Biology subjects

Guo, J.

Publications and source records attributed to Guo, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Computer Vision Methods for Spatial Transcriptomics: A Survey

Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprecedented opportunities to dissect tissue architecture and functional organization. As a relatively new omics technology, bioinformatics has driven much of the innovation in ST. However, within these frameworks, "spatial" information is often reduced to locations and relationships between molecular profiles, without fully leveraging the wealth of submicron morphological detail and histological knowledge available. Advances in computer vision-based artificial intelligence (AI) are opening exciting new avenues beyond conventional bioinformatics approaches by modeling complex histological patterns and linking morphology to molecular states. More excitingly, they bring fresh perspectives to potentially address key limitations of ST, including its high cost, limited clinical applicability, and reliance on twodimensional (2D) analysis of inherently three-dimensional (3D) tissues. For instance, models that predict ST directly from histology images enable "virtual sequencing," drastically reducing costs while integrating morphological insights from pathology with molecular biomarkers, thus accelerating clinical translation. Moreover, computer vision techniques can reconstruct pixel-aligned 3D tissue models, overcoming the technical barriers of 2D acquisition and advancing 3D spatial omics analytics. In this paper, we present the first systematic survey of computer vision AI models for ST analytics, categorizing approaches across architectures, learning paradigms, tasks, and datasets, and tracing their technological evolution. We highlight key challenges and future directions, offering a panoramic perspective on vision-driven ST and its potential to transform both basic research and clinical practice. The curated collection of vision-driven spatial transcriptomics papers is available at https://github.com/hrlblab/computer_vision_spatial_omics.

bioinformatics↗

bioRxiv2025

CNS-penetrant NLRP3 inhibitor achieves durable weight loss and reverses hypothalamic inflammation in diet-induced obesity

The NLRP3 inflammasome is a key mediator of innate immunity that integrates inflammatory and metabolic stress signals. Increased and/or chronic activation of this critical pathway has been implicated in obesity, with hypothalamic neuroinflammation linked to dysregulation of energy balance. TN-783 is an investigational, CNS-penetrant, small-molecule NLRP3 inhibitor that potently suppressed inflammasome activation across multiple in vitro assays. In diet-induced obese (DIO) mice, only TN-783, and not the peripherally restricted NLRP3 inhibitor TN-101, produced progressive and sustained weight loss, underscoring the requirement for central target engagement. Weight loss was driven by a persistent reduction in food intake across both acute and chronic phases, without altering energy expenditure. This effect was further characterized by selective reduction of fat mass, with minimal impact on lean tissues. Mechanistically, NLRP3 inhibition attenuated DIO-induced hypothalamic neuroinflammation and partially reversed obesity-associated molecular changes based on transcriptomic and proteomic profiling of the hypothalamus. Beyond monotherapy, TN-783 enhanced the effects of the GLP-1 receptor agonist semaglutide by amplifying weight loss, reinitiating weight loss after semaglutide effect had plateaued, and maintaining the weight loss benefit after semaglutide withdrawal. Discontinuation of TN-783 resulted in reversal of both weight and feeding effects, indicating that its therapeutic activity requires ongoing target engagement rather than permanent remodeling of metabolic pathways. Collectively, these observations support central NLRP3 inhibition as a distinct and promising approach for obesity treatment, offering robust induction and sustained maintenance of weight loss while preserving reversibility.

neuroscience↗

bioRxiv2025

Structure and sequence evolution in the pennycress (Thlaspi arvense) pangenome

O_LIEukaryotic genomes harbor many forms of variation, including nucleotide diversity and structural polymorphisms, which experience natural selection and contribute to genome evolution and biodiversity. However, harnessing this variation for agriculture hinges on our ability to detect, quantify, catalog, and utilize genetic diversity. C_LIO_LIHere, we explore seven complete genomes of the emerging biofuel crop pennycress (Thlaspi arvense) drawn from across the speciess current genetic diversity to catalogue variation in genome structure and content. C_LIO_LIAcross this new pangenome resource, we find contrasting evolutionary modes in different genomic regions. Gene-poor, repeat-rich pericentromeric regions experience frequent rearrangements, including repeated centromere repositioning. In contrast, conserved gene-dense chromosome arms maintain large-scale synteny across accessions, even in fast-evolving immune genes where microsynteny breaks down across species but the macrosynteny of gene cluster positioning is maintained. C_LIO_LIOur findings highlight that multiple elements of the genome experience dynamic evolution that conserves functional content on the chromosome scale but allows rearrangement and presence-absence variation on a local scale. This diversity is invisible to classical reference-based approaches and highlights the strength and utility of pangenomic resources. These results provide a valuable case study of rapid genomic structural evolution within a species and powerful resources for crop development in an emerging biofuel crop. C_LI

genomics↗

bioRxiv2025

Liver Viscosity Decreases Before the Onset of Fibrosis in Metabolic Dysfunction-Associated 1 Steatohepatitis (MASH)

