Scalable tools for consistent identification of neuronal cell types in mouse and human

项目摘要 拟议的工作将通过以下方式解决我们对神经元表型和细胞类型的理解中的一个关键空白： 开发机器学习算法和基于云的软件，以整合多种模态 表征小鼠和人类皮层神经元的大型和不断增长的数据集。通过优化和 创新地使用可能不完整的数据，并强调自动形态学表征， 所提出的工具将能够从转录组学、解剖学或基因组学的角度对神经元进行更丰富和一致的表征。 电生理分析 虽然BICCN资助的大规模细胞类型研究项目依赖于独特神经元身份的概念， 它决定了细胞在不同观察模式下的表型， 生理学、解剖学和分子表征仍然难以捉摸。尽管这些大型项目 成功地生成了广泛的多模态数据集，缺乏原则性，准确性和广泛性。 可用的计算对齐和推理工具对整个系统的成功提出了障碍。 程序.第二个问题是，尽管解剖学表征是经典的方法， 了解细胞类型，在通量方面明显落后于分子和生理学方法。 本文提出的研究旨在通过建立在耦合自动编码器上来解决对准问题 的方法，它提出了一个有效的优化框架为中心的无处不在的神经元身份。 重要的是，所提出的软件可以利用不完全表征的数据点，这在 实践，产生统一的可视化和分析抽象的神经元身份。这个工具既灵活 (e.g.,特征集可以改变）和可扩展（例如，可以增加更多的观察方式， 对齐）。对齐的表示使得能够跨神经元群体的一致聚类。 不同的观察方式，这是现代神经科学中的一个紧迫问题。 我们建议用端到端的计算管道来解决解剖吞吐量问题，从原始的 局部神经元主干的图像与解剖学描述符，其可以容易地由解剖学描述符对齐和解释。 耦合自动编码器软件。通过利用我们广泛的黄金标准手动重建，我们将训练 有监督的深度人工神经网络，以在稀疏标记场景中分割神经元乔木。浓设定 训练的例子，加上算法的创新，将赋予这种自动化的上级概括性。 分割工具，加速科学的光学显微镜为基础的研究。

Project Summary The proposed work will address a critical gap in our understanding of neuronal phenotypes and cell types by developing machine learning algorithms and cloud-based software for the integration of multiple modality characterizations large and growing datasets of cortical neurons in mouse and human. Through optimal and innovative use of potentially incomplete data and emphasis on automated morphological characterization, the proposed tools will enable richer and consistent characterizations of neurons from transcriptomic, anatomical, or electrophysiological profiling. While large-scale, BICCN-funded cell type research programs rely on the notion of unique neuronal identity which determines the cell’s phenotype across different observation modalities, overarching agreements across physiological, anatomical and molecular characterizations remain elusive. Although these large-scale programs succeeded in generating extensive multiple modality datasets, the lack of principled, accurate and widely available computational alignment and inference tools presents a roadblock to the success of the overall program. A second issue is that anatomical characterization, despite being the classical approach to understanding cell types, lags significantly behind molecular and physiological methods in terms of throughput. The research proposed here aims to address the alignment problem by building on the coupled autoencoder approach, which presents an efficient optimization framework centered on the ubiquity of neuronal identity. Importantly, the proposed software can utilize incompletely characterized data points, which is common in practice, to produce unified visualization and analysis of abstract neuronal identity. This tool will be both flexible (e.g., the feature set can be changed) and extensible (e.g., more observation modalities can be added for joint alignment). The aligned representations enable consistent clustering of the neuronal population across the different observation modalities, which is a pressing problem in modern neuroscience. We propose to address the anatomical throughput issue with an end-to-end computational pipeline, from the raw image of local neuronal arbors to the anatomical descriptor that can be readily aligned and interpreted by the coupled autoencoder software. By utilizing our extensive gold-standard manual reconstructions, we will train supervised deep artificial neural networks to segment neuronal arbors in sparse labeling scenarios. The rich set of training examples, together with algorithmic innovations, will endow superior generalizability of this automated segmentation tool, accelerating science for light microscopy-based studies.