Spatial transcriptomics is a rapidly advancing field in biomedical research, with over 2,000 institutions actively conducting studies in this area, according to the National Library of Medicine (NLM).
In the past decade, single-cell RNA sequencing (scRNA-seq) has provided scientists insights into cellular diversity within tissues and organs. However, this traditional approach requires isolating cells from tissues, which can damage their structure and remove important spatial information.
To overcome this limitation, new technology like spatial transcriptomics was introduced. This technology has allowed researchers to analyze gene expression while preserving the spatial information of cells within tissues intact.
Let’s try to understand spatial transcriptomics in depth, including its background, techniques, technologies, experiments, and more.
What is Spatial Transcriptomics?
Spatial transcriptomics allows scientists to study gene activity while preserving a cell’s original composition inside tissue. It also enables gene expression analysis while keeping the cell’s natural state intact. This has helped uncover how cells interact, how diseases progress, and how different regions of tissue function.
This approach transforms research in developmental biology, cancer, immunology, and neuroscience. By combining gene expression data with spatial information, you can gain a deeper understanding of cellular behavior, tissue structure, and disease mechanisms.
Let’s explore this topic more and understand how spatial transcriptomics works, the different methods available, and how you can use it to gain new insights into human biology and disease.
Background of Spatial Transcriptomics in Biomedical Research
The study of cells at the ‘-omic’ level—where examining their gene expression, proteins, and other molecular components takes place—has transformed biomedical research. The concept of the cell as the fundamental unit of life dates back to the 1600s when Robert Hooke first described it- a foundation that remains unchanged.
The advancement in genomics has allowed scientists to explore this field in depth. Scientists can now study cellular functions, interactions, and disease mechanisms with unprecedented detail. By analyzing cells through multi-omic approaches, you can gain deeper insights into the molecular basis of human health and disease, uncovering critical mechanisms that drive biological processes.
Below is an overview of the spatial transcriptomics technique, which enables a deeper understanding of cellular interactions, disease progression, and tissue cell diversity.
Overview of Spatial Transcriptomics Techniques
SourcePMC
Spatial transcriptomics (ST) includes multiple advanced transcriptomics techniques. Below are the 3 techniques that allow you to preserve the spatial gene expression data while getting high-resolution insights into tissue structure. Let’s explore these 3 for a better understanding of these techniques:
1. In Situ Hybridization (ISH): High-Precision Targeted Detection
Although silver in situ hybridization (SISH) and fluorescent in situ hybridization (FISH) are older technologies not typically associated with the more modern spatial transcriptomics revolution, these are the first demonstrations of ST, albeit at lower multiplexing and resolution. In Situ, hybridization (ISH), fluorophore-labeled probes or special molecules are used to detect specific genes or RNA in cells. This allows direct visualization without the need for cell isolation. Techniques like seqFISH and merFISH enable high-resolution mapping but are limited by cost, labor intensity, and constraints on the number of targets.
2. In Situ Sequencing (ISS): High-Resolution Gene Expression Mapping
In Situ Sequencing (ISS) is a high-resolution technique for mapping gene expression directly within tissues while preserving spatial context. It uses barcoded padlock probes, which are specialized DNA probes that bind to specific RNA or cDNA sequences. These probes undergo looping and copying, allowing for in situ sequencing through methods like STARmap and FISSEQ.
3. In Situ Capturing (ISC): Large-Scale Transcriptome Profiling
Captures mRNA using spatially barcoded RT primers on slides, followed by reverse transcription and next-generation sequencing (NGS). Technologies such as 10X Genomics Visium, Slide-seq, and Seq-Scope provide large-scale transcriptome spatial mapping, though they face challenges in balancing resolution and RNA capture efficiency.
As we have explored the spatial transcriptomics technique, the next step is understanding the key technologies that enable this powerful approach. Below are the technologies that drive spatial transcriptomics, each offering unique advantages for studying gene expression.
Spatial Transcriptomics Technologies
Spatial transcriptomics technologies differ from each other in terms of capturing spatial information. The four main classifications are sequencing-based, probe-based, imaging-based, and image-guided spatially resolved methods. Below, you’ll explore these methods in detail.
Source: NIH
- Sequencing-Based Methods
Sequencing-based methods use next-generation sequencing (NGS), which preserves the spatial structure of a tissue sample. The process begins with tissue sectioning, which is cut into thin slices and placed on a specialized platform containing spatially barcoded arrays. Each spatial region on the platform is linked to a unique barcode to help researchers know the original location of captured RNA molecules.
Once the RNA is captured, it is processed into a sequencing library. Then, sequencing starts, where RNA is analyzed and matched to a reference genome, allowing researchers to map gene activity across the tissue. Some well-known technologies in this category include 10X Genomics Visium, Slide-seq, Stereo-seq, and Light-seq.
