Fluorescent tools for the standardized work in Gram-negative bacteria

Journal article

Standardized and thoroughly characterized genetic tools are a prerequisite for studying cellular processes to ensure the reusability and consistency of experimental results. The discovery of fluorescent proteins (FPs) represents a milestone in the development of genetic reporters for monitoring transcription or protein localization in vivo. FPs have revolutionized our understanding of cellular dynamics by enabling the real-time visualization and tracking of biological processes. Despite these advancements, challenges remain in the appropriate use of FPs, specifically regarding their proper application, protein turnover dynamics, and the undesired disruption of cellular functions. Here, we systematically compared a comprehensive set of 15 FPs and assessed their performance in vivo by focusing on key parameters, such as signal over background ratios and protein stability rates, using the Gram-negative model organism Salmonella enterica as a representative host. We evaluated four protein degradation tags in both plasmid- and genome-based systems and our findings highlight the necessity of introducing degradation tags to analyze time-sensitive cellular processes. We demonstrate that the gain of dynamics mediated by the addition of degradation tags impacts the cell-to-cell heterogeneity of plasmid-based but not genome-based reporters. Finally, we probe the applicability of FPs for protein localization studies in living cells using standard and super-resolution fluorescence microscopy. In summary, our study underscores the importance of careful FP selection and paves the way for the development of improved genetic reporters to enhance the reproducibility and reliability of fluorescence-based research in Gram-negative bacteria and beyond.

Authors: Delgadillo-Guevara, M; Halte, M; Erhardt, M; Popp, PF

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Journal of Biological Engineering
• Volume: 18
• Issue: 1
• Pages: 25-25
• Publication date: 2024-04-08
• Acceptance date: 2024-03-18
• DOI: 10.1186/s13036-024-00420-9
• EISSN: 1754-1611
• ISSN: 1754-1611
• Language: English
• Keywords: Computational biology; Biology; Computer science