Abstract
Ratio imaging is playing an increasingly important role in modern cell biology. Combined with ratiometric dyes or fluorescence resonance energy transfer (FRET) biosensors, the approach allows the detection of conformational changes and molecular interactions in living cells. However, the approach is conducted increasingly under limited signal-to-noise ratio (SNR), where noise from multiple images can easily accumulate and lead to substantial uncertainty in ratio values. This study demonstrates that a far more serious concern is systematic errors that generate artificially high ratio values at low SNR. Thus, uneven SNR alone may lead to significant variations in ratios among different regions of a cell. Although correct average ratios may be obtained by applying conventional noise reduction filters, such as a Gaussian filter before calculating the ratio, these filters have a limited performance at low SNR and are prone to artefacts such as generating discrete domains not found in the correct ratio image. Much more reliable restoration may be achieved with multi-resolution denoising filters that take into account the actual noise characteristics of the detector. These filters are also capable of restoring structural details and photometric accuracy, and may serve as a general tool for retrieving reliable information from low-light live cell images.