bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting nanocarrier permeation across the human intestinein vitro: Model matters
Authors: Jung Nathalie , Schreiner Jonas , Baur Florentin , Vogel-Kindgen Sarah , Windbergs Maike 
Published: March 2024

AbstractFor clinical translation of oral nanocarriers, simulation of the complex intestinal microenvironment is crucial to evaluate interactions and transport across the intestinal mucosa for predicting the drug’s bioavailability. However, permeation studies are often conducted using simplistic cell culture models, overlooking key physiological factors such as tissue composition, morphology, and additional diffusion barriers as constituted by mucus. This oversight may potentially lead to an incomplete evaluation of the nanocarrier-tissue interactions and an overestimation of permeation. In this study, we systematically investigated different 3D tissue models of the human intestine under static cultivation and dynamic flow conditions with respect to tissue morphology, mucus production, and their impact on nanocarrier permeation. Our results revealed that the cell ratio between the different cell types (enterocytes and goblet cells), as well as the choice of culture conditions, had a notable impact on tissue layer thickness, mucus secretion, and barrier impairment, all of which were increased under dynamic flow conditions. Permeation studies with polymeric nanocarriers (PLGA and PEG-PLGA) elucidated that the amount of mucus present in the respective model was the limiting factor for the permeation of PLGA nanocarriers, while tissue topography represented the key factor influencing PEG-PLGA nanocarrier permeation. Furthermore, both nanocarriers exhibited diametrically opposite permeation kinetics in a direct comparison to soluble compounds. In summary, these findings reveal the critical role of the implemented test systems on permeation assessment and emphasize that, in the context of preclinical nanocarrier testing, the choice ofin vitromodel matters.
Key link between iron and the size structure of three main mesoplanktonic groups (Crustaceans, Rhizarians, and colonial N2-fixers) in the Global Ocean
Authors: Dugenne Mathilde , Corrales-Ugalde Marco , Luo Jessica Y. , Stemmann Lars , Irisson Jean-Olivier , Lombard Fabien , O’Brien Todd , Stock Charles ,   PSSdb data contributors consortium, Kiko Rainer 
Published: March 2024

AbstractSize is commonly used as a master trait to characterize pelagic organisms as it affects a range of processes and impact marine biogeochemical cycles and services. Yet, a holistic understanding of what environmental factors shape size structure is lacking for most mesoplankton. As part of the Pelagic Size Structure database, we explore the linkages between environmental factors and global compilations of Rhizarian, colonial N2-fixer, and Crustacean size spectra measured from Underwater Vision Profilers or benchtop scanners. We found that iron, alongside temperature, plays a disproportionate role in shaping their spectral biogeography. Our results highlight the effect of dust on N2-fixers and Rhizarians while total iron, comprising organic and mineral compounds, explained most of the variance in Crustaceans size structure. Using machine learning models, we predicted their size structure at the global scale with relatively high R2of 0.93, 0.84, and 0.66. We hope our predictions can support further assessment of their role in biogeochemical processes under present and future forcings.
CytoSignal Detects Locations and Dynamics of Ligand-Receptor Signaling at Cellular Resolution from Spatial Transcriptomic Data
Authors: Liu Jialin , Manabe Hiroaki , Qian Weizhou , Wang Yichen , Gu Yichen , Yan Chu Angel Ka , Gadhvi Gaurav , Song Yuxuan , Ono Noriaki , Welch Joshua D. 
Published: March 2024

AbstractNearby cells within tissues communicate through ligand-receptor signaling interactions. Emerging spatial transcriptomic technologies provide a tremendous opportunity to systematically detect ligand-receptor signaling, but no method operates at cellular resolution in the spatial context. We developed CytoSignal to infer the locations and dynamics of cell-cell communication at cellular resolution from spatial transcriptomic data. CytoSignal is based on the simple insight that signaling is a protein-protein interaction that occurs at a specific tissue location when ligand and receptor are expressed in close spatial proximity. Our cellular-resolution, spatially-resolved signaling scores allow several novel types of analyses: we identify spatial gradients in signaling strength; separately quantify the locations of contact-dependent and diffusible interactions; and detect signaling-associated differentially expressed genes. Additionally, we can predict the temporal dynamics of a signaling interaction at each spatial location. CytoSignal is compatible with nearly every kind of spatial transcriptomic technology including FISH-based protocols and spot-based protocols without deconvolution. We experimentally validate our resultsin situby proximity ligation assay, confirming that CytoSignal scores closely match the tissue locations of ligand-receptor protein-protein interactions. Our work addresses the field’s current need for a robust and scalable tool to detect cell-cell signaling interactions and their dynamics at cellular resolution from spatial transcriptomic data.
A knockoff calibration method to avoid over-clustering in single-cell RNA-sequencing
Authors: DenAdel Alan , Ramseier Michelle L. , Navia Andrew W. , Shalek Alex K. , Raghavan Srivatsan , Winter Peter S. , Amini Ava P. , Crawford Lorin 
Published: March 2024

