Background Gastroparesis is characterized by delayed gastric emptying without mechanical obstruction, but remains difficult to diagnose and distinguish from other GI disorders. Gastroparesis affects the gastric slow wave, but noninvasive assessment has been limited to the electrogastrogram (EGG), which reliably characterizes temporal dynamics but does not provide spatial information. Methods We measured gastric slow wave parameters from the EGG and magnetogastrogram (MGG) in patients with gastroparesis and in healthy controls. In addition to dominant frequency (DF) and percentage power distribution (PPD), we measured the propagation velocity from MGG spatiotemporal patterns and the percentage of slow wave coupling (%SWC) from EGG. Key Results No significant difference in DF was found between patients and controls. Gastroparesis patients had lower percentages of normogastric frequencies (60 ± 6% vs 78 ± 4%, p<0.05), and higher brady- (9 ± 2% vs 2 ± 1%, p<0.05) and tachygastric (31 ± 2 % vs 19 ± 1%, p<0.05) frequency content postprandial, indicative of uncoupling. Propagation patterns were substantially different in patients and longitudinal propagation velocity was retrograde at 4.3 ± 2.9 mm/s vs anterograde at 7.4 ± 1.0 mm/s for controls (p < 0.01). No difference was found in %SWC from EGG. Conclusions & Inferences Gastric slow wave parameters obtained from MGG recordings distinguish gastroparesis patients from controls. Assessment of slow wave propagation may prove critical to characterization of underlying disease processes. Future studies should determine pathologic indicators from MGG associated with other functional gastric disorders, and whether multichannel EGG with appropriate signal processing also reveals pathology.
This Feature describes the use a Cameca NanoSIMS instrument for directly imaging specific lipid and protein species in the plasma membranes of mammalian cells with approximately 100 nm-lateral resolution and discusses how these analyses have already begun to transform fundamental concepts in the field of membrane biology. Secondary ion generation is discussed with emphasis on the constraints that affect the detection and identification of membrane components, and then the sample preparation methodologies and data analysis strategies that address these constraints are described.
The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.HuBMAP was founded with the goal of establishing state-of-the-art frameworks for building spatial multiomic maps of non-diseased human organs at single-cell resolution 1 . During the first phase (2018)(2019)(2020)(2021)(2022), the priorities of the project included the validation and development of assay platforms; workflows for data processing, management, exploration and visualization; and the establishment of protocols, quality control standards and standard operating procedures. Extensive infrastructure was established through a coordinated effort among the various HuB-MAP integration, visualization and engagement teams, tissue-mapping centres, technology and tools development and rapid technology implementation teams and working groups 1 . Single-cell maps, predominantly consisting of two-dimensional (2D) spatial data as well as data from dissociated cells, were generated for several organs. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) was established for open access to experimental tissue data and reference atlas data.The infrastructure was augmented with software tools for tissue data registration, processing, annotation, visualization, cell segmentation and automated annotation of cell types and cellular neighbourhoods from spatial data. Computational methods were developed for integrating multiple data types across scales and interpretation 2 . Standard reference terminology and a common coordinate framework spanning anatomical to biomolecular scales were established to ensure interoperability across organs, research groups and consortia 3 . Guidelines to capture high-quality multiplexed spatial data 4 were established including validated panels of cell-and structure-specific antibodies 5 . The first phase produced a large number of manuscripts (https://commonfund.nih.gov/ publications?pid=43) including spatially resolved single-cell maps [6][7][8][9][10][11] .The production phase of HuBMAP was launched in the autumn of 2022. The focus is on scaling data production spanning diverse biological variables (for example, age and ethnicity) and deployment and enhancement of analytical, visualization and navigational tools to generate high-resolution 3D accessible maps of major functional tissue units from more than 20 organs. This phase involves over 60 institutions and 400 researchers with opportunities for active intra-and inter-consortia collaborations and building a foundational resource for new biological insights and precision medicine. Below, ...
Strategies that do not require additional characterization to be performed on the sample or the collection of additional secondary ion signals are needed to depth correct 3D SIMS images of cells. Here, we develop a depth correction strategy that uses the pixel intensities in the secondary electron images acquired during negative-ion NanoSIMS depth profiling to reconstruct the sample morphology. This morphology reconstruction was then used to depth correct the 3D SIMS images that show the components of interest in the sample. As a proof of concept, we applied this approach to NanoSIMS depth profiling data that show the 15N-enrichment and 18O-enrichment from 15N-sphingolipids and 18O-cholesterol, respectively, within a metabolically labeled Madin–Darby canine kidney cell. Comparison of the cell morphology reconstruction to the secondary electron images collected with the NanoSIMS revealed that the assumption of a constant sputter rate produced small inaccuracies in sample morphology after approximately 0.66 μm of material was sputtered from the cell. Nonetheless, the resulting 3D renderings of the lipid-specific isotope enrichments better matched the shapes and positions of the subcellular compartments that contained 15N-sphingolipids and 18O-cholesterol than the uncorrected 3D SIMS images. This depth correction of the 3D SIMS images also facilitated the detection of spherical cholesterol-rich compartments that were surrounded by membranes containing cholesterol and sphingolipids. Thus, we expect this approach will facilitate identifying the subcellular structures that are enriched with biomolecules of interest in 3D SIMS images while eliminating the need for correlated analyses or additional secondary ion signals for the depth correction of 3D NanoSIMS images.
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