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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2022

Unraveling membrane properties at the organelle-level with LipidDyn

Cellular membranes are formed from many different lipids in various amounts and proportions depending on the subcellular localization. The lipid composition of membranes is sensitive to changes in the cellular environment, and their alterations are linked to several diseases, including cancer. Lipids not only form lipid-lipid interactions but also interact with other biomolecules, including proteins, profoundly impacting each other. Molecular dynamics (MD) simulations are a powerful tool to study the properties of cellular membranes and membrane-protein interactions on different timescales and at varying levels of resolution. Over the last few years, software and hardware for biomolecular simulations have been optimized to routinely run long simulations of large and complex biological systems. On the other hand, high-throughput techniques based on lipidomics provide accurate estimates of the composition of cellular membranes at the level of subcellular compartments. The community needs computational tools for lipidomics and simulation data effectively interacting to better understand how changes in lipid compositions impact membrane function and structure. Lipidomic data can be analyzed to design biologically relevant models of membranes for MD simulations. Similar applications easily result in a massive amount of simulation data where the bottleneck becomes the analysis of the data to understand how membrane properties and membrane-protein interactions are changing in the different conditions. In this context, we developed LipidDyn, an in silico pipeline to streamline the analyses of MD simulations of membranes of different compositions. Once the simulations are collected, LipidDyn provides average properties and time series for several membrane properties such as area per lipid, thickness, diffusion motions, the density of lipid bilayers, and lipid enrichment/depletion. The calculations exploit parallelization and the pipelines include graphical outputs in a publication-ready form. We applied LipidDyn to different case studies to illustrate its potential, including membranes from cellular compartments and transmembrane protein domains. LipidDyn is implemented in Python and relies on open-source libraries. LipidDyn is available free of charge under the GNU General Public License from https://github.com/ELELAB/LipidDyn.

bioinformatics↗

bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗

bioRxiv2020

Ethylenediaminetetraacetic acid promotes the accumulation of nitric oxide

Ethylenediaminetetraacetic acid (EDTA) is a well-established chelating agent used in industry, agriculture, food, and medicine. However, the analysis of an EDTA-sensitive Arabidopsis thaliana mutant revealed that EDTA can significantly promote nitric oxide (NO) accumulation, indicating that EDTA has unexpected biological functions beyond its chelating activity. This finding challenges our current understanding about the effects of EDTA on biological systems. One Sentence SummaryAn analysis of ethylenediaminetetraacetic acid (EDTA)-sensitive mutants suggests that EDTA can promote the accumulation of nitric oxide.

molecular biology↗

bioRxiv2020

The molecular basis of Acinetobacter baumannii cadmium toxicity and resistance

Acinetobacter species are ubiquitous Gram-negative bacteria that can be found in water, soil and as commensals of the human skin. The successful inhabitation of Acinetobacter species in diverse environments is primarily attributable to the expression of an arsenal of stress resistance determinants, which includes an extensive repertoire of metal ion efflux systems. Although metal ion homeostasis in the hospital pathogen Acinetobacter baumannii is known to contribute to pathogenesis, insights into its metal ion transporters for environmental persistence are lacking. Here, we studied the impact of cadmium stress on A. baumannii. Our functional genomics and independent mutant analyses revealed a primary role for CzcE, a member of the cation diffusion facilitator (CDF) superfamily, in resisting cadmium stress. Further, we show that the CzcCBA heavy metal efflux system also contributes to cadmium efflux. Analysis of the A. baumannii metallome under cadmium stress showed zinc depletion and copper enrichment, which are likely to influence cellular fitness. Overall, this work expands our understanding of the role of membrane transporters in A. baumannii metal ion homeostasis. IMPORTANCECadmium toxicity is a widespread problem, yet the interaction of this heavy metal with biological systems is poorly understood. Some microbes have evolved traits to proactively counteract cadmium toxicity, which includes Acinetobacter baumannii. Here we show that A. baumannii utilises a dedicated cadmium efflux protein in concert with a system that is primarily attuned to zinc efflux, to efficiently overcome cadmium stress. The molecular characterization of A. baumannii under cadmium stress revealed how active cadmium efflux plays a key role in preventing the dysregulation of bacterial metal ion homeostasis, which appeared to be the primary means by which cadmium exerts toxicity upon the bacterium.

