cellular heterogeneity - uab · c1 mrna seq data combined with monocle identify key regulators of...
TRANSCRIPT
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Cellular Heterogeneity
Averaging DOES mask individual cell differences in heterogenous populations
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Single-Cell Genomics
“The Population Average is a Lie.”
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Single-Cell Genomics
Peer-reviewed publications have shown that samples are heterogeneous and that
variations are hidden in small sub populations
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C1 single cell RNA seq and Monocle algorithm define transcriptional dynamics of cell differentiation processes
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EXPERIMENTAL APPROACH
Primary Human Myoblasts were cultured and switched to low serum. Single cells were captured on C1 at 24h intervals and processed with our RNA seq protocol. Sequenced to an average depth of 4 million reads per cell. Used Monocle to order RNA seq data of differentiating myoblasts in pseudotime.
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C1 mRNA seq data combined with Monocle identify key regulators of myoblats differentiation
Many of the transcription factors identified by C1 mRNA seq/ Monocle approach were not considered before to be important for muscle development. Authors performed knockdown experiments (silencing by shRNA) on bunch of transcription factors to confirm that these factors have important role in muscle differentiation. The results confirmed that transcription factors identified by Monocle indeed Influenced myoblasts differentiation.
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SINGLE CELL GENE EXPRESSION ENABLES BETTER UNDERSTANDING OF CELLULAR REPROGRAMMING
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QUESTION
The authors wanted to understand molecular events/ genes that drive reprogramming of MEFs to iPSCs
“Because the molecular changes occurring at the different stages during the reprogramming process were based upon the analysis of heterogeneous cell populations, it has not been possible to clarify the events that occur in the rare single cells that eventually form iPSCs” During cellular reprogramming only small fractions of cells become iPSCs
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WORKFLOW
Capture the cells at early and intermediate stages of reprogramming
Thousands of single cells against 48 genes
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STUDYING LATER STAGES OF IPSCs FORMATION
Use the cells also from later stages of reprogramming
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PRINCIPAL COMPONENT (PCA) : projection of individual cells and each of 48 genes
“To globally visualize data we used PCA- a technique that reduces dimensionality of data by finding linear combinations (in this case the number of genes) of the original data ranked by their importance”
Expression of Efssrb, Utf1, Lin28 and Dppa2 is a better predictor of cells to progress Into iPSCs than expression of previously suggested: Fbxo15, Fgf4 and Oct4
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BIOLOGY VALIDATES SINGLE CELL GENE EXPRESSION SCREENING
MEFS were infected with combinations of transcription factors: used different combinations of transcription factors Excluded Exogenous Oct4, Sox2 or Nanog Showed that various combinations of factors could induce pluripotency even in the absence of exogenous Oct4, Sox2 and Nanog Activation of pluripotency is possible even in the absence of generic Yamanaka factors
TESTING COMBINATIONS OF KEY PLURIPOTENCY GENES THAT INDUCE REPROGRAMMING
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THANKS TO SINGLE CELL GENE EXPRESSION DATA AUTHORS PROPOSE STOCHASTIC AND DETERMINISTIC REPROGRAMMING OF MEFs TO IPSCs
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by
Innovation for Single-Cell genomics
C1™ Single-Cell AutoPrep System
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What is the C1 system?
