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WO2025142500A1 - Procédé et dispositif de quantification de colorant fluorescent multiple - Google Patents

Procédé et dispositif de quantification de colorant fluorescent multiple Download PDF

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Publication number
WO2025142500A1
WO2025142500A1 PCT/JP2024/043902 JP2024043902W WO2025142500A1 WO 2025142500 A1 WO2025142500 A1 WO 2025142500A1 JP 2024043902 W JP2024043902 W JP 2024043902W WO 2025142500 A1 WO2025142500 A1 WO 2025142500A1
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WIPO (PCT)
Prior art keywords
fluorescent dyes
spectral
filter
modulation
multiple fluorescent
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Application number
PCT/JP2024/043902
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English (en)
Japanese (ja)
Inventor
和真 藤原
卓哉 舩冨
和哉 北野
友貴 藤村
康博 向川
博 落合
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Kyushu University NUC
Nara Institute of Science and Technology NUC
Original Assignee
Kyushu University NUC
Nara Institute of Science and Technology NUC
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Publication of WO2025142500A1 publication Critical patent/WO2025142500A1/fr
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Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/27—Regression, e.g. linear or logistic regression

Definitions

  • the present invention relates to a technology that can quantify multiple fluorescent dyes, and in particular to a technology that can simultaneously use and identify multiple fluorescent dyes in bioimaging such as spatial omics analysis, which quantitatively analyzes the gene expression of each cell using tissue slices.
  • RNA molecules can be hybridized with fluorescently labeled DNA probes to detect them.
  • overlap of fluorescence spectra is a limitation.
  • RNA fluorescent molecular localization method which is widely used in spatial transcriptome analysis, RNA is observed as fluorescent molecules to obtain positional information at the molecular level.
  • absorption filters the number of dyes that can be obtained in a single staining operation is limited, and multiple staining and imaging are required to identify a large number of molecules.
  • the SeqFish+ method which efficiently detects a large number of mRNAs by repeating molecular hybridization and deprobing.
  • the SeqFish+ method since it is difficult to distinguish between 20 fluorescent dyes using a fluorescent microscope, a large number of mRNAs are identified by barcoding (a reading sequence in which the light pattern is uniquely designed for each mRNA) using 20 types of pseudocolors through 20 hybridizations. For example, it is possible to analyze the localization of about 10,000 types of mRNA by taking 80 images.
  • one hybridization takes just under 20 minutes, and experimental operations are required each time, so repeated data acquisition (hybridization and reprobing) takes a lot of time, which is problematic in that it is time-consuming and labor-intensive.
  • a nucleic acid array method includes a spatial modulation module that uses a spatial random phase modulator to realize random modulation of a light field to obtain a speckle image of a fluorescent signal, a liquid crystal spatial light modulator that constructs a specific two-dimensional code matrix, and an area array detector that detects a multi-color fluorescent two-dimensional intensity measurement matrix (see Patent Document 1).
  • Patent Document 2 also discloses spectral analysis in which the spectral information of a sample is input into a learning model to make an estimate.
  • spectral information is the intensity of electromagnetic waves, including light, that characterize the stimulus and response, as well as temperature, mass, and the number of counts of fragments with a specific mass.
  • spectral information cannot be obtained by observing the intensity of light that passes through a filter whose spectral transmittance is changed (modulated) over time.
  • Patent Document 4 also discloses a process for calculating spectral data of a measurement object by acquiring spectral data in each of a plurality of unit areas a plurality of times and averaging the spectral data acquired a plurality of times in at least one unit area on the measurement object and in a unit area adjacent to the one unit area.
  • a tunable filter provided in front of a plurality of pixels changes the transmission wavelength over time, so that spectral data can be acquired in each of a plurality of unit areas on the measurement object in a plurality of pixels.
  • the tunable filter in Patent Document 4 changes the transmission wavelength over time, but does not change the spectral transmittance over time, which is clearly different from modulation.
  • Patent Document 4 does not disclose any description of quantifying fluorescent dyes using a trained regression model, as in Patent Document 3.
