Fast and accurate sCMOS noise correction for fluorescence microscopy

hydroSIM: super-resolution speckle illumination microscopy with a hydrogel diffuser

Super-resolution microscopy has emerged as an indispensable methodology for probing the intricacies of cellular biology. Structured illumination microscopy (SIM), in particular, offers an advantageous balance of spatial and temporal resolution, allowing for visualizing cellular processes with minimal disruption to biological specimens. However, the broader adoption of SIM remains hampered by the complexity of instrumentation and alignment. Here, we introduce speckle-illumination super-resolution microscopy using hydrogel diffusers (hydroSIM). The study utilizes the high scattering and optical transmissive properties of hydrogel materials and realizes a remarkably simplified approach to plug-in super-resolution imaging via a common epi-fluorescence platform. We demonstrate the hydroSIM system using various phantom and biological samples, and the results exhibited effective 3D resolution doubling, optical sectioning, and high contrast. We foresee hydroSIM, a cost-effective, biocompatible, and user-accessible super-resolution methodology, to significantly advance a wide range of biomedical imaging and applications.

Ultra-high spatio-temporal resolution imaging with parallel acquisition-readout structured illumination microscopy (PAR-SIM)

Structured illumination microscopy (SIM) has emerged as a promising super-resolution fluorescence imaging technique, offering diverse configurations and computational strategies to mitigate phototoxicity during real-time imaging of biological specimens. Traditional efforts to enhance system frame rates have concentrated on processing algorithms, like rolling reconstruction or reduced frame reconstruction, or on investments in costly sCMOS cameras with accelerated row readout rates. In this article, we introduce an approach to elevate SIM frame rates and region of interest (ROI) coverage at the hardware level, without necessitating an upsurge in camera expenses or intricate algorithms. Here, parallel acquisition-readout SIM (PAR-SIM) achieves the highest imaging speed for fluorescence imaging at currently available detector sensitivity. By using the full frame-width of the detector through synchronizing the pattern generation and image exposure-readout process, we have achieved a fundamentally stupendous information spatial-temporal flux of 132.9 MPixels · s-1, 9.6-fold that of the latest techniques, with the lowest SNR of -2.11 dB and 100 nm resolution. PAR-SIM demonstrates its proficiency in successfully reconstructing diverse cellular organelles in dual excitations, even under conditions of low signal due to ultra-short exposure times. Notably, mitochondrial dynamic tubulation and ongoing membrane fusion processes have been captured in live COS-7 cell, recorded with PAR-SIM at an impressive 408 Hz. We posit that this novel parallel exposure-readout mode not only augments SIM pattern modulation for superior frame rates but also holds the potential to benefit other complex imaging systems with a strategic controlling approach.

Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy

Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

A head-mounted photoacoustic fiberscope for hemodynamic imaging in mobile mice

A miniaturized photoacoustic fiberscope has been developed, featuring a lateral resolution of 9 microns and a lightweight design at 4.5 grams. Engineered to capture hemodynamic processes at single-blood-vessel resolution at a rate of 0.2 Hz, this device represents an advancement in head-mounted tools for exploring intricate brain activities in mobile animals.

Noise correction in differential phase contrast for improving phase sensitivity

Differential phase contrast (DPC) imaging relies on computational analysis to extract quantitative phase information from phase gradient images. However, even modest noise level can introduce errors that propagate through the computational process, degrading the quality of the final phase result and further reducing phase sensitivity. Here, we introduce the noise-corrected DPC (ncDPC) to enhance phase sensitivity. This approach is based on a theoretical DPC model that effectively considers most relevant noise sources in the camera and non-uniform illumination in DPC. In particular, the dominating shot noise and readout noise variance can be jointly estimated using frequency analysis and further corrected by block-matching 3D (BM3D) method. Finally, the denoised images are used for phase retrieval based on the common Tikhonov inversion. Our results, based on both simulated and experimental data, demonstrate that ncDPC outperforms the traditional DPC (tDPC), enabling significant improvements in both phase reconstruction quality and phase sensitivity. Besides, we have demonstrated the broad applicability of ncDPC by showing its performance in various experimental datasets.

Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices

We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base our approach on a closed-form analytic expression for the characteristic function (Fourier-transform of the probability mass function) for the image counts recorded in an EMCCD camera, which takes into account both stochasticity in the arrival of photons at the imaging camera and subsequent noise induced by the detection system of the camera. The only assumption in our method is that the background photon arrival to the imaging system is described by a stationary Poisson process (we make no assumption about the photon statistics for the signal). We estimate the background photon statistics parameter, λbg, from an image which contains both background and signal pixels by use of a novel truncated fit procedure with an automatically determined image count threshold. Prior to this, the camera noise model parameters are estimated using a calibration step. Utilizing the estimates for the camera parameters and λbg, we then introduce a probabilistic thresholding method, where, for the first time, the fraction of misclassified pixels can be determined a priori for a general image in an unsupervised way. We use synthetic images to validate our a priori estimates and to benchmark against the Otsu method, which is a popular unsupervised non-probabilistic image thresholding method (no a priori estimates for the error rates are provided). For completeness, we lastly present a simple heuristic general-purpose segmentation method based on the thresholding results, which we apply to segmentation of synthetic images and experimental images of fluorescent beads and lung cell nuclei. Our publicly available software opens up for fully automated, unsupervised, probabilistic photophysical image analysis.

