Comparison between charged aerosol detection and light scattering detection for the analysis of Leishmania membrane phospholipids

Journal of Chromatography A, 1209 (2008) 88–94

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Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma

Comparison between charged aerosol detection and light scattering detection for the analysis of Leishmania membrane phospholipids R. Godoy Ramos a,c , D. Libong a , M. Rakotomanga b , K. Gaudin a , P.M. Loiseau b , P. Chaminade a,∗ a

Univ. Paris-Sud, EA 4041, IFR 141, Faculté de Pharmacie, Châtenay-Malabry, France Univ. Paris-Sud, UMR CNRS 8076, IFR 141, Faculté de Pharmacie, Châtenay-Malabry, France c Universidad de Concepción, Facultad de Farmacia, Concepción, Chile b

a r t i c l e

i n f o

Article history: Received 11 April 2008 Received in revised form 17 July 2008 Accepted 24 July 2008 Available online 31 July 2008 Keywords: Charged aerosol detection Evaporative light-scattering detection Phospholipids Leishmania

a b s t r a c t The performance of charged aerosol detection (CAD) was compared to evaporative light scattering detection (ELSD) for the analysis of Leishmania membrane phospholipid (PL) classes by NP-HPLC. In both methods, a PVA-Sil column was used for the determination of the major Leishmania membrane PLs, phosphatidic acid, phosphatidylglycerol, cardiolipin, phosphatidylinositol, phosphatidylethathanolamine, phosphatidylserine, lysophosphatidylethathanolamine, phosphatidylcholine, sphingomyelin and lysophosphatidylcholine in the same analysis. Although the response of both detection methods can be fitted to a power function, CAD response can also be described by a linear model with determination coefficients (R2 ) ranging from 0.993 to 0.998 for an injected mass of 30 ng to 20.00 ␮g. CAD appeared to be directly proportional when a restricted range was used and it was found to be more sensitive at lowest mass range than ELSD. With HPLC-ELSD the limits of detection (LODs) were between 71 and 1195 ng and the limits of quantification (LOQs) were between 215 and 3622 ng. With HPLC-CAD, the LODs were between 15 and 249 ng whereas the limits of quantification (LOQs) were between 45 and 707 ng. The accuracy of the methods ranged from 62.8 to 115.8% and from 58.4 to 110.5% for ELSD and CAD, respectively. The HPLC-CAD method is suitable to assess the influence of miltefosine on the composition of Leishmania membrane phospholipids. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Leishmaniasis is a parasitic endemic disease widespread in many countries in both New and Old Worlds. The disease affects humans in four main forms: visceral leishmaniasis (VL), cutaneous leishmaniasis (CL), mucocutaneous leishmaniasis (MCL) and diffuse cutaneous leishmaniasis (DCL) [1]. The causative agent is a unicellular parasite of the genus Leishmania, transmitted to humans by sandflies who are primarily infected by mammal reservoir hosts (wild and domestic animals and also humans). The leishmaniases, as a complex of diseases, are as yet impossible to control with a single approach or tool. In fact, the disease persists due to technical, managerial, financial and political constraints. Moreover, the human immunodeficiency virus (HIV) co-infection is changing the epidemiology contributing to the disease dissemination. The treatment for Leishmania infections consists in the administration of first-line drugs (pentavalent antimonials, Sb+5 ), or

∗ Corresponding author. Tel.: +33 1 4683 5790; fax: +33 1 4683 5458. E-mail address: [email protected] (P. Chaminade). 0021-9673/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chroma.2008.07.080

second-line drugs like amphotericin B and AmBisome (Astellas Pharma Us, Deerfield, IL, USA) (amphotericin B in liposomes). But the main disadvantages are the availability and the high cost of treatment. Moreover, most available drugs require long treatment regimes (4 weeks) and parenteral administration, and the resistance is making the mainstay of treatment with pentavalent antimony agents. Recently, miltefosine [hexadecylphosphocholine (HePC)] has been proved to be the first effective and safe oral treatment for Indian VL and has been used to treat patients with antimony-resistant VL [2–5]. However, development of other drugs in clinical phases (paromomycin and sitamaquine) is slow and drug combinations must be developed to prevent drug resistance [6,7]. Miltefosine has membrane affinity due to its phospholipidlike structure, and can insert itself easily within a phospholipid membrane monolayer and moreover binds with membrane sterols [7]. The miltefosine uptake uses a transporter in Leishmania [8]. In addition, some of us have reported that miltefosine is able to induce an apoptotic-like cell death process by indirect mechanisms as phosphatidylserine exposure in parasite’s membranes [9]. Thereby, there are evidences of the participation of miltefosine in the cell death as a consequence of its action on the membrane phospholipid distribution since these molecules are

