Spatial Transcriptomics vs DBiT-seq vs Slide-seq Comparison Guide

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This article focuses on a comprehensive comparison of three leading spatial transcriptomics platforms: Visium (10x), DBiT-seq, and Slide-seq (including V2). We analyze each technology across multiple dimensions - technical workflow, spatial resolution, sensitivity, efficiency, and cost - drawing on data from the latest Nature Methods (2024) cross-platform benchmarking study, which evaluated these methods across three reference tissues in 35 independent experiments. The goal is to provide research users with data-driven insights and practical recommendations to support informed decision-making during experimental design.

Background for Comparison: Why Evaluate Visium, DBiT-seq, and Slide-seq?

Spatial transcriptomics (ST) has emerged as a transformative approach for mapping gene expression patterns directly within the histological context of tissues. Among sequencing-based ST platforms, Visium (10x), DBiT-seq, and Slide-seq/Slide-seq V2 have become widely adopted due to their distinct technical principles and accessibility for different research scenarios.

In July 2024, a collaborative study involving researchers from the Guangzhou Laboratory, Westlake University, the University of Melbourne, and Harvard University was published in Nature Methods under the title Systematic comparison of sequencing-based spatial transcriptomic methods. This large-scale benchmark assessed 11 ST platforms, including the three highlighted in this article, using three reference tissue types - mouse hippocampus, retina, and olfactory bulb - across 35 experiments (Nature Methods, 2024).

The study evaluated performance based on several critical parameters:

• mRNA capture efficiency (sensitivity to detect low-abundance transcripts)
• Spatial resolution and lateral diffusion control (ability to preserve transcript localization)
• Accuracy of cell type annotation and clustering

The results revealed that no single platform excelled in all aspects; instead, each showed strengths and limitations depending on the tissue type, resolution requirements, and downstream analysis goals. This reinforces the importance of matching the technology to the specific research question rather than relying on a one-size-fits-all approach.

Detailed Technical Workflows: Sample Preparation, Barcoding Strategies, and Data Output

While all three platforms aim to generate spatially resolved transcriptomic profiles, their workflows differ substantially in terms of sample handling, spatial barcoding, and data granularity. Understanding these differences is crucial for selecting a method that aligns with your tissue type, resolution needs, and downstream analysis goals.

Overview of experimental design and data processing pipeline. (You, Y., et al., 2024, Nature methods)

1. Visium (10x Genomics) – Microarray-Based Capture with Poly-A or Probe Designs

• Sample Preparation: Works with both fresh frozen and FFPE tissue sections mounted on glass slides pre-patterned with capture spots. Tissue permeabilization releases mRNA directly onto these capture areas.
• Barcoding Strategy: Each spot (~55 μm in diameter) contains oligonucleotides with a unique spatial barcode, poly-dT capture sequence, and sequencing adapters. The poly-A chemistry captures polyadenylated transcripts, while probebased chemistry targets pre-selected genes for higher sensitivity in degraded RNA (e.g., FFPE).
• Data Output: Produces spot-level count matrices, where each spot contains a pooled transcript profile from multiple cells, enabling whole-tissue gene expression maps (10x Genomics Technical Note).

2. DBiT-seq – Microfluidic Channel-Based Spatial Barcoding

• Sample Preparation: Fixed tissue sections are placed beneath a polydimethylsiloxane (PDMS) microfluidic chip containing parallel microchannels. A first set of spatial barcodes is flowed across the tissue in one orientation, followed by a second orthogonal set to generate a 2D grid.
• Barcoding Strategy: Each intersection point of the microchannels receives a unique combination of "X" and "Y" barcodes, creating a coordinate for each spatial position. DBiT-seq can be adapted to capture both mRNA and proteins using DNAconjugated antibodies, enabling multimodal profiling.
• Data Output: Generates spatially indexed gene expression matrices, optionally integrated with protein expression data for multi-omic spatial analysis.

3. Slide-seq / Slide-seq V2-Based High-Density Spatial Capture

• Sample Preparation: Utilizes a "puck" or glass slide coated with randomly arranged barcoded microbeads. Each bead carries a unique spatial barcode and capture oligonucleotide. Fresh frozen tissue sections are transferred onto the bead array, allowing mRNA to bind directly to bead-bound oligos.
• Barcoding Strategy: The array is first "decoded" by sequencing the barcodes and mapping their positions using a reference image. The bead size and arrangement determine resolution—Slide-seq V2 achieves ~10 μm spot spacing for near-single-cell resolution.
• Data Output: Produces bead-level expression matrices, offering the highest spatial granularity among the three platforms (Stickels et al., Science, 2021).

Compared side-by-side, Visium prioritizes standardized workflows and reproducibility, DBiT-seq offers unique multi-modal flexibility, and Slide-seq V2 pushes spatial resolution to the microscale—often at the cost of increased complexity and sequencing depth requirements.

