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Whole brain optical imagingLeonardo Sacconi, PhDEuropean Laboratory for Non-Linear SpectroscopyNational Institute of Optics (INO-CNR)

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whole brain neuroanatomy

The investigation of the brain requires several challenges in imaging technology, since brain activity spans many orders of magnitude both spatially (from nanometers to centimeters) and temporally (from milliseconds to months).

Thanks to their flexibility in terms of spatial and temporal resolution optical methodologies have become very useful in neuroscience research. Whole mouse braincm scalePyramidal neuronmm scaleSynapsesm scaleOptical techniques to explore the brain

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Light sheet microscopy (LSM)Key advantages:

from J. Huisken and D. Y. Stainier (Development, 2009)Optical sectioning with wide field detection scheme Fast high resolution 3D imaging

Optical sectioning with low-NA optics (having longer WD)

Imaging of large specimens without sample sectioning.

Only the observed plane is illuminated

Reduced photobleaching.

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Traditional clearing protocols are based on the substitution of water with a refractive-index-matching liquid, as Benzyl Alcohol/Benzyl Benzoate (BABB) or Dibenzyl Ether (DBE) [Becker et al. 2012].Chemical clearing of entire brainsSilvestri et al. JoVe 2013.In large specimens as whole mouse brains, a substantial amount of light scattering persist even after clearing. This expands the illumination beam, leading to out-of-focus background fluorescence and blurring of images.

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We recently developed a new implementation of confocal slit detection (confocal light sheet microscopy, CLSM) in which the out-of-focus background rejection is assured by a spatial filter. Confocal light sheet microscopy (CLSM)Silvestri et al. Optics Express 2012

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SV

CV

TV1010Cerebellum from a P10 L7-GFP mouse cleared in BABBTotal volume 73 mm, voxel size 0.80.81 m, acquisition time 24 h (1.3 MegaVoxels/s)Whole brain imaging

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PV: PV-dTomato mouse (parvalbuminergic neurons labeled)GAD: GAD-dTomato mouse (GABAergic neurons labeled)PI: propidium iodide staining (all nuclei labeled)Whole brain imagingMulleibroich et al. Neurophotonics 2013

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Correlative two-photon and light sheet microscopy

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Ex vivo light sheetIn vivo reflectanceIn vivo two-photonLight sheet microscopy of mouse brains is essentially an ex vivo technique. It can be combined with in vivo two-photon imaging to gain a more comprehensive multi-scale view of the brain.

To find back in the cleared brain the same field of view imaged in vivo, blood vessels can be used as a reference map.Correlative two-photon and light sheet microscopy

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The same neuron imaged in vivo with two-photon microscopy can be found again in the large-scale optical tomography obtained with CLSM

The side of the red cube is 100 mCorrelative two-photon and light sheet microscopySilvestri et al. Methods 2014

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Correlative two-photon and light sheet microscopy

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Blood vessel staining

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A 2 mm thick block of a formalin-fixed tissue of a patient with hemimegalencephaly (HME), treated with passive CLARITY protocol immunostained with different antibody and cleared with 47% TDE/PBS

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Human Brain ImagingCostantinie et al. Sci Report 2015

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Adapted from Kasthuri and Lichtman (2007)

A data floodData production: about 5 TB per week10 Gb/s dedicated connection from LENS to CINECAConnection from LENS to Juelich via CINECA (using PRACE infrastructure)

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To image an entire brain many parallel stacks of images are acquired. They are subsequently merged with a custom-made software suited to work with very large data sets (~ 1 TB)Tera Stitcher Bria et al., BMC Bioinformatics 2012

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TeraFly Bria et al., Nat. Meth. (2016)

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Very large samples (cm-sized)Large variability of contrast in different areasTB-sized datasetsNave methods (as thresholding or clustering) are poorly effective as usually depend on sensitive parametersAdvanced methods usually requires the calculation of multiple features per each image voxel, leading to a data multiplication which is not manageable with large datasets

Automatic 3D cell localization

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Semantic deconvolution uses a supervised neural network, to enhance selected features of the image reducing the intensity of other structures. This method creates a more uniform image where significant structures (hence the name semantic) are well visible.

In the deconvolved image, easy low-cost localization algorithms (e.g. clustering) can achieve very good performances.

Original imageIdeal imageDeconvolved image

Manual soma localizationNeural network with 2 hidden layers

Supervision

Only on a small subsetSemantic deconvolutionFrasconi et al., Bioinformatics 2014

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224222 Purkinje cells automatically localized in the cerebellum of an L7-GFP mouse. Precision (true positives/all localized cells) is 95%, recall (true positives/all real cells) 97%~ 1 day of computation on a 16-cores workstation to analyze 120 GvoxelsWhole brain quantitative neuroanatomySilvestri et al., Frontiers in Neuroanatomy 2015

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Acknowledgements

Ludovico SilvestriIrene costantiniFrancesco S. Pavone

Antonino Paolo Di Giovanna