Protein detection apparatus and method

Abstract

The present invention relates to a method of characterising a protein (4) or part thereof comprising: extracting a one or more protein (4) and adsorbing the protein (4) onto a nanoparticle (14). A substrate (16) is provided having one or more well (18) configured to receive a nanoparticle (14) comprising the protein (4), the well (18) and the respective nanoparticle (14) configured to provide a plasmonic hotspot. Raman spectroscopy is used to characterise the protein (4) located on the nanoparticle (14).

Description

[0001] Protein detection apparatus and method

[0002] The present invention relates to a method of detecting and / or quantifying proteins (e.g. with single-molecule sensitivity).

[0003] Background

[0004] Current methods of quantifying proteins typically use mass spectroscopy. Such a method requires relatively large sample sizes due to low sensitivity of the apparatus. Mass spectroscopy apparatuses are bulky in size and are therefore not portable. Low detection sensitivity of proteins means said proteins cannot be easily quantified. Additionally, such analysis cannot be performed on live cells due to the low volume of proteins from a given cell. Alternative methods include immunoassay techniques, however, such techniques rely on antigen binding to capture and fluorophore labelling to analyse limited types of proteins.

[0005] An improvement would be welcome. It is an aim to overcome and / or ameliorate one or more of the above problems.

[0006] Statement of Invention

[0007] According to a first aspect of the invention, there is provided: a method of characterising a protein or part thereof comprising: extracting one or more protein (e.g. from a biological source); adsorbing the protein onto a nanoparticle; providing a substrate having one or more well configured to receive a nanoparticle comprising the protein, the well and the respective nanoparticle configured to provide a plasmonic hotspot; and using Raman spectroscopy to characterise the protein located on the nanoparticle.

[0008] The method may comprise extracting a plurality of proteins. The proteins may comprise the same proteins and / or different proteins and / or their post- translational modifications. The proteins may be deposited / adsorbed on a respective nanoparticle. The substrate may comprise a plurality of wells. The opening shape of the well may be circular, elliptical, square or rectangular. The nanoparticles may be received / entrapped in respective wells. The substrate may comprise an array of at least ten wells; preferably, at least one hundred wells. Each well may be individually / sequentially characterised using Raman spectroscopy.

[0009] The substrate may comprise an inert (e.g. insulating or dielectric) material. The substrate may comprise a coating. The coating may comprise a noble metal.

[0010] The noble metals may include one or more of: gold, silver and / or platinum. The material of the nanoparticle may comprise noble metal. The shape of the nanoparticle may be spherical, elliptical, rod shaped, cube shaped, or star shaped. The noble metal may include one or more of: gold, silver and / or platinum. The coating and the nanoparticles in the wells may collectively define the plasmonic hotspot. The well and the nanoparticle may enhance the plasmonic hotspot.

[0011] The proteins may comprise peptides, amino acids and / or post-translational modifications of them.

[0012] In order to characterise the protein, a Raman spectrum of a protein is compared to computational model of one or more protein. The computational model comprises machine learning or artificial intelligence model. The computational model may comprise a deep learning model.

[0013] Characterising the protein may comprise identifying the protein and / or a quantity thereof. Quantifying a given protein may comprises determination the absolute or relative number of nanoparticles comprising said protein (e.g. a proportion of the total number of nanoparticles comprising a protein).

[0014] Quantifying a given protein may comprises determination the absolute or relative number of nanoparticles on which said protein is deposited or adsorbed. Quantifying a given protein may comprise providing a relative number of nanoparticles comprising the protein relative to one or more other protein.

[0015] The method may comprise training the computational model. The model may be trained using a plurality of Raman spectra from known or reference proteins. The method may comprise training the computational model using a plurality of Raman spectra from characterising the protein located / adsorbed on the nanoparticle.

[0016] Extraction of the protein may be performed a plurality of times. Extraction may be performed over a period of time. The extracted proteins may be characterised over said time period. A protein profile of the cell may be provided over said time period. The protein profile may indicate the identified proteins and / or the quantity thereof over said time period.

