Name: 使用 DeepSpaceDB 挖掘空间转录组学数据集 (视频) | JoVE Journal
Uploaded: 2025-09-05T14:20:00.000+00:00
Duration: 10 min 16 s
Description: 了解如何使用DeepSpaceDB数据库进行空间转录组学数据分析，探索小鼠组织中基因表达模式与疾病机制的关系。该平台支持多种空间转录组技术，无需编程技能即可生成假设并下载数据。

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The authors would like to thank Y. Harada for secretarial assistance. This work was supported by JST NBDC (Grant Number JPMJND2303, A.V.) and AMED (Grant Number JP24gm2010003, A.V.) This work was also supported by JSPS KAKENHI (20H03451, 24K02236, and 24KK0147; S.K.), JST FOREST (JPMJFR2062; S.K), JST Moonshot (JPMJMS2011-61; S.K). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

1. Example 1: Analysis of a mouse brain sample
NOTE: In this section, the analysis of a mouse brain sample is illustrated, navigating through the different features and plots available in DeepSpaceDB (a link to the database is available in the Table of Materials).
<ol>
	<li>Sample selection
	<ol>
		<li>Click on the Database tab and use the filter to select the organism mouse, the organ brain, and the source zenodo. Move through the resulting samples, and select sample DSID001557. Alternatively, use the search box to search the database for the term &#34;DSID001557&#34; and select this sample.</li>
		<li>Click on the sample and confirm the description as 2 &#215; 106 cells in 100 &#181;L saline-NK cell (i.v. injection once a week for a total of 5 times).</li>
	</ol>
	</li>
	<li>Quality analysis
	<ol>
		<li>Click on the Quality tab to evaluate the quality of the selected sample. From the quality measures drop-down menu, select different options like Detected Genes (Figure 1A), Read Count (Figure 1B), and Mito (Figure 1C), to visualize the respective parameters in each spot across the sample slice.</li>
	</ol>
	</li>
	<li>Image annotation
	<ol>
		<li>Navigate to the Image annotation tab for identifying the different regions of the sample slice.</li>
		<li>Move the mouse cursor over the sample slice. Annotations predicted by a large language model (LLM) are displayed for parts of the sample image in a grid-based manner, with information about the anatomy and associated condition8.</li>
	</ol>
	</li>
	<li>Cluster analysis
	<ol>
		<li>For a deeper understanding of the cell type clusters in the sample slice, navigate to the Clusters tab. A 2D embedding of the clusters will be displayed, along with a representation of color-coded clusters across the spots on the sample slice (Figure 1E).</li>
	</ol>
	</li>
	<li>Spatially variable genes and pathways
	<ol>
		<li>Navigate to the Genes tab and take note of the spatially variable genes (SVG; genes whose expression levels differ across tissue locations) in the sample9,10. These SVGs are predicted using the singleCellHaystack function, which adopts the Kullback-Leibler Divergence measure (D_KL in the table) to evaluate how distinct the expression pattern of each gene is from what would be expected at random (Figure 2). Genes with a low p-value (large negative log.p.adj in the table) are listed as the SVGs. 
		NOTE: Gene expression data was normalized using the default parameters used in the Seurat R (version 5) package11. In practice, the reads for each gene at each spot were divided by the total count of reads in that spot, and multiplied by the scale factor 10,000. Next, the natural logarithm was calculated after addition of 1, to avoid problems with log(0). The plot shown in the Genes tab shows this normalized data.</li>
		<li>Click on some of the top genes in the list. This generates a spatial plot for the genes across the tissue slice, with spots color-coded for the expression level (Figure 2). Top-scoring genes have clearly distinct spatial patterns of expression.</li>
		<li>Navigate further to the Pathways tab to inspect the activity of sets of genes (e.g., genes associated with a common biological pathway) rather than individual genes. Spatially variable pathways are listed in a similar way to the SVGs discussed above (Figure 3). Pathway activities are estimated based on the expression levels of the genes associated with them7,11. 
		NOTE: Pathway activities were estimated using the Seurat R package function addModuleScore11. In brief, this function takes as input a set of genes (e.g., a set of genes involved in a common pathway) and returns their average expression levels, after several processing steps. In practice, positive values imply a higher than average activity, and negative values a lower than average activity. The plot shown in the Pathways tab shows this module score data.</li>
		<li>Click on some of the top pathways in the list. This generates a spatial plot for the pathways across the tissue slice, with spots color-coded for the activity level. Several pathways have distinct spatial patterns of activity (Figure 3).</li>
	</ol>
	</li>
	<li>Intra-sample gene expression comparison
	<ol>
		<li>Navigate to the Tissue Explorer tab and select Manual Selection (if it has not yet been selected). Next, use the mouse cursor to select the spots in the hippocampal region of the mouse brain slice, on the left side. Click on set 1, and select add to set. This will highlight all the selected spots on the slice on the right side (Figure 4A).</li>
		<li>Now click on set 2, and use the mouse cursor to select the spots in the hypothalamic region of the mouse brain slice. Click on add to set, which will highlight all the selected spots on the slice on the right side (Figure 4A).</li>
		<li>After completing the spot selection process, click on the Compare gene expression button. This will generate a table with the average gene expression values of the selected spots between both regions, along with a scatterplot representation. Move the cursor over individual spots to confirm the gene names and the average expression of genes in both regions.</li>
