Plasmonic Nanoprobes for in Vivo Multimodal Sensing and

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Biological and Medical Applications of Materials and Interfaces

Plasmonic nanoprobes for in vivo multimodal sensing and bioimaging of microRNA within plants Bridget M Crawford, Pietro Strobbia, Hsin-Neng Wang, Rodolfo Zentella, Maxim I. Boyanov, Zhen-Ming Pei, Tai-ping Sun, Kenneth M. Kemner, and Tuan Vo-Dinh ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.8b19977 • Publication Date (Web): 29 Jan 2019 Downloaded from http://pubs.acs.org on January 30, 2019

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ACS Applied Materials & Interfaces

Plasmonic nanoprobes for in vivo multimodal sensing and bioimaging of microRNA within plants Bridget M. Crawford1,2,‡ ; Pietro Strobbia1,2,‡ ; Hsin-Neng Wang1,2; Rodolfo Zentella3; Maxim I. Boyanov4,5; Zhen-Ming Pei3; Tai-Ping Sun3; Kenneth M. Kemner5; Tuan Vo-Dinh1,2,6(*) 1

Fitzpatrick Institute for Photonics, 2Department of Biomedical Engineering, 3Department of

Biology, Duke University Durham, NC, USA. 4

Bulgarian Academy of Sciences, Institute of Chemical Engineering, Sofia, 1113, Bulgaria

5

Biosciences Division, Argonne National Laboratory, Argonne, IL 60439, USA

6

Department of Chemistry, Duke University, Durham, NC, USA.

‡

B.M.C. and P.S. contributed equally to this work

*

Corresponding author: [email protected]

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ABSTRACT Monitoring gene expression within whole plants is critical for many applications ranging from plant biology to agricultural biotechnology and biofuel development; however, no method currently exists for in vivo monitoring of genomic targets in plant systems without requiring sample extraction. Herein, we report a unique multimodal method based on plasmonic nanoprobes capable of in vivo imaging and biosensing of microRNA biotargets within whole plant leaves by integrating three different and complementary techniques: surface-enhanced Raman scattering (SERS), X-ray fluorescence (XRF), and plasmonics-enhanced two-photon luminescence (TPL). The method developed uses plasmonic nanostars, which not only provide large Raman signal enhancement, but also allow for localization and quantification by XRF and plasmonics-enhanced TPL, owing to gold content and high two-photon luminescence cross-sections. Our method uses inverse molecular sentinel (iMS) nanoprobes for SERS bioimaging of microRNA within Arabidopsis thaliana leaves to provide a dynamic SERS map of detected microRNA targets while also quantifying nanoprobe concentrations using XRF and TPL. The nanoprobes were observed to occupy the intercellular spaces upon infiltration into the leaf tissues. This report lays the foundation for the use of plasmonic nanoprobes for in vivo functional imaging of nucleic acid biotargets in whole plants, a tool that will revolutionize bioengineering research by allowing the study of these biotargets with previously unmet spatial and temporal resolution, 200μm and 30 minutes respectively. KEYWORDS: plasmonics, bioimaging, gold nanostar, surface enhanced Raman scattering, microRNA


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1. Introduction There is a critical need to monitor gene expression in whole plants for a wide variety of important applications, ranging from fundamental plant biology research to biomass development for renewable energy. A class of small non-coding RNAs, called microRNAs (miRNAs), has been shown to regulate gene expression in plant developmental processes.1 In Arabidopsis, miR156 regulates the timing of the juvenile-to-adult transition by coordinating the expression of several pathways (e.g., endogenous flowering pathway). The timing to flower is one of the key determinants to plant biomass accumulation and agricultural yields, therefore the knowledge over its regulation mechanism can allow plant engineering to favor growth over reproduction and produce more biomass. However, traditional approaches for RNA analysis do not allow for in vivo monitoring of the expression of these floral genes in living plant tissues with appropriate spatial and temporal resolution in order to understand the regulation of this pathway throughout the whole organism. Such methods include RNA blot analysis, quantitative reverse transcription PCR (qRTPCR), microarrays, and RNA ligase-mediated amplification of cDNA ends.2-6 Although some of these methods provide quantitative data, sample extraction may be required, thereby limiting the ability to provide information regarding variations in gene expression across space and time. Recent work has highlighted the promising incorporation of nanomaterials into plant bioengineering research.7-8 Particularly, plasmonics-active metallic nanostructures and nanoparticles have allowed for the trace detection of biotargets in many fields, ranging from biomedical to defense applications.9-17 Among other characteristics, this class of materials has been associated with a large enhancement (i.e., 107-10) of Raman scattering of molecules adsorbed onto or in-close-proximity to the metal surface.16, 18-19 This phenomenon, known as surface-enhanced 3 ACS Paragon Plus Environment


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Raman scattering (SERS), has enabled the development of plasmonic nanosensors that allow for highly multiplexed and sensitive detection of biotargets, due to the narrow bandwidth of Raman spectral features.15-16,

20

A unique class of SERS nanobiosensors, termed inverse molecular

sentinels (iMS), for direct detection of nucleic acid biotargets.

21-24

The iMS design utilizes

plasmonic-active silver-coated gold nanostars (AuNS@Ag) functionalized with DNA strands. The functional DNA is designed to change conformation in the presence of a specific biotarget, exploiting the distance dependence of the SERS signal as a transduction mechanism. The diagnostic capabilities of these nanobiosensors have been demonstrated by detecting variations in expression of miRNA in different cancer cell lines,22 as well as in a “smart tattoo” biosensor where iMS nanoprobes were used to detect synthetic nucleic acid targets in a large animal model in vivo.24 Herein, we demonstrate the feasibility of our multifunctional plasmonic-active nanoprobes for in vivo SERS imaging as well as for monitoring spatial and temporal miRNA biomarker concentration profiles, particularly related to studies of plant biosystems. An important aspect for accurate mapping of spatial and temporal variations in target concentration is the awareness of the number of probes in each imaged pixel. Current in vitro detection methods assume isotropically distribution of nanoprobes within the sample under analysis; however, when performing imaging in vivo, particle distribution is unpredictable and possibly far from isotropic. Therefore, a procedure to normalize the nanobiosensor response to the number of nanoprobes observed in each pixel is required. This result can be achieved by combining our SERS biosensing technique with nanoparticle localization strategies based on phenomena orthogonal to the sensing mechanism, such as XRF and plasmonics-enhanced TPL.25-26


