细胞生物学

Multicellular organization relies on reciprocal interactions between molecular events, such as gene expression and protein state, and higher-scale properties, such as spatial patterning and tissue architecture. Understanding these processes requires methods that enable quantitative measurements at subcellular resolution, while maintaining the three-dimensional tissue organization. Conventional immunofluorescence imaging captures spatial information but is limited to the number of fluorescence markers that can be imaged simultaneously, whereas dissociation-based single-cell approaches can profile multimodal cellular states but lack positional information. Here, we describe 3D in toto iterative immunofluorescence imaging, termed 3D-4i, which enables up to ten-plex protein and protein state measurements in early zebrafish embryos. Leveraging sample immobilization on 96-well plates together with a gentle liquid handling system and high-content spinning disc confocal microscopy, this method comprises repeated rounds of antibody staining, optical clearing, confocal imaging, and antibody elution. Subsequent image analysis allows segmentation of nuclei and cells, extraction of quantitative single-cell features, and integration of molecular measurements with spatial context. Altogether, 3D-4i provides a scalable platform for investigating diverse biological processes in intact embryos, while maintaining both subcellular resolution and three-dimensional context.

Isogenic populations of Saccharomyces cerevisiae exhibit significant proliferative heterogeneity, with individual cells within a clonal culture displaying divergent growth rates and metabolic states. Investigating the origins of this variation requires a method to reconstruct the individual histories of cells within the population. This protocol describes a method for single-cell microencapsulation in alginate microspheres to create a physically stable, traceable, three-dimensional genealogical environment. By utilizing the alginate matrix to prevent daughter cell migration, the replicative history of a founder cell can be mathematically reconstructed. This is achieved by correlating the total cell count (N) within a developed microcolony with the total number of accumulated bud scars (n) visualized via confocal microscopy.

Polyploid hepatocytes are one of the unique features of the liver. Some polyploid hepatocytes have chromosomes in a single nucleus (e.g., 1x4n, 1x8n), while others separate their chromosomes into two nuclei (e.g., 2x2n, 2x4n). In ploidy research, hepatocytes are typically sorted according to their cellular ploidy, revealing their contribution to tumorigenesis and cellular senescence. However, the conventional sorting method fails to distinguish 1x4n from 2x2n, or 1x8n from 2x4n cells, leaving it unclear whether hepatocytes with the same cellular ploidy but different nuclear configurations are identical or phenotypically different. Here, we describe a detailed protocol for fractionating mononucleated and binucleated hepatocytes. First, we present the method for isolating primary mouse hepatocytes and staining them with the DNA dye Hoechst 33342. Flow cytometry is then used to detect fluorescence differences between mononucleated and binucleated hepatocytes. This protocol enables the discrimination of hepatocyte subpopulations with identical cellular ploidy, providing a useful tool to investigate the functional heterogeneity of polyploid hepatocytes.

While cell hashing enhances single-cell RNA sequencing (scRNA-seq) efficiency and minimizes batch effects, commercial mouse hashtags often fail in FVB/N and several other strains due to antibody-epitope incompatibility. We describe a robust alternative utilizing biotinylated antibody cocktails and streptavidin-conjugated oligos to enable reliable sample multiplexing. This approach was validated in FVB/N lung tissues, yielding high-quality single-cell libraries. Our protocol offers a practical solution for researchers requiring strain-specific or custom-designed multiplexing strategies for single-cell transcriptomics.

Natural killer (NK) cells are crucial innate immune effectors, mediating cytotoxicity against cancer and infected cells through receptors such as NKG2D. Reliable quantification of NK cell subsets is essential for evaluating NK cell-based immune responses in cancer research. Unlike other assays, including traditional flow cytometry used in assessing NK cells, imaging flow cytometry (IFC) is a simple and direct method for quantitative analysis of NK cells. This protocol describes the necessary procedures, including harvesting splenocytes, acquiring these cells labeled with NKG2D antibodies, and analyzing IFC data with IDEAS^® software. We applied this protocol to quantitatively assess the number of splenic NKG2D⁺ NK cells in mice injected with SVTneg2 cancer cells (which carry the p53 G242A missense mutation) and compared them to mice injected with EMT6 cancer cells (which have wild-type p53) or normal fibroblasts. We found that the SVTneg2 cancer cells significantly decreased the number of NKG2D⁺ NK cells in mice by approximately 2-fold (933 cells vs. 2360 cells, p < 0.001) compared with mice injected with EMT6 cancer cells. This IFC protocol can be applied to directly quantify NK cells in vivo. This quantitative protocol allows novices to quickly handle the analysis of cytotoxic NK cells with a single NKG2D marker. Further multicolor flow cytometry and cytokine assay may be required to precisely define the subtypes and effects of NK cells in anticancer immunity.

