Accessible Published by De Gruyter November 6, 2019

Single-cell RNA-Sequencing in Neuroscience

Simone Mayer ORCID logo, Shokoufeh Khakipoor, Maxim A. Drömer and Daniel A. Cozetto
From the journal Neuroforum

Summary

Technical innovations in the last decade have allowed to sequence transcriptomes of single cells. Single-cell RNA-sequencing (scRNA-seq) has since then opened the window to a deeper understanding of cellular identity and is becoming a widely used method in molecular biology. In neuroscience, scRNA-seq has broad applications, for example in determining cellular diversity in different brain regions and in revealing transcriptomic variations across brain disorders. The method consists of several steps: isolation and lysis of single cells, reverse transcription of RNAs, amplification of cDNAs, and next-generation sequencing. The large datasets can subsequently be analysed using different bioinformatic tools to deduce biological meaning. Current developments aim to integrate scRNA-seq into cellular network analysis through multimodal analysis, spatial localisation and perturbation experiments, in order to understand brain physiology and pathology.

Zusammenfassung

Durch technische Innovationen wurde es in den letzten zehn Jahren möglich, das Transkriptom einzelner Zellen zu sequenzieren. Die Einzelzell-RNA-Sequenzierung (scRNA-seq) liefert ein tieferes Verständnis der zellulären Identität und ist inzwischen eine Standardmethode der Molekularbiologie geworden. In den Neurowissenschaften wird scRNA-seq beispielsweise zur Bestimmung der zellulären Diversität verschiedener Hirnregionen und zur Analyse transkriptomischer Variationen bei Hirnerkrankungen angewandt. Die folgenden Schritte sind nötig: Isolierung und Lyse einzelner Zellen, reverse Transkription von RNAs, Amplifikation von cDNAs und Next-Generation-Sequenzierung. Die Datensätze werden anschließend mit bioinformatischen Methoden analysiert, um sie zu interpretieren. Aktuelle Entwicklungen zielen darauf ab, scRNA-seq durch multimodale Analyse, räumliche Lokalisierung und Störungsexperimente in die zelluläre Netzwerkanalyse zu integrieren, um Neurophysiologie und -pathologie besser zu verstehen.

Introduction

The physiological, morphological, and molecular properties of an individual cell define its role in neural networks. Molecular properties contribute to physiological and morphological features and can be determined in high throughput by analysing the global ensemble of mRNA transcripts, also known as the transcriptome. For many years, analysing the transcriptomes of specific brain regions was made possible through microarrays and bulk transcriptomic analysis (Figure 1A). These studies have been extremely informative, for example, in revealing transcriptomic differences between brain regions, biological states, or healthy and diseased tissue samples. However, bulk RNA sequencing requires homogenization of entire tissue samples, masking differences between cells. Addressing cellular diversity is crucial for understanding the brain, where dozens of cell types are found in close proximity. In 2009, the first single-cell RNA-sequencing (scRNA-seq) protocol was developed (Tang et al., 2009). Since then, scRNA-seq has transformed several research fields rendering it an essential tool in modern molecular biology. In this review, we aim to introduce the technical foundations of scRNA-seq and highlight some of its applications in basic and translational neuroscience research. In doing so, we hope to orient those new to the field and also those eager to start planning scRNA-seq experiments.

Fig. 1: Overview of the development of scRNA-seq in the last decade. (A) Overview of Bulk RNA-seq workflow, (B) overview of single-cell RNA-seq workflow, (C) schematic of current developments in single cell analysis.

Fig. 1:

Overview of the development of scRNA-seq in the last decade. (A) Overview of Bulk RNA-seq workflow, (B) overview of single-cell RNA-seq workflow, (C) schematic of current developments in single cell analysis.

