During the past decade, there have been many exciting advances in the fields of cellular therapies, regenerative medicine, and tissue engineering. However, current cryopreservation strategies and protocols result in inferior product after thawing. Thus, novel cryoprotectants and protocols capable of meeting the high‐quality product(s) necessary for these therapies are urgently required. The search for new and improved cryoprotectants has been ongoing but novel small molecule ice recrystallization inhibitors, originally developed from naturally occurring antifreeze proteins, have demonstrated tremendous promise and will play a significant role in fully translating cellular and regenerative therapies into the clinical environment.

Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in –omic study of complex samples. While TMT‐based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the additional reagent cost. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile solvents, which may increase the sample‐to‐sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre‐aliquoted dry TMT reagents in a 96 well plate or 12‐tube strip. As little as 50 μg dry TMT per channel was used to label 6–12 μg peptides, yielding high TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre‐aliquoted, dried, and stored for robust quantitative proteomics and metaproteomics in high throughput applications.

The jadomycins are a family of secondary metabolites produced by S. venezuelae ISP5230. Specific jadomycins have been shown to possess a variety of anticancer, antifungal, and antibacterial properties, with different molecular mechanisms of action. Herein we demonstrate qualitative and quantitative direct binding between the validated anticancer target human topoisomerase IIβ and jadomycin DS using WaterLOGSY NMR spectroscopy. Additionally, we report for the first time, that jadomycin DS also binds a variety of other proteins, likely in a non-specific manner. Such interactions may rationalize the potential polypharmacology of jadomycin DS.

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Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in -omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the increased cost due to the use of labeling reagents. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile organic solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50 μg dry TMT per channel effectively labels 6-12 μg peptides, yielding efficient TMT labeling efficiency (~99%) in both microbiome and mammalian cell line samples. This streamlined workflow decreases reagent loss and reduces inter-sample variations. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for streamlined and robust quantitative proteomics and metaproteomics in high throughput applications.