Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 74,094 bioRxiv papers from 322,357 authors.
Most downloaded bioRxiv papers, since beginning of last month
72,719 results found. For more information, click each entry to expand.
565 downloads cell biology
Contractile actomyosin networks generate intracellular forces essential for the regulation of cell shape, migration, and cell-fate decisions, ultimately leading to the remodeling and patterning of tissues. Although actin filaments aligned in bundles represent the main source of traction-force production in adherent cells, there is increasing evidence that these bundles form interconnected and interconvertible structures with the rest of the intracellular actin network. In this study, we explored how these bundles are connected to the surrounding cortical network and the mechanical impact of these interconnected structures on the production and distribution of traction forces on the extracellular matrix and throughout the cell. By using a combination of hydrogel micropatterning, traction-force microscopy and laser photoablation, we measured the relaxation of the cellular traction field in response to local photoablations at various positions within the cell. Our experimental results and modeling of the mechanical response of the network revealed that bundles were fully embedded along their entire length in a continuous and contractile network of cortical filaments. Moreover, the propagation of the contraction of these bundles throughout the entire cell was dependent on this embedding. In addition, these bundles appeared to originate from the alignment and coalescence of thin and unattached cortical actin filaments from the surrounding mesh.
564 downloads genomics
Understanding the dynamic interactions between malignant cells and the tumor stroma is a major goal of cancer research. Here we developed a Bayesian model that jointly infers both cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 85 single-cell and 1,412 bulk RNA-seq datasets in primary human glioblastoma, head and neck squamous cell carcinoma, and melanoma. We identified cell types correlated with clinical outcomes and explored regional heterogeneity in tumor state and stromal composition. We redefined common molecular subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T-cells, fibroblasts, and endothelial cells across multiple tumor types. Our work provides a new lens that we used to measure cellular composition and expression in a statistically powered cohort of three primary human malignancies.
564 downloads bioengineering
Auxin-Inducible Degron (AID) technology enables conditional depletion of targeted proteins. However, the applicability of the AID in vertebrate cells has been limited due to cytotoxicity caused by high auxin concentrations. Here, we establish an improved AID system using an engineered orthogonal auxin-TIR1 pair, which exhibits over 1,000 times stronger binding. With ~1,000-fold less auxin concentration, we achieved to generate the AID-based knockout cells in various human and mouse cell lines in a single transfection.
561 downloads systems biology
A challenge in stem cell biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Though the development of single cell assays allows for the capture of progenitor cell states in great detail, these assays cannot definitively link those molecular states to their long-term fate. Here, we use expressed DNA barcodes to clonally trace single cell transcriptomes dynamically during differentiation and apply this approach to the study of hematopoiesis. Our analysis identifies functional boundaries of cell potential early in the hematopoietic hierarchy and locates them on a continuous transcriptional landscape. Additionally, we find that the monocyte lineage differentiates through two distinct transcriptional and clonal routes, leaving a persistent imprint on mature cells. Finally, we use our approach to reflect on current methods of dynamics inference from single-cell snapshots. We find that for in vitro hematopoiesis, published fate prediction algorithms do not detect lineage priming in early progenitors, and provide evidence that there are hidden properties that influence cell fate but are not detectable with current single-cell sequencing methods.
560 downloads bioinformatics
The diagnosis of cancer is typically based on histopathological assessment of tissue sections, and supplemented by genetic and other molecular tests–. Modern computer vision algorithms have high diagnostic accuracy and potential to augment histopathology workflows–. Here we use deep transfer learning to quantify histopathological patterns across 17,396 hematoxylin and eosin (H&E) stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal distinction. Automatically learned computational histopathological features correlate with a large range of recurrent genetic aberrations pan-cancer. This includes whole genome duplications, which display universal features across cancer types, individual chromosomal aneuploidies, focal amplifications and deletions as well as driver gene mutations. There are wide-spread associations between bulk gene expression levels and histopathology, which reflect tumour composition and enables localising transcriptomically defined tumour infiltrating lymphocytes. Computational histopathology augments prognosis based on histopathological subtyping and grading and highlights prognostically relevant areas such as necrosis or lymphocytic aggregates. These findings demonstrate the large potential of computer vision to characterise the molecular basis of tumour histopathology and lay out a rationale for integrating molecular and histopathological data to augment diagnostic and prognostic workflows. : #ref-1 : #ref-6 : #ref-7 : #ref-9
560 downloads neuroscience
Morlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal precision and frequency precision. It is typically defined as the "number of cycles," but this parameter is opaque, and often leads to uncertainty and suboptimal analysis choices, as well as being difficult to interpret and evaluate. The purpose of this paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (as full-width at half-maximum). This formulation provides clarity on an important data analysis parameter, and should facilitate proper analyses, reporting, and interpretation of results. MATLAB code is provided.