Technical basics: Single-cell RNA-Sequencing Workflow

Over the past decade, scRNA-seq has undergone a process of commercialisation and diversification (Chen et al., 2019; Hwang et al., 2018; Lafzi et al., 2018). Here, we will discuss the basic steps shared by all methods (Figure 1B), an understanding of which is essential when selecting a method for one’s own experimental question.

1 Dissociation

Single cells are isolated from their tissue context, removing their contacts to each other and the extracellular matrix. A combination of mechanical and enzymatic strategies is used to generate a single-cell suspension (Hedlund and Deng, 2018). Depending on the experimental question, in some studies, authors also include fluorescence activated cell sorting (FACS) in order to isolate cell populations of interest. The isolation of neural cells can be especially challenging due to their high degree of connectivity in networks and their complex morphologies. Therefore, locally translated dendritic and axonal RNAs may be lost (Cajigas et al., 2012).

Additionally, stress induced by dissociation and FACS may influence gene expression leading to upregulation of immediate early genes and heat shock proteins introducing technical artifacts (van den Brink et al., 2017). Dissociation protocols may be adapted to reduce stress, for example, by a transcription inhibitor (Hrvatin et al., 2018; Wu et al., 2017). Alternatively, transcriptomes can be determined from frozen tissue samples through extraction of single nuclei (snRNA-Seq) (Grindberg et al., 2013; Habib et al., 2016; Lacar et al., 2016).

2 Single-cell capture

Single cells can be captured using different methods that are characterized by specific advantages (for a comprehensive review on this topic, see (Wang et al., 2019)). In short, three main systems are commercially available: microfluidics (e. g. Fluidigm C1, (Pollen et al., 2014)), nanowell-based system (e. g. Clontech iCell8 (Goldstein et al., 2017)), and nano-droplets (e. g. 10x Genomics (Klein et al., 2015; Macosko et al., 2015)). 10x Genomics is currently widely used due to its high throughput of several thousand single cells analysed per experiment.

3 Preparation for the sequencing

After capturing single cells, cells are lysed, and reverse transcription of the RNA is performed. Oligo (dT)-primers are used to generate cDNA of all poly-A-tagged RNA, which is then further amplified through different PCR reactions. Amplification by PCR may cause bias as the exponential amplification distorts the frequency of transcripts towards shorter and GC-rich templates. Finally, sequencing libraries are produced by adding adapters at the 3’ and the 5’ end of the cDNA for subsequent Next Generation Sequencing (most commonly Illumina Sequencing).

4 Bioinformatics analysis

The raw sequencing data is processed and analysed using bioinformatics tools to extract meaningful biological information (AlJanahi et al., 2018; Chen et al., 2019; Hwang et al., 2018; Luecken and Theis, 2019; Nguyen and Holmes, 2019). While there is ongoing development, open-source software packages, including Seurat (Butler et al., 2018) and Scanpy (Wolf et al., 2018) (written in R and Python programming language respectively) provide a good starting point for such analyses. First, sequencing reads are mapped to the genome, and quality control measures are implemented (AlJanahi et al., 2018; Luecken and Theis, 2019). The data is high dimensional since every gene in each cell has a specific expression value, thus making analysis mandatory for biological interpretation. In clustering approaches, cells with similar gene expression are grouped in clusters and mapped to known cell types through marker gene expression (Chen et al., 2019; Hwang et al., 2018). As these clusters appear in high-dimensional spaces, it is not possible to visualise them without dimensionality reduction (Nguyen and Holmes, 2019). Simple projections onto certain coordinates lead to loss of information about similarity, which is often better preserved by more complex projections such as t-stochastic neighbour embedding (t-SNE) which provides two-dimensional plots as shown in Figure 1B (van der Maaten and Hinton, 2008). As these transformations condense information from the original data, it is crucial to interpret with caution. An alternative analysis strategy is to investigate the progression of cell types in a lineage or a disease pathology with pseudotime analysis where an emphasis is put on the continuity of cell states in the population (Hwang et al., 2018).

