Calibrating single plasmonic nanostructures for quantitative biosening

Abstract

A method for calibrating multiple nanostructures in parallel for quantitative biosensing using a chip for localized surface plasmon resonance (LSPR) biosensing and imaging. The chip is a glass coverslip compatible for use in a standard microscope with at least one array of functionalized plasmonic nanostructures patterned onto it using electron beam nanolithography. The chip is used to collect CCD-based LSPR imagery data of each individual nanostructure and LSPR spectral data of the array. The spectral data is used to determine the fractional occupancy of the array. The imagery data is modeled as a function of fractional occupancy to determine the fractional occupancy of each individual nanostructure.

Claims

1. A method for calibrating multiple nanostructures in parallel for quantitative biosensing, comprising: fabricating at least one array of functionalized plasmonic nanostructures for localized surface plasmon resonance (LSPR) biosensing and imaging on a glass coverslip compatible for use in a standard microscope using electron beam nanolithography; simultaneously measuring CCD-based LSPR imagery data of each individual nanostructure and LSPR spectral data of the array; analyzing the LSPR spectral data of the array to determine the fractional occupancy of the entire array; analyzing the imagery data to determine the fractional occupancy of individual nanostructures; creating an image map to show a deviation between the fractional occupancy of the entire array and the fractional occupancy of individual nanostructures; and calibrating for quantitative biosensing only individual nanostructures within a defined deviation while ignoring nanostructures outside of the defined deviation.

2. The method of claim 1, wherein the functionalized plasmonic nanostructures comprise functionalized gold plasmonic nanostructures.

3. The method of claim 1, wherein the pitch between the arrays is in the range from 150 to 1000 nm.

4. The method of claim 1, wherein the size of the patterned nanostructures is in the range from 50 to 150 nm.

5. The method of claim 1, wherein the shape of the nanostructures comprises rectangles, squares, discs, ovals, or any combination thereof.

6. The method of claim 1, wherein the nanostructure arrays can be regenerated.

7. The method of claim 6, wherein the nanostructure arrays are regenerated by plasma ashing.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1(a) is a diagram of the imaging and spectroscopy setup in which P1 and P2 are crossed polarizers, BS is a 50/50 beam splitter and LP is a long pass filter with a 593 nm cutoff. The inset shows an AFM scan of a witness array fabricated on the same chip. FIG. 1(b) shows two spectra from a specific-binding study in which 200 nM of anti-c-myc was introduced over the c-myc functionalized array (10 L/min). The f.sub.initial spectrum (black) was taken before the anti-c-myc was introduced and the f.sub.final spectrum (gray) after one hour of exposure. FIG. 1(c) shows normalized spectra from 18 individual nanostructures taken in air. Individual spectra are superposed (gray curves) and compared to the ensemble average (black curve).

(2) FIG. 2 shows LSPR imaging time-course measurement from a 200 nM anti-c-myc specific binding study. FIG. 2(a) shows mean intensity for the entire array (square light gray ROI, 8484 pixels). FIG. 2(b) shows mean intensity of a single nanostructure (square black ROI, 44 pixels). FIG. 2(c) shows a comparison of the normalized responses of the whole array and the single nanostructure. Also plotted in FIGS. 2(a) and (b) are the results of a drift study that preceded the introduction of analyte (black squares). The inset images in FIGS. 2(a) and (b) show a contrast-enhanced CCD image of the array that highlights the variations in nanostructure intensities. All studies were conducted in serum-free medium at a flow rate of 10 L/min.

(3) FIG. 3(a) shows a background response map, B(r.sub.i) and FIG. 3(b) shows a linear response map, A(r.sub.i), of the array for the anti-c-myc harvested antibody study, calculated for every pixel in the image (ROI: 11 pixel). The calibration was conducted immediately following the harvested antibody study by injecting 250 nM of commercial anti-c-myc in serum-free medium. A grid of 44 pixel squares has been superimposed to demarcate the location of the nanostructures in the imagery.

