Cytometry Part A

BackgroundImaging flow cytometry (IFC) provides a high quantity of single-cell morphological data, yet the field lacks open access tools for designing interpretable, bespoke parameters. In particular, rare and atypical cell populations where well annotated data is limited, are negatively affected. ResultsWe present Flow cytometry Feature Importance (FlowFI), an open-source graphical software for bespoke image parameter design and analysis. FlowFI provides a suite of image parameter options combining data across multiple channels and markers, tailored digital noise reduction (reducing noise resulting from common flow cytometry ultra-high image acquisition modalities), and a scalable, unsupervised feature selection pipeline that allows experimentalists to refine image-derived parameters iteratively, with a novel ensemble subsampling approach that provides robust feature importance scoring. We validated FlowFI using data from a rare and heterogenous bone marrow cell type, megakaryocytes, demonstrating that the tool can successfully identify novel, discriminatory morphological features to improve the purity of selected cell populations and gating strategy. ConclusionFlowFIs core functionalities are interacted with through an intuitive user interface for researchers with options to export data directly to common image and flow cytometry software formats. With this in mind, FlowFI offers a scalable way to both feature design, and feature refinement using a range of approaches to manifold learning, augmented by a data efficient bootstrap subsampling approach for unsupervised parameter recommendations in the big data regime. The software also introduces a new feature selection measures based on common manifold learning methods in the space inspired by the Uniform Manifold Approximation and Projection (UMAP) algorithm and finds performance comparable to existing methods. FlowFI provides a versatile testing ground for future developments in broad and dynamically developing areas of research including single cell analysis, label-free sorting and intra- and inter-cellular interaction analysis, while ensuring interoperability with current research workflows. Desktop installation options as well as detailed documentation can be found at https://github.com/EarlhamInst/FlowFI

Introduction The complete blood count with differential (CBD) is one of the most commonly performed blood tests worldwide, used in nearly all areas of medicine. Although modern CBD analyzers generate flow cytometry based single cell measurements,the resultant CBD markers are limited to coarse summary features, such as total cell counts and average cell sizes. This means, the markers cannotdetect subtle cell population shifts that may signal early stage pathogenesis. To test this, we evaluate whether AI based analysis of the raw single cell data underlying the CBD can be used to develop novel, clinically prognostic biomarkers, across patient settings. Method We developed two complementary methods for biomarker discovery using CBD tests and evaluated them with longitudinal data from an academic medical center. To create interpretable biomarkers, we clustered cells into physiologically meaningful subpopulations and performed robust statistical summarization. In tandem, self supervised autoencoders were developed to extract novel nonlinear markers. We evaluated the utility of these clustering (CLS) and autoencoder (AE) markers for patient prognostication across a range of outcomes (mortality, inpatient admission, and future disease development). Results Our study included 242,623 CBD samples from 127,545 patients. Both clustering and embedding approaches successfully generated hundreds of new clinical biomarkers. Many biomarkers showed strong prognostic associations for all cause mortality, inpatient admission, and development of anemia, cancer, or cardiovascular disease, with associations remaining significant after adjustment for demographics and clinical CBD markers. A large subset of these prognostic markers also showed high novelty, having low correlations to existing CBD markers, while also exhibiting significant correlations with broader physiologic signals, such as inflammatory, hormonal, infectious, and coagulopathic markers. Conclusion Collectively, these results demonstrate how modern AI techniques can allow for deeper phenotyping of routine clinical blood counts, generating novel biomarkers that capture more subtle physiologic signals than what are currently clinically utilized.

