Shack-Hartmann wave front measurements in cortical tissue for deconvolution of large three-dimensional mosaic transmitted light brightfield micrographs

We present a novel approach for deconvolution of 3D image stacks of cortical tissue taken by mosaic/optical-sectioning technology, using a transmitted light brightfield microscope. Mosaic/optical-sectioning offers the possibility of imaging large volumes (e.g. from cortical sections) on a millimetre scale at sub-micrometre resolution. However, a blurred contribution from out-of-focus light results in an image quality that usually prohibits 3D quantitative analysis. Such quantitative analysis is only possible after deblurring by deconvolution. The resulting image quality is strongly dependent on how accurate the point spread function used for deconvolution resembles the properties of the imaging system. Since direct measurement of the true point spread function is laborious and modelled point spread functions usually deviate from measured ones, we present a method of optimizing the microscope until it meets almost ideal imaging conditions. These conditions are validated by measuring the aberration function of the microscope and tissue using a Shack-Hartmann sensor. The analysis shows that cortical tissue from rat brains embedded in Mowiol and imaged by an oil-immersion objective can be regarded as having a homogeneous index of refraction. In addition, the amount of spherical aberration that is caused by the optics or the specimen is relatively low. Consequently the image formation is simplified to refraction between the embedding and immersion medium and to 3D diffraction at the finite entrance pupil of the objective. The resulting model point spread function is applied to the image stacks by linear or iterative deconvolution algorithms. For the presented dataset of large 3D images the linear approach proves to be superior. The linear deconvolution yields a significant improvement in signal-to-noise ratio and resolution. This novel approach allows a quantitative analysis of the cortical image stacks such as the reconstruction of biocytin-stained neuronal dendrites and axons

3D Reconstruction and Standardization of the Rat Vibrissal Cortex for Precise Registration of Single Neuron Morphology

Author Summary For studying the neural basis of perception and behavior, it would be ideal to directly monitor sensory-evoked excitation streams within neural circuits, at sub-cellular and millisecond resolution. To do so, reverse engineering approaches of reconstructing circuit anatomy and synaptic wiring have been suggested. The resulting anatomically realistic models may then allow for computer simulations (in silico experiments) of circuit function. A natural starting point for reconstructing neural circuits is a cortical column, which is thought to be an elementary functional unit of sensory cortices. In the vibrissal area of rodent somatosensory cortex, a cytoarchitectonic equivalent, designated as a ‘barrel column’, has been described. By reconstructing the 3D geometry of almost 1,000 barrel columns, we show that the somatotopic layout of the vibrissal cortex is highly preserved across animals. This allows generating a standard cortex and registering neuron morphologies, obtained from different experiments, to their ‘true’ location. Marking a crucial step towards reverse engineering of cortical circuits, the present study will allow estimating synaptic connectivity within an entire cortical area by structural overlap of registered axons and dendrites

The impact of neuron morphology on cortical network architecture

The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons, depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons’ axo-dendritic projections patterns, the packing density, and the cellular diversity of the neuropil. The higher these three factors are, the more recurrent is the topology of the network. Conversely, the lower these factors are, the more feedforward is the network’s topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular, and network scales from the specific morphological properties of its neuronal constituents

former title: A theory for the emergence of neocortical network architecture

Developmental programs that guide neurons and their neurites into specific subvolumes of the mammalian neocortex give rise to lifelong constraints for the formation of synaptic connections. To what degree do these constraints affect cortical wiring diagrams? Here we introduce an inverse modeling approach to show how cortical networks would appear if they were solely due to the spatial distributions of neurons and neurites. We find that neurite packing density and morphological diversity will inevitably translate into non-random pairwise and higher-order connectivity statistics. More importantly, we show that these non-random wiring properties are not arbitrary, but instead reflect the specific structural organization of the underlying neuropil. Our predictions are consistent with the empirically observed wiring specificity from subcellular to network scales. Thus, independent from learning and genetically encoded wiring rules, many of the properties that define the neocortex’ characteristic network architecture may emerge as a result of neuron and neurite development

