Dissect brain complexity at the single-cell level
Understanding the brain requires in-depth knowledge of its components. Advanced single-cell sequencing technologies allow researchers to explore the secrets of this complex and mysterious organ in unprecedented detail.
The human brain and spinal cord contain billions of different cells and connections that form complex neural networks. Studying the building blocks of the brain is a fundamental step in understanding how it works and what can go wrong and cause disease.
“The brain is very complex – and we need to start at the molecular level to understand how it works,” says Jiaqian Wu, associate professor at UTHealth Houston, McGovern Medical School, Texas.
By measuring multiple molecular signatures in thousands to millions of individual cells, single-cell sequencing can comprehensively characterize the diversity of brain cell types and provide insight into the relationships between different cell populations. Single-cell transcriptomics allows the analysis of the abundance and sequences of RNA molecules, while epigenomics is the genome-wide mapping of DNA methylation, histone protein modification, chromatin accessibility and chromosome conformation.
“We can code individual brain cells and look at things like gene expression or epigenetic changes to understand how each cell is regulated and how it responds to external stimuli,” says Sarah Marzi, Edmond and Lily Safra researcher at UK DRI from Imperial College London. .
Rapid developments in experimental and computational methods of single-cell technologies are providing new insights into the differences between and within the cells that make up the brain – revealing cellular diversity, identifying rare subpopulations of interest, and uncovering unique cell characteristics. individual. Acting as a bridge between neuroscience, computational biology and systems biology, these sophisticated new tools hold the key to probing the brain’s internal circuitry in health and disease.
Single cell sequencing platforms
The two most common types of cells in the central nervous system are neurons, which send and receive electrical and chemical signals, and glial cells, which are necessary for neurons to function properly. These different cell types are further divided into further subclasses. But despite recent progress, a complete consensus or taxonomy of brain cell types is still lacking
“The brain is made up of many different cell types that perform very different functions,” Marzi says. “Understanding cell identity requires molecular profiling to reveal tiny distinctions between cells.”
In the past, people were limited to profiling whole tissue samples. While these “mass sequencing” approaches can provide valuable insights, they don’t tell the whole story.
“Because there are so many different cell types, the molecular signals are averaged across the cell population,” says Wu. individual cell. We use computational methods to group cells into different cell subtypes based on their molecular signatures.
Single cell sequencing technologies provide researchers with powerful tools to extract genomic, transcriptomic or epigenomic information at the level of an individual cell. Over the past decade, advances in technology have led to an exponential increase in the number of cells that can be studied, allowing the analysis of hundreds of thousands of cells in a single experiment. Many of these analyzes focus on examining gene activity in single cells using RNA sequencing (RNA-seq) – but there are still drawbacks compared to bulk approaches.
“Most single-cell technologies still have lower sensitivity than bulk sequencing approaches,” says Wu. we can’t capture as many of these types of molecules if they’re expressed at a low level.”
The first and most important step in most single cell sequencing experiments is the isolation of single cells from a tissue sample. Although such approaches can shed light on cellular relationships based on shared molecular characteristics, they provide no information about how cells are organized relative to each other in a tissue. But revolutionary spatially resolved transcriptomic methods are about to revolutionize the understanding of how cells are assembled in 3D within their microenvironment.
“These new methods are incredibly exciting, but there’s still room for improvement,” says Wu.
Even the most resolute methods can now achieve a resolution of around three to five cells in a tissue – and therefore it is still difficult to disentangle where these molecular signals are coming from at the level of a single cell. Overcoming these remaining technological barriers will open up a host of new opportunities for researchers to map gene expression in a spatial context in brain tissue – as well as to measure enzymatic processes and interactions between cells, between genes and between proteins. .
“The study of the blood-brain barrier is an important example”, considers Marzi. “You need this spatial resolution of which cell layers on which and what is happening in those cells as they respond to pathological changes in the brain – or when they develop pathology and the barrier becomes penetrable.”
Researchers are using more holistic approaches to capture increasingly rich information from individual brain cells. Many of them combine RNA-seq with epigenetic methods – such as the transposase-accessible chromatin assay by sequencing (ATAC-Seq) and chromatin immunoprecipitation with massively parallel sequencing (ChIP-Seq) – to simultaneously capture multiomics information about gene expression with clues about how genes are regulated at the single-cell level. But while the combination of single-cell technologies offers unique opportunities to probe the complexity of the brain, it creates computational challenges around integrating and interpreting the huge multiple datasets generated.
Wu’s lab combines neuroscience, stem cell biology and systems biology involving genomics, bioinformatics and functional testing to unravel gene transcription and regulatory mechanisms in the brain and spinal cord.
“We study gene expression and regulation using single-cell sequencing methods – and integrate different data sets to gain a more complete understanding,” says Wu. My lab is self-sufficient – we are split into two halves; one half is a wet lab and the other half is a dry lab. We have set up our own bioinformatics pipeline to analyze the different types of data and make sense of it.
Marzi’s lab uses a combination of wet and computational genomics approaches to understand the regulatory consequences of environmental and genetic risk factors for Alzheimer’s disease and Parkinson’s disease, two neurodegenerative diseases.
“It’s an area where you have to use a lot of data science and quantitative approaches to learn new things – because the datasets we create are so large and complicated that you have to apply sound statistical methods to them. analyze,” she explains. .
Given the remarkable advancements in machine learning technology, such techniques are also being introduced for single-cell analysis to overcome challenges and utilize its results more effectively – with encouraging results so far.
Driving a New Era in Neuroscience
Since the first single-cell RNA-seq study was published in 2009, there has been an explosion of conducting such studies in biomedical research – and the field of neuroscience is no exception. New single-cell sequencing technologies are beginning to uncover the full landscape of brain cell type diversity – and should lead to huge advances in understanding this complex organ in the years to come.
Scientists are applying these methods to create detailed atlases of every type of cell in the brain – across time, from development to adulthood. For example, a recent study performed RNA sequencing in developing human brain regions to provide comprehensive molecular and spatial analysis of the early stages of brain and cortical development. Another applied whole-brain spatial transcriptomics to derive a molecular atlas of the adult mouse brain. These resources will be extremely valuable to researchers studying normal brain development and disease pathology.
“Single-cell approaches are really powerful,” says Marzi. “They provide us with the tools to identify the key players behind unhealthy cellular responses and find ways to change them.”