AbstractStandard single-cell RNA-sequencing (scRNA-seq) pipelines nearly always include unsupervised clustering as a key step in identifying biologically distinct cell types. A follow-up step in these pipelines is to test for differential expression between the identified clusters. When algorithms over-cluster, downstream analyses will produce inflatedP-values resulting in increased false discoveries. In this work, we presentcallback(CalibratedClustering viaKnockoffs): a new method for protecting against over-clustering by controlling for the impact of reusing the same data twice when performing differential expression analysis, commonly known as “double-dipping”. Importantly, our approach can be applied to a wide range of clustering algorithms. Using real and simulated data, we show thatcallbackprovides state-of-the-art clustering performance and can rapidly analyze large-scale scRNA-seq studies, even on a personal laptop.
Graph Contrastive Learning of Subcellular-resolution Spatial Transcriptomics Improves Cell Type Annotation and Reveals Critical Molecular Pathways
Authors: Lu Qiaolin , Ding Jiayuan , Li Lingxiao , Chang Yi , Tang Jiliang , Qiu Xiaojie 
Published: March 2024

AbstractImaging based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution. The subcellular localization of RNA molecules plays a crucial role in the compartmentalization-dependent regulation of genes within individual cells. Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type. However, current cell type annotation approaches of iST primarily utilize gene expression information while neglecting the spatial distribution of RNAs within cells. In this work, we introduce a semi-supervised graph contrastive learning method called Focus, the first method, to the best of our knowledge, that explicitly models RNA’s subcellular distribution and community to improve cell type annotation. Focus first constructs gene neighborhood networks based on the subcellular colocalization relationship of RNA transcripts. Next, the subcellular graph of each cell can be augmented by adding important edges and nodes or removing trivial edges and nodes. Focus then aims to maximize the similarity between positive pairs from two augmented views of the same cell and minimize the similarity between negative pairs from different cells within a common batch. Guided by a limited amount of labeled data, Focus is capable of assigning cell type identities for the entire datasets at high accuracy. Extensive experiments demonstrate the effectiveness of Focus compared to existing state-of-the-art approaches across a range of spatial transcriptomics platforms and biological systems. Furthermore, Focus enjoys the advantages of revealing intricate cell type-specific subcellular spatial gene patterns and providing interpretable subcellular gene analysis, such as defining the gene importance score. Importantly, with the importance score, Focus identifies genes harboring strong relevance to cell type-specific pathways, indicating its potential in uncovering novel regulatory programs across numerous biological systems. Focus is freely accessible athttps://github.com/OmicsML/focus.
Compensatory Mechanisms for Preserving Speech-in-Noise Comprehension Involve Prefrontal Cortex in Older Adults
Authors: Li Zhuoran , Liu Yi , Zhang Xinmiao , Kou Nuonan , Zhao Xiaoying , Jiang Xiangru , Engel Andreas K. , Zhang Dan , Wang Shuo 
Published: March 2024

AbstractThe capacity of comprehending others amidst noise is essential for human communication. However, it presents significant challenges for the elderly who often face progressive declines in the peripheral auditory system and the whole brain. While previous studies have suggested the existence of neural reserve and neural compensation as potential mechanisms for preserving cognitive abilities in aging, the specific mechanisms supporting speech-in-noise comprehension among the elderly remain unclear. To address this question, the present study employs an inter-brain neuroscience approach by analyzing the neural coupling between brain activities of older adults and those of speakers under noisy conditions. Results showed that the neural coupling encompassed more extensive brain regions of older listeners compared to young listeners, with a notable engagement of the prefrontal cortex. Moreover, the neural coupling from prefrontal cortex was coordinated with that from classical language-related regions. More importantly, as background noise increases, the older listener’s speech comprehension performance was more closely associated with the neural coupling from prefrontal cortex. Taken together, this study reveals the compensatory recruitment of neurocognitive resources, particularly within the prefrontal cortex, to facilitate speech processing in the aging brain, and further highlights the critical role of prefrontal cortex in maintaining the elderly’s ability to comprehend others in noisy environments. It supports the neural compensation hypothesis, extending the knowledge about the neural basis that underlies cognitive preservation in the aging population.
Nonlinear Elastic Bottlebrush Polymer Hydrogels Modulate Actomyosin Mediated Protrusion Formation in Mesenchymal Stromal Cells
Authors: Ohnsorg Monica L. , Mash Kayla M. , Khang Alex , Rao Varsha V. , Kirkpatrick Bruce E. , Bera Kaustav , Anseth Kristi S. 
Published: March 2024