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.

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.

Single-cell and single-nucleus RNA sequencing are revolutionizing our understanding of cellular biology. The identification of molecular markers, single-cell transcriptomic profiling, and differential gene expression at the cellular level has revealed key functional differences between cells within the same tissue. However, tissue dissociation remains challenging for non-model organisms and for tissues with unique biochemical properties. For example, the mosquito fat body, which serves functions analogous to mammalian adipose and liver tissues, consists of trophocytes—large, adipocyte-like cells whose cytoplasm is filled with lipid droplets. Conventional enzymatic dissociation methods are often too harsh for these fragile cells, and their high lipid content can interfere with reagents required for single-cell transcriptomic analysis. Single-nucleus RNA sequencing (snRNA-seq) offers an alternative strategy when intact cells with high-quality RNA cannot be obtained by enzymatic or mechanical dissociation. Here, we present an optimized reproducible methodology for nuclei isolation from the fat body of Anopheles gambiae mosquitoes, enabling high-quality snRNA-seq. Our approach involves tissue fixation and lipid removal, followed by cell lysis and nuclei purification using a sucrose cushion. We validated this protocol on both sugar-fed and blood-fed samples, established quality metrics to remove potential ambient RNA contamination, and demonstrated that snRNA-seq using this method yields high-quality sequencing results.

Accurate identification of cell cycle stages is essential for investigating fundamental biological processes such as proliferation, differentiation, and tumorigenesis. While flow cytometry remains a widely used technique for such analyses, it is limited by its lack of single-cell resolution and its requirement for large sample sizes due to its population-based approach. These limitations underscore the need for alternative or complementary methods that offer single-cell precision with compatibility for small-scale applications. We present ImmunoCellCycle-ID, an immunofluorescence-based method that leverages the spatial distribution of endogenous markers, such as DNA, proliferating cell nuclear antigen (PCNA), centromere protein F (CENP-F), and centromere protein C (CENP-C), to reliably distinguish G1, early S, late S, early G2, late G2, and all mitotic sub-stages. This technique does not rely on precise signal quantification and utilizes standard immunofluorescence protocols alongside conventional laboratory microscopes, ensuring broad accessibility. Importantly, ImmunoCellCycle-ID detects endogenous proteins without the need for genetic modification, making it readily applicable to a wide range of human cell lines. Beyond its utility for single-cell resolution, the method can be scaled for population-level analyses, similar to flow cytometry. With its precision, versatility, and ease of implementation, ImmunoCellCycle-ID offers a powerful tool for high-resolution cell cycle profiling across diverse experimental platforms.

Single-cell RNA sequencing has revolutionized molecular cell biology by enabling the identification of unique transcription profiles and cell transcription states within the same tissue. However, tissue dissociation presents a challenge for non-model organisms, as commercial kits are often incompatible, and current protocols rely on tissue enzymatic digestion for extended periods. Tissue digestion can alter cell transcription in response to temperature and the stress caused by enzymatic treatment. Here, we propose a protocol to stabilize RNA using a deep eutectic solvent (Vivophix, Rapid Labs) prior to tissue dissociation, thereby avoiding transcription changes induced by the process and preventing RNase activity during incubation. We validated this methodology for three medically important insect vectors: Anopheles gambiae, Aedes aegypti, and Lutzomyia longipalpis. Single-cell RNA sequencing using our insect midgut dissociation protocol yielded high-quality sequencing results, with a high number of cells recovered, a low percentage of mitochondrial reads, and a low percentage of ambient RNA—two hallmark standards of cell quality.