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PubMed2026

Single-cell expression quantitative trait locus Mendelian randomization reveals immune cell-specific causal regulatory networks and actionable targets in polycystic ovary syndrome.

ObjectiveTo systematically investigate whether the pathogenesis of polycystic ovary syndrome (PCOS) is causally related to dysregulated gene expression in specific immune cell subsets, and to evaluate the potential of these causal genes as actionable drug targets.MethodsThis study employed a two-sample Mendelian randomization (MR) framework using publicly available genome-wide association study (GWAS) summary statistics. The participant data included 797 PCOS cases and 140,558 controls (no direct patient recruitment was involved). Instrumental variables were derived from high-resolution immune cell-specific single-cell expression quantitative trait locus (sc-eQTL) data (OneK1K project) across 14 immune cell types. Primary analyses utilized the inverse-variance weighted (IVW) method. Shared causal variants were validated using Bayesian colocalization. Phenome-wide association analysis (PheWAS), external transcriptomic dataset validation (GSE8157), and DrugBank database screening were conducted for pleiotropy assessment and drug repositioning.ResultsMR analysis revealed genome-wide significant causal associations for GLIPR1 in non-classical monocytes (Mono NC) and XBP1 in CD4+ effector memory T cells (CD4 ET) with PCOS risk. Higher GLIPR1 expression was associated with a decreased PCOS risk (OR = 0.669, P = 4.34×10-6), whereas higher XBP1 expression was associated with an increased risk (OR = 1.406, P = 9.53×10-8). Colocalization analysis confirmed that GLIPR1 shares a causal variant with PCOS (PP.H4 = 96.73%). PheWAS and external validation confirmed the safety profile and significant upregulation (P = 0.03) of GLIPR1. Drug repositioning identified SOT-107, a Phase III protein therapy drug, as a potential interacting agent for GLIPR1.ConclusionsThis sc-eQTL MR study reveals immune cell-specific causal regulatory networks in PCOS. GLIPR1 in non-classical monocytes represents a high-confidence protective target, while XBP1 provides suggestive evidence for immune-mediated pathogenesis. The candidate drug SOT-107 highlights theoretical repositioning opportunities, though rigorous preclinical validation remains required.

Female↗

PubMed2026

scRNA-seq and bulk RNA-seq reveal the characteristics of macrophage copper metabolism and establish a risk signature in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent malignancy with an urgent need for improved prognostic stratification and treatment-response prediction. This study aimed to explore a macrophage copper metabolism-associated prognostic model and to investigate the relationship between this risk model and the tumor immune microenvironment. METHODS: The FindClusters function was used to analyze cell clusters, and CellChat and CellPhoneDB/LIANA were employed for cell-cell communication analysis. Copper metabolism-related genes were sourced from the MSigDB database. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis, and a nomogram was constructed by integrating the prognostic model with clinicopathological factors. Additional analyses were performed to map the seven model genes in single-cell data, assess model uncertainty and robustness, evaluate macrophage/copper/cuproptosis-related transcriptional programs, and examine the correlations between risk score, immune infiltration and predicted drug sensitivity. RESULTS: Using single-cell RNA sequencing (scRNA-seq) data, we identified four macrophage subpopulations. Macrophages with high SPP1 expression showed close interaction with T cell populations and were associated with copper ion metabolism. By incorporating 141 copper metabolism-related genes and using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, we constructed a seven-gene risk prediction model. Additional single-cell mapping showed that the model genes were detectable in the HCC single-cell dataset and showed a macrophage-associated expression pattern. The model showed moderate prognostic discrimination in TCGA-LIHC, whereas its external performance was heterogeneous and remained evaluable across external cohorts, with performance varying among datasets. Immune and mechanism-related analyses suggested that the risk signature was associated with macrophage-related infiltration, copper metabolism and cuproptosis-related transcriptional programs. Drug sensitivity analysis nominated Daporinad as a computationally predicted candidate compound, supporting Daporinad as a pharmacogenomic candidate for follow-up investigation. CONCLUSIONS: By integrating scRNA-seq and bulk RNA sequencing (RNA-seq) data, we constructed a macrophage copper metabolism-associated prognostic signature for HCC. The risk score was associated with survival, immune microenvironment features and predicted drug response, providing a transcriptomic framework for risk stratification and therapeutic hypothesis generation.

