Computer screen photo-assisted detection of complementary DNA strands using a luminescent zwitterionic polythiophene derivative

Sensors and Actuators B 113 (2006) 410–418

Computer screen photo-assisted detection of complementary DNA strands using a luminescent zwitterionic polythiophene derivative b , P. Nilsson b , O. Ingan¨ ˚ D. Filippini a,∗ , P. Asberg as b , I. Lundstr¨om a b

a Applied Physics, IFM, Link¨ oping University, SE-581 83 Linkping, Sweden Biomolecular and Organic Electronics, IFM, Link¨oping University, SE-581 83 Linkping, Sweden

Received 4 January 2005; accepted 10 March 2005 Available online 6 June 2005

Abstract The computer screen photo-assisted technique (CSPT), a practical method for the evaluation of assays using computer screens as light sources and web cameras as detectors, has been used to detect the attachment of complementary DNA strands (20-mer, 5 -CAT GAT TGA ACC ATC CAC CA-3 ) to a complex of single DNA strands and a polythiophene derivative (poly(3-[(S)-5-amino-5-carboxyl-3-oxapentyl]2,5-thiophenylene hydrochloride (POWT)). The complex is a highly sequence specific indicator, based on non-covalent coupling of DNA to a water-soluble, zwitterionic, electroactive and photoactive polymer able to produce a combined absorption-emission signal readable by CSPT. The observed CSPT signal retains key spectral features of the complex spectrum, distinguishing the DNA attachment, as well as other stimuli, such as pH regulation at concentrations of 30 ␮M POWT and 15 ␮M DNA. A CSPT time resolved (linked to temperatures between 8 and 18 ◦ C) approach is demonstrated as a complementary source of discrimination and for testing the robustness of the achieved classification. © 2005 Elsevier B.V. All rights reserved. Keywords: Computer screen photo-assisted technique; Complementary DNA detection; POWT; Biosensing; Fluorescence fingerprinting

1. Introduction The computer screen photo-assisted technique (CSPT) is a practical method for the evaluation of colorimetric or fluorescent assays, which only demand a standard computer set and a web camera as measuring platform. Naturally, within the potential applications of CSPT [1–4], are the remote evaluation of medical diagnostic or environmental tests at homes, using the already available computers and Internet connections. Different existing sensing technologies are suitable with CSPT, as it has been demonstrated so far [1,2], but indeed one of the most attractive alternatives is the use of assays based on the recognition of specific DNA strands, in a similar way as complementary DNA (cDNA) microarrays perform for tracing human diseases to its very genetic cause. ∗

Corresponding author. Tel.: +46 13281282; fax: +46 12288969. E-mail address: [email protected] (D. Filippini).

0925-4005/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2005.03.112

Although CSPT is a versatile method, it does not compete with dedicated analytical methods in detection limit or accuracy. For exploiting its own advantages, CSPT requires the aid of a suitable chemistry providing a response readable by the platform. In this way, a very promising selective fluorometric DNA hybridization detection method has been recently demonstrated [5], in this case giving highly sequence-specific information, based on non-covalent coupling of DNA to a water-soluble, zwitterionic, electroactive and photoactive polythiophene derivative [6]. This alternative offers a novel way to create DNA assays without using covalent attachment to a receptor (or labeling of the analyte). In this work, we demonstrate the feasibility of CSPT for detecting the attachment of a 20 mer DNA strand to its complementary chain complex with the referred polythiophene derivative. The robustness of the detection is verified by a time resolved experiment linked to a temperature change between ∼8 and 18 ◦ C.