High-parameter flow cytometry is essential for dissecting the intricate landscape of T-cell diversity. In this study, we directly compare conventional flow cytometry (CFC) and spectral flow cytometry (SFC) for high-dimensional T-cell phenotyping, assessing how spectral detection and panel-design strategies influence analytical performance. Using peripheral blood mononuclear cells from healthy donors stained with both an established (v1) and an optimized (v2) fluorochrome-labelled antibody panel, and analyzed through manual gating and unsupervised approaches, we found that CFC reliably identified major T-cell subsets. However, spectral acquisition consistently delivered clear technical advantages, including improved signal-to-noise ratios, higher staining index values, and superior resolution of low-intensity and co-expressed markers. These improvements translated into more sharply delineated multidimensional clusters and a markedly enhanced resolution of T-cell differentiation states. Moreover, the optimized spectral panel enhanced the unsupervised detection of rare populations, such as cytotoxic CD4 T-cells (PD-1GZMB). However, despite the overall increase in data quality achieved with SFC, the selection of antibody clones may influence the measured frequencies of the identified populations. Finally, SFC - particularly when coupled with rational panel optimization and the use of advanced fluorophores - consistently delivers superior, higher-quality measurements and improved multidimensional resolution, thereby substantially enhancing the robustness and sensitivity of high-parameter T-cell phenotyping for comprehensive immunological studies.

Flow cytometry is widely used for high-throughput single-cell analysis. However, its data analysis relies on either costly commercial software or programming-intensive open-source tools. To bridge this gap, we developed FlowWeb, a freely accessible, web-based platform that combines the flexibility of the R/Bioconductor ecosystem with an intuitive graphical user interface. FlowWeb enables integrated workflows for data handling, quality control, gating, visualization and statistical analysis within a unified environment. FlowWeb integrates raw data, metadata, and analytical state within synchronized Bioconductor structures, enabling coherent analysis and visualization workflows. FlowWeb supports both manual and automated data-driven gating workflows. To evaluate its performance, we applied FlowWeb to an in-house flow cytometry dataset and compared its automated cell cycle and gating workflows to established commercial tools. FlowWebs automated cell cycle workflow produced consistent and reproducible results across replicates and demonstrated high concordance with reference analyses, highlighting the platforms robustness. FlowWebs advanced visualization tools include a wide range of fully customizable individual, overlay, and statistical plots. To enhance usability and reproducibility, the FlowWeb platform provides optional user-accounts that allow storage of reusable configurations, including quality control presets, gating definitions, and plot templates. By lowering technical barriers without compromising analytical rigor, FlowWeb facilitates accessible, reproducible, and scalable flow cytometry data analysis for a broad range of users in research and clinical settings.

Peripheral Blood transcriptome analysis evaluated the bulk transcript abundance (TA) covering all leukocyte cell populations. However, there are 2 main problems in using bulk expression as biomarkers: (1) A long list of differential expression genes (DEGs) was found, and (2) DEGs cannot be attributed to a host response of any specific cell-type. TA assays after conventional cell sorting, as the gold-standard method, is too tedious for routine use. Recently, we showed that by using a ratio-based biomarker, RBB (ratio of two stringently selected genes), it is feasible to interrogate the gene expression of a single cell-type (monocyte and B lymphocyte) in peripheral whole blood (WB) directly. Here, we apply this in-silico cell sorting algorithm (DIRECT LS-TA, Direct Leukocyte Single cell-type Transcript Abundance) to granulocytes in WB samples to reveal RBBs specific to granulocytes. This DIRECT LS-TA approach without the need for cell-sorting was applied to public datasets to differentiate the 2 types of infection (bacterial vs viral infection). The following RBBs measured in WB correlate with the expression of target (numerator) genes in purified granulocytes, thus cell-sorting can be avoided by using these RBBs: ARG1/SRGN, ANXA3/SRGN, RSAD2/SRGN. Together with monocyte DIRECT LS-TA biomarkers, IFI27/PSAP, direct quantification of 4 genes provided optimal differentiation of viral from bacterial infection. Meta-analysis and unsupervised machine learning classification confirmed the superior performance of DIRECT LS-TA biomarkers. These RBBs found by prior In-silico cell-sorting identified pairs of genes that are used to formulate as ratio-based biomarkers (RBBs) to represent gene expression of granulocytes inside whole blood cell-mixture samples which was useful to triage febrile patients into two major categories of febrile diseases between viral and bacterial infection with high degree of sensitivity and specificity.

