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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

An open-source experimental framework for automation of cell biology experiments

Modern data analysis methods, such as optimisation algorithms or machine and deep learning, have been successfully applied to a number of biological, biotechnological and medical questions. For these methods to be efficient, a large number of high quality experiments need to be conducted, which requires a high degree of automation. Here we report an open-source hardware that allows for automatic high-throughput generation of large amounts of cell biology data. The hardware consists of an automatic XY-stage for moving a multiwell plate containing growing cells; a perfusion manifold allowing application of up to 8 different solutions; and a small epifluorescent microscope. It is extremely cheap (approximately {pound}400 without and {pound}2500 with a fluorescent microscope) and is easily customizable for individual experimental needs. We demonstrate the usability of this platform with high-throughput Ca2+ imaging and large-scale labelling experiments. Key points- We present an open source framework for automation of cell biology experiments - The framework consists of an XY platform, application of up to 8 solutions and a small epifluorescent microscope with autofocusing - Very cheap ({pound}400 without a fluorescent microscope and {pound}2500 with a fluorescent microscope), customisable, - Can be used in a variety of biological applications such as imaging of fluorescent reporters, optimisation of treatment conditions and fluorescent labelling

cell biology↗

bioRxiv2017

Unlocking the biological potential of Euglena gracilis: evolution, cell biology and significance to parasitism

Photosynthetic euglenids are major components of aquatic ecosystems and relatives of trypanosomes. Euglena gracilis has considerable biotechnological potential and great adaptability, but exploitation remains hampered by the absence of a comprehensive gene catalogue. We address this by genome, RNA and protein sequencing: the E. gracilis genome is >2Gb, with 36,526 predicted proteins. Large lineage-specific paralog families are present, with evidence for flexibility in environmental monitoring, divergent mechanisms for metabolic control, and novel solutions for adaptation to extreme environments. Contributions from photosynthetic eukaryotes to the nuclear genome, consistent with the shopping bag model are found, together with transitions between kinetoplastid and canonical systems. Control of protein expression is almost exclusively post-transcriptional. These data are a major advance in understanding the nuclear genomes of euglenids and provide a platform for investigating the contributions of E. gracilis and its relatives to the biosphere.

evolutionary biology↗

bioRxiv2026

Avl9 defines a family of GTPase-activating proteins that regulate diverse cell biological functions

Ras-related GTPases are molecular switches regulating hundreds of signaling and trafficking pathways in cells. Many GTPase regulators remain to be identified despite extensive genetic and biochemical screens. Here we present the results of computational protein-protein interaction screens and functional experiments identifying the DENN domain protein Avl9 as a GTPase-activating protein for Arf1. Avl9 is involved in secretion and cell migration, but its molecular function has not been characterized. We determined that Avl9 possesses robust Arf-GAP activity and is recruited to secretory vesicles by Rab8. We find that Avl9 function is conserved in humans and enhances cell migration. We propose that several other DENN domain proteins are also candidate GAPs, and we demonstrate that one candidate previously characterized as a Rab-GEF, DENND6A, exhibits strong Arf-GAP activity towards ARL8B, explaining its role in lysosome positioning. Collectively, this work uncovers a family of monomeric DENN GAP proteins that regulate diverse cell biological pathways.

cell biology↗

bioRxiv2026

An Advanced Mobile Laboratory to enable field-based microbial ecology and cell biology across scales

Microbial biodiversity is central to ecosystem function, yet mechanistic insights into the cell biology of environmental organisms remain limited. The underlying challenges are twofold: most microbes remain uncultivable, and a persistent gap exists between field sampling and laboratory analyses. Here, we introduce the Advanced Mobile Laboratory (AML), a field-deployable platform that integrates confocal microscopy, image-enabled cell sorting, and cryo-preparation for expansion and electron microscopy. This setup enables immediate, standardized processing and analysis of environmental communities directly at the sampling site. We demonstrate its capability using marine eukaryotic plankton, showing how the AML enables multiscale investigations, from live imaging of natural communities to enabling ultrastructural and single-cell omics analyses, while minimizing sample degradation and enabling on-site experimentation. By bringing high-end sample preparation and analytical capacity into the field, the AML enables studying life in its natural context to mechanistically understand lifes diversity in the environment.

