bluuebunny/crossref_metadata_2025_split huggingface dataset:
Koina: Democratizing machine learning for proteomics research
Authors: Lautenbacher Ludwig , Yang Kevin L. , Kockmann Tobias , Panse Christian , Chambers Matthew , Kahl Elias , Yu Fengchao , Gabriel Wassim , Bold Dulguun , Schmidt Tobias , Li Kai , MacLean Brendan , Nesvizhskii Alexey I. , Wilhelm Mathias 
Published: June 2024

AbstractRecent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.
FORECAST-RBD: Forecasting Phenoconversion Risks and Its Clinical Phenotype in Patients with Isolated RBD Using Machine Learning and Explainable AI
Authors: Shin Yong Woo , Byun Jung-Ick , Kim Han-Joon , Jung Ki-Young 
Published: June 2024

AbstractBackground and ObjectivesIsolated Rapid Eye Movement (REM) sleep behavior disorder (iRBD) is a sleep disorder associated with neurodegenerative diseases such as Parkinson’s disease and dementia with Lewy bodies. Predicting which iRBD patients will phenoconvert to neurodegenerative diseases is crucial for prognosis and management. The objective of this study is to develop a machine learning model using clinical markers to predict phenoconversion in patients with RBD.MethodsAnalyzing a cohort of 178 iRBD patients over a median follow-up period of 3.6 years, during which 30 patients converted to neurodegenerative conditions, we leveraged an comprehensive dataset encompassing demographics, medication history, cognitive assessments, sleep quality, autonomic symptoms, and parkinsonian signs. We explored a variety of feature selection methods and survival models. Additionally, separate models to predict the subtype of photoconversion—whether motor-first or cognition-first—were developed.ResultsThe extreme gradient boosting survival embeddings-Kaplan neighbors (XGBSE-KN) model demonstrated the best performance, achieving a concordance index of 0.823 and integrated Brier score of 0.123 on 10-fold cross-validation. Explainable AI methods provided insights into prediction rationales and key risk factors including age, RBDQ-KR factor 2 (behavioral factors), weight, antidepressant, coffee use, and UPDRS III excluding tremor score. For subtype classification, the RandomForestClassifier utilizing three features (PSQI-TST, MoCA, and age), emerged as the most effective, achieving a Matthews Correlation Coefficient of 0.697 in 100 repeated 5-fold cross-validations. These models have been deployed on a server for physician access.DiscussionThese models can aid prognosis and enable personalized management in RBD patients, potentially improving patient care and outcomes. While these findings are promising, further external validation of the models is necessary to confirm their efficacy and reliability in clinical settings. Future research should focus on incorporating additional biomarkers and exploring the models’ performance in larger, diverse cohorts.
Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs
Authors: Song Tianci , Cosatto Eric , Wang Gaoyuan , Kuang Rui , Gerstein Mark , Min Martin Renqiang , Warrell Jonathan 
Published: June 2024

AbstractMotivationSpatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images, provides a scalable alternative approach to decoding tissue complexity.ResultsHere, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest.Availabilityhttps://github.com/song0309/asGNN/
Optimal sequencing depth for measuring the concentrations of molecular barcodes
Authors: Ocari Tommaso , Zin Emilia A. , Tekinsoy Muge , Van Meter Timothé , Cammarota Chiara , Dalkara Deniz , Nemoto Takahiro , Ferrari Ulisse 
Published: June 2024

AbstractIn combinatorial genetic engineering experiments, next-generation sequencing (NGS) allows for measuring the concentrations of barcoded or mutated genes within highly diverse libraries. When designing and interpreting these experiments, sequencing depths are thus important parameters to take into account. Service providers follow established guidelines to determine NGS depth depending on the type of experiment, such as RNA sequencing or whole genome sequencing. However, guidelines specifically tailored for measuring barcode concentrations have not yet reached an accepted consensus. To address this issue, we combine the analysis of NGS datasets from barcoded libraries with a mathematical model taking into account the PCR amplification in library preparation. We demonstrate on several datasets that noise in the NGS counts increases with the sequencing depth; consequently, beyond certain limits, deeper sequencing does not improve the precision of measuring barcode concentrations. We propose, as rule of thumb, that the optimal sequencing depth should be about ten times the initial amount of barcoded DNA before any amplification step.
Enrichment of glycoRNAs using galactose oxidase, hydrazide chemistry, and glycosidase digestion
Authors: Miao Xinyu , Deng Jianbo , Wang Xiaotong , Wen Sheng-Ye , Zhou Zeyang , Li Shuwei , Yang Xiaodong , Yang Shuang 
Published: June 2024

