bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accelerated spike-triggered non-negative matrix factorization reveals coordinated ganglion cell subunit mosaics in the primate retina
Authors: Zapp Sören J. , Khani Mohammad H. , Schreyer Helene M. , Sridhar Shashwat , Ramakrishna Varsha , Krüppel Steffen , Mietsch Matthias , Protti Dario A. , Karamanlis Dimokratis , Gollisch Tim 
Published: April 2024

ABSTRACTA standard circuit motif in sensory systems is the pooling of sensory information from an upstream neuronal layer. A downstream neuron thereby collects signals across different locations in stimulus space, which together compose the neuron’s receptive field. In addition, nonlinear transformations in the signal transfer between the layers give rise to functional subunits inside the receptive field. For ganglion cells in the vertebrate retina, for example, receptive field subunits are thought to correspond to presynaptic bipolar cells. Identifying the number and locations of subunits from the stimulus–response relationship of a recorded ganglion cell has been an ongoing challenge in order to characterize the retina’s functional circuitry and to build computational models that capture nonlinear signal pooling. Here we present a novel version of spike-triggered non-negative matrix factorization (STNMF), which can extract localized subunits in ganglion-cell receptive fields from recorded spiking responses under spatiotemporal white-noise stimulation. The method provides a more than 100-fold speed increase compared to a previous implementation, which can be harnessed for systematic screening of hyperparameters, such as sparsity regularization. We demonstrate the power and flexibility of this approach by analyzing populations of ganglion cells from salamander and primate retina. We find that subunits of midget as well as parasol ganglion cells in the marmoset retina form separate mosaics that tile visual space. Moreover, subunit mosaics show alignment with each other for ON and OFF midget as well as for ON and OFF parasol cells, indicating a spatial coordination of ON and OFF signals at the bipolar-cell level. Thus, STNMF can reveal organizational principles of signal transmission between successive neural layers, which are not easily accessible by other means.
Impact of phylogeny on the inference of functional sectors from protein sequence data
Authors: Dietler Nicola , Abbara Alia , Choudhury Subham , Bitbol Anne-Florence 
Published: April 2024

AbstractStatistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.Author SummaryProteins perform crucial functions in the cell. The biological function of a protein is encoded in its amino-acid sequence. Natural selection acts at the level of function, while mutations arise randomly on sequences. In alignments of sequences of homologous proteins, which share common ancestry and common function, the amino acid usages at different sites can be correlated due to functional constraints. In particular, groups of collectively correlated amino acids, termed sectors, tend to emerge due to selection on functional traits. However, correlations can also arise from the shared evolutionary history of homologous proteins, even without functional constraints. This may obscure the inference of functional sectors. By analyzing controlled synthetic data as well as natural protein sequence data, we show that two very different methods allow to identify sectors and mutational effects in a way that is most robust to phylogeny. We suggest that considering both of these methods allows a better identification of functionally important sites from protein sequences. These results have potential impact on the design of new functional sequences.
demuxSNP: supervised demultiplexing scRNAseq using cell hashing and SNPs
Authors: Lynch Michael P. , Wang Yufei , Gatto Laurent , Culhane Aedin C. 
Published: April 2024

AbstractBackgroundMultiplexing single-cell RNA sequencing experiments reduces sequencing cost and facilitates larger scale studies. However, factors such as cell hashing quality and class size imbalance impact demultiplexing algorithm performance, reducing cost effectivenessFindingsWe propose a supervised algorithm, demuxSNP, leveraging both cell hashing and genetic variation between individuals (SNPs). The supervised algorithm addresses fundamental limitations in demultiplexing with only one data modality. The genetic variants (SNPs) of the subset of cells assigned with high confidence using a probabilistic hashing algorithm are used to train a KNN classifier that predicts the demultiplexing classes of unassigned or uncertain cells. We benchmark demuxSNP against hashing (HTODemux, cellhashR, GMM-demux, demuxmix) and genotype-free SNP (souporcell) methods on simulated and real data from renal cell cancer. Our results demonstrate that demuxSNP outperformed standalone hashing methods on low quality hashing data, improving overall classification accuracy and allowing more high RNA quality cells to be recovered. Through varying simulated doublet rates, we show genotype-free SNP methods are unable to identify biological samples with low cell counts at high doublet rates. When compared to unsupervised SNP demultiplexing methods, demuxSNP’s supervised approach was more robust to doublet rate in experiments with class size imbalance.ConclusionsdemuxSNP is a performant demultiplexing approach that uses hashing and SNP data to demultiplex datasets with low hashing quality where biological samples are genetically distinct. Unassigned cells (negatives) with high RNA quality can be recovered, making more cells available for analysis, especially when applied to data with low hashing quality or suspected misassigned cells. Pipelines for simulated data and processed benchmarking data for 5-50% doublets are publicly available. demuxSNP is available as an R/Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.demuxSNP).
Reinforcement learning enhances training and performance in skilled alpine skiers compared to traditional coaching instruction
Authors: Magelssen Christian , Gilgien Matthias , Tajet Simen Leithe , Losnegard Thomas , Haugen Per , Reid Robert , Frömer Romy 
Published: April 2024

