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Universal autofocus for quantitative volumetric microscopy of whole mouse brains

Nature Methods volume 18, pages 953–958 (2021)Cite this article

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Abstract

Unbiased quantitative analysis of macroscopic biological samples demands fast imaging systems capable of maintaining high resolution across large volumes. Here we introduce RAPID (rapid autofocusing via pupil-split image phase detection), a real-time autofocus method applicable in every widefield-based microscope. RAPID-enabled light-sheet microscopy reliably reconstructs intact, cleared mouse brains with subcellular resolution, and allowed us to characterize the three-dimensional (3D) spatial clustering of somatostatin-positive neurons in the whole encephalon, including densely labeled areas. Furthermore, it enabled 3D morphological analysis of microglia across the entire brain. Beyond light-sheet microscopy, we demonstrate that RAPID maintains high image quality in various settings, from in vivo fluorescence imaging to 3D tracking of fast-moving organisms. RAPID thus provides a flexible autofocus solution that is suitable for traditional automated microscopy tasks as well as for quantitative analysis of large biological specimens.

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Fig. 1: RAPID autofocusing in high-resolution LSM.
Fig. 2: Whole-brain quantitative analysis of cell distribution.
Fig. 3: Microglia shape analysis in multiple brain areas.
Fig. 4: RAPID applications beyond LSM.

Data availability

The datasets acquired for this study are available at https://doi.org/10.25493/AV5J-M46.

Code availability

RAPID stand-alone code is publicly available at https://github.com/lens-biophotonics/RAPID-AF. Code used to control RAPID-enabled LSM is publicly available at https://github.com/ludovicosilvestri/RAPID_CLSM. ZetaStitcher and ilastik are available at https://github.com/lens-biophotonics/ZetaStitcher and https://www.ilastik.org, respectively. BCFind software is available at https://github.com/lens-biophotonics/BCFind2.1.

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Acknowledgements

The authors thank F. Del Gallo from the University of Padova (Italy) for providing mouse brain with labeled microglia, I. Tolić from the Ruđer Bošković Institute (Croatia) for providing the fluorescent Schizosaccharomyces pombe, and R. Cicchi from the National Institute of Optics (Italy) for providing the histological slides used in this study. This project received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements no. 785907 (Human Brain Project) and 871124 (Laserlab-Europe), and from the H2020 EU program Excellent Science in conjunction with the European Research Council (ERC) under grant agreement ID no. 692943 (BrainBIT). The project has also been supported by the Italian Ministry for Education, University, and Research within the framework of the Flagship Project NanoMAX and of the Advance Lightsheet Microscopy Italian Mode of Euro-Bioimaging ERIC, and by Ente Cassa di Risparmio di Firenze (a private foundation). M.C.M. acknowledges a Marie Skłodowska-Curie fellowship (MSCA-IF-EF-ST grant agreement no. 842893).

Author information

Authors and Affiliations

  1. Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy

    L. Silvestri & F. S. Pavone

  2. European Laboratory for Non-linear Spectroscopy (LENS), Sesto Fiorentino, Italy

    L. Silvestri, M. C. Müllenbroich, I. Costantini, A. P. Di Giovanna, G. Mazzamuto, A. Franceschini, C. Checcucci, L. Sacconi & F. S. Pavone

  3. National Institute of Optics, National Research Council, Sesto Fiorentino, Italy

    L. Silvestri, M. C. Müllenbroich, I. Costantini, G. Mazzamuto, L. Sacconi & F. S. Pavone

  4. School of Physics and Astronomy, University of Glasgow, Glasgow, UK

    M. C. Müllenbroich

  5. Department of Biology, University of Florence, Sesto Fiorentino, Italy

    I. Costantini

  6. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

    D. Kutra & A. Kreshuk

  7. Department of Information Engineering (DINFO), University of Florence, Sesto Fiorentino, Italy

    C. Checcucci, L. O. Toresano & P. Frasconi

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  1. L. Silvestri
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Contributions

L. Silvestri devised RAPID; L. Silvestri, L. Sacconi and F.S.P. designed the experiments; L. Silvestri and M.C.M. implemented RAPID; L. Silvestri and G.M. developed RAPID standalone software; I.C. prepared the yeast and nematode samples; A.P.D.G. and I.C. prepared the cleared mouse brains; L. Silvestri, I.C. and A.P.D.G. performed the experiments; L. Silvestri and M.C.M. analyzed data; L. Silvestri, L.O.T., A. F. and I.C. manually annotated ground-truth data for cell localization; C.C., L.O.T., G.M. and P.F. analyzed whole-brain SST+ datasets, and performed stitching and cell localization; D.K. and A.K. analyzed microglia morphology data; and L. Silvestri and M.C.M. wrote the paper with contributions from all of the authors.

Corresponding author

Correspondence to L. Silvestri.

Ethics declarations

Competing interests

L. Silvestri, M.C.M., L. Sacconi and F.S.P. are inventors of patent IT201600132604A1, which is related to RAPID. All other authors have no competing interests.

