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differential scanning calorimeter (DSC) measurement of membranes typical of the clusters. The results

ane med inous sig

a bthat of the Lb phase, but its fast axial rotation and high lateralmobility are similar to that of the La state. The lipid raft is assumedto exist as an lo phase membrane in the plasma membrane.

A mixture of three different lipids, lipids with a high phasetransition temperature (Tm) (e.g., lipid with saturated acyl chains),lipids with a low Tm (e.g., lipid with unsaturated acyl chains), andCh, is required to generate membrane domains. Such membraneshave been widely used to mimic plasma membranes to elucidatethe formation or structures of lipid rafts [9,11,12,14,15]. Eventhough these membranes are much simpler than biological mem-

Abbreviations: CFM, confocal uorescence microscopy; Ch, cholesterol; DOPC,1,2-dioleoyl-sn-glycero-3-phosphocholine; DPH, 1,6-diphenyl-1,3,5-hexatriene;DPPC, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine; DSC, differential scanningcalorimeter; FRET, Frster resonance energy transfer; GPI, glycosylphosphatidyli-nositol; La, liquid-disordered phase; Lb, solid-ordered phase; lo, liquid-orderedphase; MVS, multivariate spline interpolation; RSM-S, response surface methodincorporating multivariate spline interpolation; SM, sphingomyelin; SOM, Koho-nens self-organizing maps; Tm, phase transition temperature.* Corresponding author. Fax: +81 3 5498 5783.

Journal of Colloid and Interface Science 343 (2010) 628633

Contents lists availab

Journal of Colloid an

www.elsevier .coE-mail address: onuki@hoshi.ac.jp (Y. Onuki).as glycosylphosphatidylinositol (GPI)-anchored proteins [1,2] andreceptor- [3,4] or nonreceptor-type tyrosine kinases [46] origi-nate from these domains, the lipid raft is thought to act as a plat-form for protein segregation and signal transduction in the plasmamembrane. Lipid-driven lateral separation of immiscible liquidphases is likely to be a crucial factor in the formation of lipid raftsin cell membranes [7]. Although the concept of a lipid raft has re-cently been widely accepted, its mode of formation in the cellmembrane remains controversial.

as well as in cell membranes [812].In general, lipid bilayers are classied into three different

phases in order of increasing uidity: a solid-ordered phase (Lb),a liquid-ordered phase (lo), and a liquid-disordered phase (La)[7,13]. The Lb and La phases are also called gel and liquid crystallinephases, respectively. Ordered and tight packing are typical of the Lbphase membrane, whereas fast axial rotation and high lateralmobility are observed in the La phase membrane. The Ch-richmembrane exists as an lo phase membrane; this phase is interme-diate between the L and L phases. Its ordered packing is similar to1. Introduction

A lipid raft is dened as a membrlesterol (Ch) and sphingolipid locatplasma membrane. Because numer0021-9797/$ - see front matter 2009 Elsevier Inc. Adoi:10.1016/j.jcis.2009.12.009indicated that the membranes consisted of several phase domains (i.e., La, Lb, lo phase domains), andthe clusters were distinguished by differences in the type and content of membrane domain. This methodis accurate because the clusters were determined based on experimental values. This technique is usefulfor elucidating the phase behavior of ternary lipid membranes. These ndings contribute to claricationof domain formation.

2009 Elsevier Inc. All rights reserved.

icrodomain rich in cho-the outer leaet of thenaling molecules such

Model membranes such as liposomes are effective tools for elu-cidating the formation of lipid rafts. Their lipid composition can bemanipulated to suit the purpose of the experiment, and results ob-tained in this way are likely to be consistent. In addition, severalmembrane domains are known to coexist in model membranesKeywords:Lipid raftLiposome

response surface method and Kohonens self-organizing map. Thus, we generated a scatter plot indicatingthe distribution of membranes with similar membrane properties. The scatter plot showed that the SM/DOPC/Ch membranes resolved into six clusters with distinct membrane properties. We then conductedPhase behavior in a ternary lipid membrresponse surface method and Kohonens

