Spatial biology is a growing field in modern life science with a significant future potential. We dive deep into this exciting subject to keep you updated with the latest developments in the field.
Find out about state-of-the art spatial biology techniques and their uses in different areas of life science. Stay current with leading-edge software solutions combining insights from spatial biology with automated bioimage analysis technology.
Defining Spatial Biology
Progress in laboratory technologies like immunohistochemistry IHC or in situ hybridization in the early 1990s first allowed researchers to characterize cells or molecules present in a patient-derived tissue specimen down to the cellular level. This was the era that defined the development of the first targeted therapies in oncology, among others the now standard therapy for breast cancer Herceptin (Carter, 1992).
The most recent advances like multiplex IHC or immunofluorescence IF allow researchers to go one step further. Novel technologies now enable the further characterization of cells based on the local presence of multiple markers i.e. „co-localization“ or “co-expression.“
There is a continuous interaction of cells with their microenvironment, with cells of the surrounding connective tissue, immune cells and also with non-cellular components. In addition, not only cells of the same type, but also of very different types interact and communicate with each other, either directly or through secreted substances (hormones). The latest spatial biology techniques provide a better understanding of these interactions.
Spatial biology simply adds a new dimension to existing analysis practices: space. A spatial context is often required to establish a biological correlation.
Did you know?
Spatial biology is an emerging field of research that aims to extract information on the position of different types of cells and even molecules within a sample.
This information is critical to uncovering the complex underlying mechanisms of many diseases.
The number of studies on the uses of spatial biology has more than doubled over the past few years. Not very surprisingly, and having in mind the growing importance of the subject, spatially resolved transcriptomics (i.e., assigning cell types to particular locations in tissue sections) have been titled Method of the Year 2020 (Marx, 2021).
In this article we take a closer look at how novel image analysis technology transforms the field of spatial biology. Keep reading to find out how these methods find effective use in different areas of current research.
Areas of use, approaches and technologies
Spatial biology technology is on its way into routine diagnostic use, but not yet ready for full implementation. The current main application of spatial biology is in the area of novel target identification in research settings. While target identification requires a huge number of biomarkers to be analyzed at the same time, in routine diagnostic procedures the latter needs to be reduced to a smaller number for more efficient testing. With the help of spatial biology techniques remarkable improvements in therapy response prediction can already be achieved with an average of 2 to 3 markers examined (Lu, 2019).
Novel spatial biology tools successfully demonstrated their utility in the fields like oncology (Friebel, 2020), neuroscience and neurodegenerative disease research (Midema, 2020), infectious disease biology (Butler, 2021; Tans, 2021) and many more.
In oncology, the interaction of the tumor with the host immune system has become a central focus of study, driven by the introduction of checkpoint-inhibitor anti-PD-L1/PD-1 targeted therapies (Garon, 2015; Yu, 2016).
The prognostic relevance of the presence or absence of different types of immune cells is now well researched. For example, this has been proven in the case of macrophages, cytotoxic “killer” T-cells and also regulatory T-cells, across various tumor types (Chakiryan, 2021; Cereceda, 2022).
Spatial phenotyping methods measuring the density of T-cells in the tumor microenvironment or their activation status have proven to outperform stand-alone PD-L1 IHC and other biomarker tests in anti-PD-L1/PD-1 immunotherapy response prediction (Lu, 2019). While T-lymphocytes are currently the focus of oncology research, also tumor-infiltrating B-cells and the so-called tertiary lymphoid structures resembling lymph nodes are also becoming of more interest since their mechanism of interaction with the tumor and tumor microenvironment needs to be unveiled (Laumont, 2022).
Did you know?
Summing up, in addition to the presence of potentially interacting cell populations, it is also the distance between them that needs to be taken into account.
Novel anticancer drug therapies aim to inhibit or enhance the interaction between cells, especially between tumor and immune cells or between regulatory and cytotoxic T-cells (Kubli, 2021). These modern therapeutic molecules are designed as bi- or multivalent antibodies, with the capacity both to activate and inactivate target cells. Further, they can also foster direct interaction by promoting contact between the involved components. Such developments would not have been possible without the insights gained with modern spatial biology technology.
Spatial Biology Techniques On The Rise
Spatial omics is a term used to describe the spatial quantification of tissue organization and cellular communication (Palla et al., 2022). Briefly, the various analytical approaches fall into a few main categories.
The single-cell omics approach initially gained attention with the development of advanced RNA sequencing methods, used to examine transcriptomic heterogeneity and reveal previously unknown cell types or cell states in complex tissue samples. After dissociating a sample into individual cells, scRNA-seq also allows for gene expression profiling at a cellular- and even sub-cellular level. Advanced barcoding technologies currently offer unprecedented opportunities to profile DNA, RNA and proteins at a single-cell resolution (Gohil, 2021).
Multi-omics approaches on the other hand involve the integration of available data on multiple levels such as genome, epigenome, transcriptome, proteome and metabolome. The spectrum of omics can be even further extended to the lipidome, phosphoproteome, and glycol-proteome. Availability of multi-omics data has revolutionized the field of medicine and biology by paving the way for integrated system-level approaches (Subramanian, 2020).
