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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

Fluorescence exclusion: a rapid, accurate and powerful method for measuring yeast cell volume

Cells exist in an astonishing range of volumes across and within species. However, our understanding of cell size control remains limited, due in large part to the challenges associated with accurate determination of cell volume. Much of our comprehension of size regulation derives from models such as budding and fission yeast, but even for these morphologically stereotypical cells, assessment of cell volume has relied on proxies and extrapolations from two-dimensional measurements. Recently, the fluorescence exclusion method (FXm) was developed to evaluate the size of mammalian cells, but whether it could be applied to smaller cells remained unknown. Using specifically designed microfluidic chips and an improved data analysis pipeline, we show here that FXm reliably detects subtle difference in the volume of fission yeast cells, even for those with altered shapes. Moreover, it allows for the monitoring of dynamic volume changes at the single-cell level with high time resolution. Collectively, our work reveals how coupling FXm with yeast genetics will bring new insights into the complex biology of cell growth. SUMMARY STATEMENTFluorescence exclusion provides a unique method to accurately measure the volume of yeast cells at both the population and single-cell levels.

cell biology↗

bioRxiv2024

Cell-context dependent in silico organelle localization in label-free microscopy images

In silico labeling prediction of organelle fluorescence from label-free microscopy images has the potential to revolutionize our understanding of cells as integrated complex systems. However, out-of-distribution data caused by changes in the intracellular organization across cell types, cellular processes or perturbations, can lead to altered label-free images and impaired in silico labeling. We demonstrated that incorporating biological meaningful cell contexts, via a context-dependent model that we call CELTIC, enhanced in silico labeling prediction and enabled downstream analysis of out-of-distribution data such as cells undergoing mitosis, and cells located at the edge of the colony. These results suggest a link between cell context and intracellular organization. Using CELTIC to generate single cell images transitioning between different contexts enabled us to overcome inter-cell variability toward integrated characterization of organelles alterations in cellular organization. The explicit inclusion of context has the potential to harmonize multiple datasets, paving the way for generalized in silico labeling foundation models.

cell biology↗

bioRxiv2021

Evolutionary conservation of maternal RNA localization in fishes and amphibians revealed by TOMO-Seq

Asymmetrical localization of biomolecules inside the egg, results in uneven cell division and establishment of many biological processes, cell types and the body plan. However, our knowledge about evolutionary conservation of localized mRNAs is still limited to a few candidates. Our goal was to compare localization profiles along the animal-vegetal axis of mature eggs from four models, Xenopus laevis, Danio rerio, Ambystoma mexicanum and Acipenser ruthenus using the spatial expression method called TOMO-Seq. We revealed that RNAs of many known important genes such as germ layer determinants, germ plasm factors and members of key signalling pathways, are localized in completely different profiles among the models. There was also a poor correlation between vegetally localized genes but a relatively good correlation between animally localized genes. These findings indicate that the regulation of embryonic development within the animal kingdom is highly diverse and cannot be deduced based on a single model.

developmental biology↗

bioRxiv2018

Profiling the surface proteome identifies actionable biology for TSC1 mutant cells beyond mTORC1 signaling

Loss of the TSC1/TSC2 complex leads to constitutively high mTORC1 signaling; however, pharmacological inhibition of mTORC1 in this setting produces a broad spectrum of clinical responses. We report herein several cell surface proteins upregulated by inactivation of TSC1 that present therapeutic alternatives or adjuvants to direct mTORC1 inhibition. A proteomics screen revealed that TSC1 loss most dramatically induced the expression of neprilysin (NEP/CD10) and aminopeptidase N (APN/CD13). The survival of TSC1 null human cancer cells was dependent on NEP expression, and TSC1 mutation sensitized cells to biochemical inhibition of APN. Remarkably, NEP and APN upregulation occurred via a TSC2- and mTORC1-independent mechanism; therefore, the antiproliferative effects of mTORC1 inhibition could be augmented by co-suppression of APN activity.\n\nStatement of significanceThese data introduce a non-canonical biological role for TSC1 beyond regulating mTORC1 signaling, which also enabled several immediately translatable therapeutic strategies for clinically problematic cells with TSC1 mutations.