R.G. Ramos et al. / J. Chromatogr. A 1209 (2008) 88–94

important components of any biological membranes. Due to the complexity and the number of phospholipid classes it is necessary to develop reliable separation and detection methods to assess the phospholipid distribution in different Leishmania extracts [7]. The first application of HPLC to separate phospholipids was performed in 1975 [10]. Typically, the phospholipid class separations have been carried out by normal phase HPLC. Phospholipids elute by order of increasing polarity: phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylethanol (PE), phosphatidylserine (PS), phosphatidylcholine (PC) and lyso forms elute after their corresponding parent phospholipid. Initially, two basic solvent systems were developed: hexane/2-propanol/water and acetonitrile/water (with methanol) mixtures [11]. Later, other authors worked with a chloroform/methanol/water (ammonium hydroxide) system [12]. Ionization modifiers have been employed to improve the response for acidic phospholipids when using YMC PVA-Sil as a stationary phase [13]. For detection purposes, the usual optical methods (UV and fluorescence) are not well adapted to the direct analyses of phospholipids. At present, two detection methods are used for the direct analysis of phospholipid classes, evaporative light scattering detection (ELSD) and corona charged aerosol detection (Corona CAD; ESA, Chelmsford, MA, USA). The principle of ELSD was described by Charlesworth [14] in the 1970s whereas CAD detection is much more recent [15,16]. Both detection methods share the same principle of operation: (1) the mobile phase leaving the column is sprayed using a pneumatic nebulizer, (2) the droplets enter a heated tube where the solvent (partially) evaporates, (3) the solute particles enter into the detection chamber. Hence, the principal difference between the two detection methods is the technology used for solute particles detection: light diffusion for ELSD and aerosol charging for CAD. A considerable amount of literature is available to understand the key feature of ELSD. The ELSD baseline signal is scarcely affected by solvent changes [17] and thus allows the use of gradient elution needed to manage complex separations. However, the ELSD response is affected by parameters of the nebulization step and particularly the mobile phase composition that affects the size of the droplets. The aerosol produced by a pneumatic nebulizer is polydispersed with a large number of small particles. The size of particular droplet changes during solvent evaporation in the transfer tube and the diameter of resulting solute particle is d = D0

 3

C/

(1)

where D0 is the initial droplet diameter at the nebulizer exhaust, C the solute concentration and  solute density. Depending on the ratio between the wavelength of the incident light and the diameter (d) of a given particle, different light scattering mechanisms may take place. Rayleigh, Mie and reflection–refraction mechanisms are involved. The smaller the particle, the lesser the amount of light scattered and the higher the predominance of the Rayleigh scattering. When particles grow, e.g. during peak rise or for increasing amounts of solute injected, Mie and even reflection–refraction may take place. These two mechanisms lead to an increased intensity of the light scattered. Additionally, the amount of detectable particles increases with solute concentration that also reinforces the response. The response shape of an ELSD system is thus complex and difficult to predict. For this reason, the general purpose “power function” is used to model the response [18]: Y = Amb

(2)