- Probe-Based Methods
In probe-based spatial transcriptomics (most commonly implemented in FISH and SISH), specialized barcoded RNA probes (such as RNAscope probes) bind to target RNA molecules within the tissue. The sample undergoes tissue staining, allowing researchers to highlight cellular structures for better visualization. Next, the tissue is imaged, and specific regions of interest (ROI) are selected for further analysis.
Using UV cleavage, barcodes corresponding to each selected ROI are released and collected, preserving spatial gene expression data. Finally, the data is processed and visualized, enabling researchers to map gene expression across the tissue rapidly. One example of this technology is GeoMx™.
- Imaging-Based Methods
Image-based spatial transcriptomics is a microscopy-based technique that allows researchers to detect genes using immunostaining or probe hybridization/labeling, which marks specific RNA or protein molecules within the tissue. The sample is then examined through microscopic imaging, capturing high-resolution spatial data.
Finally, the collected information undergoes data analysis, enabling researchers to visualize and interpret gene expression patterns across different regions of the tissue. An example of this technology is CosMx SMI, which provides high-throughput spatial insights at the single-molecule level.
- Image-guided spatially resolved Method
In image-guided spatial transcriptomics, gene expression is analyzed using spatially resolved immunostaining, where specific molecules within the tissue are labeled. The sample is then examined through microscopic imaging, allowing for photoselection of target cells or regions of interest (ROIs).
Next, photoactivation-based cell sorting isolates selected cells for further study. The activated cells undergo sequencing, data analysis, and visualization, providing high-resolution spatial insights. An example of this technology is Spatially Annotated FUNseq, which enables precise gene expression mapping at the single-cell level.
With a deeper understanding of spatial technologies, the next step is designing spatial transcriptomic experiments.
Below, we explore key considerations and strategies for planning and executing successful experiments to obtain accurate and meaningful spatial gene expression data.
Designing Spatial Transcriptomic Experiments
Before beginning any experiment, you should ask yourself some questions. It is crucial to know the aim of your experiment, as it will allow you to choose which methods to pursue to accomplish your research goal.
Below are the steps you need to follow before starting your experiment.
1. Start By Selecting the Right Tissue Model
Choosing the proper tissue is crucial for the experiment’s success, as the suitability of the biological model and tissue type determines its success. Some tissues, such as the brain, tumors, and structured epithelial tissues, exhibit clear spatial structures and are ideal for spatial analysis. Others, like immune tissues, require specialized techniques.
2. Choose Between Hypothesis Testing and Hypothesis Generation
Spatial transcriptomics methods can be broadly classified based on whether the experiment is hypothesis-driven or exploratory. To conduct your experiment, you need to consider which ones suit the requirements of your experiment:
- Hypothesis Testing: When researchers aim to study specific genes or pathways with high spatial accuracy, targeted approaches like in situ hybridization (ISH) and in situ sequencing (ISS) are preferred. These methods are efficient and spatially precise.
- Hypothesis Generation: For unbiased discovery of new biological mechanisms, array-based and microdissection-based techniques are ideal. These methods capture whole transcriptomes, generating large datasets without focusing on predefined targets.
3. Know Your Instrumentation and Technical Requirements
Each spatial transcriptomics technique requires specialized equipment. Start by understanding your technical requirements. Below are the methods from which you can choose:
- ISH and ISS methods rely on advanced imaging systems (e.g., MERSCOPE, Esper).
- Array-based methods like Visium require access to NGS platforms but do not need dedicated imaging instruments.
- Some methods, like GeoMx and CosMx, require additional instruments for mRNA capture.
Since many of these technologies are still evolving, their comparative costs and capabilities may vary based on commercial availability.
4. Why is Integrating Spatial Transcriptomics Better Than scRNA-seq?
Integrating spatial transcriptomics (ST) instead of single-cell RNA sequencing (scRNA-seq) in your experiment provides a better understanding of gene expression by addressing the limitations of scRNA-seq alone. Here’s why integrating ST is superior:
- Spatial transcriptomics preserves spatial context without impacting the tissue architecture or spatial relationships between different cell types.
- It offers better cell-type identification accurately in complex tissues and improves annotation accuracy.
- It can seamlessly capture cell-cell interactions and reveal how gene expression is influenced by neighboring cells.
- It gives a better understanding of disease mechanisms in cells and, with it can also provide bulk-level expression data at spatial coordinates, improving the detection of low-expressed genes.
- Avoids biases from enzymatic dissociation, preserving native gene expression patterns.
This section highlights how this powerful technology transforms various research fields, from disease studies to drug discovery and precision medicine.
Spatial Transcriptomics Applications in Biomedical Research
According to Grand View Research, the global spatial transcriptomics market was valued at approximately USD 337.5 million in 2023 and is expected to expand at a compound annual growth rate (CAGR) of 15.23% from 2024 to 2030.
Spatial transcriptomics (ST) is an advanced technology that provides valuable insights into cells. This is why this technology has several applications; below, you’ll explore its applications in cancer research, neuroscience, and developmental biology.
