Sample to Insight

Why Single Cell Genomics?

Learn why scientists are interested in single cell analysis

Sample to Insight

Cells differ at the genomic level

Genome variations occur in health and disease

(1) Iourov, I.Y. et al. (2010) Somatic Genome Variations in Health and Disease, Curr Genomics 11(6)(2) Bushman, D.M. (2013) Semin Cell Dev Biol. 24(4)

• Types of somatic genome variations:◦ Aneuploidy

◦ Structural rearrangements

◦ Copy number variations

◦ Gene mutations

• Somatic genome variations: ◦ Occur during normal development

and aging

◦ Contribute to pathogenesis

◦ Can be the cause of diseases such as cancer, autoimmune, brain and other disorders

Examples of genome variations:

• Aneuploidy occurs in 15–91% of pre-implantation embryos samples(1)

• Genomic mosaicism in brain: the genomic variation caused by aneuploidy in the developing brain reaches 30-35%(2)

• Almost all cancers are caused by different types of genome variations including aneuploidy/polyploidy, structural rearrangements, gene amplifications or gene mutations(1)

8Single cell genomics by QIAGEN, 2016

Sample to Insight

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Seemingly identical cells may have unique transcriptional patterns

Cells change their transcription pattern

• The transcriptome of a cell is dynamic

• The transcriptome reflects the ◦ Function of the cell

◦ Type of the cell

◦ Cell stage

• Gene expression is influenced by both intrinsic and extrinsic factors (signaling response, stress response)

• Only at the level of single cells can you get:◦ Real (not average) transcriptome/gene

expression data

◦ Allelic expression data

◦ A deeper understanding of the transcription dynamics within a cell

Heat map of single cell RNA-seq data for selected pluripotency regulators (1)

(1) Kumar L.M. et al. (2014) Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 4;516

Single cell genomics by QIAGEN, 2016

Sample to Insight

Analyzing single cells enables new insights

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Bulk sample analysis isjust like putting a fruit salad into a blender - the taste is an average of all ingredients.

Analyzing single cells is like tasting each individual piece of fruit to gain a much more nuanced understanding of the composition of the fruit salad

Single cell genomics by QIAGEN, 2016

Sample to Insight

Key applications of single cell genomics

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Various research areas, such as developmental

biology, neuroscience and

immunology

Cancer research

Circulating tumor cells

Infectious disease and microbiology

research

Aneuploidy and mutation analysis

research for genetic testing of

embryos

Each cell is unique and samples are heterogeneousNew biological insights instead of average results

Only one to few embryo cells may be

available

Single cell genomics by QIAGEN, 2016

Sample to Insight

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Single cell analysis is advancing research in many areas

Cancer research

Neuroscience and stem cell research

Embryonic genetic research

NGSSuperior variant calling, analysis of SNVs and CNVs and genomic rearrangements with census-based low-pass single cell sequencing

Identifying clonal and mutational evolution and variants or structural rearrangements in cancer cells

Identifying rare cells and characterizing liquid biopsy researchCirculating tumor cells

Aneuploidy analysis, genome-wide SNP typing and genetic screens

Analyzing cellular functions and mechanisms

Environmental microbiology

Profiling microbial genomes cell-by-cell and profiling genomes from difficult to culture organisms

Infectious disease and microbial research

Determination of bacterial or viral genotypes and cell variations within hosts

Single cell genomics by QIAGEN, 2016

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