Background and AimMetabolic dysfunction-associated steatohepatitis (MASH) is an increasingly prevalent condition worldwide, associated with biomechanical liver changes and detectable by magnetic resonance elastography (MRE). This study explored the pathophysiological features and their biomechanical manifestations at different stages of MASH in a mouse dietary model. MethodsUsing MRE on a clinical 3 Tesla MRI scanner, we measured liver stiffness, viscosity, fat fraction and water diffusion in 45 male mice. These values were correlated with histopathology and proteomics analyses to further characterize the liver microstructural and metabolic changes during MASH progression. ResultsWe found in a high-fat, low amino-acid model that early MASH was marked by fat accumulation and increasing inflammatory activity, while later stages showed a reduction in fat despite persistent inflammation. These changes in microstructure were associated with biomechanical adaptations, including a progressive decrease in hepatic viscosity and the water diffusion. Notably, viscosity was inversely correlated with lobular inflammation, cell adhesion, antioxidant activity, and metabolic adaptations such as enhanced ketone body synthesis. These findings, which precede the onset of fibrosis and tissue stiffening, show that tissue viscosity is highly sensitive to early microstructural and metabolic alterations in MASH. ConclusionSteatosis and inflammation significantly alter liver biophysical properties, particularly viscosity, in a mouse dietary model of MASH, even in the absence of fibrosis. These findings suggest that viscosity is a potential early and clinically translatable biomarker for the development and progression of MASH.

biophysics↗

bioRxiv2025

Experimental demonstration of daisy chain gene drive and modelling of daisy suppression systems

CRISPR-based gene drive can address ecological problems by biasing their inheritance coupled with an effector for either population modification of suppression. However, the risk of uncontrolled spread impedes some applications of gene drive. Daisy chain drives have received much attention as a potential approach to overcome this problem. They potentially allow efficient spread in a target population but are ultimately self-limiting. This is achieved by splitting a standard gene drive into multiple dependent elements, where each element can bias the inheritance of another, except one non-driving element. With the successive loss of each chain link, spread of transgenic elements will slow down and eventually stop. Here, we use modelling to assess the population dynamics of suppression daisy chain drives in both panmictic and continuous space models. We find that achieving population elimination through a single release of daisy chain gene drives is possible but difficult, with relatively high requirements for drive performance and release size. These effects are substantially amplified in spatial models. We also constructed two configurations of daisy chain gene drives in Drosophila melanogaster as a proof-of-principle. One is a rescue drive for population modification, and the other aims for population suppression by targeting a female fertility gene. Both functioned within expectations at moderate efficiency in individual crosses. However, the system failed to spread in cage populations because of higher than expected fitness costs. Overall, our study demonstrates that daisy chain systems may be promising candidates for both modification and suppression, but challenges remain in both construction and potential deployment in large regions.

genetics↗

bioRxiv2025

CSRefiner: A lightweight framework for fine-tuning cell segmentation models with small datasets

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in spatial omics technologies have enabled transcriptome profiling at subcellular resolution. By performing cell segmentation on nuclear or membrane staining images, researchers can acquire single cell level spatial gene expression data, which in turn enables subsequent biological interpretation. Although deep learning-based segmentation models achieve high overall accuracy, their performance remains suboptimal for whole-tissue analysis, particularly in ensuring consistent segmentation accuracy across diverse cell populations. Existing fine-tuning approaches often require extensive retraining or are tailored to specific model architectures, limiting their adaptability and scalability in practical settings. To address these challenges, we present CSRefiner, a lightweight and efficient fine-tuning framework for precise whole-tissue single-cell spatial expression analysis. Our approach incorporates support for fine-tuning widely uaed segmentation models in the field of spatial omics, including recent published model Cellpose-SAM, while achieving high accuracy with very limited annotated data. This study demonstrates CSRefiners superior performance across various staining types and its compatibility with multiple mainstream models. Combining operational simplicity with robust accuracy, our framework offers a practical solution for real-world spatial transcriptomics applications.

