Biology subjects

Hayat, S.

Publications and source records attributed to Hayat, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Autoantibodies from recovered Guillain-Barre syndrome patients exhibit altered effector functions that prevent subsequent neuron degeneration

Guillain-Barre syndrome (GBS) is an autoimmune polyneuropathy that is the leading cause of nonpoliovirus-associated acute flaccid paralysis worldwide. In most cases, GBS occurs following an infection, most commonly Campylobacter jejuni, through the induction of antibodies recognizing bacterial ganglioside-mimicking lipooligosaccharides that cross-react with human neuronal gangliosides. This post-infectious autoimmune cascade causes neuropathy, from which patients can recover as their anti-ganglioside antibody titers diminish and immunostimulation decreases. In this study, we find 10% of clinically confirmed GBS patients maintain high titers of circulating anti-ganglioside antibodies more than one decade after recovery. These antibodies no longer cause neuropathy compared to acute sera from the same patients using a human pluripotent stem cell-derived sensory neuron model with human complement. We found both IgG subclass and glycoform differences between paired acute and recovered GBS patient sera, including anti-inflammatory modifications on isolated anti-GM1 ganglioside antibodies. Together, these data suggest that patients with GBS select for non-pathogenic variants of autoantibodies that are no longer capable of damaging their neurons, but may still protect against C. jejuni infection.

immunology↗

bioRxiv2026

Deep Visual Proteomics links vascular smooth muscle cell phenotypes to atherosclerotic plaque stability

Vascular smooth muscle cells (VSMCs) drive atherosclerosis through phenotypic switching, yet their spatial organization and protein signatures within plaques remain poorly characterized. Here, we applied Deep Visual Proteomics (DVP) to dissect more than 500 VSMC neighborhoods across 24 human carotid plaques and profile VSMC plasticity in disease. To functionally interpret these tissue proteomes, we built a reference atlas of primary VSMCs driven toward five phenotypes by TGF-{beta}, PDGF-BB, osteogenic stimuli, IL-1{beta}, or cholesterol, quantifying over 10,000 proteins. We integrated tissue and reference proteomes with a deep-learning framework that assigns functional phenotypes to each neighborhood. This revealed spatially distinct phenotype distributions and a shift toward dedifferentiated states in unstable plaques. Knockdown of four candidates (TNC, TNFAIP2, AEBP1, PLK1) validated operational roles during phenotypic switching. Our approach functionally annotates spatial proteomes and links VSMC plasticity to plaque instability in carotid artery disease.

systems biology↗

bioRxiv2026

Meditation Styles Are Highly Discriminable from EEG at the Subject Level With Limited Generalization Across the Population: A Machine-Learning Study

Meditation has been associated with improvements in attention, emotional regulation, and mental well-being, motivating increasing interest in objective methods for assessing meditative states. In this study, we investigate whether EEG-based machine learning can reliably distinguish between multiple meditation styles and mind-wandering states. EEG data were recorded from experienced meditators performing three meditation styles, Shamatha, Vipassana, and Metta, together with an eyes-closed mind-wandering condition. EEG signals were preprocessed to remove artifacts, and features were extracted from frequency, time-frequency, and time domains. Classification was evaluated using both intra-subject and inter-subject strategies with multiple machine learning classifiers. Results demonstrate high intra-subject classification accuracy across meditation-versus-mind-wandering and meditation-style comparisons, indicating strongly discriminative subject-specific neural signatures. In contrast, inter-subject performance decreased substantially, particularly for distinguishing meditation styles, suggesting considerable inter-individual variability in meditation-related EEG patterns. Furthermore, temporal analysis revealed that classification performance increase over time, indicating that the neural distinctions between meditation states become increasingly pronounced over time. Additionally, t-SNE visualization showed clear within-subject clustering but increased overlap across subjects, explaining the reduced inter-subject generalization. Overall, these findings highlight the potential of EEG-based machine learning for personalized assessment and monitoring of meditative states while emphasizing the challenges of developing subject-independent meditation classification systems.

neuroscience↗

bioRxiv2026

Polyploid cardiomyocytes define disease-specific transcriptional states in the mammalian heart

