BMC Methods

Context: Cytological specimens allow minimally invasive, repeated sampling for diagnosis and molecular testing. However, cytology biobanking protocols have primarily been optimized for morphology, and the effects of preanalytical handling and storage on intracellular proteins remain unclear. Objective: To assess how fixation, staining, storage temperature, and duration affect intracellular protein preservation in cytological specimens and to identify conditions suitable for in situ proximity ligation assay (isPLA). Design: Human cancer cell lines were processed under different preanalytical conditions. Intracellular proteins and protein protein interactions were assessed by isPLA, fluorescence microscopy, and quantitative image analysis. Slides were stored at room temperature or minus 20 Celsius, with or without MayGrunwaldGiemsa (MGG) staining. Results: Storage temperature had the greatest effect on protein signal. Unstained slides stored at minus 20 Celsius retained strong signals for up to 24 months, whereas room temperature storage reduced detectable protein interactions by approximately 95%. MGG staining further reduced signal intensity during prolonged storage. Nevertheless, stained slides remained suitable for isPLA when staining, microscopic evaluation, destaining, and antibody labeling were completed on the same day. Prolonged room temperature storage of stained slides caused loss of antigenicity, increased autofluorescence, and false negative findings. Conclusions: Low temperature storage and standardized preanalytical handling preserve intracellular protein signals in cytological specimens. These conditions support reliable single cell protein profiling of archived samples and their use in biomarker research and translational pathology.

Preprocessing of spatial transcriptomics (ST) count matrices is critical for removing technical variation while preserving biological signals, but optimal strategies remain unclear across diverse platforms. We systematically benchmarked 371 preprocessing workflows across 45 data spanning 11 mainstream ST platforms, evaluating their performance using specifically designed complementary metrics. To further assess preprocessing factors upstream of count-matrix generation, we additionally evaluated image preprocessing, including cell segmentation and post-segmentation transcript processing, across representative imaging-based ST platforms. We found that no universal count-matrix preprocessing workflow performed optimally across all platforms, while data from the same platforms exhibited striking consistency in optimal count-matrix preprocessing workflows. We identified the key data structure characteristics and count-matrix preprocessing steps that critically influence preprocessing performance. We developed platform-specific and data structure-guided recommendations to assist users in selecting the optimal count-matrix preprocessing workflow, validated across a broader range of downstream tasks. We demonstrated that our data-driven recommendations uncover subtle biologically meaningful spatial patterns in new data that were obscured by suboptimal count-matrix preprocessing. Our findings suggest best practices for ST data count-matrix preprocessing.

Studying algae requires specialized equipment to characterize culture growth and physiological performance and non-invasive monitoring of these cultures typically relies on optical density (OD) measurements. Currently, OD is often measured using standard spectrometers that suffer from detector saturation at high cell densities, which requires time consuming sample dilutions and lacks compatibility with flat-walled cell culture flasks like Roux bottles. Here we describe a 3D-printable device to measure optically dense algal cultures directly within these cultivation vessels. This instrument combines light attenuation and scattering, resulting in a dual-sensor system that utilizes near-infrared light (950 nm) to precisely quantify biomass without interference from changing photosynthetic pigments. Cultures that reach high cell densities can be assumed to be accurately quantified in situ using the scattered signal, preserving the integrity of the culture and eliminating the need for subsampling. This method is adaptable to the most common geometries of Roux bottles (T-25, T-75, and T-175) and could be used to monitor small-scale batch cultures without contamination risks. A cyanobacteria algal species (Limnospira platensis) and three flask sizes were analysed on light attenuation linearity and scattering detection limits at different cell concentration, demonstrating that the light attenuation module can reliably measure biomass up to an OD of 3.1 (equivalent to >1.5 g/L) without requiring dilution.

Many biological quantities in omics are inherently periodic or directional, including circadian phase, cell-cycle position, and cellular orientation. Treating such quantities as ordinary linear variables can introduce artificial discontinuities and obscure biological interpretation. Circular methods are well developed across directional statistics, bioinformatics, and spatial statistics, but practical guidance for omics applications remains limited. We present key considerations for circular data analysis in single-cell and spatial omics, including the provenance and observational unit of the circular quantity, the inferential or predictive objective, the distinction between tissue composition and residual spatial dependence, and the choice of appropriate models. We describe relevant approaches for circular representation, inference, clustering, regression, machine learning, and spatial analysis, together with representative R software. Their practical use is illustrated through spatial transcriptomic case studies of derived circadian peak phase in a mouse model of Alzheimer's disease and latent cell-cycle position in human ovarian cancer. These examples demonstrate how the definition of the angle, its observational unit, and tissue heterogeneity shape appropriate analysis and interpretation, including when spatial modeling is not warranted. We conclude with practical recommendations and directions for future methodological development.

