StereoMap v4

A key module of StereoMap for exploring Stereo-seq dataset. It enables interactive investigation of feature expression distribution across tissue within bins or cells. It also offers the ability to integrate images or multiple omics data for co-visualization or side-by-side visualization to inform downstream analysis.

Entrance to Visual Explore

There are several ways to open visualization data:

In Task Management module, find the tasks generated by SAW-ST-V8 workflow and click the "Visualization" button to enter the Visual Explore interface.
In Tool module, enter the visualization from Visual Explore in StereoMap V4.0. You can select the task folder of SAW-ST-V8 on the data management page, directly select the.stereo file in the "Task--visualization" folder, or individually select the visualization files you want to view.

Visual Explore Input Files

Check the SAW output directory (visualization), you will find a .stereo manifest file. The .stereo manifest file is required to open any visualization data in StereoMap. It contains information about where to find the visualization files in the SAW output visualization directory. To properly open data by manifest file, please follow the rules:

Highly recommend keeping the default file structure of the SAW output directory. All the files are managed in the same directory path as the manifest file by default. If a file has been moved to another place or the file name has been changed, remember to modify the corresponding file path in the manifest file.
At least a feature expression matrix file (.gef) or an image pyramid file (.rpi) must be available in the directory for the Visual Exploreopen in new windowmodule.

File extension	Description
`.stereo`	A manifest file in JSON format that includes experiment and pipeline information, basic analysis statistics, and references to image and spatial matrix files in the SAW output visualization file folder. Find this `_.stereo_` manifest file in the SAW-generated directory (`_visualization_`). Support to open any SAW pipeline output in StereoMap.
`.gef`	The feature expression matrix file in HDF5 format for visualization. It contains the MID count for each gene of each spot. A spot is a binning unit that has a fixed-sized square shape in which the expression value in this square is accumulated. By default, a visualization `.gef` includes spot sizes of bin 1, 5, 10, 20, 50, 100, 200.
`.cellbin.gef`	The cell-feature expression matrix file in HDF5 format for visualization. It contains the spatial location and area of each cell, the MID count for each gene of each cell, and the cluster the cell belongs to. In `.cellbin.gef`, the cell is the smallest data unit. Only applicable to SAW running processes with graphs
`.rpi`	`.rpi` file saves one or multiple images in pyramidal format for better visualization. Each image is downsampled into several resolutions and each resolution layer is chopped into 256 pixels × 256 pixels tiles. If the size of a tile or a layer is smaller than 256 pixels × 256 pixels, it will remain intact. A typical SAW-generated `.rpi` organizes images and tiles sequentially by staining type (ssDNA, DAPI, H&E, or IF name) -> image type (registered image, tissue segmentation binary mask, or cell segmentation binary mask) -> resolution (equivalent resolution size of bin 2, bin 10, bin 50, and bin 100). Required to open any image in StereoMap Visual Exploreopen in new window.
`<SN>.bin<N>_<leiden_res>.h5ad`	A file stores clustering information from the spatial transcriptomics dataset in the AnnData file format. A `<SN>.bin<N>_<leiden_res>.h5ad` file is only allowed to contain the analysis results in one bin size. In the file name, `<SN>` stands for the Stereo-seq chip serial number, `<N>` for bin size, and `<leiden_res>` for leiden resolution. In a typical SAW pipeline, the spatial clustering analysis is processed with a spatial coordinate bin size of 200 and the Leiden resolution of 1.0.
`<SN>.cellbin_<leiden_res>*.h5ad`	The result of cell clustering information in AnnData file format. In the file name, `<SN>` represents the Stereo-seq chip serial number, `<leiden_res>` corresponds to the Leiden resolution, and `` denotes optional content that indicates the cell boundary correction status. If the `` part is absent, the `.h5ad` file contains clustering results based on the feature expression matrix of cell nuclei-covered regions. However, if the `*` is replaced by `.adjusted`, the `.h5ad` file records clustering results based on the feature expression matrix of cell nuclear expression distance, considering a 10-pixel radius until another cell boundary is encountered. In a typical SAW pipeline, the cell clustering analysis is processed with the Leiden resolution of 1.0. _Only available when the image was processed in SAW pipelines since the cell location information is derived from microscope imag_es.

Visual Explore Interface

The following image shows the layout of the Visual Explore interface.

Canvas

The Canvas shows the spatial feature expression data as spots overlaid on the ssDNA image of the tissue section, and the workspace tools are floating on the Canvas.