560 downloads bioinformatics
Background: The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug-target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which are not a natural way to represent molecules. Methods: We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug-target affinity. We test 4 graph neural network variants, including GCN, GAT, GIN, and a combined GAT-GCN architecture, for the task of drug-affinity prediction. We benchmark the performance of these models on the Davis and Kiba datasets. Results: We show that graph neural networks not only predict drug-target affinity better than nondeep learning models, but also outperform competing deep learning methods. Of note, the GIN method performs consistently well for two separate benchmark datasets and for two key performance metrics. In a post-hoc analysis of our model, we find that a graph neural network can learn the importance of known molecular descriptors without any prior knowledge. We also examine the model's performance and find that a handful of drugs contribute disproportionately to the total prediction error. Conclusions: Our results confirm that deep learning models are appropriate for drug-target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Although we focus on drug-target affinity prediction, our GraphDTA model is a generic solution for any collaborating filtering or recommendation problem where either data input can be represented as a graph.
558 downloads animal behavior and cognition
To study brain function, preclinical research relies heavily on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by providing accurate tracking of animals, yet they often struggle with the analysis of ethologically relevant behaviors and lack the flexibility to adapt to variable testing environments. In the last couple of years, substantial advances in deep learning and machine vision have given researchers the ability to take behavioral analysis entirely into their own hands. Here, we directly compare the performance of commercially available platforms (Ethovision XT14, Noldus; TSE Multi Conditioning System, TSE Systems) to cross-verified human annotation. To this end, we provide a set of videos - carefully annotated by several human raters - of three widely used behavioral tests (open field, elevated plus maze, forced swim test). Using these data, we show that by combining deep learning-based motion tracking (DeepLabCut) with simple post-analysis, we can track animals in a range of classic behavioral tests at similar or even greater accuracy than commercial behavioral solutions. In addition, we integrate the tracking data from DeepLabCut with post analysis supervised machine learning approaches. This combination allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, thus outperforming commercial solutions. Moreover, the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, outperforming commercial systems at a fraction of the cost.
558 downloads neuroscience
Dopamine (DA) neurons are known to encode reward prediction error (RPE), in addition to other signals, such as salience. While RPE is known to support learning, the role of salience in supporting learning remains less clear. To address this, we recorded and manipulated VTA DA neurons in mice during fear extinction, a behavior we observed to generate spatially segregated RPE and salience signals. We applied deep learning to identify mouse freezing, eliminating the need for human scoring. Our fiber photometry recordings showed that DA neurons in medial and lateral VTA had distinct activity during fear extinction: medial VTA more closely reflected RPE, while lateral VTA reflected a salience-like signal. Optogenetic inhibition of DA neurons in either region slowed fear extinction, with the relevant time period for inhibition differing across regions. Our results indicate that salience-like signals can have similar downstream consequences to RPE-like signals, although with different temporal dependencies.
555 downloads cancer biology
Chenchen Pan, Oliver Schoppe, Arnaldo Parra-Damas, Ruiyao Cai, Mihail Ivilinov Todorov, Gabor Gondi, Bettina von Neubeck, Alireza Ghasemi, Madita Alice Reimer, Javier Coronel, Boyan K. Garvalov, Bjoern Menze, Reinhard Zeidler, Ali Ertürk
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of tumor cells more than 100-fold by applying the vDISCO method to image single cancer cells in intact transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantifications in a model of spontaneous metastasis using human breast cancer cells allowed us to systematically analyze clinically relevant features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in whole mice. DeepMACT can thus considerably improve the discovery of effective therapeutic strategies for metastatic cancer.
