Localizer:fast, accurate, open-source, and modular software package for superresolution microscopy

We present Localizer, a freely available and open source software package that implements the computational data processing inherent to several types of superresolution fluorescence imaging, such as localization (PALM/STORM/GSDIM) and fluctuation imaging (SOFI/pcSOFI). Localizer delivers high accuracy and performance and comes with a fully featured and easy-to-use graphical user interface but is also designed to be integrated in higher-level analysis environments. Due to its modular design, Localizer can be readily extended with new algorithms as they become available, while maintaining the same interface and performance. We provide front-ends for running Localizer from Igor Pro, Matlab, or as a stand-alone program. We show that Localizer performs favorably when compared with two existing superresolution packages, and to our knowledge is the only freely available implementation of SOFI/pcSOFI microscopy. By dramatically improving the analysis performance and ensuring the easy addition of current and future enhancements, Localizer strongly improves the usability of superresolution imaging in a variety of biomedical studies

Quantitative comparison of camera technologies for cost-effective super-resolution optical fluctuation imaging (SOFI)

Van den Eynde R, Sandmeyer A, Vandenberg W, et al. Quantitative comparison of camera technologies for cost-effective super-resolution optical fluctuation imaging (SOFI). Journal of Physics: Photonics. 2019;1(4): 044001

Model-free uncertainty estimation in stochastical optical fluctuation imaging (SOFI) leads to a doubled temporal resolution

Stochastic optical fluctuation imaging (SOFI) is a super-resolution fluorescence imaging technique that makes use of stochastic fluctuations in the emission of the fluorophores. During a SOFI measurement multiple fluorescence images are acquired from the sample, followed by the calculation of the spatiotemporal cumulants of the intensities observed at each position. Compared to other techniques, SOFI works well under conditions of low signal-to-noise, high background, or high emitter densities. However, it can be difficult to unambiguously determine the reliability of images produced by any superresolution imaging technique. In this work we present a strategy that enables the estimation of the variance or uncertainty associated with each pixel in the SOFI image. In addition to estimating the image quality or reliability, we show that this can be used to optimize the signal-to-noise ratio (SNR) of SOFI images by including multiple pixel combinations in the cumulant calculation. We present an algorithm to perform this optimization, which automatically takes all relevant instrumental, sample, and probe parameters into account. Depending on the optical magnification of the system, this strategy can be used to improve the SNR of a SOFI image by 40% to 90%. This gain in information is entirely free, in the sense that it does not require additional efforts or complications. Alternatively our approach can be applied to reduce the number of fluorescence images to meet a particular quality level by about 30% to 50%, strongly improving the temporal resolution of SOFI imaging

Caracterización del remodelado del colágeno asociado a la edad en el ventrículo izquierdo humano de donantes vivos y sus implicaciones en la generación de arritmias

La edad es un factor de riesgo arrítmico que, en especies animales, está asociado a la remodelación del colágeno. En este trabajo caracterizamos la dinámica tisular del colágeno asociada a la edad en el ventrículo izquierdo humano y utilizamos estos resultados para simular su efecto sobre la generación de arritmias ventriculares

Fluorescent protein optimization for advanced microscopy applications: Photochromism and super-resolution ,,

