A method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multi-modal imaging

Abstract

The application discloses a method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multi-modal imaging, which comprises the following steps: sampling and CT scanning, threshold segmentation to obtain a CT pore image; continuing to fix, rinse, dehydrate, embed and slice the soil sample; CLSM scanning of the slice to obtain a CLSM microorganism image; superimposing, multi-channel fusing and visualizing reconstruction of the CT pore image and the CLSM microorganism image. The application realizes in-situ observation of the distribution of microorganisms in pores without destroying the original structure of the soil. Compared with the method for observing the dynamic process of microorganisms relying on simulated soil structure, the application can more truly reflect the soil condition and will not disturb the composition and structure of the microorganisms, thereby laying a foundation for in-depth research of soil pore science and microbiology.

Description

Technical Field

[0001] This invention relates to the field of soil science and technology, and specifically to a method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging. Background Technology

[0002] Soil microorganisms are a crucial component of the soil ecosystem. Through the construction of complex interaction networks, they form stable communities and collectively drive key ecological processes such as nutrient cycling, environmental purification, and host health, playing an irreplaceable role in soil structure formation, organic matter decomposition, and nutrient transformation. Soil pores are the core spatial carriers for microbial habitation, metabolism, and community building; their heterogeneity is a significant factor influencing the dynamic changes of soil microbial communities. However, due to the opacity of soil and the complexity of microbial interactions, comprehensive observation methods are still lacking to quantify microbial colonization and distribution at the pore interface.

[0003] Traditional methods for quantifying soil microbial parameters mainly involve physical sectioning, staining microorganisms with specific dyes, and then using optical or fluorescence microscopy to distinguish between live and dead bacteria to quantify the microbial quantity. For example, existing literature 1: application number 202511497193.0, entitled "A Method and Apparatus for Quantitative Detection of Active Microorganisms in Solid / Semi-solid Samples," uses propidium bromide azide (PMA) to remove DNA from dead bacteria, combined with qPCR technology to quantify the number of live bacteria. However, solid / semi-solid samples are complex sample systems containing a large amount of organic matter, sugars, alcohol, and other microorganisms. These components may interfere with the PMA processing and subsequent qPCR detection, and these methods are time-consuming and difficult to accurately reflect their quantity and state in the soil.

[0004] With the development of microfluidic chip technology, its application in pore-scale microbial research is becoming increasingly widespread. Existing literature 2: Application number CN202511899633.5, invention titled "A Microchip Model and Application for Visualizing Microbial Fermentation," which directly monitors and records the fermentation state of microorganisms with long fermentation cycles and the synthesis process of fermentation products. This method only achieves visualization of microorganisms in a controlled environment; however, in the complex environment of real soil, achieving in-situ observation of microorganisms at the soil pore scale remains a pressing problem. Summary of the Invention

[0005] The purpose of this invention is to conduct in-situ observations of the distribution of microorganisms in real soil pores without damaging the original soil structure, providing an important means for in-depth research on the interaction processes of soil microorganisms at the pore scale.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging includes the following steps:

[0008] Step S1: In-situ sampling is performed in the soil area to be tested. Fluorescent staining agent is added to the sample, micro-scratches are made on the sample surface, and the sample is placed in a CT scanning device for scanning. The scanned image is then reconstructed.

[0009] Step S2: Preprocess the image reconstructed in step S1;

[0010] Step S3: Perform threshold segmentation on the image preprocessed in step S2 to obtain a CT aperture image;

[0011] Step S4: Add paraformaldehyde fixative to the soil column after CT scanning, fix it under light-protected conditions, then wash the fixed soil sample with phosphate buffer and perform gradient dehydration with ethanol solution.

[0012] Step S5: The dehydrated soil sample is embedded and solidified with epoxy resin to form a solid embedded block, and then the embedded block is cut into thin slices.

[0013] Step S6: Perform CLSM scanning on the slices from step S5, and preprocess the scanned images to obtain CLSM microbial images;

[0014] Step S7: Overlay the CT pore image obtained in step S3 with the CLSM microbial image obtained in step S6. Specifically, using the micro-scratches on the sample observation surface as a spatial positioning reference, perform image preprocessing on the CT pore image and the CLSM microbial image, and then perform image rigid registration and image elastic registration.

[0015] Step S8: Perform multi-channel fusion and visualization reconstruction of the registered CT pore images and CLSM microbial images.

[0016] Soil samples were constructed into soil columns, stained, and then subjected to CT scanning to obtain the three-dimensional pore structure. CLSM scanning of the sections was then performed to obtain microbial fluorescence distribution information. Image registration was used to achieve spatial alignment between the CT pore images and the CLSM microbial images, enabling the integration of multimodal information. This method allows for in-situ observation of the relationship between soil pore structure and microbial distribution, providing a high-precision spatial basis for subsequent quantitative microbial analysis.

[0017] Further, in step S7, the CT pore image and the CLSM microbial image undergo image preprocessing, specifically including the following steps:

[0018] Step S711: Import the CT pore images and CLSM microbial images into Avizo, Fiji, or Imaris software for processing;

[0019] Step S712: The CT pore image is denoised by 3×3×3 median filtering and Gaussian filtering, and binarized segmentation is completed by Otsu automatic thresholding combined with manual fine-tuning to distinguish the pore phase and soil solid phase. The region of interest with the same size as the CLSM microbial image is then cropped.

[0020] Step S713: The CLSM microbial image is subjected to the rolling ball algorithm to remove background noise, and a binary mask of microbial distribution is generated by threshold segmentation.