Hepatocellular carcinoma (HCC)↗

PubMed2026

Moving Beyond Morphology to Multiplexed Molecular Imaging as the Next Frontier in Diagnostic Pathology.

Diagnostic pathology has long relied on the morphologic interpretation of hematoxylin and eosin-stained tissues to guide diagnosis and assess prognostic features. Although pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histologic and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathologic perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.

Humans↗

PubMed2026

Multimodal computational framework resolves B cell maturation in autoimmunity and ageing.

Identification of the origin of pathogenic immune cells is crucial for therapeutic interventions and diagnosis but pseudotime methods struggle to trace immune cells accurately. Current trajectory inference methods for B cell development and response in health and disease either ignore or underutilize antigen receptor sequence information, limiting their ability to resolve developmental pathways, particularly for pathogenic populations. Widely used methods such as Monocle 3 reconstruct developmental paths from transcriptomic similarity alone, discarding the features from immune receptors. Dandelion has combined the immune receptor features with transcriptomics but it struggles to simulate the trajectory path of B cells. Here we present ClonoTrace, a computational framework that integrates BCR sequence features with transcriptomic trajectory inference through gated fusion of multimodal embeddings. In fetal B cell development and germinal centre development, ClonoTrace demonstrates closer concordance with the canonical reference ordering than Monocle 3 and Dandelion. Applied to systemic lupus erythematosus, ClonoTrace indicates a memory B cell extrafollicular maturation route alongside the naïve B cell route, accompanied by induction of ZEB2 with a concomitant decline of BACH2 along the trajectory, as a candidate alternative route to pathogenic double negative 2 B cells (DN2) in systemic lupus erythematosus (SLE) patients. In healthy ageing, ClonoTrace resolved three candidate age-related B cell maturation routes, from naïve, IgM+ memory and switched-memory B cells, each passing through a DN2-associated transcriptional state that is ordered before age-associated B cells along the inferred trajectory. ClonoTrace's fate probability algorithm indicated that IgM+ memory B cell to ABC transition as the leading candidate age-associated transition, which may be distinct from SLE DN2 maturation. ClonoTrace provides a generalizable framework for receptor-informed trajectory inference, describing candidate developmental routes of pathogenic B cell populations in autoimmunity and ageing.

Humans↗

PubMed2023

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.

BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data. METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best. RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis. CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

Humans↗

PubMed2026

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.

SUMMARY: Image-based spatial transcriptomics (iST) deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in (iST) dataset, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. This highly scalable tool comprehensively segments tissue into spatial domains, aiding in biological interpretation of iST data and its underlying molecular microenvironments. AVAILABILITY AND IMPLEMENTATION: The SpatialRNA package is freely accessible from online repository https://github.com/ruqianl/spatialrna and can be installed via pip. Comprehensive tutorials, guidance on parameter selection, and complete workflows of case studies are available from the documentation website https://ruqianl.github.io/spatialrna_docs/, and uploaded on Zenodo with a DOI 10.5281/zenodo.17339575.

Neural Networks, Computer↗

PubMed2006

Comparative analysis of Hox downstream genes in Drosophila.

Functional diversification of body parts is dependent on the formation of specialized structures along the various body axes. In animals, region-specific morphogenesis along the anteroposterior axis is controlled by a group of conserved transcription factors encoded by the Hox genes. Although it has long been assumed that Hox proteins carry out their function by regulating distinct sets of downstream genes, only a small number of such genes have been found, with very few having direct roles in controlling cellular behavior. We have quantitatively identified hundreds of Hox downstream genes in Drosophila by microarray analysis, and validated many of them by in situ hybridizations on loss- and gain-of-function mutants. One important finding is that Hox proteins, despite their similar DNA-binding properties in vitro, have highly specific effects on the transcriptome in vivo, because expression of many downstream genes respond primarily to a single Hox protein. In addition, a large fraction of downstream genes encodes realizator functions, which directly affect morphogenetic processes, such as orientation and rate of cell divisions, cell-cell adhesion and communication, cell shape and migration, or cell death. Focusing on these realizators, we provide a framework for the morphogenesis of the maxillary segment. As the genomic organization of Hox genes and the interaction of Hox proteins with specific co-factors are conserved in vertebrates and invertebrates, and similar classes of downstream genes are regulated by Hox proteins across the metazoan phylogeny, our findings represent a first step toward a mechanistic understanding of morphological diversification within a species as well as between species.