		<li>Based on the gene expression comparison results, identify differentially expressed genes and re-navigate to the Genes tab to visualize their expression across the sample slice (Figure 4B,C). 
		NOTE: Through the steps detailed above, DeepSpaceDB can be used to investigate the features of a mouse brain spatial transcriptomics sample.</li>
	</ol>
	</li>
</ol>
2. Example 2: Identification and annotation of differentially expressed genes associated with immune activity in metastatic regions of colorectal origin in mouse livers
NOTE: An intra-sample comparison is explored in the current section. This is illustrated through the identification and annotation of differentially expressed genes between metastatic regions of colorectal origin, and distant regions of healthy tissue within a liver section, based on two different samples. The spatial expression of specific dysregulated genes relevant to immune activity is further visualized in the tissue sections.
<ol>
	<li>Database navigation and sample selection
	<ol>
		<li>Click on the Database tab and use the filter to select the organism mouse, the organ liver, and the condition cancer. From the resulting samples, select sample DSID001005. Click on the sample and confirm the description stating that the sample is from a mouse liver containing metastasis of colorectal cancer origin.</li>
		<li>Navigate to the Tissue Explorer tab and select Manual Selection. Next, using the mouse cursor, select the spots in the tumor region (colorectal metastases) of the liver sample DSID001005, identified based on the positive expression of the Epcam marker (Figure 5A). Click on set 1, and select add to set. This highlights all the selected spots on the slice on the right side (Figure 5C).</li>
		<li>Now click on set 2, and use the mouse cursor to select the spots in the distant non-tumor region of the liver sample. Click on add to set, which will highlight all the selected spots on the slice on the right side (Figure 5C).</li>
	</ol>
	</li>
	<li>Comparison of gene expression between selected spots
	<ol>
		<li>After completing the spot selection process, click on the Compare gene expression button. This generates a table with the average gene expression values of the selected spots between both regions, along with a scatterplot representation. Move the mouse cursor over individual spots and inspect the gene names and the average expression of genes in both regions.</li>
		<li>To conduct a deeper analysis with the gene expression data, select the Download CSV option. This generates a Comma-Separated Values (CSV) file of the gene expression data for the two regions of the sample.</li>
		<li>Repeat steps 2.1.1-2.1.3 and 2.2.1-2.2.2 for sample &#34;DSID001007&#34;. Confirm its description as another slice from a mouse liver containing metastases of colorectal cancer origin.</li>
	</ol>
	</li>
	<li>Data analysis with R programming
	<ol>
		<li>Confirm that the steps above resulted in 2 CSV files, one from sample DSID001005 and one from sample DSID001007. Both files contain 2 columns representing the average gene expression in the 2 selections (tumor tissue and non-tumor tissue) that were made in each sample.</li>
		<li>Read the CSV files into R and merge them for further downstream analysis with two replicates per condition (i.e., tumor region with colorectal cancer metastases, and distant healthy tissue in the liver). Refer to the R script and data files in the Supplementary materials.</li>
		<li>Use the limma package (version 3.62.2) in R (version 4.4.2)12 to conduct differential expression analysis for the data, categorizing the colorectal metastases regions of both samples as cancer, and the distant, healthy regions of both samples as control. Obtain the upregulated genes with a filter of logFC &#62; 0.5 and adjusted p-value &#60; 0.05. Similarly, obtain the downregulated genes with a filter of logFC &#60; -0.5 and adjusted p-value &#60; 0.05. 
		NOTE: These sets of genes are used to identify biological pathways that are affected by the tumor in the next step (Figure 6A,B).</li>
		<li>Use the clusterProfiler package (version 4.14.6) in R13 to conduct the analysis of pathways of the Kyoto Encyclopedia of Genes and Genomes (KEGG)14 for the downregulated and upregulated genes. Based on a stringent filter of q-value &#60; 0.05, identify the significant pathways associated with the downregulated and upregulated genes. Focus on genes associated with immunological pathways, immune activities, or relevant signatures (Figure 6B).</li>
	</ol>
	</li>
	<li>Gene-specific data mining
	<ol>
		<li>Next, search for gene names in the Spatially Variable Genes section to confirm the spatial expression of the target genes. Click on a gene name to generate a spatial plot for the gene across the tissue slice, with spots color-coded for the expression level (Figure 7).</li>
		<li>Identify specific genes with spatial patterns of expression at the site of colorectal metastases, as against the distant, healthy liver tissue. The functional relevance of the genes, or their expression in other organs or conditions can be further explored in the database.</li>
		<li>Select the Search tab, and choose the species as mouse. Click on the search by gene option, and type in a gene name. An overview of the organ and condition distribution of the genes will be displayed and can be further analyzed. 
		NOTE: Through the steps detailed above, DeepSpaceDB can be used to investigate gene expression patterns between metastatic and non-metastatic regions in mouse liver spatial transcriptomics samples.</li>
	</ol>
	</li>
</ol>

