Polydiacetylene

Article pubs.acs.org/ac

Array-Based Disease Diagnostics Using Lipid/Polydiacetylene Vesicles Encapsulated in a Sol−Gel Matrix S. Kolusheva,†,∇ R. Yossef,‡,∇ A. Kugel,‡ M. Katz,† R. Volinsky,§ M. Welt,‡ U. Hadad,‡ V. Drory,∥ M. Kliger,⊥ E. Rubin,‡,# A. Porgador,*,‡,# and R. Jelinek*,† †

The Ilse Katz Institute, Faculty of Natural Sciences, Ben Gurion University of the Negev, Beer Sheva 84105, Israel The Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel § Helsinki Biophysics and Biomembrane Group, Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland ∥ Department of Neurology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ⊥ Medasense Biometrics Ltd., 7 Bezalel St, Ofakim, 87516, Israel # The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel ‡

S Supporting Information *

ABSTRACT: We demonstrate a novel array-based diagnostic platform comprising lipid/polydiacetylene (PDA) vesicles embedded within a transparent silica-gel matrix. The diagnostic scheme is based upon the unique chromatic properties of PDA, which undergoes blue-red transformations induced by interactions with amphiphilic or membrane-active analytes. We show that constructing a gel matrix array hosting PDA vesicles with different lipid compositions and applying to blood plasma obtained from healthy individuals and from patients suffering from disease, respectively, allow distinguishing among the disease conditions through application of a simple machine-learning algorithm, using the colorimetric response of the lipid/PDA/gel matrix as the input. Importantly, the new colorimetric diagnostic approach does not require a priori knowledge on the exact metabolite compositions of the blood plasma, since the concept relies only on identifying statistically significant changes in overall disease-induced chromatic response. The chromatic lipid/PDA/gel array-based “fingerprinting” concept is generic, easy to apply, and could be implemented for varied diagnostic and screening applications.

T

he fundamental means of disease diagnostics through blood screening (sera or plasma) have hardly changed in decades and are generally based upon identification of molecular markers indicating disease state. Specifically, the continuous contact between the blood and infected tissues gives rise to changes in blood molecular patterns originating either directly by disease agents or indirectly through varied physiological response mechanisms. In recent years many technology-based “omics” approaches - proteomics, metabolomics, glycomics, and others have been proposed, so far with limited success, for identifying disease patterns in blood components, such as cells, serum, or plasma.1−3 Unfortunately, it has become increasingly clear that biological, physiological, and technical parameters significantly complicate biomarker discovery and validation and often lead to “false discoveries”.1−3 We present here a radically different approach of disease diagnostics. Instead of trying to identify specific biomarkers in plasma, we base the diagnosis methodology upon reactions of plasma with an array of artificial biomimetic detectors comprising lipid/polydiacetylene (PDA) vesicles embedded within a © 2012 American Chemical Society

transparent sol−gel matrix. PDA is a conjugated polymer which exhibits unique color and fluorescence properties. The initial phase of the polymer is intense blue and visible to the naked eye, while dramatic color transformations, accompanied by fluorescence changes, are induced by external stimuli particularly interactions with soluble amphiphilic or membraneactive molecules.4−9 In essence, in such PDA-based platforms, the conjugated polymer acts as a built-in reporter of lipophilicity and membrane affinity of soluble molecules, measurable by a chromatic change in both the visible absorption and fluorescence emission spectra. In the context of plasma-membrane interactions, the chromatic signals induced by amphiphilic components within plasma constitute the fundamental means for distinguishing between normal and disease conditions. Recently we demonstrated that lipid/PDA vesicles underwent chromatic transformations induced by lipoproteins extracted from blood plasma, Received: February 15, 2012 Accepted: June 19, 2012 Published: June 19, 2012 5925

