Spatial Metabolism
Spatial Metabolism - Related Videos
08:52
Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
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Cited by 1 •
2025
This protocol details the key steps to enable rigorous and reproducible measurement of both glycogen and N-linked glycans using mass spectrometry imaging.
05:41
Metabolic Profile Analysis of Zebrafish Embryos
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Cited by 42 •
2013
Zebrafish represent a powerful vertebrate model that has been under-utilised for metabolic studies. Here we describe a rapid way to measure the in vivo metabolic profile of developing zebrafish that allows the comparison of different mitochondrial function parameters between genetically or pharmacologically manipulated embryos, thereby increasing the applicability of this organism.
00:52
What is Metabolism?
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2019
Overview Metabolism represents all of the chemical activity in a cell, including reactions that build molecules (anabolism) and those that break molecules down (catabolism). Anabolic reactions require energy, whereas catabolic reactions provide it. Thus, metabolism describes how cells transform energy through a variety of chemical reactions, which are often made more efficient with the help of enzymes. Metabolism Is the Sum of All of the Chemical Reactions That Take Place in an Organism
07:51
Spatial Cueing
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Attention refers to the limited human ability to select some information for processing at the expense of other stimuli in the environment. Attention operates in all sensory modalities: vision, hearing, touch, even taste and smell. It is most often studied in the visual domain though. A common way to study visual attention is with a spatial cueing paradigm. This paradigm allows researchers to measure the consequences of focusing...
08:44
Assessing Spatial Learning and Memory in Small Squamate Reptiles
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Cited by 3 •
2017
This paper describes a modification of the Barnes maze, a standard rodent paradigm used to assess spatial memory and learning, for use in small squamate reptiles.
FAQs
Local measurements preserve relationships between metabolic activity and position. In contrast, a bulk measurement can combine signals from different locations, obscuring organization that may be important for interpretation. Mapping separate regions therefore helps connect energy use and pathway behavior to local biochemical context within cells, tissues, organs, or microbial communities.
Each method contributes a different layer of spatial information. Imaging mass spectrometry maps metabolites, metabolite sensors indicate molecular activity in selected locations, and spatial transcriptomics provides spatially organized information about gene expression. Combining these approaches with biochemical analysis can relate molecular distributions to pathway activity rather than treating any single measurement as a complete picture.
Nutrient availability, enzyme localization, cell-cell interactions, and microenvironmental conditions can all shape local metabolic function. Their effects may differ across neighboring regions, so spatial analysis can associate a pathway or metabolite pattern with the conditions where it occurs. This connection helps explain why the same tissue or community may not use nutrients uniformly.
Spatial metabolism links where metabolites, enzymes, and related molecular signals occur with how biochemical pathways operate in that location. A map can therefore be interpreted alongside information about nutrient access, cellular neighbors, and the surrounding microenvironment. This approach supports models that explain metabolic function through coordinated local conditions rather than isolated molecular measurements.
A study begins by defining the locations to compare, such as regions within a tissue or members of a microbial community. Researchers then apply spatially resolved methods, including imaging mass spectrometry, metabolite sensors, or spatial transcriptomics, and integrate the resulting maps with biochemical analysis. The combined data can identify location-specific pathway activity and energy use.
It is useful when metabolic behavior may differ across a biological system and those differences matter to the research question. Supported applications include tumor metabolism, tissue development, neural energetics, and host-microbe interactions. In each case, spatial context can connect local molecular patterns with biological function and help develop more precise models of disease or physiology.
Spatially resolved metabolic information can show how pathway activity and energy use vary within diseased tissue or other relevant biological locations. In tumor research, for example, local maps can connect metabolic patterns with their surrounding context. Such information supports more precise disease models and can help frame investigations of how biological systems respond to therapeutic intervention.