AbstractRibonucleic acid (RNA), essential for protein production and immune function, undergoes glycosylation, a process that attaches carbohydrates to RNA, creating unique glycoRNAs. These sugar-coated RNA molecules regulate immune responses and may be related to immune disorders. However, studying them is challenging due to RNA’s fragility. Therefore, a robust method for identifying glycosylated RNA is important. To address this, we optimized methods for enriching and identifying glycoRNAs, opening doors to explore their potential interactions with immune receptors and tumor suppression. Our approach involved investigating factors such as preservation solutions, enzyme buffers, digestion temperature, and incubation time. We successfully achieved efficient digestion of both N-linked and O-linked glycoRNAs at room temperature using 25 mM ammonium bicarbonate, demonstrating the effectiveness of this method. Additionally, RNA preservation in RNAlater at -80°C allows controlled release of glycoRNAs within hours. While sequential digestion of different glycoRNA types is possible, significant degradation occurs after the first enzyme step. Thus, we recommend separate harvesting for each type of glycoRNA. These optimized protocols, utilizing SPCgRNA and TnORNA methods, pave the way for further research on N- and O-glycoRNAs in health and disease.
Parental thermal conditions affect the brain activity response to alarm cue in larval zebrafish
Authors: Sourisse Jade M. , Semmelhack Julie L. , Schunter Celia 
Published: June 2024

AbstractWith temperature being a crucial factor affecting the physiology of ectothermic animals, global warming will likely impact neural mechanisms aquatic organisms use to perceive their environment over generations. However, exposure to elevated temperature during specific life stages and across generations may confer fish resilience through phenotypic plasticity. In this study, we investigate the effects of developmental and parental temperature on brain activity response to an olfactory cue in the larval zebrafish,Danio rerio. We exposed parents during reproduction and their offspring during development to control (28°C) or elevated temperature (30°C) and observed the response of the larval telencephalon to an alarm cue using live calcium imaging. Parental exposure to elevated temperature decreased the time till maximum brain activity response regardless of the offspring’s developmental temperature, revealing that parental thermal conditions can affect the excitability of the offspring’s neural circuitry. Furthermore, brain activity duration was affected by the interaction between parental and offspring thermal conditions, tending to last longer when either parents or offspring were exposed to elevated temperature, yet more similar to control when elevated temperature was experienced by both parents and offspring. This could represent an anticipatory parental effect influencing the offspring’s brain response to match the parental environment, or an early developmental effect occurring within a susceptible short time window post-fertilization. Overall, our results suggest that future predicted warming can alter processes involved in brain transmission and show that parental conditions could aid in the preparation of their offspring to respond to olfactory stimuli in a changing environment.
A novel hybrid high speed mass spectrometer allows rapid translation from biomarker candidates to targeted clinical tests using 15N labeled proteins
Authors: Wahle Maria , Remes Philip M. , Albrecht Vincent , Mueller-Reif Johannes , Steigerwald Sophia , Heymann Tim , Niu Lili , Lössl Philip , Horning Stevan , Jacob Cristina C. , Mann Matthias 
Published: June 2024

AbstractRecent developments in affinity binder or mass spectrometry (MS)-based plasma proteomics are now producing panels of potential biomarker candidates for diagnosis or prognosis. However, clinical validation and implementation of these biomarkers remain limited by the reliance on dated triple quadrupole MS technology. Here, we evaluate a novel hybrid high-speed mass spectrometer, Stellar MS, which integrates the robustness of triple quadrupoles with the enhanced capabilities of an advanced linear ion trap analyzer. This instrument allows for extremely rapid and sensitive parallel reaction monitoring (PRM) and MS3 targeting. The Stellar MS allowed targeting thousands of peptides originally measured on Orbitrap Astral MS, achieving high reproducibility and low coefficients of variation (CV) as well as sensitivity and specificity sufficient for more than the top 1000 plasma proteins. Furthermore, we developed targeted assays for alcohol-related liver disease (ALD) biomarkers, showcasing the potential of Stellar MS in clinical applications. Absolute quantification is typically a requirement for clinical assays and we explore the use of15N labeled protein standards in a rapid, streamlined and generic manner. Our results indicate that Stellar MS can bridge the gap between proteomics discovery and routine clinical testing, enhancing the diagnostic and prognostic utility of protein biomarkers.
Automated classification of cellular expression in multiplexed imaging data with Nimbus
Authors: Rumberger J. Lorenz , Greenwald Noah F. , Ranek Jolene S. , Boonrat Potchara , Walker Cameron , Franzen Jannik , Varra Sricharan Reddy , Kong Alex , Sowers Cameron , Liu Candace C. , Averbukh Inna , Piyadasa Hadeesha , Vanguri Rami , Nederlof Iris , Wang Xuefei Julie , Van Valen David , Kok Marleen , Hollmann Travis J. , Kainmueller Dagmar , Angelo Michael 
Published: June 2024

AbstractMultiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use athttps://github.com/angelolab/Nimbus-Inference.
Polygenic Resilience Scores are Associated with Lower Penetrance of Schizophrenia Risk Genes, Protection Against Psychiatric and Medical Disorders, and Enhanced Mental Well-Being and Cognition
Authors: Hess Jonathan L. , Barnett Eric J. , Hou Jiahui , Faraone Stephen V. , Glatt Stephen J. 
Published: June 2024

ABSTRACTIn the past decade, significant advances have been made in finding genomic risk loci for schizophrenia (SCZ). This, in turn, has enabled the search for SCZ resilience loci that mitigate the impact of SCZ risk genes. Recently, we discovered the first genomic resilience profile for SCZ, completely independent from the established risk loci for SCZ. We posited that these resilience loci protect against SCZ for those having a heighted genomic risk for SCZ. Nevertheless, our understanding of genetic resilience remains limited. It remains unclear whether resilience loci foster protection against adverse states associated with SCZ risk related to clinical, cognitive, and brain-structural phenotypes. To address this knowledge gap, we analyzed data from 487,409 participants from the UK Biobank, and found that resilience loci for SCZ afforded protection against lifetime psychiatric (schizophrenia, bipolar disorder, anxiety, and depression) and non-psychiatric medical disorders (such as asthma, cardiovascular disease, digestive disorders, metabolic disorders, and external causes of morbidity and mortality). Resilience loci also protected against self-harm behaviors, improved fluid intelligence, and larger whole-brain and brain-regional sizes. Overall, this study sheds light on the range of phenotypes that are significantly associated with resilience loci within the general population, revealing distinct patterns separate from those associated with SCZ risk loci. Our findings indicate that resilience loci may offer protection against serious psychiatric and medical outcomes, co-morbidities, and cognitive impairment. Therefore, it is conceivable that resilience loci facilitate adaptive processes linked to improved health and life expectancy.
Young Women’s Preferences for a Self-Sampling Intervention to Diagnose Sexually Transmitted Infections: A Discrete Choice Experiment
Authors: Jaya Ziningi N. , Mapanga Witness , Ogunsakin Ropo , Mashamba-Thompson Tivani P 
Published: June 2024

AbstractThe high rates of sexually transmitted infections (STIs) in young women in South Africa warrant the use of innovative interventions like self-sampling to diagnose both symptomatic and asymptomatic infections. Although proven as an effective measure in the fight against STIs, there is limited evidence on the preferred attributes of this intervention. We conducted a discrete choice experiment (DCE) to understand young women’s preferred attributes for self-sampling which included accessibility and convenience of self-sampling kits, education and normalisation, confidentiality and communication of results, self-sampling collection method, cost, and youth-friendliness as developed using a nominal group technique. A total of 206 young women aged between 18 – 24 years residing in underserved communities in Ethekwini Metropolitan Municipality, in KwaZulu-Natal, participated in the study. Study findings highlighted young women’s preference for enhanced accessibility, comprehensive education on STIs and self-sampling, confidential result communication, autonomy in self-collection method selection, and youth-friendly healthcare environments. The design of effective self-sampling interventions that promote STI testing thereby reducing transmission of infection, should address these preferences. Policymakers and healthcare providers should engage youth in the design of such initiatives and promote patient-centred healthcare to meet their preferences and improve STI-related health outcomes in this population.
Endogenous Promotor-Driven Split Nanoluciferase Biosensor for Assessing G Protein Recruitment
Authors: Humphrys Laura J. , Höring Carina , Gattor Albert O. 
Published: June 2024

AbstractHEK293 cells are a common immortal cell line used in biological research, and their popularity has led to different distinct lineages across the world. Commonly used for overexpression of proteins, HEK293 cells also natively express biological targets, such as G protein coupled receptors (GPCRs) and their downstream signalling partners, G proteins, although this often confounds rather than compliments research. CRISPR/Cas9 gene editing can be used to harness these native proteins and make use of their presence. Here, a cost- and time-effective, plasmid-based CRISPR/Cas9 approach is used to tag well-characterised GPCRS – the β-adrenoceptors 1 and 2 – with one part of a split Nanoluciferase and replace the Gαscoupling partner with the complimentarily tagged minimal Gsprotein in HEK293T cells. Compared to untagged proteins, the CRISPR/Cas9 cells allow for better selective-ligand characterisation at the native β-adrenoceptors. Overexpressed tagged systems produce similar results to the CRISPR/Cas9 cells, however subtle changes in the characterisation of partial agonists, such as salbutamol, demonstrate the potential for utilising tagged native receptors in analysing biological effectors.Summary StatementFor the first time, a split-luciferase tagged minimal Gs protein and β1AR is inserted under endogenous promotors in HEK293T cells using CRISPR/Cas9 gene modification, avoiding protein overexpression in the assay.
Single-cell profiling of penta- and tetradactyl mouse limb buds identifies mesenchymal progenitors controlling digit numbers and identities
Authors: Palacio Victorio , Pancho Anna , Morabito Angela , Malkmus Jonas , He Zhisong , Soussi Geoffrey , Zeller Rolf , Treutlein Barbara , Zuniga Aimée 
Published: June 2024