  • the applied voltage used to observe the feature vector can be selected according to the spectral distribution group of the fluorescent dye by using a method of determining the minimum set that can maintain sufficient fluorescence quantification accuracy from a sufficiently large group of voltage candidates, or by randomly determining N types of voltages (N-dimensional voltage vectors) in advance and using optimization techniques such as machine learning or gradient descent to determine the best voltage vector that maximizes the quantification accuracy.
  • the obtained observations are converted into a 200-dimensional feature vector, and a regression model 6 trained on the feature vectors under modulation of seven types of fluorescent dyes is used to quantify the fluorescent dyes (pigments) and reconstruct an intensity image of each fluorescence.
  • Figure 5 shows an example of the spectral transmittance obtained by a spectral modulator.
  • the horizontal axis indicates wavelength and the vertical axis indicates transmittance, showing how the spectral transmittance characteristics change from (1) to (4).
  • the spectral transmittance can be modulated by changing the voltage applied to the liquid crystal variable retarder 31 in the filter control unit 2.
  • 200 types of modulation were used to quantify fluorescent dyes with seven different fluorescent spectra.
  • FIG. 6 is an explanatory diagram of encoding, in which (1) shows the spectral distribution observed at various voltages for two types of fluorescent dyes with different fluorescence spectra, and (2) shows a comparison graph when the spectral transmittance is changed in a spectral modulator.
  • FIG. 7 shows the observation results of intensity integrated in the wavelength direction using a sensor array through a filter with modulated spectral transmittance, where (1) shows the result when photographing with a voltage v1 applied, and (2) shows the result when photographing with a voltage v2 applied.
  • the fluorescence spectra of dyes d2 and d3 appear to be similar, but when the spectral transmittance of the spectral modulator 3 is changed to v1 and v2 and images are taken as shown in Fig. 6(2), when the voltage v1 is applied and images are taken, the intensity at the peak position of dye d2 is 0.9 or more, whereas the intensity at the peak position of dye d3 is about 0.4, as shown in Fig. 7(1). Also, when the voltage v2 is applied and images are taken, the intensity at the peak position of dye d2 is about 0.5, whereas the intensity at the peak position of dye d3 is about 1.0, as shown in Fig. 7(1). In this way, even if the dyes have different fluorescent spectra, by observing while changing the spectral transmittance using the spectral modulator 3, the differences in the obtained spectral distributions become clear, making it easy to distinguish and quantify the fluorescent dyes.
  • FIG. 9 is a diagram illustrating sparse modeling in which feature vectors are converted into a dictionary matrix.
  • a group of 200 observed images observed under 200 types of modulation are used as the feature vectors on the horizontal axis, and a matrix Y can be created based on the number of pixels on the vertical axis and the feature vectors.
  • the matrix X represents the amount of fluorescence (brightness value) of each pixel
  • the number of rows represents the number of pixels
  • the number of columns represents the number of fluorescent dyes.
  • the seven types of fluorescence are quantified and expressed as follows: the brightness value of fluorescence number 0 is 7, the brightness value of fluorescence number 2 is 3, and so on.
  • Matrix Y is obtained by observation, and the dictionary matrix D can be created by specifying the fluorescent dye to be used. From these two, it is sufficient to solve matrix X. By solving the linear equation in this way, it is possible to analyze how much of each fluorescent dye is present in each pixel.
  • FIG. 10 shows an explanatory diagram of a simulation using the multiple fluorescent dye quantification method of Example 1.
  • the fluorescent images are actual observation images of seven types of RNA.
  • Seven types of RNA are labeled with different fluorescent dyes and observed under various voltages to obtain a group of images.
  • the images can be synthesized by multiplying the intensity of each pixel in the RNA observation image by the feature vector of the fluorescent dye used for labeling.
  • the images are the sum of the observation images of each fluorescence obtained under that voltage. Since noise is also included in actual observations, Gaussian noise with an intensity of about 3% is added depending on the brightness of the observation image.
  • Fig. 11 shows the simulation results of the multiple fluorescent dye quantification method of Example 1.
  • a group of observation images was created using the method shown in Fig. 10, and fluorescence decoding was performed, resulting in the intensity image shown in Fig. 11.
  • the peak signal to noise ratio (PSNR) which is the ratio of maximum power to noise, was 50 dB or higher in all cases.
  • PSNR peak signal to noise ratio