Current clinical management of multiple myeloma (MM) relies on bone marrow (BM) biopsies for minimal residual disease (MRD) assessment. While BM biopsies are the gold standard, their invasive nature and potential to miss extramedullary or patchy disease necessitate sensitive, non-invasive liquid biopsy platforms. In this study, we evaluated the analytical performance of the CellSearch CMMC assay to determine its utility for deep-MRD monitoring. Using a standard 4 mL whole blood input, the assay achieves a WBC-normalized sensitivity of 2.45 x 10-7, supported by a limit of quantitation of 5 cells per run. Given this high analytical sensitivity, the assay provides a robust negative predictive value, rendering false-negative findings highly unlikely in populations with detectable peripheral disease. These findings characterize the CellSearch CMMC assay as a highly sensitive, analytically validated platform for non-invasive deep-MRD level longitudinal surveillance monitoring. When integrated into a clinical workflow that accounts for its specificity profile, the platform offers a patient-friendly complement to serial BM biopsies, with the potential to reduce their frequency in appropriate clinical contexts.

Tracking mature neutrophils remains challenging due to the lack of reliable cell surface markers. Although CD101 is a promising candidate for mature neutrophils, its stability under pathological conditions is unclear. Using a CD101-tdTomato reporter mouse model, we confirmed that the reporting system does not alter CD101 expression, and tdTomato fluorescence is predominantly expressed in mature neutrophils across peripheral tissues. Further analysis revealed that CD101+ and tdTomato+ neutrophils display identical characteristics of mature neutrophil, including poly-segmented nuclei, cell size, and key functions under homeostasis. By comparing tdTomato fluorescence with CD101 protein levels, we demonstrate that reduced CD101 expression under pathological states was not attributed to shedding or degradation. Our finding enhances CD101 as a robust and reliable marker of neutrophil maturity, providing a foundation for future applications in spatial transcriptomics and lineage tracing studies to dissect neutrophil heterogeneity and function. Highlights of the studyO_LIIn CD101-tdTomato homozygous mice, tdTomato is predominantly expressed in neutrophils and labels nearly 100% of mature neutrophils, aligning with the phenotype of CD101+ mature neutrophils; C_LIO_LIThe CD101-tdTomato reporting system does not interrupt CD101 expression or neutrophil functions; C_LIO_LICD101 remains a stable and reliable cell surface marker for labeling mature neutrophils, even under pathological conditions. C_LI

SignificanceDiffuse in vivo flow cytometry (DiFC) is an emerging technique for enumerating rare, fluorescentlylabeled circulating tumor cells (CTCs) in small animals without drawing blood samples. DiFC uses detection of transient fluorescent peaks in time-series data. Previously, we used a simple amplitude threshold-based method for identifying peak candidates, but it ignores potentially useful information in peak shape that could reduce false-positive detections from instrument noise and increase detection efficiency of lower-amplitude peaks. AimTo develop a machine learning (ML)-integrated signal processing approach for improved CTC enumeration using DiFC by distinguishing CTC peaks from artifacts. ApproachWe developed an ML-integrated approach that incorporates a convolutional neural network (CNN) classifier. The CNN was trained to distinguish CTC peaks from artifacts by analyzing peak amplitude and temporal shape characteristics. Performance was validated on in-silico, control, and CTC-bearing mouse datasets. ResultsThe CNN classifier achieved accuracy, precision, sensitivity, and specificity exceeding 98% on test data. Compared with our previously published threshold-based approach, the ML-integrated method increased the number of correctly identified CTCs and their flow direction while reducing false detections across validation datasets. ConclusionsThe ML-integrated approach significantly improves DiFC CTC enumeration, enabling robustness against artifacts in noisy conditions.

Automated analysis of murine bronchoalveolar lavage fluid (BALF) cytology is important for preclinical respiratory research, yet progress has been limited by the lack of publicly available, well-annotated mouse BALF image datasets. We present MurineCyto-Det, a high-resolution murine BALF cytology dataset comprising 333 image tiles of size 1024x1024 pixels, annotated across five cytological categories with both pixel-level segmentation masks and one-to-one matched bounding boxes. The dataset contains 14,551 annotated cell instances and supports two complementary analysis tasks: morphology-oriented cell segmentation and object-level cell detection. To establish reproducible benchmark baselines, we evaluated representative segmentation and detection models. The results demonstrate the practical utility of MurineCyto-Det while highlighting realistic challenges arising from class imbalance, small object size, irregular cell morphology, and ambiguous debris-like structures. MurineCyto-Det provides a standardized resource for developing, evaluating, and comparing automated methods for murine BALF cytology analysis. The dataset is publicly available at https://doi.org/10.5281/zenodo.17608677.

