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.

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

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Advantages of Single-Cell RNA-Seq

Single-cell and ultra-low-input RNA-Seq are powerful tools for studying the transcriptome in an unbiased manner from minimal input.

  • 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 methods

Single-Cell Sequencing and Analysis Workflow

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 the Illumina workflow for single-cell sequencing, from tissue preparation through analysis.

Launch Modal

Single-Cell Sequencing Approaches

Quality Control for Single-Cell Sequencing Experiments

Learn best practices for preparing cell suspensions with sample preparation solutions from Miltenyi Biotec

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Empowering Single-Cell Experiments with Multiplexing

Researchers from UCSF discuss MULTI-Seq, a sample barcoding strategy for single-cell and single-nucleus RNA sequencing.

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Combinatorial Single-Cell Technologies

We highlight several applications of fully supported workflows that can take you from single-cell suspensions to analyzed data.

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Single-Cell Analysis is Advancing Insights in Developmental Biology

Single-Cell RNA-Seq is Advancing Insights in Developmental Biology

Cole Trapnell, PhD, is the principal developer of TopHat, Cufflinks, and other widely used bioinformatics tools. He shared with us his views on the importance of understanding cell lineage, his lab's experience with single-cell RNA sequencing, and his application of combinatorial indexing.

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Single-Cell RNA Sequencing Applications

Considerations, Trends and Future of Single-Cell Sequencing

From Crohn's disease and peanut alergies to cell therapies and immunotherapies, the lab at NIHR Guys' and St. Thomas' Biomedical Research Centre is using Illumina technology for single-cell genomics investigations.

Considerations, Trends and Future of Single-Cell Sequencing

Single-Cell Sequencing Research Articles

The Weizmann Institute Uses NovaSeq for Single-Cell Research

By analyzing one cell at a time, Professor Amit is improving our understanding of biological systems in health and disease.

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Exploring the Tumor Microenvironment

Single-cell sequencing proves invaluable in detecting intracellular communication in tumors.

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Single-Cell Research Review

See an overview of peer-reviewed publications using Illumina technology for single-cell sequencing.

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Immune Profiling of Human Tumors Identifies CD73 as a Combinatorial Target in Glioblastoma

Single-Cell RNA Sequencing in Immunotherapy Research

Swetha Anandhan from the MD Anderson Cancer Center joins Illumina and 10x Genomics for this webinar. She highlights the use of single cell RNA-sequencing to identify a unique population of macrophages in glioblastoma multiforme that persists after treatment with immune checkpoint inhibitors.

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Workflows for RNA Sequencing Guide

Learn about Illumina solutions for next-generation RNA sequencing applications.

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Workflows for RNA Sequencing Guide

High- and Low-Throughput Methods

Single-cell sequencing methods can be distinguished by cell throughput. Low-throughput methods include mechanical manipulation or cell sorting/partitioning technologies and are able to process dozens to a few hundred cells per experiment.

Recent advances in microfluidic technologies have enabled high-throughput single cell profiling where researchers can examine hundreds to tens of thousands of cells per experiment in a cost-effective manner. Both the high- and low-throughput methods utilize Illumina sequencing by synthesis (SBS) chemistry, the most widely adopted NGS technology, which generates approximately 90% of sequencing data worldwide.*

The Singular Neuron

James Eberwine explains how single-cell RNA sequencing can be used in vivo to understand how individual cells function in their microenvironment within a complex organism.

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High-Throughput Workflow for Ultra-Low-Input and Single-Cell RNA-Seq

The Illumina Bio-Rad Single-Cell RNA Sequencing Solution combines the highly innovative Bio-Rad Droplet Digital™ technology (ddSEQ™) with Illumina NGS library preparation, sequencing, and analysis technologies. Gain valuable insight into gene expression with this sensitive, scalable, and cost-effective high-throughput workflow.

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.

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ATAC Sequencing

ATAC-Seq is a widely used method that uses the hyperactive transposase Tn5 to assess chromatin accessibility. It can be performed on single cells at high resolution.

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Cancer RNA Sequencing

Detect cancer gene expression and transcriptome changes and identify novel cancer transcripts with RNA-Seq.

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Cellular and Molecular Biology

Expand cell and molecular biology research beyond conventional methods with next-generation sequencing.

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Frequently Purchased Together

Featured Publications

Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

Researchers used single-cell RNA-Seq to characterize cell-to-cell communication via ligand-receptor interactions across cell types in a tumor microenvironment.

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Human haematopoietic stem cell lineage commitment is a continuous process

Researchers used single-cell RNA-Seq to demonstrate that hematopoietic stem cell lineage commitment is a gradual process without differentiation into discrete progenitors.

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Aging increases cell-to-cell transcriptional variability upon immune stimulation

Researchers used single-cell RNA-Seq to explore the effects of aging on the immune system, observing that age-related cell-to-cell transcriptional variability is a hallmark of aging.

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Additional Resources

*Data calculations on file. Illumina, Inc., 2015