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Results for “cell biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Perturb-seq identifies co-regulated gene programs shaping hematopoietic stem and progenitor cell function

To sustain blood formation, hematopoietic stem and progenitor cells (HSPCs) coordinate a multitude of cell biological processes, from cell cycle control and stress responses to lineage priming. While many genetic regulators of high-level HSPC function have been identified, how HSPCs coordinate more basal cell biological programs, and how such programs relate to stem cell function, remains incompletely understood. Here we use Perturb-seq to profile the transcriptional consequences of targeting 520 genes by CRISPRi in primary mouse HSPC cultures. We developed an analytical strategy to separate perturbation-induced changes in cell-state abundance and clonal heterogeneity from cell-state-local transcriptional effects. From these local perturbation signatures, we identified 19 gene regulatory programs (GRPs) that are defined by co-regulation in response to genetic perturbation, in contrast to co-expression or human curation, and align well with cell biological processes. By decomposing gene expression data from functional and clinical studies into program activity, we show that GRP activities associate with, and predict, phenotypes such as clonal output after transplantation, as well as survival and drug response in retrospective acute myeloid leukemia (AML) cohorts. Together, our study establishes perturbation-derived co-regulation programs as an interpretable framework for linking genetic regulators, cell-biological processes and stem-cell-associated phenotypes.

genomics↗

bioRxiv2017

Quantitative hybrid modeling reveals predictable mechanical stress response of growing tumor spheroids

Model simulations indicate that the response of growing cell populations on mechanical stress follows the same functional relationship and is predictable over different cell lines and growth conditions despite the response curves look largely different. We develop a hybrid model strategy in which cells are represented by coarse-grained individual units calibrated with a high resolution cell model and parameterized measurable biophysical and cell-biological parameters. Cell cycle progression in our model is controlled by volumetric strain, the latter being derived from a bio-mechanical relation between applied pressure and cell compressibility. After parameter calibration from experiments with mouse colon carcinoma cells growing against the resistance of an elastic alginate capsule, the model adequately predicts the growth curve in i) soft and rigid capsules, ii) in different experimental conditions where the mechanical stress is generated by osmosis via a high molecular weight dextran solution, and iii) for other cell types with varying doubling times. Our model simulation results suggest that the growth response of cell population upon externally applied mechanical stress is the same, as it can be quantitatively predicted using the same growth progression function.\n\nAuthor summaryThe effect of mechanical resistance on the growth of tumor cells remains today largely unquantified. We studied data from two different experimental setups that monitor the growth of tumor cells under mechanical compression. The existing data in the first experiment examined growing CT26 cells in an elastic permeable capsule. In the second experiment, growth of tumor cells under osmotic stress of the same cell line as well as other cell lines were studied. We have developed and agent-based model with measurable biophysical and cell-biological parameters that can simulate both experiments. Cell cycle progression in our model is a Hill function of cell volumetric strain, derived from a bio-mechanical relation between applied pressure and cell compressibility. After calibration of the model parameters within the data of the first experiment, we are able predict the growth rates in the second experiment. We show that that the growth response of cell populations upon externally applied mechanical stress in the two different experiments and over different cell lines can be predicted using the same growth progression function once the growth kinetics of the cell lines in abscence of mechanical stress is known.

biophysics↗

bioRxiv2025

The time of the quantitative biology in cell level: Increasing stomatal density to an intermediate level through over-expressing FSTOMAGEN improved the biomass of plants