SignificanceStandard methods to characterize peripheral blood mononuclear cells (PBMCs) are often destructive, lack metabolic information, or do not provide single-cell resolution. Label-free tools that non-destructively measure single-cell metabolism within PBMCs can provide new layers of information to characterize disease state and cell therapy potential. AimDetermine whether non-destructive fluorescence lifetime imaging microscopy (FLIM) of endogenous metabolic co-factors NAD(P)H and FAD, or optical metabolic imaging (OMI), can identify immune cell subsets and activation state within heterogeneous PBMC cultures. ApproachOMI measured single-cell metabolism of PBMCs from 3 different human donors in the quiescent or activated (phorbol 12-myristate 13-acetate and ionomycin) state. Fluorescent antibodies were used as ground truth labels for single-cell classifiers of immune cell subtypes. ResultsOMI identified quiescent vs. activated PBMCs with 93% accuracy at only 2 hours post-stimulation, identified monocytes within quiescent and activated PBMCs with 96% and 88% accuracy, respectively, and identified NK cells within quiescent and activated PBMCs with 74% accuracy. ConclusionOMI identifies activation state and immune cell subpopulations within PBMCs, enabling single-cell and label-free measurements of metabolic heterogeneity within complex PBMC samples. Therefore, OMI could enhance PBMC immunophenotyping for diagnostic and therapeutic applications. Statement of DiscoveryWe demonstrate that autofluorescence lifetime imaging can resolve functional and phenotypic metabolic subpopulations within a mixed culture of immune cells from human blood. This provides a new technique to characterize metabolic activity within immune cells from the peripheral blood of patients, which could improve disease diagnostics and the production of cell therapies.

While high-dimensional flow cytometry plays critical roles in resolving complex cellular networks, there remains a scarcity of comprehensive panels for the simultaneous profiling of diverse mouse cell types, primarily due to the inherent difficulty of multiplexing. To address this technical gap and resolve diverse cell populations in murine models, we designed a 27-color flow cytometry panel optimized for 3-laser spectral flow cytometers. This optimized panel enables broad and simultaneous detection of 16 distinct cell subsets from both lymphoid and myeloid lineages--including T cells, B cells, plasma cells, NK cells, innate lymphoid cells, dendritic cells, monocytes, macrophages, neutrophils, eosinophils, basophils, mast cells--along with non-immune cells, such as epithelial, endothelial, fibroblast, and neuronal cells. The panel has been successfully applied to various tissues, including spleen, thymus, bone marrow, peripheral blood, mesenteric lymph nodes, peritoneal lavage fluid, gut epithelium, and lamina propria. Applying this panel to a poly(I:C) model, we successfully tracked dynamic shifts in monocyte and neutrophil populations and identified a previously unrecognized, glucocorticoid-producing cell subset via reporter expression. This panel will facilitate high-dimensional immune profiling on standard 3-laser cytometers, providing a robust tool for dissecting cellular dynamics across diverse contexts.

BackgroundNatural killer (NK) cell degranulation is a key immune defense mechanism where exposure to tumor or virus-infected cells triggers the fusion of cytoplasmic granules containing apoptotic proteins, perforin, and granzyme with the cell membrane. This process transiently expresses CD107a on the NK cell surface, and measuring CD107a is a standard method to assess NK cell activity. MethodsWe compared two stimulation protocols differing only in duration (6-hour vs. 18-hour) using K562 target cells to induce NK cell degranulation. Isolated PBMCs without stimulation served as controls to assess spontaneous degranulation. Anti-CD107a-PE antibody was present throughout stimulation in both test and control samples. After stimulation, cells were stained with anti-CD45, anti-CD3, and anti-CD56 and analyzed by flow cytometry. ResultsFor 6 of 7 healthy controls, results from both methods fell within 2 standard deviations. Notably, longer (18-hour) stimulation resulted in lower CD107a expression than the 6-hour assay. Interlaboratory comparisons of two samples showed no significant difference (p>0.05). In a suspected hemophagocytic lymphohistiocytosis (HLH) case, two labs reported similarly reduced CD107a expression (9% and 7%). Inter-day variability was observed in a donor across both time points. The 6-hour assay showed higher sensitivity and specificity than the 18-hour assay. A resting period before ex vivo PBMC assays was found necessary. ConclusionStimulation periods beyond 6 hours are unsuitable for clinical NK degranulation assays. Screening for HLH should include multiple stimulants to improve assay reliability.