[0021] Image preprocessing was performed on CT pore images and CLSM microbial images, including filtering, thresholding, and background subtraction, to achieve consistency in spatial scale and resolution between the two images, providing high-quality data for subsequent registration and fusion.

[0022] Furthermore, in step S7, if Avizo software is used, the image rigid registration operation in Avizo software specifically includes:

[0023] Step S721: Select corresponding feature points; Select multiple obvious feature points in the soil column cross-section image, and use the marking tool to mark these feature points one by one in the CT pore image and CLSM microbial image;

[0024] Step S722: Initial manual alignment; perform manual fine-tuning on the CT pore image and CLSM microbial image, including rotation angle and translation distance, to ensure that the marked feature points roughly overlap in the two types of images;

[0025] Step S723: Determine the fixed and floating images; set the CT pore image as the fixed image and the CLSM microbial image as the floating image;

[0026] Step S724: Set registration parameters; select trilinear interpolation as the interpolation method to ensure the continuity of image edges, and use mean square error as the similarity measure to adapt to signal differences;

[0027] Step S725: Import and optimize feature point coordinates; Import the coordinates of the same feature points marked in step S721, and iterate through the least squares method to calculate the optimal rigid transformation matrix, including the X / Y axis translation, rotation angle and uniform scaling factor.

[0028] Step S726: Three-dimensional affine transformation correction; Select a three-dimensional affine transformation model to correct X / Y / Z axis translation, three-dimensional rotation, image scaling and shearing; Adjust the overall sample offset to make the CT pore image and CLSM microbial image more accurately aligned;

[0029] Step S727: Multi-resolution layer registration; Set 4 resolution levels, with downsampling coefficients of 8, 4, 2, and 1, and use Gaussian filtering for smoothing, with smoothing coefficients of 4, 2, 1, and 0 for each level to weaken detail interference;

[0030] Step S728: Set optimizer parameters; the optimizer uses regular gradient descent, with a maximum of 300 iterations and a convergence threshold of 1×10⁻⁶. -7 Convergence is defined as the condition that the parameters do not change significantly for 15 consecutive iterations.

[0031] Step S729: Start the registration operation; start the program and wait for the registration to complete. Output the rigidly registered CT pore image and CLSM microbial image for subsequent fusion or analysis.

[0032] Achieving global spatial alignment between CT and CLSM images provides a preliminary alignment basis for flexible registration and multimodal fusion.

[0033] Furthermore, in step S7, the image elastic registration operation in the Avizo software specifically includes:

[0034] Step S731: Import images; import the rigidly registered CLSM microbial images and CT pore images into Avizo software;

[0035] Step S732: Open the flexible registration plugin; launch the bUnwarpJ registration plugin, set the registration mode to flexible mode, select B-spline for image interpolation mode, and adjust the accuracy level to the highest.

[0036] Step S733: Set the interpolation and optimizer parameters; select trilinear interpolation as the interpolation method, LBFGS optimizer as the optimizer, set the number of iterations to 500, and set the gradient convergence threshold to 1×10⁻⁶. -7 The number of correction items is set to 8;

[0037] Step S734: Set the transformation model and mesh parameters; select the 3D B-spline free deformation model, and set the mesh size to 10×10×8;

[0038] Step S735: Multi-resolution layer registration; set 3 resolution levels with downsampling coefficients of 4, 2, and 1 respectively; use Gaussian smoothing with smoothing coefficients of 2, 1, and 0 for each level;

[0039] Step S736: Start automatic registration operation; the software generates a two-dimensional smooth deformation field to perform local micro-stretching, compression and misalignment correction on the CLSM microbial image, and matches the local structure of the CT image pixel by pixel;

[0040] Step S737: Output results: Elastically registered CLSM microbial images, horizontal and vertical deformation field files.

[0041] This method achieves pixel-by-pixel local precision alignment of microbial images with CT pore images, preserving the true relationship between microorganisms and pore space, and providing a precise basis for fusion and quantitative analysis.

[0042] Furthermore, step S8 specifically includes:

[0043] S81: Resample the elastically registered CLSM microbial image and CT pore image obtained in step S7. Use the CT pore image as a reference image to perform spatial resampling on the CLSM microbial image. During the resampling process, a trilinear interpolation algorithm is used. The image spatial size, voxel spacing, and coordinate origin are all set uniformly according to the CT image parameters. After resampling, the no-signal area is filled with zero values ​​to keep the original pixel format of the CLSM image unchanged, thus obtaining a microbial image with a unified spatial coordinate system with the CT pore image.

[0044] S82: Simultaneously load CT pore images and resampled CLSM microbial images to establish a multi-channel fusion model;

[0045] Specifically: CT pore images are assigned to the green channel; CLSM microbial images are assigned to the blue channel; and they are fused according to preset weights. ,

[0046] Where: α=0.7; β=0.3; The grayscale values ​​of the CT aperture image. The grayscale values ​​of the CLSM microbial image are... To fuse image grayscale values;

[0047] S83: Adjust the transparency and display the composite image; wherein: the opacity of the CT pore image channel is set to 50%, the opacity of the CLSM microbial image channel is set to 50%, and a two-dimensional composite superimposed image is generated that simultaneously characterizes the relationship between pore structure and microbial spatial distribution.

[0048] S84: Import two-dimensional composite overlay images into three-dimensional visualization software, and perform three-dimensional volume rendering based on the registered CT pore images and CLSM microbial images.

[0049] The registered CT and CLSM images were spatially resampled in the software to ensure consistency in voxel size and coordinate system between the two images. A multi-channel fusion model was established to weight and fuse the CT pore images and microbial images according to set weights. The opacity was adjusted to generate a two-dimensional composite overlay image. This allows for simultaneous visualization of pore structure and microbial distribution, ensuring that the two-dimensional overlay image accurately reflects spatial relationships and provides a foundation for three-dimensional visualization and subsequent quantitative analysis.