dx.doi.org/10.1021/ac300449u | Anal. Chem. 2012, 84, 5925−5931

Analytical Chemistry

Article

subsequently probe-sonicated at 40W in 70 °C for 6 min. The vesicle solution was subsequently cooled at room temperature, diluted with Tris buffer pH 7.5 (1:1), and used for sol−gel preparation. Silica sol−gel preparation was based upon an alcohol-free method.13,14 Briefly, a mixture of all gel constituents was prepared (TMOS:water:0.62 M HCl at 4.41:2.16:0.06 volume ratio). The mixture was stirred vigorously for 1 h at 4 °C to obtain a homogeneous solution. To remove the alcohol, the solution was further diluted with water 1:1 and evaporated by rotor evaporator (Buchi, Germany) for approximately 6 min at a pressure of 60 mbar. The lipid/diacetylene vesicles were then added to the silica solution (volume ratio of 1:1) and immediately placed in the wells of 384-well ELISA plates (Grainer, flat bottom). Each well contained 15 μL silica/ liposomes mixture solutions. Gelation was carried out for 30 min at room temperature followed by addition of Tris pH 7.5 for long-term storage in the refrigerator. Polymerization of the diacetylene monomers to produce the blue-phase lipid/PDA/gel construct was carried out after overnight refrigeration using 2 min irradiation at 254 nm in a 80W Cross-Linker BLX-254 (Vilber Lourmat, France). Human Patients. Experimental procedures involving human subjects were conducted in conformance with the policies and principles contained in the Helsinki Declaration according to National Health Regulations (Medical Experimentation in Human Beings, 1980) and in accordance with GCP-ICH regulations. This study was approved by both the Sourasky and Soroka Medical Center Helsinki Committees. Plasma samples were collected from (i) 20 ALS patients (11 males); the average patient age at sampling was 60.35 ± 13.34 years (range 28−80); (ii) 27 IBD patients (13 males); the average patient age at sampling was 48.2 ± 13.8 years (range 23−69); (iii) 14 neurological non-ALS patients (5 males); the average patient age at sampling was 41.92 ± 17.78 years (range 19−71); and (iv) 23 controls (12 males); the average age at sampling was 37.8 ± 11.45 years (range 24−55). Gender distribution and the average patient age at sampling are summarized at Table 2. 11of the IBD patients had Crohn’s disease and the other 16 had ulcerative colitis (UC). Most of the neurological non-ALS patients suffered either from multiple sclerosis (MS) or cerebrovascular accident (CVA). Plasma Harvesting, Handling, and Processing. Plasma samples were obtained from patients and healthy controls as follows: 5 mL of blood was drawn into a vacuette lithium heparin tube (Cat #455084, Greiner Bio One, Kremsmuenster, Austria) which was subsequently centrifuged at 2500xg for 15 min at room temperature. The separated plasma was placed in 0.25 mL aliquots in sterile protein LoBind tubes (Cat #022431081, Eppendorf, Hamburg, Germany) and stored at −70 °C until further processing. Prior to the experiments the plasma samples were thawed on ice for 60 min, then for each sample 200 μL was diluted 1:1 with 50 mM Tris pH 8.5, and 5 μL samples were placed in each well containing the lipid/PDA/gel. All measurements were done in triplicates. Confocal Fluorescence Microscopy. Confocal microscopy of the lipid/PDA/gel matrix was carried out on a PerkinElmer UltraVIEW system (PerkinElmer Life Sciences Inc., MA, USA) equipped with Axiovert-200 M (Zeiss, Germany) microscope and a Plan-Neofluar 63×/1.4 oil objective. The excitation wavelength of 488 nm was produced by an argon laser. Emitted light was passed through a barrier filter

and the extent of the chromatic transitions were different between lipoproteins separated from plasma of healthy individuals and diabetic patients.10 The approach we describe here relies upon the colorimetric transformations of silica gel-embedded lipid/PDA vesicles induced by plasma. Specifically, the new method aims to exploit variations in plasma content between disease-bearing and healthy patients for disease diagnosis, through monitoring the interactions of the plasma with arrays of vesicles containing dif ferent lipid molecules and PDA. Indeed, we show that through a simple statistical algorithm which analyzes the plasma-induced color transformations one can distinguish between healthy controls and sick patients, respectively, as well as between different diseases. In this study we examined the application of the lipid/PDA sol−gel matrix as a diagnostic platform for detecting and distinguishing among three diseases - inflammatory bowel disease (IBD), amyotrophic lateral sclerosis (ALS), and nonALS neurological disorder patients, in comparison to healthy individuals. These three diseases inflict distinct physiological damages and exhibit significant challenges in term of accurate diagnosis and monitoring. In the case of IBD, invasive procedures are required today for accurate diagnosis. Furthermore, despite extensive efforts in recent years to develop noninvasive biomarkers for diagnosing and monitoring response to IBD treatments, still no well-established biomarkers have been identified.11 For ALS, no available diagnostic test exists to confirm or exclude it; the primary diagnosis method is by ruling out other neurodegenerative syndromes. Moreover, diagnosis of ALS is based entirely on clinical features; patients in whom a diagnosis of ALS is suspected on clinical grounds usually undergo electrophysiological monitoring designed to document lower motor dysfunction in clinically involved and uninvolved regions and secondarily to exclude other disease processes.12

