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Unlocking the Potential: How Spatial Transcriptomics Transforms Liver Disease Research

In the relentless pursuit of better treatments and a deeper understanding of liver diseases, the scientific community has consistently sought innovative technologies to unlock the mysteries of cellular interactions within tissues. One such groundbreaking technique that has revolutionized liver disease research is Spatial Transcriptomics. This cutting-edge technology combines the power of traditional transcriptomics with spatial information, enabling scientists to analyze gene expression patterns in their native tissue context.


What is Spatial Transcriptomics?


Spatial Transcriptomics is an emerging field that bridges the gap between gene expression analysis and tissue spatial organization. Unlike traditional transcriptomics, which provides gene expression profiles in bulk tissue, Spatial Transcriptomics is akin to having a high-resolution map of a city, offering researchers the ability to not only pinpoint the locations of genes but also discern their precise activities within distinct regions of liver tissue. This enables a more comprehensive understanding of liver function, much like urban planners optimizing different city regions based on specific activities and industries. This technology provides a spatial dimension to gene expression data, revealing vital information about cell types and their interactions in the liver microenvironment.


Unraveling Liver Heterogeneity:


The liver is a highly complex organ comprising various cell types, including hepatocytes, Kupffer cells, stellate cells, and endothelial cells. Spatial Transcriptomics enables researchers to unravel this cellular heterogeneity within liver tissues, identifying specific gene expression profiles in different regions. By identifying differentially expressed genes and mapping them to a specific region of the liver, spatial transcriptomics has been used to identify zonation patterns in the liver. Zonation refers to the localization of metabolic functions in specific regions [1]. Varied gene expression profiles have been identified among hepatocytes in distinct locations and the findings corroborate that the primary cause of spatial heterogeneity in liver tissue stems from transcriptional disparities between zones along the lobular axis, spanning from the portal to central veins [1]. By understanding the spatial organization of cell types in healthy and diseased liver tissue, researchers gain valuable insights into disease pathology and progression.


Diagnostics and Identification of Novel Biomarkers:


Accurate and early detection of liver diseases is crucial for effective treatment outcomes. Spatial Transcriptomics has the potential to identify novel biomarkers that may be specific to certain liver diseases or disease stages. Researchers can investigate changes in gene expression during different stages of fibrosis, identify gene signatures associated with disease severity, and track the evolution of cell populations over time. By studying gene expression patterns in diseased tissues compared to healthy tissues, researchers can pinpoint unique biomarkers that may serve as diagnostic tools or therapeutic targets.


One study employed spatial transcriptomics to compare NASH, NAFL, and healthy patients [2]. This approach enabled the detection of divergent cell types in terms of expression, along with their respective spatial distribution. The NASH cohort exhibited increased proportions of hepatic stellate cells (HSCs) and myofibroblasts within the lobule and the portal region adjacent to the fibrotic region✝, accompanied by heightened infiltration of Kupffer cells within the fibrotic region✝ [2].


✝The NASH fibrotic region was distributed mainly in lobules, with a small amount found in the portal area.


One of the main advantages of spatial transcriptomics is the combination of location with the identification of differentially expressed genes. In the same study, AEBP1, DPT, CCL19, and NOTCH3 were found to be highly expressed in the fibrotic area, with AEBP1 and DPT being specific to myofibroblasts [2]. AEBP1 and DPT-positive myofibroblasts were found to be involved in the activation of HSCs and the formation of fibrosis [2]. In fact, AEBP1 has been found to be positively related to the severity of NASH fibrosis and promotes the progression of NAFL to NASH via the WNT pathway [2,3].


This study is the first to identify the potential of AEBP1 and DPT to be the key genes driving the activation of HSCs into myofibrils. Neither of these genes has been targeted in NASH treatment research, but spatial transcriptomics data support them as important targets that warrant further investigation. The questions spatial transcriptomics is able to address are vital for developing targeted therapies and identifying potential biomarkers for early diagnosis. It does so in a way that even opens up an avenue to personalized medicine.


Challenges and Future Directions:


Spatial Transcriptomics, despite its remarkable insights into liver diseases, presents its own set of challenges. Successfully harnessing this technology necessitates the utilization of advanced computational tools and data analysis techniques to accurately decipher the intricacies of spatial data. Furthermore, ongoing efforts aim to enhance the technique by improving spatial resolution and sensitivity, making it more accessible and readily available. A noteworthy example of such advancements is Stereo-seq, a commercially available approach that provides single-cell resolution without the need for complex slide preparation or microscopy. In fact, with Stereo-seq, you can simply send out your samples, and both slide preparation and sequencing will be handled for you. Another recent addition to the market, Curio Seeker, offers a comprehensive kit that equips you with all the essentials for performing Slide-seq, a technique with higher resolution compared to older commercially available methods. The availability of pre-made slides is particularly valuable, greatly simplifying the otherwise complex process of preparing slides for Spatial Transcriptomics analysis.


As technology advances, Spatial Transcriptomics is poised to become an indispensable tool for researchers studying liver diseases and other complex tissues. Spatial Transcriptomics is revolutionizing liver disease research by providing a comprehensive understanding of gene expression patterns within the liver tissue microenvironment. This innovative technology is driving the discovery of new biomarkers, enhancing drug development efforts, and shedding light on the intricate cellular interactions that underlie liver diseases. As Spatial Transcriptomics continues to evolve and integrate with other omics technologies, we can anticipate significant advancements in liver disease diagnostics, therapeutic strategies, and ultimately, improved patient outcomes. The exciting journey of Spatial Transcriptomics in liver disease research has only just begun, and the future holds great promise for the field.


Further Reading


If you want to learn more about the different methods available check out the article below which includes a table of many spatially resolved transcriptomics methods, including their strengths and limitations: 


Duan, H., Cheng, T., & Cheng, H. (2022). Spatially resolved transcriptomics: advances and applications. Blood science (Baltimore, Md.), 5(1), 1–14.

Written by Christine Mowad & AGED Diagnostics.



1. Chung, B. K., Øgaard, J., Reims, H. M., Karlsen, T. H., & Melum, E. (2022). Spatial transcriptomics identifies enriched gene expression and cell types in human liver fibrosis. Hepatology communications, 6(9), 2538–2550.

2. Minran Li, Jin-zhong Li, Li-hong Ye et al. Spatial and single-cell transcriptomics reveal the regional division of the spatial structure of NASH fibrosis, 25 May 2023, PREPRINT (Version 1) available at Research Square []


3. Gerhard, G. S., Hanson, A., Wilhelmsen, D., Piras, I. S., Still, C. D., Chu, X., Petrick, A. T., & DiStefano, J. K. (2019). AEBP1 expression increases with severity of fibrosis in NASH and is regulated by glucose, palmitate, and miR-372-3p. PloS one, 14(7), e0219764.

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