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In this work, we demonstrate the combination of Raman imaging of SERS-tags and iMS nanobiosensors with XRF and plasmonics-enhanced TPL to localize the nanoparticles and detect miRNA biotargets. Initially, we characterize iMS nanoprobes designed for the detection of miR156 in vitro. Using TEM, the nanoprobes were observed to occupy the intercellular spaces upon infiltration into the leaf tissues, allowing the sensing of extracellular miRNA. To characterize the capability of a multimodal technique, we used SERS nano-tags, showing that SERS, TPL and XRF mapping can be used to detect nanostars. Further studies demonstrate how the same method can be used to map the iMS nanoprobes in vivo while detecting the presence of target miRNA. These studies involved the infiltration of nanoprobes and target (ON) or only nanoprobes (OFF). The nanoparticles concertation was then used to normalize the SERS signal for accurate target detection. Finally, we demonstrated how the iMS nanoprobes can be used to detect an increase in target concentration in vivo, comparing the signal obtained from OFF nanoprobes with that of OFF nanoprobes exposed to increasing target concentration. This research sets the basis for functional in vivo imaging of nucleic acids in plants, a tool to potentially revolutionize bioengineering research by allowing the study of such biotargets with previously unmet spatial and temporal resolution. These studies contribute significantly to the field of bioengineering by providing a new tool to study the pathways responsible for plant behavior in response to stimuli. Such a tool will assist design of strategies to control flowering time and increase biomass accumulation, with clear implications for the field of biofuels production. 2. Results and Discussion 2.1 SERS-tag and iMS nanoprobe characterization in vitro



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Gold nanostars (AuNS) and silver-coated gold nanostars (AuNS@Ag), with respective diameters of 53 nm and 70 nm as measured using nanoparticle tracking analysis (NTA 2.1, build 0342), were synthesized by a surfactant-free method with morphologies consistent with previous reports.27-28 TEM micrographs were acquired using the FEI Tecnai G2 twin transmission electron microscope (Hillsboro, OR) to confirm morphology consistent as previously reported.27 To demonstrate stability of SERS-tags, and iMS ‘OFF’ and ‘ON’ nanoprobes over the time employed throughout the optical measurements, the particle absorbance in buffer was monitored over a period of 24 hours. Additionally, the stability of the nanoprobes within cell lysate was monitored over 24 hours to ensure no degradation was occurring. No significant shift in absorption spectra was observed over time as demonstrated in Figure S1 in Supporting Information. To demonstrate the detection of miRNAs using the iMS technique, an iMS nanoprobe labeled with Cy7 dye was designed to detect miR156, a miRNA associated with the shift between life phases in Arabidopsis. As shown in Figure 1, in the absence of miR156 synthetic targets (labeled as ‘iMS OFF’), the SERS signal of the iMS nanoprobes is low, indicating that the placeholder strand effectively keeps the Cy7 Raman-active dye away from the AuNS@Ag nanoparticle surface. In contrast, in the presence of miR156 target (labeled as ‘iMS ON’), the SERS signal is significantly increased, indicating the successful strand displacement of the placeholder-probe complex and formation of stem-loop conformation, thereby turning the SERS signal ‘ON.’ The specificity of the iMS was also investigated by incubating miR156 iMS nanoprobes in solutions containing the miR156 target, miR858 (i.e., another miRNA found in Arabidopsis), or the buffer


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(blank control). As shown in Figure S2 in Supporting Information, miR156 iMS nanoprobes change conformation to turn the SERS signal ‘ON’ only in the presence of miR156 target.

Figure 1. (A) A schematic representation of the iMS sensing mechanism. (B) Detection of miR156 target using iMS Nanoprobes labeled with Cy7. SERS response of iMS nanoprobes in absence of target (OFF, blue spectrum) and in the presence of 2 μM synthetic miR156 target DNA (ON, red spectrum).

The timing for iMS nanoprobes to hybridize to target and change from a linear conformation to a stem-loop structure is an important characteristic as it will influence the temporal resolution of the technique. We investigated this timing by monitoring the iMS nanoprobe SERS signal over a period of 45 min, after target addition in vitro. As shown in Figure S3 in Supporting Information, conformation change occurs for most of the probes in solution within 15 minutes, observable as the full signal change. Therefore, to ensure that the nanoprobes have sufficient time to change conformation, the following studies employed a 30 minutes time period for target incubation prior to Raman measurements. Another important sensing aspect of the nanoprobes is the limit of optical detection (LOD) of the SERS sensing technique for miRNA detection, using the iMS nanoprobes. The LOD is defined as the lowest amount of analyte producing a signal equal to 3 times the standard deviation (SD) of 7 ACS Paragon Plus Environment


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the background noise. This LOD represents the ultimate detection limit for sensing miR156 using iMS nanoprobes, taking into account only optical sources of variability. Figure 2 shows the spectral region containing the Cy7 peaks of the blank-subtracted SERS spectrum for iMS nanoprobes incubated with 100 fM of synthetic target (a) and a peak-free spectral region of the blank spectrum, displaying the noise level in the background (b). As shown in Figure 2, the peak intensity observed for 564 cm-1 was of 151 counts (Figure 2a) whereas the SD of the blank spectrum was 29 counts (Figure 2b). Through extrapolation, the actual LOD for the technique was estimated to be 60 fM.

Figure 2. (A) Blank-subtracted SERS spectra for iMS nanoprobes incubated with 100 fM of synthetic target within the spectral region containing the Cy7 peaks of interest. (B) Peak-free spectral region of the blank spectrum, displaying the noise level in the spectrum.

It is noteworthy that the concentration of miRNA found in animal cells is within the 100 pM to

1nM range.29 Although much less work has been dedicated to quantify concentration of miRNA in plants, a report by Huang et al. detected miRNA via qRT-PCR in crops tissues and found a concentration range of approximately 0.1-10 pM.30 This range is within the of the current 8 ACS Paragon Plus Environment

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technique. Furthermore, miRNA can be upregulated in response to a stimulus, as is the case for transgenically modified plants that are used to study regulatory pathways in plants. For instance, in biomass-optimization studies, the level of transgenic overexpression for miR156 can reach a factor of more than 100 relative to its original concentration.31 The current design for iMS nanoprobes is irreversible and will detect an increase of target miRNA concentration over time, but not a knockdown in expression. For proof-of-concept of the developed technique, a target microRNA known to be overexpressed during plant development, miR156 was selected. The overexpression of this miRNA can favor plant growth of biomass over reproduction, making the ability to monitor its expression important for biofuel research. 2.2 Nanoprobe subcellular localization within Arabidopsis leaves To characterize the nanoprobes in planta, a suspension of nanoprobes was infiltrated into the apoplastic spaces of Arabidopsis leaves. To confirm the presence of AuNS-based nanoprobes within

plant

tissue,

transmission

electron microscopy (TEM) imaging was performed. Figure 3 shows the presence of AuNS within the middle lamella and other intercellular spaces within leaves. To further validate the successful

infiltration

of

iMS

Figure 3. Transmission electron microscopy image of AuNS-based nanoprobes within the middle lamella of Arabidopsis leaf sections. B and D are zoom-in micrographs of regions from A and C, respectively showing more clearly the location and shape of the nanoprobes. The red arrows identify the cell walls of two adjacent cells.