■

MATERIALS AND METHODS Materials. The following lipids, 1,2-dimyristoyl-sn-glycero-3phosphocholine (DMPC), 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPE), 1,2-dimyristoyl-sn-glycero-3-phospho-(1′-racglycerol) (DMPG), 1,2-dipalmitoyl-sn-glycero-3-phosphothioethanol (PTE), 1,2-dipalmitoyl-sn-glycero-3-succinate (DGS), 3β-[N-(N′,N′-dimethylaminoethane)-carbamoyl]cholesterol hydrochloride (DCChl), 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-(cap biotinyl) (sodium salt) (CAPPE), elaedic acid (EA), 1,2-dioleoyl-3-trimethylammonium-propane (chloride salt) (DTP), 1,2-dimyristoyl-sn-glycero-3-phosphate (DMPA), trymyristoyl-glycerol (TM), L-α-phosphatidylserine (Brain, Porcine) (PS), L-α-phosphatidylinositol (Liver, Bovine) (PI), Sphingomyelin (Brain, Porcine) (SM), and cholesterol (bovine wool) (Chl), were purchased from Avanti Polar Lipids (Alabaster, AL). The diacetylenic monomer 10,12-tricosadiynoic acid was purchased from Alfa Aesar (Karlsruhe, Germany). The diacetylene powder was washed in chloroform and purified through a nylon 0.45 μm filter (Whatman) before use. Tris(hydroxymethyl)aminomethane (TRIZMA base buffer, C4H11NO3) and tetramethoxysilane (TMOS) were purchased from Sigma-Aldrich. Silica Sol−Gel-Containing Vesicles. Chromatic vesicles comprising the diacetylene monomer 10,12-tricosadiynoic acid and different lipids were dissolved in chloroform/ethanol (1:1) and dried together in vacuo to constant weight, followed by addition of deionized water to a final concentration of 7 mM and 5926



Article

Figure 1. Lipid/PDA/silica-gel assembly and properties. A. Synthesis scheme for production of sol−gel-embedded lipid/PDA vesicles (initially gray, blue following polymerization). For details see text. B. Colorimetric properties of the lipid/PDA/silica-gel. Left: initial blue color; middle and right: after incubation for a few minutes with triton (middle), polymyxin-B (right). C. Slice of a confocal microscopy image of the red-phase (fluorescent) lipid/ PDA/gel matrix. The bright spots correspond to the fluorescent PDA. One unit = 6.2 μm.

(500−700 nm). The microscopy image was plotted using Velocity 3D Image Analysis software (PerkinElmer). Chromatic Analysis. Aliquots of the tested solutions were placed in the multiwell plate and incubated at 37 °C for 45 min. Following incubation the multiwell plates were scanned in transmitted mode on an Epson 4990 Photo scanner to produce 2400 dpi, 24 bit color depth RGB images. Digital colorimetric analysis (DCA) was carried out by extracting RGB channels values for each pixel within the sample spots in the scanned images, and the color change values were calculated using Matlab R2010 scientific software (The Mathworks, Inc., MA, USA) as detailed previously.15,16 Briefly, DCA utilizes the standard “red-green-blue” (sRGB) model essentially translating any color signal into three distinct values corresponding to the intensities of red (R), green (G), and blue (B) color channels. Accordingly, the relative intensity of a particular RGB component in a scanned image can be defined as the chromaticity level. For example, the red chromaticity level (r) in each pixel was calculated as15 r=