Hematopoietic progenitors downstream of hematopoietic stem cells (HSC) are now recognized as the main drivers of day-to-day hematopoiesis. While embryonic and adult HSC fates have been studied in detail, less information exists on stages downstream from HSC, notably in the multipotent progenitor compartment. The early postnatal period represents an important growth phase of the animal and its immune system. Developing immune lineages must be generated in large numbers rapidly, and populate expanding organ niches. To shed light on this critical period, we focused our experiments on early postnatal Flt3+ hematopoietic progenitors, and combined genetic single progenitor barcoding using Polylox with Flt3-driven, inducible fate mapping. Key immune cell types, including T and B lymphocytes (lymphocytes), innate lymphocytes (ILC) 1-3, NK cells, and granulocytes and monocytes (myeloid) emerged from Flt3+ hematopoietic progenitors. Barcode analysis revealed that about 75% of Flt3+ hematopoietic progenitors had unipotent fates for lymphocytes, or ILC or myeloid cells, while the remaining fraction showed unprecedented fate combinations for these lineages. Focusing on ILC only, we uncovered clonal fate restriction towards ILC1, or ILC2, or ILC3 in tissues. These data indicate early tissue seeding by progenitors, and further differentiation towards discrete subsets in situ. In addition to these fate analyses, induction of fluorescent marker at this intermediate stage of hematopoiesis showed that Flt3+ progenitors generated a wave of progeny lasting for over one year. The washout of these cells over time provided kinetic data of cell turnover in major immune cell compartments (in the circulation and in tissues) in vivo. In conclusion, we tracked the fate of large numbers (in the order of hundreds) of Flt3+ progenitor clones in situ. These intermediate progenitors downstream of HSC displayed mostly lineage-restricted fates as well as strong fate complexity, thus serving as a source for early tissue seeding and durable immune lineage.

FSFC is an emerging technology that can greatly enhance our understanding of the single-cell proteomic landscape. However, its application to cells derived from solid tissues has been hampered by their complex autofluorescence signatures and lack of optimized tools for non-immune cells. Here, we present a protocol and discuss key controls that minimize the impact of unmixing errors enabling us to resolve multiple EC subpopulations isolated from different tissues in models of chronic tissue injury. Research Topic(s)Vascular biology, cell heterogeneity, full spectrum flow cytometry Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/695385v2_ufig1.gif" ALT="Figure 1000"> View larger version (43K): org.highwire.dtl.DTLVardef@1745181org.highwire.dtl.DTLVardef@1930db9org.highwire.dtl.DTLVardef@16a0b3dorg.highwire.dtl.DTLVardef@107ec29_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsOptimisation of a FSFC panel to enable in-depth phenotyping of tissue- and model-specific endothelial subpopulations from solid tissues. Discussion of appropriate controls to minimize the impact of tissue autofluorescence and enhance the signal-to-noise ratio for cell phenotyping in complex models of inflammation and fibrosis. Trajectory analysis to track cellular plasticity over time. Application of full spectrum cell sorting to isolate rare endothelial subpopulations with complex phenotypes.

