JP2013195127A - Image analyzer, image analysis method, and program for biological sample - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image analyzer, an image analysis method, and a program for a biological sample which are capable of synthetically analyzing functions of a molecule for each cell.SOLUTION: An image analysis method for a biological sample includes the steps of: obtaining a plurality of distribution images of molecules of a biological sample over times; analyzing a moving speed and a moving direction of each of the molecules for a plurality of first small sections set on the distribution images, on the basis of the obtained distribution images; and analyzing the number of the molecules and a diffusing speed of each of the molecules for a plurality of second small sections set on the distribution images, on the basis of the obtained distribution images.

Description

  The present invention relates to a biological sample image analysis apparatus, an image analysis method, and a program.

Transcription factors such as NFAT (Nuclear factor of activated T cell) regulate transcription activity by various enzymes. NFAT is dephosphorylated by calcineurin in accordance with changes in intracellular calcium concentration and moves into the nucleus. This nuclear translocation is inhibited by a decrease in intracellular calcium concentration and immunosuppressive agents. Quantifying signal transduction associated with these transcriptional activities using nuclear translocation as an index is important in examining the effects of immunosuppressants and the like.
When cells labeled in this way move cells due to changes in conditions such as stimuli, an image processing method that has been analyzed in the past is to specify multiple small regions before and after the molecules move, and signal within these small regions. A method of quantifying the degree of movement by associating a change in value with time has been used (see, for example, Patent Documents 1 and 2).

However, since this method has low accuracy in measuring dynamic molecular movement, it has not been possible to grasp how molecules move with time.
Thus, STICS (Spatio-temporal Image Correlation Spectroscopy) has been proposed as a method for measuring the amount of change when a molecule moves in a certain direction (see, for example, Non-Patent Document 1).

  Further, as measurement methods in the case of free diffusion of molecules, diffusion measurement methods such as FCS (Fluoresce Correlation Spectroscopy), FRAP, RICS (Raster-scan Image Correlation Spectroscopy) are known (for example, Non-Patent Documents 2 and 3). reference).

JP 2009-250652 A JP 2005-519625 A

“Probing the integrin-actin linkage using high-resolution protein velocity mapping.”, J. Cell Society, 2006, 119, 5204-14, Brown CM. Et.al. “Measuring fast dynamics in solutions and cells with a laser scanning microscope.”, Biophysic. J., 2005, 89 (2). “Cell Nucleus: Organization & MechanoBiology”, “Fluorescence Fluctuation Microscopy to Reveal 3D Architecture and Function in the Cell Nucleus.”, Thorsten Lenser, et.al., METHODS IN CELL BIOLOGY, VOL. 98, 2-33, 2010.

  However, even if we try to analyze the behavior of molecules using these measurement methods, the characteristics of each are not utilized effectively. It was. For example, RICS can obtain molecular size information by quantifying molecular diffusion in a specific region, but cannot obtain information on the movement direction of the molecule. On the other hand, in STICS, the molecular direction and moving speed are required, but the molecular size is not known. Thus, although information on molecules that move with time changes of cells is grasped by STICS, association with the time-dependent changes of molecules locally by RICS measurement is not sufficient.

  The present invention has been made in view of such circumstances, and provides a biological sample image analysis apparatus, an image analysis method, and a program capable of comprehensively analyzing the functions of molecules of individual cells. Is a solution issue.

  The present invention for solving the above-described problems acquires a distribution image of molecules of a plurality of biological samples as time elapses, and sets a plurality of first small regions set in the distribution image based on the acquired distribution image. Analyzing the moving speed and moving direction of the molecule, and analyzing the number of molecules and the diffusion speed of each of the plurality of second small regions set in the distribution image based on the acquired distribution image This is an image analysis method.

  According to the present invention, it is possible to provide an image analysis apparatus, an image analysis method, and a program for a biological sample capable of comprehensively analyzing the molecular functions of individual cells.

