Spatial Mapping
Spatial Mapping - Related Videos
11:52
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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2017
The segmentation clock drives oscillatory gene expression across the pre-somitic mesoderm (PSM). Dynamic Notch activity is key to this process. We use imaging and computational analyses to extract temporal dynamics from spatial expression data to demonstrate that Delta ligand and Notch receptor expression oscillate in the vertebrate PSM.
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.
11:03
Motor Maps
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California One principle of brain organization is the topographic mapping of information. Especially in sensory and motor cortices, adjacent regions of the brain tend to represent information from adjacent parts of the body, resulting in maps of the body expressed on the surface of the brain. The primary sensory and motor maps in the brain surround a prominent sulcus known as the central sulcus. The cortex...
04:18
Enhanced Spatial Mapping of Mouse Gastric Muscle Layers Using a Modified Swiss Roll Technique
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2025
This protocol outlines a whole-stomach immunostaining method modified from the "Swiss roll" technique originally developed for the murine intestine.
09:56
Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
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Cited by 4 •
2019
The method presented here uses micropatterning together with quantitative imaging to reveal spatial organization within mammalian cultures. The technique is easy to establish in a standard cell biology laboratory and offers a tractable system to study patterning in vitro.
FAQs
Spatial mapping makes biological measurements interpretable as relationships rather than isolated values. When location is retained, researchers can examine whether particular cell identities, gene-expression signals, species, or environmental conditions cluster, form gradients, or occur near one another. These patterns can reveal spatially regulated processes and generate hypotheses that a spatially uniform sample could conceal.
Removing positional information can make distinct biological regions appear equivalent, even when their contents or activities differ. Retaining position allows observations to be associated with specific tissues, cells, ecosystem areas, or environmental locations. This distinction helps researchers relate biological variation to local organization and identify patterns that would disappear if all measurements were pooled together.
The same analytical principle can be applied from ecosystems to tissues and individual cells, but the observations and tools change with scale. Geographic information systems can support ecosystem-level representations, whereas microscopy, molecular profiling, and computational analysis can characterize organization within tissues or cells. Comparing these scales helps connect local biological patterns with broader structure and change.
A spatial map links positional information with measurements such as species distributions, cell identities, gene expression, or environmental conditions. Computational analysis can then organize and compare these observations across defined regions. This integration makes it possible to evaluate whether biological features coincide, vary gradually, or form spatial neighborhoods instead of treating every observation as independent.
Researchers first define the space or biological region to be examined, then collect observations together with their locations. Depending on the question, measurements may come from microscopy, molecular profiling, geographic information systems, or related approaches. Computational analysis can organize the resulting information and help identify distributions, gradients, neighborhoods, and other spatial relationships.
Tool selection depends mainly on the biological scale and the type of information being mapped. Geographic information systems suit distributions across ecosystems, while microscopy can describe tissue or cellular organization and molecular profiling can associate locations with gene expression or cell identities. Computational analysis is useful for combining these positional and biological measurements into interpretable patterns.
This approach is valuable when location may influence biological behavior or interpretation. In biology, it supports investigations of tissue organization, development, disease, biodiversity, and ecological change. Across these applications, mapping can show where features occur and how they relate to nearby or surrounding conditions, providing evidence for hypotheses about spatially regulated biological processes.
A study can produce more than a visual distribution of biological features. By preserving position, researchers may identify clusters, gradients, neighborhoods, and relationships among entities or activities. These outcomes help distinguish organized spatial variation from broadly uniform patterns and can guide the generation and testing of hypotheses about development, disease, ecosystems, or other biological processes.