Nanoparticle distribution detection method in plants based on terahertz spectroscopy imaging

Abstract

The present invention relates to a method for detecting the distribution of nanoparticles in a plant body based on terahertz spectral imaging, and belongs to the field of terahertz spectral imaging detection applications. The method comprises: S1: using terahertz spectral imaging technology to detect and analyze the spectral characteristics of a nanoparticle aqueous solution; S2: using terahertz spectral imaging technology to detect and analyze the spectral characteristics of a cell model with nanoparticles; S3: establishing a test plant model, using terahertz spectral imaging technology to detect and analyze, and determining the terahertz spectral characteristics related to the distribution of nanoparticles in the test plant model; S4: actual sample detection verification feedback improvement. The present invention can realize in vivo label-free and nondestructive detection.

Description

Technical Field

[0001] The invention belongs to the field of terahertz spectral imaging detection applications and relates to a method for detecting nanoparticle distribution in a plant based on terahertz spectral imaging technology. Background Art

[0002] In the agricultural field, nanomaterials can be used as carriers to load and coat pesticide compounds, effectively preventing the loaded pesticides from being degraded by factors such as pH and temperature in the environment, and have great research value and application potential. However, after the active ingredients of pesticides are loaded by nanomaterials, a new drug-loaded particle is formed, which may have different effects on the growth of target crops than traditional formulations. This issue is a core scientific issue that restricts the development and application of new pesticide nanotechnology, and is also the key to whether new pesticide formulations can be implemented.

[0003] Nanoagriculture is still in its infancy. If the basic interactions and mechanisms between nanomaterials and plants can be properly understood, then nanoagriculture has a promising future. Currently, most of the research on nanomaterial-based biomolecule plant systems focuses on evaluating the final efficacy. Relatively speaking, there is little research on the interaction between nanomaterials and plant tissues and cells, and the transportation, transport and uptake mechanisms of nanomaterials in plants.

[0004] At the same time, nanomaterials can be enriched through nutrient transfer in the food chain, causing toxicological effects. The types and quantities of nanomaterials in the soil environment are increasing year by year. Studying the combined toxic effects and long-term toxic effects of various nanomaterials on soil organisms is an important direction for future research. Among them, detecting the distribution of nanomaterials in plants is an important experimental basis.

[0005] Fluorescence microscopy and electron microscopy are currently the main means of observing the distribution of nanomaterials in plants. They cannot be detected without labeling or damage, and thus cannot be monitored dynamically.

[0006] Terahertz (THz, 1THz = 1×10 12 THz) is an electromagnetic wave between infrared and microwave in the electromagnetic spectrum, with a frequency range of 0.1-10THz, a corresponding wavelength of 3mm-30μm, and a photon energy of 0.41meV-41meV. Theoretically, the energy range of THz overlaps greatly with the vibration of the biological molecular skeleton and the twisting and rotation energy of the functional groups; at the same time, the hydrogen bonds and van der Waals interactions involved in the interaction process of biological molecules also fall within the THz energy range, so biological samples have a good response to THz. THz technology can be used to non-destructively and conveniently obtain rich chemical composition, physical properties and structural information of biological samples under label-free conditions, revealing sample information that cannot or is difficult to obtain with other existing optical technologies (such as infrared, Raman, fluorescence, X-ray, etc.).

[0007] At present, in the agricultural field, terahertz technology is mainly used for qualitative and quantitative research on water content. Taking advantage of the strong absorption of water to terahertz waves, water acts like a contrast agent during the imaging process. Slight differences in water content in samples can appear significantly different in terahertz band detection. But at the same time, the water content in organisms can reach 70%, and plants are even higher. The difference in water content is the main distinguishing mechanism of terahertz imaging, but the high absorption of terahertz waves by water molecules will also lead to a reduction in the final contrast.

[0008] The concept of nanocontrast agents for terahertz imaging has proven that the contrast of terahertz imaging in living organisms can be effectively improved through functional nanoparticles. The first contrast agent used in terahertz imaging was gold nanorods (GNRs). They are the most commonly used metals for making contrast agents in terahertz medical imaging due to their ability to undergo biological binding processes and strong scattering around the localized surface plasmon resonance (LSPR) wavelength. Typically, GNRs irradiated with near-infrared (NIR) lasers can excite surface plasmon polaritons (SPPs), thereby increasing the temperature of nearby water molecules and THz signal enhancement. Under NIR laser irradiation, the amplitude of the terahertz waveform reflected in the GNRs sample increases, and the reflected signal increases with increasing laser intensity and irradiation time.