The figure which shows the structure of the laser microscope system to which the image-analysis method of this Embodiment is applied. The figure for demonstrating the image-analysis method of this Embodiment. The figure for demonstrating the image-analysis method of this Embodiment. The figure for demonstrating an example of the analysis method of this Embodiment. The figure which shows two examples which analyzed the moving direction and moving speed of the actin molecule by cytochalasin D by the analysis method of this Embodiment. The schematic diagram which shows various quantities relevant to the accurate measurement of the image analysis method of this Embodiment. The figure explaining the frame acquisition operation | movement of the image analysis method of this Embodiment. The figure for demonstrating the content of the spatial correlation calculation of the image analysis method of this Embodiment. The figure which shows the correlation function figure of the image analysis method of this Embodiment. The figure for demonstrating the fitting method of the image analysis method of this Embodiment. The figure which shows the image acquired with the image analysis method of this Embodiment. The figure which shows the result of having performed STICS analysis among the image analysis methods of this Embodiment. The figure which shows the image used for RICS analysis among the image analysis methods of this Embodiment. The figure which shows the result of having performed RICS analysis among the image analysis methods of this Embodiment. The flowchart which shows the general | schematic procedure of the image analysis method of this Embodiment.

FIG. 1 is a diagram showing a configuration of a laser microscope system to which the image analysis method of the present embodiment is applied.
The laser microscope system includes a microscope main body 1, a laser combiner 2, a scan unit 3, a detector unit 4, a control unit 5, a display device 6, and a storage device 7.

  Laser light emitted from one laser light source provided in the laser combiner 2 is reflected by a dichroic mirror (not shown) and then enters the scan unit 3. The scanning unit 3 deflects and scans the optical axis in the X-axis and Y-axis directions and enters an objective lens (not shown) of the microscope body 1. As a result, the measurement region (focal region) can be positioned at an arbitrary position of the fluorescently labeled sample specimen on the focal plane in the field of view.

  Fluorescence emitted from the fluorescent molecules in the focal region is captured by the same objective lens and guided to the dichroic mirror through the reverse optical path. The dichroic mirror is designed to transmit fluorescence having a longer wavelength than the excitation light, and the fluorescence reaches the detector unit 4. The detector unit 4 is preferably an APD (avalanche photodiode) or a photomultiplier tube.

  The fluorescence intensity signal photoelectrically converted by the detector unit 4 is input to the control unit 5. In the control unit 5, analysis (STICS, RICS) is performed by analysis software. The control unit 5 analyzes the fluorescence intensity signal from the detector unit 4 and the scan position information from the scan unit 3 in association with each other. The analysis result is appropriately displayed on the display device 6 and stored in the storage device 7.

In this laser microscope system, a plurality of fluorescent images having different wavelengths can be obtained using a plurality of laser light sources. In the following description, an analysis is performed using a fluorescent image obtained by one laser light source. explain.
Whereas RICS is basically a correlation analysis that is calculated based on a scan image of one frame, STICS is a spatio-temporal correlation analysis that measures molecular movement from continuous frame images. It is characterized by the fact that information can be obtained about the function, and it is possible to analyze the function of the signal transduction system from the direction and speed of molecular flow in the cell.

RICS is a technique for measuring the time-space correlation between two fluorescent intensities by utilizing the difference in time at which each pixel in an image is acquired. By using RICS, for example, the diffusion coefficient of a fluorescence-modified molecule can be measured.
In molecular diffusion measurement represented by STICS and RICS, it is often performed to select a part to be analyzed from an image and perform numerical processing within the range. A part of the image is called a region of interest or ROI (Region of Interest). By paying attention to a small area in the ROI and obtaining molecular diffusion information in the area, it is possible to evaluate an appropriate molecular dynamic that reflects a complicated cell morphology.

