Fundamental properties of light unavoidably impose features on images collected using fluorescence microscopes. Accounting for these features is often critical in quantitatively interpreting microscopy images, especially those gathering information at scales on par with or smaller than light's emission wavelength. Here the optics responsible for generating fluorescent images, fluorophore properties, and microscopy modalities leveraging properties of both light and fluorophores, in addition to the necessarily probabilistic modeling tools imposed by the stochastic nature of light and measurement, are reviewed.

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For a long time, the resolution of light microscopy was restricted to approximately 200 nm, as described by Abbe’s diffraction limit. Single-Molecule Localization Microscopy (SMLM) overcomes this limit by capturing many frames of a sample labeled with blinking fluorophores, where each frame shows a different subset of molecules. The fluorophores are located by fitting a model of their point spread function (PSF) and these localizations are combined to create an image with 20-50 nm resolution. Localizing a fluorophore is only possible when no other fluorescent molecules are active in the surrounding diffraction-limited area so that its PSF appears as an isolated blob. However, high-density regions unavoidably contain overlapping PSFs, which lead to localization errors as the number of emitters in the region of interest is unknown. To prevent this, the density of active fluorophores is typically kept between 0.01-0.1 μm−2 and data acquisition can take up to days, making dynamic imaging impossible. This theoretical research explores the possibility of enabling high-density SMLM by using Single-Photon Avalanche Diode (SPAD) arrays, detecting every incident photon with picosecond timing precision, instead of capturing the total intensity in a 10-100 ms interval like the conventionally used sCMOS cameras. The photon arrival times from simulated SPAD measurements are used to determine the number of emitters in the field of view, which is directly related to the second-order quantum coherence of the signal. This coherence can be calculated by dividing the arrival times into discrete time bins and convoluting the signal with itself. To correct the number of emitters for the bias that is introduced by the discretization of the data, an analytical expression is derived and validated with simulations. Using this correction, the number of Alexa647 fluorophores can be determined from a 0.1 ms simulated measurement with a relative standard error of 1% independent of emitter count, using a laser intensity of 330 kWcm−2 and 100% detection efficiency. Considering an experimental setting in which 10 kWcm−2 intensity and 10% detection efficiency are more realistic, a 1.5 s measurement is needed to obtain the same accuracy, and a 15 ms interval is required to obtain a standard error of 10%. The newly acquired information about the number of emitters will make it possible to locate fluorophores with overlapping point spread functions for multi-emitter fitting. Consequently, SPAD arrays will provide the ability to image high emitter densities, which enables faster data acquisition and dynamic imaging.

Biodesign plays a critical role in developing innovative solutions, such as carbon capturing with living materials that incorporate photosynthetic organisms. Team effectiveness is a crucial aspect of successful collaboration and has been extensively studied in various fields. However, research on team effectiveness in the emerging field of Biodesign remains limited. This study aims to fill this gap by examining the impact of transdisciplinary collaboration on team effectiveness and proposing strategies for its enhancement in the future.
In scientific teams, factors such as individual expertise, disciplinary composition, and social dynamics significantly influence team effectiveness. Issues related to team leadership, coordination, and communication have been identified as major contributors to problems in many industries.
This study focuses on exploring how the convergence of different scientific backgrounds and individual perspectives within transdisciplinary Biodesign teams influences team effectiveness. By gaining valuable insights into the dynamics at play, this research aims to lay the foundation for the development of a tool that can enhance team effectiveness across Biodesign projects.
To address the research objective, several sub-questions are proposed. The first sub-question explores themes such as team processes, social networks, hierarchy, disciplinarity, and participatory design that impact team effectiveness in scientific teams. A comprehensive literature study will shed light on this sub-question. The second sub-question investigates the specific themes that affect overall team effectiveness in the Biodesign field. An exploratory case study, involving six expert interviews, will provide insights into this sub-question. Finally, the third sub-question explores the potential of leveraging these identified themes to enhance the team effectiveness of a Biodesign research group. This sub-question will be answered by connecting the findings from the literature study to the data obtained from the case study.
In the final phase of this thesis, a tool called the Dual Feedback System (DFS) was developed with the purpose of enhancing team effectiveness in Biodesign and other academic teams. DFS comprises two components: the Satisfaction Survey and the Feedback Meeting. It involves BEP, MEP, or PhD students and their supervisors who complete the survey to provide feedback on different aspects of the ongoing project, including project satisfaction, teamwork, and supervision. The survey results serve as the foundation for the subsequent Feedback Meeting, which aims to facilitate open communication and bridge the existing hierarchical gap between participants, fostering a collaborative and constructive environment.
Overall, this thesis contributes to our understanding of team effectiveness in the Biodesign field and provides valuable insights in how collaborations can be improved and team effectiveness in transdisciplinary research teams can be enhanced. The findings serve as the basis for the development of DFS, designed to enhance team effectiveness in Biodesign and other academic teams. The implementation of this tool within teams at TU Delft will further amplify its impact, particularly in important areas such as sustainability.

Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington’s disease, which is caused by a genetic mutation in exon 1 of the Huntingtin protein (Httex1). The fluorescent labels commonly used to visualize and monitor the dynamics of protein expression have been shown to alter the biophysical properties of proteins and the final ultrastructure, composition, and toxic properties of the formed aggregates. To overcome this limitation, we present a method for label-free identification of NDD-associated aggregates (LINA). Our approach utilizes deep learning to detect unlabeled and unaltered Httex1 aggregates in living cells from transmitted-light images, without the need for fluorescent labeling. Our models are robust across imaging conditions and on aggregates formed by different constructs of Httex1. LINA enables the dynamic identification of label-free aggregates and measurement of their dry mass and area changes during their growth process, offering high speed, specificity, and simplicity to analyze protein aggregation dynamics and obtain high-fidelity information.

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High-NA light sheet illumination can improve the resolution of single-molecule localization microscopy (SMLM) by reducing the background fluorescence. These approaches currently require custom-made sample holders or additional specialized objectives, which makes the sample mounting or the optical system complex and therefore reduces the usability of these approaches. Here, we developed a single-objective lens-inclined light sheet microscope (SOLEIL) that is capable of 2D and 3D SMLM in thick samples. SOLEIL combines oblique illumination with point spread function PSF engineering to enable dSTORM imaging in a wide variety of samples. SOLEIL is compatible with standard sample holders and off-the-shelve optics and standard high NA objectives. To accomplish optimal optical sectioning we show that there is an ideal oblique angle and sheet thickness. Furthermore, to show what optical sectioning delivers for SMLM we benchmark SOLEIL against widefield and HILO microscopy with several biological samples. SOLEIL delivers in 15 μm thick Caco2-BBE cells a 374% higher intensity to background ratio and a 54% improvement in the estimated CRLB compared to widefield illumination, and a 184% higher intensity to background ratio and a 20% improvement in the estimated CRLB compared to HILO illumination.

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The microscope is an essential tool for biologists. Since the late 16th century, it has given researchers a better understanding of cell processes and greatly advanced healthcare. In this century, Single molecule localization microscopy (SMLM) has revolutionized optical microscopy by breaking the optical diffraction limit. Sparsely activating emitters in a sample labeled with fluorophores, the object can be reconstructed by estimating their positions using the system point spread function (PSF). These localization algorithms are the state of the art
in optical imaging, using unbiased estimators to reach the theoretical minimum uncertainty, or Cramér-Rao lower bound (CRLB).
While SMLM works well when emitters are sparsely activated, overlap of the emitter images is inevitable for thick or densely labeled samples. When SMLM is used on such images, the estimates become biased and the algorithm cannot find the correct number of emitters. Most techniques also make a deterministic estimate and are incapable of representing the uncertainty of estimates for dense samples.
A three-dimensional, Bayesian multiple emitter fitting algorithm is constructed using reversible jump Markov chain Monte Carlo (RJMCMC). While following the structure of Bayesian multiple-emitter fitting (BAMF), novel RJMCMC moves are designed to sample the parameters. The algorithm also jumps through models, estimating the number of emitters. It asymptotically samples from the posterior, revealing uncertainties in three-dimensional imaging that other techniques are incapable of imaging.
The algorithm was tested with astigmatic and biplane imaging. It has proven capable of consistently finding the correct model when a prior on emitter intensity is used. When separating two emitters, posterior density reconstruction revealed non-Gaussian emitter position uncertainties. Upon further investigation, the posterior density was found to be multimodal, with
both modes representative of the data and indistinguishable in terms of likelihood. This shows the algorithm can quantify three-dimensional PSF degeneracy and can become a vital tool for researchers to analyze their imaging setup. We also expect it to be especially effective when combined with modulation-enhanced localization microscopy (meLM) techniques.

Single-molecule localization microscopy (SMLM) enables the high-resolution visualization of organelle structures and the precise localization of individual proteins. However, the expected resolution is not achieved in tissue as the imaging conditions deteriorate. Sample-induced aberrations distort the point spread function (PSF), and high background fluorescence decreases the localization precision. Here, we synergistically combine sensorless adaptive optics (AO), in-situ 3D-PSF calibration, and a single-objective lens inclined light sheet microscope (SOLEIL), termed (AO-SOLEIL), to mitigate deep tissue-induced deteriorations. We apply AO-SOLEIL on several dSTORM samples including brains of adult Drosophila. We observed a 2x improvement in the estimated axial localization precision with respect to widefield without aberration correction while we used synergistic solution. AO-SOLEIL enhances the overall imaging resolution and further facilitates the visualization of sub-cellular structures in tissue.@en

