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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

Mass-spectrometry based proteomics reveals mitochondrial supercomplexome plasticity

Mitochondrial respiratory complex subunits assemble in supercomplexes. Studies of supercomplexes have typically relied upon antibody-based protein quantification, often limited to the analysis of a single subunit per respiratory complex. To provide a deeper insight into mitochondrial and supercomplex plasticity, we combined Blue Native Polyacrylamide Gel Electrophoresis (BN-PAGE) and mass spectrometry to determine the supercomplexome of skeletal muscle from sedentary and exercise-trained mice. We quantified 422 mitochondrial proteins within ten supercomplex bands, in which we showed the debated presence of complex II and V. Upon exercise-induced mitochondrial biogenesis, non-stoichiometric changes in subunits and incorporation into supercomplexes was apparent. We uncovered the dynamics of supercomplex-related assembly proteins and mtDNA-encoded subunits within supercomplexes, as well as the complexes of ubiquinone biosynthesis enzymes and Lactb, a mitochondrial-localized protein implicated in obesity. Our approach can be applied to broad biological systems. In this instance, comprehensively analyzing respiratory supercomplexes illuminates previously undetectable complexity in mitochondrial plasticity. HighlightsO_LIComprehensive quantification of respiratory subunits within supercomplexes C_LIO_LIComplex II and V assemble within supercomplexes C_LIO_LIMitochondrial-encoded subunits display elevated upregulation upon exercise training C_LIO_LIExercise increases ubiquinone biosynthesis enzyme complexes C_LI

molecular biology↗

bioRxiv2026

Dimensionally traceable 3D microstructures for multimodal microscope calibration

High-resolution microscopy techniques are used across research and industry to analyse biological systems, from biomolecules to subcellular organelles, multicellular models and tissues. As multimodal imaging workflows and quantitative analysis of bioimaging data become increasingly widespread, there is a growing need for materials and methods to calibrate imaging systems and evaluate the fidelity of generated image data. Here, we present three-dimensional microscopy phantoms fabricated using two-photon photolithography from transparent resins that exhibit both broadband visible autofluorescence and Raman scattering across the fingerprint and C-H stretching regions. Suitable for analysis using optical profilometry, the phantoms were dimensionally calibrated with SI traceability using a metrological confocal microscope. Immersible in air and common aqueous imaging media, the phantoms are compatible with a wide variety of optical microscopy techniques, including one and two-photon excited fluorescence and coherent Raman scattering microscopy. We employed a forked wedge design to validate image deconvolution results and a stacked lattice phantom to recover image distortion matrices under realistic biological imaging conditions. We demonstrate the impact of correcting chromatic offsets and axial scaling errors for a representative application: analysis of a cell seeded scaffold using confocal laser scanning fluorescence microscopy. These phantoms provide a versatile platform for calibration, quality control and validation of multimodal imaging pipelines and improved quantitative optical microscopy.

bioengineering↗

bioRxiv2024

Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location and histology using GCN

Spatial transcriptomics technology allows for the detection of cellular transcriptome information while preserving the spatial location of cells. This capability enables researchers to better understand the cellular heterogeneity, spatial organization and functional interactions in complex biological systems. However, current technological methods are limited by low resolution, which reduces the accuracy of gene expression levels. Here, we propose scstGCN, a multimodal information fusion method based on Vision Transformer (ViT) and Graph Convolutional Network (GCN) that integrates histological images, spot-based spatial transcriptomics data and spatial location information to infer super-resolution gene expression profiles at single-cell level. We evaluated the accuracy of the super-resolution gene expression profiles generated on diverse tissue ST datasets with disease and healthy by scstGCN along with their performance in identifying spatial patterns, conducting functional enrichment analysis, and tissue annotation. The results show that scstGCN can predict super-resolution gene expression accurately, aid researchers in discovering biologically meaningful differentially expressed genes and pathways. Additionally, scstGCN can segment and annotate tissues at a finer granularity, with results demonstrating strong consistency with coarse manual annotations. Key PointsO_LIscstGCN combines multi-modal information including histology image, spot-based spatial transcriptomics (ST) data, and physical spatial location through deep learning methods to achieve single-cell resolution of spot-based ST data without requiring single-cell references. C_LIO_LIscstGCN employs GCN to capture complex relationships between neighboring cells, facilitating the integration of multimodal feature information based on single-cell level, and then accurately infers single-cell resolution spatial gene expression. C_LIO_LIscstGCN can infer single-cell resolution gene expression across the entire tissue region. Through transfer learning, gene expression in three-dimensional tissues can be characterized efficiently. Furthermore, it demonstrates outstanding performance in spatial patterns enhancement, functional enrichment analysis, and annotate tissues at the high-resolution. C_LI