cell biology↗

bioRxiv2022

Modeling Cell Biological Features of Meiotic Chromosome Pairing

During meiosis, homologous chromosomes become associated side by side in a process known as homologous chromosome pairing. Pairing requires long range chromosome motion through a nucleus that is full of other chromosomes. It remains unclear how the cell manages to align each pair of chromosomes quickly while mitigating and resolving interlocks. Here, we use a coarse-grained molecular dynamics model to investigate how specific features of meiosis, including motor-driven telomere motion, nuclear envelope interactions, and increased nuclear size, affect the rate of pairing and the mitigation/resolution of interlocks. By creating in silico versions of three yeast strains and comparing the results of our model to experimental data, we find that a more distributed placement of pairing sites along the chromosome is necessary to replicate experimental findings. Active motion of the telomeric ends speeds up pairing only if binding sites are spread along the chromosome length. Adding a meiotic bouquet significantly speeds up pairing but does not significantly change the number of interlocks. An increase in nuclear size slows down pairing while greatly reducing the number of interlocks. Interestingly, active forces increase the number of interlocks, which raises the question: How do these interlocks resolve? Our model gives us detailed movies of interlock resolution events which we then analyze to build a step-by-step recipe for interlock resolution. In our model, interlocks must first translocate to the ends, where they are held in a quasi-stable state by a large number of paired sites on one side. To completely resolve an interlock, the telomeres of the involved chromosomes must come in close proximity so that the cooperativity of pairing coupled with random motion causes the telomeres to unwind. Together our results indicate that computational modeling of homolog pairing provides insight into the specific cell biological changes that occur during meiosis. Author SummaryEarly in meiosis, homologous chromosomes must find each other within the crowded nuclear space and become aligned along their entire length in a process known as homologous chromosome pairing. It remains unclear how the cell manages to align each pair of chromosomes quickly while mitigating and resolving interlocks. Here, we study this process by using a computational model. Our model attempts to capture the large-scale cell biological picture of meiotic pairing including the random initial 3D search, active motion of the chromosome ends, and meiosis specific constraints such as telomere attachment to the nuclear envelope. We use our model to study how these different features of meiosis affect the rate of pairing and the mitigation/resolution of interlocks. Importantly, our model gives us detailed movies of interlock resolution events, which we then analyze to build a step-by-step recipe for interlock resolution. We believe computational modeling of homolog pairing provides valuable insight into this complex biological process.

biophysics↗

bioRxiv2025

Fast scanning small angle X-ray scattering of hydrated biological cells

Due to their high penetration depth, X-rays enable us to obtain information from the interior of whole, unsliced cells. Scanning small angle X-ray scattering (SAXS), in particular, reveals real-space images in dark field representation as well as structural information in reciprocal space. However, obtaining information on anisotropy and orientation from cells in an aqueous, close-to-physiological environment remains challenging. Here, we extend the recently introduced fast scanning SAXS mode with short exposure times of few milliseconds to such hydrated samples by combining a newly developed, X-ray compatible microfluidic sample chamber and innovative data analysis that includes an effective noise-filtering method. This strategy enables the systematic analysis of radiation damage by quantifying the SAXS signal. Our results demonstrate that scanning SAXS can be used to obtain intracellular information of fixed-hydrated cells and the approach may in the future be applicable to living cells as well. SynopsisFast scanning SAXS on biological cells in aqueous environment reveals intracellular anisotropy and orientation and allows for systematic assessment of radiation damage caused by the measurements.

biophysics↗

bioRxiv2023

An open-source FACS automation system for high-throughput cell biology

Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions ("gating") to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instruments software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology.

bioengineering↗

bioRxiv2016

Nanog fluctuations in ES cells highlight the problem of measurement in cell biology

