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Single-Cell and Ultra-Low-Input RNA-Seq

Introduction to single-cell RNA sequencing

Complex biological systems are determined by the coordinated functions of individual cells. Conventional methods that provide bulk genome or transcriptome data are unable to reveal the cellular heterogeneity that drives this complexity. Single-cell sequencing is a next-generation sequencing (NGS) method that examines the genomes or transcriptomes of individual cells, providing a high-resolution view of cell-to-cell variation.

Ultra-low-input and single-cell RNA sequencing (scRNA-Seq) methods enable researchers to explore the distinct biology of individual cells in complex tissues and understand cellular subpopulation responses to environmental cues. Highly sensitive scRNA-Seq approaches enhance the study of cell function and heterogeneity in time-dependent processes such as differentiation, proliferation, and tumorigenesis. 

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Single-cell sequencing eBook

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Advantages of single-cell RNA-Seq

Single-cell and ultra-low-input RNA-Seq methods are powerful tools for studying the transcriptome in an unbiased manner from minimal input. Single-cell RNA sequencing can be applied across diverse research areas, with the potential to transform our understanding of cellular function in health and disease. 

  • Robust transcriptome analysis down to single-cell input levels for high-quality samples
  • Integrated protocol proceeds directly from whole cells and preserves sample integrity
  • High resolution analysis enables discovery of cellular differences usually masked by bulk sampling and bulk RNA-Seq methods

Single-cell sequencing and analysis workflow video

Single-cell sequencing can reveal the cell types present and how individual cells are contributing to the function of complex biological systems. See how you can use Illumina single-cell sequencing workflows, from tissue preparation through analysis.

Launch Modal

Difference between bulk and single-cell RNA-Seq

Bulk RNA-Seq excels at providing insights into the entire tissue. It can help researchers understand the big picture and may be used as an untargeted approach for new discoveries. However, bulk RNA-Seq may fail to capture transcripts from rare but biologically relevant subpopulations, such as stem cells or circulating tumor cells. In addition, a low-expressing gene identified in bulk RNA-Seq may instead be robustly expressed in a rare cell type.

In contrast, single-cell RNA sequencing data are generated for individual cells, enabling deeper insights into the nuanced distinctions between cells within the same sample. The variation between individual cells can be immense, even when examining the same cellular subpopulation. This is especially true of the transcriptome, a more reactive and dynamic -ome compared to the relative stability of the genome and epigenome.  Examining complex organs and tissues at single-cell resolution is critical to advancing our understanding of many diseases and systems. 

High- and low-throughput scRNA-Seq methods

Single-cell RNA sequencing methods can be distinguished by cell throughput.

High-throughput single-cell profiling methods are recommended for researchers wishing to examine hundreds to millions of cells per experiment in a cost-effective manner. 

Low-throughput methods are recommended for scientists who need to process dozens to a few hundred cells per experiment. Low-throughput approaches generally include mechanical manipulation or cell sorting/partitioning technologies.

Explore workflows for both high- and low-throughput single-cell RNA-Seq methods below. Both methods utilize proven Illumina sequencing by synthesis (SBS) chemistry. Illumina sequencing systems offer high data accuracy with flexible throughput to deliver a proven NGS solution for single-cell sequencing studies, regardless of scale.

High-throughput workflow for ultra-low-input and single-cell RNA-Seq

Gain valuable insight into gene expression with this sensitive, scalable, and cost-effective high-throughput scRNA-Seq method.

Low-throughput workflow for ultra-low-input and single-cell RNA-Seq

The low-throughput method below is recommended for researchers who wish to process small numbers of cells for a particular study, such as dozens to a few hundred cells per experiment.

Single-cell RNA-Seq data analysis and insights

DRAGEN single-cell RNA Pipeline

Accessible and highly scalable single-cell RNA-Seq solution for mRNA capture, barcoding, and library prep with a simple manual workflow that doesn't require a cell isolation instrument.

  • RNA-Seq alignment and matching to annotated genes for transcript reads
  • Cell-barcode and UMI error correction for the barcode reads
  • Genotype-based and genotype-free sample demultiplexing
Partek Flow

Partek Flow provides an on-premises stand-alone tertiary multiomic analysis option and supports a variety of commercial assays.

  • Import your own data or publicly available data from popular online repositories
  • Remove batch effects to discover biological information
  • Perform pseduo bulk analysis
Illumina Connected Multiomics

A powerful cloud-based multiomic analysis and interpretation software, enabling sample to insight workflow with interactive visualizations, infrastructure scalability, and secure data management.