Stomata are the pores on plant surface, and these tiny pores are responsible for the flow of gas between plants and atmosphere. Currently, whether quantifying stomatal density to a moderately high level via genetical manipulation could improve Arabidopsis biomass remains unknown. The Arabidopsis transgenic lines with increased stomatal density were acquired through overexpressing a gene (the homologs of STOMAGEN, FSTOMAGEN, the homologs in Flaveria). The biomass of Arabidopsis transgenic lines with intermediate stomatal density increased. Compared with the other lines, the biomass of the transgenic lines with excessive stomatal density exhibited no difference or decrease. There was a positive and significant correlation between biomass and relative water content. During the earlier phase of growth, the leaf growth rate and leaf area of the lines with higher stomatal density was faster and larger respectively, and there was a significant linear relationship between stomatal density and leaf growth rate and a strong linear relationship between stomatal density and leaf area, which was in agreement with the notion that more stomata indicated faster CO2 assimilation and then faster accumulation of biomass, although it occurs in earlier phase. This suggested that the increased biomass was attributed to the earlier phase. Results indicated that finely tuning stomatal density could improve Arabidopsis biomass by genetically manipulating FSTOMAGEN. The functions of STOMAGEN homologs are extremely conserved in the evolution, thus it is promising that this genetically modified strategy could be applied in other species, which benefits the reduction of the concentration of atmosphere CO2 and the production.

cell biology↗

bioRxiv2019

Interpretable Machine Learning for Perturbation Biology

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides an informative data resource for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in an enormously complex multi-dimensional solution space and to mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of dynamic cell biological processes with a machine learning framework, implemented in Tensorflow. We tested the modeling framework on a perturbation-response dataset for a melanoma cell line after drug treatments. The models can be efficiently trained to accurately describe cellular behavior, as tested by cross-validation. Even though completely data-driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The main predictive application is the identification of combinatorial candidates for cancer therapy. The approach is readily applicable to a wide range of kinetic models of cell biology.

systems biology↗

bioRxiv2025

Cation-controlled assembly, activity, and organisation of biomimetic DNA receptors in synthetic cell membranes

Biological cells use cations as signalling messengers to regulate a variety of responses. Linking cations to the functionality of synthetic membranes is thus crucial to engineering advanced biomimetic agents, such as synthetic cells. Here, we introduce bio-inspired DNA-based receptors that exploit non-canonical G-quadruplexes for cation-actuated structural and functional responses in synthetic lipid membranes. Membrane confinement grants cationdependent control over receptor assembly and, when supplemented with hemin co-factors, their peroxidase DNAzyme activity. Cationmediated control extends to receptor lateral distribution to localise DNA-based catalysis within phase-separated membrane domains of model synthetic cells, imitating the localisation of multimeric membrane complexes to signalling hubs in living cells. Our modular strategy paves the way for engineering from the bottom-up cation-responsive pathways for sensing, signalling, and communication in synthetic cellular systems.

biophysics↗

bioRxiv2020

An endometrial organoid model of Chlamydia-epithelial and immune cell interactions

Our understanding of how the obligate intracellular bacterium Chlamydia trachomatis reprograms the cell biology of host cells in the upper genital tract is largely based on observations made in cell culture with transformed epithelial cell lines. Here we describe a primary spherical organoid system derived from endometrial tissue to recapitulate epithelial cell diversity, polarity, and ensuing responses to Chlamydia infection. Using high-resolution and time-lapse microscopy, we catalogue the infection process in organoids from invasion to egress, including the reorganization of the cytoskeleton and positioning of intracellular organelles. We show this model is amenable to screening C. trachomatis mutants for defects in the fusion of pathogenic vacuoles, the recruitment of intracellular organelles, and inhibition of cell death. Moreover, we reconstructed a primary immune cell response by co-culturing infected organoids with neutrophils, and determined that the effector TepP limits the recruitment of neutrophils to infected organoids. Collectively, our model details a system to study the cell biology of Chlamydia infections in three dimensional structures that better reflect the diversity of cell types and polarity encountered by Chlamydia upon infection of their animal hosts. Summary statement3D endometrial organoids to model Chlamydia infection and the role of secreted virulence factors in reprogramming host epithelial cells and immune cell recruitment