Click to select a desirable resolution for the spatial feature expression heatmap.

Display Options

Three options for adjusting the canvas panel and information pane floating on the canvas. From left to right:

Setting: show or hide information panes, spot or cell tooltips, canvas navigator, or rotate canvas.
Undo: undo the last selection action. The undo step is limited to 10 steps.
Reset: discard all your actions and reset the canvas to its initial state.

Mouse Tools

Four tools from left to right are:

Cursor: the mouse will toggle to the click-and-drag mouse action.
Lasso: draw a freehand shape with the lasso selection tool for selecting regions of interest (ROIs). You will need to press Ctrl on your keyboard and use your mouse to draw the shape (release the mouse and press Enter to complete the selection). If you need to draw discontinuous regions, first release and then press Ctrl and mouse again between each draw. To remove an area, hold Alt or Option and draw the region to remove. The Lasso function can be employed alongside the creation of spot groups (see Group Menuopen in new windowfor more information). This approach allows for better interpretability and the ability to group related features together. See Characterize Substructure and Generate New Heatmapopen in new windowor Region Annotation Based on H&E Imageopen in new windowfor more about lasso function.
Reference trackline template: click to display the trackline template on the canvas. This is useful in checking whether the microscope-acquired tracklines are accurately overlaid with the reference tracklines derived from a sequencing-based spatial feature expression matrix. See Check Image Alignmentopen in new windowfor more information.
Measure: measure the distance between two mouse clicks in pixels.

Load and Download

Two options for loading or exporting files.

Load file: click to load a complementary file that corresponds to the dataset.
- Select Load a Lasso Record to upload a .lasso.geojson file, then you can modify the lasso area.
- Select Load CSV File to upload your differential expression analysisopen in new windowresult. A new window will open with your CSV file.
Download file: click to open download window for exporting images.
- You can customize your image prefix name in the file name enter box.
- You have two options for saving your screen displayed on the canvas:
  - Screenshot Image captures everything currently displayed on the canvas. The quality of the screenshot depends on your display resolution.
  - HD Image saves the image in a higher resolution. If the legend is displayed, it will be saved as a separate image.
When you click Export, your image will be downloaded via the webpage.

Zoom bar

Toggle the zoom bar to zoom in and out the canvas.

The Feature menu displays the summarized feature count data. Click the menu bar to expand/collapse the panel. The panel lists the feature name, total MID count, and E10 value. By default, the list is sorted in descending order by MID count, but you can also click the small arrow on the right of each table header to sort by the selected column in ascending or descending order. The E10 score is a measure of how clustered the expression pattern of a feature is. A high E10 value indicates that although the feature is distributed across the tissue region, the significant expression spots are only found in a small area.

You can explore the expression of specific features by selecting feature names.

Search features: You can look for the specific feature in the search bar. The search is case-insensitive and supports fuzzy search. You can search for multiple features by separating names with commas.

Select one feature: Click the feature name and the feature expression distribution will then be displayed on Canvas.
Select multiple features: If you want to select multiple features, just check the checkboxes in front of their feature names. This will allow you to view a summarized expression heatmap for all the selected features. Instead of showing a summarized heatmap, you can explore the co-expression of features by viewing them in different colors (see Co-expression of Selected Genesopen in new windowfor more information).

The Layer menu is responsible for controlling how the data is displayed in the Canvas.

Layers are grouped as Image Layer and Main Analysis Layer, and the bin size panel controls the resolution of the feature density map.

Image Layer has staining images and segmentation masks registered with the feature expression matrix. Image adjustments include opacity, normalization, brightness, contrast, and color. Options vary based on image type. Normalization adjusts the maximum and minimum value of the image, which helps visualize tracklines. The computation formula is:

𝑛𝑜𝑟𝑚=[𝑋𝑖−𝑚𝑖𝑛(𝑥)]/[𝑚𝑎𝑥(𝑥)−𝑚𝑖𝑛(𝑥)]

Main Analysis Layer includes feature expression matrix view in heatmap, clusters, or UMAP. The cluster or UMAP view is only applicable for limited bin sizes. See Main Analysis Layer Display Optionsopen in new windowfor more information. The layers listed in this category can be opened in a new linked window by clicking in front of the layer name. The windows are linked by the spot coordinates. See Microorganism and Host Genesopen in new windowfor more information.
The available bin sizes are 1, 5, 10, 20, 50, 100, 150, 200, and cell bin (only applicable if the dataset contains cell segmentation output). Bin 1 represents one DNB per bin, while Bin 5 represents 5 x 5 DNBs as a binning unit. Cell bin means binning DNBs based on the cell covered regions. There's also an Auto-binsize switch that you can toggle. When you turn on the Auto-binsize mode, the canvas resolution will automatically adjust based on the zoom-in and zoom-out magnification.