microbiology↗

bioRxiv2021

ExoBow: A transgenic strategy to study CD63 exosomes in vivo

Exosomes are described as central players in a myriad of biological processes. However, the available methodologies to study their function in complex biological systems in vivo are still very limited. The biodistribution of endogenously produced exosomes, the ability to trace their spontaneous flow in order to identify the cell types they interact with, remains a major challenge. New tools to identify comprehensive networks of communication established by exosomes originated in distinct cell types in vivo, are fundamental for a better understanding of their biology. Here, we describe the development of a genetically engineered mouse model that allows the expression of the mouse CD63 exosomal marker fused with one (monocolor) or up to four fluorescent proteins (multireporter), the ExoBow. The genetic design of the ExoBow transgene allows the conditional expression of the reporters in any tissue/cell-type in an inducible or non-inducible fashion. In addition, communication mediated by CD63 positive (CD63+) exosomes can be identified amongst the same tissue/cell types using the multireporter version of the model, in order to map intra-organ/tissue communication. We demonstrate the applicability of the ExoBow transgene in normal physiological conditions and in the context of cancer, using pancreas as a working model. The ExoBow comprises a unique strategy to identify intra- and inter-organ/cell-type communication mediated by CD63+ exosomes. We believe this tool will contribute for a better understanding of the complex interactions occurring in vivo that underly the biology of exosomes in health and disease.

cell biology↗

bioRxiv2024

Dispersion indices for universal quantification of fluorescently-labelled subcellular structure spatial distributions

Image analysis of subcellular structures and biological processes relies on specific, context-dependent pipelines, which are labor-intensive, constrained by the intricacies of the specific biological system, and inaccessible to broader applications. Here we introduce the application of dispersion indices, a statistical tool traditionally employed by economists, to analyze the spatial distribution and heterogeneity of subcellular structures. This computationally efficient high-throughput approach, termed GRID (Generalized Readout of Image Dispersion), is highly generalizable, compatible with open-source image analysis software, and adaptable to diverse biological scenarios. GRID readily quantifies diverse structures and processes to include autophagic puncta, mitochondrial clustering, and microtubule dynamics. Further, GRID is versatile, applicable to both 2D cell cultures and 3D multicellular organoids, and suitable for high-throughput screening and performance metric measurements, such as half-maximal effective concentration (EC50) values. The approach enables mechanistic analysis of critical subcellular structure processes of relevance for diseases ranging from metabolic and neuronal diseases to cancer as well as a first-pass screening method for identifying biologically active agents for drug discovery. Statement of SignificanceCurrent methods for image analysis in microscopy are tailored to specific biological contexts, which creates challenges in implementation and efficiency for researchers studying a diverse range of subcellular processes. Our application of dispersion indices, traditionally used in economics, offers a universal framework for high throughput quantification of biological structures, enabling easier and more consistent analysis across various biological contexts. By transforming pixel intensity and count into meaningful statistical measures, our method simplifies the quantification of subcellular structures such as autophagic puncta, microtubule dynamics, and mitochondrial clustering. This approach accelerates the quantitative analysis of sub-cellular processes in disease as well as imaged-based drug discovery. Classification: Bioengineering, Cell Biology, Applied Biological Sciences

cell biology↗

bioRxiv2025

An Integrative Multitiered Computational Analysis for Better Understanding the Structure and Function of 85 Miniproteins

