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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2024

Breaking out of the cell cycle: Including quiescence in cell cycle classification

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The ccAFv2 classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state (Neural G0), and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We demonstrate that the ccAFv2 classifier effectively generalizes the S, S/G2, G2/M, and M/Early G1 states across cell types derived from all three germ layers. While the G0, G1, and Late G1 states perform well in neuroepithelial cell types, their accuracy is lower in other cell types. However, misclassifications are confined to the G0, G1, and Late G1 states. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat or a PyPI package integrated with SCANPY. We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.

cell biology↗

bioRxiv2024

Substrate stiffness regulates neuronal maturation via Piezo1-mediated TTR activity

During brain development, neurons extend axons to connect to their target cells while initiating a maturation process during which neurons start expressing voltage-gated ion channels, form synapses, express synaptic transmitters and receptors, and start communicating via action potentials. Little is known about external factors regulating this process. Here, we identified environmental mechanics as an important regulator of neuronal maturation, and a molecular pathway linking tissue stiffness to this process. Using patch clamp electrophysiology, calcium imaging and immunofluorescence, we found that neurons cultured on stiffer substrates showed a delay in voltage-gated ion channel activity, spontaneous and evoked action potentials, and synapse formation. RNA sequencing and CRISPR/Cas9 knockdown revealed that the mechanosensitive ion channel Piezo1 supresses transthyretin (TTR) expression on stiffer substrates, slowing down synapse formation and consequently electrical maturation. In agreement, the stiffness of developing Xenopus laevis brain negatively correlated with local synapse densities, and stiffening of brain tissue resulted in a significant delay of synaptic activity in vivo. Our data indicate that environmental stiffness represents a fundamental regulator of neuronal maturation, which is important for the development of normal circuitry in the brain, and potentially for neurodevelopmental disorders. Furthermore, the mechanical regulation of TTR downstream of Piezo1 may be found in many other biological systems.

developmental biology↗

bioRxiv2019

Simulation with RADinitio Improves RADseq Experimental Design and Sheds Light on Sources of Missing Data

Restriction-site Associated DNA sequencing (RADseq) has become a powerful and versatile tool in modern population genomics, enabling large-scale genomic analyses in otherwise inaccessible biological systems. With its widespread use, different variants on the protocol have been developed to suit specific experimental needs. Researchers face the challenge of choosing the optimal molecular and sequencing protocols for their experimental design, an often-complicated process. Strategic errors can lead to improper data generation that has reduced power to answer biological questions. Here we present RADinitio, simulation software for the selection and optimization of RADseq experiments via the generation of sequencing data that behaves similarly to empirical sources. RADinitio provides an evolutionary simulation of populations, implementation of various RADseq protocols with customizable parameters, and thorough assessment of missing data. Using the software, we test its efficacy using different RAD protocols across several organisms, highlighting the importance of protocol selection on the magnitude and quality of data acquired. Additionally, we test the effects of RAD library preparation and sequencing on allelic dropout, observing that library preparation and sequencing often contributes more to missing alleles than population-level variation.

genomics↗

bioRxiv2026

HTLV-1-Induced Neuroimmunome Correlates with Disease Progression and Severity

Human T-cell lymphotropic virus type 1 (HTLV-1) is a retrovirus that infects approximately 5-10 million people worldwide. While most individuals remain asymptomatic, a significant subset develops debilitating neuroinflammatory or malignant disorders, including adult T-cell leukemia/lymphoma (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). To unravel the systemic molecular mechanisms underlying HTLV-1 pathogenesis, we employed a multi-dimensional systems biology approach, integrating bulk transcriptomic data from total PBMCs (n = 200) with single-cell RNA sequencing (scRNA-seq) from 233,093 peripheral blood mononuclear cells (PBMCs). Our analysis revealed a consistent and clinically relevant neuroimmune signature within leukocytes, termed the neuroimmunome, comprising a set of differentially expressed genes shared across the nervous and immune systems. Through dimensionality reduction and machine learning techniques, such as PCA, gradient boosting, and MANOVA with bootstrapping, we identified potential biomarkers predictive of HTLV-1-driven leukemogenesis, which were subsequently validated across ATL, HAM/TSP, and asymptomatic cohorts via flow cytometry. Notably, expression levels of proteins such as ATF4 and SKIL were strongly correlated with proviral load, suggesting that sustained neuroimmune dysregulation may contribute to disease progression. These findings highlight a previously underappreciated neuroimmunological layer, redefining HTLV-1-associated disease as a condition deeply rooted in neuroimmune network disruption within leukocytes and offering potential novel targets.