Main Analysis Layer Display Options

Main analysis layers offer varied display options based on projection type and binning, for easy data exploration.

Options to adjust the display of the heatmap layers:

Heatmap Options	Explanation	Cell Bin
Color	Choose the color scheme of the heatmap for better visualization.
Spot Size	Adjust the spot size.	NA
Opacity	Adjust the heatmap opacity for simultaneously visualize image layers.
MID Filter	Filter spots based on MID count. See MID Filteringopen in new windowfor more information. Only applicable on selected features.	NA
Color Bar	Show or hide the color bar or define the expression range for coloring. By default, the color range goes from 0 to the highest value of any spot in the given bin size. However, you can customize the color range to better visualize bins that fall within a limited feature expression value range.
Boundaries	Show or hide tissue boundary generated from image or expression matrix. The boundary can be displayed as the outline, filled polygon, or both. The opacity of the filled polygon is adjustable.	NA
Display Schemes	Show expression distribution of features in summarized heatmap or discrete multi-color. See Co-expression of Selected Genesopen in new windowfor more information.	NA

Options to adjust the display of the clustering layers:

Cluster & UMAP Options	Explanation	BinN	Cell Bin
Opacity	Adjust the clustering layer opacity for simultaneously visualize image layers.
Form of cells & Outline color	Set cells to appear filled, outlined, or both. The acceptable choices for cell border colors (outlines) include colors assigned by clusters, white, black, and green.	NA

The Group menu lists the spot/region groups. The group includes two types, SAW-generated groups and custom groups. SAW-generated groups list the clusters computed in SAW pipelines (SAW-ST-V8,,SAW-ST-V8-realign,SAW-ST-V8-clusteringandSAW-ST-V8-diffexp).

You can access the cluster by first choosing the bin size that has been performed clustering, and showing the layer in Cluster. By default, the SAW-generated groups show the clustering in bin size of 200 or cell bin (if the tissue has been segmented into cells based on the image) with the Leiden resolution of 1.0.

Edit the Optimization percentage value or drag the slider to adjust the display of the image layer.
Click the checkbox in front of the cluster name to hide or show the clusters.
Click the color dot to edit the cluster color.

You can create a new group coupled with the Lasso functionopen in new window. Use the lasso to select a region, name the region name, and assign the label to the group. Or you can create new groups by clicking + Create a new group and assign the label to the created new group while saving lasso labels.

Click after the group name to edit name or export region coordinates in GeoJSON format to Run Lasso. You can also export differential expression analysis required parameters by clicking Run differential expression. See Differential Expression Analysisopen in new windowfor more information.

Click the label name lights up the selected region. The yellow region outline The selected label will be highlighted in yellow, and the corresponding statistic is displayed in the floating panel.

Bioinformatics Analysis

Stereo-seq T FF

Check Image Alignment

The tracklines on the chip surface act as markers to help with image registration. They are created when the capturing probe is unloaded and will show up as narrow lines on the spatial feature expression density heatmap. A good alignment is achieved when the tracklines perfectly overlap with the lines visible on the image. Highly recommend zooming in on the tissue edges to check the quality of the alignment.

A small tip for examining alignment, begin by inspecting the two diagonal fields of view. If these views perfectly overlap, it’s likely that the overall alignment is suitable.

However, if most of the track lines do not overlap, you will need to realign the image manually. Refer to the Navigation for Image Processingopen in new window for instructions on how to do this.

::: If the tracklines overlap perfectly in one field of view but are mismatched in the diagonal view, it could indicate an issue due to stitching problems in your microscope image.

:::

Co-expression of Selected Genes

To compare the expression distribution of features, you can visualize them in different colors.

Start by selecting the interested genes, the Canvas shows a summarized expression heatmap.

Next, click the Layer menu to expand the panel and open the Gene Heatmap layer setting window. Select the Multi-colored option under the Display Schemes, you can now compare and contrast the location of the selected genes. Note that the two selected genes are not co-expressed in the tissue.

If the color assigned to the gene or display setting is not optimal, click the color dot next to the selected gene to open the feature display setting window. You can change the color profile or adjust any settings.

