Resources

We aim to build tools that are not only useful for addressing our own scientific questions, but also broadly useful to the research community. We continuously optimize our imaging platforms, molecular tools, computational pipelines, and experimental workflows for robustness, scalability, and ease of use, with the goal of enabling broad deployment.

Whenever possible, we share plasmids, protocols, software, datasets, and technical resources so that others can adapt and extend these tools for their own research.

The impact of a new technology is measured not only by the discoveries it enables in our own lab, but by the discoveries it enables elsewhere.

Machine Learning for Spatial Omics

What it does: Automated cell-type discovery and tissue-scale mapping of cell-type-specific spatial architectures from cycleHCR data using machine learning.

ML-guided spatial omics pipeline (Lian, Adjavon et al., 2026, bioRxiv):

https://github.com/liulabspatial/Omics-cycleHCR-QuAC

cycleHCR: Deep-Tissue Spatial Transcriptomics

What it does: Multiplexed imaging of RNA and protein targets in thick tissue volumes (>300 µm) with subcellular resolution.

Data processing pipeline (Gandin, Kim et al., 2025, Science) - GitHub: github.com/liulabspatial/CycleHCR-Pipeline — Archive: doi: 10.5281/zenodo.14780420

Gene Co-Expression Analysis

What it does: Quantification of single-cell gene co-expression patterns from combined imaging and genomics data.

Data and code (Dong et al., 2024, Nature Genetics) - Archive: doi: 10.5281/zenodo.11406939

3D ATAC-PALM: Super-Resolution Genome Imaging

What it does: Nanoscale 3D maps of the accessible genome in single cells using ATAC-seq chemistry combined with lattice light-sheet PALM microscopy.

Analysis code (Xie, Dong et al., 2020, Nature Methods) - GitHub: github.com/ammondongp/3D_ATAC_PALM

Molecular simulation code (Xie, Dong et al., 2022, Nature Genetics) - GitHub: github.com/ZhangGroup-MITChemistry/Chrom_cluster

Created by Dall-E