Neuronal networks are at the core of the brain's vast computational capacities. Different strategies have each brought deep insights into their architecture. Yet, each method is limited in its capacity to resolve even simple subnetworks of single cells, due to the current compromise between throughput, resolution, and coverage. Viral projection tracing helps establish regional connectomes of mammalian brains. In combination with additional modification, the method can reveal cell-type-specific projection patterns. However, these maps lack single-neuron resolution. Monosynaptic connectivity tracing with glycoprotein (G)-deleted rabies virus (dG-RV) establishes retrograde synaptic connectivity, but imaging labeled networks cannot distinguish them at single-cell resolution.We developed ROInet-seq, an accessible spatial method using dG-RV . First, we generated genetically tagged (barcoded) viral particles, by inserting 21nt-long random sequences into the RV genome, which are amenable to readout by RNA-sequencing. We optimized library complexity and uniformity, such that detection of specific barcodes can reliably distinguish multiple infected neurons and their monosynaptic input networks. Then, we infected the cortex (SSp) or hippocampus (CA1), imaged the brain's full volume, registered and microdissected RV-infected regions of interest (ROI). We used RNA-seq to detect network barcodes for each ROI (ROInet-seq) and revealed monosynaptic input networks of single neurons. In the cortex, we found preserved regional network motives, including co-inputs to single cortical neurons from distant and local sites. Towards improved spatial resolution and simultaneous detection of the single-cells transcriptomes and networks, we applied commercial spatial transcriptomics assays on barcoded (G)-deleted rabies virus hippocampus and revealed details of the regions' local network architecture.