Virally encoded connectivity transgenic overlay RNA sequencing (VECTORseq) defines projection neurons involved in sensorimotor integration

Behavior arises from concerted activity throughout the brain. Consequently, a major focus of modern neuroscience is defining the physiology and behavioral roles of projection neurons linking different brain areas. Single-cell RNA sequencing has facilitated these efforts by revealing molecular determinants of cellular physiology and markers that enable genetically targeted perturbations such as optogenetics, but existing methods for sequencing of defined projection populations are low-throughput, painstaking, and costly. We developed a straightforward, multiplexed approach, Virally Encoded Connectivity Transgenic Overlay RNA sequencing (VECTORseq). VECTORseq repurposes commercial retrogradely infecting viruses typically used to express functional transgenes, e.g., recombinases and fluorescent proteins, by treating viral transgene mRNA as barcodes within single-cell datasets. VECTORseq is compatible with different viral families, resolves multiple populations with different projection targets in one sequencing run, and identifies cortical and subcortical excitatory and inhibitory projection populations. Our study provides a roadmap for high-throughput identification of neuronal subtypes based on connectivity.

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Classifying Drosophila Olfactory Projection Neuron Subtypes by Single-cell RNA Sequencing

10.1101/145045 ◽

2017 ◽

Cited By ~ 2

Author(s):

Hongjie Li ◽

Felix Horns ◽

Bing Wu ◽

Qijing Xie ◽

Jiefu Li ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Neuronal Cell ◽

Projection Neuron ◽

Projection Neurons ◽

Open Questions ◽

Olfactory Glomeruli ◽

Single Cell Rna Sequencing ◽

Genes Encoding ◽

Circuit Assembly

AbstractHow a neuronal cell type is defined and how this relates to its transcriptome are still open questions. The Drosophila olfactory projection neurons (PNs) are among the best-characterized neuronal types: Different PN classes target dendrites to distinct olfactory glomeruli and PNs of the same class exhibit indistinguishable anatomical and physiological properties. Using single-cell RNA-sequencing, we comprehensively characterized the transcriptomes of 40 PN classes and unequivocally identified transcriptomes for 6 classes. We found a new lineage-specific transcription factor that instructs PN dendrite targeting. Transcriptomes of closely-related PN classes exhibit the largest difference during circuit assembly, but become indistinguishable in adults, suggesting that neuronal subtype diversity peaks during development. Genes encoding transcription factors and cell-surface molecules are the most differentially expressed, indicating their central roles in specifying neuronal identity. Finally, we show that PNs use highly redundant combinatorial molecular codes to distinguish subtypes, enabling robust specification of cell identity and circuit assembly.

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Model-based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior

10.1101/2020.12.26.424452 ◽

2020 ◽

Author(s):

Jin-Hong Du ◽

Ming Gao ◽

Jingshu Wang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Developmental Trajectories ◽

Probabilistic Method ◽

Synthetic Data ◽

Developmental Trajectory ◽

Projection Neurons ◽

Single Cell Rna Sequencing ◽

Mixture Prior ◽

Inference Methods

AbstractTrajectory inference methods analyze thousands of cells from single-cell sequencing technologies and computationally infer their developmental trajectories. Though many tools have been developed for trajectory inference, most of them lack a coherent statistical model and reliable uncertainty quantification. In this paper, we present VITAE, a probabilistic method combining a latent hierarchical mixture model with variational autoencoders to infer trajectories from posterior approximations. VITAE is computationally scalable and can adjust for confounding covariates to integrate multiple datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. We also apply VITAE to jointly analyze two single-cell RNA sequencing datasets on mouse neocortex. Our results suggest that VITAE can successfully uncover a shared developmental trajectory of the projection neurons and reliably order cells from both datasets along the inferred trajectory.

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