(4) FIG. 4(a) shows an error estimate map of .sub.i for the anti-c-myc harvested antibody study, as calculated by a 44 pixel (410410 nm) sliding ROI window, plotted for deviations between f.sub.I and f.sub.S ranging from 0 to 0.1. Four plots of select ROIs are shown in FIGS. 4(b)-(e) to illustrate the deviations associated with a given .sub.i. The color of the ROI data points are matched with that of the ROI square label on the response map with (b) .sub.i=0.03 (c) .sub.i=0.05 (d) .sub.i=0.10 and (e) .sub.i=0.42. The vertical dashed line indicates when the 250 nM of commercial anti-c-myc was injected.

DETAILED DESCRIPTION OF THE INVENTION

(5) The present invention provides a localized surface plasmon resonance (LSPR) imaging and analysis technique that enables the calibration of hundreds of individual gold nanostructures in parallel. The calibration enables the mapping of the fractional occupancy of surface-bound receptors at individual nanostructures with a temporal resolution of 225 ms. Some features of this invention are (1) the fabrication of the arrays by electron-beam lithography for the production of highly uniform nanostructures, as confirmed by both size and spectral characterizations (2) the simultaneous measurement of spectra and imagery and (3) the combination of the spectral and imagery data into an analysis formalism that enables the determination of the fractional occupancy of surface bound receptors at individual nanostructures, f(r, t), where r denotes the location on the substrate and t denotes time. In addition, the technique is fully compatible with fluorescence and DIC microscopy techniques and can be used in complex fluid environments like serum free medium without degradation of the sensors.

(6) Using the present invention, the imagery of single nanostructures can encode the same information as the ensemble measurement of the entire array as measured spectroscopically. In other words, if the array is subjected to a uniform spatial distribution of analyte, the optical response of nanostructure-sized regions of interest (ROIs) can be calibrated to that of the ensemble fractional occupancy found by spectroscopy. It is not obvious that this should be possible since the image sums up all the spectral information and individual nanostructures can be subject to stochastic processes that average away when the entire array is used. Using an array of four hundred nanostructures, it was demonstrated that this technique allows for the qualitative detection of commercially available anti-c-myc antibodies with single nanostructure resolution using only a CCD camera. Using the same array of nanostructures, the calibration methodology that enables the quantification of the CCD-based measurements for the determination of f(r,t) is detailed. As a demonstration of this technique's applicability to molecular and cell biology, the calibrated array was used for quantitative LSPR imaging of anti-c-myc antibodies that were harvested from the hybridoma cell line 9E10 without the need for their further purification or processing. All experiments were conducted in the same serum free medium (SFM) used for cell culturing applications.

(7) Fabrication and Functionalization of the Nanostructures

(8) The nanostructures were fabricated on No. 1.5, 25.4 mm diameter borosilicate glass coverslips by electron-beam lithography (EBL). The structures were circular in cross section with a diameter 705 nm, 752 nm in height, and patterned into 2020 arrays with a spacing of 400 nm. The chip was cleaned by plasma ashing in 5% hydrogen, 95% argon mixture and then functionalized by immersion in a two-component thiol solution (0.5 mM), consisting of a 3:1 ratio of SH(CH.sub.2).sub.8-EG.sub.3-OH (SPO) to SH(CH.sub.2).sub.11-EG.sub.3-NH.sub.2 (SPN), for 18 hours (Prochimia, Poland). The SPN component of the SAM layer was first reacted with a 10 mg/mL solution of the heterobifunctional crosslinker sulfo-N-succinimidyl-4-formylbenzamide (Sulfo-S-4FB, Solulink) in 100 mM phosphate buffered saline (100 mM PBS, Thermo Scientific) and then conjugated to the c-myc peptide (HyNic-c-myc-tag, Solulink) according to the manufacturer's instructions. Anti-c-myc secreting hybridoma cells (MYC1-9E10.2, ATCC) were adapted to growth in serum-free medium containing 1% antibiotic/antimycotic by the sequential, stepwise reduction in fetal bovine serum content over a one-month culture period. The secretion of the 9E10.2 monoclonal antibodies (anti-c-myc) was confirmed and quantified by enzyme-linked immunosorbent assay (ELISA) using a BSA-c-myc peptide conjugate (7 c-myc peptides per BSA) as the immobilized antigen (coated overnight at 5 g/mL in 100 mM PBS at 4 C.). The details of the nanostructure fabrication by EBL, plasma ashing procedure and application of the SAM layer are described in M. P. Raphael et al., A New Methodology for Quantitative LSPR Biosensing and Imaging, Anal. Chem., 84(3), 1367-73 (2012).

