Cytometry Part A

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.

Here we describe a 16-parameter, 14-color surface staining panel optimized for murine bronchoalveolar lavage cells that enables reproducible identification of major innate and adaptive immune populations relevant to pulmonary infections or other inflammatory conditions of the airways. The panel enables confident identification of neutrophils, eosinophils, B cells, T cells and subtypes, NK cells, and distinguishes between tissue resident and monocyte-derived macrophages populations. The panel was carefully designed for BAL samples that vary in cell number, are rich in debris, and often autofluorescent. Antibody concentrations are optimized to provide reproducible results regardless of sample-variable cell numbers allowing for the preparation of a single antibody cocktail master mix and rapid sample staining time, thereby cutting down on sample preparation and optimizing cell viability of analyzed samples. The panel facilitates robust cross-sectional and longitudinal comparison of airway inflammation across different airway inflammatory conditions, infections by different respiratory pathogens, impact of vaccination or therapeutics on the inflammatory landscape, and more. It facilitates hypothesis generation by revealing recruitment kinetics and remodeling of myeloid compartments, supports downstream sorting for transcriptomic or functional assays, and provides a standardized baseline for labs to adopt or extend for activation or intracellular cytokine analyses. We have successfully utilized this panel to identify differential host responses to different respiratory Mycoplasma pathogens as well as to longitudinally track the progression of inflammatory response to Mycoplasma pneumoniae over a 21-day time course study. This panel provides an economic immunophenotyping option by utilizing only 14 markers and a two laser (Blue and Violet) full spectrum cytometer to provide comprehensive immunophenotyping power of both myeloid and lymphoid cells. Furthered by lacking the requirement for advanced unmixing for sample analysis, the panel can be easily adopted by the community, enabling comparative meta-analyses of host responses across murine respiratory infection models.

Accurate spectral unmixing is a critical step for flow cytometry data analysis and requires a single stain control for every fluorescent parameter used in an experiment. Currently, compensation particles are often used for making single stain controls when a target protein is of low abundance or a cell type is of low frequency. However, compensation particles introduce incongruencies in emission spectra compared to cells resulting in spectral unmixing or compensation errors. To enable the use of cells regardless of the abundance of target proteins or immune cell type, we generated a bispecific antibody that links a human anti-CD45 and mouse anti-IgG variable region. We refer to this new bispecific tool as CaptureBody (CB) and highlight the benefits of its final nanobody-based design. We provide all sequences and methods necessary for the in-house expression of a CaptureBody to disseminate their use for spectral flow cytometry experiments.

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

Autofluorescence (AF) in biological tissues arises from the natural emission of light by intra- and extracellular molecules upon light absorption. Conventional flow cytometry cannot correct for cellular AF, leading to distorted signals and measurement errors. While spectral flow cytometry enables AF visualization and extraction, accurately correcting for AF remains challenging in complex biological samples containing multiple cell types with distinct AF properties, such as the liver. Additionally, pathological processes such as inflammation and fibrosis can alter tissue composition and activate specific cell types, further modifying AF characteristics across experimental conditions. Macrophages are among the most autofluorescent immune cells, exhibiting fluorescence emission across the entire spectrum of light. Recent studies have demonstrated substantial heterogeneity in the phenotypes of resident and recruited macrophages both in the healthy liver and during Metabolic Dysfunction-Associated Steatohepatitis (MASH). Given their critical role in liver disease pathophysiology, we developed a spectral flow cytometry approach to identify and analyze all macrophage subpopulations in healthy and MASH murine livers. Our findings show that healthy, steatotic and MASH livers exhibit distinct and heterogeneous AF signatures. Furthermore, inadequate AF extraction compromised accurate quantification of hepatic macrophages and differentiation of macrophage subsets.

