Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25418866.fastq.gz | Fastqsanger.gz | |||
| SRR25418867_1.fastq.gz | Fastqsanger.gz | |||
| SRR25418867_2.fastq.gz | Fastqsanger.gz |
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Project description:Deciphering patterns of connectivity between neurons in the mammalian brain is a critical step toward understanding brain function. Conventional imaging based neuroanatomical tracing methods identify area-to-area or sparse neuron-to-neuron connectivity patterns, but with extremely limited throughput. Recently developed barcode-based connectomics methods can efficiently map large numbers of single-neuron projections, but linking these data to single-cell transcriptomics remains a challenge. Here, we established a retro-AAV barcode-based multiplexed tracing method called MERGE-seq (Multiplexed projection neuRons retroGrade barcodE sequencing), which is capable of simultaneously characterizing the projectome and transcriptome at the single neuron level. We uncovered dedicated and collateral projection patterns of ventromedial prefrontal cortex (vmPFC) neurons to five downstream targets (AI, DMS, BLA, MD and LH). We found that projection-defined vmPFC neurons are molecularly heterogeneous, which are composed of different neuronal subtypes. We further identified transcriptional signatures of various dedicated and bifurcated vmPFC neurons, and verified Pou3f1 as the marker gene of neurons sending collateral axons to DMS and LH. Finally, we fitted our single-neuron connectome/transcriptome data into a machine learning-based model and revealed groups of genes that were predictive of certain projection pattern. In summary, we have developed a new multiplexed technique whose paired connectome and gene expression data can help reveal organizational principles that form neural circuits and process information.
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