(9) Optical Setup

(10) CCD-based LSPR imaging, as well as LSPR spectra, were collected in a reflected light geometry with an inverted microscope (Zeiss Axio Observer) using Koehler illumination, a 63, 1.4 numerical aperture (NA) oil-immersion objective and crossed-polarizers to reduce the background contribution from substrate-scattered light. Imagery and spectra were obtained simultaneously by placing a beam splitter at the output port of the microscope (FIG. 1a) and a long-pass filter with a 593 nm cut-off wavelength was placed before the CCD camera. For the spectral measurements, the focused image of the entire nanostructure array was projected on to the end of a 600 m diameter optical fiber and the spectra were subsequently measured with a thermoelectrically-cooled, CCD-based spectrophotometer (Ocean Optics QE65000). The spectrophotometer integration time was 4 seconds. For image acquisition, the focused image of the array was projected on to a thermoelectrically-cooled CCD camera with 6.456.45 m sized pixels (Hamamatsu ORCA R.sup.2) and a frame integration time of 225 ms. Details of how the above setup was optimized for high contrast imaging of the gold nanostructures are described in M. P. Raphael et al., A New Methodology for Quantitative LSPR Biosensing and Imaging, Anal. Chem., 84(3), 1367-73 (2012). Analyte was introduced under continuous flow conditions using a custom-made microfluidic cell at a flow rate of 10 L/min. The microscope stage was equipped with a temperature controlled insert which kept the stage temperature and optical light train at 28.00.04 C. (PeCon GmbH). Under these conditions, the drifts in the x, y and z directions were less than 3 nm/min. For data analysis, all frames were aligned in x and y using a commercially available image-processing alignment algorithm (Zeiss Axiovision).

(11) Qualitative Biosensing with Single Nanostructures

(12) The imagery as well as LSPR spectra were simultaneously acquired by passing the reflected light through a 50/50 beam splitter (BS) as shown in FIG. 1(a). FIG. 1(b) shows two spectra from a specific-binding study in which 200 nM of anti-c-myc was introduced over a c-myc functionalized array at a flow rate of 10 L/min. The f.sub.initial spectrum (black) was taken before the anti-c-myc was introduced and the f.sub.final spectrum (gray) was taken after one hour of exposure. The characteristic gray-shift of the peak position and the corresponding increase in counts from 605 nm to 750 nm are indicative of a local change in the dielectric constant of the medium caused by the specific binding of the anti-c-myc antibody. (A. B. Dahlin et al., Improving the instrumental resolution of sensors based on localized surface plasmon resonance, Anal. Chem., 78, (13), 4416-23 (2006) and W. P. Hall et al., A Conformation- and Ion-Sensitive Plasmonic Biosensor, Nano Lett., 11, (3), 1098-1105 (2011)). While monitoring the peak shift is currently the most common method of detecting analyte binding, it has been shown that it is the increase in the scattered intensity over such a large portion of the resonance spectrum that allows for the fractional occupancy of the array to be determined spectroscopically, while simultaneously enabling LSPR imaging via the CCD camera. (M. P. Raphael et al., A New Methodology for Quantitative LSPR Biosensing and Imaging, Anal. Chem., 84(3), 1367-73 (2012)).

