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Institute
Even one century after Santiago Ramón y Cajal’s groundbreaking contribu- tions to neuroscience, one of the most fundamental questions in the field is still largely open, namely understanding how the shape of a dendrite is adapted to its specific biological function. A systematic investigation of this problem is challenging both technically and conceptually because neurons have diverse genetic, molecular, morphological, connectional and functional properties.
In the light of the preceding, dendritic arborisation (da) neurons of the Drosophila melanogaster larva PNS have proven to be an excellent model system for the study of such growth and patterning processes. Structure and function in these cell classes are intimately intertwined, as class type-specific dendritic arbour differentiation processes are required to satisfy a given phys- iological need. Also, there is a remarkable genetic toolkit that enables one to selectively and reproducibly label, image and manipulate each one of these sensory neuron classes. In this thesis, I address the aforementioned open problem by linking single-cell patterning, information processing and wiring optimisation in sensory da neurons to behaviour in Drosophila larva.
In particular, I study Class I ventral peripherical dendritic arborisation (c1vpda) neurons. These are a class of proprioceptive neurons that relay information on the position of the larva’s body back to the CNS during crawling behaviour to assure proper locomotion. Their stereotypical comb- like shaped dendritic branches spread along the body-wall, and they get noticeably deformed during crawling behaviour. The bending of the den- dritic branches is hypothesised to be a possible mechanism to transduce the mechanosensory inputs arising from cuticle folding. Interestingly, c1vpda neurons do not necessarily satisfy optimal wiring constraints since they are required to pattern into a specific shape to fulfil their function. Therefore, I considered the da system to study how the specific functional requirements may be combined with optimal wiring constraints during development.
Although the molecular machinery of dendrite patterning in c1vpda neurons is well studied, the precise elaboration of the comb-like shaped dendrites of these cells remains elusive. Moreover, even though a lot of work has been put into the description and quantification of growth processes of the nervous system, there are still few solid and standardised models of arbour staging and patterning. Importantly, the defining parameters that determine the dendrite elaboration program that in turn is responsible for creating the final arbour morphology are still unknown. As a result, unraveling possible universal stages of dendrite elaboration shared between different model systems and cell types is challenging.
Thus, in order to understand the development of the fine regulation of branch outgrowth that leads to the observed terminal arbour morphology in the mature cell, I collected in vivo, long-term, non-invasive high temporal res- olution time-lapse recordings of dendritic trees during the differentiation process in the embryo and its maturation phase in the larva. For further analysis, I developed new algorithms that quantified the structural changes in dendrite morphology in the time-lapse videos. My approach provides a framework to analyse such developmental data, or any dataset comprising continuous morphological dynamical processes in an unbiased way. Using these newly developed methods, I examined the development of a sample of c1vpda cells and identified five stages of differentiation in these data: initial stem polarization, extension, pruning, stabilization, and isometric stretching during larval stages.
The beginning of the growth process is marked by the polarisation of the main stem. Subsequently, during the extension phase, branches emerge interstitially from the existing main stem. Later, higher-order branches sprout from pre-existing lateral branches, increasing arbour complexity. This is followed by a pruning stage where developmental intermediate dendritic branches are removed. This step leads to a spatial rearrangement of the dendritic tree. The end of the pruning step is followed by a stabilisation period where arbour morphology remains virtually unaltered in the embryo. After hatching, c1vpda dendrites experience an isometric scaling, with their branching complexity and pattern being invariant across all larval stages.
After dissecting the c1vpda dendrites spatiotemporal differentiation process, I established a link between dendritic shape and behaviour. I measured intra- cellular Ca++ activity in the dendrite branches of l1 larvae during forward locomotion, while simultaneously recording branch deformation using a dual genetic line. I reported that post-embryonic c1vpda dendrites Ca++ responses increased in freely crawling larvae. Furthermore, I showed strong correlations between Ca++ signal and deformation of the comb-like dendritic ranches during body-wall contractions.
Then, using a geometrical model, I provided evidence that the pruning stage could reorganise the dendrite morphology to maximise mechanosensory re- sponses during body wall contraction. I showed that the angle orientation of each side branch correlates with the bending curvature and thus with the me- chanical displacement of the cell membrane during locomotion. During the pruning phase, I observed a preferential reduction of less efficient branches with low bending curvature, influencing the mechanisms of dendritic sig- nal integration of c1vpda sensory neurons. I proceeded to quantify branch dynamics at single tip resolution during pruning, providing evidence that a simple random pruning mechanism is sufficient to remodel the tree structure compatible with the observed way.
