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Abstract
Super resolution microscopy methods have been designed to overcome the physical barrier of the diffraction limit and push the resolution to nanometric scales. A recently developed super resolution technique, super-resolution radial fluctuations (SRRF) [Nature communications , 7, 12471 (2016) [CrossRef] ], has been shown to super resolve images taken with standard microscope setups without fluorophore localization. Herein, we implement SRRF on emitters in the near-infrared (nIR) range, single walled carbon nanotubes (SWCNTs), whose fluorescence emission overlaps with the biological transparency window. Our results open the path for super-resolving SWCNTs for biomedical imaging and sensing applications.
© 2022 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Fluorescence microscopy is a commonly used microscopy method for observing microstructures at real time [1]. However, diffraction, which is a basic property of light, creates a major obstacle in resolving structures sized less than approximately half the wavelength of light [2]. Aiming to overcome this physical barrier, many super-resolution microscopy techniques have been developed, pushing the resolution barrier to nanometric scales [3]. Among these methods is structured illumination microscopy (SIM) [4], which is based on frequency shifting with patterned wide-field illumination followed by mathematical reconstruction [3], often requiring specialized optical components [5]. Other methods which rely on single molecule switching, known as single-molecule localization microscopy (SMLM) [3], include photoactivated localization microscopy (PALM) [6], and stochastic optical reconstruction microscopy (STORM) [7] which are considered camera based super-resolution approaches [5] and generally require standard equipment such as a wide-field microscope, continuous wave lasers for excitation and activation and a camera for the detection of single molecules [8]. An additional super resolution technique is super-resolution optical fluctuation imaging (SOFI) which preforms higher-order statistical analysis of temporal fluctuations and can be applied with a conventional wide-field microscope equipped with a CCD camera, however requires the fluorescent label to exhibit at least two different emission states [9].
Recently, a new analytical approach, termed super-resolution radial fluctuations (SRRF), was presented [5]. This method allows for super resolving a sequence of images without the need for fluorophore detection and localization [5]. For an input sequence of images, SRRF magnifies each pixel into subpixels and then measures a value termed ‘radiality’ which relates to the probability of it containing the center of a fluorophore [5,10]. The calculation of the radiality, which takes into consideration the spatial information, is based on the radial symmetry within the image, resulting from the microscope's point spread function (PSF), and is performed for every subpixel in the sequence creating a ‘radiality stack’ [10]. Temporal correlations within the radiality stack are then used to create the final SRRF image [5,10]. SRRF provides a single analytical framework that can be a applied with a standard widefield or total internal reflection fluorescence (TIRF) microscope [5].
Since its introduction, SRRF has been widely used for a variety of applications such as imaging cell processes [10–19], distinguishing the DNA base-pair distance [20], calcium imaging [21], ultrasound microvascular imaging [22], and traction force microscopy [23].
For super resolving structures within biological tissue, imaging in the nIR, with an emission wavelength of λ>900 nm, is favorable due to the “optical transparency window” where tissues and biological samples have reduce scattering, absorption, and autofluorescence [24,25]. SWCNT are one-dimensional carbon materials shaped as hollow cylindrical nanotubes with ∼1 nm in diameter [26–28]. In particular, the semiconducting SWCNTs have inherent fluorescence within the nIR window [24,26] and they do not photobleach nor blink [29], making them desirable for biomedical applications [28,30–36]. Imaging and monitoring moving SWCNTs in fluids [37,38] or gels [39] as well as fixed samples [40–43] can be beneficial for applied science [44]. SWCNTs have been successfully used as imaging probes in various applications such as within plants [45–48], live cells [49–53], whole animals [54–57], brain tissue [58] and the brain extracellular space (ECS) [59] . However, the diffraction of the long nIR wavelengths limits the resolution creating an additional challenge, compared to the visible range, when attempting to observe internal structures. Specifically, SWCNTs fluorescence emission occurs primarily between 900–1600 nm [26] resulting in a diffraction limit of ∼450-800 nm whereas for commonly used dyes the resolution limit is in the range of 250-300 nm [60]. Thus, applying super-resolution microscopy techniques using nIR fluorescent probes, such as SWCNTs, can be highly beneficial owing to the deeper sample penetration as well as sub-diffraction resolution.
