Single-Cell Analysis: Considerations & Applications

Variety is the spice of life. And living cells agree. Billions of cells comprise the thousands of species and people that live on our planet. Each cell within a cell population can look and act differently, even within the same tissue. The many cell types that comprise our body’s tissues affect how cells respond to treatment and other environmental stimuli.

So, how do we characterize these responses?

That’s where single-cell assays come in. These assays feature workflows to isolate and profile individual cells. The workflows are classified within the ‘omics and make molecular profiling at a high-throughput rate possible for single cells. From proteins to nucleic acids, researchers can tease apart a cell’s molecular traits and leverage them to treat disease.

In this article, we will introduce these methods and show how Excedr can help you propel your ‘omics efforts for single-cell profiling.

Multiomics Characterization: Introducing the ‘Omics

The ‘omics suffix has been around for decades. It was first applied for biomedical research in 1986 when Dr. Thomas H. Roderick helped name a new journal specializing in genome research: Genomics. Since then, researchers have expanded the ‘omics suffix to other datasets. The three most common of these are:

• Genomics: Researchers in genomics seek to study an organism’s genome sequence and how it works. Studying an organism’s genome provides context to the potential phenotypes they can express. Here, researchers can also explore the epigenome, the array of DNA modifications a cell’s genome possesses.

• Transcriptomics: Whereas genomics shows an organism’s potential, the transcriptome provides gene expression profiles of the cells present within a sample. This data is generated by sequencing the cell’s mRNA showing the parts of the genome being expressed at a point in time.
• Proteomics: A proteome profiles the complete set of proteins produced within an organism. They demonstrate the complete set of tools that a cell has available to live and respond to environmental stimuli.

Today, the ’omics have been expanded to study phenotypes at a single-cell level. To this end, researchers modified existing workflows with single-cell technologies. These modifications enable single-cell isolation and lysis, biomolecule extraction and labeling, and single-cell bioinformatics analysis. We will cover each of these aspects as we explore each ‘omics.

Single-Cell Genomics & Epigenomics: Identifying Genetic Variability & Gene Modifications

Cellular diversity begins at the genome. In single-cell genomics, researchers sequence the genomes of different cells with sequencing techniques. Single-cell genome sequencing reveals genomic differences between cells, a key driver of cellular heterogeneity. Some of these differences arise from mutations that drive diseases such as breast cancer. However, these differences create the need to determine which mutations are more likely to drive disease. To answer this question, researchers can leverage genome-wide association studies (GWAS). With GWAS, researchers have linked loci in individual cells to cardiac diseases and stem cell differentiation.

DNA can undergo biochemical modifications that affect which genes are expressed. Below are just some of the modifications that DNA can harbor:

• DNA methylation: Methylation is among the earliest DNA modifications detected within the epigenome. Methylation occurs when enzymes called DNA methyltransferases transfer a methyl group to a cytosine nucleotide residue.
• Histone modifications: Histones are the proteins that form chromosomes into compact, wound structures inside the nucleus. Many of these modifications arise after the cells produce the histone proteins, known as post-translational modifications. Some modifications that arise include phosphorylation (adding a phosphate group), acetylation (adding an acetyl group), and ubiquitination (adding ubiquitin proteins).

Genomic modifications in individual cells can also occur beyond a genome sequence. The study and collection of these modifications are known as epigenetics and epigenomics, respectively. Such efforts allow researchers to obtain a comprehensive picture of a cell’s phenotypic potential.

A single-cell genomics and epigenomics study commonly begins with extracting the chromosomes from single cells. These cells are obtained with microfluidics, inspired partly by fluorescence-associated cell sorting (FACS). Much like FACS, researchers can produce liquid systems that isolate single cells for extracting DNA (insert Excedr article link). The workflows then prepare libraries to sequence the DNA.

Library preparation protocols for single-cell epigenomics also feature several alterations. These include:

• Bisulfite sequencing: In bisulfite sequencing, bisulfites are added and bound to unmethylated cytosines. These cytosines would then be converted to uracil residues. The uracils would then be read as thymine sequences, leaving any cytosine residues as methylation sites.
• ChIP-seq: Chromatin immunoprecipitation before library preparation (ChIP-Seq) that determines where proteins bind to DNA. ChIP-Seq does so with antibodies that bind to histones and other proteins interacting with DNA. Such efforts help identify histone modifications at single-cell level when combined with cell sorting techniques.

