Proteomic Landscape
Proteomic Landscape - Related Videos
24:02
The ChroP Approach Combines ChIP and Mass Spectrometry to Dissect Locus-specific Proteomic Landscapes of Chromatin
0 Views •
Cited by 27 •
2014
By combining native and crosslinking chromatin immunoprecipitation with high-resolution Mass Spectrometry, ChroP approach enables to dissect the composite proteomic architecture of histone modifications, variants and non-histonic proteins synergizing at functionally distinct chromatin domains.
09:29
Skeletal Muscle Gender Dimorphism from Proteomics
0 Views •
Cited by 4 •
2011
A straight-forward set of methods to isolate and determine the identity of the most abundant proteins expressed in skeletal muscle. About 800 spots are discerned on a two-dimensional gel from 10 mg muscle; this allows for the determination of gender-specific protein expression. These methods will give equivalent results in most tissues.
09:20
Proteomic Sample Preparation from Formalin Fixed and Paraffin Embedded Tissue
0 Views •
Cited by 48 •
2013
Archival formalin fixed and paraffin embedded (FFPE) clinical samples are valuable material for investigation of diseases. Here we demonstrate a sample preparation workflow allowing in-depth proteomic analysis of microdissected FFPE tissue.
14:23
Bottom-up and Shotgun Proteomics to Identify a Comprehensive Cochlear Proteome
0 Views •
Cited by 7 •
2014
Proteome analysis of the cochlear sensory epithelium can be challenging due to its small size and because membrane proteins are difficult to isolate and identify. Both membrane and soluble proteins can be identified by combining multiple preparative methods and separation techniques along with high-resolution mass spectrometry.
05:37
An R-Based Landscape Validation of a Competing Risk Model
0 Views •
Cited by 2 •
2022
The present protocol describes codes in R for evaluating the discrimination and calibration abilities of a competing risk model, as well as codes for the internal and external validation of it.
FAQs
Changes in protein abundance do not simply mirror changes in gene sequence. A proteomic landscape can also capture protein modifications, interaction patterns, and spatial distribution, allowing genetic effects to be traced beyond gene regulation alone. Examining these layers helps explain how a genotype produces altered cellular functions or molecular phenotypes.
Comparing proteomic landscapes across genotypes can reveal proteins whose abundance or molecular state differs with genetic variation. These differences provide a connection between genomic information and observable molecular phenotypes. Researchers can therefore use genotype-based comparisons to investigate how gene regulation influences cellular function and to prioritize molecular changes for further study.
Protein abundance alone may not describe the full functional state of a cell. Modifications can alter a protein’s molecular condition, interactions can indicate relationships among proteins, and spatial distribution shows where protein patterns occur. Considering these features together gives a more informative view of cellular changes associated with development, disease, or genetic variation.
Genomic information indicates the underlying genetic context, whereas proteomic measurements show molecular features that emerge in cells, tissues, or organisms. Linking the two helps researchers connect genetic variation and gene regulation with observable protein-level phenotypes. This relationship can clarify cellular function and support the interpretation of changes associated with disease states.
A typical analysis begins by extracting proteins from the selected cell, tissue, or organism. Molecular components may then be separated or enriched before identification and quantification, commonly with mass spectrometry. Computational analysis is used alongside these measurements to organize and interpret protein abundance, modifications, interactions, or spatial patterns.
Mass spectrometry provides a means to identify and quantify proteins after extraction and any separation or enrichment steps. Computational analysis then helps interpret the resulting measurements across samples or conditions. Together, these approaches turn complex protein-level observations into comparisons that can be related to genotype, developmental stage, or disease state.
Researchers can apply proteomic landscape comparisons to genotypes, developmental stages, or disease states. The resulting patterns may help identify biomarkers, clarify disease mechanisms, and prioritize targets for functional studies or therapeutic development. In genetics, the approach is especially useful for examining how gene regulation and variation become measurable molecular changes in cells.