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Before “spatial biology” became a defined field, researchers were already exploring how to visualize molecular processes within tissue context. The earliest breakthroughs came from Fluorescence In Situ Hybridization (FISH), which enabled visualization of individual RNA or DNA sequences in fixed samples (Levsky & Singer, 2003). ¹
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Another milestone came with Laser Capture Microdissection (LCM), which allowed researchers to extract precise microscopic regions of tissue for downstream molecular profiling (Emmert-Buck et al., 1996).² These innovations laid the groundwork for the idea that spatial location is a critical variable in biology but they were limited to a handful of genes at a time, offering low throughput and modest multiplexing capability.
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The concept of spatial transcriptomics emerged between 2015 and 2018, shifting biology from static imaging to quantitative spatial genomics. In 2016, Ståhl et al. introduced the first comprehensive spatial transcriptomics method capable of capturing thousands of genes directly from tissue sections (Science, 2016).³
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Other methods soon followed: osmFISH (Chen et al., 2015) leveraged iterative hybridization to map hundreds of RNA species with high precision,⁴ while Geo-seq (Zhu et al., 2018) fused RNA-seq with spatial coordinates to achieve regional gene expression profiling.⁵ Together, these tools provided the first real glimpses into genome-wide expression within tissue context — albeit at relatively coarse (~100 µm) resolution.
2019 was the year spatial biology went mainstream. Rodriques et al. (2019) unveiled Slide-seq, a barcoded bead-based system achieving near-single-cell spatial resolution (~10 µm) (Science, 2019). Soon after, 10x Genomics released its Visium platform, combining imaging and RNA capture for high-throughput, user-friendly spatial analysis.⁶
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In parallel, NanoString Technologies launched GeoMx Digital Spatial Profiler (DSP), bringing multiplexed RNA and protein quantification to defined tissue regions. Together, these commercial platforms democratized access to spatial tools and standardized workflows across academic and industrial labs.⁷
By the early 2020s, spatial biology evolved beyond transcriptomics. DBiT-seq (Liu et al., 2020) introduced deterministic barcoding using microfluidics to map RNA and proteins simultaneously within the same tissue section.⁸ NanoString’s CosMx SMI, launched soon after, provided true subcellular (~500 nm) resolution for spatial RNA and protein co-detection.⁹
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These multi-omic integrations made it possible to correlate transcriptional and proteomic data directly, offering deeper insight into cell states, signaling pathways, and tissue microenvironments.
Between 2023 and 2024, innovation shifted toward ultra-high resolution and computational scalability. MERFISH combined with Expansion Microscopy (Wang et al., 2018; Chen et al., 2023) pushed molecular imaging to the nanoscale,¹⁰ while Stereo-seq v1.3 (Chen et al., 2022) used DNA nanoball arrays to map entire embryos at subcellular resolution.¹¹
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10x Genomics’ Visium HD further improved capture density and coverage, allowing whole-organ spatial profiling with unprecedented clarity. Together, these systems blurred the line between microscopy and sequencing, turning tissues into data-rich landscapes.¹²
As datasets exploded in complexity, artificial intelligence became essential. Emerging tools like VORTEX and MOSAIK now reconstruct 3D spatial transcriptomic atlases from limited 2D inputs, integrating data across CosMx, Xenium, and Visium HD platforms.¹³
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Meanwhile, frameworks like PersiST introduce topology-aware analysis, enabling time-series (“4D”) insights across tissue architecture. These AI-driven pipelines are redefining what it means to see biology — reconstructing entire organs and even disease progression from raw molecular coordinates.
Nature Methods (2022). Museum of Spatial Transcriptomics. https://www.nature.com/articles/s41592-022-01409-2
The American Journal of Pathology (2025). Spatial Transcriptomics Technologies Review. https://ajp.amjpathol.org/article/S0002-9440%2824%2900276-1/fulltext
Nucleic Acids Research (2025). Multi-slice Spatial Transcriptomics Integration. https://academic.oup.com/nar/article/53/12/gkaf536/8174767
he last decade of spatial biology has redefined how scientists perceive life’s complexity. As AI, imaging, and multi-omics continue to converge, the next frontier will likely move beyond static maps toward dynamic biological atlases — real-time, 4D models of tissues in motion.
To see these tools in action, meet the researchers shaping this revolution at the 3rd Spatial Biology Congress Asia, happening in Singapore on 10–11 November 2025.
🔗 Event details and registration
² Emmert-Buck, M.R. et al. Laser Capture Microdissection. Science, 1996
⁹ NanoString Technologies. CosMx Spatial Molecular Imager: Technical Overview. White Paper, 2022
¹² Lambda, M. et al. Museum of Spatial Transcriptomics. Nature Methods, 2022
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