Spatial imaging technologies provide an expansive view of tissue microenvironments through high-plex profiling of protein and molecular targets in situ. Imaging mass cytometry (IMC; Standard BioTools) is a trusted method for defining immune phenotypes based on up to 40 protein targets, whilst Xenium in situ spatial transcriptomics (Xenium; 10x Genomics) is an emerging platform that can measure up to 5000 mRNA markers simultaneously. Although these platforms can reveal valuable insights on their own, there is an increasing need to analyse samples using a multi-omics approach to further our understanding of complex biological processes. To address this, we have assessed a novel dual-platform workflow that combines Xenium and IMC on a single formalin-fixed paraffin-embedded tissue section to enable the spatial profiling of both mRNA and protein targets at single-cell resolution. The feasibility of the workflow was determined by comparing the staining quality of IMC performed after Xenium to that of IMC performed alone on an adjacent tissue section, confirming that Xenium has little to no negative impact on subsequent IMC protein staining. Although the location of transcripts picked up by Xenium correlated with the corresponding proteins picked up by IMC at a global scale, discrepancies between the two technologies were apparent at the single-cell level. This is to be expected, as biologically transcript expression does not always correlate with protein, and both platforms have their own technical limitations. However, when we analyse T cells identified by both technologies, as opposed to T cells identified by Xenium or IMC alone, it produces the most biologically meaningful results at both the transcript and protein level for specific T cell markers. These results highlight how integration of the two platforms, identifying the presence of both RNA and protein, can foster a more comprehensive view of cellular landscapes and provide a greater depth of functional capabilities and cellular interactions.

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.

Early achievement of deep remission improves patients outcome in chronic myeloid leukemia (CML) treatment, highlighting the need for predictive indicators before therapy initiation. This study aimed to develop a tool to predict CML treatment responses to guide optimal therapy selection. Using hierarchical clustering of complete blood count (CBC) data at diagnosis, patients were stratified into two clusters. Patients in Cluster 1 had higher BCR::ABL1IS mRNA levels at 3 and 6 months post-treatment and lower rates of major molecular response compared to cluster 2. Cluster 1 also showed increased granulocyte and immature white blood cell counts and decreased erythroid parameters. Flow cytometric analysis of bone marrow mononuclear cells revealed that cluster 1 had a significant increase in hematopoietic stem cell fractions and a higher ratio of granulocyte-macrophage progenitors to megakaryocyte-erythroid progenitors compared to cluster 2. These findings suggest that differences in bone marrow progenitor cell differentiation affect peripheral blood profiles. Artificial intelligence-driven ghost cytometry (GC) was evaluated for its ability to comprehensively capture these changes and successfully distinguished patients with poorer treatment responses, with GC scores at diagnosis strongly correlating with BCR::ABL1IS mRNA levels at 3 and 6 months post-treatment initiation. The study indicates that multivariate analysis of CBC or GC analysis may enable simple, early prediction of CML treatment efficacy, potentially contributing to effective and individualized CML therapy.

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.

Advances in spectral flow cytometry have enabled the simultaneous measurement of dozens of markers across millions of cells within a single experiment. Despite the increasing maximum perplexity achievable in spectral panels, panel design remains constrained. A central obstacle is signal spread-- unmixed fluorescence signal misattributed to unrelated channels--which reduces the resolution of cell populations. Here we introduce the Residual Model, a robust, scalable, and interpretable model-based approach for spread prediction during panel design. The Residual Model integrates statistical features derived from single-color controls and predicts spread under Ordinary Least Squares unmixing, the most widely used unmixing method. We demonstrate its reliable predictive performance across 141 single-color control samples measured on two instruments. To facilitate practical application, we developed the USERM R package, which implements the Residual Model and provides an out-of-box solution for interactive spread prediction and visualization.

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.

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.

Extracellular vesicles are key intercellular messengers that modulate the function of target cells by carrying effectors, either at their surface or in their lumen. In the latter case, their action depends on the ability to deliver their content into the cytosol of target cells. How efficiently EVs deliver their content upon interaction with their target cell is thus a central question for understanding the functional impact of this mode of action. To address this question, signal-driven bimolecular interactions between two partners located respectively in the EV lumen and the target cell cytosol have become a widely used strategy to detect the cytosolic delivery EV content. However, the detection of cytosolic delivery with these assays was often tributary to the artificial enhancement of the fusion between EV and cell membranes, through for instance VSV-G fusogenic protein expression. Here we provide a robust and quantitative LUCiferase-based complementation assay (HiBiT/LgBiT), to quantify the Internalization and cytosolic Delivery of EV content: LUCID-EV. By optimizing the signal-to-noise ratio of the assay, the method for loading HiBiT fragment into EVs (fusion to a lipid-binding domain rather than to tetraspanins), and the intracellular position of LgBiT (associated to membranes), we could quantify cytosolic delivery from various non-VSV-G-expressing EVs into target immune dendritic cells. Importantly, this delivery did not involve the acidic late endosomes environment required for VSV-G-dependent EV cytosolic delivery. The limited efficacy of the process highlights the need for highly sensitive assays like the one described here. Further development of the LUCID-EV assay could help identifying EV/target cells pairs with enhanced cytosolic delivery properties and characterize the cellular route for delivery.