R R+G+B

RCS =

rsample − r0 rmax − r0

*100%

(Eq. 2)

where rsample is the average red chromaticity level of all pixels in the scanned surface, r0 is the average red level calculated in a blank surface (blue sensor well left without treatment), and rmax is the average red chromaticity level of the maximal blue-red transition, an area of the sensor well in which the most pronounced blue-red transition was induced (positive control, usually achieved by incubation with strong base (NaOH 1 M) solution). In essence, RCS is the normalized change in the red chromaticity level within the sensor well surface on which the tested sample was deposited. Statistical Analysis. Statistical analysis and data visualization was conducted with the R project for statistical computations. The Mann−Whitney statistics were calculated with the “wilcox.test” method, disregarding missing values. Machine learning approach to data analysis employed the SVM models that were built using the “svm” object from the “e1071” package and using the “tot.accuracy” feature reported with each SVM model for 10-fold cross-validation.17,18 For a complete description of the SVM algorithm for binary classification, see the cited refs 17 and 18. Briefly, it selects a binary margin classifier which seeks the optimal hyperplane in a high-dimensional feature space that maximizes the margin between data samples of the two classes after transforming the data with a kernel function. We used the linear kernel function throughout this work. The method of 10-fold cross-validation used to evaluate the accuracy of the prediction is a classification model validation method that

(1)

where R (red), G (green), and B (blue) are the three primary color components. For a defined surface area within a PDA-based sensor well, we classify a quantitative parameter denoted red chromaticity shift (RCS) that represents the total blue-red transformations of the pixels encompassed in the area 5927



Article

Figure 2. Schematic colorimetric analysis of a lipid/PDA/gel multiwell plate. A. The multiwell plate is incubated with the analytes tested and scanned. B. Scanned image of the multiwell plate showing the different blue-red transitions in the wells (depending upon the analytes placed in each well). C. Quantification of the blue-red transitions using the digital color analysis (DCA) algorithm (details in the Experimental Section).

using fluorescence microscopy (Figure 1C). The confocal microscopy slice image (excitation 488 nm, emission 560 nm) in Figure 1C highlights the complete immobilization of the lipid/ PDA vesicles within the transparent gel matrix. The lipid/PDA/gel construct can be readily assembled in a multiwell plate configuration and employed for rapid simultaneous analysis of a large number of analytes using a simple image analysis procedure. 15 Figure 2 depicts the results of a representative experiment which includes data acquisition and colorimetric quantification. Figure 2A shows a multiwell plate containing DMPC/PDA/silica-gel in which different amphiphilic analytes and solvents were added to the wells. Following 45 min incubation the plate was placed on a simple desktop scanner and the color image of the entire plate was recorded (Figure 2A). The scanned image in Figure 2B clearly shows that distinct colorimetric transitions were induced within the plate; in some wells the analytes which were added gave rise to pronounced blue-red transitions, other analytes induced purple colors, while the blue color of PDA was retained in some of the wells. The differences in color transformations reflect the degrees of interactions of the analyte molecules with the embedded vesicles.15 The scanned image was further processed using a Mathematica-based algorithm which essentially quantifies the degree of blue-red transformations within the area of each well.15 The quantitative analysis essentially provides numerical values which reflect both the intensity and abundance of red PDA within the gel matrix in each well. Figure 2C, for example, shows a three-dimensional depiction of part of the multiwell plate following incubation with different analytes; the heights of the individual bars in Figure 2C correspond to the extent of red color within each well. Principles of Disease Diagnostics Using Lipid/PDA/Gel Array Analysis. The colorimetric array-based diagnostic concept is depicted in Figure 3. The hypothesis underlying our approach is that molecular variations of plasma are related to disease onset and progression. In particular, we hypothesize that the changes, however slight, in molecular composition of plasma would entail overall different colorimetric transformations upon interactions of the plasma with the gel-embedded lipid/PDA

randomly assigns the observations (samples) to one of 10 partitions such that the partitions are near-equal size. Subsequently, a set containing all but one of the partitions is used for training, and the remainder data-points are used to test its preformance. The generalization error is assessed for each of the 10 test sets and summarized to estimate the mean error rate.