[0017] The cell may be live (i.e. has not been destroyed or lysed) at two or more of said plurality of times.

[0018] The proteins may be extracted from the cell using a nanotube. The nanotube may be provided on a substrate. The cell may be adhered and cultured on the substrate. The nanotube is sized such that the cell is not destroyed when proteins are extracted therefrom. The substrate may comprise gold. Electrodes may be provided to electroporate the cell. The nanotube may be connected to electrodes to electroporate the cell.

[0019] The nanoparticles may be sized such that only a single protein can be bound to the nanoparticle. The ratio between the concentrations of nanoparticles and proteins may be controlled such that one or zero protein is bound to a given nanoparticle. The nanoparticles may be sized such that only a single nanoparticle is received / entrapped in a well. According to a further aspect of the invention, there is provided: a system for characterising a protein or part thereof comprising: a device configured to extract one or more protein (e.g. from a biological source) and adsorb the protein onto a nanoparticle; and a substrate having one or more well configured to receive a nanoparticle comprising the protein, the well and the nanoparticles configured to provide a plasmon ic hotspot such that Raman spectroscopy measurement may be performed to characterise the protein located in the well in use.

[0020] According to a further aspect of the invention, there is provided: an assay device for use with the method or system of any preceding claim comprising: a substrate, the substrate comprising an array of wells configured to receive a corresponding nanoparticle, the well comprising noble metal (e.g. gold, silver or platinum); and where the wells and the nanoparticles are configured to provide a plasmonic hotspot in use.

[0021] Any aspect of the invention may be combined with any other aspect of the invention where practicable.

[0022] List of Drawings

[0023] Embodiments of the present invention are described below, by way of example only, with reference to the accompanying drawings:

[0024] Figure 1 shows a schematic view of a protein extraction device: Figure 2 shows a substrate for protein measurement by Raman spectroscopy;

[0025] Figure 3 shows a schematic example of a Raman spectrum;

[0026] Figure 4 shows an example of Raman spectra of single-molecule protein over time and at a given time.

[0027] Figure 5 shows a schematic view of a protein characterisation method;

[0028] Figure 6 shows a schematic view of a protein profile of a cell; Figure 7 shows a Raman spectral map of the peptide (2000 spectra) with an extracted spectral map of the region 179.0-189.0 s.

[0029] Figure 8 shows a peak assignment diagram of valine in the peptide. Figure 9 shows an electronic microscopic image of a Particle-in- Well sensor.

[0030] Description

[0031] The following embodiments are only examples. Although the specification may refer to “an” embodiment in several locations, this does not necessarily mean that each such reference is to the same embodiment(s), or that the feature only applies to a single embodiment.

[0032] The articles “a” and “an” give a general sense of entities, structures, components, compositions, operations, functions, connections or the like in this document. Note also that singular terms may include pluralities.

[0033] Single features of different embodiments may also be combined to provide other embodiments. Furthermore, words "comprising" and "including" should be understood as not limiting the described embodiments to consist of only those features that have been mentioned and such embodiments may also contain features / structures that have not been specifically mentioned. All combinations of the embodiments are considered possible if their combination does not lead to structural or logical contradiction.

[0034] A protein extraction apparatus 2 is shown in figure 1 . The apparatus is configured to extract one or more protein 4 or part thereof (e.g. a peptide) from a biological source. Typically, the source comprises a cell 6 or similar host. A substrate 8 is provided on which the cell 6 may be provided. A nanotube 10 may extend through the substrate 8. The nanotube is able to extract molecules including proteins 4 from the cell 6. The nanotube 10 comprises a channel 12 extending therethrough, such that the proteins 4 can pass through the substrate 8. Such a technique allows extractions or proteins or other molecules from the cells without destruction of the cell 6. The cell 6 may therefore be live. The nanotube 10 may comprise gold. The length of the nanotube 10 may be between 1000 nm and 2000 nm. The diameter of the nanotube may be between 100 nm and 500 nm.

