Anatomical diversity of the adult corticospinal tract revealed by single cell transcriptional profiling.

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 sub-optimal in part due to underexplored molecular heterogeneity within the adult tract. Here we combine spinal retrograde CST tracing with single-cell RNA sequencing 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 anatomical 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 non-spinally terminating layer 5 neurons in M1, and separately identify limb specific CSNs. Utilizing these tools, CSN subtypes can be differentially identified to study postnatal patterning of the CST in vivo , leveraged to screen for novel limb-specific axon growth survival and growth activators in vitro , and ultimately exploited to repair the damaged CST after injury and disease. Significance Statement Therapeutic interventions designed to repair the damaged corticospinal tract (CST) after spinal cord injury have remained functionally sub-optimal in part due to an incomplete understanding of the molecular heterogeneity among subclasses of corticospinal tract neurons (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 M1 to study the development, patterning, homeostasis, and response to injury and disease, and ultimately target streamlined repair strategies to this critical motor pathway.