Tool For Discovery Research Articles

RegioneReloaded: evaluating the association of multiple genomic region sets.

Next-generation sequencing methods continue improving the annotation of genomes in part by determining the distribution of features such as epigenetic marks. Evaluating and interpreting the association between genomic regions and their features has become a common and challenging analysis in genomic and epigenomic studies. With regioneR we provided an R package allowing to assess the statistical significance of pairwise associations between genomic region sets using permutation tests. We now present the R package regioneReloaded that builds upon regioneR's statistical foundation and extends the functionality for the simultaneous analysis and visualization of the associations between multiple genomic region sets. Thus, we provide a novel discovery tool for the identification of significant associations that warrant to be tested for functional interdependence. regioneReloaded is an R package released under an Artistic-2.0 License. The source code and documentation are freely available through Bioconductor: http://www.bioconductor.org/packages/regioneReloaded.

From correlation to causation using directed topological overlap matrix: Applications in genomics.

Most causal discovery tools assume the local causal Markov condition. However, the theoretical assumptions that underlie the local causal Markov condition are often not met in practice. This is especially marked in genomics, where the unwanted presence of measurement errors, averaging effects, and feedback loops significantly undermine the legitimacy of the local causal Markov condition. Furthermore, these causal discovery algorithms require very large samples, orders above what is often available. In this paper, relaxing the local causal Markov condition and using Reichenbach's common cause principle instead, we present a more flexible approach to causal discovery, the directed topological overlap matrix (DTOM). DTOM is robust w.r.t the presence of measurement errors, averaging effects, feedback loops, and is significantly more sample efficient. We study the utility of DTOM for discovering causal relations in biological data using three real gene expression data-sets. We first examine if DTOM can help distinguish the Myostatin mutation in the Piedmontese cattle by contrasting the muscle transcriptomes of the Piedmontese and Wagyu crosses: the Myostatin mutation is the cause of the double-muscling the Piedmontese cattle are famous for. We then consider a large-scale gene deletion study in yeast. We show that DTOM allows us to distinguish the deleted gene in a sample knowing only the set of differentially expressed genes in that sample. We then examine the progression of Alzheimer's disease (AD) under the lens of DTOM. The genes implicated as having a causal role in the progression of AD by our DTOM analysis were significantly enriched in cellular components that had been repeatedly implicated in the progression of AD.

Late-Stage C-H Activation of Drug (Derivative) Molecules with Pd(ll) Catalysis.

This review comprehensively analyses representative examples of Pd(II)-catalyzed late-stage C-H activation reactions and demonstrates their efficacy in converting C-H bonds at multiple positions within drug (derivative) molecules into diverse functional groups. These transformative reactions hold immense potential in medicinal chemistry, enabling the efficient and selective functionalization of specific sites within drug molecules, thereby enhancing their pharmacological activity and expanding the scope of potential drug candidates. Although notable articles have focused on late-stage C-H functionalization reactions of drug-like molecules using transition-metal catalysts, reviews specifically focusing on late-stage C-H functionalization reactions of drug (derivative) molecules using Pd(II) catalysts are required owing to their prominence as the most widely utilized metal catalysts for C-H activation and their ability to introduce a myriad of functional groups at specific C-H bonds. The utilization of Pd-catalyzed C-H activation methodologies demonstrates impressive success in introducing various functional groups, such as cyano (CN), fluorine (F), chlorine (Cl), aromatic rings, olefin, alkyl, alkyne, and hydroxyl groups, to drug (derivative) molecules with high regioselectivity and functional-group tolerance. These breakthroughs in late-stage C-H activation reactions serve as invaluable tools for drug discovery and development, thereby offering strategic options to optimize drug candidates and drive the exploration of innovative therapeutic solutions.

Comparative analysis of computational based motif refinement methods

Finding patterns in DNA sequences, a challenging task in molecular biology and computer science, plays a crucial role in understanding gene expression and transcription regulation. This article explores various computational methods for identifying transcription factor binding sites TFBS and regulatory motifs in DNA sequences. We discuss the significance of these motifs in gene expression and disease susceptibility research. The article categorizes motif discovery algorithms into enumerative, probabilistic, combinatorial, and nature-inspired approaches, highlighting their strengths and limitations. It emphasizes the need for a comprehensive motif discovery tool that can handle diverse motif types, perform global searches efficiently, and reduce user-defined parameters.