1. Spatial Transcriptomics in Tumor Research
Spatial transcriptomics helps scientists study different tumor parts and how the immune system interacts with them. The tumor immune microenvironment (TIME) is complex, making it difficult to diagnose cancer; this technology will allow you to predict how it will progress and what treatment to choose.
For example, a study published in Nature Communications (2024), provides how spatial transcriptomics can uncover critical mechanisms in cancer progression, particularly in lung adenocarcinoma. By analyzing spatial RNA profiles from 30 lung adenocarcinoma patients at both non-invasive and invasive stages, the study highlights the significant role of the tumor microenvironment in cancer development.
2. Spatial Transcriptomics in Neuroscience and Brain Development
The human brain comprises many different types of cells, each with a unique role and location. Spatial transcriptomics (ST) has changed how you can study the brain by mapping the location of different cells and their interactions. ST can also help us understand how diseases affect brain structure.
For example, according to a study by Nature (2020), spatial transcriptomics and related technologies can be applied to neuroscience and developmental biology. This work offers valuable insights into the dynamic gene expression during mammalian embryogenesis, with a particular focus on understanding how genes regulate the development of different tissues and organs in a developing mouse embryo.
3. Spatial Transcriptomics in Developmental Biology
Developmental biology explores how a complex organism grows from a single fertilized cell. Traditional methods like scRNA-seq help track gene activity in developing embryos, but they don’t show where these changes happen in the tissue. Spatial transcriptomics (ST) fills this gap by allowing you to see both gene expression and its exact location.
For example, Nature Medicine (2024) showed how spatial transcriptomics and single-cell RNA sequencing can advance our understanding of the human brain’s cellular landscape. The research presents the Brain Cell Atlas, a comprehensive reference that integrates single-cell transcriptomic data from over 70 human and 103 mouse studies across major developmental stages. The atlas includes data from more than 26.3 million cells or nuclei sourced from both healthy and diseased brain tissues.
4. Spatial Transcriptomics in Other Pathologies
Beyond cancer and neuroscience, spatial transcriptomics (ST) has broad applications in studying complex diseases. In cardiovascular diseases (CVD), ST has been instrumental in learning how the heart changes and heals after a heart attack. In skin diseases, ST has helped characterize immune cell diversity in inflammatory conditions such as psoriasis and eczema.
For example, a study published in npj Regenerative Medicine (2024) and Nature is an excellent example of how spatial transcriptomics is being used to explore heart regeneration at both the organismal and single-cell levels. The research focuses on how different cardiac cell populations communicate with one another and how these interactions drive the heart’s response to injury and its potential for regeneration.
As you have explored the application of spatial transcriptomics along with its real-world examples below, you’ll explore the limitations that this technology currently encompasses.
Limitations of spatial transcriptomics
As you’ve seen with the application of spatial transcriptomics, there are several limitations to it that you’ll explore further below.
- High Cost: ST is more expensive compared to bulk RNA sequencing, making large-scale studies less feasible. In contrast, bulk RNA sequencing (such as that offered by Biostate) provides a more cost-effective solution for transcriptome-wide analysis.
- Limited Gene Detection: ST typically captures far fewer genes than bulk RNA-seq, often missing rare or lowly expressed transcripts. Biostate’s bulk RNA-seq can detect over 27,000 genes.
- Incomplete Coverage of Non-Coding RNA: ST primarily focuses on protein-coding genes and does not effectively capture the 300,000+ non-coding RNA species that play essential roles in gene regulation, disease progression, and cellular function. Bulk RNA-seq, as provided by Biostate, ensures broader transcriptome coverage, including these critical non-coding RNA species.
- Requirement for Fresh Tissue Samples: Most ST platforms require fresh-frozen or optimally preserved samples to maintain spatial integrity. Biostate’s bulk RNA-seq overcomes this by enabling better transcriptomic analysis of historical samples, expanding research possibilities.
While spatial transcriptomics has limitations, integrating bulk RNA sequencing and AI-driven analysis provides a more comprehensive, cost-effective solution. This synergy enhances data depth and expands research possibilities, driving impactful discoveries in biomedical science.
Conclusion
In this article, we have uncovered everything from the definition of spatial transcriptomics (ST) to its application in biomedical research. This tool has emerged as a transformative technology, providing in-depth insights into tissue architecture, cellular interactions, and disease progression.
ST’s ability to preserve spatial context while analyzing gene expression has transformed cancer research, neuroscience, developmental biology, and various pathological conditions. It has also bridged the gap between molecular profiling and histology.
However, this newly emerged technology has some limitations, so integrating AI-driven solutions becomes essential for unlocking deeper insights into RNA and DNA studies as research advances.
So, if you are looking for high-precision RNA-Seq services, then Biostate.ai is a great choice for you. It offers cost-effective sequencing solutions and AI-powered insights for clinical trials. Get a Quote on Biostate AI Today