[0035] Nanoparticles 14 are provided. The molecules including proteins 4 that have passed through the nanotube 10 are then adsorbed onto the nanoparticles 14. The concentration of the nanoparticles 14 is adjusted according to the concentration of the molecules such one single or zero molecule adheres or is deposited thereto. Thus, a single nanoparticle 14 comprises a single protein at most. The nanoparticle 14 may comprise noble metals including gold, silver and / or platinum. The nanoparticle surface may be covered with surfactants to stabilize the nanoparticles in liquid. The adsorption of the protein 4 on the nanoparticle 14 may cause replacement of the surfactants by the protein. Such replacement may depend on the electric charge of the protein and its electric affinity to the nanoparticle surface.

[0036] The extracted proteins 4 may comprise different characteristics. For example, the proteins 4 may comprise different proteins, shapes, sizes, or conformations etc. thereof. Thus, the nanoparticles 14 may comprise proteins of different characteristics accordingly. The technique may be suitable for extraction of proteins 4 from a single cell. In other embodiments, proteins may be extracted from a plurality of cells. The cells may comprise the same or different cell type.

[0037] Electrode 15C is connected to the substrate 8 and the nanotube 10 such that the nanotube 10 may electroporate the cell. Electrodes 15A and 15B are provided on either side of the substrate to generate an electric field to drive the proteins 4 toward the nanoparticles. When the cell heals or closes the pores on its membrane, the extraction process is ended and the nanoparticles 14 are collected. As shown in figure 2, the nanoparticles 14 are deposited on a substrate 16. The substrate 16 may comprises an inert base, for example, silicon nitride. A coating may be provided in the substrate 16. In some embodiments, the coating may be noble metals including gold, silver or platinum. The substrate 16 comprises a plurality of pores, wells or holes 18. In use, a nanoparticle 14 is configured to reside in a well 18. The wells 18 are sized such that only a single nanoparticle 14 is able to fit inside each well 18. The wells 18 may comprise a diameter less than or equal to 200% of the diameter of the nanoparticles 14 (i.e. such that the diameter of the nanoparticle is half the size of the well 18). The preferred diameter ratio between the well and the nanoparticle may be 2:1 . The wells 18 and the nanoparticles 14 comprise the material (e.g. gold).

[0038] The well 18 and the particle 14 in the well 18 together are illuminated by the laser 20 to induce localized surface plasmon resonance (LSPR) that generates a strong and localized electromagnetic field often referred to as a plasmonic “hotspot” on the particle 18. The hotspot creates an area in which Raman scattering from a molecule is greatly enhanced, thereby increasing the detectability of Raman scattering within said molecule. The particle 14 and well 18 act in concert to amplify the Raman scattering of protein 4. This allows easier characterisation of the protein 4.

[0039] Raman spectroscopy apparatus (not shown) is configured to analyse the protein 4 on the nanoparticle 14 within the well 18. The Raman apparatus is configured to illuminate the well 18 using a laser 20 or the like. The laser 20 may be sufficiently focused to analyse a single well 18 at a time. The Raman apparatus may therefore characterise a single protein 4 at a time. The power density of the laser may be between 1 and 100 mW per 1 square micron. The wavelength of the laser depends on the materials of the nanoparticle and well, for example, 633 nm for silver. Optical radiation (i.e. the laser) is “squeezed” or concentrated into the hotspot on the nanoparticle to excite the protein, causing the protein to emit Raman radiation. Such Raman radiation may be the result of inelastic scattering via one or more vibrational mode. This radiation can then be measured to determine a characteristic vibrational spectrum of a molecule or functional group etc. The Raman spectroscopy apparatus is otherwise conventional and will not be described in detail.

