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Results for “Cell Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Red blood cell dynamics in extravascular biological tissues modelled as canonical disordered porous media

The dynamics of blood flow in the smallest vessels and passages of the human body, where the cellular character of blood becomes prominent, plays a dominant role in the transport and exchange of solutes. Recent studies have revealed that the micro-haemodynamics of a vascular network is underpinned by its interconnected structure, and certain structural alterations such as capillary dilation and blockage can substantially change blood flow patterns. However, for extravascular media with disordered microstructure (e.g., the porous intervillous space in the placenta), it remains unclear how the mediums structure affects the haemodynamics. Here, we simulate cellular blood flow in simple models of canonical porous media representative of extravascular biological tissue, with corroborative microfluidic experiments performed for validation purposes. For the media considered here, we observe three main effects: first, the relative apparent viscosity of blood increases with the structural disorder of the medium; second, the presence of red blood cells (RBCs) dynamically alters the flow distribution in the medium; third, increased structural disorder of the medium can promote a more homogeneous distribution of RBCs. Our findings contribute to a better understanding of the cellscale haemodynamics that mediates the relationship linking the function of certain biological tissues to their microstructure.

biophysics↗

bioRxiv2020

Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells

Cell motility is a crucial biological function for many cell types, including the immune cells in our body that act as first responders to foreign agents. In this work we consider the amoeboid motility of human neutrophils, which show complex and continuous morphological changes during locomotion. We imaged live neutrophils migrating on a 2D plane and extracted unbiased shape representations using cell contours and binary masks. We were able to decompose these complex shapes into low-dimensional encodings with both principal component analysis (PCA) and an unsupervised deep learning technique using variational autoencoders (VAE), enhanced with generative adversarial networks (GANs). We found that the neural network architecture, the VAE-GAN, was able to encode complex cell shapes into a low-dimensional latent space that encodes the same shape variation information as PCA, but much more efficiently. Contrary to the conventional viewpoint that the latent space is a "black box", we demonstrated that the information learned and encoded within the latent space is consistent with PCA and is reproducible across independent training runs. Furthermore, by including cell speed into the training of the VAE-GAN, we were able to incorporate cell shape and speed into the same latent space. Our work provides a quantitative framework that connects biological form, through cell shape, to a biological function, cell movement. We believe that our quantitative approach to calculating a compact representation of cell shape using the VAE-GAN provides an important avenue that will support further mechanistic dissection of cell motility. AUTHOR SUMMARYDeep convolutional neural networks have recently enjoyed a surge in popularity, and have found useful applications in many fields, including biology. Supervised deep learning, which involves the training of neural networks using existing labeled data, has been especially popular in solving image classification problems. However, biological data is often highly complex and continuous in nature, where prior labeling is impractical, if not impossible. Unsupervised deep learning promises to discover trends in the data by reducing its complexity while retaining the most relevant information. At present, challenges in the extraction of meaningful human-interpretable information from the neural networks nonlinear discovery process have earned it a reputation of being a "black box" that can perform impressively well at prediction but cannot be used to shed any meaningful insight on underlying mechanisms of variation in biological data sets. Our goal in this paper is to establish unsupervised deep learning as a practical tool to gain scientific insight into biological data by first establishing the interpretability of our particular data set (images of the shapes of motile neutrophils) using more traditional techniques. Using the insight gained from this as a guide allows us to shine light into the "black box" of unsupervised deep learning.

cell biology↗

bioRxiv2016

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

Single-cell RNA-sequencing (scRNA-seq) allows heterogeneity in gene expression levels to be studied in large populations of cells. Such heterogeneity can arise from both technical and biological factors, thus making decomposing sources of variation extremely difficult. We here describe a computationally efficient model that uses prior pathway annotation to guide inference of the biological drivers underpinning the heterogeneity. Moreover, we jointly update and improve gene set annotation and infer factors explaining variability that fall outside the existing annotation. We validate our method using simulations, which demonstrate both its accuracy and its ability to scale to large datasets with up to 100,000 cells. Moreover, through applications to real data we show that our model can robustly decompose scRNA-seq datasets into interpretable components and facilitate the identification of novel sub-populations.