At-home sampling can address obstacles to participation in transcriptomics research. We previously developed the homeRNA and homeRNAmax platforms; these custom-designed tubes containing RNA stabilizer interface with a blood tube compatible with upper-arm blood collection devices, allowing for blood collection and RNA stabilization to occur from a participant's home. In this work we introduce homeRNAdrop, an insert for a commercially available blood tube (BD Microtainer) that divides the tube into two compartments: the top compartment holds 200 L blood and the bottom compartment holds ~590 L liquid stabilizer (RNAlater). By combining blood collection and RNA stabilization into the same tube, homeRNAdrop streamlines sample preparation for participants and allows for more compact sample storage for the lab. In a remote pilot study, n=25 participants from across the United States tested the homeRNAdrop kit. The n=23 samples successfully received by the lab show that samples collected using homeRNAdrop are of sufficient RNA quality (all RINs > 6, a common cutoff for analysis) and yield (mean yield=1.44 g) for downstream gene expression analysis. Almost all participants who successfully collected blood using the Tasso Mini device found the homeRNAdrop device easy to use (n=23/24). Overall, this work adds a simplified, user-friendly platform to the growing suite of tools for remote blood RNA stabilization.

Cell-level analysis of high-resolution spatial transcriptomics depends on accurate segmentation and transcript assignment, yet current workflows often trade transcript capture for boundary purity and can require substantial image-analysis expertise. We developed MCseg, a downloadable no-code platform whose fixed segmentation engine was derived by an AI-agent-guided search in which an AI agent iteratively proposed and evaluated combinations of image-processing and segmentation operations against Xenium-derived cell boundaries. In a lung adenocarcinoma development set, fixed-parameter MCseg increased mean panoptic quality from 0.432 to 0.472 relative to an Optuna-tuned two-diameter Cellpose baseline, while a reference-guided calibration analysis reached 0.554. In an independent expert-annotated colorectal cancer region, MCseg showed higher lineage recall and micro-F1 than the StarDist-based ENACT workflow among cells covered by both methods. Across 15 colorectal cancer regions, MCseg increased neighborhood expression discordance and reduced lineage-exclusive co-expression relative to Space Ranger at similar UMI density. The fixed workflow also transferred to fresh-frozen breast cancer without tissue-specific architecture search, illustrating an agent-guided route to reproducible, locally deployable cell-level spatial transcriptomic analysis.

Extracellular vesicles (EVs) are nanosized, lipid bilayer-enclosed particles that mediate intercellular communication and hold considerable promise as biomarkers and therapeutic agents. However, their functional heterogeneity remains incompletely understood, largely due to the absence of reliable methods for isolating specific EV subpopulations. Here, we established a Nanoscale Flow sorting (NanoFACS) workflow and evaluated its potential for EV detection, sorting, and molecular characterization. We assessed critical parameters, including fluorescent staining and labeling approaches, background signal discrimination, dilution and flow-rate effects, and scatter triggering. We achieved high-purity sorting of CFSE-stained and antibody-labeled EVs, preserving vesicle integrity and sufficient RNA yields for small RNA sequencing of sorted populations. We identified EV subtype-specific small RNAs despite partial cargo overlap in single-marker sorted populations. Our results establish a reproducible antibody-based NanoFACS workflow that defines key operational parameters for reliable EV detection and sorting, with potential adaptability across conventional and spectral flow cytometry platforms. By reducing background and non-EV-associated events, the workflow enables the isolation of phenotypically defined EV subpopulations suitable for downstream molecular analysis, providing a practical approach to investigate EV heterogeneity.