SUMMARYThe cellular mechanisms controlling digit numbers and identities have remained elusive. Profiling of wild-type (pentadactyl) andGrem1tetradactyl mouse limb buds identifies cellular changes affecting two limb bud mesenchymal progenitor (LMP) populations. In mutant limb buds, the anteriorly biased distribution of peripheral LMPs (pLMPs) is lost and the population expanded, while the distal-posterior LMP (dLMP) population is reduced from early stages onward. Analysis of LMP signature genes in wildtype and mutant mouse limb buds with digit loss or gain establishes that pLMPs are positively regulated by BMP signaling, while dLMPs require GREM1-mediated BMP antagonism. dLMPs encompass digit progenitors and altering their population size prefigures changes in digit numbers. The anteriorly biased pLMP distribution is linked to digit asymmetry as loss of this bias in tetradactyl mouse and pig limb buds underlies middle digit symmetry and identity loss. This study indicates that variable spatialGrem1expression in mutant and evolutionary diversified limb buds tunes BMP activity, impacting both LMP populations in a complementary manner.
MONFIT: Multi-omics factorization-based integration of time-series data sheds light on Parkinson’s disease
Authors: Mihajlović Katarina , Malod-Dognin Noël , Ameli Corrado , Skupin Alexander , Pržulj Nataša 
Published: June 2024

AbstractParkinson’s disease (PD) is a severe and complex multifactorial neurodegenerative disease with still elusive pathophysiology preventing the development of curative treatments. Molecular deep phenotyping by longitudinal multi-omics is a promising approach to identify mechanisms of PD aetiology and its progression. However, the heterogeneous data require new analysis frameworks to understand disease progression across biological entities and processes. Here, we present MONFIT, a holistic analysis pipeline that integrates and mines time-series single-cell RNA-sequencing data with bulk proteomics and metabolomics data by non-negative matrix tri-factorization, enabling prior knowledge incorporation from molecular networks. First, MONIFT integrates time-point-specific data and then holistically mines the integrated data across time points. By applying MONFIT to longitudinal multi-omics data of differentiation of PD and control patient-derived induced pluripotent stem cells into dopaminergic neurons, we identify novel PD-associated genes, emphasize molecular pathways that play important roles in PD pathology, and suggest new intervention opportunities using drug-repurposing. MONFIT is fully adaptable to other multi-omics data sets.
Epithelial tubule interconnection driven by HGF-Met signaling in the kidney
Authors: López-García Isabel , Oh Sunhee , Chaney Chris , Tsunezumi Jun , Drummond Iain , Oxburgh Leif , Carroll Thomas , Marciano Denise K. 
Published: June 2024

SUMMARYThe formation of functional epithelial tubules is a central feature of many organ systems. Although the process of tubule formation by epithelial cells is well-studied, the way in which tubules connect with each other (i.e. anastomose) to form functional networks bothin vivoandin vitrois not well understood. A key, unanswered question in the kidney is how the renal vesicles of the embryonic kidney connect with the nascent collecting ducts to form a continuous urinary system. We performed a ligand-receptor pair analysis on single cell RNA-seq data from embryonic mouse kidney tubules undergoing anastomosis to select candidates that might mediate this processin vivo. This analysis identified hepatocyte growth factor (HGF), which has known roles in cell proliferation, migration, and tubulogenesis, as one of several possible candidates. To test this possibility, we designed a novel assay to quantitatively examine epithelial tubule anastomosisin vitrousing epithelial spheroids with fluorescently-tagged apical surfaces to enable direct visualization of anastomosis. This revealed that HGF is a potent inducer of tubule anastomosis. Tubule anastomosis occurs through a proliferation-independent mechanism that acts through the MAPK signaling cascade and matrix metalloproteinases (MMPs), the latter suggestive of a role in extracellular matrix turnover. Accordingly, treatment of explanted embryonic mouse kidneys with HGF and collagenase was sufficient to induce kidney tubule anastomosis. These results lay the groundwork for investigating how to promote functional interconnections between tubular epithelia, which have important clinical implications for utilizingin vitrogrown kidney tissue in transplant medicine.