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Cell Suspension
C1 Single-Cell AutoPrep Workflow
Export, analyze • qPCR (BioMark HD) • Next Gen Sequencing
Capture Single Cells
Multistep chemistry • RT-STA • mRNA-Seq Prep • Whole Genome Amp
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Preamplification Implementation
in Chip
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Pipetting Map for C1 System
Outputs
Wast
e
Wast
e
#1
#2
#3
#4
#5
#6
#7
#8
C1 Cell Wash Buffer
LIVE/DEAD Staining Solution
Lysis Mix
RT Mix
Cell Input PreAmp Mix C1 Preloading Reagent
C1 Harvest Reagent
C1 Harvest Reagent
C1 Harvest Reagent Cell Outlet
Outputs
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C1 featured in peer reviewed publications
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Innovation for Single-Cell genomics C1 and Biomark as a tool to identify on single cell level transcription factors for transformation of fibroblasts to cardiomyocyte like cells
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Single cell gene expression confirms essential factors for fibroblasts reprogramming
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Live dead Staining
Ethidium homodimer-1
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Single IPSC cell captured at the C1 nest
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The single prostate cancer cell captured at C1 nest
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Real Time PCR from single prostate cancer cells
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C1 capture of human neuroprogenitor cells
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Biomark heat map results for neuroprogenitor cells
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Success Built on Technology
Fluid Line
Control Line
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Enrich Load
& Capture
Wash &
Stain Isolate
Lyse, RT & Amplify
Prepare Library
Sequence
A simplified workflow
C1 Single-Cell Auto Prep System
Analyze
Any Illumina System
mRNA seq Workflow
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NATURE METHODS
Assessing the performance of C1 mRNA seq method
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Nanoliter sample preparation improves RNA seq sensitivity
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mRNA Amplification
• Works directly from cell lysate
• No RNA Fragmentation
• PolyA+ RNA
• Produces long ds cDNA
SMARTer (Clontech) Template-Switching Method
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Library preparation Nextera XT (Illumina) Transposase-based Method
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The capture of primary mice epithelial cell and generation of full length cDNA
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Single Liver Cancer Cell Captured at C1 Nest
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The quality of full length (Smarter kit) double stranded cDNAs confirmed by Real Time PCR on Biomark HD
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Reveal Hidden Variation: C1™ Single-Cell DNA Sequencing Workflow
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There Isn’t an “Average” Cell Population
Random, low-abundance mutations are currently inaccessible by standard high-throughput sequencing approaches because they cannot be distinguished from sequencing errors. One way to circumvent this problem and simultaneously account for the mutational heterogeneity within tissues is whole genome sequencing of a representative number of single cells.
Michael Gundry, PhD. Albert Einstein College of Medicine Nucleic Acid Research (March 2012)
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Three Major Single-Cell DNA Applications
Single-Cell
Whole Genome Sequencing
Comprehensive approach to
discover all possible somatic
mutations in both functional and
regulatory regions of the
genome.
Single-Cell
Whole Exome Sequencing
Faster and more cost effective
alternate approach to WGS to
discover causal variants in protein
coding regions of the genome,
most biological activity
Single-Cell
Targeted Resequencing
Screen for known mutations or
identify signatures that may
identify disease susceptibility,
progress or therapeutic impact.
Discovery Validation Screening
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• Random hexamer primed
• Isothermal amplification
• Average product length ~12kb
• Typical yield from C1™ IFC: 65-200 ng DNA
Multiple Displacement Amplification
1 2 3
4 5
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Flexible platform: • One universal sample prep workflow for targeted & whole genome
sequencing • Enables SNP, small indel, and translocation detection
Sensitive and robust
• Single-cell sensitivity • Broad sample compatibility
Efficient workflow:
• Easy to use • Rapid time to results • Multiple samples per run
Reliable data quality:
• Good genomic coverage and uniformity with low GC bias
C1TM DNA Seq: One workflow to discover genetic variants in
individual cells C1 DNA seq workflow for discovering genetic variants in cells
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WGA implementation on the C1TM WGA
IFC
4.5nl 9 nl 9 nl 9 nl 135 nl 135 nl
Lysis MDA Capture &
Wash Neutralization
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C1 PROTOCOL GENERATOR TOOL
A software tool you can use to develop new protocols on C1 Define load, lyse, wash and thermal cycling paramters
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C1 Script Builder Overview
The C1 Script Builder software is a standalone desktop software application). User will use the software to create custom scripts for C1 Open App IFC and import them to the C1 to run.
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C1 Script Builder Features
• Fluidigm provides STA, mRNA Seq, and DNA Seq templates for customer to modify:
• Users can modify reagent incubation temperature and duration (single temperature or multiple temperature steps)
• Script Builder validates the entire script workflow and estimates script lengths
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nature
Understand gene expression in seemingly identical cells in response to stimulation Sequenced 1770 single dendrytic cells
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C1 enabled interrogation of dynamic immune response at the single cell level
After the capture , stimulated individual Cells with LPS for 4h and then continued With lysis and amplification