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2. Experimental The CSPT light source used in this work was a 130 × 80 mm2 area of a standard computer screen (a LCD monitor, Sony Vaio PCG-FX 505, operating at XGA resolution) displaying a 50-color illuminating sequence at a rate of 1 color/s. This particular color sequence, which resembles the human perception of the visible spectrum, is repeated continuously during 30 min (36 times) until the end of the experiment. Along the experiment, the screen illuminates the whole array of samples at the same time (in this case, a standard 96-well microtiterplate containing the samples in the center) by contrast to sequential inspection in 2D scanned systems. The microplate is inserted in a removable holder attached to the frame of the computer screen that receives illumination from beneath (allowing the required horizontal positioning of the plate), using a 45◦ aluminium-coated mirror to guide the light from the monitor. An opaque black box provides light shield from external sources and support for the web camera (Philips PCVC740K ToU Cam Pro, with a CCD detector operating at a resolution of 320 × 240 pixels), which is directed downwards focussed on the microplate. The light emerging from the microplate creates an image of the array under each particular color illumination, which is subsequently acquired by the web camera operating at a capture rate of 1 frame/s. The result of the measurement is a digital video file (AVI format) of the array under the different color illuminations. The acquired video file (or video stream in the case of an on line measurement) is decomposed in their individual frames and the information contained in the wells is extracted. A digital mask is used to extract the information from regions of interest (the wells). The mask extracts the information from circular areas centered on the image of the wells, which avoids the borders where meniscus formation might introduce a spurious reflectance. In this work, the image of each well is evaluated over 200 pixels on each frame of the video. The digital values of each color level (red, green and blue) of the pixels composing the image of each well are averaged giving the intensity (per color channel) for the particular illumination of the frame. All the wells in the frame are evaluated in the same way and the procedure is repeated for all the frames of the video. As a result, distinctive intensity versus color curves of the different samples are obtained. In order to highlight the sample characteristics from those of the solvent, the measurement is repeated with all the wells filled with distilled water and the corresponding intensity curves subtracted from the sample ones. The different diagrams (e.g. in Fig. 2) are normalized in the red channel (that of the smaller amplitude for these samples), whereas the values in the green and blue channel are referred to the red one. In the present samples, poly(3-[(S)-5-amino-5-carboxyl3-oxapentyl]-2,5-thiophenylene hydrochloride) (POWT) was synthesized as previously reported [6]. A stock solution containing 1 mg ml−1 POWT in deionized water was

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prepared (diluted 10 times in S1, Fig. 1a) and incubated for 30 min. The stock 100 mM, pH 7.0, phosphate buffer (PB) was also prepared at this time. All samples are prepared in a 96-well plate to a final volume of 200 ␮l. S1 is prepared by mixing 20 ␮l of the polymer solution with 180 ␮l deionized water. S2 is a reference sample for S3 and S4 and was prepared by mixing 20 ␮l of the stock POWT solution with 40 ␮l stock PB; this was incubated for 15 min at room temperature. Then deionized water was added up to 200 ␮l before put on ice until measuring with CSPT. Samples S3 and S4 are realized by taking 20 ␮l of the stock polymer solution and mix it with the appropriate amount of single-stranded DNA (ssDNA-solution, 100 nmol ml−3 , 5 -CAT GAT TGA ACC ATC CAC CA-3 ) to give the desired ratio between concentrations of single-stranded oligonucleotide and POWT. In this case 1:0.5 (30 ␮M POWT:15 ␮M ssDNA). After 15 min of incubation, the samples were diluted with a stock buffer solution incubated another 15 min, and then, transferred onto ice (for 20 min). Immediately before measuring with CSPT, an equal amount of complementary ssDNA (100 nmol ml−3 , 5 -TGG TGG ATG GTT CAA TCA TG-3 ) was added to S4 and then both samples were diluted with deionized water to the final volume of 200 ␮l containing 20 mM sodium phosphate and 30 ␮M POWT:15 ␮M double-stranded DNA (dsDNA). In this study, the chosen concentrations are rather high in order to demonstrate the detection with CSPT at the present stage of development of the platform; however, the detection limit of POWT can be as low as 10−8 M [5]. For reference measurements, all samples were diluted four times and incubated at room temperature for 5 min before measuring absorbance and fluorescence. The emission spectra were recorded with an ISA Jobin-Yvon spex FluoroMax-2 fluorescence spectrometer using photo-excitation at 400 nm. Spectroscopic absorption measurements were recorded on a Perkin-Elmer λ 9 UV/VIS/NIR spectrophotometer using 1 cm plastic cuvettes. Photo-excitation at 400 nm is optimal for the POWT-DNA detection system since the inter-sample variations are small and the absorption is similar at this wavelength.