Additional information

Peer review information Nature Methods thanks Adam Glaser and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Lateral shift of pupil-split images in brightfield microscopy.

Mutual lateral displacement of the two pupil-split images in brightfield illumination settings, for two different objectives (a and b). For these measurements, histological keloid samples stained with hematoxylin and eosin were used. Left panels show examples of the image shifts, with a dashed red line to help the reader visualizing the small lateral displacements. Right panels show image shifts determined by automatic image analysis as a function of defocus; different colors identify distinct defocus stacks. A linear fit with the measured slope and a measure of fit goodness (the adjusted R2 score) is also shown.

Extended Data Fig. 2 Lateral shift of pupil-split images in epifluorescence microscopy.

Mutual lateral displacement of the two pupil-split images in epifluorescence illumination settings, for two different objectives (a and b). For these measurements, mouse brain slices stained with SYTOX Green Nucleic Acid Stain (ThermoFisher) were used. Left panels show examples of the image shifts, with a dashed red line to help the reader visualize the small lateral displacements. Right panels show image shifts determined by automatic image analysis as a function of defocus; different colors identify distinct defocus stacks. A linear fit with the measured slope and a measure of fit goodness (the adjusted R2 score) is also shown.

Extended Data Fig. 3 Lateral shift of pupil-split images in light-sheet fluorescence microscopy.

Mutual lateral displacement of the two pupil-split images in light-sheet illumination settings. For these measurements, cleared mouse brains with vasculature fluorescent filling were used. Left panel shows examples of the image shifts, with a dashed red line to help the reader visualize the small lateral displacements. Right panel shows image shifts determined by automatic image analysis as a function of defocus; different colors identify distinct defocus stacks. A linear fit with the measured slope and a measure of fit goodness (the adjusted R2 score) is also shown. Note that quite good linearity is observed over a range of 140 µm, about 70 times larger than the objective depth of focus (≈ 2 µm).

Extended Data Fig. 4 Defocus variability in clarified mouse brains.

a, Virtual slab (500 µm thick) from the brain of a thy1-GFP-M mouse. b, Average defocus for each tile, denoting a clear focal difference between the two light sheets used to illuminate the two halves of the brain, as well as variable defocusing across tiles. Scale bar, 1 mm.

Extended Data Fig. 5 RAPID-enabled LSM on uDISCO cleared mouse brain.

a, Virtual slab extracted from the whole brain of a Thy1-GFP-M mouse cleared with uDISCO. For imaging, the sample was immersed in BABB-D10 (refractive index n = 1.56). The imaging chamber surrounding the sample cuvette was filled with TDE (n = 1.52). b, defocus range (that is absolute value of maximum variation in defocus) in the different image stacks collected to reconstruct the entire volume. c, 3D rendering of a portion of stack, highlighted with the orange square in (a), together with example images taken at different depths either with or without RAPID focus correction. d, Contrast enhancement obtained using RAPID and defocus as a function of depth for the image stack shown in c, e, violin plot of the contrast enhancement shown in (d). This distribution is significantly higher than 1, with p-value < 10−5 (one-sample Student t-test, n = 2490 images). f, violin plot of the defocus across the entire tomography volume (n = 1040 defocus positions). Scale bars: 1 mm a, and 100 μm c,. In the violin plots shown in e,f, data are also presented as box plots with minima at 5 percentile, maxima at 95 percentile, center at 50 percentile, and bounds of box at 25 and 75 percentile, respectively.

Extended Data Fig. 6 RAPID-enabled LSM on PEGASOS cleared mouse brain.

a, Virtual slab extracted from the whole brain of a Thy1-GFP-M mouse cleared with PEGASOS. For imaging, the sample was immersed in BB-PEG (refractive index n = 1.543). The imaging chamber surrounding the sample cuvette was filled with TDE (n = 1.52). b, defocus range (that is absolute value of maximum variation in defocus) in the different image stacks collected to reconstruct the entire volume. c, 3D rendering of a portion of stack, highlighted with the orange square in (a), together with example images taken at different depths either with or without RAPID focus correction. d, Contrast enhancement obtained using RAPID and defocus as a function of depth for the image stack shown in (c) e, violin plot of the contrast enhancement shown in d, This distribution is significantly higher than 1, with p-value < 10−5 (one-sample Student t-test, n = 1600 images). f violin plot of the defocus across the entire tomography volume (n = 1248 defocus positions). Scale bars: 1 mm (a), and 100 μm c, In the violin plots shown in (e,f), data are also presented as box plots with minima at 5 percentile, maxima at 95 percentile, center at 50 percentile, and bounds of box at 25 and 75 percentile, respectively.

