Sequencing-based spatial transcriptomics with high sensitivity
Abstract
The advent of spatial transcriptomics has dramatically expanded our ability to study the vast network of cell-cell interactions at the molecular level in tissue. Among current methods, sequencing-based approaches have great potential in discovery because of their unbiased capture of the whole transcriptome. In the last couple of years, the spatial resolution for the capture addresses has been improved from ~100 μm to <1 μm, well below the size of a mammalian cell. However, the capture efficiency of sequencing-based methods is generally low, which diminishes the effect of high capture resolution and negatively impacts subsequent data interpretation. Here, we introduce a spatial transcriptomic system, which provides ~1 μm capture resolution and a much higher capture efficiency. We demonstrate that as high as ~14,000 UMIs can be captured from mouse testis sample per 10 × 10 μm² area at a sequencing saturation of 0.43. Rare cells organized in single-cell-layers can be recovered when the data was analyzed at an effective resolution of 2 μm.
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BibTeX
@article{liao2026sequencingbased,
title = {{Sequencing-based spatial transcriptomics with high sensitivity}},
author = {Renjie Liao and Defeng Fu and Zaoxu Xu and Han Liang and Xiaoran Zhou and Yiling Chen and Xueqi Liu and Jiajun Cheng and Chen Li and Huihua Xia and Gailing Li and Diewen Feng and Wei Chen and Yang Chen and Longchao Chen and Zihang Huang and Qingbin Chen and Dexia Chen and Shuguang Xuan and Changling Zhang and Yongyi Lu and Hui Wang and Taiqing Feng and Yuanye Bao and Luyang Zhao and Erkai Liu and Gufeng Wang},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78854-6},
url = {https://www.nature.com/articles/s41467-026-78854-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Sequencing-based spatial transcriptomics with high sensitivity
AU - Renjie Liao
AU - Defeng Fu
AU - Zaoxu Xu
AU - Han Liang
AU - Xiaoran Zhou
AU - Yiling Chen
AU - Xueqi Liu
AU - Jiajun Cheng
AU - Chen Li
AU - Huihua Xia
AU - Gailing Li
AU - Diewen Feng
AU - Wei Chen
AU - Yang Chen
AU - Longchao Chen
AU - Zihang Huang
AU - Qingbin Chen
AU - Dexia Chen
AU - Shuguang Xuan
AU - Changling Zhang
AU - Yongyi Lu
AU - Hui Wang
AU - Taiqing Feng
AU - Yuanye Bao
AU - Luyang Zhao
AU - Erkai Liu
AU - Gufeng Wang
PY - 2026
DA - 2026/10/21
JO - Nature Communications
DO - 10.1038/s41467-026-78854-6
UR - https://www.nature.com/articles/s41467-026-78854-6
AB - The advent of spatial transcriptomics has dramatically expanded our ability to study the vast network of cell-cell interactions at the molecular level in tissue. Among current methods, sequencing-based approaches have great potential in discovery because of their unbiased capture of the whole transcriptome. In the last couple of years, the spatial resolution for the capture addresses has been improved from ~100 μm to <1 μm, well below the size of a mammalian cell. However, the capture efficiency of sequencing-based methods is generally low, which diminishes the effect of high capture resolution and negatively impacts subsequent data interpretation. Here, we introduce a spatial transcriptomic system, which provides ~1 μm capture resolution and a much higher capture efficiency. We demonstrate that as high as ~14,000 UMIs can be captured from mouse testis sample per 10 × 10 μm² area at a sequencing saturation of 0.43. Rare cells organized in single-cell-layers can be recovered when the data was analyzed at an effective resolution of 2 μm.
SN - 2041-1723
ER -