There are many different techniques available to assist your spatial biology research. It can be challenging to select the best method for your project. Above all, you have to consider whether you are interested in gathering information on the transcriptomic or the proteomic level (Rad et al. 2021), or whether you want to focus on other metabolic cellular processes.
Tips and tricks
When choosing the right technique for your spatial biology project the structural level of analysis and the biological functions in focus are the key factors.
Apart from genomics, we are going to focus on three central spatial biology fields of research depending on the biological function examined:
As an emerging field in cell biology, spatial metabolomics allows the precise localization of metabolites, lipids, and drugs in tissue sections. It is thus possible to track single molecules within cells and tissues, but also on an organ- and organism level.
Metabolomics uses particularly sensitive methods like imaging mass spectrometry. Simplified, molecules are ablated from the sample and simultaneously ionized with a laser, most commonly by means of matrix-assisted laser desorption/ionization (MALDI). Molecules can then be characterized e.g., by the time needed to reach the signal detector (time of flight, TOF). Each detected signal can be used for visualization.
The resulting imaging data represents a collection of mass spectra together with their associated x-y pixel/laser position (Alexandrov, 2020). Such procedures allow monitoring changes in a biological system which could not even be detected on the transcriptomic or proteomic level. Spatial metabolomics techniques also offer new insights to oncology researchers. So far, multiple metabolic profiles have been identified allowing physicians to classify tumors and align therapeutic concepts accordingly (Griffin, 2004; Tennant, 2010).
Spatial transcriptomics approaches extract information on gene expression combined with insights into the spatial context of tissue. Spatial transcriptomics techniques on one hand rely on Next-Generation Sequencing (NGS), where positional information is connected to transcripts. Further, these are imaging approaches based either on in situ sequencing or in situ hybridization, where labeled probes bind to their targets in the tissue (Rao, 2021). The respective signals are then used for visualization and imaging.
Protein expressions are the actual target of most drugs. Proteome analysis is believed to reveal the most accurate representation of a cell’s current state in the context of therapy development. Mass spectrometry-based approaches were among the first published spatial proteomics procedures. Major advances in imaging also facilitated the development of powerful spatial proteomics approaches for determining localization of proteins on a whole-cell, sub-cellular or organelle scale. (Borner, 2020).
Spatial biology using mass spectrometry-based approaches also enables scientists to demonstrate changes in the signaling pathway activity of a certain protein within cells (Martinez-Val, 2021). However, such procedures require complex laboratory workflows and equipment and may not yet be applicable for standard routine diagnostics. This gap is becoming smaller with the introduction of fluidics-based in situ labeling and imaging technologies allowing for simultaneous capturing of dozens to hundreds of biomarkers at the same time.
Among others, multiplex immunofluorescence (mIF) staining and imaging have become standard spatial biology techniques (Rad, 2021). Procedures using either direct-labeled primary antibodies or enzymatically catalyzed fluorophore staining reactions can be applied to standard routine pathology specimens and are close to well-established methods in pathology lab routine.
Specialized software solutions for more efficiency in spatial biology analysis
With the help of advanced software products you can obtain valuable insights into the spatial architecture of your tissue samples. This often requires statistics software in addition to image analysis solutions.
Although various digital solutions from different providers are commercially available, the underlying principles and analysis workflow remain very similar and involve software powered by artificial intelligence algorithms for data extraction and analysis. The exact procedure must of course always be tailored to the specific intended purpose.
But how to generate spatial biology data from e.g., mIF-stained slides? After the preparation of digital whole-slide images, the first steps include the segmentation of the tissue into areas , i.e. tumor-, inflammation- and normal tissue area. In addition, object or cell detection must be performed based on known structures or general markers.
Most often, nuclei detection and segmentation is performed at this stage. Combining the identified objects with additional information, i.e. the expression of biomarkers, allows researchers to classify them according to morphological properties. This process is nowadays called “phenotyping.” The labeling of all objects within a tissue section results in creating a landscape of different phenotypes, which also introduces the term “phenomapping” for this process.
With the availability of information regarding different tissue compartments and the presence of different types of cells, it is simple to count objects of different classes, allocate them to the respective compartments and to determine e.g. the density per phenotype and compartment.
Tips and tricks
For gathering data on the spatial relationships of morphological features, instance segmentation is the right way to go.
Instance segmentation is an image analysis approach sensitive to spatial information that combines information on the nature and position of single objects in the image. This finally allows for distance measurements and, in turn, for creation of interaction landscapes based on these distance measurements. This data may also be a subject of subsequent statistical analyses.
In addition to that, putting cell phenotypes into their spatial context often involves advanced statistical methods for clustering and also visualization of the resulting datasets. Such methods include nearest neighbor analysis, t-distributed stochastic neighbor embedding (t-SNE) or principal component analysis (PCA) (Parra, 2021).
Written by Elisa Opriessnig and Fanny Dobrenova
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