Linking single-neuron morphology and gene expression using deep learning

使用深度学习将单神经元形态与基因表达联系起来

• 批准号: 10534571
• 负责人: Hojae Lee
• 金额: $ 3.91万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10534571
• 关键词: Axon; BRAIN initiative; Benchmarking; Brain; Cell Nucleus; Cells; Cellular Morphology; Censuses; Characteristics; Cholinergic Receptors; Complex; Data; Data Set; Databases; Development; Electrophysiology (science); Eye; Fibrinogen; Gene Expression; Gene Expression Profile; Genes; Growth; High-Throughput Nucleotide Sequencing; Image; Individual; Learning; Link; Measurement; Measures; Methods; Modality; Modeling; Morphology; Motor Cortex; Myelin Sheath; Neurons; Neurophysiology - biologic function; Phenotype; Property; Role; Semantics; Software Tools; Structure; Taxonomy; Techniques; Testing; Time; Training; Variant; brain cell; cell type; deep learning; fluorescence imaging; generative adversarial network; insight; learning network; microscopic imaging; multimodality; network models; neural circuit; novel; nuclear imaging; public database; relating to nervous system; single-cell RNA sequencing; transcriptome; transcriptomics

Abstract Characterizing neuronal cell identity in terms of transcriptomics, electrophysiology, and morphology is an essential component for understanding neural circuits and function. A multi-modal understanding of cell-to-cell variation in both single-cell transcriptional profiles and morphological phenotypes is needed to understand functional characteristics and the emergence of complexities in the brain. High-throughput single-cell measurements of neuronal gene expression are available, but the relationship between morphology and gene expression is not well explored due to the challenge of measuring both modalities from the same cells. We hypothesize that gene expression influences neuronal morphology, and thus that single-cell gene expression can be used to predict single-cell morphology. We propose to leverage recent advances in deep generative models to predict the distribution of single-cell morphology images from single-cell gene expression. We will: (1) develop deep generative to learn the relationship between single-cell gene expression and morphology; (2) train MorphGAN to generate morphologies for unseen neuronal single-cell gene expression profiles.; and (3) identify morphological axes of variation and key genes that predict morphology using MorphGAN. Completion of these aims will produce publicly available software tools and a public database of predicted single-cell neuron morphology images. Ultimately, linking transcriptomic and morphological characteristics of single neurons would be invaluable in capturing the diversity of brain cells and delineating neuronal cell types.

摘要 在转录组学、电生理学和形态学方面表征神经元细胞身份是一个重要的研究领域。 了解神经回路和功能的重要组成部分。对细胞到细胞的多模式理解 需要了解单细胞转录谱和形态表型的变化 功能特征和大脑复杂性的出现。高通量单细胞 神经元基因表达的测量是可用的，但是形态学和基因表达之间的关系是不确定的。 由于测量来自相同细胞的两种模式的挑战，没有很好地探索表达。我们 假设基因表达影响神经元形态，因此单细胞基因表达 可用于预测单细胞形态。我们建议利用最近在深度生成方面的进展， 模型来预测单细胞基因表达的单细胞形态图像的分布。 我们将：（1）开发深层生成，以了解单细胞基因表达与 形态学;（2）训练MorphGAN生成看不见的神经元单细胞基因表达的形态 轮廓。以及（3）使用以下方法识别形态变异轴和预测形态的关键基因： MorphGAN。 这些目标的完成将产生公开可用的软件工具和预测的公共数据库。 单细胞神经元形态学图像。最终，将转录组学和形态学特征联系起来， 单个神经元在捕捉脑细胞的多样性和描绘神经元细胞类型方面将是无价的。