@article {70293, title = {Machine learning-guided spatial omics for tissue-scale discovery of cell-type-specific architectures}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 13}, abstract = {

Multiplexed protein imaging enables spatially resolved analysis of molecular organization in tissues, but existing spatial proteomics platforms remain constrained in scalability, throughput, and integration with RNA measurements and interpretable computational analysis. Here, we present an integrated spatial omics framework that combines highly multiplexed protein and RNA imaging with explainable machine learning to map cell-type-specific molecular and structural architectures at tissue scale. Using this platform, we simultaneously profiled up to 46 proteins and 79 RNA species across \~370,000 cells in intact mouse brain tissue at diffraction-limited subcellular resolution (\~260 nm). We developed a scalable, open-source computational pipeline for large-scale image processing and analysis, and show that nuclear protein and chromatin features alone are sufficient to accurately classify brain cell types and their spatial organization. Incorporation of explainable deep learning further enabled identification of human-interpretable, cell-type-specific subnuclear structural features directly from imaging data, with independent quantitative validation. Together, this integrated experimental and computational framework enables tissue-scale spatial proteomics-based cell-type classification and structural feature discovery, providing a broadly applicable platform for mechanistic studies, high-content screening, and translational applications.

}, doi = {10.64898/2026.02.12.705598}, url = {https://www.biorxiv.org/content/early/2026/02/13/2026.02.12.705598}, author = {Lian, Yumin and Adjavon, Diane-Yayra and Kawase, Takashi and Kim, Jun and Fleishman, Greg and Preibisch, Stephan and Funke, Jan and Liu, Zhe J.} } @article {69457, title = {Deep-tissue transcriptomics and subcellular imaging at high spatial resolution}, journal = {Science}, year = {2025}, month = {2025 Feb 20}, abstract = {

Limited color channels in fluorescence microscopy have long constrained spatial analysis in biological specimens. Here, we introduce cycle Hybridization Chain Reaction (HCR), a method that integrates multicycle DNA barcoding with HCR to overcome this limitation. cycleHCR enables highly multiplexed imaging of RNA and proteins using a unified barcode system. Whole-embryo transcriptomics imaging achieved precise three-dimensional gene expression and cell fate mapping across a specimen depth of ~310 μm. When combined with expansion microscopy, cycleHCR revealed an intricate network of 10 subcellular structures in mouse embryonic fibroblasts. In mouse hippocampal slices, multiplex RNA and protein imaging uncovered complex gene expression gradients and cell-type-specific nuclear structural variations. cycleHCR provides a quantitative framework for elucidating spatial regulation in deep tissue contexts for research and potentially diagnostic applications.

bioRxiv preprint:\ 10.1101/2024.05.17.594641

}, doi = { 10.1126/science.adq2084}, url = {https://www.science.org/doi/10.1126/science.adq2084}, author = {Gandin, Valentina and Kim, Jun and Yang, Liang-Zhong and Lian, Yumin and Kawase, Takashi and Hu, Amy and Rokicki, Konrad and Fleishman, Greg and Tillberg, Paul and Aguilera Castrejon, Alejandro and Stringer, Carsen and Preibisch, Stephan and Liu, Zhe J.} } @article {69954, title = {High-resolution imaging of RNA and proteins in thick tissues using cycleHCR.}, journal = {Nat Rev Genet}, year = {2025}, month = {2025 May 28}, issn = {1471-0064}, doi = {10.1038/s41576-025-00860-z}, url = {https://doi.org/10.1038/s41576-025-00860-z}, author = {Kim, Jun and Liu, Zhe J} }