The spatial organization of chromatin inside the nucleus plays a crucial role in genome function, and disease related outcomes¹. Aberrations in chromatin folding have been linked to gene dysregulation, developmental disorders, neuropsychiatric diseases and cancers². Traditionally, genomics methods utilize proximity ligation assays^3,4 to infer the 3D genome through indirect measurement of DNA sequence contacts. However, since indirect measurements are not optimal, there was an urgent need to develop methods that can directly measure DNA contacts. Chromatin tracing is an emerging microscopy-based technology that can visualize bright spots corresponding to individual targeted genomic segments arrayed along chromatin fibers and map their physical location in 3-dimensional space. Unlike the proximity-ligation assays, chromatin tracing provides direct measurement of Euclidean distances between targeted genomic segments⁵. As chromatin tracing technology has been on the rise in the past decade, numerous similar direct-measurement imaging-based techniques have been developed including, but not limited to, multiplexed DNA fluorescence in situ hybridization (FISH)^6,7, DNA-MERFISH⁸, DNA seqFISH+^9,10, ORCA¹¹, MINA¹², Hi-M¹³, OligoFISSEQ¹⁴ and IGS¹⁵. These techniques resolve the spatial location of discrete targeted genomic segments with tens of nanometer precision in single cells.

Chromatin loops are pairs of genomic loci with closer spatial proximity compared to other pairs of loci in the local neighborhood^4,16. They are the key structural feature of chromatin spatial organization and provide the structural basis of gene regulation. All currently available computational methods to identify chromatin loops are designed for genomic data generated from proximity-ligation assays, which utilize count-based statistical methods to model chromatin contact frequency¹⁷. However, these methods cannot be applied to continuous Euclidean distances from imaging-based technologies. Therefore, no method exists to identify chromatin loops for these recently developed microscopy-based technologies. Our goal was to develop the first such method and to provide an accurate yet intuitive analysis to advance our understanding of chromatin structure.

To fill in the methodological gap, we developed Single-Nucleus Analysis Pipeline for multiplexed DNA FISH data (SnapFISH). The SnapFISH algorithm is simple yet powerful. First, SnapFISH collects the 3D localization coordinates of each genomic segment targeted by FISH in each cell, then it computes the pairwise Euclidean distances between all imaged segments. SnapFISH compares the pairwise Euclidean distances between the pair of interest and its local neighborhood region using a two-sample T-test. Then, SnapFISH uses the false discovery rate (FDR) <10% to select loop candidates. Lastly, SnapFISH groups nearby loop candidates into clusters identifies the cluster summit as the final list of chromatin loops.

We rigorously evaluated the effectiveness of SnapFISH using various datasets including multiplexed DNA FISH, ORCA, and DNA seqFISH+ experiments in mouse embryonic stem cells⁹ and mouse excitatory neurons¹⁰. We have successfully detected chromatin loops in these datasets that were in line with results obtained from high-depth bulk Hi-C data⁴. These successes highlight the robustness and adaptability of SnapFISH across different single cell DNA imaging technologies. SnapFISH is a powerful and valuable addition to available loop callers, and the first computational pipeline to identify de novo chromatin loops from single cell multiplexed DNA FISH data, paving the road for revealing cell-type-specific and single cell 3D chromatin conformation structure.

The development team of SnapFISH consists of members from Ming Hu lab at Cleveland Clinic and Yun Li lab at the University of North Carolina, Chapel Hill. Postdoctoral fellow Lindsay Lee at the Hu lab has led the work, involving other talented researchers including undergraduate student Hongyu Yu and high school student Duan Dennis Wang. We are grateful to many labs including the Strambio-De-Castillia lab at University of Massachusetts, the Boettiger lab at Stanford University, and the Ren lab at University of California San Diego, as well as the 4D Nucleome Consortium to provide the latest large-scale multiplexed DNA imaging data.

To find more about SnapFISH, the full article can be found here (https://www.nature.com/articles/s41467-023-40658-3).  SnapFISH is freely available on GitHub: https://github.com/HuMingLab/SnapFISH.

Reference

1          Schmitt, A. D., Hu, M. & Ren, B. Genome-wide mapping and analysis of chromosome architecture. Nature reviews. Molecular cell biology 17, 743-755 (2016). https://doi.org:10.1038/nrm.2016.104

2          Krijger, P. H. & de Laat, W. Regulation of disease-associated gene expression in the 3D genome. Nat Rev Mol Cell Biol 17, 771-782 (2016). https://doi.org:10.1038/nrm.2016.138

3          Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289-293 (2009). https://doi.org:10.1126/science.1181369

4          Rao, Suhas S. P. et al. A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell 159, 1665-1680 (2014).

5          Hafner, A. & Boettiger, A. The spatial organization of transcriptional control. Nat Rev Genet (2022). https://doi.org:10.1038/s41576-022-00526-0

6          Wang, S. et al. Spatial organization of chromatin domains and compartments in single chromosomes. Science 353, 598-602 (2016). https://doi.org:10.1126/science.aaf8084

7          Bintu, B. et al. Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science 362 (2018). https://doi.org:10.1126/science.aau1783

8          Su, J. H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin. Cell 182, 1641-1659.e1626 (2020). https://doi.org:10.1016/j.cell.2020.07.032

9          Takei, Y. et al. Integrated spatial genomics reveals global architecture of single nuclei. Nature 590, 344-350 (2021). https://doi.org:10.1038/s41586-020-03126-2

10        Takei, Y. et al. Single-cell nuclear architecture across cell types in the mouse brain. Science 374, 586-594 (2021). https://doi.org:10.1126/science.abj1966

11        Mateo, L. J. et al. Visualizing DNA folding and RNA in embryos at single-cell resolution. Nature 568, 49-54 (2019). https://doi.org:10.1038/s41586-019-1035-4

12        Liu, M. et al. Multiplexed imaging of nucleome architectures in single cells of mammalian tissue. Nat Commun 11, 2907 (2020). https://doi.org:10.1038/s41467-020-16732-5

13        Cardozo Gizzi, A. M. et al. Microscopy-Based Chromosome Conformation Capture Enables Simultaneous Visualization of Genome Organization and Transcription in Intact Organisms. Mol Cell 74, 212-222.e215 (2019). https://doi.org:10.1016/j.molcel.2019.01.011

14        Nguyen, H. Q. et al. 3D mapping and accelerated super-resolution imaging of the human genome using in situ sequencing. Nat Methods 17, 822-832 (2020). https://doi.org:10.1038/s41592-020-0890-0

15        Payne, A. C. et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371 (2021). https://doi.org:10.1126/science.aay3446

16        Liu, W. et al. Understanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization. Genes 13 (2022). https://doi.org:10.3390/genes13040586

17        Forcato, M. et al. Comparison of computational methods for Hi-C data analysis. Nat Methods 14, 679-685 (2017). https://doi.org:10.1038/nmeth.4325