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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Capybara: A computational tool to measure cell identity and fate transitions

Transitions in cell identity are fundamental to development, reprogramming, and disease. Single-cell technologies enable the dissection of tissue composition on a cell-by-cell basis in complex biological systems. However, highly-sparse single-cell RNA-seq data poses challenges for cell-type identification algorithms based on bulk RNA-seq. Single-cell analytical tools are also limited, where they require prior biological knowledge and typically classify cells in a discrete, categorical manner. Here, we present a computational tool, Capybara, designed to measure cell identity as a continuum, at single-cell resolution. This approach enables the classification of discrete cell entities but also identifies cells harboring multiple identities, supporting a metric to quantify cell fate transition dynamics. We benchmark the performance of Capybara against other existing classifiers and demonstrate its efficacy to annotate cells and identify critical transitions within a well-characterized differentiation hierarchy, hematopoiesis. Our application of Capybara to a range of reprogramming strategies reveals previously uncharacterized regional patterning and identifies a putative in vivo correlate for an engineered cell type that has, to date, remained undefined. These findings prioritize interventions to increase the efficiency and fidelity of cell engineering strategies, showcasing the utility of Capybara to dissect cell identity and fate transitions. Capybara code and documentation are available at https://github.com/morris-lab/Capybara.

genomics↗

bioRxiv2021

Relevance of network topology for the dynamics of biological neuronal networks

Complex random networks provide a powerful mathematical framework to study high-dimensional physical and biological systems. Several features of network structure (e.g. degree correlation, average path length, clustering coefficient) are correlated with descriptors of network dynamics and function. However, it is not clear which features of network structure relate to the dynamics of biological neuronal networks (BNNs), characterized by non-linear nodes with high in- and out degrees, but being weakly connected and communicating in an event-driven manner, i.e. only when neurons spike. To better understand the structure-dynamics relationship in BNNs, we analysed the structure and dynamics of > 9, 000 BNNs with different sizes and topologies. In addition, we also studied the effect of network degeneration on neuronal network structure and dynamics. Surprisingly, we found that the topological class (random, small-world, scale-free) was not an indicator of the BNNs activity state as quantified by the firing rate, network synchrony and spiking regularity. In fact, we show that different network topologies could result in similar activity dynamics. Furthermore, in most cases, the network activity changes did not depend on the rules according to which neurons or synapses were pruned from the networks. The analysis of dynamics and structure of the networks we studied revealed that the effective synaptic weight (ESW) was the most crucial feature in predicting the statistics of spiking activity in BNNs. ESW also explained why different synapse and neuron pruning strategies resulted in almost identical effects on the network dynamics. Thus, our findings provide new insights into the structure-dynamics relationships in BNNs. Moreover, we argue that network topology and rules by which BNNs degenerate are irrelevant for BNN activity dynamics. Beyond neuroscience, our results suggest that in large networks with non-linear nodes, the effective interaction strength among the nodes, instead of the topological network class, may be a better predictor of the network dynamics and information flow.

neuroscience↗

bioRxiv2021

The Incubascope : a simple, compact and large field of view microscope for long-term imaging inside an incubator

Optical imaging has rapidly evolved in the last decades. Sophisticated microscopes allowing optical sectioning for 3D imaging or sub-diffraction resolution are available. Due to price and maintenance issues, these microscopes are often shared between users in facilities. Consequently, long term access is often prohibited and does not allow to monitor slowly evolving biological systems or to validate new models like organoids. Preliminary coarse long-term data that do not require acquisition of terabytes of high-resolution images are important as a first step. By contrast with expansive all-in-one commercialized stations, standard microscopes equipped with incubator stages offer a more cost-effective solution despite imperfect long run atmosphere and temperature control. Here, we present the Incubascope, a custom-made compact microscope that fits into a table-top incubator. It is cheap and simple to implement, user-friendly and yet provides high imaging performances. The system has a field of view of 5.5 x 8 mm2, a 3 m resolution, a 10 frames/second acquisition rate, and is controlled with a Python-based graphical interface. We exemplify the capabilities of the Incubascope on biological applications such as the hatching of Artemia salina eggs, the growth of the slime mold Physarum polycephalum and of encapsulated spheroids of mammalian cells.

biophysics↗

bioRxiv2024

Functional yeast promoter sequence design using temporal convolutional generative language models

