bluuebunny/crossref_metadata_2025_split huggingface dataset:
Visual and auditory object representations in ventral visual cortex after restoring sight in humans
Authors: Rączy Katarzyna , Linke Madita , van den Hurk Job , Heitmann Carolin , Guerreiro Maria J. S. , Zhan Minye , Kekunnaya Ramesh , Goebel Rainer , Röder Brigitte 
Published: November 2024

AbstractVisual category-selective representations in human ventral occipital temporal cortex (VOTC) seem to emerge early in infancy. Surprisingly, the VOTC of congenitally blind humans features category-selectivity for auditory and haptic objects. Yet it has been unknown whether VOTC would show category-selective visual responses if sight were restored in congenitally blind humans. Assuming competition for synaptic space during development, cross-modal activation of VOTC as a consequence of congenital blindness might interfere with visual processing in sight-recovery individuals. To test this hypothesis, we investigated adults who had suffered a transient phase of congenital blindness due to bilateral dense cataracts before their sight was restored by cataract-removal surgery. In a functional magnetic resonance imaging (fMRI) study, participants watched movies of faces, scenes, body parts and other objects in the visual condition, while in the auditory condition they listened to the corresponding sounds. The most prominent group difference was the reduced face-selectivity in individuals with reversed congenital cataracts compared to age- and sex-matched normally-sighted individuals. In addition, a double dissociation was found: only sight-recovery individuals demonstrated significant decoding accuracy of visual categories based on auditory category representations in VOTC, while only normally-sighted individuals’ VOTC decoded auditory categories based on visual category representations. The present results uncovered the neural mechanisms of previously observed face processing impairments in individuals with reversed congenital blindness. We suggest that lower face-selectivity in the sight recovery group might arise from selective deficits in the cortical representation of the central visual field in lower-tier visual areas. Additionally, we speculate that in higher-order visual areas cross-modal activity might facilitate – rather than interfere – with visual functional recovery after congenital blindness.
Robust and cost-efficient single-cell sequencing through combinatorial pooling
Authors: Gawron Johannes , Cunha Luís , Borgsmüller Nico , Beerenwinkel Niko 
Published: November 2024

AbstractSingle-cell sequencing is widely used to study molecular cell-to-cell heterogeneity. Even though the cost of sequencing has dropped throughout the last decades, single-cell assays remain expensive, because they require strategies to index molecules by cells. The high costs of indexing can be mitigated by pooling samples prior to sequencing library preparation. Computational methods have been developed to leverage molecular features that are distinct between different samples to separate the pools into distinct datasets. However, since all multiplexed samples are processed in the same way, information on the origin of each demultiplexed dataset is lost. To map datasets to their sample of origin, additional information such as molecular indexing or additional genotyping is needed. Here, we propose a class of experimental designs that allows identifying the sample of origin of each demultiplexed dataset, only relying on the genetic profiles of the samples and the composition of pools. Our approach is based on splitting and pooling samples in specific combinations. We find a most cost-efficient experimental design in this class and prove its optimality. We present a dynamic programming algorithm to iteratively simplify an optimal experimental design by breaking it into several independent designs while maintaining optimality. Furthermore, we propose a subclass of experimental designs which allow robust sample identification even under partial failure of the experiment and present a provably optimal design in this subclass. We provide an implementation for automatic sample identification under these optimal combinatorial pooling strategies and demonstrate its functionality in a simulation study.
Estimating hierarchicalF–statistics from Pool–Seq data
Authors: Gautier Mathieu , Coronado-Zamora Marta , Vitalis Renaud 
Published: November 2024

AbstractIntroduced over seventy years ago,F–statistics have been and remain central to population and evolutionary genetics. Among them,FSTis one of the most commonly used descriptive statistics in empirical studies, notably to characterize the structure of genetic polymorphisms within and between populations, to shed light on the evolutionary history of populations, or to identify marker loci under differential selection for adaptive traits. However, the use ofFSTin simplified population models can overlook important hierarchical structures, such as geographic or temporal subdivisions, potentially leading to misleading interpretations and increasing false positives in genome scans for adaptive differentiation. HierarchicalF–statistics have been introduced to account for multiple predefined levels of population structure. Several estimators have also been proposed, including robust ones implemented in the popular R packagehierfstat. Nevertheless, these were primarily designed for individual genotyping data and can be computationally intensive for large genomic datasets. In this study, we extend previous work by developing unbiased method-of-moments estimators for hierarchicalF–statistics tailored for Pool–Seq data, a cost-effective alternative to individual genome sequencing. These Pool–Seq estimators have been developed in ananovaframework, using definitions based on identity-in-state probabilities. The new estimators have been implemented in an updated version of the R packagepoolfstat, together with estimators for sample allele count data derived from individual genotyping data. We validate and compare the performance of these estimators through extensive simulations under a hierarchical island model. Finally, we apply these estimators to real Pool–Seq data fromDrosophila melanogasterpopulations, demonstrating their usefulness in revealing population structure and identifying loci with high differentiation within or between groups of subpopulations and associated with spatial or temporal genetic variation.
Assessing changes in whole-brain structural connectivity in the unilateral 6-hydroxydopamine rat model of Parkinson’s Disease using diffusion imaging and tractography
Authors: Moshchin Mikhail , Schultz Roger J , Cheng Kevin P. , Osting Susan , Koeper James , Laluzerne Matthew , Trevathan James K. , Brzeczkowski Andrea , Luu Cuong P. , Yu John-Paul J , Betzel Richard , Lake Wendell B. , Hurley Samuel A. , Ludwig Kip A. , Suminski Aaron J. 
Published: November 2024

