An interactive framework for whole-brain maps at cellular resolution

Abstract

To deconstruct the architecture and function of brain circuits, it is necessary to generate maps of neuronal connectivity and activity on a whole-brain scale. New methods now enable large-scale mapping of the mouse brain at cellular and subcellular resolution. We developed a framework to automatically annotate, analyze, visualize and easily share whole-brain data at cellular resolution, based on a scale-invariant, interactive mouse brain atlas. This framework enables connectivity and mapping projects in individual laboratories and across imaging platforms, as well as multiplexed quantitative information on the molecular identity of single neurons. As a proof of concept, we generated a comparative connectivity map of five major neuron types in the corticostriatal circuit, as well as an activity-based map to identify hubs mediating the behavioral effects of cocaine. Thus, this computational framework provides the necessary tools to generate brain maps that integrate data from connectivity, neuron identity and function.

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Fig. 1: A reference atlas based on vector graphics.
Fig. 2: A framework for standardizing and sharing neuroanatomical data.
Fig. 3: Method for segmentation and registration.
Fig. 4: Compatibility of the framework with different imaging systems.
Fig. 5: Mapping of molecular identity and neuron types.
Fig. 6: Retrograde monosynaptic tracing of corticostriatal networks.
Fig. 7: Decoding motor behavior by immediate early gene activity.

Change history

  • 18 December 2017

    In the version of this article initially published online, Daniel Fürth was not listed as a corresponding author. The error has been corrected in the print, PDF and HTML versions of this article.

References

  1. 1.

    Alivisatos, A. P. et al. The brain activity map project and the challenge of functional connectomics. Neuron 74, 970–974 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. 2.

    Mitra, P. P. The circuit architecture of whole brains at the mesoscopic scale. Neuron 83, 1273–1283 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. 3.

    Denk, W., Briggman, K. L. & Helmstaedter, M. Structural neurobiology: missing link to a mechanistic understanding of neural computation. Nat. Rev. Neurosci. 13, 351–358 (2012).

    Article  PubMed  CAS  Google Scholar 

  4. 4.

    Bohland, J. W. et al. A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale. PLOS Comput. Biol. 5, e1000334 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. 5.

    Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. 6.

    Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. 7.

    Osten, P. & Margrie, T. W. Mapping brain circuitry with a light microscope. Nat. Methods 10, 515–523 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. 8.

    Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).

    Article  PubMed  CAS  Google Scholar 

  9. 9.

    Tomer, R. et al. SPED light sheet microscopy: fast mapping of biological system structure and function. Cell 163, 1796–1806 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. 10.

    Freeman, J. et al. Mapping brain activity at scale with cluster computing. Nat. Methods 11, 941–950 (2014).

    Article  PubMed  CAS  Google Scholar 

  11. 11.

    Hunnicutt, B. J. et al. A comprehensive thalamocortical projection map at the mesoscopic level. Nat. Neurosci. 17, 1276–1285 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. 12.

    Hintiryan, H. et al. The mouse cortico-striatal projectome. Nat. Neurosci. 19, 1100–1114 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. 13.

    Renier, N. et al. Mapping of brain activity by automated volume analysis of immediate early genes. Cell 165, 1789–1802 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. 14.

    Ye, L. et al. Wiring and molecular features of prefrontal ensembles representing distinct experiences. Cell 165, 1776–1788 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. 15.

    Kim, Y. et al. Mapping social behavior-induced brain activation at cellular resolution in the mouse. Cell Rep. 10, 292–305 (2015).

    Article  PubMed  CAS  Google Scholar 

  16. 16.

    R Development Core Team. R: a Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2008).

    Google Scholar 

  17. 17.

    Ahrens, M. B. et al. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471–477 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. 18.

    Wickersham, I. R. et al. Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639–647 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. 19.

    Ng, L. et al. An anatomic gene expression atlas of the adult mouse brain. Nat. Neurosci. 12, 356–362 (2009).

    Article  PubMed  CAS  Google Scholar 

  20. 20.

    Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  PubMed  CAS  Google Scholar 

  21. 21.

    Mallat, S. G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989).

    Article  Google Scholar 

  22. 22.