bioinformatics↗

bioRxiv2025

Versatile HIV Rev-dependent reporter cell system for stringent and sensitive quantification of viral reservoirs, neutralizing antibodies, and restriction factors

Detecting and measuring HIV reservoirs, neutralizing antibodies, and restriction factors are important for HIV cure research and the development of new therapeutics and vaccines. Here we describe the development and validation of several HIV Rev-dependent indicator cell lines for these purposes. These reporter cells come from different T-lymphoblast cell lines, including MOLT-4-R5, SupT1-R5, CEM-SS, A3R5, and from the adherent TZM cell platform based on HeLa clone JC53. These cells express CD4, CXCR4, and various levels of CCR5. We compared these cell lines for responsiveness to both X4 and R5-tropic viruses, and confirmed that reporter expression in these cells is not affected by stimulation from mitogens but is responsive to HIV Tat and Rev, reducing non-specific reporter induction from the leaky LTR promoter. To validate the sensitivity of the Rev-dependent reporter cell systems, we conducted a viral dilution assay with three primary HIV-1 clade C swarms from an adult in Malawi. We also validated the systems for quantifying antibody neutralization and screening restriction factors; these systems are also sensitive for viral outgrowth assays for quantifying viral reservoirs in clinical and basic research settings. Given that the systems can measure HIV accurately in complex environments with mitogens or other substances, they can be used for versatile applications, such as quantifying latent reservoirs, testing inhibitory compounds, conducting neutralizing antibody assays, and identifying new restriction factors.

microbiology↗

bioRxiv2025

β-cell-specific Non-invasive Ultrasound Stimulation to Enhance Insulin Release and Glucose Control in Mice

AbstractsDiabetes poses a significant global health burden, with complications such as cardiovascular disease, stroke, and kidney failure. While insulin therapy is central to type 2 diabetes (T2D) management, its limitations--including rapid degradation and the need for frequent injections--highlight the demand for non-invasive alternatives. Here, we present an ultrasound (US)-mediated approach to enhance insulin release by selectively stimulating pancreatic {beta}-cells via targeted microbubbles (MBs). In vitro experiments using RINm5F {beta}-cells demonstrated that US-MB stimulation induces significant calcium influx and subsequent insulin release. In addition, this method effectively decreased blood glucose levels in mice by promoting insulin release. Mechanistic studies revealed that mechanosensitive ion channels play a pivotal role, as their inhibition (via GdCl3) abolished the ultrasonic effect. Importantly, the approach exhibited high biosafety, with no detectable cell death or tissue damage. Our findings establish ultrasound-stimulated {beta}-cell targeting as a promising non-invasive strategy for diabetes treatment, offering a potential alternative to conventional insulin therapy.

bioengineering↗

bioRxiv2025

Longitudinal in vivo MR elastography reveals whole-liver viscoelastic involvement in a murine model of hepatocellular carcinoma

Cancer cells actively shape their microenvironment and adapt the physical and biomechanical properties of the host tissue. However, for organs like the liver, especially in vivo, it remains unclear at what rate and spatial extent macroscopic viscoelastic properties change during formation of a cancer-permissive environment. Using clinical multifrequency magnetic resonance elastography (MRE) in a mouse model of hepatocellular carcinoma (HCC), we identified surprisingly early and large-scale viscoelasticity changes leading to whole-liver biomechanical involvement stretching far beyond the local cancer microenvironment. Widespread liver softening began two weeks after HCC inoculation, followed by a decrease in tissue viscosity and fluidity two weeks later, preceding any macroscopic evidence of local tumor growth. In contrast, local lesions with stiff-rigid biomechanical properties were not detectable by standard MRI and MRE until five to six weeks post-injection. Furthermore, tumor viscoelasticity correlated with that of the host liver, also suggesting a possible widespread adaptation of the biomechanical properties beyond the tumor margins and its local niche during early liver colonization. The observed large-scale viscoelastic signature, detectable just two weeks after tumor cells injection, could serve as a non-invasive imaging biomarker to inform physicians about tumor niche formation and liver cancer progression long before any macroscopic manifestation of solid tumors.

biophysics↗

bioRxiv2025

Development of LYTACs via incorporating a nucleolin-targeting and lysosome-directed aptamer

