Cleavable Biotin Probes for Labeling of Biomolecules via Azide−Alkyne Cycloaddition

The azide−alkyne cycloaddition provides a powerful tool for bio-orthogonal labeling of proteins, nucleic acids, glycans, and lipids. In some labeling experiments, e.g., in proteomic studies involving affinity purification and mass spectrometry, it is convenient to use cleavable probes that allow release of labeled biomolecules under mild conditions. Five cleavable biotin probes are described for use in labeling of proteins and other biomolecules via azide−alkyne cycloaddition. Subsequent to conjugation with metabolically labeled protein, these probes are subject to cleavage with either 50 mM Na_2S_2O_4, 2% HOCH_2CH_2SH, 10% HCO_2H, 95% CF_3CO_2H, or irradiation at 365 nm. Most strikingly, a probe constructed around a dialkoxydiphenylsilane (DADPS) linker was found to be cleaved efficiently when treated with 10% HCO_2H for 0.5 h. A model green fluorescent protein was used to demonstrate that the DADPS probe undergoes highly selective conjugation and leaves a small (143 Da) mass tag on the labeled protein after cleavage. These features make the DADPS probe especially attractive for use in biomolecular labeling and proteomic studies

A BODIPY-Cyclooctyne for Protein Imaging in Live Cells

Cellular proteins that bear reactive azides can be imaged by fluorescence microscopy following strain-promoted ligation to cyclooctyne dyes. Here we describe BODIPY-cyclooctyne (BDPY), a membrane-permeant fluorophore that can be used to label intracellular proteins in live mammalian cells. Flow cytometry reveals fluorescence signals more than 25-fold above background after labeling of azide-tagged cells with BDPY

Live-Cell Imaging of Cellular Proteins by a Strain-Promoted Azide–Alkyne Cycloaddition

Live and let dye: Three coumarin-cyclooctyne conjugates have been used to label proteins tagged with azidohomoalanine in Rat-1 fibroblasts. All three fluorophores labeled intracellular proteins with fluorescence enhancements ranging from eight- to 20-fold. These conjugates are powerful tools for visualizing biomolecule dynamics in living cells

Introduction to the Analysis of Survival Data in the Presence of Competing Risks

Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. When fitting regression models in the presence of competing risks, researchers can choose from 2 different families of models: modeling the effect of covariates on the cause-specific hazard of the outcome or modeling the effect of covariates on the cumulative incidence function. The former allows one to estimate the effect of the covariates on the rate of occurrence of the outcome in those subjects who are currently event free. The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time. The former family of models may be better suited for addressing etiologic questions, whereas the latter model may be better suited for estimating a patient’s clinical prognosis. We illustrate the application of these methods by examining cause-specific mortality in patients hospitalized with heart failure. Statistical software code in both R and SAS is provided