bioinformatics↗

bioRxiv2019

Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces

Single-cell RNA-Seq (scRNA-seq) has become an invaluable tool for studying biological systems in health and diseases. While dimensionality reduction is a crucial step in interpreting the relation between cells based on scRNA-seq, current methods often are hampered by "crowding" of cells in the center of the latent space, biased by batch effects, or inadequately capture developmental relationships. Here, we introduced scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces, as a more accurate representation of the data. ScPhere resolves cell crowding, corrects multiple, complex batch factors, facilitates interactive visualization of large datasets, and gracefully uncovers pseudotemporal trajectories. We demonstrate scPhere on six large datasets in complex tissue from human patients or animal development, demonstrating how it controls for both technical and biological factors and highlights complex cellular relations and biological insights.

bioinformatics↗

bioRxiv2018

Refining interaction search through signed iterative Random Forests

Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically \"black-boxes,\" learning statistical interactions that are difficult to translate into testable hypotheses. The iterative Random Forest (iRF) algorithm took a step towards bridging this gap by providing a computationally tractable procedure to identify the stable, high-order feature interactions that drive the predictive accuracy of Random Forests (RF). Here we refine the interactions identified by iRF to explicitly map responses as a function of interacting features. Our method, signed iRF (s-iRF), describes \"subsets\" of rules that frequently occur on RF decision paths. We refer to these \"rule subsets\" as signed interactions. Signed interactions share not only the same set of interacting features but also exhibit similar thresholding behavior, and thus describe a consistent functional relationship between interacting features and responses. We describe stable and predictive importance metrics (SPIMs) to rank signed interactions in terms of their stability, predictive accuracy, and strength of interaction. For each SPIM, we define null importance metrics that characterize its expected behavior under known structure. We evaluate our proposed approach in biologically inspired simulations and two case studies: predicting enhancer activity and spatial gene expression patterns. In the case of enhancer activity, s-iRF recovers one of the few experimentally validated high-order interactions and suggests novel enhancer elements where this interaction may be active. In the case of spatial gene expression patterns, s-iRF recovers all 11 reported links in the gap gene network. By refining the process of interaction recovery, our approach has the potential to guide mechanistic inquiry into systems whose scale and complexity is beyond human comprehension.

genomics↗

bioRxiv2023

Speedy-PASEF: Analytical flow rate chromatography and trapped ion mobility for deep high-throughput proteomics

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs and facilitate new approaches in systems biology and biomedical research. Here we propose Speedy-PASEF, a combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition and data analysis with the DIA-NN software suite, for conducting fast, high-quality proteomic experiments that require only moderate sample amounts. For instance, using a 500-l/min flow rate and a 3-minute chromatographic gradient, Speedy-PASEF quantified 5,211 proteins from 2 g of a mammalian cell-line standard at high quantitative accuracy and precision. We further used Speedy-PASEF to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-minute chromatographic gradient and alternating column regeneration on a dual pump system, for processing 398 samples per day. Speedy-PASEF delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates. Speedy-PASEF thus facilitates acquisition of high-quality proteomes in large numbers.

