Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing

Abstract

Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to achieve circuit mapping at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNAseq, and 4,130 retrogradely labeled cells and 2,914 transsynaptically labeled cells in situ. We determined the transcriptomic identities of rabies virus-infected cells robustly using both single-cell RNA-seq and in situ sequencing. We then distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus thus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.

Brain-wide monosynaptic connectivity mapping with ROInet-seq

“…At the loss of spatial context, single-cell RNA-seq could be applied to sequence BC-ΔG-RV+ neurons across the full brain. Yet, similar to our own experience, (Zhang et al 2023) report that insufficient cell recovery in dissociation makes reconstruction of neuronal networks too sparse. Further, the quality of RV-infected transcriptomes was compromised, likely also because of RV-infected neurons’ sensitivity to the dissociation protocol.…”

“…Chen et al 2019; Kebschull et al 2016), several groups recently developed a variant of ΔG-RV that carries genetic barcodes to increase the number of distinguishable markers that uniquely label individual networks in a single experiment. This allowed to track glial interactions (Clark et al 2021) and monosynaptic retrograde neuronal connectivity in vitro (Saunders et al 2022) and in vivo (Zhang et al 2023). For example, (Zhang et al 2023) used in situ -sequencing to read network barcode sequences, and characterize the cells’ molecular identities, besides providing valuable spatial context of single cells in the network.…”

“…This allowed to track glial interactions (Clark et al 2021) and monosynaptic retrograde neuronal connectivity in vitro (Saunders et al 2022) and in vivo (Zhang et al 2023). For example, (Zhang et al 2023) used in situ -sequencing to read network barcode sequences, and characterize the cells’ molecular identities, besides providing valuable spatial context of single cells in the network. This method is practically limited to small areas or local networks, and highly costly to implement at scale.…”

Deciphering the Genetic Code of Neuronal Type Connectivity: A Bilinear Modeling Approach

“…To address this, future research endeavors will also explore adaptations of our model to other available datasets, such as those that combine single-cell transcriptomic profiling with long-range neuronal projection mapping [89, 90]. Furthermore, our model is amenable to integration with trans-synaptic tracer-based sequencing methods [91, 92], expanding its utility in studies where detailed connectomic information is limited. Pursuing these avenues is pivotal in broadening the model’s utility and ensuring its relevance across a wider spectrum of brain connectivity research, making it an invaluable tool in the quest to unravel the complexities of neural circuitry.…”

“…To enhance the model’s fidelity and applicability, we propose several advancements. First, we advocate for the integration of auxiliary data types, including electrophysiological data, neuron tracing data, and an array of omics data such as proteomics and epigenetics data, to augment and enrich the model’s training base [49, 91, 92, 93, 94]. These data modalities offer complementary insights into neuronal function and connectivity, providing valuable context that can inform and refine the model’s predictions.…”