Introduction to spatial biology
Mapping the landscape of disease
Spatial biology tools and techniques
Cancer: precision diagnosis and treatment
Clinical implementation and future perspectives
References
Further reading
Spatial biology is the study of the spatial organization of biological systems. It has immense clinical applications in disease diagnosis and treatment. By analyzing the spatial arrangement of cells and tissues, spatial biology provides a deeper understanding of disease progression and helps identify potential treatment targets.
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Introduction to spatial biology
Spatial biology is the analysis of the three-dimensional arrangement of biological systems to gain insight into their functions and interactions. Its importance in biomedicine lies in its ability to provide a more precise view of molecular processes within cells and tissues that may indicate disease progression or response to treatment.
Some commonly used approaches in spatial biology include spatial omics technologies and their various bioinformatics tools to process and interpret spatial data, identify spatial patterns, and infer cellular interactions. Among the different approaches, spatial transcriptomics has gained significant popularity and is widely used in research.
The development of techniques such as 10x Genomics' Visium Spatial Gene Expression and NanoString's GeoMx have contributed to the widespread adoption of spatial transcriptomics in the study of spatial biology. These companies also offer technologies for spatial proteomics.
Mapping the landscape of disease
Spatial biology techniques are increasingly being used to study the spatial aspects of various diseases, such as cancer, by revealing the spatial heterogeneity within tumors and their microenvironment; autoimmune and neurodegenerative diseases, by mapping the distribution of protein aggregates, inflammatory markers, and cellular degeneration within affected tissues; and Infectious diseases, by visualizing the spatial distribution of pathogens, the host immune response, and tissue damage. Spatial biology helps to understand the complex dynamics of these diseases and to develop targeted therapies.
In addition to providing information on gene expression, spatial transcriptomics also uses spatial location data to localize the pathogenic causes of disease physically. Using this approach, Misrielal et al. (2022) found that the acute stage of development of experimental autoimmune encephalomyelitis in mice, as well as the center of mixed active/inactive injury in postmortem human MS brain tissue, both showed a significant reduction in the expression of autophagy-related genes (ATG). These findings contributed to the understanding of the role of autophagy in the pathological stages of MS and in determining the severity and course of the disease.
Another study examined the cellular characteristics of granulomas in human leprosy lesions. The researchers focused on the study of reversal reactions (RR), a process by which some patients with disseminated lepromatous leprosy transition to self-limiting tuberculoid leprosy and mount effective antimicrobial responses.
By applying single-cell and spatial sequencing to leprosy biopsy samples, the researchers identified genes involved in antimicrobial responses that were differentially expressed in RR compared to lepromatous leprosy lesions. These genes were regulated by IFN-γ and IL-1β. By integrating the spatial coordinates of key cell types and antimicrobial gene expression, the researchers constructed a map that revealed the organized architecture of granulomas and how macrophages, T cells, keratinocytes, and fibroblasts contribute to this antimicrobial response.
Spatial biology tools and techniques
Single-cell methods such as scRNA-seq are key to studying the heterogeneity and dynamic changes of eukaryotic cells. Still, these methods require cells to be released from tissues intact and viable, which can be challenging for certain cell types. To address this issue, spatial biology techniques have been developed.
There are two broad methods for profiling transcriptomes while preserving spatial information: imaging-based and sequencing-based spatial transcriptomics. Imaging-based methods use microscopy to image mRNAs in situ.
Two different approaches are used to distinguish different mRNAs: hybridization of mRNAs to fluorescently labeled gene-specific probes (in situ hybridization, ISH) and in situ sequencing (ISS) of amplified mRNAs, in which transcripts are sequenced directly within a tissue section using sequencing by ligation (SBL) technology.
The second method is sequencing-based spatial transcriptomics, in which mRNAs are extracted from the tissue while preserving spatial information by microdissection and microfluidics or by ligation of mRNAs to spatially encoded probes in a microarray. They're then profiled using next-generation sequencing (NGS) techniques.
Spatial proteomics techniques include mass spectrometry analysis of fractionated organelles to identify enriched proteins, affinity purification mass spectrometry to identify protein interactions and imaging-based proteomics such as matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI).
In addition to these broad approaches, antibody-dependent methods such as t-CycIF, CODEX or mass cytometry are also used. Emerging techniques such as multiplexed ion beam time-of-flight imaging (MIBI-TOF) and imaging mass cytometry (IMC) offer high resolution for profiling multiple proteins.
Some multiomics approaches, such as the Spatial Multi-Omics (SM-Omics) platform, have also been developed to combine and spatially resolve transcriptomics and antibody-based protein measurements.
Cancer: precision diagnosis and treatment
Spatial biology has been particularly valuable in cancer research. Researchers have gained insight into tumor heterogeneity, immune cell infiltration, and treatment resistance by analyzing the spatial organization of tumor cells and their microenvironment. This information can guide the development of personalized treatment strategies and improve patient outcomes.
One example is a study led by Threne et al. (2018), who used spatial transcriptomics to delve into the transcriptomes of more than 2,200 tissue domains within melanoma lymph node biopsies. Through unsupervised analysis, the researchers uncovered a complex intratumoral composition of melanoma metastases, revealing distinct gene expression profiles within each individual biopsy.
The study also revealed the coexistence of multiple melanoma signatures within a single tumor region, as well as common profiles for lymphoid tissue based on spatial location and gene expression patterns. Factor analysis was used to decompose the information into 20 factors describing gene expression profiles. Ultimately, the study highlights the importance of incorporating spatial information into the analysis of tumor progression and treatment outcomes, paving the way for improved understanding and targeted therapies in the future.
Clinical implementation and future perspectives
Spatial biology techniques offer several advantages over traditional methods: multiplexing, spatial context, quantitative analysis, and high-throughput capabilities. Nevertheless, translating spatial technologies into clinical practice poses several challenges, such as developing robust bioinformatics workflows, staff training and support, reproducibility, and validation. However, it is certainly noticeable that work is being done in this direction for the successful clinical application of spatial technologies.
References
- Ma F, et al. (2021). The cellular architecture of the antimicrobial response network in human leprosy granulomas. Nature Immunology, 22(7), 839–850. https://doi.org/10.1038/s41590-021-00956-8
- Misrielal C, et al. (2022). Transcriptomic changes in autophagy-related genes are inversely correlated with inflammation and are associated with multiple sclerosis lesion pathology. Brain, Behavior, & Immunity - Health, 25, 100510. https://doi.org/10.1016/j.bbih.2022.100510
- Thrane K, et al. (2018). Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma. Cancer Research, 78(20), 5970–5979. https://doi.org/10.1158/0008-5472.can-18-0747
- Vickovic S, (2022). SM-OMICS is an automated platform for high-throughput spatial multi-omics. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-28445-y
- Williams C, (2022). An introduction to spatial transcriptomics for biomedical research. Genome Medicine, 14(1). https://doi.org/10.1186/s13073-022-01075-1
- An introduction to spatial biology and spatial profiling. (2022). NanoString. [Online] https://nanostring.com/blog/an-introduction-to-spatial-biology-and-spatial-profiling/ (Accessed on September 2023)
Further Reading