cycleHCR imaging of 8 protein targets in hippocampal slice

3D cell-fate map of an E6.5-7.0 mouse embryo based on spatial transcriptome data across a depth of ~310 microns

A new platform for multiplexed RNA and protein imaging in thick tissues

Our genome-imaging work revealed that 3D chromatin organization controls gene co-expression programs in single cells. To understand how these programs are deployed across tissues, in different cell types, tissue compartments, and developmental stages, we needed a way to measure expression of hundreds of genes and proteins simultaneously, deep inside intact tissue, with subcellular resolution. No existing method could do all of this. So we built one.

The technology: cycleHCR is a highly multiplexed, deep-tissue spatial transcriptomics and proteomics platform that combines iterative hybridization chain reaction (HCR) amplification with automated fluidics and high-resolution confocal imaging (Gandin & Kim et al., Science 2025). Key engineering advances include:

  • Scale: Simultaneous imaging of 254+ RNA targets and multiple protein markers per tissue volume.

  • Depth: Imaging through >300 µm of intact tissue, roughly 10× deeper than standard multiplexed FISH methods, while maintaining subcellular resolution and morphological detail.

  • Throughput: A 20-fold improvement in acquisition speed over prior HCR-based approaches, enabled by optimized probe chemistry and imaging cycles.

  • Automation: A fully automated fluidics system that executes multi-day imaging runs with minimal user intervention, designed and built in-house.

Instrument design and custom engineering. cycleHCR runs on a custom-built imaging platform with automated fluidics, environmental control, and microscope control software developed in our lab. This engineering capability is central to our lab's identity.

Applications. We are applying cycleHCR to construct 3D cell-fate maps of early mouse embryos, decode spatial gene-expression networks in the adult brain (hippocampus, cortex), and profile cell-type-specific transcriptional programs in disease models. Combined with genetic perturbations and machine-learning-based analysis, these experiments aim to identify the spatial regulatory logic that governs cell identity and tissue organization.

Related Publications

1. Gandin, V.*, Kim, J.*, Yang, L., Lian, Y., Kawase, T., Hu, A., Rokicki, K., Fleishman, G., Tillberg, P., Castrejon, A.A., Stringer, C., Preibisch, S. Liu, Z.@ (2025) Deep-tissue transcriptomics and subcellular imaging at high spatial resolution. Science, 10.1126/science.adq2084

2.  Machine learning-guided spatial omics for tissue-scale discovery of cell-type-specific architectures. Yumin Lian, Diane Adjavon, Takashi Kawase, Jun Kim, Greg Fleishman, Stephan Preibisch, Jan Funke, Zhe J. Liu, bioRxiv, 2026.02.12.705598;