BackgroundMiniproteins, defined as polypeptides containing fewer than 50 amino acids, have recently elicited significant interest due to an emerging understanding of their diverse roles in fundamental biological processes. In addition, miniprotein dysregulation underlies human diseases and is a significant focus for biotechnology and drug development. Notably, the human genome project revealed the existence of many novel miniproteins, most of which remain uncharacterized. This study reports an approach for analyzing and scoring previously uncharacterized miniproteins by integrating knowledge from classic sequence-based bioinformatics, computational biophysics, and system biology annotations. Our results demonstrate that these approaches provide novel information on the structure-function relationship of these molecules with a particular focus on their biomedical relevance. MethodsWe identified 85 human miniproteins using a simple multi-tier approach. First, we performed a sequence-based analysis of these proteins using several algorithms to identify regions of structural and functional importance. Protein-protein interactions and gene ontology annotations were used to analyze miniprotein function. Then, we predicted miniprotein three-dimensional structures using AI-based methods and peptide modeling to determine their relative yields for these understudied polymers. Subsequently, we used several computational biophysics methods and structure-based calculations to annotate and evaluate results from both algorithms. ResultsWe find several relations between predicted structure and functional properties to assign these proteins to several groups with similar properties. Sequence-based analysis leads us to identify motifs and residues that link structure-to-function for most of these proteins. We suggest novel miniprotein functions, such as thymosin beta proteins regulating the shelterin complex through TERF1 and POT1 interactions, FAM86JP and FAM66E participating in endocytic processes, and BAGE1 influencing chromatin remodeling through interaction with nuclear proteins. Further, known functions of miniproteins, such as STRIT1, STMP1, and SLN, were supported. Finally, structure-based scoring led us to build 3D models that provided complementary information to ontologies. We identify that structural propensity is not strictly dependent on polymer length. In fact, in this dataset, peptide-based algorithms may have advantages over AI-based algorithms for certain groups of miniproteins. ConclusionThis analytic approach and resulting identification and annotation of miniproteins adds much to what is currently known about miniproteins. Our determination of novel properties of miniproteins bears significant mechanistic and biomedical relevance. We propose novel functions of miniproteins, which expands our understanding of their potential roles in cellular processes. And, we practically identify which sequence and structure-based tools provide the most information, aiding future studies of miniproteins.

bioinformatics↗

bioRxiv2025

A Multi-Valued Logic Model of T Cell Activation and Cytokine Response

T cell activation results from the integration of multiple concurrent signals, including antigen engagement through the T cell receptor (TCR), co-stimulatory cues and inhibitory modulation. While traditional computational models often rely on Boolean logic to describe immune decision-making, these binary representations oversimplify the nuanced, reversible and gradated nature of actual immune responses. To address this limitation, we present a multi-valued logic framework that models T cell activation using three discrete levels--low, intermediate, and high--for each regulatory input. Focusing on cytokine production as a functional readout, our model uses threshold-based logic rules to compute output levels based on combinations of TCR, co-stimulatory and inhibitory signals. We analyzed the system through both deterministic state-space exploration and stochastic asynchronous simulations, capturing both static outcomes and dynamic trajectories. Results reveal that cytokine output scales proportionally with activating inputs, while inhibitory signals modulate these responses in a graded, context-sensitive manner. The model avoids bi-stability or irreversible attractors, instead reflecting the flexible and reversible behavior observed in biological systems. Comparative analysis with experimental data from literature supports key assumptions of the model, including dose-dependent cytokine production and the enhancing effect of co-stimulation. In contrast to purely Boolean or continuous models, our multi-valued approach strikes a balance between interpretability and computational efficiency. It provides a flexible and tractable framework for representing immune logic and is well-suited for integration into larger, systems-level models of immune function.

immunology↗

bioRxiv2026

uniSynK: a ratio-optimized bicistronic chemically induced dimerization tool for robust induction of dendritic spine enlargement