3. Results and discussion Fig. 1a shows the absorption and emission characteristics of the present samples (for narrowband excitation at 400 nm). The absorption and emission spectra of POWT were recorded after diluting samples S1, S2, S3 and S4 four times; this gives a polymer concentration of 0.025 mg/ml (7.5 ␮M). The S2 sample shows an absorption maximum around 440 nm and a shoulder in the region at 550 nm, associated with a planar polymer and aggregation of polymer chains, respectively [7]. When the polymer is associated with ssDNA it is red-shifted around 20 nm and has a more pronounced shoulder at 550 nm. Once the complementary strand is added to the POWT/ssDNA (S3) to form a POWT/dsDNA (S4)

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complex, the polymer chains are deplanarized and separated from each other, this is recorded as a slight blue shift and a decreased shoulder compared with the ssDNA case [5]. If the polymer is prepared in a plain deionized water solution (S1), it adopts a helical conformation [7], which is observed as a blue shift and the disappearance of the shoulder in the 550 nm region. Because of an intra-chain event, associated with a nonplanar helical conformation, emission at 540 nm is highest in the S1 sample where it also has its maximum. The S2, S3 and S4 samples show less emission than S1 due to aggregation and is also red-shifted compared to S1 because of the planarization of the polymer chain. In the 650 nm region, a shoulder is observed due to an inter-chain event caused by contact between polymer chains and because of planarization of the polymer [8]. These phenomena will reduce the fluorescence quantum yield due to both radiative and non-radiative de-excitation. A comparison between the POWT/ssDNA (S3) and POWT/dsDNA (S4) samples reveals that these two samples have differences in both the intra- and inter-chain events. In the S4 sample, the shoulder at 540 nm is increased and at 650 nm it is slightly decreased, both of these are interpreted as deplanarization and deaggregation compared to S3. The detection of DNA interaction is robust and the detection is not sequence dependent except for the case where the oligonucleotide sequences hybridizes with itself (unpublished results). Using fluorescent spectroscopy, POWT can selectively detect one, two or three single-nucleotide mismatches during the hybridization process [5]. POWT has also been used as a DNA hybridization detector in a surface plasmon resonance setup [9]. Fig. 1b depicts the standard human perception of colors [10], which is used here to represent the spectral response of color channels in web cameras in general, since naturally tri-chromatic recording media try to resemble the human vision. The color bar indicates both the perceived colors for the different wavelengths and also the sequence of 50 polychromatic colors used for illumination. Schematically, the screen spectral radiances are represented by Gaussian functions In Fig. 1c. The weighted sum of these radiances allows displaying up to 224 (in true color monitors) different colors on a computer screen. The weights are triplets of rgb-values ([r(i)g(i)b(i)]) that identifies each color in particular and can be referred as one particular color index (i) of the illuminating sequence [4,11]. Accordingly, in CSPT experiments, the spectral radiances of the color sequences can be expressed as: C(i, λ) = r(i) × R(λ) + g(i) × G(λ) + b(i) × B(λ)

Fig. 1. (a) Absorption and emission spectra of samples S1 (POWT in water), S2 (POWT in 20 mM PB buffer), S3 (S2 + 20 mer ssDNA) and S4 (S3 + complementary DNA strand). (b) Standard color matching function and color bar indicating both the approximate perceived color of the different wavelengths and the 50 polychromatic illuminating colors used in CSPT

(1)

This light illuminates the samples (which have characteristic transmittance T(λ) and emission spectra E(λ)), and the experiments. (c) Indicative spectral radiances of primary colors representative of LCD screens, schematically illustrated using Gaussian functions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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emerging light which contains the spectral signature of the samples, passes through the filters of the web camera (FR (λ), FG (λ) and FB (λ) symbolized by the standard perception in Fig. 1b). After the filters, the light excites the detector (of spectral response D(λ)) and is integrated along the complete range of visible wavelengths, providing an intensity value from each channel of the web camera [1,12]. To highlight the features of the samples the intensity collected from a reference substance (distilled water in this case) is subtracted, producing the sensing signal I, which e.g. for the red channel is:
IR = C(i, λ) × T (λ) × FR (λ) × D(λ) × dλ λ




+ λ


E(i, λ) × FR (λ) × D(λ) × d λ   fluorescence

C(i, λ) × Tref (λ) × FR (λ) × D(λ) × dλ

−

(2)