The adult mammalian heart has a limited regenerative capacity. Following injury, cardiomyocytes undergo a hypertrophic response accompanied by polyploidization, which has been described as a barrier to proliferation and regeneration of the heart1,2. However, the unique molecular programs of polyploidy, or genome multiplied cardiomyocytes, and their influence on the disease-related myocardial remodelling process remains unclear. Here, we integrate single-nuclei and high-resolution spatial multi-omics across human, rat, and mouse hearts to define novel cardiac cell states and their tissue niches in ischemic and non-ischemic heart disease. Computational analysis across scales allowed us to generate detailed networks of the cardiac tissue remodelling process as well as tissue and sub-cellular environments uniquely enriched in polyploid cardiomyocytes or their diploid origins. We identify a conserved, dichotomous transcriptional program distinguishing diploid from polyploid cardiomyocytes. Polyploid cardiomyocytes demonstrated rewired metabolic and chromatin-remodeling transcriptional programs and recapitulate the gene signature of immature human fetal cardiomyocytes. Notably, we observe that polyploid cardiomyocytes--rather than the general myocyte population--are the primary sites of enrichment for major heart-failure drug targets, including the mineralocorticoid, {beta}1-adrenergic, and glucagon-like peptide-1 receptors. Based on our cross-species dataset we further identified TNIK, a Wnt-pathway regulator expressed in polyploid cardiomyocytes across species, as a potential therapeutic target and demonstrate that pharmacological TNIK inhibition improves cardiac function after myocardial infarction in rats. Together, this species-spanning, disease-resolved study redefines cardiomyocyte heterogeneity in heart disease and suggests a therapeutic path to heart failure treatment by targeting polyploid cardiomyocytes.

genomics↗

bioRxiv2025

Pathway-Centric Visualization of Cell-Cell Communication in Single-Cell Transcriptomics Data

ObjectiveSingle-nuclei transcriptomics enables investigating ligand-receptor mediated cell-cell communication between different cell-types. However, current tools do not allow for non-programmatic means to access this analysis. Additionally, most methods can not account for molecular pathways involved in downstream receptor signaling in cell-cell communication. MethodsWe developed scVizComm, a ShinyApp based web-portal that can be used to interactively visualize ligand-receptor networks across cell-types and different conditions. ResultsscVizComm can be used to host pathway centric pre-calculated cell-cell communication results. Using human kidney and heart single-nuclei transcriptomics data, we showcase the utility of scVizComm in understanding ligand-receptor interactions involved in fibrosis related pathways. ConclusionscVizComm allows interactive visualisation and pathway centric prioritisation of cell-cell communication analyses.

bioinformatics↗

bioRxiv2025

Immunomodulatory endothelial cells contribute to T cell recruitment and activation through antigen presentation on MHC class II

AimsA subset of endothelial cells referred to as immunomodulatory endothelial cells (IMEC) has been proposed to regulate T cell responses in atherosclerosis, but their phenotype and function remain poorly understood. Here, we characterized the inflammation-induced emergence of IMEC and their crosstalk with T cells. Methods and ResultsAn in vitro model to study IMEC was established and characterized using flow cytometry and proteomics. Single-cell transcriptome data from human atherosclerotic arteries as well as single cell transcriptome and endothelial cell-specific translatome data from a murine atherogenesis model were used to determine pathophysiological relevance. Immunopeptidomics was performed to detect antigen presentation. T cell chemotaxis, adhesion and activation were assessed through flow cytometry and microscopy. IMEC were induced by treating human endothelial cells with interleukin-1{beta}, interferon-{gamma}, and transforming growth factor-{beta}2. These cells expressed lower levels of classical endothelial cell markers but expressed major histocompatibility complex (MHC) class II, proteins involved in antigen processing and presentation (CD83, CD80 and CD86) and pro-inflammatory cytokines as well as chemokines, including CXCL9. An endothelial cell subpopulation with similar immunomodulatory features was identified in a mouse model of accelerated atherogenesis as well as in human atheromas. Conditioned medium from IMEC enhanced the migration of peripheral blood mononuclear cells and induced T cell chemotaxis, the latter being partially inhibited by antagonizing CXCL9. Proteins related to glycosaminoglycan degradation were significantly downregulated in IMEC which was relevant inasmuch as the glycocalyx plays a key role in the establishment of chemokine gradients. Indeed, the accumulation of heparan sulfates in IMEC contributed to the adhesion of T cells. Notably, IMEC that had been exposed to monocyte lysates presented 627 peptide antigens on MHC class II and induced T cell activation. ConclusionOur data demonstrate the role of IMEC as non-professional antigen-presenting cells that potentially contribute to T cell-mediated immune responses in cardiovascular disease. Translational PerspectiveThis study characterizes immunomodulatory endothelial cells (IMEC) as critical mediators of vascular inflammation through their capacity to process and present exogenous antigens and activate T cells. Induced by pro-atherogenic cytokines (IFN-{gamma}, IL-1{beta}, TGF-{beta}2), IMEC upregulate MHC class II and costimulatory molecules, promote leukocyte chemotaxis, and enhance T cell adhesion through surface heparan sulfate. The identification of IMEC-like populations in both murine models and human atherosclerotic plaques indicates a conserved immunological function in atherogenesis. These findings position IMEC as novel, non-professional antigen-presenting cells and potential therapeutic targets to modulate vascular immune responses in atherosclerotic cardiovascular disease.