Unlike the previous selection, here we select genes that exhibit co-expression in blended colors of yellow and violet.

Characterize Substructure and Generate New Heatmap

To identify substructure within tissue samples, the Lasso selection function can be a useful tool. You can manually delineate the regions of interest within the tissue samples. These selected regions can be scattered or continuous. The regions labeled with the same name are grouped together.

If you have exited lasso mode after saving the label, but realize that you need to cover another region, you can simply use the same label name to lasso select the remaining region.

Once the regions have been well-labeled, the coordinate information can be saved and passed to SAW to obtain the spatial feature expression matrix for the chosen region. Click to the right of group or label name and choose Run lasso to export the lasso GeoJSON file and submit SAW-ST-V8-reanalyze-lassoworkflow.

You can find <SN>.YYYYMMDDHHMMSS.lasso.geojson file in Data Management under the **/Files/ManualData/StereoMap/StereoMap_V4.0/**Lasso, and use it to submit theSAW-ST-V8-reanalyze-lassoagain in Workflow module.

::: The lasso GeoJSON stores the coordinates of the region contour, rather than the spots, allowing it to be used as input for square bin or cell bin computation. :::

MID Filtering

Preprocessing spatially resolved transcriptomic expression data is essential to eliminate noise before downstream analyses. The MID filtering function is specifically designed to manually remove under- or over-expressed spots of each selected feature, allowing for a focus on its spatial pattern.

The filtering function is applied to each selected feature individually, allowing for separate adjustments and different filtering thresholds. The filtering thresholds represent the lower and upper limits of the MID count and vary with bin sizes. Therefore, it is highly recommended to first switch to the intended bin size that you plan to use in the subsequent analyses before making adjustments to the MID filtering.

The output matrix of SAW-ST-V8-MIDFilterconcatenates the filtered matrix of each feature and can be used in downstream analyses.

Differential Expression Analysis

::: New feature! Compatible with SAW >= V8.0. :::

Differential expression analysis is conducted on spot groups, such as clusters, or spatial regions, such as lasso labels.

For clusters, click -> Run differential expression and submitSAW-ST-V8-diffexp.

For lasso labels, you need first to create at least two labeled regions in a group.

Then, click located to the right of the group name and select Run differential expression. In the pop-up window, select the analysis method, and submitSAW-ST-V8-diffexpworkflow.

Two differential expression methods are available:

Label vs. others: To identify features that are differentially expressed between a specific label (cluster) and all other clusters combined.
Label vs. label: To identify features that distinguish a specific label (cluster) from each other label within the same group.

You will find the differential expression analysis parameters recorded in the <SN>.YYYYMMDDHHMMSS.diffexp.geojson file in Data Management under /Files/ManualData/StereoMap/StereoMap_V4.0/Diffexp directory.

You can also run theSAW-ST-V8-diffexpworkflow again in Workflow module, using the path to the above file as a parameter for subsequent analysis.

SAW outputs include a <bin_size>_marker_features.csv file which is a formatted CSV file containing differential expression analysis result for visualization in StereoMap. Open it by Load CSV file (see Load and Save).

The differential expression analysis result table will be open in a linked new window. You can reorder the table by clicking the “up” and “down” arrows of log2 fold change (L2FC) or p-values of each gene and cluster to see the significant features.

Clicking on a feature name in the table will reveal the corresponding gene expression distribution on the canvas in summarized heatmap. Additionally, for multiple features, you can explore their co-expressed relationshipopen in new window by showing them in multi-color mode.

Stereo-seq N FFPE

Region Annotation Based on H&E Image

Spatial transcriptomics allows for the visualization and quantification of gene expression data in the context of the original tissue architechture. It generates a gene expression heatmap that characterizes the gene’s activity over the tissue. This can provide insights into the functional organization of tissues at the molecular level. On the other hand, H&E staining provides a detailed view of tissue architecture and histologic information. It allows pathologiest to easily differentiate between the nuclear and cytoplasmic parts of a cell. The overall patterns of coloration from the stain show the general layout and distribution of cells.

To annotate regions of interest from tissue and extract corresponding gene expression data to gain a comprehensive understanding of both the structural and functional aspects of tissue, you may prefer to start by label on H&E image based on pathohistologic features.

First, adjust the opacity of the feature expression heatmap to ensure the H&E image is visible.

Following that, use the lasso functionopen in new window to annotate on the H&E image (similar to Characterize Substructure and Generate New Heatmapopen in new window). Press Enter to complete the selection and click Save to naming the annotation.

