Patent Application
20230288337
The present disclosure relates generally to methods for tuning the time-domain emissive profile of single upconversion nanoparticles using a number of different techniques so as to increase the coding capacity at the nanoscale. The disclosure also relates to time-resolved wide-field imaging and deep-learning techniques to decode the nanoparticle fingerprints.
Any discussion of the prior art throughout this specification should in no way be considered as an admission that such prior art is widely known or forms part of the common general knowledge in the field.
It is the ultimate goal of nanotechnology to manipulate structures with unprecedented accuracy and to tune their functions to precisely match the parameters required at the single nanoparticle level. Optical multiplexing with increased capacity will advance the ongoing development of next-generation enabling technologies, spanning from high-capacity data storage, anti-counterfeiting, large-volume information communication, to high-throughput screening of multiple single molecular analytes in a single test and super-resolution imaging of multiple cellular compartments.
Super-capacity optical multiplexing challenges our ability in creating multiplexed codes in orthogonal dimensions, e.g. intensity, colour, polarization and decay time, assigning them to the microscopic and nanoscale carriers, and decoding them in high throughput fashion with sufficient accuracy in the orthogonal optical dimensions. Though desirably the size of material that carries the optical barcodes can be pushed from microscopic to the nanoscopic range, it sacrifices the overall amount of emissive photons (brightness), and therefore limits the number of detectable codes, e.g. typically three to four colour channels or brightness levels. The amount of signal emitted from a nanoscale object can drop exponentially and their size is often below the optical diffraction limit. This prevents the conventional filter optics and detection process from decoding them with sufficient spectral-spatial resolutions.
This unmet need poses significant challenges for material sciences to pursue fabrication strategies and the precise control in producing uniform nanoscopic carriers, and further challenges the photonics community to maximize the number of emissive photons and to explore the diversity of optical information that can be produced in multiple orthogonal dimensions, such as emission colours (spectrum), lifetime, polarization and angular momentum.
Lanthanide-doped upconversion nanoparticles (UCNPs) absorb low-energy near-infrared photons to emit high-energy emissions in the visible and UV regions. Single UCNPs are uniform, photo-stable for hours and allow single nanoparticle tracking experiments in live cells. Recently, the core-shell-shell design of each single UCNP has been reported as emitting ˜200 photons per second under a low irradiance of 8 W/cm3, and intensity uniform UCNPs have enabled the single-molecule (digital) immuno assay. The colour-based multiplexing of UCNPs can be realized by tuning the dopants, core-shell structure or excitation pulse durations, but all colour-based approaches are intrinsically limited by cross-talk in the spectrum domain. Major advances have been made in the ensemble lifetime measurements of microsphere arrays, time-domain contrast agents for deep-tissue tumour imaging and high-security-level anticounterfeiting applications. Though lifetime multiplexing with single nanoparticle sensitivity was possible, the relatively low brightness and point scanning confocal microscopy have limited the readout throughput.
In a first aspect the present disclosure provides a method for tuning a time-domain emissive profile of an upconversion nanoparticle, the method comprising the step of manipulating a rising, decay and/or peak moment of an excited state population.
Manipulation of the rising, decay and/or peak moment of the excited state population may be achieved by altering interfacial energy migration in the nanoparticles.
Interfacial energy migration may be altered by exposing the nanoparticle to different excitation wavelengths.
The nanoparticles may be UCNPs.
The UCNPs may comprise one or more of: neodymium, ytterbium, thulium, erbium, lanthanum, cerium, praseodymium, neodymium, promethium, samarium, europium, gadolinium, terbium, dysprosium, holmium, lutetium, scandium and yttrium.
The UCNPs may comprise neodymium, ytterbium, thulium and/or erbium.
The UCNPs may contain a host material selected from an alkali fluoride, an oxide or an oxysulfide.
The alkali fluoride may be NaGdF4, Ca2F, NaYF4, LiYF4, NaLuF4 or LiLuF4, KMnF3, and the oxide may be Y2O3. Mixtures of these materials are also contemplated. In one embodiment, the host material is NaYF4.
Where the UCNPs are crystalline, the NaYF4 may be hexagonal phase, or any other crystal phase.