immunology↗

bioRxiv2025

Targeting Modulated Vascular Smooth Muscle Cells in Atherosclerosis via FAP-Directed Immunotherapy

Vascular smooth muscle cell (VSMC) and immune cell diversification play a central role in driving atherosclerotic coronary artery disease (CAD)1-3. However, the molecular mechanisms governing cell state transitions within the neo-intima in human CAD remain poorly understood, and no lipid-independent therapies are currently approved for its treatment. Here, we performed multi-omic single-cell gene expression profiling, epitope mapping, and spatial transcriptomics from 27 human coronary arteries. Our analysis identified fibroblast activation protein (FAP) as a marker of modulated VSMCs within the neo-intima. Genetic lineage tracing in mice confirmed that FAP cells in the plaque originate from medial VSMCs. Additionally, non-invasive positron emission tomography (PET) imaging in patients with CAD revealed focal FAP uptake in atherosclerotic lesions. Spatial transcriptomics further delineated the distinct localization of VSMC and immune cell subsets within plaques, with FAP states enriched in the neo-intima. To explore the therapeutic potential of targeting de-differentiated VSMCs, we developed an anti-FAP bispecific T-cell engager (BiTE) and demonstrated that it significantly reduced the plaque burden in multiple mouse models of atherosclerosis. Collectively, our study provides the first single-cell and spatially resolved map of human CAD, establishes FAP as a marker of modulated smooth muscle cells, and demonstrates the broader potential of immunotherapeutics for lipid independent targets in atherosclerotic CAD.

genomics↗

bioRxiv2025

SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data

Rapid technological advancements have made it possible to generate single-cell data at a large scale. Several laboratories around the world can now generate single-cell transcriptomic data from different tissues. Unsupervised clustering, followed by annotation of the cell type of the identified clusters, is a crucial step in single-cell analyses. However, there is no consensus on the marker genes to use for annotation, and celltype annotation is currently mostly done by manual inspection of marker genes, which is irreproducible, and poorly scalable. Additionally, patient-privacy is also a critical issue with human datasets. There is a critical need to standardize and automate celltype annotation across datasets in a privacy-preserving manner. Here, we developed SwarmMAP that uses Swarm Learning to train machine learning models for cell-type classification based on single-cell sequencing data in a decentralized way. SwarmMAP does not require any exchange of raw data between data centers. SwarmMAP has a F1-score of 0.93, 0.98, and 0.88 for cell type classification in human heart, lung, and breast datasets, respectively. Swarm Learning-based models yield an average performance of 0.907 which is on par with the performance achieved by models trained on centralized data (p-val=0.937, Mann-Whitney U Test). We also find that increasing the number of datasets increases cell-type prediction accuracy and enables handling higher cell-type diversity. Together, these findings demonstrate that Swarm Learning is a viable approach to automate cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP.

