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High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling

ABSTRACT: High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling

PROVIDER: PRJNA864622 | ENA |

REPOSITORIES: ENA

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High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling [scRNA-seq]

Project description:High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling [scRNA-seq]

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Project description:High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling [bulk RNA-seq]

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High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling [scRNA-seq]

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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High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labelling

Project description:This SuperSeries is composed of the SubSeries listed below.

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Single cell sequencing and retrograde barcode tracing reveal human DA neuron identities based on projection patterns in stem cell-derived grafts

Project description:Groundbreaking research in the last decade has definitively established human pluripotent stem cells (hPSCs) as a powerful source for the unlimited generation of dopamine (DA) neurons used in cell-based therapy in Parkinson’s disease (PD). However, DA neurons constitute a heterogeneous group of cells with different molecular identities and, consequently, different functions and innervation targets. Moreover, the inaccessibility of the human brain has so far hindered our understanding of the highly complex human DA system and, therefore, the design of DA neuron subtype-specific differentiation protocols for more effective cell replacement therapies. Currently, DA neurons – located in human ventral midbrain – can only be reliably distinguished based on their projection patterns. Here, we used hPSCs to derive and then transplant DA neurons into a rat model of PD. After one year of transplantation, we performed single nucleus RNA sequencing (snRNAseq) of ~80,000 human nuclei to transcriptionally define cell type composition and obtained a comprehensive map of DA neuron molecular identities. Exploiting the fact that transplanted DA neurons innervated and integrated within the host circuitry, we then combined snRNAseq with axonal retrograde AAV barcoding to trace the projection patterns of molecularly distinct human DA neuron subtypes in the grafts. We thus defined human DA neuron transcriptional profiles based on their target-specific outgrowth in the host brain. The resulting datasets provide an unprecedented resource both for elucidating the molecular basis of human DA neuron diversity as well as for developing more targeted cell-based therapies, with major implications for outcomes of PD patients.

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