts

The quantitation of trace levels of analytes from tissueextracts presents some challenges, namely, successful ex-traction while minimizing effects from interfering proteinsand peptides. By adding methanol to the extraction media,we not only preserved the analytes from enzymatic degra-dation by deactivation of the enzymes, but sufficiently low-ered the conductivity to provide field-amplified stacking inorder to improve LODs and increase separation efficiencies.While CE is tolerant of complex sample environments,excess amounts of proteins can coat the walls of the capil-lary, degrading performance. This was mitigated in threeways. First, the total amount of protein present in the samplewas reduced by using a 10-kDa molecular weight cut-offfilter. Second, a low pH separation buffer was chosen. Atlow pH, the silanol groups on the capillary surface areprotonated, limiting the attraction between the peptidesand the surface. Finally, conditioning of the capillary surfacewith 0.1 N of NaOH between each analysis removed anycontaminants. The amount of sodium hydroxide used tocondition the capillary was kept at a minimum so as topreserve the capillary and reduce the frequency of replace-ment. Flushing the capillary for 1 min at 15 psi with 0.1 N ofNaOH yielded reproducible electropherograms for the tissueextract.

The analyses of rat brain stem tissue demonstrated theability of the automated CE-LINF instrument and describedmethod to achieve reproducible quantitation from CNS tis-sue extracts. Comparison electropherograms, extracted atthe same wavelength for the tissue extracts, are shown forboth the automated CE-LINF (Fig. 5a) and WR-CE-LINF(Fig. 5b) systems. A full wavelength-resolved electrophero-gram is shown in Fig. 6. The peaks quantified using theautomated CE system are labeled in Fig. 5a: serotonin andtryptophan, and one indolamine-like, unidentified peak.Results for the automated system include serotonin, 1.25(± 0.12)nmol/g; tryptophan, 10.6 (± 0.37)nmol/g; and thesignal for an unidentified peak, 3.64 (± 0.42)A.U. While thetwo electropherograms are similar, the minor differencesbetween them can be attributed to differences in the fluo-rescence responses between the two excitation wavelengths.The utility of tunable emission wavelength monitoring isfurther demonstrated by the wavelength-resolved electro-pherogram in Fig. 6. The varying analyte bands detectedhere have spectra ranging from 300 to 500 nm. Selecting

optimal wavelengths for specific analytes of interest simpli-fies the electropherogram, reducing potentially interferingsignals.

Conclusions

We introduce an automated, cost-effective, and sensitiveCE-LINF platform for the routine analysis of tryptophanand tyrosine metabolites in complex sample matrixes. Theability to monitor a particular wavelength of fluorescenceemission enables fast and efficient data processing com-pared with WR systems, which result in more complex datarequiring correspondingly more complex data analysismethods. In contrast to charge-coupled devices or arraydetection platforms, using a single wavelength for detectionlowers instrument cost and complexity. The system is mademore versatile by a programmable spectrograph that allowsthe optimal detection wavelength to be selected and tailoredto the analytes of interest. We look forward to the commer-cial availability of this instrument for both research andclinical applications. While metabolite detection has beenemphasized here, CE-LINF with DUV is applicable to manyareas, including general characterization of peptides andproteins [25, 29, 43, 44]. The advantages of fluorescenceinclude its fairly constant response for many Try and Trp-containing proteins and ease of quantitation. We expect theuse of LINF to greatly expand with the availability of thisdetection modality for CE.

Acknowledgments The project described was supported by the fol-lowing awards: R01 NS031609 from the National Institute of Neuro-logical Disorders and Stroke, P30 DA018310 from National Instituteon Drug Abuse, and CHE-11-11705 from the National Science Foun-dation. The content is solely the responsibility of the authors and doesnot necessarily represent the official views of the award agencies. Wewould also like to thank Beckman Coulter, Inc. for kindly providingthe prototype automated CE-LINF platform for this study, and WilliamHug from Photon Systems, and Bruno de Vandiere from Flowgene, fortheir efforts in creating this system.

