Single Cell Sequencing

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Go beyond averages and get the most accurate picture of your cells’ transcriptomes with single cell sequencing. Using state-of-the-art 10x Genomics Chromium technology, we offer accurate, reliable data – including immune cell profiling – at various throughput levels to suit your requirements.

Download our single cell RNA-seq white paper

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Single cell RNA sequencing enables you analyze the transcriptome of thousands of individual cells at once. The resulting data gives you a clear picture of the variation in gene expression levels between cells – rather than average values seen in bulk RNA sequencing.

The resulting data can help you to get clear insights into the existence of clusters and subpopulations of cells within a single sample. These insights are instrumental in a wide range of studies.

In the field of oncology, research into cancer risk, development and progression is an important area in which single cell events can have major implications. Many cancers develop through an accumulation of mutations – caused by genomic instability – which means that subpopulations with unique expression profiles are common. This heterogeneity is often a driver of metastasis and resistance to therapy and can be studied in detail with single cell sequencing.

Immunology is another field of research where cell heterogeneity holds critical information on many important biological questions. May populations of immune cells cannot be studied adequately when analyzed as a single, uniform group. A single cell approach is needed to understand the underlying genomic mechanisms in many immunology-related research projects.

Library preparation kits

  • 10x Genomics Chromium 3' single cell gene expression
  • 10x Genomics Chromium 5' single cell immune profiling (BCR, TCR)

Sequencing platforms

  • Illumina: NovaSeq 6000, HiSeq

Data analysis

Single cell population analysis

  • preprocessing and quality checks
  • clustering
  • finding top expressed cluster biomarkers
  • functional annotation for cluster biomarkers
  • finding DEGs between two groups for each cluster
  • functional annotation for DEGs across groups by cluster
  • cell composition by cluster
  • cell type classification
  • trajectory and pseudo-time analysis

Single cell immune profiling

  • paired full-length V(D)J gene expression
  • clonality and diversity of TCR/BCR

Single cell ATAC-seq analyses

  • chromatin accessibility profiles