However, what is spatial biology, and how can scientists use its instruments to address the increasing complexity of biological issues in the post-omics age? The main research concerns in this ever-evolving subject of spatial biology and associated technology are briefly summarized in this overview article.
Describe Spatial Biology.
The study of how molecules, cells, and tissues are arranged, interact, and relate to one another in their natural 2D or 3D spatial setting is known as spatial biology. Researchers can investigate interactions within native tissue microenvironments using spatial biology, which reveals the spatial architecture and variety of cellular phenotypes.
How can I produce data on spatial biology?
Various technological ways, or sometimes a mix of these approaches, are used to create spatial biology data. Some of the more well-known approaches are as follows:
Image-based methods like transcriptomics and proteomics employing RNAScope or antibody-based multiplexing
workflows that combine mass spectrometry, mass cytometry, or sequencing methods with complementing strategies, including laser microdissection, which preserves spatial context while isolating regions—even individual cells—for omics analysis later on.
All of these strategies are supplemented by AI-based analysis for quantifying spatial correlations, which is required to extract valuable insights from intricate datasets.
Genomics and Spatial Transcriptomics
investigates the transcriptome, or gene expression, in geographical context using automated counting and profiling techniques like as microscopy, RNA sequencing, in-situ hybridization, and others. RNA sequencing readings can be linked to particular physical sites using downstream analysis that follows microdissection procedures for increased sensitivity.
Proteomics in Space
use mass spectrometry or antibody-based multiplex imaging techniques to comprehend the dynamics and location of proteins; the primary distinction between these methods is the level of sensitivity needed. Using microdissected tissue areas, imaging is increasingly being coupled with downstream mass spectrometry. The processed data is then realigned back to the reference pictures to offer spatial context.
Metabolomics in Space
uses exact measurement standards to provide quantitative spatial information on non-protein metabolites, including pharmacological compounds, lipids (lipidomics), and sugars (glycomics), in order to get a thorough understanding of human tissue chemistry.
Spatial Multiomics: What About It?
A new field called spatial multiomics blends many -omics technologies to provide even more context and profound understanding. For instance, integrating information about localization within tissue sections with proteomics and transcriptome data.
Tissue Research: The Importance of Spatial Biology
Tumor microenvironment (TME) and other heterogeneous tissues are intricate collections of cells with a wide range of phenotypic differences. Therefore, a spatial multiplexed imaging study technique is necessary to investigate the organization and interactions between tumor, stroma, and immune cells; high sensitivity and specificity antibodies are needed for this purpose. Because multiplexed imaging makes a lot more biomarkers visible than standard microscopy, it can extract more information from human tissue samples.
Biomarker Multiplex Imaging
It is possible to identify and evaluate complicated tissue and cell phenotypes by concurrently monitoring many biomarkers. Researchers may map normal and sick tissue by cell type, biomarker expression profile, and particular characteristics (often referred to as spatial phenotyping) using antibody-based multiplex imaging, which also allows them to examine when and where proteins are expressed. Finding biomarkers requires a greater understanding of the tissue landscape and disease development, which is provided by the spatial mapping and context-based cell profiling.
What kinds of multiplexed imaging are there?
There are differences in “plexity,” or the quantity of analytes examined in a sample, between microscopy-based methods. While high-plex approaches examine hundreds or thousands of biomarkers, low- or mid-plex methods examine fewer. For greater resolution imaging of tissue samples, researchers may choose for lower plex imaging-based methods like traditional light microscopy. When the sample has a complicated three-dimensional structure, such thick tissue or spheroids, this is essential.
There are now several multiplexed imaging techniques available, and each one employs a unique strategy to increase plexity. The primary classifications are:
single-round staining (one pass) for a small number of targets, as well as single-step staining and imaging.
Iterative staining and integrated multi-omics procedures allow for a thorough examination of the distribution and interactions of proteins in tissue samples.
Target area dissection for specialized Spatial Biology Analysis
It is frequently necessary to isolate certain regions of tissue samples, or even individual cells, either for more sensitive downstream analysis (such as omics-based techniques) or for additional study of a particular portion free from contamination from neighboring cells. In cancer biology, for instance, there are noticeable molecular variations both inside the tumor and between tumor and non-tumor areas. Only by separating certain areas of these locations can these distinctions be understood. In spatial biology, laser microdissection (LMD), often referred to as laser capture microdissection (LCM), is being utilized more and more to separate and dissect individual target cells or complete tissue regions from a wide range of materials. This can be used with methods directed by artificial intelligence (AI) to automatically identify areas of interest (ROI) that are going to be dissected.
Artificial Intelligence in Spatial Biology
Technologies used in spatial biology may provide enormous volumes of data, frequently in the form of pictures, which include the answers to several study questions. However, analysis is difficult due to the large amount and complexity of this data. Furthermore, overcoming the subjectivity of analysis can be a major challenge when examining hundreds of data points and various classifications. These elements are contributing to the expanding trend of using multiplexed image analysis driven by AI to extract significant and measurable insights from geographical data. For instance, distinct and hitherto unknown cell phenotypes and their distribution within the tissue microenvironment can be discovered by AI-driven machine learning analysis of diverse tissue samples. Researchers may be able to classify tumor types more accurately and predict treatment responses more precisely with the use of these findings, which might eventually enhance patient outcomes.
In conclusion
In order to get positional information and improve our comprehension of tissue architecture and spatial interactions, spatial biology uses a variety of tools, techniques, and analyses, such as -omics technologies and imaging-based approaches. Researchers will be able to tackle a wider variety of topics as these technologies develop. These discoveries have the potential to have a big influence on complicated fields including developmental biology, neurology, and cancer biology. It will be easier to integrate these techniques and encourage researchers to try new things in their experimental systems if they adopt the spatial biology mentality.