Performance Metrics: Spatial Resolution, Sensitivity, Efficiency, and Cost

Choosing between Visium, DBiT-seq, and Slide-seq often comes down to how each platform performs in core technical areas. The differences can be subtle on paper but significant in practice, especially when working with challenging tissue types or when the research goal demands a specific balance between resolution and sensitivity.

Comparison of the sensitivity of data generated by different platforms. (You, Y., et al., 2024, Nature methods)

Spatial Resolution

Resolution defines how finely the platform can map transcripts to their original locations in the tissue.

• Slide-seq V2 delivers the highest spatial resolution, with beads spaced about 10 µm apart—small enough to approximate the dimensions of many mammalian cells. This makes it particularly suitable for dissecting cell–cell interactions in densely packed tissues like the brain.
• DBiT-seq resolution depends on the width of the microfluidic channels, which can be adjusted, but it generally does not reach the bead-level granularity of Slide-seq V2.
• Visium offers a more modest resolution (~55 µm spots), pooling transcripts from multiple cells per capture area. While this limits single-cell precision, it is often sufficient for broader tissue architecture studies.

Sensitivity (Capture Efficiency)

Sensitivity refers to how effectively a platform can detect low-abundance transcripts.

• In benchmark tests, Slide-seq V2 consistently detected more transcripts per spot than the other two platforms, especially in neural tissues.
• Visium showed solid midrange performance, particularly in its probe-based format for degraded RNA, making it a reliable option for FFPE samples.
• DBiT-seq tended to recover fewer transcripts overall, though its multi-modal capability can compensate for this in studies that also require protein expression data.

Efficiency and Throughput

• Visium benefits from highly standardized reagents, streamlined protocols, and strong vendor support, making it efficient for routine use and reproducible results.
• Slide-seq V2 is more technically demanding—bead array preparation and decoding require precision—but it offers unmatched throughput in terms of spatial spots per experiment.
• DBiT-seq requires custom microfluidic setup and alignment, which can be a limiting factor in labs without microfabrication resources.

Comparison on downstream performance. (You, Y., et al., 2024, Nature methods)

Cost Considerations

Cost is influenced not only by reagents and consumables but also by the required sequencing depth.

• Visium provides predictable, welldocumented cost structures and sequencing recommendations.
• Slide-seq V2 can become more expensive due to the need for deeper sequencing to fully exploit its high resolution.
• DBiT-seq may have higher upfront setup costs if microfluidic equipment is not already available, though per-sample reagent costs can be competitive.

Key takeaway:

If your study demands the finest possible spatial detail, Slide-seq V2 is the clear leader. For routine, high-throughput tissue profiling with standardized workflows, Visium offers the best balance. If integrating mRNA and protein mapping is essential, DBiT-seq provides capabilities that the others lack.

Practical Insights: Controlling for Success and Avoiding Pitfalls

While published protocols for Visium, DBiT-seq, and Slide-seq provide a solid foundation, the reality in the lab is that small details can make the difference between high-quality data and an inconclusive dataset. Drawing from benchmark studies and real-world project experience, here are several practical considerations.

Match the Technology to the Tissue

Not all tissues behave the same during permeabilization or barcode capture.

• Highly vascularized tissues can cause transcript diffusion beyond intended capture areas, blurring spatial resolution. In Nature Methods' cross-platform tests, this was more noticeable in certain probe-based methods and microfluidic approaches. Slide-seq V2 tended to control lateral diffusion better, likely due to its dense bead arrangement.
• For tissues with high autofluorescence (e.g., retina), imaging steps in DBiT-seq and bead decoding in Slide-seq can be more challenging. Adjusting imaging filters or pre-clearing background signal can help.

Plan Sequencing Depth Strategically

Benchmark data show that when read depth is reduced, most major cell types remain detectable. However, rare or closely related subpopulations—such as specific neuronal subtypes—can be lost without sufficient coverage.

• For Slide-seq V2, under-sequencing wastes its resolution advantage.
• For Visium, the loss is less dramatic, but spatial clustering precision still benefits from adequate depth.

Control Barcoding Precision

• DBiT-seq relies on precise alignment of two microfluidic channel arrays; even minor misalignment can generate mixed or "blurred" coordinates. Including control lanes with known markers can help detect this early.
• Slide-seq requires an even and well-decoded bead array—clusters of defective beads can create "holes" in the spatial map. Careful puck preparation and quality control are essential.

Optimize Tissue Preparation

• Frozen tissue should be sectioned at consistent thickness to ensure uniform capture efficiency.
• FFPE samples for Visium's probe-based chemistry must be fully deparaffinized and rehydrated, or probe binding efficiency will drop significantly.