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<li>Zhang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Association+between+NRGN+gene+polymorphism+and+resting-state+hippocampal+functional+connectivity+in+schizophrenia%5BTitle%5D)+AND+%22BMC+Psychiatry%22%5BJournal%5D)">Association between NRGN gene polymorphism and resting-state hippocampal functional connectivity in schizophrenia.</a> BMC Psychiatry. 19 (1), 108(2019).</li>
<li>Sreedharan, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Glutamate%2C+aspartate+and+nucleotide+transporters+in+the+SLC17+family+form+four+main+phylogenetic+clusters%3A+evolution+and+tissue+expression%5BTitle%5D)+AND+%22BMC+Genomics%22%5BJournal%5D)">Glutamate, aspartate and nucleotide transporters in the SLC17 family form four main phylogenetic clusters: evolution and tissue expression.</a> BMC Genomics. 11, 17(2010).</li>
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<li>Li, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Benchmarking+spatial+and+single-cell+transcriptomics+integration+methods+for+transcript+distribution+prediction+and+cell+type+deconvolution%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution.</a> Nat Methods. 19 (6), 662-670 (2022).</li>
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</ol>

The authors have nothing to disclose.

Here, we introduced two comprehensive protocols outlining the navigation, retrieval, and analysis of spatial transcriptomics data in DeepSpaceDB. While most spatial omics databases focus on the collection of data from a large number of samples, generated using various platforms3,4,5,6, DeepSpaceDB focuses on the development of interactive tools that allow users to deeply and efficiently explore spatial transcriptomic features. To enable this level of functionality, the current release focuses exclusively on the Visium platform. With the emergence of high-resolution platforms, we plan to expand DeepSpaceDB accordingly, developing new strategies for the processing and integration of such data in a user-friendly manner.
DeepSpaceDB enables users to assess sample quality metrics (e.g., gene counts, read depth) and compare them across datasets. The database includes multi-layered annotations: unsupervised clustering across the full database with assigned labels, LLM-based detection of structural and pathological features from histological images, and expert histology annotations for a growing subset of samples. Moreover, users can interactively select regions of interest within or across samples to compare gene expression, enabling studies of spatial contrasts between regions like tumor versus stroma or diseased versus healthy regions. Such features are generally lacking in other databases3,4,5,6. Other features, such as spatially variable genes and pathways, cell type predictions, and clustering results, are also available. Taken together, this database significantly lowers the barriers to exploring spatial transcriptomics data. Samples from a wide range of tissues and conditions are freely accessible, and users can navigate them through simple point-and-click interactions; no advanced bioinformatics expertise required. That said, some prior knowledge of marker genes and tissue architecture is likely necessary for the accurate interpretation of expression patterns and for selecting regions of interest in the Tissue Explorer tool.
Although not introduced here, users can also upload their own samples and apply many of the same tools to analyze them. The database also supports inter-sample comparisons between 2 different tissue slices, allowing, for example, comparisons between diseased tissues and healthy control tissues. Finally, raw and processed data, along with all derived analysis outputs, are available for download, supporting downstream workflows and custom analyses. For several of these tools, short tutorial videos are available on the tutorial page of the database.
There are still aspects of the database that require improvement. One is the accurate prediction of cell types and cell type compositions at each location within the tissue slices. In the current version of DeepSpaceDB (version 1.0), we predicted the cell type composition of each Visium spot using a method called robust cell type decomposition (RCTD)21. RCTD performed relatively well in a recent benchmark study22. Predictions made by RCTD could also be experimentally validated in our recent study of the livers of cancer-bearing mice23. However, a comprehensive evaluation of the accuracy of cell type predictions has not been conducted. A related issue is that RCTD and other cell type prediction methods require a reference dataset with annotated cell types. In general, cell types (or cell type compositions) at each spatial location are predicted through the comparison with gene expression patterns in this reference dataset. However, selecting a suitable reference for each Visium sample is not always straightforward. References might lack key cell types, or, conversely, might include cell types that are not present in the tissue slice24. Moreover, within one cell type, cells can be in drastically different states, such as inactive versus activated immune cells25.&#160;The cell states present in reference datasets don&#39;t necessarily match those of spatial samples, which are often obtained from disease models of patients. Both issues are likely to result in inaccurate predictions. We hope to address this issue in the future.
As the field of spatial transcriptomics continues to evolve rapidly, a growing number of computational tools are being developed to analyze diverse aspects of spatial data, including cell-cell interactions, spatial domains, and prediction of spatially variable genes (see, for example 26,27,28). While this proliferation reflects the field&#39;s dynamism, it also presents a challenge for curating and integrating tools into this database. To ensure that the most robust and broadly applicable methods are included, there is a pressing need for systematic benchmark studies that evaluate tool performance across datasets and analysis tasks22,29,30. Such efforts will be essential to guide informed selection and prioritization of tools for inclusion in the database.
While other spatial transcriptomics databases attempt to collect large numbers of samples of many different platforms, in DeepSpaceDB we have decided to use a different strategy: focus on a few popular platforms and implement interactive and intuitive tools that allow the user to easily explore the data in more detail. Although our database contains only Visium samples in the current version 1.0, we plan to also include samples from other platforms in a future update.