biochemistry↗

bioRxiv2025

pH-Responsive Phase Separation Dynamics of Intrinsically Disordered Peptides

Liquid-liquid phase separation of biomolecules is crucial for maintaining the functional organization in biological systems. Intrinsically disordered proteins are particularly prone to form phase-separated condensates in response to various physicochemical triggers. While the effect of ionic strength and temperature on phase separation dynamics have been studied extensively, the influence of pH is less explored. Here, we study a model glycine-rich protein present in the tick bioadhesive, given its capability to undergo phase separation. After confirming its disordered nature through spectroscopy, we investigated its pH dependence and underlying molecular mechanisms. Our findings reveal that pH significantly influences the protein hydrophobicity via ionic residues, driving notable variations in the coacervation behavior (propensity, progression) and in shaping the material properties (viscosity, interfacial activity) of the formed condensates. Given the ubiquitous presence of disordered proteins in biology, this study provides valuable insights about the broad implications of the pH-dependent behavior of intrinsically disordered proteins.

biochemistry↗

bioRxiv2026

Benchmarking Next-Generation Sequencing Platforms: A Comprehensive Comparison Of Single-Cell RNA-Seq from Ultima UG 100 vs. Illumina NovaSeq X Plus

Single-cell RNA sequencing (scRNA-seq) has become an essential technology for dissecting cellular heterogeneity in complex biological systems, with Illumina platforms serving as the dominant NGS provider1-3. The recent emergence of the Ultima Genomics UG 100 platform offers a high-throughput and potentially more cost-effective alternative, warranting a rigorous comparative analysis. Here, we conducted a comprehensive head-to-head comparison of the Ultima Genomics UG 100 and Illumina NovaSeq X Plus platforms, generating deep-coverage scRNA-seq data from human PBMCs and stimulated T-cell samples. Our multi-level analysis revealed highly concordant performance in both cell type identification and gene expression. We found that both platforms robustly captured all major immune lineages and demonstrated good transcriptional agreement. Minor observed discrepancies were primarily correlated with low UMI counts, suggesting stochastic biological effects rather than technical variance. Overall, our study establishes that the Ultima UG 100 delivers performance highly comparable to the Illumina NovaSeq X Plus for single-cell transcriptomics.

bioinformatics↗

bioRxiv2019

Correlative three-dimensional super-resolution and block face electron microscopy of whole vitreously frozen cells

Living cells function through the spatial compartmentalization of thousands of distinct proteins serving a multitude of diverse biochemical needs. Correlative super-resolution (SR) fluorescence and electron microscopy (EM) has emerged as a pathway to directly view nanoscale protein relationships to the underlying global ultrastructure, but has traditionally suffered from tradeoffs of structure preservation, fluorescence retention, resolution, and field of view. We developed a platform for three-dimensional correlative cryogenic SR and focused ion beam milled block-face EM across entire vitreously frozen cells that addresses these issues by preserving native ultrastructure and enabling independent SR and EM workflow optimization. Application to a variety of biological systems revealed a number of unexpected protein-ultrastructure relationships and underscored the value of a comprehensive multimodal view of ultrastructural variability across whole cells.

cell biology↗

bioRxiv2026

Rod shape bacterial motion in 2D confinement to decouple hydrodynamic and steric wall effects

