Odor substance measuring device, and odor substance measuring method and measuring system using the same.

Abstract

To provide a means for measuring odor substances that can identify and measure multiple odor substances, is highly portable, and allows for easy measurement outside of a laboratory. [Solution] The odor substance measuring device includes one or more first-shaped hydrogel pieces having a first shape and one or more second-shaped hydrogel pieces having a second shape different from the first shape. The first-shaped hydrogel pieces contain first odor sensor cells that emit a signal in the presence of a first odor substance, and the second-shaped hydrogel pieces contain second odor sensor cells that emit a signal in the presence of a second odor substance different from the first odor substance.

Description

Technical Field

[0001] The present invention relates to an odorant measurement device capable of identifying and measuring a plurality of types of odorants, as well as an odorant measurement method and measurement system using the same.

Background Art

[0002] In general, methods for detecting odors include methods utilizing the olfactory sense of humans or animals, methods using chemical and physical sensors, and methods using gas chromatography. However, methods utilizing the olfactory sense of humans or animals have problems with reproducibility, quantitativeness, and safety in odor detection by humans or animals, and the latter two methods also have problems related to the cost of large-scale apparatuses and portability. On the other hand, humans and animals have olfactory receptors that selectively accept various odorants. In recent years, attempts have been made to utilize cells that express these olfactory receptors possessed by various animals for odorant detection (Non-Patent Document 1). Up to now, it has been reported that olfactory receptor-expressing odor sensor cells are encapsulated in a hydrogel, and used to identify odorants in a liquid. There are also reports that hydrogels encapsulating sensor cells are arranged at specific positions. Furthermore, a method of labeling hydrogel particles to perform immunoassay on multiple types of test substances (Non-Patent Document 2), and a method of labeling hydrogel particles to discriminate each particle (Non-Patent Document 3) are also known. In addition, odor sensor cells that emit a signal in the presence of a specific odorant are also known (Non-Patent Document 4).

Prior Art Literature

Non-Patent Literature

[0003]

Non-Patent Document 1

Non-Patent Document 2

[0004] Among methods for identifying and measuring multiple odor molecules, the chromatography method is difficult to perform outside the laboratory, and it is not easy to perform measurements in outdoor environments where the odor molecules to be measured are present. Furthermore, the method of encapsulating olfactory receptor-expressing odor sensor cells in hydrogels and arranging each hydrogel piece at a specific position for each measurable odor molecule is time-consuming to arrange and has difficulties in reproducibility during quantification.

[0005] Therefore, the object of the present invention is to provide a means for measuring odor substances that can identify and measure multiple odor substances, is highly portable, and can be easily used for measurements outside of a laboratory. [Means for solving the problem]

[0006] As a result of diligent research, the inventors of the present invention have conceived of the ability to distinguish and measure odor substances in a test sample by using hydrogel pieces of multiple shapes, each containing odor sensor cells that can measure different odor substances, exposing each hydrogel piece to an odor substance-containing test sample, and investigating which shape of hydrogel piece emits a signal. This has led to the present invention.

[0007] In other words, the present invention provides the following: (1) An odor substance measuring device comprising one or more first-shaped hydrogel pieces having a first shape and one or more second-shaped hydrogel pieces having a second shape different from the first shape, wherein the first-shaped hydrogel pieces contain first odor sensor cells that emit a signal in the presence of a first odor substance, and the second-shaped hydrogel pieces contain second odor sensor cells that emit a signal in the presence of a second odor substance different from the first odor substance. (2) The odor substance measuring device according to (1), further comprising hydrogel pieces of one or more shapes different from the first and second shapes, wherein each hydrogel piece of shape contains a sensor cell that emits a signal in the presence of an odor substance different from the first and second odor substances, and each odor sensor cell emits a signal in the presence of an odor substance different from that of each hydrogel piece of shape. (3) The odor substance measuring device according to (1) or (2), wherein the signal is fluorescent. (4) The step of preparing an odor substance measuring device as described in any of (1) to (3), The steps include bringing a test sample containing one or more odor substances into contact with the odor substance measuring device, A method for measuring odor substances, including a step of determining which shape of hydrogel piece emitted a signal. (5) The method according to (4), comprising measuring the signal emitted by each hydrogel piece. (6) The method according to (5), wherein the signal is fluorescence, and comprises measuring the fluorescence emitted by each hydrogel piece. (7) The method according to (5), comprising a computer equipped with a neural network trained on the shapes of each hydrogel piece, an image analysis means connected to the computer for examining the shapes of each hydrogel piece, and a signal measuring device connected to the computer for measuring signals from each hydrogel piece, to automatically measure the signals emitted by each hydrogel piece of each shape. (8) An odor substance measuring system comprising the odor substance measuring device described in (1) or (2), a computer equipped with a neural network trained on the shapes of each hydrogel piece, an image analysis means connected to the computer for examining the shapes of each hydrogel piece, and a signal measuring device connected to the computer for measuring signals from each hydrogel piece. (9) The odor substance measurement system according to (8), wherein the signal is fluorescence and the signal measuring device is a fluorescence measuring device. [Effects of the Invention]

