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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

Vascular lung triculture organoid via soluble extracellular matrix suspension

Micropatterned suspension culture creates consistently sized and shaped cell aggregates but has not produced organotypic structures from stable cells, thus restricting its use in accurate disease modeling. Here, we show that organotypic structure is achieved in hybrid suspension culture via supplementation of soluble extracellular matrix (ECM). We created a viable lung organoid from epithelial, endothelial, and fibroblast human stable cell lines in suspension culture. We demonstrate the importance of soluble ECM in organotypic patterning with the emergence of lumen-like structures with airspace showing feasible gas exchange units, formation of branching, perfusable vasculature, and long-term 70-day maintenance of lumen structure. Our results show a dependent relationship between enhanced fibronectin fibril assembly and the incorporation of ECM in the organoid. We successfully applied this technology in modeling lung fibrosis via bleomycin induction and test a potential antifibrotic drug in vitro while maintaining fundamental cell-cell interactions in lung tissue. Our human fluorescent lung organoid (hFLO) model represents features of pulmonary fibrosis which were ameliorated by fasudil treatment. We also demonstrate a 3D culture method with potential of creating organoids from mature cells, thus opening avenues for disease modeling and regenerative medicine, enhancing understanding of lung cell biology in health and lung disease.

bioengineering↗

bioRxiv2021

Identification of electroporation sites in the complex lipid organization of the plasma membrane

The plasma membrane of a biological cell is a complex assembly of lipids and membrane proteins, which tightly regulate transmembrane transport. When a cell is exposed to a strong electric field, the membrane integrity becomes transiently disrupted by formation of transmembrane pores. This phenomenon, termed electroporation, is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrythmias. However, the molecular mechanisms of electroporation are not yet sufficiently well understood; in particular, it is unclear where exactly pores form in the complex organization of the plasma membrane. In this study we combine coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis to identify how formation of pores depends on the local lipid organization. We show that pores do not form homogeneously across the membrane, but colocalize with domains that have specific features, the most important being high density of polyunsaturated lipids. We further show that knowing the lipid organization is sufficient to reliably predict poration sites with machine learning. However, by analysing poration kinetics with Bayesian survival analysis we then show that poration does not depend solely on local lipid arrangement, but also on membrane mechanical properties and the polarity of the electric field. Finally, we discuss how the combination of atomistic and coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis can guide the design of future experiments and help us to develop an accurate description of plasma membrane electroporation on the whole-cell level. Achieving this will allow us to shift the optimization of electroporation applications from blind trial-and-error approaches to mechanistic-driven design.

biophysics↗

bioRxiv2021

Single-cell Atlas Unveils Cellular Heterogeneity and Novel Markers in Human Neonatal and Adult Intervertebral Discs

The origin, composition, distribution, and function of cells in the human intervertebral disc (IVD) has not been fully understood. Here, cell atlases of both human neonatal and adult IVDs have been generated and further assessed by gene ontology pathway enrichment, pseudo-time trajectory, histology, and immunofluorescence. Comparison of cell atlases revealed the presence of two sub-populations of notochordal cells (NCs) and their associated markers in both the neonatal and adult IVDs. Developmental trajectories predicted 7 different cell states that describe the developmental process from neonatal to adult cells in IVD and analyzed the NCs role in the IVD development. A high heterogeneity and gradual transition of annulus fibrosus cells (AFCs) in the neonatal IVD was detected and their potential relevance in IVD development assessed. Collectively, comparing single-cell atlases between neonatal and adult IVDs delineates the landscape of IVD cell biology and may help discovering novel therapeutic targets for IVD degeneration.

developmental biology↗

bioRxiv2021

Activity-dependent endoplasmic reticulum Ca2+ uptake depends on Kv2.1-mediated endoplasmic reticulum/plasma membrane junctions to promote synaptic transmission