In this work, we apply the SRRF algorithm to individually dispersed SWCNT imaged in the nIR using a microscope setup either with epi-illumination or TIRF-illumination. The radiality stack created by SRRF preserves information in the gradient field which would be discarded by other localization techniques [5]. As such the radiality map on its own can already improve the resolution prior to the temporal analysis as we demonstrated by applying SRRF to a single frame of a SWCNT (Fig. S1). We show that the method can successfully super resolve the SWCNTs in a variety of different nanotube densities. Further, the SRRF algorithm can be used on short or long SWCNT samples (up to 3-4 µm). Lastly, we demonstrate the use of SRRF on freely diffusing SWCNT samples allowing for super-resolution videos. For the various imaging conditions, we received an improvement of up to 4.8 times in resolution. This work paves the way for super resolving images taken within complex biological samples in the nIR range using SWCNT as fluorescent imaging probes.
2. Methods
2.1 SWCNT suspension
1 mg mL-1 HiPCO SWCNTs (NanoIntegris) were suspended in 2 wt% Sodium Cholate (SC) (Sigma-Aldrich) by applying bath sonication (80 Hz for 10 minutes, Elma P-30H) and direct tip sonication (12 W for 60 minutes, QSonica Q125). Next, the suspension was ultracentrifuged (160,000 rcf for 4 hours, OPTIMA XPN-80) to allow for separating the individually suspended SWCNTs from aggregates and impurities [34]. Long SWCNT samples were created by mixing 1 mg mL-1 HiPCO SWCNTs with 2 wt% Dodecylbenzenesulfonic acid sodium salt (SDBS) (Sigma-Aldrich) followed by bath sonication (80 Hz for 10 minutes) and a short period of direct tip sonication (∼8 W for 7 seconds). The suspension was then centrifuged twice (16,100 rcf for 90 min, Eppendorf) where following each cycle 80% of the supernatant was collected. The absorption spectra were recorded using an ultraviolet-visible-nIR (UV-Vis-nIR) spectrophotometer (Shimadzu UV-3600 PLUS). The fluorescence spectra were acquired with the use of a nIR microscope coupled to an InGaAs detector, utilizing a spectrograph (PyLoN-IR 1024-1.7 and HRS-300SS, Princeton Instruments, Teledyne Technologies). A super-continuum white-light laser (NKT-photonics, Super-K Extreme) coupled to a tunable bandpass filter (NKT-photonics, Super-K varia, Δλ = 20 nm) was used for excitation.
2.2 SWCNT immobilization
Microscope coverslips were immersed in 0.01% poly-L-lysine (PLL) (Sigma-Aldrich) solution in H2O for five minutes and then washed with water. Subsequently, 1 mg L-1 SC-SWCNTs were placed beneath the PLL coated coverslips for the duration of 3, 5 or 7 minutes. For comparing long and short SWCNT samples, 0.5 mg L-1 SC-SWCNTs or SDBS-SWCNTs were placed beneath the PLL coated coverslips for the duration of 7 min. The coverslip was then washed with water, placed above a glass slide, and sealed.
2.3 Diffusing SWCNT samples
0.5 mg L-1 SDBS-SWCNTs were diluted in 90% glycerol (Bio Lab) in water. The solution was then placed on a glass slide and sealed with a coverslip. Particle tracking of the SWCNT was done with the use of TrackMate ImageJ plugin [61]. The mean square displacement (MSD) was calculated with the help of msdanalyzer MATLAB per-value class [62].