Cationic cholesterol-dependent LNP delivery to lung stem cells, the liver, and heart

Adding a cationic helper lipid to a lipid nanoparticle (LNP) can increase lung delivery and decrease liver delivery. However, it remains unclear whether charge-dependent tropism is universal or, alternatively, whether it depends on the component that is charged. Here, we report evidence that cationic cholesterol-dependent tropism can differ from cationic helper lipid-dependent tropism. By testing how 196 LNPs delivered mRNA to 22 cell types, we found that charged cholesterols led to a different lung:liver delivery ratio than charged helper lipids. We also found that combining cationic cholesterol with a cationic helper lipid led to mRNA delivery in the heart as well as several lung cell types, including stem cell-like populations. These data highlight the utility of exploring charge-dependent LNP tropism.

Light-field flow cytometry for high-resolution, volumetric and multiparametric 3D single-cell analysis

Imaging flow cytometry (IFC) combines flow cytometry and fluorescence microscopy to enable high-throughput, multiparametric single-cell analysis with rich spatial details. However, current IFC techniques remain limited in their ability to reveal subcellular information with a high 3D resolution, throughput, sensitivity, and instrumental simplicity. In this study, we introduce a light-field flow cytometer (LFC), an IFC system capable of high-content, single-shot, and multi-color acquisition of up to 5,750 cells per second with a near-diffraction-limited resolution of 400-600 nm in all three dimensions. The LFC system integrates optical, microfluidic, and computational strategies to facilitate the volumetric visualization of various 3D subcellular characteristics through convenient access to commonly used epi-fluorescence platforms. We demonstrate the effectiveness of LFC in assaying, analyzing, and enumerating intricate subcellular morphology, function, and heterogeneity using various phantoms and biological specimens. The advancement offered by the LFC system presents a promising methodological pathway for broad cell biological and translational discoveries, with the potential for widespread adoption in biomedical research.

Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy

BACKGROUND

A common goal of scientific microscopic imaging is to determine if a spatial correlation exists between two imaged structures. This is generally accomplished by imaging fluorescently labeled structures and measuring their spatial correlation with a class of image analysis algorithms known as colocalization. However, the most commonly used methods of colocalization have strict limitations, such as requiring overlap in the fluorescent markers and reporting requirements for accurate interpretation of the data, that are often not met. Due to the development of novel super-resolution techniques, which reduce the overlap of the fluorescent signals, a new colocalization method is needed that does not have such strict requirements.

RESULTS

In order to overcome the limitations of other colocalization algorithms, I developed a new ImageJ/Fiji plugin, Colocalization by cross-correlation (CCC). This method uses cross-correlation over space to identify spatial correlations as a function of distance, removing the overlap requirement and providing more comprehensive results. CCC is compatible with 3D and time-lapse images, and was designed to be easy to use. CCC also generates new images that only show the correlating labeled structures from the input images, a novel feature among the cross-correlating algorithms.

CONCLUSIONS

CCC is a versatile, powerful, and easy to use colocalization and spatial correlation tool that is available through the Fiji update sites. Full and up to date documentation can be found at https://imagej.net/plugins/colocalization-by-cross-correlation . CCC source code is available at https://github.com/andmccall/Colocalization_by_Cross_Correlation .

MEMS-based two-photon microscopy with Lissajous scanning and image reconstruction under a feed-forward control strategy

Two-photon microscopy (TPM) based on two-dimensional micro-electro-mechanical (MEMS) system mirrors shows promising applications in biomedicine and the life sciences. To improve the imaging quality and real-time performance of TPM, this paper proposes Lissajous scanning control and image reconstruction under a feed-forward control strategy, a dual-parameter alternating drive control algorithm and segmented phase synchronization mechanism, and pipe-lined fusion-mean filtering and median filtering to suppress image noise. A 10 fps frame rate (512 × 512 pixels), a 140 µm × 140 µm field of view, and a 0.62 µm lateral resolution were achieved. The imaging capability of MEMS-based Lissajous scanning TPM was verified by ex vivo and in vivo biological tissue imaging.

Physics-informed deep neural network for image denoising

Image enhancement deep neural networks (DNN) can improve signal to noise ratio or resolution of optically collected visual information. The literature reports a variety of approaches with varying effectiveness. All these algorithms rely on arbitrary data (the pixels' count-rate) normalization, making their performance strngly affected by dataset or user-specific data pre-manipulation. We developed a DNN algorithm capable to enhance images signal-to-noise surpassing previous algorithms. Our model stems from the nature of the photon detection process which is characterized by an inherently Poissonian statistics. Our algorithm is thus driven by distance between probability functions instead than relying on the sole count-rate, producing high performance results especially in high-dynamic-range images. Moreover, it does not require any arbitrary image renormalization other than the transformation of the camera's count-rate into photon-number.

Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy

Fluorescence microscopy has become an indispensable tool for revealing the dynamic regulation of cells and organelles. However, stochastic noise inherently restricts optical interrogation quality and exacerbates observation fidelity when balancing the joint demands of high frame rate, long-term recording and low phototoxicity. Here we propose DeepSeMi, a self-supervised-learning-based denoising framework capable of increasing signal-to-noise ratio by over 12 dB across various conditions. With the introduction of newly designed eccentric blind-spot convolution filters, DeepSeMi effectively denoises images with no loss of spatiotemporal resolution. In combination with confocal microscopy, DeepSeMi allows for recording organelle interactions in four colors at high frame rates across tens of thousands of frames, monitoring migrasomes and retractosomes over a half day, and imaging ultra-phototoxicity-sensitive Dictyostelium cells over thousands of frames. Through comprehensive validations across various samples and instruments, we prove DeepSeMi to be a versatile and biocompatible tool for breaking the shot-noise limit.