Extended Data Fig. 7 Multi-color RAPID autofocusing.

a, On the left, 3D rendering of a stack imaged in two channels (GFP and PI, with RAPID autofocusing operating on PI channel). On the right, example images of both channels taken at different depths, either with or without RAPID focus correction. Scale bars: 200 μm (rendering), 100 μm (insets). b, RAPID defocus in the stack, computed using the GFP or PI channel, respectively. c, Violin plot of the discrepancy between defocus computed using the two channels (n = 40 defocus points). In the same panel, data are also presented as box plots with minima at 5 percentile, maxima at 95 percentile, center at 50 percentile, and bounds of box at 25 and 75 percentile, respectively. The same graph reports also the thickness of the light sheet.

Extended Data Fig. 8 RAPID autofocusing allows robust cell localization.

a,Virtual slab extracted from the whole brain of a FosTRAP mouse. b, Difference in cell counts between stacks imaged with RAPID autofocus and the same imaged without defocus correction. c, defocus range in the different image stacks collected to reconstruct the entire volume. d, 3D rendering of a stack, highlighted with the green square in (b). e, high-resolution 3D rendering of the portion of stack highlighted with the red parallelepiped in (d), imaged with RAPID autofocus. (f) the same volume shown in e, imaged without defocus correction. g, difference in cell counts (RAPID – No AF) versus defocus, calculated for subvolumes of 1 × 1 × 0.4 mm3. h, relative difference in cell counts (RAPID – No AF) versus defocus, calculated on the same subvolumes. The graph reports also Pearson’s correlation coefficient r between the two variables and the probability p that the observed correlation is due to chance. Scale bars: 1 mm (a, b, c, d) and 100 μm (e, f).

Extended Data Fig. 9 Spatial distribution of SST+ cells.

Coronal slices taken at different anterior/posterior positions, showing density heatmap (left half), original grayscale image (right half, gamma corrected to reduce dynamic range). Contours from Allen brain Common Coordinate Framework (CCF v3) are superimposed. Consistently with previous reports18, SST cortical neurons are mainly located in layer 4. Numbers represents A/P bregma. Scale bar, 1 mm.

Extended Data Fig. 10 Microglia segmentation across multiple brain areas.

Representative images of microglia cells from different mouse brain areas, together with the results of the Ilastik segmentation. Somata are depicted in magenta, processes in green. Scale bar, 50 μm; scale is the same for all images.

Supplementary information

Supplementary Information

Supplementary Notes 1–8, Methods, Tables 1–4, Figures 1–11, Video Legends, References in Supplementary Notes and Methods.

Reporting Summary

Supplementary Video 1

Mutual lateral motion of the two pupil-split images as a function of defocus. The amount of defocus is indicated in the upper left corner. A reference target (R1L3S2P, Thorlabs) was used to avoid any potential specimen-induced artifact.

Supplementary Video 2

Image stack from a cleared mouse brain with vasculature stained, acquired with light-sheet microscopy, using RAPID (left) or with a fixed focal position (right). The stack is the same shown in Fig. 1h (main text). Imaging depth inside the tissue is reported in the upper left corner. Scale bar, 20 μm.

Supplementary Video 3

Image stack from the cleared brain of a transgenic mouse expressing tdTomato in SST+ neurons, acquired with RAPID-enabled LSM. Imaging depth inside the tissue is reported in the upper right corner. Scale bar, 100 µm.

Supplementary Video 4

Image stack from the cleared brain of a transgenic mouse expressing GFP in microglia cells, using RAPID (left) or with a fixed focal position (right). Imaging depth inside the tissue is reported in the upper right corner. Scale bar, 100 µm.

Supplementary Video 5

Image stack from the brain of a thy1-GFP-M mouse, cleared with uDISCO, using RAPID (left) or with a fixed focal position (right). The stack is the same shown in Extended Data Fig. 5. Imaging depth inside the tissue is reported in the upper right corner. Scale bar, 100 µm.

Supplementary Video 6

Image stack from the brain of a thy1-GFP-M mouse, cleared with PEGASOS, using RAPID (left) or with a fixed focal position (right). The stack is the same shown in Extended Data Fig. 6. Imaging depth inside the tissue is reported in the upper right corner. Scale bar, 100 µm.

Supplementary Video 7

Long-term bright-field imaging of S. cerevisiae cell culture using RAPID (left) or with a fixed focal position (right). Elapsed time is indicated in the upper left corner as hours:minutes. Scale bar, 1 µm.

Supplementary Video 8

Long-term epi-fluorescence imaging of S. pombe cell culture using RAPID (left) or with a fixed focal position (right). Elapsed time is indicated in the upper left corner as hours:minutes. Scale bar, 1 µm.

Supplementary Video 9

Tracking of freely moving C. elegans in bright-field illumination, using RAPID (left) or with a fixed focal position (right). In the RAPID panel the axial position of the stage is used to reconstruct the 3D trajectory of the worm. The movie is shown at double speed with respect to real time.

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Silvestri, L., Müllenbroich, M.C., Costantini, I. et al. Universal autofocus for quantitative volumetric microscopy of whole mouse brains. Nat Methods 18, 953–958 (2021). https://doi.org/10.1038/s41592-021-01208-1

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