[0009] Gadolinium oxide (Gd2O3) nanoparticles were originally considered as a contrast agent suitable for MRI and have also been reported as a useful contrast agent in terahertz imaging. Using a mechanism different from the thermal effect, terahertz waves interact with Gd2O3 nanoparticles more strongly than with water, with an absorption rate three orders of magnitude higher, making them suitable for terahertz medical imaging. The power absorption of Gd2O3 nanoparticles is proportional to their frequency and concentration.

[0010] Carbon nanoparticles have also been proposed as potential contrast agents for terahertz imaging of breast cancer in the form of nanosized diamonds (nanodiamonds; NDs) and onion-like carbon (OLC, also known as carbon nanoonions). These carbon nanoparticles do not require the use of an external non-terahertz source to enhance contrast. Results showed that there was a clear interaction signal between OLC and terahertz, indicating that OLC is suitable for selectively targeted cancer cell imaging. However, the biotoxicity of OLC remains a challenge and requires further investigation.

[0011] The above proves that terahertz imaging can theoretically distinguish between tissues containing nanoparticles and those not containing nanoparticles, and is expected to detect the location and transport of nanoparticles in plants without labeling and non-destructive in vivo, especially in leaf structures. Summary of the invention

[0012] In view of this, the object of the present invention is to provide a method for detecting the distribution of nanoparticles in plants based on terahertz spectral imaging, which can realize label-free and non-destructive detection in vivo.

[0013] In order to achieve the above object, the present invention provides the following technical solutions:

[0014] A method for detecting nanoparticle distribution in plants based on terahertz spectral imaging specifically comprises the following steps:

[0015] S1: Use terahertz spectral imaging technology to detect and analyze the spectral characteristics of nanoparticle aqueous solutions;

[0016] S2: Using terahertz spectral imaging technology to detect and analyze the spectral characteristics of cell models with nanoparticles;

[0017] S3: Establish a test plant model, use terahertz spectral imaging technology to detect and analyze, and determine the terahertz spectral characteristics related to the distribution of nanoparticles in the test plant model;

[0018] S4: Actual sample testing, verification, feedback and improvement.

[0019] Further, in step S1, the spectral characteristics of the nanoparticle aqueous solution are detected and analyzed, which specifically includes the following steps:

[0020] S11: Use terahertz attenuated total reflection to obtain and analyze the terahertz spectrum characteristic information of the liquid detection object;

[0021] For a nanomaterial, the terahertz spectrum of different ions, concentrations, pH and temperature conditions is detected; the detection conditions are based on the physiological water environment and are appropriately expanded;

[0022] S12: Compare the terahertz spectral characteristics of aqueous solution without nanoparticles and aqueous solution with nanoparticles, and obtain the terahertz spectral changes of water environment caused by nanoparticles; wherein, the terahertz spectral characteristics include time domain spectral information, frequency domain spectral information and further analysis results based on this; the terahertz spectral changes include relative size, the influence of pH and temperature changes, etc.;

[0023] S13: Screen the features with obvious differences based on the numerical differences of the terahertz spectral features in the solution under the same conditions.

[0024] Further, in step S2, the spectral characteristics of the cell model with nanoparticles are detected and analyzed, which specifically includes the following steps:

[0025] S21: Use cell models to simulate near-physiological conditions, specifically including: nanomaterials are made to appear inside or outside cells through co-culture or microinjection (reference is made to existing research data and TEM, Raman spectroscopy, etc. to determine the location where a certain type of nanomaterial appears under certain culture conditions); among them, the cell model selects suspended cell lines or dispersed thin-walled cells or epidermal cells, etc.;

[0026] S22: Detect cells by terahertz near-field microscopic imaging to obtain terahertz spectral characteristics of the detected cells; wherein the terahertz spectral characteristics include time domain spectral information, frequency domain spectral information and further analysis results based on these to screen for characteristics with obvious differences; at least 10 samples are detected for each type of cell;

[0027] S23: By comparing with normal cells, the spectral imaging characteristics of the cell model with nanoparticles are analyzed to obtain the influence of nanoparticles on the terahertz characteristic information of cells;

[0028] S24: Analyze the difference in the effects of nanoparticles present inside and outside cells.

