小鼠视觉皮层中单细胞转录组与投射组的连接
小鼠视觉皮层中单细胞转录组与投射组的连接
作者： 小柯机器人 发布时间：2026/7/3 11:21:14
本期文章：《自然》：Online/在线发表
小鼠视觉皮层中单细胞转录组与投射组的连接，这一成果由美国艾伦脑科学研究所Staci A. Sorensen研究组经过不懈努力而取得。相关论文发表在2026年7月1日出版的《自然》杂志上。
为了解决这个问题，该课题组研究人员收集了来自同主题视觉皮层的两个数据集，包括：（1）1,528个兴奋性Patch-seq神经元，从每个细胞收集局部形态学，电生理和转录组学数据；（2）341个兴奋性全神经元形态学。从Patch-seq数据中，课题组定义了17种形态电转录组类型，并构建了一个多步分类器，将细胞类型分配与整个神经元形态结合起来，并询问跨模态关系。
该研究团队发现形态电转录组类型内部和之间的转录组变异与形态和电生理表型相对应。此外，这些基因表达模式，以及细胞的解剖位置，可以预测单个神经元的投射目标。该研究组观察到第5层脑内和脑外神经元新的多模态细胞类型特征，并揭示了它们的轴突电路，包括半球间脑内投射。通过这种方法，研究人员建立了一个全面、综合的皮层、兴奋性神经元类型分类，并创建了一个高维细胞类型分类系统，该系统可以扩展到整个大脑，甚至可能跨物种。
研究人员表示，哺乳动物的大脑由具有不同功能的不同类型的神经元组成。最近的单细胞RNA测序方法导致了转录组定义的细胞类型的全脑分类学。通过将转录组谱与局部形态和电生理特性联系起来，Patch-seq实验增强了这些细胞类型的描述。然而，将转录组特性与远程轴突投射联系起来仍然是一个主要的未解决的挑战。
附：英文原文
Title: Connecting single-cell transcriptomes to projectomes in the mouse visual cortex
Author: Sorensen, Staci A., Gouwens, Nathan W., Wang, Yun, Mallory, Matt, Budzillo, Agata, Dalley, Rachel, Lee, Brian R., Gliko, Olga, Kuo, Hsien-chi, Kuang, Xiuli, Mann, Rusty, Ahmadinia, Leila, Alfiler, Lauren, Baftizadeh, Fahimeh, Baker, Katherine S., Bannick, Sarah, Bertagnolli, Darren, Bickley, Kris, Bohn, Phil, Bomben, Jasmine, Bowman, Chris, Boyer, Gabriella, Brouner, Krissy, Brown, Dillan, Cahoon, Alex, Chen, Natalie, Chen, Chao, Chen, Kai, Chvilicek, Maggie, Collman, Forrest, Daigle, Tanya L., Dawes, Tim, de Frates, Rebecca, Dee, Nick, DePartee, Maxwell, Egdorf, Tom, El-Hifnawi, Laila, Enstrom, Rachel, Esposito, Luke, Farrell, Colin, Gala, Rohan, Gamlin, Clare, Gary, Amanda, Glomb, Andrew, Gerasymchuk, Olena, Goldy, Jeff, Gu, Hong, Hadley, Kristen, Hawrylycz, Mike, Henry, Alex, Hill, Dijon, Hirokawa, Karla E., Huang, Zili, Johnson, Katelyn, Juneau, Zoe, Kebede, Sara, Kim, Lisa, Kruse, Lauren, Lee, Changkyu, Leon, Arielle L., Lesnar, Phil, Lheureux, Quinn, Li, Anan, Li, Yaoyao, Liang, Elizabeth, Link, Katie, Maxwell, Michelle, McGraw, Medea, McMillen, Delissa A., Mukora, Alice, Ng, Lindsay, Ochoa, Thomas, Oldre, Aaron, Park, Daniel, Pom, Christina Alice
Issue&Volume: 2026-07-01
Abstract: The mammalian brain consists of diverse neuron types with various functions. Recent single-cell RNA sequencing approaches have led to a whole-brain taxonomy of transcriptomically defined cell types1. Patch-seq experiments augment these cell-type descriptions by linking transcriptomic profiles with local morphological and electrophysiological properties2,3,4,5,6,7. However, linking transcriptomic identities to long-range axonal projections remains a major unresolved challenge. Here, to address this, we collected two datasets from the mouse visual cortex consisting of: (1) 1,528 excitatory Patch-seq neurons, with local morphological, electrophysiological and transcriptomic data collected from each cell, and (2) 341 excitatory, whole-neuron morphologies. From the Patch-seq data, we defined 17 morphoelectric–transcriptomic types and built a multistep classifier to integrate cell-type assignments with whole-neuron morphology and interrogate cross-modality relationships. We find that transcriptomic variation within and across morphoelectric–transcriptomic types corresponds with morphological and electrophysiological phenotypes. In addition, these gene expression patterns, along with the anatomical location of the cell, can be used to predict projection targets of individual neurons. We observed novel multimodal cell-type signatures for layer 5 intratelencephalic and extratelencephalic neurons and shed new light on their axonal circuitry, including interhemispheric intratelencephalic projections. With this approach, we establish a comprehensive, integrated taxonomy of cortical, excitatory neuron types, and create a system for high-dimensional cell-type classification that can be extended to the whole brain and potentially across species.
DOI: 10.1038/s41586-026-10424-8
Source: https://www.nature.com/articles/s41586-026-10424-8