Morpho Mso 1300 E2 Driver Download
Delia Sagastume
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Knowledge of the cell-type-specific composition of the brain is useful in order to understand the role of each cell type as part of the network. Here, we estimated the composition of the whole cortex in terms of well characterized morphological and electrophysiological inhibitory neuron types (me-types). We derived probabilistic me-type densities from an existing atlas of molecularly defined cell-type densities in the mouse cortex. We used a well-established me-type classification from rat somatosensory cortex to populate the cortex. These me-types were well characterized morphologically and electrophysiologically but they lacked molecular marker identity labels. To extrapolate this missing information, we employed an additional dataset from the Allen Institute for Brain Science containing molecular identity as well as morphological and electrophysiological data for mouse cortical neurons. We first built a latent space based on a number of comparable morphological and electrical features common to both data sources. We then identified 19 morpho-electrical clusters that merged neurons from both datasets while being molecularly homogeneous. The resulting clusters best mirror the molecular identity classification solely using available morpho-electrical features. Finally, we stochastically assigned a molecular identity to a me-type neuron based on the latent space cluster it was assigned to. The resulting mapping was used to derive inhibitory me-types densities in the cortex.
Copyright: 2023 Roussel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Code is under open sourcing process and is publicly available at -features-to-mo-ID-mapping. Downloading of Allen Institute for Brain Science data was not incorporated in the code and is left at users discretion due to license issues. We recommend to use allensdk package ( ) to obtain the Allen Institute for Brain Science data. More details are provided in the README.md file of our public repository.
Here, we developed the method and applied it to assign a molecular identity to BBP morpho-electrical models (me-models) using a dataset from the Allen Institute for Brain Science (AIBS) [13], which provides a molecular identity to its mouse cells, bridging the gap. Since both datasets come from different species (rat and mouse), we proceeded through several normalization steps to ensure maximum alignment between the datasets to better predict common molecular identities. We then validated the approach using labels common to both datasets to verify homogeneity in cross-species resulting clusters. Finally, we used the results to perform a broader prediction: the composition of cortical brain regions in terms of established morphological and/or electrical types from recordings of marker densities. We combined the resulting mapping with molecular marker densities extracted from the BBCAv2 to infer BBP me-type densities across the whole cortex.
The AIBS mouse dataset [13] served as a reference to link morpho-electrical features to molecular identity. We focused on inhibitory neurons from layer 1 to layer 6 with morphological reconstructions and electrophysiological recordings made available through the AllenSDK [19]. We collected ME data of 157 layer 2/3 to layer 6 neurons, and another set of 15 layer 1 neurons for a total of 172 neurons. We did not incorporate layer 1 neurons into the mapping process because of the low number of exemplars and because layer 1 has a specific cellular composition of types not mapped in this study [20]. Additionally, we retrieved the corresponding morphological, electrical and morpho-electrical labels from the supplementary information of the Gouwens et al. 2019 paper [13]. We defined the molecular identity (ID) as the expressed molecular marker among Lamp5, VIP, PV or SST. These four markers are known to describe a complete partition of interneurons into non-overlapping subpopulations [5,10,13,18,21]. In their supplementary information, Gouwens and colleagues provided a mapping between their native me-types classification and the 4 molecular markers Lamp5, VIP, PV and SST [13]. Thus, we could easily attribute a molecular ID to the neurons.
BBP data is both electrical and morphological. The electrical data consist of a collection of electrophysiological features (e-features). Each e-feature (e.g. action potential amplitude, inter-spike interval, etc.) was previously extracted from a series of electrophysiological recordings of neurons that were expertly labeled using 10 electrical types (e-types, e.g. cNAD, bIR) [7]. Thus, for each feature of a given e-type, we have a mean and a standard deviation that characterizes the distribution of values. A morphological dataset of 250 morphologies for inhibitory neurons from layer 2/3 to layer 6 allowed classification into nine morphological types (m-types) with representatives in each layer. We had an additional set of 85 morphologies classified into six m-types for layer 1. In total, we collected 335 morphologies classified into 15 inhibitory m-types (38 different m-types if we distinct them per layer).
Since the AIBS dataset comes from adult mouse visual cortex and the BBP dataset comes from juvenile rat somatosensory cortex we expect that most of the variance across datasets is due to differences in animal species and, to a lesser extent, differences in cortical areas and developmental variability (juvenile or adult). We thus need to normalize electrophysiological and morphological data across datasets so that the extracted features can be compared.
An effective normalization process needs some invariants that are used to build a framework in which data will be expressed. An invariant is a mathematical object that is conserved after transformation. In our case, the transformation is the change in animal model and/or cortical region. Since morphologies are geometrical descriptions of neurons, spatial cues that are conserved across animals and cortical areas should be used as invariants. Fortunately, in mammals, the cortex is consistently divided into layers that are homologous across species [22,23]. We thus used the layer separation of the cortex as the invariant between datasets and expressed the spatial coordinates of reconstructed morphologies in relation to these layer limits. Layer limits were derived using layers thicknesses values taken from literature [7,23,24].
A. Morphological features extraction. Original morphologies(i) were realigned to be contained within cortical limits. X and Y coordinates were normalized by cortical depth and corresponding layer thickness, respectively resulting in normalized morphologies(ii). Density moments of order 0, 1 and 2 were extracted separately for axon and dendrites and for each layer individually(iii). Moments of order 0 can be related to total neurite length (bar chart on the right side of the panel) whereas moments of order 1 and 2 can be linked to mean and standard deviation of the density (ellipses). Axon in blue, dendrites in orange. B. Electrophysiological recordings from step currents were used to extract a collection of electrophysiological features. C. Extracted features were stored in tables and used to produce a knowledge graph (D) grouping all pieces of information that can be extracted from both datasets. Nodes are cell IDs, morpho-electrical features or other pieces of information and edges highlight the existence of a link between these pieces of information. E. A parameter α controlling the weighting between m and e features was introduced when computing the me-features matrix (i). We optimized α to find the value that yielded the most homogeneous clusters in terms of molecular IDs (ii). F. We used ward hierarchical clustering results using the optimal α and (G) we established the optimal clustering distance dopt as a compromise between averaged cluster homogeneity with respect to molecular IDs (blue curve) and ratio of BBP cells belonging to mixed clusters (red curve). H. We proceeded to probabilistic mapping using datasets native labels (see Methods) and the new assigned clusters.
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