References

1. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B,Hau DD, Psychogios N, Dong E, Bouatra S, Mandal R, SinelnikovI, Xia J, Jia L, Cruz JA, Lim E, Sobsey CA, Shrivastava S, HuangP, Liu P, Fang L, Peng J, Fradette R, Cheng D, Tzur D, ClementsM, Lewis A, De Souza A, Zuniga A, Dawe M, Xiong Y, Clive D,Greiner R, Nazyrova A, Shaykhutdinov R, Li L, Vogel HJ,Forsythei I (2009) HMDB: a knowledgebase for the human metab-olome. Nucleic Acids Res 37(suppl 1):603–610

2. Xiao JF, Zhou B, Ressom HW (2012) Metabolite identificationand quantitation in LC-MS/MS-based metabolomics. TrendsAnalyt Chem 32:1–14


3. Bharti SK, Roy R (2012) Quantitative 1H NMR spectroscopy.Trends Analyt Chem 35:5–26

4. Lapainis T, Sweedler JV (2008) Contributions of capillary electro-phoresis to neuroscience. J Chromatogr 1184(1-2):144–158

5. Wallingford RA, Ewing AG (1987) Capillary zone electrophoresiswith electrochemical detection. Anal Chem 59(14):1762–1766

6. Jorgenson JW, Lukacs KD (1983) Capillary zone electrophoresis.Science 222(4621):266–272

7. Chang HT, Yeung ES (1995) Determination of catecholamines insingle adrenal medullary cells by capillary electrophoresis andlaser-induced native fluorescence. Anal Chem 67(6):1079–1083

8. Cheng YF, Dovichi NJ (1988) Subattomole amino acid analysis bycapillary zone electrophoresis and laser-induced fluorescence.Science 242(4878):562–564

9. Kautz RA, Lacey ME, Wolters AM, Foret F, Webb AG, KargerBL, Sweedler JV (2001) Sample concentration and separation fornanoliter-volume NMR spectroscopy using capillary isotachopho-resis. J Am Chem Soc 123(13):3159–3160

10. Liu CC, Zhang J, Dovichi NJ (2005) A sheath-flow nanosprayinterface for capillary electrophoresis/mass spectrometry. RapidCommun Mass Spectrom 19(2):187–192

11. Azmitia EC (2010) Evolution of serotonin: Sunlight to suicide. In:Christian PM, Barry LJ (eds) Handbook of BehavioralNeuroscience, vol 21. Elsevier, pp 3–22

12. Shear JB (1999) Multiphoton-excited fluorescence. Anal Chem 71(17):598A–605A

13. Li Q, Seeger S (2010) Autofluorescence detection in analyticalchemistry and biochemistry. Appl Spectroscopy Rev 45(1):12–43

14. Chattopadhyay A, Rukmini R, Mukherjee S (1996) Photophysicsof a neurotransmitter: ionization and spectroscopic properties ofserotonin. Biophys J 71(4):1952–1960

15. Schulze P, Belder D (2009) Label-free fluorescence detection incapillary and microchip electrophoresis. Anal Bioanal Chem 393(2):515–525

16. Lillard SJ, Yeung ES, McCloskey MA (1996) Monitoring exocy-tosis and release from individual mast cells by capillary electro-phoresis with laser-induced native fluorescence detection. AnalChem 68(17):2897–2904

17. Yeung ES (1999) Study of single cells by using capillary electropho-resis and native fluorescence detection. J Chromatogr 830(2):243–262

18. Gooijer C, Kok SJ, Ariese F (2000) Capillary electrophoresis withlaser-induced fluorescence detection for natively fluorescent ana-lytes. Analusis 28(8):679–685

19. Zhang X, Sweedler JV (2001) Ultraviolet native fluorescencedetection in capillary electrophoresis using a metal vapor NeCulaser. Anal Chem 73(22):5620–5624

20. Zhang X, Stuart JN, Sweedler JV (2002) Capillary electrophoresiswith wavelength-resolved laser-induced fluorescence detection.Anal Bioanal Chem 373(6):332–343

21. Miao H, Rubakhin SS, Sweedler JV (2003) Analysis of serotoninrelease from single neuron soma using capillary electrophoresisand laser-induced fluorescence with a pulsed deep-UV NeCu laser.Anal Bioanal Chem 377(6):1007–1013

22. Benturquia N, Couderc F, Sauvinet V, Orset C, Parrot S, Bayle C,Renaud B, Denoroy L (2005) Analysis of serotonin in brain micro-dialysates using capillary electrophoresis and native laser-inducedfluorescence detection. Electrophoresis 26(6):1071–1079

23. Fuller RR, Moroz LL, Gillette R, Sweedler JV (1998) Singleneuron analysis by capillary electrophoresis with fluorescencespectroscopy. Neuron 20(2):173–181

24. Timperman AT, Khatib K, Sweedler JV (1995) Wavelength-resolved fluorescence detection in capillary electrophoresis. AnalChem 67(1):139–144