    Suzuki, S. & Abe, K. Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30, 32–46 (1985).

    Article  Google Scholar 

  23. 23.

    Friston, K. J., Penny, W. D., Ashburner, J., Kiebel, S. J. & Nichols, T. E. Statistical Parametric Mapping: the Analysis of Functional Brain Images. (Academic Press, London, 2006).

    Google Scholar 

  24. 24.

    Johnson, G. A. et al. Waxholm space: an image-based reference for coordinating mouse brain research. Neuroimage 53, 365–372 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Allen Mouse Common Coordinate Framework. Technical white paper: Allen mouse common coordinate framework. http://help.brain-map.org/download/attachments/2818171/MouseCCF.pdf (2015).

  26. 26.

    Mitra, J. et al. A thin-plate spline based multimodal prostate registration with optimal correspondences. Signal-Image Technol. Internet-Based Syst. SITIS 2010 Sixth Int. Conf. 7–11 (2010).

  27. 27.

    van Kaick, O., Hamarneh, G., Zhang, H. & Wighton, P. Contour correspondence via ant colony optimization. in Proc. 15th Pacific Conference on Computer Graphics and Applications (PG’2007) 271–280 (2007).

  28. 28.

    Bookstein, F. L. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989).

    Article  Google Scholar 

  29. 29.

    Quan, T. et al. NeuroGPS-Tree: automatic reconstruction of large-scale neuronal populations with dense neurites. Nat. Methods 13, 51–54 (2016).

    Article  PubMed  CAS  Google Scholar 

  30. 30.

    Watabe-Uchida, M., Zhu, L., Ogawa, S. K., Vamanrao, A. & Uchida, N. Whole-brain mapping of direct inputs to midbrain dopamine neurons. Neuron 74, 858–873 (2012).

    Article  PubMed  CAS  Google Scholar 

  31. 31.

    Pollak Dorocic, I. et al. A whole-brain atlas of inputs to serotonergic neurons of the dorsal and median raphe nuclei. Neuron 83, 663–678 (2014).

    Article  PubMed  CAS  Google Scholar 

  32. 32.

    Jordan, M. I. & Jacobs, R. A. Hierarchical mixtures of experts and the EM algorithm. Neural Comput. 6, 181–214 (1994).

    Article  Google Scholar 

  33. 33.

    Graybiel, A. M. The basal ganglia and chunking of action repertoires. Neurobiol. Learn. Mem. 70, 119–136 (1998).

    Article  PubMed  CAS  Google Scholar 

  34. 34.

    Wall, N. R., De La Parra, M., Callaway, E. M. & Kreitzer, A. C. Differential innervation of direct- and indirect-pathway striatal projection neurons. Neuron 79, 347–360 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. 35.

    Guo, Q. et al. Whole-brain mapping of inputs to projection neurons and cholinergic interneurons in the dorsal striatum. PLoS One 10, e0123381 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. 36.

    Sagar, S. M., Sharp, F. R. & Curran, T. Expression of c-fos protein in brain: metabolic mapping at the cellular level. Science 240, 1328–1331 (1988).

    Article  PubMed  CAS  Google Scholar 

  37. 37.

    Graybiel, A. M., Moratalla, R. & Robertson, H. A. Amphetamine and cocaine induce drug-specific activation of the c-fos gene in striosome-matrix compartments and limbic subdivisions of the striatum. Proc. Natl. Acad. Sci. USA 87, 6912–6916 (1990).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. 38.

    Pich, E. M. et al. Common neural substrates for the addictive properties of nicotine and cocaine. Science 275, 83–86 (1997).

    Article  PubMed  CAS  Google Scholar 

  39. 39.

    Zheng, Z. et al. A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Preprint at bioRxiv  https://doi.org/10.1101/140905 (2017).

  40. 40.

    Eichler, K. et al. The complete connectome of a learning and memory centre in an insect brain. Nature 548, 175–182 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. 41.

    White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. Lond. B 314, 1–340 (1986).

    Article  CAS  Google Scholar 

  42. 42.

    Helmstaedter, M. Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nat. Methods 10, 501–507 (2013).

    Article  PubMed  CAS  Google Scholar 

  43. 43.

    McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. 44.

