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Revealing nanostructures in brain tissue via protein decrowding by iterative expansion microscopy

Nature Biomedical Engineering volume 6, pages 1057–1073 (2022)Cite this article

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Abstract

Many crowded biomolecular structures in cells and tissues are inaccessible to labelling antibodies. To understand how proteins within these structures are arranged with nanoscale precision therefore requires that these structures be decrowded before labelling. Here we show that an iterative variant of expansion microscopy (the permeation of cells and tissues by a swellable hydrogel followed by isotropic hydrogel expansion, to allow for enhanced imaging resolution with ordinary microscopes) enables the imaging of nanostructures in expanded yet otherwise intact tissues at a resolution of about 20 nm. The method, which we named ‘expansion revealing’ and validated with DNA-probe-based super-resolution microscopy, involves gel-anchoring reagents and the embedding, expansion and re-embedding of the sample in homogeneous swellable hydrogels. Expansion revealing enabled us to use confocal microscopy to image the alignment of pre-synaptic calcium channels with post-synaptic scaffolding proteins in intact brain circuits, and to uncover periodic amyloid nanoclusters containing ion-channel proteins in brain tissue from a mouse model of Alzheimer’s disease. Expansion revealing will enable the further discovery of previously unseen nanostructures within cells and tissues.

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Fig. 1: ExR, a technology for decrowding of proteins through isotropic protein separation.
Fig. 2: Validation of ExR using synapses and comparison with DNA-PAINT.
Fig. 3: Validation of ExR enhancement and effective resolution in synapses of mouse cortex.
Fig. 4: ExR reveals how calcium channel distributions participate in trans-synaptic nanoarchitecture.
Fig. 5: ExR reveals periodic nanoclusters of Aβ42 peptide in the fornix of Alzheimer’s model 5xFAD mice.
Fig. 6: ExR reveals co-localized clusters of Aβ42 peptide with potassium and sodium ion channels in the fornix of Alzheimer’s model 5xFAD mice.
Fig. 7: Analysis of Aβ42 peptide and potassium ion channel nanoclusters in shapes and their relationships.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

The custom code used in this study is available on Zenodo at https://doi.org/10.5281/zenodo.6383293 and on GitHub at https://github.com/schroeme/ExR.

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Acknowledgements

We thank T. Biederer, C. Zhang and Y. Liu for antibody advice, S. Alon and K. Piatkevich for trainings and discussions; B. Kang for decrowding analysis advice; and D. Goodwin for instructions and scripts for manual image registration. D.S. acknowledges funding from NIH K99/R00 Pathway to independence Award (K99GM126277 and R00GM126277); M.E.S was supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1745302 and the MathWorks Science Fellowship; A.-H.T. acknowledges funding from NARSAD; E.D.N. acknowledges funding from Alana Down Syndrome Center and Robert A. and Renee E. Belfer Foundation; P.Y. acknowledges funding from NIH RF1 MH124606 and NIH Director’s Pioneer Award DP1 GM133052; L.-H.T. acknowledges funding from Robert A and Renee E Belfer Family Foundation, Alana Down Syndrome Center, Ludwig Family Foundation, the JPB Foundation, NIH Grants RO1 NS102730 and RF1 AG054321; T.A.B acknowledges funding from NIMH R37MH080046 and R01MH119826; E.S.B. acknowledges funding from the Ludwig Family Foundation, the Open Philanthropy Project, John Doerr, Lisa Yang and the Tan-Yang Center for Autism Research at MIT, the US Army Research Laboratory and the US Army Research Office under contract/grant number W911NF1510548; Charles Hieken, Tom Stocky, Kathleen Octavio, Lore McGovern, Good Ventures; NIH Director’s Pioneer Award 1DP1NS087724, NIH R01MH110932, NIH R01EB024261, NIH U24NS109113, NIH R37MH08004613, NIH 1RM1HG008525, NIH 1R01AG070831, NIH 1R56AG069192 and NIH R01MH124606; funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 835102); and HHMI. We also thank B. Chow and E. Betzig, as well as many current and past members of the Synthetic Neurobiology group, for discussions. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Deblina Sarkar, Jinyoung Kang, Asmamaw T Wassie.

