Interdepartmental Neuroscience Program, Yale University School, New Haven, Connecticut 06511.
Department of Neurology, Yale University School, New Haven, Connecticut 06511.
J Neurosci. 2023 Nov 22;43(47):7929-7945. doi: 10.1523/JNEUROSCI.0811-22.2023.
The corticospinal tract (CST) forms a central part of the voluntary motor apparatus in all mammals. Thus, injury, disease, and subsequent degeneration within this pathway result in chronic irreversible functional deficits. Current strategies to repair the damaged CST are suboptimal in part because of underexplored molecular heterogeneity within the adult tract. Here, we combine spinal retrograde CST tracing with single-cell RNA sequencing (scRNAseq) in adult male and female mice to index corticospinal neuron (CSN) subtypes that differentially innervate the forelimb and hindlimb. We exploit publicly available datasets to confer anatomic specialization among CSNs and show that CSNs segregate not only along the forelimb and hindlimb axis but also by supraspinal axon collateralization. These anatomically defined transcriptional data allow us to use machine learning tools to build classifiers that discriminate between CSNs and cortical layer 2/3 and nonspinally terminating layer 5 neurons in M1 and separately identify limb-specific CSNs. Using these tools, CSN subtypes can be differentially identified to study postnatal patterning of the CST , leveraged to screen for novel limb-specific axon growth survival and growth activators , and ultimately exploited to repair the damaged CST after injury and disease. Therapeutic interventions designed to repair the damaged CST after spinal cord injury have remained functionally suboptimal in part because of an incomplete understanding of the molecular heterogeneity among subclasses of CSNs. Here, we combine spinal retrograde labeling with scRNAseq and annotate a CSN index by the termination pattern of their primary axon in the cervical or lumbar spinal cord and supraspinal collateral terminal fields. Using machine learning we have confirmed the veracity of our CSN gene lists to train classifiers to identify CSNs among all classes of neurons in primary motor cortex to study the development, patterning, homeostasis, and response to injury and disease, and ultimately target streamlined repair strategies to this critical motor pathway.
皮质脊髓束(CST)构成了所有哺乳动物的自主运动器官的核心部分。因此,该通路内的损伤、疾病和随后的退行性变导致慢性、不可逆的功能缺陷。目前修复受损 CST 的策略并不理想,部分原因是对成人束内未充分探索的分子异质性。在这里,我们结合脊髓逆行 CST 示踪和成年雄性和雌性小鼠的单细胞 RNA 测序(scRNAseq),对皮质脊髓神经元(CSN)亚型进行索引,这些亚型对前肢和后肢进行不同的神经支配。我们利用公开可用的数据集来推断 CSN 之间的解剖学专业化,并表明 CSN 不仅沿着前肢和后肢轴分离,而且还沿着上运动轴突侧支化分离。这些解剖定义的转录数据使我们能够使用机器学习工具构建分类器,区分 M1 中的 CSN 和皮质层 2/3 和非脊髓终止的层 5 神经元,并分别识别肢体特异性 CSN。使用这些工具,可以区分 CSN 亚型以研究 CST 的出生后模式,利用这些工具筛选新型肢体特异性轴突生长存活和生长激活剂,并最终在损伤和疾病后修复受损的 CST。旨在修复脊髓损伤后受损 CST 的治疗干预在功能上仍然不尽如人意,部分原因是对 CSN 亚类之间的分子异质性缺乏全面了解。在这里,我们结合脊髓逆行标记和 scRNAseq,并通过其初级轴突在颈或腰脊髓中的终止模式和上运动轴突侧支终端场对 CSN 进行注释。我们使用机器学习验证了我们的 CSN 基因列表的真实性,以训练分类器来识别初级运动皮层中所有神经元类别的 CSN,以研究发育、模式形成、动态平衡以及对损伤和疾病的反应,并最终将简化的修复策略针对这一关键的运动通路。