We still lack a detailed map of the anatomical disposition of neurons in the human brain. A complete map would be an important step for deeply understanding the brain function, providing anatomical information useful to decipher the neuronal pattern in healthy and diseased conditions. Here, we present several important advances towards this goal, obtained by combining a new clearing method, advanced Light Sheet Microscopy and automated machine-learning based image analysis. We perform volumetric imaging of large sequentially stained human brain slices, labelled for two different neuronal markers NeuN and GAD67, discriminating the inhibitory population and reconstructing the brain connectivity.
We still lack a detailed map of the anatomical disposition of neurons in the human brain. A complete map would be an important step for deeply understanding the brain function, providing anatomical information useful to decipher the neuronal pattern in healthy and diseased conditions. Here, we present several important advances towards this goal, obtained by combining a new clearing method, advanced Light Sheet Microscopy and automated machine-learning based image analysis. We perform volumetric imaging of large sequentially stained human brain slices, labelled for two different neuronal markers NeuN and GAD67, discriminating the inhibitory population and reconstructing the brain connectivity.
Deciphering brain architecture at a system level requires the ability to quantitatively map its structure with cellular and subcellular resolution. Besides posing significant challenges to current optical microscopy methods, this ambitious goal requires the development of a new generation of tools to make sense of the huge number of raw images generated, which can easily exceed several TeraBytes for a single sample. We present an integrated pipeline enabling transformation of the acquired dataset from a collection of voxel gray levels to a semantic representation of the sample. This pipeline starts with a software for image stitching that computes global optimal alignment of the 3D tiles. The fused volume is then accessed virtually by means of a dedicated API (Application Programming Interface). The virtually fused volume is then processed to extract meaningful information. We demonstrate two complementary approaches based on deep convolutional networks. In one case, a 3D conv-net is used to ‘semantically deconvolve’ the image, allowing accurate localization of neuronal bodies with standard clustering algorithms (e.g. mean shift). The scalability of this approach is demonstrated by mapping the spatial distribution of different neuronal populations in a whole mouse brain with singlecell resolution. To go beyond simple localization, we exploited a 2D conv-net estimating for each pixel the probability of being part of a neuron. The output of the net is then processed with a contour finding algorithm, obtaining reliable segmentation of cell morphology. This information can be used to classify neurons, expanding the potential of chemical labeling strategies.
The three-dimensional reconstruction of large volumes of the human neural networks at cellular resolution is one of the biggest challenges of our days. Commonly, fine slices of samples marked with colorimetric techniques are individually imaged. This approach in addition to being time-consuming does not consider space cell organization, leading to loss of information. The aim of this work was to develop a methodology that allows analyzing the cytoarchitecture of the human brain in three dimensions at high resolution. In particular, we exploit the possibility of combining high-resolution 3D imaging techniques with clearing methodologies. We successfully integrate the SWITCH immunohistochemistry technique with the TDE clearing method to image a large volume of human brain tissue with two-photon fluorescence microscopy. In conclusion, this new approach enables to characterize large human brain specimens with high-resolution optical techniques, giving the possibility to expand the histological studies to the third dimension.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.