Different transcriptomics methods offer different levels of detail, cost, and usefulness depending on the type of sample and the research question. Transcriptomics refers to the study of gene expression by analyzing the RNA transcripts produced in a cell or tissue. The main approaches, Bulk RNA sequencing (Bulk-seq), single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics, each provide unique advantages based on the complexity of the sample and the type of information required.
At Single Cell Discoveries (SCD), transcriptomics projects are planned in collaboration with researchers to ensure that the chosen method aligns with the scientific goals, available samples, and budget.
This blog article outlines when each method is most appropriate and when combining scRNA-seq and spatial transcriptomics may offer added value.
Sequencing-Based Spatial Transcriptomics
Sequencing-based spatial transcriptomics captures RNA transcripts from tissue sections using spatially barcoded arrays or beads. Each transcript is assigned a barcode corresponding to its physical location and later sequenced via cDNA synthesis and next-generation sequencing. Computational reconstruction then generates a spatial transcriptome map. These methods provide unbiased, transcriptome-wide coverage, making them particularly suitable for discovery-driven research. Representative platforms include Visium HD (10x Genomics) and Stereo-seq (STOmics).
Watch our webinar on spatial profiling of drug-tolerant cells in colorectal cancer models with Visium HD
Single-cell spatial insights using Visium HD
Imaging-Based Spatial Transcriptomics
Imaging-based approaches detect RNA molecules directly in tissue sections using fluorescent probes that target specific genes. High-resolution microscopes capture the resulting fluorescent signals, marking individual mRNA molecules at defined spatial coordinates. Common examples include Xenium (10x Genomics), MERFISH 2.0 (Vizgen), and CosMx (Nanostring).
Sequencing vs. Imaging: Key Differences
Selecting between sequencing-based and imaging-based spatial transcriptomics depends on specific technical and practical considerations (Figure 1).
Figure 1: Comparison of sequencing-based and imaging-based spatial transcriptomics methods. A summary of key differences between the two approaches.
Spatial Resolution
Sequencing-based platforms provide multi-cell to single-cell resolution, depending on the array’s spot size. For example, Visium HD achieves single-cell resolution.
Imaging-based platforms offer higher spatial resolution, enabling detection of transcripts at single-cell or subcellular levels.
Sensitivity and Accuracy
Sequencing-based approaches can detect thousands of genes across the tissue, offering broad transcriptomic coverage and accurate transcriptomic identification. However, capture and amplification biases may lead to underrepresentation of low-abundance transcripts, and spatial accuracy can be limited when transcripts from several cells are captured in a single spot.
Imaging-based methods detect labeled transcripts directly in the tissue, providing high sensitivity and precise localization for targeted genes. Accuracy may be affected by optical crowding (overlapping signals in dense transcript regions) and by the quality of probe design.
Slides and Gene Throughput:
- Slides Throughput
Sequencing-based workflows can process several slides in parallel. cDNA libraries generated from different slides can be pooled in a single sequencing run, making these methods easier to scale for large studies.
Imaging-based approaches require multiple hybridization and imaging cycles, limiting the number of slides that can be processed simultaneously.
- Gene Throughput
Sequencing-based methods provide unbiased transcriptome coverage. They can detect thousands of genes in a single run.
Imaging-based methods are typically targeted, requiring a predefined panel of genes that can range from hundreds to several thousand genes, depending on the platform.
Time and Cost Differences
Sequencing-based workflows use standardized library preparation and sequencing pipelines, making them a more scalable and cost-effective solution option for projects with multiple samples.
Imaging-based experiments require specialized equipment, custom probe panels, and longer imaging times, which can increase overall time and cost per sample.
Data Output and Analysis
Sequencing-based methods produce transcriptome-wide gene expression matrices with spatial coordinates, similar to scRNA-seq data, thereby simplifying integration with well-established, trusted transcriptomics analysis pipelines.
Imaging-based methods generate large image datasets that require extensive image processing, as well as high-performance computing resources and dedicated software.
Because the output formats differ, the potential downstream analyses also differ. Sequencing-based data enables broader discovery-focused analyses, such as identifying spatially variable genes, mapping cell types through integration with scRNA-seq, and segmenting tissues into molecular domains.
Imaging-based data with molecule-level counts for targeted genes support high-resolution localization studies and precise spatial pattern analysis.
Choosing a Method Based on Research Goals
Spatial transcriptomics can serve two primary research purposes: discovery or validation. Each goal aligns naturally with a different technological approach (Figure 2).
Sequencing-based spatial transcriptomics is ideal for discovery. By profiling the entire transcriptome without prior selection, it enables identification of new markers, pathways, and spatial relationships, particularly in early exploratory stages.
Imaging-based approaches are well-suited for validation. Once key genes or pathways are identified, imaging can precisely measure their spatial distribution at single-cell or subcellular resolution.
Figure 2. Decision tree for choosing between sequencing-based and imaging-based spatial transcriptomics methods.This diagram helps determine the most suitable approach based on whether the target genes are known.
For the most complete view, spatial transcriptomics is often combined with scRNA-seq:
- To address a common challenge in spatial transcriptomics methods, that is, when transcripts from multiple cells are captured within the same spot or pixel and produce mixed signals. scRNA-seq data provide high-resolution molecular profiles that can be used computationally to resolve these mixed spatial signals.
- For imaging-based methods, performing scRNA-seq beforehand helps select relevant genes for probe panels and ensures biological relevance.
- For sequencing-based methods, spatial data can be aligned with scRNA-seq profiles to assign cell types to locations and reconstruct tissue architecture.
Other factors to consider include sample preservation type, compatibility with earlier or future planned experiments, and project-specific budget constraints.
Contact our team to discuss your research goals and explore the best spatial transcriptomics strategy for your research goal.
How Single Cell Discoveries Supports Spatial Transcriptomics Projects
At Single Cell Discoveries (SCD), spatial transcriptomics projects are performed using Visium HD from 10x Genomics. This sequencing-based platform delivers whole-transcriptome profiling at single-cell resolution and can be combined with scRNA-seq to provide both molecular detail and spatial localization.
While scRNA-seq defines individual cell types and their transcriptomic profiles, Visium HD maps those same populations within intact tissues. Together, they offer a powerful, integrated view of tissue organization and gene expression.
Single Cell Discoveries supports every stage of the workflow: from experimental design and sample preparation to data analysis and interpretation, ensuring each project is scientifically and technically robust and aligned with the researcher’s goals.
Conclusion
Sequencing-based and imaging-based spatial transcriptomics each offer unique strengths. Understanding their differences helps researchers select the method that aligns best with their scientific questions. Ultimately, rather than competing, these approaches are complementary.
Contact the SCD team to discuss which spatial strategy best fits your project.