bioinformatics↗

bioRxiv2024

Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as {Delta}{Delta}G). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate {Delta}{Delta}G of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

biophysics↗

bioRxiv2024

mcBERT: Patient-Level Single-cell Transcriptomics Data Representation

Single-cell RNA sequencing (scRNA-seq) transcriptomics improves our understanding of cellular heterogeneity in healthy and pathological states. However, most scRNA-seq analyses remain confined to single cells or distinct cell populations, limiting their clinical applicability. Addressing the need to translate single-cell insights into a patient-level disease understanding, we introduce mcBERT, a new method that leverages scRNA-seq data and a transformer-based model to generate integrative patient representations using a self-supervised learning phase followed by contrastive learning to refine these representations. Our evaluations of mcBERT across 7 million cells from 1223 individuals encompassing diverse disease states in heart, kidney, blood cell, and lung tissues show that learned representations facilitate a robust identification of disease cohorts and enable comparisons of patient similarity. Moreover, our findings indicate that mcBERT can accurately classify disease phenotypes, also in previously unseen biospecimens and patients. Independent of the specific tissue, mcBERT extends the utility of scRNA-seq data from cellular analysis to potentially actionable patient-centric applications.

bioinformatics↗

bioRxiv2024

RFX3 is essential for the development and maturation of human pancreatic islets derived from pluripotent stem cells

RFX3 in human pancreatic islet development has not been explored. This study aims to investigate the function of RFX3 in human pancreatic islet development using human islet organoids derived from iPSCs, hypothesizing that RFX3 regulates human islet cell differentiation. We generated RFX3 knockout (RFX3 KO) iPSC lines using CRISPR/Cas9 and differentiated them into pancreatic islet organoids. Various techniques were employed to assess gene expression, cell markers, apoptosis, proliferation, and glucose-stimulated insulin secretion. Single-cell RNA sequencing (scRNA-seq) datasets from hESC-derived pancreatic islets were re-analyzed to investigate RFX3 expression in specific cell populations at various developmental stages. Furthermore, bulk RNA sequencing was conducted to further assess transcriptomic changes. RFX3 was found to be highly expressed in pancreatic endocrine cell populations within pancreatic progenitors (PPs), endocrine progenitors (EPs), and mature islet stages derived from iPSCs. scRNA-seq further confirmed RFX3 expression across different endocrine cell clusters during differentiation. RFX3 loss disrupted pancreatic endocrine gene regulation, reduced hormone-secreting islet cells, and impaired beta-cell function and insulin secretion. Despite a significant reduction in pancreatic islet hormones, the pan-endocrine marker CHGA remained unchanged at both EP and islet stages, likely due to an increase in enterochromaffin cells (ECs). This was supported by our findings of high EC marker expression in RFX3 KO EPs and islets. In addition, RFX3 loss led to smaller islet organoids, elevated TXNIP levels, and increased apoptosis in EPs and islets. These findings underscore the crucial role of RFX3 in regulating human islet cell differentiation and its role in suppressing enterochromaffin cell specification. These insights into RFX3 function have implications for understanding islet biology and potential diabetes susceptibility.

developmental biology↗

bioRxiv2024

PICASO: Profiling Integrative Communities of Aggregated Single-cell Omics data

Various single-cell modalities covering transcriptomics, epigenetic and spatio-temporal changes in health and disease phenotypes are used in an exploratory way to understand biological systems at single-cell resolution. However, the vast amount of such single-cell data is not systematically linked to existing biomedical data. Networks have previously been used to represent harmonized biomedical data. Integrating various resources of biomedical data in networks has recently received increasing attention. These aggregated networks can provide additional insight into the biology of complex human diseases at cell-type level, however, lack inclusion of single cell expression data. Here, we present the PICASO framework, which incorporates single-cell gene expression data as an additional layer to represent associations between cell types, disease phenotypes, drugs and genes. The PICASO network includes several standardized biomedical databases such as STRING, Uniprot, GeneOntology, Reactome, OmniPath and OpenTargets. Using multiple cell type-specific instances of the framework, each annotated and scored with their respective expression data, comparisons between disease states can be made by computing respective sub-networks and comparing the expression scores between conditions. Ultimately, these group-specific networks will allow the identification of relevant genes, processes and potentially druggable targets, as well as the comparison of different measured groups and thus the identification of group-specific communities and interactions.

bioinformatics↗

bioRxiv2024

scDecorr - Feature decorrelation representation learning with domain adaptation enables self-supervised alignment of multiple single-cell experiments