Spatial transcriptomics is a new technology that enables researchers to analyze gene expression while retaining spatial information inside a tissue section, thus enabling the study of tissue architecture, cellular heterogeneity, and microenvironmental influences at unprecedented resolution1,2. However, despite the potential of this technology, access and analysis remain limited, spatial transcriptomics is cost-prohibitive for many laboratories, and the data analysis requires advanced bioinformatics skills.
Developing public databases is one way to broaden access to this emerging experimental modality. Several spatial transcriptomics databases have been created. The first was SpatialDB, but it contains only a limited number of samples and has not been updated3. The SODB, SOAR, and STOmicsDB databases include large numbers of samples from many different platforms and serve a great role as data repositories4,5,6. However, analysis tools are limited and lack interactivity. To address this issue, we have recently developed DeepSpaceDB, a curated, user-friendly database of publicly available spatial transcriptomics datasets designed to lower technical barriers and expand accessibility7. This article illustrates several tools in this database, including searching the database, inspecting sample quality, visualization tools, and the comparison of interactively selected regions within tissue slices. It presents detailed protocols using two representative examples: the analysis of a mouse brain sample and a murine liver with colorectal metastases to demonstrate these tools in practical contexts. Through these tools, DeepSpaceDB empowers a broader range of researchers to leverage spatial transcriptomics without needing their own data or in-house bioinformatics capacity. A comprehensive description of the data collection, quality control, processing workflow, as well as the data and features included in DeepSpaceDB, is provided in detail by Honcharuk et al7.

<table><tbody><tr><td>clusterProfiler</td><td></td><td>R package - version 4.14.6</td><td></td></tr><tr><td>DeepSpaceDB</td><td></td><td>version &gt; 1.0</td><td>A link to the database: www.deepspacedb.com</td></tr><tr><td>limma</td><td></td><td>R package - version 3.62.2</td><td></td></tr><tr><td>R</td><td></td><td>version 4.4.2</td><td></td></tr><tr><td>RStudio</td><td>Posit</td><td>version 2024.12</td><td></td></tr></tbody></table>

mining spatial transcriptomics datasets using deepspacedb

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Spatial transcriptomics is a rapidly evolving technology that enables the capture of gene expression patterns in tissue samples while preserving positional information. It has wide-ranging applications in biological research and bioinformatics, allowing researchers to investigate and track spatial variations in gene expression across different tissues, conditions, and diseases. With spatial transcriptomics data analysis gaining traction, the number of publicly available datasets is rising. However, spatial transcriptomics remains a highly specialized experimental technique, with significant technical and financial constraints. To facilitate access to spatial data, we have recently developed DeepSpaceDB, a comprehensive and dynamic database for spatial transcriptomics data exploration. This article presents detailed workflows outlining the components of the database and its navigation with the help of a few examples. First, the analysis of a mouse brain sample is demonstrated, exploring quality indicators, spatially variable genes and pathways, and gene expression variations between the hippocampus and hypothalamus. Next, the identification and annotation of differentially expressed genes associated with immune activity is further explored by comparing metastatic regions of colorectal origin with distant areas of healthy tissue in murine livers. DeepSpaceDB, with its advanced tools and interactive features, serves as a valuable resource for spatial transcriptomics research, enabling deeper exploration of tissue organization and disease biology.

