Integration of electrophysiological recordings with single-cell RNA-sequencing
The brain is comprised of a famously diverse menagerie of specialised cell types, enabling highly refined electrophysiological behaviour, as well as fulfilling brain nutrient needs and defence against pathogens. Functional specialization allows fine-tuning of circuit dynamics and decoupling of support functions such as energy supply, waste removal, and immune defence. Cells in the nervous system have historically been classified using location, morphology, target specificity, and electrophysiological characteristics, often combined with molecular markers 1,2. Systematic in situ hybridization has revealed extensive regional heterogeneity 3. However, none of these properties carry enough information to result in a definitive cell type identification 4.
The morphology, excitability, connectivity and neurotransmitter utilization of individual neurons underlie the distinct computations each neuronal circuit can perform in the nervous system 1,2. Thus, the identification of distinct subclasses of neurons remains a key challenge in neuroscience. Neuronal taxonomy based on a combination of developmental, morphological and neurophysiological traits is well accepted 5. These classification systems primarily rely on candidate marker analysis by a mixture of patch-clamp electrophysiology and single-cell semiquantitative PCR (qPCR) 6. More recently, advances in single-cell RNA sequencing (RNA-seq) in the central nervous system led to the identification of novel cell types 7,8.
Combining patch-clamp electrophysiology and post-hoc morphological reconstructions with the resolution of quantitative RNA-seq in single neurons would present a potentially critical advance for neuronal classification as it can resolve transcriptome-wide variations in gene expression to reveal cell type-specific determinants of neuronal cytoarchitecture and biophysical properties.
Historically, there has been considerable interest in focusing on a neuron’s complement of ion channels it expresses, and in finding genetic signatures that correlate with a firing phenotype in general. This led to the development of post-recording, single-cell profiling techniques, in which the cytosol of recorded cells is aspirated through the patch pipette, collected in a buffer, and the mRNA is subsequently isolated (patch/aspirate) 9,10.
Before the development of these technologies, studies of gene expression and function in the brain were restricted to targeted assay of a relatively small number of genes for any given study. Now it is possible to obtain a more panoramic view of gene expression, and potentially to understand the molecular underpinnings of brain function from the viewpoint of gene networks rather than from a viewpoint dominated by the effects of single genes. However, for a given experimental condition, gene expression changes occurring in rare cell types may go undetected, as they contribute to only a small fraction of the total tissue RNA. Hence, directly measuring the transcriptomes of specific cell types is crucial to understanding the intracellular gene networks that underlie cellular phenotypes.
Cell type-specific transcriptomics requires completion of four tasks (Figure 1):
- Cell type of interest must be identified and (typically) labelled
- RNA from the targeted cells must be extricated from that in surrounding cell types
- RNA is amplified
- Isolated sequences must be identified through sequencing or hybridization
Briefly, cell types are commonly identified by electrophysiological properties from acute brain slices using whole-cell patch-clamp recordings, by projection target using retrograde or anterograde tracers, by cell type-specific markers using immunostaining, or through transgenic labelling approaches.
Figure 1: Typical workflow of a cell type-specific transcriptional profiling experiment. Each node represents a key step and the arrows indicate the sequence of steps. Purification methods are indicated by coloured nodes. Notice that there are multiple possible paths for some methods 11.
Patch-seq is a term used for performing RNA-seq on the same neurons that are characterized by patch-clamp electrophysiology 12 (Figure 2).
- After testing the neurons in a series of current-clamp and voltage-clamp protocols within 20–25 min, their entire somatic compartment is aspirated into the recording pipette.
- By applying a continuum of positive voltage pulses (to membrane potential +20 mV from holding potential −5 mV, each 5 ms in length, at 5-ms intervals) the loss of RNA is reduced by most efficiently holding negatively charged RNA molecules in the pipette solution.
- The samples (0.8–0.9 μl) are ejected into lysis buffer (0.6 μl).
- RNA-seq is then performed.
Figure 2: Workflow diagram of Patch-seq procedures. (a) Coronal cutting plane of a mouse brain to access the somatosensory cortex. (b) Ex vivo brain slice anatomy with the somatosensory cortex highlighted in yellow and orange. (c) Whole-cell patch-clamp recording of DsRed+/GFP+ dual-tagged interneurons. (d) Aspiration of neuronal somata was followed by square voltage pulses from −5 mV (holding potential) to +20 mV, while maintaining negative pressure. (e) The sample was expelled into lysis buffer, which allowed for in-tube reverse transcription by PCR. (f) Single-cell RNA sequencing performed on an Illumina Hiseq2000 instrument 12.