555 downloads cancer biology
The WNT pathway is a fundamental regulator of intestinal homeostasis and hyperactivation of WNT signaling is the major oncogenic driver in colorectal cancer (CRC). To date, there are no described mechanisms that bypass WNT dependence in intestinal tumors. Here, we show that while WNT suppression blocks tumor growth in most organoid and in vivo CRC models, the accumulation of CRC-associated genetic alterations enables drug resistance and WNT-independent growth. In intestinal epithelial cells harboring mutations in KRAS or BRAF, together with disruption of p53 and SMAD4, transient TGFβ exposure drives YAP/TAZ-dependent transcriptional reprogramming and lineage reversion. Acquisition of embryonic intestinal identity is accompanied by a permanent loss of adult intestinal lineages, and long-term WNT-independent growth. This work delineates genetic and microenvironmental factors that drive WNT inhibitor resistance, identifies a new mechanism for WNT-independent CRC growth and reveals how integration of associated genetic alterations and extracellular signals can overcome lineage-dependent oncogenic programs.
554 downloads neuroscience
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation.
554 downloads systems biology
Single-cell transcriptomics promises to revolutionize our understanding of the vasculature. Emerging computational methods applied to high dimensional single cell data allow integration of results between samples and species, and illuminate the diversity and underlying developmental and architectural organization of cell populations. Here, we illustrate these methods in analysis of mouse lymph node (LN) lymphatic endothelial cells (LEC) at single cell resolution. Clustering identifies five well-delineated subsets, including two medullary sinus subsets not recognized previously as distinct. Nearest neighbor alignments in trajectory space position the major subsets in a sequence that recapitulates known and suggests novel features of LN lymphatic organization, providing a transcriptional map of the lymphatic endothelial niches and of the transitions between them. Differences in gene expression reveal specialized programs for (1) subcapsular ceiling endothelial interactions with the capsule connective tissue and cells, (2) subcapsular floor regulation of lymph borne cell entry into the LN parenchyma and antigen presentation, and (3) medullary subset specialization for pathogen interactions and LN remodeling. LEC of the subcapsular sinus floor and medulla, which represent major sites of cell entry and exit from the LN parenchyma respectively, respond robustly to oxazolone inflammation challenge with enriched signaling pathways that converge on both innate and adaptive immune responses. Integration of mouse and human single-cell profiles reveals a conserved cross-species pattern of lymphatic vascular niches and gene expression, as well as specialized human subsets and genes unique to each species. The examples provided demonstrate the power of single-cell analysis in elucidating endothelial cell heterogeneity, vascular organization and endothelial cell responses. We discuss the findings from the perspective of LEC functions in relation to niche formations in the unique stromal and highly immunological environment of the LN.
554 downloads synthetic biology
Therapeutic antibody optimization is time and resource intensive, largely because it requires low-throughput screening (10^3 variants) of full-length IgG in mammalian cells, typically resulting in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-specificity from a massively diverse sequence space to identify globally optimized antibody variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small libraries (10^4) produced high quality data capable of training deep neural networks that accurately predict antigen-binding based on antibody sequence. Deep learning is then used to predict millions of antigen binders from an in silico library of ~10^8 variants, where experimental testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set of in silico predicted binders is then subjected to multiple developability filters, resulting in thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate high-dimensional protein sequence space, deep learning offers great potential for antibody engineering and optimization.
553 downloads neuroscience
Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. But in spite of extensive research, it has remained open how they can learn through synaptic plasticity to carry out complex network computations. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A new mathematical insight tells us how these pieces need to be combined to enable biologically plausible online network learning through gradient descent, in particular deep reinforcement learning. This new learning method – called e-prop – approaches the performance of BPTT (backpropagation through time), the best known method for training recurrent neural networks in machine learning. In addition, it suggests a method for powerful on-chip learning in novel energy-efficient spike-based hardware for AI.
553 downloads genetics
C. elegans is exposed to many different bacteria in its environment, and must distinguish pathogenic from nutritious bacterial food sources. Here, we show that a single exposure to purified small RNAs isolated from pathogenic Pseudomonas aeruginosa (PA14) is sufficient to induce pathogen avoidance, both in the treated animals and in four subsequent generations of progeny. The RNA interference and piRNA pathways, the germline, and the ASI neuron are required for bacterial small RNA-induced avoidance behavior and transgenerational inheritance. A single non-coding RNA, P11, is both necessary and sufficient to convey learned avoidance of PA14, and its C. elegans target, maco-1, is required for avoidance. A natural microbiome Pseudomonas isolate, GRb0427, can induce avoidance via its small RNAs, and the wild C. elegans strain JU1580 responds similarly to bacterial sRNA. Our results suggest that this ncRNA-dependent mechanism evolved to survey the microbial environment, use this information to make appropriate behavioral decisions, and pass this information on to its progeny.
