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Rupprecht, P.

Publications and source records attributed to Rupprecht, P..

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PURELIGHT: a quantitative photon-counting framework unifying intensity and lifetime imaging at video rate across detector technologies

Quantitative fluorescence microscopy requires photon-efficiency, speed and accurate intensity and lifetime measurements. Time-correlated single-photon counting (TCSPC) simultaneously captures intensity and lifetime, but photon pile-up distorts both signals at high count rates, preventing fast acquisitions. Existing corrections discard photons, distort intensity, or require specialized detectors. Here we introduce PURELIGHT, an integrated hardware and software framework that simultaneously recovers undistorted intensities and lifetimes at count rates far beyond conventional pile-up limits. PURELIGHT works with hybrid photodetectors, silicon photomultipliers and photomultiplier tubes while retaining over three times more photons than alternative approaches. Using two-photon imaging, we showcase PURELIGHT's superior accuracy and spatial contrast, demonstrating video-rate subcellular lifetime imaging in awake mice, a unique lifetime-calibrated ratiometric modality and crosstalk-free temporal multiplexing. By removing the limits that have confined TCSPC to low-signal applications, PURELIGHT promotes the adoption of quantitative, photon-efficient microscopy across the life sciences.

neuroscience

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience