bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reconstruction of single cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition
Authors: Cheng Yu-Chen , Zhang Yun , Tripathi Shubham , Harshavardhan BV , Jolly Mohit Kumar , Schiebinger Geoffrey , Levine Herbert , McDonald Thomas O. , Michor Franziska 
Published: September 2023

AbstractExploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and intricate mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport (OT) analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of theEEDandEZH2genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointingITGB4,LAMA3, andLAMB3as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found thatCENPF,CKS1B, andMKI67showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the latter group of genes represents newly identified regulators, shedding light on potential targets for modulating EMT trajectories.Significance StatementIn our study, we investigated cellular trajectories during EMT using a time-series scRNAseq dataset. OT analysis was used to infer cell-to-cell connections from scRNAseq data, allowing us to predict cell linkages and overcome limitations of sequencing such as the need to sacrifice cells for each measurement. This approach allowed us to identify diverse EMT responses under uniform treatment, a significant advancement over previous studies limited by the static nature of scRNAseq data. Our analysis identified a broad set of genes involved in the EMT process, uncovering novel insights such as the upregulation of cell cycle genes in cells predisposed to a high EMT state and the enhancement of cell adhesion marker genes in cells veering towards a partial EMT state. This work enriches our understanding of the dynamic processes of EMT, showcasing the varied cellular fates within the same experimental setup.
Identifying Temporal Correlations Between Natural Single-shot Videos and EEG Signals
Authors: Yao Yuanyuan , Stebner Axel , Tuytelaars Tinne , Geirnaert Simon , Bertrand Alexander 
Published: September 2023

AbstractObjectiveElectroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored.ApproachWe collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features.Main ResultsWe demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch (MM) task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects.SignificanceThis work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.
pyHiM, a new open-source, multi-platform software package for spatial genomics based on multiplexed DNA-FISH imaging
Authors: Xavier Devos , Jean-Bernard Fiche , Marion Bardou , Olivier Messina , Christophe Houbron , Julian Gurgo , Marie Schaeffer , Markus Götz , Thomas Walter , Florian Mueller , Marcelo Nollmann 
Published: September 2023

AbstractThe three-dimensional (3D) nuclear organization of chromatin in eukaryotes plays a crucial role in gene regulation, DNA replication, and DNA damage repair. While genome-wide ensemble methods have enhanced our understanding of chromatin organization, they lack the ability to capture single-cell heterogeneity and preserve spatial information. To overcome these limitations, a new family of imaging-based methods has emerged, giving rise to the field of spatial genomics. In this study, we present pyHiM, an open-source and modular software toolbox specifically designed for the robust, automatic analysis of sequential spatial genomics data. pyHiM enables the reconstruction of chromatin traces in individual cells from raw, multicolor images, offering novel, robust and validated algorithms, extensive documentation, and tutorials. Its user-friendly graphical interface and command-line interface allow for easy installation and execution on various hardware platforms. The software employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM supports preprocessing, spot detection, mask detection, and trace generation, generating human-readable reports and intermediate results for data validation and further analysis. Moreover, it offers additional features for data formatting, result display, and post-processing. pyHiM’s scalability and parallelization capabilities enable the analysis of large, complex datasets in a reasonable time frame. Overall, pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis, foster collaborative developments, and promote the growth of a user community to drive discoveries in the field of chromatin organization.
SENSE-PPI reconstructs protein-protein interactions of various complexities, within, across, and between species, with sequence-based evolutionary scale modeling and deep learning
Authors: Volzhenin Konstantin , Bittner Lucie , Carbone Alessandra 
Published: September 2023