    Kebschull, J. M. et al. High-throughput mapping of single-neuron projections by sequencing of barcoded RNA. Neuron 91, 975–987 (2016).

    Article  PubMed  CAS  Google Scholar 

  45. 45.

    Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. 46.

    Larsson, C., Grundberg, I., Söderberg, O. & Nilsson, M. In situ detection and genotyping of individual mRNA molecules. Nat. Methods 7, 395–397 (2010).

    Article  PubMed  CAS  Google Scholar 

  47. 47.

    Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  CAS  Google Scholar 

  48. 48.

    Wickersham, I. R., Sullivan, H. A. & Seung, H. S. Production of glycoprotein-deleted rabies viruses for monosynaptic tracing and high-level gene expression in neurons. Nat. Protoc. 5, 595–606 (2010).

    Article  PubMed  CAS  Google Scholar 

  49. 49.

    Sena-Esteves, M., Tebbets, J. C., Steffens, S., Crombleholme, T. & Flake, A. W. Optimized large-scale production of high titer lentivirus vector pseudotypes. J. Virol. Methods 122, 131–139 (2004).

    Article  PubMed  CAS  Google Scholar 

  50. 50.

    Gong, S. et al. Targeting Cre recombinase to specific neuron populations with bacterial artificial chromosome constructs. J. Neurosci. 27, 9817–9823 (2007).

    Article  PubMed  CAS  Google Scholar 

  51. 51.

    Bodenhofer, U., Kothmeier, A. & Hochreiter, S. APCluster: an R package for affinity propagation clustering. Bioinformatics 27, 2463–2464 (2011).

    Article  PubMed  CAS  Google Scholar 

  52. 52.

    Edelstein, A., Amodaj, N., Hoover, K., Vale, R. & Stuurman, N. Computer control of microscopes using µManager. Curr. Protoc. Mol. Biol. Chapter 14, 20 (2010).

    PubMed  Google Scholar 

  53. 53.

    Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. 54.

    Eddelbuettel, D. et al. Rcpp: seamless R and C++ integration. J. Stat. Softw. https://doi.org/10.18637/jss.v040.i08 (2011).

  55. 55.

    Urbanek, S. png: Read and write PNG images. https://CRAN.R-project.org/package=png (2013).

  56. 56.

    Murrell, P. Importing vector graphics: The grImport package for R. J. Stat. Softw. https://doi.org/10.18637/jss.v030.i04 (2009).

  57. 57.

    Lang, D.T. & The CRAN Team. XML: Tools for Parsing and Generating XML Within R and S-Plus. https://www.rdocumentation.org/packages/XML/versions/3.98-1.9 (2017).

  58. 58.

    Adler, D. & Murdoch, D. rgl: 3D Visualization Using OpenGL. https://CRAN.R-project.org/package=rgl (2017).

  59. 59.

    Feng, D. & Tierney, L. Computing and displaying isosurfaces in R. J. Stat. Softw. 28 https://doi.org/10.18637/jss.v028.i01 (2008).

  60. 60.

    Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: a grammar of data manipulation. https://CRAN.R-project.org/package=dplyr (2017).

  61. 61.

    Bradski, G. The OpenCV library. Dr. Dobbs J. Softw. Tools Prof. Program. http://www.drdobbs.com/open-source/the-opencv-library/184404319# (2000).

  62. 62.

    Frigo, M. & Johnson, S. G. The Design and Implementation of FFTW3. Proc. IEEE 93, 216–231 (2005).

    Article  Google Scholar 

  63. 63.

    Guennebaud, G. & Jacob, B. Eigen v3. (2010). Available at: http://eigen.tuxfamily.org.

  64. 64.

    Holschneider, M., Kronland-Martinet, R., Morlet, J. & Tchamitchian, P. A Real-time algorithm for signal analysis with the help of the wavelet transform. In: P. J.-M. Combes, P. A. Grossmann, P. P. Tchamitchian eds.. Wavelets (pp. 286–297. Springer Berlin, Heidelberg, Germany, 1990).

    Google Scholar 

  65. 65.

    Huang, L.-K. & Wang, M.-J. J. Image thresholding by minimizing the measures of fuzziness. Pattern Recognit 28, 41–51 (1995).