If you need a group contains multiple annotated regions, remember to label them under the identical group name.

You are now able to leave the lasso mode and modify the heatmap’s opacity to view the annotations on different layers.

After annotation, if you would like to obtain the spatial feature expression matrix of the selected regions, click to the right of the group or label name and choose Run lasso to submitSAW-ST-V8-reanalyze-lassoworkflow. Or, if you would like to understand the differences between annotated regions, choose Run differential expressoin.

You can find the output<SN>.YYYYMMDDHHMMSS.lasso.geojsonfile under **/Files/ManualData/StereoMap/StereoMap_V4.0/**Lasso directory, or <SN>.YYYYMMDDHHMMSS.diffexp.geojsonfile under /Files/ManualData/StereoMap/StereoMap_V4.0/Diffexp directory. Then use them to run the corresponding workflows in Workflow module.

See Characterize Substructure and Generate New Heatmapopen in new window and Differential Expression Analysisopen in new window for more details .

Microorganism and Host Genes

Stereo-seq N FFPE product capture total RNA information by free probe design. This design also allows for efficient capturing of microorganisms. Select the reference of Microorganism and Host for the RefLibrariesparameter in SAW-ST-V8, it will output the host's spatial gene expression matrix and microorganism distribution matrix.

Open .stereo in StereoMap Visual Explore, microorganism distribution matrix can be accessed in layer menu under Microorganism category. Click in front of the layer name to open it in a new linked window.

The main window and the linked window are connected based on spot coordinates. In the main window, you can choose clusters or use lasso selection to highlight specific regions. These selected spots will then be highlighted in the linked window, along with their corresponding content.

In the example below, the main window depicts spatial clusters in bin 200, while the linked window illustrates the distribution of microorganisms in the same bin. When you choose Cluster 1 in the main window, the linked window will exclusively display spots with corresponding coordinates. In the linked window, you can utilize the lasso function once more. Doing so will allow the statistic panel to show the components of the selected spots.

Microorganisms are classified into taxonomic levels using a hierarchy followed by a double underline and the microbial species name. These levels are represented by abbreviations: p (phylum), c (class), o (order), f (family), g (genus), and s (species). For instance, the genus Mycobacterium is represented as g__Mycobacterium in the feature and statistics panel.

A core function for manipulating images. It allows for the manual alignment of an image with the feature expression matrix, and the execution of tissue and cell segmentation either manually or through the importation of result from external tools. The result can subsequently be transferred to the SAW analysis workflow for standard co-analysis.

Why Use Images in Spatial Analysis

The expression level of features (such as mRNA and proteins) on clinical tissues might be uneven, making it challenging to identify tissue boundaries accurately solely based on the spatial feature expression density heat map. However, microscope images of cell nuclei (such as ssDNA fluorescent staining or DAPI staining) or tissue hematoxylin and eosin (H&E) staining can clearly show the whole tissue region. The use of staining images can significantly improve the outlining of tissue or even cells. After determining the boundaries, precisely align the image with the density map and use the boundary information to obtain a subset density map of tissue or cell region for further analysis.

Image types and formats

The following is a description of the data formats supported by StereoMap V4.0 and SAW-ST-V8:

Image type	Image format	Objective lens magnification	Stereo-seq chip size
Nuclear staining images, such as: ssDN or DAPI	Single grayscale image at 8 or 16 bits	10X	<=2 cm x 3 cm
Nuclear staining + immunofluorescence image For example: DAPI + up to 6 IF images	Single grayscale image at 8 or 16 bits	10X	<= 1 cm x 1 cm
Hematoxylin-eosin (H&E) staining images	24-bit deep color images	10X	<= 1 cm x 1 cm

The surface of the Stereo-seq chip has periodic trajectory lines (horizontal and vertical directions), which will be displayed as narrow lines on the spatially expressed heat map. Since there are no capture probes in the trajectory line area, it can assist matrix and image tracking. alignment. Please strictly refer to the Standard Operating Guidelines (SOPs) for tissue staining and imaging of Stereo-seq technology to minimize its impact on downstream mRNA capture rates and improve visibility of trace lines in microscopy images. These trajectory lines can be seen simultaneously on the spatially represented heat map and microscopy image and can be used as position markers for image alignment.

Below are examples of trace lines on images and heatmaps. (The brightness of the image has been adjusted so that the trajectory lines can be clearly displayed.)