AbstractAb initiocomputational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights on cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging latest generation protein language models and recurrent neural networks, we presentSENSE-PPI, a sequence-based deep learning model that efficiently reconstructsab initioPPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins.SENSE-PPIdemonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions. Its performance decreases for phylogenetically more distant model and non-model organisms, but signal alteration is very slow.SENSE-PPIis state-of-the-art, outperforming all existing methods. In this regard, it demonstrates the important role of parameters in protein language models.SENSE-PPIis very fast and can test 10,000 proteins against themselves in a matter of hours, enabling the reconstruction of genome-wide proteomes.Graphical abstractSENSE-PPIis a general deep learning architecture predicting protein-protein interactions of different complexities, between stable proteins, between stable and intrinsically disordered proteins, within a species, and between species. Trained on one species, it accurately predicts interactions and reconstructs complete specialized subnetworks for model and non-model organisms, and trained on human-virus interactions, it predicts human-virus interactions for new viruses.
Sex Chromosomes and Gonads Shape the Sex-Biased Transcriptomic Landscape in Tlr7-Mediated Demyelination During Aging
Authors: Lopez-Lee Chloe , Kodama Lay , Fan Li , Wong Man Ying , Foxe Nessa R. , Jiaz Laraib , Yu Fangmin , Ye Pearly , Zhu Jingjie , Norman Kendra , Torres Eileen Ruth , Kim Rachel D. , Mousa Gergey Alzaem , Dubal Dena , Liddelow Shane , Luo Wenjie , Gan Li 
Published: September 2023

AbstractDemyelination occurs in aging and associated diseases, including Alzheimer’s disease. Several of these diseases exhibit sex differences in prevalence and severity. Biological sex primarily stems from sex chromosomes and gonads releasing sex hormones. To dissect mechanisms underlying sex differences in demyelination of aging brains, we constructed a transcriptomic atlas of cell type-specific responses to illustrate how sex chromosomes, gonads, and their interaction shape responses to demyelination. We found that sex-biased oligodendrocyte and microglial responses are driven by interaction of sex chromosomes and gonads prior to myelin loss. Post demyelination, sex chromosomes mainly guide microglial responses, while gonadal composition influences oligodendrocyte signaling. Significantly, ablation of the X-linked gene Toll-like receptor 7 (Tlr7), which exhibited sex-biased expression during demyelination, abolished the sex-biased responses and protected against demyelination.One-sentence summaryCell type-specific processes underlying aged demyelination are sex-biased and mediated byTlr7.
Mineralocorticoid receptor-independent activation of ENaC in bile duct ligated mice
Authors: Wang Xue-Ping , Mutchler Stephanie M. , Carrisoza-Gáytan Rolando , Al-Bataineh Mohammad , Baty Catherine J. , Vandevender Amber , Srinivasan Priyanka , Tan Roderick J. , Jurczak Michael J. , Satlin Lisa M. , Kashlan Ossama B. 
Published: September 2023

AbstractSodium and fluid retention in liver disease is classically thought to result from reduced effective circulating volume and stimulation of the renin-angiotensin-aldosterone system (RAAS). Aldosterone dives Na+retention by activating the mineralocorticoid receptor and promoting the maturation and apical surface expression of the epithelial Na+channel (ENaC), found in the aldosterone-sensitive distal nephron. However, evidence of fluid retention without RAAS activation suggests the involvement of additional mechanisms. Liver disease can greatly increase plasma and urinary bile acid concentrations and have been shown to activate ENaCin vitro. We hypothesize that elevated bile acids in liver disease activate ENaC and drive fluid retention independent of RAAS. We therefore increased circulating bile acids in mice through bile duct ligation (BDL) and measured effects on urine and body composition, while using spironolactone to antagonize the mineralocorticoid receptor. We found BDL lowered blood [K+] and hematocrit, and increased benzamil-sensitive natriuresis compared to sham, consistent with ENaC activation. BDL mice also gained significantly more body water. Blocking ENaC reversed fluid gains in BDL mice but had no effect in shams. In isolated collecting ducts from rabbits, taurocholic acid stimulated net Na+absorption but had no effect on K+secretion or flow-dependent ion fluxes. Our results provide experimental evidence for a novel aldosterone-independent mechanism for sodium and fluid retention in liver disease which may provide additional therapeutic options for liver disease patients.SignificanceAdvanced liver disease is often complicated by renal sodium retention, leading to fluid retention, poor outcomes, and increased mortality. This is currently thought to be driven by increased levels of the hormone aldosterone, although numerous published reports demonstrate normal aldosterone levels in many liver patients with volume overload. Management of these patients relies on diuretics and Na+restriction, or more invasive procedures with increased risks for diuretic-resistant patients. Here, we report a novel mechanism for fluid retention in liver disease, which provides a basis to develop new strategies to treat fluid retention in these patients.
Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19
Authors: Bugrova Anna E. , Strelnikova Polina A. , Kononikhin Alexey S. , Zakharova Natalia V. , Diyachkova Elizaveta O. , Brzhozovskiy Alexander G. , Indeykina Maria I. , Kurochkin Ilya N. , Averyanov Alexander V. , Nikolaev Evgeny N. 
Published: September 2023