Fluorescence microscopy is a powerful method to study living systems with high spatial and temporal resolution. However, the resolution of a conventional microscope is limited by diffraction, which precludes the direct visualization of many biological processes occurring at their small scale. During the last decade, several super-resolution fluorescence microscopy methods have been developed that break this diffraction limit and offer a new revolutionary view on structures with a size in the range of 100 nm and smaller. One of these techniques is pcSOFI, which distills sub-diffraction information out of a statistical analysis of blinking fluorophores. The necessary blinking for pcSOFI is most easily generated by using reversibly switchable fluorescent proteins (RSFPs), a class of photochromic derivatives of the green fluorescent protein (GFP), discovered in 1962. The special feature of these genetically encoded fluorophores is their capacity to reversibly switch between a fluorescent on-state and a non-fluorescent off-state, depending on the light with which they are irradiated. Continuous switching between the two states results in fluorescence blinking, suitable for pcSOFI analysis. Because of the key-role “photophysically smart labels”, such as RSFPs, play in super-resolution imaging, optimal performance of the methods is largely dependent on the quality of the used fluorophores. The development of optimized variants is thus a crucial step towards achieving the full potential of sub-diffraction microscopy. Within this dissertation, I outline the basics of how to create and characterize improved fluorescent proteins, and describe my efforts in developing RSFPs with beneficial properties for advanced imaging. In Chapter 2, I describe a series of mutants based on Dronpa and Dronpa2, which are a slow and a fast photoswitcher. By creating structural variation and optimizing the expression, I paved the way for the creation of refSOFI, a complementation approach that allows the visualization of protein-protein interactions with super-resolution. Other variants of Dronpa and Dronpa2 were shown to exhibit more fluorescence when expressed at 37°C and maintained efficient photoswitching characteristics. In Chapter 3, I introduce the rsGreens, which were developed using a strategy especially suited for optimizing “smart” fluorescent proteins. I provide an in depth characterization of a range of mutants, in terms of spectroscopic, photochromic and biological properties. The work on rsGreens is continued in Chapter 4, where I describe the structural analysis of rsGreen0.7 and the lessons learned about biological performance and photoswitching. This information is subsequently used for the structure-guided development of new rsGreen variants with significantly altered photoswitching characteristics. The final results chapter, Chapter 5, provides an extensive description of pcSOFI and includes an analysis of the method’s performance under different imaging conditions. I also present the first results obtained with multi-tau (mt) pcSOFI, which is a pcSOFI approach that can separate multiple spectrally similar fluorophores based on their blinking behavior. The development of a large number of new RSFPs, with beneficial biological and photochromic properties significantly increases the number of available “smart probes”. The characterization of the created RSFPs also contributes to a better understanding of the mechanisms behind all the different processes, which may benefit further developments. By aiding the development of refSOFI, by showing the potential of mt-pcSOFI and by performing the quality assessment of regular pcSOFI, I hope to have broadened the application area of pcSOFI and super-resolution microscopy in general.nrpages: 196status: publishe

Super-resolution imaging goes fast and deep

Advances in image scanning microscopy move super-resolution imaging deeper into tissues with faster visualization and finer details.status: publishe

Diffraction-unlimited fluorescence microscopy of living biological samples using pcSOFI

The complex microscopic nature of many live biological processes is often obscured by the diffraction limit of light, requiring diffraction-unlimited fluorescence microscopy to resolve them. Because of the vast range of different processes that can be studied, sub-diffraction imaging should work efficiently under many different conditions. Photochromic stochastic optical fluctuation imaging (pcSOFI) is a recent addition to the field of diffraction-unlimited fluorescence microscopy. This robust and versatile method employs a statistical analysis of random fluctuations in the emission of single labels, in this case reversibly switchable fluorescent proteins (RSFPs), to retrieve super-resolution information. Added to the resolution enhancement, pcSOFI also offers contrast enhancement and background reduction in a practical and convenient way. Here, we describe the necessary steps to obtain diffraction-unlimited images, including multicolor and three-dimensional imaging, and highlight the advantages of pcSOFI together with the circumstances under which pcSOFI can be favorably applied.status: publishe

Localizer: fast, accurate, open-source, and modular software package for superresolution microscopy

We present Localizer, a freely available and open source software package that implements the com- putational data processing inherent to several types of superresolution fluorescence imaging, such as localization (PALM/STORM/GSDIM) and fluctuation imaging (SOFI/pcSOFI). Localizer delivers high accuracy and performance and comes with a fully featured and easy-to-use graphical user interface but is also designed to be integrated in higher-level analysis environments. Due to its modular design, Localizer can be readily extended with new algo- rithms as they become available, while maintaining the same interface and performance. Weprovide front-ends for running Localizer from Igor Pro, Matlab, or as a stand-alone program. We show that Localizer performs favorably when compared with two existing superresolution packages, and to our knowledge is the only freely available implementation of SOFI/pcSOFI microscopy. By dramatically improving the analysis performance and ensuring the easy addition of current and future enhancements, Localizer strongly improves the usability of superresolution imaging in a variety of biomedical studies.status: publishe

Localizer: fast, accurate, open-source, and modular software package for superresolution microscopy

We present Localizer, a freely available and open source software package that implements the computational data processing inherent to several types of superresolution fluorescence imaging, such as localization (PALM/STORM/GSDIM) and fluctuation imaging (SOFI/pcSOFI). Localizer delivers high accuracy and performance and comes with a fully featured and easy-to-use graphical user interface but is also designed to be integrated in higher-level analysis environments. Due to its modular design, Localizer can be readily extended with new algorithms as they become available, while maintaining the same interface and performance. We provide front-ends for running Localizer from Igor Pro, Matlab, or as a stand-alone program. We show that Localizer performs favorably when compared with two existing superresolution packages, and to our knowledge is the only freely available implementation of SOFI/pcSOFI microscopy. By dramatically improving the analysis performance and ensuring the easy addition of current and future enhancements, Localizer strongly improves the usability of superresolution imaging in a variety of biomedical studies