Significance: Fluorescence imaging remains largely qualitative and device-specific, limiting reproducibility and intersystem comparisons. SI-traceable radiometric characterization requires calibrated sources suitable for transferring known radiance to imaging systems. Aim: Establish and evaluate a compact, calibrated solid-state radiometric emitter target (RET) for SI-traceable radiance transfer, and assess methods for evaluating radiance invariance, imager responsivity, and extension of radiance transfer to digitally defined regions of interest (ROIs). Approach: The radiance invariance of an LED-based RET was evaluated by measuring its radiance across varying source-to-aperture distances and collection apertures. A previously described radiance-transfer formalism based on calibrated source radiance and collection geometry was implemented to determine imager responsivity (R{lambda}), which was evaluated across varying working distances and lens apertures. Grubbs and MAD outlier tests and one-way ANOVA were used to assess radiance and responsivity invariance. Finally, responsivity obtained using digitally defined ROIs was compared with matched physical apertures to assess ROI-based radiance transfer. Results: The measured RET radiance remained invariant across the tested source-to-aperture distances and collection apertures, with no significant differences observed across distance (p = 0.88) or aperture (p = 0.15). Imager responsivity remained stable across working distances for both tested lens configurations (p = 0.48 and p > 0.99). Responsivity was also invariant across lens apertures, except for deviations at f/11 that were consistent with practical aperture tolerances. Digitally defined ROIs reproduced responsivity values obtained using matched physical apertures, demonstrating that the radiance-transfer approach can be extended to image-defined regions. Conclusions: The presented methods provide an approach for evaluating compact LED-based sources for invariant-radiance emission and suitability for SI-traceable radiance transfer. The characterized RET supported stable imager responsivity across changes in imaging geometry and extension to digitally defined ROIs. These methods can be adapted to other LED-based sources, with further angular characterization and uncertainty analysis needed to establish broader applicability.

Emerging efforts to engineer multicellular kidney tissues for drug discovery, disease modeling, and therapeutic repair require scalable generation of kidney organoids derived from human induced pluripotent stem cells (hiPSCs). Here, we report a method for generating kidney ureteric bud and collecting duct epithelial organoids in stirred tank bioreactors. We demonstrate that extracellular matrix microcarriers facilitate organoid expansion and are compatible with suspension bioreactor methods. The combination of cell-coated matrix microcarriers with stirred bioreactors promotes maturation marker expression compared to static controls comprising matrix-embedded organoids. Our method offers promise for generating epithelial organoids that serve as multicellular models and organ building blocks for scalable biofabrication.

Extracellular vesicles (EV) are cell-secreted, nanosized biological particles that are involved in most physiological and pathophysiological processes. Their composition and properties reflect the pathophysiological state of the producing cell, tissue, and organ. Released into biological fluids, they may be used as a potential source of non-invasive biomarkers for diagnosis, prognosis or therapeutic follow-up in several diseases such as cancer. However, despite the development of single-EV analysis technologies, the sorting of EV subpopulations for downstream analysis remains a major challenge. Here, we describe a novel instrument that combines a highly sensitive optical detection system with an advanced lab-on-a-chip device. Using engineered fluorescent small extracellular vesicles (sEV), we demonstrate that the Hekat NanoSorter enables sensitive, scalable, and reproducible detection and sorting of individual sEVs for downstream analysis by cryo-EM. Furthermore, detailed characterization of the sorted EV populations, together with the detection of sEVs labeled with an in vitro pathophysiological marker, demonstrates the ability of the NanoSorter to monitor and enrich clinically relevant EV-associated biomarkers. These findings highlight the clinical potential of extracellular vesicle sorting for their use as biomarkers for therapeutic monitoring and treatment outcome assessment.