molecular biology↗

bioRxiv2020

PHENSIM: Phenotype Simulator

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach in order to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules and does so by exploiting signaling pathways. Our tools applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approachs reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.atlas.dmi.unict.it/.

bioinformatics↗

bioRxiv2024

Quantification of subcellular RNA localization through direct detection of RNA oxidation

Across cell types and organisms, thousands of RNAs display asymmetric subcellular distributions. The study of this process often requires quantifying abundances of specific RNAs at precise subcellular locations. To analyze subcellular transcriptomes, multiple proximity-based techniques have been developed in which RNAs near a localized bait protein are specifically labeled, facilitating their biotinylation and purification. However, these complex methods are often laborious and require expensive enrichment reagents. To streamline the analysis of localized RNA populations, we developed Oxidation-Induced Nucleotide Conversion sequencing (OINC-seq). In OINC-seq, RNAs near a genetically encoded, localized bait protein are specifically oxidized in a photo-controllable manner. These oxidation events are then directly detected and quantified using high-throughput sequencing and our software package, PIGPEN, without the need for biotin-mediated enrichment. We demonstrate that OINC-seq can induce and quantify RNA oxidation with high specificity in a dose- and light-dependent manner. We further show the spatial specificity of OINC-seq by using it to quantify subcellular transcriptomes associated with the cytoplasm, ER, nucleus, and the inner and outer membranes of mitochondria. Finally, using transgenic zebrafish, we demonstrate that OINC-seq allows proximity-mediated RNA labeling in live animals. In sum, OINC-seq together with PIGPEN provide an accessible workflow for the analysis of localized RNAs across different biological systems.

molecular biology↗

bioRxiv2025

ABaCo: Addressing Heterogeneity Challenges in Metagenomic Data Integration with Adversarial Generative Models

The rapid advancement of high-throughput metagenomics has produced extensive and heterogeneous datasets with significant implications for environmental and human health. Integrating these datasets is crucial for understanding the functional roles of microbiomes and the interactions within microbial communities. However, this integration remains challenging due to technical heterogeneity and the inherent complexity of these biological systems. To address these challenges, we introduce ABaCo, a generative model that combines a Variational Autoencoder (VAE) with an adversarial discriminator specifically designed to handle the unique characteristics of metagenomic data. Our results demonstrate that ABaCo effectively integrates metagenomic data from multiple studies, corrects technical heterogeneity, outperforms existing methods, and preserves taxonomic-level biological signals. We have developed ABaCo as an open-source, fully documented Python library to facilitate, support and enhance metagenomics research in the scientific community.

bioinformatics↗

bioRxiv2019

Deciphering the ‘m6A code’ via quantitative profiling of m6A at single-nucleotide resolution

N6-methyladenosine (m6A) is the most abundant modification on mRNA, and is implicated in critical roles in development, physiology and disease. The ability to map m6A using immunoprecipitation-based approaches has played a critical role in dissecting m6A functions and mechanisms of action. Yet, these approaches are of limited specificity, unknown sensitivity, and unable to quantify m6A stoichiometry. These limitations have severely hampered our ability to unravel the factors determining where m6A will be deposited, to which levels (the m6A code), and to quantitatively profile m6A dynamics across biological systems. Here, we used the RNase MazF, which cleaves specifically at unmethylated RNA sites, to develop MASTER-seq for systematic quantitative profiling of m6A sites at 16-25% of all m6A sites at single nucleotide resolution. We established MASTER-seq for orthogonal validation and de novo detection of m6A sites, and for tracking of m6A dynamics in yeast gametogenesis and in early mammalian differentiation. We discover that antibody-based approaches severely underestimate the number of m6A sites, and that both the presence of m6A and its stoichiometry are hard-coded via a simple and predictable code within the extended sequence composition at the methylation sites. This code accounts for ~50% of the variability in methylation levels across sites, allows excellent de novo prediction of methylation sites, and predicts methylation acquisition and loss across evolution. We anticipate that MASTER-seq will pave the path towards a more quantitative investigation of m6A biogenesis and regulation in a wide variety of systems, including diverse cell types, stimuli, subcellular components, and disease states.