Yoshinori Onuki *, Kozo TakayamaDepartment of Pharmaceutics, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 14

a r t i c l e i n f o

Article history:Received 27 October 2009Accepted 2 December 2009Available online 6 December 2009

a b s t r a c t

A novel method for investiSixty-ve model membranand cholesterol (Ch) were pObserved uorescence anill rights reserved.e estimated using a nonlinearlf-organizing map

01, Japan

ng phase behavior in a ternary lipid membrane was developed and tested.composed of sphingomyelin (SM), dioleoyl phosphatidylcholine (DOPC),ared, and uorescence anisotropy between 25 C and 60 C was measured.opy curves as functions of temperature were analyzed using a nonlinear

le at ScienceDirect

d Interface Science

m/locate / jc is

aration on membrane surfaces, this method has limitations. First,

tify the lipid phase behavior of membranes [14,1619]. Because

number of untested lipid compositions for the prediction was

d anthese measurements do not require preparation of a giant unila-mellar vesicle, a wider range of lipid compositions can be exam-ined than with CFM. In addition, measurements based onuorescence analysis enable objective and quantitative evaluationof domain formation, and the experimental procedure is not verycomplicated. However, large data sets are required to fully under-stand the relationship between lipid composition and phasebehavior and, in most cases, the collection of such large data setsis impractical.

To overcome this problem and elucidate the phase behavior ofternary lipids, we applied a response surface method incorporatingmultivariate spline interpolation (RSM-S) and a data mining tech-nique (Kohonens self-organizing maps; SOMs). Firstly, liposomescomposed of sphingomyelin (SM), dioleoyl phosphatidylcholine(DOPC), and Ch were prepared and their uorescence anisotropywas measured. Using data based on uorescence anisotropy, wedeveloped a scatter plot indicating the distribution of membraneswith similar membrane properties. Response surface methods anda data mining technique were used to compensate for the lack ofexperimental data. Using these methods, we successfully resolvedthe membranes into several clusters. This is the rst technical re-port on the phase behavior of lipids conducted using response sur-face and data mining methods.

2. Experimental procedure

2.1. Materials

Ch was purchased from Wako (Osaka, Japan). Chicken egg SM(Coatsome NM-10) and DOPC (Coatsome MC-8181) were pur-chased from Nippon Oil & Fat (Tokyo, Japan). More than 70% ofthe SM is 16:0 SM. 1,6-diphenyl-1,3,5-hexatriene (DPH) was pur-chased from Aldrich (Milwaukee, WI, USA). All other chemicalswere of analytical grade and are commercially available.

2.2. Preparation of liposomes

Liposomes composed of SM, DOPC, and Ch were prepared asreported previously [19]. In brief, designated amounts of lipidsdissolved in chloroform were transferred to a ask, and the chlo-roform was removed by evaporation at room temperature undera stream of nitrogen. This procedure resulted in the formationbecause giant unilamellar vesicles are not obtained from all lipidcompositions, whole-phase behavior can never be determinedwith this method. Second, because the border region betweenmembrane domains is thought to be an ambiguous structure, itis difcult to distinguish membrane domains by subjective evalu-ation. Third, because the phase behavior of ternary lipids is verycomplicated and is substantially changed by slight differences inlipid composition, a bunch of model membranes differing in lipidcomposition should be examined to elucidate phase behavior.