Synaptic plasticity is widely implicated in learning and brain-state regulation. Although correlations between synaptic modifications and diverse brain functions have been extensively reported, establishing causality requires tools that can directly and selectively perturb synapses in vivo. Recently, the chemogenetic actuator SYNCit-K (SynK) enabled causal induction of dendritic spine enlargement and synaptic potentiation. However, SynK relies on the co-expression of two independent components delivered by separate viral vectors, making it difficult to reliably achieve appropriate expression levels of both components within the same neuron. Here, using mouse neocortical dissociated culture, we sought to develop a unified, single-vector version of SynK (uniSynK) that enables robust and safe induction of dendritic spine enlargement. We first found that each SynK component induces distinct, concentration-dependent abnormalities in neuronal morphology when overexpressed, underscoring the need for precise control of their expression ratio. To address this challenge, we engineered a single construct in which the two components are linked by internal ribosome entry site (IRES) variants with graded translational efficiencies. Systematic screening of IRES variants identified an optimized configuration that preferentially drives each component within its respective concentration range that preserves normal spine morphology. The optimized uniSynK efficiently induces dendritic spine enlargement, while exhibiting fewer off-target morphological abnormalities than observed in the multi-vector system when expression levels were not properly controlled. By simplifying experimental design, this single-vector, ratio-optimized synaptic chemogenetic tool provides a practical and broadly applicable platform that should facilitate causal interrogation of synaptic plasticity across neural circuits and behavior. Significance StatementSynaptic plasticity is thought to shape learning, brain states, and neuropsychiatric disease, but testing its causal roles requires tools that can directly manipulate synapses with minimal side effects. Here, we developed uniSynK, a single-vector chemogenetic system that induces dendritic spine enlargement by coordinating the expression of two functional components. By defining safe expression ranges and using IRES variants to optimize their ratio, uniSynK achieves robust spine enlargement while reducing unintended morphological abnormalities associated with imbalanced expression. This work provides both a practical tool for synaptic manipulation and a general strategy for optimizing multi-component biological systems.

neuroscience↗

bioRxiv2026

A dCas9 Proximity Reporter for Investigating DNA Linear and Rotational Dynamics

The dCas9 system has rapidly been developed into many tools to explore different aspects of the human genome; the high binding specificity, coupled with the inactive nuclease enzyme, allows for precise recruitment of molecules to specific sequences of DNA. We sought to exploit these capabilities to create a tool for assessing the real-time proximity of two DNA sequences within a biological system. By incorporating aptamers into gRNAs, dCas9 molecules can be used to recruit the {beta}9 or {beta}10 strands of split-NanoLuc(R) to specific DNA sequences and quantify the proximity of those sequences based on their ability to complex with the luciferase fragment ({Delta}11S) and produce luminescence. While many tools exist to detect a single DNA sequence, this system is uniquely capable of assessing how two DNA sequences interact with each other. As expected, we found that the interaction of two dCas9 molecules was affected by their linear distance from each other on dsDNA. Surprisingly, we found that their interaction was also strongly influenced by rotational orientation, even for sequences that are close together in linear space. This finding indicates that dCas9 rotational alignment is an important consideration for designing dCas9 systems that target multiple DNA sequences simultaneously. Beyond the findings presented herein, we believe this DNA proximity detection tool has the potential to be adapted for applications involving the proximity and orientation of two DNA sequences.

bioengineering↗

bioRxiv2017

Genetic networks of the oxytocin system in the human brain: A gene expression and large-scale fMRI meta-analysis study

