Image analysis techniques provide objective and reproducible statistics for interpreting microscopy data. At higher dimensions, three-dimensional (3D) volumetric and spatiotemporal data highlight additional properties and behaviors beyond the static 2D focal plane. However, increased dimensionality carries increased complexity, and existing techniques for general segmentation of 3D data are either primitive, or highly specialized to specific biological structures. Borrowing from the principles of 2D topological data analysis (TDA), we formulate a 3D segmentation algorithm that implements persistent homology to identify variations in image intensity. From this, we derive two separate variants applicable to spatial and spatiotemporal data, respectively. We demonstrate that this analysis yields both sensitive and specific results on simulated data and can distinguish prominent biological structures in fluorescence microscopy images, regardless of their shape. Furthermore, we highlight the efficacy of temporal TDA in tracking cell lineage and the frequency of cell and organelle replication.

Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This particle-based stochastic simulation method is very flexible and can be seen as a generalization of well-established standard particle-based generators. In comparison, our approach allows us: (1) to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g., Brownian) to another (e.g., a directed motion); (2) to take into account finely the appearance over time of new trajectories and their disappearance, these events possibly depending on the cell regions but also on the current spatial configuration of all existing particles. This flexibility enables to generate more realistic dynamics than standard particle-based simulation procedures, by for example accounting for the colocalization phenomena often observed between intracellular vesicles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. As an illustration, based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane, including the well-known colocalization occurrence between these two types of vesicles. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences.

In vivo fluorescence microscopy is a powerful tool to image the beating heart in its early development stages. A high acquisition frame rate is necessary to study its fast contractions, but the limited fluorescence intensity requires sensitive cameras that are often too slow. Moreover, the problem is even more complex when imaging distinct tissues in the same sample using different fluorophores. We present Paired Alternating AcQuisitions, a method to image cyclic processes in multiple channels, which requires only a single (possibly slow) camera. We generate variable temporal illumination patterns in each frame, alternating between channel-specific illuminations (fluorescence) in odd frames and a motion-encoding brightfield pattern as a common reference in even frames. Starting from the image pairs, we find the position of each reference frame in the cardiac cycle through a combination of image-based sorting and regularized curve fitting. Thanks to these estimated reference positions, we assemble multichannel videos whose frame rate is virtually increased. We characterize our method on synthetic and experimental images collected in zebrafish embryos, showing quantitative and visual improvements in the reconstructed videos over existing nongated sorting-based alternatives. Using a 15 Hz camera, we showcase a reconstructed video containing two fluorescence channels at 100 fps.

High-content screening (HCS) provides an excellent tool to understand the mechanism of action of drugs on disease-relevant model systems. Careful selection of fluorescent labels (FLs) is crucial for successful HCS assay development. HCS assays typically comprise (a) FLs containing biological information of interest, and (b) additional structural FLs enabling instance segmentation for downstream analysis. However, the limited number of available fluorescence microscopy imaging channels restricts the degree to which these FLs can be experimentally multiplexed. In this article, we present a segmentation workflow that overcomes the dependency on structural FLs for image segmentation, typically freeing two fluorescence microscopy channels for biologically relevant FLs. It consists in extracting structural information encoded within readouts that are primarily biological, by fine-tuning pre-trained state-of-the-art generalist cell segmentation models for different combinations of individual FLs, and aggregating the respective segmentation results together. Using annotated datasets that we provide, we confirm our methodology offers improvements in performance and robustness across several segmentation aggregation strategies and image acquisition methods, over different cell lines and various FLs. It thus enables the biological information content of HCS assays to be maximized without compromising the robustness and accuracy of computational single-cell profiling.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

The dynamics and fusion of vesicles during the last steps of exocytosis are not well established yet in cell biology. An open issue is the characterization of the diffusion process at the plasma membrane. Total internal reflection fluorescence microscopy (TIRFM) has been successfully used to analyze the coordination of proteins involved in this mechanism. It enables to capture dynamics of proteins with high frame rate and reasonable signal-to-noise values. Nevertheless, methodological approaches that can analyze and estimate diffusion in local small areas at the scale of a single diffusing spot within cells, are still lacking. To address this issue, we propose a novel correlation-based method for local diffusion estimation. As a starting point, we consider Fick’s second law of diffusion that relates the diffusive flux to the gradient of the concentration. Then, we derive an explicit parametric model which is further fitted to time-correlation signals computed from regions of interest (ROI) containing individual spots. Our modeling and Bayesian estimation framework are well appropriate to represent isolated diffusion events and are robust to noise, ROI sizes, and localization of spots in ROIs. The performance of BayesTICS is shown on both synthetic and real TIRFM images depicting Transferrin Receptor proteins.

