Single-Cell Sequencing: Definition, Process, Uses, Cost and More

Single-cell sequencing refers to isolating individual cells within a population and sequencing these cells’ DNA or RNA to obtain detailed information about each cell’s genetic characteristics. The purpose of single-cell sequencing is to provide a higher resolution view of cell populations compared to bulk genetic sequencing, helping researchers identify rare cell types with important functions and analyze these cells’ characteristics. Single-cell sequencing is important for better understanding complex biological processes like disease and plays a key role in rare disease and cancer research.

The different types of single-cell sequencing are single-cell genome sequencing, single cell transcriptome sequencing (or RNA seq, which is RNA sequencing) and single-cell DNA epigenomic sequencing; in addition, single-cell multi-omics combines multiple sequencing techniques for greater insight. The most common single-cell applications include obtaining and studying microbial genome sequences without cultivation and cancer sequencing to find amplified therapeutic targets. 

There are many key advantages of single-cell sequencing over traditional bulk sequencing, such as the ability to find cellular heterogeneity, identify rare cell types and observe cellular development and lineage. However, the greater detail and focus of single-cell sequencing is also cost-intensive and has limitations like reduced genome coverage, greater bias in amplification and limited sensitivity to low-abundance transcripts. The cost of single-cell sequencing can also be significant, ranging from $1,700 to $3,800 per sample in library preparation depending on factors like the type of sequencing, number of cells per sample and number of reads per cell. By the end of this blog, you will understand the definition and importance of single-cell sequencing and understand how single-cell sequencing benefits life sciences research.

What is single-cell sequencing?

Single-cell sequencing is a research technique that isolates and identifies the genome, transcriptome or epigenome of individual cells, enabling higher resolution analysis that uncovers cellular heterogeneity and other details that bulk genomic analysis would miss.

According to “Single-cell sequencing techniques from individual to multiomics analyses” by Kashima et al. (2020), single-cell sequencing refers to advanced methods that isolate and sequence nucleic acids from single cells to reveal genomic, transcriptomic and epigenomic diversity within cell populations. Single cell sequencing techniques like single-cell DNA-seq, scRNA‑seq and single-cell ATAC‑seq can be applied individually or combined in multi-omics approaches. These methods offer researchers high-resolution analysis of individual cells for greater insight into the cells’ molecular profiles and functions in health and disease.

As an example of how single-cell sequencing benefits real-world research, a pulmonary research team used scRNA‑seq on 7,662 mouse tracheal epithelial cells and 2,970 primary human bronchial epithelial cells (HBECs) differentiated at an air-liquid-interface (ALI). This led to the discovery of rare ionocyte cells in airway epithelium, which are small cellular subpopulations crucial for cystic fibrosis research. While these ionocytes were unseen in bulk tissue, single-cell transcriptomics revealed them and demonstrated how this method finds rare but important cell types.

What is the purpose of single-cell sequencing?

Single-cell sequencing enables scientists to analyze the genetic material (DNA or RNA) of individual cells, and working with this higher resolution analysis can uncover variations that bulk sequencing can’t detect. These variant cell populations can behave differently and have significant effects on complex biological systems. This makes single-cell sequencing essential for understanding cancer, immunology, neurodevelopment or rare disease. By revealing cellular heterogeneity and lineage relationships, single-cell sequencing helps researchers gain useful insights into disease mechanisms and tissue development to discover new therapeutic targets.

Why is single-cell sequencing important?

Biological tissues are composed of all kinds of cell types, each with their own roles, states and genetic profiles. Bulk genome sequencing generates information about millions of cells at once, effectively averaging these signals for high level analysis. However, this masks critical variations, especially in heterogeneous systems like tumors or inflamed tissues. Single-cell sequencing enables researchers to find these critical variations by profiling each cell individually. This is crucial for studying rare diseases, tracing developmental pathways and identifying pathogenic subpopulations in cancer.

For example, glioblastoma researchers used single-cell sequencing to reveal that a single tumor can contain multiple transcriptionally distinct cell populations. Each of these cell populations could potentially respond differently to therapy. This example shows how, by unlocking cell-level insights, single-cell sequencing empowers more precise diagnostics and novel therapeutic targets that lead to personalized treatment.

What are the different types of single-cell sequencing?

There are three primary types of single-cell sequencing that focus on different genetic material in a cell, and single-cell multi-omics combines more than one sequencing technique to gain greater insight. Here is a chart comparing the three primary single-cell sequencing modalities:

Let’s do a comparison of single-cell sequencing technologies, examining each in turn:

Single-cell genome sequencing:

Single-cell genome sequencing refers to a method of sequencing the complete DNA profile of one isolated cell. Researchers often follow sequencing with whole-genome amplification (WGA) techniques like multiple displacement amplification (MDA) or Strand-seq to increase the quantity of DNA for analysis. After amplifying the single cell’s genome, the next steps are constructing sequencing libraries and applying advanced DNA sequencing like Illumina dye sequencing or Ion semiconductor sequencing. In addition to this process’s complexity, the limitations of single-cell genome sequencing include the risk of amplification bias that can result in uneven coverage and higher error rates.