Trajectory lines on fluorescence images	Trajectory lines on color image	Trajectory lines on gene expression heatmap
The trajectory line is a black line	The trajectory line is a light white line	The trajectory line is a black line

The Stereo-seq analysis workflow software package (SAW) embeds automated image processing algorithms to identify tissue and cell boundaries and can detect trajectory lines on the Stereo-seq chip to align the image with the feature expression matrix. If trace lines cannot be detected or tissue/cell boundaries are unclear, you may need to process manually.

Image Processing User Roadmap

The recommended image processing user roadmap is as follows:

● Check the quality of the microscope image. In order to simplify subsequent image analysis, it is strongly recommended to perform image QC during the experiment. The purpose is mainly to confirm whether the trajectory line can be detected, whether the image is accurately spliced, whether the tissue is visible, and ultimately provide a reference for whether the image can be automatically processed by SAW.

::: The cloud platform StereoMap V4.0 does not currently support QC. Please use offline software to complete the QC steps. To obtain offline software and information about QC evaluation indicators and standards, please refer to Image QC for more details. :::

● Registration of images to expression matrix heatmaps. By overlapping the image with the heat map of the expression matrix, the direction, scale, and angle of the image can be adjusted to ensure that it is consistent with the expression matrix. For multiple immunofluorescence (IF) images, check the registration results for each image.

● Circle the tissues and cells. Use drawing tools to extract ROI regions of interest or upload tissue/cell masks generated by third-party tools. For immunofluorescence IF images, the ROI area can be obtained by adjusting the threshold interval, because the area with weak signal is likely to be the background. Selecting accurate tissue/cell regions is crucial to generating high-quality image data, which can reduce the impact of image background noise on subsequent analysis.

● Export operation record files. Manual recording can be used to extract the spatial expression matrix corresponding to the tissue or cell region through the SAW-ST-V8-realign process.

The following page will display the image data supported by Stereo-seq in a step by step manner. Please refer to the following operation guide:

Image processing entrance

Image processing can be turned on in the following ways:

In the task management module, find the task generated by the SAW-ST-V8 process and click the "Image Processing" button to enter the image processing interface.
In the interactive analysis module, enter image processing from Image Processing on the StereoMap V4.0 startup page. On the data management page, you can select the .tar.gz or .stereo file output by the SAW-ST-V8 process.

Choose the type that matches your image, three image types are currently supported.

Image processing input file

SAW is embedded with automatic image processing algorithms for image stitching (small FOV stitching into large images), tissue segmentation, cell segmentation and identification of tracklines on the Stereo-seq chip, which can combine images with the same tracklines The feature expression matrix is aligned. For data with blurred tissue/cell boundaries or where the algorithm cannot automatically detect trajectory lines, you may need to manually select or align tissue/cell outlines.

The image processing module provides a series of operations for manual image processing, mainly including functions such as manually selecting tissue/cell areas, importing Mask results from third-party segmentation tools, and manually aligning images with feature expression matrices. The input data for manual image processing is the data after running the SAW automatic image algorithm.

file extension describe

.tar.gz Store original microscope images and image quality control information. Offline gadget->Image quality control->File generated by Image QC.

.stereo A visualization manifest file in JSON format, including image result files and expression matrix files automatically analyzed by SAW. The manifest file of .stereo can be found in the SAW output folder (visualization). If .stereo is used as the input file of the image processing module, the manifest file contains at least the image (.tar.gz) file and the expression matrix (.gef) file. path. The image compressed file .tar.gz output by SAW records the results of automatic algorithm segmentation and registration.

file extension	describe
`.tar.gz`	Store original microscope images and image quality control information. Offline gadget->Image quality control->File generated by Image QC.
`.stereo`	A visualization manifest file in JSON format, including image result files and expression matrix files automatically analyzed by SAW. The manifest file of .stereo can be found in the SAW output folder (visualization). If .stereo is used as the input file of the image processing module, the manifest file contains at least the image (.tar.gz) file and the expression matrix (.gef) file. path. The image compressed file .tar.gz output by SAW records the results of automatic algorithm segmentation and registration.

Nuclear Staining Image Processing Guide

Why do we need nuclear stain images?

Nuclear staining has no tissue separation bias in tissue sections and is of high value for determining tissue regions and cell locations. Stereo-seq experiments and bioanalysis tools are compatible with both ssDNA and DAPI nuclear staining reagents.

