Embedded Fluorescence Lifetime Determination for High-Throughput, Low-Photon-Number Applications
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Time-resolved fluorescence (TRF) analysis is considered to be among the primary research tools in biochemistry and biophysics. One application of this method is the investigation of biomolecular interactions with promising applications for biosensing. For the latter context, time-correlated single photon counting (TCSPC) is the most sensitive, hence preferred implementation of TRF. However, high throughput applications are presently limited by the maximum achievable photon acquisition rate, and even more by the data processing rate. The latter rate is actually limited by the computational complexity to estimate accurately the fluorescence lifetime from TCSPC data. Here we propose a solution that would enable the implementation of TRF detection for fluorescence-activated droplet sorting (FADS), a particularly high throughput, microfluidic-based technology that can be used for drug discovery and thus help finding new cures for diseases. Most fluorescence lifetime algorithms require a large number of detected photons for an accurate lifetime computation. This paper presents an implementation based on a maximum likelihood estimator (MLE), enabling high precision estimation with a limited number of detected photons, significantly reducing the total measurement time. This speedup rapidly increases the input data rate. As a result, off-the-shelf embedded products cannot handle the data rates produced by current TCSPC units that are used to measure the fluorescence. Therefore, a configurable real-time capable hardware architecture is implemented on a field-programmable gate array (FPGA) that can handle the data rates of future TCSPC units, rendering high throughput droplet sorting with microfluidics possible. The presented hardware architecture is validated with experimental input data and produces high precision results.
KeywordsFPGA Time-resolved fluorescence Fluorescence lifetime Microfluidics Embedded signal processing
This work was financially supported by the Research Training Group 1773 “Heterogeneous Image Systems”, funded by the German Research Foundation (DFG).
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