[0050] Further, step S84 specifically involves: importing the fused CT pore image and CLSM microbial image into the Surpass module of the Imaris software for three-dimensional volume rendering, wherein: the CT pore image is set to green display, the rendering mode is Blend mode, and the opacity is set to 30%–50%, making the pore network a semi-transparent structure; at the same time: the CLSM microbial image is set to blue display, the opacity is set to 80%–100%, and the target area of ​​microorganisms is extracted according to the fluorescence intensity threshold; and a three-dimensional spatial distribution map of microorganisms in the soil pore network is obtained through volume rendering.

[0051] Two-dimensional composite images are imported into the Imaris Surpass 3D module. Color, opacity, and threshold are set for CT pore and CLSM microbial images respectively. 3D volume rendering is then used to display the spatial distribution of microorganisms within the pore network. This provides a visually intuitive 3D spatial display of microorganisms within the pore network, allowing observation of microbial colonization locations and pore space utilization.

[0052] Further, step S4 specifically involves adding 4% paraformaldehyde fixative to the soil column after CT scanning, fixing it at 4°C for 4-6 hours under light-protected conditions, then rinsing the fixed soil sample three times with phosphate buffer for 10 minutes each time, followed by gradient dehydration with ethanol solutions of 30%, 50%, 70%, 80%, 90%, and 100% by volume, with each concentration gradient treatment lasting 15 minutes.

[0053] The soil column samples were cross-linked and fixed using 4% paraformaldehyde fixative to protect the microbial structure. Rinsing with phosphate-buffered saline (PBS) was to remove excess fixative, as residual fixative would automatically fluoresce, making the CLSM background too bright for the microorganisms. PBS is an isotonic solution that will not deform or rupture the microorganisms, maintaining their morphology. Using ethanol directly without first rinsing with PBS would cause the microbial cells to rupture.

[0054] Further, step S5 specifically involves: embedding the dehydrated soil sample in epoxy resin, allowing it to infiltrate under vacuum for 24 hours, then placing it in a mold, curing it at 37°C for 12 hours, and then at 50°C for 24 hours to form a solid embedded block; and using an ultrathin slicer to cut the embedded block into slices with a thickness of 50~100μm.

[0055] Soil samples are embedded in epoxy resin to form a solid mass. Ultrathin sectioning is then used to obtain uniformly thick sample sections, facilitating fluorescence imaging. This ensures the stability of microorganisms and pore structure, providing samples suitable for high-resolution CLSM scanning.

[0056] Furthermore, a method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging further includes step S9: quantitative analysis of the spatial relationship between microorganisms and pores, specifically including: calculating the proportion of microorganisms in pores of different sizes, pore preference index, distance from microorganisms to pore walls, microorganism-pore wall adhesion rate, microorganism-pore colocation index, and microorganism-pore coupling degree.

[0057] To achieve in-situ quantitative analysis of soil microorganisms and reveal their preferences and distribution patterns for different pore sizes and spatial structures.

[0058] Furthermore, the CLSM scan in step S6 includes the following steps:

[0059] Step S61: Sample preparation: Transfer the slide to a glass slide, add anti-fluorescence quenching mounting medium, cover with a coverslip, and gently press to remove air bubbles;

[0060] Step S62: The DAPI nuclear dye is excited by a 405nm laser with a pinhole of 1 Airy Unit and a laser power of 3%. The scanning mode is Z-axis layer scanning. The scanning range covering the target observation area is set along the Z-axis direction, the layer scanning step size is set to 0.3μm, and the scanning speed is normally controlled at 400-800Hz. For high-quality imaging, it is adjusted to 200-400Hz. At the same time, 2-4 scans are accumulated and averaged to reduce the background noise of the image.

[0061] Step S63: First, find the target observation field of view through bright field or wide field imaging mode and complete the coarse focusing operation; switch to laser confocal imaging mode, finely adjust the Z-axis focal length, and lock the clear imaging layer;

[0062] Step S64: Adjust the PMT gain and offset parameters channel by channel to ensure that the fluorescence signal is not overexposed or has no black field, and that the signal intensity is within the linear detection range;

[0063] Step S65: During 3D imaging, start the Z-axis automatic tomography program to complete the acquisition of images across the entire plane. After acquisition, save the image files in .lsm, .czi, or .tif format, preserving complete imaging metadata.

[0064] The GFP and Alexa Fluor 488 green fluorescent probes are excited by a 488nm laser. This allows for the acquisition of high signal-to-noise ratio three-dimensional fluorescent images of microorganisms, ensuring the accuracy of microbial spatial localization and quantitative analysis.

[0065] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0066] This invention acquires the three-dimensional structure of soil pores using CT scanning and the fluorescence distribution of microorganisms using CLSM scanning, thus achieving multimodal data acquisition of pore structure and microorganisms. A combination of rigid and flexible registration methods is employed to achieve precise global and local alignment between CT pore images and CLSM microbial images. By fusing the CT pore images and CLSM microbial images, and through two-dimensional overlay and three-dimensional volume rendering, the spatial distribution of microorganisms in the pore network is displayed, revealing the colonization patterns of microorganisms in the pores and achieving high-precision in-situ quantification.

[0067] This invention enables in-situ observation of the distribution of microorganisms in pores without damaging the original soil structure. Compared to methods that rely on simulating soil structure to observe the dynamic processes of microorganisms, this invention can more realistically reflect soil conditions without disturbing the composition and structure of microorganisms, laying the foundation for in-depth research in soil porosity and microbiology. Attached Figure Description

[0068] Figure 1 This is an 8-bit grayscale image of a soil sample CT scan from Example 2, after reconstruction (before inoculation).