Functional promoter sequence design plays a crucial role in accurately controlling gene expression processes that are one of the most fundamental mechanisms in biological systems. Thanks to the recent community effort, we are now able to elucidate the associations between yeast promoter sequences and their corresponding expression levels using advanced deep learning methods. This milestone boosts the further development of many downstream biological sequence research tasks including synthetic DNA sequence design. In this work, we propose a novel synthetic promoter sequence generation method, namely Gen-DNA-TCN, which exploits a pre-trained sequence-to-expression predictive model to facilitate its autoregressive generative model training. A large-scale evaluation confirms that Gen-DNA-TCN successfully generates a large number of unique, diverse and functional synthetic yeast promoter sequences that also encode similar transcription factor binding site distributions compared with real yeast promoter sequences.

bioinformatics↗

bioRxiv2025

The Hidden Biology of Wellbeing: A Multi-Omics Analysis Across Genomic, Epigenomic, and Transcriptomic Layers.

Biological systems are composed of multiple molecular layers, including the genome, epigenome, transcriptome, and metabolome. These layers are interconnected and interact, but are often studied independently. Multi-omics approaches aim to integrate these layers to better understand shared biological processes and their roles in complex traits, such as wellbeing. This project used data from 2,320 participants from the Netherlands Twin Register (NTR), which includes genome, epigenome, and transcriptome data from the same individuals, providing a rare opportunity to study interactions between omics layers. Multi-Omics Factor Analysis (MOFA) was applied, an unsupervised dimensionality reduction method, to identify latent factors that capture shared variation across omics layers. These factors may reflect underlying biological mechanisms not directly observed in individual omics layers. The phenotypic focus of this study is wellbeing, a multifactorial trait involving emotional, psychological, and social dimensions. Following MOFA modeling, one latent factor showed to be statistically significantly associated with wellbeing. After studying the latent factor, it mainly seemed to capture epigenetic variation. The association between the latent factor and wellbeing did not remain significant after correcting for relevant covariates. The association was mainly driven by age, a well-known confounder of epigenetic signal. By adopting a multi-omics framework, this study managed to move beyond traditional single-omics, phenotype-driven approaches and provided a more integrated approach of how biological layers may jointly influence (or not) complex traits like wellbeing.

molecular biology↗

bioRxiv2016

Widespread Historical Contingency in Influenza Viruses

In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular disease-causing mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally-validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains, a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.

Evolutionary Biology↗

bioRxiv2020

Cluster similarity spectrum integration of single-cell genomics data

Technologies to sequence the transcriptome, genome or epigenome from thousands of single cells in an experiment provide extraordinary resolution into the molecular states present within a complex biological system at any given moment. However, it is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, timepoints and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, Cluster Similarity Spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid single-cell transcriptomic data, and to integrate data across experimental conditions and human individuals. We compare CSS to other integration algorithms and show that it can outperform other methods in certain integration scenarios. We also show that CSS allows projection of single-cell genomic data of different modalities to the CSS-represented reference atlas for visualization and cell type identity prediction. In summary, CSS provides a straightforward and powerful approach to understand and integrate challenging single-cell multi-omic data.

bioinformatics↗

bioRxiv2021

Enhanced laterally resolved ToF-SIMS and AFM imaging of the electrically conductive structures in cable bacteria

Cable bacteria are electroactive bacteria that form a long, linear chain of ridged cylindrical cells. These filamentous bacteria perform centimeter-scale long-range electron transport through parallel, interconnected conductive pathways of which the detailed chemical and electrical properties are still unclear. Here, we combine ToF-SIMS (time of flight secondary ion mass spectrometry) and AFM (atomic force microscopy) to investigate the structure and composition of this naturally-occurring electrical network. The enhanced lateral resolution achieved allows differentiation between the cell body and the cell-cell junctions that contain a conspicuous cartwheel structure. Three ToF-SIMS modes were compared in the study of so-called fiber sheaths (i.e., the cell material that remains after removal of cytoplasm and membranes and which embeds the electrical network). Among these, fast imaging delayed extraction (FI-DE) was found to balance lateral and mass resolution, thus yielding multiple benefits in the study of structure-composition relations in cable bacteria: (i) it enables the separate study of the cell body and cell-cell junctions, (ii) by combining FI-DE with in-situ AFM, the depth of Ni-containing protein - key in the electrical transport - is determined with greater precision, and (iii) this combination prevents contamination, which is possible when using an ex-situ AFM. Our results imply that the interconnects in extracted fiber sheaths are either damaged during extraction, or that their composition is different from fibers, or both. From a more general analytical perspective, the proposed methodology of ToF-SIMS in FI-DE-mode combined with in-situ AFM holds great promise for studying the chemical structure of other biological systems.