The endoplasmic reticulum (ER) forms a continuous and dynamic network throughout a neuron, extending from dendrites to axon terminals, and axonal ER dysfunction is implicated in several neurological disorders. In addition, tight junctions between the ER and plasma membrane (PM) are formed by several molecules including Kv2 channels, but the cellular functions of many ER-PM junctions remain unknown. Dynamic Ca2+ uptake into the ER during electrical activity plays an essential role in synaptic transmission as failure to allow rapid ER Ca2+ filling during stimulation activates stromal interaction molecule 1 (STIM1) and decreases both presynaptic Ca2+ influx and synaptic vesicle exocytosis. Our experiments demonstrate that Kv2.1 channels are necessary for enabling ER Ca2+ uptake during electrical activity as genetic depletion of Kv2.1 rendered both the somatic and axonal ER unable to accumulate Ca2+ during electrical stimulation. Moreover, our experiments show that the loss of Kv2.1 in the axon impairs synaptic vesicle fusion during stimulation via a mechanism unrelated to modulation of membrane voltage. Thus, our data demonstrate that the non-conducting role of Kv2.1 in forming stable junctions between the ER and PM via ER VAMP-associated protein (VAP) binding couples ER Ca2+ uptake with electrical activity. Our results further suggest that Kv2.1 has a critical function in neuronal cell biology for Ca2+-handling independent of voltage and reveals a novel and critical pathway for maintaining ER lumen Ca2+ levels and efficient neurotransmitter release. Taken together these findings reveal an essential non-classical role for both Kv2.1 and the ER-PM junctions in synaptic transmission. SignificanceThe endoplasmic reticulum (ER) extends throughout the neuron as a continuous organelle, and its dysfunction is associated with several neurological disorders. During electrical activity, the ER takes up Ca2+ from the cytosol which has been shown to support synaptic transmission. This close choreography of ER Ca2+ uptake with electrical activity suggests functional coupling of the ER to sources of voltage-gated Ca2+ entry through an unknown mechanism. Here we report a non-conducting role for Kv2.1 through its ER binding domain that is necessary for ER Ca2+ uptake during neuronal activity. Loss of Kv2.1 profoundly disables neurotransmitter release without altering presynaptic voltage suggesting that Kv2.1-mediated signaling hubs play an important neurobiological role in Ca2+ handling and synaptic transmission independent of ion conduction.

neuroscience↗

bioRxiv2021

Chromosome-scale assemblies of Acanthamoeba castellanii genomes provide insights into Legionella pneumophila infection-related chromatin re-organization

The unicellular amoeba Acanthamoeba castellanii is ubiquitous in aquatic environments, where it preys on bacteria. The organism also hosts bacterial endosymbionts, some of which are parasitic, including human pathogens such as Chlamydia and Legionella spp. Here we report complete, high quality genome sequences for two extensively studied A. castellanii strains, Neff and C3. Combining long- and short-read data with Hi-C, we generated near chromosome-level assemblies for both strains with 90% of the genome contained in 29 scaffolds for the Neff strain and 31 for the C3 strain. Comparative genomics revealed strain-specific functional enrichment, most notably genes related to signal transduction in the C3 strain, and to viral replication in Neff. Furthermore, we characterized the spatial organization of the A. castellanii genome and showed that it is reorganized during infection by Legionella pneumophila. Infection-dependent chromatin loops were found to be enriched in genes for signal transduction and phosphorylation processes. In genomic regions where chromatin organization changed during Legionella infection, we found functional enrichment for genes associated with metabolism, organelle assembly, and cytoskeleton organization, suggesting that changes in chromosomal folding are associated with host cell biology during infection.

genomics↗

bioRxiv2021

Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

Experimental studies of cell growth, inheritance, and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking--assigning identical cells on consecutive images to a track--is often challenging due to imperfect segmentation, moving cells, or focus drift, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance measures the similarity between cells in two consecutive images by comparing the structural information contained in the low frequencies of a Fourier transform. We show that it is broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses the concept to reject unlikely assignments, thereby substantially increasing tracking performance on published and newly generated long-term data sets from various species. For S. cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator. It demonstrates how highly precise tracking can help uncover previously undescribed single-cell biology.

systems biology↗

bioRxiv2021

DeepBacs: Bacterial image analysis using open-source deep learning approaches

Deep Learning (DL) is rapidly changing the field of microscopy, allowing for efficient analysis of complex data while often out-performing classical algorithms. This revolution has led to a significant effort to create user-friendly tools allowing biomedical researchers with little background in computer sciences to use this technology effectively. Thus far, these approaches have mainly focused on analysing microscopy images from eukaryotic samples and are still underused in microbiology. In this work, we demonstrate how to use a range of state-of-the-art artificial neural-networks particularly suited for the analysis of bacterial microscopy images, using our recently developed ZeroCostDL4Mic platform. We showcase different DL approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the DL capacity to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. To aid in the training of novice users, we provide a purposefully-built database of training and testing data, enabling bacteriologists to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of novel tools for bacterial cell biology and antibiotic research.

microbiology↗

bioRxiv2021

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

microbiology↗

bioRxiv2021

Towards a structurally resolved human protein interaction network

All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized. Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure-based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data. We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signalling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies. Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease.

bioinformatics↗

bioRxiv2021

A nuclear cAMP microdomain suppresses tumor growth by Hippo pathway inactivation

cAMP signaling pathways are critical for both oncogenesis and tumor suppression. cAMP signaling is localized to multiple spatially distinct microdomains, but the role of cAMP microdomains in cancer cell biology is poorly understood. We developed a tunable genetic system that allows us to activate cAMP signaling in specific microdomains. We uncovered a previously unappreciated nuclear cAMP microdomain that functionally activates a tumor suppressive pathway in a broad range of cancers by inhibiting YAP, a key effector protein of the Hippo pathway, inside the nucleus. We show that nuclear cAMP induces a LATS-dependent pathway leading to phosphorylation of nuclear YAP solely at serine 397, export of YAP from the nucleus, without YAP protein degradation. Thus, nuclear cAMP inhibition of nuclear YAP is distinct from other known mechanisms of Hippo regulation. Pharmacologic targeting of specific cAMP microdomains remains an untapped therapeutic approach for cancer, and since Hippo pathway deregulation can lead to oncogenesis and chemotherapeutic resistance, drugs directed at the nuclear cAMP microdomain may provide new avenues for the treatment of cancer.

cancer biology↗

bioRxiv2021

Cryo-EM of the yeast VO complex reveals distinct binding sites for macrolide V-ATPase inhibitors

Vacuolar-type adenosine triphosphatases (V-ATPases) are proton pumps found in almost all eukaryotic cells. These enzymes consist of a soluble catalytic V1 region that hydrolyzes ATP and a membrane-embedded VO region responsible for proton translocation. V-ATPase activity leads to acidification of endosomes, phagosomes, lysosomes, secretory vesicles, and the trans-Golgi network, with extracellular acidification occurring in some specialized cells. Small molecule inhibitors of V-ATPase have played a crucial role in elucidating numerous aspects of cell biology by blocking acidification of intracellular compartments, while therapeutic use of V-ATPase inhibitors has been proposed for treatment of cancer, osteoporosis, and some infections. Here, we determine structures of the isolated VO complex from Saccharomyces cerevisiae bound to two well-known macrolide inhibitors: bafilomycin A1 and archazolid A. The structures reveal different binding sites for the inhibitors on the surface of the proton-carrying c ring, with only a small amount of overlap between the two sites. Binding of both inhibitors is mediated primarily through van der Waals interactions in shallow pockets and suggests that the inhibitors block rotation of the ring. Together, these structures indicate the existence of a large chemical space available for V-ATPase inhibitors that block acidification by binding the c ring.

biochemistry↗

bioRxiv2021

Laser Ablation and Fluid Flows Show a Single Force Mechanism Governs Spindle Positioning