[0040] A plurality of wells 18 are provided as an array or grid. This allows analysis of a plurality of proteins accordingly. Raman spectroscopy is performed on each well 18 individually (e.g. the wells 18 are analysed sequentially). The wells 18 may be spaced by less than or equal to 100pm; preferably, less than or equal to 50pm; preferably, less than or equal to 20pm; preferably, less than or equal to 10pm. The wells 18 may be spaced by greater than or equal to 1 pm; preferably, greater than or equal to 3pm. The substrate 16 may be greater than 1 mm is diameter / width; preferably, greater than 5mm. For example, the substrate 16 may be 1cm in width. The substrate 16 may therefore contain tens or hundreds of thousands of wells 18, or even millions of wells 18. Therefore, as many nanoparticles 14 and proteins 4 may be characterised accordingly.

[0041] Deposition of the nanoparticles 14 onto the substrate 16 may be achieved using any suitable means. In the present embodiment, the nanoparticles are held in solution. A wiper or doctor blade is used to spread the solution across the substrate 16. Control of the spreading speed of the wiper controls the drying speed of the solution, and the nanoparticles are deposited into the wells 18 after the solvent evaporates.

[0042] An example of a Raman scattering spectrum 22 is shown in figure 3. It can be appreciated that figure 3 is merely an arbitrary example to aid with the understanding of the invention and is not limiting on the invention. The spectrum 22 comprises peaks 24 indicative of Raman scattering. A plurality of peaks 24 may be provided where multiple Raman scattering modes (e.g. vibrational modes) are provided in a given molecule. The peaks 24 (i.e. the intensity and / or position thereof) provide a fingerprint of the protein, thereby allowing identification or characterisation thereof (e.g. identification of functional groups thereof). The spectrum 22 may be compared to spectra of known / reference proteins to allow identification thereof. The use of nanoparticle-in-well arrangement ensures that only a single protein or peptide is provided per well 18, thus helping to prevent mixing or contamination of Raman scattering measurements that may occur if more than one protein is located in a single well. A spectrum 22 for each well may be provided accordingly.

[0043] In the present embodiment, the Raman spectroscopy is used to identify a whole protein. In some embodiments, Raman spectroscopy may be used to identify a part of the protein (e.g. a peptide) or amino acid groups in the protein.

[0044] A specific example of Raman spectra of a single well is shown in figure 4. The left-hand side of figure 4 shows the Raman spectra as a function of time (i.e. the spectra are stacked), with the relative colour indicating intensity. The diagram is variable as a function of time as the single protein diffuses on the nanoparticle surface and part of it moves into the hotspot. A snapshot of a specific time is shown in on the right-hand side of figure 4, indicating the Raman spectrum of part of the protein in the hot spot. The diagram shows a number of peaks indicative of a number of amino acid residues, thus indicating structure of the protein / peptide. Such spectra can be analysed for each time / well accordingly.

[0045] The method of characterising and monitoring protein levels for a cell is described with reference to figure 5. In steps 26-30, the proteins 4 are extracted onto nanoparticles, the particles 14 are distributed into the well 18 array, and Raman spectroscopy is used to characterise the proteins / peptides as previously described. The Raman spectrum or data relating thereto may be stored in electronic memory.

[0046] In some cases, a protein will not adhere to portion of the nanoparticles. Thus, such nanoparticles will produce a null result (i.e. no Raman peaks of proteins are detected). In such a case data relating to the null result is stored. In step 32, the spectroscopy data in input into computational model. The computational model may comprise a machine learning or artificial intelligence model. For example, the computational model may comprise a neural network or regression model. The computational model may be provided by any suitable hardware and / or software. The hardware may comprise one or more: a processor; microprocessor; SoC; RAM; non-volatile memory; GPU or suitable computing device. The computing device may comprise a desktop, laptop, mobile phone, tablet, supercomputer, or distributed computing system. It can be appreciated that the exact form the computational model and the hardware / software thereof is not pertinent to the invention at hand, and many configurations may be used.