(13) The calibration of hundreds of nanostructures in batch mode requires that the spectral properties of individual nanostructures closely resemble that of the array ensemble average. Topological studies of the nanostructures by AFM revealed small variations in nanostructure shape due to the fabrication process (FIG. 1(a) inset). To investigate the corresponding spectral variations amongst individual nanostructures, single-nanostructure spectroscopy using dark-field microspectroscopy was conducted in air on a witness row of 18 nanostructures fabricated on the same chip as the array used for biosensing. The results, summarized in FIG. 1(c), show that while small variabilities in the shape, amplitude, and resonant wavelength could be discerned, the spectrum of nearly every nanostructure fell close to the ensemble average. In the CCD imagery, these distributions can be manifested as a distribution of intensities, as shown in the FIG. 2 insets, which have been contrast enhanced to highlight the intensity variations.

(14) FIG. 2 details the time course of a 200 nM anti-c-myc specific binding study as measured by LSPR imagery and demonstrates a straightforward image analysis technique for qualitatively monitoring the kinetics down to the single nanostructure. FIG. 2(a) shows the enhanced counts from binding for the entire array (8484 pixels) as calculated from the mean intensity of the pixels bounded within the light gray region of interest (ROI) square:

(15) $\begin{array}{cc}I\left(r_i,t_n\right)=\frac{1}{m_i}\underset{\stackrel{.fwdarw.}{x}{r}_i}{\overset{}{.Math.}}{I}_{\mathrm{image}}\left(\stackrel{->}{x},t_n\right)& \left(1\right)\end{array}$
where m.sub.i is the number of pixels in the ROI denoted as r.sub.i and t.sub.n is the time point. Also shown is a drift study that preceded the introduction of analyte (black squares) in which SFM flowed over the array for 30 minutes at 10 L/min. In contrast to simple buffers, SFM typically contains anywhere from 50 mg/L to 1000 mg/L of additional proteins such as albumin, transferrin and insulin, which can potentially bio-foul the sensors. Despite this presence, the measurements demonstrated minimal drift, and sensitivity to analyte was retained. Additional studies demonstrating minimal non-specific binding studies between the antibody and the SAM-functionalized surface were conducted using a Bio-Rad XPR36 surface plasmon resonance instrument.

(16) FIG. 2(b) plots the same two experiments but with the ROI now comprising only a single nanostructure, as selected by a 44 pixel (410410 nm) square ROI shown in black near the center of the array. The relative response of the nanostructure is compared directly to that of the entire array in FIG. 2(c) by plotting the normalized counts: [I(r.sub.i,t.sub.n)I(r.sub.i, t.sub.o)]/[I(r.sub.i,t.sub.f)I(r.sub.i,t.sub.o)] where I(r.sub.i,t.sub.o) is the average of the first twenty time points and I(r.sub.i,t.sub.f) is the average of the last twenty time points. This same straightforward ROI approach can be applied to any nanostructure in the array, giving four hundred independent and label-free nanosensors within the 8 m8 m area.

(17) FIG. 2(c) highlights that the response within the nanoscale ROIs are typically in excellent agreement with that of the entire array. Thus, if the entire array can be calibrated for the determination of the fractional occupancy, f(t), this homogeneous response in principle can be utilized to simultaneously calibrate the individual nanostructures. This at first can seem surprising given the likelihood that the surface-bound receptors on any given nanostructure will have an inhomogeneous spatial distribution and response to analyte. (K. M. Mayer et al., A single molecule immunoassay by localized surface plasmon resonance, Nanotechnology, 21, (25) (2010) and G. J. Nusz et al., Label-free plasmonic detection of biomolecular binding by a single gold nanorod, Anal. Chem., 80, (4), 984-989 (2008)). It is reasonable, however, given that both the theory of random sequence adsorption (E. L. Hinrichsen et al., Geometry of random sequential adsorption, J. Stat. Phys., 44, (5-6), 793-827 (1986)) and experimental estimates using similarly functionalized gold surfaces (S. Chen et al., Plasmon-Enhanced Colorimetric ELISA with Single Molecule Sensitivity, Nano Lett., 11, (4), 1826-1830 (2011) and L. S. Jung et al., Binding and dissociation kinetics of wild-type and mutant streptavidins on mixed biotin-containing alkylthiolate monolayers, Langmuir, 16, (24), 9421-9432) (2000)) are in agreement that nanostructures of this size can accommodate hundreds of proteins, thus averaging out the effect of such inhomogeneities. Next is the discussion on how this qualitative observation can be expanded into a data analysis formalism that allows for the quantitative determination of the fractional occupancy within the imagery.