cancer biology↗

bioRxiv2026

Inferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes

1Single-cell RNA sequencing (scRNA-seq) reveals heterogeneity in circadian clock states across individual cells, yet accurately inferring circadian phase and distinguishing biological desynchrony from technical noise remains challenging. Here, we introduce scRitmo, a probabilistic framework that infers single-cell circadian phases from mRNA count data, providing both a point estimate and a posterior uncertainty for each cell. A simulationcalibrated variance decomposition separates the observed phase dispersion into biological and technical components, enabling direct estimation of intercellular desynchrony. We validate scRitmo using deeply sequenced unsynchronized fibroblasts, where inferred transcriptomic phases accurately predict protein-level oscillations of a circadian reporter. Applied to murine scRNA-seq datasets from liver, aorta, and skin, scRitmo outperforms existing methods and reveals cell-type-specific levels of phase coherence. In SABER-FISH time-series data, the method recovers the progressive accumulation of desynchrony following synchronization, and in Drosophila clock neurons it captures cell-type-specific phase shifts and the expected increase in phase dispersion under constant darkness relative to light-dark entrainment. Together, scRitmo provides a principled approach for quantifying circadian (de)synchrony from transcriptomic data, decoupling biological phase variability from measurement noise across tissues, organisms, and experimental conditions.

bioinformatics↗

bioRxiv2016

Mitochondrial DNA Density Homeostasis Accounts for the Threshold Effect in Human Mitochondrial Disease

Mitochondrial dysfunction is involved in a wide array of devastating diseases but the heterogeneity and complexity of these diseases symptoms challenges theoretical understanding of their causation. With the explosion of -omics data, we have the unprecedented ability to gain deep understanding of the biochemical mechanisms of mitochondrial dysfunction. However, there is also a need to make such datasets interpretable, and quantitative modelling allows us to translate such datasets into intuition and suggest rational biomedical treatments. Working towards this interdisciplinary goal, we use a recently published large-scale dataset, and develop a mathematical model of progressive increase in mutant load of the MELAS 3243A>G mtDNA mutation to develop a descriptive and predictive biophysical model. The experimentally observed behaviour is surprisingly rich, but we find that a simple, biophysically-motivated model intuitively accounts for this heterogeneity and yields a wealth of biological predictions. Our findings suggest that cells attempt to maintain wild-type mtDNA density through cell volume reduction, and thus energy demand reduction, until a minimum cell volume is reached. Thereafter, cells toggle from demand reduction to supply increase, upregulating energy production pathways. Our analysis provides further evidence for the physiological significance of mtDNA density, and emphasizes the need for performing single-cell volume measurements jointly with mtDNA quantification. We propose novel experiments to verify the hypotheses made here, to further develop our understanding of the threshold effect, and connect with rational choices for mtDNA disease therapies.\n\nAuthor SummaryMitochondria are organelles which produce the major energy currency of the cell: ATP. Mitochondrial dysfunction is associated with a multitude of devastating diseases, from Parkinsons disease to cancer. Large volumes of data related to these diseases are being produced, but translation of these data into rational biomedical treatment is challenged by a lack of theoretical understanding. We develop a mathematical model of progressive increase of mutant load in mitochondrial DNA, for the mutation associated with MELAS (the most common mitochondrial disease), to address this. We predict that cells attempt to maintain the ratio of healthy mtDNA to cell volume by reducing their cell volume until they reach a minimum cell volume. As mutant load continues to increase, cells switch strategy by increasing their energy supply pathways. Our work accounts for large-scale experimental data and makes testable predictions about mitochondrial dysfunction. It also provides support for increasing mitochondrial content, as well as reduction in dependence upon mitochondrial metabolism via the ketogenic diet, as relevant treatments for mitochondrial disease.