A number of important pluripotency regulators, including the transcription factor Nanog, are observed to fluctuate stochastically in individual embryonic stem (ES) cells. By transiently priming cells for commitment to different lineages, these fluctuations are thought to be important to the maintenance of, and exit from, pluripotency. However, since temporal changes in intracellular protein abundances cannot be measured directly in live cells, these fluctuations are typically assessed using genetically engineered reporter cell lines that produce a fluorescent signal as a proxy for protein expression. Here, using a combination of mathematical modeling and experiment, we show that there are unforeseen ways in which widely used reporter strategies can systemically disturb the dynamics they are intended to monitor, sometimes giving profoundly misleading results. In the case of Nanog we show how genetic reporters can compromise the behavior of important pluripotency-sustaining positive feedback loops, and induce a bifurcation in the underlying dynamics that gives rise to heterogeneous Nanog expression patterns in reporter cell lines that are not representative of the wild-type. These findings help explain the range of published observations of Nanog variability and highlight a fundamental measurement problem in cell biology.

Cell Biology↗

bioRxiv2024

An evolutionary cell biology perspective into the diverging mechanisms of clathrin-mediated endocytosis in dikarya fungi

Clathrin-mediated endocytosis is an ancient eukaryotic trafficking pathway, which transports plasma membrane and associated cargo into the cell and is involved in numerous cell- and tissue-level processes. Cargo selection and clathrin-coated vesicle formation is mediated by over 60 proteins that assemble in a regular and sequential manner at the plasma membrane. Decades of endocytosis studies have followed the tenet that uncovering the conserved core molecular mechanisms is sufficient to understand a cellular process. However, this approach also revealed a number of cell type or species-related variations that challenge a universal conserved, core mechanism. In this paper, we refocus on the endocytic diversity to understand how evolution shapes endocytic mechanisms. We define a comparative evolutionary cell biology approach that uses dikarya fungi as a model clade and live-cell fluorescence microscopy to study endocytosis dynamics in three species: Saccharomyces cerevisiae, Schizosaccharomyces pombe and Ustilago maydis. Our results quantitatively define several phenotypic differences between the species. We uncover several differences that impact the endocytic early phase, the protein assembly order, actin regulation, membrane invagination and scission. These findings demonstrate a mosaic evolution of endocytic traits, suggest ancestral states and direction of changes. We also investigate the phenotypic plasticity and robustness against environmental conditions. Lastly, we demonstrate that relatively minor evolutionary changes can majorly impact endocytic phenotypes. These studies force an appreciation of endocytic variation as not auxiliary, but vital to mechanistic understanding of this conserved cellular pathway.

cell biology↗

bioRxiv2026

Structural cell biology by mega-expansion microscopy

Structural characterization of macromolecular assemblies within intact cells remains one of the central challenges in cell biology. While cryo-electron tomography provides unparalleled structural information, its applicability is limited by sample thickness, imaging throughput, and accessibility. Fluorescence microscopy offers molecular specificity and compatibility with intact biological specimens but has so far lacked the spatial resolution required to visualize cellular ultrastructure. Here we introduce Mega-expansion microscopy (Mega-ExM), a fluorescence imaging approach that enables structural visualization of whole cells using conventional confocal microscopes. Mega-ExM combines iterative hydrogel expansion with whole-proteome NHS-dye labeling and post-expansion immunostaining to achieve tunable expansion factors of up to [~]1,500-fold while preserving ultrastructure. At expansion factors of 40-260x, Mega-ExM resolves centrioles, mitochondrial cristae, protein-dense domains within mitochondrial cristae consistent with respiratory-chain supercomplexes, the synaptonemal complex, and nuclear pore complexes (NPCs) with high fidelity. Particle averaging of [~]200x expanded NPCs yields reconstructions with a structural resolution of [~]35 [A], approaching what cryo-electron tomography has achieved for selected protein assemblies. By combining molecular specificity, large imaging volumes, and nanoscale structural resolution on conventional fluorescence microscopes, Mega-ExM establishes a broadly accessible platform for in situ structural biology.