One example of how single-cell genome sequencing has advanced cancer research was a 2014 study of childhood acute lymphoblastic leukemia that revealed distinct co-dominant clones in patient samples. This finding illuminated how unique genetic events like subclonal structures can contribute to malignancy and treatment resistance.

Single-cell transcriptome sequencing (scRNA-seq):

Single-cell transcriptome sequencing profiles the RNA expression of individual cells then uses these gene expression patterns to uncover potentially undiscovered cell types. Researchers begin this process by isolating single cells then lysing them to release RNA. Next the RNA is reverse-transcribed into complementary DNA (cDNA), which is then amplified using polymerase chain reaction (PCR) or in vitro transcription (IVT). Researchers add unique molecular identifiers (UMIs) and cell barcodes during library preparation to trace transcripts back to their cell of origin. Some challenges of single-cell RNA sequencing include that it is difficult to preserve the initial amount of mRNA in a cell, as it only captures 10–20% of transcripts. This can lead to dropout and amplification bias. In addition, this limited transcript capture means long-transcript cells like neurons may be underrepresented. 

As an example of how scRNA-seq is used, researchers applied single-cell RNA sequencing to profile 101 mouse embryos to “show how single-cell profiling of whole embryos can enable the systematic molecular and cellular phenotypic characterization of mouse mutants with unprecedented breadth and resolution.” By learning more about how mutations occur, this study gained insight into developmental disorders and how they could be treated.

Single-cell DNA epigenomic sequencing:

Single-cell DNA epigenomic sequencing is a technique for analyzing epigenetic modifications inside individual cells at a high resolution that reveals each cell’s unique epigenetic landscape, which can include features like chromatin accessibility, DNA methylation, histone modifications or 3D genome architecture. Single-cell epigenomic sequencing encompasses several methods. The most common approach, scATAC‑seq, uses a Tn5 transposase to tag and fragment open chromatin regions in single cells, which are then preferentially amplified and sequenced. Other techniques include single-cell bisulfite sequencing (scBS‑seq) for single-cell DNA methylation mapping, scChIP‑seq for profiling histone modifications using antibody enrichment and single-cell Hi‑C to explore 3D chromatin interactions. 

The limitations of single-cell DNA epigenomic sequencing are that data tends to be extremely sparse per cell, which forces researchers to rely on data aggregation. In particular, scChIP‑seq often suffers from high background and poor signal-to-noise, with many assays requiring large cell numbers or pooling for researchers to detect meaningful patterns.

Single-cell DNA epigenomic sequencing enabled researchers “to identify cell-type-specific chromatin accessibility patterns in the developing human brain.” By performing scATAC‑seq on  tissue samples from the developing human forebrain, these researchers identified thousands of cell-type-specific open chromatin loci, characterized differences between cortical areas and mapped developmental changes in enhancer activity during neurogenesis.

What are the applications of single-cell sequencing?

The higher resolution analysis of single-cell sequencing has empowered researchers to better understand many complex biological systems. Some of the most important single-cell applications include studying microbial genome sequences without cultivation as well as cancer research, where sequencing helps find amplified targets for new therapies.

Microbial genomics is a key single-cell application in which researchers can isolate and sequence the genomes of uncultivable microorganisms directly from environmental or host-associated samples. This approach is critical for microbiome and infectious disease research into microbial species that cannot be grown in lab conditions. By applying single-cell sequencing to soil, marine, organismal, subsurface and other microbiomes, scientists can explore questions around microbial ecology and evolution to advance public health and biotechnology research. 

Cancer research has been a vital single-cell application, helping to reveal tumor heterogeneity, track clonal evolution, identify subpopulations that may resist therapy and develop new targeted therapies. Identification of amplified oncogenes or therapeutic targets has been especially important, as these genes are overexpressed in some malignant cell types but not others. Identifying these genes enables clinicians and researchers to design more precise and targeted treatment strategies than if they had to rely on bulk genetic sequencing. 

What is the single-cell sequencing process?