Authors and Affiliations

  1. Media Lab, MIT, Cambridge, MA, USA

    Deblina Sarkar

  2. MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA

    Deblina Sarkar & Edward S. Boyden

  3. MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA

    Jinyoung Kang, Margaret E. Schroeder, Demian Park & Edward S. Boyden

  4. Department of Biological Engineering, MIT, Cambridge, MA, USA

    Asmamaw T. Wassie & Edward S. Boyden

  5. Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA

    Margaret E. Schroeder, Zhuyu Peng, Jennie Z. Young, Li-Huei Tsai & Edward S. Boyden

  6. The Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA

    Zhuyu Peng, Emily D. Niederst, Jennie Z. Young & Li-Huei Tsai

  7. Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA

    Tyler B. Tarr, Ai-Hui Tang & Thomas A. Blanpied

  8. CAS Key Laboratory of Brain Function and Disease, Biomedical Sciences and Health Laboratory of Anhui Province, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China

    Ai-Hui Tang

  9. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Hanquan Su & Peng Yin

  10. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Hanquan Su & Peng Yin

  11. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Li-Huei Tsai

  12. Program in Neuroscience, University of Maryland School of Medicine, Baltimore, MD, USA

    Thomas A. Blanpied

  13. Koch Institute, MIT, Cambridge, MA, USA

    Edward S. Boyden

  14. Howard Hughes Medical Institute, Cambridge, MA, USA

    Edward S. Boyden

  15. Media Arts and Sciences, MIT, Cambridge, MA, USA

    Edward S. Boyden

  16. K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA

    Edward S. Boyden

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  1. Deblina Sarkar
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Contributions

D.S. initiated work, developed the ExR technology, contributed key ideas, designed and performed experiments and interpreted data for all projects, and wrote and edited the manuscript. J.K. contributed key ideas, designed and performed experiments for all projects, performed analysis for decrowding, and wrote and edited the manuscript. A.T.W. co-developed approaches to measuring decrowding, contributed key ideas to ExR technology development and decrowding analysis, designed and performed experiments, analysed data for all projects, and wrote and edited the manuscript. M.E.S contributed key ideas, designed and implemented analysis, visualization and statistical tests for all projects, created the schematic in Fig. 1 with input from others, and wrote and edited the manuscript. Z.P. contributed key ideas, designed and performed experiments, and conducted analysis for the Alzheimer’s project. T.B.T. contributed key ideas, designed experiments, created software, analysed and interpreted data related to synapses, and helped in writing and editing the manuscript. A.-H.T. created software and analysed data related to synapses. E.D.N. designed experiments, interpreted experimental data for the Alzheimer’s project, and helped in writing and editing the manuscript. J.Z.Y. oversaw the Alzheimer’s project and designed experiments for it. H.S. performed DNA-PAINT validation experiments. D.P. prepared cultured neurons. P.Y. contributed key ideas and designed experiments for DNA-PAINT validation. L.-H.T. conceptualized and initiated the Alzheimer’s project, provided oversight and funding, contributed key ideas and designed experiments, and helped in writing and editing the manuscript. T.A.B. contributed key ideas, designed experiments and interpreted data for the synapse project, and helped in writing and editing the manuscript. E.S.B. supervised the project, initiated work, contributed key ideas, designed experiments, helped with data analysis and interpretation, and wrote and edited the manuscript.

Corresponding authors

Correspondence to Li-Huei Tsai, Thomas A. Blanpied or Edward S. Boyden.

Ethics declarations

Competing interests

D.S., A.T.W., J.K. and E.S.B. are co-inventors on a patent application for ExR (US 2020/0271556 A1). E.S.B. is co-founder of a company seeking to deploy applications of ExM-related technologies. The other authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Laurent Nguyen, Markus Sauer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of synaptic nanostructures imaged using DNA-PAINT and ExR in cultured neurons.

(a-c) Three representative fields of view imaged using DNA-PAINT (i) and ExR (ii) after rigid body registration to DNA-PAINT images. Scale bar = 5 μm. (iii-iv) Processed, binary versions of (i) and (ii) used to automatically count synaptic puncta number and pairwise distances. Scale bar, 5μm. (d) Representative manually-cropped matched synaptic ROIs for DNA-PAINT (top row) and ExR (bottom row), used for the distortion analysis shown in Fig. 2h,i (scale bar = 250 nm). (e) Pixel-wise correlation between min-max normalized ExR and DNA-PAINT channels as a function of shift distance in x- and y-directions for two randomly selected synaptic ROIs. (f) Pixel-wise autocorrelation between min-max normalized DNA-PAINT (PAINT-PAINT, magenta), ExR (ExR-ExR, yellow), and pixel-wise correlation between DNA-PAINT and ExR (PAINT-ExR, black) as a function of shift distance in x- and y-directions for the synaptic ROIs shown in (e). (g) Representative manually-cropped pairs of synaptic puncta used to generate the data shown in Fig. 2j and panel i. Shown is an overlay of DNA-PAINT (green) and ExR (magenta) binary masks (scale bar = 250 nm). (h) Histogram of difference in number of synaptic puncta counted after thresholding pairs of synaptic puncta. (i) Difference in radial distance between pairs of synaptic puncta, DNA-PAINT – ExR (mean = -0.008854, 95% CI [-0.05419, 0.03649]). (j) Total number of synaptic puncta for the five fields of view imaged using DNA-PAINT and ExR (two-sided paired t-test, p = 0.9271, t = 0.09735, df = 4). All data are from 5 ROIs from 3 wells of one cultured neuron batch. Shown are images from one representative experiment from two independent replicates.