cell biology↗

bioRxiv2024

NOODAI: A webserver for network-oriented multi-omics data analysis and integration pipeline

Omics profiling has proven of great use for unbiased and comprehensive identification of key features that define biological phenotypes and underlie medical conditions. While each omics profile assists characterization of specific molecular components relevant for the studied phenotype, their joint evaluation can offer deeper insights into the overall mechanistic functioning of biological systems. Here, we introduce an approach where starting from representative traits (e.g., differentially expressed elements) obtained for each omics profile, we construct and analyze joint interaction networks. The resulting networks rely on the existing knowledge of confident interactions among biological entities. We use these maps to identify and describe central elements, which connect multiple entities characteristic for the studied phenotypes and we leverage MONET network decomposition tool in order to highlight functionally connected network modules. In order to enable broad usage of this approach, we developed the NOODAI software platform, which enables integrative omics analysis through a user-friendly interface. The analysis outcomes are presented both as raw output tables as well as high-quality summary plots and written reports. Since the MONET tool enables the use of algorithms with strong performance in identifying disease-relevant modules, the NOODAI software platform can be of a high value for the analysis of clinical multi-omics datasets. Availability and ImplementationThe platform is available as a web application freely accessible at https://omics-oracle.com. The source code is freely available from GitHub under the GPL3 license at: https://github.com/TotuTiberiu/NOODAI.

bioinformatics↗

bioRxiv2025

High-throughput screening for class I peptide MHC binding via yeast surface display

T cells rely on short peptides presented by highly polymorphic major histocompatibility complexes (MHCs) to selectively initiate adaptive immune responses. Despite its importance, few techniques can systemically evaluate stable peptide presentation across diverse MHC alleles. Here, we describe a yeast display pipeline that can be deployed to rapidly screen proteomic space to identify class I pMHC binders across many alleles. Through this, we capture unique biological phenomena such as interference with peptide presentation via type IV drug-induced hypersensitivity. We apply this approach to multiple pathogen proteomes (Mtb Type 7S substrates, SARS-CoV-2, Dengue, and Zika) to create a high-resolution catalog of potential T cell antigens. Altogether, this platform acts as a flexible tool to generate large unbiased datasets for class I peptide presentation at a speed and scale competitive with the biological systems they represent.

immunology↗

bioRxiv2026

Quorum-Sensing-Mediated Extracellular Electron Transfer Enables Hydrogel Morphogenesis

Engineered living materials seek to capture the sensitivity, responsiveness, and programmable characteristics of biological systems. One emergent property of living systems is genetically driven spatial patterning, which controls cell differentiation and the development of complex multicellular organisms. Inspired by this capability, we use bacteria to spatially control material assembly. In our system, extracellular electron transfer (EET) flux from Shewanella oneidensis drives hydrogel synthesis via copper-catalyzed radical polymerization. We first construct a recombinant quorum sensing system in S. oneidensis that allows for cell-cell communication between "sender" and "receiver" cells through an autoinducer. We then examine controlled gene expression and EET-driven chemical transformation in various synthetic consortia. Via diffusion through agarose, we examine 2D patterns of gene expression relative to localized sender cell populations and demonstrate controlled hydrogel crosslinking in predictable patterns. Finally, we apply computational methods and NOT logic in "receiver" cells towards more complex patterns of gene expression. Our results highlight the potential of bacteria to program material systems with life-like properties including self-assembly, environmental responsiveness, and patterned differentiation.