In situ sequencing methods provide subcellular-resolution gene expression data while preserving spatial context, enabling the integrated analysis of genomics and tissue morphology. However, many clustering workflows represent spatial transcriptomic structure through hard labels, imposing sharp boundaries between transcriptional domains and limiting the visualisation of gradual transitions, mixed signals, and uncertainty in cluster assignment, resulting in potentially misleading interpretations of tissue organisation. Here we present SFUMATO (Segmentation-Free Uncertainty Mapping and Analysis of Transcriptomic Organisation), a GPU-accelerated Bayesian probabilistic clustering and visualisation framework for spatial transcriptomics. SFUMATO builds on segmentation-free, multi-scale transcript binning and applies a Bayesian Gaussian mixture model to infer posterior probabilities over transcriptional components at each spatial location. These posterior probabilities provide a unified representation for hierarchical clustering, uncertainty-aware visualisation, and quantitative estimation of transcript-defined area fractions. SFUMATO assigns related colours to transcriptionally related components and blends colours according to posterior probabilities, producing maps in which sharp boundaries, gradual transitions, and ambiguous regions are represented directly in the visualisation. Classical hard labels remain recoverable from the same posterior distributions, preserving compatibility with downstream analyses that require discrete clusters. We evaluate SFUMATO on public 10x Genomics Xenium mouse brain and human breast cancer datasets. In mouse brain, SFUMATO preserves local transcriptional similarity in its colour encoding, maintains hierarchically consistent visualisations across different numbers of clusters, and recovers cell-associated spatial organisation without requiring prior cell segmentation. In breast cancer, SFUMATO-derived posterior maps can be converted into semantic masks concordant with pathology-associated regions, particularly invasive carcinoma, while highlighting the greater heterogeneity of DCIS-associated tissue. Together, these results show that SFUMATO provides an interpretable probabilistic representation of spatial transcriptomics that connects clustering, visualisation, and semantic segmentation across cell-associated and niche-level scales. More broadly, SFUMATO also provides a compact and continuous representation of highly multiplexed spatial transcriptomic information, offering a flexible input for future downstream analyses and multimodal integration.

Cell-type marker genes are definitional characteristics of a cell type, and in single-cell RNA sequencing atlases they are commonly nominated through differential expression (DE) testing. The Wilcoxon rank-sum test is the default method for marker gene discovery in Seurat and a recommended method in Scanpy, two widely used computational software packages for scRNA-seq analysis. Structurally, Wilcoxon's standardized statistic grows with sample size at a fixed underlying effect, meaning computed rankings across clusters of unequal size are incomparable. We quantified this effect across three atlas datasets - lung, kidney, and brain. Mean Wilcoxon score scaled with log cluster size at Pearson correlation r = 0.90-0.95 in each organ. In contrast, a classification-based comparator, NS-Forest, showed low correlation with cluster size (r = 0.10-0.28). This DE-cluster size relationship remained essentially unchanged after controlling for classification performance through partial correlation, decayed monotonically under cluster size downsampling, and was reproduced in simulation with marker quality fixed. The Wilcoxon rank-sum test can be transformed into a rank-based effect size by normalizing by the target-background pair count, removing the positive size dependence. However, this effect size nominates an identical set of candidate genes as the original statistic, indicating the cluster size dependence and poor specificity of DE nominated genes are separate problems. Genes nominated under default settings were substantially less specific to their target clusters, with median on-target fractions of 0.204 (lung), 0.166 (kidney), and 0.083 (brain), compared to NS-Forest's marker genes (0.580, 0.687, 0.364), revealing the relatively poor specificity that highly scoring DE genes exhibit.

Raman spectroscopy (RS) has been shown to differentiate various disease states in tissue samples, yet the impact of sample substrate on spectral quality and downstream machine-learning (ML) performance is under-explored. We collected 960 spectra from 36 formalin-fixed, paraffin-embedded colonic specimens mounted in parallel on calcium fluoride (CaF2) and mirrored stainless-steel (SS) slides. Raw SS spectra were more intense than CaF2, and routine dewaxing left prominent paraffin peaks on CaF2 that were largely absent on SS. Extending CaF2 xylene immersion to 48 h reduced key paraffin peaks by 11 to 23%, aligning its biochemical profile with SS. Nested cross-validated classifiers were built to distinguish true disease (T1) from epithelial misplacement (EM). The highest accuracies were observed on SS (0.65 - 0.66), while CaF2 improved from 0.41 - 0.57 to 0.65 - 0.69 after extended dewaxing, chiefly via gains in recall. Although optimal dewaxing renders CaF2 diagnostically non-inferior, SS still offers superior accuracy, faster preparation, at approximately 4% of the cost, supporting its integration into scalable, AI-enabled RS pathology workflows.

Lymphatic endothelial cell (LEC) function is strongly tied to transitions between button- and zipper-like junctions that control fluid flow into vessels. Published methods quantifying junction morphology are limited to single cell analysis or use few quantitative metrics to describe junctions across LEC monolayers. Therefore, a novel 3-step image analysis pipeline was developed to identify new metrics that quantify differences in button- and zipper-like junctions in LECs. The pipeline used Cellpose, CellProfiler, and Fiji to analyze VE-cadherin expression across lymphatic vessels within a 3D microfluidic chip and quantify VE-cadherin radial intensity, number of objects, object size, branchpoints, end-points, and junction thickness. Tumor necrosis factor (TNF)- exposure produced but-ton-like junctions, and unexposed cells were zippered. Radial intensity was heightened at the cell membrane in zipper-like junctions while perinuclear intensity increased for but-ton-like junctions. Increased branchpoints, endpoints, objects per cell, and junction thick-ness were associated with junction buttoning, while increased object size was associated with junction zippering. Principle component analysis results reinforced these trends and identified metric combinations that represented each junctional state. Overall, this semi-automated pipeline quantified differences in VE-cadherin junction morphology across LEC monolayers and generated new quantitative metrics to expand the tools available for studying junction morphology in lymphatic vasculature.