[0069] Figure 2 This is a grayscale image of the soil sample from Example 2 after preprocessing (before inoculation).

[0070] Figure 3 The image shows the pores (green) after splitting the soil sample image before inoculum application in Example 2.

[0071] Figure 4 This is an example of a soil sample CT scan image from Example 2 that has been reconstructed and converted into an 8-bit grayscale image (after inoculation).

[0072] Figure 5 This is a grayscale image of the soil sample from Example 2 after preprocessing (after inoculum application).

[0073] Figure 6 The image shows the pores (green) after the soil sample was decomposed following the application of the bacteria in Example 2.

[0074] Figure 7 This is a reconstruction of the CLSM scan image of the soil sample from Example 3;

[0075] Figure 8This is a two-dimensional image (CT microstructure and CLSM microbial image overlay) of a soil sample from Example 3.

[0076] Figure 9 This is a three-dimensional image (CT microstructure and CLSM microbial image overlaid on a soil sample from Example 3). Detailed Implementation

[0077] To make the technical solution and advantages of the invention clearer, the invention will be further described below with reference to specific examples and accompanying drawings.

[0078] The soil samples used in the following examples were all taken from the experimental base of the Red Soil Ecological Experimental Station in Yingtan City, Jiangxi Province. Topsoil samples (0-20 cm) were randomly collected. Peanut seeds were purchased online; the variety was Ganhua 18.

[0079] Example 1

[0080] The purpose of this embodiment is to observe whether the addition of fluorescent staining agent will cause the loss of microbial activity and whether it will affect the abundance of microorganisms.

[0081] In this embodiment, the soil sample was completely submerged in water, and plant residues were removed from the soil using a 250μm sieve. The soil was then air-dried and passed through a 2mm sieve for later use.

[0082] Sterilize peanut seeds with 0.1% mercuric chloride for 5 minutes, then rinse them 5 times with deionized water. After sterilization, soak the peanuts in clean water for 8-12 hours until they are fully swelled. Take them out and place them on a damp gauze, then cover them with another damp cloth and place them in a warm, dark place for germination treatment. When the sprouts are about 0.5cm long, select peanut seedlings with uniform growth for later use.

[0083] The sieved soil was filled into a PVC pipe with a diameter of 5cm and a height of 10cm. Because the soil expands after being moistened, the soil column was only filled to a height of 9cm, with a bulk density of 1.3g / cm³. 3 The required soil mass for each soil column was 212.60 g, totaling 2551.2 g. Peanut seeds were transplanted into the soil columns at a planting depth of approximately 2 cm. Two treatment groups were set up: 1) with added synthetic microbial community and no DAPI nuclear dye staining agent (CK); 2) with added synthetic microbial community and DAPI nuclear dye staining agent (TWY). Each treatment group included 6 replicates, filling a total of 12 soil columns.

[0084] Twelve soil columns were cultured simultaneously, and the soil moisture content was kept constant by adding water every three days during the culture process. During the peanut planting period, the soil moisture content was maintained at 60% field capacity. Destructive sampling was carried out on the 7th, 14th, and 21st days of culture, and two replicate samples were taken from each treatment group for the determination of microbial activity and abundance or biomass.

[0085] The experimental results showed that the staining agent had no significant effect on the activity and abundance of microorganisms and the labeling was clear. Therefore, DAPI dye was used to perform fluorescent staining of microorganisms.

[0086] In this embodiment, a microbial community synthesized in our laboratory was fluorescently labeled and then applied to the soil. The colonization and distribution of the synthesized bacteria in the plant rhizosphere were observed. If plant residues were present in the original soil, the plants might not be able to secrete rhizosphere compounds due to death, resulting in poor colonization of the synthesized bacteria. Therefore, a 250μm sieve was used to remove the plant residues from the soil.

[0087] Example 2

[0088] Six soil columns were filled in the same manner as in Example 1, i.e., sieved soil was filled into PVC pipes with a diameter of 5 cm and a height of 10 cm, with only 9 cm of soil added at a time. Peanuts were planted in the same manner as in Example 1. The filled soil columns were then marked by creating micro-scratches on the sample observation surface to provide reference points for subsequent image registration. The aforementioned six samples were scanned using a micro-CT scanner (Phoenix Nanotom, Germany) at a scanning voltage of 100 kV, a current of 90 μA, and an exposure time of 1.25 s. Each soil column was rotated 360° uniformly on the sample stage. During this process, 3301 to 3334 images were acquired for each sample. In this example, the PVC pipe diameter for the six samples was 5 cm, and the CT scan image resolution was 30 μm. Image reconstruction was performed using Datos | x2 Rec software. The number of images obtained for each sample is shown in Table 1 below, and the images were stored in TIFF format. Figure 1 As shown.

[0089] Table 1. Sample weight and corresponding number of scanned images

[0090]

[0091] For each sample, the following identical operations were performed on the 8-bit grayscale images obtained from CT scans. Taking sample CK1 as an example: 3301 grayscale images of sample CK1 were imported into the open-source software Image J / Fiji. First, the brightness and contrast of the images were adjusted using Brightness / Contrast. Then, 2200 cylindrical Regions of Interest (ROIs) with a diameter of 1700 pixels were selected. The ROI selection was completed using Set Oval - Make Inverse - Clear Outside. Noise reduction was performed using the non-local mean filter in the Biomed group plugin, with parameters set to noise variance 15 (Sigma = 15) and smoothing factor 1 (δ = 1). Next, edge enhancement was performed by applying an UnsharpMask Filter with a radius of 1 to reduce local volumetric effects caused by image blurring. The processed image was then saved. Figure 2 As shown. The segmented image from ilastik is imported into Fiji software, and Color-Split Channels is used to split the image, segmenting the soil pores, as shown. Figure 3 As shown.