    Article  Google Scholar 

  66. 66.

    Scharr, H. Optimal Operators in Digital Image Processing. (University of Heidelberg, Germany, 2000).

    Google Scholar 

  67. 67.

    Rezakhaniha, R. et al. Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy. Biomech. Model. Mechanobiol. 11, 461–473 (2012).

    Article  PubMed  CAS  Google Scholar 

  68. 68.

    Zhang, T. Y. & Suen, C. Y. A fast parallel algorithm for thinning digital patterns. Commun. ACM 27, 236–239 (1984).

    Article  Google Scholar 

  69. 69.

    Arganda-Carreras, I., Fernández-González, R., Muñoz-Barrutia, A. & Ortiz-De-Solorzano, C. 3D reconstruction of histological sections: Application to mammary gland tissue. Microsc. Res. Tech. 73, 1019–1029 (2010).

    Article  PubMed  Google Scholar 

  70. 70.

    Wearne, S. L. et al. New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales. Neuroscience 136, 661–680 (2005).

    Article  PubMed  CAS  Google Scholar 

  71. 71.

    Rodriguez, A., Ehlenberger, D. B., Dickstein, D. L., Hof, P. R. & Wearne, S. L. Automated three-dimensional detection and shape classification of dendritic spines from fluorescence microscopy images. PLoS One 3, e1997 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. 72.

    Wang, Y., Narayanaswamy, A., Tsai, C.-L. & Roysam, B. A broadly applicable 3-D neuron tracing method based on open-curve snake. Neuroinformatics 9, 193–217 (2011).

    Article  PubMed  Google Scholar 

  73. 73.

    Rose, A. Vision: Human and Electronic. (Plenum Press, New York, 1973).

    Google Scholar 

  74. 74.

    Preacher, K. J. & Kelley, K. Effect size measures for mediation models: quantitative strategies for communicating indirect effects. Psychol. Methods 16, 93–115 (2011).

    Article  PubMed  Google Scholar 

  75. 75.

    Carpenter, B. et al. Stan: A probabilistic programming language. J. Stat. Softw. https://doi.org/10.18637/jss.v076.i01 (2017).

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Acknowledgements

D.F. thanks J. Bernardi and J.H. Lee for fruitful discussions about the software or the paper. K.M. acknowledges financial support from the Swedish Research Council (VR 2012-02049), from the Karolinska Institutet (KID-funding supporting D.F., O.T., A.M.), from the Strategic Neuroscience Area at Karolinska Institutet (StratNeuro) for rabies virus production and from the Swedish Brain Foundation (Hjärnfonden). Additional financial support for the project was from a National Institute of Mental Health grant (MH109795 to G.R., K.M. and C.A.M.) and a National Institute on Drug Abuse grant (DA0036376, to C.A.M.).

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Affiliations

Authors

Contributions

D.F. conceived and developed the computational framework, performed experiments and contributed to data collection, analyzed data and wrote the paper. T.V. contributed to the computational framework, performed experiments, collected data and contributed to writing of the paper. O.T., Y.X., A.M., I.L. and G.S. performed experiments and contributed to data collection. G.F. supervised experiments on corticostriatal tracing. R.T. and K.D. developed and performed COLM experiments. M.C. supervised experiments and contributed to writing of the paper. C.A.M. supervised experiments and contributed to the computational framework and to writing of the paper. G.R. supervised experiments and contributed to the computational framework and to writing of the paper. K.M. conceived and supervised the project and wrote the paper.

Corresponding authors

Correspondence to Daniel Fürth or Konstantinos Meletis.

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D.F. is a stakeholder in Histohub AB. The other authors declare no competing financial interests.

