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Confocal Airy beam oblique light-sheet tomography for brain-wide cell type distribution and morphology

Nature Methods volume 22, pages 2622–2630 (2025)Cite this article

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An Author Correction to this article was published on 19 November 2025

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Abstract

Advanced brain-wide mapping is critical for addressing complex questions in neuroscience. However, current imaging methods are limited by throughput, resolution and signal-to-noise ratio, constraining their broader applicability. Here, we present confocal Airy beam integrated with single-photon oblique light-sheet tomography (CAB-OLST): a system that integrates single-photon excitation with a scanned Airy beam light sheet, virtual slit detection and automated mechanical sectioning. CAB-OLST enables high-throughput, high-resolution and high-signal-to-noise ratio volumetric imaging, achieving an optical resolution of 0.77 μm × 0.49 μm × 2.61 μm. This allows for mouse brain-wide cell type distribution mapping at a voxel size of 0.37 μm × 0.37 μm × 1.77 μm in 10 h and single-neuron projectome imaging with a voxel size of 0.26 μm × 0.26 μm × 1.06 μm over 58 h. Compared to existing light-sheet and point-scanning systems, CAB-OLST provides a scalable and robust platform for comprehensive neuronal morphology reconstruction and high-precision cell atlas generation.

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Fig. 1: Schematic and performance of CAB-OLST.
Fig. 2: Brain-wide imaging of fine neuronal morphology using CAB-OLST with integrated mechanical sectioning.
Fig. 3: Dual-channel colocalization imaging of reporter expression and an endogenous marker.
Fig. 4: High-precision, brain-wide mapping of cell type distribution with high-axial resolution sampling and 3D cell counting.
Fig. 5: Brain-wide tracing of long-projecting neurons reveals diverse projection patterns of single neurons.

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Data availability

Processed, downsampled image data sufficient to reproduce all figures and analyses are publicly available at Zenodo (https://doi.org/10.5281/zenodo.17191164)59. Due to their substantial size (4–60 TB per dataset), the raw, high-resolution datasets have been deposited in the specialized Brain Image Library (https://www.brainimagelibrary.org/) archive as part of the Brain Initiative Cell Census Network.

Code availability

All custom code developed for this study is released under the MIT License and is archived on Zenodo to ensure long-term availability and reproducibility. The archived versions correspond to the exact code used for the analyses in this paper. The specific packages are as follows: cell type distribution analysis, https://doi.org/10.5281/zenodo.17170411 (ref. 60) and https://github.com/coreyelowsky/OLSTv2; stripe removal, provided as Supplementary Code 1, https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/mBrainAligner; 3D U-Net for cellular segmentation, https://doi.org/10.5281/zenodo.17172303 (ref. 61) and https://github.com/rmunozca/CAB-OLST_Analysis; SmartStitcher, https://doi.org/10.5281/zenodo.17178922 (ref. 62) and https://github.com/polya1998/SmartStitcher.git. The publicly available software Vaa3D (version 4.001), including Vaa3D-TeraVR and Vaa3D-TeraFly, is accessible at http://www.vaa3d.org/.

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Acknowledgements

This work was mainly supported by National Institutes of Health grants U01MH114824 (to P.O.) and U19MH114821 (to Z. Josh Huang).H.P. is a New Cornerstone Investigator and a Shanghai Academy of Natural Sciences Senior Investigator. Additional support was provided by National Natural Science Foundation of China grants 62401009 (to Y.L.) and 62271003 (to L.Q.), in part by the Sci-Tech Innovation 2030 Agenda (2022ZD0205200 and 2022ZD0205204). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper. We thank J. Daniels (ASI), F. Albeanu (Cold Spring Harbor Laboratory) and J. Mizrachi (Cold Spring Harbor Laboratory) for their discussions during microscopy development. We also thank R. Eifert (Cold Spring Harbor Laboratory) for the custom fabrication of microscopy components and R. Campbell (Advanced Microscopy Facility, Sainsbury Wellcome Centre, UCL) for providing the vibratome design specifications.

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Author notes

  1. These authors contributed equally: Xiaoli Qi, Rodrigo Muñoz-Castañeda, Yuanlei Yue, Sheng Xiao.

Authors and Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Xiaoli Qi, Rodrigo Muñoz-Castañeda, Arun Narasimhan, Corey Elowsky, Jason Palmer, Rhonda Drewes, Jianjun Sun & Pavel Osten

  2. National Institute of Biological Sciences, Beijing, China

    Xiaoli Qi

  3. Changping Laboratory, Beijing, China

    Yuanlei Yue

  4. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Sheng Xiao

  5. New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China

    Liya Ding, Xin Chen & Lei Qu

  6. Appel Alzheimer’s Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA

    Wei Wang & Zhuhao Wu

  7. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China

    Yuanyuan Li & Lei Qu

  8. Beijing Information Science and Technology University, Beijing, China

    Jinglei Wu

  9. New Cornerstone Science Laboratory, Institute for Brain and Intelligence, Fudan University, Shanghai, China

    Hanchuan Peng

  10. Shanghai Academy of Natural Sciences, Fudan University, Shanghai, China

    Hanchuan Peng

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Contributions

P.O. and H.P. supervised and coordinated the project. X.Q. conceptualized and led the project under the supervision of P.O. and H.P. X.Q. designed and constructed the microscope with input from A.N. and performed data acquisition. X.Q. and Y.Y. drafted the manuscript with guidance from P.O. and H.P. S.X. developed the Airy deconvolution algorithm and reviewed the optical design. H.P., R.M.-C. and X.Q. jointly supervised image processing. Z.W. and W.W. contributed to sample preparation, including optical clearing and immunostaining. R.D. and J.S. performed animal surgeries including virus injections and brain embedding. C.E., J.P., L.Q. and Y.L. developed computational tools for imaging analysis. L.D. and X.C. conducted single-cell neuronal tracing under the supervision of H.P. J.W. performed data visualization for Fig. 5c,d. All authors participated in the interpretation of results and editing the manuscript.