■

RESULTS AND DISCUSSION Chromatic Lipid/PDA/Silica Gel Matrix. Figure 1 depicts the procedure for assembling the lipid/PDA/sol−gel transparent matrix. Briefly, vesicles comprising lipids and diacetylene monomers were prepared and interspersed within alcohol-free hydrolyzed quaternary silicate monomers (Figure 1A). Following gel condensation in a slightly basic pH, the resultant lipid/ diacetylene/silica gel was kept in a buffer environment, and polymerization of the blue polydiacetylene through UV irradiation at 254 nm was carried out just prior to analyte addition and colorimetric analysis. Importantly, PDA retained its colorimetric properties while encapsulated within the gel framework (Figure 1B). Initially, the lipid/PDA/silica-gel matrix appeared intense blue; the blue color was stable for long time periods (weeks) prior to analyte testing. The rapid polymerization of the gel-embedded PDA guest domains and absence of colorimetric transformations of the bluephase PDA indicates that the incorporated lipid/PDA vesicles by themselves do not exhibit interactions with the sol−gel framework. Indeed, silica gel matrixes have been previously shown to constitute inert, nonreactive hosts for diverse guest species.19 Furthermore, the sol−gel matrix provides enhanced stability for the embedded lipid/PDA vesicles, as compared to vesicles in solution. Figure 1B shows that brief incubation of the lipid/PDA/gel matrix with amphiphilic or membrane-active substances resulted in the blue-red transformations ascribed to interaction of the analytes with the lipid/PDA vesicles.7,8 The dramatic color change depicted in Figure 1B indicates that the porous gel framework allowed diffusion of amphiphilic analytes and consequent interactions with the gel-embedded lipid/PDA vesicles. The transformation of the blue-phase PDA to the red f luorescent phase allows examination of the composite matrix 5928



Article

which is essentially a sum of all contributions of the individual components in the mixture (Figure 3C). As depicted in Figure 3, the lipid variability within the gelembedded vesicles is the core feature facilitating the diversity of signals generated in the chromatic system. Essentially, the plasma samples are applied to an array of lipid/PDA vesicles comprising PDA and different lipid molecules (chromatic vesicles a-c in Figure 3B). Each plasma sample induces a distinct colorimetric transition when added to a particular lipid/PDA vesicle, since each plasma solution comprises a different molecular composition. Importantly, the overall color transformation will depend upon the distinct affinities of all plasma components to lipids having different structures, headgroup charges, membrane packing, and other molecular properties. Accordingly, incubation of the tested plasma sample with the lipid/PDA/gel array will result in a chromatic pattern or “fingerprint” (each row in Figure 3C), in which the number of components is determined by the different vesicle compositions employed in the experiment. Crucially, as described in detail below, we find that statistically significant distinct color patterns (e.g., chromatic fingerprints) can be discerned through application of simple algorithms analyzing the colorimetric arrays induced by plasma from healthy and disease-inflicted individuals, respectively. Application of the Lipid/PDA/Gel System to Disease Diagnostics. Figures 3 and 4 present chromatic experiments and statistical analysis of plasma sample aliquots obtained from 84 subjects belonging to four clinical groups: patients suffering from amyothrophic lateral sclerosis (ALS), non-ALS neurological disorders, inflammatory bowel disease (IBD), and healthy controls (Table 1). The lipid/PDA/gel matrix employed in the

Figure 3. Disease diagnosis using array-based chromatic lipid/PDA/gel concept. A. Three plasma samples (i-iii), each containing a different combination of molecular components. B. Each plasma sample is incubated with lipid/PDA vesicles embedded within the silica gel. The lipid composition in each embedded vesicle is different (a-c). C. The colorimetric matrix resulting from the array experiment; the different colors result from the overall interactions between the plasma and lipid/ PDA/gels.

vesicles. Importantly, no a priori knowledge of the actual plasma composition (or putative disease-induced changes in such composition) is required to perform the analysis. Our hypothesis is schematically outlined in the generic experiment in Figure 3, in which three plasma are examined (plasma i-iii). For the sake of simplification, each plasma can be perceived as a mixture of three amphiphilic/membrane-active components (shown in different colors in Figure 3A). Each plasma is incubated with an array of lipid/PDA/gel constructs, in which each construct contains a dif ferent lipid composition (Figure 3B, vesicles a-c); the actual experiments we carried out (see below) employed a significantly larger array of lipid/PDA vesicles. Upon interactions with each array component (e.g., lipid/PDA composition), the plasma induces a chromatic signal

Table 1. Plasma Screened Using the Lipid/PDA/Gel Assay no. of samples control IBD ALS non-ALS