Synthetic Biology↗

bioRxiv2020

Lyophilized cell-free systems display tolerance to organic solvent exposure

Cell-free systems offer a powerful way to deliver biochemical activity to the field without cold chain storage. These systems are capable of sensing as well as biosynthesis of useful molecules at the point of need. So far, cell-free protein synthesis (CFPS) reactions have been studied as aqueous solutions in test tubes or absorbed into paper or cloth. Embedding biological functionality into broadly-used materials, such as plastic polymers, represents an attractive goal. Unfortunately, this goal has for the most part remained out of reach, presumably due to the fragility of biological systems outside of aqueous environments. Here, we describe a surprising and useful feature of lyophilized cell-free lysate systems: tolerance to a variety of organic solvents. Screens of individual CFPS reagents and different CFPS methods reveal that solvent tolerance varies by CFPS reagent composition. Tolerance to suspension in organic solvents may facilitate the use of polymers to deliver dry cell-free reactions in the form of coatings or fibers, or allow dosing of analytes or substrates dissolved in non-aqueous solvents, among other processing possibilities.Competing Interest StatementThe authors have declared no competing interest.AbbreviationsCFPScell-free protein synthesisGFPgreen fluorescent proteinDCMdichloromethaneDMFdimethylformamideDMSOdimethylsulfoxideTHFtetrahydrofuranView Full Text

synthetic biology↗

bioRxiv2024

Social interactions are impacted by food availability, food type, and group size

Social interactions are important for how societies function, conferring robustness and resilience to environmental changes. The structure of social interactions can shape the dynamics of information and goods transmission. In addition, the availability and type of resources that are transferred might impact the structure of interaction networks. For example, storable resources might reduce the required speed of distribution and altering interaction structure can facilitate such change. Here we use ants as a model system to examine how social interactions are impacted by group size, food availability, and food type. We compare global- and individual-level network measures across experiments in which groups of different sizes received limited or unlimited food that is either favorable and cannot be stored (carbohydrates), or unfavorable but with a potential of being stored (protein). We found that as group size increased, individuals interacted with more social partners and interaction networks became more compartmentalized. Furthermore, group compartmentalization increased when food was limited and when transferring storable goods. Our findings highlight how biological systems can adjust their interaction networks in ways that relate to their function. The study of such biological flexibility can inspire novel and important solutions to the design of robust and resilient supply chains.

animal behavior and cognition↗

bioRxiv2026

Resolving multivalent antibody binding states with defined spatial antigen-patterning

Multivalent interactions are fundamental to many biological systems, including how antibodies bind to their respective antigens. Still, their accumulated binding strength, i.e. avidity, resulting from binding and rebinding events of multiple interacting points, remains difficult to measure. Classical assay platforms like ELISA and SPR lack control over antigen positioning, limiting the resolution of the binding characteristics that shape biological outcomes at nanoscale. Here, we present PANMAP, a planar and plate-based assay inspired by ELISA that uses DNA origami to present antigens at defined nanoscale patterns, enabling direct measurements of spatially resolved binding events, termed avidity profiles. Using IgG antibodies as a model system, PANMAP distinguishes between monovalent and bivalent binding states by combining equilibrium absorbance measurements with a simple biophysical model. The avidity profiles reveal how the balance shifts between monovalent and bivalent interactions as a function of antibody concentration and antigen spacing, with intermediate separation distances favoring bivalent binding events and excluded at both near and far distances. This spatial profiling allows decoupling of affinity dependency from avidity profiles to reveal how spatial constraints influence binding equilibria. Our approach fills a longstanding gap in multivalent interaction measurement and offers a new tool for antibody engineering, development of multivalent reagents, therapeutic screening, and mechanistic immunology.

immunology↗

bioRxiv2025

DynPerturb:Dynamic Perturbation Modeling for Spatiotemporal Single-Cell Systems

Perturbation responses in multicellular systems are inherently spatiotemporal, governed by temporal dynamics, perturbation intensity, and tissue context. However, existing approaches rarely capture explicit spatiotemporal perturbation dynamics, limiting the ability to reconstruct how perturbations propagate over time, spread across space, and induce feedback at multiple biological scales. Here we introduce DynPerturb, a dynamic inference framework for single-cell and spatial transcriptomics that systematically models the "when-how strong-where" dimensions of perturbations. DynPerturb incorporates temporal encoding, spatial adjacency, and memory modules to capture nonlinear signal propagation and cross-regional feedback with mechanistic interpretability. Across temporal benchmarking datasets, DynPerturb consistently outperformed existing methods in regulatory link prediction, demonstrated zero-shot generalization to unseen nodes, and preserved accuracy under varying sparsity conditions and temporal resolutions. Applied to representative biological systems, DynPerturb uncovers dynamic regulatory rewiring in kidney disease, highlighting a mid-age sensitivity window and potential reversibility with early intervention; delineated a temporal fate-switching boundary between megakaryocyte-erythroid progenitors (MEPs) and granulocyte-monocyte progenitors (GMPs) during hematopoiesis; and revealed spatiotemporal interchamber signaling in murine heart development, where Igf2 perturbations induced spatial feedback shaping chamber proportioning. Collectively, these results establish DynPerturb as a unified and versatile system for dissecting dynamic perturbation effects and guiding precision intervention strategies in multicellular systems.