cell biology↗

bioRxiv2022

High level and molecular nature of transcriptional noise in yeast cells

"Biological noise" is defined as functionally insignificant events that occur in living cells due to imperfect fidelity of biological processes. Distinguishing between biological function and biological noise is often difficult, and experiments to measure biological noise have not been performed. Here, we measure biological noise in yeast cells by analyzing chromatin structure and transcription of an 18 kb region of DNA whose sequence was randomly generated and hence lacks biological function. Nucleosome occupancy on random-sequence DNA is comparable to that on yeast genomic DNA. However, nucleosome-depleted regions are much less frequent, and there are fewer well-positioned nucleosomes and shorter nucleosome arrays. Steady-state levels of RNAs expressed from random-sequence DNA are comparable to those of typical yeast mRNAs, although transcription and mRNA decay rates are higher. Transcriptional initiation (5 ends) from random-sequence DNA occurs at numerous sites at low levels, indicating very low intrinsic specificity of the Pol II machinery. In contrast, poly(A) profiles (relative levels and clustering of 3 isoforms) of random-sequence RNAs are roughly comparable to those within 3 untranslated regions of yeast mRNAs, suggesting limited evolutionary constraints on poly(A) site choice. RNAs expressed from random-sequence DNA show higher cell-to-cell variability than RNAs expressed from yeast genomic DNA, suggesting that functional elements limit the variability among individual cells within a population. These observations indicate that transcriptional noise occurs at high levels in yeast, and they provide insight into how chromatin and transcription patterns arise from the evolved yeast genome.

molecular biology↗

bioRxiv2024

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

Mass spectrometry (MS)-based single-cell proteomics (SCP) has gained massive attention as a viable complement to other single cell approaches. The rapid technological and computational advances in the field have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell at reasonable proteome depth to characterize biological phenomena remains a challenge. To address some of those limitations we present a combination of fully automated single cell sample preparation utilizing a dedicated chip within the picolitre dispensing robot, the cellenONE. The proteoCHIP EVO 96 can be directly interfaced with the Evosep One chromatographic system for in-line desalting and highly reproducible separation with a throughput of 80 samples per day. This, in combination with the Bruker timsTOF MS instruments, demonstrates double the identifications without manual sample handling. Moreover, relative to standard high-performance liquid chromatography, the Evosep One separation provides further 2-fold improvement in protein identifications. The implementation of the newest generation timsTOF Ultra with our proteoCHIP EVO 96-based sample preparation workflow reproducibly identifies up to 4,000 proteins per single HEK-293T without a carrier or match-between runs. Our current SCP depth spans over 4 orders of magnitude and identifies over 50 biologically relevant ubiquitin ligases. We complement our highly reproducible single-cell proteomics workflow to profile hundreds of lipopolysaccharide (LPS)-perturbed THP-1 cells and identified key regulatory proteins involved in interleukin and interferon signaling. This study demonstrates that the proteoCHIP EVO 96-based SCP sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.

biochemistry↗

bioRxiv2024

NeuroSCAN: Exploring Neurodevelopment via Spatiotemporal Collation of Anatomical Networks

Volume electron microscopy (vEM) datasets such as those generated for connectome studies allow nanoscale quantifications and comparisons of the cell biological features underpinning circuit architectures. Quantifying cell biological relationships in the connectome yields rich, multidimensional datasets that benefit from data science approaches, including dimensionality reduction and integrated graphical representations of neuronal relationships. We developed NeuroSC (also known as NeuroSCAN) an open source online platform that bridges sophisticated graph analytics from data science approaches with the underlying cell biological features in the connectome. We analyze a series of published C. elegans brain neuropils and demonstrate how these integrated representations of neuronal relationships facilitate comparisons across connectomes, catalyzing new insights into the structure-function relationships of the circuits and their changes during development. NeuroSC is designed for intuitive examination and comparisons across connectomes, enabling synthesis of knowledge from high-level abstractions of neuronal relationships derived from data science techniques to the detailed identification of the cell biological features underpinning these abstractions.

neuroscience↗

bioRxiv2020

No evidence that plasmablasts transdifferentiate into developing neutrophils in severe COVID-19 disease