[0029] Further, in step S3, the terahertz spectrum characteristics related to the distribution of nanoparticles in the test plant model are detected and analyzed, which specifically includes the following steps:

[0030] S31: Plant samples are preferably clones from the same strain or the same mother plant. Through large-scale cultivation, uniform plant seedlings are selected, free of basic diseases, and in good growth condition;

[0031] S32: Normal culture is used as the control group, and nanomaterial treatment is used as the experimental group; the treatment conditions are summarized by steps S1 and S2 and reference research data, and different methods are used for different materials, including leaf spraying, culture medium incorporation, etc.; the control group and the laboratory test objects are selected as much as possible with the same leaf position, the same height, similar size, and uniform growth. The target objects are subjected to in vivo terahertz spectral imaging detection at intervals of 1 hour, and then the traditional method is used to verify the distribution characteristics of the nanoparticles;

[0032] S33: The same treatment conditions are repeated at least 3 times, and there is no special requirement for the terahertz detection environment;

[0033] S34: Terahertz spectral data analysis uses different spectral features to reconstruct a variety of terahertz spectral images with different information characteristics;

[0034] S35: Image based on the terahertz spectral characteristics of nanoparticles in solution / cells, compare normal leaves with treated photos, analyze tissue terahertz imaging, refer to the detection results of other methods, and determine the terahertz spectral characteristics related to nanoparticle distribution detection;

[0035] S36: using image processing to highlight feature information in imaging;

[0036] S37: Differentiate between different leaf regions (mesophyll and veins) based on spectral features.

[0037] Further, in step S4, actual sample detection, verification, feedback and improvement are performed, specifically including the following steps:

[0038] S41: The test object is a new model sample, positive control, negative control, etc., which is used to determine the applicable conditions and other information;

[0039] S42: Further expand the database in practice: increase the types of nanoparticles to be identified; increase the types of plants; optimize the characteristic parameters of a certain type of sample;

[0040] S43: After the sample size is increased, machine learning and other means can be used to improve the recognition method; and based on the evaluation results, each set can be expanded, training can be iterated, and feedback can be used to adjust the machine learning efficiency.

[0041] The beneficial effects of the present invention are:

[0042] (1) As a spectroscopy method, compared with the immunoassay, the present invention is label-free and non-destructive, is not limited by the type of antibodies, is not affected by the antibody titer, and is not affected by the sample processing effect.

[0043] (2) Compared with electron microscopy, the method of the present invention is label-free and non-destructive, has simple processing conditions, and can be observed in vivo and continuously.

[0044] (3) The method of the present invention can image the same target over time and monitor the transport of nanoparticles. In practice, more application scenarios may be developed based on the principles of this invention patent.

[0045] (4) The present invention utilizes the concept of terahertz imaging nanocontrast agents, and can effectively improve the contrast of terahertz imaging of living organisms through functional nanoparticles. The present invention is an innovative application of this experimental fact, which will deepen people's understanding of the interaction between nanoparticles and leaf tissues, and provide new means and information for the research and development of nanopesticides and biological effects.

[0046] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0048] Figure 1 The present invention is a flow chart of the method for detecting the distribution of nanoparticles in plants based on terahertz spectral imaging. DETAILED DESCRIPTION

[0049] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0050] See also Figure 1 This embodiment provides a method for detecting the distribution of nanoparticles in watermelon based on terahertz spectral imaging, which specifically includes the following steps:

[0051] Step 1: Analyze the spectral characteristics of Au nanorod aqueous solution (preparation process).

[0052] (1) Prepare Au NP-double distilled water and Au NP-PBS buffer solutions, pH 6-8, and select 5-7 conditions within the temperature range of 15-30°C.

[0053] (2) The terahertz attenuated total reflection (ATR) was used to detect the terahertz spectra of the solution containing Au NP particles and the solution without Au NP particles to obtain the terahertz time-domain spectral information of the samples.

[0054] (3) Refer to the terahertz dielectric constant of the solution (A review of terahertz time-domain spectroscopy and frequency-domain spectroscopy, Hirori H, Yamashita, Nagai M, et al. Japanese Journal of Applied Physics. Part a, Letters Express Letters, 2004, 43(10A): L1287) and calculate the absorption coefficient, refractive index, etc.