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Integrated Supplementary Information

Supplementary Figure 1 Atlas-based mechanical or laser dissection of tissue

(a) An open-source brain block designed from our 3D atlas printed which can be directly ordered from (http://www.shapeways.com/shops/wholebrain) with all profit going to further development of the atlas. Alternatively, the brain block can be downloaded (https://github.com/tractatus/brain-blocks) modified for any needs and 3D printed by the user itself. (b) The brain block is designed to be used with a standard and can cut coronal sections anchored in the atlas with 1 mm apart, ranging from stereotactic anterior-posterior coordinates +5 mm to −11 mm relative to bregma (white arrow). (c) Top view. (d) Close-up, the small circular bevel marks bregma 0 mm (black and red arrow). (e) Specimen holder for the Leica LMD system scanning stage accommodating 3 slides, with only the middle one loaded with one membrane slide mounted with a single brains section. (f) Entire membrane microscope slide imaged with bright-field at 10× optical magnification and stitched by Leica LMD, this overview image is exported as raw TIF and used as input to WholeBrain registration. (g) Close up on brain section after registration to matched coordinate (+0.2 mm from bregma) (h). The user can select any region desired to laser capture with the get.region() command, in this case selecting SSp (left hemisphere), MOp2/3 (right hemisphere), SSp-bfd5 (right hemisphere), ADP, SI, MS i.e. get.regions(acronym=c("SSp","MOs2/3","SSp-bfd5","ADP","SI","MS"), right.hemisphere=c(0,1,1,2,2,2) registration=regi), where regi is the registration output. (i) Registration result superimposed on the microscope slide. (j) with regions to be laser captured.

Supplementary Figure 2 Multiresolution decomposition

a) Description of manual and automatic work flow as well as output from an automated WholeBrain pipeline. (b) Schematic of a wavelet filter bank in two-dimensions as well as the wavelet lifting scheme. (c-e) Maximum intensity projection of confocal images of a single EGFP labeled medium spy neuron in striatum imaged with three different objectives; (c) 20×, (d) 63×, and (e) 100×, red point and diamond indicate center of mass and white outlines are manually drawn soma perimeters at different z-plane. (f) Estimated soma area as a function of imaging objectives and pixel resolution, measurements of soma area from drawn perimeters obtained from (c-e), colored squares indicate estimated soma area for each objective. Inset: the average fractal dimension, D, of the soma in (c-e) as a function of microscope objective and pixel resolution. As the optical resolution approaches the pixel density the fractal dimension approaches one (Manhattan geometry). (g) A single image is decomposed into a sequence of nested subspaces. Low-resolution images are contained in high-resolution images. (h) As the image is filtered through the wavelet bank and the scale period approaches that of the lower resolution objectives the measured soma area will approach the estimate obtained from that objective, non-filled circles indicate area estimates form individually drawn contours. (i) Soma area of the same neuron estimated from images taken either with 10x (orange) or 63x (blue) objectives can be directly compared by transforming both images into the wavelet detail coefficients where the sampling period is close to the spatial scale that defines typically the soma size (10–20 μm). In this case for the 63x objective this corresponds to d6 where the sampling period is 17.85 μm whereas for the 10x objective this corresponds to d3 where the sampling period is 14.06 μm. Individual dots display areas estimated from individual contours drawn. Probability densities from these data points are obtained by kernel density estimation.

Supplementary Figure 3 Generation of equally spaced correspondence points along a contour by principal components analysis

a) The contour (black thin line) is generated by segmentation of a binary mask following1. The first (thick black lines) and the second (dashed black line) principal components (PC) are drawn intersecting at the centroid of the contour. The two intersections for each PC with the contour is computed (black points) and are added to the set of correspondence points, p. For each two pair of points (p i and p i+1) generate an equal distance midpoint q (white circle) between these two points. (b) At the next level start from the midpoints q and draw a line perpendicular to the midline between p i and p i+1 where this line intersects the contour add that point to the set of correspondence points. Iterate the procedure for as many levels as the user wants, e.g. (c) three levels 16 points, (d) four levels 32 points. This scheme follows2. 1.Suzuki, S. & be, K. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing 30, 32–46 (1985). 2.Mitra, J. et al. A Thin-Plate Spline Based Multimodal Prostate Registration with Optimal Correspondences. 7–11 (2010). doi:10.1109/SITIS.2010.12.

Supplementary Figure 4 Registration error

(a) Regions of interests for measuring registration error before and after manual landmark correction. Regions were selected based on their difference in proximity to the center of the tissue section. Scale bar 1 mm. (b) Root Mean Square Error (RMSE) in micrometers as a function of brain nuclei in four different brains and 64 landmarks along the contour of each nuclei. (c) Mean RMSE as a function of average distance from center of tissue section.