Due to the various artifacts that can be introduced in any of the steps from dissociation to bioinformatics analysis, validation by orthogonal methods is crucial. Validation can be performed for example through qRT-PCR, in situ hybridization or immunohistochemistry approaches in the tissue, or functionally through perturbation experiments.

Applications in basic neuroscience research – atlasing and beyond

scRNA-seq has been used to address diverse questions in basic neuroscience. For instance, scRNA-seq has been used to catalogue cell types in different brain areas (Mayer et al., 2018; Nowakowski et al., 2017; Zeisel et al., 2015).

The successful use of scRNA-seq in characterising all cell types in a tissue on the molecular level has led to the initiation of the Cell Atlas, a global effort aiming to catalogue all the cells in the human body including the brain through scRNA-seq (Regev et al., 2017). Current efforts are seeking to mimic human brain development and physiology in vitro through the generation of organoids (Kadoshima et al., 2013; Lancaster et al., 2013; Pasca et al., 2015). The characterisation and benchmarking of organoid protocols has heavily relied on scRNA-seq since this method allows the molecular analysis of cells in a heterogeneous tissue (Camp et al., 2015; Pollen et al., 2019; Velasco et al., 2019).

In most studies, transcriptomic findings remain to be linked to the physiological function of cells. One study has already demonstrated the power of scRNA-seq by showing transcriptomic responses to light exposure in dark-reared mice in all cell types, including those associated with the vasculature (Hrvatin et al., 2018). In another study, scRNA-seq has revealed cell-type-specific responses to morphine treatment and revealed substantial changes in glial cells (Avey et al., 2018). Further research on short and long-term consequences in gene expression in response to external stimuli will contribute to a deeper understanding of neural plasticity and learning processes.

Current developments in single-cell analysis

New technologies have been developed in order to integrate single-cell transcriptomics into traditional methods allowing the implementation of more complex experimental designs (Figure 1C). Here, we highlight recent developments that integrate scRNA-seq into other experimental approaches.

Multimodal analysis

One criticism of scRNA-seq is that the transcriptome is an incomplete proxy of cell type or cell state. Instead, cellular classification by other features that are more permanent such as the epigenetic landscape, or have a more direct functional importance, such as the proteome, would be preferable. In the last years, several developments have started to enable multimodal analysis, which means that more than one molecular feature is analysed at the single-cell level in high throughput. Now methods are becoming available to combine the transcriptome with the epigenome (Lake et al., 2018) or the proteome (Schenk et al., 2019). Additionally, it is possible to analyse intracellular signalling cascade activation, together with the transcriptome in fixed cells (Gerlach et al., 2019).

Integrating physiology with scRNA-seq

Neuronal cells are organized in networks in order to realize complex behaviours and cognitive tasks. The individual physiological properties of a neuron are thus of great importance for its function. For a comprehensive understanding of neuronal function, combining electrophysiological recordings with scRNA-seq is therefore essential. In 2016, this feat was first achieved by combining patch-clamp recordings with scRNA-seq through the aspiration of the cytoplasm after the electrophysiological recordings and subsequent transcriptome analysis (Cadwell et al., 2016; Fuzik et al., 2016). This approach, called Patch-seq, combines classical physiological recordings with scRNA-seq as well as connectivity and morphology in intact tissue slices with low throughput (Pfeffer and Beltramo, 2017).