Multiplane HiLo microscopy with speckle illumination and non-local means denoising

Significance

HiLo microscopy synthesizes an optically sectioned image from two images, one obtained with uniform and another with patterned illumination, such as laser speckle. Speckle-based HiLo has the advantage of being robust to aberrations but is susceptible to residual speckle noise that is difficult to control. We present a computational method to reduce this residual noise without undermining resolution. In addition, we improve the versatility of HiLo microscopy by enabling simultaneous multiplane imaging (here nine planes).

Aim

Our goal is to perform fast, high-contrast, multiplane imaging with a conventional camera-based fluorescence microscope.

Approach

Multiplane HiLo imaging is achieved with the use of a single camera and z-splitter prism. Speckle noise reduction is based on the application of a non-local means (NLM) denoising method to perform ensemble averaging of speckle grains.

Results

We demonstrate the capabilities of multiplane HiLo with NLM denoising both with synthesized data and by imaging cardiac and brain activity in zebrafish larvae at 40 Hz frame rates.

Conclusions

Multiplane HiLo microscopy aided by NLM denoising provides a simple tool for fast optically sectioned volumetric imaging that can be of general utility for fluorescence imaging applications.

Reorganization of the Flagellum Scaffolding Induces a Sperm Standstill During Fertilization

Mammalian sperm delve into the female reproductive tract to fertilize the female gamete. The available information about how sperm regulate their motility during the final journey to the fertilization site is extremely limited. In this work, we investigated the structural and functional changes in the sperm flagellum after acrosomal exocytosis and during the interaction with the eggs. The evidence demonstrates that the double helix actin network surrounding the mitochondrial sheath of the midpiece undergoes structural changes prior to the motility cessation. This structural modification is accompanied by a decrease in diameter of the midpiece and is driven by intracellular calcium changes that occur concomitant with a reorganization of the actin helicoidal cortex. Although midpiece contraction may occur in a subset of cells that undergo acrosomal exocytosis, live-cell imaging during in vitro fertilization showed that the midpiece contraction is required for motility cessation after fusion is initiated. These findings provide the first evidence of the F-actin network's role in regulating sperm motility, adapting its function to meet specific cellular requirements during fertilization, and highlighting the broader significance of understanding sperm motility.

Significant statement

In this work, we demonstrate that the helical structure of polymerized actin in the flagellum undergoes a rearrangement at the time of sperm-egg fusion. This process is driven by intracellular calcium and promotes a decrease in the sperm midpiece diameter as well as the arrest in motility, which is observed after the fusion process is initiated.

Pupil engineering for extended depth-of-field imaging in a fluorescence miniscope

Significance

Fluorescence head-mounted microscopes, i.e., miniscopes, have emerged as powerful tools to analyze in-vivo neural populations but exhibit a limited depth-of-field (DoF) due to the use of high numerical aperture (NA) gradient refractive index (GRIN) objective lenses.

Aim

We present extended depth-of-field (EDoF) miniscope, which integrates an optimized thin and lightweight binary diffractive optical element (DOE) onto the GRIN lens of a miniscope to extend the DoF by 2.8× between twin foci in fixed scattering samples.

Approach

We use a genetic algorithm that considers the GRIN lens' aberration and intensity loss from scattering in a Fourier optics-forward model to optimize a DOE and manufacture the DOE through single-step photolithography. We integrate the DOE into EDoF-Miniscope with a lateral accuracy of 70  μm to produce high-contrast signals without compromising the speed, spatial resolution, size, or weight.

Results

We characterize the performance of EDoF-Miniscope across 5- and 10-μm fluorescent beads embedded in scattering phantoms and demonstrate that EDoF-Miniscope facilitates deeper interrogations of neuronal populations in a 100-μm-thick mouse brain sample and vessels in a whole mouse brain sample.

Conclusions

Built from off-the-shelf components and augmented by a customizable DOE, we expect that this low-cost EDoF-Miniscope may find utility in a wide range of neural recording applications.

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images

Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

Volumetric live-cell autofluorescence imaging using Fourier light-field microscopy

This study introduces a rapid, volumetric live-cell imaging technique for visualizing autofluorescent sub-cellular structures and their dynamics by employing high-resolution Fourier light-field microscopy. We demonstrated this method by capturing lysosomal autofluorescence in fibroblasts and HeLa cells. Additionally, we conducted multicolor imaging to simultaneously observe lysosomal autofluorescence and fluorescently-labeled organelles such as lysosomes and mitochondria. We further analyzed the data to quantify the interactions between lysosomes and mitochondria. This research lays the foundation for future exploration of native cellular states and functions in three-dimensional environments, effectively reducing photodamage and eliminating the necessity for exogenous labels.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

Fluorescence-Based Portable Assays for Detection of Biological and Chemical Analytes

Fluorescence-based detection techniques are part of an ever-expanding field and are widely used in biomedical and environmental research as a biosensing tool. These techniques have high sensitivity, selectivity, and a short response time, making them a valuable tool for developing bio-chemical assays. The endpoint of these assays is defined by changes in fluorescence signal, in terms of its intensity, lifetime, and/or shift in spectrum, which is monitored using readout devices such as microscopes, fluorometers, and cytometers. However, these devices are often bulky, expensive, and require supervision to operate, which makes them inaccessible in resource-limited settings. To address these issues, significant effort has been directed towards integrating fluorescence-based assays into miniature platforms based on papers, hydrogels, and microfluidic devices, and to couple these assays with portable readout devices like smartphones and wearable optical sensors, thereby enabling point-of-care detection of bio-chemical analytes. This review highlights some of the recently developed portable fluorescence-based assays by discussing the design of fluorescent sensor molecules, their sensing strategy, and the fabrication of point-of-care devices.