Fluorescence analyses such as Frster resonance energy trans-fer (FRET) and uorescence anisotropy are also employed to iden-branes, their phase behavior remains complicated. Despitenumerous research studies, consensus on membrane phasebehavior has yet to be reached. Veatch et al. generated a ternaryphase diagram by observing the surface of giant unilamellar ves-icles using confocal uorescence microscopy (CFM) and describeda condition in which phase separation occurs [11,12]. Althoughmicroscopic observation can be used to directly detect phase sep-

Y. Onuki, K. Takayama / Journal of Colloiof a thin lipid lm on the wall of the ask. The lm was storedovernight in a vacuum desiccator to ensure complete evaporationof the chloroform. Puried water (10 mL) was added to the ask,5041. Lipid composition and corresponding uorescence anisot-ropy values for every 5 C from 25 C to 60 C were regarded asan input data set. Namely, 5106 data sets, including experimentaland predicted data, were used for SOM clustering. SOM clusteringwas performed using Viscovery software, SOMine version 4.0(Eudaptics Software, Vienna, Austria). The number of nodes in2.4. Differential scanning calorimeter measurement

The liposome suspensions were lyophilized using a freeze dryer(FD-1; Tokyo Rikakikai, Tokyo, Japan) under reduced pressure. Thefreeze-dried liposomes (5 mg) were placed in aluminum pans forthe DSC measurement. DSC measurements were performed usinga Thermo Plus DSC 8230 instrument (Rigaku, Tokyo, Japan)equipped with a refrigerated circulator (Rigaku, Tokyo, Japan).The scan rate was set to 1 C/min.

2.5. Clustering of SM/DOPC/Ch membranes into membranes withsimilar properties

The procedure for clustering membranes is shown in Fig. 1.Sixty-ve model liposomes with different lipid compositions wereprepared (Fig. 2). Fluorescence anisotropy was measured at tem-peratures ranging from 25 C to 60 C. dataNESIA software, version3.0 (Yamatake Corp., Tokyo, Japan) was used for RSM-S. The ob-served uorescence anisotropy values at 25, 30, 35, 40, 45, 50,55, and 60 C and the differences in values from 25 C to 60 C wereused as tutorial data for generating response surfaces using RSM-S.Fluorescence anisotropy values of untested lipid compositionswere predicted by reading points on the response surfaces. Ther IVV GIVHIVV 2GIVH 1

where r is anisotropy and IVV and IVH are the intensities measuredparallel and perpendicular to the polarized exciting light, respec-tively. The G factor was dened as IVV/IVH, which is equal to the ratioof the sensitivities of the detection system for vertically and hori-zontally polarized light. The G factor of our detection system was1.202.and the lipids were hydrated for 30 min. The total lipid concen-tration was adjusted to 10 mM. The suspension was sonicatedfor 10 min at about 60 C using a bath-type sonicator. After thesamples were cooled to room temperature, they were stored atroom temperature for maximum of 2 days before use in theexperiments.

2.3. Fluorescence anisotropy measurement

The liposome was labeled with DPH by adding 10 lL of 10 mMfreshly prepared DPH stock solution in tetrahydrofuran to 1000 lLof liposome suspension and then incubating the mixture at 37 Cfor 2 h in the dark to complete the labeling. The samples were di-luted 50 times with puried water. The uorescence anisotropy ofDPH in the liposomes was measured using a uorescence spectro-photometer (F-450; Hitachi, Tokyo, Japan) at an excitation wave-length of 351 nm and an emission wavelength of 430 nm. Thetemperature range during measurement was 2560 C. Steady-state uorescent anisotropy was calculated using the followingequation:

d Interface Science 343 (2010) 628633 629the output was set at 2000. Thus, a scatter plot indicating the dis-tribution of distinct membranes was developed using referencevectors for each cluster.

anCollecting experimental data(Fluorescence anisotropy measurement)

630 Y. Onuki, K. Takayama / Journal of Colloid3. Results

3.1. Estimation of the distribution of membranes with similarmembrane properties

Fluorescence anisotropy curves as functions of temperature ob-tained from 65 model membranes differed markedly in shape andvalue, indicating the diversity of the model membranes (data not

The values of SM-rich membranes (neighborhood of the right-bot-

cluster 6 was the most abundant in DOPC. Cluster 3 was an SM-richGenerating a ternary phase diagram

Generating response surface by RSM-S (Correlation model)

Predicting characteristics of untested lipid compositions

Classification of lipid composition into several clusters by SOM

Estimating lipid composition belonging to each cluster

Fig. 1. Flow chart for estimating a ternary phase diagram for SM/DOPC/Ch byintegrating RSM-S and SOM.