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nanoprobes within plant tissues, a 2D projection of a TPL image across the entire depth of an Arabidopsis leaf containing iMS nanoprobes is provided in Figure S4 in Supporting Information. The presence of nanoprobes in the middle lamella, or apoplastic space, allows for these SERS sensors to detect miRNA targets when transported apoplastically. The apoplastic transport process refers to the transport of biomolecules through the cell walls as opposed to the symplastic pathway where transport occurs through the cytoplasm via plasmodesmata. Non-coding nucleic acids, such as miRNA, are used for signaling purposes in plants and are transported throughout the organism for this purpose. Current literature has suggested that these molecules are transported both apoplastically and through plasmodesmata; however, due to a lack of tracking techniques for miRNA biotargets in vivo, these questions remain unanswered.32-33 We believe that our nanoprobes can be used to answer such questions regarding transport of miRNA within plant tissues. 2.3 In vivo Raman detection of SERS-tags and co-registration via XRF and TPL AuNS functionalized with Cy7.5 (referred to herein as SERS-tags) were used for proof-ofconcept validation of the co-localization through multimodal imaging of leaves. To demonstrate the co-localization of nanoparticles through multimodal imaging, Arabidopsis leaves were infiltrated with SERS-tags and mapped with three different imaging techniques (SERS, TPL and XRF). Infiltration was accomplished through needleless injection of a 1 nM suspension of SERStags. The Raman signal from the entire infiltrated leaf was mapped using the lab-built Raman microscope with a resolution of 600 µm, correlating the map with a bright field image of the leaf, followed by TPL imaging of the same area. After the completion of the optical measurements, the leaf was encapsulated within Kapton tape and imaged by XRF. Results from multiple leaves (not all shown) demonstrate that the nanoparticles distribution did not shift between measurements. 10 ACS Paragon Plus Environment

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Figure 4 shows the SERS-tag infiltrated leaf imaged with the various techniques. Figure 4a shows the superposition of the Raman intensity map of the leaf, calculated as the background subtracted intensity of the 927 cm-1 peak of Cy7.5 in each pixel, over the bright field image of the leaf. Figure 4b shows the spectra from two different pixels of the Raman intensity map: one from a location infiltrated with SERS-tags and one from a non-infiltrated region of the leaf. The spectra show that the characteristic peaks of Cy7.5 are only observable where the particles are present. Figures 4c and 4d provide the TPL and XRF images, respectively, to demonstrate location and quantification of SERS-tags.

Figure 4. Multifunctional SERS-tags within an A. thaliana leaf. (A) Superposition of the Raman intensity map of the infiltrated leaf, calculated as the background subtracted intensity of the 927 cm-1 peak of Cy7.5 in each pixel, over the bright field image of the leaf. (B) Spectra from two different pixels of the Raman intensity map: one from the location infiltrated with SERS-tags (purple) and one from a non-infiltrated region of the leaf (black). (C) TPL map superimposed over bright field image of the leaf (SERStags appear white) and (D) Au XRF image to demonstrate location and quantification of SERS-tags.

These results establish the potential of plasmonic-active nanoparticles to be successfully colocalized and quantified with different imaging techniques. The AuNS-based SERS-tags were chosen for these initial co-localization experiments due to the greater TPL cross-sections of AuNS 11 ACS Paragon Plus Environment


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compared to silver-coated gold nanostars (AuNS@Ag). Following experiments utilized AuNS@Ag, which allow for greater Raman enhancement for iMS nanoprobe sensing within leaves. 2.4 In vivo SERS detection of iMS nanoprobes and co-registration via XRF and TPL The feasibility of sensing miRNA with iMS nanoprobes while conserving spatial information was demonstrated by imaging the ‘OFF’ and ‘ON’ iMS nanoprobes within Arabidopsis leaves. The leaf was infiltrated with ‘OFF’ nanoprobes at a concentration of 1 nM and mapped with the Raman system and XRF. As evidenced by Figure 1, in the ‘open’ state, iMS nanoprobes have very low Raman signal from the reporter molecule (Cy7), due to the distance of the molecule from the metallic nanoparticle surface. To produce an ‘ON’ signal, the ‘OFF’ iMS nanoprobes were exposed to 0.2 µM miR156 synthetic target for 30 min before infiltration into the leaf of Arabidopsis. Figure 5a and 5d show the superimposed Raman intensity map of the background subtracted intensity of the 564 cm-1 peak of Cy7 in each pixel over the bright field image of the leaf for a leaf infiltrated with ‘OFF’ and ‘ON’ iMS nanoprobes, respectively. These Raman maps have the same resolution of the previous experiment (i.e., 600µm). Figure 5b and 5e provide spectra from two different pixels within each Raman intensity map for iMS OFF (b) and iMS ON (e). One spectrum was taken from the location infiltrated with iMS nanoprobes and the other from a non-infiltrated region of the leaf. The spectra from the region of the leaf without infiltration did not show any Raman peaks, while the infiltrated areas show the characteristic Cy7 Raman peaks, but with large differences in intensities. The comparison of these spectra with that shown in Figure 1 demonstrates that the function of the iMS nanoprobe sensing mechanism has been conserved 12 ACS Paragon Plus Environment

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following infiltration. Figure 5c and 5f display the Au XRF maps of the infiltrated leaves following encapsulation within Kapton tape. As can be observed, the Au XRF images demonstrate that both leaves contain the AuNS@Ag nanoparticles of iMS nanoprobes; however, only the Raman map relative to the ‘ON’ iMS nanoprobes shows significant response and agreement with the XRF image. This result establishes how these particles can potentially be used for sensing of miRNA while conserving spatial information (i.e., functional imaging), as only the particles that were in contact with the target nucleic acids provide significant Raman signal.

Figure 5. Multifunctional iMS nanoprobes for detection of miR156 within an A. thaliana leaf. Superimposed Raman intensity map of the background subtracted intensity of the 564 cm-1 peak of Cy7 in each pixel over the bright field image for a leaf infiltrated with (A) ‘OFF’ (- miR156) and (B) ‘ON’ (+ miR156) iMS nanoprobes. Spectra from two different pixels within each Raman intensity map for iMS OFF (C) and iMS ON (D): one from the location infiltrated with iMS nanoprobes and one from a noninfiltrated region of the leaf. Au XRF images to demonstrate location and quantification of iMS nanoprobes in the leaves for ‘OFF’ (E) and ‘ON’ (F) nanoprobes.