553 downloads neuroscience
Despite numerous studies, there is little agreement about what brain changes accompany motor sequence learning, partly because of a general publication bias that favors novel results. We therefore decided to systematically reinvestigate proposed functional magnetic resonance imaging (fMRI) correlates of motor learning in a preregistered longitudinal study with four scanning sessions over 5 weeks of training. Activation decreased more for trained than untrained sequences in premotor and parietal areas, without any evidence of learning-related activation increases. Premotor and parietal regions also exhibited changes in the fine-grained, sequence-specific activation patterns early in learning, which stabilized later. No changes were observed in the primary motor cortex (M1). Overall, our study provides evidence that human motor sequence learning occurs outside of M1. Furthermore, it shows that we cannot expect to find activity increases as an indicator for learning, making subtle changes in activity patterns across weeks the most promising fMRI correlate of training-induced plasticity.
553 downloads molecular biology
Changes in cell identities and positions underlie tissue development and disease progression. Although, single-cell mRNA sequencing (scRNA-Seq) methods rapidly generate extensive lists of cell-states, spatially resolved single-cell mapping presents a challenging task. We developed SCRINSHOT ( S ingle C ell R esolution IN S itu H ybridization O n T issues), a sensitive, multiplex RNA mapping approach. Direct hybridization of padlock probes on mRNA is followed by circularization with SplintR ligase and rolling circle amplification (RCA) of the hybridized padlock probes. Sequential detection of RCA-products using fluorophore-labeled oligonucleotides profiles thousands of cells in tissue sections. We evaluated SCRINSHOT specificity and sensitivity on murine and human organs. SCRINSHOT quantification of marker gene expression shows high correlation with published scRNA-Seq data over a broad range of gene expression levels. We demonstrate the utility of SCRISHOT by mapping the locations of abundant and rare cell types along the murine airways. The amenability, multiplexity and quantitative qualities of SCRINSHOT facilitate single cell mRNA profiling of cell-state alterations in tissues under a variety of native and experimental conditions.
553 downloads bioinformatics
Single-cell transcriptomic studies of diverse and complex systems are becoming ubiquitous. Algorithms now attempt to integrate patterns across these studies by removing all study-specific information, without distinguishing unwanted technical bias from relevant biological variation. Integration remains difficult when capturing biological variation that is distributed across studies, as when combining disparate temporal snapshots into a panoramic, multi-study trajectory of cellular development. Here, we show that a fundamental analytic shift to gene coexpression within clusters of cells, rather than gene expression within individual cells, balances robustness to bias with preservation of meaningful inter-study differences. We leverage this insight in Trajectorama, an algorithm which we use to unify trajectories of neuronal development and hematopoiesis across studies that each profile separate developmental stages, a highly challenging task for existing methods. Trajectorama also reveals systems-level processes relevant to disease pathogenesis within the microglial response to myelin injury. Trajectorama benefits from efficiency and scalability, processing nearly one million cells in around an hour.
553 downloads neuroscience
The vestibular system broadcasts head-movement related signals to sensory areas throughout the brain, including visual cortex. These signals are crucial for the brain's ability to assess whether motion of the visual scene results from the animal's head-movements. How head-movements impact visual cortical circuits remains, however, poorly understood. Here, we discover that ambient luminance profoundly transforms how mouse primary visual cortex (V1) processes head-movements. While in darkness, head movements result in an overall suppression of neuronal activity, in ambient light the same head movements trigger excitation across all cortical layers. This light-dependent switch in how V1 processes head-movements is controlled by somatostatin expressing (SOM) inhibitory neurons, which are excited by head movements in dark but not in light. This study thus reveals a light-dependent switch in the response of V1 to head-movements and identifies a circuit in which SOM cells are key integrators of vestibular and luminance signals.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
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- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
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