[0008] According to the present invention, multiple odor substances can be easily identified and measured even outside of a laboratory. [Brief explanation of the drawing]

[0009] [Figure 1] The following examples show (A) the procedure for preparing intracellular encapsulated hydrogel pieces of odor sensors, (B) biopsy trephines with circular, triangular, and square tips used for hollowing out the intracellular encapsulated hydrogel, and (C) the prepared intracellular encapsulated hydrogel pieces of odor sensors. [Figure 2] The following examples show: (A) the training procedure for the convolutional neural network (CNN) model used for shape classification of intracellular hydrogels in odor sensors; (B) the behavior of the loss function (categorical cross-entropy) during training and validation; (C) the behavior of the classification accuracy (correctness) during training and validation; (D) the confusion matrix; and (E) the constructed hydrogel shape classification algorithm. [Figure 3] The following examples show: (A) an overview of the three types of odor sensor cells used in the responsiveness analysis based on shape recognition; (B) the procedure for the odor substance addition experiment; (C) an example of the shape classification result; (D) the confusion matrix of the shape classification results in the four experiments; and (EH) the change in fluorescence intensity of each shape identification gel obtained in the four odor substance addition experiments. ΔI / I0 represents the relative amplification factor from the initial fluorescence intensity, and is shown by subtracting the fluorescence intensity I0 at time 0 from the fluorescence intensity I at each time, and normalizing by I0. The maximum value of ΔI / I0 is plotted in the far right panel along with the type of odor substance added in each experiment. The black line in the far right panel is the median, and the black arrows represent misclassification of the shape. [Modes for carrying out the invention]

[0010] As described above, the odor substance measuring device of the present invention includes one or more first-shaped hydrogel pieces having a first shape and one or more second-shaped hydrogel pieces having a second shape different from the first shape. The first-shaped hydrogel piece contains a first odor sensor cell that emits a signal in the presence of a first odor substance, and the second-shaped hydrogel piece contains a second odor sensor cell that emits a signal in the presence of a second odor substance different from the first odor substance. The odor substance measuring device may include hydrogel pieces of three or more shapes. For example, in the following embodiment, three types of hydrogel pieces with planar shapes of circular, triangular, and square are used. The number of shapes may be four or more. As the hydrogel, a well-known one commonly used for cell encapsulation can be used, such as low-melting-point agarose or alginic acid.

[0011] Each hydrogel piece encloses an olfactory cell sensor that emits a signal in the presence of an odorant. The first-shape hydrogel piece encloses a first olfactory sensor cell that emits a signal in the presence of a first odorant, and the second-shape hydrogel piece encloses a second olfactory sensor cell that emits a signal in the presence of a second odorant different from the first odorant. When there are three or more types of hydrogel piece shapes, each shape type encloses an olfactory sensor cell that emits a signal in the presence of a different odorant. For example, in the following examples, the hydrogel piece with a circular planar shape encloses olfactory sensor cells that emit green fluorescence in the presence of phenol, the hydrogel piece with a triangular planar shape encloses olfactory sensor cells that emit green fluorescence in the presence of 6-methyl-5-hepten-2-one, and the hydrogel piece with a quadrilateral planar shape encloses olfactory sensor cells that emit green fluorescence in the presence of acetophenone.