The R&D team of STOmics tested and compared a variety of commercial staining reagents and found that ssDNA staining has the least impact on the downstream mRNA capture rate. DAPI is highly specific for staining cell nuclei and can be used with other fluorescent reagents. It is a commonly used dye. Trajectory lines can be seen with both staining methods.

Notes on Nuclear Staining Images

Nuclear staining images processed in StereoMap and SAW are single-channel 8-bit/16-bit deep grayscale images, or RGB color images. (For more information, see Image Types and Formats for more information.)

Fluorescence image	Data types and formats
grayscale image	8 / 16 bit single channel image

Step 1: Upload an image

Click Choose file in the selection box and select a compatible image file. The nuclear staining image type allows only one image file to be uploaded.

Please refer to Image Processing Input Files for more image file information.

Selecting a file will trigger a file parsing process. This process will not only read the image, but also obtain the necessary information from the input. The parsing time will vary depending on the file type and image size. During the image processing step, the Stereo-seq chip serial number (SN) and microscope configuration provide important reference information. If the input file is .tar.gz or .stereo, the information has been written to the input file.

If image quality control is complete, you can view the status of the indicator before each QC indicator. These indicators are related to the image processing results, and you may see tooltips on step labels indicating potential risks for a step.

icon	illustrate
	Finish. You have completed this step.
	warn. You may want to pay special attention to this step. Image quality is not ideal for automatic operation. For example, if you see this warning during image registration in step 2, this may indicate that there may be an error in the automatic registration in SAW. You need to check the results or make manual adjustments.
	mistake. There is a problem with your image that cannot be processed in this step.

Step 2: Image Registration

In this step, you adjust the orientation, angle, and scale of the image so that it aligns with the spatial feature representation matrix.

If you uploaded a .stereo file in the first step, you can see the image and spatial feature expression matrix at the same time when entering the second step; if you uploaded a .tar.gz image file, you need to select a .stereo file to specify the spatial feature expression matrix. You can click to select the .stereo file. In addition, if you uploaded a .stereo file (including image files) in the first step and want to change the spatial feature expression matrix file displayed in the second step, you can click to reload a new matrix file, but still use the first step to upload .stereo image.

The manual registration process consists of two stages: roughly matching the orientation of the image according to the tissue morphology, and then finely adjusting the position and scale of the image to ensure complete overlap with the spatial feature expression matrix. You can also use Chip trackline to assist with fine alignment.

To roughly align the images, you need to transform the microscope image to align with the feature matrix orientation.

Use the flip tool to mirror and flip the image around the Y axis.
Click the Rotate button to rotate the image in the same direction.

When the image orientation is consistent with the expression matrix orientation, you can proceed to fine alignment.

When doing fine registration, you need to move the image to an area covered by tissue.

Use the translation panel to set the step size and the four directions of movement (up, down, left, right).
Since the size of the image data captured by the microscope is different from the feature matrix, you can use the zoom tool to adjust the zoom ratio.

You can check Chip trackline to display a reference trackline template to assist in aligning images. At this time, the reference trajectory line template can be used as a representation of the matrix. If the trajectory line is dark, the tool buttons such as normalization, contrast, brightness, and opacity of the image can be manually adjusted.

::: Adjust the image so that the trajectory lines are clearly visible. :::

::: Saturation adjustments are not available for grayscale images. :::

Images that have been registered will be marked as completed。

::: If you have done manual registration, you can skip steps 3 and 4. You can export the manual tar.gz image file and input it into SAW to run automatic tissue segmentation and cell segmentation.

In addition, the *regist.tif image file will be output in the fifth step of the SAW-ST-V8-realign process. This file is a registered image. Its shape and direction match the gene expression matrix. This image can be used as a tissue segmentation Or the starting point of cell segmentation. If you consider using a third-party segmentation tool, it is strongly recommended to use this image file as input to avoid displacement, angle, and scale deviations between the image and the Mask. :::

Step 3: Tissue Segmentation

::: Tissue segmentation is a skippable step. :::

In this step, you need to identify tissue regions. Accurate identification of tissue boundaries can reduce the impact of background interference on the clustering results. The image-based tissue segmentation results will be mapped to the spatial feature expression matrix to generate an expression heat map of the tissue region.

::: If you uploaded the .stereo file in the first step, you can see a semi-transparent tissue mask on the registered image layer.

In the case of a .tar.gz image file, you will need to use hand tools to draw the tissue areas. :::

In this step you can edit the previously recorded tissue mask or create a new mask. Segmentation masks recorded in a .tar.gz or .stereo file will be marked RECORD in the Segmentation mask drop-down menu, while masks created by manual drawing or import will be marked CUSTOM . To change the mask image displayed on the canvas, simply select the toggle from the Segmentation mask drop-down menu.