A recent study by Wilk et al. of the transcriptome of peripheral blood mononuclear cells (PBMCs) in seven patients hospitalized with COVID-19 described a population of "developing neutrophils" that were "phenotypically related by dimensionality reduction" to plasmablasts, and that these two cell populations represent a "linear continuum of cellular phenotype"1. The authors suggest that, in the setting of acute respiratory distress syndrome (ARDS) secondary to severe COVID-19, a "differentiation bridge from plasmablasts to developing neutrophils" connected these distantly related cell types. This conclusion is controversial as it appears to violate several basic principles in cell biology relating to cell lineage identity and fidelity. Correctly classifying cells and their developmental history is an important issue in cell biology and we suggest that this conclusion is not supported by the data as we show here that: (1) regressing out covariates such as unique molecular identifiers (UMIs) can lead to overfitting; and (2) that UMAP embeddings may reflect the expression of similar genes but not necessarily direct cell lineage relationships.

genomics↗

bioRxiv2019

Clickable methionine as a universal probe for labelling intracellular bacteria

Despite their clinical and biological importance, the cell biology of obligate intracellular bacteria is less well understood than that of many free-living model organisms. One reason for this is that they are mostly genetically intractable. As a consequence, it is not possible to engineer strains expressing fluorescent proteins and therefore fluorescence light microscopy - a key tool in host-pathogen cell biology studies - is difficult. Strain diversity limits the universality of antibody-based immunofluorescence approaches. Here, we have developed a universal labelling protocol for intracellular bacteria based on a clickable methionine analog. Whilst we have applied this to obligate intracellular bacteria, we expect it to be useful for labelling free living bacteria as well as other intracellular pathogens.

microbiology↗

bioRxiv2025

Diatom ultrastructural diversity across controlled and natural environments

Diatoms are ubiquitous aquatic microalgae critical to our planet, that were amongst the pioneer model organisms in cell biology for their large and transparent cell structure. However, their robust silica cell wall renders diatoms impermeable to many dyes and antibodies, and complicates the intracellular delivery of gene editing tools - driving in part the eventual decline of diatoms as mainstream model species despite their unique cellular physiology and remarkable ecological success. Here, we demonstrate that cryo-fixation combined with ultrastructural expansion microscopy (cryo-ExM) can overcome the silica barrier across diverse diatom species spanning over 80 million years of evolutionary time. We illustrate the potential of cryo-ExM to provide scalable, cost-effective volumetric imaging of diatom ultrastructure in laboratory cultures, as well as field-collected samples from the pan-European TREC expedition. We first reveal striking similarities in interphase microtubule organization across diverse diatom species by characterizing cytoskeletal arrangements throughout cell cycles and populations, uniting both pennate and centric morphologies under shared principles. We further unveil diatom photosynthetic diversity through qualitative and quantitative comparative analysis of chloroplast and pyrenoid morphologies, demonstrating that each diatom species architects unique photosynthetic machinery. Using cryo-ExM on environmental samples further exposes intricate diatom symbioses, revealing tight spatial organisation of ecological interactions. This methodology makes diatoms more accessible for modern and comparative cell biology research, providing new opportunities to investigate the cellular mechanisms of one of Earths most successful photosynthetic groups. HighlightsO_LICryo-ExM successfully overcomes diatom frustule barriers to achieve consistent, high-resolution immunofluorescence across evolutionarily diverse species - allowing for comparative cell biology in one of the most important phytoplankton groups on the planet. C_LIO_LICharacterizing microtubule organisation in asynchronously cycling single cells and colonial chains, reveals core features of interphase microtubule organisation across multiple pennate and centric diatom species. C_LIO_LIComparative analysis of chloroplast and pyrenoid morphologies across species provides qualitative and quantitative insights on photosynthetic diversity, suggesting that photosynthetic architectures are unique to each diatom. C_LIO_LIApplied to environmental samples, cryo-ExM provides detailed insight on spatial organisation of diatom symbioses. C_LI

cell biology↗

bioRxiv2026

From prediction to engagement: defining tumor-reactive T cells through biological interaction