[0012] The signal emitted by the olfactory sensor cell in the presence of a specific odorant is preferably a visually observable one, more preferably fluorescence, color development or the like, and particularly preferably fluorescence. Olfactory sensor cells that emit green fluorescence in the presence of a specific odorant are well known, and olfactory sensor cells that react to various odorants are known. Such known olfactory sensor cells can be preferably used. Examples of such olfactory sensor cells include cells obtained by introducing genes for an olfactory receptor (OR), an olfactory co-receptor (Orco), and a calcium-sensitive green fluorescent protein (GCaMP) into insect cells to allow their constitutive expression, as described in Non-Patent Document 4. When a specific odorant binds to the OR, the calcium ion channel composed of the OR and Orco opens, and calcium ions flow into the cells. This induces the green fluorescence of intracellular GCaMP.

[0013] The hydrogel pieces are preferably flat plate-shaped, as this shape facilitates easy shape recognition and simple production. In this case, the thickness of the hydrogel pieces is not particularly limited, but is usually about 0.1 mm to 1.0 mm, preferably about 0.4 mm to 0.6 mm. In addition, the size of the hydrogel pieces is preferably a size that is visible to the naked eye; for a circular shape, the diameter, and for a polygonal shape, the length of one side, is usually about 1 mm to 5 mm, preferably about 2 mm to 4 mm. The density of cells encapsulated in the hydrogel pieces is not particularly limited, but is usually 1 x 10 7 cells / mL to 2 x 10 8 cells / mL, preferably 5 x 10 7 cells / mL to 1 x 10 8 cells / mL. The number of hydrogel pieces of each shape may be 1 or more, but is usually about 3 to 100, preferably about 5 to 30.

[0014] The odor substance to be measured is not limited as long as measurable odor sensor cells are available, and examples include, but are not limited to, phenol, 6-methyl-5-hepten-2-one, and acetophenone measured in the examples, as well as benzaldehyde and the like.

[0015] When measuring an odor substance, the aforementioned odor substance measurement device is exposed to a test sample containing the odor substance to be measured. It is simple and preferable to arbitrarily place the odor substance measurement device on a flat plate such that the hydrogel pieces do not overlap each other and expose it to the test sample, but the exposure mode is not necessarily limited thereto. The exposure time is not particularly limited, but is usually about 1 minute to 10 minutes, preferably about 2 minutes to 5 minutes. The temperature during exposure may be ambient temperature (surrounding temperature), and usually no heating or cooling is required. The "measurement" referred to in the present invention includes all of detection, semi-quantitation, and quantitation.

[0016] If a test sample contains a measurable odorant, odor sensor cells that emit a signal in the presence of that odorant will emit a signal. For example, if the signal is green fluorescence, only odor sensor cells that emit green fluorescence in the presence of that odorant will emit green fluorescence. Therefore, when excitation light (usually around 460 nm to 500 nm in the case of green fluorescent protein) is shone on each hydrogel piece after exposure to the test sample, only the hydrogel pieces containing odor sensor cells that react to the odorant in the test sample will emit green fluorescence. Thus, by visually observing which shapes of hydrogel pieces emit green fluorescence, the odorant in the test sample can be identified and detected from other odorants. For example, in the odorant measuring device fabricated in the example below, if the odorant in the test sample is phenol, the circular hydrogel piece will emit green fluorescence, while the other shapes of hydrogel pieces will not. If the odorant in the test sample is 6-methyl-5-hepten-2-one, the triangular hydrogel piece will emit green fluorescence, while hydrogel pieces of other shapes will not. If the odorant in the test sample is acetophenone, the square hydrogel piece will emit green fluorescence, while hydrogel pieces of other shapes will not. In this way, by visually observing which shape of hydrogel piece emits green fluorescence, the odorant in the test sample can be identified and detected from other odorants. If the test sample contains multiple types of measurable odorants, multiple hydrogel pieces of different shapes corresponding to each odorant will emit green fluorescence. Furthermore, since the emitted fluorescence correlates with the concentration of the odorant in the test sample, semi-quantification is possible by observing the fluorescence intensity. In addition, quantitative analysis of the odorant is possible by measuring the fluorescence intensity with a fluorescence analyzer. For quantitative and semi-quantitative analysis, a calibration curve can be created by first exposing standard samples containing odor substances of known concentrations to an odor substance measuring device and measuring their fluorescence intensity. The fluorescence intensity measured for the actual test sample can then be applied to this calibration curve to quantify the odor substance in the unknown test sample.