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These results were further explored using higher resolution Raman mapping (200µm resolution) in combination with co-registering nanoparticles not only by XRF, but also through TPL. Similar to Figure 5, Figures 6 and 7 demonstrate multimodal imaging of Arabidopsis leaves infiltrated with the negative and positive controls of ‘OFF’ and ‘ON’ iMS nanoprobes, respectively. Figures 6a and 7a show the superimposed Raman intensity map of the background subtracted intensity of the 516 cm-1 peak of Cy7 in each pixel over the bright field image of the leaf for a leaf infiltrated with ‘OFF’ and ‘ON’ iMS nanoprobes, respectively. Both the 516 and the 564 cm-1 peaks are specific to Cy7 and sensitive to the target presence. In the high resolution Raman maps, the higher intensity 516 cm-1 peak was chosen for analysis to ensure that the infiltrated area could be visualized via the baseline signal from the ‘OFF’ iMS. The difference in SERS response when using high resolution as compared to low resolution is caused by lower sensitivity of high

Figure 6. Multifunctional iMS nanoprobes for detection of miR156 within an A. thaliana leaf. (A) Superimposed Raman intensity map of the background subtracted intensity of the 516 cm-1 peak of Cy7 in each pixel over the bright field image for a leaf infiltrated with ‘OFF’ (- miR156) iMS nanoprobes. (B) Spectra from two different pixels of the Raman intensity map: one from the location infiltrated with ‘OFF’ iMS nanoprobes (blue) and one from a non-infiltrated region of the leaf (black). (C) TPL map of the infiltrated leaf (iMS nanoprobes appear gold) and (D) Au XRF image to demonstrate location and quantification of the nanoprobes.


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resolution images due to the decreased laser power used to form a smaller laser spot. Figures 6c and 7c demonstrate the TPL capability of AuNS@Ag as well as strong correlation between TPL and XRF (Figures 6d and 7d). TPL imaging does not require fixing of the leaves between Kapton tape and can therefore be accomplished in a nondestructive manner. Incorporating TPL within this study alongside XRF will allow for future use of TPL mapping to account for particle concentration variation in SERS signal. As it can be observed, while both the leaves show the presence of the iMS nanoprobes from XRF and TPL data, only the leaf containing the target shows strongly enhanced Raman signal.

Figure 7. Multifunctional iMS nanoprobes for detection of miR156 within an A. thaliana leaf. (A) Superimposed Raman intensity map of the background subtracted intensity of the 516 cm-1 peak of Cy7 in each pixel over the bright field image for a leaf infiltrated with ‘ON’ (+ miR156) iMS nanoprobes. (B) Spectra from two different pixels of the Raman intensity map: one from the location infiltrated with ‘ON’ iMS nanoprobes (red) and one from a non-infiltrated region of the leaf (black). (C) TPL map of the infiltrated leaf (iMS nanoprobes appear gold) and (D) Au XRF image to demonstrate location and quantification of the nanoprobes.



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To demonstrate the effect of the normalization on the imaging of SERS nanobiosensors, we compared three areas from the SERS images with and without the normalization process. Two areas were selected from the leaf infiltrated with iMS nanoprobes and target (iMS ON, Figure 7) and one from the leaf infiltrated with iMS nanoprobes only (iMS OFF, Figure 6). The specific areas in the iMS ON leaf were selected in order to have varied concentrations of iMS nanoprobes, as observed using XRF. The SERS signals from each area (a 9 pixel square, 600 µm × 600 µm) were averaged and are shown in Figure 8a. These values were normalized according to the average number of particles in the corresponding areas from the XRF gold content map. The normalized values are shown in Figure 8b.

Figure 8. (A) Bar graph showing the average SERS signal from 2 regions of the leaf containing the ‘ON’ nanoprobes (ON high concentration, ON low concatenation) and from a region of the leaf containing the ‘OFF’ nanoprobes (OFF). (B) Bar graph representing the signal from the same regions as in A normalized by the average number of particles of the region, quantified through XRF. The error bars represent the standard deviation given by the signal variation across each area.

The normalization was observed to resolve differences in SERS signal due to target concentration from those due to particle concentration. The values for the average SERS intensities of the selected areas (Figure 8a) show a gradient of SERS signal, rather than the expected ‘OFF’ or ‘ON’ binary response. This observation is due to the convolution between the iMS signal (‘ON’ 16 ACS Paragon Plus Environment

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or ‘OFF’) with the number of particles relative to the specific area. High concentration of ‘ON’ iMS nanoprobes show strong SERS signal intensity, while the low concentration of ‘ON’ iMS nanoprobes demonstrates signal intensity closer to the ‘OFF’ iMS nanoprobes. Without an adequate normalization, these results can be read as inaccurate target concentrations. Adding the XRF imaging information allows for the deconvolution of the two factors affecting the SERS signal intensity, particle concentration and sensor response, and restores the binary response of the sensor to the target miRNA. As it can be observed in Figure 8b, the normalized SERS signal intensities accurately show the two states of the nanobiosensor (‘ON’ and ‘OFF’) independently of the particles concentration. Note that the target was added to the iMS nanoprobes prior to the infiltration into the leaf, thereby the concentration of target per nanoprobes can be assumed constant throughout the leaves. These results demonstrate that XRF and TPL can be incorporated in a multimodal imaging concept to track the presence and concentration of nanoprobes in the leaves, enabling the use of SERS-based nanobiosensors to detect biotargets in plants in vivo. Important aspects in the development of this technique are its temporal and spatial resolution. With the experiments shown herein, we demonstrate that the spatial resolution of the Raman mapping can be of 200 µm, while conserving a field of view of 6 mm (for a single image). We believe that the spatial resolution for the multimodal technique corresponds to the SERS mapping resolution since this is its limiting step. The temporal resolution of this technique will instead depend on two factors: the rate at which the iMS changes conformation in the presence of the target and the SERS measurements. A suspension of nanoprobes were observed to change conformation in vitro under 15 min (Figure S3, Supporting Information) and each pixel in a SERS image can be detected in under 5 s. Currently, measurements over gene expression in leaf tissues 17 ACS Paragon Plus Environment


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are performed by cutting and homogenizing the tissue before performing qRT-PCR on extracted RNA. This process has very limited spatial resolution, which is defined by the cutting technique and the amount of sample needed for PCR, and its temporal resolution is not quantifiable because gene expression in a single leaf can only be detected at a single time-point. The technique developed herein has improved resolution (both spatial and temporal) with respect to the current capabilities for the dynamic detection of gene expression via PCR. In addition, we believe that our technique will further contribute to the field of plant studies by offering a mean for directly monitoring miRNA in vivo without sample extraction. 2.5 In vivo dynamic SERS detection of target miRNA To demonstrate the possibility of dynamic detection of target miRNA inside plant tissue, we observed the turning ON process of the nanoprobes within Arabidopsis leaves. To this end, two Arabidopsis leaves were infiltrated with OFF iMS nanoprobes and one with ON iMS nanoprobes. The SERS signal was measured from the infiltrated areas. The leaf containing ON nanoprobes and one of the leaves containing OFF nanoprobes were then overlayed with infiltration solution (1 mM MgCl2 with 0.01% Tween-20). The other leaf containing OFF nanoprobes was overlayed with infiltration solution containing 2 µM miR156 synthetic target. After 30 min incubation, the SERS signal from the infiltrated areas was measured again. Tween-20 was used in the buffer to facilitate the diffusion of the solution inside the leaf. This technique is commonly used for the topical delivery of chemicals into living plant tissues. Furthermore, a 1 mM MgCl2 buffer was chosen for the nanoprobes suspension, rather than PBS, to be of similar composition as the environment within leaf tissues.


