Embedded Fluorescence Lifetime Determination for High-Throughput, Low-Photon-Number Applications
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).
- 1.DE0-Nano-SoC User Manual. 9F., No.176, Sec.2, Gongdao 5th Rd, East Dist, Hsinchu City, 30070. Taiwan (2015). http://www.terasic.com.tw/attachment/archive/941/DE0-Nano-SoC_User_manual.pdf.
- 2.TimeHarp 260. Rudower Chaussee 29, 12489 Berlin, Germany (2016). http://www.picoquant.com/images/uploads/downloads/timeharp260.pdf.
- 3.Baret, J.C., Miller, O.J., Taly, V., Ryckelynck, M., El-Harrak, A., Frenz, L., Rick, C., Samuels, M.L., Hutchison, J.B., Agresti, J.J., Link, D.R., Weitz, D.A., Griffiths, A.D. (2009). Fluorescence-activated droplet sorting (fads): efficient microfluidic cell sorting based on enzymatic activity. Lab on a Chip, 9, 1850–1858. https://doi.org/10.1039/B902504A.CrossRefGoogle Scholar
- 4.Digman, M.A., Caiolfa, V.R., Zamai, M., Gratton, E. (2008). The phasor approach to fluorescence lifetime imaging analysis. Biophysical Journal, 94(2), L14–L16. https://doi.org/10.1529/biophysj.107.120154. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2157251/. 120154[PII].
- 5.Franch, N., Alonso, O., Canals, J., Vilà, A., Herms, A., Dieguez, A. (2016). A low cost fluorescence lifetime measurement system based on spad detectors and fpga processing. In 2016 Conference on design of circuits and integrated systems (DCIS) (pp. 1–6). https://doi.org/10.1109/DCIS.2016.7845266
- 9.Ioanna Skilitsi, A., Turko, T., Cianfarani, D., Barre, S., Uhring, W., Hassiepen, U., Leonard́, J. (2017). Towards sensitive, high-throughput, biomolecular assays based on fluorescence lifetime. Methods and Applications in Fluorescence, 5(3), 034,002. https://doi.org/10.1088/2050-6120/aa7f66.
- 10.Köllner, M., & Wolfrum, J. (1992). How many photons are necessary for fluorescence-lifetime measurements? Chemical Physics Letters, 200(1), 199–204. https://doi.org/10.1016/0009-2614(92)87068-Z. http://www.sciencedirect.com/science/article/pii/000926149287068Z.
- 12.Léonard, J., Dumas, N., Causse, J.P., Maillot, S., Giannakopoulou, N., Barre, S., Uhring, W. (2014). High-throughput time-correlated single photon counting. Lab on a Chip, 14, 4338–4343. https://doi.org/10.1039/C4LC00780H.
- 13.Li, D.D.U., Arlt, J., Tyndall, D., Walker, R., Richardson, J., Stoppa, D., Charbon, E., Henderson, R.K. (2011). Video-rate fluorescence lifetime imaging camera with cmos single-photon avalanche diode arrays and high-speed imaging algorithm. Journal of Biomedical Optics, 16(9), 096,012–096,012–12. https://doi.org/10.1117/1.3625288.
- 14.Li, D.U., Arlt, J., Richardson, J., Walker, R., Buts, A., Stoppa, D., Charbon, E., Henderson, R. (2010). Real-time fluorescence lifetime imaging system with a 32 × 32 0.13 μm cmos low dark-count single-photon avalanche diode array. Optics Express, 18 (10), 10,257–10,269. https://doi.org/10.1364/OE.18.010257. http://www.opticsexpress.org/abstract.cfm?URI=oe-18-10-10257.
- 15.Lieske, T., Uhring, W., Dumas, N., Léonard, J., Fey, D. (2017). Embedded fluorescence lifetime determination for high throughput real-time droplet sorting with microfluidics. In 2017 Conference on design and architectures for signal and image processing (DASIP).Google Scholar
- 16.Liu, J., Sun, Y., Qi, J., Marcu, L. (2012). A novel method for fast and robust estimation of fluorescence decay dynamics using constrained least-squares deconvolution with laguerre expansion. Physics in Medicine and Biology, 57(4), 843. http://stacks.iop.org/0031-9155/57/i=4/a=843.
- 17.Maus, M., Cotlet, M., Hofkens, J., Gensch, T., De Schryver, F.C., Schaffer, J., Seidel, C.A.M. (2001). An experimental comparison of the maximum likelihood estimation and nonlinear least-squares fluorescence lifetime analysis of single molecules. Analytical Chemistry, 73(9), 2078–2086. https://doi.org/10.1021/ac000877g. PMID: 11354494.CrossRefGoogle Scholar
- 19.Rowley, M.I., Barber, P.R., Coolen, A.C.C., Vojnovic, B. (2011). Bayesian analysis of fluorescence lifetime imaging data. https://doi.org/10.1117/12.873890.
- 20.Schmid, L., Weitz, D.A., Franke, T. (2014). Sorting drops and cells with acoustics: acoustic microfluidic fluorescence-activated cell sorter. Lab Chip, 14, 3710–3718. https://doi.org/10.1039/C4LC00588K.
- 21.Swaminathan, R., & Periasamy, N. (1996). Analysis of fluorescence decay by the maximum entropy method: Influence of noise and analysis parameters on the width of the distribution of lifetimes. Proceedings of the Indian Academy of Sciences - Chemical Sciences, 108(1), 39. https://doi.org/10.1007/BF02872511.Google Scholar
- 23.Tyndall, D., Rae, B.R., Li, D.D.U., Arlt, J., Johnston, A., Richardson, J.A., Henderson, R.K. (2012). A high-throughput time-resolved mini-silicon photomultiplier with embedded fluorescence lifetime estimation in 0.13 μm cmos. IEEE Transactions on Biomedical Circuits and Systems, 6(6), 562–570. https://doi.org/10.1109/TBCAS.2012.2222639.
- 24.Whitesides, G.M. (2006). The origins and the future of microfluidics. Nature, 442(7101), 368–373. https://doi.org/10.1038/nature05058.