Patch-seq: Past, Present, and Future

Abstract

Single-cell transcriptomic approaches are revolutionizing neuroscience. Integrating this wealth of data with morphology and physiology, for the comprehensive study of neuronal biology, requires multiplexing gene expression data with complementary techniques. To meet this need, multiple groups in parallel have developed "Patch-seq," a modification of whole-cell patch-clamp protocols that enables mRNA sequencing of cell contents after electrophysiological recordings from individual neurons and morphologic reconstruction of the same cells. In this review, we first outline the critical technical developments that enabled robust Patch-seq experimental efforts and analytical solutions to interpret the rich multimodal data generated. We then review recent applications of Patch-seq that address novel and long-standing questions in neuroscience. These include the following: (1) targeted study of specific neuronal populations based on their anatomic location, functional properties, lineage, or a combination of these factors; (2) the compilation and integration of multimodal cell type atlases; and (3) the investigation of the molecular basis of morphologic and functional diversity. Finally, we highlight potential opportunities for further technical development and lines of research that may benefit from implementing the Patch-seq technique. As a multimodal approach at the intersection of molecular neurobiology and physiology, Patch-seq is uniquely positioned to directly link gene expression to brain function.

Keywords: electrophysiology; multi-modal; neuronal morphology; patch-clamp; single cell; transcriptomics.

Figure 1.

Schematic workflows of Patch-seq using various experimental model systems. A, On acute brain slices, electrophysiological recordings are performed on identified neurons, and the cell processes are filled with dye for subsequent morphologic reconstruction. During sample collection, most of the cytoplasmic contents are aspirated (generally including the nucleus) and transferred to an individual tube containing lysis buffer. The single-cell transcriptomes are then obtained using scRNAseq protocols. B, Electrophysiological recordings can also be performed on neuronal cultures (e.g., iPSC-derived neurons). Simultaneous, live imaging of the recorded neuron is possible if the recording pipette contains a fluorescent dye. After recording and imaging, the whole cell is collected using the patch pipette and the transcriptome obtained via scRNAseq. C, In vivo patch-clamp recordings are used to study the responses of individual neurons to sensory stimuli. Using the patch pipette, each neuron can then be collected and processed for scRNAseq and for in vivo Patch-seq.

Figure 2.

Diagrams of typical analytic workflows for Patch-seq experiments. A, Patch-seq allows the direct targeting of identified cells of interest (e.g., genetically labeled, retrogradely labeled by targeting the projection site or by their unique responses to a stimulus). B, The gene expression profile of Patch-seq samples can be used to map cells onto a reference scRNAseq dataset, thus assigning a multimodal phenotype to transcriptomically identified cell clusters. C, Multimodal Patch-seq data can be used to train a machine learning algorithm that can be used to predict the functional state of cells collected in independent scRNAseq datasets. D, Patch-seq data can be used to identify gene expression patterns that correlate with morphoelectric properties and other phenotypes.