Example 1 demonstrated the analysis of a mouse brain sample, validating parameters like read count, spatially variable genes and pathways, and gene expression variations between the hippocampus and cortex. First, the quality of the mouse brain sample DSID001557 was assessed against several quality measures: "Detected Genes" (Figure 1A), "Read Count" (Figure 1B), and "Mito" (the percentage of mitochondrial reads; Figure 1C). This clearly highlighted a region with lower quality on the left side of the brain sample, based on the low number of detected genes and the low read count. To understand the relative quality of the sample against all other samples, the Relative quality of sample tab in the database was clicked, which displayed a graph of the Count versus No. of genes detected per spot (Mean). For the sample being analyzed, there were between 3500-4000 genes detected per spot (Figure 1D). The anatomical features of the sample were further analyzed using the Image annotation tab. As a general note, these annotations have been generated by cutting tissue images into smaller parts and asking an LLM to describe the observable features8. They are rough indications to assist the interpretation of the sample and need to be interpreted with care. For a subset of samples (especially human breast cancer samples), annotations by a human specialist are also available. However, considering the lower quality of Visium H&amp;E images compared to images used for routine diagnosis, the provided annotations are for research purposes only. For sample DSID001557, move the cursor over the slice displayed annotations of the different regions of the mouse brain, such as the hippocampal region, cortical layers, dense cellular layers with gliosis, etc. From understanding the basic anatomical features of the sample slice, detailed features like cell type clusters and spatially variable genes and pathways were further explored. The mouse brain sample had 15 clusters in total, which were represented with color coding across the sample slice (Figure 1E). Some of the top spatially variable genes associated with the sample are Nrgn, Slc17a7, Ly6h, and Ddn (Figure 2). Nrgn exhibited high expression in the hippocampal region, in accordance with the literary evidence that indicates the role of the Nrgn-encoded protein (neurogranin) in mediating synaptic plasticity and spatial learning15. Slc17a7, a gene coding for a vesicular glutamate transporter crucial for neurotransmission in glutaminergic neurons16, and Ddn, a gene coding for a protein that modulates the structure of the post-synaptic cytoskeleton17, were also highly expressed in the hippocampal region. In contrast, the expression of gene Ly6h was localized in the cortical region, in accordance with the literature that indicates the restrictive synaptic role of Ly6h in the membranes of cortical cells18. In a similar way, the activity of pathways was visualized across the sample slice (Figure 3). The spatially variable pathways were observed to be activated in concordance with the functional roles of the spatially variable genes, with regulation of synaptic plasticity and neurotransmitter activity in the hippocampal region, and neuropeptide signaling in the cortical region.
Finally, to identify differentially expressed genes between the hippocampal region and the hypothalamus of the mouse brain sample, the Tissue Explorer tab was utilized. Spots associated with the regions of interest were selected with guidance from the image annotation (Figure 4A). From the scatterplot generated, some of the differentially expressed genes identified were among the top spatially variable genes (Nrgn, Slc17a7, Ddn), in addition to a few others, such as Pmch and Ttr. The expression of these genes was visualized in the sample slice. Pmch was specifically overexpressed in the lateral hypothalamic region (Figure 4B; compare with the green selected area in Figure 4A). This gene encodes the precursor of the melanin concentrating hormone, and is involved in the maintenance of energy homeostasis19. In contrast, the gene Ttr was specifically expressed in the hippocampal region (Figure 4C; compare with the red selected area in Figure 4A), in accordance with its functional role in learning and spatial memory20. By conducting intra-sample comparisons between different mouse brain regions using this database, we were able to highlight region-specific functional features based on spatial gene expression and pathway activity.
In example 2, the database was utilized for the identification of immune signatures associated with colorectal metastases in the liver. Intra-sample comparison was conducted between the tumor region with colorectal metastases and the distant, healthy liver tissue, through appropriate spot selection for the two samples: DSID001005 (Figure 5A-C) and DSID001007 (Figure 5D-F). The data were re-analyzed with two replicates per condition using R. Differential expression analysis conducted between the tumor region with colorectal metastasis and the healthy liver tissue revealed the downregulation of 138 genes and the upregulation of 115 genes, based on the selected parameters (Figure 6A,B). KEGG pathway analysis demonstrated the enrichment of the downregulated genes' pathways, like drug metabolism and chemical carcinogenesis (Figure 6C), while the upregulated genes exhibited signatures corresponding to leukocyte trans-endothelial