细胞生物学

Time-lapse into immunofluorescence (TL into IF) imaging combines the wealth of information acquired during live-cell imaging with ease of access for static immunofluorescence markers. In the field of mechanobiology, connecting live and static imaging to visualize cell biology dynamics is often troublesome. For instance, nuclear blebs are deformations of the nucleus that often rupture spontaneously, leading to changes in the molecular composition of the nucleus and the nuclear bleb. Current techniques to connect cellular dynamics and their downstream effects via live-cell imaging, followed by immunofluorescence, often require third-party analysis programs or stage position measurements to accurately track cells. This protocol simplifies the connection between live and static imaging by utilizing a gridded imaging dish. In our protocol, cells are plated on a dish with an engraved coordinate plane. Individual cells are then matched from when the time-lapse ends to the immunofluorescence images simply by their known coordinate location. Overall, TL into IF offers a straightforward method for connecting dynamic live-cell with static immunofluorescence imaging, in an easy and accessible tool for cell biologists.

The deep learning revolution has accelerated discovery in cell biology by allowing researchers to outsource their microscopy analyses to a new class of tools called cell segmentation models. The performance of these models, however, is often constrained by the limited availability of annotated data for them to train on. This limitation is a consequence of the time cost associated with annotating training data by hand. To address this bottleneck, we developed Cell-APP (cellular annotation and perception pipeline), a tool that automates the annotation of high-quality training data for transmitted-light (TL) cell segmentation. Cell-APP uses two inputs—paired TL and fluorescence images—and operates in two main steps. First, it extracts each cell’s location from the fluorescence images. Then, it provides these locations to the promptable deep learning model μSAM, which generates cell masks in the TL images. Users may also employ Cell-APP to classify each annotated cell; in this case, Cell-APP extracts user-specified, single-cell features from the fluorescence images, which can then be used for unsupervised classification. These annotations and optional classifications comprise training data for cell segmentation model development. Here, we provide a step-by-step protocol for using Cell-APP to annotate training data and train custom cell segmentation models. This protocol has been used to train deep learning models that simultaneously segment and assign cell-cycle labels to HeLa, U2OS, HT1080, and RPE-1 cells.

Repetitive increases of intracellular calcium ions (Ca²⁺ oscillations) control cellular functions in various biological events, including meiotic resumption after fertilization. Sperm-derived substances enter the cytoplasm of mature oocytes by sperm fusion, causing Ca²⁺ oscillations. Sperm-independent Ca²⁺ oscillations are also induced in immature oocytes isolated from the ovaries of neonatal to adult mice. The presence of Ca²⁺ oscillations may contribute to subsequent oocyte quality; however, its physiological role and molecular mechanism are unclear. Here, we describe a method of collecting immature oocytes from the ovaries of juvenile (12, 15, and 21 days after birth) and adult mice and monitoring their Ca²⁺ oscillations. Since mouse oocytes are larger than other types of cells, they are a useful model for studying spatiotemporal patterns and the mechanism of Ca²⁺ oscillations in various types of cells. This method can be applied to other rodents due to similarities in oocyte size and developmental processes. Furthermore, the use of various fluorescent probes enables visualization of organelle rearrangement. The mechanism of interaction between oocytes and somatic cells differs between juvenile and adult mice. Therefore, two distinct methods are employed for oocyte collection.

Neuronal survival in vitro is usually used as a parameter to assess the effect of drug treatments or genetic manipulation in a disease condition. Easy and inexpensive protocols based on neuronal metabolism, such as 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), provide a global view of protective or toxic effects but do not allow for the monitoring of cell survival at the single neuronal level over time. By utilizing live imaging microscopy with a high-throughput microscope, we monitored transduced primary cortical neurons from 7–21 days in vitro (DIV) at the single neuronal level. We established a semi-automated analysis pipeline that incorporates data stratification to minimize the misleading impact of neuronal trophic effects due to plating variability; here, we provide all the necessary commands to reproduce it.

Plasma membrane–associated condensates driven by liquid–liquid phase separation represent a novel mechanism of receptor-mediated signaling transduction, serving as mesoscale platforms that concentrate signaling molecules and modulate reaction kinetics. Condensate formation is a highly dynamic process that occurs within seconds to minutes following receptor activation. Here, we present methods for de novo reconstituting liquid-like condensates on supported lipid bilayers and assessing the condensate fluidity using fluorescence recovery after photobleaching (FRAP). This protocol encompasses supported lipid bilayer preparation, condensation imaging, and FRAP analysis using total internal reflection fluorescence (TIRF) microscopy. Supported lipid bilayers provide a membrane-mimicking environment for receptor signaling cascades, offering mechanistic insights into protein–protein and lipid–protein interactions amid micron-scale condensates. The protocol can also be adapted to study condensates associated with the internal membranes of the Golgi apparatus, mitochondria, and other organelles.