I used these time-lapse data to constrain a new computational noisy growth model with random pruning based on optimal wiring principles. This model is able to generate highly realistic synthetic c1vpda morphologies. The model furthermore requires few parameters to generate highly accurate temporal development trajectories and morphologies at single-cell level. Utilising this data and model enabled me to investigate upon the hypothesis that a noisy dendrite growth and random pruning mechanism synergise to achieve den- dritic trees efficient in terms of both wiring and function. My findings show how single neurons can create functionally specialised dendrites while min- imising wiring costs, elucidating how general principles of self-organisation may be involved in the generation of these structures.
Connectomic analysis of apical dendrite innervation in pyramidal neurons of mouse cerebral cortex
(2020)
The central goal of this study was to generate synapse-resolution maps of local and long-range innervation on apical dendrites (AD) in mouse cerebral cortex. We used three-dimensional electron microscopy (3D-EM) to first measure the cell-type specific balance in the excitatory and inhibitory input on ADs. Further, we found two inhibitory axon populations with preference for apical dendrites originating from layer 2 and 3/5. Additionally, we used a combination of large-scale volumetric light and electron microscopy to investigate the innervation preference of long-range cortical projections onto ADs. To generate such large-scale 3D-EM datasets, we also developed a software package to automate aberration adjustment.
The balance of excitation and inhibition defines the computational properties of neurons. We, therefore, generated 6 datasets and annotated 26,548 excitatory and inhibitory synapses to map the relative inhibitory strength on the AD of pyramidal neurons in layers 1 and 2 (L1 and 2) of the cortex. We found consistent and cell-type specific patterns of inhibitory strength along the apical dendrite of L2-5 pyramidal neurons in primary somatosensory (S1), secondary visual (V2), posterior parietal (PPC) and anterior cingulate (ACC) cortices. L2 and L5 pyramidal neurons had inhibitory hot-zones at their main bifurcation and distal apical dendrite tuft, respectively. In contrast, L3 neurons had a baseline (~10%) level of inhibition along their apical dendrite. As controls, we quantified the effect of synapse strength (size), dendrite diameter, AD classification and synapse identification methods on the cell-type specific synapse densities. To classify L5 pyramidal subtypes, we performed hierarchical clustering using morphological properties that were described to differentiate slender- and thick-tufted L5 neurons.
We also investigated the distance to soma as a predictor of fractional inhibition around the main bifurcation of apical dendrites. Interestingly, we found a strong exponential relationship that was absent in density of either synapse type. This suggests a distance dependent control mechanism designed specifically for the balance (in synapse numbers) of excitation and inhibition.
Next, we focused on the inhibitory innervation preference for apical dendrite of pyramidal neuron. We, therefore, annotated 5,448 output synapses of AD-targeting inhibitory axons and found two populations specific for either L2 or L3/5 apical dendrites. Together with previous findings on preferential innervation of sub-cellular structures by inhibitory axons, this suggests two distinct inhibitory circuits for control of AD activity in L2 vs. deep-layer pyramidal neurons. This innervation preference was surprisingly consistent across S1, V2, PPC and ACC cortices.
3D-EM data acquisition is a laborious process that is made easier and more popular everyday by technical progress in the laboratory and industrial settings. To make data acquisition robust using our custom-built 3D-EM microscopes, an automatic aberration software was implemented to adjust the objective lens and the stigmators of the electron microscope. This method was used in multiple month-long experiments across 2 microscopes and 10 datasets. The aberration adjustment used the reduction in image details (high-frequency elements) to estimate the level of deviation from optimal focus and stigmator parameters. However, large objects in EM micrographs such as blood vessel and nuclei cross-sections generated anomalous results. We, therefore, added image processing routines based on edge detection combined with morphological operations to exclude such large objects.
Finally, we performed a correlative three-dimensional (3D) light (LM) and electron (EM) microscopy experiment to map the long-range primary visual (V1) and secondary motor (M2) cortical input to ADs in layer 1 of PPC using the “FluoEM” approach. This method allows for identification of the long-range source of projection axons in EM volumes without the need for EM-dense label conversion or heat-induced markings. The long-range source of an axon in EM is identified based on the fluorescent protein that is expressed in its LM counterpart. In comparison to M2 input, Long-range axons from V1 had a higher tendency to target L3 pyramidal neurons in PPC according to our preliminary analysis. In combination with the difference observed in the synapse composition of L2 and L3 apical dendrites, this suggests the need for separate functional and structural analysis of L2 and 3 pyramidal neurons.