23 27 20 14

age ± SD 37.8 ± 11.45 48.2 ± 13.84 60.35 ± 13.34 41.92 ± 17.78

% male 52 48 55 36

Figure 4. Comparisons of plasma-induced colorimetric transitions of different disease states. Radar plots showing plasma-induced color transitions recorded in DMPE/PI/PDA/silica-gel (A) and in DMPC/DMPG/PDA/silica-gel (B). Two disease conditions were compared in each plot: non-ALS neurological condition (gray surface); IBD (red); healthy controls (green). Each axis in the plots corresponds to the number of measurements (plasma samples and repeats) corresponding to the disease condition which induced the %RGB indicated (for example, in the case of DMPE/PI/PDA/silica-gel IBD samples yielded 30 colorimetric measurements in which the %RGB was 45 ± 5, 15 measurements were of the order of 55 ± 5, 16 measurements of 65 ± 5, and 10 color measurements of 75 ± 5). 5929



Article

chromatic detector, we tested the response distribution according to gender for all of 84 samples per each chromatic detector. No significant differences have been found between males and females (Table 1, Supporting Information). While the male/female distributions in the clinical groups were similar, age variations were apparent among patients belonging to each disease condition (Table 1, above). Accordingly, we tested the age-dependent response distribution within each clinical group per each chromatic detector, deviding the patients into two groups based on the age median. Importantly, we find that age was not a significant factor in affecting response within a clinical group for 14 of the 17 lipid compositions examined (Table 1, Supporting Information). Compositions 7, 8, and 12 (Table 2) showed significant age-related differences (p < 0.05) within the clinical groups corresponding to IBD, ALS, and control, respectively. Thus we assume that, except for lipid compositions 7, 8, and 12 which were subsequently discarded in the statistical analyses, the clinical-based differences observed for a specific lipid composition did not stem from age or gender. Mann−Whitney analysis designed to determine whether statistically significant differences were observed between the disease states per each lipid composition was carried out (Table 2, Supporting Information file). Although some statistically significant differences between patterns of chromatic responses among the clinical groups were observed for some lipid compositions, none of these array members could serve as a single marker for reliable separation between clinical groups due to overlap in their value distribution (Table 2, Supporting Information). Therefore, we decided to employ machine learningbased protocols for generating reliable prediction classifiers for clinical groups. Specifically, we implemented the support vector machine (SVM) algorithm18,17 due to its robustness and statistical strength. However, because SVM is a binary classification algorithm, separate machine-learning experiments were performed for each pair of the clinical groups examined. Overall, the colorimetric result from each specific gel composition was employed as a feature in the SVM analysis, thus yielding 14 features per plasma from each of the tested clinical groups (note that compositions 7, 8, and 12 were discarded due to identification of the age-related colorimetric effects, above). Building a successful classifier using machine learning algorithms usually requires that an informative subset of features is selected for model development (i.e., feature selection). We took the approach of exhaustive analysis employing all possible combinations of 1 to 5 features (in the case here the features being the different lipid compositions). Essentially, for each subset of selected features an SVM model was trained and evaluated using a 10-fold cross-validation protocol (Experimental Section). Overall, classifiers were obtained for each pair of clinical groups (e.g., classes) and tested on the other two classes. Table 3 shows the best obtained combination of chromatic detctors according to 10-fold cross-validation for combinations of 3, 4, or 5 features. The prediction values outlined in Table 3 attest to the remarkable success of the array-based colorimetric assay in conjunction with the SVM algorithm to distinguish among the clinical groups tested. To further validate the statistical significance of the results, a shuffling experiment was conducted in which the clinical class names of the different samples were randomly rearranged. Specifically, for each combination of two groups, the samples were randomly assigned “group 1” and “group 2” labels; the entire binary classification process was then

analysis contained 17 different combinations of lipids (Table 2), designed to span a broad range of lipid properties, such as size Table 2. Lipid Compositions Employed in the Lipid/PDA/ Gel Matrixa no.

a

no.