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Figure

9a

shows

the

SERS

intensity

over

time

of

the

three

leaves,

ON nanoprobes incubated with buffer, OFF nanoprobes incubated with buffer containing synthetic target, and OFF nanoprobes incubated with buffer, respectively labeled as ‘ON’, ‘turned ON’ and ‘OFF’. The plotted intensities represent the SERS peak intensity at 516 cm-1 normalized for the intensity of the ON nanoprobes to account for changes in the SERS intensity of nanoprobes due to dilution from the additional buffer solution and/or photobleaching. Figure 9b shows background subtracted SERS spectra for the different leaves after incubation. Figure 9a clearly shows that the OFF nanoprobes incubated with target have an increase in SERS signal after 30 minutes of incubation, while the OFF nanoprobes incubated with buffer alone did not show significant change in the SERS signal. Incubation of leaves containing OFF miR156 iMS nanoprobes with a nonspecific target (miR858) also did not show a significant change in the SERS signal (Figure S5, Supporting Information).The same results can be confirmed with spectra in Figure 9b, in which the spectrum of the OFF nanoprobes incubated with target (turned ON) resemble that of the ON nanoprobes (ON), rather than the spectrum from OFF nanoprobes incubated with buffer (OFF).

Figure 9. (A) Average SERS signal from leaves containing ON nanoprobes incubated with buffer (ON), OFF nanoprobes incubated with target (turned ON), and OFF nanoprobes incubated with buffer (OFF), at time 0 and after 30 min of incubation. The error bars represent the standard deviation (n=4). (B) Spectra from the infiltrated area in the leaves (ON, turned ON and OFF) after incubation. Schematics over the spectra represent the experimental conditions for the different leaves.



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Although statistically significant, the results shown in Figure 9a have large standard deviations. This is due to the fact that the SERS intensity from the leaves were not normalized for the concentration of nanoprobes using XRF imaging data, but rather by using the fluorescence signal from the reporter (i.e., Cy7). We found that the fluorescence intensity from the reporter molecule is a less accurate mean to normalize for the nanoprobe concentration as compared to XRF. However, even without the aid of the XRF normalization, this study demonstrates the potential of iMS nanoprobes for dynamic detection of changes in miRNA concentration within plant tissues. 3. CONCLUSIONS Our study demonstrates the feasibility of multimodal imaging using optical biosensors within living whole leaves of Arabidopsis thaliana, setting the basis for SERS-based functional imaging of miRNA in plants. By characterizing the iMS nanoprobes, we show how these SERS nanobiosensors are ideal candidates for the detection of biotargets in vivo, due to their homogeneous, label-free detection mechanism (i.e., they do not require washing steps) and their OFF-to-ON signal response, which increases the diagnostic accuracy. Provided in the results (Figure 3), we demonstrate the fate of these nanoprobes within leaf tissue. The nanoprobes were observed to occupy the middle lamella, the apoplastic space between cell walls. We believe that our nanoprobes can be used to answer such questions regarding transport of miRNA within plant tissues. The proposed novel multimodal imaging technique was first validated with SERS-tags in Arabidopsis leaves, showing agreement between the different imaging modalities (SERS, TPL and XRF). After initial validation, iMS nanoprobes developed for the detection of miR156, a miRNA associated with the phase transitions in Arabidopsis thaliana, were detected and imaged in leaves 20 ACS Paragon Plus Environment

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using this multimodal imaging technique. The results show that iMS nanoprobes exposed to target miRNA were only visible through Raman imaging, while TPL and XRF were able to detect the particles regardless of the presence of target. These findings demonstrate that iMS nanoprobes and Raman mapping have potential for use in the detection of a target nucleic acids, while XRF and TPL modalities allow for concentrations of sensors per pixel to be accounted for in Raman mapping. This aspect of the multimodal imaging technique was further validated by using XRF particle concentration data to normalize the SERS signal (Figure 8). Doing so re-established the binary response of nanoprobes to the miRNA concentration, whereas raw SERS results showed a vast gradient of sensor responses. This aspect is of key importance for this novel technique as current research on nanosensors for biotarget detection is limited in detecting nanoparticle concentration (e.g., in vivo particle tracking for diagnostics) or nanoparticle response to target concentration. Herein, we show proof-of-concept of a multimodal technique that enables the use of nanosensors for in vivo detection of biotargets. Furthermore, we show the possibility to resolve changes over time in target concentration using iMS nanoprobes in leaves in vivo (Figure 9). While this experiment involved the detection of target introduced within the leaf though its pores and not released by the leaf itself, it clearly verifies that increases in target concentration can be detected. The technique developed herein was shown to have a spatial resolution as low as 200 µm and temporal resolution of approximately 15 min. These figures are unmet by the current technology used for detection of gene expression in plant tissues (i.e., qRT-PCR), which requires tissue isolation, homogenization and extraction before RNA analysis. Also, the possibility of measuring the gene expression in a tissue at multiple time-points is a feature currently unique for our technique. At the present state, the developed technique can be used for the detection of differences 21 ACS Paragon Plus Environment


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in miRNA in transgenic plants overexpressing a target miRNA, and possibly to measure changes in miRNA expression in different locations on a transgenic plant in response to a predetermined stimulus. Such study can determine how miRNA is expressed and transported in different parts of the plant. Furthermore, this technique has great potential to study dynamic gene expression in nontransgenic plants, with applications in biomass production optimization. As miR156 regulates the timing of endogenous flowering pathway, which is one of the key determinants to plant biomass accumulation, this technique could provide an important tool to study and improve plant products for biofuel research. In summary, we have developed the first technique to address functional imaging of target nucleic acids in plants in vivo. Our method has potential to be used for direct imaging of nucleic acids within whole plant systems. Using this multimodal imaging technique for direct imaging of miRNAs will allow for monitoring of plant development and regulation, such as phase transitions, by tracking the flux of nucleic acids in relation to external stimuli with necessary spatial and temporal resolutions. 4. MATERIALS AND METHODS 4.1 Chemicals and Materials. Gold(III) chloride trihydrate (HAuCl4ꞏ3H2O), L(+)-ascorbic acid (AA), trisodium citrate dihydrate, sodium borohydride (NaBH4), 1 N hydrochloric acid solution (HCl), Dulbecco’s phosphate buffered saline (PBS), Tween-20 and 6-mercapto-1-hexanol (MCH) were purchased from Sigma-Aldrich (St. Louis, MO) at the highest purity grade available. Silver nitrate (AgNO3, 99.995%) was supplied by Alfa Aesar (Ward Hill, MA). Thiol PEG (mPEG-SH, MW 5000) was purchased from Nanocs (New York, NY). Cy7.5 PEG Thiol (Cy7.5-PEG-SH, MW 1000) and NHS-Cy7 were purchased from Lumiprobe (Hunt Valley, MD). Ammonium hydroxide 22 ACS Paragon Plus Environment