Cell Biology↗

bioRxiv2021

The Necroptosis Effector MLKL drives Small Extracellular Vesicle Release and Tumour Growth in Glioblastoma

Extracellular vesicles (EVs) are lipid-based nano-sized particles that convey biological material from donor to recipient cells. They play key roles in tumour progression, notably in glioblastoma in which the subpopulation of Glioblastoma Stem-like Cells (GSCs) might represent a meaningful source of tumour-derived EVs. However, the mechanisms involved in the production and release of EVs by GSCs are still poorly understood. Here, we report the identification of MLKL, a crucial effector of cell death by necroptosis, as a regulator of the constitutive secretion of small EVs from GSCs. The targeting of MLKL by genetic, protein depletion or chemical approaches alters endosomal trafficking and EV release and reduces GSC expansion in vitro. This function ascribed to MLKL appears independent of its role during necroptosis. In vivo, pharmacological inhibition of MLKL triggers a reduction of both the tumour burden in xenografted mice and of the level of plasmatic EVs. This work reinforces the idea of a non-deadly role for MLKL in endosomal trafficking and suggests that interfering with EV biogenesis is a promising therapeutic option to sensitize glioblastoma cells to death.

cell biology↗

bioRxiv2024

Human cytomegalovirus glycoprotein variants governing viral tropism and syncytium formation in epithelial cells and macrophages

Human cytomegalovirus (HCMV) displays a broad cell tropism, and the infection of biologically relevant cells such as epithelial, endothelial, and hematopoietic cells supports viral transmission, systemic spread, and pathogenesis in the human host. HCMV strains differ in their ability to infect and replicate in these cell types, but the genetic basis of these differences has remained incompletely understood. In this study, we investigated HCMV strain VR1814, which is highly infectious for epithelial cells and macrophages and induces cell-cell fusion in both cell types. A VR1814-derived bacterial artificial chromosome (BAC) clone, FIX-BAC, was generated many years ago but has fallen out of favor because of its modest infectivity. By sequence comparison and genetic engineering of FIX, we demonstrate that the high infectivity of VR1814 and its ability to induce syncytium formation in epithelial cells and macrophages depends on VR1814-specific variants of the envelope glycoproteins gB, UL128, and UL130. We also show that UL130-neutralizing antibodies inhibit syncytium formation, and a FIX-specific mutation in UL130 is responsible for its low infectivity by reducing the amount of the pentameric glycoprotein complex in viral particles. Moreover, we found that a VR1814-specific mutation in US28 further increases viral infectivity in macrophages, possibly by promoting lytic rather than latent infection of these cells. Our findings show that variants of gB and the pentameric complex are major determinants of infectivity and syncytium formation in epithelial cells and macrophages. Furthermore, the VR1814-adjusted FIX strains can serve as valuable tools to study HCMV infection of myeloid cells. ImportanceHCMV is a major cause of morbidity and mortality in transplant patients and the leading cause of congenital infections. HCMV infects various cell types, including epithelial cells and macrophages, and some strains induce the fusion of neighboring cells, leading to the formation of large multinucleated cells called syncytia. This process may limit the exposure of the virus to host immune factors and facilitate its spread. However, the reason why some HCMV strains exhibit a broader cell tropism and why some induce cell fusion more than others is not well understood. We compared two closely related HCMV strains and provided evidence that small differences in viral envelope glycoproteins can massively increase or decrease the virus infectivity and its ability to induce syncytium formation. The results of the study suggest that natural strain variations may influence HCMV infection and pathogenesis in humans.

microbiology↗

bioRxiv2024

Kaizen: Decomposing cellular images with VQ-VAE

A fundamental problem in cell and tissue biology is finding cells in microscopy images. Traditionally, this detection has been performed by segmenting the pixel intensities. However, these methods struggle to delineate cells in more densely packed micrographs, where local decisions about boundaries are not trivial. Here, we develop a new methodology to decompose microscopy images into individual cells by making object-level decisions. We formulate the segmentation problem as training a flexible factorized representation of the image. To this end, we introduce Kaizen, an approach inspired by predictive coding in the brain that maintains an internal representation of an image while generating object hypotheses over the external image, and keeping the ones that improve the consistency of internal and external representations. We achieve this by training a Vector Quantised-Variational AutoEncoder (VQ-VAE). During inference, the VQ-VAE is iteratively applied on locations where the internal representation differs from the external image, making new guesses, and keeping only the ones that improve the overall image prediction until the internal representation matches the input. We demonstrate Kaizens merits on two fluorescence microscopy datasets, improving the separation of nuclei and neuronal cells in cell culture images.