Optical microscopes allow us to study highly dynamic events from the molecular scale up to the whole animal level. However, conventional three-dimensional microscopy architectures face an inherent tradeoff between spatial resolution, imaging volume, light exposure and time required to record a single frame. Many biological processes, such as calcium signalling in the brain or transient enzymatic events, occur in temporal and spatial dimensions that cannot be captured by the iterative scanning of multiple focal planes. Snapshot volumetric imaging maintains the spatio-temporal context of such processes during image acquisition by mapping axial information to one or multiple cameras. This review introduces major methods of camera-based single frame volumetric imaging: so-called multiplane, multifocus, and light field microscopy. For each method, we discuss, amongst other topics, the theoretical framework; tendency towards optical aberrations; light efficiency; applicable wavelength range; robustness/complexity of hardware and analysis; and compatibility with different imaging modalities, and provide an overview of applications in biological research.@en

A variety of modern super-resolution microscopy methods provide researchers with previously inconceivable biological sample imaging opportunities at a molecular resolution. All of these techniques excel at imaging samples that are close to the coverslip, however imaging at large depths remains a challenge due to aberrations caused by the sample, diminishing the resolution of the microscope. Originating in astro-imaging, the adaptive optics (AO) approach for wavefront shaping using a deformable mirror is gaining momentum in modern microscopy as a convenient approach for wavefront control. AO has the ability not only to correct aberrations but also enables engineering of the PSF shape, allowing localization of the emitter axial position over several microns. In this study, we demonstrate remote focusing as another AO benefit for super-resolution microscopy. We show the ability to record volumetric data (45 × 45 × 10 μm), while keeping the sample axially stabilized using a standard widefield setup with an adaptive optics addon. We processed the data with single-molecule localization routines and/or computed spatiotemporal correlations, demonstrating subdiffraction resolution.

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Sub-diffraction or super-resolution fluorescence imaging allows the visualization of the cellular morphology and interactions at the nanoscale. Statistical analysis methods such as super-resolution optical fluctuation imaging (SOFI) obtain an improved spatial resolution by analyzing fluorophore blinking but can be perturbed by the presence of non-stationary processes such as photodestruction or fluctuations in the illumination. In this work, we propose to use Whittaker smoothing to remove these smooth signal trends and retain only the information associated to independent blinking of the emitters, thus enhancing the SOFI signals. We find that our method works well to correct photodestruction, especially when it occurs quickly. The resulting images show a much higher contrast, strongly suppressed background and a more detailed visualization of cellular structures. Our method is parameter-free and computationally efficient, and can be readily applied on both two-dimensional and three-dimensional data.

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Small-molecule fluorophores enable the observation of biomolecules in their native context with fluorescence microscopy. Specific labeling via bio-orthogonal tetrazine chemistry combines minimal label size with rapid labeling kinetics. At the same time, fluorogenic tetrazine-dye conjugates exhibit efficient quenching of dyes prior to target binding. However, live-cell compatible long-wavelength fluorophores with strong fluorogenicity have been difficult to realize. Here, we report close proximity tetrazine-dye conjugates with minimal distance between tetrazine and the fluorophore. Two synthetic routes give access to a series of cell-permeable and -impermeable dyes including highly fluorogenic far-red emitting derivatives with electron exchange as the dominant excited-state quenching mechanism. We demonstrate their potential for live-cell imaging in combination with unnatural amino acids, wash-free multicolor and super-resolution STED, and SOFI imaging. These dyes pave the way for advanced fluorescence imaging of biomolecules with minimal label size.

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High-resolution live-cell imaging is necessary to study complex biological phenomena. Modern fluorescence microscopy methods are increasingly combined with complementary, label-free techniques to put the fluorescence information into the cellular context. The most common high-resolution imaging approaches used in combination with fluorescence imaging are electron microscopy and atomic-force microscopy (AFM), originally developed for solid-state material characterization. AFM routinely resolves atomic steps, however on soft biological samples, the forces between the tip and the sample deform the fragile membrane, thereby distorting the otherwise high axial resolution of the technique. Here we present scanning ion-conductance microscopy (SICM) as an alternative approach for topographical imaging of soft biological samples, preserving high axial resolution on cells. SICM is complemented with live-cell compatible super-resolution optical fluctuation imaging (SOFI). To demonstrate the capabilities of our method we show correlative 3D cellular maps with SOFI implementation in both 2D and 3D with self-blinking dyes for two-color high-order SOFI imaging. Finally, we employ correlative SICM/SOFI microscopy for visualizing actin dynamics in live COS-7 cells with subdiffraction-resolution.

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Localization microscopy is a super-resolution imaging technique that relies on the spatial and temporal separation of blinking fluorescent emitters. These blinking events can be individually localized with a precision significantly smaller than the classical diffraction limit. This sub-diffraction localization precision is theoretically bounded by the number of photons emitted per molecule and by the sensor noise. These parameters can be estimated from the raw images. Alternatively, the resolution can be estimated from a rendered image of the localizations. Here, we show how the rendering of localization datasets can influence the resolution estimation based on decorrelation analysis. We demonstrate that a modified histogram rendering, termed bilinear histogram, circumvents the biases introduced by Gaussian or standard histogram rendering. We propose a parameter-free processing pipeline and show that the resolution estimation becomes a function of the localization density and the localization precision, on both simulated and state-of-the-art experimental datasets.

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