2.4 nIR fluorescence imaging
TIRF imaging of SWCNTs was preformed using an inverted fluorescence microscope (Olympus IX83) with a 100× TIRF objective (Olympus UAPON 100XOTIRF). Epi-illumination imaging was performed using 100×, 1.3 NA objective (Plan FL). SWCNT suspensions were excited with a 730 nm CW laser (MDL-MD-730-1.5W, Changchun New Industries) and a dichroic mirror (900 nm long-pass, Chroma) was used to direct the excitation light at the sample. The nIR emission was detected after a 900 nm long-pass emission filter (Chroma, ET900lp) using an InGaAs-camera (Raptor, Ninox 640 VIS-nIR). For immobilized SWCNTs at varying densities, 100 frames were acquired in TIRF mode at a frame rate of 5 frames per second (fps) and an exposure time of 190 ms. For comparing long and short SWCNT samples, 100 frames were acquired in TIRF mode at a frame rate of 9 fps and an exposure of 100 ms. Diffusing SWCNT videos were taken at a frame rate of 40 fps with 15 ms exposure.
2.5 SRRF analysis
Images were pre-processed using ImageJ. A 3X3 median filter was applied to the image to remove noise. The background was then removed from the images using the rolling ball algorithm [63], and the full width half maximum (FWHM) of the SWCNTs before SRRF was calculated. SRRF analysis was preformed using the ImageJ plugin [5] where the ring radius was set to 0.5, Radiality magnification to 5 and Axes in ring to 6. Temporal analysis was done using temporal radiality average (TRA). Intensity weighting was preformed to enhance radiality peaks [5]. For comparing the SWCNT density, and long vs. short SWCNT samples, SRRF images were created using 100 frames. For super-resolving diffusing SWCNTs, SRRF images were created for every 10 frames resulting in a super-resolution video of moving SWCNTs.
3. Results
3.1 SWCNT suspension characterization
SC-SWCNT suspensions were characterized with a UV-Vis-nIR spectrophotometer showing clear absorption peaks (Fig. 1(a)) The suspension displayed fluorescence emission in the nIR range, under a variety of excitation wavelengths, with distinguishable peaks corresponding to the different SWCNT chiralities in the suspension (Fig. 1(b)).
Fig. 1. SC-SWCNT characterization. (a) Absorption spectra of SC-SWCNTs (b) Excitation–emission map of SC-SWCNTs
3.2 Varying SWCNT densities
The SRRF algorithm is capable of analyzing high-density data sets with minimal reconstruction artifacts as compared to other super-resolution algorithms [5]. To examine this ability of the SRRF algorithm to analyze high-density data sets, SC-SWCNT were immobilized to PLL-coated coverslips and imaged in TIRF mode at three different densities (Fig. 2(a), (b), (c)). The change in density was controlled by altering the incubation time of the negatively charged SC-SWCNTs and the positively charged PLL-coated coverslips, where a longer incubation time led to higher density data set owing to electrostatic binding. SRRF analysis was then performed on the three data sets (Fig. 2(d), (e), (f)). For 10 randomly chosen SWCNTs within the data set, a cross section of the SWCNTs was fit to a standard gaussian and the FWHM was calculated (Fig. 2(g), (h), (i)) before and after SRRF. Before applying the SRRF algorithm, the calculated FWHM was 0.54 ± 0.09 µm, 0.49 ± 0.07 µm, and 0.61 ± 0.07 µm, for the low, medium, and high-density data sets, respectively (Fig. 2(j)). Following the SRRF analysis, the calculated FWHM was 0.17 ± 0.06 µm, 0.11 ± 0.03 µm, and 0.13 ± 0.05 µm for the low, medium, and high-density data sets, respectively (Fig. 2(j)), corresponding to an average improvement for the FWHM of times 3.8 ± 2.2, 4.7 ± 1.7, and 4.9 ± 0.1 for the low, medium, and high-density data sets, respectively. (Figure 2(k)). For the different SWCNT density images the signal to noise ratio (SNR) was calculated before and after SRRF. Before SRRF the average SNR of the images was 45 ± 4 dB and following SRRF 54 ± 2 dB showing an improvement in the SNR. Further, within the high-density image, we were also able to demonstrate the ability of the SRRF algorithm to separate two neighboring SWCNTs (Figure 2(l)). The FWHM of the join spot before SRRF was 1.04 µm, whereas the FWHM of the two separated peaks following SRRF were 0.13 µm and 0.17 µm. These results show the successful application of the SRRF algorithm on images which vary in density.