biophysics↗

bioRxiv2026

Stack: In-Context Learning of Single-Cell Biology

Foundation models trained on single-cell transcriptomic data offer the promise of identifying and predicting the diversity of cellular phenotypes across species, diseases, and other biological conditions. However, the current models are limited to their supervised training conditions and tasks, which limits their utility for biological discovery. Here, we present SO_SCPLOWTACKC_SCPLOW, a foundation model trained on 149 million uniformly preprocessed human single cells that leverages tabular attention to generate representations for each cell informed by the cells in its context. SO_SCPLOWTACKC_SCPLOW offers substantial improvements for downstream tasks in the zero-shot setting compared to baselines, whether they are zero-shot, fine-tuned, or trained from scratch on the target dataset. SO_SCPLOWTACKC_SCPLOW can perform in-context learning from unlabeled cells representing arbitrary conditions, such as a chemical perturbation or a different donor, and predict the effect of those conditions on a target cell population without requiring data-specific fine-tuning. We apply SO_SCPLOWTACKC_SCPLOW to generate Perturb Sapiens, the first human whole-organism atlas of perturbed cells, spanning 28 tissues, 40 cell types, and 892 drug, cytokine, and genetic perturbations. We validated subsets of Perturb Sapiens using in vitro stimulation profiles. SO_SCPLOWTACKC_SCPLOW uniquely empowers prioritization of donor-specific perturbation effects, a capability we validated in our newly collected DiseasePert-3M data, comprising T cells from 40 donors across 14 diseases, stimulated with 11 cytokines. Overall, SO_SCPLOWTACKC_SCPLOW presents a new modeling framework where cells themselves act as guiding examples at inference time, unlocking general-purpose in-context learning capabilities for single-cell biology.

bioinformatics↗

bioRxiv2020

Cell biological studies of ethanologenic bacterium Zymomonas mobilis

Alphaproteobacterium Zymomonas mobilis exhibits extreme ethanologenic physiology, making this species a promising biofuel producer. Numerous studies have investigated its biology relevant to industrial applications and mostly at the population level. However, the organization of single cells in this industrially important, polyploid species has been largely uncharacterized. In the present study, we characterized basic cellular behaviour of Z. mobilis strain Zm6 at a single cell level. We observed that growing Z. mobilis cells often divided at non mid-cell position, which contributed to variant cell size at birth. Yet, the cell size variance was regulated by a modulation of cell cycle span, mediated by a correlation of bacterial tubulin homologue FtsZ-ring accumulation with cell growth. The Z. mobilis culture also exhibited heterogeneous cellular DNA contents among individual cells, which might have been caused by asynchronous replication of chromosome that was not coordinated to cell growth. Furthermore, slightly angled divisions might have rendered temporary curvatures of attached Z. mobilis cells. Overall, the presented study uncovered a novel bacterial cell organization in Z. mobilis, the metabolism of which is not favoured for biosynthesis to build biomass. ImportanceWith increasing environmental concerns about the exhausting use of fossil fuels, a development of sustainable biofuel production platform has been attracting significant public attention. Ethanologenic Z. mobilis species are endowed with an efficient ethanol-fermentation capacity that surpass, in several aspects, that of the bakers yeast Saccharomyces cerevisiae, the most used microorganism for ethanol productions. For a development of Z. mobilis culture-based biorefinery, an investigation of its uncharacterized cell biology is important, because bacterial cellular organization and metabolism are closely associated with each other in a single cell compartment. In addition, the current work highlights that polyploid bacterium Z. mobilis exhibits a distinctive mode of bacterial cell organization, reflecting its unique metabolism that do not prioritize incorporation of nutrients to cell growth. Thus, another significance of presented work is to advance our general understanding in the diversity of bacterial cell architecture.