Acute myeloid leukemia (AML) is a heterogeneous malignancy whose characterization relies on immunophenotyping and molecular profiling. While hemolysis is recommended for leukocyte isolation in clinical diagnostics, Ficoll-based density gradient centrifugation is widely used in research and biobanking. Here, we evaluated the impact of Ficoll isolation on commonly performed analyses of AML samples. Ficoll altered flow cytometry-based characterization by systematically enriching lymphocytes and AML blasts while depleting granulocytes. The increased T-cell content impaired AML engraftment in NSG mice, as T cells mediated terminal graft-versus-host disease. Although Ficoll had minimal impact on ex vivo AML blast expansion or chemotherapy response, RNA sequencing identified 1,136 differentially expressed genes compared with hemolysis, with Ficoll-processed samples notably leading to an overestimation of leukemic stem cell gene set expression. Immunogenomic deconvolution highlighted that Ficoll leads to an overestimation of CD8+ T-cell and monocyte abundances in sequenced samples. Mutation calling from RNA-seq data revealed substantial discrepancies between methods, including failure to detect a clinically relevant DNMT3A R882 mutation in a Ficoll-processed sample. Together, these findings support the systematic use of hemolysis to preserve cellular diversity and avoid unpredictable biases introduced by Ficoll-based isolation.

Circulating tumor cells (CTCs), and especially CTC-clusters, are linked to poor prognosis and may reveal mechanisms of metastasis and treatment resistance. Therefore, developing unbiased methods for the functional characterization of CTCs in liquid biopsies is an urgent need. Here, we present an evaluation of multiplex imaging mass cytometry (IMC) to analyze CTCs in mice with human xenograft tumors. In a single-step process, IMC uses metal-labeled antibodies to simultaneously detect a large number of proteins/modifications within minimally manipulated small volumes of blood from the tail vein or heart. We used breast cancer cell lines and a patient-derived xenograft (PDX) to assess antibodies for cross-species interpretation. Along with manual verification, HALO-AI-based cell segmentation was used to identify CTCs and quantify markers. Despite some limitations regarding human-specificity, this technology can be used to investigate the effect of genetic and pharmacological interventions on the properties of single and cluster CTCs in tumor-bearing mice.

Holographic imaging in microscopy enables label-free quantitative information of biological specimens and has found applications across a wide range of biomedical studies, from cell morphology to particle dynamics; yet its widespread adoption is often limited by the lack of accessible and standardized analysis software. We present HoloBio, an open-source, Python-based graphical user interface developed to address this issue. This software offers two primary operational modes: a Real-Time mode that enables live processing of holograms at video frame rates, and an Offline mode designed for post-processing previously recorded holograms. HoloBio is compatible with holograms recorded using both lens-based and lensless systems, supporting off-axis architectures in telecentric and non-telecentric configurations, as well as slightly off-axis and in-line optical setups. The software incorporates tools for cell tracking, phase profiling, thickness estimation, and morphological analysis, including cell counting and object area quantification. HoloBio is designed to be accessible for users without coding expertise, offering a reproducible, high-throughput environment tailored for researchers in biology, biophotonics, and biomedical imaging.

