µQFA:
Like QFA, only more awesomer

Conor Lawless
SBSB March 2012

http://lwlss.net/talks/uqfa

What is QFA?

• An extremely high-throughput technology: screens fitness of thousands of independent microbial cultures
• Time-lapse photography of solid agar plates arrayed with up to 308 independent cultures captures rates of change in culture cell density
• Colonyzer gives cell density estimates from pixels
• Dilute culture inoculum allows capture of exponential growth rate from growth curves (fitting logistic model)
• Incubating plates at different temperatures allows investigation of temp. sensitivity (e.g. TS mutations)
• Adding drugs & nutrients to agar or irradiating plates allows investigation of their effects on growth
• Two phenotypes: population growth rate and yield

QFA Growth curves by photographing agar plates

$d(t) = \frac{K d_0 e^{rt}}{K + d_0 \left( e^{rt} - 1\right)}$

Logistic population growth model

QFA epistasis plot - interaction with query mutation

What is µQFA?

• Plain old high-throughput technology for simultaneous screening of the fitness of up to 308 microbial cultures
• Same principles as QFA, but uses microscopy instead of photography to estimate cell density
• Individual cells detected immediately on inoculation
• Growth rate for sick cells which only divide a few times
• Growth rates during both lag and exponential phases
• Growth rate heterogeneity between lineages (cell viability)
• Opportunity for examining fluorescence markers in cell lineages
• Several simultaneous phenotypes: very rich data

Preparing to inoculate a µQFA plate

Preparing to inoculate a µQFA plate

Main µQFA experimental components

• Warm room to house microscope (temperature control)
• Nikon 4x Plan APO objective lens
• Computer-controlled stage (x,y & z axes)

Main µQFA software components

• Micromanager - open source stage control, autofocus and image capture with Python API
• OpenCV - open source C++ computer vision library with Python API

µQFA growth rate distributions

µQFA growth rate distributions

Conclusions

• µQFA experimental protocol & analysis are working
• Tracks cell lineages & provides growth rate estimates immediately after inoculation
• Estimates population growth rate distributions & identifies proportion of viable cells
• Could be used to investigate correlation between growth rate and protein expression
• Validate dynamic, stochastic, population models of growth or statistical models of genetic interaction

Acknowledgements

David Lydall, Peter Banks, Alan Leake

A common model of microbial growth - evidence for lag phase in µQFA observations?