Few techniques are available for elucidating the nature of forces that drive subcellular behaviors. Here we develop two complementary ones: 1) femtosecond stereotactic laser ablation (FESLA), which rapidly creates complex cuts of subcellular structures, thereby allowing precise dissection of when, where, and in what direction forces are generated; and 2) assessment of subcellular fluid flows, by comparing direct flow measurements, using microinjected fluorescent nanodiamonds, to large-scale fluid-structure simulations of different models of force transduction. We apply these to study centrosomes in Caenorhabditis elegans early embryos, and use the data to construct a biophysically-based model of centrosome dynamics. Taken together, we demonstrate that cortical pulling forces provide a general explanation for many behaviors mediated by centrosomes, including pronuclear migration/centration and rotation, metaphase spindle positioning, asymmetric spindle elongation and spindle oscillations. In sum, this work establishes new methodologies for disentangling the forces responsible for cell biological phenomena.

biophysics↗

bioRxiv2021

Comparative proteogenomics deciphers the origin and evolution of eukaryotic chromatin

Histones and associated chromatin proteins have essential functions in eukaryotic genome organization and regulation. Despite this fundamental role in eukaryotic cell biology, we lack a phylogenetically-comprehensive understanding of chromatin evolution. Here, we combine comparative proteomics and genomics analysis of chromatin in eukaryotes and archaea. Proteomics uncovers the existence of histone post-translational modifications in Archaea. However, archaeal histone modifications are scarce, in contrast with the highly conserved and abundant marks we identify across eukaryotes. Phylogenetic analysis reveals that chromatin-associated catalytic functions (e.g., methyltransferases) have pre-eukaryotic origins, whereas histone mark readers and chaperones are eukaryotic innovations. We show that further chromatin evolution is characterized by expansion of readers, including capture by transposable elements and viruses. Overall, our study infers detailed evolutionary history of eukaryotic chromatin: from its archaeal roots, through the emergence of nucleosome-based regulation in the eukaryotic ancestor, to the diversification of chromatin regulators and their hijacking by genomic parasites.

evolutionary biology↗

bioRxiv2021

mCherry contains a fluorescent protein isoform that interferes with its reporter function

Fluorescent proteins are essential reporters in cell biology and molecular biology. Here, we reveal that red-fluorescent proteins possess an alternative translation initiation site that produces a short functional protein isoform. The short isoform creates significant background fluorescence that biases the outcome of expression studies. Our investigation identifies the short protein isoform, traces its origin, and determines the extent of the issue within the family of red fluorescent protein. Our analysis shows that the short isoform defect of the red fluorescent protein family may affect the interpretation of many published studies. Finally, we provide a re-engineered mCherry variant that lacks background expression as an improved tool for imaging and protein expression studies.

molecular biology↗

bioRxiv2021

Structural mechanism for bi-directional actin crosslinking by T-plastin

To fulfill the cytoskeletons diverse functions in cell mechanics and motility, actin networks with specialized architectures are built by crosslinking proteins, which bridge filaments to control micron-scale network geometry through nanoscale binding interactions via poorly defined structural mechanisms. Here, we introduce a machine-learning enabled cryo-EM pipeline for visualizing active crosslinkers, which we use to analyze human T-plastin, a member of the evolutionarily ancient plastin/fimbrin family of tandem calponin-homology domain (CHD) proteins. We define a sequential bundling mechanism which enables T-plastin to bridge filaments in both parallel and anti-parallel orientations. Our structural, biochemical, and cell biological data highlight inter-CHD linkers as key structural elements underlying flexible but stable crosslinking which are likely to be disrupted by mutations causing hereditary bone diseases. Beyond revealing how plastins are evolutionary optimized to crosslink dense actin networks with mixed polarity, our cryo-EM workflow will broadly enable analysis of the structural mechanisms underlying cytoskeletal network construction. One sentence summaryCryo-EM, biochemical, and cellular studies reveal how the crosslinking protein T-plastin bridges actin filaments in two opposing orientations.

biophysics↗

bioRxiv2021

CRISPR knockouts of pmela and pmelb engineered a golden tilapia by regulating relative pigment cell abundance

