Gene expression profiling in single cells Anders Ståhlberg Department of Chemistry & Biosciences / Molecular Biotechnology Chalmers University of Technology
Outline Technical considerations Gene expression profiling Outline
Why single cells? Background
Workflow single cell analysis Ce llcollection c olle c tion methods Cell Lysis Patch clamp capillaries Heat Lysis 80 d e g re e s 5 m in Reverse transcription RT e nzym e Flow cytometry RTp rim e rs Detergents (Igepal, Reproducibility RNa se inhib ito r Triton...) Laser dissection RT Buffe r Sufficient cdna yield Physical disruption RT Background RNA Pre-amplification 30-50 d e g re e s Data analysis QPCR 60-95 d e g re e s appropiate statistics number of cells Da ta a na lysis Linear amplification at Real-time PCR mrna level > 3h Gene specific assays amplification at Exponential C DNA cdna PCR inhibition level Limited amount PCR m a ste r m ix of with p rim ematerial rs for biological ta rg e t g e ne s Suitable controls Gene Gene expression expression profiling profiling in in single single cells cells Technical Technical considerations considerations
Gene expression in single cells 1600 1400 Gene expression 1200 1000 800 600 400 200 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Cell number
mrna distribution 20 40 Cell count 30 20 10 Cell count 15 10 5 0 0 0 1 2 3 4 5 6 7 8 9 10 11-1.0 12 13 14-0.5 0.0 0.5 1.0 Relative transcript levels Relative transcript levels Linear scale Lognormal scale
The median cell Geometric mean Arithmetic mean 20 15 Cell count 10 5 0-1.0-0.5 0.0 0.5 1.0 Relative transcript levels Lognormal scale
Effect of glucose Ins1 Ins2 20 mm Arithmetic Geometric ActB 3.3 4.9 Ins1 5.5 37 Ins2 2.6 43 5 mm Abcc8 1.4 1.5 Kcnj11 1.1 1.3
Models of gene expression Without e nha nc e r With e nh a nc e r Gene Gene E ( N N mrna 1 + 1+ 1+ mrna ) total = N mrna = 1+ 1+ 1+ 1+ 1+ 1+ = 6 Cell Rhe o sta tic m o d e ( N mrna N) total mrna = = 0 + N0 mrna + 0 + = 60 + 0 + 0 + 6 + 0 + 0 = Cell 6 Bina ry m o d e
Distribution skewness Skewness = - 0.70 Skewness = - 0.70 Skewness = 0 Skewness = 0.06 Skewness = - 0.12
Gene regulation at single cell level Arithmetic Geometric ActB 3.3 4.9 Ins1 5.5 37 Ins2 2.6 43 Abcc8 1.4 1.5 Kcnj11 1.1 1.3
Gene regulation -case I Gene 1 Gene 1 Intermediete Low expression Gene 2 expression Gene 2 High expression
Gene regulation -case II Gene 1 Gene 1 Intermediete Low expression Gene 2 expression Gene 2 High expression
Gene regulation in individual cells ActB Ins1 Ins2 Sur1 Kcnj11 ActB 1 Ins1 0.15 1 Ins2 0.12 0.90 1 Sur1-0.02-0.01 0.06 1 Kcnj11 0.11-0.02 0.24-0.15 1
Gene regulation - Ins1 & Ins2 Ins1 Ins1 Intermediete Low expression Ins2 expression Ins2 High expression
Gene regulation - Ins1 & ActB / Ins2 & ActB Ins1/2 Ins1/2 Intermediete Low expression ActB expression ActB High expression
Conclusions The combination of patch-clamp capillaries and reverse transcription realtime PCR is suitable for gene expression studies in individual cells Gene expression is lognormally distributed at a single cell level Gene regulation at single cell level do not necessary correlate at cell population level Bengtsson M. et al. Genome Research, in press Conclusions
Acknowledgments Martin Bengtsson Mikael Kubista Patrik Rorsman TATAA Biocenter