Supplementary Figure 5 Comparison of quantifications at different sectioning planes

(a-f) Representative sections (n = 6) from a total of 53 imaged and quantified sections representing a single mouse brain stained for FOXP2. Left: Original fluorescent image with atlas fit superimposed (purple). Right: segmented FOXP2+ nuclei transformed into stereotactic reference atlas where individual nuclei are color coded according to brain region. (g-l) Representative section (n = 6) from a total of 29 sagittal mouse brain sections also stained for FOXP2. Top: Original fluorescent image with atlas fit superimposed (purple). Bottom: segmented FOXP2+ nuclei transformed into stereotactic reference atlas where individual nuclei are color coded according to brain region. (m) 3D reconstruction of 53 individual FOXP2stained coronal sections. (n) 3D reconstruction of FOXP2 segmented nuclei obtained in the sagittal plane from a single hemisphere and then mirrored to obtain a whole-brain representation at almost half the imaging, sectioning and mounting time. (o) A more complete 3D reconstruction can be achieved by combining (m) and (n). Scale bars: 1 mm.

Supplementary Figure 6 FOXP2 cortical cell counts from sagittal versus coronal sectioning plane

Absolute cell counts of FOXP2+ neurons in sagittal sectioning plane (blue) versus coronal sectioning plane (red). Ordered from top to bottom according to absolute difference in sagittal versus coronal sectioning plane per cortical region. Right panel: measurement error defined as square root error in proportion of total number of segmented FoxP2+ cells.

Supplementary Figure 7 FoxP2 subcortical cell counts from sagittal versus coronal sectioning plane

(a) Absolute cell counts of FOXP2+ neurons in sagittal sectioning plane (blue) versus coronal sectioning plane (red) in striatal/pallidal regions. Ordered from top to bottom according to absolute difference in sagittal versus coronal sectioning plane per cortical region. (b) Absolute cell counts of FOXP2+ neurons in sagittal sectioning plane (blue) versus coronal sectioning plane (red) in thalamic/hypothalamic regions. Ordered from top to bottom according to absolute difference in sagittal versus coronal sectioning plane per cortical region. Right panels: measurement error defined as square root error in proportion of total number of segmented FOXP2+ cells.

Supplementary Figure 8 Accuracy in arbitrary sectioning planes

(a) Projection of coronal whole mouse brain reconstructed in Supplementary Figure 7 onto horizontal plane (orange horizontal line in inset with side view of brain). All FOXP2+ cells at +/- 100 μm dorso/ventral from horizontal plane were projected onto the plane. Right panel shows the kernel density estimation of segmented FOXP2+ cells. (b) Same as in (a) but for sagittal plane. (c) To further demonstrate the limitations in resolution using sectioned tissue we projected the coronal and sagittal reconstructed brains onto three sections at 45° relative to the coronal and sagittal plane. (d-e) Atlas rendering of the 45° sectioning plane. (g-i) Fit of (d-f) onto FoxP2 stained brain cut at the 45° angle plane. (j) Coronal reconstructed brain superimposed on the 45° angle plane. (k) Sagittal reconstructed brain superimposed on the 45° angle plane. (l) Comparison of sagittal and coronal reconstruction on the arbitrary 45° angle plane.

Supplementary Figure 9 Single-molecule fluorescent in situ hybridization for mapping of molecular identity and neuron types