Lysosomal targeting chimeras (LYTACs) represent an emerging class of bifunctional molecules that bridge extracellular target proteins with intrinsic lysosome-targeting receptors (LTRs) on the cell surface, facilitating endocytic internalization and subsequent lysosomal degradation of the targets. However, the therapeutic potential of LYTACs has been limited by the scarcity of suitable intrinsic LTRs. We previously identified an aptamer, SAPT8, that selectively targets nucleolin, a shuttling protein overexpressed on the surface of pathogenic FLSs in rheumatoid arthritis (RA), and induced its lysosomal degradation. In this study, we repurposed SAPT8 as a tumor-targeting and lysosome-directed ligand, leveraging the elevated expression of NCL on tumor cell surfaces. By conjugating SAPT8 with either the c-Met-binding aptamer SL1 or the small molecule inhibitor Tepotinib, we engineered novel LYTACs that demonstrated potent tumor-targeting capability and induced concurrent degradation of both c-Met and NCL, leading to significant antitumor effects. Furthermore, fusion of SAPT8 with VEGFR-2-targeting aptamer Apt02 generated LYTACs that simultaneously degraded VEGFR-2 and NCL, effectively suppressing RA-FLS activity. These results establish SAPT8 as a versatile platform for developing next-generation LYTACs, overcoming current limitations in extracellular protein degradation by circumventing dependence on endogenous LTRs. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/672993v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@975b74org.highwire.dtl.DTLVardef@f55565org.highwire.dtl.DTLVardef@b9639corg.highwire.dtl.DTLVardef@13aaba7_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2025

A Spatially Resolved Atlas of Alternative Polyadenylation Across 18 Human Tissues and 76 Disease States

Alternative polyadenylation (APA) is a key regulator of gene expression and cellular dynamics, yet systematic investigations of spatially resolved APA across diverse human tissues remain limited. Here, we developed SpatialAPA (https://github.com/Omicslab-Zhang/spatialAPA), a framework that benchmarks multiple APA identification methods and integrates spatial APA data with gene expression and cellular dynamics at spatial resolution. Applying SpatialAPA to 363 spatial transcriptomic data from 56 projects across 18 human tissues and 76 diseases, we constructed a spatially resolved APA atlas comprising 346,932 APA events across 52,175 genes. This atlas reveals organ-specific APA patterns and provides new insights into how APA regulates tissue homeostasis and disease progression beyond transcriptional control. To ensure cross-sample comparability, we applied batch correction, while spatial cell deconvolution uncovered cell-type-specific dynamics and interactions. In triple-negative breast cancer, integrated spatial and single-cell analyses identified TSPAN8-positive epithelial subpopulations whose distinct APA regulation and transcriptional programs drive differentiation and malignant progression. To facilitate community access, we developed an online platform (http://www.biomedical-web.com/spatialAPAdb/home) for exploring APA, gene expression, and cellular dynamics in health and disease. Together, this study establishes the first comprehensive spatial APA atlas, providing a valuable resource and analytical framework for investigating molecular mechanisms and therapeutic targets.

bioinformatics↗

bioRxiv2025

The Pdgfd-Pdgfrb axis orchestrates tumor-nerve crosstalk in pancreatic cancer

Nerves are an integral component of the tumor microenvironment, contributing to cancer progression, metastasis, morbidity, and mortality. In pancreatic ductal adenocarcinoma (PDAC), worse clinical outcomes are associated with perineural invasion (PNI), a process by which cancer cells surround and invade nerves. Here, we employed whole-transcriptome and single-cell spatial transcriptomics to identify candidate tumor-nerve interactions that promote PNI. We discovered that Pdgfd signaling promotes key features of nerve invasion. Mechanistically, Pdgfd stimulated cancer cell invasiveness, neurite outgrowth, and direct physical engagement with glia. Pharmacological blockade of this axis reduced each of these processes in vitro as well as PNI in vivo. Thus, Pdgfd-Pdgfrb signaling mediates PNI by coordinating multifaceted cancer-neuron-glia interactions and represents a promising therapeutic strategy aimed at disrupting harmful cancer-nerve crosstalk.

cancer biology↗

bioRxiv2025

ABCG transporter gene PstABCG2 contributes to multidrug resistance in Puccinia striiformis

