Xenium

Xenium platform allows researchers to assess spatial gene expression in both fresh frozen and FFPE tissue sections, providing a powerful tool for understanding the intricate biology of normal liver tissue. 

Questions can be asked on Xenium normal liver tissue data analysis: 

1. Cellular Composition:

   • What are the predominant cell types present in the normal liver tissue?

   • Can we identify hepatocytes, Kupffer cells, endothelial cells, and other specialized liver cell populations?

2. Spatial Distribution:

   • How are different cell types distributed within the liver tissue? Are there specific regions where certain cell types cluster?

   • What is the proximity of hepatocytes to blood vessels or other cellular components?

3. Gene Expression Patterns:

   • Which genes are highly expressed in hepatocytes compared to other cell types?

   • Are there specific genes associated with liver function, metabolism, or detoxification pathways?

4. Functional Relationships:

   • Can we correlate gene expression levels with cellular function and location?

   • How do gene expression patterns change across different liver lobes or zones?

5. Comparison with Disease States:

   • How does the normal liver spatial transcriptome differ from that of liver diseases (e.g., hepatocellular carcinoma)?

   • Are there specific genes or pathways that show altered expression in disease states?

6. Cellular Interactions:

   • Can we infer interactions between neighboring cells based on their spatial proximity?

   • Are there signaling pathways active in specific cell clusters?

Colorectal Cancer CRC Data Analysis

Xenium CRC analysis, cell segmentation, and CXCL10 cytokine expression:

-Xenium CRC Analysis: Utilizes 10x Genomics Xenium technology to analyze colorectal cancer (CRC) samples, focusing on high-resolution spatial profiling of gene expression within the tissue.

-Cell Segmentation: Involves detailed segmentation of individual cells within the CRC sample, allowing for precise identification and analysis of cellular structures and boundaries.

-CXCL10 Cytokine Expression: Highlights the expression of CXCL10, a cytokine involved in immune responses, within the CRC sample. The analysis shows the spatial distribution of CXCL10 expression across different cells, providing insights into its role in cancer progression and potential therapeutic targets.

This comprehensive approach helps in understanding the spatial organization and functional roles of different cell types within the CRC microenvironment, aiding in the development of targeted therapies.

The image illustrates the process of Xenium cell segmentation, which involves aligning images, segmenting regions of interest (ROI), and analyzing cell segmentation along with transcript expression. This is relevant for understanding cellular structures and functions in CRC cancer research.  

Xenium normal colon analysis:

• Microscopic View: The analysis includes microscopic view of the colon tissue, highlighting various cellular components or markers.

• Marker Identification: Specific markers such as MS4A1 are identified within the tissue, indicating the presence of certain proteins or cellular components.

• Histological Image: A histological H&E stained image provides structural details of the colon tissue.

• Zoomed-In View: A zoomed-in view of the tissue shows detailed cellular structures, with annotations indicating the markers used (e.g., MS4A1, DAPI).

This comprehensive analysis allows for a detailed examination of the cellular and molecular composition of normal colon tissue, which is crucial for understanding normal colon function and identifying any abnormalities or diseases.

Spatial Distribution and Co-Expression of IL7R, MS4A1, and CXCL13 in the Tumor Microenvironment of Colon Cancer 

The figure illustrates (below) MS4A1 (CD20), IL7R (Interleukin-7 Receptor), and CXCL13 co-express in the tumor microenvironment (TME) of colon cancer. This co-expression can have significant implications for the immune landscape and the behavior of immune cells within the TME.

1. Immune Cell Interaction: The co-expression of these markers suggests the presence of both B cells (marked by MS4A1), T cells (marked by IL7R), and chemokine activity (marked by CXCL13) in the TME. This indicates a complex interplay between different immune cell types, which can influence the overall immune response to the tumor.

2. Immune Surveillance and Response: IL7R is involved in the survival and homeostasis of T cells, including memory T cells. Its presence in the TME can enhance the survival and function of T cells, potentially improving the immune system's ability to recognize and attack cancer cells. MS4A1+ B cells can contribute to antigen presentation and the activation of T cells, further enhancing the anti-tumor immune response. CXCL13, a chemokine, plays a role in attracting B cells and T follicular helper cells to the tumor site, promoting an organized immune response.