89

where m is the injected amount of solute, and A and b numerical coefficients. The b term of the equation has a special importance because it is connected to the predominant light scattering phenomenon. b values are from 2/3 when reflection–refraction prevails to 2 when Rayleigh scattering predominates. A is associated with the amount of light scattered per unit of solute injected but is not truly sensitivity since sensitivity varies along the calibration curve with a non-linear detector. CAD, like ELSD, allows HPLC detection of non-volatile or semivolatile compounds and it has been used recently for lipid analysis [19] and also in the assessment of adjuvant of Leishmania vaccine [20]. As underlined above, the principle of operation of a CAD system is thus similar to that of an ELSD system. In their pioneering publication, Dixon and Peterson [15] connected a TSI electrical aerosol size analyzer (EAA) at the outlet of a drift tube used to perform the size reduction of the wet aerosol generated by a pneumatic nebulizer. The principle of this particle counter is based on aerosol charging and is incorporated in the commercial CAD: a stream of nitrogen (or air) is ionized by corona discharge and directed into a chamber at the exhaust of the drift tube. In this chamber, impacting the positively charged nitrogen ionizes the particles. After particles of less than 10 nm are eliminated by an ion trap, charged particles are detected by an electrometer. According to Dixon and Peterson, the sensitivity of the particle counter varies with particle size and the prediction of the response of the whole detector is complex to establish. For this reason, they also used the power function to empirically model the response. Obviously, the b coefficient does not have any particular meaning in this case and reflects the non-linearity of the detector response. b values close to 1 were reported by these authors that suggest this detector could be used as a directly proportional detector. The aim of this study was to show the potential of CAD for the determination and the quantification of Leishmania PLs. In a first step, a normal-phase HPLC comparative study with two detection modes (ELSD and CAD) was made and two possible response types were assessed considering typical validation parameters. Finally, real leishmania samples were analyzed in order to evaluate the applicability of the method. 2. Experimental 2.1. Sample preparation Promastigote forms of Leishmania donovani LV9 (MHOM/ET/67/L82) wild-type (WT) were kindly provided by Professor S.L. Croft (Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK). This L. donovani clone was cultivated in vitro at 26 ◦ C in M199 medium (Sigma, St. Quentin Fallavier, France) supplemented with 10% heat inactivated foetal calf serum, HEPES 40 mM [4(2-hydroxyethyl)-1-piperazineethanesulfonic acid, VWR, Paisley, UK], adenosine 100 ␮M (Sigma) and hemin (0.2% of 250 ␮g/mL stock) (Sigma) in the presence of 50 ␮g/mL gentamycine. Promastigotes were harvested during the late logarithmic phase of growth after three centrifugation-washing cycles. Thus, old medium was eliminated after the first centrifugation at 4000 × g during 10 min at 4 ◦ C and parasites were washed three times using cold Tris-buffered saline (10 mM Tris–HCl, 145 mM NaCl, pH 7). After a last centrifugation at 4000 × g for 10 min, the pellet of parasites containing about 5 × 109 parasites was frozen at −80 ◦ C. These parasites were called non-treated (NT) parasites. Treated (T) parasites were samples treated with 10 ␮M of miltefosine for a 48 h period. Resistant (R) parasites were the parasites resistant to 40 ␮M of miltefosine.

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2.2. Chemicals

Table 1 Solvent program gradient

2-Propanol, n-heptane, chloroform and methanol (all HPLC grade) were obtained from VWR International, Strasbourg, France. Acetic acid 99–100% and triethylamine 99% were analytical reagent grade purchased from Sigma. l-␣-Phosphatidylcholine (PC), l-␣-phosphatidylethanolamine (PE), l-␣-phosphatidyl-l-serine (PS), l-␣-lysophosphatidylcholine (LPC), l-␣-phosphatidylinositol (PI), l-␣-phosphatidyglycerol (PG), l-␣-lysophosphatidylethanolamine (LPE), Cardiolipin (CL), sphingomyelin (SM) and l-␣-phosphatidic acid (PA) with purity of approximately 98% were purchased from Sigma. Standards were dissolved in a chloroform:methanol (2:1) mixture, at a concentration close to 2.0 mg/mL and then further diluted in accordance with their distribution in biological membranes.

Mobile phases

2.3. Apparatus

Time (min)

Aa (%)

Bb (%)

Cc (%)

0 22 42 44 46 48 68

100 12 0 0 0 100 100

0 88 60 100 100 0 0

0 0 40 0 0 0 0

a

n-Heptane/2-propanol (98:2). Chloroform/2-propanol (65:35). c Methanol/water (95:5). All mobile phases containing 0.08% TEA and 1.00% CH3 COOH (v/v). b

nitrogen gas. The samples were resuspended in 300 ␮L of a mixture chloroform/methanol (2:1) and subsequently analyzed.

The chromatographic system included a PU 980 pump (Jasco, Nantes, France) and a HPLC 360 Autosampler (Kontron Instruments, Basel, Switzerland) with an injection valve, fitted with a 20 ␮L sample loop. The separations were performed on a 5 ␮m YMC PVASil analytical column (150 mm × 2.0 mm i.d.) and guard column (10 mm × 2.0 mm i.d.) (YMC, Kyoto, Japan), supplied by Interchim (Montluc¸on, France). The column temperature was thermostatically controlled with a column oven (DuPont Instruments, Newton, CN, USA). The ELSD and Corona CAD systems (both from ESA, Chelmsford, MA, USA) were supplied by Eurosep (Cergy, France). The signal was acquired and processed with Kroma System 2000 software (Kontron Instruments, St. Quentin en Yvelines, France). For the statistical treatment of the data, Matlab 6.0 R12 (MathWorks, Natick, MA, USA) software was used.