The lack of non-invasive, quantitative methods to track the location and proliferation of transplanted cells hampers the clinical translation of cardiac regenerative therapies. Here, we establish a PET reporter for tracking human induced pluripotent stem cell (hiPSC)-derived cardiac cells across complementary two-dimensional (2D) and three-dimensional (3D) cardiac tissue models. Methods hiPSCs were genetically engineered using CRISPR-Cas9 to express a human-derived, anticalin-based, bioorthogonal PET reporter (DTPA-R) that specifically binds radiolabeled DTPA-metal complexes. The engineered hiPSCs were differentiated into ventricular progenitors (HVPs) and cardiomyocytes (CMs). Stability of reporter gene expression and its effects on cellular differentiation and function were evaluated. Uptake of [18F]F-DTPA*Tb by DTPA-R-positive and -negative HVPs was analyzed in vitro, and DTPA-R HVPs were evaluated in ex vivo cultured porcine myocardial slices with and without radiofrequency ablation (RFA) injury. Phantom studies were performed to evaluate PET detectability under clinically relevant large-animal and human imaging conditions and to assess how activity distribution affects signal detection in relation to DTPA-R cardiac cell-equivalent numbers. Results DTPA-R expression was maintained during the differentiation stages of hiPSC-derived cardiac cells and had no measurable impact on migration, differentiation, and functional integration. PET imaging enabled the specific detection of DTPA-R HVPs in intact and injured myocardial slices. At the initial phantom scan, the measured activity corresponded to cell-equivalent numbers of approximately 3.2 x 10^6 HVPs or 5.5 x 10^6 CMs. The compact 200 uL source remained clearly detectable at 6 h, whereas the more dispersed 1,000 uL source progressively approached background activity, indicating that PET detectability depended strongly on local activity concentration and distribution. Conclusion This study establishes a bioorthogonal PET reporter gene platform for quantitative imaging of hiPSC-derived cardiac cells, with no measurable adverse effects on the biological parameters examined, and provides a methodological foundation for future translational and in vivo imaging studies. The approach may also be applicable to other hiPSC-derived cell products beyond cardiac cell lineages.

Background: Induced pluripotent stem cell (iPSC)-derived kidney organoids hold great promise for disease modeling, drug screening, and regenerative medicine. However, since these organoids are synthetic constructs, it is essential to evaluate how faithfully their constituent cell populations recapitulate the corresponding cell types in the native kidney. Single-cell transcriptomic label-transfer tools, which assign cell-type labels by integrating cells with well-annotated reference data and classifying them, are increasingly used for this purpose. Yet the reliability of the resulting labels - particularly for immature or partially differentiated cells that might have no clear in vivo counterpart - has not been systematically addressed. Methodology: To quantify the reliability of cell-type labels assigned by label-transfer algorithms, we introduce two complementary robustness measures for each cell type - the classification stability score, which assesses label stability under simulated Poisson noise, and the prediction specificity score, which assesses how well the predicted cell-type label probabilities match the assigned labels. We apply this methodology to scRNA-seq datasets from the human fetal kidney and four iPSC-derived kidney organoid protocols, using a mouse fetal kidney dataset as a reference. Results: We find that label-transfer robustness scores are generally high in most human fetal kidney cell types, except for cells of immature epithelial structures. In the organoid-derived cell subpopulations, robustness scores are high for the cap mesenchyme but vary for epithelial lineages according to each protocol's specific differentiation strategy. These observations are consistent across both classification stability and prediction specificity scores, which are generally well correlated with each other. We further validate our approach through a simulated cell-removal experiment, confirming that the proposed measures correctly identify unreliable classifications. Significance: These robustness measures provide a generalizable framework for assessing the reliability of cell-type label transfer in single-cell transcriptomic datasets, and can be extended to other biomedical settings that rely on automated single-cell classification.

