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Volumetric interferometric lattice light-sheet imaging

Abstract

Live cell imaging with high spatiotemporal resolution and high detection sensitivity facilitates the study of the dynamics of cellular structure and function. However, extracting high-resolution 4D (3D space plus time) information from live cells remains challenging, because current methods are slow, require high peak excitation intensities or suffer from high out-of-focus background. Here we present 3D interferometric lattice light-sheet (3D-iLLS) imaging, a technique that requires low excitation light levels and provides high background suppression and substantially improved volumetric resolution by combining 4Pi interferometry with selective plane illumination. We demonstrate that 3D-iLLS has an axial resolution and single-particle localization precision of 100 nm (FWHM) and <10 nm (1σ), respectively. We illustrate the performance of 3D-iLLS in a range of systems: single messenger RNA molecules, nanoscale assemblies of transcription regulators in the nucleus, the microtubule cytoskeleton and mitochondria organelles. The enhanced 4D resolution and increased signal-to-noise ratio of 3D-iLLS will facilitate the analysis of biological processes at the sub-cellular level.

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Fig. 1: Principles of 3D-iLLS, optical setup schematic and comparison of PSF properties of 3D-iLLS versus conventional LLS.
Fig. 2: 3D-iLLS outperforms conventional LLS in 3D sub-cellular imaging.
Fig. 3: 3D-iLLS-SIM enables cellular imaging with improved resolution compared to conventional LLS-SIM and dithered LLS.
Fig. 4: 3D-iLLS-SIM enables two-color imaging with extended resolution.
Fig. 5: 3D-iLLS-SIM enables time lapse imaging with extended resolution.
Fig. 6: 3D-iLLS and modulation interferometry enable improved axial localization and 3D single-particle tracking.

Data availability

Datasets that support results in the paper are available in the Zenodo repository: https://doi.org/10.5281/zenodo.4795421.

Code availability

Custom-written analysis code is available in the Zenodo repository: https://doi.org/10.5281/zenodo.4795421. Data acquisition and instrument control software can be requested for academic use from the corresponding author, after executing material transfer agreements with Memorial Sloan Kettering Cancer Center.

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Acknowledgements

We thank G. Ayzenberg (Department of Medical Physics, Memorial Sloan Kettering Cancer Center) for expert machining, D. Mazover for assistance with CAD and L. Lavis for dye reagents. This work was supported by a NYSTEM Postdoctoral Training Award (C32599GG; J.L.), a National Cancer Institute grant (P30 CA008748), a National Institutes of Health (NIH) Director’s New Innovator Award (1DP2GM105443-01; A.P.), the Louis V. Gerstner, Jr. Young Investigators Fund (A.P.) and the National Institute of General Medical Sciences of the NIH (1R01GM135545-01 and 1R21GM134342-01; A.P.).

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Authors

Contributions

A.P. conceived, designed and supervised the study. A.P. and B.C. built the experimental apparatus. B.C. developed the data acquisition software, wrote analysis code and validated the optical performance of the 3D-iLLS setup. S.C. implemented 3D-iLLS-SIM techniques and performed experiments. J.L. developed the protocols for preparation and imaging of cell samples. G.W. performed numerical calculations. A.P. performed experiments, analyzed and interpreted the data and wrote the manuscript.

Corresponding author

Correspondence to Alexandros Pertsinidis.

Ethics declarations

Competing interests

Memorial Sloan Kettering Cancer Center has filed patent applications (WO2018106678A1, 62/430117 and 63/070125) relating to this work, with A.P. and G.W. listed as inventors.

Additional information

Peer review information Nature Biotechnology thanks Reto Fiolka, Jonas Ries and Lothar Schermelleh for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Numerical calculation of 3D-iLLS PSFs.

a, Simulation pipeline. b, Near uniform sampling of 214 orientations viewed from three angles.

Extended Data Fig. 2 Numerical 3D-iLLS PSFs for different excitation lattices and comparison with conventional LLS.

Overall PSFs are calculated for 2pi detection and for 4pi constructive and destructive detection. Simulation parameters are given in Supplementary Tables 1 and 2.

Extended Data Fig. 3 Three-objective 3D-iLLS configuration, liquid sample cell, sample holder and sample mounting geometry.

a, Photograph of 3D-iLLS setup, highlighting the three-objective configuration, the liquid sample cell and the top-immersion sample holder. b, Photograph of the pincher-grip sample holder with mounted EM grid. c, Sample mounting geometry.

Extended Data Fig. 4 Localization of Brd4 clusters in reconstructed 3D-iLLS images.

a-d, Axial profiles of individual Brd4 clusters. Thick lines show non-linear least-squares fits to equations of the form \(B + \frac{A}{2}\left( {1 \pm \cos \left( {k\left( {z - z_0} \right) + \theta } \right)} \right)e^{ - \frac{{(z - z_0)^2}}{{2\sigma _z^2}}}\) for Cam0 and Cam1, respectively. Global fitting is performed, with shared k,θ and σz parameters. We obtain two separate localization measurements of the parameter z0 that indicates the center position of the cluster, estimated independently from Cam0 and Cam1. e, Oscillation wave-vector k is 0.02375±0.00059 nm−1 (mean±SD), indicating a relative error σk/k of ≈2.5%. f, The center position z0 shows a systematic offset between the two cameras of dz0=25 nm and an r.m.s localization error σdz0=10 nm. These systematic and random errors relative to the oscillation period (2π/k=265 nm) are ≈9% and ≈4% respectively.

