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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Statistical Integration of Multiple Omics Datasets Using GO2PLS

BackgroundNowadays, multiple omics data are measured on the same samples in the belief that these different omics datasets represent various aspects of the underlying biological systems. Integrating these omics datasets will facilitate the understanding of the systems. For this purpose, various methods have been proposed, such as Partial Least Squares (PLS), decomposing two datasets into joint and residual subspaces. Since omics data are heterogeneous, the joint components in PLS will contain variation specific to each dataset. To account for this, Two-way Orthogonal Partial Least Squares (O2PLS) captures the heterogeneity by introducing orthogonal subspaces and better estimates the joint subspaces. However, the latent components spanning the joint subspaces in O2PLS are linear combinations of all variables, while it might be of interest to identify a small subset relevant to the research question. To obtain sparsity, we extend O2PLS to Group Sparse O2PLS (GO2PLS) that utilizes biological information on group structures among variables and performs group selection in the joint subspace. ResultsThe simulation study showed that introducing sparsity improved the feature selection performance. Furthermore, incorporating group structures increased robustness of the feature selection procedure. GO2PLS performed optimally in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to datasets from two studies: TwinsUK (a population study) and CVON-DOSIS (a small case-control study). In the first, we incorporated biological information on the group structures of the methylation CpG sites when integrating the methylation dataset with the IgG glycomics data. The targeted genes of the selected methylation groups turned out to be relevant to the immune system, in which the IgG glycans play important roles. In the second, we selected regulatory regions and transcripts that explained the covariance between regulomics and transcriptomics data. The corresponding genes of the selected features appeared to be relevant to heart muscle disease. ConclusionsGO2PLS integrates two omics datasets to help understand the underlying system that involves both omics levels. It incorporates external >group information and performs group selection, resulting in a small subset of features that best explain the relationship between two omics datasets for better interpretability.

bioinformatics↗

bioRxiv2017

Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders

The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumors response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors. Variational autoencoders (VAEs) are a deep neural network approach capable of generating meaningful latent spaces for image and text data. In this work, we sought to determine the extent to which a VAE can be trained to model cancer gene expression, and whether or not such a VAE would capture biologically-relevant features. In the following report, we introduce a VAE trained on TCGA pan-cancer RNA-seq data, identify specific patterns in the VAE encoded features, and discuss potential merits of the approach. We name our method \"Tybalt\" after an instigative, cat-like character who sets a cascading chain of events in motion in Shakespeares \"Romeo and Juliet\". From a systems biology perspective, Tybalt could one day aid in cancer stratification or predict specific activated expression patterns that would result from genetic changes or treatment effects.

bioinformatics↗

bioRxiv2022

Photoselective sequencing: microscopically-guided genomic measurements with subcellular resolution

In biological systems, spatial organization is interconnected with genome function and regulation. However, methods that couple high-throughput genomic and epigenomic profiling with spatial information are lacking. Here, we developed Photoselective Sequencing, a spatially-informed DNA sequencing method to assay collections of cells or subcellular regions that share a unifying morphological trait. In Photoselective Sequencing, we prepare a blocked fragment library within a fixed biological specimen. Guided by fluorescence imaging, we remove the block in specific regions of interest using targeted illumination with near-UV light, ultimately allowing high-throughput sequencing of the selected fragments. To validate Photoselective Sequencing, we profile chromatin openness in fluorescently-labeled cell types within the mouse brain and demonstrate strong agreement with published single-cell ATAC-seq data. Using Photoselective Sequencing, we characterize the accessibility profiles of oligodendrocyte-lineage cells within the cortex and corpus-callosum regions of the brain. We develop a new computational strategy for decomposing bulk accessibility profiles by individual cell types, and report a relative enrichment of oligodendrocyte-progenitor-like cells in the cortex. Finally, we leverage Photoselective Sequencing for unbiased profiling of DNA at the nuclear periphery, a key chromatin organizing region. We compare and contrast the Photoselective Sequencing profile with lamin ChIP-seq data, and identify features beyond lamin interaction that are correlated with positioning at the nuclear periphery. These results collectively demonstrate that Photoselective Sequencing is a flexible and generalizable platform for exploring the interplay of spatial structures with genomic and epigenomic properties.

genomics↗

bioRxiv2022

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.