Single-Cell Transcriptomics: Teasing Apart Gene Expression

A typical RNA sequencing (RNA-Seq) study profiles the mRNA produced by all cells within a sample. The dataset would reflect the average gene expression patterns within a cell population. Nonetheless, gene expression varies greatly between cells, whether mammalian or microbial. Substantial heterogeneity also arises in cells from different tissues and among cells comprising the same tissue. Hence, new technologies that extend existing RNA-Seq workflows are needed to conduct single-cell transcriptome analysis.

To meet this need, researchers have developed single-cell RNA sequencing (scRNA-seq). Using scRNA-seq helped discover rarer phenotypes that may contribute to worsening disease phenotypes. For instance, scRNA-seq can help with diagnostics research by identifying cancer stem cell subpopulations that are correlated with the progression of human fatal renal cancer cell carcinomas. scRNA-seq can also identify phenotypes characteristic of breast cancer cell subtypes that remain dormant before inducing breast cancer relapse.

The success of scRNA-seq workflows in biomedical research lies in extending existing RNA-seq methods to profile a single cell’s mRNA transcripts. Most of these workflows comprise the following steps before any data analysis is performed:

• Single-cell isolation and preservation: This step obtains the individual cells from whom gene expression profiles are obtained. As discussed, this step is partly inspired by flow cytometry, where fluidics systems separate the cells for further profiling. Researchers can then preserve the cells’ mRNA with preservatives such as DMSO to minimize changes in gene expression.
• mRNA capture: After isolating and preserving the cells, the mRNA must be obtained for sequencing. Single nucleus RNA sequencing (snRNA-seq) represents one such approach. snRNA-seq is useful when cells are difficult to isolate, where cells are immediately lysed to minimize the effects of cell stresses while keeping the nuclei intact. snRNA-seq can also be combined with the single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) to study the regulation of mRNA transcription through transcription factor binding.
• Library preparation and sequencing: Like a typical RNA-seq workflow, the process typically involves the reverse transcription of mRNA into complementary DNA (cDNA) before PCR amplification of the cDNA from the individual cells. The pipeline must also integrate barcodes to associate pieces of mRNA with a specific cell. These barcodes must be carefully prepared to prevent mislabeling and barcode switching. Such errors can cause researchers to assign the wrong gene expression profiles to cells.

Researchers can also contextualize single-cell gene expression within the tissues where they reside with spatial transcriptomics. Here, researchers obtain positional information about the cells they are sequencing and map gene expression back to those cells. This data can generate cell atlases that differentiate cell states and cell types within tissues. Such efforts have aided the visualization of tumor cell subpopulations that populate the tumor microenvironment. Single-cell transcriptomics has also helped researchers identify changes in immune cell composition with tumor progression.

Single-Cell Proteomics: Profiling a Cell’s Tools

Researchers have also begun to appreciate the collection of proteins unique to individual cells. However, conventional proteomics assays fail to account for heterogeneity in protein levels and identify lower-abundance proteins. Single-cell proteomics addresses this by characterizing protein abundances in each cell with two techniques. They are:

• Immunoassays: An immunoassay measures the concentrations of biomolecules, or analytes, using antibodies specific to the analyte. Enzymes or fluorescent dyes are typically added to the antibodies as a label to measure analyte concentrations. However, scientists have, over time, made advances in immunoassay efforts. For one, they implemented microfluidics to obtain accurate quantifications of protein and mRNA concentrations in single cells.
• Mass spectrometry (MS): MS facilitates the quantification of thousands of proteins in samples. This is done by analyzing a molecule’s molecular weight through mass-to-charge ratio measurements. A typical MS workflow first separates molecules of interest through chromatography. Then, the molecules are ionized, which provides a charge to the molecules. When accelerated through the analyzer, the velocity at which the molecules travel indicates their size.

