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Bone marrow (BM) cellularity assessment is a crucial step in the evaluation of BM trephine biopsies for hematological and non-hematological disorders. Clinical assessment is based on a labor-intensive, semi-quantitative visual estimation of the hematopoietic and adipocytic components by hematopathologists, which does not provide quantitative information on other stromal compartments. In this study, we developed and validated MarrowQuant 2.0, an efficient user-friendly digital hematopathology workflow integrated within QuPath software which serves as BM quantifier for five mutually-exclusive compartments (bone, hematopoietic, adipocytic, interstitial/microvasculature areas, and “Other”) to derive cellularity of human BM trephine biopsies. Instance segmentation of individual adipocytes is realized via adaptation of the machine-learning-based algorithm StarDist. We calculated BM compartments and adipocyte size distributions of haematoxylin and eosin (H&E) images obtained from a total of 250 bone specimens, from control and acute myeloid leukemia or myelodysplastic patients at diagnosis or follow-up, then measured the agreement of cellularity estimates by MarrowQuant 2.0 against visual scores from four hematopathologists. The algorithm was capable of robust BM compartment segmentation with average mask accuracy of 86%, maximal for bone (99%), hematopoietic (92%) and adipocyte (98%) areas. MarrowQuant 2.0 cellularity score and hematopathologist estimations were highly correlated (R2 = 0.92–0.98, Intraclass-Correlation-Coefficient ICC = 0.98; inter-observer ICC 0.96). BM compartment segmentation quantitatively confirmed reciprocity of the hematopoietic and adipocytic compartments. MarrowQuant 2.0 performance was additionally tested for cellularity assessment of specimens prospectively collected from clinical routine diagnosis. After special consideration for the choice of the cellularity equation in specimens with expanded stroma, performance was similar in this setting (R2 = 0.86, n = 42). We thus conclude that MarrowQuant 2.0 can be applied in a clinical setting. We expect this workflow will contribute to improving the speed and ease of diagnosis in hematopathology and serve as a clinical research tool to explore novel biomarkers related to BM stromal components.

Human cells produce thousands of lipids that change during cell differentiation and can vary across individual cells of the same type. However, we are only starting to characterize the function of these cell-to-cell differences in lipid composition. Here, we measured the lipidomes and transcriptomes of individual human dermal fibroblasts by coupling high-resolution mass spectrometry imaging with single-cell transcriptomics. We found that the cell-to-cell variations of specific lipid metabolic pathways contribute to the establishment of cell states involved in the organization of skin architecture. Sphingolipid composition is shown to define fibroblast subpopulations, with sphingolipid metabolic rewiring driving cell-state transitions. Therefore, cell-to-cell lipid heterogeneity affects the determination of cell states, adding a new regulatory component to the self-organization of multicellular systems.

Bone marrow (BM) cellularity assessment is a crucial step in the evaluation of BM trephine biopsies for hematologic and nonhematologic disorders. Clinical assessment is based on a semiquantitative visual estimation of the hematopoietic and adipocytic components by hematopathologists, which does not provide quantitative information on other stromal compartments. In this study, we developed and validated MarrowQuant 2.0, an efficient, user-friendly digital hematopathology workflow integrated within QuPath software, which serves as BM quantifier for 5 mutually exclusive compartments (bone, hematopoietic, adipocytic, and interstitial/microvasculature areas and other) and derives the cellularity of human BM trephine biopsies. Instance segmentation of individual adipocytes is realized through the adaptation of the machine-learning-based algorithm StarDist. We calculated BM compartments and adipocyte size distributions of hematoxylin and eosin images obtained from 250 bone specimens, from control subjects and patients with acute myeloid leukemia or myelodysplastic syndrome, at diagnosis and follow-up, and measured the agreement of cellularity estimates by MarrowQuant 2.0 against visual scores from 4 hematopathologists. The algorithm was capable of robust BM compartment segmentation with an average mask accuracy of 86%, maximal for bone (99%), hematopoietic (92%), and adipocyte (98%) areas. MarrowQuant 2.0 cellularity score and hematopathologist estimations were highly correlated (R 2 = 0.92-0.98, intraclass correlation coefficient [ICC] = 0.98; interobserver ICC = 0.96). BM compartment segmentation quantitatively confirmed the reciprocity of the hematopoietic and adipocytic compartments. MarrowQuant 2.0 performance was additionally tested for cellularity assessment of specimens prospectively collected from clinical routine diagnosis. After special consideration for the choice of the cellularity equation in specimens with expanded stroma, performance was similar in this setting (R 2 = 0.86, n = 42). Thus, we conclude that these validation experiments establish MarrowQuant 2.0 as a reliable tool for BM cellularity assessment. We expect this workflow will serve as a clinical research tool to explore novel biomarkers related to BM stromal components and may contribute to further validation of future digitalized diagnostic hematopathology workstreams.