[0055] (4) Compare the terahertz spectral characteristics of aqueous solutions without nanoparticles and those with nanoparticles, including time-domain spectral information, frequency-domain spectral information, and further analysis results based on this, including indicators such as relative size, the effects of pH and temperature changes, etc.

[0056] (5) Based on the numerical differences of the THz spectral features in the w / o Au NP solution under the same conditions, the features with obvious differences are determined as candidate indicators for further imaging analysis.

[0057] Step 2: Analyze the terahertz spectral imaging characteristics of watermelon pulp cells in the presence of Au nanoparticles (preparation process).

[0058] (1) Isolate watermelon pulp cells (refer to Single cell imaging with near-field terahertzscanning microscopy, Cell Proliferation, 2020, 53(4): 0-e12788).

[0059] (2) Place the parts in a PBS buffer solution containing Au NPs. The concentration is based on the concentration with the most obvious difference in step 1, and the solution is appropriately extended to both sides. The immersion time is determined in a preliminary experiment. On the premise of ensuring that the cells do not rupture, set 3-5 time points from 0 h.

[0060] (3) The untreated part was directly subjected to terahertz near-field super-resolution microscopy imaging (see Single cell imaging with near-field terahertz scanning microscopy, Cell Proliferation, 2020, 53(4): 0-e12788) to detect 5-10 cells.

[0061] (4) The treated cells were subjected to TEM to determine the location of Au in the pulp cells.

[0062] (5) Terahertz near-field microscopy was used to detect AuNP-treated cells (see Single cell imaging with near-field terahertz scanning microscopy, Cell Proliferation, 2020, 53(4): 0-e12788). At least 10 samples were tested for each type of cell.

[0063] (6) Terahertz spectral imaging (see Single cell imaging with near-field terahertz scanning microscopy, Cell Proliferation, 2020, 53(4): 0-e12788). By comparing watermelon pulp cells with or without Au NPs, the effect of the presence of nanoparticles on the terahertz spectral imaging characteristics was obtained, and the differences between different treatment conditions were analyzed.

[0064] Step 3: Establish a model of Au NP-treated citrus plants, perform THz imaging detection and analysis, and determine the THz spectral imaging features related to the distribution of Au nanoparticles.

[0065] (1) Using the same strain of trifoliate orange, select seedlings with uniform growth and culture them normally as the control group.

[0066] (2) Au nanoparticles were injected into the stem. The concentration conditions were determined by steps 1 and 2, and the concentrations were relatively different.

[0067] (3) The control group and the laboratory test objects were selected to have leaves of the same leaf position, height, size, and uniform growth as much as possible. The target objects were subjected to in vivo terahertz spectroscopy imaging detection at intervals of 1 hour. There were no special requirements for the terahertz detection environment. The thickness of the test leaves at different positions was detected by a spiral micrometer. The leaves were then picked and the distribution characteristics of the nanoparticles were verified by fluorescence microscopy and TEM methods, and the water content of the leaves was determined by weighing method.

[0068] (4) The same treatment conditions were repeated at least 3 times.

[0069] (5) Terahertz spectral data analysis (using different characteristic parameters, refer to CN202010814290.9, CN201910860639.X, Temporal and Spatial Variability of Water Status in Plant Leaves by Terahertz Imaging, IEEE Transactions on Terahertz Science and Technology, 2018, 8(5): 520-527), using different spectral features to reconstruct terahertz spectral images with different information characteristics

[0070] (6) Based on the effect of solution / intracellular nanoparticles on terahertz spectral characteristics, normal leaves with similar structure and water content were compared with treated photos, tissue terahertz imaging was analyzed, and the detection results of other methods were referred to to determine the terahertz spectral characteristics related to nanoparticle distribution detection.

[0071] (7) Spectral characteristics distinguish different regions of the leaf (mesophyll and veins) and the effects of different times.

[0072] Step 4: Actual sample testing, verification, feedback and improvement.

[0073] (1) Image processing is used to highlight the feature information in the image.

[0074] (2) The test objects are new model samples, positive controls, negative controls, etc., which are used to determine applicable conditions and other information.

[0075] (3) Further expand the database in practice: increase the types of nanoparticles that can be identified; increase the types of plants; and optimize the characteristic parameters of a certain type of sample.