The solvent program used for phospholipids separation is summarized in Table 1, together with the composition of solvents A, B and C. 1% acetic acid and 0.08% triethylamine (TEA) were added in all phases to modify the ionization of lipids and their retention. The flow rate was 0.4 mL/min and the column temperature was maintained at 35 ◦ C. The total time analysis was 68 min, including 20 min for column re-equilibration. ELSD nebulizer and evaporation temperatures were set at 35 and 45 ◦ C, respectively, while the air pressure was maintained at 1 bar. The signal filter was set in the medium position. CAD experiments were performed at 35 psi (1 psi = 6894.76 Pa) air pressure and with a “medium filter” setting.

2.4. Lipid extraction

3. Results and discussion

A modified Folch method [21] was used to extract the lipids. The pellet of parasites previously obtained as described in section 2.1 was thawed and suspended in 1 mL Tris-buffered saline solution. Total lipids were extracted by adding 2.50 mL of chloroform and 1.25 mL of methanol to the parasite suspension. After at least an hour of incubation time, the mixture was sonicated five times for 30 s at 4 ◦ C using a sonifier cell disruptor. After centrifugation at 3000 × g for 5 min, the lower phase containing total lipids was collected and evaporated to dryness at room temperature under

The HPLC method employed enabled baseline separation of all the major phospholipid classes. A chromatogram of a standard PLs mixture obtained with both ELSD and CAD detector is shown in Fig. 1. Phospholipids eluted as well-defined peaks and the order of elution was PA, PG, CL, PI, PE, PS, LPE, PC, SM and LPC. The PVA-Sil column allowed the complete separation of the 10 phospholipids classes in a single run. When separating lipid classes that consist of several molecular species, it is of particular importance to use standards of biological origin [22] and to allow large elution windows to

2.5. Chromatographic conditions

Fig. 1. Separation of phospholipid standards on PVA-Sil column with (A) ELSD detection and (B) CAD detection (0.05 ␮g/␮L of PA (1), PG (2), CL (3), PS (6), LPE (7) and SM (9); 0.10 ␮g/␮L of PI (4) and LPC (10); 0.50 ␮g/␮L for PE (5) and 1.00 ␮g/␮L for PC (8)).

R.G. Ramos et al. / J. Chromatogr. A 1209 (2008) 88–94

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Table 2 Calibration curves and response model for phospholipid analysis by HPLC-ELSD PLs

PA PG CL PI PE PS LPE PC SM LPC

Range (␮g)

0.25–2.00 0.13–2.00 0.13–2.00 0.06–4.00 0.64–20.00 0.25–2.00 0.06–2.00 1.28–20.00 0.06–2.00 0.25–4.00

Power model (y = Amb )

Linear model (y = a1 x + a0 ) A1 (±A1 )x + a0 (±a0 )

p value, / 0 a0 =

r2

2.00 (±0.06)x − 0.35 (±0.07) 10.52 (±0.38)x − 1.75 (±0.44) 5.99 (±0.29)x − 1.21 (±0.30) 14.39 (±0.38)x − 2.57 (±0.72) 18.57 (±0.49)x − 22.00 (±4.59) 4.25 (±0.24)x − 1.43 (±0.28) 7.76 (±0.35)x − 1.01 (±0.35) 14.35 (±0.44)x − 13.82 (±4.54) 7.12 (±0.19)x − 0.44 (±0.19) 11.11 (±0.23)x − 1.32 (±0.51)

0.00 0.00 0.00 0.01 0.00 0.00 0.02 0.01 0.04 0.03

0.991 0.987 0.970 0.989 0.989 0.969 0.974 0.988 0.991 0.996

Freg (1)

Fadj

A(±A )xb(±b )

p value, b = / 1

r2

Freg (1)

Fadj

1134 747 414 1419 1455 316 494 1065 1469 2375

4.56 (p = 0.05) 16.95 (p = 0.00) 64.5 (p = 0.00) 38.93 (p = 0.00) 6.78 (p = 0.00) 27.2 (p = 0.00) 221.0 (p = 0.00) 112.7(p = 0.00) 2.92 (p = 0.09) 25.95 (p = 0.00)