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular het-erogeneity in complex biological systems. However, analyzing and integrating scRNA-seq data poses unique computational challenges due to sparsity, high variability, and technical batch effects. Here, we propose a novel framework called scDecorr for robust representation learning and data integration for scRNA-seq analysis. Our approach leverages the idea of feature decorrelation-based self-supervised learning (SSL) to obtain efficient low-dimensional representations of individual cells without relying on negative samples. By maximizing similarity among distorted embeddings while decorrelating their components, scDecorr captures the biological signature while eliminating technical noise. Furthermore, scDecorr incorporates unsupervised domain adaptation to bridge the gap between batches with different probability distributions, enabling effective integration of scRNA-seq data from diverse sources. Our framework achieves domain-invariant representations by learning cell embeddings independently across domains and employing domain-specific batch normalization. We evaluate scDecorr on a variety of single cell datasets and demonstrate its ability to integrate batches without losing the inherent biological variance, thereby facilitating optimal clustering. The representations generated by scDecorr also exhibit robustness in label transfer tasks, allowing for effective transfer of cell-type labels from reference to query datasets. Overall, scDecorr offers a powerful tool for efficient analysis and integration of large and complex scRNA-seq datasets, advancing our understanding of cellular processes and disease mechanisms. The code is available here https://github.com/hayatlab/scdecorr.

bioinformatics↗

bioRxiv2024

Deletion of RFX6, a Diabetes-Associated Gene, Impairs iPSC-Derived Islet Organoid Development and Survival, With No Impact on the Generation of PDX1+/NKX6.1+ Progenitors

RFX6 is essential for pancreatic development and insulin secretion, while its role in diabetes pathogenesis is unclear. Here, RFX6 expression was detected in PDX1+ cells in the hESC-derived posterior foregut (PF). However, in the pancreatic progenitors (PPs), RFX6 did not co-localize with PDX1 and NKX6.1, but instead with NEUROG3, NKX2.2, and islet hormones in the endocrine progenitor (EPs) and islets. Single-cell analysis revealed high RFX6 expression in endocrine clusters across various hESC-derived pancreatic differentiation stages. Upon differentiating iPSCs lacking RFX6 into pancreatic islets, a significant decrease in PDX1 expression at the PF stage was observed, although it did not affect PPs co-expressing PDX1 and NKX6.1. RNA sequencing showed the downregulation of essential genes involved in pancreatic endocrine differentiation, insulin secretion, and ion transport due to RFX6 deficiency. Furthermore, RFX6 deficiency resulted in the formation of smaller islet organoids due to increased cellular apoptosis, linked to reduced Catalase (CAT) expression, implying a protective role for RFX6. Overexpression of RFX6 reversed defective phenotypes in PPs and EPs. These findings suggest that pancreatic hypoplasia and reduced islet cell formation associated with RFX6 mutations are not due to alterations in PDX1+/NKX6.1+ PPs but instead result from cellular apoptosis and downregulation of pancreatic endocrine genes.

developmental biology↗

bioRxiv2024

Shift in cellular crosstalk reveals the immunosuppressive environment in cholangiocarcinoma patients.

Background and aimsCholangiocarcinoma (CCA) is a deadly disease, and this cancer entity is characterized by an abundant stroma. The tumor microenvironment (TME) plays an important role in aggressive behavior and poor response to therapeutics; however, underlying pathways are unknown. MethodsTo fill this gap, we used multiplexed immunohistochemistry, high-dimensional phenotyping, and transcriptomics to analyze human CCA samples and identify cell cluster crosstalk in patients with a poor prognosis. ResultsOur findings confirmed the presence of Tregs and the lack of effector memory cells in the tumor. New findings are the spatiality of the effector memory cells being more present in the peripheral tissue, for some reason these immune cells fail to reach the tumor niche. We revealed cancer crosstalk with macrophages and stromal cells and identified responsible genes in the poor prognosis group. Amongst the responsible ligand pairs are GAS6-AXL belonging to the TAM family. We then identified VCAN-TLR2 to be present and influencing the ECM in a way to supports immune exhaustion. Last, EGFR-TGF-{beta} is expressed in macrophages and this finding is important in Tregs induction and blocking cytotoxic T cell function. ConclusionsThe multiple mechanisms leading to the exclusion of relevant immune cells needed for an anti-cancer response and mechanisms leading to active immune suppression are part of complex cell-cell crosstalk. This study provides a deeper insight into the immune exhausted phenotype in CCA.