[0047] The computational model may generate of a model of a specific protein or peptide using the Raman spectra data (i.e. the model is trained using the Raman spectra data). Known or reference spectra may be used to initialise the model. Further training data may be generated using the apparatus of figure 2 with known proteins (i.e. measured data is input into the model). This may generate training data specific to the apparatus, thus increasing the accuracy thereof. In some embodiments, an already trained model may be used (e.g. an “off-the-shelf’ solution). Further data collection may be used to retrain or refine the trained model. In some embodiments, the model may be distributed, allowing data from multiple apparatus or systems to train the model.

[0048] The Raman spectrum 22 is suitably parameterised for input into the computational model. Parameters relating to a measured and / or training Raman spectrum may comprise one or more of: the number of peaks 24; the position (e.g. Raman shift) of the peaks 24; the ratio of two or more peaks 24; amplitude (absolute or relative) of one or more peaks 24; a characteristic shape of a one or more peaks 24; and / or a characteristic shape of the complete spectrum 22 or a portion thereof. In some embodiments, the computational model may be trained to detect defective proteins or peptides. Thus, spectra relating to said defective proteins may be used to train the model. Such defective proteins may include protein that have undergone “post-translational modification” (PTM). The proteins or defective proteins may include biomarkers for one or more pathology.

[0049] Once the model is sufficiently trained (i.e. the model can characterise the measured protein within a specific degree of accuracy), in step 34 a specific protein may be characterised or identified using a single or small number of Raman spectra. The measured spectrum is compared to the computational model to determine the specific protein in the measured spectrum. For example, the Raman shift and amplitude of one or more peak 24 in the measured spectrum is compared to Raman peaks in the machine learning model, and the closest peak to said measured peak is determined. This may be repeated over a plurality of peaks, and thus a closest match protein or peptide can be determined accordingly. For example, the number and positions (i.e. Raman shift) of the peaks. It can be appreciated that the machine model may be significantly more complex, and the system may find a closest match across the whole “fingerprint” of the Raman spectrum.

[0050] In some embodiments, the system may be configured to determine if multiple proteins are provided on single nanoparticles. For example, Raman spectra with high amplitude and random fluctuation may be associated with multiple proteins on a single nanoparticle. In such a case, then the measured data may be disregarded and / or not input into the computational model.

[0051] In step 36, the identification process is repeated for each well 18. The specific protein is identified for each well 18 accordingly. This data may then be collated to calculate the proportion of proteins detected for each sample (i.e. the number of wells 18 containing a specific protein against the total number of wells 18). This allows the type and the concentration of a given protein to be determined for a given sample, for example, via the Poisson distribution. Given the large number of nanoparticles 14, the quantity of wells 18 containing a specific protein may be broadly representative of the proportion or concentration of proteins 4 in a given cell 6. This allows determination of a “protein profile” with the cell 6.

[0052] In order to satisfy the Poisson distribution, any null results may be taken into account when quantifying the proteins. For example, to make sure one or zero protein adsorbed on one nanoparticle, the probability of 2 proteins on one nanoparticle may be less than 0.1 %. This means 90% of the particles have no proteins (i.e. null results) and 9.8% of the nanoparticles have 1 protein.

[0053] Given the cell 6 is not destroyed during the protein extraction stage, the protein identification and quantification steps can be repeated over a given time period. This allows a time dependent determination of the protein profile. An arbitrary example of a protein profile of a cell is shown in figure 6. A sample of the cell 6 is taken at times T1 , T2, T3 and T4 respectively. The proteins are identified and quantified at the respective times. This allows the user to observe the specific proteins present and their respective ratios etc. For example, at time T1 , Protein A is present in large quantities than Proteins B and C. At time T4, quantity of Protein A has decreased, whereas the amount of proteins B and C have increased. It can be seen that the measured quantities of the proteins change over time, thus allowing the user to observe different protein environments within the cell at that time.

[0054] The present process can be used to observe the physiology of the cell at a given time, and how said physiology may change over time. For example, this may be used to observe a protein or PTM that is indicative of a pathology. The present process may be used to measure how a cell responds to external stimuli. For example, how a cell responds to a specific drug or medicament.