ABSTRACTThe nonlinear elasticity of many tissue-specific extracellular matrices is difficult to recapitulate without the use of fibrous architectures, which couple strain-stiffening with stress relaxation. Herein, bottlebrush polymers are synthesized and crosslinked to form poly(ethylene glycol)-based hydrogels and used to study how strain-stiffening behavior affects human mesenchymal stromal cells (hMSCs). By tailoring the bottlebrush polymer length, the critical stress associated with the onset of network stiffening is systematically varied, and a unique protrusion-rich hMSC morphology emerges only at critical stresses within a biologically accessible stress regime. Local cell-matrix interactions are quantified using 3D traction force microscopy and small molecule inhibitors are used to identify cellular machinery that plays a critical role in hMSC mechanosensing of the engineered, strain-stiffening microenvironment. Collectively, this study demonstrates how covalently crosslinked bottlebrush polymer hydrogels can recapitulate strain-stiffening biomechanical cues at biologically relevant stresses and be used to probe how nonlinear elastic matrix properties regulate cellular processes.
Effectiveness and efficiency: label-aware hierarchical subgraph learning for protein-protein interaction
Authors: Zhou Yuanqing , Lin Haitao , Huang Yufei , Wu Lirong , Li Stan Z. , Chen Wei 
Published: March 2024

AbstractProtein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods for PPIs prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they usually suffer from performance degradation under complex real-world scenarios. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis. By modeling the PPIs as a graph with protein as nodes and interactions as edge types, the prevailing models tend to learn the pattern of nodes’ degrees rather than intrinsic sequence-structure profiles, leading to the problem termed topological shortcut. In addition, with the emergence of high-throughput experimental methods such as mass spectrometry and protein chip technology, the amount of available PPI data is exploding. The huge data growth leads to intensive computational costs and challenges computing devices, causing infeasibility in practice. To address the discussed problems, we propose alabel-aware hierarchical subgraphlearning method (laruGL-PPI) that can effectively infer PPIs while being both interpretable and generalizable. Specifically, we introduced edge-based subgraph sampling to effectively alleviate the problems of topological shortcuts and high computing costs. Besides, the inner-outer connections of PPIs are modeled as a hierarchical graph, together with the dependencies between interaction types constructed by a label graph. Extensive experiments on PPI datasets of different scales demonstrate that laruGL-PPI outperforms state-of-the-art PPI prediction methods, particularly in the testing of unseen proteins. Also, our model can recognize crucial sites of proteins, such as surface sites for binding and active sites for catalysis.
Choroid plexus enlargement in acute neuroinflammation is tightly interrelated to the tyrosine receptor signalling
Authors: Luessi Felix , Schiffer Julia , Gonzalez-Escamilla Gabriel , Fleischer Vinzenz , Engel Sinah , Ciolac Dumitru , Koeck Thomas , Wild Philipp S. , Gruchot Joel , Ruck Tobias , Othmann Ahmed , Bittner Stefan , Meuth Sven G. , Zipp Frauke , Stüve Olaf , Groppa Sergiu 
Published: March 2024

AbstractThe choroid plexus (ChP) plays a crucial function in neuroinflammation of the central nervous system and in the immune response of the brain during neurodegeneration. Recent studies described a massive ChP enlargement in patients with multiple sclerosis (MS) and active disease courses, but also in several other neuroinflammatory and neurodegenerative conditions. Nevertheless, the exact basis and pathophysiology behind ChP hypertrophy remains unclear. This study was designed to evaluate the association of cerebrospinal fluid (CSF) proteomic spectra with brain MRI-derived volumetric measures of ChP in two independent cohorts of MS patients, and to translationally validate the related molecular mechanisms in the transcriptomic analysis of the ChP properties in a mouse model of experimental autoimmune encephalomyelitis (EAE). Our analysis revealed five enriched proteins(NTRK2, ADAM23, SCARB2, CPM, CNTN5)significantly associated with the ChP volumes in both of the MS cohorts. These proteins relate closely to mechanisms of cellular communication, function (e.g. transmembrane tyrosine receptor signalling (RTK) and vascular endothelial growth) and pathways involved in the regulation of cellular plasticity (e.g. neuron differentiation, axonal remodelling and myelin regulation) as depicted by molecular function analysis and validation of the results in the transcriptome from ChP tissue specific for EAE. This work provides conclusive new evidence for the role of ChP in the context of neuroinflammation and neurodegeneration, demonstrating the intriguing relationships between ChP enlargement, CSF dynamics, and the development of neuroinflammatory and neurodegenerative diseases. Our results are encouraging for the development of new therapeutic avenues (i.e. targeting RTK signalling).One sentence summaryTyrosine receptor signalling is tightly associated with choroid plexus enlargement and is key in CSF dynamics during a neuroinflammatory attack in MS
Single-character insertion-deletion model preserves long indels in ancestral sequence reconstruction
Authors: Jowkar Gholamhossein , Pěcerska Jūlija , Gil Manuel , Anisimova Maria 
Published: March 2024