As we know, bacterial motion navigates complex environments in natural settings, providing a basis for scientific study to understand their dynamics. Hydrodynamics drive wall alignment and accumulation, but research remains unclear about the extent to which confinement alone, without hydrodynamics, modulates bacterial dynamics. Therefore, we develop a 2D model to study the effects of isolated steric and hydrodynamic forces on bacterial motion in 10- and 50-m microchannels. We used bacteria to model self-propelled rigid rods with run-and-tumble motion, and then compared dry systems (purely steric wall effects) with wet systems (hydrodynamic effects). We found that bacterial speeds and their orientation are independent of channel dimensions in dry systems. In wet systems, we observed strong wall-hugging and alignment due to wall-induced hydrodynamic interactions, enhanced residence times, and a slight increase in the observable effective speed in 10 m microchannels compared to 50 m channels (where bacteria-maintained bulk-like dynamics). Our study thus emphasises when confinement affects bacterial motion solely due to pure dry geometry, and when hydrodynamics play an essential role. This study provides a template for microfluidics-based experimental prediction of bacterial dynamics and could be applied to an analysis of antibiotic resistance. Significance StatementMicrobes rely on self-propelled motion to navigate complex environmental systems and survive and persist. In such an environment, physical interactions with surrounding boundaries play a key role in how bacteria move, orient, or settle in complex spaces. Although many studies show that hydrodynamic forces near walls influence how bacteria swim in liquid, there is still confusion about whether confinement, with or without hydrodynamic effects, can change the complete bacterial pattern. Our 2D model shows that confinement (purely steric wall effects, dry limit) alone does not alter bacterial motion patterns. It is the hydrodynamics (wet limit) that drives bacteria to orient toward walls, remain near boundaries, and direct motion in narrow channels. This work clarifies when confinement and hydrodynamics actually affect bacterias motion and provides a practical way to understand bacterial behaviour in biological systems, including in microfluidic applications and studies of antibiotic resistance strategies.

biophysics↗

bioRxiv2023

New tools reveal PCP-dependent polarized mechanics in the cortex and cytoplasm of single cells during convergent extension

Understanding biomechanics of biological systems is crucial for unraveling complex processes like tissue morphogenesis. However, current methods for studying cellular mechanics in vivo are limited by the need for specialized equipment and often provide limited spatiotemporal resolution. Here we introduce two new techniques, Tension by Transverse Fluctuation (TFlux) and in vivo microrheology, that overcome these limitations. They both offer time-resolved, subcellular biomechanical analysis using only fluorescent reporters and widely available microscopes. Employing these two techniques, we have revealed a planar cell polarity (PCP)-dependent mechanical gradient both in the cell cortex and the cytoplasm of individual cells engaged in convergent extension. Importantly, the non-invasive nature of these methods holds great promise for its application for uncovering subcellular mechanical variations across a wide array of biological contexts. SummaryNon-invasive imaging-based techniques providing time-resolved biomechanical analysis at subcellular scales in developing vertebrate embryos.

developmental biology↗

bioRxiv2023

1.7 GHz long-term evolution radiofrequency electromagnetic field with efficient thermal control has no effect on the proliferation of different human cell types

Long-term evolution (LTE) radiofrequency electromagnetic field (RF-EMF) is widely used in communication technologies. As a result, the influence of RF-EMF on biological systems is a major public concern, and its physiological effects remain controversial. In our previous study, we showed that continuous exposure of various human cell types to 1.7 GHz LTE RF-EMF at specific absorption rate (SAR) of 2 W/Kg for 72 h can induce cellular senescence. To understand the precise cellular effects of LTE RF-EMF, we elaborated the 1.7 GHz RF-EMF cell exposure system used in the previous study by replacing the RF signal generator and developing a software-based feedback system to improve the exposure power stability. This refinement of the 1.7 GHz LTE RF-EMF generator facilitated the automatic regulation of RF-EMF exposure, maintaining target power levels within a 3% range and a constant temperature even during the 72-h exposure period. With the improved experimental setup, we examined the effect of continuous exposure to 1.7 GHz LTE RF-EMF at up to SAR of 8 W/Kg of adipose tissue-derived stem cells and Huh7, HeLa, and B103 cells. Surprisingly, the proliferation of all cell types, which displayed different growth rates, did not change significantly compared with that of the unexposed controls. However, when the thermal control system was turned off and the subsequent temperature increase induced by the RF-EMF was not controlled during continuous exposure to SAR of 8 W/Kg LTE RF-EMF, cellular proliferation increased by 35.2% at the maximum. These observations strongly suggest that the cellular effects attributed to 1.7 GHz LTE RF-EMF exposure were primarily due to the induced thermal changes, rather than the RF-EMF exposure itself.

cell biology↗

bioRxiv2025

Brain glucodynamic variability is an essential feature of the metabolism-cognition relationship