The emergence of triadimefon resistance in Puccinia striiformis f. sp. tritici (Pst), the causal agent of wheat stripe rust, poses a growing threat to global food security. While the novel SDHI fungicide flubeneteram exhibits high efficacy against Pst, the molecular mechanisms governing resistance to both fungicides remain largely unknown. Here, we identify the ABC transporter PstABCG2 as a key mediator of multidrug resistance (MDR) through combined transcriptomic, genetic, and biochemical analyses. First, time-series transcriptomics revealed sustained upregulation of PstABCG2 under fungicide stress, with silencing via in planta RNAi and HIGS significantly enhancing Pst sensitivity to both triadimefon and flubeneteram. Heterologous expression of PstABCG2 in a Fusarium graminearum {Delta}FgABCG2 mutant restored fungicide tolerance, confirming its efflux function. Crucially, saturation mutagenesis and structural modeling pinpointed E1184 as a critical residue for fungicide binding: the E1184Y mutation disrupted triadimefon affinity (validated by MST/ITC, {Delta}G reduced from -7.1 to -6.5 kcal/mol) but minimally impacted flubeneteram binding due to adaptive halogen-bond networks. Furthermore, we discovered that the GATA-family transcription factor PstGATA directly activates PstABCG2 expression by binding to promoter cis-elements, and silencing PstGATA phenocopied the hypersensitivity of PstABCG2-silenced isolates. The results may provide a theoretical foundation for elucidating multidrug resistance mechanisms in Pst, field resistance management, and novel fungicide target development.

pathology↗

bioRxiv2025

Engineering anaerobic fungal-bacterial consortia for medium-chain fatty acid production from lignocellulosic biomass

Lignocellulosic biomass is a renewable feedstock for sustainable fuels and chemicals, yet industrial conversion remains constrained by carbohydrate solubilization. Inspired by herbivore rumen microbiomes, we engineered an anaerobic fungal-bacterial consortium converting native lignocellulose into medium-chain fatty acids (MCFAs) without pretreatment. Systematic screening identified a newly isolated anaerobic fungus, Neocallimastix sp. FC1, in co-culture with Megasphaera hexanoica as a top-performing pair, achieving a lignocellulose-to-MCFA yield of 21.0 % (carbon-to-carbon basis) through tight lactate cross-feeding without competition for soluble sugars. Because fungal lactate production rate constrained the growth of M. hexanoica, the bacterium reallocated protein from growth toward chain elongation, resulting in increased MCFAs production over butyrate. These results demonstrate that high lignocellulose-to-MCFA conversion by the consortium requires high lactate-producing capability and operating regimes sustaining low lactate concentrations at high flux. Technoeconomic analysis further identifies the cost and yield thresholds required for economically viable deployment, establishing quantitative design targets for pretreatment-free fungal-bacterial lignocellulose upgrading.

bioengineering↗

bioRxiv2025

Phylogenetic Diversity Across the Complete Tree of Life

In the face of rapid biodiversity loss, many approaches have been developed to measure biodiversity in ways that go beyond species richness. One prominent example is Phylogenetic Diversity (PD), which, on a dated phylogenetic tree, measures evolutionary history by summing the branch lengths measured in time required to connect a set of species. PD may also capture other biodiversity measures by proxy such as the richness of biological features and their potential benefits for humanity, known as future options. The total global PD is known for some well-studied groups, such as most vertebrates, but PD estimates are lacking for the majority of the tree of life. Here, we characterize the distribution of PD across the complete tree of life with over 2.2 million species. To do this we use data from the Open Tree of Life and a smoothing method to interpolate between nodes without date information. We estimate that the PD represented by all described species together is between 30 and 33 trillion years (95% confidence interval). We characterize the distribution of evolutionary distinctiveness, a measure of the fair share of PD captured by individual species, across all life and within selected clades. This Evolutionary Distinctiveness metric has previously been used as the basis for conservation prioritization schemes such as EDGE (Evolutionary Distinct and Globally Endangered) which synthesizes phylogenetic tree data with extinction risk data from the IUCN Red List of threatened species. We use our results to estimate EDGE scores for over 130,000 species. We also estimate the amount of threatened phylogenetic diversity across the complete tree of life. We hope this work will pave the way for more complete and automated analyses of PD, Evolutionary Distinctiveness, threatened evolutionary history, and EDGE scores across the complete tree of life.

