Biomarker Expression Mapping
Biomarker Expression Mapping - Related Videos
07:54
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
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Cited by 27 •
2011
Here we describe a method to quantify molecular heterogeneity in histological sections of tumor material using quantitative immunofluorescence, image analysis, and a statistical measure of heterogeneity. The method is intended for use in clinical biomarker development and analysis.
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.
13:19
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Cited by 16 •
2013
Human endogenous retroviruses (HERV), which occupy 8% of the human genome, retain scarce coding capacities but a hundred thousand long terminal repeats (LTRs). A custom Affymetrix microarray was designed to identify individual HERV locus expression and was used on prostate cancer tissues as a proof of concept for future clinical studies.
08:29
Biomarkers in an Animal Model for Revealing Neural, Hematologic, and Behavioral Correlates of PTSD
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Cited by 29 •
2012
We describe a rat model of post traumatic stress disorder (PTSD) that reveals the persistent alterations in neuroendocrine function and the delayed long-term, exaggerated fear response, characteristic of PTSD patients. The animal model and methods described here are useful for correlating biomarkers in brain nuclei, which are mechanistic but cannot be measured in patients, with biomarkers in peripheral white blood cells, which can.
09:45
Mapping and Application of Enhancer-trap Flippase Expression in Larval and Adult Drosophila CNS
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Cited by 7 •
2011
We describe a Flippase-induced intersectional Gal80/Gal4 repression (FINGR) method, allowing tissue-specific FLP to determine Gal80 expression patterns. Wherever Gal4 and FLP overlap, Gal4 expression is turned on (Gal80 flipped out) or off (Gal80 flipped in). The FINGR method is versatile for clonal analysis and neural circuit mapping.
FAQs
Spatial context shows whether a molecular signal is concentrated in particular cells, tissue regions, or organs rather than distributed uniformly. This distinction can reveal differences between neighboring cell populations and help separate normal from pathological tissue. In medical research, retaining location therefore supports more precise tissue classification and improves interpretation of disease-related molecular patterns.
Antibodies, nucleic-acid probes, and other detection reagents provide different ways to identify selected molecular indicators. The reagent generates a detectable signal where the biomarker occurs, allowing investigators to associate molecular presence with specific cellular or tissue locations. Choosing an appropriate detection reagent is central to connecting the observed signal with the biomarker being studied.
Signal intensity indicates how strongly a biomarker is detected, while location identifies the cells or regions associated with that signal. Considering both features prevents interpretation based only on overall abundance. A biomarker may be concentrated in a particular pathological region or cell population, making its spatial distribution as informative as the measured signal level.
A typical workflow selects molecular indicators relevant to the health or disease question, applies antibodies, nucleic-acid probes, or other detection reagents, and measures the resulting signals. Investigators then visualize signal distribution and relate intensity and location to defined cells, tissue regions, or organs. This sequence converts molecular detection into spatially interpretable tissue information.
The approach is useful when researchers need to distinguish normal tissue from pathological tissue or examine variation within a disease. It can characterize disease heterogeneity by showing that molecular patterns differ across regions or cell populations. These findings may support tissue classification, clarify disease-associated organization, and guide investigation of clinically relevant biomarker distributions.
Mapped patterns can connect biomarker distribution with clinically meaningful questions, including diagnosis, prognosis, and treatment response. Spatially localized signals may also identify regions or cell populations that contain potential therapeutic targets. In medicine, these observations support the development of more precise strategies for patient care by preserving molecular information together with tissue context.