Adoptive T-cell therapy (ACT), particularly tumor-infiltrating lymphocyte (TIL) therapy, demonstrates that durable regression of solid tumors can occur when polyclonal T cells target authentic tumor antigens, including private neoantigens. Yet scalable identification of tumor-reactive clonotypes remains limited because the relevant cells are rare, patient-specific, and poorly captured by indirect markers, expansion behavior, or predictive algorithms. We examined whether productive engagement between T cells and tumor cells or antigen-presenting cells provides a more direct organizing principle for tumor-reactive T-cell discovery. Across published tumor and blood datasets reanalyzed here, T cells recovered through physical clustering, target-cell extraction, or antigen-dependent activation are enriched for clonotypes occupying antigen-experienced, progenitor-like states with restrained cytotoxic differentiation. Productive engagement can therefore serve as a practical enrichment variable, enabling recovery of rare neoantigen-reactive T cells from circulation while preserving developmental states associated with persistence and therapeutic responsiveness. These analyses support a shift in tumor-specific T-cell discovery from prediction-first nomination to interaction-first recovery: enrich for T cells bearing evidence of prior tumor-antigen encounter in vivo, recover clonotypes preserved in a restrained progenitor-like state, expand them before culture competition erases them, and reinforce them therapeutically with matched antigenic information.

immunology↗

bioRxiv2025

A new framework for analyzing mass transport in cortical brain tissue at <10 nm resolution

Transmission electron microscopy of brain tissue yields high resolution maps of the spatial organization of cells in the central nervous system. Automated segmentation identifies distinct biological cells and the resulting segmented images are easily converted into surface meshes. The surface meshes are structural models of cell surfaces, providing a framework for modeling of mass transport within the interstitial fluid of brain tissue that surrounds the cells. Our goal is to model the production and clearance of proteins implicated in the development of Alzheimers Disease. This work introduces a new custom computational framework to allow massive parallelization of the solution of the mathematical equations of mass transfer. The diffusion equation was solved directly on the surface meshes of multiple biological cells in parallel, with exchange of mass across co-localized faces at the end of each time step. Mass transfer across the interfaces of multiple analysis volumes was incorporated using a semi-implicit approach. To demonstrate the capabilities of the framework, unsteady mass transfer along cell surfaces was modeled in an array of eight 4 x 4 x 4 m analysis volumes containing 2175 biological cells at 8 nm resolution, consisting of 313 million face elements. Areas of enhanced and hindered diffusive transport were identified, suggesting structural motifs that may contribute to the development of insoluble plaques. The tortuosity and fractional anisotropy were consistent with Diffusion Tensor Imaging (DTI) measurements in cortical tissue. This new framework allows modeling of diffusive mass transport while preserving anatomical details at <10 nm resolution. Author summaryA framework was developed to model mass transport around thousands of biological cells in parallel. This may be useful to identify structural features in cortical tissue that are more prone to the development of amyloid plaques.

bioengineering↗

bioRxiv2025

Microbial Cell-Adaptive Segmentation in Quantitative Phase Imaging

Quantitative phase imaging (QPI) enables label-free microscopy with exceptional signal-to-background contrast and provides direct measurements of key biophysical parameters such as cell mass. Despite these advantages, QPI lacks a generalized, accurate, and computationally efficient segmentation method, limiting its use in high-throughput single-cell biology. Here, we introduce microbial Cell-Adaptive Segmentation (mCAS), a purpose-built framework for QPI. mCAS is an object-based, modular pipeline that leverages the robust phase-edge signal to achieve accurate segmentation without Fourier filtering, deconvolution, or deep learning. It runs efficiently on standard hardware, is readily implemented in common software platforms, and requires no curated training data. mCAS consistently outperformed both intensity-based and deep learning approaches, achieving over 98% accuracy with up to tenfold faster processing. By eliminating the segmentation bottleneck, mCAS enables scalable, high-fidelity analysis of QPI datasets and provides a broadly applicable foundation for label-free, high-throughput single-cell biology. We validate its generality across diverse bacterial species, morphologies, and intracellular complexities. Significance StatementQuantitative phase imaging (QPI) enables label-free, noninvasive measurement of cellular biophysics, yet its broader adoption has been limited by the absence of a generalized and computationally efficient segmentation strategy. We present microbial Cell-Adaptive Segmentation (mCAS), a framework purpose-built for QPI that achieves high accuracy without training, specialized hardware, or intensive preprocessing. mCAS generalizes across microbial species and morphologies, outperforming state-of-the-art deep learning approaches while processing thousands of single-cell images in minutes. By eliminating the segmentation bottleneck, mCAS makes QPI broadly accessible for high-throughput, label-free single-cell biology and expands the computational toolkit for quantitative imaging in microbiology.