cancer biology↗

bioRxiv2023

Encompassing view of spatial and single-cell RNA-seq renews the role of the microvasculature in human atherosclerosis

Atherosclerosis is a pervasive contributor to cardiovascular diseases including ischemic heart disease and stroke. Despite the advance and success of effective lipid lowering-therapies and hypertensive agents, the residual risk of an atherosclerotic event remains high and improving disease understanding and development of novel therapeutic strategies has proven to be challenging. This is largely due to the complexity of atherosclerosis with a spatial interplay of multiple cell types within the vascular wall. Here, we generated an integrative high-resolution map of human atherosclerotic plaques by combining single-cell RNA-seq from multiple studies and novel spatial transcriptomics data from 12 human specimens to gain insights into disease mechanisms. Comparative analyses revealed cell-type and atherosclerosis-specific expression changes and associated alterations in cell-cell communication. We highlight the possible recruitment of lymphocytes via different endothelial cells of the vasa vasorum, the migration of vascular smooth muscle cells towards the lumen to become fibromyocytes, and cell-cell communication in the plaque, indicating an intricate cellular interplay within the adventitia and the subendothelial space in human atherosclerosis.

bioinformatics↗

bioRxiv2023

Novel YAP1/TAZ pathway inhibitors identified through phenotypic screening with potent anti-tumor activity via blockade of GGTase-I / Rho-GTPase signaling

This study describes the identification and target deconvolution of novel small molecule inhibitors of oncogenic YAP1/TAZ activity with potent anti-tumor activity in vivo. A high-throughput screen (HTS) of 3.8 million compounds was conducted using a cellular YAP1/TAZ reporter assay. Target deconvolution studies identified the geranylgeranyltransferase-I (GGTase-I) complex, as the direct target of YAP1/TAZ pathway inhibitors. The novel small molecule inhibitors block the activation of Rho-GTPases, leading to subsequent inactivation of YAP1/TAZ and inhibition of cancer cell proliferation in vitro. Multi-parameter optimization resulted in BAY-593, an in vivo probe with favorable PK properties, which demonstrated anti-tumor activity and blockade of YAP1/TAZ signaling in vivo. SIGNIFICANCEYAP1/TAZ have been shown to be aberrantly activated oncogenes in several human solid tumors, resulting in enhanced cell proliferation, metastasis and provision of a pro-tumorigenic microenvironment, making YAP1/TAZ targets for novel cancer therapies. Yet, the development of effective inhibitors of these potent oncogenes has been challenging. In this work, we break new ground in this direction through the identification of novel inhibitors of YAP1/TAZ activity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=192 HEIGHT=200 SRC="FIGDIR/small/555331v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@4aac3forg.highwire.dtl.DTLVardef@728b6forg.highwire.dtl.DTLVardef@203a2eorg.highwire.dtl.DTLVardef@1cbc5f9_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LINovel YAP1/TAZ pathway inhibitors identified by phenotypic high-throughput screen C_LIO_LITarget deconvolution identifies GGTase-I as the direct target of the novel YAP1/TAZ pathway inhibitors C_LIO_LIGGTase-I inhibitors block Rho-GTPase signaling and downstream YAP1/TAZ C_LIO_LIGGTase-I inhibitor BAY-593 demonstrates potent anti-tumor activity in vivo C_LI

cancer biology↗

bioRxiv2023

MASIv2 enables standardization and integration of multi-modal single-cell and spatial omics data with one general framework

Data annotation and integration are two common tasks in large-scale and collaborative single-cell research. Rapid technological advancements have made diverse single-cell and spatial data modalities available. This data deluge brought up great challenges in data annotation and integration. Though different biological modalities preserve shared features to define the same cellular system, they often present unique angles to unravel a multi-level understanding about this system. Here, we present one general framework that uses modality-shared and -specific features for annotation and integration of single-cell and spatial omics data. We benchmark our framework with existing methods across different datasets and demonstrate its application in two real world tasks.

bioinformatics↗