Understanding gene expression in its natural tissue environment is important to understand complex biological processes. Traditional RNA sequencing (RNA-seq) captures gene activity but breaks tissues apart, losing spatial information. This loss of context makes it harder to study how cells interact in organs or diseased tissues.
Spatial transcriptomics preserves this context by visualizing gene expression in intact tissue sections. Highly multiplexed RNA sequencing in situ hybridization merges RNA sequencing with imaging.
Advanced methods like DART-FISH and FISSEQ profile thousands of genes in large tissue samples, aiding research on tissue architecture, cellular diversity, and disease progression.
With the spatial genomics and transcriptomics market projected to reach $995.7 million by 2029, the demand for these technologies is soaring. But how do they work, and what makes them so powerful?
In this article, we discuss the science behind highly multiplexed RNA in situ hybridization, its key applications, and its growing role in biomedical research.
What is Multiplexed RNA Sequencing In Situ Hybridization and How it Works?
Overview of Multiplexed RNA Sequencing In Situ Hybridization
Highly multiplexed RNA sequencing in situ hybridization method allows the simultaneous detection of a large number of RNA species directly within intact tissue sections.
Techniques such as Decoding Amplified taRgeted Transcripts with Fluorescence In Situ Hybridization (DART-FISH) and Fluorescent In Situ RNA Sequencing (FISSEQ) are at the forefront of this field.
These approaches use padlock probes that are designed to bind to complementary cDNA sequences produced by reverse transcription. Once bound, the probes are circularized and then amplified through Rolling Circle Amplification (RCA), creating clusters of repeated sequences known as “rolonies.”
These rolonies emit a bright fluorescent signal, making them visible under a microscope. In the case of FISSEQ, the amplified DNA nanoballs are sequenced directly in situ by a process called sequencing-by-ligation.
Key Technical Innovations
Recent innovations in RNA sequencing have transformed spatial transcriptomics by enhancing precision and signal amplification. Padlock probe design ensures high specificity, while RCA boosts signal strength for improved detection. Isothermal decoding simplifies the process by eliminating thermal cycling, making spatial RNA sequencing faster and more efficient.
Now, let’s have a closer look at these key innovations.
- Padlock Probe Design: Probes are engineered to bind with high specificity to their target sequences. Their circularization after binding creates a closed-loop structure that is ideal for RCA.
- Rolling Circle Amplification (RCA): It is a robust method to amplify the signal. The RCA process generates multiple copies of the target sequence, increasing the fluorescent signal enough to overcome the diffraction limits of light microscopy.
- Isothermal Decoding: Unlike traditional methods that rely on thermal cycling, these protocols use enzyme-free, isothermal procedures for probe decoding, thereby reducing processing time.
- Omni-cell Type Cytoplasmic Staining: This innovation improves cell body segmentation, a crucial step for mapping gene expression to individual cells within heterogeneous tissues.
- Computational Deconvolution: Sophisticated algorithms are applied to resolve overlapping signals (optical crowding) and accurately assign reads to their spatial origins.
With a strong foundation in these principles, we now turn to the Technical Workflow and Methodologies, understanding the step-by-step processes that make these breakthroughs possible.
Technical Workflow and Methodologies
A clear understanding of the technical workflow is essential to appreciate how these spatial transcriptomics platforms achieve their high-resolution and multiplexing capabilities.
Here are the steps involved in the process:
- Sample Preparation
Successful mapping of gene expression begins with careful tissue collection and preparation. Tissues are rapidly collected and preserved, commonly by Fixation with Paraformaldehyde (PFA), to maintain both morphology and RNA integrity.
In many cases, tissue sections are cut using a cryostat or microtome to produce thin slices (typically 5–10 micrometers thick) that can be mounted on glass slides.
Before hybridization, an omni-cell type cytoplasmic stain is applied. It enhances cell segmentation by highlighting the overall cellular architecture, ensuring that gene expression signals can be assigned to the correct cell bodies.
- Reverse Transcription and Probe Hybridization
Once the tissue is prepared, RNA is reverse-transcribed into complementary DNA (cDNA). The process is initiated by using tagged random hexamers, which bind to multiple regions of the mRNA. The resulting cDNA contains adapter sequences that are essential for subsequent steps.
Next, padlock probes are introduced. These are single-stranded DNA molecules designed with two ends that are complementary to adjacent sequences on the target cDNA. When the padlock probe binds, it forms a loop and is ligated to create a closed circle. This circularization is crucial for the next phase of amplification.
- Signal Amplification via Rolling Circle Amplification (RCA)
After successful probe circularization, RCA is performed. During RCA, a DNA polymerase extends the circularized probe repeatedly, generating a long, concatenated single-stranded DNA molecule called a rolony.