Oxytocin is a neuropeptide involved in animal and human reproductive and social behaviour, with potential implications for a range of psychiatric disorders. However, the therapeutic potential of oxytocin in mental health care suggested by animal research has not been successfully translated into clinical practice, partly due to a poor understanding of the expression and distribution of the oxytocin signaling pathway in the human brain, and its complex interactions with other biological systems. Among the genes involved in the oxytocin signaling pathway, three genes have been frequently implicated in human social behavior: OXT (structural gene for oxytocin), OXTR (oxytocin receptor), and CD38 (central oxytocin secretion). We characterized the distribution of the OXT, OXTR, and CD38 mRNA across the brain, identified putative gene pathway interactions by comparing gene expression patterns across 29131 genes, and assessed associations between gene expression patterns and cognitive states via large-scale fMRI meta-analysis. In line with the animal literature, oxytocin pathway gene expression was enriched in central, temporal, and olfactory regions. Across the brain, there was high co-expression of the oxytocin pathway genes with both dopaminergic (DRD2) and muscarinic acetylcholine (CHRM4) genes, reflecting an anatomical basis for critical gene pathway interactions. Finally, fMRI meta-analysis revealed that oxytocin pathway maps correspond with motivation and emotion processing, demonstrating the value of probing gene expression maps to identify brain functional targets for future pharmacological trials.

neuroscience↗

bioRxiv2018

CONFESS: Fluorescence-based single-cell ordering in R

Modern high-throughput single-cell technologies facilitate the efficient processing of hundreds of individual cells to comprehensively study their morphological and genomic heterogeneity. Fluidigms C1 Auto Prep system isolates fluorescence-stained cells into specially designed capture sites, generates high-resolution image data and prepares the associated cDNA libraries for mRNA sequencing. Current statistical methods focus on the analysis of the gene expression profiles and ignore the important information carried by the images. Here we propose a new direction for single-cell data analysis and develop CONFESS, a customized cell detection and fluorescence signal estimation model for images coming from the Fluidigm C1 system. Applied to a set of HeLa cells expressing fluorescence cell cycle reporters, the method predicted the progression state of hundreds of samples and enabled us to study the spatio-temporal dynamics of the HeLa cell cycle. The output can be easily integrated with the associated single-cell RNA-seq expression profiles for deeper understanding of a given biological system. CONFESS R package is available at Bioconductor (http://bioconductor.org/packages/release/bioc/html/CONFESS.html).

bioinformatics↗

bioRxiv2020

Distinct expression of select and transcriptome-wide isolated 3'UTRs suggests critical roles in development and transition states

Mature mRNA molecules are typically considered to be comprised of a 5UTR, a 3UTR and a coding region (CDS), all attached until degradation. Unexpectedly, however, there have been multiple recent reports of widespread differential expression of mRNA 3UTRs and their cognate coding regions, resulting in the expression of isolated 3UTRs (i3UTRs); these i3UTRs can be highly expressed, often in reciprocal patterns to their cognate CDS. Similar to the role of other lncRNAs, isolated 3UTRs are likely to play an important role in gene regulation but little is known about the contexts in which they are deployed. To begin to parse the functions of i3UTRs, here we carry out in vitro, in vivo and in silico analyses of differential 3UTR/CDS mRNA ratio usage across tissues, development and cell state changes both for a select list of developmentally important genes as well as through unbiased transcriptome-wide analyses. Across two developmental paradigms we find a distinct switch from high i3UTR expression of stem cell related genes in proliferating cells compared to newly differentiated cells. Our unbiased transcriptome analysis across multiple gene sets shows that regardless of tissue, genes with high 3UTR to CDS ratios belong predominantly to gene ontology categories related to cell-type specific functions while in contrast, the gene ontology categories of genes with low 3UTR to CDS ratios are similar and relate to common cellular functions. In addition to these specific findings our data provide critical information from which detailed hypotheses for individual i3UTRs can be tested-with a common theme that i3UTRs appear poised to regulate cell-specific gene expression and state. Significance StatementThe widespread existence and expression of mRNA 3 untranslated sequences in the absence of their cognate coding regions (called isolated 3UTRs or i3UTRs) opens up considerable avenues for gene regulation not previously envisioned. Each isolated 3UTR may still bind and interact with micro RNAs, RNA binding proteins as well as other nucleic acid sequences, all in the absence or low levels of cognate protein production. Here we document the expression, localization and regulation of i3UTRs both within particular biological systems as well as across the transcriptome. As this is an entirely new area of experimental investigation these early studies are seminal to this burgeoning field.