Source data

Extended Data Fig. 2 Analysis of the ExR decrowding effect.

(a, b) Quantification of decrowding in a set of manually identified synapses. Statistical significance was determined using Sidak’s multiple comparisons test on two-sided t-tests following ANOVA (*P ≤ 0.05, **P ≤ 0.01, ***P < 0.001, ****P < 0.0001, here and throughout the paper, and plotted is the mean, with error bars representing standard error of the mean (SEM), here and throughout the paper). (a) Mean signal intensity inside and outside of (that is, nearby to) dilated reference ROIs for pre- and post-expansion stained manually-identified synapses (Supplementary Table 2 for numbers of technical and biological replicates)). Data points represent the mean across all synapses from a single field of view. (b) Total volume (in voxels; 1 voxel = 17.16 × 17.15 × 40, or 11,779, nm3) of signals inside and outside of dilated reference ROIs, in both cases within cropped images containing one visually identified synapse (Supplementary Fig. 3h), for pre- and post-expansion stained manually-identified synapses (Supplementary Table 2 for numbers of biological and technical replicates). Data points represent the mean across all synapses for 3 fields of view from 3 biological replicates (n = 9 fields of view total per protein). (c) Mean voxel size and (d) mean signal-to-noise (SNR) ratio of pre- and post-expansion immunostaining showing 7 proteins in somatosensory cortex regions L1, L2/3, and L4 of 3 mice. Plotted is mean and SEM. To compare the 3D voxel size and SNR of pre- and post-expansion stained synapses for each of the seven proteins, three two-sided t-tests (one for each layer) were run (n = 49–70 puncta per layer from 3 mice; Supplementary Table 2 for exact n values). Statistical significance was determined using multiple t-tests corrected using the Holm-Sidak method, with alpha = 0.05. (e) Population distribution (violin plot of density, with a dashed line at the median and dotted lines at the quartiles) of the fractional difference in the number of synaptic puncta between post- and pre-expansion staining channels for Homer1 and Shank3 (n = 480 synapses from 3 mice). (f) Population distribution of the difference in distance (in nm) between the shift at which the correlation is half maximal half-maximal shift for pre-pre autocorrelation and post-pre correlation (calculated pixel-wise between intensity values normalized to the minimum and maximum of the image, see Methods) for x-, y-, and z-directions (x- and y-directions being transverse, z-direction being axial) for Homer1 and Shank3 (n = 458 synapses, 3 directions each, from 3 mice). (g) Same as (f), for post-post autocorrelation and pre-post correlation.

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Extended Data Fig. 3 Analysis of the distortion caused by post-expansion staining, as compared to classical pre-expansion staining.

(a-b) Representative background-subtracted and binary images of Homer1 (a) and Shank3 (b) in pre- and post-expansion staining channels (top row (yellow): pre-expansion channel, second row (black/white): binary pre-expansion channel, third row (magenta): post-expansion channel, bottom row (black/white): binary post-expansion channel). (c) Number of synaptic puncta for pre- and post-expansion staining channels, after filtration, for the images in (a-b). (d) Population distribution (violin plot of density, with a dashed line at the median and dotted lines at the quartiles) of the number of synaptic puncta in the pre-expansion staining channel for Homer1 and Shank3 (see Supplementary Table 5 for statistics for this figure). (e) Population distribution of the number of synaptic puncta in the post-expansion staining channel for Homer1 and Shank3. (f) Difference in the number of synaptic puncta between post- and pre-expansion staining channels normalized to the number of synaptic puncta in the pre-expansion staining channel. (g-h) Pixel-wise autocorrelation between pre-expansion (pre-pre, yellow), post-expansion (post-post, magenta), and pixel-wise correlation between pre- and post-expansion (pre-post, black) as a function of shift distance in x- (left column), y- (middle column), and z- (right column) directions for Homer1 (g) and Shank3 (h). The mean across all synapses is shown in the top row, and representative synapses are shown in the second through fourth rows. These values were used to calculate the linearized error measure shown in Extended Data Fig. 2f, g. (i-j) Pixel-wise correlation between mean-normalized, masked pre- and post-expansion channels as a function of shift distance in x- and y-directions (z = 1) for Homer1 (i) and Shank3 (j). The mean across all synapses is shown in the top left, standard deviation across all synapses shown in second from the top left, and representative synapses are shown in the remaining plots. (k-l) Mutually overlapped volume between pre- and post-expansion stained synaptic puncta, normalized to total puncta volume in the pre-expansion staining channel, as a function of shift distance in x- and y-directions (z = 1) for Homer1 (k) and Shank3 (l). The mean across all synapses is shown in the top left, standard deviation across all synapses shown in second from the top left, and representative synapses are shown in the remaining plots. Analysis was conducted on 480 (before exclusion based on size) synapses for Shank3 and Homer1 from 3 mice (see Supplementary Table 5 for exact numbers).

