Why Nuclei-staining?
Nuclei-staining has no bias in labeling cells in a tissue slice, making it invaluable for determining tissue area and cell location. Stereo-seq experiments and bioinformatics analysis tools are compatible with two stains, ssDNA and DAPI.
STOmics R&D team has compared and tested various commercialized staining reagents and found that ssDNA staining has the least effect on the downstream mRNA capture rate. The blue-fluorescent DAPI nucleic acid stain is a commonly used nuclear counterstain with high specificity in staining nuclei and can be used alongside other fluorescent reagents. In addition, both ssDNA and DAPI staining allow visualization of tracklines.
Considerations for Nuclei-staining Images
Processing nuclei-staining images in StereoMap and SAW requires either an individual acquisition stored in a single-page grayscale file at 8 or 16-bit depth or an RGB color image. (See Image Types and Format for more information)
Fluorescence Image
Data Type and File Format
One 8 or 16-bit grayscale single-page image file.
Step 1: Upload Image
Click on Choose file in the drag-and-drop box to select a compatible image file from your file system. You can also drag your selected file and drop it in the box. Only one image file is allowed for this image type.
Please visit Image Processing Input Files for detailed image file information.
Selecting a file triggers a file-parsing process that not only reads the image but also acquires necessary data from the input. Depending on the file type and image size, the parsing time can vary. Stereo-seq chip serial number (SN) and microscope setting provide important information in the image processing steps. The .tar.gz or .stereo the input file contains these data. However, if the input file format is .tif or .tiff, you need to enter the required information specified in this step to begin the image parsing and quality check. Specifically, you’ll be asked to input details about the microscope by choosing a microscope configuration. Refer to the Microscope Settings for additional details.
Step 2: Image Registration
In this step, you will need to adjust the orientation, position, and scale of the image to align it with the spatial feature expression matrix.
If you upload the .stereo file in step 1, you can see the image and the spatial feature expression matrix once enter step 2. For .tar.gz or .tif/.tiff image file, you will need to add a .stereo file for specifying a spatial feature expression matrix as the reference. Click 
to select the .stereo file. Also, if your image is in the .stereo file, but you want to change the reference expression matrix, you can click the 
and load a new file. This action will reload the matrix, but use the image from step 1.
The registration process involves two stages, roughly matching the orientation of the image based on the morphology and finely adjusting the position and scale for exact overlap with the the spatial feature expression map. You can also choose "Chip tracklines" to display tracklines that are derived from the spatial feature expression matrix to help with fine alignment.
To roughly align the image, you need to match the microscope image and the feature density map in orientation.
Use the Flip tool 
to mirror the image.
Click on 
and use the Control knob 
to rotate the image in the same direction.
Once the orientation of the image and the spatial feature expression matrix have matched, you can move on to the fine-alignment steps.
In the fine-alignment stage, you will need to move the image to the place where the tissue can overlap.
Use the Move panel 
by setting the step size and pan move in four directions.
The dimension of the microscope image might differ from the spatial feature expression map, you can adjust scales using Scale tool
.
You can align the image by checking "Chip tracklines" to show the reference trackline template and let the tracklines fall directly overlapped. At this time, the chip trackline template is a representation of the matrix. If the tracklines are dim, the Normalization 
, Contrast 
, Brightness 
, and Opacity 
of the image can be adjusted manually.
Example of trackline overlapping The image that has been adjusted will be marked as complete by 
.
Step 3: Tissue Segmentation
In this step, you will identify the tissue regions. Accurately identifying the boundaries of the tissue can significantly reduce the interference from the background in the clustering result. The image-based tissue segmentation result will be mapped onto the sequencing-based spatial feature expression matrix to create a feature density map of the tissue region.
In the Tissue Segmentation step, you have the option to edit the mask that was previously recorded or create a new one. Tissue mask that was recorded in the .tar.gz or .stereo file will be labeled as "RECORD" in the Segmentation mask dropdown menu, while the mask created by drawing or importing will be labeled as "CUSTOM". To change the active image displayed in the canvas, simply select from the Segmentation mask dropdown.
To select or edit the tissue region, use a combination of Lasso 
, Brush 
, and Eraser 
tools. Lasso is typically used for selecting or deselecting large areas, while the Brush and Eraser tools are more suitable for smaller areas, such as regions around tissue or small holes in the tissue.
If you have created a .tif format binary mask file using a third-party tool, you can import it by clicking on the Segmentation mask dropdown on the right panel and then clicking . If the imported result is unsatisfactory, you can replace it by clicking to import a new mask.
Show the file name of the imported mask Step 4: Cell Segmentation
Cell identification and segmentation are the core steps in generating single-cell spatial resolved feature expression data.
Similar to Tissue Segmentation, you have the option to edit the mask that was previously recorded (tagged with "RECORD") or create a new one (tagged with "CUSTOM"). Change the active mask by selecting from the Segmentation mask dropdown.
Use the Lasso , Brush , and Eraser tools to select or edit cells. Lasso is best for use in deselecting large areas such as background, while the Brush and Eraser tools are more suitable for smaller areas, such as marking cells or separating cell clusters.
Lasso to deselect some cells It is recommended to import a.tif format cell mask file created from the registered image by any third-party tools. You can import it by clicking on the Segmentation mask dropdown and then accessing your file system by clicking . If the imported result is unsatisfactory, you can replace it by clicking to import a new mask.
Show the file name of the imported mask The final step is to export the results of image registration, tissue segmentation, and cell segmentation. Click Export image processing record to generate a .tar.gz file.
This output file can be located in your file system, specifically under the StereoMapWorkspace -> Processing folder, which is in your designated saving path. The saving path can be modified in Setting. If you have manually adjusted the image alignment with the matrix, you will also find a *regist.tif file in your output folder. Alternatively, this TIFF file can be located in the SAW output directory under the /outs/ folder.
The .tar.gz file contains the original image and records of the manual process. This file will be used in SAW to analyze sequencing data and images together. The internal structure of the .tar.gz is fixed and modification of the structure or any of the files in it is not recommended.
The *regist.tif file is a registered image that has been cropped and resized to match the dimension of the feature expression matrix. It can be utilized in any third-party tool, and the resulting data can be re-imported to StereoMap.
Pass the TAR.GZ to SAW Pipeline
There are two options for transferring the output of Image Processing to SAW.
One option is to use the --image-tar argument to feed the .tar.gz file into the SAW count pipeline. This will process the image together with the Stereo-seq FASTQ files and produce the HTML summary report.
Another choice is to use the --realigned-image-tar argument to input the .tar.gz file into the SAW realign pipeline. SAW realign will bypass the CID mapping and genome alignment steps, and re-generate aligned images, obtain the feature expression matrix at tissue and cell level, and produce an updated HTML report.
will not affect the grayscale image.
