Spatial Protein Expression
Spatial Protein Expression - 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.
08:46
Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli
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Cited by 34 •
2015
A streamlined approach to screening for the expression of recombinant membrane proteins in Escherichia coli based on fusion to green fluorescent protein is presented.
08:54
Imaging Spatial Reorganization of a MAPK Signaling Pathway Using the Tobacco Transient Expression System
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Cited by 4 •
2016
At the subcellular level, signaling events are dynamically modulated by developmental and environmental cues. Here we describe a protocol that employs the tobacco transient expression system to monitor dynamic protein-protein interaction and to disclose spatial organization of signal transduction in plant cells.
09:19
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Cited by 2 •
2022
Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 µm.
07:47
Staining of Proteins in Gels with Coomassie G-250 without Organic Solvent and Acetic Acid
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Cited by 94 •
2009
A short protocol for protein staining with Coomassie Brilliant Blue (CBB) G-250 in polyacrylamide gels is described without using organic solvents or acetic acid as in the classical staining procedures with CBB.
FAQs
Preserving tissue architecture links protein abundance to the cells, tissue regions, or subcellular locations where it occurs. This spatial context distinguishes molecular signals that may appear similar in an averaged tissue measurement but arise from different anatomical sites. In medicine, that distinction helps connect protein changes with pathology, tissue organization, and disease-associated pathways rather than treating the specimen as molecularly uniform.
Cellular heterogeneity means that neighboring cells or regions can differ in their protein profiles and biological roles. Spatial protein expression makes those differences visible by assigning signals to anatomical coordinates. This can reveal localized disease-associated pathways and interactions that conventional measurements may obscure, supporting more precise interpretation of tissue biology and improving the search for clinically informative biomarkers.
These approaches detect spatial protein signals through different measurement strategies. Antibody-based imaging identifies selected targets visually, while multiplexed staining expands the number of proteins assessed within the same tissue context. Spatially resolved mass spectrometry provides another route for mapping molecular signals to locations. Their shared value is connecting protein measurements with preserved anatomy, while their measurement formats differ.
A basic workflow preserves the tissue architecture, detects selected proteins or broader molecular signals, and assigns those measurements to precise anatomical coordinates. Depending on the method, detection may use antibodies, multiplexed staining, or spatially resolved mass spectrometry. The resulting map can then be examined alongside tissue organization and pathology to interpret where molecular changes occur.
Medical researchers can apply these maps to biomarker discovery, diagnosis, patient stratification, and treatment-response assessment. The approach is especially useful when the location of a protein signal may influence its interpretation. By connecting molecular changes with pathology, spatial analysis can help distinguish disease-relevant tissue regions and support comparisons among patients or treatment conditions.
Within tumors and immune microenvironments, spatial maps can show how molecular signals are distributed among distinct tissue regions and cellular populations. They can expose cellular heterogeneity, tissue organization, disease-associated pathways, and local interactions. These observations provide context for studying disease mechanisms and may help relate tissue-level protein patterns to biomarker behavior or changes observed after treatment.