  • Fig. 12 is a graph showing the correlation between the number of observations and the restoration quality. As shown in Fig. 12, for each of the dyes (d1 to d7), the peak S/N ratio increases as the number of observations increases, but even when the number of observations is about 25, the peak S/N ratio is generally 50 dB or more, and sufficient restoration quality can be obtained.
  • Fig. 13 is an explanatory diagram of a method for optimizing the number of observations.
  • matrix ⁇ is a matrix in which the rows and columns of the dictionary matrix in Fig. 9 are interchanged, and the number of rows is the number of fluorescent dyes, and the number of columns is the optimal number of observations.
  • the fluorescence intensity image (X) corresponding to each dye can be expressed by taking a linear sum using a small number of images (Y) observed under various voltages.
  • the coefficient matrix in this linear sum is ⁇ .
  • regularization is performed so that the elements of many columns are all zero. Observations corresponding to columns where all elements are zero can be considered unnecessary for estimating the fluorescence intensity image.
  • the strength of regularization the number of observations can be increased or decreased, and the error can be estimated by evaluating the difference between XT and ⁇ YT at that time.
  • Figure 14 shows a graph comparing the correlation between the number of observations and the error when optimizing using sparse modeling and when selecting randomly. That is, the pixel where each dye appears is expressed by a linear combination of the observed images, and sparse modeling is used to restore where and how much dye is present using as few observed images as possible.
  • the multiple fluorescent dye quantification method of the present invention sufficiently reduces error with a small number of observations, such as 7 to 8, and it was found to have higher performance than random selection even with the same number of observations.
  • Fig. 15 shows a functional block diagram of the multiple fluorescent dye quantitative measurement apparatus of Example 4.
  • the multiple fluorescent dye quantitative measurement apparatus 1a includes a filter control unit 2, a filter 3, a sensor array 4, a memory unit 5, a regression model 6, and a multi-order wave plate 7.
  • the filter control unit 2, the filter 3, the sensor array 4, the memory unit 5, and the regression model 6 are similar to those of the multiple fluorescent dye quantitative measurement apparatus 1 of Example 1.
  • the multiple fluorescent dye quantification device 1a has a multi-order wave plate 7 between the filter 3 and the observation target 8.
  • the multi-order wave plate 7 is a wave plate in which the retardance of the optical path shifts by an integer multiple of the wavelength in addition to the design retardance, and has the advantage of being able to be implemented at a lower cost than a zero-order wave plate.
  • the sensor array 4 observes the observation target 8 containing multiple fluorescent dyes through the spectral modulator 3 and the multi-order wave plate 7. By providing the multi-order wave plate 7 between the filter 3 and the observation target 8, it is possible to perform observation offset to a high frequency, and quantify the fluorescent dyes with higher accuracy.
  • Figure 17 is a graph showing a comparison of the spectral transmittance between a case where the multi-order wave plate 7 is provided in front of the variable retarder 31 and a case where the multi-order wave plate 7 is not provided in front of the variable retarder 31.
  • Figure 17 (1) shows the case where the applied voltage is 1V. Compared to the spectral transmittance when only the liquid crystal variable retarder 31 is used, the spectral transmittance changes with wavelength more drastically when the multi-order wave plate 7 is provided in front of the liquid crystal variable retarder 31. The more drastic the change, the more effective it is for discriminating and quantifying fluorescent dyes with small spectral differences.
  • Figure 17 (2) shows the case where the applied voltage is 10V.
  • the spectral transmittance when only the liquid crystal variable retarder 31 is used changes very slowly with respect to wavelength, but the spectral transmittance changes greatly with respect to wavelength when the multi-order wave plate 7 is provided in front of the variable liquid crystal retarder 31.
  • the pattern of the spectral transmittance which is important for discriminating fluorescent dyes, depends on the type of fluorescent dye to be discriminated or quantified.
  • the number of types of spectral transmittance that can be selected can be increased, which makes it possible to improve the performance of discrimination and quantification.
  • deep learning may be used to learn the trends at each pixel based on the proximity of pixels.
  • the present invention is useful as a technology for simultaneously using and identifying multiple fluorescent dyes in bioimaging, including spatial omics analysis.