migration, focal adhesion, and cell cycle, among others (Figure 6D). Focusing on the relevance of leukocyte trans-endothelial migration for immune activity, top genes detected in the category were identified, and their spatial expression was observed in DeepSpaceDB. Interestingly, genes Cldn7, Cldn4, and Actg1 detected under the category of leukocyte trans-endothelial migration, exhibited upregulation at the tumor region (Epcam+ site) of the samples, and not in the distant region with healthy liver tissue (Figure 7). This provided insights into the nature of the immune activity driven at the tumor site of the liver, with the active recruitment of leukocytes. In summary, intra-sample analysis using DeepSpaceDB enables the extraction of diverse biological insights. By comparing spatial transcriptomic data through interactive tools and re-analysis workflows, researchers can generate and validate hypotheses regarding tissue-specific gene expression and functional heterogeneity.
<img alt="Spatial gene expression maps and histogram; clustering analysis; brain sample; gene count; read metrics." class="xfigimg" src="/files/ftp_upload/68892/68892fig1.jpg" data-altupdatedat="1783396841000" data-alt="Figure 1"> 
Figure 1: Quality measures of the sample. (A) Number of detected genes, (B) read count, and (C) percentage mitochondrial reads per spot. (D) The average number of detected genes per spot in this sample, compared to the distribution of all other samples in the database. (E) Spot clusters across the tissue slice. <a href="https://www.jove.com/files/ftp_upload/68892/68892fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Gene expression heatmaps showing Nrgn, Slc17a7, Ly6h, Ddn; brain section analysis, color-coded." class="xfigimg" src="/files/ftp_upload/68892/68892fig2.jpg" data-altupdatedat="1783396841000" data-alt="Figure 2"> 
Figure 2: Expression of top spatially variable genes. (A) Nrgn, (B) Slc17a7, (C) Ly6h, and (D) Ddn. <a href="https://www.jove.com/files/ftp_upload/68892/68892fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Brain activity heatmaps; A) Neuropeptide signaling, B) Synaptic plasticity, C) Neurotransmitter transport." class="xfigimg" src="/files/ftp_upload/68892/68892fig3.jpg" data-altupdatedat="1783396841000" data-alt="Figure 3"> 
Figure 3: Activity of top spatially variable pathways. (A) Neuropeptide signaling, (B) Regulation of synaptic plasticity, (C) Neurotransmitter transport. <a href="https://www.jove.com/files/ftp_upload/68892/68892fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Tissue gene expression heatmap; cluster analysis by region; Pmch, Ttr comparison; data visualization." class="xfigimg" src="/files/ftp_upload/68892/68892fig4.jpg" data-altupdatedat="1783396841000" data-alt="Figure 4"> 
Figure 4: Comparison of gene expression patterns between two selected regions of the mouse brain. (A) Spot selection in hypothalamic and hippocampal regions for intra-sample comparisons. Selected region 1 is shown in red, and region 2 in green. Spatial expression patterns of differentially expressed genes (B) Pmch and (C) Ttr between hypothalamic and hippocampal regions. <a href="https://www.jove.com/files/ftp_upload/68892/68892fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Spatial transcriptomics maps; Epcam expression, spot clustering, tissue region identification." class="xfigimg" src="/files/ftp_upload/68892/68892fig5.jpg" data-altupdatedat="1783396841000" data-alt="Figure 5"> 
Figure 5: Properties of two metastatic mouse liver samples. For sample DSID001005: (A) Epcam marker expression, (B) spot clusters, and (C) selected regions in cancerous and distant regions for intra-sample comparisons. For sample DSID001007: (D) Epcam marker expression, (E) spot clusters, and (F) selected regions in cancerous and distant regions for intra-sample comparisons. For both samples, tumor spots are in the regions shown in red, and non-tumor spots are in the regions shown in green. <a href="https://www.jove.com/files/ftp_upload/68892/68892fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Gene expression analysis workflow diagram; includes visualization, data normalization, KEGG analysis." class="xfigimg" src="/files/ftp_upload/68892/68892fig6.jpg" data-altupdatedat="1783396841000" data-alt="Figure 6"> 
Figure 6: Re-analysis results. (A) Schematic summary of the workflow used in the re-analysis. (B) Volcano plot representing the differentially expressed genes between cancerous and distant regions. KEGG pathway enrichment of (C) upregulated genes and (D) downregulated genes. <a href="https://www.jove.com/files/ftp_upload/68892/68892fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Gene expression heatmaps, diagrams A-F; analyzing Cldn7, Cldn4, Actg1 expressions; experimental result." class="xfigimg" src="/files/ftp_upload/68892/68892fig7.jpg" data-altupdatedat="1783396841000" data-alt="Figure 7"> 
Figure 7: Spatial expression of genes. (A) Cldn7, (B) Cldn4, and (C) Actg1 in tissue slice DSID001005. Spatial expression of genes. (D) Cldn7, (E) Cldn4, and (F) Actg1 in tissue slice DSID001007. <a href="https://www.jove.com/files/ftp_upload/68892/68892fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary files 1-4: Data files and R script for liver metastasis example. <a href="https://www.jove.com/files/ftp_upload/68892/Supplementary.zip" target="_blank">Please click here to download this File.</a>