Premelanosome protein (pmel) is a key gene for melanogenesis in vertebrates. Mutations in this gene are responsible for white plumage in chicken, but its role in pigmentation of fish remains to be demonstrated. In this study we found that most fishes have two pmel genes arising from the teleost-specific whole genome duplication. Both pmela and pmelb were expressed at high levels in the eyes and skin of Nile tilapia. We mutated both genes in tilapia using CRISPR/Cas9 gene editing. Homozygous mutation of pmela resulted in yellowish body color with weak vertical bars and a hypo-pigmented retinal pigment epithelium (RPE) due to significantly reduced number and size of melanophores. In contrast, we observed an increased number and size of xanthophores in mutants compared to wild-type fish. Homozygous mutation of pmelb resulted in a similar, but milder phenotype than pmela-/- mutants, without effects on RPE pigmentation. Double mutation of pmela and pmelb resulted in loss of additional melanophores compared to the pmela-/- mutants, and also an increase in the number and size of xanthophores, producing a strong golden body color without bars in the trunk. The RPE pigmentation of pmela-/-;pmelb-/- was similar to pmela-/- mutants, with much less pigmentation than pmelb-/- mutants and wild-type fish. Taken together, our results indicate that, while both pmel genes are important for the formation of body color in tilapia, pmela plays a more important role than pmelb. To our knowledge, this is the first report on mutation of pmelb or both pmela;pmelb in fish. Studies on these mutants suggest new strategies for breeding golden tilapia, and also provide a new model for studies of pmel function in vertebrates. Author SummaryMelanophores, the most common pigment cell type, have been studied for nearly 150 years. Many genes are involved in melanoblast migration, melanophore differentiation, and melanin biosynthesis. Pmel is fundamental for melanosome development by directing melanin biosynthesis and melanosome phase transition. Specifically, PMEL can form a fibrillar structure within the melanosome upon which melanin is deposited. We identified two pmel genes in Nile tilapia arising from the teleost-specific whole genome duplication. Disruption of either pmela or pmelb in tilapia leads to significant hypo-pigmentation. PMEL disrupted fish showed not only a reduction in melanin and tiny melanophores, but also a significant increase in the number of xanthophores, and even guanine-filled melanophores, which led to a golden tilapia with hypo-pigmented RPE. Our study confirmed the role of pmel in melanin biosynthesis and maturation, and also highlighted its effects on melanophore number and size. These results provide new insights into pigment cell biology and will help us better understand the mechanisms of color patterning in teleosts. Knockout of both pmela and pmelb provide a new strategy for engineering a golden tilapia, which might provide a foundation for developing new strains in the tilapia industry.

genetics↗

bioRxiv2021

imageseg: an R package for deep learning-based image segmentation

O_LIConvolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications. CNNs can perform very well in various tasks, especially for visual tasks and image data. Image segmentation (the classification of all pixels in images) is one such task and can for example be used to assess forest vertical and horizontal structure. While such methods have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. C_LIO_LIHere, we present R package imageseg which implements a workflow for general-purpose image segmentation using CNNs and the U-Net architecture in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with two models for forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 3288 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1468 understory vegetation images. C_LIO_LIOverall classification accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively), indicating robustness to variation in input images and good generalization strength across forest types and biomes. C_LIO_LIThe package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with multiple classes and based on color or grayscale images, e.g. in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond. C_LI

ecology↗

bioRxiv2021

Directing Min protein patterns with advective bulk flow

The Min proteins constitute the best-studied model system for pattern formation in cell biology. We theoretically predict and experimentally show that the propagation direction of in vitro Min protein patterns can be controlled by a hydrodynamic flow of the bulk solution. We find downstream propagation of Min wave patterns for low MinE:MinD concentration ratios, upstream propagation for large ratios, but multistability of both propagation directions in between. Whereas downstream propagation can be described by a minimal model that disregards MinE conformational switching, upstream propagation can be reproduced by a reduced switch model, where increased MinD bulk concentrations on the upstream side promote protein attachment. Our study demonstrates that a differential flow, where bulk flow advects protein concentrations in the bulk, but not on the surface, can control surface-pattern propagation. This suggests that flow can be used to probe these features and to constrain mathematical models for pattern-forming systems.

biophysics↗