bioinformatics↗

bioRxiv2020

scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures

The most fundamental challenge in current single-cell RNA-seq data analysis is functional interpretation and annotation of cell clusters. The biological pathways in distinct cell types have different activation patterns, which facilitates understanding cell functions in single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptomic data analysis based on prior biological pathway knowledge. Here, we introduce scTPA (http://sctpa.bio-data.cn/sctpa), which is a web-based platform providing pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Together, scTPA is a comprehensive tool to identify pathway activation signatures for dissecting single cell heterogeneity.

genomics↗

bioRxiv2024

A β-catenin:MSI2 axis regulates the expression of LEF1 and subsequent human haematopoietic stem/progenitor cell proliferation

Wnt/{beta}-catenin signaling is important for normal hematopoietic stem/progenitor cell (HSPC) biology and heavily implicated in acute myeloid leukaemia (AML). The central mediator {beta}-catenin is an attractive therapeutic target in AML however its targeting has been hampered by poor characterisation of its molecular interactions in haematopoietic cells. Our previous {beta}-catenin interactome study identified the significant enrichment of RNA-binding proteins (RBP) implying post-transcriptional roles for {beta}-catenin in myeloid cells. To identify {beta}-catenin-associated mRNAs we performed {beta}-catenin RNA-immunoprecipitation coupled to RNA-sequencing (RIP-seq) and identified significantly enriched Wnt signalling pathway transcripts. Using {beta}-catenin cross-linking immunoprecipitation (CLIP) we demonstrated a limited capacity for {beta}-catenin to bind RNA directly implying dependence on other RBPs. {beta}-Catenin was found to interact with MSI2 in both myeloid cell lines and AML patient samples, where expression was significantly correlated. MSI2 knockdown reduced Wnt signalling output (TCF/LEF activity), through suppression of LEF-1 expression and nuclear localization. Through both RIP and CLIP we demonstrate MSI2 binds LEF1 mRNA in a partly {beta}-catenin dependent fashion, and may impact the post-transcriptional control of LEF-1 expression. Finally, we show that MSI2-mediated expansion of human HSPCs could be partly driven through LEF1 regulation. This is the first study to show functional crosstalk between MSI2 and Wnt signalling in human cells, and indicates potential novel post-transcriptional roles for {beta}-catenin in a haematological context.

cell biology↗

bioRxiv2022

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA reveals novel spatial biology

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine learning algorithm, CELESTA, which uses both cells protein expression and spatial information to identify cell type of individual cells. We demonstrate the performance of CELESTA on multiplexed immunofluorescence in situ images of colorectal cancer and head and neck cancer. Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, which we validate in an independent cohort. By coupling our in situ spatial analysis with single-cell RNA-sequencing data on proximal sections of the same tissue specimens, we identify and validate cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of spatial biology to reveal clinically-relevant cellular interactions.

bioinformatics↗

bioRxiv2025

An Automated End-to-End Workflow for Production of Secreted Proteins in Transfected Mammalian Cells

The advancement of automation technologies has helped to enable a surge in large-scale screening efforts across fields such as molecular biology, protein biochemistry, cell biology, and structural biology. In the context of this "omics"-driven research, there is a need to generate automation platforms that are more flexible and less expensive, so that they can be utilized for basic research conducted by small groups. A key challenge in automation lies in developing methods that can replicate fine motor techniques that are normally performed manually by researchers at the bench. We are engaged in a large-scale project to map interactions among human cell-surface and secreted proteins and assess their effects on cells. This project involves production of a library of more than 2000 recombinant His-tagged fusion proteins secreted from transfected Expi293 cells. To execute such a project with a small group at an academic institution required construction of an affordable automated system that could also be used by other investigators. This led us to develop a high-throughput, 96-well format automation platform for end-to-end protein production. The workflow includes transformation of E. coli, plasmid DNA preparation, transient transfection, protein purification, desalting and buffer exchange, protein quantification, and normalization of protein concentrations, resulting in assay-ready proteins. The system is built around an in-house engineered modular robotic platform that integrates liquid handling with a suite of interchangeable plug-and-play mobile enclosed device modules. Housed within a BSL-2 sterile environment, the platform enables flexible, fully automated workflows and can be readily customized for diverse user-defined protocols.

