A highly sensitive method for measuring gene expression from single cells

Generating RNA libraries from ultra-low-input samples

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

Highly sensitive RNA sequencing (RNA-Seq) methods now enable gene expression analysis of very low-input samples and even single cells. Single-cell sequencing is a method that examines the genomes or transcriptomes of individual cells, providing a high-resolution view of cell-to-cell variation.

With ultra-low-input and single-cell RNA-Seq, you can explore the distinct biology of different cells within an organ or a tumor, and understand subpopulation responses to environmental cues. These assays enhance study of cell function and heterogeneity in time-dependent processes such as differentiation, proliferation, and tumorigenesis.

Ultra-low-input and single-cell RNA sequencing are powerful tools for studying the full transcriptome, including splice junctions and alternative splicing, from the lowest amount of source material.

  • Robust transcriptome analysis down to single-cell input levels in high-quality samples
  • Protocol works directly on whole cells and preserves sample integrity
  • High resolution enables discovery of cellular differences usually masked by bulk sampling methods
Advantages of Ultra-Low-Input and Single-Cell RNA-Seq

Dr. Matt Huentelman, Ph.D., a neurogeneticist at the Translational Genomics Research Institute (TGen), is working to understand the genetic and neurological variables that affect learning and memory. His latest project, MindCrowd, uses the power of crowdsourcing to ask users to take online evaluations, complete questionnaires, and submit DNA samples. See how Dr. Huentelman is using the power of single-cell RNA sequencing to understand how individual neurons are involved in the formation of human memories.

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Single-Cell RNA-Seq, the Internet, and Memory

Single-cell sequencing studies are on the rise with applications in a myriad of fields. Here we provide an introduction to single-cell sequencing.

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Single-Cell Sequencing: Needle in a Haystack

Illumina sequencing by synthesis (SBS) chemistry is the most widely adopted NGS technology, generating approximately 90% of sequencing data worldwide.*

In addition to our industry-leading data quality, Illumina offers integrated ultra-low-input and single-cell RNA sequencing workflows that simplify the entire process, from library preparation to data analysis and biological interpretation.

Click on the below to view products for each workflow step.

SMART-Seq® Ultra® Low Input RNA Kit

Synthesize full-length cDNA from only 1–1000 whole cells or 10 pg–10 ng high-quality total RNA. Sold and supported by Clontech, and optimized for Illumina sequencing.

Nextera XT DNA Library Preparation Kit

Optimal for preparing an Illumina RNA sequencing library from cDNA generated with the Clontech SMART-Seq Ultra Low Input RNA kit.

NextSeq Series

Flexible power and simplicity for whole genome, exome, or transcriptome sequencing. 3-10 total RNA samples per run.

HiSeq 2500 System

Power and efficiency to sequence 8-96 total RNA samples per run.

HiSeq 3000/HiSeq 4000 Systems

High-throughput RNA-Seq, with up to 50 human transcriptomes per run on the HiSeq 3000 and 100 transcriptomes on the HiSeq 4000.

Platform Comparison Tool

Compare sequencing platforms and identify the best system for your lab and applications.

Sequencing Reagents

Find reagents, flow cells, and buffers tailored to each Illumina sequencing system.

RNA Express App

Aligns RNA-Seq reads with the STAR aligner and assigns aligned reads to genes, followed by differential expression with DESeq2.

TopHat Alignment App

Maps reads, performs abundance estimations of reference genes and transcripts, calls variants, and offers optional fusion calling.

Cufflinks Assembly & Differential Expression (DE) App

Assembles novel transcripts and performs differential expression of novel and reference transcripts.

The Broad’s IGV

A genome browser developed by the Broad Institute of MIT and Harvard that displays NGS data for complex variant analysis.

Genomatix Pathway System (GePS)

Associates single gene or list of genes with annotation data for pathways, diseases, tissues, and small molecules.

iPathway Guide

Differential gene expression, drug interaction, and disease analysis.

BaseSpace Sequence Hub

The Illumina genomics computing environment for NGS data analysis and management.

BaseSpace Correlation Engine

A growing library of curated genomic data to support researchers in identifying disease mechanisms, drug targets, and biomarkers.

Marine Biology: Uncover Hidden Diversity
Marine Biology: Uncover Hidden Diversity

Dr. Ramunas Stepanauskas, Ph.D., a senior research scientist at Bigelow Laboratory for Ocean Sciences, uses whole-genome, single-cell RNA sequencing to study bacteria inhabiting the surface layers of the ocean. Learn more about single-cell RNA-Seq in marine research.

RNA-Seq for Cancer Research
Cancer Research: Study Individual Clonal Populations

Tumors commonly contain several clonal populations that reflect the ongoing accumulation of mutations. Evaluating transcriptome profile differences in different areas of a tumor can enhance our understanding of relapse and metastasis. Learn more about cancer RNA-Seq.

Featured Publications
Transcriptional regulation by nicotine in dopaminergic neurons.

Biochem Pharmacol 86 1074-832013

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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.

Nat Biotechnol 32 381-62014

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Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq.

Nature 509 371-52014

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RNA-Seq Data Analysis
RNA-Seq Data Analysis

User-friendly software tools simplify RNA-Seq data analysis for biologists, regardless of bioinformatics experience.

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Review of Single Cell Research
Review of Single Cell Research

See selected publications using Illumina technology for single-cell sequencing.

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Single-Cell RNA-Seq and Stem Cell Research
Single-Cell RNA-Seq and Stem Cell Research

See how ultra-low-input RNA-Seq is transforming our view of cellular development in stem cells.

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Mapping Neural Diversity with Single-Cell mRNA-Seq
Mapping Neural Diversity with Single-Cell mRNA-Seq

The ability to analyze gene expression signatures from individual cells is transforming the way neurons are classified.

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