molecular biology↗

bioRxiv2019

GIP: A Gene network-based integrative approach for Inferring disease-associated signaling Pathways

Dysregulation or crosstalk of signal transduction pathways contributes to disease development. Despite the initial success of identifying causal links between source and target proteins in simple or well-studied biological systems, it remains challenging to investigate alternative pathways specifically associated with a disease. We develop a Gene network-based integrative approach for Inferring disease-associated signaling Pathways (GIP). Specifically, we identify alternative pathways given source and target proteins. GIP was applied to human breast cancer data. Experimental results showed that GIP identified biologically meaningful pathway modules associated with antiestrogen resistance.

bioinformatics↗

bioRxiv2019

Asymmetrical diversification of the receptor-ligand interaction controlling self-incompatibility in Arabidopsis

How two-components genetic systems accumulate evolutionary novelty and become diversified in the course of evolution is a fundamental problem in evolutionary systems biology. In the Brassicaceae, self-incompatibility (SI) is a spectacular example of a diversified allelic series in which numerous highly diverged receptor-ligand combinations are segregating in natural populations. However, the evolutionary mechanisms by which new SI specificities arise in the first place have remained elusive. Using in planta ancestral protein resurrection, we demonstrate that two allelic variants currently segregating as distinct receptor-ligand combinations diverged through an asymmetrical process whereby one variant has retained the same recognition specificity as the (now extinct) ancestor, while the other has functionally diverged and now represents a novel specificity no longer recognized by the ancestor. Examination of the structural determinants of the shift in binding specificity suggests that allosteric changes may be an important source of evolutionary novelty in this highly diversified receptor-ligand system.

evolutionary biology↗

bioRxiv2025

Moving from Association to Causation: Instrumental factor models for causal inference in high-dimensional multi-omics data

Distinguishing true causal relationships from mere association is a core challenge in bioinformatics and science in general. Causal inference, in particular in "-omics" studies, is hindered by high dimensionality, correlated measurements, and pervasive endogeneity arising from unmeasured confounding and complex interventions. We introduce Factor IV, a supervised instrumental variable (IV) framework for identifying causal effects in high-dimensional biological systems. FactorIV constructs low-dimensional instrumental factors via a sparse low-rank decomposition of the instrument-exposure map, yielding an identifiable first stage even when either/both endogenous and instrumental variables are high-dimensional. Under standard IV assumptions, these factors remain orthogonal to outcome noise while capturing coordinated, perturbation-driven biological variation. The framework supports generalized first-stage models, including Gaussian, Bernoulli and negative binomial likelihoods. Simulation studies demonstrate accurate recovery of factor-level and feature-level causal effects under linear and generalized settings, with robustness to correlated errors and hidden confounding. To illustrate biological discovery, we applied FactorIV to a mouse hepatocellular carcinoma model and the DIABIMMUNE infant cohort. We demonstrate how FactorIV uncovers mechanistically interpretable causal structure beyond association-based analyses.

bioinformatics↗

bioRxiv2026

A Crossed Laser Phase Plate for CryoEM

The laser phase plate (LPP) enables phase-contrast imaging in cryogenic electron microscopy (cryoEM), enhancing image contrast without compromising high-resolution information. Here we report the implementation of a crossed laser phase plate (xLPP) comprising two optical cavities oriented orthogonally, installed in a ThermoFisher Scientific Krios G4 microscope equipped with a newly designed transfer lens module. We demonstrate the expected, strong contrast enhancement and stable, additive phase shifts of 90{degrees}, with a contrast transfer function (CTF) that closely matches theory. Single-particle analysis (SPA) of apoferritin, a standard benchmark sample, reached a resolution of 1.79 [A], demonstrating the system is capable of acquiring high-resolution cryoEM data. When imaging thick E. coli cells ([~]350 nm), the xLPP enhances contrast and increases low-frequency template-matching signal. Together, these results establish the feasibility of the xLPP and highlight its potential for high-contrast, high-resolution cryoEM imaging of biological systems.