1.51 (±0.04)x1.29 (± 0.04) 7.77 (±0.18)x1.35 (± 0.04) 3.76 (±0.11)x1.59 (± 0.04) 9.33 (±0.23)x1.31 (± 0.02) 7.79 (±0.97)x1.28 (± 0.04) 2.11 (±0.12)x1.81 (± 0.08) 5.15 (±0.09)x1.57 (± 0.03) 6.88 (±0.67)x1.24 (± 0.03) 6.15 (±0.19)x1.19 (± 0.05) 8.81 (±0.25)x1.16 (± 0.02)

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.995 0.998 0.996 0.999 0.993 0.996 0.998 0.993 0.995 1.000

2390 3950 4690 18200 2720 1710 17100 3400 2560 9460

0.08 (p = 0.92) 0.01 (p = 0.99) 2.82 (p = 0.09) 0.30 (p = 0.87) 2.25 (p = 0.12) 1.92 (p = 0.21) 3.31 (p = 0.06) 33.30 (p = 0.00) 0.27 (p = 0.85) 3.54 (p = 0.08)

prevent peak overlap. The situation is even more complicated when using universal detectors where a multi-channel detection is not possible. The performance of both ELSD and CAD modes with the above HPLC separation was compared and evaluated, as described in the following sections.

dard deviations. The intercept of the linear model is tested for difference with zero and the b value of the power model is tested for difference with one. Obviously, if the intercept of the linear model is zero and the b coefficient of the power model is one, the two models resume to a direct proportionality. The statistical parameters [25] involved in the regression comparison are: the determination coefficient r2 , the F value for regression (Freg ) and lack of fit (Flof ). The determination coefficient r2 is computed as the ratio of the regression sum of squares (SSreg ) relative to the total sum of squares (SST ). The determination coefficient is thus related to the amount of y variance explained by the fitted model. Freg expresses the ratio between the regression and residual (non-modeled) variance. The information provided by Freg is fairly similar to r2 , except a comparison is done between explained and unexplained y-variance and a level of significance (p value) is easily computed. Unexplained variance has two origins, i.e. the experimental error and the lack of fit of the model. Calculating FLof allows comparing the two corresponding variances. FLof is the most stringent statistical parameter since a lack of fit significantly higher than the experimental error, means that a better model should be used to model data. Table 2 shows the results obtained with ELSD. The r2 obtained with the linear model are high together with the Freg values. The p values associated with Freg are all ≤0.00, which confirm that the model encompasses the vast majority of the variance. However, the p values associated with FLof show an evident lack of fit of the linear model for 8 out of 10 phospholipids (p < 0.05). In addition, the intercept of the linear model is statistically different from zero for all phospholipids (p < 0.05). The r2 and Freg calculated for the power model show even higher values for each phospholipids. The power model appears to be validated since the test for lack of fit is acceptable for 9 PLs out of 10 where p > 0.05. In addition, b was found different from one for all phospholipids.

3.1. Response model Calibration standards were diluted with the appropriate solvent, considering the solubility of the phospholipids, i.e. chloroform/methanol (2:1) mixture. Triplicate 20 ␮L injections of each solution were used with variable amounts according to the phospholipid analyzed. For PLs present in low concentrations in Leishmania membranes, a calibration range of 0.03–4.00 ␮g was investigated for both ELSD and CAD systems. The remaining PLs were investigated between 0.16 and 40.00 ␮g for both detection modes. To study the relationship between the individual lipid amount and the ELSD or CAD response (peak area), linear and power models were evaluated in order to find which better describes the detector response. Linear regression was used to fit ELSD or CAD data to the linear model whereas non-linear, regression using Marquardt’s algorithm [23] was used to assess the power model. In the latter case, nonlinear regression was preferred over the classical log–log transformation of both x and y data. Plotting log y = log A + b log x is often used to linearize the ELSD response function. Linearizing transformations distort the experimental error and the estimated parameters are, in fact, inaccurate [24]. In addition, the log–log transformation changes the scale of both x and y and hinders the side-by-side comparison of the regression statistics of the two models. Tables 2 and 3 compare the linear and power model for both ELSD and CAD, respectively. The estimated coefficients of the two possible response functions are presented together with their stanTable 3 Calibration curves and response model for phospholipid analysis by HPLC-CAD PLs

PA PG CL PI PE PS LPE PC SM LPC a

Range (␮g)

0.13–2.00 0.06–2.00 0.06–1.00 0.03–4.00 0.32–10.00 0.13–2.00 0.03–2.00 1.28–20.00 0.06–2.00 0.25–4.00

Power model (y = Amb )