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Extended Data Fig. 4 ExR and unexpanded tissue confocal images showing immunolabeling of Aβ42.

ExR confocal images (single z-slices) showing immunolabeling of Aβ42 with two different monoclonal antibodies (a) D54D2 + 6E10 and (b) D54D2 + 12F4 with SMI co-staining in the fornix of 5xFAD mouse (n = 3 fields of view of 2 slices from 2 mice). Scale bar, 10 μm (top row), 1 μm (i, ii panels). (c) Unexpanded tissue confocal image, a single z-slice, showing pre-expansion Aβ42 (yellow) and SMI (cyan) staining in the fornix of WT (upper panel) and 5xFAD mice (lower panel) (n = 3 fields of view of 1 slice from 1 mouse per WT and 5xFAD, respectively). Scale bar, 30 μm (left panel) and 6 μm (panels i, ii)).

Extended Data Fig. 5 ExR confocal images showing immunolabeling of PLP, SMI, and 12F4 in 5xFAD and WT fornix.

ExR reveals relative localization of Aβ42 peptide and myelin in the fornix of Alzheimer’s model 5xFAD and WT mice (n = 2 fields of view of 1 slice from 2 mice per WT and 5xFAD, respectively). (a) ExR confocal image (max intensity projections, 900–1000 nm thickness) showing post-expansion Aβ42 (magenta), SMI (cyan) and PLP (green) staining in the fornix of 5xFAD mice. Leftmost panel, merged low-magnification image; right images show individual channels. Insets (i-iii) show close-up views of the boxed regions highlighted in the upper left image. (b) ExR confocal image (max intensity projections, 1.72 μm thickness) showing post-expansion Aβ42 (magenta), SMI (cyan) and PLP (green) staining in the fornix of wild-type mice. Leftmost panel, merged image. All images were subtracted background using Fiji’s Rolling Ball algorithm with radius 50 pixels, and adjusted with auto-contrast. Scale bar = 500 nm. (c) Comparison of 12F4, SMI, and PLP intensity levels along axons in 5xFAD fornix with and without 12F4. To analyze axonal amyloid beta deposition with myelination and SMI intensity, we measure the (i) 12F4, (ii) SMI and (iii) PLP intensity levels along 10 axons with (12F4+) and without 12F4 (12F4-) from the same field of view (n = 3 fields of view of 2 slices from 2 5xFAD). All images were subtracted background with 50 pixels, and adjusted with auto-contrast for analysis by ImageJ. On each axon, three lines were drawn cross-sectionally across each axon in Image J and averaged intensity levels of PLP, 12F4 and SMI from different channels were measured respectively along these lines. For 12F4 + axons, each line was drawn across the centroid of amyloid beta deposition. For 12F4- axons, lines were positioned randomly along the axon. We then compared PLP, 12F4 and SMI312 intensity levels between 12F4 + and 12F4- axons. Plotted is the mean, with error bars representing standard error of the mean (SEM). Two-sided paired t-test, (i) ****p < 0.0001, t = 6.112, df = 18, (ii) p = 0.0595, t = 2.012, df = 18, (iii) p = 0.6580, t = 0.4502, df = 18.

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Sarkar, D., Kang, J., Wassie, A.T. et al. Revealing nanostructures in brain tissue via protein decrowding by iterative expansion microscopy. Nat. Biomed. Eng 6, 1057–1073 (2022). https://doi.org/10.1038/s41551-022-00912-3

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