Completing the ‘Omics: Data Analysis with Bioinformatics

Irrespective of the kinds of biomolecules assayed and the insights to be gained, single-cell assays require computational tools to analyze the data. To meet this need, researchers have developed numerous bioinformatics tools to assess the quality of the data and glean biological insights from individual cells. Across all bioinformatics workflows, some features arise:

• Barcode identification: All single-cell assays must delineate the cells from where the biomolecules are profiled. Any single-cell genomics and single-cell RNA-seq analysis uses nucleic acid barcode sequences. That way, multiple single cells can be profiled through multiplex labeling and sequencing. In single-cell proteomics, researchers typically use a label-free approach where single cells are sent for lysis during sample preparation and then quantified. However, researchers have also prepared isobaric labeling. In it, peptides with identical masses adopt different isotopic configurations to label peptides from individual cells.
• Data normalization: Single cells will not have the same amount of biomolecules most of the time. Data normalization is most commonly applied in single-cell RNA-seq analysis, where transcript abundances must be normalized between cells. This helps correct for batch effects such as personnel and time differences when the experiments were conducted.

Excedr leases the technologies you need to conduct a single-cell study

We’ve covered two kinds of machines you will need to conduct a single-cell ‘omics study: the next-generation sequencer and the mass spectrometer. If you’re in need of a new or refurbished unit, Excedr can lease exactly what you need, regardless of brand or manufacturer. . This is because we don’t carry any inventory, and instead provide a brand agnostic service that puts your needs first. Rather than be tied to a specific vendor or reseller, you can simply let us know what unit you’re interested in and we can procure it for you and your lab.

Here are just some of the mass spectrometer manufacturers we have leased from in the past:

• Sciex: Sciex has been well-positioned to conduct single-cell proteomics research. Their CESI 8000 Plus system uses electrospray ionization (ESI) to pulse ionized samples into the gaseous phase. In single-cell proteomics, researchers can pulse a cell’s contents with minimal sample preparation. The CESI 8000 Plus system was recently used to profile single-cell proteomes of a single HeLa cell line, reducing sample costs for oncology research.
• Bruker: Bruker’s mass spectrometers have also been used for single-cell proteomics research. Their timsTOF Pro represents the flagship line of mass spectrometers. timsTOF Pro uses a trapped ion mobility spectrometry (TIMS) device to accumulate and concentrate ions of a specific mass and mobility. This technology can help analyze hundreds of cells as low as 150 pg of sample. The timsTOF SCP can also complement scRNA-seq research.

We have also helped sequencing facilities set up their operations by supplying sequencers from the following brands:

• Illumina: The Illumina NextSeq 550, 1000, and 2000 sequencing platforms are currently among the most popular high-throughput sequencers in the market. The sequencers can produce robust single-cell transcriptome and genomics libraries by sequencing short reads that can be assembled into full transcripts and genomes.
• PacBio: PacBio is a fast-growing company that helped make long-read sequencing possible. HiFi sequencing sits at the heart of the PacBio Revio and Sequel IIe Systems. In it, libraries are prepared directly from high-quality double-stranded DNA, removing the need to fragment the DNA. Long-read sequencing improves the accuracy of a genome assembly for individual cells, enhancing variant identification in single-cell genomics efforts.
• Oxford Nanopore: Along with PacBio, Oxford has also developed a long-read sequencer: the Nanopore sequencer. These sequencers use flow cells that contain nanopores. The nanopores contain a channel and sensor chip that measures electric current for elucidating DNA and cDNA (from RNA) sequences in real-time. Like the PacBio sequencer, the Nanopore can generate long reads from single cells while minimizing the risk of PCR bias, improving genome and transcriptome assembly. The Nanopore can also produce full-length transcripts for distinguishing RNA transcript isoforms in individual cells.

Lease the tools to enhance your single-cell assays

Cellular life is teeming with diverse cell types and subpopulations. Profiling these populations requires tools that separate the cells and characterize them with high resolution and accuracy. Tools typically used to characterize whole samples are also capable of profiling individual cells. By using mass spectrometers, sequencers, and other specialized instrumentation, researchers have made great clinical strides in studying biological phenomena at the single-cell level.

Excedr’s leasing program can help you establish your single-cell assays by leasing you the equipment your lab needs to study the heterogeneity of single cells. From reducing upfront costs to extending cash runway, speak with our team today to learn exactly how we can help.