λ

E(i,λ) represents the emission of the substance excited by the different polychromatic illuminations i. Although this function is unknown in the CSPT context where the platforms spectral characteristics are ignored, its emission fingerprint is embedded in I, and contributes to the purpose of the CSPT evaluation, namely to retain as much as possible of the sample’s spectral characteristics using a simple setup. Although spectral imagers [13] and spectral reconstruction techniques [14] (e.g. like those used for accurate color recording of art work [15] or spectral sampling of satellite images [16]) can be considered as an imaging alternative to spectrofluorometry, these methods, even when they use trichromatic cameras, incorporate discrete or tunable filters and assume all the components spectrally characterized. These aspects make them inadequate for home-based testing, especially when compared with the reduced cost of CSPT. By contrast, the CSPT method is aimed at operating on any possible combination of screens and cameras, still retaining key spectral features of the substances. Such a simple setup satisfying these requirements is feasible due to the chance of evaluating image patterns, which are known a priori. In this way, well-addressed sample spots can be interrogated by a collection of tens or hundreds of pixels instead of individual ones (relaxing the demand on the quality of the camera). Additionally, CSPT is intended to recognize substances among a constrained set of possibilities (as commonly occurs in bioassays), which reduces the number of spectral features to be recognized. It is important to stress that CSPT is not intended to compete with state of the art analytical techniques for substance characterization, but to recognize expected responses through their spectral features and to classify such substances. In this context, to partially separate emission from absorption signals should be considered as a strategy to enhance selectivity rather than a goal in itself.

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An additional procedure is used to verify the robustness of the determinations, which consists of implementing a time resolved measurement (in the present case linked with the temperature of the samples). Thus, the samples are introduced at ∼8 ◦ C in the CSPT platform which is at room temperature and along the 30 min of the measurement the temperature changes affecting the involved substances but still generating classification patterns able to distinguish between ssDNA and the complex with the complementary strand. Fig. 2 shows the CSPT signatures unfolded on the red, green and blue channels of the camera for the samples S3 (ssDNA) and S4 (dsDNA). The rest of the samples are simultaneously evaluated, since the whole microtiterplate is illuminated at the same time by contrast to sequential 2D scanning systems. Thus, the measuring time in this snap-shot approach is independent of the number of samples, being just limited by the number of pixels rendering each sample in the acquired image. For instance, using the web camera at the present resolution (320 × 240 pixels per frame), each well is evaluated by 200 pixels, so the same area can be covered by 384 wells plate and still being evaluated by 50 pixels per well. Arrays of smaller total areas can also be evaluated by placing them at closer distances to the camera, consequently allowing smaller wells to be evaluated by the same amount of pixels. The first row of Fig. 2 (surface graphics) shows IR , IG and IB versus the illuminating color, for all the different repetitions of the measurement along the 30 min experiment. Positive I values are related to a noticeable contribution of emission in the total signal whereas negative values correspond to predominant absorption. Considering the camera filters (represented by the standard color perception in Fig. 1b), it is clear that the red channel is mostly capturing the fluorescent of the substances (Fig. 1a), a combination of absorption and emission is observed with the green channel, and predominant absorption in the blue channel. Although in the present setup absorption is more efficiently detected than emission (since no special separation of the emitted signal from the transmitted background is introduced), it is still possible to register emission features in good correlation with the emission spectra (Fig. 1a) as analyzed later. The contour graphics in the two lower rows of Fig. 2 display the time evolution of the measurements along the x-axes, uncovering a different behavior of samples S3 (ssDNA) and S4 (dsDNA). The time sequence in this example is related to the temperature of the samples, but it could eventually track any other action coordinate depending on the assay. Already in these signals is possible to observe qualitative differences like, e.g. in S3 (observed in the red channel) that show a widening of the contours at a lower temperature than for S4. Similar behavior can also be observed in the other color channels. The smooth transition of these features along the measurement dismisses the presence of artifacts and the temperature