The limitations of 2D microscopy constrain our ability to observe and understand tissue-wide networks that are, by nature, 3-dimensional. Optical projection tomography enables the acquisition of large volumes (ranging from micrometres to centimetres) in various tissues, with label-free capacities for the observation of auto-fluorescent signals as well fluorescent-labelled targets of interest in multiple channels. We present a multi-modal workflow for the characterization of both structural and quantitative parameters of the mouse small intestine. As proof of principle, we evidence its applicability for imaging the mouse intestinal immune compartment and surrounding mucosal structures. We quantify the volumetric size and spatial distribution of Isolated Lymphoid Follicles (ILFs) and quantify density of villi throughout centimetre long segments of intestine. Furthermore, we exhibit the age- and microbiota-dependence for ILF development, and leverage a technique that we call reverse-OPT for identifying and homing in on regions of interest. Several quantification capabilities are displayed, including villous density in the autofluorescent channel and the size and spatial distribution of the signal of interest at millimetre-scale volumes. The concatenation of 3D image acquisition with the reverse-OPT sample preparation and a 2D high-resolution imaging modality adds value to interpretations made in 3D. This cross-modality referencing technique is found to provide accurate localisation of ROIs and to add value to interpretations made in 3D. Importantly, OPT may be used to identify sparsely-distributed regions of interest in large volumes whilst retaining compatibility with high-resolution microscopy modalities, including confocal microscopy. We believe this pipeline to be approachable for a wide-range of specialties, and to provide a new method for characterisation of the mouse intestinal immune compartment.

The number of grey values that can be displayed on monitors and be processed by the human eye is smaller than the dynamic range of image-based sensors. This makes the visualization of such data a challenge, especially with specimens where small dim structures are equally important as large bright ones, or whenever variations in intensity, such as non-homogeneous staining efficiencies or light depth penetration, becomes an issue. While simple intensity display mappings are easily possible, these fail to provide a one-shot observation that can display objects of varying intensities. In order to facilitate the visualization-based analysis of large volumetric datasets, we developed an easy-to-use ImageJ plugin enabling the compressed display of features within several magnitudes of intensities. The Display Enhancement for Visual Inspection of Large Stacks plugin (DEVILS) homogenizes the intensities by using a combination of local and global pixel operations to allow for high and low intensities to be visible simultaneously to the human eye. The plugin is based on a single, intuitively understandable parameter, features a preview mode, and uses parallelization to process multiple image planes. As output, the plugin is capable of producing a BigDataViewer-compatible dataset for fast visualization. We demonstrate the utility of the plugin for large volumetric image data.