composition

1

PTE/PDA

composition

mole ratio 2:3

10

2

DMPC/Chl/PDA

1:1:3

11

3 4

DMPC/Chl/PDA DMPE/PS/PDA

1.5:0.5:3 1:1:3

12 13

5 6 7

DMPE/PI/PDA DOPC/Chl/PDA DGS/PDA

1:1:3 1:1:3 2:3

14 15 16

8 9

DMPC/PDA DMPC/DCChl/ PDA

2:3 1.5:0.5:3

17

DMPC/CAPPE/ PDA DMPC/DMPG/ PDA DMPC/EA/PDA DMPC/DTP/ PDA SM/PDA DMPA/PDA DMPC/DMPA/ PDA DMPC/TM/PDA

mole ratio 1:1:3 1:1:3 1:1:3 1.5:0.5:3 2:3 2:3 1:1:3 1.5:0.5:3

Complete abbreviations are provided in the Materials section.

and charge of the head-groups, alkyl-chain saturation, and transition temperatures.20 Following incubation of the gel with the plasma sample aliquots from the clinical groups the blue-red transformations were calculated per each plate through application of the colorimetric quantification algorithm (see the Experimental Section for a more detailed description). Figure 4 depicts two representative radar plots corresponding to the colorimetric experiments (the complete database of colorimetric results, obtained for the four disease states and 17 lipid compositions, is presented in box-plot format in the Supporting Information). Each radar plot accounts for a specific lipid composition (in the lipid/PDA/gel matrix) and includes the colorimetric transformations induced by plasma associated with two medical conditions (which are shown in different colors). The radar plots in Figure 4 indicate that some overlap in colorimetric signals exist, although different disease conditions seem to induce distinct distribution of colorimetric signals. For example, examination of the DMPE/PI/PDA/gel results in Figure 4A reveals that the colorimetric measurements recorded from IBD plasma (red surface) yielded relatively lower RGB values compared to non-ALS plasma (gray surface). In case of DMPC/DMPG/PDA/gel (Figure 4B), measurements of plasma from healthy patients also induced generally lower RGB values (green surface) compared to non-ALS plasma (gray). It should be emphasized that the differences in chromatic responses both between vesicle compositions as well as between disease conditions apparent in Figure 4 correspond to the underlying molecular phenomena responsible for the colorimetric transformations. Specifically, the plasma contains a multitude of membrane-active and amphiphilic compounds, including enzymes, lipids, small molecules, and ions. These molecular components exhibit distinct interactions with the embedded lipids depending on lipid properties, giving rise to somewhat different overall chromatic transitions per vesicle type. Similarly, the disease-modified plasma molecular composition is the likely factor giving rise to different chromatic responses upon incubation with the lipid/PDA/gel constructs, as shown in Figure 4. To investigate the biological significance of the colorimetric data we carried out several statistical analyses. First, to verify that gender difference did not alter the response of a specific 5930



■

Table 3. Best Predictive Combination of Lipid Compositions According to 10-Fold Cross-Validation class 1

class 2

neurogenic non-ALS

ALS

IBD

ALS

control

ALS

control

IBD

neurogenic non-ALS

IBD

neurogenic non-ALS

control

no. of chromatic detectors

chromatic detectors

10-fold accuracy (percent)

5 4 3 5 4 3 5 4 3 5 4 3 5 4 3 5 4 4

n1,n2,n5,n9,n14 n1,n5,n13,n14 n4,n10,n17 n2,n3,n5,n10,n16 n5,n10,n15,n16 n4,n5,n10 n2,n11,n13,n14,n17 n2,n4,n5,n11 n2,n4,n14 n1,n2,n4,n5,n10 n2,n5,n6,n10 n5,n10,n17 n2,n3,n5,n14,n16 n5,n11,n16,n17 n5,n11,n16 n1,n2,n11,n14,n16 n2,n10,n11,n14 n4,n11,n14

82 88 76 96 96 91 86 84 81 94 94 86 98 95 95 97 95 95

AUTHOR INFORMATION

Corresponding Author

*E-mail R.J.: [email protected], A.P.: [email protected]. Author Contributions ∇

S.K. and R.Y. contributed equally.

Notes

The authors declare no competing financial interest.