The UCNPs may be core-multi-shell UCNPs.
The core-multi-shell UCNPs may comprise a core, a migration layer and a sensitisation layer.
The migration layer may comprise Yb3+.
The sensitization layer may comprise Yb3+ and Nd3+.
The core may comprise Yb3+, Er3+ and/or Tm3+.
The core may comprise Yb3+ and Er3+ or Yb3+ and Tm3+.
The UCNPs may be selected from: corea-multi-shell β-NaYF4: Nd3+, Yb3+, Tm3+ UCNPs and core-multi-shell β-NaYF4: Nd3+, Yb3+, Er3+ UCNPs.
The UCNPs may have a coefficient of variation (CV) value less than about 15%, or less than about 10%, or less than about 5%.
In a second aspect the present invention provides a multiplex assay method for identifying a luminescent probe in a multiplex assay, the method comprising: stimulating the luminescent probe to produce luminescence, and measuring the rising time, peak moment and/or decay time of the luminescence.
The multiplex array may be a suspension array.
The method may further comprise: stimulating a plurality of luminescent probes to produce luminescence; measuring the rising times, peak moments and/or decay times of the luminescence, and identifying one or more probes based on differences in the rising times, peak moments and/or decay times.
The rising time, peak moment and/or decay time of the luminescence may provide one or more codes.
The luminescent probe may be a nano-tag, sphere, particle or carrier.
The luminescent probe may include one or more nanoparticles.
The luminescent probe may include one or more nanoparticles as described above in connection with the first aspect.
In a third aspect the present invention provides a method for performing a multiplex assay, the multiplex assay including using, as probes, a plurality of nanoparticles having luminescence profiles possessing different rising times, peak moments and/or decay times, wherein the probes are distinguished from one another based on their differing rising times, peak moments and/or decay times.
The luminescent probe may include one or more nanoparticles as described above in connection with the first aspect.
In a fourth aspect the present invention provides a method for preparing a library of spectrally distinct nanoparticles comprising:
In one embodiment, at least three different classes of nanoparticles are prepared, and each class comprises at least 10 different types of nanoparticles.
The nanoparticles may be UCNPs.
The different classes of UCNPs may be classes of UCNPs having different combinations of activators and/or sensitisers.
The UCNPs may comprise one or more of: neodymium, ytterbium, thulium, erbium, lanthanum, cerium, praseodymium, neodymium, promethium, samarium, europium, gadolinium, terbium, dysprosium, holmium, lutetium, scandium and yttrium.
The UCNPs may comprise neodymium, ytterbium, thulium and/or erbium.
The UCNPs may contain a host material selected from an alkali fluoride, an oxide or an oxysulfide.
The alkali fluoride may be NaGdF4, Ca2F, NaYF4, LiYF4, NaLuF4 or LiLuF4, KMnF3, and the oxide may be Y2O3. Mixtures of these materials are also contemplated. In one embodiment, the host material is NaYF4.
Where the UCNPs are crystalline, the NaYF4 may be hexagonal phase, or any other crystal phase.
In one embodiment, the plurality of different classes of UCNPs includes at least one class having core-multi-shell UCNPs.
The core-multi-shell UCNPs may comprise a core, a migration layer and a sensitisation layer.
The migration layer may comprise Yb3+.
The sensitization layer may comprise Yb3+ and Nd3+.
The core may comprise Yb3+, Er3+ and/or Tm3+.
The core may comprise Yb3+ and Er3+ or Yb3+ and Tm3+.
In one embodiment, the plurality of different classes of UCNPs includes the following: core-multi-shell β-NaYF4: Nd3+, Yb3+, Tm3+ UCNPs, core-multi-shell β-NaYF4: Nd3+, Yb3+, Er3+ UCNPs and β-NaYF4: Yb3+, Tm3+ UCNPs.
The UCNPs may have a coefficient of variation (CV) value less than about 15%, or less than about 10%, or less than about 5%.
In one embodiment, all of the parameters are varied.
In a fifth aspect the present invention provides a library of spectrally distinct nanoparticles when obtained by the method of the fourth aspect.