cancer biology↗

bioRxiv2023

The positioning mechanics of microtubule asters in Drosophila embryo explants

Microtubule asters are essential in localizing the action of microtubules in processes including mitosis and organelle positioning. In large cells, such as the one-cell sea urchin embryo, aster dynamics are dominated by hydrodynamic pulling forces. However, in systems with more densely positioned nuclei such as the early Drosophila embryo, which packs around 6000 nuclei within the syncytium in a crystalline-like order, it is unclear what processes dominate aster dynamics. Here, we take advantage of a cell cycle regulation Drosophila mutant to generate embryos with multiple asters, independent from nuclei. We use an ex vivo assay to further simplify this biological system to explore the forces generated by and between asters. Through live imaging, drug and optical perturbations, and theoretical modelling, we demonstrate that these asters likely generate an effective pushing force over short distances. Significance StatementUsing cytosolic explants from Drosophila syncytial embryos combined with quantitative microscopy and perturbations, de-Carvalho et al., reveal the mechanical forces separating Drosophila microtubule asters. Aster separation drives precise nuclear positioning in multinucleated embryo cells, a vital process for tissue formation and gene expression during subsequent embryo development.

biophysics↗

bioRxiv2023

Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation

Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.

molecular biology↗

bioRxiv2020

Tracing the in vivo fate of nanoparticles with a "non-self" biological identity

ABSTRACTNanoparticles can acquire a biomolecular corona with a species-specific biological identity. However, “non-self” incompatibility of recipient biological systems is often not considered, for example, when rodents are used as a model organism for preclinical studies of biomolecule-inspired nanomedicines. Using zebrafish embryos as an emerging model for nano-bioimaging, here we unraveled the in vivo fate of intravenously injected 70 nm SiO2 nanoparticles with a protein corona pre-formed from fetal bovine serum (FBS), representing a non-self biological identity. Strikingly rapid sequestration and endolysosomal acidification of nanoparticles with the pre-formed FBS corona were observed in scavenger endothelial cells within minutes after injection. This led to loss of blood vessel integrity and inflammatory activation of macrophages over the course of several hours. As unmodified nanoparticles or the equivalent dose of FBS proteins alone failed to induce the observed pathophysiology, this signifies how the corona enriched with a differential repertoire of proteins can determine the fate of the nanoparticles in vivo. Our findings thus reveal the adverse outcome triggered by incompatible protein coronas and indicate a potential pitfall in the use of mismatched species combinations during nanomedicine development.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2019

Detecting regions of differential abundance betweenscRNA-Seq datasets

Traditional cell clustering analysis used to compare the transcriptomic landscapes between two biological states in single cell RNA sequencing (scRNA-seq) is largely inadequate to functionally identify distinct and important differentially abundant (DA) subpopulations between groups. This problem is exacerbated further when using unsupervised clustering approaches where differences are not observed in clear cluster structure and therefore many important differences between two biological states go entirely unseen. Here, we develop DA-seq, a powerful unbiased, multi-scale algorithm that uniquely detects and decodes novel DA subpopulations not restricted to well separated clusters or known cell types. We apply DA-seq to several publicly available scRNA-seq datasets on various biological systems to detect differences between distinct phenotype in COVID-19 cases, melanomas subjected to immune checkpoint therapy, embryonic development and aging brain, as well as simulated data. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies, but also reveals new DA subpopulations that were not described before. Analysis of these novel subpopulations yields new biological insights that would otherwise be neglected.

bioinformatics↗

bioRxiv2019

Capturing Protein-Ligand Recognition Pathways in Coarse-grained Simulation

Protein-substrate recognition is highly dynamic and complex process in nature. A key approach in deciphering the mechanism underlying the recognition process is to capture the kinetic process of substrate in its act of binding to its designated protein cavity. Towards this end, microsecond long atomistic molecular dynamics (MD) simulation has recently emerged as a popular method of choice, due its ability to record these events at high spatial and temporal resolution. However, success in this approach comes at an exorbitant computational cost. Here we demonstrate that coarse grained models of protein, when systematically optimised to maintain its tertiary fold, can capture the complete process of spontaneous protein-ligand binding from bulk media to cavity, within orders of magnitude shorter wall clock time compared to that of all-atom MD simulations. The simulated and crystallographic binding pose are in excellent agreement. We find that the exhaustive sampling of ligand exploration in protein and solvent, harnessed by coarse-grained simulation at a frugal computational cost, in combination with Markov state modelling, leads to clearer mechanistic insights and discovery of novel recognition pathways. The result is successfully validated against three popular protein-ligand systems. Overall, the approach provides an affordable and attractive alternative of all-atom simulation and promises a way-forward for replacing traditional docking based small molecule discovery by high-throughput coarse-grained simulation for searching potential binding site and allosteric sites. This also provides practical avenues for first-hand exploration of bio-molecular recognition processes in large-scale biological systems, otherwise inaccessible in all-atom simulations.

