Biology subjects

Marioni, J.

Publications and source records attributed to Marioni, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Archetype analysis of multiplexed antibody imaging reveals interpretable morphological programs in optical pooled screens

High-content imaging-based screens with small molecule or genetic perturbations rely on rich optical phenotypes, yet how distinct subcellular markers contribute complementary biological information remains unclear. Here, we introduce CellMAPP, a multiplexed organelle imaging platform that uses optical pooled screens with iterative antibody staining to map cellular architecture under perturbation. Using 17 markers across 524 genetic perturbations in MCF-7 cells, we systematically examined how individual, and combinations of, markers capture diverse morphological responses. We find that markers provide complementary views of perturbation effects, with increased multiplexing improving signal and the breadth of hit recovery, although some phenotypes are better recovered from a subset of features rather than a full profile. Because non-molecular image-based profiles can be challenging to link with interpretable biology, we developed CHART (available at https://github.com/Genentech/CHART), an archetype analysis framework that identifies distinct reference morphological states for each marker and uses multimodal large language models (MLLMs) to automatically annotate them, connecting cellular images to recognizable organelle phenotypes. Integrating archetypes across multiple markers revealed modular morphological programs that describe coordinated organelle reorganization in response to perturbation. This framework identified regulators of defined organelle states such as micronuclei, nominated novel gene functions and revealed complex patterns of multi-organelle reposition. Together, optical profiling with MAPP and CHART provides an interpretable, scalable framework for image-based screens that sheds light on rules governing cellular structural reprogramming.

systems biology↗

bioRxiv2026

An Integrated Atlas of the Human Kidney Spanning Health and Diseases

The human kidney contains highly specialized cell populations. Despite numerous single-cell and single-nucleus transcriptomics studies, differences in cohorts, technologies, analytical pipelines, and annotation frameworks have limited the ability to define consensus kidney cell states, identify disease-associated populations and interpret kidney disease genetic susceptibility. Here, we assembled 18 human kidney single-cell and single-nucleus RNA-sequencing datasets spanning 232 donors and five major disease contexts into a uniformly processed and computationally integrated Human Kidney Cell Atlas (HKCA), comprising over one million high-quality cells (816,895) and nuclei (215,308). The HKCA resolves 63 cell types and 120 harmonized cell states, including rare epithelial and stromal populations associated with kidney diseases . Integration with spatial transcriptomics, intercellular communication networks, and human genetic association data further defined the anatomical context and disease relevance of these populations. The HKCA also provides a framework for automated annotation of independent kidney human and mouse datasets. Together, the HKCA establishes a comprehensive reference for human kidney biology, enabling disease interpretation and genetic risk localization at cellular resolution.

genomics↗

bioRxiv2025

Promising Inhibitor of Flavivirus: Evaluating the Antiviral Activity of Nordihydroguaiaretic Acid against West Nile Virus Replication

Mosquito-borne Arboviruses, including Alphaviruses and Flaviviruses, are responsible for millions of human infections worldwide. The absence of specific antivirals for arbovirus infections underscores the urgent need for novel research to develop effective treatments. Natural products have emerged as promising candidates. Nordihydroguaiaretic acid (NDGA), a natural lignan predominantly found in Larrea spp., has shown antiviral activity against several viruses, including Dengue. This study aimed to investigate the antiviral potential of NDGA against West Nile virus (WNV), assessing its mechanism of action and cellular interactions. Using plaque-forming unit reduction and immunofluorescence assays in LLC-MK2 cells, we determine the reduction in viability of WNV viral particles at different times post-infection. The potential involvement of the sterol regulatory element-binding proteins (SREBP) and 5-lipoxygenase pathways in WNV infection was evaluated. Additionally, we analyzed the cytoskeleton integrity in treated cells. NDGA, had the greatest inhibitory effect on WNV replication when added 1 and 2 h post-viral internalization (PI), losing activity when added after 3 h PI, thus suggesting it targets early viral infection events. The SREBP pathway seems to be involved in the inhibition of WNV by NDGA, likely due to its lipogenic activity. Furthermore, the antiviral activity of NDGA could be related to its ability to modify the cytoskeleton of LLC-MK2 cells. These findings highlight NDGA potential as a therapeutic candidate against WNV and other flaviviruses. Plant metabolites are among the leading therapeutic resources globally. Further research is necessary to comprehensively assess the therapeutic promise of NDGA. ImportanceMosquito-borne arboviruses pose significant global health threats due to their widespread distribution and the absence of specific antiviral treatments. The emergence of virulent strains and recurrent outbreaks underscore the urgent need for novel antiviral agents. Nordihydroguaiaretic acid, a natural phenolic compound, has shown promising antiviral activity against flaviviruses such as West Nile virus. Understanding nordihydroguaiaretic acids mechanism of action not only reveals potential therapeutic strategies but also highlights the broader importance of plant-derived compounds in combating viral infections. Our research focuses on further elucidating nordihydroguaiaretic acids antiviral effects against West Nile virus, aiming to contribute to the development of effective therapies. Continued investigation of nordihydroguaiaretic acids therapeutic potential is essential for advancing antiviral drug discovery and addressing the persistent challenges posed by mosquito-borne diseases worldwide.