Ambient RNA contamination confounds expression analysis in droplet-based single-cell and single-nucleus RNA sequencing. Here we present AmbiDose, which combines an ambient profile estimated from empty droplets with cell-specific nominal removal budgets and expression-based protection of lineage-enriched genes. Its ambient dose guides count removal rather than estimating a calibrated contamination fraction. In a held-out human-mouse GEM-X mixture, AmbiDose removed 92.0% of cross-species UMIs while retaining 99.6% of same-species UMIs. It reduced off-target marker expression in kidney-tumor and fetal-liver cohorts and yielded the lowest proximal-tubule marker leakage among the tested methods across five strain-mixed kidney libraries. Simulations supported the contribution of cell-specific dose estimates but identified reduced endogenous retention or incomplete removal when ambient and endogenous expression overlapped. AmbiDose is available as a Python package compatible with Scanpy.

Image scanning microscopy (ISM) is emerging as the natural evolution of confocal laser-scanning microscopy, combining optical sectioning and a two-fold improvement in lateral resolution with high signal-to-noise ratio. Single-photon avalanche diode (SPAD) array detectors enable an effective implementation of ISM by recording a micro-image at each scan position with single-photon sensitivity, negligible readout noise, and without the fixed frame-readout constraints of conventional cameras. Nevertheless, SPAD array-based ISM remains largely confined to laboratories with expertise in microscope development. Here, we present BrightEyes-Scope, a reference implementation of a SPAD array-based ISM system. We provide step-by-step guidelines for optical design, system construction, alignment, calibration, data acquisition, and image reconstruction. Presented as a complete ISM microscope, BrightEyes-Scope also serves as a reference for upgrading existing laser-scanning microscopes. We anticipate that it will accelerate the adoption of ISM for quantitative super-resolution imaging.

Spatial profiling is moving from method development into a central platform for biological discovery and clinical translation, but analyzing such complex data has become the bottleneck of scientific discovery. Translating spatial transcriptomic measurements into biological insight requires reproducible workflows that integrate diverse analytical methods. Although language-model agents can plan and execute multistep analyses, fragmented data and software ecosystems hinder their use as reliable research assistants. In this study, we introduce SpatialOmicsLab, an integrated research environment for spatial transcriptomics comprising 113 curated computational tools, 63 reference datasets and 4,614 task specifications. We also introduce ST-Coscientist, an AI co-scientist that uses this environment to assemble, execute and refine spatial transcriptomic analyses. Within SpatialOmicsLab, ST-Coscientist outperformed expert-led workflows and other scientific agents across six benchmark settings spanning spatial clustering, cell-type deconvolution and spatially variable gene detection. It constructed alignment-aware workflows for three-dimensional MERFISH data and addressed biological questions by selecting and analysing relevant reference datasets. Procedural memory increased the success rate of integrating and validating additional analytical tools from 48% to 95%. Together, SpatialOmicsLab and ST-Coscientist establish an extensible ecosystem for reproducible spatial transcriptomic research, enabling AI co-scientists to address evolving biological questions with an expanding analytical repertoire.

Protein labelling using genetically encoded fluorescent protein fusions has revolutionized the study of protein localization and function. However, visualizing endogenous proteins in specific cell types remains challenging, as existing approaches often require overexpression or laborious genetic engineering. To address these limitations, we developed a library of fluorescent protein binders using existing nanobodies and single-chain variable fragments that recognize fluorescent proteins and short peptide tags, allowing for modular, cell type-specific labelling of genetically tagged endogenous proteins. We found that fluorescent protein binders generate background fluorescence in the absence of the target protein, reducing signal specificity. To overcome this limitation, we generated destabilized fluorescent protein binders directed against GFP, ALFA, GCN4 peptide and V5. These binders display increased antigen-dependent stability and reduced background fluorescence, thereby improving the signal-to-noise ratio of endogenous protein visualization. We demonstrate the detection of endogenous proteins in drosophila larvae and embryos, zebrafish embryos, as well as in specific neuronal populations of the adult drosophila brain. In the brain, tissue-specific labelling revealed previously unknown protein expression and subcellular localization in sleep-regulatory neurons. Together, our panel of tag-stabilized fluorescent protein binders provides a versatile, ready-to-use toolkit for background-free, cell type-specific visualization of endogenous proteins.