FIG. 2 is a diagram for explaining the image analysis method of the present embodiment.
In the image analysis method of the present embodiment, STICS analysis and RICS analysis are performed in cooperation on the acquired biological sample image.
In the STICS analysis, the entire measurement period is divided into N time segments, one image is acquired in each time segment, and analysis is performed on the acquired multiple (N) images. On the other hand, in the RICS analysis, n images are acquired in each time segment, and the analysis is executed on the n images. In such image analysis method, STICS analysis analyzes the movement of molecules in a long time span, while RICS analysis analyzes the molecular diffusion in a short time. It is a thing.

Subsequently, processing contents of STICS analysis and RICS analysis will be described.
The STICS and RICS technology described below is not the same as the known STICS and RICS. In order to solve the technical problem of the present application, an improvement is added to a known technique. However, for the purpose of explanation, the terms STICS and RICS are used.

[STICS analysis]
In this embodiment, for the sake of explanation, the spatial coordinates x and y and the time coordinate t of the continuous image are handled as integers. By multiplying the proportionality factor determined from the photographing magnification and the photographing interval, it is possible to easily convert the actual position of the sample and the photographing time. Further, although the image is assumed to be two-dimensional for the sake of explanation, it can be easily extended to a three-dimensional image having stereoscopic information photographed by a confocal microscope or the like.

FIG. 3 is a diagram for explaining the image analysis method of the present embodiment.
A biological sample is photographed at a fixed time interval with an image having a size of M × M (pixels), and N consecutive images are acquired. Image correlation calculation is performed on two (one set) images having a time interval τ from the N consecutive images. From N consecutive images, image correlation calculation is performed on (N−τ) sets of images. FIG. 3 shows a case where τ = 2.

Next, an average image correlation image obtained by averaging (N−τ) correlation images obtained by the image correlation calculation is obtained. The average image correlation image can be obtained by adding the image data of the corresponding pixel positions of each correlation image and calculating the average value.
The above process is expressed by a mathematical expression.
When the signal intensity of the image at the coordinates (x, y) acquired at time t is I (x, y; t), the average image correlation is expressed by Equation (1).

<I (x, y; t)> in the equations (1) and (2) indicates that the image data is averaged with respect to x and y of an image having a size of M × M (pixels). . That is, for the function F of x, y, t, <F (x, y; t)> is defined by equation (3).

  For x and y, if F (x + nM, y + mM; t) = F (x, y; t) is satisfied for the integers n and m, the numerator of formula (1) The described cross-correlation function can be calculated at high speed using Fourier transform.

In addition, in order to catch the movement of the expression distribution in the sample with time, it is possible to remove in advance a component that is immobile or a component that is slow in the time segment to be analyzed. The removal method may remove the low frequency component of the signal intensity change for each coordinate. Alternatively, the steady component may be removed. That is, the moving component I _mov may be calculated according to Equation (4), and I _mov may be used instead of I (x, y; t) in Equation (1).

Next, an analysis method using the average image correlation image will be described.
FIG. 4 is a diagram for explaining an example of the analysis method of the present embodiment.
When the distribution image in the continuous image moves in a specific direction with time, the average image correlation image R (ξ, η, τ) obtained by the above method is the center of the image (M / 2, M / 2). A distribution image having a local maximum value at a position deviating from the range. As one method for determining the position of the local maximum value, there is a method of fitting a cross-correlation image assuming a Gaussian distribution.

That is, the Gaussian represented by the equation (5) is fitted to the average image correlation image by the least square method with A, β, p, q, and C as variable parameters.

P and q obtained by fitting are the maximum positions (p _τ and q _τ ) of the average image correlation image in the time interval τ. Therefore, the maximum position (p _τ , q _τ ) of the average image correlation image is determined for each delay time τ (= 1 to N−1).
[Moving Speed]
The moving speed of the distribution in the image _(v x, _{v y),} the proportional coefficient of the delay time and maximum position of the formula (6) _v x, is determined as _{v y.}

In order to derive statistically accurate v _x and v _y , as shown in FIG. 4, the proportional coefficients v _x and v _y may be calculated by regression analysis using a least square method or the like.
[Movement speed distribution]
By performing the above analysis on the image in the m × m (pixel) partial area included in the entire M × M (pixel) image, the local movement speed in the partial area is derived. I can do it. That is, image correlation is calculated for the partial area A _i where x is from x _i to x _i + m−1 and y is from y _i to y _i + m−1, and the moving speed for the partial area is determined. it can. By obtaining the local moving speed for a large number of partial images, the moving speed distribution can be obtained.