Manufacturing red blood cells (RBCs) from human induced pluripotent stem cells (iPSCs) can improve our understanding of embryonic erythropoiesis, foster innovative treatments for RBC-related diseases, and ultimately address clinical blood supply shortages. However, existing systems face low efficiency, enucleation failure, and uncertainty about the develop-mental wave of cultured RBCs. We successfully used self-organized hemanoids to improve iPSC-derived RBC generation. Based on the hypothesis that cellular interactions and 3D organization promote hematopoietic cell fate, we aimed to thoroughly characterize hemanoids. We visualized the spatiotemporal emergence of hematopoiesis by generating a CD43-GFP reporter iPSC line. Imaging and spatial transcriptomics analysis provided de-tailed insight into the hemanoid architecture, identifying stromal cells and hepatoblasts as potential erythropoiesis-supportive elements. The developmental stage mirrors extraembryonic hematopoiesis. Given the difficulties of accessing these early stages in vivo, our system offers a platform not only for further clinical translation but also for exploring hu-man embryonic blood wave dynamics.

BackgroundCompetitive transplantation is essential for defining intrinsic repopulating capacity of murine hematopoietic stem and progenitor cells (HSPCs), yet comparable assays for human cells have been limited by the lack of a robust in vivo platform. MethodsHere, we describe a novel competitive transplantation method in humanized NOD.Cg-KitW-41J Tyr + Prkdcscid Il2rgtm1Wjl/ThomJ (NBSGW) mice that enables simultaneous engraftment and longitudinal tracking of distinct human grafts within a shared microenvironment. ResultsUsing human leukocyte antigen-mismatched donor CD34+ cells, this method facilitates standard flow cytometry panels to track multiple donor cell chimerism, lineage output, and HSPC composition. The experimental framework may be adapted to different mouse models, conditioning strategies, donor sources, and treatments. ConclusionsOverall, this humanized competitive repopulation assay fills a critical translational gap and offers a flexible foundation for advancing mechanistic discovery in human hematopoietic biology and improving clinical strategies for stem cell transplantation.

Current evaluation of male fertility is largely based on indirect sperm parameters such as viability, concentration, morphology, and motility; however, each of these parameters, alone or combined, has been shown to have limited predictive value for successful fertilization. To address this problem, we introduce hSPICER (human SPerm-Induced CEll-cell fusion Requiring JUNO), an assay that evaluates sperm function based on their ability to induce fusion of somatic cells expressing human JUNO (hJUNO), the egg-specific sperm receptor. Similarly to our previous discovery in mice, we found that human sperm can fuse with somatic cells expressing hJUNO on their surface (pseudo-eggs) and promote content mixing between cells in culture, as measured using a split GFP system. The assay is sensitive, specific, and species-dependent, requiring hJUNO for optimal signal. We generated a stable cell line expressing hJUNO, enhancing reproducibility and sensitivity. We also show that hSPICER is compatible with cryopreserved sperm and consistent over different days. Importantly, hSPICER values correlate with fertilization outcomes of patients during fertility treatments, indicating its potential as a functional diagnostic tool. Beyond diagnostic uses, hSPICER establishes a platform to explore sperm fusion mechanisms and to screen for therapeutic compounds and interventions to treat low fertility, enhance fertilization, and develop non-hormonal contraceptives for males and females, as well as quality assessment of semen samples in fertility clinics and sperm banks.

Metastasis remains the major cause of cancer-related mortality, and circulating tumor cells (CTCs) are both candidate liquid-biopsy biomarkers and plausible intermediates of metastatic dissemination. Because CTCs are extremely rare in peripheral blood, platform comparisons have often focused solely on recovery. That focus is insufficient for applications that depend on the quality of the recovered material, including single-cell profiling, short-term culture, and functional testing. Here, we compared four CTC isolation approaches: TellDx CTC System, Genesis System, RosetteSep, and flow cytometry, using spike-in experiments in human blood. Capture efficiency was evaluated across all four platforms; purity was assessed for TellDx, Genesis, and RosetteSep; and post-isolation GFP signal persistence in culture was assessed for TellDx and Genesis as an exploratory proxy for short-term post-isolation preservation. Under the conditions tested, TellDx showed the highest recovery (88.1% {+/-} 3.7%), followed by Genesis (40.6% {+/-} 12.1%), RosetteSep (36.5% {+/-} 9.0%), and flow cytometry (7.6% {+/-} 4.5%). TellDx also showed the highest purity score (3.76), whereas Genesis (2.25) and RosetteSep (2.09) did not differ substantially. In the short-term culture assay, TellDx-derived samples retained a higher normalized GFP signal than Genesis-derived samples at 48 h and 72 h. To synthesize these readouts, we propose the Recovery Performance Index (RPI), a composite score integrating recovery, purity, and post-isolation signal persistence. Within this experimental framework, TellDx achieved the highest RPI. These data support two conclusions. First, platform benchmarking for CTC workflows benefits from multidimensional evaluation rather than recovery alone. Second, under this spike-in model and within the specific workflows used here, TellDx performed best among the platforms tested. The principal contribution of this study is therefore the establishment of a practical benchmarking framework that can be expanded in future work using clinical samples, multiple CTC phenotypes, and orthogonal viability assays.

