细胞生物学

Extracellular vesicles (EVs) circulating in blood serve as non-invasive “liquid biopsies,” carrying molecular cargo that reflects the physiological and pathological state of distant cells. Their analysis is crucial for understanding disease mechanisms and discovering novel biomarkers. Clinically, blood EVs hold significant promise for early disease diagnosis, prognostic assessment, and monitoring treatment response in diverse areas such as organ transplantation, cancer, and neurological disorders. Current EV isolation techniques, beyond ultracentrifugation, include size exclusion chromatography (separation by size for high purity) and immunoaffinity capture (using antibodies for high specificity). Here, we present a simplified, rapid, and reproducible method for isolating EVs from small-volume blood samples. This protocol consistently yields a concentrated EV pellet covering 50–300 nm EVs, amenable to direct downstream analysis. Developed and validated in our laboratory using human, porcine, and murine blood samples, this method has proven instrumental in identifying EV-based biomarkers for predicting outcomes related to organ transplantation. The protocol’s adaptability and reliance on readily prepared, cost-effective reagents further enhance its utility. This scalable approach can be further integrated with subsequent purification or enrichment steps to optimize sample preparation for protein and nucleic acid assays.

Organelle abundance is a key microscopic readout of organelle formation and, in many cases, function. Quantification of organelle abundance using confocal microscopy requires estimating their area based on the fluorescence intensity of compartment-specific markers. This analysis usually depends on a user-defined intensity threshold to distinguish organelle regions from the surrounding cytoplasm, which introduces potential bias and variability. To address this issue, we present a machine learning–assisted algorithm that allows for the quantification of organelle density using the open-source Fiji platform and WEKA segmentation. Our method enables the automated quantification of organelle number, area, and density by learning from training data. This standardizes threshold selection and minimizes user intervention. We demonstrate the utility of this approach for both membrane and non-membrane organelles, such as peroxisomes, lipid droplets, and stress granules, in human cells and whole fish samples.

Obtaining articular cartilage-derived cells (chondroprogenitors) by explant methodology is a reliable approach for isolating migratory progenitor cells that retain strong chondrogenic potential. This method allows cells to emerge naturally from small cartilage fragments without enzymatic digestion. The procedure consists of plating cartilage explants on a plastic surface with culture medium, from which cells subsequently migrate and adhere to the substrate. Compared with enzymatic isolation, the explant approach minimizes cellular stress and better reproduces the physiological microenvironment of cartilage tissue. This protocol can be applied to both osteoarthritic and non-osteoarthritic samples, enabling comparative studies on disease-related phenotypic differences. Overall, this technique offers a reproducible, straightforward, and minimally invasive strategy for obtaining functional chondroprogenitor cells suitable for cartilage regeneration research.

Endocytosis is an essential membrane transport mechanism that is indispensable for the maintenance of life. It is responsible for the selective internalization and subsequent degradation or recycling of specific extracellular proteins and nutrients, thereby facilitating cellular nutrient supply, modulation of receptor signaling, and clearance of foreign substances. However, methods for the quantitative analysis of lysosomal degradation of extracellular proteins via endocytosis remain limited. This protocol describes a method for purifying the protein-of-interest (POI)–red fluorescent protein (RFP)–green fluorescent protein (GFP) fusion protein, which is modified with specific mammalian cell glycans or other modifications, from the conditioned medium of mammalian cell cultures. Subsequently, the protocol details a quantitative approach for evaluating its internalization and lysosomal degradation within cells using the RFP–GFP tandem fluorescent reporter. Following the addition of POI-RFP-GFP to the medium, cells can be subjected to cell biological assays, such as flow cytometry, as well as biochemical analyses, such as immunoblotting. This protocol is broadly applicable to studies of the internalization of extracellular proteins.

Prostate carcinoma (PCa) progression is strongly influenced by the surrounding tumor microenvironment, where cancer-associated fibroblasts (CAFs) represent the most abundant and functionally relevant stromal population. Despite their importance, the lack of stable cell lines representing CAF phenotypes limits the study of stromal–tumor interactions. To address this limitation, we provide an optimized protocol for isolating CAFs from fresh human PCa biopsies based on a mechanical procedure exploiting the specific CAF ability to migrate out from the tumor explants. This approach preserves tissue architecture and maintains CAF viability and phenotype. The resulting ex vivo CAF cultures provide a suitable model to investigate CAF biology within the tumor microenvironment.

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.

Mitophagy is a highly conserved process among eukaryotic cells, playing a primordial role in mitochondrial quality control and overall cellular homeostasis. In Saccharomyces cerevisiae, Atg32 is the only identified mitophagy receptor localized to the mitochondrial outer membrane, making this yeast a particularly powerful model for molecular studies of mitophagy that require the isolation of intact mitochondria. However, traditional methods for isolating mitochondria from yeast often rely on enzymatic cell wall digestion and homogenization, which can compromise the stability of mitochondrial surface proteins such as Atg32. In this protocol, we describe an optimized mechanical approach for yeast cell disruption using glass beads in a cold, protease-inhibited buffer to preserve mitochondrial integrity and facilitate the detection of Atg32. Subsequent differential centrifugation and washing steps yield mitochondrial fractions suitable for downstream biochemical analyses. This workflow eliminates enzymatic digestion steps, reduces sample variability, and allows parallel processing of multiple strains or experimental conditions. Overall, this method offers a rapid, low-cost, and reproducible alternative for crude mitochondrial isolation, ensuring excellent preservation of Atg32 and broad compatibility with quantitative and comparative studies.

Obesity is a risk factor for many diseases. The 3T3-L1 cell line is often used to obtain mature adipocytes, but these lack the inflammatory phenotype observed in obesity. Using a cocktail of cytokines that mimics the secretome of macrophages found in the inflammatory adipose tissue, we developed a protocol for obtaining mature inflammatory adipocytes. This model was validated at gene (RT-qPCR) and protein levels (multiplex adipokine array) as we found a decrease of adipogenic markers (C/EBPα, PPARУ, adiponectin, and CD36) and an increase of pro-inflammatory cytokines (IL-6, IL-1β, CXCL1, CXCL10, TNF-α, ICAM-1, and lipocalin-2). We provide a relevant in vitro model for studying the impact of low-grade chronic inflammation caused by obesity and its downstream effects on metabolic disorders and tumor microenvironments.

This protocol describes an easy, quick, cheap, and effective method for the purification and concentration of bacteriophages (phages) produced in rich culture media, meeting the quality criteria required for structural analyses. It is based on a tube dialysis system that replaces the classical but expensive and tedious density gradient ultracentrifugation step. We developed this protocol for the Oenococcus oeni bacteriophage OE33PA from its amplification to imaging by negative stain electron microscopy (NS-EM). The host bacterium, O. oeni, is a lactic acid bacterium that lives in harsh oenological ecosystems and grows only in rich and complex media such as Man–Rogosa–Sharpe (MRS) or fruit juice-based media in laboratory conditions. This raises experimental challenges in pure and concentrated phage preparations for further uses such as structure-function studies.

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.