Each rolony contains thousands of tandem repeats of the target sequence, providing a robust and localized fluorescent signal.
For methods such as FISSEQ, the amplified product remains stably cross-linked to the tissue, allowing multiple rounds of imaging and even re-hybridization with sequencing probes.
- Sequencing and Imaging
The amplified signals (rolonies) are then imaged using high-resolution fluorescence microscopy. In the case of FISSEQ, the sequencing is performed in situ using sequencing-by-ligation (SOLiD technology).
Here, fluorescently labeled oligonucleotides are cyclically ligated to the rolonies, reading out short sequences (e.g., 27 bases) from thousands of genes across the tissue.
A key innovation is the partition sequencing approach. By varying the length of the sequencing primers, researchers can control the density of readable spots. This balance is essential as too many signals cause overlap, whereas too few reduce statistical significance.
- Data Analysis and Computational Deconvolution
The raw imaging data contain thousands of fluorescent spots that must be mapped back to their spatial locations and corresponding gene identities. Automated pipelines perform image deconvolution to distinguish between individual rolonies, even in areas with dense signals and assign a sequence to each spot.
Statistical analyses compare in situ sequencing data with bulk RNA-seq or microarray data, ensuring that the spatial method accurately reflects the underlying gene expression profiles.
These computational methods also differentiate between nuclear and cytoplasmic signals, identify alternative splicing events and correlate gene expression with cellular structures.
By combining molecular precision with advanced imaging and computational analysis, these methodologies enable a clear view of gene expression in its native spatial context.
Now, let’s discuss some of the applications where spatial transcriptomics is transforming our understanding of development, disease, and therapeutic responses.
Applications in Mapping Human Tissues
Highly multiplexed RNA sequencing in situ hybridization is best illustrated through its diverse applications in biomedical research.
Let’s have a look at some of the widely used applications:
1. Mapping the Human Neocortex
One of the landmark applications of this technology is in mapping the human neocortex. Researchers have applied padlock probe-based techniques to profile hundreds of genes in large sections of the neocortex.
By generating spatial transcriptomic maps, they have been able to delineate distinct neuronal and non-neuronal subclasses based on their gene expression patterns. These maps reveal the cytoarchitecture of the brain, showing how different cell types are organized and interact within the tissue.
2. Profiling Diseased Tissues
Another significant application is the profiling of diseased tissues. For example, mapping gene expression in human kidney tissue has allowed researchers to identify different cell states, both healthy and pathological.
By detecting subtle changes in gene expression, including the upregulation of specific extracellular matrix (ECM) components and the activation of remodeling pathways.
These studies help pinpoint diseased niches within tissues. This information can be vital for understanding disease progression and for identifying potential biomarkers for diagnosis or therapeutic targets.
3. Wound Healing and Cellular Response
In vitro wound healing assays have also benefited from these advanced techniques. By simulating tissue injury and capturing spatial gene expression before, during, and after cell migration, researchers can study dynamic cellular responses.
For example, changes in alternative splicing of key genes such as FN1 (fibronectin) have been observed during wound healing, indicating shifts in cellular behavior and ECM remodeling. This level of detail is crucial for designing targeted therapies that promote efficient tissue repair.
4. Other Research Applications
Beyond the brain, kidney, and wound healing, these technologies are applicable to various tissues and experimental models:
- Cancer Research: Mapping tumor heterogeneity and identifying subpopulations of cancer cells.
- Developmental Biology: Charting the spatial organization of gene expression during embryogenesis to understand developmental processes.
- Immunology: Revealing the spatial distribution of immune cell populations in tissues to understand their roles in infection and inflammation.
As these applications demonstrate, spatial transcriptomics is transforming our understanding of tissue organization, disease progression, and cellular dynamics. However, like any technology, it comes with both strengths and challenges.
Advantages and Limitations
Highly multiplexed RNA in situ hybridization has revolutionized spatial transcriptomics by enabling precise gene expression mapping within intact tissues. While this technology offers significant advantages, such as spatial preservation and high multiplexing, it also comes with challenges like optical crowding and technical complexity.
Understanding these factors is important to optimize their applications.
Advantages
Highly multiplexed RNA in situ hybridization offers several advantages. Here are a few of the most prominent benefits:
- Preservation of Spatial Context
Unlike traditional RNA-seq, these methods retain the spatial arrangement of cells, allowing researchers to correlate gene expression with tissue architecture and cellular interactions.
- High Multiplexing Capability
The use of padlock probes and rolling circle amplification allows for the simultaneous detection of hundreds to thousands of genes. This high level of multiplexing is essential for comprehensive tissue profiling.
- Subcellular Resolution
Techniques such as FISSEQ enable the localization of transcripts at subcellular levels, distinguishing between nuclear and cytoplasmic expression patterns. It is invaluable for understanding RNA processing and localization.