Building Digital Capabilities with DigitalEd.ie: Learnings from the Digital Transformation Journey at ATU

In 2019, the Atlantic Technological University (ATU) Ireland (previously known as GMIT, IT Sligo and LYIT) were awarded a HEA Innovation and Transformation award to build digital capabilities across 8 campuses. The project is called iNOTE and it provides opportunities to transform the higher education experience in the CUA institutes (2019-2022). The Teaching and Learning Centre (Galway-Mayo) is leading the development of DigitalEd.ie Knowledge Platform and digital teaching and learning development initiatives (i.e., Work Package 2 of the iNOTE project).
 
 DigitalEd.ie, is a digital teaching and learning knowledge platform, which provides access to professional development pathways and a suite of digital resources, to build digital capabilities and pedagogic expertise, so that educators can design, deliver and support flexible and online learning programmes effectively.
 
 This paper will discuss: the development journey of DigitalEd.ie; building a digital education community; designing digital learning pathways; creating a digital resources directory; insights and impact of building digital teaching and learning capabilities during the pandemic; and the creation of a suite of digital education services and resources, that is transforming the higher education learning experience.
 
 The development journey of DigitalEd.ie knowledge platform uncovered several resources and learnings, including a discovery tool, digital tools to support online student engagement, meaningful and robust assessment strategies, digital badges and learning pathways, and teaching and learning techniques moving from online to hybrid delivery. In addition, 20 digital teaching and learning impact case studies were developed across eight themes (https://DigitalEd.ie/book), outlining how and why educators transformed their teaching and learning practice and the impact on student engagement. 
 
 This paper will conclude with feedback from participants who undertook digital learning opportunities through the DigitalEd.ie monthly training programme, the Discovery Tool, Digital Badge pathways, and an accredited postgraduate Certificate in Digital Teaching and Learning. The feedback gathered through the action research study provides insights on the value of Continuous Professional Development (CPD) and considerations for the digital learning environment in higher education, post Covid-19.

Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening.

Structure-based virtual screening has been a crucial tool in drug discovery for decades. However, as the chemical space expands, the existing structure-based virtual screening techniques based on molecular docking and scoring struggle to handle billion-entry ultralarge libraries due to the high computational cost. To address this challenge, people have resorted to machine learning techniques to enhance structure-based virtual screening for efficiently exploring the vast chemical space. In those cases, compounds are usually treated as sequential strings or two-dimensional topology graphs, limiting their ability to incorporate three-dimensional structural information for downstream tasks. We herein propose a novel deep learning protocol, GEM-Screen, which utilizes the geometry-enhanced molecular representation of the compounds docking to a specific target and is trained on docking scores of a small fraction of a library through an active learning strategy to approximate the docking outcome for yet nontraining entries. This protocol is applied to virtual screening campaigns against the AmpC and D4 targets, demonstrating that GEM-Screen enriches more than 90% of the hit scaffolds for AmpC in the top 4% of model predictions and more than 80% of the hit scaffolds for D4 in the same top-ranking size of library. GEM-Screen can be used in conjunction with traditional docking programs for docking of only the top-ranked compounds to avoid the exhaustive docking of the whole library, thus allowing for discovering top-scoring compounds from billion-entry libraries in a rapid yet accurate fashion.

