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TASBE Synthetic Biology Tools: Calibrated Flow Cytometry

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1 TASBE Synthetic Biology Tools: Calibrated Flow Cytometry
Jacob Beal, Aaron Adler, Fusun Yaman Revised June, 2014

2 Curriculum Why Single Cell Measurements?
Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

3 Distributions Matter Are these groups of cells the same?
But they've got the same mean fluorescence…?

4 Engineering must consider variation
Biological systems exhibit large variation in behavior due to many classes of cause, including: Inherent process stochasticity e.g., transcription, translation, replication, … Cell-to-cell differences e.g., size, cycle state, health, mutations, location, … Protocol stochasticity e.g., transfection variation, insertion site, … Protocol execution issues e.g., reagent variation, contamination, instrument drift, ... System engineering must handle each class differently! Largely uncorrelated effects on individual system elements Highly correlated effects on individual system elements Predictable distributions, affected by choice of protocol parameters Unpredictable, must be detected and appropriately compensated

5 Example: Metabolic Process Composition
Enzyme 1 Enzyme 2 A B C Input Intermediate Product What is the effect of variation in enzyme performance? Different classes of variation have different effects: uncorrelated per-cell variation  decreased overall variation correlated per-cell varation  increased variation, selection pressure protocol variation  batch-to-batch "fickleness"

6 Example: Detecting Protocol Failure
Bad Transfection Good Samples Bad Samples Distribution Model By modeling transfection distribution, can detect failure of batches or individual samples [Beal et al., 2012; Kiani et al., 2014; Davidsohn et al, submitted]

7 Per-Cell Measurements Reveal Variation
Example: RNA Replicon Cotransfection Example: Constitutive mKate in HEK293 Cell Variation Dose Variation Quantization Dose Efficiency Spectral Bleed Autofluorescence Dose/Resource Variation Transfection Efficiency [Beal et al., 2012] [Beal et al., submitted] Quantifying variation components requires per-cell measurements of large populations of cells

8 Curriculum Why Single Cell Measurements?
Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

9 Output R2 R1 First, some metrology… Unit mismatch! [Output] [R2] [R1]
Arbitrary Red Arbitrary Blue [R2] [R1] [Output] R2 R1 Unit mismatch!

10 Good Engineering Needs Absolute Units
Metrology 101: precision measurement enables: Comparison of results across experiments and labs Deeper insight into the behavior of devices Effective dissemination of materials and methods Testing and validation of materials and systems Establishment of commercial & industrial services Safety assurance and traceability of responsibility Many biological measurements are only relative!

11 How Flow Cytometry Works
Challenges: Autofluorescence Variation in measurements Spectral overlap Time Contamination Lots of data points! Different protein fluorescence Individual cells behave (very) differently

12 Metrology vs. Flow Cytometry
Flow cytometry great for per-cell measurements, but… Arbitrary unit output depends on… Instrument brand, configuration Interference from other colors Choice of instrument settings Run-to-run calibration drift These are not physical units! Blue (Arbitrary Units) Fortunately, this can be corrected… Yellow (Arbitrary Units)

13 Calibrated Flow Cytometry
Standardized Units (MEF) Autofluorescence Subtraction [Roederer, 2002; Wang et al., 2008; NIST/ISAC, 2012; Beal et al., 2012; Kiani et al., 2014; Davidsohn et al, submitted] Non-Distorted Compensation ColorEquivalence Mapping Result: replicable measurements in absolute units (MEFL), rather than non-replicable relative units as usual practice.

14 Example: Predicting Repressor Cascades
Precision dose-response measurement allows high-precision prediction with quantitative models pCAG Dox T2A rtTA3 VP16Gal4 pTRE EBFP2 R1 pUAS-Rep1 pUAS-Rep2 EYFP R2 mkate Prediction of Repressor Cascade Range vs. Error for 6 Cascades TAL14  TAL21 +: experimental o: predicted Each line is a dose/response curve for a different relative number of circuit copies. Subpopulation identified by color on inset mKate histogram [Davidsohn et al., submitted]

15 How much does calibration matter?
8x tighter range just by calibration! (2.8x better high errors, 2.8x better low errors) [Davidsohn et al., submitted]

16 Example: Engineering Replicon Expression
Per-cell measurement of dose-response gives model allowing high-precision control of expression mVenus nsP1-4 SGP Example: Prediction of fluorescence vs. time for novel mixtures of 3 Sindbis RNA replicons Mix 1: 0.1Y, 0.1R, 0.1B Mix 2: 0.3Y, 0.3R, 0.3B Mix 3: 0.1Y, 0.5R, 0.4B Mix 4: 0.2Y, 0.2R, 0.6B Mix 5: 0.01Y, 0.1R, 0.5B Mix 6: 0.4Y, 0.02R, 0.02B mKate nsP1-4 SGP EBFP2 nsP1-4 SGP Example Prediction of 3-RNA Replicon Mix: Range vs. Error for 6 Mixtures Mix Number [Beal et al., 2014]

