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Single Cell Long Read Transcriptome

Single-cell sequencing has traditionally relied on short-read sequencing, which is effectively in providing insights into single-cell gene expression, but falls short in capturing information about alternative splicing, splicing regulation, transcriptomic complexity, and isoform diversity.

The incorporation of long-read sequencing into single-cell assays addresses this shortfall observed in traditional short-read sequencing methodologies. This integration offers insights into molecular mechanisms, enabling the identification of intricate structural variants, comprehensive exploration of whole transcript alternative splicing events, and the expression of cell-type-specific mRNA isoforms at the single-cell level.


  • Isoform-level gene expression of RNA transcripts.
  • Analyze different isoform, alternative splicing, fusion genes, etc.
  • Characterization of transcript isoforms relevant to health, development, and disease in single cell level.

* : Only acceptable in AMEA (Asia pacific, Middle East and Africa)

Sample Requirement

Sample Type

Sample Amount




Single cell suspension*

≥ 1,000,000

≥500,000 cells/mL

Cell viability: >80%
Cell size: <30 μm

cDNA from 10x


≥ 50 ng

≥ 2 ng/ul

Peak Size: 1-1.8 kb

< 2 months under -20℃/-80℃

* For more information on the detailed sample requirements, please contact your local sales.

Specifications: Sequencing and Analysis

Sequence platform Nanopore PromethION llumina NovaSeq PE150


  • Full length long reads
  • Get the full-length information of mRNA
  • Analyze different mRNA isoform, alternative splicing, fusion genes, etc.
  • 3’ to 5’ short reads
  • Gene expression information only
  • Impossible to analyze the differences in the isoform of transcripts between cells
Read length Median read length: ~700-1000bp Paired-end 150bp
Data Output
1 PromethION cell
~ 100 M total reads/cell
50,000 pair reads/cell
100 -120Gb

Data QC
Standard Analysis


  • Data QC
  • Identify the cell barcode and UMI sequences present in Nanopore sequencing reads
  • Summary metrics (read quality, number of cells, genes and transcripts identified within each sample, median genes per cell, and sequence saturation)
  • UMAP projections

Cell Ranger

  • Demultiplex BCL files from a sequencer into FASTQs
  • Summary metrics (sequencing quality, number of cells detected, the mean reads per cell, and the median genes detected per cell et al.)
  • Alignment of reads to genome
  • Gene expression quantification
  • Clustering analysis
  • Differentially expression analysis between clusters
  • Visualization

Standard Analysis

  • Data QC
  • Mapping and Quantification
  • Dimensionality reduction, clustering, and differential analysis
  • Base on gene
    Base on transcripts
  • GO/KEGG/Reactome Enrichment Analysis
  • Alternative Splicing
  • Demultiplex BCL files from a sequencer into FASTQs
  • Alignment, UMI counting, Metrics summary
  • Identification of highly variable gene (HVGs)
  • Cell Subpopulation Identification
    Principal component analysis (PCA)
    Identify clusters of cells
    Dimensionality reduction and Visualization
  • Marker gene detection (Differentially expression analysis between clusters)
  • GO/KEGG/Reactome Enrichment
    Functional Annotation of Transcription Factor
    Protein-Protein Interaction Network Analysis

Project Workflow

From sample preparation, library preparation, sequencing and data quality control, to bioinformatics analysis, Novogene provides high-quality products and professional services. Each step is performed in agreement with a high scientific standard and meticulous design to ensure high-quality research results.

Featured publications of Single Cell Long Read Transcriptome

Demo Results

Fig 1 wf-single-cell diagnostic plots.

A. Knee plot is a quality control for RNA-seq data and illustrates the procedure used to filter invalid cells. The X-axis represents cells ranked by number of reads and the Y-axis reads per barcode. The vertical dashed line shows the cutoff. Cells to the right of this are assumed to be invalid cells, including dead cells and background from empty droplets. B. Gene saturation: Genes per cell as a function of depth. C. UMI saturation: UMIs per cell as a function of read depth. D. Sequencing saturation: This metric is a measure of the proportion of reads that come from a previously observed UMI, and is calculated with the following formula: 1 – (number of unique UMIs / number of reads).

Fig 2. UMAP plots. (A) Integration grouping (B) Individual grouping.

UMAP plots show high consistency of the cell annotation grouping results in both short reads and long reads sequencing data.

Fig 3. Differential gene violin plot.

The figure displays four marker genes. The x-axis represents the cluster number, and the y-axis represents the average expression level of the genes.