In a six aspect the present invention provides use of the library of spectrally distinct nanoparticles of the fifth aspect in a multiplex assay, wherein the nanoparticles are used as probes.
The probes may be distinguished from one another based on at least differing rising times, peak moments and/or decay times of their luminescence profiles.
The probes may be decoded using wide-field time-resolved microscopy or deep learning.
In the context of this specification the term “about” is understood to refer to a range of numbers that a person of skill in the art would consider equivalent to the recited value in the context of achieving the same function or result.
In the context of this specification the terms “a” and “an” are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.
Throughout this specification, unless the context requires otherwise, the word “comprise”, or variations such as “comprises” or “comprising” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. Thus, in the context of this specification, the term “comprising” means “including principally, but not necessarily solely”.
The present inventors have discovered that the time-domain emissive profile from single upconversion nanoparticles, including the rising, decay and peak moment of the excited state population (τ2 profile) can be arbitrarily tuned by upconversion schemes, including interfacial energy migration, concentration dependency, energy transfer, and isolation of surface quenchers. This allows a significant increase in the coding capacity at the nanoscale. It has also been found that at least three orthogonal dimensions, including the excitation wavelength, emission colour and τ2 profile, can be built into the nanoscale derivative τ2-dots. These high-dimensional optical signatures can be pre-selected to build a vast library of single-particle nano-tags. These high-dimensional optical fingerprints provide a new horizon for applications spanning from sub-diffraction-limit data storage, security inks, to high-throughput single-molecule digital assays and super-resolution imaging.
The applicant has demonstrated that the morphology of both active core @ inert shell UCNPs and active core @ energy migration shell @ sensitization shell @ inert shell UCNPs can be highly controlled, and that once the single UCNP is sufficiently bright under wide-field microscopy it displays its characteristic optical signatures in the time domain. Surprisingly, not only is the decay time of each batch of UCNPs tunable, but also the rising time, decay time and peak moment of the excited state population from a single nanoparticle. The inventors have found that the rising time, decay time and peak moment can be further manipulated by a multi-interfacial energy transfer process and orthogonal excitation wavelengths. Accordingly, in one aspect the present invention provides a method for tuning a time-domain emissive profile of an upconversion nanoparticle, the method comprising manipulation of a rising, decay and/or peak moment of an excited state population. In one embodiment, manipulation of the rising, decay and/or peak moment of the excited state population may be achieved by altering interfacial energy migration (IEM) in the nanoparticles. In one embodiment, IEM may be altered by exposing the nanoparticle to different excitation wavelengths.
To demonstrate the role of IEM in manipulating the rising, decay and/or peak moment of an excited population, a series of core-multi-shell β-NaYF4: Nd3+, Yb3+, Tm3+ UCNPs (
The ability to tune the rising time, decay time and peak moment opens the possibility for this dimension to be used in multiplexing assays. Accordingly, in another aspect the present invention provides a multiplex assay method for identifying a luminescent probe in a multiplex assay, the method comprising: stimulating the luminescent probe to produce luminescence, and measuring the rising time, peak moment and/or decay time of the luminescence. In a further aspect the present invention provides a method for performing a multiplex assay, the multiplex assay including using, as probes, a plurality of nanoparticles having luminescence profiles possessing different rising times, peak moments and/or decay times, wherein the probes are distinguished from one another based on their differing rising times, peak moments and/or decay times.