Fig. 2. The effect of SWCNT density of SRRF prefomance. Red lines represent the cross section used for calcualting the FWHM of individaul SWCNTs. Panels a-f: scale bar stands for 10 µm. Panel l: scale bar stands for 1 µm. (a) Low density TIRF image of SWCNTs. (b) Medium density TIRF image of SWCNTs. (c) High density TIRF image of SWCNTs. (d) Corresponding SRRF image of the low density SWCNTs. (e) Corresponding SRRF image of the medium density SWCNTs. (f) Corresponding SRRF image of the high density SWCNTs. (g) Representative FWHM analysis before and after SRRF in the low density images. (h) Representative FWHM analysis before and after SRRF in the medium density images. (i) Representative FWHM analysis before and after SRRF in the high density images. (j) Mean FWHM calculated for 10 individual SWCNTs before and after SRRF analysis. (k) Imrovement factor of the FWHM. (l) 2 SWCNTs which could not be resolved before the algorithm are super resolved following SRRF.
3.3 Images of long SWCNTs
The length distribution of the emitting SWCNTs can vary based on the preparation and processing method [40,64]. To challenge the SRRF algorithm and show its applicability to fluorophores of different aspect-ratios, we compared the performance on SWCNT samples composed of short nanotubes (Fig. 3(a)) as opposed to SWCNT sample enriched with long nanotubes (∼3-4 µm) (Fig. 3(c)). The SRRF algorithm was applied to both data sets (Fig. 3(b), (d)) and the FWHM was calculated for 5 random SWCNTs (Fig. 3(e), (f)). The average FWHM of the short SWCNTs prior to the SRRF analysis was 0.48 ± 0.04 µm and following SRRF 0.14 ± 0.04 µm (Fig. 3(g)) corresponding to an improvement by a factor of 3.8 ± 1.4 in the FWHM (Fig. 3(h)). In comparison, the average FWHM of the long SWCNTs prior to the SRRF analysis was 0.5 ± 0.03 µm and following SRRF 0.12 ± 0.04 µm (Fig. 3(g)) corresponding to an improvement of times 4.6 ± 1.2 in the FWHM (Fig. 3(h)). These results show comparable improvement factor in the cases of long and short SWCNTs (Fig. 3(h)), supporting that the algorithm can be applied to long SWCNTs as well as short ones. Further, the calculated SNR prior to SRRF was 39.9 dB and 43.7 dB for the short and long SWCNTs, respectively. Following SRRF, the calculated SNR was 54.2 dB and 55.4 dB for the short and long SWCNTs, respectively, showing an improvement in the SNR for both data sets.
Fig. 3. The effect of SWCNT length on SRRF prefomance. Scale bar stands for 5 µm. (a) TIRF image of short SWCNTs, enlargement of the image is marked by the yellow box. (b) SRRF image of short SWCNTs, enlargement of the image is marked by the yellow box, red lines represent the cross section used for calculating the FWHM on individaul SWCNTs. (c) TIRF image of long SWCNTs, enlargement of the image is marked by the yellow box. (d) SRRF image of long SWCNTs, enlargement of the image is marked by the yellow box, red lines represent the cross section used for calculating the FWHM on individaul SWCNTs. (e) Representative FWHM analysis before and after SRRF algorithm for the SWCNT marked by the yellow box in Fig. 3 a,b (short SWCNT) (f) Representative FWHM analysis before and after SRRF algorithm for the SWCNT marked by the yellow box in Fig. 3 c,d (long SWCNT). (g) Mean FWHM calculated for 5 individual SWCNTs as marked by the red lines in TIRF and SRRF images before and after SRFF analysis. (h) Improvement in the FWHM calcualtion for the long and short SWCNTs images as a result of the SRRF analysis.