Active fluorescent modulation for low-noise super-resolution microscopy

Extracting the position of individual molecular probes with high precision is the basis and core of super-resolution microscopy. However, with the expectation of low-light conditions in life science research, the signal-to-noise ratio (SNR) decreases and signal extraction faces a great challenge. Here, based on temporally modulating the fluorescence emission at certain periodical patterns, we achieved super-resolution imaging with high sensitivity by largely suppressing the background noise. We propose simple bright-dim (BD) fluorescent modulation and delicate control by phase-modulated excitation. We demonstrate that the strategy can effectively enhance signal extraction in both sparsely and densely labeled biological samples, and thus improve the efficiency and precision of super-resolution imaging. This active modulation technique is generally applicable to various fluorescent labels, super-resolution techniques, and advanced algorithms, allowing a wide range of bioimaging applications.

Self-adaptive fusion method for scientific CMOS image sensors with variable gain ratios and background values

Image diagnosis is an important technique in transient process research of high-energy physics. High dynamic range scenes require high linear dynamic range imaging systems. Scientific CMOS (sCMOS) image sensors have widely been used in high-energy physics, nuclear medical imaging, and astronomical observation because of their advantages in the high linear dynamic range. In this paper, we study the gain ratio variation and background value variation of commercial sCMOS image sensors. A self-adaptive fusion method is proposed to realize the fusion of high linear dynamic range images. The proposed method only uses the high gain image and the low gain image of the sCMOS image sensor to evaluate the gain ratio and the background compensation. The measured results show that the error rates of the evaluated gain ratio and background compensation are less than 2% and 6%. Test results show that the self-adaptive fusion method realizes well the fusion effects, which efficiently avoids the influence of gain ratio variation and background value variation.

Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data

Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands of neurons in mammalian brains at video rate. However, tissue scattering and background contamination results in signal deterioration, making the extraction of neuronal activity challenging, laborious and time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings and effectively works on experimental data to achieve high-fidelity neuronal extraction. Equipped with systematic background contribution priors, DeepWonder conducts neuronal inference with an order-of-magnitude-faster speed and improved accuracy compared with alternative approaches. DeepWonder removes background contaminations and is computationally efficient. Specifically, DeepWonder accomplishes 50-fold signal-to-background ratio enhancement when processing terabytes-scale cortex-wide functional recordings, with over 14,000 neurons extracted in 17 h.

Advantages of holographic imaging through fog

In this paper, we demonstrate digital holographic imaging through a 27-m-long fog tube filled with ultrasonically generated fog. Its high sensitivity makes holography a powerful technology for imaging through scattering media. With our large-scale experiments, we investigate the potential of holographic imaging for road traffic applications, where autonomous driving vehicles require reliable environmental perception in all weather conditions. We compare single-shot off-axis digital holography to conventional imaging (with coherent illumination) and show that holographic imaging requires 30 times less illumination power for the same imaging range. Our work includes signal-to-noise ratio considerations, a simulation model, and quantitative statements on the influence of various physical parameters on the imaging range.

Single-molecule tracking (SMT): a window into live-cell transcription biochemistry

How molecules interact governs how they move. Single-molecule tracking (SMT) thus provides a unique window into the dynamic interactions of biomolecules within live cells. Using transcription regulation as a case study, we describe how SMT works, what it can tell us about molecular biology, and how it has changed our perspective on the inner workings of the nucleus. We also describe what SMT cannot yet tell us and how new technical advances seek to overcome its limitations. This ongoing progress will be imperative to address outstanding questions about how dynamic molecular machines function in live cells.

Super-resolution SRS microscopy with A-PoD

Stimulated Raman scattering (SRS) offers the ability to image metabolic dynamics with high signal-to-noise ratio. However, its spatial resolution is limited by the numerical aperture of the imaging objective and the scattering cross-section of molecules. To achieve super-resolved SRS imaging, we developed a deconvolution algorithm, adaptive moment estimation (Adam) optimization-based pointillism deconvolution (A-PoD) and demonstrated a spatial resolution of lower than 59 nm on the membrane of a single lipid droplet (LD). We applied A-PoD to spatially correlated multiphoton fluorescence imaging and deuterium oxide (D2O)-probed SRS (DO-SRS) imaging from diverse samples to compare nanoscopic distributions of proteins and lipids in cells and subcellular organelles. We successfully differentiated newly synthesized lipids in LDs using A-PoD-coupled DO-SRS. The A-PoD-enhanced DO-SRS imaging method was also applied to reveal metabolic changes in brain samples from Drosophila on different diets. This new approach allows us to quantitatively measure the nanoscopic colocalization of biomolecules and metabolic dynamics in organelles.

Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.