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  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Immunology (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Health & Medical Sciences (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)

Abstract

L'invention concerne un procédé et un dispositif de quantification rapide et stable de l'intensité spectrale d'émissions fluorescentes par augmentation du nombre de colorants fluorescents qui peuvent être utilisés simultanément. Le dispositif comprend une unité de commande de filtre 2, un filtre (modulateur spectral) 3, un réseau de capteurs 4, une unité de stockage 5 et un modèle de régression 6. L'unité de commande de filtre 2 commande le modulateur spectral 3. Le modulateur spectral 3 module la transmittance spectrale, et comprend un premier polariseur 32a, un retardateur variable à cristaux liquides 31 et un second polariseur 32b. Le réseau de capteurs 4 observe un objet observé 8 comprenant une pluralité de colorants fluorescents à travers le modulateur spectral 3. L'unité de stockage 5 stocke les valeurs de luminance de pixels de caméra capturés sous N types de modulation, en tant que vecteurs de caractéristiques à N dimensions. Le modèle de régression 6 est entraîné à l'aide des vecteurs de caractéristiques sous la modulation de chaque colorant fluorescent, et quantifie les colorants fluorescents pour reconstruire l'image d'intensité 9 de chaque fluorescence.
PCT/JP2024/043902 2023-12-27 2024-12-11 Procédé et dispositif de quantification de colorant fluorescent multiple Pending WO2025142500A1 (fr)

Applications Claiming Priority (2)

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JP2023221014 2023-12-27
JP2023-221014 2023-12-27

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003067230A1 (fr) * 2002-02-07 2003-08-14 Fuji Electric Holdings Co.,Ltd. Procede et dispositif de mesure d'une image fluorescente
JP2005308688A (ja) * 2004-04-26 2005-11-04 Olympus Corp エアギャップ可変式分光透過率可変素子のエアギャップ基準位置調整方法、エアギャップ可変式分光透過率可変素子のエアギャップ基準位置調整構造、及びエアギャップ可変式分光透過率可変素子のエアギャップ基準位置調整構造を備えた光学装置
WO2022244645A1 (fr) * 2021-05-20 2022-11-24 パナソニックＩｐマネジメント株式会社 Dispositif d'imagerie

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003067230A1 (fr) * 2002-02-07 2003-08-14 Fuji Electric Holdings Co.,Ltd. Procede et dispositif de mesure d'une image fluorescente
JP2005308688A (ja) * 2004-04-26 2005-11-04 Olympus Corp エアギャップ可変式分光透過率可変素子のエアギャップ基準位置調整方法、エアギャップ可変式分光透過率可変素子のエアギャップ基準位置調整構造、及びエアギャップ可変式分光透過率可変素子のエアギャップ基準位置調整構造を備えた光学装置
WO2022244645A1 (fr) * 2021-05-20 2022-11-24 パナソニックＩｐマネジメント株式会社 Dispositif d'imagerie

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