bioinformatics↗

bioRxiv2026

Far-from-equilibrium assembly of multimers through DNA-based catalytic templating

On-demand assembly of arbitrary, sequence-defined polymers from a pool of monomers is a major challenge in modern chemistry, towards which limited progress has been made. By contrast, biological systems routinely use information-bearing DNA and RNA templates to catalytically synthesize a precise, far-from-equilibrium ensemble of nucleic acid and protein sequences from the available pools of nucleoside triphosphates or aminoacyl-tRNAs. Inspired by these biological examples, we introduce an enzyme-free DNA strand displacement network in which single-stranded DNA sequences template the assembly of specific non-covalent DNA multimers of up to length five, under isothermal and autonomous conditions. The templates overcome product inhibition and can thereby catalyse the formation of a far-from-equilibrium ensemble of long-lived metastable products that are not otherwise addressable.

biophysics↗

bioRxiv2022

Mechanistic Deep Learning Framework on Cell Traits Derived from Single-Cell Mass Cytometry Data

Germline genetic variations can alter cellular differentiation, potentially impacting the response of immune cells to inflammatory challenges. Current variant- and gene-based methods in mouse and human models have established associations with disease phenotypes; however, the underlying mechanisms at the cellular level are less well-understood. Immunophenotyping by multi-parameter flow cytometry, and more recently mass cytometry, has allowed high-resolution identification and characterization of hematopoietic cells. The obtained characterization yields increased dimensionality; however, conventional analysis workflows have been inefficient, incomplete, or unreliable. In this work, we develop a comprehensive machine learning framework - MDL4Cyto - that is tailored to the analysis of mass cytometry data, incorporating statistical, unsupervised learning, and supervised learning models. The statistical modeling can be used to illuminate cell fate decision and cell-type dynamics. The unsupervised learning models along with complementary marker enrichment analyses highlight genetic perturbations that are significantly associated with alterations in cell populations in the hematopoietic system. Furthermore, our supervised learning models, including deep learning and tree-based algorithms, address the bottleneck to data pre-processing that characterizes conventional workflows and generate inferences (e.g., on marker/cell-type interactions) from raw experimental characterization. Notably, we reveal a close relationship among network design, prediction performance, and the underlying biological context. We show that the network architecture extracted from the differentiation cascade of the investigated biological system yields enhanced prediction performance. The presented methodology will enable new insights into hematopoietic differentiation at baseline and following perturbation. HighlightsO_LIAnalysis pipeline on mass cytometry data with high-performance implementation of statistical, unsupervised learning, and supervised learning models C_LIO_LIConcordance of machine learning results with biological contexts C_LIO_LIBiologically-informed neural network designs enhance prediction performance C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/504669v3_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@10e35cborg.highwire.dtl.DTLVardef@1edc8ecorg.highwire.dtl.DTLVardef@220726org.highwire.dtl.DTLVardef@35916_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Controlled exchange of protein and nucleic acid signals from and between synthetic minimal cells

Synthetic minimal cells are a class of small liposome bioreactors that have some, but not all functions of live cells. Here, we report a critical step towards the development of a bottom-up minimal cell: cellular export of functional protein and RNA products. We used cell penetrating peptide tags to translocate payloads across a synthetic cell vesicle membrane. We demonstrated efficient transport of active enzymes, and transport of nucleic acid payloads by RNA binding proteins. We investigated influence of a concentration gradient alongside other factors on the efficiency of the translocation, and we show a method to increase product accumulation in one location. We demonstrate the use of this technology to engineer molecular communication between different populations of synthetic cells, to exchange protein and nucleic acid signals. The synthetic minimal cell production and export of proteins or nucleic acids allows experimental designs that approach the complexity and relevancy of natural biological systems.

synthetic biology↗