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(NH4OH, 29.5%), carbon-coated copper TEM grids and 1 mL disposable syringes, 27G × 1 /2 in. needles were obtained through VWR (Radnor, PA). All oligonucleotides were purchased from Integrated DNA Technologies, Inc (Coralville, IA). All glassware and stir bars were thoroughly cleaned with aqua regia and dried prior to use. Ultrapure water (18 MΩꞏcm) was used in all preparations. 4.2 Synthesis of gold nanostars. Gold Nanostars (AuNS) were synthesized with a previously described procedure.34 Briefly, 12nm gold seed solution was first prepared using a modified Turkevich method. AuNS were then synthesized by the simultaneous addition of 50 μL of 2 mM AgNO3 and 50 μL of 0.1 M ascorbic acid to a solution containing 10 mL of 0.25 mM HAuCl4, 10 μL of 1 N HCl, and 100 μL of the 12 nm gold seed solution under gently stirring at room temperature. The process was completed in less than a minute along with color change from a light orange to dark blue within 10 seconds, indicating formation of AuNS. The stock concentration of AuNS is approximately 0.1 nM, as determined by nanoparticle tracking analysis (NTA). 4.3 SERS-tag synthesis. SERS-tags were made by functionalization of the AuNS with 1µM HS-PEG(1k)-Cy7.5 for 1 h, followed by the addition of 1µM HS-mPEG(5k) to improve the stability of the AuNS. After a total incubation time of 2 h, the particles were centrifugal washed (3500rcf, 10min) to remove excess reagents and weakly-bound surface species. The SERS-tags were resuspended at a concentration of 1 nM. 4.4 Synthesis of silver-coated gold nanostars. The silver-coated gold nanostars (AuNS@Ag) were prepared as previously described.27 For synthesis of AuNS@Ag, unfunctionalized AuNS were kept stirring and 50 µL of AgNO3 0.1 M and 10 µL of NH4OH were added to the solution. The color of the solution changed from blue to dark brown. The obtained solution was used for 23 ACS Paragon Plus Environment


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further functionalization without purification. The AuNS@Ag were functionalized 2 h after the synthesis to obtain iMS-nanoprobes for nucleotide detection and imaging. 4.5 Inverse Molecular Sentinel (iMS) nanoprobe design. Inverse Molecular Sentinel (iMS) nanoprobes were designed for the detection of miR156. The sequences for the molecular sentinel (MS) and the placeholder (PH) were chosen to maximize the sensitivity by optimizing the melting temperatures of the probe-placeholder and placeholder-target hybrid complexes. The MS and PH sequences were designed as 5’- AA AAA TCT CTT AAA AAA AAA ATG ACA GAA GAG A -3’ and 5'- CTC ACT CTC TTC TGT CAT TTT T -3', respectively. The molecular sentinel sequence is also thiol-terminated on the 5’ end and amine-terminated on the 3’ end. This is to allow for binding of the 3’ end of the MS to the metallic surface of nanoparticles and to bind Ramanactive NHS ester Cyanine7 dye to the 3’ end. 4.6 Synthesis of SERS iMS nanoprobes. The molecular sentinel sequence was bound to the Cyanine7 NHS ester Raman-active reporter following the reporter manufacturer suggested procedure. In brief, the amine-terminated MS sequence was incubated with NHS-Cy7 for 4 h in a bicarbonate buffer (pH 8) under constant shaking. The solution was then purified using a desalting column (NAP-5; GE Healthcare, Little Chalfont UK). The purified MS-Cy7 solution was stored at -4°C and used within 30 days. The iMS nanoprobes were synthesized as described in a previously optimized procedure.22 In brief, 20µL of purified MS solution was added to the AuNS@Ag as synthesized and incubated overnight in 0.25 mM MgCl2. To stabilize the nanoprobes, 1 µM of HS-mPEG(5k) was added to the solution and left for 30 min. The solution was then centrifugal washed (3,500 rcf, 10 min) and resuspended in Tris-HCl buffer (10 mM, pH 8.0) containing 0.01% Tween-20. The metallic surface of AuNS@Ag was then passivated using 24 ACS Paragon Plus Environment

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0.1 mM 6-mercapto-1-hexanol (MCH) for 10 min at 37ºC followed by three additional centrifugal washing steps. To turn the iMS SERS signal ‘OFF,’ the nanoprobes (0.1 nM) were incubated with the placeholder strand (0.2 µM) in PBS buffer solution containing 0.01% Tween-20 overnight at 37ºC. The excess placeholder strands were removed using repeated centrifugation and finally redispersed in PBS Tween-20 buffer. To turn the iMS SERS signal ‘ON,’ the ‘OFF’ iMS nanoprobes were incubated with synthetic target analytes (2 µM) at 37ºC for 1 hr. 4.7 SERS-tag and iMS nanoprobe characterization. To test the stability of the nanostars and nanoprobes, absorption spectra were collected with a FLUOstar Omega plate reader (BMG LABTECH GmbH, Ortenberg, Germany). Particle hydrodynamic size distribution, concentration and ζ-potential were determined by nanoparticle tracking analysis (NTA) on a NanoSight NS500 (Nanosight Ltd. Amesbury, UK). While all the SERS-tags and nanoprobes were used within few days of the synthesis, it was observed that functionalized SERS-tags and nanoprobes stored at 20ºC for multiple weeks does not have a significant effect on the SERS signal or iMS nanoprobe sensing capabilities. To ensure nanoprobes were not aggregated, the absorption spectra were monitored, and the size distributions were evaluated by NTA. No significant shift in absorption spectra or increase in hydrodynamic size was observed over time. Transmission electron microscopy (TEM) micrographs were acquired using the FEI Tecnai G2 twin transmission electron microscope (Hillsboro, OR) to confirm morphology consistent as previously reported.27 4.8 Plant material and growth conditions. Rosette leaves of four-week old pants of Arabidopsis thaliana ecotype Columbia (Col-0) were used for this study. Seeds were sown directly on soil (Pro-Mix B Biofungicide, Premier Horticulture) in growing trays with 10-cell inserts. Plants were grown at 22°C under long day conditions (16h light, 8h darkness). 25 ACS Paragon Plus Environment