Introducing outdoor office work to reduce stress among office workers in Sweden

Abstract Background Office workplace stress is concerning as it leads to adverse and chronic health outcomes. Outdoor office work (OOW), an innovative practice may address these challenges as being outdoors has potential benefits. This study aims to assess stress among office workers that are working in their normal indoor office compared to practicing OOW and identifying facilitators and barriers for doing OOW. Methods In a feasibility study, 9 office workers were asked to work indoors the 1st week and OOW for at least 30 minutes per day during the 2nd week. They wore a heart rate variability (HRV) monitor (FirstBeat BG3) for 2 weeks while at work to assess objective stress. Subjective stress was assessed via daily surveys using a scale of 1 to 10 from “not stressed at all” to “very stressed”. They were instructed to use the app “Our Voice Discovery Tool” to take photos of facilitators or barriers to practicing OOW. Subjective stress data was analysed using paired sample t-test in SPSS 28. Photos were categorised into themes. Analysis of HRV time and frequency parameters is underway. Results Preliminary findings show statistically significant decrease in stress scores between week 1 (M = 4.42, SD = 1.43) and week 2 (M = 3.74, SD = 1.31) t(7)=1.91, p=.049 (one-tailed), mean decrease in self-rated stress was .68 with a 95% confidence interval ranging from -.12 to 1.43. The Cohen's d (.67) indicated a medium effect size. Picture analysis revealed themes related to “convenience/comfort”, equipment/facilities”, and “weather” as barriers. Out of 76 pictures, most themed “nature”, both indoor and outdoor, were marked as positive experiences. Conclusions Reduction in subjective stress from week 1 to week 2 suggests OOW's potential as useful method to reduce work-related stress. Picture analysis suggests, to ensure a healthier milieu for office workers, conditions conducive to OOW such as outdoor work facilities and green spaces in offices’ vicinity may need to be facilitated by employers. Key messages • Outdoor office work (OOW) may reduce workplace stress, potentially improving employee health, well-being, and productivity. • Employers should facilitate outdoor workspaces and green areas to promote healthier office environments.

O041 Exploring the Landscape of Sleep Data Resources: A Literature Survey

Abstract Introduction The complex physiologic interdependencies of sleep make sleep medicine a data-rich discipline. Emerging big data approaches hold promise for novel discoveries, however, scholars have suggested these advances may be hindered by dataset limitations. We conducted a review of the field to assess the drawbacks of available sleep data resources. Methods A systematic literature survey on PubMed. A keyword search identified studies based on sleep datasets which combined polysomnography records and contextual patient health information. For eligible studies, we extracted information on data provenance (existing repositories versus self-collected), data accessibility for onward research, dataset size, cohort characteristics, data/metadata inclusions, and use of systematic nomenclatures. Results Only approximately 40% of identified studies used public datasets, suggesting that these repositories only partially met current research needs. Alternative, self-collected cohorts were typically small, with just 20% having populations greater than 1000 subjects. Most were closed-source or lacking data access indications, which may impact the strength and reproducibility of study results. Large-size resources focussed predominantly on sleep apnoea, limiting the reach of research efforts targeting less prevalent conditions. Datasets featuring comprehensive medical histories and long-term follow-up information were rare. Where available, this information generally lacked systematisation of nomenclatures and metadata, impairing the ability of users to extract meaning from data entities and their interrelationships. Discussion Limitations in current sleep data practices were substantiated by our review. There is a clear need to develop larger, diverse, multimodal and clinically representative sleep data collections that can be more easily shared, and analysed using emergent data-driven discovery tools.

Transcriptome analysis reveals molecular signature and cell-type difference of Homo sapiens endothelial-to-mesenchymal transition.

Endothelial-to-mesenchymal transition (EndoMT), a specific form of epithelial-to-mesenchymal transition, drives a growing number of human (Homo sapiens) pathological conditions. This emerging knowledge opens a path to discovering novel therapeutic targets for many EndoMT-associated disorders. Here, we constructed an atlas of the endothelial-cell transcriptome and demonstrated EndoMT-induced global changes in transcriptional gene expression. Our gene ontology analyses showed that EndoMT could be a specific checkpoint for leukocyte chemotaxis, adhesion, and transendothelial migration. We also identified distinct gene expression signatures underlying EndoMT across arterial, venous, and microvascular endothelial cells. We performed protein-protein interaction network analyses, identifying a class of highly connected hub genes in endothelial cells from different vascular beds. Moreover, we found that the short-chain fatty acid acetate strongly inhibits the transcriptional program of EndoMT in endothelial cells from different vascular beds across tissues. Our results reveal the molecular signature and cell-type difference of EndoMT across distinct tissue- and vascular-bed-specific endothelial cells, providing a powerful discovery tool and resource value. These results suggest that therapeutically manipulating the endothelial transcriptome could treat an increasing number of EndoMT-associated pathological conditions.

Analytical Strategy for Oxylipin Annotation by Combining Chemical Derivatization-Based Retention Index Algorithm and Feature Tandem Mass Spectrometric Fragmentation as a Biomarker Discovery Tool.