3.4 SRRF videos of diffusing SWCNTs
The SRRF algorithm has been previously demonstrated on live-cells allowing for super-resolution videos of dynamic processes in cells [5,10,12,14]. When attempting to image diffusing SWCNTs in water, the dynamics of the SWCNTs were extremely rapid [65] (Visualization 1) owing to the ∼1 μm2 s-1 diffusion coefficient [66,67]. With the need to balance between the high repetition rate needed to capture the fast diffusion as well as super resolve the sequence, and the signal to noise ratio, the resulting SRRF video was unsatisfying (Visualization 1) most likely due to the noise having a large effect on the radiality peaks which was not resolved with intensity weighting [5]. In order to slow down the diffusion [66], we chose to demonstrate the dynamics of the SWCNTs in 90% glycerol (Visualization 2, Fig. 4(a), (b), (c)). The viscosity of 90% glycerol is ∼250 times higher compared to water [68], which significantly slows-down the SWCNT dynamics [66]. We generated super-resolution videos at 4 super resolution frames per second, where every 10 frames were used to create a SRRF image (Visualization 2, Fig. 4(d), (e), (f)). This allowed for capturing the bending dynamics [37] of the SWCNTs at a sub-diffraction resolution. Three random SRRF frames within the super-resolved video were chosen and the FWHM was calculated before and after the SRRF analysis (Fig. 4(g), (h), (i)). The average FWHM was 0.46 ± 0.15 µm and 0.12 ± 0.01 µm before and after SRRF, respectively, manifesting a 4 ± 1.3 improvement factor in the FWHM. The MSD was calculated for the videos of SWCNTs in glycerol in order to probe their diffusion coefficient (Fig. 4(j)). For the video prior to SRRF, the calculated diffusion coefficient was 0.0086 ± 0.0002 µm2s-1. Following SRRF, we received a similar diffusion coefficient of 0.011 ± 0.001 µm2s-1. Our results agree with previous findings of the diffusion coefficient of SWCNT in a water-glycerol mixture [66], further validating the results of the SRRF.
Fig. 4. Scale bar stands for 2 µm (a) Frames 1-10 of a single diffusing SWCNT in 90% glycerol (b) Frames 51-60 of a single diffusing SWCNT in 90% glycerol (c) Frames 271-280 of a single diffusing SWCNT in 90% glycerol. (d) SRRF image created from frames 1-10 (e) SRRF image created from frames 51-60 (f) SRRF image created from frames 271-280. (g) FWHM calculation before and after SRRF analysis for frames 1-10. (h) FWHM calculation before and after SRRF analysis for frames 51-60. (i) FWHM calculation before and after SRRF analysis for frames 271-280. (j) MSD for diffusing SWCNT before and after SRRF
4. Discussion
Over the past few years, super resolution microscopy has enabled the study of biological processes at the nanoscale [69]. Being able to apply such methods in the nIR can be highly beneficial for in vivo imaging, as it enables deeper tissue penetration with higher spatial resolution due to reduced light scattering [70]. SWCNTs are ideal to be applied as luminescent probes in biological imaging, with substantial brightness and photostability in water [71]. However, nIR fluorescence microscopy of SWCNTs is restricted by a higher diffraction limit compared to the visible range [72]. Previously, a cyanine labeled SWCNT was super-resolved using SRRF only within the visible range [73]. Within the nIR, previous research has shown the ability to super resolve SWCNTs by preforming localization, averaging and fitting [72]. Further, by tracking individual SWCNTs over time, and monitoring how the SWCNTs interact with their environment, sub-diffraction accuracy regarding the space in which the SWCNTs are emitting can be achieved [71,74]. Another study engineered photoswitchable SWCNTs for super-resolution microscopy in the nIR using SMLM techniques [75]. The SRRF algorithm, however, benefits from the ability to super resolve images without fluorophore detection and localization [5]. Our results show the ability to apply SRRF to SWCNT images, thus receiving sub-diffraction resolution within the nIR range without the need for localization nor special equipment. The algorithm performs well on low density as well as high density SWCNT data sets removing the restriction to maintain the density of fluorophores emitting in each frame as needed by some super resolution methods [5].