biochemistry↗

bioRxiv2019

Rosa26 docking sites for investigating genetic circuit silencing in stem cells

ABSTRACTApproaches in mammalian synthetic biology have transformed how cells can be programmed to have reliable and predictable behaviour, however, the majority of mammalian synthetic biology has been accomplished using immortalized cell lines that are easy to grow and easy to transfect. Genetic circuits that integrate into the genome of these immortalized cell lines remain functional for many generations, often for the lifetime of the cells, yet when genetic circuits are integrated into the genome of stem cells gene silencing is observed within a few generations. To investigate the reactivation of silenced genetic circuits in stem cells, the Rosa26 locus of mouse pluripotent stem cells was modified to contain docking sites for site-specific integration of genetic circuits. We show that the silencing of genetic circuits can be reversed with the addition of sodium butyrate, a histone deacetylase inhibitor. These findings demonstrate an approach to reactivate the function of genetic circuits in pluripotent stem cells to ensure robust function over many generations. Altogether, this work introduces an approach to overcome the silencing of genetic circuits in pluripotent stem cells that may enable the use of genetic circuits in pluripotent stem cells for long-term function.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cell biology↗

bioRxiv2026

GraphHDBSCAN*: Graph-based Hierarchical Clustering on High Dimensional Single-cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is widely used to resolve cellular heterogeneity across thousands to millions of cells. A major challenge is to identify biologically meaningful cell populations while preserving their hierarchical organization, because broad cell types frequently split into more specialized subtypes. However, state-of-the-art approaches mostly focus on flat partitions and ignore the hierarchical structure of single-cell data. Here we introduce GraphHDBSCAN*, a graph-based, hyperparameter-free extension of HDBSCAN* that performs hierarchical density-based clustering on a graph representation of the data, enabling robust recovery of both single-level and hierarchical relationships in high-dimensional and sparse datasets. We evaluate GraphHDBSCAN* across multiple scRNA-seq datasets and show that it recovers biologically meaningful hierarchies that reveal fine-grained structure in complex data, including monocyte subpopulations. In addition, the method yields high-quality flat partitions that outperform widely used community-detection methods.

bioinformatics↗

bioRxiv2026

Epithelial Reprogramming and Transition during Pulmonary Bioengineering

Recent research has emphasized the critical role of cell state transitions in tissue homeostasis. In lung biology, transitional cells are recognized as a feature of tissue-scale processes during both normal physiology and disease. The precise way that transitional cell states emerge and are regulated remains to be determined. Engineered tissues, built in a laboratory through bioengineering approaches, allow detailed study of cellular states that are not commonly found in native biology, and allow opportunities to directly induce and manipulate cellular transitions. The following study explores and characterizes epithelial cell states that emerge via cellular reprogramming in a tissue engineering context. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/701406v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@1d456f3org.highwire.dtl.DTLVardef@1989ba5org.highwire.dtl.DTLVardef@127dd7org.highwire.dtl.DTLVardef@3dd7a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2021

The origin of universal cell shape variability in a confluent epithelial monolayer

Cell shape is fundamental in biology. The average cell shape can influence crucial biological functions, such as cell fate and division orientation. But cell-to-cell shape variability is often regarded as noise. In contrast, recent works reveal that shape variability in diverse epithelial monolayers follows a nearly universal distribution. However, the origin and implications of this universality are unclear. Here, assuming contractility and adhesion are crucial for cell shape, characterized via aspect ratio (AR), we develop a mean-field analytical theory for shape variability. We find that a single parameter, , containing all the system-specific details, describes the probability distribution function (PDF) of AR; this leads to a universal relation between the standard deviation and the average of AR. The PDF for the scaled AR is not strictly but almost universal. The functional form is not related to jamming, contrary to common beliefs, but a consequence of a mathematical property. In addition, we obtain the scaled area distribution, described by the parameter {micro}. We show that and {micro} together can distinguish the effects of changing physical conditions, such as maturation, on different system properties. The theory is verified in simulations of two distinct models of epithelial monolayers and agrees well with existing experiments. We demonstrate that in a confluent monolayer, average shape determines both the shape variability and dynamics. Our results imply the cell shape variability is inevitable, where a single parameter describes both statics and dynamics and provides a framework to analyze and compare diverse epithelial systems.