Circuit-selective cell-autonomous regulation of inhibition in pyramidal neurons by Ste20-like kinase

Maintaining an appropriate balance between excitation and inhibition is critical for neuronal information processing. Cortical neurons can cell-autonomously adjust the inhibition they receive to individual levels of excitatory input, but the underlying mechanisms are unclear. We describe that Ste20-like kinase (SLK) mediates cell-autonomous regulation of excitation-inhibition balance in the thalamocortical feedforward circuit, but not in the feedback circuit. This effect is due to regulation of inhibition originating from parvalbumin-expressing interneurons, while inhibition via somatostatin-expressing interneurons is unaffected. Computational modeling shows that this mechanism promotes stable excitatory-inhibitory ratios across pyramidal cells and ensures robust and sparse coding. Patch-clamp RNA sequencing yields genes differentially regulated by SLK knockdown, as well as genes associated with excitation-inhibition balance participating in transsynaptic communication and cytoskeletal dynamics. These data identify a mechanism for cell-autonomous regulation of a specific inhibitory circuit that is critical to ensure that a majority of cortical pyramidal cells participate in information coding

Novel 3D Microscopic Analysis of Human Placental Villous Trees Reveals Unexpected Significance of Branching Angles

The villous trees of human placentas delineate the fetomaternal border and are complex three-dimensional (3D) structures. Thus far, they have primarily been analyzed as thin, two-dimensional (2D) histological sections. However, 2D sections cannot provide access to key aspects such as branching nodes and branch order. Using samples taken from 50 normal human placentas at birth, in the present study we show that analysis procedures for 3D reconstruction of neuronal dendritic trees can also be used for analyzing trees of human placentas. Nodes and their branches (e.g., branching hierarchy, branching angles, diameters, and lengths of branches) can be efficiently measured in whole-mount preparations of isolated villous trees using high-end light microscopy. Such data differ qualitatively from the data obtainable from histological sections and go substantially beyond the morphological horizon of such histological data. Unexpectedly, branching angles of terminal branches of villous trees varied inversely with the fetoplacental weight ratio, a widely used clinical parameter. Since branching angles have never before been determined in the human placenta, this result requires further detailed studies in order to fully understand its impact

Automatic alignment of stacks of filament data

We present a fast and robust method for the alignment of image stacks containing filamentous structures. Such stacks are usually obtained by physical sectioning a specimen, followed by an optical sectioning of each slice. For reconstruction, the filaments have to be traced and the sub-volumes aligned. Our algorithm takes traced filaments as input and matches their endpoints to find the optimal transform. We show that our method is able to quickly and accurately align sub-volumes containing neuronal processes, acquired using brightfield microscopy. Our method also makes it possible to align traced microtubuli, obtained from electron tomography data, which are extremely difficult to align manually

Number and Laminar Distribution of Neurons in a Thalamocortical Projection Column of Rat Vibrissal Cortex

This is the second article in a series of three studies that investigate the anatomical determinants of thalamocortical (TC) input to excitatory neurons in a cortical column of rat primary somatosensory cortex (S1). Here, we report the number and distribution of NeuN-positive neurons within the C2, D2, and D3 TC projection columns in P27 rat somatosensory barrel cortex based on an exhaustive identification of 89 834 somata in a 1.15 mm3 volume of cortex. A single column contained 19 109 ± 444 neurons (17 560 ± 399 when normalized to a standard-size projection column). Neuron density differences along the vertical column axis delineated “cytoarchitectonic” layers. The resulting neuron numbers per layer in the average column were 63 ± 10 (L1), 2039 ± 524 (L2), 3735 ± 905 (L3), 4447 ± 439 (L4), 1737 ± 251 (L5A), 2235 ± 99 (L5B), 3786 ± 168 (L6A), and 1066 ± 170 (L6B). These data were then used to derive the layer-specific action potential (AP) output of a projection column. The estimates confirmed previous reports suggesting that the ensembles of spiny L4 and thick-tufted pyramidal neurons emit the major fraction of APs of a column. The number of APs evoked in a column by a sensory stimulus (principal whisker deflection) was estimated as 4441 within 100 ms post-stimulus