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Fig. 2. Surface graphics of the CSPT evaluation of sample S3 (upper row), unfolded in the signals of the three-color channels of the web camera. The intensity relative to the reference substance (distilled water) is displayed in the vertical axis, whereas the other two axes represent the illuminating color sequence and time (linked to a temperature rise from 0 to 20 ◦ C). Positive values of I indicate predominant emission meanwhile negative values correspond to a main contribution of transmitted light. The two lower rows collect the results as color-codified contour plots for S3 and S4. Two particular time intervals of interest, t1 and t2 (corresponding to ∼12 and 16 ◦ C, respectively), displaying distinctive substance behavior are highlighted for further analysis. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

transition becomes a simple way of assessing the robustness of the achieved classification. For a conceptual discussion of the signals, we will consider two particular times: t1 is a compromise of maximum feature expression for both samples, associated to a threshold temperature where interactions leading to conformational changes in POWT begin to be noticeable (12 ◦ C), and t2 a steady-state regime (>16 ◦ C), which in practice should be the reporting level. Fig. 3 displays the two chosen time intervals for S3 and S4 (for t2, the areas enclosed by the curves have been hatched as an aid to the eye for distinguishing the different features). In the red channel, the S3 signal (Fig. 3a, black line) indicates emission, as intuitively expected from the discussion above. The particular pattern displayed for the different illuminating colors are due to the E(i,λ) fingerprint. Hence, violet or bluish illuminating colors contain enough proportion of short wavelengths (combinations of mostly blue and

red spectral radiances as in Fig. 1c) to excite fluorescence in S3, meanwhile greenish light has neither the intensity, nor the spectral components required for exciting emission, by contrast with yellowish colors (which are composed by the three screen primaries and at large intensities). Reddish light is little absorbed by the sample and the red filter of the camera, displays increasing absorbance at darker hues (basically lower intensities of the same red primary which also contribute with too large wavelengths for exciting emission) as can be intuitively expected. Through the green channel of the web camera, a combination of absorption and emission features is acquired, resulting in an expected predominant contribution from the transmitted signal. For illuminating colors between light blue and yellow the contribution of emitted signal is more relevant (smaller absolute value of I) and for reddish light fluorescence is not excited.

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Fig. 4. CSPT signals vs. illuminating colors unfolded in the three-color channels of the web camera, for samples S3 (solid green line), S2 (solid red line) and S1 (solid black line). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

Fig. 3. CSPT signals vs. illuminating colors, unfolded in the three-color channels of the web camera, for samples S3 and S4 at the specific intervals t1 (solid black lines, 5 ◦ C) and t2 (solid red lines and hatched areas, 16 ◦ C). The light yellow background indicates pure emission. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

For all of the tested samples (Fig. 1a) is evident that the blue channel of the camera (Fig. 1b) can only render absorption, as is corroborated by the signals in Fig. 3. At time t2 (red lines and hatched areas in Fig. 3) the steady sample features of S3 are completely defined, displaying in the red channel emission between indexes 30 and 40, and a reduced absorption peak for greenish light. The green channel reflects the larger emission as a decrease in absorbance for the considered illuminating colors. The blue channel renders a decrease in short wavelength absorption at t2. The comparison between S3 and S4 (Fig. 3) demonstrates the detection of the attachment of a complementary DNA strand using a CSPT setup. As can be seen in Fig. 1a (S3 and S4), the emission differences are quite subtle and it is by the combined analysis of absorption and emission spectra that this phenomenon can be discriminated. At t2 in the red channel of the web camera, S4 (red lines and hatched areas in Fig. 3) shows similar emission to S3 for violet-bluish illumination as might be expected since the emission spectra are quite similar for S3 and S4, but for greenish illumination the smaller absorption of S4 with respect to

S3 is observed as emission (at least predominating to absorption) in Fig. 3. In the green channel (Fig. 3, red lines and hatched areas) S4 exhibits less absorption than S3 (as could be predicted from Fig. 1a and b). Finally, in the blue channel the spectral crossing depicted in Fig. 1a (at short wavelengths S4 is more absorbing than S3) is also retained, showing for S3 an absorption maximum of −3.5 meanwhile for S4 it is −3.8. In addition to the steady CSPT features, the transition between t1 and t2 recordings are also distinctive as can be appreciated comparing the blank spaces between the curves in Fig. 3. Other substances can also be distinguished; Fig. 4 shows the CSPT profiles at t2 of S1 and S2 together with sample S3 included for comparison (the colors of the lines are the same as in Fig. 1a). Sample S1 is POWT in water, which has its emission peak (Fig. 1a) at a shorter wavelength than S3, actually better centered in the green channel of the camera than in the red one (Fig. 1b). Accordingly, in the red channel S1 displays positive values for greenish illuminations, whereas the signal in the red channel is more absorbing than that for S3 for reddish illuminations. In the green channel the contribution of the fluorescent signal is stronger for S1 and consequently, the CSPT signal looks less absorbing. Finally, in good agreement with Fig. 1a, the blue channel reports a more absorbing signal for S1 than for S3. The S2 absorption peak at shorter wavelength than for S3 (Fig. 1b) correlates to the positive peak for greenish illumination observed in the red channel (Fig. 4), whereas the lower emission intensity of S2 (Fig. 1b) is consistent with the absorbance for reddish illumination (instead of the emission observed for S3 in Fig. 3). The lower absorbance of S2 with respect to S1 and S3 in the window of the green filter (Fig. 1a and b) is observed as a small absorbance in the CSPT profile for the green channel. Although not intuitive, the smaller absorbance for S2 in the blue channel (Fig. 3, blue channel)