[0076] (4) After the sample size is increased, machine learning and other means can be used to improve the recognition method. Based on the evaluation results, each set can be expanded, training can be iterated, and feedback can be used to adjust the efficiency of machine learning.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for detecting the distribution of nanoparticles in plants based on terahertz spectral imaging, characterized in that: The method specifically comprises the following steps: S1: Using terahertz spectral imaging technology to detect and analyze the spectral characteristics of nanoparticle aqueous solution, specifically including the following steps: S11: Use terahertz attenuated total reflection to obtain and analyze terahertz spectrum characteristic information of the liquid detection object; For a nanomaterial, the terahertz spectrum of different ions, concentrations, pH and temperature changes is detected; the detection conditions are based on the physiological water environment and are appropriately expanded; S12: Compare the terahertz spectral characteristics of the aqueous solution without nanoparticles and the aqueous solution with nanoparticles, and obtain the terahertz spectral changes of the water environment caused by the nanoparticles; wherein the terahertz spectral characteristics include time domain spectral information, frequency domain spectral information and further analysis results based on these; the terahertz spectral changes include relative size, and the influence of pH and temperature changes; S13: Screening out features with obvious differences based on the differences in the numerical values ​​of the terahertz spectral features in the solution under the same conditions; S2: Using terahertz spectral imaging technology, the spectral characteristics of the cell model with nanoparticles are detected and analyzed, which specifically includes the following steps: S21: Using cell models to simulate near-physiological conditions, specifically including: nanomaterials are made to appear inside or outside cells by co-culture or microinjection; the cell model is a suspended cell line or dispersed parenchyma cells or epidermal cells; S22: Detect cells by terahertz near-field microscopic imaging to obtain terahertz spectral characteristics of the detected cells; wherein the terahertz spectral characteristics include time domain spectral information, frequency domain spectral information and further analysis results based on these to screen for characteristics with obvious differences; at least 10 samples are detected for each type of cell; S23: By comparing with normal cells, the spectral imaging characteristics of the cell model with nanoparticles are analyzed to obtain the influence of nanoparticles on the terahertz characteristic information of cells; S24: Analyze the difference in the effects of nanoparticles inside and outside cells; S3: Establish a test plant model, use terahertz spectral imaging technology to detect and analyze, and determine the terahertz spectral characteristics related to the distribution of nanoparticles in the test plant model; S4: Actual sample testing, verification, feedback and improvement.

2. The method for detecting the distribution of nanoparticles in plants according to claim 1, characterized in that: In step S3, the terahertz spectrum characteristics related to the distribution of nanoparticles in the test plant model are detected and analyzed, which specifically includes the following steps: S31: Plant samples are preferably clones from the same strain or the same mother plant. Through large-scale cultivation, uniform plant seedlings are selected, free of basic diseases, and in good growth condition; S32: Normal culture was used as the control group, and nanomaterial treatment was used as the experimental group. The control group and the laboratory test objects were selected from leaves with the same leaf position, height, similar size, and uniform growth. In vivo terahertz spectroscopy imaging detection was performed on the target objects at intervals of 1 hour, and then the traditional method was used to verify the distribution characteristics of the nanoparticles. S33: The same treatment conditions are repeated at least 3 times, and there is no special requirement for the terahertz detection environment; S34: Terahertz spectral data analysis uses different spectral features to reconstruct a variety of terahertz spectral images with different information characteristics; S35: Image based on the terahertz spectral characteristics of nanoparticles in solution / cells, compare normal leaves with treated photos, analyze tissue terahertz imaging, refer to the detection results of other methods, and determine the terahertz spectral characteristics related to nanoparticle distribution detection; S36: using image processing to highlight feature information in imaging; S37: Differentiate different regions of leaves based on spectral features.

3. The method for detecting the distribution of nanoparticles in plants according to claim 1, characterized in that: In step S4, actual sample testing, verification, feedback and improvement are performed, which specifically includes the following steps: S41: The test objects are new model samples, positive controls, and negative controls, which are used to determine the applicable conditions; S42: Further expand the database in practice: increase the types of nanoparticles to be identified; increase the types of plants; optimize the characteristic parameters of a certain type of sample; S43: After the sample size is increased, use machine learning to improve the recognition method; and based on the evaluation results, expand each set, iterate training, and use feedback to adjust the machine learning efficiency.

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