microbiology↗

bioRxiv2024

GLP1R agonists activate human POMC neurons

Drugs like semaglutide (a.k.a. Ozempic/Wegovy) that activate the glucagon-like peptide-1 receptor (GLP-1R) are a promising therapy for obesity and type 2 diabetes (T2D). Animal studies suggest that these drugs likely function by stimulating GLP-1R on appetite-suppressing neuron populations in the brain, but it is still unclear how they act to reduce food intake in humans. We therefore generated appetite-regulatory hypothalamic neurons from human pluripotent stem cells (hPSCs) to study their responses to GLP-1R agonists by calcium imaging and electrophysiology. We found that hPSC-derived proopiomelanocortin (POMC) and other hypothalamic neuron subtypes expressed GLP1R mRNA, and many of these neurons robustly responded to GLP-1R agonists by membrane depolarization, increased action potential firing, and extracellular calcium influx that persisted long after agonist withdrawal. The observed GLP-1R-induced response was likely mediated by the activation of PKA and L-type calcium channels, and led to significant changes in gene expression. These findings provide mechanistic insight into how GLP-1R agonists may suppress appetite in humans.

neuroscience↗

bioRxiv2023

High-throughput phenotyping of single nucleotide variants by linking transcriptomes to genotypes in single cells

CRISPR screens with single-cell transcriptomic readouts are a valuable tool to understand the effect of genetic perturbations, but are currently limited because genotypes are inferred from the guide RNA identity. We have developed a technique that couples single-cell genotyping to transcriptomics of the same cells to enable screening for the effects of single nucleotide variants. Analysis of variants tiling across the JAK1 gene demonstrates the importance of determining the precise genetic perturbation and classifies missense variants into three functional categories.

genetics↗

bioRxiv2023

Time, Space and Single-Cell Resolved Molecular Trajectory of Cell Populations and the Laterality of the Body Plan at Gastrulation

Understanding of the molecular drivers of lineage diversification and tissue patterning during primary germ layer development requires in-depth knowledge of the dynamic molecular trajectories of cell lineages across a series of developmental stages of gastrulation1-7. Through computational modeling, we constructed at single-cell resolution a spatio-temporal compendium of the molecular trajectories of germ-layer derivatives in gastrula-stage mouse embryos. This molecular atlas infers the developmental trajectories of single-cell populations and the molecular network activity underpinning the specification and differentiation of the germ-layer lineages. Analysis of the heterogeneity of cellular composition of cell populations at defined positions in the epiblast revealed progressive diversification of cell types, mirroring the process of lineage allocation during gastrulation. A novel observation is the difference in the contribution of cells on contralateral sides of the epiblast to mesoderm derivatives of the early organogenesis embryo, and the enhanced BMP signaling activity in right-side mesoderm of E7.5 embryo. Perturbation of BMP signaling activity at late gastrulation led to randomization of left-right (L-R) molecular asymmetry in the lateral mesoderm of early-somite-stage embryo. Our findings indicate the asymmetric BMP activity during gastrulation may be critical for the symmetry breaking process associated with specification of L-R body asymmetry ahead of the acquisition of functionality of the L-R organizer.

developmental biology↗

bioRxiv2023

A Human Breast Cell Atlas Mapping the Homeostatic Cellular Shifts in the Adult Breast