17 Example: Device Engineering
Absolute comparisons, segmentation vs. constitutive fluorescence allow better design and debugging of devices Example: CRISPR Device Design Distribution analysis identifies introns as a problem area Original intronic design: Modified design to fix intron issues: Device ig-b Normal TRE induction Expected high-copy leak Device ig-c Max ~5x repression Max ~20x repression no response & no leaky expression  intron failure [Kiani et al., 2014]

18 Curriculum Why Single Cell Measurements?
Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

19 TASBE Public Web Interface
https://synbiotools.bbn.com/ On first use, you will have to terms of service Your data is secure, and can’t be shared on site. FireFox recommended; Chrome has an image-display bug. Register: individual accounts or group account? Anonymous access also available (but not private)

20 Online Help Tutorial FAQ / Debug Support

21 TASBE Flow Cytometry Workflow

22 Setting up your Flow Cytometer
Only needs to be done once Exactly as it appears in machine For your records - not used by tools Add every channel you might ever want to use

23 Curriculum Why Single Cell Measurements?
Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

24 Fluorescent Beads  Absolute Units
SpheroTech RCP-30-5A Tool currently only supports SpheroTech RCP-30-5A Run beads every time: flow cytometers drift up to 20 percent! Also can detect instrument problems, mistakes in settings

25 Compensating for Autofluorescence
Use your negative control for this

26 Compensating for Spectral Overlap
Use a strong positive control for each color Note: only linear when autofluorescence subtracted [cf. Roederer 2002]

27 Translating Fluorescence to MEFL
Only FITC channel (e.g. GFP) goes directly Others obtained from triple/dual constitutive controls Must have exact same constitutive promoter! Must have a FITC control protein! This is a new control that you need to add

28 Creating a Color Model (1/5)
When you pick the machine, wait a moment and available channels will fill in Pick all channels used

29 Creating a Color Model (2/5)
Negative control Calibration beads Similar entries for single positive controls

30 Creating a Color Model (3/5)
Must have at least FITC-X for each other fluorescence X If you only have two colors, repeat one here

31 Creating a Color Model (4/5)
The same control set can be run with different parameters Make sure you pick this right! Leave these alone unless there are model problems Free choice of labels

32 Creating a Color Model (5/5)
Results: Lots of graphs for you to check result validity Online model for one-variable experiments Matlab .mat file that you can use for calibration by hand

33 Bead problems? Usually means either:
you didn’t run enough beads, or your low peaks are being affected by sensor noise, or you've got a serious flow cytometer hardware problem First step: adjust peak & minimum thresholds

34 Curriculum Why Single Cell Measurements?
Why Calibrated Flow Cytometry? https://synbiotools.bbn.com Color Models One-Variable Experiments

35 One-Variable Characterization
Rapid, high-precision device characterization with reproducible absolute units Output Dox R1 pCAG Dox T2A rtTA3 VP16Gal4 pTRE EBFP2 R1 pUAS-Rep1 EYFP mkate Transfer curve for TAL 14 Transfer curve for TAL 21 Each line is a dose/response curve for a different relative number of circuit copies. Subpopulation identified by color on inset mKate histogram R1 = TAL14 R1 = TAL21

36 Experiment Assumptions
Each functional unit on its own plasmid (but you can work with combined systems too) 3 colors: input, output and constitutive (but you can kludge your way down to two colors) Significant variation in circuit copy number (but you can get population statistics too)

37 TASBE Characterization Method
Output Dox R1 pCAG Dox T2A rtTA3 VP16Gal4 pTRE EBFP2 R1 pUAS-Rep1 EYFP mkate Transient cotransfection of 5 plasmids Calibrated flow cytometry Analysis by copy-count subpopulations

38 Multi-plasmid cotransfection!?!
Avoids all problems with adjacency, plasmid size, sequence validations Variation appears to be independent

39 One-Variable Analysis (1/5)
Make sure you'll be able to understand these several months from now

40 One-Variable Analysis (2/5)
If you only have 2 FPs, make Input = Output. If you only have 1 FP, make all the same

41 One-Variable Analysis (3/5)
Each column is a replicate Each row is an independent variable value Adjust rows, columns Units ignored - you can do time, voltage, whatever

42 One-Variable Analysis (4/5)
Expected range of fluorescence (log10) Generally leave these alone

43 One-Variable Analysis (5/5)
Results: Matlab .mat file, CSV file, lots of graphs Three key graphs, others are preliminary: Input Induction Constitutive Histogram Input/Output Population graphs too, but they are delicate… better to compute from .mat

44 Summary Calibrated flow cytometry enables order-of-magnitude increase in accuracy TASBE "color model" tool  calibrated units TASBE "characterization" tool does single-variable experiments Best with 3-color mammalian cotransfections Also works with other organisms, less colors, etc. Online tools produce graphs and Matlab files


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