Enhancing Weak-Signal Extraction for Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) has been widely used in biological imaging due to its ultrahigh spatial resolution. However, due to the strategy of reducing photodamage to living cells, the fluorescence signals of emitters are usually weak and the detector noises become non-negligible, which leads to localization misalignments and signal losses, thus deteriorating the imaging capability of SMLM. Here, we propose an active modulation method to control the fluorescence of the probe emitters. It actually marks the emitters with artificial blinking character, which directly distinguishes weak signals from multiple detector noises. We demonstrated from simulations and experiments that this method improves the signal-to-noise ratio by about 10 dB over the non-modulated method and boosts the sensitivity of single-molecule localization down to -4 dB, which significantly reduces localization misalignments and signal losses in SMLM. This signal-noise decoupling strategy is generally applicable to the super-resolution system with versatile labeled probes to improve their imaging capability. We also showed its application to the densely labeled sample, showing its flexibility in super-resolution nanoscopy.

Advances in Microscopy and Its Applications with Special Reference to Fluorescence Microscope: An Overview

The microscope has revolutionized the understanding of an organism's structural details and cellular functions. With the invention of highly evolved microscopes, the diagnosis and treatment of diseases has gained momentum. Technology has immensely helped demonstrate cellular events like phagocytosis, cell movement, cell division, etc. with enhanced temporal and spatial resolution. One of these advanced inventions is the fluorescent microscope which has enabled scanning through various physiological activities of the cell. A fluorescence microscope uses the property of fluorescence to create an image. In addition to visualizing the structural details of the cells, a fluorescence microscope also aids in witnessing cellular activities. With an immunofluorescence microscope, cellular antigens can be localized. This chapter highlights the basics of microscopy, types of microscopes, principles, and types of fluorescence microscopes, and recent advances in microscopy and its application.

Obtaining 3D super-resolution images by utilizing rotationally symmetric structures and 2D-to-3D transformation

Super-resolution imaging techniques have provided unprecedentedly detailed information by surpassing the diffraction-limited resolution of light microscopy. However, in order to derive high quality spatial resolution, many of these techniques require high laser power, extended imaging time, dedicated sample preparation, or some combination of the three. These constraints are particularly evident when considering three-dimensional (3D) super-resolution imaging. As a result, high-speed capture of 3D super-resolution information of structures and dynamic processes within live cells remains both desirable and challenging. Recently, a highly effective approach to obtain 3D super-resolution information was developed that can be employed in commonly available laboratory microscopes. This development makes it both scientifically possible and financially feasible to obtain super-resolution 3D information under certain conditions. This is accomplished by converting 2D single-molecule localization data captured at high speed within subcellular structures and rotationally symmetric organelles. Here, a high-speed 2D single-molecule tracking and post-localization technique, known as single-point edge-excitation sub-diffraction (SPEED) microcopy, along with its 2D-to-3D transformation algorithm is detailed with special emphasis on the mathematical principles and Monte Carlo simulation validation of the technique.

Extending resolution within a single imaging frame

The resolution of fluorescence microscopy images is limited by the physical properties of light. In the last decade, numerous super-resolution microscopy (SRM) approaches have been proposed to deal with such hindrance. Here we present Mean-Shift Super Resolution (MSSR), a new SRM algorithm based on the Mean Shift theory, which extends spatial resolution of single fluorescence images beyond the diffraction limit of light. MSSR works on low and high fluorophore densities, is not limited by the architecture of the optical setup and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image stacks, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other SRM approaches. Along with its wide accessibility, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.

Model of dynamic speckle evolution for evaluating laser speckle contrast measurements of tissue dynamics

We introduce a dynamic speckle model (DSM) to simulate the temporal evolution of fully developed speckle patterns arising from the interference of scattered light reemitted from dynamic tissue. Using this numerical tool, the performance of laser speckle contrast imaging (LSCI) or speckle contrast optical spectroscopy (SCOS) systems which quantify tissue dynamics using the spatial contrast of the speckle patterns with a certain camera exposure time is evaluated. We have investigated noise sources arising from the fundamental speckle statistics due to the finite sampling of the speckle patterns as well as those induced by experimental measurement conditions including shot noise, camera dark and read noise, and calibrated the parameters of an analytical noise model initially developed in the fundamental or shot noise regime that quantifies the performance of SCOS systems using the number of independent observables (NIO). Our analysis is particularly focused on the low photon flux regime relevant for human brain measurements, where the impact of shot noise and camera read noise can become significant. Our numerical model is also validated experimentally using a novel fiber based SCOS (fb-SCOS) system for a dynamic sample. We have found that the signal-to-noise ratio (SNR) of fb-SCOS measurements plateaus at a camera exposure time, which marks the regime where shot and fundamental noise dominates over camera read noise. For a fixed total measurement time, there exists an optimized camera exposure time if temporal averaging is utilized to improve SNR. For a certain camera exposure time, photon flux value, and camera noise properties, there exists an optimized speckle-to-pixel size ratio (s/p) at which SNR is maximized. Our work provides the design principles for any LSCI or SCOS systems given the detected photon flux and properties of the instruments, which will guide the experimental development of a high-quality, low-cost fb-SCOS system that monitors human brain blood flow and functions.