cell biology↗

bioRxiv2025

AOP Net: An AI-Enhanced Software Platform for the Visualization and Analysis of Complex Toxicological Pathways

AO_SCPLOWBSTRACTC_SCPLOWThe Adverse Outcome Pathway (AOP) framework is a cornerstone of 21st-century toxicology, providing a structured method for organizing mechanistic knowledge to support risk assessment. However, the inherent complexity of biological systems, characterized by interconnected signaling networks, reveals the limitations of simple, linear AOP representations. This has created a pressing need for advanced computational tools that can visualize and analyze these intricate AOP networks. This study introduces the AOP Network Viewer, a novel software platform designed to address the critical challenges of scalability, interactivity, and deep analysis in toxicological research. The system is built on a scalable client-server architecture with a graph database backend, supporting sophisticated interactive features such as multi-pathway selection, dynamic path-finding, and advanced filtering. The principal innovation of the AOP Network Viewer is its integrated AI Analysis Panel, a Large Language Model (LLM)-powered engine that provides a deep, context-aware analysis of selected network components. This AI system utilizes a unique three-layer prompt architecture to deliver domain-specific insights across seven specialized analytical modes. Case studies demonstrate the platforms utility in developing alternative testing strategies for endocrine disruptors, interpreting high-throughput screening data, and generating hypotheses for toxicity assessment of mixtures. The AOP Network Viewer is engineered to translate complex user queries into focused, interpretable biological networks, transforming AOP network visualization from a passive viewing experience into an interactive and intelligent analytical process, thereby accelerating mechanistic discovery and enhancing evidence-based regulatory decision-making. The app is source code is available at https://github.com/abhik1368/aop-visualizer-clean.

bioinformatics↗

bioRxiv2026

netPCF: Geometry-Aware Pair Correlation Functions for Spatial Biology

Spatial organisation is a defining feature of biological systems, underpinning cellular interactions, tissue function, disease progression and therapeutic response. Identifying and quantifying spatial organisation may require methods that resolve relationships across spatial scales. The pair correlation function (PCF) quantifies spatial dependence between points across multiple length scales, but its standard Euclidean formulation is poorly suited to data defined on irregular, curved or otherwise structured domains, where tissue geometry may constrain biological organisation and distort Euclidean distances. Here, we introduce netPCF, a geometry-aware extension of the PCF for quantifying spatial organisation on complex biological domains. By representing tissue structures, anatomical surfaces and other constrained geometries as spatial networks, netPCF generalises the PCF beyond extrinsic Euclidean settings. The framework derives the expected behaviour of the statistic under complete spatial randomness using interpretable finite-support kernels, provides bootstrap-based uncertainty quantification, and includes practical criteria for assessing domain discretisation adequacy. We further extend netPCF to marked (labelled) biological data using feature kernels for categorical and continuous attributes, enabling unified analysis of cell identities, marker intensities, phenotypic states, gene expression and other quantitative features on structured domains in any spatial dimension. All methods are implemented in the open-source Python package spacenet. Synthetic studies show that netPCF recovers classical Euclidean behaviour on sufficiently resolved networks and is robust to common imaging noise. We demonstrate its utility in two biological applications. In three-dimensional imaging mass cytometry data from HER2+ breast carcinoma, netPCF separates tissue architecture-driven proximity from biologically meaningful endothelial and immune cell organisation. In reconstructed surfaces of developing murine embryos, netPCF identifies a transition in the Wnt1 -Wnt6 relationship from short-range co-localisation at E9.5 to spatial exclusion at E11.5, a pattern of ectodermal boundary refinement not captured by prior voxel-wise co-expression analysis. Overall, netPCF provides a statistically grounded and practical framework for quantifying spatial organisation on complex biological domains. Author summarySpatial organisation is central to many biological processes, but it is often measured using distances that ignore the shape of the tissue or structure being studied. We introduce netPCF, a method for quantifying multiscale spatial correlation in data that lie on complex biological domains, including irregular, curved, or branching structures. netPCF reconstructs the domain as a distance-preserving spatial network and estimates pair correlation along this intrinsic geometry, allowing spatial associations to be interpreted relative to the structure in which they occur. The framework includes uncertainty estimates and extensions for categorical and continuous markers, supporting analysis of cell types, marker intensities, phenotypic states, and gene expression patterns. In synthetic data, netPCF recovers expected spatial behaviour on well-resolved networks. In biological imaging data, it distinguishes apparent cell proximity caused by breast carcinoma tissue architecture from biologically meaningful cell organisation, and reveals a developmental transition in Wnt gene organisation over the surface of a murine embryo that direct co-expression analysis does not capture. netPCF is available in the open-source Python package spacenet, supplemented with online tutorials supporting practical use across spatial biology applications.

