Understanding how proteins work inside our cells is key to figuring out disease mechanisms and developing new treatments. A recent study at the University of Washington introduces LABEL-seq, a novel tool that makes it easier to study large numbers of protein variants simultaneously, helping scientists understand how tiny changes in proteins can impact their function and behavior.
What is LABEL-seq?
LABEL-seq is a method that uses RNA barcodes to tag protein variants. You can think of these RNA barcodes like name tags that allow researchers to track specific versions of proteins inside human cells. Here’s the basic idea:
Each protein variant is linked to a unique RNA barcode.
These proteins are fused with an RNA-binding domain (RBD), a tool that helps the protein latch onto the matching RNA barcode.
By pulling out the RBD-protein fusions, scientists can also collect the RNA barcodes and figure out which protein variant is present using high-throughput sequencing.
This innovative system allows researchers to measure how proteins behave directly in cells by looking at multiple properties like protein stability, activity, and how well the protein interacts with other molecules.
Overview of LABEL-seq and development of the RNA and protein components
a, Schematic of the LABEL-seq platform. Intracellular self-assembly of proteins tagged with tdMCP, an RBD with high affinity for the MS2 stem loop, with a single circRNA containing the MS2 stem loop and a short barcode sequence facilitates the identification of a co-expressed protein variant. Intracellular variant properties or functions such as abundance (top), activity (middle) or protein–protein interactions (bottom) are quantified after cell lysis by performing an appropriate affinity enrichment of tdMCP-tagged protein fusions, followed by quantification of co-enriched MS2-circRNAs using high-throughput sequencing. IP, immunoprecipitation. b, Two MS2-circRNA architectures containing MS2 RNA hairpins and degenerate 16-nucleotide barcodes. c, In-gel fluorescence of total RNA extracted from cells expressing MS2-circRNAs 1 or 2 and stained with DFHBI-1T (n = 1). Molecular weight markers have units of nucleotides. d, Enrichment of tdMCP–Lck protein fusions complexed with MS2-circRNAs 1 or 2 with the immobilized ATP-competitive inhibitor dasatinib are shown. FT, flow through of enrichment. The ‘+’ indicates the presence of a free dasatinib competitor (n = 3 independent experiments). Molecular weight markers have units of kDa and nucleotides for protein and RNA gels, respectively.
Why Study So Many Protein Variants?
Many diseases, such as cancer, are driven by mutations—small changes in proteins that alter their normal behavior. In this study, the researchers applied LABEL-seq to analyze 1,600 different versions of the BRaf protein (a protein often mutated in cancers) and measured the impact of nearly 20,000 mutations.
They found something surprising:
Mutations in regions frequently altered in cancer didn’t affect how much of the protein was present in the cell.
However, these mutations did affect how the protein works, influencing its activity, interactions with other proteins, and even whether it could be targeted by drugs.
What Did We Learn?
By comparing many variants at once, LABEL-seq gave new insights into how BRaf mutations impact cell growth and response to drugs. For example:
Some mutations disrupted critical protein-protein interactions, which could explain how they drive cancer development.
Others made the protein more or less sensitive to drugs, helping predict which variants might resist treatment.
The technique also highlighted networks of mutations that behaved similarly, providing a deeper understanding of how proteins function as complex systems.
Why is LABEL-seq Important?
LABEL-seq offers a powerful way to study proteins in their natural environment, which is critical for understanding disease mechanisms more accurately. Traditional methods often require studying proteins in artificial settings, but LABEL-seq allows measurements directly in living cells.
This approach could lead to:
Better cancer treatments by predicting which mutations will respond to certain drugs.
New drug targets by identifying protein regions that affect interactions and activity.
Improved understanding of diseases beyond cancer, as the same strategy can be applied to other proteins linked to conditions like neurological disorders or metabolic diseases.
Final Thoughts
LABEL-seq represents a major leap forward in protein research by combining the power of RNA barcoding with the precision of high-throughput sequencing. By enabling scientists to explore how thousands of protein variants behave in real time, this tool could accelerate the development of new therapies and provide deeper insights into complex diseases.
With methods like LABEL-seq, the future of biomedical research looks brighter, offering new ways to untangle the mysteries of proteins and design better treatments tailored to specific mutations.