bioinformatics↗

bioRxiv2025

SPACE: spatially resolved multiomic analysis for high-throughput CRISPR screening in 3D models

Current spatial CRISPR screening technologies are limited by targeted readouts and high costs, restricting the scope of biological discovery. Here we present SPAtial Cell Exploration (SPACE), a spatial CRISPR screening platform that integrates whole-transcriptome profiling ([~]18,000 genes), multiplexed protein detection ([~]68 markers), and CRISPR perturbation mapping at subcellular resolution. SPACE significantly reduces whole-transcriptome profiling costs compared to sequencing methods while preserving spatial context. We demonstrate SPACE by screening 43 CRISPR knockouts (KOs) across [~]100,000 cells in hundreds of cancer-associated fibroblast (CAF)-tumor spheroids, obtaining whole-transcriptome and multiplexed protein readout from the same exact cells. SPACE revealed previously unknown regulatory mechanisms on tumor extracellular matrix (ECM) remodeling, and identified spatially-resolved ligand-receptor interactions and perturbation-specific spatial gene signatures that are not detectable with dissociation-based methods. This scalable, cost-effective platform provides a transformative framework for high-throughput spatial perturbation studies in complex tissue models.

cell biology↗

bioRxiv2026

Circulating Cell-Free Chromatin Particles Trigger a Unique Biphasic STING Signaling Program that Drives DNA Damage and Inflammation

Cell death, DNA damage, and inflammation are closely interconnected processes implicated in ageing, cancer, and inflammatory disorders, yet the endogenous mechanisms linking them remain unclear. We previously identified cell-free chromatin particles (cfChPs), released from dying cells, as biologically active entities that enter neighboring cells and induce DNA damage and inflammation. Here, we show that serum-derived circulating cfChPs are rapidly internalized by human peripheral blood mononuclear cells and trigger a previously unrecognized biphasic STING signaling response. An early phase involves rapid STING trafficking to the perinuclear region and nucleus, with activation of IRF3 and NF-{kappa}B preceding detectable DNA damage. This is followed by a later phase characterized by STING phosphorylation, puncta formation, persistent DNA damage, and robust inflammatory cytokine production. Pharmacological inhibition or genetic deletion of STING markedly attenuated these responses. These findings identify extracellular cfChPs as endogenous DNA-damaging agents and reveal biphasic STING signaling as a mechanistic link between cell death, DNA damage, and sterile inflammation. One-sentence summaryCirculating cell-free chromatin particles released from dying cells trigger a previously unrecognized biphasic STING signaling program that mechanistically links cell death to DNA damage and sterile inflammation, providing a potential basis for aging, cancer, and inflammatory diseases.

immunology↗

bioRxiv2024

Decoding the Cognitive map: Learning place cells and remapping

Hippocampal place cells are known for their spatially selective firing and are believed to encode an animals location while forming part of a cognitive map of space. These cells exhibit marked tuning curve and rate changes when an animals environment is sufficiently manipulated, in a process known as remapping. Place cells are accompanied by many other spatially tuned cells such as border cells and grid cells, but how these cells interact during navigation and remapping is unknown. In this work, we build a normative place cell model wherein a neural network is tasked with accurate position reconstruction and path integration. Motivated by the notion of a cognitive map, the networks position is estimated directly from its learned representations. To obtain a position estimate, we propose a non-trainable decoding scheme applied to network output units, inspired by the localized firing patterns of place cells. We find that output units learn place-like spatial representations, while upstream recurrent units become boundary-tuned. When the network is trained to perform the same task in multiple simulated environments, its place-like units learn to remap like biological place cells, displaying global, geometric and rate remapping. These remapping abilities appear to be supported by rate changes in upstream units. While the model does not learn grid-like units, its place unit centers form clusters organized in a hexagonal lattice in open fields. When we decode the center locations of CA1 place fields in mice, we find preliminary evidence of a similar clustering tendency. This suggests a potential mechanism for the interaction between place cells, border cells, and grid cells. Our model provides a normative framework for learning spatial representations previously reserved for biological place cells, providing new insight into place cell field formation and remapping.