Animals↗

PubMed2026

MIF-CD74 axis facilitates MDSC infiltration in the tumor microenvironment of pancreatic ductal adenocarcinoma.

Immune checkpoint inhibitors show insufficient efficacy against pancreatic ductal adenocarcinoma (PDAC). The tumor microenvironment (TME) has a remarkable influence on responsiveness to cancer immunotherapy. The aim of this study was to investigate immunosuppressive characteristics of TME in PDAC tissues. The flow cytometry (FCM) of PDAC surgical specimens revealed that the profile of tumor-infiltrating leukocytes was classified into myeloid cell- and T-cell-dominant subtypes; the myeloid subtype was associated with poorer patient outcomes. Myeloid-derived suppressor cells (MDSCs) showed the highest hazard ratio among various myeloid cell types. Single-cell RNA sequencing and FCM revealed that most MDSCs, but not lymphocytes, in PDAC tissues characteristically express CD74. Macrophage migration inhibitory factor (MIF), a CD74 ligand, was highly expressed in cancer-associated fibroblasts (CAFs) and cancer cells. Spatial transcriptomics demonstrated that the MIF-CD74+ myeloid cell interaction was recognized in CAF-dominant areas in PDAC tissue. CAFs expressing immune suppressor molecules such as MFAP5 and LRRC15 were consistent with MIF+ CAFs. Furthermore, MIF+ CAFs enhanced the migratory activity of MDSCs and promoted MDSC induction and activation. In the murine model, MDSCs were significantly increased in MIF-expressing PDAC tumors, as were CD74+ M-MDSCs per M-MDSC, confirming in vivo interaction between CD74 and MIF. MDSCs play a crucial role in creating an immunosuppressive TME in PDAC; the MIF-CD74 axis drives interactions between MDSCs and CAFs.

Humans↗

PubMed2005

Microarrays: new tools to unravel parasite transcriptomes.

The ability to monitor the expression levels of thousands of genes in a single microarray experiment is a huge progression from conventional Northern blot analysis or PCR-based techniques. Microarrays can play a pivotal role in the mass screening of genes in a wide range of fields including parasitology. The relatively few parasites that can be readily cultured or isolated from a host, as compared with cell lines or tissue sources, makes microarray technology ideal for maximizing experimental results from a limiting source of starting material. Khan et al. (1999 a) commented in an early review of microarray technology " With this system in place, one can anticipate a time when data from thousands of gene expression experiments will be available for meta-analysis........leading to more robust results and subtle conclusions". Now in 2005, microarrays represent a very powerful resource that can play an important role in the characterization and annotation of the transcriptomes of many parasites of medical and veterinary importance.

Animals↗

PubMed2005

Complex cellular responses to reactive oxygen species.

Genome-wide analyses of yeast provide insight into cellular responses to reactive oxygen species (ROS). Many deletion mutants are sensitive to at least one ROS, but no one oxidant is representative of 'oxidative stress' despite the widespread use of a single compound such as H(2)O(2). This has major implications for studies of pathological situations. Cells have a range of mechanisms for maintaining resistance that involves either induction or repression of many genes and extensive remodeling of the transcriptome. Cells have constitutive defense systems that are largely unique to each oxidant, but overlapping, inducible repair systems. The pattern of the transcriptional response to a particular ROS depends on its concentration, and 'classical' antioxidant systems that are induced by high concentrations of ROS can be repressed when cells adapt to low concentrations of ROS.

Animals↗

PubMed2005

Noncontact laser microdissection and pressure catapulting: sample preparation for genomic, transcriptomic, and proteomic analysis.