Traditionally, intracellular calcium levels are used as an indicator of neuronal activity and can be measured at single-cell resolution in tissue slices or live animals in order to examine activity in many cells in a neuronal network at the same time (Rochefort et al., 2008). Making use of a classical calcium indicator, we have recently developed an approach, where we combine calcium imaging with transcriptome analysis in dissociated cells using a microfluidic system (Mayer et al., 2019). Combining calcium imaging and scRNA-seq has allowed us to reach a higher throughput (approximately 30 cells per experiment or day) than Patch-Seq (Mayer et al., 2019). Moreover, the analysis of dissociated single cells has allowed us to analyse cell-autonomous physiological responses that do not rely on network activity. Our technique thus provides an example of multimodal single-cell analysis, where we monitor several cellular features, namely the responses to six different neurotransmitter receptor agonists and the single-cell transcriptomes at the same time (Mayer et al., 2019). We found that analysing physiological and molecular properties of single neuronal progenitor cells or immature neurons at the same time allowed us to gain a better understanding of the functional importance of transcriptomic cell type differences (Mayer et al., 2019). Further developments in this field will allow the integration of calcium imaging with scRNA-seq in the tissue context (Liu et al., 2018), thus integrating scRNA-seq with systems neuroscience.

Spatial transcriptomics

Spatial transcriptomic methods that determine RNA content in cells while preserving the tissue location have been developed in order to prevent dissociation biases and may also allow subsequent analysis of proteins through immunohistochemistry. There are several protocols available, some also commercially, which each come with their strengths and weaknesses (Eng et al., 2019; Salmen et al., 2018; Wang et al., 2018). When choosing the protocol for one’s experimental question, one should consider the spatial resolution, the number of genes studied, the sensitivity, and the throughput. Besides, it is also possible to analyze physical interactions between cells together with scRNA-seq in live tissue by using microdissection and mild dissociation (Boisset et al., 2018).

Perturbation experiments

Another advance is the possibility to perform gain and loss of function studies with the integration of CRISPR-Cas9 genome editing. This technique is especially powerful for lineage tracing in developmental biology studies, and has been applied to reveal unknown lineage connections in the brain and other organs (Alemany et al., 2018; Griffiths et al., 2018; Raj et al., 2018) and to discover molecular drivers for differentiation (Genga et al., 2019). CRISPR-based screening is also available commercially through 10x Genomics.

Clinical applications: Stratification of molecular pathology

Due to its strength in analysing molecular properties of single-cells in heterogeneous tissue with high dimensionality and throughput, scRNA-seq has proven to be beneficial in studying the molecular pathology of various disorders. For example, in a mouse model of Alzheimer’s disease, scRNA-seq has revealed that different microglial states exist (Keren-Shaul et al., 2017). snRNA-seq has opened a whole new toolbox for the analysis of human pathological processes at the molecular level, including neurological disorders (Habib et al., 2016). For instance, in human Alzheimer patient brain samples, differential gene expression was found in various sub-clusters of excitatory, inhibitory, and glial cells (Mathys et al., 2019). Cellular pathology of multiple sclerosis has also recently been investigated with snRNA-seq revealing pathological changes to specific subgroups of neurons and glial cells showing, for example, selective vulnerability of upper cortical layer excitatory neurons (Jakel et al., 2019; Schirmer et al., 2019). snRNA-seq of autism spectrum disorder samples has similarly revealed changes in upper layer excitatory neurons as well as microglial cells (Velmeshev et al., 2019). The unbiased analysis of all cell types thus reveals specific contributions of cell types that are often overlooked such as glial or senescent cells, but play an important role in disease (Baker and Petersen, 2018). These studies highlight the importance of stratification – different subtypes of cells are differentially involved in disease, and this understanding will allow identifying targets for drug development in the future.

The application of scRNA-seq to clinical research questions will have a significant impact on human health and society. On the European level, the vision of using and further developing single-cell methods has led to the launch of the LifeTime Initiative (lifetime-fetflagship.eu), which has as a mission to “Revolutionize healthcare by tracking, understanding, and treating human cells during diseases”.

Funding

Funder Name: Deutscher Akademischer Austauschdienst, Funder Id: http://dx.doi.org/10.13039/501100001655, Grant Number: IAESTE, 2019 (57423938)

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Published Online: 2019-11-06
Published in Print: 2019-11-26

© 2019 Walter de Gruyter GmbH, Berlin/Boston