AbstractSkilled athletes need powerful movement strategies to solve tasks effectively. Typically, athletes learn these strategies with instruction-based teaching methods where coaches offer athletes a correct solution. Inspired by recent evidence from decision neuroscience, we asked whether skilled athletes learn strategy choices better with an evaluation-based training strategy (reinforcement learning). To address this question, we conducted a three-day learning experiment with skilled alpine ski racers (n=98) designed to improve their performance on flat slopes on slalom with four strategies at their disposal to achieve this goal. We compared performance and strategy choices of three groups: a reinforcement learning group, that only received feedback about their race times after every run, a supervised (free choice) learning group, that received strategy instructions from their coach, and a supervised (target skill) learning group, being coached to use the theoretically optimal strategy for skiing well on flats. We found that despite making similar strategy choices, the skiers in the reinforcement learning group, showed greater improvements in their race times during the training sessions than their counterparts in the supervised (free choice) learning group and outperformed them during a subsequent retention test. Surprisingly, the skiers in the reinforcement learning group even showed descriptively (but not significantly) better performance than those in the supervised (target skill) learning group. Our findings show that reinforcement learning can be an effective training strategy for improving strategy choices and performance among skilled athletes, even among the best ones.
mini-Complexome Profiling (mCP), an FDR-controlled workflow for global targeted detection of protein complexes
Authors: Amedei Hugo , Paul Niels , Foo Brian , Neuenroth Lisa , Lehnart Stephan E. , Urlaub Henning , Lenz Christof 
Published: April 2024

AbstractIntroductionCo-fractionation mass spectrometry couples native-like separations of protein/protein complexes with mass spectrometric proteome analysis for global characterization of protein networks. The technique allows for both de novo detection of complexes and for the detection of subtle changes in their protein composition. The typical requirement for fine-grained fractionation of >80 fractions, however, translates into significant demands on sample quantity and mass spectrometric instrument time, and represents a significant barrier to experimental replication and the use of scarce sample material (ex. Patient biopsies).MethodsWe developed mini-Complexome Profiling (mCP), a streamlined workflow with reduced requirements for fractionation and, thus, biological material and laboratory and instrument time. Soluble and membrane-associated protein complexes are extracted from biological material under mild conditions, and fractionated by Blue Native electrophoresis using commercial equipment. Each fraction is analyzed by data independent acquisition mass-spectrometry, and known protein complexes are detected based on the coelution of known components using a novel R package with a controlled false discovery rate approach. The tool is available to the community on a GitHub repository.ResultsmCP was benchmarked using HEK293 cell lysate and exhibited performance similar to established workflows, but from a significantly reduced number of fractions. We then challenged mCP by performing comparative complexome analysis of cardiomyocytes isolated from different chambers from a single mouse heart, where we identified subtle chamber-specific changes in mitochondrial OxPhos complexes.DiscussionThe reduced sample and instrument time requirements open up new applications of co-fractionation mass spectrometry, specifically for the analysis of sparse samples such as human patient biopsies. The ability to identify subtle changes between similar tissue types (left/right ventricular and atrial cardiomyocytes) serves as a proof of principle for comparative analysis of mild/asymptomatic disease states.
Biases in ARG-based inference of historical population size in populations experiencing selection
Authors: Marsh Jacob I. , Johri Parul 
Published: April 2024

AbstractInferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ARG-based approaches to demographic inference in typical empirical analyses are susceptible to mis-inference due to these effects. To address this, we developed highly realistic forward simulations of human andDrosophila melanogasterpopulations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, though it could cause mis-inference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference ofD. melanogasterpopulations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid mis-inference due to the linked effects of selection.
Assessing drug uptake and response differences in 2D and 3D cellular environments using stimulated Raman scattering microscopy
Authors: Xu Fiona Xi , Sun Rui , Owens Ryan , Hu Kailun , Fu Dan 
Published: April 2024