Fluorescence microscopy techniques have experienced a substantial increase in the visualization and analysis of many biological processes in life science. We describe a semiautomated and versatile tool called Cell-TypeAnalyzer to avoid the time-consuming and biased manual classification of cells according to cell types. It consists of an open-source plugin for Fiji or ImageJ to detect and classify cells in 2D images. Our workflow consists of (a) image preprocessing actions, data spatial calibration, and region of interest for analysis; (b) segmentation to isolate cells from background (optionally including user-defined preprocessing steps helping the identification of cells); (c) extraction of features from each cell; (d) filters to select relevant cells; (e) definition of specific criteria to be included in the different cell types; (f) cell classification; and (g) flexible analysis of the results. Our software provides a modular and flexible strategy to perform cell classification through a wizard-like graphical user interface in which the user is intuitively guided through each step of the analysis. This procedure may be applied in batch mode to multiple microscopy files. Once the analysis is set up, it can be automatically and efficiently performed on many images. The plugin does not require any programming skill and can analyze cells in many different acquisition setups.

To overcome the physical barriers caused by light diffraction, super-resolution techniques are often applied in fluorescence microscopy. State-of-the-art approaches require specific and often demanding acquisition conditions to achieve adequate levels of both spatial and temporal resolution. Analyzing the stochastic fluctuations of the fluorescent molecules provides a solution to the aforementioned limitations, as sufficiently high spatio-temporal resolution for live-cell imaging can be achieved using common microscopes and conventional fluorescent dyes. Based on this idea, we present COL0RME, a method for covariance-based $ {\mathrm{\ell}}_0 $ super-resolution microscopy with intensity estimation, which achieves good spatio-temporal resolution by solving a sparse optimization problem in the covariance domain and discuss automatic parameter selection strategies. The method is composed of two steps: the former where both the emitters’ independence and the sparse distribution of the fluorescent molecules are exploited to provide an accurate localization; the latter where real intensity values are estimated given the computed support. The paper is furnished with several numerical results both on synthetic and real fluorescence microscopy images and several comparisons with state-of-the art approaches are provided. Our results show that COL0RME outperforms competing methods exploiting analogously temporal fluctuations; in particular, it achieves better localization, reduces background artifacts, and avoids fine parameter tuning.

Fluorescence microscopy is a critical tool for cell biology studies on bacterial cell division and morphogenesis. Because the analysis of fluorescence microscopy images evolved beyond initial qualitative studies, numerous images analysis tools were developed to extract quantitative parameters on cell morphology and organization. To understand cellular processes required for bacterial growth and division, it is particularly important to perform such analysis in the context of cell cycle progression. However, manual assignment of cell cycle stages is laborious and prone to user bias. Although cell elongation can be used as a proxy for cell cycle progression in rod-shaped or ovoid bacteria, that is not the case for cocci, such as Staphylococcus aureus. Here, we describe eHooke, an image analysis framework developed specifically for automated analysis of microscopy images of spherical bacterial cells. eHooke contains a trained artificial neural network to automatically classify the cell cycle phase of individual S. aureus cells. Users can then apply various functions to obtain biologically relevant information on morphological features of individual cells and cellular localization of proteins, in the context of the cell cycle.