Cells are powerful carriers that can help to improve the delivery of nanomedicines. One approach to use cells as carriers is to immobilize the nanoparticulate cargo on the cell surface. While a plethora of chemical conjugation strategies are available to bind nanoparticles to cell surfaces, only relatively little is known about the effects of particle size and cell type on the surface immobilization of nanoparticles. This study investigates the biotin-NeutrAvidin mediated immobilization of model polymer nanoparticles with sizes ranging from 40 nm to 1 mu m on two different T cell lines, viz., human Jurkat cells as well as mouse SJL/PLP7 T cells, which are of potential interest for drug delivery across the blood-brain barrier. The nanoparticle cell surface immobilization and the particle surface concentration and distribution were analyzed by flow cytometry and confocal microscopy. The functional properties of nanoparticle-modified SJL/PLP7 T cells were assessed in an ICAM-1 binding assay as well as in a two-chamber setup in which the migration of the particle-modified T cells across an in vitro model of the blood-brain barrier was studied. The results of these experiments highlight the effects of particle size and cell line on the surface immobilization of nanoparticles on living cells.

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cells contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier.Author summaryBreast cancer is the most commonly diagnosed tumor in women worldwide and its incidence in the population is increasing over time. Because our understanding of such disease has been hampered by the lack of adequate human preclinical model, efforts have been made in order to develop better approaches to model the human complexity. Recent advances in this regard were achieved with Patient-Derived Xenografts (PDXs), which entail the implantation of human-derived specimens to recipient immunosuppressed mice and are, thus far, the preclinical system best recapitulating the heterogeneity of both normal and malignant human tissues. However, histological analyses of the resulting tissues are usually confounded by the presence of cells of different species. To circumvent this hurdle and to facilitate the discrimination of human and murine cells in xenografted samples, we developed Single Cell Classifier (SCC), a deep learning-based open-source software, available as a plugin in ImageJ/Fiji, performing automated species classification of individual cells in H&E stained sections. We show that SCC can reach up to 96% classification accuracy to classify cells of different species mainly leveraging on their contextual features in both normal and tumor PDXs. SCC will improve and automate histological analyses of human-in-mouse xenografts and is open to new in-house built models for further classification tasks and applications in image analysis.

The decline of neuronal synapses is an established feature of ageing accompanied by the diminishment of neuronal function, and in the motor system at least, a reduction of behavioural capacity. Here, we have investigated Drosophila motor neuron synaptic terminals during ageing. We observed cumulative fragmentation of presynaptic structures accompanied by diminishment of both evoked and miniature neurotransmission occurring in tandem with reduced motor ability. Through discrete manipulation of each neurotransmission modality, we find that miniature but not evoked neurotransmission is required to maintain presynaptic architecture and that increasing miniature events can both preserve synaptic structures and prolong motor ability during ageing. Our results establish that miniature neurotransmission, formerly viewed as an epiphenomenon, is necessary for the long-term stability of synaptic connections., Synaptic structures disintegrate and fragment as ageing progresses. Here the authors find that miniature neurotransmission is required to maintain adult motor synapse structures in Drosophila and that increasing miniature events can preserve motor ability during ageing.

Cellular uptake and intracellular trafficking of polymer conjugates or polymer nanoparticles is typically monitored using fluorescence-based techniques such as confocal microscopy. While these methods have provided a wealth of insight into the internalization and trafficking of polymers and polymer nanoparticles, they require fluorescent labeling of the polymer or polymer nanoparticle. Because in biological media fluorescent dyes may degrade, be cleaved from the polymer or particle, or even change uptake and trafficking pathways, there is an interest in fluorescent label-free methods to study the interactions between cells and polymer nanomedicines. This article presents a first proof-of-concept that demonstrates the feasibility of NanoSIMS to monitor the intracellular localization of polymer conjugates. For the experiments reported here, poly( N-(2-hydroxypropyl) methacrylamide)) (PHPMA) was selected as a prototypical polymer-drug conjugate. This PHPMA polymer contained a 19F-label at the α-terminus, which was introduced in order to allow NanoSIMS analysis. Prior to the NanoSIMS experiments, the uptake and intracellular trafficking of the polymer was established using confocal microscopy and flow cytometry. These experiments not only provided detailed insight into the kinetics of these processes but were also important to select time points for the NanoSIMS analysis. For the NanoSIMS experiments, HeLa cells were investigated that had been exposed to the PHPMA polymer for a period of 4 or 15 h, which was known to lead to predominant lysosomal accumulation of the polymer. NanoSIMS analysis of resin-embedded and microtomed samples of the cells revealed a punctuated fluorine signal, which was found to colocalize with the sulfur signal that was attributed to the lysosomal compartments. The localization of the polymer in the endolysosomal compartments was confirmed by TEM analysis on the same cell samples. The results of this study illustrate the potential of NanoSIMS to study the uptake and intracellular trafficking of polymer nanomedicines.