If you need to edit the mask image of tissue areas, you can use tools such as the lasso, brush, and eraser. The lasso is generally used to select or eliminate large areas, while the brush and eraser tools are better suited for editing smaller areas such as around tissue or small holes in tissue.

You can import a binary mask file in .tif format created by a third-party segmentation tool by clicking the Segmentation mask drop-down menu on the right panel. If you are not satisfied with the imported results, you can click to replace it with a new one Mask file

Step 4: Cell segmentation

::: Cell segmentation is a skippable step. :::

Cell segmentation is a core step in generating single-cell spatial resolution data.

::: If you uploaded the .stereo file in the first step, you can see a red cell/nucleus mask on the registered image layer.

If it's a .tar.gz or .tif/.tiff image file, you'll need to use hand tools to draw the cells.

Since the corresponding cell segmentation results are only displayed within the tissue area, areas outside the tissue will be displayed with a black background.

:::

Similar to tissue segmentation, you can choose to edit a previously recorded mask (marked RECORD ) or create a new mask (marked CUSTOM ) by switching the mask type displayed in the canvas from the Segmentation mask drop-down menu.

Use the lasso, brush, and eraser tools to edit cells. The lasso is best for removing large areas of background, while the brush and eraser tools are better suited for smaller areas, such as marking a cell.

Delete some cells using the Lasso tool's inverse selection

It is recommended that you use the registered image to import it into a third-party segmentation tool to create a binary cell mask file in .tif format. You can import a binary mask file in .tif format created by a third-party segmentation tool by clicking the Segmentation mask drop-down menu on the right panel. If you are not satisfied with the imported results, you can click to replace it with a new one Mask file.

Import Cell Mask

Display the name of the imported cell Mask

Replace cell Mask

Step 5: Save & Run

Finally, the delivery process is based on the results of manual adjustment. Click Save&Run, the system will generate an .ipr file (save to the following path: /Files/ManualData/StereoMap/StereoMap_V4.0/ImageProcessing/), and automatically activate the SAW-ST-V8-realign process, enter the corresponding parameters, and click Just run it. In addition, users can also enter the process analysis module and use the above .ipr file to submit the SAW-ST-V8-realign process themselves.

Nuclear Staining + Immunofluorescence Image Processing Guide

Why do nuclear staining + immunofluorescence images?

Immunofluorescence (IF) is a widely used image-based technique for visualizing the subcellular distribution of proteins in cells; for example, nuclei can be stained with DAPI and T cells can be identified by CD3. Multiplex immunofluorescence (multiplex, mIF) can be labeled on tissue sections and scanned simultaneously with a fluorescence microscope. Stereo-seq biochemical workflows and bioinformatics analysis tools are compatible with DAPI and up to 6 user-defined IFs, allowing more space for tissue samples. Research.

Notes on Nuclear Staining + Immunofluorescence Images

Since each immunofluorescence (IF) has a specific fluorescence spectrum, and they are all applied to the same tissue section at the same time, there are major challenges in both the selection of IF and the microscope imaging system. It is therefore critical to manage the degree of spectral overlap between the IFs you choose, in addition to the excitation and emission wavelengths available with your imaging system.

Fluorescence microscopy scans areas of tissue labeled with IF markers by two methods: switching filters over the same scan area until the entire tissue area has been scanned, or switching filters after scanning the entire tissue area. Both methods require holding the chip still between each scan to ensure that the tissue position in the IF image remains consistent in both angular and scale directions.

DAPI + mIF images processed in StereoMap and SAW are 8-bit/16-bit deep grayscale images. (See Image Types and Formats for more informationopen in new window)

Fluorescence image	Data types and formats
grayscale image	8/16-bit single channel image

Step 1: Upload the image

Click Choose file in the selection box, select a compatible one, and drag it into the selection box on the left. .tar.gz or .stereo file.

Please refer to the Image Processing Input Fileopen in new window for more detailed image file information.

Icon	description
	Finish. You have completed this step.
	Warn. You may want to pay special attention to this step. Image quality is not ideal for automatic operation. For example, if you see this warning during image registration in step 2, this may indicate that there may be an error in the automatic registration in SAW. You need to check the results or make manual adjustments.
	Mistake. There is a problem with your image that cannot be processed in this step.