AbstractInsertions and deletions (indels) play a significant role in genome evolution across species. Realistic modelling of indel evolution is challenging and is still an open research question. Several attempts have been made to explicitly model multi-character (long) indels, such as TKF92, by relaxing the site independence assumption and introducing fragments. However, these methods are computationally expensiveOn the other hand, the Poisson Indel Process (PIP) assumes site independence but allows one to infer single-character indels on the phylogenetic tree, distinguishing insertions from deletions. PIP’s marginal likelihood computation has linear time complexity, enabling ancestral sequence reconstruction (ASR) with indels in linear time. Recently, we developed ARPIP, an ASR method using PIP, capable of inferring indel events with explicit evolutionary interpretationsHere, we investigate the effect of the single-character indel assumption on reconstructed ancestral sequences on mammalian protein orthologs and on simulated data. We show that ARPIP’s ancestral estimates preserve the gap length distribution observed in the input alignment. In mammalian proteins the lengths of inserted segments appear to be substantially longer compared to deleted segments. Further, we confirm the well-established deletion bias observed in real dataTo date, ARPIP is the only ancestral reconstruction method that explicitly models insertion and deletion events over time. Given a good quality input alignment, it can capture ancestral long indel events on the phylogeny
Non-apoptotic caspase events and Atf3 expression underlie direct neuronal differentiation of adult neural stem cells
Authors: Rosa Frédéric , Dray Nicolas , Bally-Cuif Laure 
Published: March 2024

AbstractNeural stem cells (NSCs) are key physiological components of adult vertebrate brains, generating neurons over a lifetime. In the adult zebrafish pallium, NSCs persist at long term through balanced fate decisions that include direct neuronal conversions, i.e., delamination and neurogenesis without a division. The characteristics and mechanisms of these events remain unknown. Here we reanalyze intravital imaging data of adult pallial NSCs and observe shared delamination dynamics between NSCs and committed neuronal progenitors. In a candidate approach for mechanisms predicting NSC decisions, we build an NSC-specific genetic tracer of Caspase3/7 activation (Cas3*/Cas7*)in vivoand show that non-apoptotic Cas3*/7* events occur in adult NSCs and are biased towards neuronal conversion under physiological conditions. We further identify the transcription factor Atf3 as necessary to express this fate. Finally, we show that the Cas3*/7*/Atf3 pathways are part of the processes engaged when NSCs are recruited for neuronal regeneration. These results provide evidence for the non-apoptotic caspase events occurring in vertebrate adult NSCs and link these events with the NSC fate decision of direct conversion, important for long-term NSC population homeostasis.
Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics
Authors: Ma Cong , Balaban Metin , Liu Jingxian , Chen Siqi , Ding Li , Raphael Benjamin J. 
Published: March 2024

AbstractA key challenge in cancer research is to reconstruct the somatic evolution within a tumor over time and across space. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genetic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and a spatial model of tumor evolution from SRT of tumor slices. By modeling CNA-induced perturbations in both total and allele-specific gene expression, CalicoST identifies important types of CNAs – including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs– that are invisible to total copy number analysis. On SRT data from nine patients from the Human Tumor Atlas Network (HTAN) with matched whole exome sequencing (WES) data, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. On two patients with SRT data from multiple adjacent slices, CalicoST reconstructs a tumor phylogeography that describes the spread of cancerous clones in three-dimensional space. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals five spatially coherent clones, with mirrored subclonal CNAs distinguishing clones on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space.
Detection of spatial chromatin accessibility patterns with inter-cellular correlations
Authors: Chen Xiaoyang , Li Keyi , Wu Xiaoqing , Li Zhen , Jiang Qun , Wu Yanhong , Jiang Rui 
Published: March 2024

AbstractRecent advances in spatial sequencing technologies enable simultaneous capture of spatial location and chromatin accessibility of cells within intact tissue slices. Identifying peaks that display spatial variation and cellular heterogeneity is the first and key analytic task for characterizing the spatial chromatin accessibility landscape of complex tissues. Here we propose an efficient and iterative model, Descartes, for spatially variable peaks identification based on the graph of inter-cellular correlations. Through the comprehensive benchmarking for spatially variable peaks identification, we demonstrate the superiority of Descartes in revealing cellular heterogeneity and capturing tissue structure. In terms of computational efficiency, Descartes also outperforms existing methods with spatial assumptions. Utilizing the graph of inter-cellular correlations, Descartes denoises and imputes data via the neighboring relationships, enhancing the precision of downstream analysis. We further demonstrate the ability of Descartes for peak module identification by using peak-peak correlations within the graph. When applied to spatial multi-omics data, Descartes show its potential to detect gene-peak interactions, offering valuable insights into the construction of gene regulatory networks.