I.AbstractParkinson’s disease (PD) is a multifactorial, progressive neurodegenerative disease that has a profound impact on those it afflicts. Its hallmark pathophysiology is characterized by degeneration of dopaminergic neurons in the midbrain which trigger a host of motor and non- motor symptoms. Many preclinical research efforts utilize unilateral lesion models to assess the neural mechanisms of PD and explore new therapeutic approaches because these models produce similar motor symptoms to those of PD patients. The goal of this work is to examine changes in brain structure resulting from a unilateral lesion both within the nigrostriatal system, where dopaminergic neurons are lost, and throughout the brain. Using multi-shell diffusion magnetic resonance imaging and correlational tractography, we assessed microstructural changes throughout the brain resulting from unilateral injection of 6-hydroxydopamine (6-OHDA) in the median forebrain bundle (MFB). Following lesioning, the PD phenotype was confirmed using behavioral and histological assessment. Correlational tractography found networks of fiber tracts that were either positively or negatively correlated with lesion status throughout the brain. Analyzing patterns of intra- and inter-hemispheric connectivity between the positively and negatively correlated fiber tracts revealed two separate neural networks. The first contained only negatively correlated fibers in the lesioned hemisphere consistent with the local effects of the lesion (i.e. dopaminergic depletion in the nigrostriatal system). The second contained systematically overlapping fiber tracts in the lesioned and non-lesioned hemispheres including the olfactory system and cerebellum, which we suggest are indicative of adaptive mechanisms to compensate for the lesion. Taken together, these results suggest that correlational tractography is a reasonable tool to examine whole brain structural changes in rodent models of neurodegenerative disease, and may have future translational value as a diagnostic tool for patients with PD.
New algorithms for unsupervised cell clustering from scRNA-seq data
Authors: Robles Melissa , Díaz-Riaño Jorge , Forigua Cristhian , Ojeda Sebastian , Guio Laura , Siaucho Paula , Guzman-Porras Jennifer , García-Orjuela Danilo , Naranjo Andres , Maradei Silvia , Quiroz Adolfo , Duitama Jorge 
Published: November 2024

AbstractThe identification of cell types is a basic step of the pipeline for Single-Cell RNA sequencing data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: the high dimensional nature of the data, the sparse nature of the gene expression matrix, and the presence of technical noise that can introduce false zero entries. In this study, we introduce new algorithms for clustering scRNA-seq data. The first algorithm builds ak-MST graph from distances obtained directly from the input data without dimensionality reduction. The computation follows an iterative procedure of k steps in which each step calculates and stores the edges of minimum spanning trees over different subgraphs obtained removing edges selected in previous iterations. The Louvain algorithm is executed on thek-MST graph for cell clustering. We also explored alternatives based on neural networks in which an autoencoder is used to learn the parameters of a Gaussian mixture model, aiming to improve the handling of clusters with different shapes and sizes. Benchmark experiments with simulated data and public datasets show that the algorithms proposed in this work have competitive accuracy, compared to previous solutions, but also that sequencing depth, number of cells and tissue types have important effects on the performance of the algorithms. Moreover, we performed further experiments with scRNA-data taken from a patient with refractory epilepsy. The AE-GMM model achieved the best accuracy for this dataset, and thek-MST ranked first among methods that do not require previous information on the expected number of clusters.
STEP: Deciphering Spatial Atlas at Single-Cell Level with Whole-Transcriptome Coverage
Authors: Hu Zheqi , Zhu Zirui , Cai Linghan , Zhan Yangen , Yan Xiangming , Chen Jingyun , Sun Bingqian , Du Sijing , Jiang Songhan , Wang Hongpeng , Zhang Yongbing 
Published: November 2024