Quality assessment in light microscopy for routine use through simple tools and robust metrics

Although there is a need to demonstrate reproducibility in light microscopy acquisitions, the lack of standardized guidelines monitoring microscope health status over time has so far impaired the widespread use of quality control (QC) measurements. As scientists from 10 imaging core facilities who encounter various types of projects, we provide affordable hardware and open source software tools, rigorous protocols, and define reference values to assess QC metrics for the most common fluorescence light microscopy modalities. Seven protocols specify metrics on the microscope resolution, field illumination flatness, chromatic aberrations, illumination power stability, stage drift, positioning repeatability, and spatial-temporal noise of camera sensors. We designed the MetroloJ_QC ImageJ/Fiji Java plugin to incorporate the metrics and automate analysis. Measurements allow us to propose an extensive characterization of the QC procedures that can be used by any seasoned microscope user, from research biologists with a specialized interest in fluorescence light microscopy through to core facility staff, to ensure reproducible and quantifiable microscopy results.

3D super-resolution live-cell imaging with radial symmetry and Fourier light-field microscopy

Live-cell imaging reveals the phenotypes and mechanisms of cellular function and their dysfunction that underscore cell physiology, development, and pathology. Here, we report a 3D super-resolution live-cell microscopy method by integrating radiality analysis and Fourier light-field microscopy (rad-FLFM). We demonstrated the method using various live-cell specimens, including actins in Hela cells, microtubules in mammary organoid cells, and peroxisomes in COS-7 cells. Compared with conventional wide-field microscopy, rad-FLFM realizes scanning-free, volumetric 3D live-cell imaging with sub-diffraction-limited resolution of ∼150 nm (x-y) and 300 nm (z), milliseconds volume acquisition time, six-fold extended depth of focus of ∼6 µm, and low photodamage. The method provides a promising avenue to explore spatiotemporal-challenging subcellular processes in a wide range of cell biological research.

Review on data analysis methods for mesoscale neural imaging in vivo

Significance: Mesoscale neural imaging in vivo has gained extreme popularity in neuroscience for its capacity of recording large-scale neurons in action. Optical imaging with single-cell resolution and millimeter-level field of view in vivo has been providing an accumulated database of neuron-behavior correspondence. Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely complicated, leading to laborious and challenging signal processing demands. The existing data analysis procedure remains unstandardized, which could be daunting to neophytes or neuroscientists without computational background. Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems. Approach: We divide the pipeline into two main stages. The first stage focuses on extracting high-fidelity neural responses at single-cell level from raw images, including motion registration, image denoising, neuron segmentation, and signal extraction. The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction. Results: Here, we introduce the general pipeline of processing the mesoscale neural images. We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges. Conclusions: There are great challenges and opportunities brought by the large-scale mesoscale data, such as the balance between fidelity and efficiency, increasing computational load, and neural network interpretability. We believe that global circuits on single-neuron level will be more extensively explored in the future.

Pushing the colorimetry camera-based fluorescence microscopy to low light imaging by denoising and dye combination

Colorimetry camera-based fluorescence microscopy (CCFM) is a single-frame imaging method for observing multiple biological events simultaneously. Compared with the traditional multi-color fluorescence microscopy methods based on sequential excitation or spectral splitting, the CCFM method simplifies multi-color fluorescence imaging experiments, while keeping a high spatial resolution. However, when the level of the detected fluorescence signal decreases, the image quality, the demosaicking algorithm precision, and the discrimination of fluorescence channels on the colorimetry camera will also decrease. Thus, CCFM has a poor color resolution under a low signal level. For example, the crosstalk will be higher than 10% when the signal is less than 100 photons/pixel. To solve this problem, we developed a new algorithm that combines sCMOS noise correction with demosaicking, and a dye selection method based on the spectral response characteristics of the colorimetry camera. By combining the above two strategies, low crosstalk can be obtained with 4 ∼ 6 fold fewer fluorescence photons, and low light single-frame four-color fluorescence imaging was successfully performed on fixed cos-7 cells. This study expands the power of the CCFM method, and provides a simple and efficient way for various bioimaging applications in low-light conditions.

Optimizing sampling for surface localization in 3D-scanning microscopy

3D-scanning fluorescence imaging of living tissue is in demand for less phototoxic acquisition process. For the imaging of biological surfaces, adaptive and sparse scanning schemes have been proven to efficiently reduce the light dose by concentrating acquisitions around the surface. In this paper, we focus on optimizing the scanning scheme at a constant photon budget, when the problem is to estimate the position of a biological surface whose intensity profile is modeled as a Gaussian shape. We propose an approach based on the Cramér-Rao bound to optimize the positions and number of scanning points, assuming signal-dependant Gaussian noise. We show that, in the case of regular sampling, the optimization problem can be reduced to a few parameters, allowing us to define quasi-optimal acquisition strategies, first when no prior knowledge of the surface location is available and then when the user has a prior on this location.

Photon-free (s)CMOS camera characterization for artifact reduction in high- and super-resolution microscopy

Modern implementations of widefield fluorescence microscopy often rely on sCMOS cameras, but this camera architecture inherently features pixel-to-pixel variations. Such variations lead to image artifacts and render quantitative image interpretation difficult. Although a variety of algorithmic corrections exists, they require a thorough characterization of the camera, which typically is not easy to access or perform. Here, we developed a fully automated pipeline for camera characterization based solely on thermally generated signal, and implemented it in the popular open-source software Micro-Manager and ImageJ/Fiji. Besides supplying the conventional camera maps of noise, offset and gain, our pipeline also gives access to dark current and thermal noise as functions of the exposure time. This allowed us to avoid structural bias in single-molecule localization microscopy (SMLM), which without correction is substantial even for scientific-grade, cooled cameras. In addition, our approach enables high-quality 3D super-resolution as well as live-cell time-lapse microscopy with cheap, industry-grade cameras. As our approach for camera characterization does not require any user interventions or additional hardware implementations, numerous correction algorithms that rely on camera characterization become easily applicable.