■

REFERENCES

(1) Diamandis, E. P. J. Natl. Cancer Inst. 2010, 102, 1462−1467. (2) Ransohoff, D. F. Recent Results in Cancer Research Fortschritte Der Krebsforschung Progres Dans Les Recherches Sur Le Cancer 2009, 181, 55− 59. (3) Sikaroodi, M.; Galachiantz, Y.; Baranova, A. Curr. Mol. Med. 2010, 10, 249−257. (4) Katz, M.; Ben-Shlush, I.; Kolusheva, S.; Jelinek, R. Pharm. Res. 2006, 23, 580−588. (5) Kolusheva, S.; Boyer, L.; Jelinek, R. Nat. Biotechnol. 2000, 18, 225− 227. (6) Kolusheva, S.; Shahal, T.; Jelinek, R. Biochemistry. 2000, 39, 15851−15859. (7) Kim, J. M.; Ji, E. K.; Woo, S. M.; Lee, H.; Ahn, D. J. Adv. Mater. 2003, 15, 1118−1121. (8) Kim, J.-M.; Lee, Y. B.; Yang, D. H.; Lee, J.-S.; Lee, G. S.; Ahn, D. J. J. Am. Chem. Soc. 2005, 127, 17580−17581. (9) Ji, E.-K.; Ahn, D. J.; Kim, J.-M. Bull. Korean Chem. Soc. 2003, 24, 667−670. (10) Hanin-Avraham, N.; Fuhrman, B.; Mech-Dorosz, A.; Kolusheva, S.; Porgador, A.; Aviram, M.; Jelinek, R. Biochim. Biophys. Acta 2009, 1788, 2436−2443. (11) Mendoza, J. L.; Abreu, M. T. Gastroenterol. Clin. Biol. 2009, 33 (Suppl 3), S158−73. (12) Rowland, L. P. J. Neurol. Sci. 1998, 160 (Suppl), S6−24. (13) Ferrer, M. L.; Del Monte, F.; Levy, D. Chem. Mater. 2002, 14, 3619−3621. (14) Ferrer, M. L.; Yuste, L.; Rojo, F.; Del Monte, F. Chem. Mater. 2003, 15, 3614−3618. (15) Volinsky, R.; Kliger, M.; Sheynis, T.; Kolusheva, S.; Jelinek, R. Biosens. Bioelectron. 2007, 22, 3247−3251. (16) Pratt, W. K. Image Rochester NY 2001, 5, 471. (17) Chang, C.-C.; Lin, C.-J. LIBSVM: a Library for Support Vector Machines; 2001. (18) Alpaydin, E. Introduction to Machine Learning (Adaptive Computation and Machine Learning); The MIT Press: 2004. (19) Frenkel-Mullerad, H.; Avnir, D. J. Am. Chem. Soc. 2005, 127, 8077−81. (20) Gennis, R. B. Biomembranes:- Molecular Structure and Function; Springer-Verlag: New York, 1989.

repeated with the shuffled data. Crucially, in all cases the previously employed classifiers did not yield significant prediction values (using 10-fold cross-validation). Most importantly, the number of models (e.g., vesicle lipid compositions) that predicted well was few to none as compared to the dozens of models predicting well obtained for the nonshuffled results. Conclusions. We present a simple yet powerful array-based approach for disease diagnostics using colorimetric transformations induced by blood plasma within lipid/PDA vesicles embedded in a transparent sol−gel matrix. The colorimetric response is based upon the summation of distinct blue-red transitions induced by plasma molecular components upon interactions with the lipid/PDA vesicles. Crucially, we created an array of lipid/PDA/sol−gels, in which each array member comprised a different lipid composition. Application of a machine-learning statistical algorithm for which the input was the colorimetric array matrix demonstrated a remarkable capability to distinguish among disease states. The advantages of the new colorimetric array-based concept for disease diagnostics are noteworthy. Preparation of the matrix is straightforward, and the lipid/PDA/gel assembly is robust, stable, and can be stored for months before use. Readout of the colorimetric signals can be carried out using a conventional desktop scanner, followed by computerized image analysis algorithms. In a conceptual sense, the technique does not require a priori knowledge of the identity and distribution of molecular components within plasma; the only assumption (which was clearly demonstrated here) is that disease-induced variability in plasma composition results in different colorimetric transitions in the lipid/PDA/gel matrix array, which can be distinguished through the machine learning analysis. The new array-based concept could be employed for carried disease diagnostics, monitoring, and other biological screening applications.

■

Article

ASSOCIATED CONTENT

S Supporting Information *

Figure 1, SI and Supplemental Tables 1 and 2. This material is available free of charge via the Internet at http://pubs.acs.org. 5931


Polydiacetylene

Recommend Documents