By harnessing the ability to tune the τ2 profile of UCNPs, the applicant has created a set of time-domain optical fingerprints and built a library of different batches of τ2-Dots by implementing five strategies to tailor the excited-state populations of emitters present in the UCNPs. Accordingly, in a further aspect the present invention provides a method for preparing a library of spectrally distinct nanoparticles comprising:
The exemplary library is based on three series of UCNPs as set out in Table 1, displaying three orthogonal dimensions (excitation wavelength, emission wavelength, and lifetime) of optical fingerprints. The Yb—Tm series (
As illustrated in
It will be appreciated that in preparing a library, one or more of these strategies may be adopted. In some embodiments, all five strategies are adopted. Using all five strategies (see Table 1), fourteen (1→14 in
Preferably the nanoparticles in step (a) are selected from: core-multi-shell β-NaYF4: Nd3+, Yb3+, Tm3+ UCNPs, core-multi-shell β-NaYF4: Nd3+, Yb3+, Er3+ UCNPs and core-shell β-NaYF4: Yb3+, Tm3+ UCNPs. In some embodiments, the nanoparticles in step (a) are core-multi-shell β-NaYF4: Nd3+, Yb3+, Tm3+ UCNPs, core-multi-shell β-NaYF4: Nd3+, Yb3+, Er3+ UCNPs and core-shell β-NaYF4: Yb3+, Tm3+ UCNPs, such that the library is based on three UCNP types as shown in Table 1. However, those skilled in the art will appreciate that the library may be based on other UCNPs, and indeed nanoparticles more generally, as long as their optical uniformity and tunability of optical fingerprints, e.g. in the spectrum, meet the requirement discussed herein.
The applicant has found that despite the large dynamic ranges of lifetime profiles that can be encoded in different batches of τ2-Dots, the difference between each encoded optical fingerprint can be hidden at the ensemble level. Therefore, the single nanoparticle spectroscopy method should be adopted to verify the optical uniformity of single τ2-Dots. Here, fourteen batches of τ2-Dots (namely τ2-1 to τ2-14 in Table 1) were selected in the Yb—Tm series (τ2-1 to τ2-9) and Nd—Yb—Er series (τ2-10 to τ2-14) to perform the decoding experiment at single nanoparticle level. Using a confocal microscopy setup (
Confocal scanning microscopy allows illumination power up to 106 W/cm2 to excite every single nanoparticle by scanning across each pixel, but of which the scanning mode dramatically limits the throughput in the decoding process. A wide-field microscope was therefore developed with an intensifier coupled CMOS camera for time-resolved imaging (
Compared to the conventional micron-sized beads, optical codes created on nanoscopic-sized τ2-Dots can significantly increase the capacity of coding information, which takes optical super capacity multiplexing into the region smaller than the optical diffraction limit. To illustrate this opportunity and challenge, 5 μm polystyrene beads were stained with τ2-13 dots (
Using the wide-field time-resolved microscope, the lifetime curves of more than 20 single τ2-Dots from each batch were measured and their lifetime profiles are presented in
Deep learning is an emerging technique showing strong ability to classify highly non-linear datasets. Here an opportunity was offered by both the controlled growth of highly optically uniform single nanoparticles and subsequent image analysis to obtain lifetime fingerprints of single dots, which can generate a large set of high-quality data to train the machine in deep learning. By collecting the sequences of time-dependent frames of images, we extracted the values of the normalized τ2 profiles at 75 time moments between 0-3750 μs as the data source of input for training, in which we first pre-process the as-collected images by only selecting the imaging data from single nanoparticles. As shown in
We train the machine by the database of 14 batches of τ2-Dots with two series independently (τ2-1 to 9 and τ2-10 to 14) and challenge the established neural networks to recognize every single τ2-Dot. To do this, we first collected seven sets of time-resolved sequences of images from each type of τ2-Dots sample, and each image data contains the lifetime fingerprints of 50 to 200 single nanoparticles after data preselection of single τ2-Dots. We use any six sets of imaging data from each type of τ2-Dots to train the machine first to establish a neural network, and use the last set of data as validation analytes. A typical set of visualized result for each τ2-Dot sample was displayed in
The nanoscale super-capacity optical multiplexing opens a new horizon for many applications. Using the time-domain τ2-profiles, different batches of materials emitting the same colour can be used to develop the new generation of dynamic anti-counterfeiting security inks, as illustrated in
The NaYF4 core nanoparticles were synthesized using a coprecipitation method1. In a typical procedure, 1 mmol RECl3 (RE=Y, Yb, Nd, Er, Tm) with different doped ratios together with 6 mL oleic acid and 15 mL 1-octadecene were added to a 50 ml three-neck round-bottom flask under vigorous stirring. The resulting mixture was heated at 150° C. for 40 mins to form lanthanide oleate complexes. The solution was cooled to 50° C., and 6 mL methanol solution containing 2.5 mmol NaOH and 4 mmol NH4F was added with vigorous stirring for 30 mins. Then the mixture was slowly heated to 150° C. and kept for 30 mins under argon flow to remove methanol and residual water. Next, the solution was quickly heated at 300° C. under argon flow for 1.5 h before cooling down to room temperature. The resulting core nanoparticles were collected and redispersed in cyclohexane with 5 mg/mL concentration after washing with cyclohexane/ethanol/methanol several times. Three series of core nanoparticles were synthesized (NaYF4: Yb, Tm; NaYF4: Yb, Nd, Tm; NaYF4: Yb, Er) with different doping concentrations using the same above method.