(a) Close-up of mRNA transcripts labeled by RNAscope superimposed on cell nuclei (gray: DAPI). Green: Vesicular Glutamate transporter 2 (VGlut2), red: Prodynorphin (Pdyn), magenta: Vesicular GABA transporter (VGat). Scale bar: 10 μm. (b) Segmentation of individual cell nuclei (orange contours) together with perinuclear zone (dashed orange contours). (c) Segmentation of single fluorescently labeled mRNA transcripts (Green asterisks: VGlut2, Magenta squares: VGat, Red triangles: Pdyn). (d) Affinity propagation clustering to assign single transcripts to single cell nuclei or background (82%, 204466 transcripts, could be assigned to individual nuclei). (e) Maximum intensity projection of hypothalamus imaged by 90 field of views and 15 optical z-planes at 40x optical magnification and then registered to coronal plate from the reference atlas (−1.75 mm anterior-posterior from bregma) with registration results superimposed (orange contours). Scale bar: 500 µm. (f-h) Scatter plots of 7121 individually segmented cell nuclei assigned with at least a single transcript each plotted on log-log coordinates of number of transcripts with Pearson product-moment correlation from linear scale. To classify cells we assigned cells with a transcript count of VGlut2 higher than ten as VGlut2+ (turqoise), and VGlut2+ cells who also expressed Pdyn at transcript counts higher than ten as VGlut2+/Pdyn+ cells (coral), and cells belonging to neither population as VGat+ (purple). (i) Pie chart of the three cell populations defined in (f). (j) 8703 cells from four different populations: VGlut2+ (turqoise), VGlut2+/Pdyn+ (coral), VGat+ (purple), and cell nuclei without any transcripts (dark gray) superimposed on the fit to the reference atlas. (k) Cell counts as a function of brain region. (l) Total number of transcripts as a function of brain region. Brain regions: ARH: Arcuate hypothalamic nucleus; DMHa: Dorsomedial nucleus of the hypothalamus, anterior part; DMHp: Dorsomedial nucleus of the hypothalamus, posterior part; DMHv: Dorsomedial nucleus of the hypothalamus, ventral part; HY: Hypothalamus; LHA: Lateral hypothalamic area; ME: Median eminence; PH: Posterior hypothalamic nucleus; PVi: Periventricular hypothalamic nucleus, intermediate part; RE: Nucleus of reunions; TH: Thlamus; TU: Tuberal nucleus; VMHc: Ventromedial hypothalamic nucleus, central part; VMHdm: Ventromedial hypothalamic nucleus, dorsomedial part; VMHvl: Ventromedial hypothalamic nucleus, ventrolateral part; ZI: Zona incerta.

Supplementary Figure 10 Segmentation of c-fos positive nuclei

(a) Original epifluorescent micrograph of c-fos and DAPI. (b) Wavelet filtered at the second detail coefficient scale. (c) The energy from the structure tensor. (d) Shannon entropy based binary thresholding followed by watersheding, inset shows results before and after watersheding. (e) Subtraction of the binary image from the original epifluorescence. Scale bars: 200 μm. (f) Segmentation result for DAPI with each nuclei color-coded according to mean pixel intensity, cube helix color scheme. (g) Segmentation result for c-fos with each nuclei color-coded according to mean pixel intensity. (h) Cell counts from f and g. (i) c-fos to DAPI nuclei ratio, note that it’s approximately 0.5 which is roughly the ratio of neurons to glia in the brain3. 3.Azevedo, F. A. C. et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. The Journal of Comparative Neurology 513, 532–541 (2009).

Supplementary Figure 11 Top 16 regions with highest cocaine-induced c-fos expression

Posterior estimates of average cell count with regions sorted in descending order with background color indicating anatomical parent region. Thin error bar represents the 95% credible interval (C.I.) and thick lines represents 80% C.I. (n = 4 mice).

Supplementary information

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Supplementary Figures 1–11

Life Sciences Reporting Summary

Supplementary Table 1

Comparison of contemporary approaches to fluorescent whole mouse brain mapping.

Supplementary Video 1 – 3D reconstruction of sectioned tissue form a single mouse brain

Green cells are presynaptic partners to D2-expressing striatal neurons labeled using monosynaptic cell-type specific rabies-EGFP virus in D2-cre mouse.

Supplementary Video 2 – 3D reconstruction of 161,294 neurons from five different mice

Initially labeled neurons are color coded according to brain region, later (00:18) neurons are color coded according to transgenic mouse line (dark blue: D1-Cre, red: D2-Cre, light blue: Chat-Cre, yellow: Camk2a-Cre, green: Gad2-Cre).

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Fürth, D., Vaissière, T., Tzortzi, O. et al. An interactive framework for whole-brain maps at cellular resolution. Nat Neurosci 21, 139–149 (2018). https://doi.org/10.1038/s41593-017-0027-7

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