[Vector display]
The moving speed obtained by the above analysis method is displayed on the screen, thereby visualizing the result and assisting the user in visually understanding the result. As a display method, for example, as shown in the lower right of FIG. 4, a method of superimposing and displaying a graphic representing a velocity vector on a continuous image is conceivable. As a figure representing a velocity vector, there is an arrow whose length is proportional to the magnitude of the moving velocity and whose arrowhead indicates the direction of the vector. Also, the color of the arrow can be displayed according to the magnitude of the speed.

FIG. 5 is a diagram showing an example in which a transcription factor is transferred from the cytoplasm of a cultured cell to a cell nucleus by the analysis method of the present embodiment. The moving direction and moving speed of the transcription factor are displayed superimposed on the cell image.
The diagram shown in FIG. 5 was created by the following procedure.
First, processing was performed on each ROI while shifting a small area (ROI) by a predetermined pixel in the vertical and horizontal directions with respect to a continuous image in which a light emission image was recorded.
Processing in each ROI was performed as follows.

Step 1: A cross-correlation image for two images having a time interval τ was calculated and averaged for a plurality of cross-correlation images to obtain a cross-correlation average image. This process was repeatedly performed by changing τ, and a cross-correlation average image was obtained for each τ.
Step 2: The position of the peak value was obtained from the cross-correlation average image for each τ value. The position of the peak value was determined from the position of the Gaussian vertex by fitting a two-dimensional Gaussian function to the original image by the method of least squares.

Step 3: From the position of the peak (x _τ , y _τ ) for each τ value, the signal transfer speed at that ROI was derived. The x-axis component value v _x of the signal movement speed was derived by applying x _τ = v _x × τ to the x-axis peak position x _τ for each τ value by the least square method. For the y-axis component, _vy was derived by the same method.

Step 4: When the peak position could not be identified, the speed was derived based on the data in the range of the identified time interval. If the peak position could not be identified for τ> 0, it was considered that there was no significant velocity component, and the velocity component for that ROI was undefined.
Step 5: The signal moving speed in each ROI was displayed by drawing an arrow having a length proportional to the magnitude of the speed in the direction of the speed from the center coordinates of the ROI.
This makes it possible to grasp in more detail how the transcription factor is moved.

[RICS analysis]
Next, the contents of RICS analysis will be described in detail. In this RICS analysis, the number of labeled molecules and the diffusion constant thereof are obtained by performing spatial correlation calculation on the fluorescence intensity signal for each pixel in the image obtained by laser scanning.

* Step 1: Scan with laser light so that target (fluorescent) molecules can be detected pixel by pixel.
FIG. 6 is a schematic diagram showing various quantities related to accurate measurement of the image analysis method of the present embodiment.
FIG. 6 shows the moving speed (trajectory) of the target 10, the size of the laser spot 11 irradiated on the target 10, and the pixel position (1, 2,...) Of the detection element.

  FIG. 6 shows that there is a close relationship between the moving speed of the target, the scanning speed, and the pixel size (actual size per pixel). Therefore, when the number of molecules of the target, the diffusion rate, and the like are obtained by RICS analysis, it is important that various quantities shown in FIG. 6 are set to appropriate values.

* Step 2: Scan the laser within the set frame. This scan is executed a plurality of times to obtain a plurality of frames.
FIG. 7 is a diagram for explaining the frame acquisition operation of the image analysis method of the present embodiment.
The size of one frame 12 (vertical length × horizontal length) is set by the analyst in step 1. The laser beam is scanned by the scan unit 3 in this frame area. For example, the laser beam is scanned one line from the upper left position of the frame area to the right, and then the lower line is scanned from the left to the right. This operation is repeated up to the bottom line to complete one frame scan.
This frame scan is repeated a plurality of times in the same manner to obtain a plurality of frame images (t = 0 to n).