AbstractThe COVID-19 pandemic has exposed a number of key challenges that need to be urgently addressed. In particular, rapid identification and validation of prognostic markers is required. Mass spectrometric studies of blood plasma proteomics provide a deep understanding of the relationship between the severe course of infection and activation of specific pathophysiological pathways. Analysis of plasma proteins in whole blood may also be relevant for the pandemic as it requires minimal sample preparation. Here, for the first time, frozen whole blood samples were used to analyze 189 plasma proteins using multiple reaction monitoring (MRM) mass spectrometry and stable isotope-labeled peptide standards (SIS). A total of 128 samples (FRCC, Russia) from patients with mild (n=40), moderate (n=36) and severe (n=19) COVID-19 infection and healthy controls (n=33) were analyzed. Levels of 114 proteins were quantified and compared. Significant differences between all of the groups were revealed for 61 proteins. Changes in the levels of 30 reproducible COVID-19 markers (SERPING1, CRP, C9, ORM1, APOA1, SAA1/SAA2, LBP, AFM, IGFALS, etc.) were consistent with studies performed with serum/plasma samples. Levels of 70 proteins correlated between whole blood and plasma samples. The best-performing classifier built with 13 significantly different proteins achieved the best combination of ROC-AUC (0.93-0.95) and accuracy (0.87-0.93) metrics and distinguished patients from controls, as well as patients by severity and risk of mortality. Overall, the results support the use of frozen whole blood for MRM analysis of plasma proteins and assessment of the status of patients with COVID-19.
ANS: Adjusted Neighborhood Scoring to improve gene signature-based cell annotation in single-cell RNA-seq data
Authors: Ciernik Laure , Kraft Agnieszka , Barkmann Florian , Yates Josephine , Boeva Valentina 
Published: September 2023

AbstractIn the field of single-cell RNA sequencing (scRNA-seq), gene signature scoring is integral for pinpointing and characterizing distinct cell populations. However, challenges arise in ensuring the robustness and comparability of scores across various gene signatures and across different batches and conditions. Here, we evaluated the stability of established methods such as Scanpy, UCell, and JASMINE in the context of scoring cells of different types and states. On eight cancer and healthy scRNA-seq datasets, we reported that none of the existing methods provide fair gene signature scores that can be used in unsupervised cell state annotation based on the highest signature values. Addressing this challenge, we introduced a new scoring method, the Adjusted Neighbourhood Scoring (ANS), that builds on the traditional Scanpy method and improves the handling of the control gene sets. We further exemplified the usability of ANS scoring in differentiating between cancer-associated fibroblasts and malignant cells undergoing epithelial-mesenchymal transition (EMT) in four cancer types, and evidenced excellent classification performance (AUCPR train: 0.95-0.99, AUCPR test: 0.91-0.99). In summary, our research introduces ANS as a robust and deterministic scoring approach that enables the comparison of diverse gene signatures and score-based annotation of cell types and states. The results of our study contribute to the development of more accurate and reliable methods for analyzing scRNA-seq data.
Intraspecific trait variation modulates the temperature effect on elemental quotas and stoichiometry in marineSynechococcus
Authors: Davis Austin , Garcia Nathan S , Martiny Adam C 
Published: September 2023