neuroscience↗

bioRxiv2017

The origin of animal multicellularity and cell differentiation

How animals evolved from their single-celled ancestors over 600 million years ago is poorly understood. Comparisons of genomes from animals and their closest relatives - choanoflagellates, filastereans and ichthyosporeans - have recently revealed the genomic landscape of animal origins. However, the cell and developmental biology of the first animals have been less well examined. Using principles from evolutionary cell biology, we reason that the last common ancestor of animals and choanoflagellates (the Urchoanozoan) used a collar complex - a flagellum surrounded by a microvillar collar - to capture bacterial prey. The origin of animal multicellularity likely occurred through the modification of pre-existing mechanisms for extracellular matrix synthesis and regulation of cytokinesis. The progenitors of animals likely developed clonally through serial division of flagellated cells, giving rise to sheets of cells that folded into spheres by a morphogenetic process comparable to that seen in modern choanoflagellate rosettes and calcareous sponge embryos. Finally, we infer that cell differentiation evolved in the animal stem-lineage by a combination of three mechanisms: division of labor from ancient plurifunctional cell types, conversion of temporally segregated phenotypes into spatially segregated cell types, and functional innovation.

evolutionary biology↗

bioRxiv2023

3D electron microscopy of the Leishmania mexicana cell cycle: Patterns of organelle duplication and segregation and their implications for parasite biology

The unicellular parasite Leishmania has a precisely defined cell architecture that is inherited by each subsequent generation, requiring a highly coordinated pattern of duplication and segregation of organelles and cytoskeletal structures. A framework of nuclear division and morphological changes is known from light microscopy, yet this has limited resolution and the intrinsic organisation of organelles within the cell body and their manner of duplication and inheritance is unknown. Using volume electron microscopy approaches, we have produced three-dimensional reconstructions of different promastigote cell cycle stages to give a spatial and quantitative overview of organelle positioning, division and inheritance. The first morphological indications seen in our dataset that a new cell cycle had begun were the assembly of a new flagellum, the duplication of the contractile vacuole and the increase in volume of the nucleus and kinetoplast. We showed that the progression of the cytokinesis furrow created a specific pattern of membrane indentations and sub-pellicular microtubule organisation indicates that is likely a preferred site of new microtubule insertion. The daughter cells retained these indentations in their cell body for a period post-abscission. By comparing cultured and sand fly derived promastigotes, we found an increase in the number and overall volume of lipid droplets in the promastigotes from the sand fly, reflecting a change in their metabolism to ensure transmissibility to the mammalian host. Our insights into the cell cycle mechanics of Leishmania will be invaluable for future molecular cell biology analyses of these important parasites.

cell biology↗

bioRxiv2020

Synchronised oscillations in growing cell populations are explained by demographic noise

Understanding synchrony in growing populations is important for applications as diverse as epidemiology and cancer treatment. Recent experiments employing fluorescent reporters in melanoma cell lines have uncovered growing subpopulations exhibiting sustained oscillations, with nearby cells appearing to synchronise their cycles. In this study we demonstrate that the behaviour observed is consistent with long-lasting transient phenomenon initiated, and amplified by the finite-sample effects and demographic noise. We present a novel mathematical analysis of a multi-stage model of cell growth which accurately reproduces the synchronised oscillations. As part of the analysis, we elucidate the transient and asymptotic phases of the dynamics and derive an analytical formula to quantify the effect of demographic noise in the appearance of the oscillations. The implications of these findings are broad, such as providing insight into experimental protocols that are used to study the growth of asynchronous populations and, in particular, those investigations relating to anti-cancer drug discovery. Statement of SignificanceRecent experiments have reported strong evidence of periodic oscillations in the proportion of young and old melanoma cells. The biological mechanism generating this synchronisation and the potential impact that can have on commonly used experimental protocols is still unclear. Here we studied a population of melanoma cells for which we found oscillations in the proportions of cells in each phase of the cell cycle. We demonstrate that these observations may be triggered by intrinsic demographic noise alone, rather than any active synchronisation mechanism requiring cell-cell communication. Our findings may have implications for typical experimental protocols which aim to produce asynchronous cell populations.

cell biology↗

bioRxiv2025

Nephrobase Cell+: Multimodal Single-Cell Foundation Model for Decoding Kidney Biology