biophysics↗

bioRxiv2023

Gap junctions in Turing-type periodic feather pattern formation

Periodic patterning requires coordinated cell-cell interactions at the tissue level. Turing showed, using mathematical modeling, how spatial patterns could arise from the reactions of a diffusive activator-inhibitor pair in an initially homogenous two-dimensional field. Most activators and inhibitors studied in biological systems are proteins, and the roles of cell-cell interaction, ions, bioelectricity, etc. are only now being identified. Gap junctions (GJs) mediate direct exchanges of ions or small molecules between cells, enabling rapid long-distance communications in a cell collective. They are therefore good candidates for propagating non-protein-based patterning signals that may act according to the Turing principles. Here, we explore the possible roles of GJs in Turing-type patterning using feather pattern formation as a model. We found seven of the twelve investigated GJ isoforms are highly dynamically expressed in the developing chicken skin. In ovo functional perturbations of the GJ isoform, connexin 30, by siRNA and the dominant-negative mutant applied before placode development led to disrupted primary feather bud formation, including patches of smooth skin and buds of irregular sizes. Later, after the primary feather arrays were laid out, inhibition of gap junctional intercellular communication in the ex vivo skin explant culture allowed the emergence of new feather buds in temporal waves at specific spatial locations relative to the existing primary buds. The results suggest that gap junctional communication may facilitate the propagation of long-distance inhibitory signals. Thus, the removal of GJ activity would enable the emergence of new feather buds if the local environment is competent and the threshold to form buds is reached. We propose Turing-based computational simulations that can predict the appearance of these ectopic bud waves. Our models demonstrate how a Turing activator-inhibitor system can continue to generate patterns in the competent morphogenetic field when the level of intercellular communication at the tissue scale is modulated.

developmental biology↗

bioRxiv2024

AutoXAI4Omics: an Automated Explainable AI tool for Omics and tabular data

Machine learning (ML) methods have the potential of detailed insights of complex biological systems and today are increasingly used to analyse omics data for tasks such as the discovery of novel biomarkers and phenotype prediction. It can be extremely beneficial and powerful for scientists, domain experts, to easily run sophisticated, robust, and interpretable ML pipelines without the need for an in depth understanding of the code needed to train, tune, optimise ML algorithms. They can then focus on the biological interpretation and validation of the results and insights generated by ML models. Here, we present an entirely automated open-source explainable AI tool, AutoXAI4Omics, that performs classification and regression tasks from omics and tabular numerical data. AutoXAI4Omics accelerates scientific discovery by automating processes and decisions made by AI experts, e.g., selection of the best feature set, hyper-tuning of different ML algorithms and selection of the best ML model for a specific task and dataset. Prior to ML analysis AutoXAI4Omics incorporates feature filtering options that are tailored to specific omic data types. Moreover, the insights into the predictions that are provided by the tool through explainability analysis highlight associations between omic feature values and the targets under investigation e.g., predicted phenotypes, facilitating the discovery of actionable insights. AutoXAI4Omics is at: https://github.com/IBM/AutoXAI4Omics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/586460v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@366327org.highwire.dtl.DTLVardef@a7b559org.highwire.dtl.DTLVardef@7319aforg.highwire.dtl.DTLVardef@9b5030_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Actomyosin, vimentin and LINC complex pull on osteosarcoma nuclei to deform on micropillar topography

