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Perhaps you have seen a time-lapse video of a busy city sidewalk. As people come and go, they blur together into a crowd with no distinguishing features. You could count the number of people pushing strollers in each frame, but it might be hard to tell how long one parent has been circling the same block with a colicky baby.

As proteins are made and destroyed in a cell, they tend to blur together too. Many proteomics studies measure with precision the number of copies of each protein species but not how long each one lasts. In in the journal Â鶹´«Ã½É«ÇéƬ & Cellular Proteomics, researchers in lab at the report a new approach to determining the lifespan of a great many proteins, and their alternative isoforms, in large data sets.

“Plenty of research has demonstrated that cancer, neurodegenerative diseases, age-related diseases and even aging per se are associated with altered lifespans of single proteins or a global dysregulation of the cellular recycling machinery,” said lead author . She compares a cell in which proteins are continuously made and destroyed to “a tiny protein production and recycling machinery.” With colleagues, Zecha set out to measure this factory’s output, determining the rates of production and destruction of many different proteins.

The researchers combined two techniques for telling samples apart by their mass: stable isotope labeling by amino acids in cell culture, or SILAC for short, and tandem mass tag labeling, or TMT. The primary SILAC label enabled a pulse-chase experiment, a way of measuring how much of a new amino acid is taken up after it is added to cells. By combining SILAC with TMT, the researchers could achieve high proteome coverage with high reproducibility and accurate counts of each protein. Then they looked for trends over time. For example, a protein’s rate of synthesis can be measured by how much of the new SILAC label appears over time in its spectrum, and degradation is measured by how much the old label disappears.

Other scientists previously had combined the SILAC and TMT methods, but this data set gave an unusually thorough look at protein lifetimes. The researchers found substantial variability among splice variants of proteins, which no one had yet measured in a data set of this size. Because two splice variants from the same gene have many peptides in common, a data set with many measurements at the peptide level was required.

The approach could offer a better way of understanding the basic biology of disease states with altered protein turnover. The researchers also are interested in modifications occurring after translation that may alter turnover rates.

“A proteomewide measurement of turnover rates of modified peptides is the next logical step for us,” Zecha said.