Variability ensures that complex biological systems, including the brain, are capable of responding to changing environmental demands. While the importance of neural variability in electrophysiological and haemodynamic aspects of brain activity is beginning to be understood, little is known about how variability in molecular activity influences brain function. Here we examine how temporal variability in glucodynamics, or time-varying glucose use, is related to cognition in 35 younger and 43 older adults. Stationary metabolic rates of glucose were not directly associated with cognition. Rather, higher glucodynamic variability, and its coherence into metabolic networks, was associated with better cognitive performance. Lower glucodynamic variability in ageing was associated with altered metabolic network efficiency and reduced cognitive performance. Our results demonstrate for the first time that variability in cerebral glucose metabolism is biologically and functionally relevant to cognition and the network architecture of the brain. Cognition is influenced by time-varying glucose metabolism and its coherent fluctuations in metabolic networks. A loss of glucodynamics in ageing reduces the efficiency of the metabolic connectome and contributes to reduced cognitive performance. The study of glucodynamics significantly advances our understanding of metabolic brain changes in health, ageing and disease. Significance statementThe human brain has a high energy requirement and must be able to dynamically adjust its energy supply as needed. However, the dynamic nature of brain glucose use is not well understood. In this work, we identify the variability in glucodynamics - the moment-to-moment fluctuations in glucose use - as an important biological feature supporting cognition and mediating age-related changes in brain function. We report that higher glucodynamic variability is associated with higher metabolic network efficiency and enhanced cognitive performance. Older adults exhibit reduced glucodynamic variability, reduced network efficiency and impaired cognition. Our results demonstrate that glucodynamics are important to our understanding of energy-dependent cognitive processes, the efficiency of metabolic brain networks, and the energetic basis of cognitive ageing.

neuroscience↗

bioRxiv2022

Viruses inhibit TIR gcADPR signaling to overcome bacterial defense

The Toll/interleukin-1 receptor (TIR) domain is a key component of immune receptors that identify pathogen invasion in bacteria, plants, and animals. In the bacterial antiphage system Thoeris, as well as in plants, recognition of infection stimulates TIR domains to produce an immune signaling molecule whose molecular structure remained elusive. This molecule binds and activates the Thoeris immune effector, which then executes the immune function. We identified a large family of phage-encoded proteins, denoted here Thoeris anti-defense 1 (Tad1), that inhibit Thoeris immunity. We found that Tad1 proteins are "sponges" that bind and sequester the immune signaling molecule produced by TIR-domain proteins, thus decoupling phage sensing from immune effector activation and rendering Thoeris inactive. A high-resolution crystal structure of Tad1 bound to the signaling molecule revealed that its chemical structure is 1'-2' glycocyclic ADPR (gcADPR), a unique molecule not previously described in other biological systems. Our results define the chemical structure of a central immune signaling molecule, and reveal a new mode of action by which pathogens can suppress host immunity.

microbiology↗

bioRxiv2022

A Multifunctional Anchor for Multimodal Expansion Microscopy

In situ imaging of biomolecular location with nanoscale resolution enables mapping of the building blocks of life throughout biological systems in normal and disease states. Expansion microscopy (ExM), by physically enlarging specimens in an isotropic fashion, enables nanoimaging on standard light microscopes. Key to ExM is the equipping of different kinds of molecule, with different kinds of anchoring moiety, so they can all be pulled apart by polymer swelling. Here we present a multifunctional anchor, an acrylate epoxide, that enables multiple kinds of molecules (e.g., proteins and RNAs) to be equipped with anchors in a single experimental step. This reagent simplifies ExM protocols and greatly reduces cost (by 2-10 fold for a typical multiplexed ExM experiment) compared to previous strategies for equipping RNAs with anchors. We show that this unified ExM (uniExM) protocol can be used to preserve and visualize RNA transcripts, proteins in biologically relevant ultrastructure, and sets of RNA transcripts in patient-derived xenograft (PDX) cancer tissues, and can support the visualization of other kinds of biomolecular species as well. Thus, uniExM may find many uses in the simple, multimodal nanoscale analysis of cells and tissues.