  • Suitable for both small data sets and large-scale multiomic studies, supporting multiple sample types
  • Link results to curated biological knowledge for deeper interpretation
  • Reveal insights from large, complex datasets and get publication-ready figures

Applications of single-cell sequencing

Single-cell sequencing enables a broad range of applications, allowing researchers to make significant strides in understanding complex biological systems. Examples of popular applications include:

  • Cancer research: scRNA-Seq has powered breakthrough studies of tumor heterogeneity, rare treatment-resistant cell populations, and immunotherapy responses.
  • Stem cell biology: Single-cell sequencing approaches enable characterization of transcriptional heterogeneity in stem cell subpopulations.
  • Immunology research: scRNA-Seq methods are advancing studies of immune cell development, autoimmune diseases, and rare immune cell subpopulations.
  • Neurobiology: Researchers can utilize single-cell RNA-Seq to study brain pathology and perform disease-specific transcriptome profiling studies.
Spotlight on single-cell transcriptomics

Learn more about emerging applications of scRNA-Seq and uncover deep insights into complex cellular biology.

Single-cell RNA-Seq FAQ

Single-cell RNA sequencing is used to profile gene expression at the level of individual cells, enabling a high-resolution view into complex cellular phenotypes. This method uncovers cellular heterogeneity and dynamic changes in gene expression at the single-cell level.1
scRNA-Seq has broad utility across basic, translational, and clinical research areas, including cancer research, immunology and autoimmunity, stem cell biology, developmental biology, and neuroscience. This powerful method is used to study heterogeneous cell populations, discover rare cell types, and characterize processes of disease and development at the cellular level.1,2
Unlike bulk RNA-Seq approaches, single-cell RNA-Seq provides a detailed transcript profile for a given cell within a highly heterogeneous cell population. This level of resolution is especially important in complex tissues, including tumors or in immune cell populations. Additionally, single-cell RNA-Seq is a sensitive method that can detect rare and low-abundance transcripts, identify novel transcript isoforms and regulatory mechanisms, and enable a better understanding of disease pathobiology.
Single-cell RNA sequencing provides a high-resolution snapshot of cellular identity and function by revealing which genes are being expressed in each cell, how cellular phenotypes differ based on their transcriptomic signatures, and how gene expression is influenced by their environment, disease processes, and treatments. For example, single-cell RNA sequencing can be used to identify and characterize specific immune cell subpopulations via their transcriptional signatures and reveal possible cell fate decisions.3
With technical issues and biological factors, single-cell RNA sequencing data is reported as having more noise and complexity compared to bulk RNA sequencing. The large amount of variability with scRNA-Seq data may make computational analysis and data interpretation a challenge.4 Additionally, widespread adoption of single-cell RNA sequencing has been slowed due to limited scaling, high reagent costs, and the need for specialized capital equipment.
Discover how the Illumina Single Cell 3' RNA Prep kit enables single-cell mRNA capture, barcoding, and library prep without expensive microfluidic equipment or complex workflows.
PIPseq chemistry enables scalable and simple single-cell RNA capture and barcoding. Particle-templated instant partitions (PIPs) use emulsification with template particles that include barcoded oligonucleotides bound to hydrogel beads. During sample preparation, the cell suspension of interest is mixed with template particles and oil and segregated into templated emulsions by vortexing. Cells in the emulsions are then lysed and the mRNA is captured by the barcoded templates. The emulsion is broken and cDNA is generated from the captured mRNA via reverse transcription and amplified to create a cDNA library for each individual cell. Single-cell cDNA libraries are then processed into sequencing libraries using standard library preparation methods followed by NGS.
Explore the Illumina Single Cell 3' RNA Prep kit page to learn more about the use of PIPseq chemistry for scRNA-Seq applications.
The optimal sequencing depth depends on numerous factors, including sample types with varying RNA content, desired experimental outcomes, and the throughput capabilities of the sequencing platform.
For Illumina Single Cell 3’ RNA Prep kits, recommended sequencing read depth is calculated using reads per input cell (RPIC) rather than the expected number of captured cells, due to variability in capture efficiency across sample types.
Visit the single-cell RNA prep FAQ and resources page to learn more.
The number of recommended cells for single-cell RNA sequencing depends on experimental goals and the desired method. For the Illumina Single Cell 3’ RNA Prep kit, approximately 100 to 200,000 cells are recommended for single-cell RNA sequencing.
For additional information, visit our Illumina Single Cell 3' RNA Prep kit page with simplified workflows and specification tables.
Single-cell RNA sequencing can be performed on a wide variety of sample types, including fresh, frozen, or DSP-methanol fixed cells and nuclei, as well as whole tissue samples from various species. Additionally, scRNA-Seq can be applied to various biological sources like cell cultures, blood, and organoids.5
To prepare a single-cell suspension from tissue for single-cell RNA sequencing, the tissue needs to be dissociated into individual cells. This is typically achieved through a combination of mechanical and enzymatic methods. Careful optimization is crucial to balance cell yield and viability, as harsh conditions can stress cells and affect gene expression.6
While single-cell RNA sequencing is typically performed on fresh cells, DSP-methanol fixed or frozen cells can be used, but with limitations and considerations. Freezing can cause cell death, RNA degradation, and altered gene expression profiles.
If using frozen cells, it is recommended to start with a single-cell suspension and cryopreserve cells in a suitable medium to minimize damage during freezing and thawing. For frozen tissues, single-nucleus RNA sequencing (snRNA-Seq) is often preferred over isolating single cells due to the challenges of dissociating and recovering viable cells after thawing.7
Any species that has polyadenylated RNA can be used with the Illumina Single Cell 3' RNA Prep kit.
For additional information, explore the features and benefits of our Illumina Single Cell 3' RNA Prep kits.