One of the barriers for breast cancer prevention and treatment is our poor understanding of the dynamic cellular shifts that naturally occur within the breast and how these changes contribute to tumour initiation. In this study we report the use of single cell RNA sequencing (scRNAseq) to compile a Human Breast Cell Atlas (HBCA) assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mammoplasties. The data from more than 800,000 cells identified 41 cell subclusters distributed across the epithelial, immune, and stromal compartments. We found that the contribution of these different clusters varied according to the natural history of the tissue. Breast cancer risk modulating factors such as age, parity, and germline mutation affected the homeostatic cellular state of the breast in different ways however, none of the changes observed were restricted to any one cell type. Remarkably, we also found that immune cells from BRCA1/2 carriers had a distinct gene expression signature indicative of potential immune exhaustion. This suggests that immune escape mechanisms could manifest in non-cancerous tissues during very early stages of tumour initiation. Therefore, the Atlas presented here provides the research community with a rich resource that can be used as a reference for studies on the origins of breast cancer which could inform novel approaches for early detection and prevention.

cancer biology↗

bioRxiv2023

Sensitive cluster-free differential expression testing.

Comparing molecular features, including the identification of genes with differential expression (DE) between conditions, is a powerful approach for characterising disease-specific phenotypes. When testing for DE in single-cell RNA sequencing data, current pipelines first assign cells into discrete clusters (or cell types), followed by testing for differences within each cluster. Consequently, the sensitivity and specificity of DE testing are limited and ultimately dictated by the granularity of the cell type annotation, with discrete clustering being especially suboptimal for continuous trajectories. To overcome these limitations, we present miloDE - a cluster-free framework for differential expression testing. We build on the Milo approach, introduced for differential cell abundance testing, which leverages the graph representation of single-cell data to assign relatively homogenous, neighbouring cells into overlapping neighbourhoods. We address key differences between differential abundance and expression testing at the level of neighbourhood assignment, statistical testing, and multiple testing correction. To illustrate the performance of miloDE we use both simulations and real data, in the latter case identifying a transient haemogenic endothelia-like state in chimeric mouse embryos lacking Tal1 as well as uncovering distinct transcriptional programs that characterise changes in macrophages in patients with Idiopathic Pulmonary Fibrosis. miloDE is available as an open-source R package at https://github.com/MarioniLab/miloDE.

bioinformatics↗

bioRxiv2023

A transcriptional and regulatory map of mouse somitogenesis

The mammalian body plan is shaped by rhythmic segmentation of mesoderm into somites, which are transient embryonic structures consisting of hundreds of cells that form down each side of the neural tube. We have systematically analysed the genome-wide transcriptional and chromatin dynamics occurring within nascent somites, from early inception of somitogenesis to the latest stages of body plan establishment. We created matched gene expression and open chromatin maps for the three leading pairs of somites at six time points during embryonic development. Here we show that the rate of somite differentiation accelerates as development progresses. We identified a conserved maturation programme followed by all somites after segmentation, but somites from more developed embryos concomitantly switch on differentiation programmes from derivative cell lineages soon after segmentation. Integrated analysis of the somitic transcriptional and chromatin activities revealed opposing regulatory modules controlling the onset of differentiation. We identified transcription factors expressed during early development that inhibit the activity of proteins required for commitment and differentiation of skeletal cell populations. Our results provide a powerful, high-resolution view of the molecular genetics underlying somitic development in mammals.

developmental biology↗

bioRxiv2022

Molecular characterization of Left-Right symmetry breaking in the mouse embryo

The asymmetric morphology of the mammalian heart is essential to its function as the organ of pulmonary and systemic double circulation. Left-right asymmetry is established by a leftward flow in the node that results in the asymmetric expression of Nodal. This triggers a cascade of asymmetric expression of downstream genes, such as Pitx2c, in the lateral plate mesoderm that gives rise to the first morphologically recognizable primordial heart structure, the cardiac crescent. Relatively little is known about gene expression asymmetries in the cardiac crescent that might underpin asymmetric cardiac morphogenesis. To systematically identify asymmetrically expressed genes, we performed a single-cell transcriptional analysis of manually dissected left and right halves of the cardiac crescent at stages spanning symmetry breaking. This revealed both left and right-sided genes that have not previously been implicated in left-right symmetry breaking. Some of these were expressed in multiple cell types but showed asymmetric expression in only a sub-set of cell types. We validated these findings using multiplexed in situ Hybridization Chain Reaction (HCR) and high-resolution volume imaging to characterize the expression patterns of select genes. Using Dnahiv/iv mutant embryos that show randomized situs, we established that all the genes tested tracked the asymmetric expression of Pitx2c, indicating their asymmetric expression also arose from the early asymmetries at the node. This study provides a high-fidelity molecular characterization of left-right symmetry breaking during cardiac crescent formation, providing a basis for future mechanistic studies on asymmetric cardiac morphogenesis.

