A New Method for Scalable Spatial Transcriptomics via Large-Format Microarrays: The Birth of Array-Seq Technology

Background and Research Motivation

In recent years, spatiomolecular analyses have become pivotal tools in biomedical research and clinical pathology due to their ability to study how the spatial organization of cells and molecules within tissues influences their functions and abnormalities in both health and disease. However, existing spatial transcriptomics (ST) technologies face significant limitations: high costs, complexity, small surface areas, lack of support for large-scale sample processing, and incompatibility with conventional histological staining methods like Hematoxylin and Eosin (H&E) staining. These shortcomings hinder widespread adoption and increase the difficulty and cost of applying these technologies in basic research and clinical analysis.

Early ST technologies, such as the Visium platform, achieved spatial transcriptome sequencing for specific tissue regions by attaching spatial barcodes to poly-A-tail capturing oligonucleotide probes. Nevertheless, the limited surface area and high costs of such methods remain major obstacles to widespread adoption. Meanwhile, classical oligonucleotide microarrays—once a staple of high-throughput gene expression analysis in the early 21st century—prompted researchers to hypothesize whether these mature tools could be repurposed to develop more efficient and cost-effective ST platforms. Against this backdrop, the authors of this study proposed an innovative solution: the Array-Seq method.

Publication Source and Research Team

This study, titled Repurposing large-format microarrays for scalable spatial transcriptomics, was conducted by Denis Cipurko, Tatsuki Ueda, Linghan Mei, and Nicolas Chevrier. The research was primarily carried out at the Pritzker School of Molecular Engineering, University of Chicago. The paper was published in Nature Methods in November 2024, with the online DOI: https://doi.org/10.1038/s41592-024-02501-5.

Experimental Design and Research Workflow

In this paper, the research team introduced the Array-Seq technology, which repurposes classical oligonucleotide microarrays into a high-throughput platform for spatial transcriptomics. The study’s experimental process can be divided into the following key steps:

1. Constructing Spatially Barcoded Capture Probes with Microarrays

The researchers designed custom oligonucleotide microarrays where each array spot carried a unique spatial barcode sequence and two common anchor sequences to enable spatial localization and subsequent operations. These barcode probes were synthesized on glass slides by ink-jet phosphoramidite chemistry, creating a microarray with 974,016 spots per slide. Each spot had a diameter of 30 µm and a center-to-center spacing of 36.65 µm, covering a total active surface area of 11.31 cm².

Next, a two-step reaction assembled mRNA capture probes on the microarray. In the first step, oligonucleotides hybridized to the common anchor sequences on the array. In the second step, a DNA polymerase (e.g., Phusion DNA polymerase) extended the primers, and a DNA ligase (T4 DNA ligase) linked the components to produce complete capture probes. Finally, unligated probes were removed by washing, ensuring a capture probe purity exceeding 75%.

2. Sample Processing and Spatial Transcriptomics Analysis

Using the Array-Seq platform, the researchers analyzed 2D and 3D spatial transcriptomic data from mouse and human tissue samples. Tissue sections were fixed (pre-treated with methanol), subjected to standard H&E staining to verify histological features, and then processed to capture polyadenylated mRNA. This was followed by reverse transcription to synthesize cDNA, linking spatial barcodes, unique molecular identifiers (UMIs), and target sequences.

High-throughput sequencing (Illumina platform) and computational tools (e.g., STARsolo and Scanpy) generated spatial gene expression matrices, which enabled downstream analyses such as cell-type identification, tissue region delineation, and spatial gene expression profiling.

3. Validation and Comparison of Results

The team validated Array-Seq’s performance through real-world datasets and compared the new technology to the existing Visium platform. Experiments used various samples, including mouse olfactory bulb (main olfactory bulb, MOB), kidney, and bone marrow. Key findings included:

1. High Sensitivity and Resolution: For the mouse olfactory bulb, Array-Seq detected an average of 3,582 UMIs and 1,971 genes per spot, accurately reproducing known tissue-layer features.
2. Flexible Sample Coverage: Each Array-Seq slide can capture approximately 2 million to 20 million cells, depending on the tissue type and section count.
3. 3D Analysis Capability: The researchers constructed 3D spatial transcriptomic maps of mouse kidney regions using serial tissue sections, capturing spatial alterations in tissue features with depth.
4. Cross-Platform Comparison: Compared to Visium, Array-Seq achieved an 8.1-fold increase in spot density, a 216.8-fold increase in total spots, and a 26.7-fold increase in total active surface area. Array-Seq demonstrated similar detection sensitivity and maintained precise spatial localization of marker genes.

4. Expansion to Multi-Tissue and Whole-Organ Applications

The researchers further demonstrated Array-Seq’s versatility in analyzing multi-tissue samples and entire human organs. In an experiment with a human spleen, a single longitudinal section (~12 cm²) covered 750,640 spots, revealing known functional regions (e.g., white pulp, red pulp) and cell-type distributions. Notably, the spatial localization of immune cell chemokines (e.g., CXCL13 and its receptor CXCR5) showed highly specific patterns consistent with established knowledge.

Research Conclusions and Significance

The Array-Seq platform integrates the advantages of classical microarrays and next-generation sequencing, creatively addressing issues such as high costs, low throughput, and complexity in current spatial transcriptomics methods. The platform’s main strengths include:

1. High Scalability: Array-Seq supports large-surface-area samples and dramatically reduces per-experiment costs to less than 1/20th of Visium’s (approximately $314–$628 per cm²).
2. Ease of Use: It requires no specialized equipment and is compatible with standard histological workflows, including H&E staining.
3. Scientific and Practical Value: The platform enables high-resolution data generation, aiding analyses of complex tissue structures and opening new directions for both basic and clinical research.
4. Innovative Technology Repurposing: By reusing microarray technologies, Array-Seq unlocks the potential of spatial transcriptomics, democratizing access for more research labs.

Study Highlights and Future Outlook

Key highlights of this study include: - An innovative technical design that efficiently transforms traditional devices into cutting-edge research tools; - Unparalleled capabilities for analyzing large areas, diverse sample types, and 3D tissue structures; - Greatly reduced costs for high-resolution analyses, enabling large-scale public research projects.

Despite current limitations in resolution (30 µm spot diameter) and compatibility with fixed tissue samples (e.g., paraffin-embedded), the research team plans to overcome these challenges with further technological optimizations, such as improving spot spacing and extending sample preprocessing capabilities. They also envision expanding the platform’s scope through multimodal analyses (e.g., integrated proteomics) and automation.

Array-Seq technology provides not only a novel tool for basic biological research but also a critical technological foundation for precision medicine and spatial pathology.