[0092] The scanned soil columns were placed in a light incubator for cultivation. On the 14th day of cultivation, two treatment groups were set up: a sterile water group and a synthetic bacteria group. Sterile water was added to samples CK1, CK2, and CK3 in the sterile water group, while stained synthetic bacteria were added to samples TWY-1, TWY-2, and TWY-3 in the synthetic bacteria group. Each treatment group included three replicate samples. The soil column samples were then scanned again using a CT scanner, and the images were reconstructed using the same processing method as the first CT scan. The number of images obtained for each sample is shown in Table 2 below, and the images were stored in TIFF format. The results are as follows: Figure 4 , Figure 5 , Figure 6 As shown.

[0093] Table 2. Sample weight and corresponding number of scanned images

[0094]

[0095] Professional analysis software was used to preprocess the CT images of the six samples before and after inoculum treatment (Table 1 and Table 2). First, image spatial registration was performed to ensure a one-to-one correspondence between the two sets of images: directly scanned CK1, CK2, and CK3 corresponded to those with sterile water, and directly scanned TWY-1, TWY-2, and TWY-3 corresponded to those with added stained synthetic microorganisms. Second, scanning artifacts and noise interference were removed, and grayscale normalization was performed to improve image quality and lay the foundation for subsequent pore segmentation. Based on the grayscale difference between soil pores and solid particles, a grayscale thresholding method was used to determine the segmentation critical value. The preprocessed CT images were then binarized to distinguish between soil pore regions and solid particle regions, removing isolated noise points and preserving the true soil pore structure information. Using CT analysis software, core quantitative indicators of soil pore structure before and after the application of synthetic microorganisms were extracted, including total pore volume, pore surface area, and average width. The changes in the values ​​of each parameter were clarified, and the evolution of soil pore structure under the influence of synthetic microorganisms was identified. Combined with the peanut seed germination status, the interaction between microorganisms and soil pore structure was analyzed. The results are shown in Table 3. After the application of the microorganisms, the pore volume, surface area, and porosity in the soil column increased, indicating an improvement in soil pore structure. This is conducive to the further distribution and colonization of microorganisms, forming a positive feedback effect.

[0096] Table 3. Pore structure characteristic parameters at the soil column scale

[0097]

[0098] Example 3

[0099] The soil column after CT scanning was further scanned using CLSM (laser confocal scanning microscope). The steps were as follows: 4% paraformaldehyde fixative was added to the soil containing the stained synthetic microorganisms, and the soil was fixed at 4°C for 4-6 hours in the dark. The fixed soil sample was then rinsed three times with phosphate buffered saline (PBS) for 10 minutes each time. Subsequently, the soil was dehydrated in a gradient with ethanol solutions of 30%, 50%, 70%, 80%, 90%, and 100% by volume, with each concentration gradient treatment lasting 15 minutes.

[0100] Dehydrated soil samples were embedded in epoxy resin and allowed to infiltrate under vacuum for 24 hours. They were then placed in a mold and cured at 37°C for 12 hours and then at 50°C for 24 hours to form solid embedded blocks. These blocks were then sliced ​​into thin sections of 50–100 μm thickness using an ultramicrotome. The sections were then transferred to glass slides, covered with anti-fluorescence quenching mounting medium, and covered with coverslips. Air bubbles were gently pressed out before microscopic observation.

[0101] The appropriate excitation channel was selected based on the sample's fluorescent labeling type: DAPI nuclear dyes were excited using a 405nm laser. The pinhole was set to 1 Airy Unit; this balances imaging resolution and signal-to-noise ratio, achieving optimal two-dimensional imaging. The laser power was set to 3% to reduce phototoxicity and fluorescence quenching risks, ensuring sample activity and fluorescence signal stability. Z-axis tomography was used, with continuous tomography starting at the upper surface and ending at the lower surface of the sample, with a step size of 0.3μm. The scanning speed was adjusted to 200-400Hz, and 2-4 scans were accumulated and averaged to reduce background noise and improve fluorescence signal clarity.

[0102] The target observation field is located using bright-field or wide-field imaging mode to complete coarse focusing. Switching to laser confocal imaging mode, the Z-axis focal length is finely adjusted to lock onto a clear imaging plane. The PMT gain and offset parameters are adjusted channel-by-channel to ensure the fluorescence signal is not overexposed or has no black areas, and that the signal intensity is within the linear detection range. During 3D imaging, an automatic Z-axis tomography program is initiated to complete full-plane image acquisition. After acquisition, image files are saved in .lsm, .czi, or .tif format for subsequent data analysis.

[0103] Image analysis software such as ImageJ, Fiji, and Imaris were used for post-processing and quantitative analysis. First, image preprocessing was performed, including background noise removal and brightness and contrast adjustment to optimize the visual effect without altering the signal accuracy. For Z-axis tomographic data, 3D reconstruction and maximum density projection (MIP) were used to restore the three-dimensional spatial structure of the samples, resulting in microbial 3D fluorescence images such as... Figure 7 As shown in Table 4, parameters such as cell area, perimeter, volume, and particle number in the samples were determined through morphological analysis.

[0104] Table 4 Soil microbial morphological parameters

[0105]

[0106] Example 4

[0107] The CT pore image and CLSM microbial image after the synthetic bacterial colony was introduced were superimposed. The micro-scratches on the sample observation surface were used as a spatial positioning reference for multi-device imaging to ensure that the CT pore image and the CLSM observation area of ​​the laser confocal scanning microscope were completely consistent.

