Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2.
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ABSTRACT:
SUBMITTER: Stickels RR
PROVIDER: S-EPMC8606189 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:<p><strong>BACKGROUND:</strong> Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.</p><p><strong>RESULTS:</strong> We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity.</p><p><strong>CONCLUSION:</strong> The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (<a href='http://www.bioconductor.org/' rel='noopener noreferrer' target='_blank'>http://www.bioconductor.org/</a>).</p>
2020-01-15 | MTBLS1430 | MetaboLights
Project description:Background: The molecular identification of neural progenitor cell populations that connect to establish the sympathetic nervous system (SNS) remains unclear. This is due to technical limitations in the acquisition and spatial mapping of molecular information to tissue architecture. Results: To address this, we applied Slide-seq spatial transcriptomics to intact fresh frozen chick trunk tissue transversely cryo-sectioned at the developmental stage prior to SNS formation. In parallel, we performed age- and location-matched single cell (sc) RNA-seq and 10x Genomics Visium to inform our analysis. Downstream bioinformatic analyses led to the unique molecular identification of neural progenitor cells within the peripheral sympathetic ganglia (SG) and spinal cord preganglionic neurons (PGNs). We then successfully applied the HiPlex RNAscope fluorescence in situ hybridization and multispectral confocal microscopy to visualize 12 gene targets in stage-, age- and location-matched chick trunk tissue sections. Conclusions: Together, these data demonstrate a robust strategy to acquire and integrate single cell and spatial transcriptomic information, resulting in improved resolution of molecular heterogeneities in complex neural tissue architectures. Successful application of this strategy to the developing SNS provides a roadmap for functional studies of neural connectivity and platform to address complex questions in neural development and regeneration.
2023-04-27 | GSE230776 | GEO
Project description:In this work, we present a highly-sensitive refractive index sensor based on metatronic nanocircuits operating at near-infrared spectral range. The structure is designed based on simple nanorod geometry and fabricated by nanopatterning of transparent conducting oxides. The functionality of these polarization dependent metatronic nanocircuits is enhanced by applying tunable response. This feature is investigated by depositing NH2 (Amine) groups via plasma polymerization technique on top of indium-tin-oxide nanorods. The dielectric constant of Amine groups is a function of their thickness, which can be controlled by the RF power and the time duration of the applied plasma polymerization process. The resonance wavelengths of nanocircuits shift to higher wavelength, as the dielectric constant of the deposited material increases. An excellent agreement between the design and experimental results are obtained. Our metatronic based nanosensor offers a high-sensitive performance of 1587?nm/RIU with a satisfactory figure of merit for this class of sensors.
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Project description:BackgroundLiquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.ResultsWe developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity.ConclusionThe new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (http://www.bioconductor.org/).
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