Seeing beyond the blot: A critical look at assumptions and raw data interpretation in Western blotting.
Western blots
biometrology
cell signaling
experimental design
post-translational modification
protein abundance
quantitative biology
reproducibility
rigor
undergraduate and graduate research education
Journal
Biomolecular concepts
ISSN: 1868-503X
Titre abrégé: Biomol Concepts
Pays: Germany
ID NLM: 101518829
Informations de publication
Date de publication:
01 Jan 2024
01 Jan 2024
Historique:
received:
21
05
2023
accepted:
08
03
2024
medline:
1
4
2024
pubmed:
1
4
2024
entrez:
1
4
2024
Statut:
epublish
Résumé
Rapid advancements in technology refine our understanding of intricate biological processes, but a crucial emphasis remains on understanding the assumptions and sources of uncertainty underlying biological measurements. This is particularly critical in cell signaling research, where a quantitative understanding of the fundamental mechanisms governing these transient events is essential for drug development, given their importance in both homeostatic and pathogenic processes. Western blotting, a technique developed decades ago, remains an indispensable tool for investigating cell signaling, protein expression, and protein-protein interactions. While improvements in statistical analysis and methodology reporting have undoubtedly enhanced data quality, understanding the underlying assumptions and limitations of visual inspection in Western blotting can provide valuable additional information for evaluating experimental conclusions. Using the example of agonist-induced receptor post-translational modification, we highlight the theoretical and experimental assumptions associated with Western blotting and demonstrate how raw blot data can offer clues to experimental variability that may not be fully captured by statistical analyses and reported methodologies. This article is not intended as a comprehensive technical review of Western blotting. Instead, we leverage an illustrative example to demonstrate how assumptions about experimental design and data normalization can be revealed within raw data and subsequently influence data interpretation.
Identifiants
pubmed: 38557557
pii: bmc-2022-0047
doi: 10.1515/bmc-2022-0047
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 the author(s), published by De Gruyter.
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