BackgroundLarge foundation models have revolutionized single-cell analysis, yet no kidney-specific model currently exists, and it remains unclear whether organ-focused models can outperform generalized models. The kidneys complex cellular architecture and dynamic microenvironments further complicate integration of large-scale single-cell and spatial omics data, where current frameworks trained on limited datasets struggle to correct batch effects, capture cross-modality variation, and generalize across species. MethodsWe developed Nephrobase Cell+, the first kidney-focused large foundation model, pretrained on ~100 billion tokens from ~39.5 million single-cell and single-nucleus profiles across 4,319 samples, four mammalian species (human, mouse, rat, pig), and multiple assay modalities (scRNA-seq, snRNA-seq, snATAC-seq, spatial transcriptomics). Nephrobase Cell+ uses a transformer-based encoder-decoder architecture with gene-token cross-attention and a mixture-of-experts module for scalable representation learning. ResultsNephrobase Cell+ sets a new benchmark for kidney single-cell analysis. It produces tightly clustered, biologically coherent embeddings in human and mouse kidneys, far surpassing previous foundation models such as Geneformer, scGPT, and UCE, as well as traditional methods such as PCA and autoencoders. It achieves the highest cluster concordance and batch-mixing scores, effectively removing donor/assay batch effects while preserving cell-type structure. Cross-species evaluation shows superior alignment of homologous cell types and >90% zero-shot annotation accuracy for major kidney lineages in both human and mouse. Even its 1B-parameter and 500M variants consistently outperform all existing models. ConclusionsWith organ-scale multimodal pretraining and a specialized transformer architecture, Nephrobase Cell+ delivers a unified, high-fidelity representation of kidney biology that is robust, cross-species transferable, and unmatched by current single-cell foundation models, offering a powerful resource for kidney genomics and disease research.

genetics↗

bioRxiv2019

A new role for Notch in control of polarity and asymmetric cell division of developing T cells

A fundamental question in biology is how single cells can reliably produce progeny of different cell types. Notch signalling frequently facilitates fate determination. Asymmetric cell division (ACD) often controls segregation of Notch signalling by imposing unequal inheritance of regulators of Notch. Here, we assessed the functional relationship between Notch and ACD in mouse T cell development. To attain immunological specificity, developing T cells must pass through a pivotal stage termed {beta}-selection, which involves Notch signalling and ACD. We assessed functional interactions between Notch and ACD during {beta}-selection using direct presentation of Notch ligands, DL1 and DL4, and pharmacological inhibition of Notch signalling. Contrary to prevailing models, we find Notch controls distribution of Notch1 itself and cell fate determinants, -Adaptin and Numb. Notch and CXCR4 signalling cooperated to drive polarity during division. Thus, Notch signalling directly orchestrates ACD, and Notch1 is differentially inherited by sibling cells.

cell biology↗

bioRxiv2021

Learning the rules of cell competition without prior scientific knowledge

AO_SCPLOWBSTRACTC_SCPLOWDeep learning is now a powerful tool in microscopy data analysis, and is routinely used for image processing applications such as segmentation and denoising. However, it has rarely been used to directly learn mechanistic models of a biological system, owing to the complexity of the internal representations. Here, we develop an end-to-end machine learning model capable of learning the rules of a complex biological phenomenon, cell competition, directly from a large corpus of time-lapse microscopy data. Cell competition is a quality control mechanism that eliminates unfit cells from a tissue and during which cell fate is thought to be determined by the local cellular neighborhood over time. To investigate this, we developed a new approach ({tau}-VAE) by coupling a probabilistic encoder to a temporal convolution network to predict the fate of each cell in an epithelium. Using the{tau} -VAEs latent representation of the local tissue organization and the flow of information in the network, we decode the physical parameters responsible for correct prediction of fate in cell competition. Remarkably, the model autonomously learns that cell density is the single most important factor in predicting cell fate - a conclusion that is in agreement with our current understanding from over a decade of scientific research. Finally, to test the learned internal representation, we challenge the network with experiments performed in the presence of drugs that block signalling pathways involved in competition. We present a novel discriminator network that, using the predictions of the{tau} -VAE, can identify conditions which deviate from the normal behaviour, paving the way for automated, mechanism-aware drug screening.

cell biology↗