  Here, the time for scanning one pixel is on the order of μsec, the time for scanning one line is on the order of msec, and the time for scanning one frame is on the order of sec. When one frame scanning time is 1 second, for example, and 100 frames are acquired, the measurement time is about 100 seconds.

  In principle, one frame screen is sufficient for performing spatial correlation calculations. However, in this embodiment, the average calculation regarding the spatial correlation calculation is performed by increasing the number of acquired frames. By increasing the number of frames and calculating the average value of the correlation function, it is possible to remove molecular information of slow motion.

* Step 3: Perform spatial correlation calculation.
The spatial correlation calculation is executed according to the following correlation function expression.

FIG. 8 is a diagram for explaining the contents of the spatial correlation calculation of the image analysis method according to the present embodiment.
Two identical frames 12 are superposed with a shift of ξ in the X-axis direction and ψ in the Y-axis direction. Then, a value obtained by multiplying the fluorescence intensity values at the overlapping positions is added over the entire frame. A value obtained by normalizing the addition result is set as a spatial correlation value at the point (ξ, ψ). By changing these ξ and ψ on a two-dimensional plane, a two-dimensional spatial correlation value is obtained.

Next, a two-dimensional spatial correlation value is calculated for each of the other frames. The plurality of spatial correlation values acquired in this way are averaged for each point to obtain a correlation function diagram (spatial correlation function diagram).
FIG. 9 is a diagram showing a correlation function diagram of the image analysis method of the present embodiment.
As can be seen from the above formula, the correlation function value takes the maximum value (= 1) at the origin (0, 0) located at the center of FIG.

* Step 4: A fitting operation is performed using a correlation function diagram obtained as a result of the correlation function operation.
When the fluorescence intensity values are plotted in the height direction (Z-axis direction) based on the correlation function diagram thus obtained, a mountain shape centered on the origin is obtained. The measured shape is compared with the shapes of a plurality of reference patterns to identify a reference pattern that fits well.

FIG. 10 is a diagram for explaining a fitting method of the image analysis method of the present embodiment.
On the lower side of the figure, the correlation function values obtained by the calculation are represented in three dimensions. On the upper side of the figure, the difference from a certain reference pattern is shown in three dimensions. A reference pattern that is evaluated to have the smallest difference is specified. Then, the number of molecules and the diffusion rate corresponding to the specified reference pattern can be obtained as the average number of molecules and the average diffusion rate of the sample.

Next, an example to which the image analysis method of the present embodiment is applied will be described.
A protein obtained by fusing a fluorescent protein EGFP to a transcription factor NFAT (Nuclear factor of activated T cell) (hereinafter referred to as NFAT-EGFP) is expressed in U2OS cells, and A23187 (a calcium ionophore is used to increase intracellular calcium concentration). 2 μg / ml) was added to the culture solution, and images of NFAT-EGFP moving from the cytoplasm of cultured cells (U2OS) to the cell nucleus were acquired with a laser confocal fluorescence microscope (FV1000: Olympus) and analyzed by STICS. .

  After starting the stimulation of A23187, images were obtained in a total of 60 frames at a size of 256 × 256 at intervals of 30 seconds. Then, after removing A23187 from the extracellular fluid, an image of 120 frames was obtained. In each case, STICS analysis was performed, and the moving speed and direction of the molecule were mapped with arrows. In the map, the maximum speed of 0.0015 μm / sec is indicated by a red arrow, and the lower speed is displayed according to the scale in the screen.

  In addition, RICS analysis was performed on the size change of the nuclear domain under the same conditions as in the cells, expressed proteins, stimulating drugs, and the like. Specifically, when acquiring a time-lapse image showing the movement of molecules, parameter settings suitable for RICS measurement at appropriate time intervals (scan speed: 12.5 μsec / pixel, image size: 256 × 256 pixels, pixel size: 0.05 to 0.062 μm / pixel, the number of acquired frames: 50 to 100).