Linear model (y = a1 x + a0 ) a1 (±A1 )x + a0 (±a0 )

p value, / 0 a0 =

r2

10.78 (±0.20)x + 1.39 (±0.21) 15.22 (±0.18)x + 0.11 (±0.18) 25.08 (±0.31)x + 1.01 (±0.16) 18.79 (±0.20)x + 2.00 (±0.34) 8.53 (±0.15)x + 0.92 (±0.72) 8.66 (±0.16)x − 0.63 (±0.17) 14.57 (±0.17)x − 0.22 (±0.16) 9.24 (±0.21)x − 2.37 (±2.22) 13.93 (±0.24)x + 1.87 (±0.24) 10.91 (±0.29)x − 0.61 (±0.66)

0.00 0.57 0.00 0.00 0.22 0.00 0.19 0.31 0.00 0.38

0.996 0.998 0.998 0.998 0.995 0.995 0.998 0.993 0.996 0.993

All p values are < 0.00.

a

Freg

2902 7145 6644 9269 3091 2817 7170 1849 3436 1380

Fadj

A(±A )xb(±b )

p value, b = / 1

r2

Freg a

Fadj

0.10 (p = 0.96) 2.40 (p = 0.13) 1.97 (p = 0.18) 2.81 (p = 0.06) 0.70 (p = 0.61) 2.76 (p = 0.10) 1.24 (p = 0.35) 0.24 (p = 0.87) 1.79 (p = 0.21) 6.60 (p = 0.02)

12.60 (±0.21)x0.85 (± 0.03) 15.50 (±0.17)x0.97 (± 0.02) 25.89 (±0.22)x0.89 (± 0.01) 22.20 (±0.42)x0.89 (± 0.02) 9.36 (±0.52)x0.96 (± 0.03) 7.83 (±0.18)x1.10 (± 0.04) 14.20 (±0.23)x1.03 (± 0.02) 8.29 (±0.84)x1.03 (± 0.03) 16.80 (±0.24)x0.79 (± 0.02) 9.35 (±0.53)x1.11 (± 0.04)

0.00 0.07 0.00 0.00 0.18 0.02 0.25 0.42 0.00 0.03

0.993 0.999 0.998 0.998 0.995 0.994 0.998 0.993 0.996 0.995

1960 9060 7043 10900 3140 2190 6910 1810 3650 2120

1.76 (p = 0.22) 1.19 (p = 0.36) 1.67 (p = 0.24) 1.96 (p = 0.15) 0.63 (p = 0.65) 4.50 (p = 0.03) 1.40 (p = 0.29) 0.32 (p = 0.81) 1.49 (p = 0.28) 2.91 (p = 0.11)

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Table 4 Repeatability and intermediate precision study with ELSD and CAD detection. PL

PA PG CL PI PE PS LPE PC SM LPC a

Levels N◦ (␮g)

Evaporative light scattering detector

Charge aerosol detector

1

2

3

Repeatability (RSD, %) at levels 1–2–3

Intermediate (RSD, %)

0.25 0.25 0.25 0.25 1.00 0.25 0.25 2.50 0.25 0.50

0.50 0.50 0.50 0.50 5.00 0.50 0.50 5.00 0.50 1.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.00

17.5 14 11.9 11.8 7.3 9.1 14.7 3.4 7.4 16.4

13.6 9.1 11.9 7.2 5.0 9.4 8.5 2.8 4.7 8.9

13.1 6.0 13.2 4.4 4.6 10.3 7.2 3.2 1.8 5.2

10.2 7.4 10.7 5.5 3.1 8.9 3.6 1.9 4.8 5.0

precisiona

Repeatability (RSD, %) at levels 1–2–3

Intermediate precisiona (RSD, %)

10.1 8.0 11.9 3.9 4.2 9.8 10.7 2.9 4.7 8.8

9.4 7.0 11.4 3.9 3.4 7.1 6.6 2.7 3.8 6.1

9.5 7.2 12.9 4.4 3.6 6.3 5.4 3.2 3.9 5.1

8.7 5.9 9.4 3.5 2.4 5.1 3.6 1.9 2.8 4.3

At level 2.