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4.9 Infiltration of Arabidopsis thaliana leaves. Arabidopsis thaliana leaves were infiltrated with SERS-tags, ‘OFF’ iMS nanoprobes or ‘ON’ iMS nanoprobes using a needleless syringe on the abaxial side of the leaf. To reduce the pressure required for infiltration, the wax cuticle was gently removed, thus reducing the risk of damage to the leaf. Leaves were then rinsed with deionized water to wash off surface particles and kept in a humid environment. 4.10 Raman measurements and maps. Raman maps were obtained with a lab-built Raman setup composed of an inverted microscope (Ti-U; Nikon Instruments Inc., Melville, NY) coupled to a 300 mW, 785 nm diode laser (Optoengine Midvale, UT) focused into the image plane of the microscope objective (laser spot = 500 µm). The laser was passed through and reflected through a filter cube containing a laser line filter, dichroic mirror and notch filter, all for 785 nm excitation (Semrock, Rochester, NY). The objective used in the experiment was selected to maximize the field of view (2X NA = 0.1; Thorlabs, Newton NJ). The images were obtained using a labdeveloped Labview (National Instruments, Austin, TX) code which automated the stage movements (100 mm x 120 mm travel RS-232; Zaber, Vancouver, CA) and coordinated the spectra acquisition. The maps were obtained by scanning the laser excitation through the image by moving the stage in steps. The Raman images were obtained with a spatial resolution of 600 μm with a 500 μm laser spot size obtained through critical illumination of the laser collimated from a fiber optic, except for the high resolution images, which had a resolution of 200 μm and a laser spot size of 160 μm (the lower laser spot size was associated to a reduction in the Raman signal due to the different fiber used to reduce the spot size).The Raman spectra were collected using a spectrometer (1200 grooves mm-1 grating) connected to a CCD camera (LS785 and Pixis100; Princeton Instruments, Trenton, NJ), connected to the microscope through a circular to line fiber optic bundle 26 ACS Paragon Plus Environment

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(Thorlabs, Newton, NJ). A bright field image was taken before each map with a CCD camera (ProEM 512B; Princeton Instruments) connected to the microscope, to correlate the Raman and transmission images. A schematic representation of the lab-built Raman microscope setup used can be found in Figure S6 in Supporting Information. Raman spectra of the SERS-tags as well as for iMS characterization were collected with the same system using LightField software (Princeton Instruments). The background subtraction and smoothing of the spectra was performed on Matlab using a Savitsky-Golay filter (five-point window and first-order polynomial). The Raman maps and spectra were performed with 2 different sets of parameters, depending on the particles used. For iMS nanoprobes 10 accumulations of 100 ms were used, while for the SERS-tags 5 accumulations of 1 s. 4.11 Plasmonics-enhanced two-photon luminescence (TPL) imaging. All TPL images were taken using a multiphoton microscope (Olympus FV1000; Olympus America, Center Valley, PA) at the Light Microscopy Core Facility, Duke University. Microscopic imaging was carried out using a femtosecond Ti:Sapphire laser (Chameleon Vision II; Coherent, Santa Clara, CA) with tunable wavelength ranging from 680 to 1,080 nm, 140 femtosecond (fsec) pulse width, and 80 MHz repetition rate. The laser beam was focused through a 25×1.05 NA water-immersion objective (XLPL25XWMP; Olympus America). Images were taken under 800 nM excitation and 3.7 mW. All images were collected and reconstructed using ImageJ.35 4.12 X-Ray fluorescence imaging. The synchrotron spectroscopy measurements were carried out at the MR-CAT/EnviroCAT insertion device beamline (Sector 10-ID; Advanced Photon Source).36 The energy of the X-ray beam was set to 12,000 eV, which is 81 eV above the Au LIII absorption edge (11,919 eV) and thus able to excite the Au atoms in the sample. The beamline 27 ACS Paragon Plus Environment


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energy was calibrated by a XANES scan from an Au foil (inflection point set at 11,919 eV). The imaging measurement was performed with a 140 μm (horizontal) by 200 μm (vertical) beam defined by a pin hole, producing a 200x200 μm spot on the sample which was set at 45° relative to the beam in the horizontal direction. The beam was raster scanned in 140h x 200v micron steps on the samples and the emitted fluorescence was collected and analyzed using a 4-element energydispersive Vortex detector. The x-axis of the maps was corrected for the 45° angle of the sample by scaling the positions by a factor of 1.414. The energy calibration of the detector was carried out using a NIST 1833 standard, which contains a known area density of Ti, Fe, Zn, and Pb deposited on a thin polymer film. The energy regions of interest used for the XRF maps were defined using the multi-channel XRF spectra from the leaf samples. To convert the measured X-ray fluorescence counts under the Au XRF line to area density of nanoparticles at each pixel, the following calibration procedure was applied. A solution of AuNS or AuNS@Ag nanoparticles was evaporated onto a thin Kapton film and imaged using the same procedures as described above. The resulting calibration images are provided in Figure S7, in Supporting Information. Summing the Au counts from the area of the drop and determining the total number of nanoparticles deposited in the same area via DLS enables a calibration of XRF produced per nanoparticle. Using these calibration parameters, the Au XRF images can be converted from XRF Au counts to number of nanoparticles at each pixel. Based on the background Au counts in the XRF images (ca. 1600 counts per 200 μm x 200 μm area) the sensitivity of the XRF measurement can be estimated to be 400 particles within a 200 μm x 200 μm X-ray spot size. 4.13 Limit of optical detection. The limit of optical detection of the sensing mechanism (defined as the concentration at which the sensor signal is higher than three times the standard 28 ACS Paragon Plus Environment

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deviation of the blank spectrum) was obtained by incubating the target miRNA at a specific concentration (e.g., 100 fM) with the iMS nanoprobes at a concentration of 1 pM overnight. Subsequently, the incubated sample was concentrated by centrifugation to reach a concentration of 30 pM of iMS nanoprobes. The same procedure was applied to a blank control sample not containing any miR156 (i.e., blank). The samples had three replicates and each sample was measured three separate times (integration time = 25 s), to account for assay and measurement variabilities in the average. The spectra were normalized for the fluorescence background from Cy7, background-subtracted and smoothed (as explained in the previous section), before being averaged (n = 9). The average blank spectrum was subtracted from the average 100 fM spectrum, and smoothed an additional time for visualization purposes only. It was ensured that the second smoothing process did not change the peak height in the final spectrum. The standard deviation of the blank spectrum was calculated from the region between 690 and 785 cm-1 in the blank spectrum.



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ASSOCIATED CONTENT Supporting Information. The Supporting Information is available free of charge on the ACS Publications website at DOI: AUTHOR INFORMATION Corresponding Author (*) Dr. Tuan Vo-Dinh Duke University 1427 CIEMAS, BOX 90281 Durham, NC 27708 Phone: (919) 660-8520 Email: [email protected] Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. B.M.C and P.S. contributed equally to this work.

ACKNOWLEDGEMENTS This material is based upon work supported by the U.S. Department of Energy Offices of Science, under Award Number DE-SC0014077. BMC acknowledges the support of the National Science Foundation Graduate Research Fellowship under Grant No. 1106401.

ABBREVIATIONS


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AuNS, gold nanostars; AuNS@Ag, silver-coated gold nanostars; iMS, inverse molecular sentinel; miRNA, microRNA; MS, molecular sentinel; MPM, multiphoton microscopy; NTA, nanoparticle tracking analysis; SERS, surface enhanced Raman scattering; TEM, transmission electron microscopy.

CONFLICT OF INTEREST The authors declare no conflicts of interest related to this study.



