Endogenous Tagging with Split mNeonGreen in Human iPSCs for Live Imaging Studies

Core Concepts

Efficient endogenous tagging using split mNeonGreen enables live imaging studies of cellular processes in human iPSCs.

Abstract

Abstract:

Endogenous tags are crucial for visualizing native proteins in live cells.
Split mNeonGreen facilitates large-scale endogenous tagging in human iPSCs.
Neural network-based image restoration allows live imaging of dynamic cellular processes.
Introduction:

GFP revolutionized protein visualization but has limitations.
CRISPR/Cas9 enables direct genome insertion of fluorescent markers.
Split fluorescent proteins offer efficient endogenous tagging.
Data Extraction:

"Recently, an engineered split mNeonGreen protein was used to generate a large-scale endogenous tag library in HEK293 cells."
"Large-scale endogenous tagging in HEK293 cells was recently achieved by the OpenCell project, with 1,310 proteins tagged to date."
Quotations:

"The two fragments have been engineered to form a functional fluorescent protein when co-expressed."
"This work represents the first step towards a genome-wide endogenous tag library in human stem cells."

Stats

Recently, an engineered split mNeonGreen protein was used to generate a large-scale endogenous tag library in HEK293 cells.
Large-scale endogenous tagging in HEK293 cells was recently achieved by the OpenCell project, with 1,310 proteins tagged to date.

Quotes

"The two fragments have been engineered to form a functional fluorescent protein when co-expressed."
"This work represents the first step towards a genome-wide endogenous tag library in human stem cells."

Key Insights Distilled From

Endogenous tagging using split mNeonGreen in human iPSCs for live imaging studies

by Husser,M. C.... at www.biorxiv.org 06-28-2023

https://www.biorxiv.org/content/10.1101/2023.06.28.546942v3

Deeper Inquiries

How can the use of split mNeonGreen impact other areas of biotechnology?

The use of split mNeonGreen in endogenous tagging has the potential to impact various areas of biotechnology. One significant application is in the study of protein-protein interactions. By tagging different proteins with complementary fragments of split mNeonGreen, researchers can visualize and study dynamic interactions between proteins in real-time within living cells. This approach provides a powerful tool for understanding complex biological processes and signaling pathways.
Furthermore, split fluorescent proteins like mNeonGreen can be utilized in high-throughput screening assays to identify novel drug targets or assess the efficacy of potential therapeutics. The ability to tag multiple proteins simultaneously using split tags enables researchers to perform large-scale studies on protein function and localization, which is crucial for drug discovery and development.
In addition, the versatility and efficiency of split mNeonGreen for endogenous tagging make it a valuable tool for genetic engineering applications such as CRISPR/Cas9-mediated genome editing. By incorporating these tags into specific genomic loci, researchers can track gene expression patterns, monitor cellular processes, and investigate gene function with high precision.
Overall, the use of split mNeonGreen has broad implications across various fields within biotechnology by enabling detailed visualization and manipulation of molecular events within living cells.

What are potential drawbacks or limitations of using split fluorescent proteins for endogenous tagging?

While split fluorescent proteins offer numerous advantages for endogenous tagging applications, there are also some drawbacks and limitations associated with their use:

Efficiency Variability: The efficiency of endogenous tagging with split fluorescent proteins like mNeonGreen can vary depending on the target gene locus. Some genes may have lower editing efficiencies compared to others, leading to inconsistent results across different loci.

Detection Sensitivity: Weakly expressed proteins may not produce a strong enough signal for detection by standard imaging techniques when tagged with small fragments like mNG211. This limitation could hinder accurate visualization and quantification of low-abundance proteins.

Off-Target Effects: Gene editing using CRISPR/Cas9 technology carries a risk of off-target effects that could result in unintended mutations at non-specific sites in the genome. These off-target effects may introduce unwanted genetic alterations that affect cell behavior or function.

Clonal Variation: Clonal isolation following gene editing is essential to obtain homogeneous populations; however, clonal cell lines may exhibit genetic heterogeneity due to mutations introduced during editing or inherent variability among individual clones.

Phototoxicity Sensitivity: Certain cell types like iPSCs may be more sensitive to phototoxicity during live imaging experiments due to low-endogenous protein levels requiring longer exposure times which could compromise cell viability over time.

How might advancements in live imaging technology influence future research on cellular processes?

Advancements in live imaging technology have already begun transforming our understanding of cellular processes by providing unprecedented insights into dynamic biological events at high spatiotemporal resolution:

Increased Temporal Resolution: Improved live imaging technologies enable researchers to capture rapid cellular dynamics with higher temporal resolution than ever before.

Enhanced Spatial Resolution: Advances in microscopy techniques allow scientists to visualize subcellular structures and molecular interactions at nanometer scales.

Multi-Modal Imaging: Integration of different imaging modalities (e.g., fluorescence microscopy combined with electron microscopy) offers comprehensive views from macroscopic tissue down to molecular details.

Super-Resolution Microscopy: Techniques such as STED (stimulated emission depletion), PALM (photoactivated localization microscopy), SIM (structured illumination microscopy) provide super-resolution images beyond diffraction limits.

Single-Molecule Imaging: Technologies enabling tracking single molecules inside living cells shed light on biomolecular behaviors otherwise obscured by ensemble averaging.

Machine Learning-Assisted Analysis: Utilizing artificial intelligence algorithms improves image processing speed & accuracy facilitating automated analysis & interpretation

These advancements will continue shaping future research by allowing scientists unprecedented access into intricate cellular mechanisms previously inaccessible through traditional methods alone..