- Robust Signal Amplification
Rolling circle amplification generates highly fluorescent signals from individual transcripts. This amplification makes it possible to detect low-abundance RNAs that less sensitive methods might miss.
- Cost-Effectiveness and Speed
The isothermal decoding and enzyme-free amplification methods reduce the time and expense associated with thermal cycling and complex instrumentation.
- Flexibility Across Specimen Types
These methods have been successfully applied to a range of specimens, including cell cultures, tissue sections, and even whole-mount embryos.
Limitations and Challenges
While highly multiplexed RNA in situ hybridization offers in-depth insights into spatial gene expression, it also presents technical challenges. Factors like optical crowding, reading depth limitations, and complex protocols can impact data accuracy. Understanding these limitations helps researchers refine their methods and improve the reliability of spatial transcriptomic studies.
Here are some of the limitations of highly multiplexed RNA in situ hybridization:
- Optical Crowding and Resolution Limits
In regions with high transcript density, overlapping fluorescent signals (optical crowding) can make it difficult to resolve individual molecules. The partition sequencing approach mitigates this issue but requires careful calibration.
- Read Depth and Coverage
Although in situ methods provide spatial information, they often generate fewer sequencing reads compared to conventional RNA-seq. As a result, very low-abundance transcripts may be underrepresented.
- Technical Complexity
The protocol involves multiple steps, from tissue fixation to probe hybridization, amplification, imaging, and data analysis. Each step must be precisely controlled, and small errors can affect the overall quality of the data.
- Computational Demands
Data analysis requires advanced image deconvolution and statistical algorithms to accurately assign sequences to spatial coordinates. This necessitates robust computational resources and expertise.
- Potential Biases
Differences in probe accessibility, RNA integrity, and amplification efficiency may introduce biases. Comparing in situ data with RNA-seq or microarray data can help identify and correct these biases.
While these advancements offer remarkable insights, researchers must consider technical challenges to maximize their potential. Real-world applications highlight how scientists overcome these hurdles to make groundbreaking discoveries.
Real-World Examples and Case Studies
Real-world applications of highly multiplexed in situ RNA sequencing demonstrate its power in understanding cellular complexity and disease mechanisms. From mapping brain development to studying fibroblast heterogeneity, these case studies highlight how spatial transcriptomics advances biomedical research.
Here, we discuss two key examples showcasing the impact of these technologies.
Two key examples illustrate their impact:
Case Study 1: Mapping the Human Neocortex
Understanding the cellular composition and gene expression patterns in the human neocortex is vital for deciphering brain function and development. A study employed single-cell RNA sequencing to profile individual cells from the developing human neocortex.
This approach identified distinct cell types and their gene expression signatures, providing insights into the complex architecture of the neocortex. The study’s findings contribute to our understanding of human brain development and have implications for neurological disease research.
Key findings include the following:
- Comprehensive Cell Catalog: Researchers conducted single-cell RNA sequencing on approximately 40,000 cells from the mid-gestation human neocortex, identifying a diverse array of cell types, including progenitor cells and subtypes of excitatory and inhibitory neurons.
- Regulatory Networks: The study uncovered cell-type-specific regulatory networks, linking regulatory elements and transcription factors to gene expression programs, enhancing understanding of human neurogenesis and cortical evolution.
- Disease Implications: By mapping gene expression to specific cell types, the research provides insights into the cellular basis of neuropsychiatric disorders, potentially informing future therapeutic strategies.
Case Study 2: In Situ RNA Sequencing in Fibroblasts
Fibroblasts play a crucial role in tissue repair and fibrosis. The study combined single-cell RNA sequencing with spatial transcriptomics and high-resolution multiplex fluorescence in situ hybridization.
This integrative approach characterized fibroblast subpopulations within the human oral mucosa, identifying specialized compartments and immune programs. The research enhances our understanding of fibroblast heterogeneity and its roles in chronic inflammatory diseases.
Highlights from the study:
- Spatial Transcriptomics Integration: Researchers combined single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridization to analyze the human oral mucosa in both healthy and chronic inflammatory states.
- Pro-Inflammatory Fibroblast Subpopulations: The study identified novel pro-inflammatory fibroblast subpopulations involved in lymphocyte recruitment through the expression of chemokines CXCL8 and CXCL10, shedding light on mechanisms of disease progression in chronic inflammation.
- Specialized Epithelial and Stromal Compartments: By deconvolving spatial transcriptomic data, the research defined highly specialized epithelial and stromal compartments, revealing location-specific immune programs within the oral mucosa.
Both case studies underscore how highly multiplexed in situ RNA sequencing maps gene expression and reveals functional and regulatory aspects of tissue biology that are critical for both basic research and clinical applications.
