Dissecting the Complexity of the Brain at a Single Cell Level – Technology Networks

Understanding the brain requires an in-depth knowledge of its components. Advanced single-cell sequencing technologies are enabling 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 intricate neural networks. Studying the brains building blocks is a fundamental step toward understanding how it functions and what can go wrong to cause disease.

The brain is very complex and we have 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 relationships between different cell populations. Single-cell transcriptomics enables 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 barcode individual brain cells and examine things like gene expression or epigenetic changes to understand how each cell is regulated and how they respond to external stimuli, says Sarah Marzi, Edmond and Lily Safra research fellow at the UK DRI at Imperial College London.

Rapid developments in the experimental and computational methods of single-cell technologies are providing novel insights into differences among and within the cells that make up the brain revealing cell diversity, identifying rare subpopulations of interest and discovering unique characteristics of individual cells. Acting as a bridge between neuroscience, computational biology and systems biology, these sophisticated new tools hold the key to probing the brains inner circuitry in health and disease.

The two most common cell types in the central nervous system are neurons, which send and receive electrical and chemical signals, and glial cells, which are necessary for the healthy function of neurons. These different cell types are then further divided into additional subclasses. But despite recent progress, there is still a lack of a complete consensus or taxonomy of brain cell types.

The brain is made up of many different cell types that fill vastly different functions, says Marzi. Understanding the identity of cells requires molecular profiling to reveal tiny distinctions between cells.

In the past, people were limited to profiling whole tissue samples. While these bulk sequencing approaches can provide valuable information, they dont reveal the whole story.

Because there are so many different cell types, the molecular signals are averaged out across the population of cells, says Wu. Newer single-cell technologies are allowing a more fine-grained examination of whats going on at an individual cell level. We use computational methods to cluster cells into different cell subtypes based on their molecular signatures.

Single-cell sequencing technologies are providing researchers with powerful tools to extract genomic, transcriptomic or epigenomic information at an individual cell level. Over the past decade, technological advances have fueled exponential increases in the number of cells that can be studied, enabling the analysis of hundreds of thousands of cells in a single experiment. Many of these analyses are focused on examining gene activity within individual cells using RNA sequencing (RNA-seq) but there are still some disadvantages compared to bulk approaches.

Most single-cell technologies still have a lower sensitivity than bulk sequencing approaches, explains Wu. For example, Im interested in long non-coding RNAs, which are a very important type of regulatory RNA, but we may not capture as many of these kinds of molecules if theyre expressed at a low level.

The first and most important step in most single-cell sequencing experiments is the isolation of individual cells from a tissue sample. While such approaches can shed light on cellular relationships based on shared molecular characteristics, they dont provide any information about how cells are organized relative to each other in a tissue. But groundbreaking spatially resolved transcriptomic methods are set to revolutionize understanding of how cells are assembled in 3D within their microenvironment.

These new methods are incredibly exciting, but there is still some room for improvement, says Wu.

Even the most highly resolved methods can now achieve a resolution of perhaps around three to five cells within a tissue and so disentangling where those molecular signals are coming from at a single cell level is still challenging. Overcoming these remaining technological barriers will open a wealth of new opportunities for researchers to map gene expression in a spatial context in brain tissues as well as to take measurements of enzymatic processes and the interactions between cells, among genes, and between proteins.

Studying the blood-brain barrier is an important example, envisions Marzi. You need that spatial resolution of which cell layers onto which and whats happening in these cells as they react to pathological changes in the brain or as 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 these combine RNA-seq with epigenetics methods such as assay for transposase-accessible chromatin by sequencing (ATAC-Seq), and chromatin immunoprecipitation with massively parallel sequencing (ChIP-Seq) to simultaneously capture multiomics information about gene expression along with clues about how genes are regulated at a single-cell level. But while combining single-cell technologies provides unique opportunities for probing into the complexity of the brain, it creates computational challenges around integrating and interpreting the enormous multiple datasets generated.

Wus laboratory combines neuroscience, stem cell biology and systems biology involving genomics, bioinformatics and functional assays to unravel gene transcription and regulatory mechanisms in the brain and spinal cord.

Were studying gene expression and regulation using single-cell sequencing methods and integrating different datasets to gain a more comprehensive understanding, explains Wu. My laboratory is self-sufficient were split into two halves; one half is wet lab and the other is dry lab. Weve set up our own bioinformatics pipeline to analyze the different types of data and make sense of it.

Marzis lab uses a combination of wet and computational genomics approaches to understand the regulatory consequences of environmental and genetic risk factors for Alzheimers and Parkinsons disease, both neurodegenerative disorders.

This is a field where you need to use a lot of data science and quantitative approaches to learn new things because the datasets were creating are so large and complicated that you need to apply solid statistical methods to analyze it, she explains.

Given the remarkable progress in machine learning technology, such techniques are also currently being introduced for single-cell analysis to overcome challenges and make more effective use of its results with encouraging results so far.

Since the first single-cell RNA-seq study was published in 2009, there has been an explosion in conducting such studies across biomedical research and the field of neuroscience is no exception. Novel single-cell sequencing technologies are beginning to uncover the comprehensive landscape of brain cell type diversity and are predicted to drive huge progress in understanding this complex organ in coming years.

Scientists are applying these methods to create detailed atlases of every cell type in the brain across time from development to adulthood. For example, one recent study performed RNA-seq across regions of the developing human brain to provide a comprehensive molecular and spatial analysis of the early stages of brain and cortical development. Another applied whole-brain spatial transcriptomics to deduce a molecular atlas of the adult mouse brain. Such resources will be hugely valuable for researchers studying normal brain development and disease pathology.

Single-cell approaches are really powerful, says Marzi. Theyre providing us with the tools to identify the key players behind unhealthy cell responses, and finding ways to change them.

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Dissecting the Complexity of the Brain at a Single Cell Level - Technology Networks