| 1. |
- Alseekh, Saleh, et al.
(författare)
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Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices
- 2021
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Ingår i: Nature Methods. - : Springer Science and Business Media LLC. - 1548-7091 .- 1548-7105. ; 18:7, s. 747-756
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Forskningsöversikt (refereegranskat)abstract
- This Perspective, from a large group of metabolomics experts, provides best practices and simplified reporting guidelines for practitioners of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics. Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.
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| 2. |
- Alvelid, Jonatan, et al.
(författare)
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Event-triggered STED imaging
- 2022
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Ingår i: Nature Methods. - : Springer Nature. - 1548-7091 .- 1548-7105. ; 19:10, s. 1268-1275
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Tidskriftsartikel (refereegranskat)abstract
- Monitoring the proteins and lipids that mediate all cellular processes requires imaging methods with increased spatial and temporal resolution. STED (stimulated emission depletion) nanoscopy enables fast imaging of nanoscale structures in living cells but is limited by photobleaching. Here, we present event-triggered STED, an automated multiscale method capable of rapidly initiating two-dimensional (2D) and 3D STED imaging after detecting cellular events such as protein recruitment, vesicle trafficking and second messengers activity using biosensors. STED is applied in the vicinity of detected events to maximize the temporal resolution. We imaged synaptic vesicle dynamics at up to 24 Hz, 40 ms after local calcium activity; endocytosis and exocytosis events at up to 11 Hz, 40 ms after local protein recruitment or pH changes; and the interaction between endosomal vesicles at up to 3 Hz, 70 ms after approaching one another. Event-triggered STED extends the capabilities of live nanoscale imaging, enabling novel biological observations in real time.
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| 3. |
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| 4. |
- Baker, Ethan A. G., et al.
(författare)
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In silico tissue generation and power analysis for spatial omics
- 2023
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Ingår i: Nature Methods. - : Springer Nature. - 1548-7091 .- 1548-7105. ; 20:3, s. 424-
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Tidskriftsartikel (refereegranskat)abstract
- As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization. This paper presents a statistical framework for power analysis of spatial omics studies, facilitated by an in silico tissue-generation method.
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| 5. |
- Boehm, U., et al.
(författare)
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QUAREP-LiMi: a community endeavor to advance quality assessment and reproducibility in light microscopy
- 2021
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Ingår i: Nature Methods. - : Springer Science and Business Media LLC. - 1548-7091 .- 1548-7105. ; :18, s. 1423-1426
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Tidskriftsartikel (refereegranskat)abstract
- The community-driven initiative Quality Assessment and Reproducibility for Instruments & Images in Light Microscopy (QUAREP-LiMi) wants to improve reproducibility for light microscopy image data through quality control (QC) management of instruments and images. It aims for a common set of QC guidelines for hardware calibration and image acquisition, management and analysis.
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| 6. |
- Camsund, Daniel, 1980-, et al.
(författare)
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Time-resolved imaging-based CRISPRi screening
- 2020
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Ingår i: Nature Methods. - : NATURE PUBLISHING GROUP. - 1548-7091 .- 1548-7105. ; 17:1, s. 86-92
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Tidskriftsartikel (refereegranskat)abstract
- DuMPLING (dynamic mu-fluidic microscopy phenotyping of a library before in situ genotyping) enables screening of dynamic phenotypes in strain libraries and was used here to study genes that coordinate replication and cell division in Escherichia coli. Our ability to connect genotypic variation to biologically important phenotypes has been seriously limited by the gap between live-cell microscopy and library-scale genomic engineering. Here, we show how in situ genotyping of a library of strains after time-lapse imaging in a microfluidic device overcomes this problem. We determine how 235 different CRISPR interference knockdowns impact the coordination of the replication and division cycles of Escherichia coli by monitoring the location of replication forks throughout on average >500 cell cycles per knockdown. Subsequent in situ genotyping allows us to map each phenotype distribution to a specific genetic perturbation to determine which genes are important for cell cycle control. The single-cell time-resolved assay allows us to determine the distribution of single-cell growth rates, cell division sizes and replication initiation volumes. The technology presented in this study enables genome-scale screens of most live-cell microscopy assays.
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| 7. |
- Ciric, Rastko, et al.
(författare)
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TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models.
- 2022
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Ingår i: Nature methods. - : Springer Science and Business Media LLC. - 1548-7105 .- 1548-7091. ; 19:12, s. 1568-1571
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Tidskriftsartikel (refereegranskat)abstract
- Reference anatomies of the brain ('templates') and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR-findable, accessible, interoperable, and reusable-principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species.
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| 8. |
- Edlund, Christoffer, et al.
(författare)
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LIVECell : a large-scale dataset for label-free live cell segmentation
- 2021
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Ingår i: Nature Methods. - : Nature Publishing Group. - 1548-7091 .- 1548-7105. ; 18:9, s. 1038-1045
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks.
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| 9. |
- Ekvall, Markus, et al.
(författare)
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Spatial landmark detection and tissue registration with deep learning
- 2024
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Ingår i: Nature Methods. - 1548-7091 .- 1548-7105. ; 21, s. 673-679
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Tidskriftsartikel (refereegranskat)abstract
- Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle nonlinear deformations between tissue sections and are ineffective for z-stack alignment, other modalities beyond image data or multimodal data. We address these challenges by introducing effortless landmark detection, a new unsupervised landmark detection and registration method using neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets including histology and spatially resolved transcriptomics, demonstrating superior performance in both accuracy and stability compared to existing approaches. Effortless landmark detection is an unsupervised deep learning-based approach that addresses key challenges in landmark detection and image registration for accurate performance across diverse tissue imaging datasets.
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| 10. |
- Fan, Yuhang, et al.
(författare)
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Expansion spatial transcriptomics
- 2023
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Ingår i: Nature Methods. - : Springer Nature. - 1548-7091 .- 1548-7105. ; 20:8, s. 1179-1182
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Tidskriftsartikel (refereegranskat)abstract
- Capture array-based spatial transcriptomics methods have been widely used to resolve gene expression in tissues; however, their spatial resolution is limited by the density of the array. Here we present expansion spatial transcriptomics to overcome this limitation by clearing and expanding tissue prior to capturing the entire polyadenylated transcriptome with an enhanced protocol. This approach enables us to achieve higher spatial resolution while retaining high library quality, which we demonstrate using mouse brain samples.
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