bioengineering↗

bioRxiv2023

In Vivo Biorthogonal Antibody Click for Dual Targeting and Augmented Efficacy in Cancer Treatment

Antibody-drug conjugates (ADCs) have emerged as promising therapeutics for cancer treatment; however, their effectiveness has been limited by single antigen targeting, potentially leading to resistance mechanisms triggered by tumor compensatory pathways or reduced expression of the target protein. Here, we present antibody-ADC click, an approach that harnesses bioorthogonal click chemistry for in vivo dual receptor targeting, irrespective of the levels of the tumors expression of the ADC-targeting antigen. Antibody-ADC click enables targeting heterogeneity and enhances antibody internalization and drug delivery inside cancer cells, resulting in potent toxicity. We conjugated antibodies and ADCs to the bioorthogonal click moieties tetrazine (Tz) and trans-cyclooctene (TCO). Through sequential antibody administration in living biological systems, we achieved dual receptor targeting by in vivo clicking of antibody-TCO with antibody-Tz. We show that the clicked antibody therapy outperformed conventional ADC monotherapy or antibody combinations in preclinical models mimicking ADC-eligible, ADC-resistant, and ADC-ineligible tumors. Antibody-ADC click enables in vivo dual-antigen targeting without extensive antibody bioengineering, sustains tumor treatment, and enhances antibody-mediated cytotoxicity.

cancer biology↗

bioRxiv2025

Uncovering Functional Gene Regulatory Networks in Bulk and Single-Cell Data through Robust Transcription Factor Activity Estimation and Model-Guided Experimental Validation

Reconstructing genome-scale gene regulatory networks (GRNs) remains a difficult problem in systems biology, and many experimental and computational methods have been developed to address this problem. Recent computational methods have aimed to more accurately model GRNs by estimating the hidden Transcription Factor Activity (TFA) from prior knowledge of TF target regulatory connections, encoded as an input directed graph, to relax the assumption that mRNA level of the regulator correlates with the protein activity of the regulator. However, the noise in the prior knowledge can adversely affect the estimated TFA levels and the quality of the downstream inferred GRNs. Here, we present a new approach, MERLIN+P+TFA, that uses prior knowledge-guided sparsity regularization to robustly and accurately estimate TFA and downstream GRNs. We apply our method to simulated and real expression data in yeast and mammalian systems and show improved quality of inferred GRNs for both bulk and single-cell datasets. Regularized TFA offers benefits to a variety of other GRN inference algorithms, including those that have traditionally been used with expression alone, in both bulk and scRNA-seq settings. We used the inferred GRN to prioritize key regulators for the mouse Embryonic Stem Cell (mESC) state and validate 58 regulators experimentally. We identified both known and novel regulators of the mESC state and further validate the targets of 4 known and novel regulators. Our validation experiments suggest that computationally inferred networks can capture functional targets of TFs with higher precision than estimated in current benchmarks, however, it is important to generate context-specific gold standards.

bioinformatics↗

bioRxiv2025

AlterNet: Alternative splicing-aware gene regulatory networkinference

Gene regulatory networks (GRNs) help decode biological systems by identifying how genes interact and regulate cellular processes. However, conventional GRN inference methods operate at the gene-level, overlooking transcript-level variability introduced by alternative splicing (AS). In this work, we present AlterNet, the first GRN inference and annotation pipeline which considers isoform of transcription factors as distinct regulators in a GRN. AlterNet builds on the GRNBoost2 inference algorithm, and includes a transcript plausibility and annotation workflow. We applied AlterNet to expression data from heart tissue, including samples from donors with normal heart function and from patients with different types of cardiomyopathy. The resulting isoform-level GRNs uncovered highly relevant regulatory interactions not detectable at the gene-level. Overall, AlterNet infers transcript-level regulatory network which enable the discovery of novel, biologically relevant regulatory interactions that remain hidden in gene-level GRNs. The source code of AlterNet is available on GitHub https://github.com/bionetslab/AlterNet, and can be installed as a Python package.

bioinformatics↗