FIG. 11 is a diagram showing an image acquired by the image analysis method of the present embodiment. FIG. 11 (1) shows a transition image after A23187 stimulation start (increasing calcium concentration), and FIG. 11 (2) shows a transition image after A23187 is removed.
According to FIG. 11 (1), in the NFAT-EGFP-expressing U2OS cell, NFAT localization appears only in the cytoplasm in a steady state, and almost no NFAT is present in the nucleus (frame 1). After stimulation with A23187, as NFAT moved into the nucleus with time, cytoplasmic NFAT decreased and the fluorescence intensity of NFAT-EGFP decreased (frames 20-60). In the nucleus, the fluorescence intensity by the transferred NFAT-EGFP increased, and an intranuclear domain aggregate was formed. The size of the nuclear domain increased as the amount of NFAT transferred increased.

  When it was considered that the NFAT transfer was almost completed, the cells were washed with OPTI-MEM several times in order to remove A23187 from the OPTI-MEM medium as an extracellular solution. Thereafter, when observation was continued, as shown in FIG. 11 (2), the fluorescence of the nuclear domain gradually decreased, and conversely, the appearance of recovery of cytoplasmic NFAT-EGFP was observed (frames 1 to 120). .

FIG. 12 is a diagram illustrating a result of STICS analysis performed in the image analysis method according to the present embodiment.
As a result of STICS analysis using the acquired image frame, the moving direction and speed of NFAT-EGFP could be displayed by the color and size of the arrow at several selected locations in the cell as shown in FIG. FIG. 12 (1) is a map diagram showing the direction and moving speed of NFAT-EGFP moving from the cytoplasm to the nucleus. FIG. 12 (2) is a map showing the direction and moving speed of NFAT-EGFP returning from the nucleus to the cytoplasm.
In FIG. 12 (1), there are many locations that show the direction from the cytoplasm to the nucleus, that is, the inside in the vicinity of the nucleus, whereas in FIG. The situation was seen. However, in FIG. 12 (2), the arrow from the nucleus to the outside is not so clear, and conversely, movement toward the nucleus was also observed.

FIG. 13 is a diagram showing an image used for RICS analysis in the image analysis method of the present embodiment.
Along the arrow in the figure, No. FIGS. 1 to 5 show transition images after A23187 stimulation start (increasing calcium concentration). Figures 6-11 show the transition image after A23187 is removed. A small area shown in each figure represents an ROI. No. In FIG. 1, it can be seen that the ROI is set near the boundary between the nucleus (the left part in the figure) and the cytoplasm (the right part in the figure). No. In the figures after 3, this boundary portion is unclear, but the position of the ROI is fixed in the nucleus. In the set ROI, NFAT-EGFP is represented as a bright spot.

Referring to FIG. 13, it can be seen that the size of the intranuclear domain tends to increase as NFAT moves into the nucleus, and that the size decreases again when NFAT returns to the outside of the nucleus.
FIG. 14 is a diagram illustrating a result of performing RICS analysis in the image analysis method according to the present embodiment. FIG. 14 (1) shows the transition of the diffusion coefficient D in the ROI, and FIG. 14 (2) shows the transition of the number of molecules in the ROI. Also, the dotted line in the figure shows the timing when A23187 is removed.

  In FIG. 14 (1), the number N of molecules decreased with the NFAT nuclear translocation. From this, in FIG. 13 (1-5), NFAT gathers and the apparent size increases, but the RICS analysis results suggest that the number of molecules decreases. In the case of NFAT translocation, on the contrary, FIG. 13 (6-11) shows that the accumulated NFAT is gradually dispersed and reduced in size. The number was shown to return.