biophysics↗

bioRxiv2020

Fe(III) heme sets an activation threshold for processing distinct groups of pri-miRNAs in mammalian cells

The essential biological cofactor heme is synthesized in cells in the Fe(II) form. Oxidized Fe(III) heme is specifically required for processing primary transcripts of microRNAs (pri-miRNAs) by the RNA-binding protein DGCR8, a core component of the Microprocessor complex. It is unknown how readily available Fe(III) heme is in the largely reducing environment in human cells and how changes in cellular Fe(III) heme availability alter microRNA (miRNA) expression. Here we address the first question by characterizing DGCR8 mutants with various degrees of deficiency in heme-binding. We observed a strikingly simple correlation between Fe(III) heme affinity in vitro and the Microprocessor activity in HeLa cells, with the heme affinity threshold for activation estimated to be between 0.6-5 pM under typical cell culture conditions. The threshold is strongly influenced by cellular heme synthesis and uptake. We suggest that the threshold reflects a labile Fe(III) heme pool in cells. Based on our understanding of DGCR8 mutants, we reanalyzed recently reported miRNA sequencing data and conclude that heme is generally required for processing canonical pri-miRNAs, that heme modulates the specificity of Microprocessor, and that cellular heme level and differential DGCR8 heme occupancy alter the expression of distinct groups of miRNAs in a hierarchical fashion. Overall, our study provides the first glimpse of a labile Fe(III) heme pool important for a fundamental physiological function and reveal principles governing how Fe(III) heme modulates miRNA maturation at a genomic scale. We also discuss potential states and biological significance of the labile Fe(III) heme pool.

molecular biology↗

bioRxiv2024

Identifying Representative Cell Line Models for TNBC Chemotherapy Drug Resistance via Systems Biology and Bioinformatics

Cancer cell lines, derived from tumors, have become essential tools in life science research and are commonly employed as experimental model systems in cancer research. However, researchers often overlook the similarities between clinical samples when selecting cell line models, potentially impacting the validity and applicability of their findings. In the context of triple-negative breast cancer (TNBC) chemotherapy drug resistance, our study aims to provide guidance for selecting appropriate cell line models by employing a combination of systems biology and bioinformatic approaches. These approaches, including hierarchical clustering analysis, Spearmans rank correlation, and single sample gene set enrichment analysis (ssGSEA), allowed us to identify the most representative cell models that correspond to poor chemotherapy responders among TNBC patients.

bioinformatics↗

bioRxiv2021

An improved organ explant culture method reveals stem cell lineage dynamics in the adult Drosophila intestine

In recent years, live-imaging techniques have been developed for the adult midgut of Drosophila melanogaster that allow temporal characterization of key processes involved in stem cell and tissue homeostasis. However, current organ culture techniques are limited to imaging sessions of [≤]16 hours, an interval too short to track dynamic processes such as damage responses and regeneration, which can unfold over several days. Therefore, we developed a new organ explant culture protocol capable of sustaining midguts ex vivo for up to 3 days. This was made possible by the formulation of a culture medium specifically designed for adult Drosophila tissues with an increased Na+/K+ ratio and trehalose concentration, and by placing midguts at an air-liquid interface for enhanced oxygenation. We show that midgut progenitor cells can respond to gut epithelium damage ex vivo, proliferating and differentiating to replace lost cells, but are quiescent in healthy intestines. Using ex vivo gene induction to promote stem cell proliferation, we demonstrate that intestinal stem lineages can be traced through multiple cell divisions using live imaging. Both asymmetric and symmetric divisions can be identified in the reconstructed lineages. We find that daughter cells of asymmetric divisions remain in close proximity of each other, while the progeny of symmetric divisions actively move apart, with implications for cell differentiation and tissue organization. We show that the same culture set-up is useful for imaging adult renal tubules and ovaries for up to 72 hours. By enabling both long-term imaging and real-time ex vivo gene manipulation, our simple culture protocol provides a powerful tool for studies of epithelial biology and cell lineage behavior.

cell biology↗

bioRxiv2014

Filament formation by metabolic enzymes is a specific adaptation to an advanced state of cellular starvation