systems biology↗

bioRxiv2025

Unlocking biological insight from single-cell data with an interpretable dual-stream foundation model

Deep learning foundation models are revolutionizing single-cell biology, yet learning holistic and discriminative representations from complex, high-dimensional data remains a central challenge. Although Transformer-based single-cell language models have shown significant progress, they typically rely on a single input-encoding scheme, a practice that results in the loss of critical gene expression information and hinders the effective learning of global cellular representations. To address these challenges, we introduce scDMC, an innovative single-cell Dual-stream Masked Contrastive pre-training framework designed to synergistically optimize information fidelity at both the gene and cellular levels. Pre-trained on only 2 million cells far fewer than the datasets used by mainstream models, scDMC sets a new state-of-the-art in multiple benchmark tasks, including cell annotation, clustering, and data integration. More importantly, we demonstrate that scDMC can uncover functional gene modules, infer cell-type-specific regulatory networks in a data-driven manner, and exhibits a high degree of biological interpretability.

bioinformatics↗

bioRxiv2026

Biologically grounded cell profiling across microscopy modalities

Microscopy-based cell profiling has broad applications in biological discovery, disease characterization, and phenotypic drug screening. Modern microscopy continues to push the limits of resolution, speed, depth and throughput, but better imaging does not automatically lead to better biomedical discovery and translation. A key bottleneck is feature representation: existing features are either handcrafted or learned as black-box embeddings and often lack explicit biological meaning. Here we propose biological grounding as a first principle for cell profiling and implement it through MorphAgent, an AI agent framework in which each quantitative feature originates from a biological hypothesis and is anchored to a cellular structure or process. By integrating biological knowledge, multimodal reasoning and automated validation, MorphAgent makes biologically grounded feature design systematic and scalable. We show biological grounding fundamentally changes the properties of cell profiling features. Across three microscopy modalities, MorphAgent produces compact, expressive and transferable features for drug screening, cell state characterization and disease morphology analysis. In wide-field Cell Painting, a common assay for phenotypic drug screening, it improves perturbation-retrieval mean average precision by 48% over CellProfiler and 20% over DeepProfiler while using substantially fewer dimensions. In confocal mitochondrial imaging, biologically grounded features transfer across independently acquired datasets and imaging resolutions, supporting accurate aging-state classification. In structured illumination microscopy (SIM) imaging of Tau-labeled samples, biologically grounded super resolution features reveal nanoscale morphologies inaccessible at conventional resolution, enhancing the discrimination of disease-associated mutations. Moreover, biologically grounded features provide a hierarchical organization of cellular morphology, enabling multilevel alignment with transcriptomics data and facilitating mechanistic understanding. Biologically grounded cell profiling thus brings AI reasoning closer to biological mechanisms, advancing biomedical discovery and translation.

bioinformatics↗

bioRxiv2017

Microfluidic Transfection for High-Throughput Mammalian Protein Expression

Mammalian synthetic biology and cell biology would greatly benefit from improved methods for highly parallel transfection, culturing and interrogation of mammalian cells. Transfection is routinely performed on high-throughput microarrays, but this setup requires manual cell culturing and precludes precise control over the cell environment. As an alternative, microfluidic transfection devices streamline cell loading and culturing. Up to 280 transfections can be implemented on the chip at high efficiency. The culturing environment is tightly regulated and chambers physically separate the transfection reactions, preventing cross-contamination. Unlike typical biological assays that rely on end-point measurements, the microfluidic chip can be integrated with high-content imaging, enabling the evaluation of cellular behavior and protein expression dynamics over time.

molecular biology↗