biophysics↗

bioRxiv2020

Magnesium maintains length of circadian period in Arabidopsis thaliana

The circadian clock coordinates the physiological response of a biological system to day and night rhythms through complex loops of transcriptional/ translational regulation. It can respond to external stimuli and adjust generated circadian oscillations accordingly to keep an endogenous period close to 24 h. To date, the interaction between nutritional status and circadian rhythms in plants is poorly understood. Magnesium (Mg) is essential for numerous biological processes in plants and its homeostasis is crucial to maintain optimal development and growth. Magnesium deficiency in young Arabidopsis thaliana seedlings increased the circadian period of pCCA1:LUC oscillations and dampened its amplitude in constant light in a dose-dependent manner. Although circadian period increase by Mg deficiency was light dependent, it did not depend on active photosynthesis. Mathematical modelling of the Mg input to the circadian clock reproduced the experimental increase of the circadian period and suggested that Mg is likely to affect global transcription/translation levels rather than a single component of the circadian oscillator. The model prediction was supported by a synergistic interaction between Mg deficiency and cyclohexamide, an inhibitor of translation. These findings suggest that proper Mg supply is required to support proper timekeeping in plants. One sentence summaryMagnesium maintains the circadian period in Arabidopsis seedlings and interferes with the circadian oscillator most likely through translational mechanisms.

molecular biology↗

bioRxiv2019

Computational approach to identifying universal macrophage biomarker

Macrophages are a type of white blood cell, of the immune system, that engulfs and digests cellular debris, cancer cells, and anything else that does not have the type of proteins specific to healthy body cells on its surface. Understanding gene expression dynamics in macrophages are crucial for studying human diseases. Recent advances in high-throughput technologies have enabled the collection of immense amounts of biological data. A reliable marker of macrophage is essential to study their function. Traditional approaches use a number of markers that may have tissue specific expression patterns. To identify universal biomarker of macrophage, we used a previously published computational approach called BECC (Boolean Equivalent Correlated Clusters) that was originally used to identify universal cell cycle genes. We performed BECC analysis on a seed gene CD14, a known macrophage marker. FCER1G and TYROBP were among the top candidates which were validated as strong candidates for universal biomarkers for macrophages in human and mouse tissues. To our knowledge, such a finding is first of its kind.\n\nCONTRIBUTIONS TO THE FIELDWe have developed a computational approach to identify universal biomarkers of different entities in a biological system. We applied this approach to study macrophages and identified universal biomarkers of this particular cell type. FCER1G and TYROBP were among the top candidates which were validated as strong candidates for universal biomarkers for macrophages in human and mouse tissues. The expression patterns of TYROBP and FCER1G are found to be more homogeneous compared to currently used biomarkers such as ITGAM, EMR1 (F4/80), and CD68. Further, we demonstrated that this homogeneity extends to all the tissues currently profiled in the public domain in multiple species including human and mouse. FCER1G and TYROBP expression patterns were also found to be extremely specific to macrophages found in various tissues. They are strongly co-expressed together. We believe that these two genes are the most reliable candidates of universal biomarker for macrophages.

immunology↗

bioRxiv2026

Redox hysteresis controls the NADH-dependent reduction of cytochrome b5 in rat microsomes

In enzymology, hysteresis is manifested as a time-dependent shift in the kinetic behavior of an enzyme. Through hysteresis, the activation or inhibition of a biological pathway can be regulated by a molecule or metabolite that acts as a hysteretic modulator of the enzyme within that metabolic route. This mechanism of regulation contrasts with those that act on gene expression leading to modulation of enzyme protein levels. Through hysteresis, the amplitude of natural oscillations in metabolic pathways can be adjusted according to the levels of a metabolite that might be beneficial for cells. At physiological level, the slow response of hysteretic enzymes to changes, in the cellular levels of substrates, allows a time-dependent buffering effect on certain metabolites. Understanding the mechanisms and properties of hysteretic enzymes has been important for developing new therapies and improving our understanding of these enzymes in biological systems. However, due to their complex kinetics, the study of hysteretic enzymes has remained a challenge over time. In this study, we characterized the reduction of cytochrome b5 by NADH-dependent microsomal enzymes from rat liver using recombinant purified cytochrome b5, coenzyme Q10 and coenzyme Q0, as substrates, to mimic the conditions found in biological membranes, where competition between cytochrome b5 and other substrates might influence their reduction. We found a lag-time-dependent behavior in the cytochrome b5 reduction compatible with the existence of hysteretic modulation induced by endogenous molecules present in these membranes. Our data suggest that at least for the case of coenzyme Q10, fluctuations in its levels may impact metabolic pathways in which reduced cytochrome b5 levels play a key for the function of the cytochrome b5-dependent route.