Kalanchoe delagoensis is adapted to intense solar irradiation, drought, and heat, partially due to the presence of phenols, important photo-protective compounds and antioxidants. This study aimed to evaluate the distribution of flavonoids and phenolic acid derivatives throughout the erect-tubular leaves of K. delagoensis. Specimens grown under sunny conditions were used for histochemical and high-performance liquid chromatography coupled with diode array detection (liquid HPLC-DAD) analysis. The NP (2-aminoethyl diphenylborinate) test suggested the presence of phenolic acids throughout the leaf blade below the epidermis and in chloroplasts, mainly in the leaf base. Flavonoids were detected specifically in chloroplasts, on the adaxial side of the middle third and at the leaf apex, near the meristematic cells. There was a tendency of flavonoid accumulation from the middle third to the apex, especially surrounding the gem, while phenolic acids were observed mainly in the base. This can be explained by the more exposed leaf apex and to the presence of apical buds (high production and regulation sites of ROS). The HPLC-DAD analysis showed different classes of flavonoids and phenolic acid derivatives in the leaf extracts, agreeing with the NP test results. This is the first time that the substitution of phenolic acids by flavonoids from the leaf base to the apex has been described.

Technological advancements in the areas of sample illumination, image acquisition, and image processing have significantly improved the speed and sensitivity of fluorescence microscopy. In particular, light emitting diodes (LEDs) coupled to transistor-transistor logic (TTL) circuits have reduced photo-bleaching and photo-toxicity by limiting sample illumination to the camera exposure time. Unfortunately, many microscopes still rely on bulb-based light sources that cannot be configured with TTL. Moreover, even when TTL is enabled in conventional microscope software, hardware and software delays can still contribute to photo-toxicity and lead to additional delays between subsequent images, introducing errors in time lapse image recordings. The goal of the present article is to highlight the significance of these issues.

There is often a need to locate the same cellular structure of interest in light and electron microscopy, which can be a difficult task. Here we present a method that uses only commercially available reagents and standard epi-fluorescence and transmission electron microscopy (TEM) technology to make correlative light and electron microscopy (CLEM) available to a large group of researchers without specialized CLEM hardware. This was achieved by seeding cells on photo-etched gridded cover slips and staining the protein to be localized with a secondary antibody coupled to both a fluorophore and 10 nm gold. The presence of the grid allowed for the alignment of light microscopy images with TEM images and the double-labeled antibody revealed co-localization of the fluorophore with gold particles.

Based on technologies capable of data collection between the millimeter and nanometer scales, correlative imaging has been transforming how researchers obtain molecular and spatial information from specimens. Attempts to combine multidimensional data are often met with the challenge of overcoming suboptimal sample conditions such as reduced fluorescence signal, poor specimen preservation, anisotropic specimen deformation, and low specimen contrast. These issues motivated the development and use of enhanced sample preparation procedures, as well as specialized imaging software to overcome such challenges. In this work we present three simple methods to correlate optical and scanning electron microscopy images.

Black mats are organic-rich sediments and soils that form in wet environments associated with spring discharge. Micromorphological and geochemical analyses of 25 black mats dating to the Younger Dryas Chronozone (12.9–11.7 ka) and early Holocene were conducted to determine their composition and depositional environment. Samples were collected from Arizona, New Mexico, Texas and Nevada. Micromorphological analyses were conducted on thin sections using polarized and blue fluorescent light. These analyses determined that black mats contain humic acids, fine (5–20 μm) plant fragments, diatoms, phytoliths, and gastropods. The dominant type of organic matter in black mats is derived from herbaceous plants, contradicting previous studies that supported algal or charcoal sources. Differences in the micromorphological characteristics of the samples revealed that black mats formed as three different types, organic horizons, moist soils and ponded sediments, depending on their topographic position in relation to the water table. The microscopic evidence found in black mats supports the presence of widespread wet environments in Nevada and Arizona during the Younger Dryas Chronozone, clearly indicating a sustained period of greater effective moisture, optimal for spring discharge and black mat formation.

We demonstrate that the second-Stokes output from a diamond Raman laser, pumped by a femtosecond Ti:Sapphire laser, can be used to efficiently excite red-emitting dyes by two-photon excitation at 1,080 nm and beyond. We image HeLa cells expressing red fluorescent protein, as well as dyes such as Texas Red and Mitotracker Red. We demonstrate the potential for simultaneous two-color, two-photon imaging with this laser by using the residual pump beam for excitation of a green-emitting dye. We demonstrate this for the combination of Alexa Fluor 488 and Alexa Fluor 568. Because the Raman laser extends the wavelength range of the Ti:Sapphire laser, resulting in a laser system tunable to 680–1,200 nm, it can be used for two-photon excitation of a large variety and combination of dyes.