[0017] Although the simplest case of measurement by visual inspection has been described above, it is also possible to automatically measure the signals emitted by hydrogel pieces of each shape using a system comprising a computer equipped with a neural network trained on the shapes of each hydrogel piece, an image analysis means connected to the computer to examine the shape of each hydrogel piece, and a signal measuring device connected to the computer to measure the signals from each hydrogel piece. Image analysis, learning of shape recognition using a neural network, and coordination with the signal measuring device can be basically carried out by known methods, and detailed examples are described in the embodiments below, so those skilled in the art can easily construct such a system by referring to the embodiments below. [Examples]

[0018] The present invention will be described in detail below based on examples. However, the present invention is not limited to the following examples.

[0019] 1. Construction of odor sensor cells Odor sensor cells were prepared by a known method described in Non-Patent Literature 4. Specifically, 30 mL of culture medium (Sf-900 III SFM, Thermo Fisher Scientific, USA) was added to a 125 mL flask, and ExpiSf9 cells were cultured at 27°C with shaking at 125 RPM (doubling time: 24-30 hours, density: 0.4-3.2 x 10⁻¹⁶). 6 (cells / mL). Next, the cells are 0.5 x 10 6 Odor sensor cells were prepared by diluting the cells to cells / mL, adding the transfection solution (Grace's Insect Medium unsupplemented 3 mL, approximately 10 μg of OR plasmid DNA, approximately 2 μg of Orco plasmid DNA, approximately 20 μg of GCaMP plasmid DNA, and 60 μL of TransIT-Insect Transfection Reagent Mirus Bio USA), and culturing under the above conditions for 72 hours.

[0020] The OR plasmid DNA and Orco plasmid DNA used in the above method were constructed by inserting the cDNA of OR or the cDNA of Orco into a commercially available vector (pFastBac vector, Thermo Fisher Scientific) according to a known method (Misawa, N. et al., ACS sensors, 4(3), 711-716). The GCaMP plasmid was prepared by introducing the cDNA of GCaMP (commercially available from addgene) into a commercially available pIZ vector (Thermo Fisher Scientific) according to a known method (Mitsuno, H. Biosensors and Bioelectronics, 65, 287-294). The cDNA sequences of OR, Orco, and GCaMP used are described in SEQ ID NOs. 1 to 5, respectively. Sequence ID 1 shows the cDNA sequence of OR that responds to phenol, Sequence ID 2 shows the cDNA sequence of OR that responds to 6-methyl-5-hepten-2-one, Sequence ID 3 shows the cDNA sequence of OR that responds to acetophenone, Sequence ID 4 shows the cDNA sequence of Orco, and Sequence ID 5 shows the cDNA of GCaMP.

[0021] 2. Preparation of hydrogels of various shapes containing odor sensor cells. In this example, three types of odor sensor cells that emit green fluorescence when they detect a specific odor were used. Figure 1A shows the procedure for preparing the hydrogel containing the odor sensor cells. First, the odor sensor cells were added and dispersed in a molten hydrogel (low-melting-point agarose or 3% sodium alginate aqueous solution) (cell density: 1 x 10⁻⁶). 7(Cells / mL). The three types of odor sensor cells each emit green fluorescence in the presence of phenol, 6-methyl-5-hepten-2-one, or acetophenone. This cell-containing fused hydrogel solution was spread between two silicone spacers (0.5 mm thick) attached to the bottom of a petri dish, formed into a sheet, and then gelled by cooling or by dropping 100 mM calcium chloride aqueous solution. Furthermore, using three types of biopsy trepans with circular, triangular, and square tips (Figure 1B, original inner diameter: 2 mm), gels of different shapes were cut out one by one from the intracellular encapsulation gel sheet, depending on the type of odor sensor cell. Twenty hydrogel pieces of each shape were prepared. Photographs of the three types of odor sensor intracellular encapsulation hydrogels prepared are shown in Figure 1C. The circular hydrogel pieces contained odor sensor cells that emitted green fluorescence in the presence of phenol, the triangular hydrogel pieces contained odor sensor cells that emitted green fluorescence in the presence of 6-methyl-5-hepten-2-one, and the square hydrogel pieces contained odor sensor cells that emitted green fluorescence in the presence of acetophenone.