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can be attributed to the narrower absorption spectra compared to the S3 characteristic (Fig. 1a). For practical applications, this kind of analysis becomes inconvenient, and the classification must be aided by a chemometric approach. As mentioned above, CSPT is not aiming at the characterization of unknown substances, but to the classification of expected outcomes instead. In this context a sensible way of operating CSPT is to compare possible outcomes (from reference spots embedded in the assay) with the actual determination. To verify the classification capabilities, a principal components analysis (PCA [17]) is implemented and the results compared with the classification capabilities using the spectroscopic characterization as input vectors. In the case of CSPT measurements we form a set of 23 × 4 vectors, representing the four different substances (S1–S4) for the 23 times repeated measurements between t1 and t2. Each of these vectors is formed by the concatenation of the red, green and blue channel features (I) of the corresponding samples, which are 150 element vectors formed by the 3 × 50 different illuminating colors: p

p

Sj=1...50 = Iri=1...50 , p

p

p

Sj=50+1...50×2 = Igi=1...50 , p

Sj=50×2+1...50×3 = Igi=1...50

(3) p=1...23×4

with p = 1. . .23 × 4. The input vectors Sj=1...50×3 are first normalized and then used to calculate the covariance matrix. The eigenvectors of the covariance matrix sorted according to decreasing eigenvectors values constitute the so-called feature vector used to transform original data into the principal components coordinates. The same method is applied to absorption and emission spectra. In this case, the spectra are sampled along 202 points between 400 and 700 nm. For each substance, the input vectors are composed by concatenating the emission to the absorption spectra: p

p

p

Fig. 5. (a) First three principal components plot of four samples evaluated by CSPT (open circles) and absorption/emission spectroscopy (crossed squares). In the case of CSPT, all the repetitions of the four samples along the time series of measurements between t1 and t2 are represented. In the case of the substances involved in the detection of the complementary DNA attachment (S3 and S4), the resulting clusters have been highlighted. (b) First two principal components of the same samples aiding to locate the clusters in the 3D representation.

p

Srefj=1...202 = Ai=1...202 , Srefj=202+1...202×2 = Ei=1...202 (4) In this case with p = 1. . .4. Considering that the first three principal components account for more than 85% of the information, we represent the data in a 3D PCA plot displayed in Fig. 5a. Fig. 5b displays the first two principal components and complement the information of the 3D graphic by locating the clusters on the plane. In this kind of analysis, the ability of the measuring technique to classify the different substances is given by the distances in the PCA plot. As expected, the spectroscopic characterization (crossed squares) clearly distinguish the four samples, but which is certainly remarkable is the comparable performance achieved by the CSPT measurement, which displays the samples similarly well separated (in the case of CSPT, since the whole time series is represented the plots can be seen as paths between t1 an t2). In Fig. 5, we have

highlighted the clusters corresponding to the attachment of the complementary DNA strand and the ssDNA complex for the whole time (temperature) evolution. The present results are valid for concentrations not lower than 100 nM in the used configuration, and find applications in tests where these concentrations are feasible, e.g. in primary care units or private doctor’s offices, providing in this way a ubiquitous measuring platform at a low cost.