The understanding of the molecular mechanisms of cellular metabolism and proliferation necessitates accurate identification, isolation, and finally characterization of a specific cell or a population of cells and subsequently their subsets of biomolecules. For the simultaneous analysis of thousands of molecular parameters within a single experiment, as realized by DNA, RNA, and protein microarray technologies, a defined number of homogeneous cells derived from a distinct morphological origin is required. Sample preparation is therefore a very crucial step for high-resolution downstream applications. Laser microdissection and laser pressure catapulting (LMPC) enables such pure and homogeneous sample preparation, resulting in an eminent increase in the specificity of molecular analyses. For microdissection, the force of focused laser light is used to excise selected cells or large tissue areas from object slides or from living cell culture down to a resolution of individual single cells and subcellular components like organelles or chromosomes, respectively. After microdissection this sample is directly catapulted into an appropriate collection device. As the entire process works without any mechanical contact, it enables pure sample retrieval from morphologically defined origin without cross contamination. Wherever homogenous samples are required for subsequent analysis of, e.g., cell areas, single cells, or chromosomes, the PALM MicroBeam system is an indispensable tool. The integration of image analysis platforms fully automates screening, identification, and finally subsequent high-throughput sample handling. These samples can be directly linked into versatile downstream applications, such as single-cell mRNA-extraction, different PCR methods, microarray techniques, and many others. Acceleration in sample generation vastly increases the throughput in molecular laboratories and leads to an increasing knowledge about differentially regulated mRNAs and expressed proteins, providing new insights into cellular mechanisms and therefore enabling the development of systems for tumor biomarker identification, early detection of disease-causing alterations, therapeutic targeting and/or patient-tailored therapy.

Animals↗

PubMed2005

Web-based data warehouse on gene expression in human colorectal cancer.

Based on biomedical literature databases, we tried a first step for constructing a gene expression "data warehouse" specific to human colorectal cancer (CRC). Results of genome-wide transcriptomic research were available from 12 studies, using various technologies, namely, SAGE, cDNA and oligonucleotide arrays, and adaptor-tagged amplification. Three studies analyzed CRC cell lines and nine studies of human samples. The total number of patients was 144. Out of 982 up- or down-regulated genes, 863 (88%) were found to be differentially expressed in a single study, 88 in two studies, 22 in three studies, 7 in four studies, and only 2 genes in six studies. Eight large-scale proteomics studies were published in CRC, using 2-D-, SDS- or free-flow electrophoresis, involving only 11 patients. Out of 408 differentially expressed proteins, 339 (83%) were found to be differentially expressed only in a single study, 16 in three studies, 10 in four studies, 3 in five, and 1 in eight studies. Confirmation at proteome level of results obtained with large-scale transcriptomics studies was possible in 25%. This proportion was higher (67%) for reproducing proteome results using transcriptomics technologies. Obviously, reproducibility and overlapping between published gene expression results at proteome and transcriptome level are low in human CRC. Thus, the development of standardized processes for collecting samples, storing, retrieving, and querying gene expression data obtained with different technologies is of central importance in translational research.

Colorectal Neoplasms↗

PubMed2006

Profiling of gene expression in rat liver and rat primary cultured hepatocytes treated with peroxisome proliferators.

The Toxicogenomics project has been constructing a large-scale database of about 150 compounds exposed to rat (single dose, 3, 6, 9, 24 hrs and repeated dose for 3, 7, 14 28 days with 3 dose levels) and rat hepatocytes (2, 8, 24 hr with 3 concentrations) and data of transcriptome in liver using GeneChip, and the related toxicological measures are being accumulated. In the present study, the data of three ligands of peroxisome proliferator activated receptor alpha (PPARalpha), i.e., clofibrate, WY-14643 and gemfibrozil in our database were analyzed. Many of the beta-oxidation-related genes were commonly induced in vivo and in vitro, whereas expression changes in genes related to cell proliferation, apoptosis, were detected in vivo (single and repeated dose) but not in vitro. Changes in those related to the immune response, coagulation and the stress response were also detectable exclusively in vivo. Using the genes mobilized in two or three PPARalpha agonists, hierarchical clustering was performed on 32 compounds stored in our database. In the profiling of an in vivo single dose, benzbromarone and aspirin were located in the same cluster of the three PPARalpha agonists. The clustering of in vitro data revealed that benzbromarone, three NSAIDs (aspirin, indomethacin and diclofenac sodium) and valproic acid belonged to the same cluster of PPARalpha agonists, supporting the reports that benzbromarone,valproic acid and some NSAIDs were reported to be PPARalpha agonists. Using the genes commonly up-regulated both in vivo and in vitro, principal component analysis was performed in 32 compounds, and principal component 1 was found to be the convenient parameter to extract PPARalpha agonist-like compounds from the database.

Alanine Transaminase↗

PubMed2006

Deciphering B-ZIP transcription factor interactions in vitro and in vivo.