Additionally, by using SRRF, we were able to super-resolve long SWCNT samples as well as short ones. The SWCNT length is an important and relevant parameter for a many fundamental processes and applications [76]. For the development of SWCNT bio-sensors, it is important to determine an accurate length of the SWCNT as it can provide quantification of the target analyte [77]. Further, it has been shown that there is a correlation between the SWCNTs fluorescence intensity and their length [78], showing the importance of the ability to apply super-resolution methods to SWCNT samples of different length distributions. However, the SRRF algorithm assumes that the radiality of a single fluorophore results in a conical distribution [5] which may be inexact for elongated SWCNTs, and could be the cause of slight artifacts in the super resolved images. Overcoming this challenge, by relaxing the assumption of spherical emitters, will be the subject of our future work.
We also extended the use of the SRRF algorithm to diffusing SWCNTs. SWCNTs have unique diffusion properties due to their small diameter and long length, which together with their stable nIR fluorescence make them suitable for long-term single-molecule video imaging and tracking [79]. Super-resolving diffusing SWCNTs can provide information on the local environment of the SWCNT, as was demonstrated in the brain ECS showing for the first-time super-resolution data of the ECS in live adult brain tissue [71]. It was also demonstrated that SWCNTs could be used to image and detect molecular motor proteins in embryos [80] and living cells [81], with the advantage of being non-photobleaching, non-blinking emitters. Further, SWCNTs can be used to image and detect protein efflux from cells [82] or bacteria [83] in which spatial information has significance. Applying Super-Resolution techniques to such images could be valuable for exact detection and tracking. The SRRF algorithm successfully achieved sub-diffraction resolution for the diffusing SWCNT videos. Future work will include optimizing the imaging conditions to allow for super-resolving SWCNTs in aqueous environment with lower viscosity by improving the signal-to-noise ratio. This can be done by increasing the excitation power, optimizing the SWCNT functionalization for improved photoluminescence quantum yield [84], decreasing the noise in the camera by further cooling the detector, and optimizing the optical elements for higher transmission in the nIR range.
5. Conclusion
In conclusion, we have shown the high applicability of SRRF on SWCNTs in the nIR. We demonstrated the use of the algorithm in a variety of challenging conditions such as varying SWCNT densities and lengths, as well as diffusing and immobilized SWCNTs. SWCNTs SRRF opens the path for sub-diffraction super-resolved images in the nIR which benefit from deep sample penetration and improved signal-to-noise for biomedical applications.
Funding
Zuckerman STEM Leadership Program; Israel Science Foundation (456/18); Ministry of Science, Technology and Space (3-17426); Nicholas and Elizabeth Slezak Super Center for Cardiac Research and Biomedical Engineering; Zimin Institute for Engineering Solutions Advancing Better Lives; Tel Aviv University Center for Combating Pandemics; Marian Gertner Institute for Medical Nanosystems.
Acknowledgments
Gili Bisker acknowledges the support of the Zuckerman STEM Leadership Program, the Israel Science Foundation (Grant no. 456/18), the Ministry of Science, Technology, and Space, Israel (Grant no. 3-17426), the Nicholas and Elizabeth Slezak Super Center for Cardiac Research and Biomedical Engineering at Tel Aviv University, the Zimin Institute for Engineering Solutions Advancing Better Lives, and the Tel Aviv University Center for Combating Pandemics. Roni Ehrlich was supported by the Ministry of Science, Technology, and Space, Israel and the Marian Gertner Institute for Medical Nanosystems. The authors thank Dr. Dotan Kamber for valuable discussions.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
Supplemental document
See Supplement 1 for supporting content.
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