Light microscopy offers life sciences the possibility to study a variety of organisms on widely differing spatial and temporal scales. Recent years have seen the appearance of a multitude of important light microscopy techniques. To characterise and describe biological processes, however, sheer visualisation of images is inadequate. The assembly of image-processing algorithms, so-called workflows, is needed to extract meaningful quantitative data. These workflows are as important as image acquisition and, therefore, are critical components for data acquisition and sample preparation. Here, we describe a variety of image-processing workflows covering a large part of the biological landscape pointing out common themes and outlining a generic template for workflow assembly. Key Concepts Digital image analysis is an important tool to extract meaningful data out of an image. Digital image analysis is the last component of a workflow comprising sample preparation and image acquisition. Segmentation allows to identify objects in an image. Filtering is needed to reduce noise in images from biological samples. Digital image analysis is the key for reproducible workflows in life sciences. Keywords: image processing; image analysis; segmentation; filtering; data representation; analysis workflow

Delivery of therapeutics to the central nervous system (CNS) is challenging due to the presence of the blood-brain barrier (BBB). Amongst various approaches that have been explored to facilitate drug delivery to the CNS, the use of cells that have the intrinsic ability to cross the BBB is relatively unexplored, yet very attractive. This paper presents a first proof-of-concept that demonstrates the feasibility of activated effector/memory CD4(+) helper T cells (CD4(+) T-EM cells) as carriers for the delivery of polymer nanoparticles across the BBB. This study shows that CD4(+) T-EM cells can be decorated with poly(ethylene glycol)-modified polystyrene nanoparticles using thiol-maleimide coupling chemistry, resulting in the immobilization of approximate to 105 nanoparticles per cell as determined by confocal microscopy. The ability of these cells to serve as carriers to transport nanoparticles across the BBB is established in vitro and in vivo. Using in vitro BBB models, CD4(+) T-EM cells are found to be able to transport nanoparticles across the BBB both under static conditions as well as under physiological flow. Finally, upon systemic administration, nanoparticle-modified T cells are shown to enter the brain parenchyma of mice, demonstrating the brain delivery potential of this T cell subset in allogeneic hosts.

A robust and reproducible method for culturing monolayers of adherent and well-spread primary islet cells on glass coverslips is required for detailed imaging studies by super-resolution and live-cell microscopy. Guided by an observation that dispersed islet cells spread and adhere well on glass surfaces in neuronal co-culture and form a monolayer of connected cells, we demonstrate that in the absence of neurons, well-defined surface coatings combined with components of neuronal culture media collectively support robust attachment and growth of primary human or rat islet cells as monolayers on glass surfaces. The islet cell monolayer cultures on glass stably maintain distinct mono-hormonal insulin+, glucagon+, somatostatin+ and PP+ cells and glucose-responsive synchronized calcium signaling as well as expression of the transcription factors Pdx-1 and NKX-6.1 in beta cells. This technical advance enabled detailed observation of sub-cellular processes in primary human and rat beta cells by super-resolution microscopy. The protocol is envisaged to have broad applicability to sophisticated analyses of pancreatic islet cells that reveal new biological insights, as demonstrated by the identification of an in vitro protocol that markedly increases proliferation of primary beta cells and is associated with a reduction in ciliated, ostensibly proliferation-suppressed beta cells.