  On the other hand, in FIG. 14 (2), the diffusion coefficient D in the ROI decreased with the NFAT nuclear transfer. This indicates that the movement is suppressed because the size of NFAT has increased due to aggregation. As for NFAT extranuclear translocation, the D value tended to return to the original level, but the degree was not as significant as in the intranuclear translocation. This is supported by the fact that the movement direction of NFAT for nuclear translocation is larger than the movement direction of NFAT for nuclear translocation in the map of FIG. 12 in which STICS analysis was performed. Suggested that the movement of NFAT once bound to nuclear DNA or the like is still suppressed. Thus, it was found that by combining the principles of STICS and RICS, it is possible to provide analysis data that can comprehensively determine both molecular size and dynamics that could not be discovered and / or explained by conventional analysis.

  From the above results, in the comparison between RICS and STICS, RICS can quantify molecular diffusion in a specific region, but cannot obtain information on the direction of movement of the molecule. On the other hand, in STICS, the molecular direction and the moving speed are required, but the molecular size is unknown. On the other hand, according to the above-described example using the image analysis method of the present embodiment, the moving direction and speed of NFAT that moves throughout the cell by STICS can be obtained, and NFAT molecules that have moved to the nucleus by RICS From the diffusion constant, the dynamic change of the NFAT-containing body aggregated with the nuclear domain could be captured.

As described above, by applying the two methods of STICS and RICS at the same timing for the same object, it became possible to examine the functional process of the NFAT transcription factor by combining a plurality of analysis results.
In addition, the molecule | numerator used as the object of this analysis is not restricted to the above-mentioned transcription factor, It applies to wide objects, such as a protein related to signal transduction and transcription | transfer, fluorescence, or a luminescent label protein.

FIG. 15 is a flowchart showing a schematic procedure of the image analysis method of the present embodiment. This flowchart shows the operation of the control unit 5 shown in FIG.
In step S01, the control unit 5 acquires the operating conditions of the confocal laser microscope input by the operator. For example, information such as image acquisition time, acquisition period, acquisition interval, number of acquisitions, acquisition conditions (light source type, exposure time, filter type, etc.) for STICS and RICS is input.

  In step S02, the control unit 5 controls the laser combiner 2 and the scan unit 3 according to the input operating conditions, and causes the detector unit 4 to acquire an image of the biological sample. The control unit 5 receives the continuous image sent from the detector unit 4 and stores it in the storage device 7 together with information for identifying the continuous image.

When a predetermined image acquisition operation (for example, when acquisition of a continuous image of a predetermined cycle is completed) is executed, the control unit 5 (analysis software) executes analysis of the continuous image stored in the storage device 7 To do.
In step S03, the control unit 5 performs STICS analysis based on the acquired image. The STICS analysis is performed by executing the processing of steps 1 to 5 described with reference to FIG.
In STICS analysis, a moving component is extracted from the image represented by Expression (4) (moving out a stationary component or a low-speed component) and an image correlation is calculated to obtain a high S / N result. Can do.

In step S04, the control unit 5 performs RICS analysis on the ROI designated by the operator based on the acquired image. The analysis is performed by executing the processing of steps 1 to 4 described with reference to FIGS.
In step S05, the control unit 5 outputs the analysis result. As analysis results to be output, for example, in addition to analysis result data, continuous images used for each analysis, maps of moving samples superimposed on biological sample images, diffusion coefficient transition diagrams, molecular weight transition diagrams, etc. It is.

According to the processing procedure described above, an analysis result can be obtained in a short time (immediately) after completion of image acquisition of a biological sample.
The above-described processing may be performed fully automatically, or may be configured to execute processing in accordance with the operator's instruction for each step. Furthermore, the above-described processing can be implemented in variously modified forms. For example, the operator may specify the ROI for RICS analysis based on the image acquired in step S02, and the STICS analysis in step S03 and the RICS analysis in step S04 may be performed in parallel. Further, according to the image analysis apparatus of the present embodiment, the STICS analysis in step S03 and the RICS analysis in step S04 can be performed independently.