AbstractThe architecture of cell culture—two-dimensional (2D) versus three-dimensional (3D)—significantly impacts various cellular factors, including cell-cell interactions, nutrient and oxygen gradients, metabolic activity, and gene expression profiles. This can result in different cellular responses during cancer drug treatment, with 3D-cultured cells often exhibiting higher resistance to chemotherapeutic drugs. While various genetic and proteomic analyses have been employed to investigate the underlying mechanisms of this increased resistance, complementary techniques that provide experimental evidence of spatial molecular profiling data are limited. Stimulated Raman scattering (SRS) microscopy has demonstrated its capability to measure both intracellular drug uptake and growth inhibition. In this work, we applied three-band SRS imaging to 2D and 3D cell cultures and provided a comparative analysis of drug uptake and response with the goal of understanding whether the difference in drug uptake explains the drug resistance in 3D culture compared to 2D. Our investigations revealed that despite similar intracellular drug levels in 2D and 3D A549 cells during lapatinib treatment, the growth of 3D spheroids is less impacted, supporting an enhanced drug tolerance in the 3D microenvironment. We further elucidated drug penetration patterns and the resulting heterogeneous cellular responses across different spheroid layers. Additionally, we investigated the role of the extracellular matrix in modulating drug delivery and cell response, and we discovered that limited drug penetration in 3D could also contribute to lower drug response. Our study provides valuable insights into the intricate mechanisms of increased drug resistance in 3D tumor models during cancer drug treatments.
Therapeutic Activity of Resolvin D1 (RvD1) in Murine MASH
Authors: Navarro-Corcuera Amaia , Zhu Yiwei , Ma Fanglin , Gupta Neha , Asplund Haley , Yuan Feifei , Friedman Scott , Sansbury Brian E. , Huang Xin , Cai Bishuang 
Published: April 2024

AbstractBackground and AimsRecent studies have highlighted the beneficial effect of resolvin D1 (RvD1), a DHA-derived specialized pro-resolving mediator, on metabolic dysfunction-associated steatohepatitis (MASH), but the underlying mechanisms are not well understood. Our study aims to determine the mechanism by which RvD1 protects against MASH progression.MethodsRvD1 was administered to mice with experimental MASH, followed by bulk and single-cell RNA sequencing analysis. Primary cells including bone marrow–derived macrophages (BMDMs), Kupffer cells, T cells, and primary hepatocytes were isolated to elucidate the effect of RvD1 on inflammation, cell death, and fibrosis regression genes.ResultsHepatic tissue levels of RvD1 were decreased in murine and human MASH, likely due to an expansion of pro-inflammatory M1-like macrophages with diminished ability to produce RvD1. Administering RvD1 reduced inflammation, cell death, and liver fibrosis. Mechanistically, RvD1 reduced inflammation by suppressing the Stat1-Cxcl10 signaling pathway in macrophages and prevented hepatocyte death by alleviating ER stress-mediated apoptosis. Moreover, RvD1 inducedMmp2and decreasedActa2expression in hepatic stellate cells (HSCs), and promotedMmp9andMmp12expression in macrophages, leading to fibrosis regression in MASH.ConclusionsRvD1 reduces Stat1-mediated inflammation, mitigates ER stress-induced apoptosis, and promotes MMP-mediated fibrosis regression in MASH. This study highlights the therapeutic potential of RvD1 to treat MASH.Impact and implicationsMetabolic dysfunction–associated steatohepatitis (MASH) is an increasing healthcare burden worldwide. Current treatments for MASH and its sequelae are very limited. Recent studies highlighted the therapeutic benefit of specialized pro-resolving mediators (SPMs), including resolvin D1 (RvD1), in liver diseases. However, the mechanisms underlying these beneficial effects are not well understood. Based on unbiased transcriptomic analyses using bulk and single-cell RNA sequencing in RvD1-treated MASH livers, we show that RvD1 suppresses Stat1-mediated inflammatory responses and ER stress-induced apoptosis, and induces gene expression related to fibrosis regression. Our study provides new mechanistic insight into the role of RvD1 in MASH and highlights its therapeutic potential to treat MASH.HighlightsLiver RvD1 levels are decreased in MASH patients and MASH miceRvD1 administration suppresses Stat1-mediated inflammatory responseRvD1 administration alleviates ER stress-induced apoptosisRvD1 administration induces fibrosis regression gene expression
Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery
Authors: Nie Zhiwei , Ma Yiming , Chen Jie , Liu Yutian , Liu Zhihong , Yang Peng , Xu Fan , Huang Xiansong , Yin Feng , Li Zigang , Fu Jie , Ren Zhixiang , Zhang Wen-Bin , Gao Wen , Tian Yonghong 
Published: April 2024