developmental biology↗

bioRxiv2022

Precise identification of cell states altered indisease with healthy single-cell references

Single cell genomics is a powerful tool to distinguish altered cell states in disease tissue samples, through joint analysis with healthy reference datasets. Collections of data from healthy individuals are being integrated in cell atlases that provide a comprehensive view of cellular phenotypes in a tissue. However, it remains unclear whether atlas datasets are suitable references for disease-state identification, or whether matched control samples should be employed, to minimise false discoveries driven by biological and technical confounders. Here we quantitatively compare the use of atlas and control datasets as references for identification of disease-associated cell states, on simulations and real disease scRNA-seq datasets. We find that reliance on a single type of reference dataset introduces false positives. Conversely, using an atlas dataset as reference for latent space learning followed by differential analysis against a matched control dataset leads to precise identification of disease-associated cell states. We show that, when an atlas dataset is available, it is possible to reduce the number of control samples without increasing the rate of false discoveries. Using a cell atlas of blood cells from 12 studies to contextualise data from a case-control COVID-19 cohort, we sensitively detect cell states associated with infection, and distinguish heterogeneous pathological cell states associated with distinct clinical severities. Our analysis provides guiding principles for design of disease cohort studies and efficient use of cell atlases within the Human Cell Atlas.

genomics↗

bioRxiv2022

Origin and segregation of the human germline

Human germline-soma segregation occurs during weeks 2-3 in gastrulating embryos. While direct studies are hindered, here we investigate the dynamics of human primordial germ cell (PGCs) specification using in vitro models with temporally resolved single-cell transcriptomics and in-depth characterisation to in vivo datasets from human and non-human primates, including a 3D marmoset reference atlas. We elucidate the molecular signature for the transient gain of competence for germ cell fate during peri-implantation epiblast development. Further, we show that both the PGCs and amnion arise from transcriptionally similar TFAP2A positive progenitors at the posterior end of the embryo. Notably, genetic loss of function experiments show that TFAP2A is crucial for initiating the PGC fate without detectably affecting the amnion, and its subsequently replaced by TFAP2C as an essential component of the genetic network for PGC fate. Accordingly, amniotic cells continue to emerge from the progenitors in the posterior epiblast, but importantly, this is also a source of nascent PGCs.

developmental biology↗

bioRxiv2022

A human embryonic limb cell atlas resolved in space and time

Human limbs emerge during the fourth post-conception week as mesenchymal buds which develop into fully-formed limbs over the subsequent months. Limb development is orchestrated by numerous temporally and spatially restricted gene expression programmes, making congenital alterations in phenotype common. Decades of work with model organisms has outlined the fundamental processes underlying vertebrate limb development, but an in-depth characterisation of this process in humans has yet to be performed. Here we detail the development of the human embryonic limb across space and time, using both single-cell and spatial transcriptomics. We demonstrate extensive diversification of cells, progressing from a restricted number of multipotent progenitors to myriad mature cell states, and identify several novel cell populations, including neural fibroblasts and multiple distinct mesenchymal states. We uncover two waves of human muscle development, each characterised by different cell states regulated by separate gene expression programmes. We identify musculin (MSC) as a key transcriptional repressor maintaining muscle stem cell identity and validate this by performing MSC knock down in human embryonic myoblasts, which results in significant upregulation of late myogenic genes. Through integration of multiple anatomically continuous spatial transcriptomic samples, we spatially map single-cell clusters across a sagittal section of a whole fetal hindlimb. We reveal a clear anatomical segregation between genes linked to brachydactyly and polysyndactyly, and uncover transcriptionally and spatially distinct populations of mesenchyme in the autopod. Finally, we perform scRNA-seq on murine embryonic limbs to facilitate cross-species developmental comparison at single-cell resolution, finding substantial homology between the two species.