Identifying allogeneic HLA-specific B cells in sensitized individuals is essential for defining the cellular basis of allogeneic humoral immunity but remains technically challenging due to their low frequency. To overcome this barrier, we generated a 64-plex single-HLA reporter cell (HLA64-RC) panel that provides a cost-efficient, multiplex, high-throughput platform for screening B-cell specificity. We additionally developed a companion R package, HLA64, for automated data analysis and visualization. Integrated with a streamlined high-throughput BCR discovery workflow, this platform enables reliable identification and characterization of allogeneic HLA-specific B cells from sensitized transplant candidates. In a pilot application, thirteen HLA-specific B cells were identified, enabling linked analyses of phenotype, function, and BCR genetics. These B cells exhibited an IgG+ CD24low phenotype, diverse HLA allele-specificity profiles, and recurrent heavy- and light-chain V-gene usage. In two independent B-cell lineages, clonal members within each lineage displayed divergent binding patterns despite sharing a common clonal origin. Broader application of this approach for systematic profiling of alloreactive B-cell responses will help elucidate the molecular basis of allorecognition, define immunodominant HLA eplets, and ultimately improve immunological risk assessment and allograft outcomes in transplant recipients.

Investigating single-cell dynamics and morphology in tissues and embryos requires highly accurate quantitative analysis of microscopy images. Despite significant advances in the field of bioimage analysis, even the most sophisticated segmentation and tracking algorithms inevitably produce errors (e.g. : over segmentation, missing objects, miss-connected objects). Although error rate may be small, their propagation throughout a time-lapse sequence has catastrophic effects on the accuracy of tracking and extraction of single cell parameters. Extracting single cell temporal information in the context of tissue/embryo requires thus expert curation to identify and correct segmentation errors. In the movies commonly used in developmental biology and stem cell research, both the number of imaged cells and the duration of recording are large, making this manual correction task extremely time-consuming. This has now become a major bottleneck in the fields of development, stem cell biology and bioimage analysis. We present here EpiCure (Epithelial Curation), a versatile tool designed to streamline and accelerate manual curation of segmentation and tracking in 2D movies of large epithelial tissues. EpiCure uses temporal information and morphometric parameters to automatically identify segmentation and tracking errors and provides user-friendly tools to correct them. It focuses on ergonomics and offers several visualization options to help navigating in movies of tissue covering a large number of cells, speeding up the detection of errors and their curation. EpiCure is highly interoperable and supports input from a wide range of segmentation tools. It also includes multiple export filters, enabling seamless integration with downstream analysis pipelines. In this paper, using movies from several animal models, we highlight the importance of curating cell segmentation and tracking for accurate downstream analysis, and demonstrate how EpiCure helps the curation process for extracting accurate single cell dynamics and cellular events detection, making it faster and amenable on large dataset.

AbstractThe lymphatic system is essential for maintaining fluid homeostasis, lipid transport and supporting immune function. Despite its central role in health and disease, advancements in understanding human lymphatic vasculature has been constrained, in part because primary human LECs are difficult to access and study in disease-relevant contexts. This study describes an efficient and scalable feeder-free method to differentiate human iPSCs into lymphatic endothelial cells (LECs) that are transcriptionally and phenotypically similar to primary fetal LECs. An iPSC-derived LEC system overcomes a drawback of primary cells by enabling precise genetic perturbations, supporting study of lymphatic diseases of interest in a human context. By grounding our approach in in vivo stages of lymphangiogenisis, we describe a staged protocol that recapitulates the key milestones of lymphatic development. We first adapted a published method to differentiate human iPSCs into venous endothelial cells (VECs) and then initiate transdifferentiation of VECs into LECs. Using immunocytochemistry, qPCR, as well as flow cytometry, we demonstrated expression of lymphatic-specific markers in the differentiated population. We further characterized our induced VECs (iVECs) and LECs (iLECs) through bulk RNA sequencing analysis and compared the populations to pseudobulk VEC and LEC transcriptomic datasets generated from human fetal heart endothelia at 12, 13 and 14 weeks of gestation. Through this work, we expanded the repertoire of approaches for accessing LECs, with the goal of accelerating discoveries in lymphatic biology and therapeutics. Abstract summary image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=171 SRC="FIGDIR/small/712968v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1a9a406org.highwire.dtl.DTLVardef@4faec6org.highwire.dtl.DTLVardef@15b4e73org.highwire.dtl.DTLVardef@17b9c36_HPS_FORMAT_FIGEXP M_FIG C_FIG