3. Prognostic and Therapeutic Implications: The co-expression of these markers can be associated with better prognosis and response to immunotherapy. High levels of IL7R expression have been linked to improved survival and better responses to immune checkpoint inhibitors. Similarly, MS4A1 expression can indicate the presence of B cells that support anti-tumor immunity. CXCL13 has been shown to promote the growth and invasion of colon cancer cells, but its role in the immune response can also be beneficial in attracting immune cells to the tumor site.

Overall, the co-expression of MS4A1, IL7R, and CXCL13 in the TME of colon cancer signifies a potentially robust immune response, with B cells, T cells, and chemokine activity playing crucial roles in combating the tumor. This information can be valuable for developing targeted therapies and improving the effectiveness of immunotherapy treatments.

Chemokine/Cytokine Signaling in Colon Cancer: Implications for Targeted Immunotherapy 

In the context of targeted immunotherapy for colon cancer, the roles of AQP1, CXCL10, CXCL11, and CXCR4 in cellular communication within the tumor and its microenvironment (TME) are crucial. AQP1 (Aquaporin 1) facilitates water transport and influences cell migration and proliferation, contributing to tumor growth and metastasis. CXCL10 and CXCL11 are chemokines that attract immune cells to the tumor site, enhancing the immune response. CXCR4, a chemokine receptor, binds to these chemokines, promoting cancer cell migration and invasion. Understanding these interactions helps in developing targeted therapies that modulate the immune response, inhibit tumor growth, and improve treatment outcomes in colon cancer 

Cancer-Associated Fibroblasts (CAFs):

Importance of CAF in CRC TME: CAFs, marked by ACTA2 and COL1A1, play a crucial role in tumor growth, invasion, and immune modulation in CRC, and spatial omics data help map their interactions within the TME.

Co-Expression of ACTA2 and COL1A1: ACTA2 and COL1A1 co-expression in CAFs is found in areas of fibrosis and tumor stroma, indicating their role in ECM remodeling and tumor progression.

Relationship of Co-Expressed CAFs in TME: ACTA2+COL1A1+ CAFs influence the structural integrity of the TME and interact with immune cells, impacting tumor progression and therapy response

COL1A1 is often found near areas of tumor invasion and inflammation (spatial captured in xenium colon data image below). It plays a role in the formation of the fibrotic stroma, which can be close to the tumor cells

Tumor-Associated Macrophages (TAMs): 

Importance of TAM in CRC TME: TAMs play a crucial role in tumor growth, immune evasion, and metastasis in CRC, and spatial omics data help map their interactions within the TME.

Co-Expression of CXCL13 and CD163: CXCL13 and CD163 co-expression in TAMs is found in inflammation areas, indicating immunosuppressive environments that support tumor growth.

Relationship of Co-Expressed TAMs in TME: CXCL13+CD163+ TAMs influence TIL recruitment and activation, shaping the immune landscape and impacting tumor progression and therapy response.

Tumor-Associated Macrophages (TAMs) and Cancer-Associated Fibroblasts (CAFs) play crucial roles in the tumor microenvironment (TME) of colorectal cancer (CRC). TAMs, marked by CXCL13 and CD163, influence TIL recruitment and immune modulation, shaping the immune landscape and impacting therapy response. CAFs, identified by ACTA2 and COL1A1, contribute to ECM remodeling and fibrosis, affecting TIL infiltration and tumor progression. Understanding the spatial distribution and interactions of these cells in the TME using Xenium data is essential for developing targeted immunotherapies.

Spatially Mapping TILs Activity and Leveraging LLMs to Decode the TME in CRC 

Spatially mapping Tumor-Infiltrating Lymphocytes (TILs) activity using histopathology, spatial omics, and morphology-based segmented TILs in the Tumor Microenvironment (TME) of Colorectal Cancer (CRC) can significantly enhance our understanding of the roles of Cancer-Associated Fibroblasts (CAFs), Tumor-Associated Macrophages (TAMs), TILs, Regulatory T Cells (Tregs), Dendritic Cells (DCs), and their clusters in the TME. By leveraging the capabilities of Large Language Models (LLMs), we can integrate diverse data types, such as gene expression profiles, spatial distributions, and morphological features, into a cohesive framework. This integration allows for the creation of comprehensive knowledge graphs that capture complex interactions and communication networks within the TME. These knowledge graphs can then be used to model the immune landscape and identify key regulatory pathways and cellular interactions that influence tumor progression and response to immunotherapy.