The precursors were prepared using the above procedure until the step where the reaction solution was slowly heated to 150° C. after adding NaOH/NH4F solution and kept for 30 mins. Instead of further heating to 300° C. to trigger nanocrystal growth, the solution was cooled down to room temperature to yield the shell precursors.
The core-shell and core-multi-shell nanoparticles were prepared by a layer-by-layer epitaxial growth method. The pre-synthesized NaYF4 core nanoparticles were used as seeds for shell modification. 0.2 mmol as-prepared core nanocrystals were added to a 50 ml flask containing 3 ml OA and 8 ml ODE. The mixture was heated to 150° C. under argon for 30 min, and then further heated to 300° C. Next, a certain amount of as-prepared shell precursors were injected into the reaction mixture and ripened at 300° C. for 2 mins, followed by the same injection and ripening cycles for several times to get different shell thickness. Finally, the slurry was cooled down to room temperature and the formed core-shell nanocrystals were purified according to the same procedure used for the core nanocrystals. The core-multi-shell nanoparticles were also prepared by the epitaxial growth method described above and the core-shell nanoparticles were used as the seeds.
Preparation of τ2-Dots (τ2-13) Tagged Microbeads
The polystyrene (PS) microbeads (d=5 μm; Sigma-Aldrich) solution was processed by swelling 5 μl (10% w/v) of PS beads with 137 μl of an 8% (v/v) chloroform solution in butanol. 40 μl (8 mg/ml) τ2-13 dots in cyclohexane was added to the above PS suspension. The solution was vortexed after adding the τ2-Dots. After incubating at 25° C. for 3 hours, the beads were washed four times, alternating between ethanol and cyclohexene. After washing, the τ2-Dots embedded beads were dispersed in ethanol and then one drop of the beads was air-dried on the surface of a coverslip for optical measurements.
The morphology characterization of the nanoparticles was performed by transmission electron microscopes of JEOL TEM-1400 at an acceleration voltage of 120 kV and JEOL TEM-2200FS with the 200 kV voltage. The cyclohexane dispersed UCNPs were imaged by dropping them onto carbon-coated copper grids. The surface morphology characterization of the PS beads (see
To prepare a sample slide for single nanoparticle measurement, a coverslip was washed with pure ethanol by ultrasonication, followed by air-drying. 20 μl of the τ2-dots (0.01 mg/ml) in cyclohexane was dropped onto the surface of a coverslip. After being air-dried, the coverslip was put over a clean glass slide and any air bubbles were squeezed out by gentle force before measurement.
A stage-scan confocal microscope was built for the intensity and lifetime measurements of single τ2-Dots, as shown in
For the lifetime measurement, the diode laser was modulated to produce 200 μs excitation pulses. The photon-counting SPAD was continuously switched on to capture the long-lifetime luminescence. For each time point, the gate-width is 5 μs with an accumulation of 10000 times. The pulsed excitation, time-gated data collection and the confocal scanning were controlled and synchronized using a multifunction data acquisition device (USB-6343, National Instruments) and a purpose-built LabVIEW program.