This article introduces a protocol for using DeepSpaceDB, a dynamic, interactive database for spatial transcriptomics, offering analysis workflows and examples to explore tissue organization and disease-related gene expression.

Spatial transcriptomics is a rapidly evolving technology that enables the capture of gene expression patterns in tissue samples while preserving ...

We are making a spatial transcriptomics database called DeepSpaceDB. The call is to make spatial transcriptomics data more easily accessible for biologists and bioinformaticians. Several spatial transcriptomics platforms have been developed.They allow researchers to study gene expression patterns within tissue slices. But this technology is expensive and the data analysis requires high level bioinformatics skills. We have been using the rhythm and the Xenium spatial platform, the Our Cancer CEX Research.This platform allow us to decide tumor, swelling and even the distant host tissue within the same organ context. It can help us to resolve the changes in the expression and cellular computation in each compartment separately. One of the major challenges for biologists is actually conducting data analysis.So there are many researchers that lack the necessary programming and computational skills to be able to fully interpret an increasingly large number of spatial transcriptomics data sets that are now becoming available. By making spatial transcriptomics data sets more easily accessible, the database enables users to generate new hypotheses, exploring the underlying mechanisms behind different diseases. So, for example, we can evaluate the spatial gene expression patterns associated with the tumor microenvironment.To begin, click on the database tab and select the organism as mouse, the organ as brain, and the source as Zenodo. Scroll through the resulting samples and select the sample labeled DSID001557. Then click on the selected sample and confirm that the description reads 2 million cells in 100 microliters saline NK cell.Click on the quality tab to evaluate the sample quality. From the quality measures dropdown menu, select options such as detected genes, read count, and mito to view the respective parameter distributions across the sample slice. Now, navigate to the image annotation tab to identify different regions in the sample slice.Move the mouse cursor over the sample slice to display annotations. View the grid-based annotations generated by a large language model that show anatomical features and associated conditions. Then navigate to the clusters tab to examine the cell type clusters in the sample slice.View the two dimensional embedding of the clusters and the corresponding color coded representation across spots on the sample slice. Next, navigate to the genes tab to examine the spatially variable genes in the sample. Click on some of the top genes in the list to generate spatial plots of their expression across the tissue slice.Observe the color coded expression patterns, which clearly show distinct spatial distributions for the highest scoring genes. Then navigate to the pathways tab to examine the activity of gene sets associated with common biological pathways. View the list of spatially variable pathways with pathway activity estimated based on the expression levels of related genes.Click on some of the top pathways in the list to generate spatial plots of their activity across the tissue slice. Observe the color coded patterns of pathway activity across different tissue regions. Now, go to the tissue explorer tab, which allows users to freely select regions of interest and compare gene expression patterns between them.Ensure manual selection is activated. Using the mouse cursor, select the spots in the hippocampal region on the left side of the mouse brain slice. Click on set one, and then add to set to highlight the selected spots on the right panel.Then click on set two and use the mouse cursor to select the spots in the hypothalamic region of the brain slice. Click on add to set to highlight these selected spots on the right side. After completing the spot selection, click on the compare gene expression button.This generates a table, displaying the average gene expression values for each selected region along with a scatter plot representation. Move the cursor over individual points on the scatter plot to confirm the gene names and the average expression values in both regions. Based on the comparison results, identify differentially expressed genes.Navigate back to the genes tab and visualize the expression of these genes across the tissue slice. Click on the database tab and use the filter to select the organism as mouse, the organ as liver, and the condition as cancer. From the resulting sample list, select sample DSID001005.Click on the selected sample and confirm that the description indicates that the sample is from a mouse liver, containing metastasis of colorectal cancer origin. Then navigate to the tissue explorer tab and activate manual selection mode. Using the mouse cursor, select the spots corresponding to the tumor region identified by positive expression of the EpCAM marker in sample DSID001005.Click on set one. Then select add to set to highlight the selected tumor spots on the right side. Now, click on set two and use the cursor to select the spots in the distant non-tumor region of the liver sample.Click on add to set to highlight the selected non-tumor spots on the right side of the display. To perform further analysis of gene expression data, click on the download CSV option, generating a comma separated values file of the gene expression data for the two regions of the sample. After repeating the database navigation steps for DSID001007, confirm that the description states it is another slice from a mouse liver containing metastases of colorectal cancer origin.Next, confirm that two CSV files have been generated, one each from samples DSID001005 and DSID001007, containing two columns representing average gene expression in tumor and non-tumor regions. Load both CSV files into the R programming environment. Merge the data sets to perform downstream analysis using two replicates per condition.In R, use the limma package to perform differential gene expression analysis on the merge dataset. Assign the colorectal metastases regions from both samples to the cancer group and the distant healthy regions to the control group. Filter the results to identify upregulated genes with a log fold change greater than 0.5 and an adjusted P value less than 0.05.Similarly extract downregulated genes with a log fold change less than minus 0.5 and an adjusted P value less than 0.05. A distinct low quality region was observed on the left side of the mouse brain sample, characterized by a reduced number of detected genes and a lower read count. The sample showed an average of approximately 4, 000 genes detected per spot, aligning well with the distribution of other samples in the database.15 spatial clusters were identified across the mouse brain sample with distinct boundaries representing anatomical differences. The genes NRGN, SLC17A7 and DDN showed strong expression in the hippocampal region. In contrast, LY6H expression was localized in the cortical regions, particularly the lower left and right outer edges of the slice.Neuropeptide signaling activity was notably increased in the lower cortical regions of the sample slice. Regulation of synaptic plasticity was activated across the hippocampal region, particularly in the upper middle zones. Neurotransmitter transport activity was elevated across the mid and upper right sections of the hippocampus.The gene CLDN7, CLDN4 and ACTG1 exhibited clear upregulation at the tumor region with colorectal metastasis in liver sample DSID 001005. In contrast, the expression of CLDN7, CLDN4 and ACTG1 were notably lower in the distant healthy liver tissue of sample DSID001007.