Accurate oxylipin annotation is crucial for advancing our understanding of physiological processes in health and disease and identifying biomarkers. However, a full view of oxylipins for early diagnosis needs further attention due to the lack of proper analytical methods, which may be attributed to the wide dynamic range, poor sensitivity, extreme molecular complexity, and limited commercially available standards of oxylipins. Here, we devised a novel method by combining a chemical derivatization (CD)-based retention index (RI) algorithm and feature tandem mass spectrometric fragmentation annotation (CD-RI-LC-MS/MS) for identification and quantification of oxylipins. To this end, N,N-diethyl-1,3-diaminopropane (DEPA) was used for fast labeling of oxylipin (within 0.5 min at room temperature) to improve separation resolution and detection sensitivity. The RI algorithm was established to calibrate the retention time variances and assist the identification of oxylipins during liquid chromatography-tandem mass spectrometry (LC-MS) analysis. MS/MS analysis of in total 58 DEPA derivatives of authentic oxylipin standards was subsequently employed to obtain the tandem mass spectrometric feature fragmentation rules for further structure elucidation of the unknown regio-isomers. Finally, a method based upon CD-RI-LC-MS/MS was established for profiling oxylipins from Standard Reference Material (SRM) 1950 human plasma and nonalcoholic fatty liver disease (NAFLD) mouse liver tissue samples. A total of 87 and 96 potential oxylipins including 12 and 14 unreported oxylipins were detected and identified from human plasma and mouse liver tissues, respectively. The results showed that compared to the control group, in the liver samples of the NAFLD mouse, the content levels of prostaglandin (PG) E2, PGF2a, 8-hydroxy-eicosatrienoic acid (8-HETrE), and the newly discovered 2-hydroxy-octadecatrienoic acid (2-HOTrE) were remarkably increased, while the oxidation product of n-3 PUFA (p < 0.05) and all hydroperoxy oxylipins significantly decreased. On balance, this method contributes to future studies on oxylipin screening and application in other biological samples for facilitating the understanding of oxylipin roles in metabolic regulation of numerous diseases.

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation.

This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow.

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Hybrid RNA sequencing of broad bean wilt virus 2 from faba beans.

High-throughput sequencing (HTS) is an important tool for plant virus detection and discovery. HTS technologies such as Nanopore sequencing has been rapidly developing in recent years, and offers new possibilities for fast routine diagnostic applications. This study compared MiSeq (Illumina) RNA-Seq with the MinION sequencer (Oxford Nanopore Technologies) (ONT) direct-RNA-Seq methods to detect and sequence the broad bean wilt virus 2 (BBWV2) genome. The Illumina BBWV2 RNA1 and RNA2 genome segments were intact and matched with ONT with 99.1% and 98.8% nucleotide identity (nt) match, respectively. The RNA1 genome derived from ONT had deletions within the nucleoside-triphosphate binding and protease factor open-reading frames, and the upstream of five untranslated regions. However, its RNA2 was intact, and no sequencing errors were observed. The ONT and Illumina RNA1 and RNA 2 BBWV2 genome segments clustered together phylogenetically along with other none-Fabaceae species, BBWV2 RNA1 NCBI accession KC790225, and RNA 2 MW556592 both from China. This is the first BBWV2 genome study reported in Australia and forms part of the strategy to integrate versatile diagnostic genomics tools at the border to safeguard the Australian grains industry. IMPORTANCE Globally, viral diseases impair the growth and vigor of cultivated crops such as grains, leading to a significant reduction in quality, marketability, and competitiveness. As an island nation, Australia has a distinct advantage in using its border to prevent the introduction of damaging viruses, which threaten the continental agricultural sector. However, breeding programs in Australia rely on imported seeds as new sources of genetic diversity. As such, it is critical to remain vigilant in identifying new and emerging viral pathogens, by ensuring the availability of accurate genomic diagnostic tools at the grain biosecurity border. High-throughput sequencing offers game-changing opportunities in biosecurity routine testing. Genomic results are more accurate and informative compared to traditional molecular methods or biological indexing. The present work contributes to strengthening accurate phytosanitary screening, to safeguard the Australian grains industry, and expedite germplasm release to the end users.