Then, in our experimental conditions, ELSD could not provide a linear response. In phospholipid analysis using ELSD, a linear response was reported by other authors [26–31] and also by Christie [32]. The latter author noticed however that the response deviated from linearity when the injected amount was less than 10 ␮g. In our data, the b values calculated for the power model range from 1.16 to 1.81. Such values indicate a predominant Mie or Rayleigh scattering and are consistent with the principle of operation of ELSD. Table 3 shows the results of the statistical analysis in the case of CAD. In this case, the performances of the two models appear to be nearly equivalent. When performing a line by line comparison of PLs, the r2 and Freg for the two models are similar, and the Flof value does not indicate a preference for either model. Both models appear to be valid for 9 PLs out of 10. The examination of the b value of the power model shows that in most cases, b is less than one. However, even when b < 1, the curvature is not very pronounced and the linear model is statistically valid. At last, it can be noticed that, for PLs where b = / 1, the intercept of the linear model is statistically different from zero. Hence, for 5 PLs out of 10 the response of the CAD detector can be considered as directly proportional to the amount injected. In all cases the response can be fitted to a linear equation. This feature is clearly advantageous for CAD compared to ELSD. The detector calibration should be performed with a sufficient number of concentration levels to evaluate the value of the intercept and introduce the relevant correction factor when calculating the sample concentrations. 3.2. Precision For the evaluation of repeatability three replicated injections at three concentration levels were made with both ELSD and CAD. Whereas for intermediate precision study samples were prepared at intermediate level and a triplicate injection on three different

days was made. From a general point of view, Table 4 shows that a better precision is encountered with CAD. For phospholipids at low injected mass (0.25 ␮g) the repeatability is about 10% (RSD) whereas a 5% (RSD) can be reached at higher injected amount. A repeatability of less than 5% is encountered for PE and PC, the two PLs injected at the highest concentration. The trend encountered with the ELSD is similar although the RSD values were larger than a few percent in all cases. As a consequence, the intermediate precision study shows a better overall performance for CAD compared with ELSD, with RSD values varying between 2.7 and 11.4. 3.3. Sensitivity, limits of detection (LODs) and quantitation (LOQs) The evaluation of detector sensitivity is simple in the case of linear detectors. According to IUPAC [33], the sensitivity is the slope of the calibration curve. Thus, when the calibration curve is non-linear, the sensitivity varies with the analyte concentration or amount. As the ELSD system behaves as a non-linear detector in the concentration range we investigated, sensitivity (S) can be calculated as the derivative of the power model with respect to the injected amount: S=

dy = Abxb−1 dx

(3)

As sensitivity is of major importance at low injected amounts, we decided to indicate the sensitivity for the lowest concentration used in the repeatability study. Additionally, as the power model appeared to be statistically valid for both detectors, the sensitivity of the ELSD and CAD was compared using the same calculation for the two detectors. The sensitivity values presented in Table 5 show a clear advantage for CAD. The response per unit mass injected is equal or higher (up to 10-fold) with CAD compared with ELSD.

Table 5 Sensitivity, LOD and LOQ study with both ELSD and CAD detection. PL

PA PG CL PI PE PS LPE PC SM LPC

Amount (ng)

250 250 250 250 1000 250 250 2500 250 500

ELSD

CAD

S (mV/␮g)

LOD (ng)

LOQ (ng)

S (mv/␮g)

LOD (ng)

LOQ (ng)

1.3 5.1 1.8 5.1 8.8 1.2 1.6 9.1 4.3 8.2

1195 82 202 71 221 133 183 255 77 318

3622 249 613 215 670 402 553 773 233 965

14.5 16.4 31.4 29.1 9.4 7.0 13.2 8.6 24.0 8.9

120 44 41 15 109 146 85 249 21 233

364 134 124 45 330 441 258 756 63 707

R.G. Ramos et al. / J. Chromatogr. A 1209 (2008) 88–94

93

Fig. 2. Preliminary non-treated (NT) Leishmania cultures chromatograms obtained with ELSD (A) and CAD (B) detection.

Table 5 also summarizes the LOD and LOQ values [34] obtained with both detectors. LODs and LOQs are estimated as: LOD =

3.3 S

(4)