A robust and reproducible method for culturing monolayers of adherent and well-spread primary islet cells on glass coverslips is required for detailed imaging studies by super-resolution and live-cell microscopy. Guided by an observation that dispersed islet cells spread and adhere well on glass surfaces in neuronal co-culture and form a monolayer of connected cells, we demonstrate that in the absence of neurons, well-defined surface coatings combined with components of neuronal culture media collectively support robust attachment and growth of primary human or rat islet cells as monolayers on glass surfaces. The islet cell monolayer cultures on glass stably maintain distinct mono-hormonal insulin+, glucagon+, somatostatin+ and PP+ cells and glucose-responsive synchronized calcium signaling as well as expression of the transcription factors Pdx-1 and NKX-6.1 in beta cells. This technical advance enabled detailed observation of sub-cellular processes in primary human and rat beta cells by super-resolution microscopy. The protocol is envisaged to have broad applicability to sophisticated analyses of pancreatic islet cells that reveal new biological insights, as demonstrated by the identification of an in vitro protocol that markedly increases proliferation of primary beta cells and is associated with a reduction in ciliated, ostensibly proliferation-suppressed beta cells.

The ability to specifically label subcellular structures or even proteins of interest in combination with the ability to look at live specimens turned fluorescence light microscopy into an invaluable tool. However, conventional light microscopy is diffraction limited, which restricts the lateral resolution to around 200 nm laterally and 600-800 nm axially. In 2014, the Nobel Prize in Chemistry was awarded to Eric Betzig, Stefan W. Hell, and William E. Moerner for the development of super-resolved fluorescent microscopy techniques. Since then, it has become evident that imaging techniques that enable the visualization of structures below the diffraction limit are essential for the field of life sciences. However, each one of these approaches has inherent advantages and limitations. Here, we describe an imaging workflow suitable for combining structured illumination microscopy (SIM) with direct stochastic optical reconstruction microscopy (dSTORM) data. This is invaluable, since it allows us to put highly resolved dSTORM data into its cellular context.

The use of micropatterns has transformed investigations of dynamic biological processes by enabling the reproducible analysis of live cells using time-lapse fluorescence microscopy. With micropatterns, thousands of individual cells can be efficiently imaged in parallel, rendering the approach well suited for screening projects. Despite being powerful, such screens remain challenging in terms of data handling and analysis. Typically, only a fraction of micropatterns is occupied in a manner suitable to monitor a given phenotypic output. Moreover, the presence of dying or otherwise compromised cells complicates the analysis. Therefore, focusing strictly on relevant cells in such large time-lapse microscopy dataset poses interesting analysis challenges that are not readily met by existing software packages. This motivated us to develop an image analysis pipeline that handles all necessary image processing steps within one open-source platform to detect and analyze individual cells seeded on micropatterns through mitosis. We introduce a comprehensive image analysis pipeline running on Fiji termed TRACMIT (pipeline for TRACking and analyzing cells on micropatterns through MITosis). TRACMIT was developed to rapidly and accurately assess the orientation of the mitotic spindle during metaphase in time-lapse fluorescence microscopy of human cells expressing mCherry:: histone 2B and plated on L-shaped micropatterns. This solution enables one to perform the entire analysis from the raw data, avoiding the need to save intermediate images, thereby decreasing data volume and thus reducing the data that needs to be processed. We first select micropatterns containing a single cell and then identify anaphase figures in the time-lapse recording. Next, TRACMIT tracks back in time until metaphase, when the angle of the mitotic spindle with respect to the micropattern is assessed. We designed the pipeline to allow for manual validation of selected cells with a simple user interface, and to enable analysis of cells plated on micropatterns of different shapes. For ease of use, the entire pipeline is provided as a series of Fiji/ImageJ macros, grouped into an ActionBar. In conclusion, the open source TRACMIT pipeline enables high-throughput analysis of single mitotic cells on micropatterns, thus accurately and efficiently allowing automatic determination of spindle positioning from time-lapse recordings.

