2023-02-15 スイス連邦工科大学ローザンヌ校(EPFL)
◆脳の細胞型別構成に関する知識は、ネットワークの一部としての各細胞型の役割を理解するのに役立ち、大規模な神経回路シミュレーションに取り組むのに必要であり、マウスの脳全体のデジタルモデルを正確に構築するというブルー・ブレインの長期的目標の鍵でもあるのです。それにもかかわらず、脳の細胞組成のグローバルな理解を得ることは、文献に内在する大きなばらつきだけでなく、脳を構成する多数の脳領域と細胞型のために、過度に複雑な作業となります。
◆2018年、EPFLのBlue Brainプロジェクトは、マウスの脳の組成の推定値を提供するセルアトラスの最初のモデルを発表しました。Blue Brain’s Cell Atlas(BBCAv1)のリリースにより、3Dデジタルアトラスが初めて、マウス脳の700以上の全領域における主要な細胞の種類、数、位置に関する情報を提供することになりました。BBCAv1では、各領域の神経細胞、結合組織細胞(グリア)、およびそれらのサブタイプの密度が、ナビゲーション可能で動的なフォーマットで表示され、研究者が新しいデータを提供できるようになっています。ブルー・ブレインの創設者でディレクターのヘンリー・マークラム教授は、「当時、マウスの脳の96%の領域に関する知識の大きなギャップを埋めるものでした」と語っています。
◆PLOS Computational Biology誌の2つの関連論文で発表された、セルアトラスの改良とBBCAv2の作成に使用された新しいツールと手法は、よく識別されたタイプを抑制性ニューロンのサブクラスにマッピングするために拡張され、脳組織をより正確にin silico再構成する道を開くものである。Blue Brain Cell Atlasのアップグレードに使用されたデータ、アルゴリズム、ソフトウェア、パイプラインの結果はすべて一般に公開されています。ブルー・ブレインの分子システムチームのリーダーであるDaniel Keller氏は、「このバージョンには4年間の研究が含まれており、シミュレーションに適した結果にするために、生体データからの制約が加えられています。シミュレーションに使用することで、さらに改良すべき領域を特定することができ、世代を重ねるごとに改善することができます」。
◆”このプロジェクトは、データ、ソフトウェア、ツールへのオープンアクセスにより、科学界を巻き込んで貢献することを目指しています。BBCAv2が様々な用途に使われることを期待しています」と、著者らは結んでいる。
<関連情報>
- https://actu.epfl.ch/news/new-release-of-blue-brain-project-atlas-sheds-li-2/
- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010058
- https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010739
大脳皮質抑制性神経細胞の形態電気的特徴と分子的同一性のマッピング Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons
Dimitri Rodarie ,Csaba Verasztó,Yann Roussel,Michael Reimann,Daniel Keller,Srikanth Ramaswamy,Henry Markram,Marc-Oliver Gewaltig
PLoS Computational Biology Published: January 5, 2023
DOI:https://doi.org/10.1371/journal.pcbi.1010058
Abstract
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.
Author summary
The computational abilities of the brain arise from its organization principles at the cellular level. One of these principles is the neuronal type composition over different regions. Since computational functions of neurons are best described by their morphological and electrophysiological properties, it is logical to use morpho-electrically defined cell types to describe brain composition. However, characterizing morpho-electrical properties of cells involve low-throughput techniques not very well suited to scan the whole brain. Thanks to recent progress on transcriptomic and immuno-staining techniques we are now able to get a more accurate snapshot of the mouse brain composition for molecularly defined cell types. How to link molecularly defined cell types with morpho-electrical cell types remains an open question. Several studies have explored this problem providing valuable three-modal datasets combining electrical, morphological and molecular properties of cortical neurons. The long-term goal of the Blue Brain Project (BBP) is to accurately model the mouse’s whole brain, which requires detailed biophysical models of neurons. Instead of going through the time-consuming process of producing detailed models from the three-modal datasets, we explored a time-saving method. We mapped the already available detailed morpho-electrical models from the BBP rat dataset to cells from a three-modal mouse dataset. We thus assigned a molecular identity to the neuron models allowing us to populate the whole mouse cortex with detailed neuron models.
異種データセットからマウス脳の細胞組成を推定する方法 A method to estimate the cellular composition of the mouse brain from heterogeneous datasets
Dimitri Rodarie ,Csaba Verasztó,Yann Roussel,Michael Reimann,Daniel Keller,Srikanth Ramaswamy,Henry Markram,Marc-Oliver Gewaltig
PLoS Computational Biology Published: December 21, 2022
DOI:https://doi.org/10.1371/journal.pcbi.1010739
Abstract
The mouse brain contains a rich diversity of inhibitory neuron types that have been characterized by their patterns of gene expression. However, it is still unclear how these cell types are distributed across the mouse brain. We developed a computational method to estimate the densities of different inhibitory neuron types across the mouse brain. Our method allows the unbiased integration of diverse and disparate datasets into one framework to predict inhibitory neuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron densities, gene expression data from in situ hybridization image stacks together with a wide range of values reported in the literature. Using constrained optimization, we derived coherent estimates of cell densities for the different inhibitory neuron types. We estimate that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. We find that our density estimations improve as more literature values are integrated. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of our pipeline are publicly available and update the Blue Brain Cell Atlas. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region.
Author summary
Obtaining a global understanding of the cellular composition of the brain is a very complex task, not only because of the great variability that exists between reports of similar counts but also because of the numerous brain regions and cell types that make up the brain.
Previously, we presented a model of a cell atlas, which provided an estimate of the densities of neurons, glia and their subtypes for each region in the mouse brain. Here, we describe an extension of this model to include more inhibitory neuron types. We collected estimates of inhibitory neuron counts from literature and built a framework to combine them into a consistent cell atlas. Using brain slice images, we also estimated inhibitory neuron density in regions where no literature data are available. We estimated that in the mouse brain 20.3% of all neurons are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. Our approach can be further extended to any other cell type and provides a resource to build tissue-level models of the rodent brain.