LOQ =

10 S

(5)

where  is the standard deviation of the response and S is usually referred as the slope of the calibration curve. As S expresses the sensitivity in Eqs. (4) and (5), we used the values calculated using Eq. (3) together with the standard deviation of the response estimated for the same amount injected. Here also, the results are in favor of CAD. The combination of a higher sensitivity and a better precision leads to improved LODs and LOQs values in the case of CAD compared to ELSD. Depending on the PL, CAD LODs and LOQs are equal to 5-fold better than the values achieved with ELSD. The result is even more particular for PA for which both sensitivity and precision are poor with ELSD. The LOQs as calculated with Eq. (5) are higher than the range of the calibration curve presented in Table 2. LODs calculated with Eq. (4) often lead to higher values than experimentally searching the injected amount that produces a signal 3 times higher than the background noise. The problem is the same for LOQs calculated using Eq. (5) or estimated as 10-fold the background noise. The high values obtained for PA indicate that ELSD is not adapted for our application but would better perform at higher concentrations. At the opposite, the LOQ values obtained with CAD are in accordance with the lowest bound of the calibration curves presented in Table 2. Thus, CAD is better suited for the quantitative evaluation of PLs in Leishmania. 3.4. Accuracy The accuracy expresses the closeness of agreement between the accepted reference value and the value found. Typically, accu-

racy is represented and determined by recovery studies, but there are three ways to determine accuracy: (1) by comparison to a reference standard, (2) recovery of the analyte spiked into blank matrix, or (3) standard addition of the analyte [34–36]. In this study, evaluation of accuracy was carried out by spiking the analyte in culture media at two levels of the expected concentration for individual lipid class. The concentration level for each phospholipid was different and representative of PL abundance in leishmania membranes. Accuracy values were reported as percent recovery together with the confidence intervals and they are displayed in table 6. The obtained results show that the recovery is variable with values ranging from 62.8 to 115.8% and 58.4 to 110.5% RSD for ELSD and CAD, respectively. This variability can be explained due to the differences in the physicochemical nature of each lipid class and their mass range. 3.5. Application to leishmania cultures Finally, a practical evaluation was done by injecting a single sample of each of the three different cultures of Leishmania: non-treated (NT), treated (T) and resistant (R) to miltefosine. It must be reminded here that the purpose is just to perform a detector comparison and to select the more informative system. The response of the two detection methods (Fig. 2) is illustrated by the chromatographic profile of the non-treated sample. ELSD failed to detect 4 PL classes while only PA is not detected with CAD. Although a more important number of samples will be necessary to statistically appreciate the differences in PL profiles of NT, T, and R Leishmania cultures, HPLC-CAD appears to be able to detect PL variations between cultures. These preliminary results show that the CAD keeps its advantages even with real samples where possible matrix effects can happen.

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4. Conclusion ELSD is a well-established universal detection technique widely used for lipid analysis. CAD shares the same mobile phase nebulization principle but uses a charge transfer for solute detection that provides improved performances. As shown in our example of Leishmania phospholipids profiling, CAD is more sensitive and more precise than ELSD at the lower end of the calibration curve. The resulting LODs and LOQs are, on average, three times lower with CAD than ELSD. Both detection methods are intrinsically nonlinear when calibrated over a large concentration range. It can also be concluded from our example that CAD is compatible with the use of linear calibration over a 10-fold range, and could be used as a directly proportional detection method when the calibration range is more restricted. By providing improved detection of phospholipids at low levels, the chromatographic profiles of Leishmania cultures obtained with CAD are more informative than with ELSD. Acknowledgments Franc¸oise Huteau is acknowledged for her kind technical assistance in L. donovani cultures. This work was supported by The ALBAN Program of the European Union Program of High Level Scholarships for Latin America through scholarship, No. E04D044940CL, and also by MECESUP Project UCO 0202 of the Ministry of Education and University of Concepción, Chile. References [1] P. Desjeux, Comp. Immunol. Microbiol. Infect. Dis. 27 (2004) 305. [2] T.K. Jha, S. Sundar, C.P. Thakur, P. Bachmann, J. Karbwang, C. Fischer, A. Voss, J.N. Berman, Engl. J. Med. 341 (1999) 1795. [3] S. Sundar, L.B. Gupta, M.K. Makharia, M.K. Singh, A. Voss, F. Rosenkaimer, J. Engel, H.W. Murray, Ann. Trop. Med. Parasitol. 93 (1999) 589. [4] S. Sundar, F. Rosenkaimer, M.K. Makharia, A.K. Goyal, A.K. Mandal, A. Voss, P. Hilgard, H.W. Murray, Lancet 352 (1998) 1821. [5] S. Sundar, A. Makharia, D.K. More, G. Agrawal, A. Voss, C. Fischer, P. Bachmann, H.W. Murray, Clin. Infect. Dis. 31 (2000) 1110. [6] P.J. Guerin, P. Olliaro, S. Sundar, M. Boelaert, S.L. Croft, P. Desjeux, M.K. Wasunna, A.D.M. Bryceson, Lancet Infect. Dis. 2 (2002) 494.

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