microbiology↗

bioRxiv2023

Assessing the limits of zero-shot foundation models in single-cell biology

The advent and success of foundation models such as GPT has sparked growing interest in their application to single-cell biology. Models like Geneformer and scGPT have emerged with the promise of serving as versatile tools for this specialized field. However, the efficacy of these models, particularly in zero-shot settings where models are not fine-tuned but used without any further training, remains an open question, especially as practical constraints require useful models to function in settings that preclude fine-tuning (e.g., discovery settings where labels are not fully known). This paper presents a rigorous evaluation of the zero-shot performance of these proposed single-cell foundation models. We assess their utility in tasks such as cell type clustering and batch effect correction, and evaluate the generality of their pretraining objectives. Our results indicate that both Geneformer and scGPT exhibit limited reliability in zero-shot settings and often underperform compared to simpler methods. These findings serve as a cautionary note for the deployment of proposed single-cell foundation models and highlight the need for more focused research to realize their potential.2

bioinformatics↗

bioRxiv2024

One step 4x and 12x 3D-ExM: robust super-resolution microscopy in cell biology

Super-resolution microscopy has become an indispensable tool across diverse research fields, offering unprecedented insights into biological architectures with nanometer scale resolution. Compared to traditional nanometer-scale imaging methods such as electron microscopy, super-resolution microscopy offers several advantages, including the simultaneous labeling of multiple target biomolecules with high specificity and simpler sample preparation, making it accessible to most researchers. In this study, we introduce two optimized methods of super-resolution imaging: 4-fold and 12-fold 3D-isotropic and preserved Expansion Microscopy (4x and 12x 3D-ExM). 3D-ExM is a straightforward expansion microscopy method featuring a single-step process, providing robust and reproducible 3D isotropic expansion for both 2D and 3D cell culture models. With standard confocal microscopy, 12x 3D-ExM achieves a lateral resolution of under 30 nm, enabling the visualization of nanoscale structures, including chromosomes, kinetochores, nuclear pore complexes, and Epstein-Barr virus particles. These results demonstrate that 3D-ExM provides cost-effective and user-friendly super-resolution microscopy, making it highly suitable for a wide range of cell biology research, including studies on cellular and chromatin architectures.

cell biology↗

bioRxiv2021

Single Cell Biological Microlasers Powered by Deep Learning

Cellular lasers are cutting-edge technologies for biomedical applications. Due to the enhanced interactions between light and cells in microcavities, cellular properties and subtle changes of cells can be significantly reflected by the laser emission characteristics. In particular, transverse laser modes from single-cell lasers which utilize Fabry-Perot cavities are highly correlated to the spatial biophysical properties of cells. However, the high chaotic and complex variation of laser modes limits their practical applications for cell detections. Deep learning technique has demonstrated its powerful capability in solving complex imaging problems, which is expected to be applied for cell detections based on laser mode imaging. In this study, deep learning technique was applied to analyze laser modes generated from single-cell lasers, in which a correlation between laser modes and physical properties of cells was built. As a proof-of-concept, we demonstrated the predictions of cell sizes using deep learning based on laser mode imaging. In the first part, bioinspired cell models were fabricated to systematically study how cell sizes affect the characteristics of laser modes. By training a convolutional neuron network (CNN) model with laser mode images, predictions of cell model diameters with a sub-wavelength accuracy were achieved. In the second part, deep learning was employed to study laser modes generated from biological cells. By training a CNN model with laser mode images acquired from astrocyte cells, predictions of cell sizes with a sub-wavelength accuracy were also achieved. The results show the great potential of laser mode imaging integrated with deep learning for cell analysis and biophysical studies.

biophysics↗

bioRxiv2026

Interconnecting ADC Structure with Tumor Cell Biology with Multimodal Learning

Antibody-drug conjugates (ADCs) represent a significant advancement in cancer therapy, yet their development remains constrained by high attrition rates driven by an incomplete understanding of how ADC chemical design interconnects with tumor biology. Compounding this challenge, the field has converged on a narrow set of redundant structural components, and current linker-payload systems that do not share a single mechanism of action. Existing drug response prediction frameworks cannot resolve this multidimensional complexity, relying predominantly on genomic inputs while protein-level biology is challenging to integrate. To address this, we developed a multimodal machine learning platform interconnecting ADC structural parameters with tumor cell biology across thousands of curated structure-activity datapoints, including multi-omics profiles from 1,479 human tumor cell lines and protein-level inputs from a unique model (GENCEP) that derives complete proteomic signatures. The model was validated through blinded retrospective evaluation and, critically, large coverage prospective prediction of cytotoxicity across 159 ADC-cell line combinations spanning five antigens, four mechanistically distinct linker-payload systems, and eight tumor types, most with no prior published associated ADC data. Performance surpassed industry benchmarks established for small molecule therapeutic modalities, demonstrating that protein-informed multimodal integrated framework is effective at capturing cytotoxic determinants at scale.