developmental biology↗

bioRxiv2022

Supervised spatial inference of dissociated single-cell data with SageNet

Spatially-resolved transcriptomics uncovers patterns of gene expression at supercellular, cellular, or subcellular resolution, providing insights into spatially variable cellular functions, diffusible morphogens, and cell-cell interactions. However, for practical reasons, multiplexed single cell RNA-sequencing remains the most widely used technology for profiling transcriptomes of single cells, especially in the context of large-scale anatomical atlassing. Devising techniques to accurately predict the latent physical positions as well as the latent cell-cell proximities of such dissociated cells, represents an exciting and new challenge. Most of the current approaches rely on an autocorrelation assumption, i.e., cells with similar transcriptomic profiles are located close to each other in physical space and vice versa. However, this is not always the case in native biological contexts due to complex morphological and functional patterning. To address this challenge, we developed SageNet, a graph neural network approach that spatially reconstructs dissociated single cell data using one or more spatial references. SageNet first estimates a gene-gene interaction network from a reference spatial dataset. This informs the structure of the graph on which the graph neural network is trained to predict the region of dissociated cells. Finally, SageNet produces a low-dimensional embedding of the query dataset, corresponding to the reconstructed spatial coordinates of the dissociated tissue. Furthermore, SageNet reveals spatially informative genes by extracting the most important features from the neural network model. We demonstrate the utility and robust performance of SageNet using molecule-resolved seqFISH and spot-based Spatial Transcriptomics reference datasets as well as dissociated single-cell data, across multiple biological contexts. SageNet is provided as an open-source python software package at https://github.com/MarioniLab/SageNet.

bioinformatics↗

bioRxiv2022

StabMap: Mosaic single cell data integration using non-overlapping features

Currently available single cell -omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilises mapping of single cell data by exploiting the non-overlapping features. StabMap is a flexible approach that first infers a mosaic data topology, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates disjoint mosaic data integration, and enables the use of novel spatial gene expression features for mapping dissociated single cell data onto a spatial transcriptomic reference.

bioinformatics↗

bioRxiv2022

An organoid CRISPRi screen revealed that SOX9 primes human fetal lung tip progenitors to receive WNT and RTK signals

The balance between self-renewal and differentiation in human fetal lung epithelial progenitors controls the size and function of the adult organ. Moreover, progenitor cell gene regulation networks are employed by both regenerating and malignant lung cells, where modulators of their effects could potentially be of therapeutic value. Details of the molecular networks controlling human lung progenitor self-renewal remain unknown. We performed the first CRISPRi screen in primary human lung organoids to identify transcription factors controlling progenitor self-renewal. We show that SOX9 promotes proliferation of lung progenitors and inhibits precocious airway differentiation. Moreover, by identifying direct transcriptional targets using Targeted DamID we place SOX9 at the centre of a transcriptional network which amplifies WNT and RTK signalling to stabilise the progenitor cell state. In addition, the proof-of-principle CRISPRi screen and Targeted DamID tools establish a new approach for using primary human organoids to elucidate detailed functional mechanisms underlying normal development and disease. HighlightsO_LIA pooled CRISPRi screen in human fetal lung organoids identified transcription factors controlling progenitor cell self-renewal. C_LIO_LISOX9 promotes tip progenitor cell proliferation and supresses precocious airway differentiation. C_LIO_LITargeted DamID (TaDa) identified SOX9 direct binding targets, revealing that SOX9 lies at the intersection of WNT and RTK signalling. C_LIO_LISOX9 and ETVs co-regulate the human fetal lung progenitor self-renewal programme. C_LI

developmental biology↗

bioRxiv2022

A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates

We present a multiomic cell atlas of human lung development that combines single cell RNA and ATAC sequencing, high throughput spatial transcriptomics and single cell imaging. Coupling single cell methods with spatial analysis has allowed a comprehensive cellular survey of the epithelial, mesenchymal, endothelial and erythrocyte/leukocyte compartments from 5-22 post conception weeks. We identify new cell states in all compartments. These include developmental-specific secretory progenitors and a new subtype of neuroendocrine cell related to human small cell lung cancer. Our datasets are available through our web interface (https://lungcellatlas.org). Finally, to illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signalling and transcription factor hierarchies which we test using organoid models. HighlightsO_LISpatiotemporal atlas of human lung development from 5-22 post conception weeks identifies 144 cell types/states. C_LIO_LITracking the developmental origins of multiple cell compartments, including new progenitor states. C_LIO_LIFunctional diversity of fibroblasts in distinct anatomical signalling niches. C_LIO_LIResource applied to interrogate and experimentally test the transcription factor code controlling neuroendocrine cell heterogeneity and the origins of small cell lung cancer. C_LI

developmental biology↗

bioRxiv2021

CellRegMap: A statistical framework for mapping context-specific regulatory variants using scRNA-seq

Single cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to perform genetic analyses using scRNA-seq remain based on principles established for bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize heterogeneity in allelic effects across cellular contexts of different granularity, including cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to two recent studies of differentiating iPSCs, where we uncover a previously underappreciated heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.

bioinformatics↗