[0108] Export the CT pore images as raw data in .tif, .vol, or .nc format, and save the CLSM microbial scan images in .lsm, .czi, or .tif format. Import the CT pore images and CLSM microbial images into Avizo or Fiji (ImageJ) software for processing.

[0109] Image preprocessing was performed in Fiji (ImageJ) software: CT pore images were denoised using 3×3×3 median filtering and Gaussian filtering, and binarized segmentation was completed using Otsu's automatic thresholding method combined with manual fine-tuning to distinguish the porous phase from the soil solid phase. Regions of interest (ROIs) with the same size as the CLSM microbial images were then cropped. Background noise in the CLSM microbial images was removed using the rolling ball algorithm, and a binary mask of microbial distribution was generated after thresholding segmentation.

[0110] Performing rigid image registration in Avizo software:

[0111] Five to eight corresponding feature points, including soil column cross-section edges, large pores, and soil particles with special morphologies, were selected and manually marked using a marking tool. These points were then matched one-to-one in both the CT pore image and the CLSM microbial image. Preliminary alignment of the two types of images was achieved by manually fine-tuning the rotation angle and translation distance, ensuring that the feature points roughly overlapped. The CT pore image was set as a fixed image, while the CLSM microbial image was set as a floating image. Trilinear interpolation was selected to ensure edge continuity, and the mean squared error (SSD) similarity measure was used to adapt to the differences between CT grayscale and CLSM fluorescence multimodal imaging signals. The coordinates of the marked corresponding feature points were imported, and the optimal rigid transformation matrix was calculated using the least squares iterative optimization method, including X and Y axis translations, rotation angles, and uniform scaling coefficients. A three-dimensional affine transformation model was used to correct X / Y / Z axis translations, three-dimensional rotation, image scaling, and cropping, adjusting sample offset. Multi-resolution hierarchical registration is employed: four resolution levels are set, with downsampling coefficients of 8, 4, 2, and 1. Gaussian filtering is used for smoothing, with smoothing coefficients of 4, 2, 1, and 0 for each level, to reduce detail interference. The optimizer uses regularized gradient descent, with a maximum of 300 iterations and a convergence threshold of 1×10⁻⁶. -7 Convergence is defined as 15 consecutive iterations in which the parameters show no significant change. Start the registration operation and wait for the program to complete.

[0112] Performing elastic registration of images in Avizo software:

[0113] In Avizo software, import the rigidly registered CLSM microbial image and CT pore image. Open the bUnwarpJ registration plugin, set the registration mode to Elastic, select B-spline as the image interpolation mode, and adjust the accuracy level to the highest level to ensure registration accuracy. Select trilinear interpolation as the interpolation method, choose the LBFGS optimizer, set the iteration parameter to 500, and the gradient convergence threshold to 1×10⁻⁶. -7The number of correction terms is set to 8. The transformation model is selected and mesh parameters are set: a 3D B-spline free deformation model is used, with a mesh size of 10×10×8 (X / Y / Z); multi-resolution layered registration is employed: three resolution levels are set, with downsampling coefficients of 4, 2, and 1, and Gaussian smoothing is used, with smoothing coefficients of 2, 1, and 0 for each layer to weaken detail interference. Automatic registration is initiated, and the software generates a 2D smooth deformation field to perform local micro-stretching, compression, and misalignment correction on the CLSM microbial image, matching the local structure of the CT pore image pixel-by-pixel; after registration, the elastically registered CLSM microbial image, horizontal and vertical deformation field files, and registration quality evaluation data are output.

[0114] Registration quality evaluation data is used to verify whether the images are properly registered. If the requirements are not met, manual alignment is required.

[0115] In Imaris software, the registered images are multi-channel fusion and overlay: After registration, the image resampling module is opened in the software, and the original CLSM microbial image and the final transformed field of fine registration are imported. The CT pore image is specified as the reference image, and all spatial parameters, including size, physical spacing, and coordinate origin, are configured using the CT pore image. Interpolation is maintained as trilinear interpolation, and no-signal areas after image mapping are filled with pixel values ​​of 0. The original pixel format of the CLSM microbial image is preserved without type conversion, and resampling operation is performed.

[0116] Two-dimensional fusion display: The original CT pore image and the resampled CLSM microbial image are loaded simultaneously, and the spatial parameters of the two images are completely unified. Multi-channel fusion is performed through the menu bar Image → Color → Merge Channels: a weighted fusion mode is used, with the CT pore image weighted at 0.7 and the registered CLSM microbial image weighted at 0.3. The CT pore image is assigned to the green channel, and the CLSM microbial image is assigned to the blue channel. "Create composite" and "Keepsource images" are checked to generate a composite image. The Channels Tool is opened, and the opacity of both the green (CT) and blue (microbial) channels is set to 50% to ensure that the pore structure and microbial distribution signals are clearly displayed simultaneously, generating a two-dimensional composite overlay image, such as... Figure 8 As shown.

[0117] 3D Volume Rendering: The CT pore image and CLSM microbial image used for 2D fusion are imported into the Surpass module of Imaris software. In the object pane, the CT pore image is selected, the color is set to green, the opacity is set to 50% or 30%, and the rendering mode is set to Blend. The pores become a semi-transparent green skeleton, revealing the interior. In the object pane, the CLSM microbial image is selected, the color is set to blue, the opacity is set to 80% or 100%, and the threshold is set to retain only the microbial region. Combined with maximum density projection and a 3D rotated view, the 3D spatial distribution characteristics of the pores and microorganisms are presented, such as... Figure 9 As shown.