Toxoplasma gondii is an apicomplexan parasite that infects a wide variety of eukaryotic hosts and causes toxoplasmosis. The cell cycle of T. gondii exhibits a distinct architecture and regulation that differ significantly from those observed in well-studied eukaryotic models. To better understand the tachyzoite cell cycle, we developed a fluorescent ubiquitination-based cell cycle indicator (FUCCI) system that enables real-time visualization and quantitative assessment of the different cell cycle phases via immunofluorescence microscopy. Quantitative immunofluorescence and live-cell imaging of the ToxoFUCCI^S probe with specific cell cycle markers revealed substantial overlap between cell cycle phases S, G₂, mitosis, and cytokinesis, further confirming the intricacy of the apicomplexan cell cycle.

Flagellate stages of green microalgae such as Trebouxia are only partially characterised, with recent evidence suggesting that they are involved in both sexual and asexual reproduction. Conventional methods based on fixed samples in light, confocal, or electron microscopy provide only static observations and prevent real-time monitoring of living cells. To overcome this limitation, we have developed a simple and cost-effective protocol for observing Trebouxia flagellate cells over several days by coating microscopy slides with Bold’s basal medium. The method preserves cell viability and allows repeated imaging of motile cells in the same areas so that their behaviour and development can be continuously observed. In this way, qualitative observations, such as flagellate cell release, motility, and gamete fusion, can be combined with quantitative analyses of cell morphology. The protocol has proven to be robust and reproducible and was applied to several Trebouxia species. Compared to existing techniques, it allows the monitoring of dynamic processes and provides a powerful tool to study specific life stages not only in Trebouxia but also in other unicellular and colonial green algae.

Expansion microscopy (ExM) is an innovative and cost-effective super-resolution imaging technique that enables nanoscale visualization of biological structures using conventional fluorescence microscopes. By physically enlarging biological specimens, ExM circumvents the diffraction limit and has become an indispensable tool in cell biology. Ongoing methodological advances have further enhanced its spatial resolution, labeling versatility, and compatibility with diverse sample types. However, ExM imaging is often hindered by sample drift during image acquisition, caused by subtle movements of the expanded hydrogel. This drift can distort three-dimensional reconstruction, compromising both visualization accuracy and quantitative analysis. To overcome this limitation, we developed 3D-Aligner, an advanced and user-friendly image analysis software that computationally corrects sample drift in fluorescence microscopy datasets, including but not limited to those acquired using ExM. The algorithm accurately determines drift trajectories across image stacks by detecting and matching stable background features, enabling nanometer-scale alignment to restore structural fidelity. We demonstrate that 3D-Aligner robustly corrects drift across ExM datasets with varying expansion factors and fluorescent labels. This protocol provides a comprehensive, step-by-step workflow for implementing drift correction in ExM datasets, ensuring reliable three-dimensional imaging and quantitative assessment.

Conventional Schlieren optics equipment typically operates on a large optical table, which is inconvenient for imaging small samples or thin layers of transparent materials. We describe an imaging device based on Schlieren optics, aided by a slight shift in light reflected from two surfaces. The device is designed to place the sample between a thick concave mirror and a camera next to a point-light source located at the spherical origin of the concave mirror. The compact device is portable and convenient. It is similarly capable of sensitively detecting patterns in gaseous or liquid media created by a density gradient when the optical effect is too subtle to be detectable by regular cameras and scanners. The new device is particularly suitable for detecting translucent samples, including thin fluid films on the order of micrometers, tissue slices, and other biological samples. We show two examples of how our device can be applied to imaging biological samples. The first compares images acquired using several techniques of a bacterial swarm spread over an agar plate; the second is a set of images of human cells grown on a tissue culture plate.

Small GTPases function as molecular switches in cells, and their activation triggers diverse cellular responses depending on the GTPase type. Therefore, visualizing small GTPase activation in living cells is crucial because their activity is tightly regulated in space and time, and this spatiotemporal pattern of activation often determines their specific cellular functions. Various biosensors, such as relocation-based sensors and fluorescence resonance energy transfer (FRET)-based sensors, have been developed. However, these methods rely on interactions between activated GTPases and their downstream effectors, which limits their applicability for detecting activation of GTPases with unknown or atypical effectors. Recently, we developed a novel method utilizing split fluorescence technology to detect membrane recruitment of small GTPases upon activation, designated the Small GTPase ActIvitY ANalyzing (SAIYAN) system. This approach offers a new strategy for monitoring small GTPase activation based on membrane association and is potentially applicable to a wide range of small GTPases, including those with uncharacterized effectors.