We report a reliable strategy to perform automated image cytometry of single (non-adherent) stem cells captured in microfluidic traps. The method rapidly segments images of an entire microfluidic chip based on the detection of horizontal edges of microfluidic channels, from where the position of the trapped cells can be derived and the trapped cells identified with very high precision (>97%). We used this method to successfully quantify the efficiency and spatial distribution of single-cell loading of a microfluidic chip comprised of 2048 single-cell traps. Furthermore, cytometric analysis of trapped primary hematopoietic stem cells (HSC) faithfully recapitulated the distribution of cells in the G1 and S/G2-M phase of the cell cycle that was measured by flow cytometry. This approach should be applicable to automatically track single live cells in a wealth of microfluidic systems.

Multi-user core microscopy facilities are often faced with the challenge to adapt or modify existing instruments. This is essential in order to fulfill the requirements of the user community, who wants to image a wide range of model organisms with varying stains and sample thicknesses. In recent years, lightsheet microscopy has turned into an invaluable tool for both live and cleared sample imaging of many different specimens. This brought up new challenges in terms of sample mounting as the classical approach of attachment onto a coverslip cannot be universally applied. Here we describe the development of a diversified holder which extends the range of samples which can be imaged on a Zeiss Lightsheet microscope Z1. We focus on mounting strategies of cleared specimens; however, the holder and mounting strategy can be applied to live specimens too. The proposed methodology provides very high flexibility along with numerous possibilities for adaptation based on imaging specimen size, condition and available clearing reagents. Moreover, the described mounting strategies can be applied to other light sheet microscopes that can mount 1 mL syringes.

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier.

Despite significant advances in the treatment of cardiovascular diseases, antiplatelet therapies are still associated with a high risk of hemorrhage. In order to develop new drugs, methods to measure platelet function must be adapted for the high-throughput screening (HTS) format. Currently, all assays capable of assessing platelet function are either expensive, complex, or not validated, which makes them unsuitable for drug discovery. Here, we propose a simple, low-cost, and high-throughput-compatible platelet function assay, validated for the 384-well plate. In the proposed assay, agonist-induced platelet activity was assessed by three different methods: (i) measurement of light absorbance, which decreases with platelet aggregation; (ii) luminescence measurement, based on ATP release from activated platelets and luciferin-luciferase reaction; and (iii) automated bright-field microscopy of the wells and further quantification of platelet image area, described here for the first time. Brightfield imaging results were validated by demonstrating the similarity of dose-response curves obtained with absorbance and luminescence measurements after stimulating platelets, pre-incubated with prostaglandin E1 or tirofiban, and demonstrating the similarity of dose-response curves obtained with agonists. Assay quality was confirmed using the Z'-factor, a statistical parameter used to validate the robustness and suitability of an HTS assay. The results showed that, under high rotations per minute (1200 RPM), an acceptable Z'-factor score is reached for absorbance measurements (Z'-factor - 0.58) and automated brightfield imaging (Z'-factor - 0.52), without the need of replicates, while triplicates must be used to achieve an acceptable Z'-factor score (0.54) for luminescence measurements. Using low platelet concentration (4 × 104/μl - 10 μl), the brightfield imaging test was further validated using washed platelets. Furthermore, drug screening was performed with compounds selected by structure-based virtual screening. Taken together, this study presents an optimized and validated assay for HTS to be used as a tool for antiplatelet drug discovery.

Eater is an EGF-like repeat transmembrane receptor of the Nimrod family and is expressed in Drosophila hemocytes. Eater was initially identified for its role in phagocytosis of both Gram-positive and Gram-negative bacteria. We have deleted eater and show that it appears to be required for efficient phagocytosis of Gram-positive but not Gram-negative bacteria. However, the most striking phenotype of eater deficient larvae is the near absence of sessile hemocytes, both plasmatocyte and crystal cell types. The eater deletion is the first loss of function mutation identified that causes absence of the sessile hemocyte state. Our study shows that Eater is required cell-autonomously in plasmatocytes for sessility. However, the presence of crystal cells in the sessile compartment requires Eater in plasmatocytes. We also show that eater deficient hemocytes exhibit a cell adhesion defect. Collectively, our data uncovers a new requirement of Eater in enabling hemocyte attachment at the sessile compartment and points to a possible role of Nimrod family members in hemocyte adhesion.