Single-cell sequencing resources


Single-cell webinars
How to explore single-cell data
How to explore single-cell data

This presentation introduces the basic steps in tertiary scRNA-Seq analyses, highlighting how different cell populations can react to external factors.

Single-cell RNA sequencing
Single-cell RNA sequencing across multiple sites

Technology advances enable single-cell RNA-Seq from multiple sites in one workflow. Learn about data quality, recovery of fragile cell types, and more.

Single-cell multiomics
Single-cell multiomics: Beyond RNA-Seq

Dr. Michael Kelly uses single-cell sequencing methods to study auditory development and supports research at the NCI Center for Cancer Research.

Featured application notes
NextSeq 1000 and NextSeq 2000 single-cell RNA sequencing solution

This cost-effective, flexible workflow measures gene expression in single cells and offers high-resolution analysis to discover cellular differences usually masked by bulk sampling methods.

Single-cell gene expression + ATAC-Seq solution

Unify single-cell gene expression and chromatin accessibility to help reveal cellular mechanisms driving gene regulation.

Single-cell and spatial sequencing on NextSeq 1000 and 2000 Systems

Learn how XLEAP-SBS chemistry combined with 10x Genomics single-cell and spatial solutions enable high-resolution genomics on the NextSeq 1000 and NextSeq 2000 Systems.

DRAGEN now features a Single-Cell RNA Pipeline

The DRAGEN Single-Cell RNA offers a cell x gene expression matrix output starting point for downstream single cell analysis.

Key features include:

  • Ultra-rapid. Analysis times less than 40 minutes for ~8000 cells with > 1 billion reads**
  • Widely compatible: Supports a wide range of input library types, giving a common output of cell x gene expression matrix compatible with downstream analysis tools
  • Efficient: Goes from BCL files to quantified expression per cell with a single tool
Learn More
Want to learn valuable insights about the single-cell sequencing workflow? Download ebook

Keep exploring
Cancer single-cell analysis

Single-cell sequencing powered by NGS can examine the genomes or transcriptomes of individual cancer cells, providing a high-resolution view of cell-to-cell variation.

CITE-Seq

CITE-Seq (cellular indexing of transcriptomes and epitopes) is a sequencing-based method that simultaneously quantifies cell surface protein and transcriptomic data within a single cell readout.

Transcriptomics

Profile the transcriptome for a better understanding of biology. Explore various techniques and learn how the discovery power of RNA-seq can empower high-impact research.

Multiomics

Combine data from genomics, transcriptomics, epigenetics, and proteomics to better connect genotype to phenotype.

ATAC-Seq

Evaluate regions of open chromatin across the genome, in either bulk cell populations or single cells at high resolution.

Exploring the tumor microenvironment

Dr. Alex Swarbrick discusses the advantages of single-cell sequencing for studying tumor microenvironments in breast and prostate cancers.

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References

  1. Jovic D, Liang X, Zeng H, Lin L, Xu F, Luo Y. Single‐cell RNA sequencing technologies and applications: A brief overview. Clin Transl Med. 2022;12(3):e694. doi:10.1002/ctm2.694
  2. Baccin C, Al-Sabah J, Velten L, et al. Combined single-cell and spatial transcriptomics reveals the molecular, cellular and spatial bone marrow niche organization. Nat Cell Biol. 2020;22(1):38-48. doi:10.1038/s41556-019-0439-6
  3. Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018;18(1):35-45. doi:10.1038/nri.2017.76
  4. Chen G, Ning B, Shi T. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Front Genet. 2019;10:317. doi:10.3389/fgene.2019.00317
  5. Sant P, Rippe K, Mallm JP. Approaches for single-cell RNA sequencing across tissues and cell types. Transcription. 2023;14(3-5):127-145. doi:10.1080/21541264.2023.2200721
  6. Wiegleb G, Reinhardt S, Dahl A, Posnien N. Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material. Frontiers in Zoology. 2022;19(1):27. doi:10.1186/s12983-022-00472-x
  7. Stamper CT, Marchalot A, Tibbitt CA, et al. Single-cell RNA sequencing of cells from fresh or frozen tissue reveals a signature of freezing marked by heightened stress and activation. Eur J Immunol. 2024;54(4):e2350660. doi:10.1002/eji.202350660
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