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3.2. Characterization of distinct membranes

We characterized distinct membranes according to SOM. Forthe following experiments, the centroid lipid composition of thecluster was assumed to be typical of the cluster (Table 1).

Fig. 6 shows the uorescence anisotropy curves of the clusters.The symbols in the gure represent values predicted by RSM-S andSOM. The predicted values were highly consistent with experimen-tal values (r = 0.994, data not shown). Except for cluster 3, the val-ues decreased as the amount of DOPC in the membrane increased.The highest values were observed for cluster 1, whereas the lowestvalues were observed for cluster 6 (Fig. 6). The values of cluster 3decreased markedly from 0.25 to 0.15.

Freeze-dried liposomes were used for DSC measurement toavoid a large peak caused by water. The DSC curve of cluster 3showed an endothermic peak corresponding to the Tm of SM(Fig. 7). Similarly, endothermic peaks corresponding to the Tm ofDOPC were observed in clusters 5 and 6, which are DOPC-richmembranes (Fig. 7).

4. Discussion

To clarify the phase behavior of ternary lipid membranes, SM,DOPC, and Ch were selected as components of the model mem-brane. SM, a high Tm lipid, is a component of ordered phase mem-branes, whereas DOPC, a low Tm lipid, is a component of disorderedphase membranes, respectively. SM also forms a lo phase mem-brane with Ch. Fluorescence anisotropy was used to investigatethe phase behavior of the ternary lipid mixture. The La, Lb, and lotom vertex) were markedly lower at higher temperatures, implyingstructural alteration of membranes. Prediction accuracy was eval-uated by leave-one-out cross validation (Fig. 4). The correlationcoefcient was very high (r = 0.992), indicating that the RSM-Sconstructed a reliable model of the correlation between lipid com-position and uorescence anisotropy. The response surfaces en-abled prediction of a large number of uorescence anisotropyvalues. The number of untested lipid compositions for predictionwas set at 5041.

Subsequently, we analyzed experimental and predicted uores-cence anisotropy values using SOM clustering and classied SM/DOPC/Ch membranes into clusters with similar membrane proper-ties. Viscovery software includes several clustering techniquessuch as SOM-Ward, Ward, and SOM-Single-Linkage. The SOM-Ward technique was employed for clustering because it is consid-ered the most efcient. SM/DOPC/Ch membranes were divided intosix clusters using SOM clustering. Consequently, based on the ref-erence vectors of clusters, we estimated a scatter plot indicatingthe distribution of lipid compositions with similar membraneproperties (Fig. 5). Cluster 1 was the most abundant in Ch, whereasshown). The experimental values were processed using RSM-S,and then response surfaces indicating the relationships between li-pid composition and uorescence anisotropy were generated. Onbehalf of all response surfaces generated using RSM-S, those at25, 40, and 60 C are shown in Fig. 3ac. We also generated a re-sponse surface representing differences in values from 25 C to60 C (Fig. 3d). Low values were observed for DOPC-rich mem-branes (neighborhood of the left-bottom vertex), whereas high val-ues were observed for membranes containing a large amount of Ch.

d Interface Science 343 (2010) 628633phases of lipid membranes can be distinguished according to uid-ity. Fluorescence anisotropy is commonly used to determine mem-brane uidity. Each phase membrane can be identied by analysis

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Y. Onuki, K. Takayama / Journal of Colloiof the curves because their responses differ, i.e., the uorescenceanisotropy values of Lb and lo phase membranes are much higherthan those of La phase membranes. The measurements were per-formed between 25 C and 60 C. This range includes the transitionof SM from the Lb phase to the La phase. Therefore, if a membranecontains a Lb phase membrane rich in SM, a marked decrease inuorescence anisotropy should be observed at its Tm. In contrast,La and lo phase membranes are presumed to be stable within this

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Fig. 3. Response surfaces generated by RSM-S for uorescence anisotropy values at 25 C (a), 40 C (b), and 60 C (c) and differences in values from 25 C to 60 C (d). Pointsrepresent lipid compositions of the model membrane. Experimental values at each temperature were used as tutorial data and response surfaces were generated using RSM-S.