Chloroplast division is driven by a macromolecular complex that assembles at the midplastid. The FtsZ ring (Z ring) is the central structure in this complex, and is composed of the functionally distinct cytoskeletal proteins FtsZ1 and FtsZ2. Recent studies in the heterologous Schizosaccharomyces pombe system showed that Arabidopsis FtsZ1 and FtsZ2 filaments have distinct assembly and turnover characteristics. To further analyze these FtsZs, we employed this system to compare the assembly and dynamic properties of FtsZ1 and FtsZ2 lacking their N- and/or C-termini with those of their full-length counterparts. Our data provide evidence that the N-terminus of FtsZ2 is critical for its structural dominance over FtsZ1, and that the N- and C-termini promote polymer bundling and turnover of both FtsZs and contribute to their distinct behaviors. We also assessed how ARC6 affects FtsZ2 filament dynamics, and found that it interacts with and stabilizes FtsZ2 filaments in S. pombe independent of its presumed Z-ring tethering function in planta. Finally, we generated FtsZ1-FtsZ2 coexpression constructs to facilitate reconstitution of more complex interaction networks. Our experiments yield new insight into factors influencing FtsZ behavior and highlight the utility of S. pombe for analyzing chloroplast FtsZs and their assembly regulators.

We compared the characteristics of neural cells derived from induced pluripotent stem (iPS) cells from a patient with multiple sclerosis versus neurally differentiated control iPS cells of a healthy individual. The iPS cells were differentiated toward the oligodendrocyte lineage using a four-step protocol established for the differentiation of embryonic stem cells. The resulting cell population was immunostained on day 112 of differentiation for the presence of oligodendrocytes and analyzed by transmission electron microscopy (TEM). Both patient and control samples resembled a mixed population of neural cells rather than oligodendroglia of high purity, including neural stem cell-like cells and possibly oligodendrocytes demonstrable by TEM.

Fluorescence images present low signal-to-noise ratio (SNR), are corrupted by a type of multiplicative noise with Poisson distribution, and are affected by a time intensity decay due to photoblinking and photobleaching (PBPB) effects. The noise and the PBPB effects together make long-term biological observation very difficult. Here, a theoretical model based on the underlying quantum mechanic physics theory of the observation process associated with this type of image is presented and the common empirical weighted sum of two decaying exponentials is derived from the model. Improvement in the SNR obtained in denoising when the proposed method is used is particularly important in the last images of the sequence where temporal correlation is used to recover information that is sometimes faded and therefore useless from a visual inspection point of view. The proposed PBPB model is included in a Bayesian denoising algorithm previously proposed by the authors. Experiments with synthetic and real data are presented to validate the PBPB model and to illustrate the effectiveness of the model in denoising and reconstruction results.

Podosomes are cellular adhesion structures involved in matrix degradation and invasion that comprise an actin core and a ring of cytoskeletal adaptor proteins. They are most often identified by staining with phalloidin, which binds F-actin and therefore visualizes the core. However, not only podosomes, but also many other cytoskeletal structures contain actin, which makes podosome segmentation by automated image processing difficult. Here, we have developed a quantitative image analysis algorithm that is optimized to identify podosome cores within a typical sample stained with phalloidin. By sequential local and global thresholding, our analysis identifies up to 76% of podosome cores excluding other F-actin-based structures. Based on the overlap in podosome identifications and quantification of podosome numbers, our algorithm performs equally well compared to three experts. Using our algorithm we show effects of actin polymerization and myosin II inhibition on the actin intensity in both podosome core and associated actin network. Furthermore, by expanding the core segmentations, we reveal a previously unappreciated differential distribution of cytoskeletal adaptor proteins within the podosome ring. These applications illustrate that our algorithm is a valuable tool for rapid and accurate large-scale analysis of podosomes to increase our understanding of these characteristic adhesion structures.