Extended Data Fig. 5 Reduction of axial side-lobes by deconvolution and optimization of the 3D-iLLS PSF.

a, Reduction of side-lobes in 3D-iLLS imaging using deconvolution. Top: raw z profiles of two individual mRNA molecules, from the data in Fig. 2a. Bottom: z profiles of the same mRNAs, after 10 iterations of the Richardson-Lucy deconvolution algorithm with an experimental PSF. b-i, Optimized 3D-iLLS PSF based on a fundamental rectangular 2D bound lattice. b, SLM pattern and c, corresponding intensity at rear pupil. d Annular mask. e, Intensity at rear pupil after annular mask. f, Resulting 2D bound lattice in real space and g, corresponding dithered lattice excitation pattern. h, Axial profile of conventional LLS PSFs. i, Axial profile of 3D-iLLS PSFs. Cyan: excitation; yellow: detection; gray: overall.

Extended Data Fig. 6 Comparison of OTFs obtained by conventional LLS, 3D-iLLS, conventional LLS-SIM and 3D-iLLS-SIM.

Simulation parameters are given in Supplementary Tables 1 and 2.

Extended Data Fig. 7 Line profiles of OTFs and PSFs obtained by conventional LLS, 3D-iLLS, conventional LLS-SIM and 3D-iLLS-SIM.

a, conventional LLS and 3D-iLLS based on dithered LLS excitation. b, conventional LLS-SIM and 3D-iLLS-SIM based on SIM LLS excitation. Line profiles along the kz axis (kx=0, ky=0) and z axis (x=0, y=0) are shown for OTFs and PSFs, respectively. Simulation parameters are given in Supplementary Tables 1 and 2.

Extended Data Fig. 8 Resolution and recovery of spatial frequencies of conventional LLS-SIM vs. 3D-iLLS-SIM.

a, Conventional LLS-SIM vs. 3D-iLLS-SIM of microtubules. Average z profile obtained from n=9 and 7 individual microtubules from the 3D-iLLS-SIM and conventional LLS-SIM data in Fig. 3a. b,c, Fourier transforms of 3D-iLLS-SIM vs. 3D-iLLS images of microtubules and mitochondria. Fourier transforms S(k) correspond to the real-space data shown in Fig. 4a. Maps show log(|S(k)|) in the kxky and kxkz planes. 3D-iLLS data are obtained from the 3D-iLLS-SIM data by 5-phase averaging. Two experiments were repeated independently with similar results.

Extended Data Fig. 9 Illustration of z tracking using 3D-iLLS modulation interferometry with a 4-step modulation cycle.

Data corresponds to part of the trajectory of a single mRNA molecule (shown in the second row of Fig. 4e). Top trace shows the displacement of the phase shifter. Four steps are taken, each corresponding to 1/4th of the interferometric period. The black and magenta traces show the intensity of the Cam0 and Cam1 images in each frame. In each step, two phases are measured simultaneously, one on each camera. The images from the first half of each original 4-step modulation cycle - corresponding to φ=0° and 90° measured on Cam0 and φ=180° and 270° measured on Cam1– are combined in a single modulation cycle. Similarly, the images from the second half of the original 4-step modulation cycle - corresponding to φ=180° and 270° measured on Cam0 and φ=0° and 90° measured on Cam1 – are combined in a separate second modulation cycle. The blue line shows this combined Cam0+Cam1 intensity trace. The z position is then extracted by the phase of the intensity modulation, resulting in two successive z position measurements, one each for the first and second part of the original 4-step modulation cycle.

Extended Data Fig. 10 Axial localization performance with 3D-iLLS and 4-phase modulation interferometry.

a, Signals from a 40 nm bead on Cameras 0 and 1, over 100 4-step modulation cycles. The piezoelectric phase shifter is stepped in 182.5 nm increments, corresponding to 0°, 90°, 180° and 270° relative phases. Each step lasts 25 msec, for a total of 100 msec per 4-step modulation cycle Right panel: zoom-in of the dotted region in the left panel, illustrating the anti-correlated signal modulation of Cam0 vs. Cam1. b, Signals from Cam0 and Cam1 are combined into a single modulation cycle, doubling the temporal resolution to 50 msec. Right panel: zoom-in of the dotted region in the left panel. c, Superposition of all 200 modulation cycles by collapsing the x axis in the interval [0-2π), showing excellent stability and reproducibility of the setup. Solid line: fit to a sine wave. d, Extracted phase and z coordinate, showing σz ≈ 8 nm r.m.s. localization precision. Two experiments were repeated independently with similar results.

Supplementary Information

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Cao, B., Coelho, S., Li, J. et al. Volumetric interferometric lattice light-sheet imaging. Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-01042-y

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