Corresponding authors

Correspondence to Xiaoli Qi, Hanchuan Peng or Pavel Osten.

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Competing interests

P.O. is a cofounder and the CEO of Theracast. The company had no role in or financial gain from this work. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 CAB-OLST configurations.

(a-b) Configuration for imaging 5 mm-thick tissue using long working-distance (WD) objectives. (c) Configuration showing laser illumination of a cleared mouse brain. (d) Full view of CAB-OLST prototype, with a single camera, and objectives immersed in U.Clear buffer.

Extended Data Fig. 2 Comparative analysis of Airy, deconvolved Airy, and confocal Airy microscopy.

a, Representative neurite images. b, Normalized intensity profiles of a. c, Quantitative comparison of image contrast across the three microscopy modes. Data are presented as mean ± s.e.m. (n = 5 neurites). Statistical analysis was performed using a one-way ANOVA with Tukey’s post-hoc test. Significant differences were found between the standard Airy (Control) and both Deconvolved (P = 0.0013) and Confocal (P = 0.0019) modes. No significant difference was observed between the Deconvolved and Confocal modes (P = 0.976). On the graph, asterisks denote significance levels: **P < 0.01.

Extended Data Fig. 3 XZ and YZ projections of dual-channel cell distribution data.

Representative XY, YZ, and XZ projections of a dual-channel dataset. The top row shows Channel 1 (CH1) and the bottom row shows Channel 2 (CH2). Scale bars, 80 μm.

Extended Data Fig. 4 Axial resolution and detection efficiency across the FOV.

(a) Maximum intensity projection of fluorescent microspheres across the full FOV. (b) Representative images of isolated microspheres at different Z’ positions (1: 250 μm, 2: 131 μm, 3: 491 μm). (c, e) Full-width at half maximum (FWHM) in X, Y, and Z for microspheres imaged at the original full FOV (c) and with Z-stitching (e). Original FOV (median ± s.d.): X: 0.78 ± 0.09 μm; Y: 0.46 ± 0.08 μm; Z: 2.95 ± 0.89 μm; Stitching FOV (median ± s.d.): X: 0.77 ± 0.10 μm; Y: 0.49 ± 0.08 μm; Z: 2.61 ± 0.79 μm. (d) Schematic of the Z-stitching strategy. (f) Background image from a median-filtered AVP brain. (g) Intensity profiles of microspheres versus distance from the FOV center (Z’). Data are presented as median± s.d.

Extended Data Fig. 5 Quantitative comparison of Slit Confocal Airy LS and standard Airy LS.

(a, b) Quantification of image contrast (or signal-to-noise ratio, SNR) for somata (a) and neurites (b) imaged with either Slit Confocal Airy LS or standard Airy LS. For this comparison, laser powers were adjusted to achieve the same maximum noise intensity across modalities.

Extended Data Fig. 6 Quantitative comparison with Slit Confocal Airy LS and standard Airy LS in different signal locations.

a,b, Representative images (a) and quantification of image contrast (b) for somata. c,d, Representative images (c) and quantification of image contrast (d) for neurites. All images were acquired with the same laser power to ensure equivalent maximum signal intensity. Data are presented as mean ± s.e.m. (n = 5 for both). Statistical significance was determined by a two-tailed, paired Student’s t-test (P = 0.0011 for somata; P = 0.0001 for neurites). On the graph, asterisks denote significance levels: **P < 0.01, ***P < 0.001.

Extended Data Fig. 7 Imaging comparison with and without mechanical sectioning (MS).

Representative images and corresponding intensity profiles of four somata (a) and four neurites (b), imaged with and without MS under identical conditions.

Extended Data Fig. 8 Imaging performance comparison using Depth LS and Sectional LS.

(a) Representative images comparing Depth LS (left) and Sectional LS (right) imaging of neuronal structures. Insets are magnified views. Scale bar, 50 μm. (b) Corresponding intensity profiles along the lines indicated in (a), for Depth LS (black) and Sectional LS (red).

Extended Data Fig. 9 3D U-Net segmentation workflow.

a) Overview of ground truth annotation generation process. An unsupervised segmentation generates initial cell masks, which are then refined to create three-class ground truth labels (background, cell boundary, intracellular). A representative cell (right panels) is shown at different processing stages (indicated by green arrows). b) Example of final segmentation results. Left: Raw image of clustered cell bodies with varying intensities. Middle: The segmented output, with cell boundaries shown in green and intracellular regions in red. Right: Detected cell centroids (red dots) overlaid on the original image.

Supplementary information

Supplementary Information

Supplementary Tables 1–4

Reporting Summary

Supplementary Code 1

Stripe removal code.

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Qi, X., Muñoz-Castañeda, R., Yue, Y. et al. Confocal Airy beam oblique light-sheet tomography for brain-wide cell type distribution and morphology. Nat Methods 22, 2622–2630 (2025). https://doi.org/10.1038/s41592-025-02888-9

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