AbstractProtein stability offers valuable insights into protein folding and functionality, making it an integral component of evolutionary fitness. Previous computational methods possess both strengths and weaknesses, leading to practical and inter-pretational limitations. Here, we propose an interpretable protein stability change prediction method, S3C, to anchor evolutionary fitness for protein stability with virtual chemical environment recovery. S3C first gets rid of the shackles of high-resolution protein structure data and restores the local chemical environments of the mutations at the sequence level. Subsequently, S3C promotes the evolutionary fitness of protein stability to dominate the fitness landscape under the selective pressure. Naturally, S3C comprehensively outperforms state-of-the-art methods on benchmark datasets while showing ideal generalization when migrated to unseen protein families. More importantly, S3C is demonstrated to be interpretable at multiple scales, including high-fidelity recovery of local structure micro-environment, perception of intricate interaction reconstruction, and accurate mining of rare beneficial mutations. S3C expands the boundaries of protein evolution prediction and provides an ideal candidate for large-scale optimization of protein engineering.
Differences in chromatin accessibility between renal cortex and inner medulla correlate with spatial differences in gene expression and are modulated by NFAT5 function
Authors: Engel Kristina , Chernyakov Dmitry , Nerger Katrin , Sameith Katrin , Dahl Andreas , Edemir Bayram 
Published: April 2024

ABSTRACTA spatial gene expression pattern between the cortex (CTX) and inner medulla (IM) of the kidney has been observed, but the underlying mechanisms are unclear. Understanding these mechanisms is essential for elucidating renal function. Using the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) we analyzed the open chromatin structures and the involvement of epigenetic mechanisms in mediating gene expression differences between the renal CTX and IM. We also examined the role of the nuclear factor of activated T cells 5 (NFAT5), a key regulator of hypertonicity. ATAC-seq analysis was performed on CTX and IM samples from both wild-type (WT) and NFAT5 knockout (KO) mice.This work demonstrates for the first time that these differences in gene expression between renal CTX and IM are associated with an epigenetic mechanism driven by chromatin accessibility, which is partially modulated by the nuclear factor of activated T-cells 5 (NFAT5) in mice. Furthermore, spatial localization and NFAT5-promoted chromatin accessibility correlate with differential gene expression and altered promoter binding motif enrichment in CTX and IM.This study provides new insights into the spatial and NFAT5-mediated regulation of chromatin accessibility and gene expression in CTX and IM. This work advances our understanding of kidney physiology by uncovering previously unknown epigenetic factors influencing gene expression and provides a new perspective on renal adaptive mechanisms.TRANSLATIONAL STATEMENTThe study reveals new insights into the spatial and epigenetic regulation of gene expression in the renal cortex (CTX) and inner medulla (IM) in the mouse kidney. We used the Assay for Transposase-Accessible Chromatin with High-Throughput Sequence Analysis (ATAC-seq) to identify a key role of NFAT5 in modulating chromatin accessibility and to uncover previously unknown epigenetic factors. This research enhances our understanding of renal physiology and has important implications for clinical care by providing insights into potential adaptive mechanisms in the kidney. These findings suggest future investigations targeting epigenetic signaling pathways for therapeutic intervention in renal diseases.
CTHRC1+Fibroblasts andSPP1+Macrophages Synergistically Contribute to Pro-Tumorigenic Tumor Microenvironment in Pancreatic Ductal Adenocarcinoma
Authors: Li Evan , Cheung Hoi Ching , Ma Shuangge 
Published: April 2024

AbstractPancreatic ductal adenocarcinoma (PDAC) is an extremely lethal cancer that accounts for over 90% of all pancreatic cancer cases. With a 5-year survival rate of only 13%, PDAC has proven to be extremely desmoplastic and immunosuppressive to most current therapies, including chemotherapy and surgical resection. In recent years, focus has shifted to understanding the tumor microenvironment (TME) around PDAC, enabling a greater understanding of biological pathways and intercellular interactions that can ultimately lead to potential for future drug targets. In this study, we leverage a combination of single-cell and spatial transcriptomics to further identify cellular populations and interactions within the highly heterogeneous TME. We demonstrate thatSPP1+APOE+tumor-associated macrophages (TAM) andCTHRC1+GREM1+cancer-associated myofibroblasts (myCAF) not only act synergistically to promote an immune-suppressive TME through active extracellular matrix (ECM) deposition and epithelial mesenchymal transition (EMT), but are spatially colocalized and correlated, leading to worse prognosis. Our results highlight the crosstalk between stromal and myeloid cells as a significant area of study for future therapeutic targets to treat cancer.