As these methods continue to evolve, they are shaping the future of medicine by enabling more precise diagnostics and targeted therapies.
Impact on Biomedical Research and Personalized Medicine
Highly multiplexed RNA in situ hybridization is revolutionizing biomedical research by providing spatial and molecular insights at an unprecedented scale.
By preserving tissue architecture while capturing gene expression, these techniques enable scientists to study cellular diversity, gene regulation, and tissue organization. Here, we discuss their transformative impact on biomedical research:
1. Advancing Biomedical Research
Highly multiplexed RNA in situ hybridization is transforming our understanding of tissue biology. By combining spatial context with high-throughput gene expression data, these techniques allow researchers to:
- Dissect Cellular Heterogeneity: Understanding the diversity of cell types within a tissue is essential for deciphering complex biological processes such as brain function, immune response, and organ development.
- Study Gene Regulation in Situ: Spatial localization of transcripts provides clues to regulatory mechanisms that are not apparent in bulk RNA sequencing.
- Map Tissue Architecture: Detailed spatial maps of gene expression help researchers understand how cells interact to form functional tissues and organs.
For instance, mapping the human neocortex at single-cell resolution has revealed new neuronal subtypes and regional variations that could lead to novel insights into neurodegenerative diseases or psychiatric disorders.
2. Personalized Medicine and Diagnostics
Personalized medicine is transforming healthcare by personalizing treatments for individual patients based on their unique genetic makeup and disease characteristics. Spatial transcriptomics offers detailed maps of gene expression at the tissue level, enabling more precise and effective diagnostics and therapeutic approaches.
Now, let’s have a look at how spatial transcriptomics can revolutionize biomarker discovery, targeted therapies, and precision diagnostics in clinical applications:
- Biomarker Discovery: By identifying spatially restricted expression patterns, researchers can discover novel biomarkers that are specific to certain cell types or pathological conditions.
- Targeted Therapies: Understanding the spatial distribution of gene expression in tumors can inform targeted therapy. For example, identifying niches within a tumor where drug resistance arises can help design more effective treatment strategies.
- Precision Diagnostics: Spatial maps of gene expression can aid in diagnosing diseases based on subtle changes in cellular organization or transcript abundance that are missed by traditional histopathology.
As spatial transcriptomics methods become more robust and accessible, they are poised to become standard tools in diagnostic laboratories. In time, these techniques may be integrated with routine clinical workflows to provide comprehensive molecular profiles that guide personalized treatment plans.
Conclusion
Highly multiplexed RNA sequencing in situ hybridization is revolutionizing gene expression research by preserving spatial context. Techniques like DART-FISH and FISSEQ allow scientists to analyze thousands of genes while preserving spatial context, offering unprecedented insights into cellular architecture and disease progression.
From mapping the human neocortex to identifying pro-inflammatory fibroblast subpopulations, highly multiplexed RNA sequencing in situ hybridization is transforming neuroscience, immunology, and precision medicine.
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Frequently Asked Questions
1. What is high-throughput sequencing of RNA?
High-throughput RNA sequencing (RNA-Seq) is a method that allows researchers to analyze gene expression across thousands of transcripts simultaneously. Using Next-generation Sequencing (NGS), it captures and quantifies RNA molecules, providing insights into gene activity, alternative splicing, and regulatory mechanisms in various biological conditions and diseases.
2. What does bulk RNA sequencing tell you?
Bulk RNA sequencing measures gene expression across an entire population of cells in a sample. It provides average transcript levels, making it useful for identifying differentially expressed genes, detecting alternative splicing events, and understanding cellular responses in tissues, but it lacks single-cell resolution for distinguishing individual cell types.
3. What is multiplexed single-cell in situ RNA profiling?
Multiplexed single-cell in situ RNA profiling is a method that allows the visualization and quantification of multiple RNA transcripts within intact tissues at single-cell resolution. Techniques like MERFISH, DART-FISH, and seqFISH use barcoded probes and fluorescence imaging to map spatial gene expression while preserving tissue architecture.
4. What is multiplexing in RNA-Seq?
Multiplexing in RNA-Seq allows multiple RNA samples to be sequenced simultaneously in a single run by tagging each sample with unique molecular barcodes. It increases efficiency, reduces sequencing costs, and enables comparative transcriptomic analysis across different conditions while maintaining accurate sample identification.
5. What is the DART-FISH method?
Decoding Amplified taRgeted Transcripts with Fluorescence In Situ Hybridization (DART-FISH) is a high-resolution spatial transcriptomics technique. It uses amplified and barcoded probes to detect multiple RNA targets in tissue sections. By integrating computational decoding, it provides precise gene expression mapping, making it valuable for studying complex tissues and disease microenvironments.