Facile and highly selective near-infrared emitting fluorescent indicator for hypochlorous acid signaling and bioimaging

A near-infrared emitting fluorescent chemodosimeter for hypochlorous acid (HOCl), PAM, was designed and synthesized by modifying on the sulfonate porphyrin chromophore with a diaminomaleonitrile substituent to form Schiff-base as the chemodosimetric recognition unit. PAM behaved as a sensitive fluorescence “off-on” reactor to HOCl with satisfying anti-interference capacity over other latent disturbing analytes. The drastic fluorescence enhancement originated from the HOCl-promoted intramolecular cyclization to rigidify the molecular structure to a substituted imidazole, which effectively inhibited the CN isomerization. This chemical process is specific, expeditious and will not release extra compounds other than H2O. 1H NMR, FI-IR, HRMS, and spectral analysis confirmed the proposed mechanism. In addition to facilely synthesis, photo- and chemo-stable, PAM has outstanding features of fully water-soluble, low analytic limitation (6.3 nM), and positive linearity, which makes it practical to quantify HOCl in tap water and serum samples. Furthermore, PAM was efficiently leveraged for monitoring both exogenous and endogenous HOCl in living cells, as well as for differentiating between normal and cancer cells based on differences in basal HOCl levels. The versatility of this probe definitely provided a potential discovery tool for further elucidate the HOCl-associated neurological disorders.

Genotyping-by-sequencing reveals a high number and quality of single nucleotide polymorphisms in guinea pigs (Cavia porcellus) from the Peruvian Andes.

Guinea pigs are a major source of animal protein for Peruvian Andean families. Despite the economic and cultural relevance of guinea pigs, their genomic characterization has been scarcely addressed. Genotyping-by-sequencing (GBS) has emerged as an affordable alternative to genotyping of livestock and native animals. Here, we report the use of GBS for single nucleotide polymorphism (SNP) discovery of traditionally raised guinea pigs from six regions of the Peruvian Andes and one group of breeding animals. The paired-end (2 × 150 bp) sequencing of 40 guinea pig DNA samples generated a mean of 6.4 million high-quality sequencing reads per sample. We obtained an average sequencing depth of 10× with an 88.5% mapping rate to the Cavia porcellus reference genome. A total of 279 965 SNPs (102 SNPs/Mbp) were identified after variant calling and quality filtering. Based on this SNP set, we assessed the genetic diversity and distance within our selected guinea pig populations. An overall average minor allele frequency of 0.13, an observed heterozygosity of 0.31, an expected heterozygosity of 0.35, and an F-value of 0.1 were obtained, while the SNP-based neighbor-joining tree suggests a closer genetic relationship between individuals from geographically close locations. We showed that GBS is a cost-effective tool for SNP discovery and genetic characterization of Peruvian guinea pig populations. Therefore, it may be considered as a suitable and affordable tool for genomic characterization of poorly studied native animal species.

Assessing Drug Administration Techniques in Zebrafish Models of Neurological Disease.

Neurological diseases, including neurodegenerative and neurodevelopmental disorders, affect nearly one in six of the world's population. The burden of the resulting deaths and disability is set to rise during the next few decades as a consequence of an aging population. To address this, zebrafish have become increasingly prominent as a model for studying human neurological diseases and exploring potential therapies. Zebrafish offer numerous benefits, such as genetic homology and brain similarities, complementing traditional mammalian models and serving as a valuable tool for genetic screening and drug discovery. In this comprehensive review, we highlight various drug delivery techniques and systems employed for therapeutic interventions of neurological diseases in zebrafish, and evaluate their suitability. We also discuss the challenges encountered during this process and present potential advancements in innovative techniques.

Immune-related signature of periodontitis and Alzheimer's disease linkage.

Background: Periodontits (PD) and Alzheimer's disease (AD) are both associated with ageing and clinical studies increasingly evidence their association. However, specific mechanisms underlying this association remain undeciphered, and immune-related processes are purported to play a signifcant role. The accrual of publicly available transcriptomic datasets permits secondary analysis and the application of data-mining and bioinformatic tools for biological discovery. Aim: The present study aimed to leverage publicly available transcriptomic datasets and databases, and apply a series of bioinformatic analysis to identify a robust signature of immune-related signature of PD and AD linkage. Methods: We downloaded gene-expresssion data pertaining PD and AD and identified crosstalk genes. We constructed a protein-protein network analysis, applied immune cell enrichment analysis, and predicted crosstalk immune-related genes and infiltrating immune cells. Next, we applied consisent cluster analysis and performed immune cell bias analysis, followed by LASSO regression to select biomarker immune-related genes. Results: The results showed a 3 gene set comprising of DUSP14, F13A1 and SELE as a robust immune-related signature. Macrophages M2 and NKT, B-cells, CD4+ memory T-cells and CD8+ naive T-cells emerged as key immune cells linking PD with AD. Conclusion: Candidate immune-related biomarker genes and immune cells central to the assocation of PD with AD were identified, and merit investigation in experimental and clinical research.