Mapping neuronal identities on single-cell RNA-seq data sets
The brain undoubtedly exhibits the highest level of cellular heterogeneity 13, and contains a large variety of neurons that differ in their morphology, connectivity, biophysical parameters and molecular phenotypes 3,5. The taxonomy for neurons dates back to the first pioneers of neuroanatomy (e.g., Cajal and Golgi), who exclusively used morphological features, such as the size and topography of axonal and dendritic arbours, for classification and is now based on a wide array of neurophysiology, advanced histochemistry and RNA analyses 3. Nevertheless, reliance on known candidate marks continued to dominate the literature.
Electrophysiology is inherently limited in throughput. Consequently, the molecular classification of neurons from small and/or heterogeneous groups of cells is challenging because of the resulting low statistical power. Patch-seq data can be associated to large data sets, allowing the electrophysiological properties to be accurately aligned to molecularly defined neuronal subclasses 6. Even though this approach is not mandatory for neuronal classification, the increasing availability of reference data sets for major brain regions will enhance overall classification accuracy in small-sized sample populations.
Although the current majority of cell type-specific transcriptomic data comes from microarray studies, RNA-Seq is clearly the next frontier. RNA-Seq affords a more straightforward ability to study genetic variation, uncover previously uncharacterized transcriptional start and stop sites and splice variants, and discover novel noncoding RNAs 14. However, before performing either microarray or RNA-Seq experiments, the considerable obstacle of tissue heterogeneity must be overcome.
The efficiency of mRNA capture in Patch-seq is lower than that in single-cell RNA-seq on dissociated tissues. However, it is still sufficient to efficiently sample even genes of low expression because of its extremely low rate of false-positive identification7,15. Moreover, the combination of Patch-seq with transgenic mouse technologies might facilitate positive cell identification. These methods, together with the progressive decoding of regional heterogeneity in the nervous system through large-scale RNA-seq databases 16, can increase the stringency of neuronal classification. Such reference atlases, once available, will allow for precise hierarchical landscapes to be built even when cell numbers from patch-clamp electrophysiology experiments are limited. However, Patch-seq can stand alone and give much more complete and accurate information about gene expression (∼2,000 genes per cell) in selectively probed cell contingents, compared to previous methods (e.g., qPCR for 10–20 genes/cell) 17.
Patch-seq samples somatic material upon aspiration 12. In neurons, dendrites and axons occupy large spaces and their intracellular volume is considerable. Therefore, one may argue that Patch-seq misses many mRNAs that are preferentially targeted to distant domains of axons or dendrites. Although some mRNA is actively transported into neurites48, this does not mean that those mRNA species are absent from the soma. On the contrary, most (if not all) mRNA species are more abundant in the soma than in neurites, and there is not a single known case of an mRNA that is localized exclusively outside the soma. As a result, sequencing the soma content can be expected to give a representative view of mRNA expressed by a neuron, although without information on which mRNAs are efficiently transported into the neurites.
Another technical element that needs to be tightly controlled is the length of electrophysiology recordings because electrical stimuli might alter the transcriptome. Typically, 20-25-minute electrophysiology protocols are used because the lifetimes of most mRNA molecules are on the order of many hours (median: 9 h), with none known to be shorter than 2 hours. The quickest transcriptional response known in any setting is the induction of immediate-early genes (e.g., c-Fos), which can be detectable after 30 min, but peaks at 3 hours. Thus, the impact of patch-clamp recordings that occur on a timescale shorter than 1 h can be expected to have minimal impact on the RNA transcriptome.
In conclusion, Patch-seq can be expected to facilitate the characterization of transcriptome-wide changes in many experimental settings, thus contributing to a better understanding of fundamental physiological and pathological processes.
More broadly, single-cell RNA-seq is a powerful tool for physiologists, which may assist with whole-brain and even whole-organism cell type discovery and characterization 16. Such data will deepen our understanding of the regulatory basis of cellular identity, in development, neurodegenerative disease, and regenerative medicine.
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