The single-cell sequencing workflow includes four main steps: isolating individual cells from a population, then extracting, processing and amplifying genetic material from each cell, then preparing a sequencing library using the single cell’s genetic material and finally sequencing the library of the cell with a next-generation sequencer. The process of single-cell sequencing is shown in the image below:

Here are more details into the four steps of the single-cell sequencing workflow:

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   Isolating individual cells from a population The process always begins by isolating single cells from a complex sample to ensure each cell can be uniquely profiled without contamination from neighboring cells. However, the technique to isolate the cells can vary based on the type of sample and how researchers plan to process the separated cells. Fluorescence-activated cell sorting (FACS) uses fluorescently labeled antibodies and laser-based detection to sort live cells based on surface markers. Microfluidic droplet systems encapsulate each cell into nanoliter droplets along with reagents to enable high-throughput isolation. Laser capture microdissection physically cuts out individual cells or regions of tissue under microscopic guidance, which is useful for fixed samples. Manual micromanipulation, often performed under a microscope using micropipettes, allows researchers to precisely extract individual cells but is generally low-throughput.

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   Extracting, processing and amplifying genetic material Once researchers have evaluated the quality of the cell isolation and each cell’s viability through imaging, the isolated cells are lysed. This is a process that breaks down the cell membrane using physical, chemical or enzymatic methods to release DNA (in scDNA-seq or scDNA-Met-seq) or RNA (in scRNA-seq). Because each cell contains only a tiny quantity of genetic material, researchers next perform whole-genome amplification (for DNA) or reverse transcription followed by PCR or in vitro transcription (for RNA) to generate enough input for sequencing. Amplification produces single-stranded DNA that researchers will collect into a sequencing library.

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   Preparing a sequencing library using the single cell’s genetic material Making the amplified, single-stranded DNA into a sequencing library is essential for that DNA to be properly sequenced. Researchers begin by barcoding the individual cells after amplification so they can be easily identified and add adapter sequences to the 5’ and 3’ ends of the DNA fragments. Attaching these barcodes and adapters creates a library that allows downstream association of sequencing reads with their original cell and molecule of origin. Researchers also include quality controls to make sure the library accurately reproduces the original cell’s state.

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   Sequencing the library of the cell with a next-generation sequencer Now researchers load the prepared library into a next-generation sequencing (NGS) platform like Illumina or PacBio. Because these platforms have different methods, researchers should use the platform that fits their single-cell application and desired read length. For example, Ilumina-based sequencing binds DNA fragments to a flow cell where they undergo bridge amplification to form clusters of identical molecules. The platform then sequences these clusters using sequencing-by-synthesis, in which fluorescently labeled nucleotides are incorporated one at a time and a camera captures the emitted signal to determine the base at each cycle. The output of this sequencing of the cell’s library is raw data ready for computational analysis to reveal cellular heterogeneity, expression patterns, mutations or epigenetic features.

What are the advantages of single-cell sequencing?

Single-cell sequencing has become a strong engine for discovery in life sciences research, offering many advantages over bulk genomic screening. Here are three of the most important advantages of single-cell sequencing:

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   Detect cellular heterogeneity more effectively By profiling individual cells inside complex tissues or populations, researchers can uncover subpopulations that would be masked by the averaged data of bulk genomic sequencing. This helps scientists identify small but important differences in gene expression, mutation status or the epigenetic landscape at the individual cell level. These differences can be critical in understanding disease mechanisms in cancer, autoimmune disorders and other diseases. For example, pancreatic cancer researchers used scRNA‑seq to reveal subclonal populations with different gene-expression signatures linked to chemotherapy resistance such as gemcitabine sensitivity. This enabled deeper insights into tumor behavior and treatment response.

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   Identify rare cell types Single-cell sequencing helps researchers discover rare but biologically significant cell types that can be hidden in population-level data. But while these rare cells are only a small part of a population, they can have outsized effects on complex biological systems like cancer. Detecting and analyzing rare cells can lead to breakthroughs in oncology and infectious disease. For instance, the publication Genome Biology describes how the tool CellSIUS used single-cell RNA-seq data to successfully detect rare cell subpopulations. These included rare immune or progenitor cells in peripheral blood mononuclear cell datasets that can play a key role in health and disease mechanisms.

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   Gain insight into cellular development and lineage The high-resolution analysis of single-cell sequencing enables scientists to reconstruct developmental trajectories that reveal lineage relationships between cells. By capturing gene expression or epigenetic changes across an individual cell’s developmental stages, researchers can better understand how different cell types specialize or change over time. This unlocks new insights into developmental biology that can enable novel therapies at different stages of rare disease. For example, researchers used a technique called scGESTALT integrated with CRISPR barcoding with single-cell sequencing to study development in zebrafish brains. This method traced progenitor cells across developmental time to find lineage relationships among neuronal subtypes.

What are the challenges in single-cell sequencing?

Single-cell sequencing has empowered life sciences researchers across the planet, but it also has limitations to consider. Among the most significant is its cost and technical complexity. Generating data at single-cell resolution requires specialized equipment and workflows for isolating and processing individual cells. High-throughput platforms like droplet microfluidics can reduce per-cell cost, but still require large-scale sequencing runs to produce enough material. Once the single-cell data is gathered, then researchers have to store and analyze it. Considering that a typical single cell RNA-seq dataset is represented as a gene-by-cell expression matrix with ~20,000 genes and can include millions of cells, the computational infrastructure to process such large data volumes is complex and costly. 