Bottom line: Many "failures" in spatial transcriptomics are preventable with upfront attention to tissue quality, platformspecific setup requirements, and realistic sequencing plans. Investing time in pilot runs with control tissue is often the most costeffective way to avoid expensive reruns.

Key Questions About Visium, DBiT-seq, and Slide-seq Compared

Q1: Which platform offers the highest spatial resolution—Visium, DBiT-seq, or Slide-seq?

A: Slide-seq V2 achieves the highest spatial resolution, with bead spacing around 10 µm, allowing transcript mapping at near-cellular precision. DBiT-seq resolution depends on microchannel width and is adjustable, but generally lower than Slide-seq V2. Visium spots are ~55 µm in diameter, providing lower resolution but more standardized and reproducible workflows.

Q2: Which method is most sensitive for detecting lowabundance transcripts?

A: Benchmark studies indicate Slide-seq V2 detects more transcripts per capture site than Visium or DBiT-seq, particularly in neural tissue. Visium's probe-based chemistry improves detection in degraded samples such as FFPE. DBiT-seq sensitivity is lower but offers multi-modal capability by capturing both mRNA and proteins in the same tissue section.

Q3: Can I profile both RNA and proteins simultaneously with these platforms?

A: Among the three, only DBiT-seq natively supports simultaneous RNA and protein detection through DNA-barcoded antibodies. This makes it a strong choice for multi-omic spatial studies where transcriptomic data needs to be directly integrated with spatial proteomics.

Q4: Are these platforms equally suited for all tissue types?

A: No. Tissues with high vascularization or high autofluorescence may require specific adjustments. Slide-seq V2's bead density helps control transcript diffusion, while Visium's standardized protocols make it robust for a wide range of tissues, including FFPE. DBiT-seq may require more optimization for challenging tissue architectures.

Summary and Selection Guidance

Spatial transcriptomics is no longer a niche technique; it has become a central tool for understanding gene expression within the native architecture of tissues. Visium, DBiT-seq, and Slide-seq each bring unique strengths to this field, and choosing between them depends on your experimental goals, sample type, and available resources.

• When to choose Slide-seq V2:

Opt for this platform if your study requires ultra-high spatial resolution to resolve fine cellular structures or dissect micro-environments in dense tissues. Be prepared for the higher technical demands and deeper sequencing needed to fully exploit its capabilities.

• When to choose Visium (10x):

A strong choice for projects prioritizing workflow standardization, reproducibility, and vendor support. Ideal for whole-tissue mapping, longitudinal studies, or when working with challenging samples like FFPE where probe-based chemistry can boost sensitivity.

• When to choose DBiT-seq:

Select this method if multi-omic capability is essential — for example, when you need to capture mRNA and protein profiles from the same tissue section. Keep in mind the need for microfluidic setup and careful alignment.

Final thought:

No single platform is universally "best." Instead, the most successful spatial transcriptomics projects begin with a clear definition of the biological question, followed by matching the method's strengths to that question. Pilot experiments, ideally on representative tissue, remain the most reliable way to confirm that the chosen platform will deliver the resolution, sensitivity, and data type your study needs.

Research Support Services

Looking to streamline your spatial transcriptomics project and avoid technical pitfalls? At CD Genomics Spatial Omics Lab, we offer end-to-end support tailored for research labs and institutions. From specimen embedding and sectioning (FFPE or fresh frozen) to library construction, sequencing, and optional FISH validation - our platform handles every step with standardized QC and rapid turnaround times. With dedicated project managers, expert guidance, and competitive pricing, our goal is to help you generate clear, publication-ready data efficiently.

References

1. You, Y., Fu, Y., Li, L., Zhang, Z., Jia, S., Lu, S., ... & Tian, L. (2024). Systematic comparison of sequencing-based spatial transcriptomic methods. Nature methods, 21(9), 1743-1754.
2. Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., ... & Macosko, E. Z. (2019). Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science, 363(6434), 1463-1467.
3. Stickels, R. R., Murray, E., Kumar, P., Li, J., Marshall, J. L., Di Bella, D. J., ... & Chen, F. (2021). Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nature biotechnology, 39(3), 313-319.
4. Su, G., Qin, X., Enninful, A., Bai, Z., Deng, Y., Liu, Y., & Fan, R. (2021). Spatial multi-omics sequencing for fixed tissue via DBiT-seq. STAR protocols, 2(2), 100532.
5. Gripshover, T. C., Treves, R. S., Rouchka, E. C., Chariker, J. H., Zheng, S., Hudson, E., ... & Hardesty, J. E. (2025). Visium spatial transcriptomics and proteomics identifies novel hepatic cell populations and transcriptomic signatures of alcohol‐associated hepatitis. Alcohol: Clinical and Experimental Research, 49(1), 106-116.