FLARE: a fast and flexible workflow for identifying RNA editing foci

BackgroundFusion of RNA-binding proteins (RBPs) to RNA base-editing enzymes (such as APOBEC1 or ADAR) has emerged as a powerful tool for the discovery of RBP binding sites. However, current methods that analyze sequencing data from RNA-base editing experiments are vulnerable to false positives due to off-target editing, genetic variation and sequencing errors.ResultsWe present FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I. We applied FLARE to C-to-U editing data from a RBFOX2-APOBEC1 STAMP experiment, to show that our approach attains high specificity for detecting RBFOX2 binding sites. We also applied FLARE to detect regions of exogenously introduced as well as endogenous A-to-I editing.ConclusionsFLARE is a fast and flexible workflow that identifies significantly edited regions from RNA-seq data. The FLARE codebase is available at https://github.com/YeoLab/FLARE.

Time-Lapse Observation of Cell Dynamics During Angiogenesis Using the Rat Mesentery Culture Model.

The ability to track cells and their interactions with other cells during physiological processes offers a powerful tool for scientific discovery. An ex vivo model that enables real-time investigation of cell migration during angiogenesis in adult microvascular networks would enable observation of endothelial cell dynamics during capillary sprouting. Angiogenesis is defined as the growth of new blood vessels from existing ones and involves multiple cell types including endothelial cells, pericytes, and interstitial cells. The incorporation of these cell types in a physiologically relevant environment, however, represents a challenge for biomimetic model development. Recently, our laboratory has developed the rat mesentery culture model, which enables investigation of angiogenesis in an intact tissue. The objective of this chapter is to detail a protocol for tracking cellular dynamics during angiogenesis using the rat mesentery tissue culture model. The method involves harvesting mesentery tissues from adult SD-EGFP rats, culturing them in MEM+10% fetal bovine serum, and imaging network regions over the time course of angiogenesis. In example applications, time-lapse comparison of microvascular networks in cultured tissues confirmed dramatic increases in GFP-positive capillary sprouting and GFP-positive segment density. Additionally, tracking of individual capillary sprout extensions revealed their ability to "jump" by disconnecting from one vessel segment and reconnecting to another segment in the network. GFP-positive sprouts were also capable of undergoing subsequent regression. The representative results support the use of the rat mesentery culture model for identifying and tracking cellular dynamics during angiogenesis in intact microvascular networks.

A Human Neuron/Astrocyte Co-culture to Model Seeded and Spontaneous Intraneuronal Tau Aggregation.

Communication and contact between neurons and astrocytes is important for proper brain physiology. How neuron/astrocyte crosstalk is affected by intraneuronal tau aggregation in neurodegenerative tauopathies is largely elusive. Human induced pluripotent stem cell (iPSC)-derived neurons provide the opportunity to model tau pathology in a translationally relevant in vitro context. However, current iPSC models inefficiently develop tau aggregates, and co-culture models of tau pathology have thus far utilized rodent astrocytes. In this article, we describe the co-culture of human iPSC-derived neurons with primary human astrocytes in a 96-well format compatible with high-content microscopy. By lentiviral overexpression of different mutated tau variants, this protocol can be flexibly adapted for the efficient induction of seeded or spontaneous tau aggregation. We used this novel co-culture model to identify cell type-specific disease mechanisms and to provide proof of concept for intervention by antisense therapy. These results show that this human co-culture model provides a highly translational tool for target discovery and drug development for human tauopathies. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Human neuron/astrocyte co-culture for seeded and spontaneous intraneuronal tau aggregation Support Protocol 1: Human induced pluripotent stem cell culture Support Protocol 2: Human primary astrocyte culture.