Mass spectrometry imaging (MSI) has become a significant tool for measuring chemical species in biological tissues, where much of the impact of these platforms lies in their capability to report the spatial distribution of analytes for correlation to sample morphology. As a result, enhancement of spatial resolution has become a frontier of innovation in the field, and necessary developments are dependent on the ionization source. More particularly, laser-based imaging sources may require modifications to the optical train or alternative sampling techniques. These challenges are heightened for systems with infrared (IR) lasers, as their operating wavelength generates spot sizes that are inherently larger than their ultraviolet counterparts. Recently, the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source has shown the utility of a diffractive optical element (DOE) to produce square ablation patterns, termed top-hat IR-MALDESI. If the DOE optic is combined with oversampling methods, smaller ablation volumes can be sampled to render higher spatial resolution imaging experiments. Further, this approach enables reproducible spot sizes and ablation volumes for better comparison between scans. Herein, we investigate the utility of oversampling with top-hat IR-MALDESI to enhance the spatial resolution of measured lipids localized within the head of sectioned zebrafish tissue. Four different spatial resolutions were evaluated for data quality (e.g., mass measurement accuracy, spectral accuracy) and quantity of annotations. Other experimental parameters to consider for high spatial resolution imaging are also discussed. Ultimately, 20 μm spatial resolution was achieved in this work and supports feasibility for use in future IR-MALDESI studies.
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http://dx.doi.org/10.1021/jasms.4c00219 | DOI Listing |
Brief Bioinform
November 2024
Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, No. 15 Shangxiadian Road, Cangshan District, Fuzhou 350002, China.
Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction.
View Article and Find Full Text PDFUltrasound localization microscopy (ULM) enables microvascular imaging at spatial resolutions beyond the acoustic diffraction limit, offering significant clinical potentials. However, ULM performance relies heavily on microbubble (MB) signal sparsity, the number of detected MBs, and signal-to-noise ratio (SNR), all of which vary in clinical scenarios involving bolus MB injections. These sources of variations underscore the need to optimize MB dosage, data acquisition timing, and imaging settings in order to standardize and optimize ULM of microvasculature.
View Article and Find Full Text PDFMass Spectrom (Tokyo)
December 2024
Department of Pharmaceutical Engineering, Faculty of Engineering, Toyama Prefectural University, 5180 Kurokawa, Imizu-City, Toyama 939-0398, Japan.
Matrix-assisted laser desorption/ionization (MALDI), surface-assisted laser desorption/ionization (SALDI), and time-of-flight mass spectrometry (TOFMS) imaging are used for visualizing the spatial distribution of analytes. Mass spectrometry (MS) imaging of a sample with a rough surface with a uniform distribution of an analyte does not provide uniform ion intensities in the image. A shift in the value of the analyte ions is also observed.
View Article and Find Full Text PDFSpatially variable genes (SVGs) reveal the molecular and functional heterogeneity of cells across different spatial regions of a tissue. We found that sample-wide SVGs, identified by previous methods across the whole sample, largely overlap with cell-type marker genes derived from single-cell gene expression, leaving the spatial location information largely underutilized. We developed ctSVG, a computational method specifically tailored for Visium HD spatial transcriptomics at single-cell resolution.
View Article and Find Full Text PDFThe advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms.
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