Cell deformation occurs in many critical biological processes, including cell extravasation during immune response and cancer metastasis. These cells deform the nucleus, its largest and stiffest organelle, while passing through narrow constrictions in vivo and the underlying mechanisms still remain elusive. It is unclear which biochemical actors are responsible and whether the nucleus is pushed or pulled (or both) during deformation. Herein we use an easily-tunable poly-L-lactic acid micropillar topography, mimicking in vivo constrictions to determine the mechanisms responsible for nucleus deformation. Using biochemical tools, we determine that actomyosin contractility, vimentin and nucleo-cytoskeletal connections play essential roles in nuclear deformation, but not A-type lamins. We chemically tune the adhesiveness of the micropillars to show that pulling forces are predominantly responsible for the deformation of the nucleus. We confirm these results using an in silico cell model and propose a comprehensive mechanism for cellular and nuclear deformation during confinement. These results indicate that microstructured biomaterials are extremely versatile tools to understand how forces are exerted in biological systems and can be useful to dissect and mimic complex in vivo behaviour.

cell biology↗

bioRxiv2026

There and back again: a multi-omics tale of thyroid co-expression network rewiring

The integration of multi-omics data offers unprecedented insight into complex biological systems but presents significant analytical challenges. In this study, we propose a best-practice framework for constructing simultaneous weighted gene co-expression networks (WGCNA) from transcriptomics, proteomics, and metabolomics data. Using a rodent model of thyroid toxicity induced by propylthiouracil (PTU), we analyzed thyroid tissues from control, treated, and recovery groups. We demonstrate that concatenating individually processed omics layers at the sample level--without additional scaling--preserves meaningful correlation structures and reflects best practices for biologically interpretable network construction. Co-expression networks were constructed for each group, revealing extensive disruption of molecular interactions under treatment and partial restoration during recovery. We highlight the complementary strengths of two analytical strategies: module preservation analysis identifies disrupted co-regulatory structures, while differential connectivity analysis detects feature-level rewiring events. As a methodological advance, we introduce a permutation-based approach for calculating feature-specific p-values for differential connectivity (DiffK), enabling robust statistical inference. This strategy uncovered over 4,400 significantly rewired features, many of which showed stable expression, underscoring the added value of network-based analyses. Our findings demonstrate the utility of integrated multi-omics WGCNA and differential network analysis in capturing dynamic, system-wide regulatory changes.

bioinformatics↗

bioRxiv2016

Tracking stem cell differentiation without biomarkers using pattern recognition and phase contrast imaging

Bio-image informatics is the systematic application of image analysis algorithms to large image datasets to provide an objective method for accurately and consistently scoring image data. Within this field, pattern recognition (PR) is a form of supervised machine learning where the computer identifies relevant patterns in groups (classes) of images after being trained on examples. Rather than segmentation, image-specific algorithms or adjustable parameter sets, PR relies on extracting a common set of image descriptors (features) from the entire image to determine similarities and differences between image classes.\n\nGross morphology can be the only available description of biological systems prior to their molecular characterization, but these descriptions can be subjective and qualitative. In principle, generalized PR can provide an objective and quantitative characterization of gross morphology, thus providing a means of computationally defining morphological biomarkers. In this study, we investigated the potential of a pattern recognition approach to a problem traditionally addressed using genetic or biochemical biomarkers. Often these molecular biomarkers are unavailable for investigating biological processes that are not well characterized, such as the initial steps of stem cell differentiation.\n\nHere we use a general contrast technique combined with generalized PR software to detect subtle differences in cellular morphology present in early differentiation events in murine embryonic stem cells (mESC) induced to differentiate by the overexpression of selected transcription factors. Without the use of reporters, or a priori knowledge of the relevant morphological characteristics, we identified the earliest differentiation event (3 days), reproducibly distinguished eight morphological trajectories, and correlated morphological trajectories of 40 mESC clones with previous micro-array data. Interestingly, the six transcription factors that caused the greatest morphological divergence from an ESC-like state were previously shown by expression profiling to have the greatest influence on the expression of downstream genes.

cell biology↗

bioRxiv2025

AlphaCross-XL: a seamless tool for automated and proteome-scale map-ping of crosslinked peptides onto three-dimensional protein structures