(18) Quantitative Biosensing with Single Nanostructures

(19) We have previously reported a methodology that allows for the determination of the fractional occupancy of the entire array from the spectral data, denoted here as f.sub.s(t). (M. P. Raphael et al., A New Methodology for Quantitative LSPR Biosensing and Imaging, Anal. Chem., 84(3), 1367-73 (2012)). In short, the number of counts at wavelength, , accumulated by the spectrometer during time interval t.sub.n can be written as a linear response model with Poisson counting noise, .sub.Poisson, in terms of the f.sub.s for a specifically-bound monolayer perturbing the localized surface plasmon resonance:
N.sub.(t.sub.n)=g(t.sub.n).Math.[a.sub.f.sub.S(t.sub.n)+b.sub.]+.sub.Poisson(2)
The model parameters a.sub. and b.sub. represent the wavelength-dependent dielectric response caused by the bound analyte and the initial background of the LSPR array, respectively. The overall time-dependent coefficient, g(t.sub.n), is initially set to one at the beginning of the experiment but can account for drift and jump processes that cause variations in the scattered light intensity with no wavelength dependence. Generally, g(t.sub.n)1 and can be ignored in most situations. For a given nanostructure array and experimental conditions the b.sub. is determined when no analyte is present at the beginning of the experiment and the a.sub. is determined by the injection of a saturating injection of known concentration at the end of the experiment. The time-dependent functions g(t.sub.n) and f.sub.S(t.sub.n) can be determined by non-linear regression within a Poisson noise model for the counts at each time interval, t.sub.n.

(20) To assess whether the imagery of single nanostructures can capture the fractional occupancy information found in whole-array spectroscopy, a simple generative model is proposed for how the image data is formed, similar to that used in analyzing the spectroscopy data:
I(r.sub.i,t.sub.n)=A(r.sub.i).Math.f.sub.I(r.sub.i,t.sub.n)+B(r.sub.i)+.sub.Poisson(3)
Here the model parameters a.sub. and b.sub. are analogously represented by the spatially dependent parameters A(r.sub.i) and B(r.sub.i) for the determination of the fractional occupancy from the image data, f.sub.I(r.sub.i,t.sub.n). Since the size of the array is small compared to the diffusion length of the analyte over the exposure time of the CCD camera, the concentration is effectively uniform. This allows A(r.sub.i) and B(r.sub.i) to be determined via multi-variate linear regression by setting f.sub.I(r.sub.i,t.sub.n)=f.sub.S(t.sub.n), thus, calibrating the entire array via imagery. Once the array is calibrated, inhomogeneous fractional occupancy can be estimated as:

(21) $\begin{array}{cc}{\hat{f}}_I\left(r_i,t_n\right)=\frac{I\left(r_i,t_n\right)-B\left(r_i\right)}{A\left(r_i\right)}& \left(4\right)\end{array}$
In order to determine if this relatively simple treatment of the imagery data is effective, the variance in the local response, {circumflex over (f)}.sub.I(r.sub.i,t.sub.n), of the nanostructure array from f.sub.S(t.sub.n) over the ROIs can be calculated:

(22) $\begin{array}{cc}{}^2\left(r_i\right)=\frac{1}{N}\underset{n=1}{\overset{N}{.Math.}}{.Math.{\hat{f}}_I\left(r_i,t_n\right)-f_S\left(t_n\right).Math.}^2& \left(5\right)\end{array}$
The resulting image map will show what parts of the array are capable of being calibrated for the determination of the local fractional occupancy.