[0022] 3. Development of a program for recognizing the shape of intracellular hydrogels containing odor sensors. A convolutional neural network (CNN) was used to recognize the shape of the sensor intracellular encapsulated hydrogels (see Section 5 for details on the CNN algorithm). Figure 2A shows the training procedure for the CNN model used. First, three types of odor sensor intracellular encapsulated hydrogels were mixed, and green fluorescence images were captured in a gel imaging device (Figure 2A-i). Next, segmentation of the gel fluorescence images was performed (see Section 5 for the software used), and binary masks of the hydrogels in the images were generated (Figure 2A-ii). Furthermore, using the created binary mask images and logical operations, only the parts corresponding to each hydrogel were extracted from the original gel fluorescence images (Figure 2A-iii), and the shapes of the hydrogel images were visually classified into three shapes: circular, triangular, and square (Figure 2A-iv). This created a database of approximately 2500 hydrogel images for each shape. 80% of the images collected in the database were divided into training images and the remaining 20% ​​into validation images, and the CNN model described above was repeatedly trained to generate five trained CNN models.

[0023] Figures 2B-D show the training results of the CNN model. The loss function (categorical cross-entropy) asymptotically approaches 0 after about 2-3 epochs (Figure 2B), and the classification accuracy also asymptotically approaches 1 after about 5-6 epochs (Figure 2C). Furthermore, in validation using independent data not used in training, the loss function (Figure 2B) and classification accuracy (Figure 2C) also showed asymptotic approach to 0 and 1, respectively. Moreover, looking at the confusion matrix summarizing these results, it can be seen that shape classification was successful with nearly 100% accuracy in both training and validation (Figure 2D).

[0024] Figure 2E shows the constructed hydrogel image shape classification algorithm. This algorithm calculates five Softmax probability sequences for a single input hydrogel image using five pre-trained CNN models. Next, its average value is calculated, and shape classification is performed based on the maximum average Softmax value. A threshold (0.5 in this case) is set for the maximum average Softmax sequence value, and images with low maximum values ​​are classified as "Unidentified." This reduces the possibility of misclassification that can occur when the maximum average Softmax sequence value is low.

[0025] 4. Responsiveness analysis of intracellular encapsulation hydrogels for odor sensors based on shape recognition Here, we constructed an automated program for analyzing the responsiveness of cellular odor sensors to odor substance addition, using the three types of intracellular encapsulated hydrogels for odor sensors prepared in Section 2 and the hydrogel shape recognition algorithm constructed in Section 3.

[0026] First, three types of odor sensor cells expressing olfactory receptors (OR#1-3) with specific selectivity for odor substances #1-3 were encapsulated in circular, triangular, and square hydrogels, respectively (Figure 3A). Specifically, odor substance #1 was phenol, odor substance #2 was 6-methyl-5-hepten-2-one, and odor substance #3 was acetophenone. Next, the three prepared odor sensor cell-encapsulated gels were mixed, and fluorescence time-lapse imaging was performed (Figure 3B). At time 0 minutes, one of the three odor substances, or a negative control (negacon) sample without an odor substance, was added to the mixed gel sample, and imaging was performed at 1 fps for 4 minutes. Subsequently, the gel shapes were classified from the acquired images, and the odor substance responsiveness was compiled and analyzed for each shape based on the identified gel shapes. The concentration of each odor substance that came into contact with each hydrogel piece during this imaging was 100 μM.

[0027] Figure 3C shows typical results of shape classification, and Figure 3D shows the confusion matrix summing the experimental results for the four conditions. The confusion matrix shows that shape classification was performed with an accuracy of approximately 98% (the misclassification rate was approximately 2%). Figures 3E-H show the change in GCaMP fluorescence intensity (ΔI / I0: the value obtained by subtracting the fluorescence intensity I0 at time 0 from the fluorescence intensity I at each time point, and normalizing by I0. This is the relative amplification factor from the initial fluorescence intensity) in the gels whose shapes were identified under each experimental condition. When each of the odorants #1-3 was added, a significant increase in fluorescence intensity was observed in the circular, triangular, and square gels, respectively (Figure 3E - circular, Figure 3F - triangular, Figure 3G - square). Furthermore, when the negative control sample was added, no significant increase in fluorescence intensity was observed in any gel shape. These results indicate that automatic classification of gel shapes is possible using the constructed gel shape classification algorithm, and that the added odorants were identified by performing a response analysis for each shape after classification. Furthermore, the misclassifications, which accounted for approximately 2%, were negligible after averaging with the reactivity of the numerous correctly classified gels.

