Development of the prethalamus is crucial for thalamocortical projection formationand is regulated by Olig2
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE56389 | GEO | 2014/06/04
SECONDARY ACCESSION(S): PRJNA243094
REPOSITORIES: GEO
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Project description:Thalamocortical axons pass through the prethalamus in the first step of their neural circuit formation Although it has been supposed that the prethalamus is an intermediate target for thalamocortical projection formation, much less is known about the molecular mechanisms of this targeting. The samples were grouped into 2 sample types: Olig2-KO and the wild-type. RNA was purified from the E13.5 ventral forebrain from Olig2-KO (Olig2-/-) and the wild-type (Olig2+/+). RNA samples were prepared by pooling RNAs extracted from 3 brains to constitute one replicate and each sample type was prepared in five replicates. cRNA hybridization probes were hybridized to Affymetrix Mouse Whole Genome MOE430 2.0 arrays. The R-CRAN statistical language and Bioconductor software package (Gentleman et al., 2004) were used throughout the expression analyses. Raw image files were processed using Affymetrix GCOS and the Microarray Suite (MAS) 5.0 algorithm. The probe signal levels were quantile-normalized, summarized and then transformed to the log 2 expression using GCRMA (gcrma R-package, Irizarry et al., 2003). Gene filtering was performed using the Wilcoxon signed-rank test to select probe sets that were “consistently expressed” (p <0.04) in at least one group (mas5calls R-package; Bioconductor). All samples showed good quality control according to MAS5.0 guidelines and the AffyQC report (R-package; Bioconductor).
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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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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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