A wide-field fluorescence microscope was built, as shown in
To perform single nanoparticle-based machine learning, data processing was performed to select the single nanoparticles in the collected images. The brightest frame (maximum mean brightness) from the 75 frame images was selected. Then the peak pixel of each bright spot was found. For each peak, a 40-pixel by 40-pixel region of interest (ROI) was cropped centred on the peak. In each ROI, the image was segmented with the OTSU threshold and get a binary mask. Considering that two adjacent peaks might be connected in the binary mask, watershed segmentation was employed on the binary mask to get the boundaries of each peak. Finally, all the spots were sorted by their peak intensity and divided all spots into four groups (Q1 to Q4) according to their peak intensities. The Q1-Q4 represented 4 intensity thresholds to classify the groups. The spots were counted as the single nanoparticles when the peak intensities within the statistical range of single particle intensity (eg. 8000±1000 for τ2-13, equaling to Q2 group). After filtering out all the aggregated spots, an image that only involves single nanoparticles was obtained. After that, the image sequence was transformed into multiple single nanoparticle sequences. For example, if 100 particles were identified as single nanoparticles in an image sequence, this image sequence was decomposed into 100 particle sequences.
The artificial neural networks (ANN) were implemented in python using the PyTorch package (https://pytorch.org/). We extracted the normalized time-domain fluorescence intensity sequences of single nanoparticles as the input for deep learning. We performed the aforementioned data processing for all the 14 τ2-Dots. About 500 single nanoparticles were randomly selected for each τ2-Dots as the training sets, where their lifetime features and types were known. ˜100 single nanoparticles were used as the validation sets. There were five key aspects during determining the networking architecture: 1) the number of layers in the convolutional network; 2) the number of filters in each 1D convolutional layer; 3) whether to use activation function; 4) the number of neurons in each fully connected (FC) layer; 5) the keep probability for the dropout regularization scheme. We started with the network structure of one convolutional layer with 10 filters and two fully connected layers with 10 neurons for each of them.
The number of neurons was first determined in each fully connected layer ranging from 10 to 1000. Given one convolutional layer with 10 filters, the network obtained satisfactory results when the number of neurons in each FC layer was around 500. Given the above two FC layers, we started to determine the number of convolutional layers and the number of filters for each layer. The network obtained satisfactory results when using two convolutional layers with 50 filters in the first layer and 20 filters in the second layer. Then, given the above convolutional layers, we further adjusted the number of neurons for each FC layer, and found 100-200 neurons in each layer can obtain satisfactory results. With the above conductions, the network structure was temporarily determined as two convolutional layers with 50 filters in the first layer and 20 filters in the second layer followed by two FC layers with 100 neurons for each layer. With this network structure, we validated the network performance when activation functions or/and dropout scheme was/were introduced. Three activation functions have been validated during this procedure, which were ReLU, ReLU6 and RReLU. The keep probability of the dropout scheme was determined in the range from 0.5 to 0.9. After the above adjustment of the network, we went back to adjust the number of neurons in FC layers and obtained the final network architecture as below.
The fingerprint retrieval network contained two convolutional networks and two fully connected networks. The two 1D convolutional layers used the element-wise function ReLU6(x)=min(max(0,x), 6). There were 50 filters in the first 1D convolutional layer using a kernel of size 3 and the stride size was 2. The second 1D convolutional layer has 20 filters with a kernel of size 2 and the stride size was 1. The two fully connected networks contained two layers with 150 (FC1) neurons in the first layer and 100 (FC1) neurons in the second layer, and the element-wise function was also employed for each layer. We applied a dropout regularization scheme with 80% keep probability for the fully connected part. During training, the output layer neuron whose index corresponds to the input binary number was set to “1” while the other neuron activations were kept at “0”. A variant of the stochastic gradient descent (SGD) algorithm (“Adam”) was applied to train the parameters in the network through a randomly shuffled batch of size 200. We used the categorical cross-entropy loss, a learning rate of 0.005 and train the network for 50 epochs.
The classification effectiveness of convolutional neural networks was evaluated by the mean and deviation of the classification accuracy of 50 randomly sampled experiments. We have 7 sets of image sequences of each sample and run 50 experiments of training-and-testing to compute the average error and deviation. In each experiment, we randomly selected one set of image sequences for test particles. For 14 batches of nanoparticles we selected 14 image sequences. The data of single nanoparticles in the rest image sequences were used as the training set in the training algorithm section, where their lifetime features were available, but the label was unknown until computing the model error. After one training-and-testing process, the testing error for 14 image sequences was obtained. The mean and deviation of errors were computed through 50 random selections.