Modeling Non-additive Effects in Neighboring Chemically Identical Fluorophores

Quantitative fluorescence analysis is often used to derive chemical properties, including stoichiometries, of biomolecular complexes. One fundamental underlying assumption in the analysis of fluorescence data─whether it be the determination of protein complex stoichiometry by super-resolution, or step-counting by photobleaching, or the determination of RNA counts in diffraction-limited spots in RNA fluorescence in situ hybridization (RNA-FISH) experiments─is that fluorophores behave identically and do not interact. However, recent experiments on fluorophore-labeled DNA origami structures such as fluorocubes have shed light on the nature of the interactions between identical fluorophores as these are brought closer together, thereby raising questions on the validity of the modeling assumption that fluorophores do not interact. Here, we analyze photon arrival data under pulsed illumination from fluorocubes where distances between dyes range from 2 to 10 nm. We discuss the implications of non-additivity of brightness on quantitative fluorescence analysis.

Polyoxometalate-based materials in extraction, and electrochemical and optical detection methods: A review

Polyoxometalates (POMs) as metal-oxide anions have exceptional properties like high negative charges, remarkable redox abilities, unique ligand properties and availability of organic grafting. Moreover, the amenability of POMs to modification with different materials makes them suitable as precursors to further obtain new composites. Due to their unique attributes, POMs and their composites have been utilized as adsorbents, electrodes and catalysts in extraction, and electrochemical and optical detection methods, respectively. A survey of the recent progress and developments of POM-based materials in these methods is therefore desirable, and should be of great interest. In this review article, POM-based materials, their properties as well as their identification methods, and analytical applications as adsorbents, electrodes and catalysts, and corresponding mechanisms of action, where relevant, are reviewed. Some current issues of the utilization of these materials and their future prospects in analytical chemistry are discussed.

Live-cell fluorescence spectral imaging as a data science challenge

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.

sCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy

Structured illumination microscopy (SIM) is widely applied due to its high temporal and spatial resolution imaging ability. sCMOS cameras are often used in SIM due to their superior sensitivity, resolution, field of view, and frame rates. However, the unique single-pixel-dependent readout noise of sCMOS cameras may lead to SIM reconstruction artefacts and affect the accuracy of subsequent statistical analysis. We first established a nonuniform sCMOS noise model to address this issue, which incorporates the single-pixel-dependent offset, gain, and variance based on the SIM imaging process. The simulation indicates that the sCMOS pixel-dependent readout noise causes artefacts in the reconstructed SIM superresolution (SR) image. Thus, we propose a novel sCMOS noise-corrected SIM reconstruction algorithm derived from the imaging model, which can effectively suppress the sCMOS noise-related reconstruction artefacts and improve the signal-to-noise ratio (SNR).

Standard deviation of microscopy images used as indicator for growth stages

Photoelectron emission microscopy (PEEM) and low energy electron microscopy (LEEM) can easily distinguish between organic molecules adsorbed in crystallites or in the wetting layers as well as the bare metal substrate due to their different electronic properties. Already before (and during) the condensation of such solid phases (2D islands or 3D crystallites), there is a dilute 2D gas phase. Such a 2D gas phase consists of molecules, which are highly mobile and diffuse across the surface. The individual molecules are too small to be resolved in PEEM/LEEM images. Here, we discuss, how image features below and above the resolution limit of a PEEM/LEEM affect the mean electron yield and its (normalized) standard deviation. We support our findings with two experimental examples: the deposition of cobalt phthalocyanine (CoPc) on Ag(100) and of perfluoro-pentacene on Ag(110). Our results demonstrate, how a spatial and temporal analysis of image series can be used to obtain information about molecular phases, which cannot be directly resolved in microscopy images.

Can super-resolution microscopy become a standard characterization technique for materials chemistry?

The characterization of newly synthesized materials is a cornerstone of all chemistry and nanotechnology laboratories. For this purpose, a wide array of analytical techniques have been standardized and are used routinely by laboratories across the globe. With these methods we can understand the structure, dynamics and function of novel molecular architectures and their relations with the desired performance, guiding the development of the next generation of materials. Moreover, one of the challenges in materials chemistry is the lack of reproducibility due to improper publishing of the sample preparation protocol. In this context, the recent adoption of the reporting standard MIRIBEL (Minimum Information Reporting in Bio-Nano Experimental Literature) for material characterization and details of experimental protocols aims to provide complete, reproducible and reliable sample preparation for the scientific community. Thus, MIRIBEL should be immediately adopted in publications by scientific journals to overcome this challenge. Besides current standard spectroscopy and microscopy techniques, there is a constant development of novel technologies that aim to help chemists unveil the structure of complex materials. Among them super-resolution microscopy (SRM), an optical technique that bypasses the diffraction limit of light, has facilitated the study of synthetic materials with multicolor ability and minimal invasiveness at nanometric resolution. Although still in its infancy, the potential of SRM to unveil the structure, dynamics and function of complex synthetic architectures has been highlighted in pioneering reports during the last few years. Currently, SRM is a sophisticated technique with many challenges in sample preparation, data analysis, environmental control and automation, and moreover the instrumentation is still expensive. Therefore, SRM is currently limited to expert users and is not implemented in characterization routines. This perspective discusses the potential of SRM to transition from a niche technique to a standard routine method for material characterization. We propose a roadmap for the necessary developments required for this purpose based on a collaborative effort from scientists and engineers across disciplines.

