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  • 1
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    Online Resource
    Trajectory inference across multiple conditions with condiments
    Springer Science and Business Media LLC ; 2024
    In:  Nature Communications Vol. 15, No. 1 ( 2024-01-27)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 15, No. 1 ( 2024-01-27)
    Abstract: In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments , a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell’s conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-024-44823-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2024
    detail.hit.zdb_id: 2553671-0
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  • 2
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    A multimodal cell census and atlas of the mammalian primary motor cortex
    Springer Science and Business Media LLC ; 2021
    In:  Nature Vol. 598, No. 7879 ( 2021-10-07), p. 86-102
    Details
    In: Nature, Springer Science and Business Media LLC, Vol. 598, No. 7879 ( 2021-10-07), p. 86-102
    Abstract: Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization 1–5 . First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
    URL: Article
    DOI: 10.1038/s41586-021-03950-0
    RVK:
    TA 1000
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    UA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 120714-3
    detail.hit.zdb_id: 1413423-8
    SSG: 11
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  • 3
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    A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex
    Springer Science and Business Media LLC ; 2021
    In:  Nature Vol. 598, No. 7879 ( 2021-10-07), p. 103-110
    Details
    In: Nature, Springer Science and Business Media LLC, Vol. 598, No. 7879 ( 2021-10-07), p. 103-110
    Abstract: Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain 1–3 . With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions 4 . We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
    URL: Article
    DOI: 10.1038/s41586-021-03500-8
    RVK:
    TA 1000
    RVK:
    UA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 120714-3
    detail.hit.zdb_id: 1413423-8
    SSG: 11
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  • 4
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    Trajectory-based differential expression analysis for single-cell sequencing data
    Springer Science and Business Media LLC ; 2020
    In:  Nature Communications Vol. 11, No. 1 ( 2020-03-05)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2020-03-05)
    Abstract: Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-020-14766-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2553671-0
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  • 5
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    Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects
    Elsevier BV ; 2022
    In:  Cell Reports Methods Vol. 2, No. 11 ( 2022-11), p. 100321-
    Details
    In: Cell Reports Methods, Elsevier BV, Vol. 2, No. 11 ( 2022-11), p. 100321-
    Type of Medium: Online Resource
    ISSN: 2667-2375
    URL: Article
    DOI: 10.1016/j.crmeth.2022.100321
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 3091714-1
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  • 6
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    CALDERA: finding all significant de Bruijn subgraphs for bacterial GWAS
    Oxford University Press (OUP) ; 2022
    In:  Bioinformatics Vol. 38, No. Supplement_1 ( 2022-06-24), p. i36-i44
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 38, No. Supplement_1 ( 2022-06-24), p. i36-i44
    Abstract: Genome-wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single-nucleotide polymorphisms to mobile genetic elements. This approach does not require a reference genome, making it easier to account for accessory genes. However, a same gene can exist in slightly different versions across different strains, leading to diluted effects. Results Here, we overcome this issue by testing covariates built from closed connected subgraphs (CCSs) of the de Bruijn graph defined over genomic k-mers. These covariates capture polymorphic genes as a single entity, improving k-mer-based GWAS both in terms of power and interpretability. However, a method naively testing all possible subgraphs would be powerless due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all CCSs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. Our method integrates with existing visual tools to facilitate interpretation. Availability and implementation We provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_ISMB. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btac238
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1468345-3
    detail.hit.zdb_id: 1422668-6
    SSG: 12
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  • 7
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    Improving replicability in single-cell RNA-Seq cell type discovery with Dune
    Springer Science and Business Media LLC ; 2024
    In:  BMC Bioinformatics Vol. 25, No. 1 ( 2024-05-24)
    Details
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 25, No. 1 ( 2024-05-24)
    Abstract: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. Results Here, we propose , a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results—or partitions—on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . Conclusions Cluster refinement by helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.
    Type of Medium: Online Resource
    ISSN: 1471-2105
    URL: Article
    DOI: 10.1186/s12859-024-05814-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2024
    detail.hit.zdb_id: 2041484-5
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  • 8
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    Klf5 establishes bi-potential cell fate by dual regulation of ICM and TE specification genes
    Elsevier BV ; 2021
    In:  Cell Reports Vol. 37, No. 6 ( 2021-11), p. 109982-
    Details
    In: Cell Reports, Elsevier BV, Vol. 37, No. 6 ( 2021-11), p. 109982-
    Type of Medium: Online Resource
    ISSN: 2211-1247
    URL: Article
    DOI: 10.1016/j.celrep.2021.109982
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2649101-1
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  • 9
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    Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study
    JMIR Publications Inc. ; 2021
    In:  JMIR Formative Research Vol. 5, No. 3 ( 2021-3-4), p. e20175-
    Details
    In: JMIR Formative Research, JMIR Publications Inc., Vol. 5, No. 3 ( 2021-3-4), p. e20175-
    Abstract: Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable new paradigms of clinical research. A new generation of continuous glucose monitoring (CGM) devices provides access to clinical-grade measurement of interstitial glucose levels. Adoption of these sensors has become widespread for the management of type 1 diabetes and is accelerating in type 2 diabetes. In parallel, individuals are adopting health-related smartphone-based apps to monitor and manage care. Objective We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinical study measures of glucose levels without clinic visits. Methods Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical food on glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint was incremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided a visual diet log. Results All subjects completed the study and mastered CGM device usage. Over 40 days, 3000 data points on average per subject were collected across three sensors. No adverse events were recorded, and subjects reported general satisfaction with sensor management, the study product, and the smartphone app, with an average self-reported satisfaction score of 8.25/10. Despite a lack of sufficient power to achieve statistical significance, we demonstrated that we can detect meaningful changes in the postprandial glucose response in real-world settings, pointing to the merits of larger studies in the future. Conclusions We have shown that CGM devices can provide a comprehensive picture of glucose control without clinic visits. CGM device usage in conjunction with our custom smartphone app can lower the participation burden for subjects while reducing study costs, and allows for robust integration of multiple valuable data types with glucose levels remotely. Trial Registration ClinicalTrials.gov NCT04424888; http://clinicaltrials.gov/ct2/show/NCT04424888.
    Type of Medium: Online Resource
    ISSN: 2561-326X
    URL: Article
    DOI: 10.2196/20175
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2021
    detail.hit.zdb_id: 2941716-8
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  • 10
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    Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia
    American Association for the Advancement of Science (AAAS) ; 2020
    In:  Science Advances Vol. 6, No. 31 ( 2020-07-31)
    Details
    In: Science Advances, American Association for the Advancement of Science (AAAS), Vol. 6, No. 31 ( 2020-07-31)
    Abstract: Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) – the causal agent in COVID-19 – affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.
    Type of Medium: Online Resource
    ISSN: 2375-2548
    URL: Article
    DOI: 10.1126/sciadv.abc5801
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2020
    detail.hit.zdb_id: 2810933-8
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