bioinformatics↗

bioRxiv2022

Differentiation is accompanied by a progressive loss in transcriptional memory

Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (sc-RT-qPCR and scRNA-seq). We identified that the gene transcription profiles of differentiating sister-cells are more similar to each-other than to those of non related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister-cells than between self-renewing sister-cells. Furthermore, a continuous increase in this divergence from first generation to second generation was observed when comparing differentiating cousin-cells to self renewing cousin-cells. Our results are in favor of a continuous and gradual erasure of transcriptional memory during the differentiation process.

cell biology↗

bioRxiv2023

Generating the sigmoid form in neuroscience: A threshold-crossing model

The conventional interpretation1 of a logistic curve in biology is that it indicates the presence of an infection-like mechanism bounded by a saturation limit e.g. saturation occurs when the whole population has been infected. However, a logistic form may be generated by the fluctuating approach of biological systems to, and then exceeding, an effect threshold. The commonly observed trends in frequency and degree of cognitive loss in neurodegenerative disease (NDD) are consistent with a threshold-crossing model. Whereas direct evidence of real life NDD being driven by an infection-like mechanism remains elusive2,3. Variation in susceptibility to NDD may be explained by variation in the rate of biological ageing (e.g. neurodegeneration rate, or, metabolic interference), height of threshold and initial reserve. Each is potentially modifiable and clinically informative.

neuroscience↗

bioRxiv2024

ScPectral: Spectrally Clustering HypergraphRepresentations of Transcription Networks to Identify Developmental Pathways

Transcription Networks, otherwise known as Gene Regulatory Networks (GRNs), are models of biological systems centred on Transcription Factor (TF) interactions. These models equip experimentalists with a powerful computational tool to predict the effects of different genetic perturbations. GRNs are canonically modelled using a digraph, wherein the arcs indicate activation or repression between each pair of nodes to represent the relationships among the TFs. However, gene regulation is accomplished by groups of TFs working in concert, a biological reality the pairwise model neglects. In addition to the paucity of GRN representations incorporating this known TF biology, a persisting challenge to inference of the networks themselves is in accounting for the latent dynamics of gene interactions. In considering this second point, the advent of single-cell RNA sequencing technologies, provides the high resolution data needed to begin effectively inferring temporally-aware models. Despite this, utilisation of temporally-aware statistical metrics to do so has been limited. In addressing these shortcomings to GRN inference, scPectral is introduced as a method to infer a robust dynamic representation of a common GRN motif, the cascade, in the form of a hypergraph. ScPectral is applied to the identification of developmental pathways for known processes to validate its efficacy. Given scPectrals modest success in finding key constituents of developmental pathways, and its ability to do so in a manner requiring no input or annotation of known biology, through further improvement it may develop to become a technique able to aid experimentalists exploring novel development processes. ScPectral is made available at: https://github.com/Dennis-Bersenev/scPectral.

bioinformatics↗