why scientist analyze single cells
TRANSCRIPT
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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