Crosslinking mass spectrometry (XL-MS) is an exciting proteomics technology to capture native protein conformations in real time within biological systems. Historically, however, implementation of this technology has typically been limited to single purified recombinant proteins or in vitro assembled protein complexes. These limitations are associated with inherent challenges in XL-MS analysis, including extremely low abundance of crosslinked (XL) peptides and complex deconvolution of XL peptide-derived spectral data. However, impressive recent developments in computation and instrumentation have now made it feasible to address biological questions using proteome-wide XL-MS analysis. Although some XL mapping software tools exist, these require manual input of specific Protein Data Bank (PDB) structures at the single protein level, and do not function at the high throughput scale required to analyze datasets derived from thousands of proteins. To address this need, we therefore sought to develop a strategy enabling automated mapping of XL peptides onto the three-dimensional (3D) structures of proteins, at a proteome-wide scale. Herein we describe AlphaCross-XL, a first-in-class seamless computational tool for automated mapping of XL peptides onto the protein structures for intra-protein crosslinks and loop-links. The AlphaCross-XL software first retrieves protein structures from the AlphaFold Protein Structure Database and maps all the identified crosslinks onto the 3D structure. It also calculates the Euclidian distance between the crosslinked residues and reports the violated and satisfied crosslink distances based on a user-defined distance threshold, which is visually discriminated by color in PyMOL. Lastly, the tool also supports further validation of user-submitted protein structures, that can include any computer-predicted protein structure and experimentally derived protein structures (i.e., from PDB). AlphaCross-XL is available at https://github.com/sanjyotshenoy/alphacross-xl.

bioinformatics↗

bioRxiv2026

The impact of homeostatic inhibitory plasticity in a generative biophysical model

A main characteristic of biological systems is their capacity to dynamically adapt to environmental changes. In the brain, synaptic plasticity enables the strengthening or weakening of connections between neurons, allowing neural circuits to adapt based on experience, learning, and environmental changes. Yet, it is homeostatically regulated such that it avoids excessive proliferation of synaptic contacts. These mechanisms can be studied with large-scale models of brain activity. Here, we embed a biologically grounded inhibitory-homeostatic plasticity rule into the Dynamic Mean Field (DMF) model, creating a Homeostatic Dynamic Mean Field (HDMF) model that dynamically tunes local excitation-inhibition balance. Convergence of excitatory firing rates is reached by mapping a large range of coupling strength to parameters of inhibitory synapses. The HDMF reproduces statistical observables of brain activity as well as the original DMF, and can sustain neuromodulatory perturbations without overhead computations. The HDMF can generate unprecedented sleep-like slow-wave activity, which can also coexist with wake-like asynchronous dynamics, permitting to model dissociated states of consciousness such as parasomnias. Together, these results show that a single homeostatic rule broadens the stability and expressiveness of the DMF, providing a unified platform for studying how local adaptive processes shape the diverse global dynamics of the human brain.

neuroscience↗

bioRxiv2014

In vivo generation of DNA sequence diversity for cellular barcoding

Heterogeneity is a ubiquitous feature of biological systems. A complete understanding of such systems requires a method for uniquely identifying and tracking individual components and their interactions with each other. We have developed a novel method of uniquely tagging individual cells in vivo with a genetic \"barcode\" that can be recovered by DNA sequencing. We demonstrate the feasibility of this technique in bacterial cells. This method should prove useful in tracking interactions of cells within a network, and/or heterogeneity within complex biological samples.

Synthetic Biology↗

bioRxiv2020

ATP driven diffusiophoresis: active cargo transport without motor proteins

Morphogenesis and homeostasis of biological systems are intricately linked to gradient formation through energy dissipation. Such spatial organization may be achieved via reaction-diffusion or directional cargo transport, as prominently executed by motor proteins. In contrast to these processes that rely on specific protein interactions, active transport based on a non-specific, purely physical mechanism remains poorly explored. Here, by a joint experimental and theoretical approach, we describe a hidden function of the MinDE protein system from E. coli: Besides forming dynamic patterns, this system accomplishes the active transport of large, functionally unrelated cargo on membranes in vitro. Remarkably, this mechanism allows to sort diffusive objects according to their effective size, as evidenced using modular DNA origami-streptavidin nanostructures. We show that the diffusive fluxes of MinDE and cargo couple via density-dependent friction. This non-specific process constitutes a Maxwell-Stefan diffusiophoresis, so far undescribed in a biologically relevant setting. Such nonlinear coupling between diffusive fluxes could represent a generic physical mechanism for the intracellular organization of biomolecules.

biophysics↗