In the TME of Colorectal Cancer (CRC), several cell types play crucial roles in shaping the immune landscape and influencing tumor progression. TILs, marked by CD3D, CD3E, and CD3G, are essential for anti-tumor immune responses. CAFs, identified by markers such as COL1A1 and ACTA2, contribute to extracellular matrix remodeling and fibrosis, impacting TIL infiltration and tumor growth. TAMs marked by CXCL13 and CD163, modulate immune responses and promote tumor progression. Other important players include Regulatory T Cells (Tregs), Myeloid-Derived Suppressor Cells (MDSCs), Dendritic Cells (DCs), Endothelial Cells (ECs), Hypoxia-Inducible Factors (HIFs), and Extracellular Matrix (ECM) components.

Understanding the spatial distribution and interactions of these cells using spatial omics data is crucial for developing targeted immunotherapies. Knowledge graphs can be particularly helpful in capturing and validating these interactions, providing insights into the complex communication networks within the TME.

Using GenAI approaches, we can further enhance these models by simulating various scenarios and predicting the outcomes of targeted interventions. This can provide valuable insights into the potential efficacy of different immunotherapeutic strategies and help identify novel targets for therapy. Overall, the combination of LLMs, multimodal data integration, and GenAI approaches offers a powerful toolkit for advancing our understanding of the TME in CRC and developing more effective targeted immunotherapies. This novel approach is currently being tested (as proof-of-concept model) on more samples and modalities, such as single-cell and proteomics data, to create a robust model; further details will be shared soon. 

The image below presents an overview of the Xenium Explorer, a tool for visualizing and analyzing spatial data in colon cancer FFPE samples. It highlights features such as interactive visualization, transcript localization, cell segmentation check, and downstream analysis informer. The tool allows users to explore gene expression data, pinpoint transcript locations, verify cell segmentation, and gather insights for downstream analysis.

The figure also shows specific gene expressions, such as CXCL10, CD3, CD8, IL-4, IL-6, IL-8, IL-11, IL-17A, IL-22, IL-23, IL-33, TNF, TGF-β, VEGF, FGFR2, MMP11, and OTOP2. Understanding these gene expressions is crucial for identifying potential biomarkers and therapeutic targets in colon cancer, aiding in the development of personalized treatments. Additionally, the figure includes expressions of other key genes involved in tumor progression and immune modulation, providing a comprehensive view of the molecular landscape in colon cancer.

Targeted Immunotherapy and Patient Response vs. Resistance in CRC TME: Harnessing Spatial Omics and GenAI Approach 

Targeted immunotherapy in colorectal cancer (CRC) tumor microenvironment (TME) aims to enhance the immune system's ability to recognize and destroy cancer cells. The effectiveness of such therapies can vary significantly between responders and non-responders, making it crucial to understand the underlying mechanisms. Spatial biology, through technologies like spatial omics, allows for the precise mapping of molecular markers within the tissue context, offering valuable insights into the TME's complexity and the factors influencing therapeutic responses.

For example, in this image shown below, illustrates the markers EGFR and IGFBP7 are mapped spatially within CRC tissues, revealing their distinct expression patterns. EGFR, often associated with tumor promotion, and IGFBP7, linked to tumor suppression, are shown in detailed histological sections. The spatial distribution and clustering analysis provide a deeper understanding of their roles in the TME, which is essential for designing targeted immunotherapies. By identifying specific regions with high or low marker expression, researchers can tailor treatments to target these areas more effectively, potentially improving patient outcomes.

Efforts are underway to develop a GenAI model in CRC TME for targeted immunotherapy. This involves using large language models (LLMs) for feature extraction and embedding, integrating multimodal data such as single-cell RNA sequencing, histopathology, and clinical data to create a comprehensive understanding of the TME and the roles of these markers.