4. Conclusions The ability of CSPT to detect the subtle spectral features associated with the attachment of a 20 mer complementary DNA strand to a single-stranded DNA–POWT complex has been demonstrated. This result corroborates the flexibility of CSPT for evaluating different kind of assays (colorimetric,

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fluorescent, absorbing, etc.) [1,2,4]. Thus, the sensitivity and purpose of the measurements is given by the assay itself (e.g. cell viability, hormone detection, DNA attachment), the measuring instrument is, however, always the same. In this case, this synergy has been proven feasible aiding the method with an affordable and sensitive POWT-based assay. As it has been discussed, E(i,λ) is a function of the particular color sequence chosen for illumination, and consequently optimized or adaptive sequences constitute natural alternatives to enhance instrumental capabilities. For the purpose of diagnostics, detecting subtle events such as complementary DNA attachments is significant, and becomes especially convenient if such kind of detection can be done with affordable CSPT platforms, eventually supporting self-tests or remote diagnostics in ordinary life.

Acknowledgments Financial support from the Swedish Research Council and the Swedish Agency for Innovation Systems (VINNOVA) is gratefully acknowledged by the authors.

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Biographies Daniel Filippini was born in 1968 in Buenos Aires, Argentina. He received his electrical engineer degree (MSc) from the National Technological University, Buenos Aires, Argentina, in 1993. In 1998, he obtained MSc in biomedical engineering, at Favaloro University, Argentina. From 1998 to 2000 he worked on his PhD, as a DAAD scholar, at the Institute of Physical and Theoretical Chemistry, University of T¨ubingen, Germany, obtaining his PhD from the University of Buenos Aires in 2000. Since 2003 he is an assistant professor in applied physics at the Department of Physics and Measurement Technology at Link¨oping University, Sweden. Filippini has been appointed docent in 2005. His current research interest relates to controlled light assisted sensor systems for chemical or biochemical applications, and the development of new concepts for chemical image generation. ˚ Peter Asberg was born in N¨assj¨o, Sweden, on December 1, 1973. He recieved his MSc degree in engineering biology, with specialization towards microsystem technology and biosensors, from Link¨oping University, Sweden, in 2001. At present he is working as a PhD student at the Department of Physics, Measurement Technology, Biology and Chemistry, Link¨oping University, Sweden. His research interests are development of biosensors and biochips based on luminescent conjugated polyelectrolytes. Peter Nilsson was born in Link¨oping, Sweden, on February 7, 1970. He recieved his MSc degree in Chemistry from Kalmar University, Sweden, in 2000. At present he is working as a PhD student at the Department of Physics, Measurement Technology, Biology and Chemistry, Link¨oping University, Sweden. His research interests is to develop biosensors based on luminescent conjugated polyelectrolytes. Olle Ingan¨as, Professor of Biomolecular and organic electronics, IFM, Link¨opings Universitet. Born in Huddinge 1951, received a MSc in technical physics from Chalmers University of Technology 1977, a BSc in philosophy and economics from G¨oteborg University in 1978, and a PhD in applied physics at Link¨oping University in 1984. He was appointed docent in applied physics in 1989 and a professor in 1999. Ingan¨as has focused on studies of the class of conjugated polymers (conducting and emitting polymers) throughout areas of polymer physics, electrochemistry, electronics and optics, and covering topics of chromism in soluble conjugated polymers, electronic devices with semiconducting polymers, (transistors, diodes, light emitting diodes and photodiodes/solar cells) and electrochemical devices (energy storage in batteries and supercapacitors, smart windows and actuators). He has published ca. 280 papers and holds 20 patents and patent applications, and has contributed to the creation of 3 technology companies. Ingemar Lundstr¨om was born in 1941 in Skellefte˚a, Sweden. He received his PhD (in 1970) in electrical engineering (solid-state electronics) from Chalmers University of Technology, G¨othemburg, Sweden.

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He was an assistant professor at the Research Laboratory of Electronics, Chalmers, until 1978, when he was appointed as a professor at the technical faculty of Link¨oping University, Link¨oping, Sweden, where he now heads the Laboratory of Applied Physics. The laboratory, which has an interdisciplinary research staff, conducts research on chemical sensors and biosensors, catalysis, thin films, conductive polymers, surface

modifications, biomaterials and interface biology. Lundstr¨om is presently involved, in the research and development of high temperature chemical sensors, electronic noses and tongues, surface oriented biospecific interaction analysis and natural nanosystems such as pigment containing cells for biosensing purposes. Lundstr¨om has published about 450 scientific papers.