RT Journal Article
SR Electronic
T1 High-throughput mapping of single-cell molecular and projection architecture of neurons by retrograde barcoded labelling
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.16.444258
DO 10.1101/2021.05.16.444258
A1 Peibo Xu
A1 Jian Peng
A1 Tingli Yuan
A1 Zhaoqin Chen
A1 Ziyan Wu
A1 Ting Li
A1 Xiaodong Li
A1 Luyue Wang
A1 Wu Wei
A1 Chengyu T. Li
A1 Zhen-Ge Luo
A1 Yuejun Chen
YR 2021
UL http://biorxiv.org/content/early/2021/05/17/2021.05.16.444258.abstract
AB Deciphering mesoscopic connectivity of the mammalian brain is a pivotal step in neuroscience. Most imaging-based conventional neuroanatomical tracing methods identify area-to-area or sparse single neuronal labeling information. Although recently developed barcode-based connectomics has been able to map a large number of single-neuron projections efficiently, there is a missing link in single-cell connectome and transcriptome. Here, combining single-cell RNA sequencing technology, we established a retro-AAV barcode-based multiplexed tracing method called MEGRE-seq (Multiplexed projEction neuRons retroGrade barcodE), which can resolve projectome and transcriptome of source neurons simultaneously. Using the ventromedial prefrontal cortex (vmPFC) as a proof- of-concept neocortical region, we investigated projection patterns of its excitatory neurons targeting five canonical brain regions, as well as corresponding transcriptional profiles. Dedicated, bifurcated or collateral projection patterns were inferred by digital projectome. In combination with simultaneously recovered transcriptome, we find that certain projection pattern has a preferential layer or neuron subtype bias. Further, we fitted single-neuron two-modal data into a machine learning-based model and delineated gene importance by each projection target. In summary, we anticipate that the new multiplexed digital connectome technique is potential to understand the organizing principle of the neural circuit by linking projectome and transcriptome.HighlightsMERGE-seq recovers single-neuron transcriptome and projectome simultaneouslyMERGE-seq partitions projection pattern into neuronal layer and subtypeMERGE-seq bridges projection pattern to differentially expressed genesMachine learning models interpret relationship between digital projectome and gene expressionCompeting Interest StatementThe authors have declared no competing interest.