In addition, technical limitations affect the quality and completeness of single-cell data. Because each cell contains only picogram-level quantities of DNA or RNA, extensive amplification is necessary to complete sequencing. This often leads to biased representation of certain genomic regions or transcripts. This amplification bias combined with stochastic dropout events can result in lower genome coverage and reduced sensitivity to low-abundance transcripts, especially when dealing with single-cell RNA seq data. As a result, important regulatory RNAs or subtle expression differences may go undetected or misinterpreted.

How much does single-cell sequencing cost?

Single-cell sequencing (sometimes called seq single cell) costs vary based on assay type, the number of cells analyzed, the desired sequencing depth and the sequencing platform. For example, Emory University reports that running scRNA‑seq using 10x Genomics would cost core facilities and library-prep fees ranging between $1,700 to $2,500 per sample, excluding sequencing costs. If the cost range combines library preparation and single-cell sequencing, Emory charges $2,700–$3,800 per sample. This does not include costs for computational resources like storage and cloud computing or paying bioinformaticians to analyze results.

TileDB offers a database solution built to simplify single-cell sequencing analysis by optimizing compute and storage costs. Using cloud-native multidimensional arrays, TileDB enables bioinformaticians to run queries directly from remote storage in their choice of cloud. This reduces storage costs and improves processing time by utilizing cost-efficient storage services like Amazon S3 that eliminate data wrangling and large downloads.

How do single-cell data differ from bulk RNA sequencing?

The main difference between single-cell sequencing and bulk RNA sequencing (also called bulk RNA seq) is that single-cell sequencing captures the gene expression profiles of individual cells while bulk RNA seq measures the average expression across a pooled population of cells. While both these forms of sequencing are useful for different purposes, they have key distinctions:

In short, single-cell sequencing is focused on individual cells while bulk RNA sequencing gathers an average genetic expression of all cells in a population.

How can you conduct single-cell analysis?

Single-cell analysis refers to studying the outputs of single-cell sequencing to look for insights in the genomics, transcriptomics, proteomics and metabolomics of individual cells. You can conduct single-cell analysis using computational tools and programming languages designed for high-dimensional biological data. R and Python are the most common languages for single-cell analysis, and are supported by specialized libraries such as Seurat (R), Scanpy (Python), and Bioconductor packages. These software tools for single-cell analysis enable bioinformaticians to preprocess, normalize, cluster, reduce dimensionality (e.g., PCA, UMAP) and analyze differential expression in single-cell sequencing outputs. 

TileDB’s solution for single-cell analysis helps researchers unify single-cell data by storing it as multi-dimensional arrays. By managing RNA-seq data and count matrices in this cloud-native format, research teams can store any number of samples in a compressed and lossless manner for tremendous storage savings. TileDB also offers language APIs for R, Python and C++ and supports interoperability with popular tools like Seurat, Bioconductor and Scanpy so researchers can use their preferred software. Finally, to best structure single-cell data for optimal performance, TileDB offers a novel data model built for single-cell data called TileDB SOMA. SOMA is a flexible, extensible and open-source data model designed to represent the annotated matrices often used in single cell biology.

What databases are used for single-cell sequencing data?

Single-cell databases refer to curated repositories that store, organize and share data generated from single-cell sequencing experiments. These platforms enable researchers to access single-cell data from published studies, analyze how rare cells evolve and function, explore gene expression across cell types and compare findings across tissues, organisms, populations or disease states. Single-cell database platforms often offer features like searchable metadata and visualization tools to facilitate life sciences research by supporting reproducibility and accelerating discovery. 

Some of the most commonly used databases for single-cell sequencing data include:

• Human Cell Atlas: A global consortium creating comprehensive maps of all human cells to better diagnose, monitor and treat disease. Their Single Cell Expression Atlas visualises the exploration and analysis of their single cell RNA-Seq data.  

• PanglaoDB: This database collects and integrates data from multiple studies of mouse and human single-cell RNA sequencing experiments inside a unified framework. 

• GEO (Gene Expression Omnibus): This public functional genomic data repository archives and freely distributes microarray, next-generation sequencing, and other high-throughput functional genomics data for life sciences research.

• CellxGene: This Chan Zuckerberg Initiative database offers data from 115.4 million cells to help global researchers better understand how human tissues function at the cellular level.

TileDB offers a modern data platform built for the scale and complexity of single-cell biology research. This empowers researchers to work at atlas scale by parallelizing complex workloads and creating distributed algorithms using TileDB Carrara, all while ensuring secure access and sharing to maintain compliance. To learn more about how TileDB facilitates single-cell research and analysis, visit us online.