Cell Biology

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for exploring cellular heterogeneity and host–pathogen interactions. This protocol details the Zika virus (ZIKV)-targeted scRNA-seq workflow for preparing high-quality single-cell suspensions from the whole brain tissues of neonatal mice, high-quality single-cell sorting, cDNA reverse transcription, amplification, ZIKV enrichment and host transcriptome library preparation, and sequencing dataset integration in downstream analysis to complete the quantification of ZIKV RNA in individual cells.

Human tissue samples represent the gold standard for obtaining clinically relevant and translatable insight into disease processes that in vitro systems cannot fully reproduce. However, patient-derived samples are often limited in size and availability, limiting the number of downstream assays that can be performed. To maximize the use of invaluable human samples, we present a protocol for the tandem extraction of high-quality RNA and protein from the same tissue section. This method has been optimized for 15–30 mg tissue sections, enabling more experimental conditions and technical replicates, while minimizing intrasample variability associated with heterogeneous tissues. This protocol also avoids potentially hazardous solvents present in phenol-chloroform-based methods such as TRIzol, providing a safer and more accessible workflow without compromising biomolecule integrity. This protocol was developed and validated using atherosclerotic plaque tissue from carotid endarterectomy, a very challenging tissue type to work with due to extensive calcification, necrosis, and limited surgical availability. We have also validated this method using mouse aortic tissue and cultured THP-1 cells, demonstrating its versatility across sample input types. As this protocol relies on standard column-based RNA extraction kits and commonly available reagents for protein precipitation and extraction, this methodology is widely accessible and easy to implement as a standard, streamlined workflow.

Breast cancer (BC) is the most frequently diagnosed malignancy in women and a leading cause of cancer-related mortality worldwide. Current clinical management relies on molecular classification—based on estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki67 expression—to guide prognosis and therapy. Triple-negative breast cancer (TNBC), which lacks ER, PR, and HER2 expression, represents 15%–20% of cases and is characterized by aggressive behavior, early recurrence, and a paucity of targeted treatment options. These challenges underscore the urgent need for improved preclinical models that better recapitulate tumor biology to accelerate therapeutic discovery. While conventional monolayer (2D) cultures have contributed significantly to cancer research, they fail to mimic critical features of the three-dimensional (3D) tumor microenvironment (TME), thereby limiting clinical translation. To address this gap, 3D spheroid models have emerged as a powerful intermediary, more accurately replicating in vivo conditions such as cell–cell and cell–matrix interactions, nutrient and oxygen gradients, and the development of hypoxic cores. These features make spheroids a physiologically relevant platform for studying complex processes like metastasis, drug resistance, and treatment response. Here, we present a robust, simple, and cost-effective protocol for generating uniform 3D spheroids. Our method enables consistent monitoring of spheroid formation and growth over time, with quantitative, image-based size analysis to ensure reproducibility and scalability. Designed for flexibility, the protocol is broadly applicable across diverse cell types, effectively bridging the gap between traditional 2D cultures and complex in vivo studies. By providing an accessible and reliable model of the 3D TME, this protocol opens new avenues for high-throughput drug screening, mechanistic studies of tumor progression, and the advancement of personalized medicine strategies in breast cancer and beyond.

Centrosomes are dynamic organelles critical for mitotic spindle assembly and cilia formation. Here, I describe a protocol for quantifying relative centrosomal protein abundance in Drosophila melanogaster embryos using radial profile analysis of fluorescence intensity. The method involves embryo collection, manual dechorionation, mounting for live imaging, confocal microscopy, and subsequent image analysis. Radial profiling allows quantification of relative protein abundance together with its spatial distribution at the centrosome, providing either relative or normalized intensity profiles. I then outline how this approach can be integrated with complementary techniques such as fluorescence recovery after photobleaching (FRAP) and super-resolution imaging, in this case, three-dimensional structured illumination microscopy (3D-SIM). Combining radial fluorescence profiling with these imaging modalities enables high-resolution, quantitative analysis of dynamic centrosome assembly in a genetically tractable system.

Cellulose synthase complexes (CSCs) play a central role in plant cell wall formation. Their dynamic behavior at the plasma membrane leads to the deposition of cellulose microfibrils into the apoplastic space, thereby shaping the architecture and mechanical properties of the cell wall. Although previous imaging studies have provided important insights into CSC dynamics and localization, standardized and reproducible workflows for quantitative measurements of CSC speed and density remain limited. Here, we present a reproducible live-cell imaging and analysis workflow for quantifying the speed and density of fluorescently labeled CSCs at the plasma membrane in Arabidopsis thaliana. The protocol integrates optimized spinning-disk confocal imaging, surface-based projection of z-stack recordings, automated detection of diffraction-limited CSCs foci, and kymograph-based speed measurements using freely available tools in Fiji. While selected steps, such as region of interest definition and parameter selection for spot detection or trajectory analysis, remain user-guided, these decisions are constrained to well-defined stages within an otherwise standardized pipeline, thereby reducing variability and improving reproducibility across experiments. The workflow has been validated across multiple tissues, reporter lines, genetic backgrounds, and perturbation conditions in Arabidopsis and enables robust comparative analysis of CSC dynamics. Beyond CSCs, this workflow is expected to be adaptable to other fluorescently labeled proteins that appear as diffraction-limited foci at or near the plasma membrane.

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.