The time-domain anti-counterfeiting by using three types of τ2-Dots was based on the spatial modulation of the excited patterns on the sample plane. A digital micro-mirror device (DMD) was added in the wide-field optical system as the spatial light modulator to generate excitation patterns of the ABC alphabet. The laser beam illuminated the DMD after beam collimation and expansion. Then the illuminated alphabet patterns were imaged on the sample plane
Post-synthesis surface modification was adopted to transfer the τ2-Dots into hydrophilic and biocompatible before bioconjugation with DNA oligonucleotides. Surface modification was performed via ligand exchange with a block copolymer composed of hydrophilic block poly(ethylene glycol) methyl ether acrylate phosphate methacrylate (POEGMA-b-PMAEP)2. In a typical procedure, 500 μl of OA-coated τ2-Dots (20 mg/mL) were dispersed in tetrahydrofuran (THF). Then the OA-capped τ2-Dots in THF were mixed with 5 mg copolymer ending in carboxyl group in 2 mL THF. The above mixture was sonicated for one min followed by incubation in a shaker overnight at room temperature. The polymer-coated τ2-Dots were purified four times by washing/centrifugation at 14860 rpm for 20 min with water to obtain carboxyl group modified τ2-Dots. The supernatant was removed and the nanoparticles were dispersed in water for further conjugation with DNA.
We selected five couples of pathogen-related genetic sequences in the short length of 24 bases (HBV, HCV, HIV, HPV-16, EV). The protocol of carbodiimide chemistry was adopted to conjugate the carboxyl group on the polymer with the amine groups of probe DNA molecules. The five groups of carboxyl-τ2-Dots were re-activated by the EDC (100-folder molar ratio to carboxyl-τ2-Dots) in HEPES buffer (0.2 mM, pH 7.2) with slightly shaking at room temperature for 30 mins. The five groups of NH2-DNA (100 uM) was added into the above solution with 600 rpm shaking for the reaction of 3 h, respectively. The activated carboxyl-τ2-Dots were washed/centrifuged at 14680 rpm cycle two times to remove EDC and resuspended in HEPES buffer to obtain probe DNA-polymer-τ2-Dots.
The Streptavidin with a concentration of about 0.5 μg/mL in 200 μL PBS buffer was coated on the 5 pairs of 96-well plates and incubated 4 h at room temperature. Following by removal of the supernatant, 200 pmol biotinylated-capture DNA in 200 μL PBS was added into the well and incubated overnight at 4° C. for further immobilization. Washing the plates 3 times with PBS buffer after the reaction, then 200 μL of blocking with 1% casein buffer was added to each well and incubated at room temperature for 1 h. The Target-DNA in 200 uL Tris buffer was added to five of the experimental wells and incubated at room temperature for 2 h, while the five corresponding control wells were added Tris buffer without Target-DNA. After washing 3 times with Tris buffer, 100 μL complementary DNA-functionalized τ2-Dots in reaction buffer contains 0.1% casein and 5 mM NaF in Tris were added to react 1 h. Then washing the wells 3 times and the well was ultimately dissolved in 100 μl Tris-5 mM NaF before detecting the images.
Structured illumination microscopy (SIM), as a wide-field super-resolution technique, was based on the spatial modulation of the excited patterns on the sample plane. In this work, a digital micro-mirror device (DMD, DLP 4100, Texas Instruments Inc., USA) was used as the spatial light modulator to generate excitation patterns. DMD contained an array of 1024×768 micro-mirrors on the chip. The size of each micromirror was 13.68×13.68 pmt. For each of the micro-mirrors, the physical size was slightly less than 13.68 μm due to the fill factor of 91%. Each micro-mirror can be tilted to two positions along its diagonal: ±12° tilt to deflect the incident light beam away from the optical path. These micro-mirrors can be controlled independently to modulate the amplitude of incoming light to generate arbitrary illumination patterns.
As shown in
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020902731 | Aug 2020 | AU | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/AU2021/050849 | 8/4/2021 | WO |