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Table 1Typical lipid compositions of clusters in membranes.

SM (mol%) Ch (mol%) DOPC (mol%)

Cluster 1 25.0 69.4 5.6Cluster 2 36.7 40.8 22.5Cluster 3 79.0 8.5 12.6Cluster 4 23.4 33.6 43.0Cluster 5 24.7 18.0 57.3Cluster 6 12.1 14.5 73.3

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632 Y. Onuki, K. Takayama / Journal of Colloidtemperature range. Previously, we described distinct uorescenceanisotropy curves derived from Lb, lo, and a mixture of lo and Lamembranes composed of dipalmitoyl phosphatidylcholine (DPPC),DOPC, and Ch [19]. An advantage of uorescence anisotropy is thatmeasurements can be conducted under identical conditionsregardless of the components and composition of the membranes.To make the most of this attribute, the lipid composition of themodel membranes were designed to be as diverse as possible.

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Fig. 7. Differential scanning calorimetry thermograms of freeze-dried liposomesuspensions. The temperature was scanned at 1 C/min.The maximum amounts of SM, DOPC, and Ch were 100%, 100%,and 90% of total lipids, respectively (Fig. 2).

Based on the uorescence anisotropy values of 65 model mem-branes, RSM-S was used to generate response surfaces of the rela-tionships between lipid composition and uorescence anisotropy(Fig. 3). RSM-S is a nonlinear response surface method developedin our laboratory. Multivariate spline interpolation (MVS) is inte-grated into RSM-S as a method of generating the response surface.MVS involves a boundary element method [20]. It can estimatenonlinear relationships between factors and characteristics withhigh accuracy. To date, we have applied this method to formulationoptimization of pharmaceutics, and various ndings have sug-gested that RSM-S is a promising tool for the development of phar-maceutics [2126]. The response surfaces showed that theuorescence anisotropy values changed substantially and in acomplicated manner as lipid composition and temperature chan-ged (Fig. 3). Thus, to fully understand the lipid phase behavior ofthe whole range of lipid compositions tested, a vast number of datasets would have to be compiled.

For instance, Buboltz et al. investigated phase behavior in mix-tures of DPPC, DOPC, and Ch using FRET measurements [17] andalso encountered the problem of the number of data sets requiredfor interpretation of their results. To address this issue, they exam-ined 1294 kinds of lipid compositions and prepared a ternaryphase diagram. Although their experimental procedure was muchsimpler than others, the collection of such a large amount of exper-imental data must have been very arduous. However, this strategywas not feasible with our experimental approach.

We relied on the high prediction accuracy of RSM-S to overcomethe problemof a relatively small number of data sets. Leave-one-outcross validation showed that the response surfaces generated in thisstudy have a good correlation between predicted and experimentalvalues (r = 0.992) (Fig. 4). We also evaluated the model correlationsusing data sets consisting of untested lipid compositions. In thecourse of the study, we identied clusters in six typical membranes(Table 1) andmeasured their uorescence anisotropy values (Fig. 6).Because these lipid compositions were not used in generating re-sponse surfaces, we regarded them as untested. There was a highcorrelation coefcient (r = 0.994, data not shown) between the pre-dicted and experimental values. Thus, a large number of uores-cence anisotropy values were predicted with high accuracy. As thismethod is applicable toanyexperimentaldata,weconsider it anef-cient method of compensating for a lack of experimental data.