pharmacology and toxicology↗

bioRxiv2023

Genome sequence and cell biological toolbox of the highly regenerative, coenocytic green feather alga Bryopsis

Green feather algae (Bryopsidales) undergo a unique life cycle in which a single cell repeatedly executes nuclear division without cytokinesis, resulting in the development of a thallus (> 100 mm) with characteristic morphology called coenocyte. Bryopsis is a representative coenocytic alga that has exceptionally high regeneration ability: extruded cytoplasm aggregates rapidly in seawater, leading to the formation of protoplasts. However, the genetic basis of the unique cell biology of Bryopsis remains poorly understood. Here, we present a high-quality assembly and annotation of the nuclear genome of Bryopsis sp. (90.7 Mbp, 27 contigs, N50 = 6.7 Mbp, 14,034 protein-coding genes). Comparative genomic analyses indicate that the genes encoding BPL-1/Bryohealin, the aggregation-promoting lectin, are heavily duplicated in Bryopsis, whereas homologous genes are absent in other Ulvophycean algae, suggesting the basis of regeneration capability of Bryopsis. Bryopsis sp. possesses >30 kinesins but only a single myosin, which differs from other green algae that have multiple types of myosin genes. Consistent with this biased motor toolkit, we observed that the bidirectional motility of chloroplasts in the cytoplasm was dependent on microtubules but not actin in Bryopsis sp. Unexpectedly, most genes required for cytokinesis in plants are present in Bryopsis, including those in the SNARE or kinesin superfamily. Nevertheless, a kinesin crucial for cytokinesis initiation in plants (NACK/Kinesin-7II) is hardly expressed in the coenocytic part of the thallus, possibly underlying the lack of cytokinesis in this portion. The present genome sequence lays the foundation for experimental biology in coenocytic macroalgae. Significance statementThe exceptionally coenocytic body and remarkable regeneration ability of Bryopsis have attracted biologists for years. However, molecular biological tools remain underdeveloped, partly due to the lack of genome information. Here, we report high-quality assembly and annotation of the genome, providing a crucial resource for experimental biology and genomics studies of Bryopsis. Furthermore, comparative genomic analysis reveals a unique gene repertoire that possibly underlies the highly regenerative coenocytic body.

plant biology↗

bioRxiv2020

Transcriptomic and Proteomic analysis of clear cell foci (CCF) in the human non-cirrhotic liver identifies several differentially expressed genes and proteins with functions in cancer cell biology and glycogen metabolism.

Clear cell foci (CCF) of the liver are considered to be pre-neoplastic lesions of hepatocellular adenomas and carcinomas. They are hallmarked by glycogen-overload and activation of AKT/mTOR-signaling. Here we report the transcriptome and proteome of CCF extracted from human liver biopsies by laser capture microdissection. We mainly found genes and proteins with lower expression in CCF compared to control samples. Of 14 differentially expressed transcripts only 3 were overexpressed and belong to previously uncharacterized long non-coding RNAs. At the protein level, 3 proteins were at least 1.5-fold higher expressed in CCF than in control samples, while 22 proteins were at least 1.5-fold less abundant. Using immunohistochemistry, the reduced expressions of STBD1, USP28, monad/WDR92, CYB5B and HSPE1 were validated in CCF. Knockout of STBD1, the gene coding for Starch-binding domain-containing protein 1, in mice did not have an effect on liver glycogen levels. Similiarly, Usp28 knockout in mice did not affect glycogen storage in diethylnitrosamine-induced liver carcinoma. Thus, these data indicate that reduced expression of STBD1 or USP28 in CCF is unlikely to explain glycogen overload. Our study revealed several novel proteins and RNAs that are differentially expressed in CCF and have functions relevant for carcinogenesis or the glycogen metabolism.

cancer biology↗