[0055] The present process allows characterisation of the proteins in a cell. The technique may be used to characterise said proteins without destruction of the cell, thereby allowing a time dependent profile of the proteins in the cell to be determined. The use of the particle-in-well array allows sampling of a large number of proteins, helping to provide said profile. Use of machine learning allows more accurate characterisation of the proteins.

[0056] The present arrangement provides a label-free single-molecule protein detection apparatus and method. The present arrangement relates to a surface enhanced Raman spectroscopic method of detecting and / or quantifying proteins at single-molecule sensitivity.

[0057] Experimental results of the present system are shown in figures 7-9. The figures show surface-enhanced Raman spectroscopy of single molecule valine detection in peptide. Figure 7 shows a Raman spectral map of the peptide (2000 spectra) with an extracted spectral map of the region 179.0-189.0 s. Figure 8 shows the peak assignment of valine in the peptide. Figure 9 shows an electronic microscopic image of the Particle-in-Well sensor.

Claims

Claims:1 . A method of characterising a protein (4) or part thereof comprising: extracting one or more protein (4); adsorbing one protein (4) onto a nanoparticle (14); providing a substrate (16) having one or more well (18) configured to receive a nanoparticle (14) comprising the protein (4), the well (18) and the respective nanoparticle (14) configured to provide a plasmonic hotspot; and using Raman spectroscopy to characterise the protein (4) located on the nanoparticle (14).

2. A method according to claim 1 , comprising extracting a plurality of proteins (4) and adsorbing the proteins (4) on a respective nanoparticle (18); where the substrate (16) comprises a plurality of wells (18) and the nanoparticles (14) are received in respective wells (18).

3. A method according to claim 2, where the substrate (16) comprises an array of at least ten wells (18); preferably, at least one hundred wells (18).

4. A method according to any preceding claim, where in order to characterise the protein (4), a Raman spectrum of a protein (4) is compared to computational model of one or more protein.

5. A method according to any preceding claim, where characterising the protein (4) comprises identifying the protein (4) and / or a quantity thereof.

6. A method according to claim 5, where the quantifying a given protein (4) comprises determination the absolute or relative number of nanoparticles (14) comprising said protein (4).

7. A method according to any of claims 4-6, where the computational model comprises a machine learning model.

8. A method according to any of claims 4-7, comprising training the computational model using a plurality of Raman spectra from known or reference proteins.

9. A method according to any of claims 4-8, comprising training the computational model using a plurality of Raman spectra from characterising the protein (4) located on the nanoparticle.

10. A method according to any preceding claim, where extraction of the protein (4) is performed a plurality of times over a period of time, and the extracted proteins are characterised over said time period accordingly such that a protein profile of a cell (6) is provided over said time period.

11. A method according to claim 10, where the cell (6) is live at two or more of said plurality of times.

12. A method according to any preceding claim, where the proteins (4) are extracted from the cell (6) using a nanotube (10) provided on a substrate (8).

13. A method according to any preceding claim, where the ratio between the concentrations of nanoparticles (14) and proteins (6) is controlled such that one or zero protein (4) is adsorbed to a given nanoparticle (14).

14. A system for characterising a protein (6) or part thereof comprising: a device configured to extract a one or more protein (4) and adsorb the protein (4) onto a nanoparticle (14); and a substrate (16) having one or more well (18) configured to receive a nanoparticle (14) comprising the protein (4), the well (18) and the nanoparticle (14) configured to provide a plasmonic hotspot such that Raman spectroscopy measurement may be performed to characterise the protein (6) located in the well in use.

15. An assay device for use with the method or system of any preceding claim comprising: a substrate (16), the substrate (16) comprising an array of wells (18) configured to receive a corresponding nanoparticle (14), the well (18) comprising a noble metal; andwhere the wells (18) and the nanoparticles (14) are configured to provide a plasmonic hotspot in use.5

Images