biochemistry↗

bioRxiv2018

Interaction pathways promote spliceosome module integration and network-level robustness to cascading effects

The functionality of distinct types of protein networks depends on the patterns of protein-protein interactions. A problem to solve is understanding the fragility of protein networks to predict system malfunctioning due to mutations and other errors. Spectral graph theory provides tools to understand the structural and dynamical properties of a system based on the mathematical properties of matrices associated with the networks. We combined two of such tools to explore the fragility to cascading effects of the network describing protein interactions within a key macromolecular complex, the spliceosome. Using S. cerevisiae as a model system we show that the spliceosome network has more indirect paths connecting proteins than random networks. Such multiplicity of paths may promote routes to cascading effects to propagate across the network. However, the modular network structure concentrates paths within modules, thus constraining the propagation of such cascading effects, as indicated by analytical results from the spectral graph theory and by numerical simulations of a minimal mathematical model parameterized with the spliceosome network. We hypothesize that the concentration of paths within modules favors robustness of the spliceosome against failure, but may lead to a higher vulnerability of functional subunits which may affect the temporal assembly of the spliceosome. Our results illustrate the utility of spectral graph theory for identifying fragile spots in biological systems and predicting their implications.

molecular biology↗

bioRxiv2024

Origin of Chromosome 12 Trisomy Surge in Human Induced Pluripotent Stem Cells (iPSCs)

Cultured pluripotent stem cells are unique in being the only fully diploid immortal human cell lines. However, during continued culture, they acquire significant chromosome abnormalities. Chromosome 12 trisomy is the most common whole-chromosome abnormality found during culture of human induced pluripotent stem cells (iPSCs). The conventional paradigm is that trisomy 12 occurs very rarely but provides a proliferative advantage, enabling these cells to outcompete the diploid. Here, we challenge this prevailing model by demonstrating that trisomy 12 arises simultaneously in a very high percentage of diploid cells. Using a single cell line that reproducibly undergoes transition from diploid to trisomy 12, we found that proliferation differences alone do not account for the rapid dominance of trisomic cells. Through careful mapping by fluorescent in-situ hybridization, we identified critical transition passages where trisomic cells first appeared and swiftly gained dominance. Remarkably, single trisomic cells repeatedly emerged de novo from diploid parents. Delving deeper, we discovered an extremely high incidence of chromosome 12 anaphase bridging exclusively during transition passages, along with overrepresentation of chromosome 12 chromatids in micronuclei. These micronuclei fail to replicate during S phase. Subsequently, when these micronucleated cells enter mitosis they contain an unreplicated chromosome 12 chromatids. We also found that nearly 20% of the shorter p arms of chromosome 12 but not the longer q arms exhibited loss of subtelomeric repeats during transition passages. Chromosome 12p arms were exclusively responsible for the bridging observed in anaphase cells. Our findings unveil a novel mechanism of whole-chromosome instability in human stem cells, where chromosome 12p arm-specific segregation errors occur simultaneously in a high percentage of cells. The slight yet significant growth advantage of trisomy 12 cells allows them to persist and eventually dominate the population. Our findings detailing this novel interpretation of the origin of chromosome instability in cultured of human stem cells may have broad implications for understanding the genesis of aneuploidy across diverse biological systems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/626470v3_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@dc7cc7org.highwire.dtl.DTLVardef@183fb9eorg.highwire.dtl.DTLVardef@1fc6adaorg.highwire.dtl.DTLVardef@cb4078_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2020

Unsupervised manifold learning of collective behavior

Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the systems behavior. Author summaryMany complex systems in society and nature exhibit collective behavior where individuals local interactions lead to system-wide organization. One challenge we face today is to identify and characterize these emergent behaviors, and here we have developed a new method for analyzing data from individuals, to detect when a given complex system is exhibiting system-wide organization. Importantly, our approach requires no prior knowledge of the fashion in which the collective behavior arises, or the macro-scale variables in which it manifests. We apply the new method to an agent-based model and empirical observations of fish schooling. While we have demonstrated the utility of our approach to biological systems, it can be applied widely to financial, medical, and technological systems for example.

bioinformatics↗