  Note that the functions described in the above-described embodiments are not limited to being configured using hardware, and can be realized by causing a computer to read a program describing each function using software. Each function may be configured by appropriately selecting either software or hardware.

Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage.
Various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, you may combine suitably the component covering different embodiment.

  INDUSTRIAL APPLICABILITY The present invention can be used in industries that provide an image analysis method, an image analysis apparatus, an image capturing apparatus, and a program for biological samples that can comprehensively analyze the function of molecules for individual cells.

  DESCRIPTION OF SYMBOLS 1 ... Microscope main body, 2 ... Laser combiner, 3 ... Scan unit, 4 ... Detector unit, 5 ... Control unit, 6 ... Display apparatus, 7 ... Memory | storage device.

Claims (10)

Acquisition means for acquiring distribution images of molecules of a plurality of biological samples over time;
A movement analysis means for analyzing a movement speed and a movement direction of a molecule for each of a plurality of first small regions set in the distribution image based on the acquired distribution image;
Diffusion analysis means for analyzing the number of molecules of molecules and the diffusion rate for each of a plurality of second small regions set in the distribution image based on the acquired distribution image. apparatus. The acquisition unit divides the entire acquisition period into N (an integer of 2 or more) segments, acquires n (natural number) distribution images in each segment,
The movement analysis means performs an analysis based on a plurality of distribution images extracted one for each segment,
The biological sample image analysis apparatus according to claim 1, wherein the diffusion analysis unit performs analysis based on a plurality of distribution images in one segment. The movement analysis means includes
Setting means for setting a plurality of first small areas in each acquired distribution image;
Selection means for selecting any two distribution images from the acquired plurality of distribution images;
A computing means for computing the correlation between the images of the two corresponding first small areas with respect to the plurality of first small areas for the two selected distribution images;
The analysis execution means for analyzing the movement speed and the movement direction of the molecule for each first small region by repeatedly executing the selection means and the calculation means. Image analysis device for biological samples.   The biological sample image analysis apparatus according to claim 3, wherein the movement analysis unit performs analysis based on a new distribution image obtained by removing a stationary component or a low-speed component from the distribution image.   5. The organism according to claim 4, wherein a composite image is generated by superimposing an arrow image representing a movement speed and a movement direction of a molecule for each of the first small regions at a corresponding position on the distribution image. Sample image analysis device.   6. The biological sample image analysis apparatus according to claim 5, wherein the molecule to be analyzed is a protein related to signal transmission or transcription, a fluorescent or luminescent labeled protein. Acquire molecular distribution images of multiple biological samples over time,
Analyzing the moving speed and moving direction of the molecules for each of the plurality of first small regions set in the distribution image based on the acquired distribution image,
A biological sample image analysis method, comprising: analyzing the number of molecules of molecules and the diffusion rate for each of a plurality of second small regions set in the distribution image based on the acquired distribution image. In the acquisition of the distribution image, the entire acquisition period is divided into N (integer of 2 or more) segments, and n (natural number) distribution images are acquired in each segment,
In the analysis of the movement of the molecule, an analysis is performed based on a plurality of distribution images extracted one for each segment,
The biological sample image analysis method according to claim 7, wherein in the analysis of the diffusion of molecules, the analysis is executed based on a plurality of distribution images in one segment. In the biological sample image analysis program,
Acquiring molecular distribution images of a plurality of biological samples over time,
Analyzing the moving speed and moving direction of molecules for each of a plurality of first small regions set in the distribution image based on the acquired distribution image;
A program for causing a computer to execute a step of analyzing the number of molecules and the diffusion rate for each of a plurality of second small regions set in the distribution image based on the acquired distribution image. In the distribution image acquisition step, the entire acquisition period is divided into N (integer greater than or equal to 2) segments, and n (natural number) distribution images are acquired in each segment,
In the molecular movement analysis step, an analysis is performed based on a plurality of distribution images extracted for each segment,
The program according to claim 9, wherein in the molecular diffusion analysis step, analysis is performed based on a plurality of distribution images in one segment.

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