(23) As an example of the applicability of this approach to molecular and cell biology, the determination of fractional occupancy versus time at the nanoscale to the secreted antibodies contained within the supernatant of cultured MYC1-9E10.2 hybridoma cells was demonstrated. The harvested antibodies experiment was conducted by simply centrifuging the cells at 3000 rpm for 5 minutes, collecting the supernatant and applying that solution to the nanostructures via the microfluidic setup at a flow rate of 10 L/min. The concentration of secreted anti-c-myc antibodies in the supernatant was independently determined by ELISA to be 9 nM. The nanostructure calibration was conducted as described above by introducing a known concentration of commercial anti-c-myc (250 nM) over the nanostructures immediately following the harvested antibody experiment.

(24) FIG. 3 displays the B(r.sub.i) and A(r.sub.i) response maps of a 12.412.4 m area centered about the array for every pixel in the image (ROI: 11 pixel). A grid of 44 pixel (410410 nm) squares has been superimposed over each map to demarcate the location of each nanostructure from the imagery. In FIG. 3(a), the dark regions of the coefficient map for the background term, B(r.sub.i), are highly correlated with the brightest nanostructures in the CCD image (FIG. 2 inset), as is to be expected for the background contribution to the fit. The coefficient map for the linear response term, A(r.sub.i), is shown in FIG. 3(b). Again the strongest positive responses are located within the squares of the grid and thus are correlated with the locations of the nanostructures. The optimization was repeated using a sliding ROI window of the same size as the grid (44 pixels) which had the advantage of closely approximating the size of the diffraction-limited image of the nanostructures. In addition, this larger ROI averaged out the presence of a slight drift of 2 pixels (1.7 nm/min) which occurred over the course of the 2 hour run.

(25) The results of the 44 pixel sliding ROI calibration and analysis are summarized in FIG. 4. FIG. 4(a) shows the error estimate map of which presents the deviations between f.sub.I and f.sub.S as a grey-scale map. The shape of the array is clearly reproduced on the map due to the fact that it is only within the array area that f.sub.I and f.sub.S are within reasonable agreement. In fact, by setting the scale of .sub.i on the map to be between 0 and 0.1 we show that over 75% of the area encompassed by the array can be well calibrated with the spectroscopically determined fractional occupancy. To illustrate the deviations between f.sub.I and f.sub.S associated with a given the data from specific ROIs are plotted in FIGS. 4(b)-(e) with .sub.i=0.03, .sub.i=0.05, .sub.i=0.10, and .sub.i=0.42, respectively. The color of the ROI data points are matched with that of the ROI square label on the response map and the vertical dashed line indicates the end of the harvested anti-c-myc antibody run, at which point 250 nM of commercial anti-c-myc was injected. There is excellent agreement between the two for .sub.i0.05, which deteriorates somewhat at .sub.i=0.1, while there is no statistically meaningful correlations for the ROI located outside of the array.

(26) The results in FIG. 4 demonstrate that for the majority (over 75%) of the array, f.sub.I can be determined to within 10% of f.sub.S using ROIs of a similar size to that of the diffraction limited image of each nanostructure. Even with the reproducibility of fabricating by EBL, however, it was not possible to calibrate the entire array area to within this range of error. This is not an impediment though since a great advantage to this approach is that those ROIs which do not calibrate to within a set specification can simply be ignored while those that do can be used for the quantitative analysis. As such, this LSPR imaging technique allows for label-free and quantitative characterization of cell supernatant with minimal preparation and nanomolar sensitivity, utilizing hundreds of nanostructures independently calibrated to within the user's specification. In its current form, this technology sets the stage for future applications in high density proteomics arrays as well as for imaging analyte concentration gradients in complex live cell environments.

(27) The above descriptions are those of the preferred embodiments of the invention. Various modifications and variations are possible in light of the above teachings without departing from the spirit and broader aspects of the invention. It is therefore to be understood that the claimed invention may be practiced otherwise than as specifically described. Any references to claim elements in the singular, for example, using the articles a, an, the, or said, is not to be construed as limiting the element to the singular.