Over the last 15 years, numerous studies have addressed the structural rules that regulate dimerization stability and dimerization specificity of the leucine zipper, a dimeric parallel coiled-coil domain that can either homodimerize or heterodimerize. Initially, these studies were performed with a limited set of B-ZIP proteins, sequence-specific DNA binding proteins that dimerize using the leucine zipper domain to bind DNA. A global analysis of B-ZIP leucine zipper dimerization properties can be rationalized using a limited number of structural rules [J.R. Newman, A.E. Keating, Comprehensive identification of human bZIP interactions with coiled-coil arrays, Science 300 (2003) 2097-2101]. Today, however, access to the genomic sequences of many different organisms has made possible the annotation of all B-ZIP proteins from several species and has generated a bank of data that can be used to refine, and potentially expand, these rules. Already, a comparative analysis of the B-ZIP proteins from Arabidopsis thaliana and Homo sapiens has revealed that the same amino acids are used in different patterns to generate diverse B-ZIP dimerization patterns [C.D. Deppmann, A. Acharya, V. Rishi, B. Wobbes, S. Smeekens, E.J. Taparowsky, C. Vinson, Dimerization specificity of all 67 B-ZIP motifs in Arabidopsis thaliana: a comparison to Homo sapiens B-ZIP motifs, Nucleic Acids Res. 32 (2004) 3435-3445]. The challenge ahead is to investigate the biological significance of different B-ZIP protein-protein interactions. Gaining insight at this level will rely on ongoing investigations to (a) define the role of target DNA on modulating B-ZIP dimerization partners, (b) characterize the B-ZIP transcriptome in various cells and tissues through mRNA microarray analysis, (c) identify the genomic localization of B-ZIP binding at a genomic level using the chromatin immunoprecipitation assay, and (d) develop more sophisticated imaging technologies to visualize dimer dynamics in single cells and whole organisms. Studies of B-ZIP family leucine zipper dimerization and the regulatory mechanisms that control their biological activities could serve as a paradigm for deciphering the biophysical and biological parameters governing other well-characterized protein-protein interaction motifs. This review will focus on the dimerization specificity of coiled-coil proteins, particularly the human B-ZIP transcription family that consists of 53 proteins that use the leucine zipper coiled-coil as a dimerization motif.

Amino Acid Motifs↗

PubMed2005

Advanced glycation endproduct-induced aging of the retinal pigment epithelium and choroid: a comprehensive transcriptional response.

Advanced glycation endproduct (AGE) formation is a trigger for the onset of age-related disease. To evaluate AGE-induced change in the ocular fundus, 5-mo-old C57BL/6 mice were given low-dose D-galactose (D-gal) for 8 wk and evaluated by AGE fluorescence, electroretinography (ERG), electron microscopy, and microarray analysis for 20 wk. Although AGE fluorescence was increased in D-gal-treated retinal pigment epithelium (RPE)-choroid compared with controls at all time points, ERG showed no AGE-induced functional toxicity. Progressive ultrastructural aging in the RPE-choroid was associated temporally with a transcriptional response of early inflammation, matrix expansion, and aberrant lipid processing and, later, down-regulation of energy metabolism genes, up-regulation of crystallin genes, and altered expression of cell structure genes. The overall transcriptome is similar to the generalized aging response of unrelated cell types. A subset of transcriptional changes is similar to early atherosclerosis, a chronic inflammatory disease characterized by matrix expansion and lipid deposition. These changes suggest an important contribution of a single environmental stimulus to the complex aging response.

Animals↗

PubMed2006

A comprehensive SAGE database for the analysis of gammadelta T cells.

Gammadelta T cells have been conserved since the adaptive immune system arose, yet their importance is still unclear. In an attempt to compensate for the lack of a broad knowledge base of gammadelta T cells across species, global analyses of gammadelta T cell transcriptomes have been performed using serial analysis of gene expression (SAGE). Twelve new SAGE libraries were generated from the following bovine lymphocyte populations: magnetic bead-sorted blood gammadelta T cells, spleen gammadelta T cells and enriched spleen alphabeta T cells from a single calf, both rested and Con A/IL2 stimulated, and flow cytometry-sorted blood gammadelta and alphabeta T cells each either rested, Con A/IL2, or phorbol 12 myristate 13-acetate/ionomycin stimulated. These new libraries complement two earlier SAGE libraries of circulating gammadelta T cell subsets. These databases were analyzed using new web-based bioinformatic tools, which allow the user to rapidly compare gene expression patterns within these and other SAGE and standard expressed sequence tag libraries generated from different cell types and different species. These analyses revealed striking differences between blood and spleen gammadelta T cells and how these cells respond to mitogenic stimulation. These analyses also confirm previous studies that suggested that global gene expression in gammadelta and alphabeta T cells is quite similar; however, a 5-fold increase in gammadelta T cell-specific transcripts could be induced by Con A/IL2 stimulation. These new public databases provide additional resources for the annotation/analysis of global gene expression in gammadelta T cells, which will facilitate studies of the biology of this enigmatic lymphoid cell.

