Computational methods for label-free multiphoton fluorescence lifetime imaging microscopy of fast metabolic dynamics
Abstract: Due to constraints in instrumentation bandwidth, many advanced microscopy techniques suffer from slow acquisition, restricting applications that require high throughput and/or fast dynamic imaging. To overcome this challenge, computational methods can be used to reconstruct temporal and spectral signatures from rapidly acquired optical signatures using well-defined and calibrated acquisition electronics. Using Single- and multi-photon Peak Event Detection (SPEED), time-resolved photon counts for multiphoton fluorescence intensity and lifetime images are acquired faster than ever previously demonstrated. This is achieved by bypassing slow photon-counting analog electronics, directly digitizing detector output, and determining photon counts via FPGA- and GPU-accelerated processing. This method can be used to collect label-free dynamics in a wide variety of live samples, relying on contrast from multiphoton autofluorescence intensity and lifetime of key metabolic cofactors. Rapid metabolic dynamics of samples such as apoptotic breast cancer cells and bacteria treated with antibiotics can be observed and used to classify differences in susceptibility to treatment.
Bio: Janet Sorrells is a PhD Candidate at the University of Illinois at Urbana-Champaign in the lab of Professor Stephen Boppart. Her research is on new methods and applications of optical microscopy for metabolic imaging. Specifically, her research is focused on computationally-inspired solutions for faster multimodal nonlinear optical microscopy for use in microbiology.