25. Lee TT, Yeung ES (1992) High-sensitivity laser-induced fluores-cence detection of native proteins in capillary electrophoresis. JChromatogr 595(1-2):319–325

26. Milofsky RE, Yeung ES (1993) Native fluorescence detection ofnucleic acids and DNA restriction fragments in capillary electro-phoresis. Anal Chem 65(2):153–157

27. Chan KC, Muschik GM, Issaq HJ (2000) Solid-state UV laser-induced fluorescence detection in capillary electrophoresis.Electrophoresis 21(10):2062–2066

28. Hapuarachchi S, Janaway GA, Aspinwall CA (2006) Capillaryelectrophoresis with a UV light-emitting diode source for chemicalmonitoring of native and derivatized fluorescent compounds.Electrophoresis 27(20):4052–4059

29. Sluszny C, He Y, Yeung ES (2005) Light-emitting diode-inducedfluorescence detection of native proteins in capillary electrophore-sis. Electrophoresis 26(21):4197–4203

30. Wu S, Dovichi NJ (1989) High-sensitivity fluorescence detectorfor fluorescein isothiocyanate derivatives of amino acids separatedby capillary zone electrophoresis. J Chromatogr 480:141–155

31. Lapainis T, Scanlan C, Rubakhin SS, Sweedler JV (2007) Amultichannel native fluorescence detection system for capillaryelectrophoretic analysis of neurotransmitters in single neurons.Anal Bioanal Chem 387(1):97–105

32. Zhang X, Fuller RR, Dahlgren RL, Potgieter K, Gillette R,Sweedler JV (2001) Neurotransmitter sampling and storage forcapillary electrophoresis analysis. Fresenius J Anal Chem 369(3-4):206–211

33. Bonnin C, Matoga M, Garnier N, Debroche C, de Vandiere B,Chaminade P (2007) 224 nm deep-UV laser for native fluores-cence, a new opportunity for biomolecules detection. J Chromatogr1156(1-2):94–100

34. Debroche C, Crespeau H, Vandiere BD (2009) Optical device for lightdetector. Patent Application No. 10/596,340 assigned to Flowgene

35. Park YH, Zhang X, Rubakhin SS, Sweedler JV (1999)Independent optimization of capillary electrophoresis separationand native fluorescence detection conditions for indolamine andcatecholamine measurements. Anal Chem 71(21):4997–5002

36. Squires LN, Jakubowski JA, Stuart JN, Rubakhin SS, Hatcher NG,Kim WS, Chen K, Shih JC, Seif I, Sweedler JV (2006) Serotonincatabolism and the formation and fate of 5-hydroxyindole thiazo-lidine carboxylic acid. J Biol Chem 281(19):13463–13470

37. Schappler J, Staub A, Veuthey JL, Rudaz S (2008) Highly sensi-tive detection of pharmaceutical compounds in biological fluidsusing capillary electrophoresis coupled with laser-induced nativefluorescence. J Chromatogr 1204(2):183–190

38. Kok SJ, Kristenson EM, Gooijer C, Velthorst NH, Brinkman UAT(1997) Wavelength-resolved laser-induced fluorescence detectionin capillary electrophoresis: naphthalenesulphonates in river water.J Chromatogr 771(1-2):331–341

39. Neumann M, Herten DP, Dietrich A, Wolfrum J, Sauer M (2000)Capillary array scanner for time-resolved detection and identifica-tion of fluorescently labelled DNA fragments. J Chromatogr 871(1-2):299–310

40. Heller C (2001) Principles of DNA separation with capillary elec-trophoresis. Electrophoresis 22(4):629–643

41. Zhang J, Yang M, Puyang X, Fang Y, Cook LM, Dovichi NJ(2001) Two-dimensional direct-reading fluorescence spectrographfor DNA sequencing by capillary array electrophoresis. AnalChem 73(6):1234–1239

42. Cheng YF, Wu S, Chen DY, Dovichi NJ (1990) Interaction ofcapillary zone electrophoresis with a sheath flow cuvette detector.Anal Chem 62(5):496–503

43. Shippy SA, Jankowski JA, Sweedler JV (1995) Analysis of tracelevel peptides using capillary electrophoresis with UV laser-induced fluorescence. Anal Chim Acta 307(2-3):163–171

44. Timperman AT, Oldenburg KE, Sweedler JV (1995) Native fluo-rescence detection and spectral differentiation of peptides contain-ing tryptophan and tyrosine in capillary electrophoresis. AnalChem 67(19):3421–3426


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