DeepCEL0 for 2D single-molecule localization in fluorescence microscopy

MOTIVATION

In fluorescence microscopy, single-molecule localization microscopy (SMLM) techniques aim at localizing with high-precision high-density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super resolution plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit.

RESULTS

In this work, we propose a deep learning-based algorithm for precise molecule localization of high-density frames acquired by SMLM techniques whose ℓ2-based loss function is regularized by non-negative and ℓ0-based constraints. The ℓ0 is relaxed through its continuous exact ℓ0 (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data.

AVAILABILITY AND IMPLEMENTATION

DeepCEL0 code is freely accessible at https://github.com/sedaboni/DeepCEL0.

Imaging the endocannabinoid signaling system

The endocannabinoid (eCB) system is one of the most widespread neuromodulatory systems in the mammalian brain, with a multifaceted role in functions ranging from development to synaptic plasticity. Endocannabinoids are synthesized on demand from membrane lipid precursors, and act primarily on a single G-protein coupled receptor type, CB1, to carry out diverse functions. Despite the importance of the eCB system both in healthy brain function and in disease, critically important details of eCB signaling remained unknown. How eCBs are released from the membrane, how these lipid molecules are transported between cells, and how the distribution of their receptors is controlled, remained elusive. Recent advances in optical microscopy methods and biosensor engineering may open up new avenues for studying eCB signaling. We summarize applications of superresolution microscopy using single molecule localization to reveal distinct patterns of nanoscale CB1 distribution in neuronal axons and axon terminals. We review single particle tracking studies using quantum dots that allowed visualizing CB1 trajectories. We highlight the recent development of fluorescent eCB biosensors, that revealed spatiotemporally specific eCB release in live cells and live animals. Finally, we discuss future directions where method development may help to advance a precise understanding of eCB signaling.

Rapid ensemble measurement of protein diffusion and probe blinking dynamics in cells

We present a fluorescence fluctuation image correlation analysis method that can rapidly and simultaneously measure the diffusion coefficient, photoblinking rates, and fraction of diffusing particles of fluorescent molecules in cells. Unlike other image correlation techniques, we demonstrated that our method could be applied irrespective of a nonuniformly distributed, immobile blinking fluorophore population. This allows us to measure blinking and transport dynamics in complex cell morphologies, a benefit for a range of super-resolution fluorescence imaging approaches that rely on probe emission blinking. Furthermore, we showed that our technique could be applied without directly accounting for photobleaching. We successfully employed our technique on several simulations with realistic EMCCD noise and photobleaching models, as well as on Dronpa-C12-labeled β-actin in living NIH/3T3 and HeLa cells. We found that the diffusion coefficients measured using our method were consistent with previous literature values. We further found that photoblinking rates measured in the live HeLa cells varied as expected with changing excitation power.

Nano-precision metrology of X-ray mirrors with laser speckle angular measurement

X-ray mirrors are widely used for synchrotron radiation, free-electron lasers, and astronomical telescopes. The short wavelength and grazing incidence impose strict limits on the permissible slope error. Advanced polishing techniques have already produced mirrors with slope errors below 50 nrad root mean square (rms), but existing metrology techniques struggle to measure them. Here, we describe a laser speckle angular measurement (SAM) approach to overcome such limitations. We also demonstrate that the angular precision of slope error measurements can be pushed down to 20nrad rms by utilizing an advanced sub-pixel tracking algorithm. Furthermore, SAM allows the measurement of mirrors in two dimensions with radii of curvature as low as a few hundred millimeters. Importantly, the instrument based on SAM is compact, low-cost, and easy to integrate with most other existing X-ray mirror metrology instruments, such as the long trace profiler (LTP) and nanometer optical metrology (NOM). The proposed nanometrology method represents an important milestone and potentially opens up new possibilities to develop next-generation super-polished X-ray mirrors, which will advance the development of X-ray nanoprobes, coherence preservation, and astronomical physics.

Single-molecule tracking technologies for quantifying the dynamics of gene regulation in cells, tissue and embryos

For decades, we have relied on population and time-averaged snapshots of dynamic molecular scale events to understand how genes are regulated during development and beyond. The advent of techniques to observe single-molecule kinetics in increasingly endogenous contexts, progressing from in vitro studies to living embryos, has revealed how much we have missed. Here, we provide an accessible overview of the rapidly expanding family of technologies for single-molecule tracking (SMT), with the goal of enabling the reader to critically analyse single-molecule studies, as well as to inspire the application of SMT to their own work. We start by overviewing the basics of and motivation for SMT experiments, and the trade-offs involved when optimizing parameters. We then cover key technologies, including fluorescent labelling, excitation and detection optics, localization and tracking algorithms, and data analysis. Finally, we provide a summary of selected recent applications of SMT to study the dynamics of gene regulation.

Deriving high contrast fluorescence microscopy images through low contrast noisy image stacks

Contrast in fluorescence microscopy images allows for the differentiation between different structures by their difference in intensities. However, factors such as point-spread function and noise may reduce it, affecting its interpretability. We identified that fluctuation of emitters in a stack of images can be exploited to achieve increased contrast when compared to the average and Richardson-Lucy deconvolution. We tested our methods on four increasingly challenging samples including tissue, in which case results were comparable to the ones obtained by structured illumination microscopy in terms of contrast.