mining-spatial-transcriptomics-datasets-using-deepspacedb

Research

JoVE Journal

Biology

Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 µm.

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Here, we describe a method that allows for the decalcification of freshly obtained bone tissues and the preservation of high-quality RNA. A method is also illustrated for sectioning Formalin Fixed Paraffin Embedded (FFPE) samples of non-demineralized bones to obtain good quality results if fresh tissues are not available or cannot be collected.

Methods to Enable Spatial Transcriptomics of Bone Tissues

This article presents a protocol to perform comprehensive spatial transcriptomic profiling of host, viral, fungal, and microbiome RNA from formalin-fixed paraffin-embedded tissue sections using Stereo-seq, which enables high-resolution mapping of diverse transcriptomes while preserving tissue architecture.

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

This protocol includes a step-by-step workflow for calcified vascular specimens: tissue handling, decalcification, RNA validation, calcification level detection, and region-of-interest selection strategies on the Nanostring GeoMx Digital Spatial Profiler (DSP). The goal is to present a comprehensive method for preserving vascular tissue morphology and RNA for reliable spatial transcriptomic analysis.

Calcified Artery Preparation and Processing with Preserved Morphology and RNA for Digital Spatial Profiling

This protocol presents a reproducible workflow for analyzing spatial transcriptomics data, guiding users from public data acquisition and Seurat-based quality control through integration, spatial feature detection, cell-type deconvolution, region-of-interest annotation, and cell–cell communication analysis, with practical checkpoints that support transparent execution.

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses

This study presents a spatial Trekker–CUT&amp;Tag multiome protocol combining direct spatial barcoding of a tissue section with single-cell multiome analysis. This approach enables spatially resolved single-cell profiling of gene expression and the histone H3K27ac modification simultaneously, offering mechanistic insights into gene regulation in the context of tissue microanatomy.

Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections

We present a method for generating cDNA from environmental mRNA. In general, total RNA is first collected from the environment, rRNA is selectively removed, mRNA is selectively amplified, and cDNA synthesized from the enriched mRNA pool is sequenced. Recovered sequences can be annotated using standard bioinformatics techniques to identify the expressed genes.

Analyzing Gene Expression from Marine Microbial Communities using Environmental Transcriptomics

Microscopic analysis of γH2AX foci, which form following the phosphorylation of H2AX at Ser-139 in response to DNA double-strand breaks, has become an invaluable tool in radiation biology. Here we used an antibody to mono-methylated histone H3 at lysine 4 as an epigenetic marker of actively transcribing euchromatin, to evaluate the spatial distribution of radiation-induced γH2AX formation within the nucleus.

Evaluation of the Spatial Distribution of &gamma;H2AX following Ionizing Radiation

ITC is a powerful tool for studying the binding of a ligand to its host. In complex systems however, several models may fit the data equally well. The method described here provides a means to elucidate the appropriate binding model for complex systems and extract the corresponding thermodynamic parameters.

Collecting Variable-concentration Isothermal Titration Calorimetry Datasets in Order to Determine Binding Mechanisms

了解如何使用DeepSpaceDB数据库进行空间转录组学数据分析，探索小鼠组织中基因表达模式与疾病机制的关系。该平台支持多种空间转录组技术，无需编程技能即可生成假设并下载数据。

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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