Single-Cell RNA-Seq

Overview Features Project Workflow Bioinformatics Analysis Pipeline Sample Requirements Demo Results Cases & FAQ Resources Inquiry

Single-cell RNA sequencing (scRNA-seq) is a next-generation sequencing (NGS)-based method to amplify and sequence the whole transcriptome of a single cell. It is becoming a powerful tool and has been applied to research related to stem cell differentiation, embryogenesis, whole tissue analysis, and even tumors.


Single-cell RNA-seq is a method that enables simple and comprehensive access to the transcriptome of thousands of single cells. The principle is to separate multicellular organisms into single cells, and then the RNA of single cells is efficiently amplified using mature whole genome amplification (WGA) kits before being subjected to high-throughput sequencing. Single-cell RNA-seq allows a comparison of the transcriptome of individual cells and reveals the similarities and differences of the transcriptome in cell populations. scRNA-seq involves single-cell isolation, cell lysis, reverse transcription of RNA to cDNA, optional polyA RNA selection, followed by DNA sequencing. Unlike the standard bulk RNA sequencing, scRNA-seq provides unparalleled details of different cells in a sample, allowing researchers to move from average data for large numbers of tissues to individualized cellular expression data.

From basic research to clinical applications, there are many applications for scRNA-seq. It provides transcriptome information about single cells, enabling researchers to analyze the unique gene expression patterns of each cell, understand cellular heterogeneity, and explore how different cells promote disease progression and clinical response. Currently, this technique has been changing our understanding of basic biological processes, such as development, immunity or cell-related pathology.


Any Species Single-Cell Insights High Resolution Multiple Applications
This method can be applied to any species, from microorganisms to humans. Quantitative analysis down to single-cell levels for input samples. Discovery of more cellular differences based on high resolution analysis. Understanding complex tissues, tumor heterogeneity and clonal evolution.

Project Workflow

Sample Preparation

1. Sample Preparation

Isolation of viable, single cells from a given sample

Library Preparation

2. Library Preparation

Total RNA or poly-A RNA; Smart-seq2


3. Sequencing

Illumina HiSeq; PE50/75/100/150; >10G clean data

Data Analysis

4. Data Analysis

Provide customized bioinformatics analyses and services for users.

Bioinformatics Analysis Pipeline

Bioinformatics Analysis Pipeline

Comprehensive analysis:

  • Advanced cell type characterization
  • Refinement of destination cell populations through reclustering
  • Gene set variation analysis (GSVA)
  • Integrated differential expression analysis (iDEA)
  • Evolution of cellular population states
  • Proposed timing analysis
  • Rate analysis for cellular transitions
  • Trajectory exploration
  • Investigation of cell-cell interactions
  • In-depth cell cycle analysis
  • Assessment of regulatory factors
  • Detection of single-cell mutations
  • Comprehensive single-cell copy number variation (CNV) analysis
  • Identification of subpopulation Outcomes

Integration analysis

Sample Requirements

Deliverable: FastQ, raw data, coverage summary, QC report, experiment results, custom bioinformatics analysis.

Demo Results

Data quality controlData quality control

Cell filtration and statisticsCell filtration and statistics

Dimensionality reduction and clusteringDimensionality reduction and clustering

Identification of cell subpopulationsIdentification of cell subpopulations

Differentially expressed gene analysisDifferentially expressed gene analysis

KEGG pathway functional analysisKEGG pathway functional analysis

Pseudotime trajectory analysisPseudotime trajectory analysis

Case Studies



  1. Leimkühler, Nils B., et al. "Heterogeneous bone-marrow stromal progenitors drive myelofibrosis via a druggable alarmin axis." Cell Stem Cell 28.4 (2021): 637-652.
  2. Wang, Yu, et al. "Single-cell transcriptome atlas of the leaf and root of rice seedlings." Journal of Genetics and Genomics 48.10 (2021): 881-898.
* For Research Use Only. Not for use in diagnostic procedures.

Research Areas
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