[0118] Quantitative studies were conducted based on overlaid images, calculating the proportion of microorganisms in pores of different sizes, pore preference index, distance from microorganisms to pore walls, microorganism-pore wall adhesion rate, microorganism-pore co-location index, and microorganism-pore coupling degree. These were all calculated by software. The results are shown in Table 5. Microorganisms accounted for the highest proportion (83%) in small pores (<30 μm), showing a preference for small pores and being abundantly distributed in pores smaller than 30 μm. Table 6 shows that microorganisms were uniformly distributed in the pores, with a very low adhesion rate to the pore walls, only about 0.1%. The co-location index between microorganisms and pores was relatively high, indicating that some microorganisms were located within the pores, while others were inside the solid phase or aggregates. The coupling degree between microorganisms and pores was moderate, indicating that the distribution of microorganisms was regulated by the pore structure, but was also influenced by other factors.

[0119] Table 5. Distribution ratio of microorganisms in soil pore microdomains

[0120]

[0121] Table 6. Correlation Indicators Between Microorganisms and Pore Coupling

[0122]

[0123] This invention provides an effective means for accurately identifying, segmenting, and analyzing soil microorganisms in CT digital images, which is of great significance for in-depth research on soil microorganisms. The ilastik and Image J / Fiji software used in this invention are both open-source software; only a basic understanding of their usage and image processing workflow is required, making them easy to promote and apply.

Claims

1. A method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging, characterized in that, Includes the following steps: Step S1: In-situ sampling is performed in the soil area to be tested. Fluorescent staining agent is added to the sample, micro-scratches are made on the sample surface, and the sample is placed in a CT scanning device for scanning. The scanned image is then reconstructed. Step S2: Preprocess the image reconstructed in step S1; Step S3: Perform threshold segmentation on the image preprocessed in step S2 to obtain a CT aperture image; Step S4: Add paraformaldehyde fixative to the soil column after CT scanning, fix it under light-protected conditions, then wash the fixed soil sample with phosphate buffer and perform gradient dehydration with ethanol solution. Step S5: The dehydrated soil sample is embedded and solidified with epoxy resin to form a solid embedded block, and then the embedded block is cut into thin slices. Step S6: Perform CLSM scanning on the slices from step S5, and preprocess the scanned images to obtain CLSM microbial images; Step S7: Overlay the CT pore image obtained in step S3 with the CLSM microbial image obtained in step S6. Specifically, using the micro-scratches on the sample observation surface as a spatial positioning reference, perform image preprocessing on the CT pore image and the CLSM microbial image, and then perform image rigid registration and image elastic registration. Step S8: Perform multi-channel fusion and visualization reconstruction of the registered CT pore image and CLSM microbial image; Step S8 specifically includes: S81: Resample the elastically registered CLSM microbial image and CT pore image obtained in step S7. Use the CT pore image as a reference image to perform spatial resampling on the CLSM microbial image. During the resampling process, a trilinear interpolation algorithm is used. The image spatial size, voxel spacing, and coordinate origin are all set uniformly according to the CT image parameters. After resampling, the no-signal area is filled with zero values ​​to keep the original pixel format of the CLSM image unchanged, thus obtaining a microbial image with a unified spatial coordinate system with the CT pore image. S82: Simultaneously load CT pore images and resampled CLSM microbial images to establish a multi-channel fusion model; Specifically: CT pore images are assigned to the green channel; CLSM microbial images are assigned to the blue channel; and they are fused according to preset weights. ； Where: α=0.7; β=0.3; The grayscale values ​​of the CT aperture image. The grayscale values ​​of the CLSM microbial image are... To fuse image grayscale values; S83: Adjust the transparency and display the composite image; wherein: the opacity of the CT pore image channel is set to 50%, the opacity of the CLSM microbial image channel is set to 50%, and a two-dimensional composite superimposed image is generated that simultaneously characterizes the relationship between pore structure and microbial spatial distribution. S84: Import two-dimensional composite overlay images into three-dimensional visualization software, and perform three-dimensional volume rendering based on the registered CT pore images and CLSM microbial images.

2. The method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging according to claim 1, characterized in that, Step S84 specifically involves importing the fused CT pore image and CLSM microbial image into the Surpass module of the Imaris software for three-dimensional volume rendering. Specifically, the CT pore image is set to green for display, the rendering mode is Blend, and the opacity is set to 30%–50%, making the pore network appear semi-transparent. Simultaneously, the CLSM microbial image is set to blue for display, with an opacity set to 80%–100%, and the target microbial region is extracted based on the fluorescence intensity threshold. A three-dimensional spatial distribution map of microorganisms in the soil pore network is obtained through volume rendering.

3. The method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging according to claim 1, characterized in that, In step S7, the CT pore images and CLSM microbial images undergo image preprocessing, specifically including the following steps: Step S711: Import the CT pore images and CLSM microbial images into Avizo, Fiji, or Imaris software for processing; Step S712: The CT pore image is denoised by 3×3×3 median filtering and Gaussian filtering, and binarized segmentation is completed by Otsu automatic thresholding combined with manual fine-tuning to distinguish the pore phase and soil solid phase. The region of interest with the same size as the CLSM microbial image is then cropped. Step S713: The CLSM microbial image is subjected to the rolling ball algorithm to remove background noise, and a binary mask of microbial distribution is generated by threshold segmentation.