evolutionary biology↗

bioRxiv2025

VO: The Vaccine Ontology

With the widespread use of vaccines in research and clinical settings, there is an urgent need to standardize vaccine representation, integrate information across diverse vaccine types, and support computer-assisted reasoning. Accordingly, we have since 2007 developed the community-based Vaccine Ontology (VO), which aligns with the Basic Formal Ontology and adheres to OBO Foundry principles. VO models ontologically vaccines, vaccine components, vaccine immune responses, vaccine investigation studies and other vaccine-related topics. VO represents more than 10,000 vaccines targeting 289 infectious pathogens and cancers in humans and over 30 nonhuman animal species. VO provides mappings to external resources such as RxNorm, CVX, FDA, and USDA. Various VO use cases exist. VO facilitates vaccine standardization in resources such as the VIOLIN vaccine database, ImmPort, and the Vaccine Adjuvant Compendium (VAC). Semantic queries can be made to query VO. VO has been shown to enhance experimental and clinical vaccine data analysis and vaccine literature mining. Overall, VO standardizes vaccine modeling and representation and greatly supports vaccine AI research in the Semantic Web era.

bioinformatics↗

bioRxiv2025

Cytokine Regulation of Human Antibody Responses to Influenza Vaccines

Studies show that human vaccine responses widely vary. Here, we analyzed data measuring 66 cytokines from 4 different inactivated influenza vaccine (IIV) cohorts over 5 seasons (N=581) and identified a significant correlation between baseline/day 0 serum IL-18 and IFN-{beta} concentrations and the vaccine-specific antibody response on day 28, suggesting these factors may have an adjuvant-like effect. To investigate this further, we tested the impact of 19 cytokines on the development of anti-influenza antibody response when administered together with the IIV vaccine in human tonsil and spleen organoids. We found that Type I IFNs (IFN-{beta} and others), IL-21, IL-12, IL-10, but not IL-18 or IFN-{gamma}, enhanced the antibody response. The live attenuated influenza vaccine (LAIV) induced a stronger antibody response than the inactivated one in organoids. Adding a single cytokine, IFN-{beta}, to IIV stimulation recapitulated most of the live vaccine-specific cytokine activation program. It increased the antibody response of the inactivated vaccine to that of the LAIV. Two other antibody-boosting cytokines, IL-12 and IL-21, were induced by LAIV but not by Type I IFNs, indicating different cytokines can affect different pathways leading to a robust antibody response. We then generated cytokine mRNA lipid nanoparticles (LNPs) to test the effect of cytokines on IIV response in a mouse model of immunization. We found that IL-21-LNPs augmented the quantity and breadth of the antibody responses, while IFN-{beta} LNPs enhanced durability. These findings identified parallel cytokine pathways regulating human vaccine responses and provide a rationale for using cytokines as adjuvants to mimic the effectiveness of live-attenuated vaccines without the risk of viral replication.

immunology↗

bioRxiv2025

A conserved distal element in the mouse Csf1r locus contributes to transcription in hematopoietic and trophoblast cells.

Expression of the Csf1r gene in mice is restricted to cells of the mononuclear phagocyte system and placental trophoblasts. A conserved element (Csf1r upstream regulatory element A, CUREA) in the mouse Csf1r locus contains transcription start sites utilised by trophoblasts and osteoclasts and an enhancer essential for expression of multicopy transgenic reporters in most tissue macrophages. Here we describe the impact of deletion of CUREA in the mouse genome, on the background of a knock-in Csf1r-FusionRed reporter. By contrast to the essential function in transgene expression, CUREA deletion had no effect on expression of FusionRed or differentiation of blood monocytes or tissue resident macrophages. The deletion reduced Csf1r mRNA in hematopoietic stem cells and committed myeloid progenitors (MPP3) leading to a subtle differentiation delay and also had a significant impact on microglial phenotype in the brain and the differentiation of osteoclasts. The expression of FusionRed in placenta confirmed expression of CSF1R in trophoblasts. 5RACE analysis demonstrated that the effect of CUREA deletion on Csf1r transcription in placenta was overcome by the use of cryptic upstream transcription start sites. We conclude that CUREA is a regulatory element controlling Csf1r transcription. The function overlaps with other enhancers identified in the locus and is therefore partly redundant. Key PointsO_LIA regulatory element (CUREA) in the mouse Csf1r locus has both promoter and enhancer activity. C_LIO_LIGerm-line deletion of CUREA impacts differentiation of marrow progenitors, microglia, osteoclasts and placental trophoblasts. C_LI

cell biology↗