AbstractDiverse phytoplankton modulate the coupling between the ocean carbon and nutrient cycles through life-history traits such as cell size, elemental quotas, and ratios. Biodiversity is mostly considered at broad functional levels, but major phytoplankton lineages are themselves highly diverse. As an example,Synechococcusis found in nearly all ocean regions and contain extensive intraspecific variation. Here, we grew four closely relatedSynechococcusisolates in semi-continuous cultures across a range of temperatures (16-25°C) to quantify for the relative role of intraspecific trait variation vs. environmental change. We report differences in cell size (p<0.01) as a function of strain and clade (p<0.01). The carbon (QC), nitrogen (QN), and phosphorus (QP) cell quotas all increased with cell size. Furthermore, cell size has an inverse relationship to growth rate. Within our experimental design, temperature alone had a weak physiological effect on cell quota and elemental ratios. Instead, we find systemic intraspecific variance of C:N:P, with cell size and N:P having an inverse relationship. Our results suggest a key role for intraspecific life history traits in determining elemental quotas and stoichiometry. Thus, the extensive biodiversity harbored within many lineages may modulate the impact of environmental change on ocean biogeochemical cycles.
Olfactory learning modulates a neural circuit mediating innate odor-guided behavior inDrosophila
Authors: Campetella Florencia , Huber Roman , Klappenbach Martin , Warnecke Carolin , Locatelli Fernando , Felsenberg Johannes , Hansson Bill S. , Knaden Markus , Sachse Silke 
Published: September 2023

AbstractBehavior is often categorized as being innate or learned, with the specific circuits being assigned to one of these categories. InDrosophila, neural circuits mediating an innate behavioral response are considered as being “hard-wired”, as activation of these neuronal pathways leads to stereotyped behaviors. However, only a limited number of studies assessed whether innate behaviors and their underlying neural circuits are plastic or show experience-dependent modulation. Here, we show that experience modulates second-order olfactory neurons involved in innate behavioral responses. We focus on the neural circuit defined by multiglomerular projection neurons (mPNs) that target the lateral horn, a structure relevant in the genesis of innate behavior. We show that mPNs, coding for odor attraction, are bidirectionally modulated after olfactory associative learning: when an olfactory stimulus is paired with an aversive electric shock, the activity of these neurons is decreased, while when the odor is paired with a sucrose-reward they are potentiated. We further show that this modulation requires glutamate and serotonin signaling, and that downstream third-order neurons are consequently affected. The bidirectional nature of the plasticity in these neurons is reflected in behavior: silencing mPN activity leads to odor avoidance, while artificial activation induces approach. While output from the mPNs is not required in aversive olfactory conditioning, silencing these neurons during retrieval of appetitive memories leads to a significant memory impairment. Artificially activating these neurons during odor presentation is sufficient to generate a 3 h appetitive memory. Our study in flies shows that a neural circuit coding for innate odor attraction can contribute to learned behavior, is modulated by olfactory learning and can provide reward-like reinforcement.
Dissecting Factors Underlying Phylogenetic Uncertainty Using Machine Learning Models
Authors: Rosas-Puchuri Ulises , Duarte-Ribeiro Emanuell , Khanmohammadi Sina , Arcila Dahiana , Ortí Guillermo , Betancur-R Ricardo 
Published: September 2023

AbstractPhylogenetic inference can be influenced by both underlying biological processes and methodological factors. While biological processes can be modeled, these models frequently make the assumption that methodological factors do not significantly influence the outcome of phylogenomic analyses. Depending on their severity, methodological factors can introduce inconsistency and uncertainty into the inference process. Although search protocols have been proposed to mitigate these issues, many solutions tend to treat factors independently or assume a linear relationship among them. In this study, we capitalize on the increasing size of phylogenetic datasets, using them to train machine learning models. This approach transcends the linearity assumption, accommodating complex non-linear relationships among features. We examined two phylogenomic datasets for teleost fishes: a newly generated dataset for protacanthopterygians (salmonids, galaxiids, marine smelts, and allies), and a reanalysis of a dataset for carangarians (flatfishes and allies). Upon testing five supervised machine learning models, we found that all outperformed the linear model (p < 0.05), with the deep neural network showing the best fit for both empirical datasets tested. Feature importance analyses indicated that influential factors were specific to individual datasets. The insights obtained have the potential to significantly enhance decision-making in phylogenetic analyses, assisting, for example, in the choice of suitable DNA sequence models and data transformation methods. This study can serve as a baseline for future endeavors aiming to capture non-linear interactions of features in phylogenomic datasets using machine learning and complement existing tools for phylogenetic analyses.