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(35) Schneider, C. A.; Rasband, W. S.; Eliceiri, K. W. Nih Image to Imagej: 25 Years of Image Analysis. Nature methods 2012, 9 (7), 671. (36) Segre, C.; Leyarovska, N.; Chapman, L.; Lavender, W.; Plag, P.; King, A.; Kropf, A.; Bunker, B.; Kemner, K.; Dutta, P. In The Mrcat Insertion Device Beamline at the Advanced Photon Source, AIP Conference Proceedings, AIP: 2000; pp 419‐422.


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Figure 1. (A) A schematic representation of the iMS sensing mechanism. (B) Detection of miR156 target using iMS Nanoprobes labeled with Cy7. SERS response of iMS nanoprobes in absence of target (OFF, blue spectrum) and in the presence of 2 μM synthetic miR156 target DNA (ON, red spectrum).



Figure 2. (A) Blank-subtracted SERS spectra for iMS nanoprobes incubated with 100 fM of synthetic target within the spectral region containing the Cy7 peaks of interest. (B) Peak-free spectral region of the blank spectrum, displaying the noise level in the spectrum.


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Figure 3. Transmission electron microscopy image of AuNS-based nanoprobes within the middle lamella of Arabidopsis leaf sections. B and D are zoom-in micrographs of regions from A and C, respectively showing more clearly the location and shape of the nanoprobes. The red arrows identify the cell walls of two adjacent cells.



Figure 4. Multifunctional SERS-tags within an A. thaliana leaf. (A) Superposition of the Raman intensity map of the infiltrated leaf, calculated as the background subtracted intensity of the 927 cm-1 peak of Cy7.5 in each pixel, over the bright field image of the leaf. (B) Spectra from two different pixels of the Raman intensity map: one from the location infiltrated with SERS-tags (purple) and one from a non-infiltrated region of the leaf (black). (C) TPL map superimposed over bright field image of the leaf (SERS-tags appear white) and (D) Au XRF image to demonstrate location and quantification of SERS-tags.


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Figure 5. Multifunctional iMS nanoprobes for detection of miR156 within an A. thaliana leaf. Superimposed Raman intensity map of the background subtracted intensity of the 564 cm-1 peak of Cy7 in each pixel over the bright field image for a leaf infiltrated with (A) ‘OFF’ (- miR156) and (B) ‘ON’ (+ miR156) iMS nanoprobes. Spectra from two different pixels within each Raman intensity map for iMS OFF (C) and iMS ON (D): one from the location infiltrated with iMS nanoprobes and one from a non-infiltrated region of the leaf. Au XRF images to demonstrate location and quantification of iMS nanoprobes in the leaves for ‘OFF’ (E) and ‘ON’ (F) nanoprobes.



Figure 6. Multifunctional iMS nanoprobes for detection of miR156 within an A. thaliana leaf. (A) Superimposed Raman intensity map of the background subtracted intensity of the 516 cm-1 peak of Cy7 in each pixel over the bright field image for a leaf infiltrated with ‘OFF’ (- miR156) iMS nanoprobes. (B) Spectra from two different pixels of the Raman intensity map: one from the location infiltrated with ‘OFF’ iMS nanoprobes (blue) and one from a non-infiltrated region of the leaf (black). (C) TPL map of the infiltrated leaf (iMS nanoprobes appear gold) and (D) Au XRF image to demonstrate location and quantification of the nanoprobes.


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Figure 7. Multifunctional iMS nanoprobes for detection of miR156 within an A. thaliana leaf. (A) Superimposed Raman intensity map of the background subtracted intensity of the 516 cm-1 peak of Cy7 in each pixel over the bright field image for a leaf infiltrated with ‘ON’ (+ miR156) iMS nanoprobes. (B) Spectra from two different pixels of the Raman intensity map: one from the location infiltrated with ‘ON’ iMS nanoprobes (red) and one from a non-infiltrated region of the leaf (black). (C) TPL map of the infiltrated leaf (iMS nanoprobes appear gold) and (D) Au XRF image to demonstrate location and quantification of the nanoprobes.



Figure 8. (A) Bar graph showing the average SERS signal from 2 regions of the leaf containing the ‘ON’ nanoprobes (ON high concentration, ON low concatenation) and from a region of the leaf containing the ‘OFF’ nanoprobes (OFF). (B) Bar graph representing the signal from the same regions as in A normalized by the average number of particles of the region, quantified through XRF. The error bars represent the standard deviation given by the signal variation across each area.


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Figure 9. (A) Average SERS signal from leaves containing ON nanoprobes incubated with buffer (ON), OFF nanoprobes incubated with target (turned ON), and OFF nanoprobes incubated with buffer (OFF), at time 0 and after 30 min of incubation. The error bars represent the standard deviation (n=4). (B) Spectra from the infiltrated area in the leaves (ON, turned ON and OFF) after incubation. Schematics over the spectra represent the experimental conditions for the different leaves.



Figure S1. Absorbance of (A) SERS-tags, (B) iMS ‘OFF’ nanoprobes, and (C) iMS ‘ON’ nanoprobes in buffer over 24-hour time period. (D) Absorbance of iMS ‘OFF’ nanoprobes in Arabidopsis cell lysate over 24-hour time period. In all cases, no significant shift in absorbance was observed, indicating the stability of nanoprobes during the time employed for optical measurements.


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Supplementary Figure 2. Detection of miR156 target using miR156 iMS Nanoprobes labeled with Cy7. SERS response of iMS nanoprobes in absence of target (OFF, blue spectrum), in the presence of miR858 target (non-specific, purple spectrum), and in the presence of 0.2 μM synthetic miR156 target DNA (ON, red spectrum).



Supplementary Figure 3. miR156 iMS nanoprobes in the presence of 2μM synthetic miR156 target DNA. Spectra were acquired over a 45 min time period. Reported here are the background subtracted intensities of the 564 cm-1 peak of Cy7.


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Supplementary Figure 4. Exploded Z-stack two-photon luminescence (TPL) image of AuNS within an Arabidopsis leaf obtained under multiphoton microscopy (MPM). The images shown were taken at a step size of 100µm.



Supplementary Figure 5. Average SERS signal from leaves containing OFF miR156 iMS nanoprobes incubated miR858 non-specific target at time 0 and after 30 min of incubation. The error bars represent the standard deviation (n=4).


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Supplementary Figure 6. Representation of the setup of the lab-built Raman microscope used to obtain the Raman maps of the SERS nanoprobes in leaves. A) Schematics of the optical setup in the microscope for the Raman detection. B) Schematics of the optical setup in the microscope for the wide field transmission images.



Supplementary Figure 7. Calibration image of a drop of the AuNS@Ag iMS nanoprobe solution used to convert the measured x-ray fluorescence counts under the Au XRF line to area density of iMS nanoprobes at each pixel. The image shows a 5 μL drop of iMS nanoprobes at a particle concentration of 0.387nM, yielding a total deposition of 1.165E+08 nanoprobes over the drop area. This information was used to determine the following conversion: 9.828particles/Au count.


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