AbstractRecent advances in spatial transcriptomics have revolutionized our understanding of tissue spatial architecture and biological processes. However, many of these technologies face significant challenges in achieving either single-cell resolution or comprehensive whole-transcriptome profiling, hindering their capacity to fully elucidate intercellular interactions and the tissue microenvironment. To address these limitations, we present STEP, a hybrid framework that synergistically integrates probabilistic models with deep learning techniques for spatial transcriptome analysis. Through innovations in model and algorithm design, STEP not only enhances sequencing-based spatial transcriptome data to single-cell resolution but accurately infers transcriptome-wide expression levels for image-based spatial transcriptomic. By leveraging the nuclear features extracted from histological images, STEP achieves precise predictions of cell type and gene expression and effectively diffuses the discriminative ability to serial sections for modeling solid tissue landscapes. The capability is particularly advantageous for analyzing cells with distinctive characteristics, such as cancer cells, enabling cross-sample inference. In addition, STEP simulates intercellular communication through a spatially resolved cell-cell interaction network, uncovering intrinsic biological processes. Overall, STEP equips researchers with a powerful tool for understanding biological functions and unveiling spatial gene expression patterns, paving the way for advancements in spatial transcriptomics research. Code is available athttps://github.com/childishHU/STEP.
Higher-Order Interaction Analysis via Hypergraph Models for Studying Multidimensional Neuroscience Data
Authors: Bilbao Dalma , Aimar Hugo , Torterolo Pablo , Mateos Diego M. 
Published: November 2024

AbstractHigher-Order Interaction (HOI) theory offers a powerful framework for capturing complex, non-linear relationships within multidimensional systems, moving beyond traditional pairwise graph methods to encompass multi-way interactions. This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data.
Large-scale Signal Propagation Modes in the Human Brain
Authors: Song Youngjo , Kim Pyeong Soo , Philip Benjamin A. , Kim Taewon 
Published: November 2024

AbstractThe brain’s large-scale temporal dynamics play a crucial role in understanding its operations, but developing a cohesive framework to integrate the potentially extensive array of spatiotemporal patterns remains elusive. Our work addresses this gap by identifying multiple large-scale signal propagation modes in resting-state fMRI time series under a unified methodological framework. We found five distinct modes that effectively predict future blood-oxygen-level-dependent (BOLD) signal dynamics, each reconciling transitions between well-known large-scale brain networks into coherent spatiotemporal units. By utilizing these coherent units, our approach circumvents the need to explore combinatorial explosion of transitions between potential states, enabling parsimonious modeling and effective prediction of whole-brain temporal evolution. Each mode captures specific operational dimensions of neural resource allocation, ensuring their interpretability. Importantly, we showed that complex spatiotemporal features emerge from the superposition of these few propagation modes, unifying a broad spectrum of well-known brain dynamics phenomena. Our results lay the groundwork for a unified framework to understand large-scale spatiotemporal brain organization. Moreover, individual differences in mode expression profiles correlate with general cognitive abilities, exhibit heritability, and demonstrate cross-task stability, underscoring their functional significance. This could lead to efficient methods for characterizing functional fingerprints and advancing diagnostic approaches for neurological disorders.
Mitochondrial ROS and HIF-1α signalling mediate critical period plasticity
Authors: Sobrido-Cameán Daniel , Coulson Bramwell , Miller Michael , Oswald Matthew C.W. , Pettini Tom , Baines Richard A. , Landgraf Matthias 
Published: November 2024

AbstractAs developing networks transition from spontaneous irregular to patterned activity, they undergo plastic tuning phases, termed “critical periods”; “critical” because disturbances during these phases can lead to lasting changes in network development and output. Critical periods are common to developing nervous systems, with analogous features shared from insects to mammals, yet the core signalling mechanisms that underly cellular critical period plasticity have remained elusive. To identify these, we exploited theDrosophilalarval locomotor network as an advantageous model system. It has a defined critical period and offers unparalleled access to identified network elements, including the neuromuscular junction as a model synapse. We find that manipulations of a single motoneuron or muscle cell during the critical period lead to predictable, and permanent, cell-specific changes. This demonstrates that critical period adjustments occur at a single cell level. Mechanistically, we identified mitochondrial reactive oxygen species (ROS) as causative. Specifically, we show that ROS produced by complex I of the mitochondrial electron transport chain, generated by the reverse flow of electrons, are necessary and instructive for critical period-regulated plasticity. Downstream of ROS, we identified theDrosophilahomologue of hypoxia inducible factor (HIF-1α), as required for transducing the mitochondrial ROS signal to the nucleus. This signalling axis is also sufficient to cell autonomously specify changes in neuronal properties and animal behaviour but, again, only when activated during the embryonic critical period. Thus, we have identified specific mitochondrial ROS and HIF-1α as primary signals that mediate critical period plasticity.
Effects of sex, mating status, and genetic background on circadian behavior inDrosophila
Authors: Akpoghiran Oghenerukevwe , Strich Alexandra K. , Koh Kyunghee 
Published: November 2024