One of the key questions in biology is how the metabolism of a cell responds to changes in the environment. In budding yeast, starvation causes a drop in intracellular pH, but the functional role of this pH change is not well understood. Here, we show that the enzyme glutamine synthetase (Gln1) forms filaments at low pH and that filament formation leads to enzyme inactivation. Filament formation by Gln1 is a highly cooperative process, strongly dependent on macromolecular crowding, and involves back-to-back stacking of cylindrical homo-decamers into filaments that associate laterally to form higher order fibrils. Other metabolic enzymes also assemble into filaments at low pH. Hence, we propose that filament formation is a general mechanism to inactivate and store key metabolic enzymes during a state of advanced cellular starvation. These findings have broad implications for understanding the interplay between nutritional stress, the metabolism and the physical organization of a cell.

Cell Biology↗

bioRxiv2016

Comparative assessment of fluorescent proteins for in vivo imaging in an animal model system

Fluorescent protein tags are fundamental tools used to visualize gene products and analyze their dynamics in vivo. Recent advances in genome editing have enabled precise insertion of fluorescent protein tags into the genomes of diverse organisms. These advances expand the potential of in vivo imaging experiments, and they facilitate experimentation with new, bright, photostable fluorescent proteins. Most quantitative comparisons of the brightness and photostability of different fluorescent proteins have been made in vitro, removed from biological variables that govern their performance in cells or organisms. To address the gap we quantitatively assessed fluorescent protein properties in vivo in an animal model system. We generated transgenic C. elegans strains expressing green, yellow, or red fluorescent proteins in embryos, and we imaged embryos expressing different fluorescent proteins under the same conditions for direct comparison. We found that mNeonGreen was not bright in vivo as predicted based on in vitro data, but that mNeonGreen is a better tag than GFP for specific kinds of experiments, and we report on optimal red fluorescent proteins. These results identify ideal fluorescent proteins for imaging in vivo in C. elegans embryos, and they suggest good candidate fluorescent proteins to test in other animal model systems.

Cell Biology↗

bioRxiv2020

Endothelial Cells stimulate proliferation of CD140a sorted human Glial Progenitor Cells and their specification towards astrocytic lineage.

Homeostasis in stem cell niche is established by fine-tuning between interacting signaling pathways of all resident cell types that constitute the niche. In this context, the exact contribution of human endothelial cells in biology of PDGFR positive human glial progenitor cells (hGPCs) in niche environment is not well understood. Towards the same, insert co-culture system with human umbilical vein endothelial cells (HUVECs) has been adopted. In co-culture with HUVECs, under proliferative condition, hGPCs show increased proliferation and sphere formation, while under differentiating condition hGPCs show increased differentiation to astrocytes with concomitant decrease in differentiation to oligodendrocytes with respect to no co-culture controls. Transcript assay for selected humoral factors reveals bone morphogenic proteins (BMPs), endothelin1, growth arrest specific 6 and interleukin 6 to be in higher abundance in HUVECS than hGPCs, of which BMP4 transcripts were most abundant, indicating possibilities of its being the key mediator of endothelial mediated effects. Concurrently, noggin effectively attenuates HUVEC mediated astrocytic differentiation of PDGFR sorted fetal hGPCs The results have implication towards safety of transplantation therapies with PDGFR sorted fetal hGPCs. It is postulated that proliferation and differentiation response as seen in this co-culture system could have defining implications towards the genesis of glioma.

neuroscience↗

bioRxiv2021

Bayesian metamodeling of complex biological systems across varying representations

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are (i) converted to a standardized statistical representation relying on Probabilistic Graphical Models, (ii) coupled by modeling their mutual relations with the physical world, and (iii) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic {beta}-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic {beta}-Cell Consortium. Significance StatementCells are the basic units of life, yet their architecture and function remain to be fully characterized. This work describes Bayesian metamodeling, a modeling approach that divides-and-conquers a large problem of modeling numerous aspects of the cell into computing a number of smaller models of different types, followed by assembling these models into a complete map of the cell. Metamodeling enables a facile collaboration of multiple research groups and communities, thus maximizing the sharing of expertise, resources, data, and models. A proof-of-principle is provided by a model of glucose-stimulated insulin secretion produced by the Pancreatic {beta}-Cell Consortium.

cell biology↗