Animals↗

PubMed2025

Dual-patterned pluripotent stem cells self-organize into a human embryo model with extended anterior-posterior patterning.

Human gastruloids are a powerful class of stem cell-derived models that recapitulate key features of early embryonic development, including symmetry breaking and the emergence of three germ layers1-3. However, they lack anterior embryonic structures and coordinated axial organization4-6. To address this limitation, we pre-patterned human pluripotent stem cells (hPSCs) by exposing them to either anterior (FGF2) or posterior (CHIR99021 [CHIR] & retinoic acid [RA]) cues. Upon mixing, these dual-patterned hPSCs interacted and self-organized into elongated structures with both anterior and posterior features-which we term anterior-posterior (AP) human gastruloids. Anteriorly pre-treated cells robustly intercalated into posteriorly pre-treated cells, collectively giving rise to a continuum of neural tissues-including a brain-like domain, a neural tube-like structure, and neuro-mesodermal progenitors (NMPs)-with segmented somites arrayed bilaterally. Single cell RNA sequencing (scRNA-seq) revealed that human AP gastruloids contain cell types resembling the midbrain-hindbrain boundary (MHB), regionalized hindbrain structures (i .e. rhombomeres 1-8), regionalized neural crest (i.e. cranial, vagal, trunk)7,8 and head mesoderm. Transcriptomic comparisons to primate embryos revealed that human AP gastruloids most closely resemble Carnegie stage 11 (CS11) embryos. While they lack a notochord and full dorsal-ventral polarity, human AP gastruloids recapitulate key spatial and temporal features of early neurulation and somitogenesis. Perturbation of folic acid metabolism or rho-associated kinase (ROCK) signaling induced spinal cord defects, phenocopying aspects of spina bifida and other neural tube defects, highlighting this model's potential for studying congenital disorders9. AP gastruloids may serve as a simple, robust, scalable platform for modeling coordinated human AP body axis development. More broadly, our results suggest that controlled interactions between differentially prepatterned progenitors can initiate self-organization of complex body axis features. The "pattern-and-mix" strategy may serve as a generalizable framework for assembling spatially organized stem cell models of mammalian development.

Journal Article↗

PubMed2005

Proteomic and transcriptomic analyses of macrophages with an increased resistance to oxidized low density lipoprotein (oxLDL)-induced cytotoxicity generated by chronic exposure to oxLDL.

The uptake of oxidized low density lipoprotein (oxLDL) by macrophages leads to foam cell formation and fatty streaks, which represent early sites of potential atheroma development. We developed a cell culture model of chronic oxLDL exposure to determine whether hallmark parameters of oxLDL uptake and cytotoxicity are altered during foam cell formation and to determine changes in protein and mRNA expression that distinguish acute and chronic oxLDL exposure. Although the extent of oxLDL uptake did not change, a resistance to oxLDL-induced cytotoxicity was observed in the chronically exposed cells. Macrophages that have been chronically exposed to oxLDL required a 40% higher concentration of oxLDL to achieve 50% survival in a 48-h treatment relative to macrophages subjected to a single oxLDL exposure. A main feature of the differentially expressed proteome was a series of significantly overexpressed antioxidant and antioxidant-related proteins in the oxLDL-exposed cells. A large proportion of these proteins (45%) was overexpressed in the chronically exposed cells prior to the oxLDL treatment, indicative of the unique phenotype produced by the chronic treatment. Analysis of the transcriptome also revealed a broad increase in the expression of antioxidant and antioxidant-related proteins. In addition, the transcriptome experiments found an increased inflammatory response under conditions of both acute and chronic oxLDL exposure. Overall the combined functional, proteomic, and transcriptomic experiments show that macrophages respond to oxLDL by developing an oxidative stress resistance that increases and stabilizes with chronic exposure. Furthermore this protective response and the increased foam cell survival that it supports amplifies their proatherogenic role by promoting a continued inflammatory state.

Animals↗