4. The method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging according to claim 3, characterized in that, In step S7, if Avizo software is used, the image rigid registration operation in Avizo software specifically includes: Step S721: Select corresponding feature points; Select multiple obvious feature points in the soil column cross-section image, and use the marking tool to mark these feature points one by one in the CT pore image and CLSM microbial image; Step S722: Initial manual alignment; perform manual fine-tuning on the CT pore image and CLSM microbial image, including rotation angle and translation distance, to ensure that the marked feature points roughly overlap in the two types of images; Step S723: Determine the fixed and floating images; set the CT pore image as the fixed image and the CLSM microbial image as the floating image; Step S724: Set registration parameters; select trilinear interpolation as the interpolation method to ensure the continuity of image edges, and use mean square error as the similarity measure to adapt to signal differences; Step S725: Import and optimize feature point coordinates; Import the coordinates of the same feature points marked in step S721, and iterate through the least squares method to calculate the optimal rigid transformation matrix, including the X / Y axis translation, rotation angle and uniform scaling factor. Step S726: Three-dimensional affine transformation correction; Select a three-dimensional affine transformation model to correct X / Y / Z axis translation, three-dimensional rotation, image scaling and shearing; Adjust the overall sample offset to make the CT pore image and CLSM microbial image more accurately aligned; Step S727: Multi-resolution layer registration; Set 4 resolution levels, with downsampling coefficients of 8, 4, 2, and 1, and use Gaussian filtering for smoothing, with smoothing coefficients of 4, 2, 1, and 0 for each level to weaken detail interference; Step S728: Set optimizer parameters; the optimizer uses regular gradient descent, with a maximum of 300 iterations and a convergence threshold of 1×10⁻⁶. -7 Convergence is defined as the condition that the parameters do not change significantly for 15 consecutive iterations. Step S729: Start the registration operation; start the program, wait for the registration to complete, and output the rigidly registered CT pore image and CLSM microbial image.

5. The method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging according to claim 4, characterized in that, In step S7, the image elastic registration operation in Avizo software specifically includes: Step S731: Import images; import the rigidly registered CLSM microbial images and CT pore images into Avizo software; Step S732: Open the flexible registration plugin; launch the bUnwarpJ registration plugin, set the registration mode to flexible mode, select B-spline for image interpolation mode, and adjust the accuracy level to the highest. Step S733: Set the interpolation and optimizer parameters; select trilinear interpolation as the interpolation method, LBFGS optimizer as the optimizer, set the number of iterations to 500, and set the gradient convergence threshold to 1×10⁻⁶. -7 The number of correction items is set to 8; Step S734: Set the transformation model and mesh parameters; select the 3D B-spline free deformation model, and set the mesh size to 10×10×8; Step S735: Multi-resolution layer registration; set 3 resolution levels with downsampling coefficients of 4, 2, and 1 respectively; use Gaussian smoothing with smoothing coefficients of 2, 1, and 0 for each level; Step S736: Start automatic registration operation; the software generates a two-dimensional smooth deformation field to perform local micro-stretching, compression and misalignment correction on the CLSM microbial image, and matches the local structure of the CT image pixel by pixel; Step S737: Output results: Elastically registered CLSM microbial images, horizontal and vertical deformation field files.

6. The method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging according to claim 1, characterized in that, Step S4 is as follows: Add 4% paraformaldehyde fixative to the soil column after CT scanning, fix it at 4℃ for 4-6 hours under light-protected conditions, then rinse the fixed soil sample three times with phosphate buffer for 10 minutes each time, and then perform gradient dehydration with ethanol solutions of 30%, 50%, 70%, 80%, 90% and 100% volume fractions, with each concentration gradient treatment lasting 15 minutes.

7. The method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging according to claim 1, characterized in that, Step S5 is as follows: The dehydrated soil sample is embedded in epoxy resin and infiltrated under vacuum for 24 hours. Then it is placed in a mold and cured at 37°C for 12 hours and then at 50°C for 24 hours to form a solid embedded block. The embedded block is then cut into thin slices with a thickness of 50~100μm using an ultrathin slicer.

8. The method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging according to claim 1, characterized in that, It also includes step S9: quantitative analysis of microbial-pore spatial relationship, which specifically includes: calculating the proportion of microorganisms in pores of different sizes, pore preference index, distance from microorganisms to pore walls, microbial-pore wall adhesion rate, microbial-pore colocation index, and microbial-pore coupling degree.

9. The method for in-situ quantitative distribution of microorganisms in soil pores based on CT-CLSM multimodal imaging according to claim 1, characterized in that, Step S6, the CLSM scan, includes the following steps: Step S61: Sample preparation: Transfer the slide to a glass slide, add anti-fluorescence quenching mounting medium, cover with a coverslip, and gently press to remove air bubbles; Step S62: The DAPI nuclear dye is excited by a 405nm laser with a pinhole size of 1 Airy Unit and a laser power of 3%. The scanning mode is Z-axis layer scanning, and the scanning range covering the target observation area is set along the Z-axis. The layer scanning step size is set to 0.3μm. The scanning speed is normally controlled at 400-800Hz, and adjusted to 200-400Hz for high-quality imaging. At the same time, 2-4 scans are accumulated and averaged to reduce image background noise. Step S63: First, find the target observation field of view through bright field or wide field imaging mode and complete the coarse focusing operation; switch to laser confocal imaging mode, finely adjust the Z-axis focal length, and lock the clear imaging layer; Step S64: Adjust the PMT gain and offset parameters channel by channel to ensure that the fluorescence signal is not overexposed or has no black field, and that the signal intensity is within the linear detection range; Step S65: During 3D imaging, start the Z-axis automatic tomography program to complete the acquisition of images across the entire plane. After acquisition, save the image files in .lsm, .czi, or .tif format, preserving complete imaging metadata.

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