Experimental and predicted uorescence anisotropy valueswere analyzed using SOM clustering. SOM clustering is now re-garded as a powerful tool for data mining. SOM is a feedforward-type neural network model that consists of one input layer andone output layer [27]. The SOM algorithm is based on unsuper-vised, competitive learning. The network ultimately associatesthe output nodes with groups or patterns of input vectors byrepeating the learning. This method was described in detail inour previous articles [22,25]. SOM can accommodate several vari-ables as input vectors and takes them into account in determiningclustering. We used a series of uorescence anisotropy values fordifferent temperatures as input vectors to represent the curve.Using this method, we were able to classify membranes with sim-ilar properties based on uorescence anisotropy curves obtained.

As a result of the SOM clustering analysis, SM/DOPC/Ch mem-branes were divided into six clusters (Fig. 5). Apart from cluster3, the main difference between clusters was the DOPC content ofthe total lipid fraction. The uorescence anisotropy values of typi-cal membranes decreased progressively as the amount of DOPC inthe membrane increased (Fig. 6). Endothermic peaks correspond-

d Interface Science 343 (2010) 628633ing to the Tm of DOPC were observed in DOPC-rich membranes(clusters 5 and 6) (Fig. 7). This indicates that a considerableamount of La phase membrane domains rich in DOPC are present

on their surfaces. Based on these ndings, the clusters were distin-guished according to the ratio between the disordered phase do-main and the ordered phase domain.

We intended to use differences in the uorescence anisotropyvalues between 25 C and 60 C to estimate the distribution of theLb phase domain. As anticipated, substantial changes in the valueswere observed for SM-rich membranes (Fig. 3d). As the distributionwas quite similar to that of the Lb phase domain reported by de Al-meida et al. [28], we considered the pattern shown in Fig. 3droughly indicative of the distribution of the Lb phase domain. Clus-ter 3 was abundant in membranes that exhibited a substantialchange in anisotropy. The typical membrane showed a marked de-crease in uorescence anisotropy (Fig. 6) and an endothermic peakcorresponding to the Tm of SM (Fig. 7). Thus, cluster 3 representsmembranes rich in Lb phase membrane domains. In addition, a partof cluster 5 (Ch < 10 mol%, 40 mol% < SM < 80 mol%) showed aslight but obvious difference in anisotropy value (Fig. 3d and 5).These membranes were probably composed of a mixture of Lb andLa phase domain membranes. In contrast, the uorescence anisot-ropy values of clusters 1, 2, and 4 hardly changed, even though

Acknowledgments

The authors are grateful to Yamatake Corporation for providingus with dataNESIA version 3.0. We are also grateful to Ms. Eri Imajoat Hoshi University for her kind assistance with the experimentalwork.

References

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Y. Onuki, K. Takayama / Journal of Colloid and Interface Science 343 (2010) 628633 633the temperature increased to 60 C; therefore, the most orderedphase domains were assumed to exist as an lo phase membrane.This is supported by the phase diagram of de Almida et al. [28].

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5. Conclusions

We successfully classied SM/DOPC/Ch membranes into sixclusters with similar membrane properties. Our study, whichencompassed a wide range of lipid compositions, contributes tothe elucidation of the behavior of ternary lipid mixtures. Althoughan immense amount of experimental data would have been re-quired to interpret the data using conventional means, we over-came this problem by using RSM-S and Kohonens self-organizingmap. Furthermore, because the clusters were determined basedon experimental values, the accuracy of our method is assured.Our method should be useful for related studies and contributeto elucidating the formation of lipid rafts.[10] B.D. Ladbrooke, R.M. Williams, D. Chapman, Biochim. Biophys. Acta 150 (1968)333340.

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Phase behavior in a ternary lipid membrane estimated using a nonlinear response surface method and Kohonens self-organizing mapIntroductionExperimental procedureMaterialsPreparation of liposomesFluorescence anisotropy measurementDifferential scanning calorimeter measurementClustering of SM/DOPC/Ch membranes into membranes with similar properties

ResultsEstimation of the distribution of membranes with similar membrane propertiesCharacterization of distinct membranes

DiscussionConclusionsAcknowledgmentsReferences