AbstractCircadian rhythms play a crucial role in regulating behavior, physiology, and health. Sexual dimorphism, a widespread phenomenon across species, influences circadian behaviors. Additionally, post-mating physiological changes in females are known to modulate various behaviors, yet their effects on circadian rhythms remain underexplored. Here, usingDrosophila melanogaster, a powerful model for studying circadian mechanisms, we systematically assessed the impact of sex and mating status on circadian behavior. We measured circadian period length and rhythm strength in virgin and mated males and females, including females mated to males lacking Sex Peptide (SP), a key mediator of post-mating changes. Across four wild-type and control strains, we found that males consistently exhibited shorter circadian periods than females, regardless of mating status, suggesting that circadian period length is a robust sexually dimorphic trait. In contrast, rhythm strength was influenced by the interaction between sex and mating status, with female mating generally reducing rhythm strength in the presence of SP signaling. Notably, genetic background significantly modulated these effects on rhythm strength. Our findings demonstrate that while circadian period length is a stable sex-specific trait, rhythm strength is shaped by a complex interplay between sex, mating status, and genetic background. This study advances our understanding of how sex and mating influence circadian rhythms inDrosophilaand provides a foundation for future research into sexually dimorphic mechanisms underlying human diseases associated with circadian disruptions.
Sex-specific perturbations of neuronal development caused by mutations in the autism risk geneDDX3X
Authors: Mossa Adele , Dierdorff Lauren , Lukin Jeronimo , Park Yeaji , Fiorenzani Chiara , Akpinar Zeynep , Garcia-Forn Marta , De Rubeis Silvia 
Published: November 2024

ABSTRACTDDX3Xis an X-linked RNA helicases that escapes X chromosome inactivation and is expressed at higher levels in female brains. Mutations inDDX3Xare associated with intellectual disability (ID) and autism spectrum disorder (ASD) and are predominantly identified in females. Using cellular and mouse models, we show thatDdx3xmediates sexual dimorphisms in brain development at a molecular, cellular, and behavioral level. During cortical neuronal development,Ddx3xsustains a female-biased signature of enhanced ribosomal biogenesis and mRNA translation. Female neurons display higher levels of ribosomal proteins and larger nucleoli, and these sex dimorphisms are obliterated byDdx3xloss.Ddx3xregulates dendritic outgrowth in a sex- and dose-dependent manner in both female and male neurons, and dendritic spine development only in female neurons. Further, ablatingDdx3xconditionally in forebrain neurons is sufficient to yield sex-specific changes in developmental outcomes and motor function. Together, these findings poseDdx3xas a mediator of sexual differentiation during neurodevelopment and open new avenues to understand sex differences in health and disease.
Alanine-scanning of the yeast killer toxin K2 reveals key residues for activity, gain-of-function variants, and supports prediction of precursor processing and 3D structure
Authors: Prins Rianne C. , Marinus Tycho , Dafni Eyal , Yacoby Iftach , Billerbeck Sonja 
Published: November 2024

AbstractYeast killer toxins (YKTs) are antimicrobial proteins secreted by yeast with potential applications ranging from food preservation to therapeutic agents in human health. However, the practical use of many YTKs is limited by specific pH requirements, low temperature stability, low production yields, and narrow target specificity. While protein engineering could potentially overcome these challenges, progress is hindered by a lack of detailed knowledge about sequence-function relationships and structural data for these often multi-step processed proteins. In this study, we focused on the YKT K2, encoded by the M2 dsRNA satellite virus inSaccharomyces cerevisiae. Using alanine scanning mutagenesis of the full open reading frame and structure predictions combined with molecular dynamics simulations, we generated a comprehensive sequence-function map, refined the model for the proteolytic processing of the K2 precursor, and predicted the mature toxin structure. Our findings also demonstrate that K2 can be engineered towards enhanced toxicity and altered target specificity through single-site mutations. Furthermore, we identified structural homology between K2 and the SMK toxin from the yeastMillerozyma farinosa. Our cost-effective workflow provides a platform to broadly map YKT sequence-structure-function relationships, facilitating the engineering towards toxin-based technologies. The workflow could also serve as a template to resolve the processing and conformations of other proteins within the secretory pathway – a dynamic multi-step process that is challenging to structurally capture by purification and solving structures of intermediates.