[0028] 5. Reagents, materials, equipment, software, and algorithms used in the experiment • Odor sensor cells: These are cells created by introducing genes for the olfactory receptor (OR), olfactory coreceptor (Orco), and calcium-sensitive green fluorescent protein (GCaMP) into ExpiSf9 cells (ThermoFisher Scientific) prepared by the method described in item 1 above, and then constitutively expressing these genes. When a specific odor substance binds to OR, a calcium ion channel consisting of OR and Orco opens, and calcium ions flow into the cell. This induces green fluorescence from intracellular GCaMP. • Low-melting point agarose: Sigma-Aldrich A2576 Agarose Ultra-low Gelling Temperature, molecular biology grade Sodium Alginate: Wako 194-13321 80-120 cP • Biopsy Punch: KAI BP20F Biopsy Punch Regular Type • Gel imaging system: BioTools DP-T130z Hardware used for image analysis: MacBook Pro 14-inch 2023 (Chip: Apple M2 Max, Memory: 64GB, OS: macOS 14.5) Software used for image analysis: Python 3.10, TensorFlow 2.16, Keras 3.3 • Structure of the Convolutional Neural Network (CNN) Model: The Convolutional Neural Network (CNN) model took a single 128x128 pixel hydrogel image as input and applied two 3x3 kernel convolutional layers (16→32 filters), followed by two 2x2 max pooling layers. After the convolution operation, the output was flattened and passed through a 64-unit fully connected layer before performing 3-class classification using the Softmax activation function. The Adam optimization algorithm was used, and the categorical cross-entropy was used as the loss function. The training batch size was set to 128, and the number of epochs to 20. Software used for segmentation of intracellular gel-encapsulated images: Cellpose 2.2.3

Claims

1. An odor substance measuring device comprising one or more first-shaped hydrogel pieces having a first shape and one or more second-shaped hydrogel pieces having a second shape different from the first shape, wherein the first-shaped hydrogel pieces contain first odor sensor cells that emit a signal in the presence of a first odor substance, and the second-shaped hydrogel pieces contain second odor sensor cells that emit a signal in the presence of a second odor substance different from the first odor substance.

2. The odor substance measuring device according to claim 1, further comprising hydrogel pieces of one or more shapes different from the first and second shapes, wherein each hydrogel piece of shape contains a sensor cell that emits a signal in the presence of an odor substance different from the first and second odor substances, and each odor sensor cell emits a signal in the presence of an odor substance different from that of each hydrogel piece of shape.

3. The odor substance measuring device according to claim 1 or 2, wherein the signal is fluorescence.

4. A step of preparing the odor substance measuring device according to claim 1 or 2, The steps include bringing a test sample containing one or more odor substances into contact with the odor substance measuring device, A method for measuring odor substances, including a step of determining which shape of hydrogel piece emitted a signal.

5. The method according to claim 4, comprising measuring the signal emitted by each hydrogel piece.

6. The method according to claim 5, wherein the signal is fluorescence, and the method includes measuring the fluorescence emitted by each hydrogel piece.

7. The method according to claim 5, comprising a computer equipped with a neural network trained on the shapes of each hydrogel piece, an image analysis means connected to the computer for examining the shapes of each hydrogel piece, and a signal measuring device connected to the computer for measuring signals from each hydrogel piece, wherein the system is used to automatically measure the signals emitted by hydrogel pieces of each shape.

8. An odor substance measuring system comprising: an odor substance measuring device according to claim 1 or 2; a computer having a neural network trained to learn the shapes of each hydrogel piece; an image analysis means connected to the computer for examining the shapes of each hydrogel piece; and a signal measuring device connected to the computer for measuring signals from each hydrogel piece.

9. The odor substance measurement system according to claim 8, wherein the signal is fluorescence and the signal measuring device is a fluorescence measuring device.

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