The biological efficacy of an antibody is largely defined by its affinity. Moreover, because the binding affinity of an antibody can span orders of magnitude, each antigen-specific B cell would not be expected to contribute equally to humoral defense: high-affinity antibodies are likely to possess increased potency in comparison to those with lower affinities. Hence, assessing the affinity spectrum of a persons antigen-specific B cell repertoire would provide valuable information on their immune competence. Currently, cloning and expression of large numbers of monoclonal antibodies (mAbs) per test subject would be required to gain such insights, but this is impractical in the context of large-scale immune monitoring efforts. Here, we introduce a variant of the B cell ImmunoSpot assay that can simultaneously assess the relative affinity distribution of hundreds of individual B cells in a test sample. Additionally, we also demonstrated its suitability for high-throughput assay workflows that require minimal labor and exploit machine-assisted image analysis software tools. Specifically, as proof of principle, we verified that B cell hybridomas secreting mAbs of different affinities for the SARS-CoV-2 Spike protein could readily be distinguished through simple titration of the soluble antigen detection probe. Furthermore, using this assay methodology we provide evidence for affinity maturation within the Spike-specific memory B cell repertoire following a second COVID-19 mRNA vaccination. Collectively, we introduce a high-throughput suitable and scalable methodology with the potential of filling a major gap in the immune monitoring field: characterizing the affinity distribution of antigen-specific B cells in large study cohorts.

The binding of transmembrane (TM) ligands to their cognate TM receptors on neighboring cells governs intercellular adhesion and direct cell-cell communication. However, these interactions are difficult to study in vitro because they depend on membrane presentation, ligand orientation, receptor clustering, and avidity, features often not captured by soluble recombinant ligands or cell-free assays. Here, we describe a flow cytometry-based assay using fluorescent, lentiviral-derived virus-like particles (VLPs) displaying TM ligands to quantify binding to their receptors on target cells. Fluorescent VLPs are generated in-house by plasmid transfection in HEK293T cells and enable direct fluorescent detection without fluorochrome-conjugated secondary antibodies. The system is modular and readily accommodates engineered ligand constructs, including patient-derived variants. We applied this platform to generate ICAM-1-displaying fluorescent VLPs and to study human LFA-1 function in patient-derived leukocytes. This protocol provides a detailed workflow for VLP production and in vitro binding assays, offering a simple, quantitative, and cost-effective approach for studying TM ligand-receptor interactions in a membrane context. The system is well suited for mechanistic studies, functional assessment of patient-derived variants, and direct binding assays using patient-derived cells. Integrating the assay into multicolor flow cytometry panels enables simultaneous immunophenotyping and quantification of up to four ligand-receptor interactions at single-cell resolution. Key featuresO_LIQuantifies TM ligand-receptor binding in a membrane context using fluorescent VLPs and flow cytometry. C_LIO_LIFully in-house, modular system based on plasmid transfection in HEK293T cells, without reliance on recombinant ligands or fluorochrome-conjugated secondary antibodies. C_LIO_LISupports testing of engineered ligand variants, including patient-derived alleles, and direct functional studies on patient-derived cells. C_LIO_LICompatible with multicolor flow cytometry panels, enabling simultaneous immunophenotyping and quantification of up to four ligand-receptor interactions at single-cell resolution. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/725198v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@a43069org.highwire.dtl.DTLVardef@166491borg.highwire.dtl.DTLVardef@49c7d4org.highwire.dtl.DTLVardef@1de36a0_HPS_FORMAT_FIGEXP M_FIG C_FIG