Stanislav Sokolenko

The high cost of conventional cell culture media has driven demand for sustainable, cost‐effective serum‐free formulations in biopharmaceutical manufacturing and cultivated meat production. Although plant‐ and yeast‐derived hydrolysates are generally considered to be promising additives, significant variability in raw materials and processing conditions results in substantial compositional and batch‐to‐batch differences between products, making it challenging to predict effectiveness in cell culture a priori . This review examines hydrolysate media additives through the specific lens of comprehensive multi‐omic characterization as a means of addressing this uncertainty. While typical characterization is often limited to metrics such as degree of hydrolysis or color, detailed metabolomic and peptidomic profiling remains uncommon. We identify a gap in systematic, high‐resolution characterization and argue that integrating compositional and bioactivity analyses is essential to transition hydrolysates from undefined inputs toward semi‐defined media components.

The simultaneous detection of tens to hundreds of metabolites in a single metabolomic timecourse sample offers a unique but often unrealized opportunity for quantification validation. An individual timecourse fit for each metabolite fundamentally convolutes measurement noise with systematic sample bias (stemming from, for example, variable sample dilution, extraction, and normalization). However, since systematic bias, by its definition, influences all metabolites within a sample in a similar fashion, it can be identified and corrected through the simultaneous fit of all detected metabolites in a single timecourse model. This study presents a nonlinear B‐spline mixed‐effects model as a convenient formulation capable of estimating and correcting such bias. The proposed model was successfully applied to real cell culture data and validated using simulated timecourse data perturbed with varying degrees of random noise and systematic bias. The model was able to accurately correct systematic bias of 3%–10% to within 0.5% on average for typical data. An R package for the correction model has been developed to facilitate model adoption and use. The proposed nonlinear B‐spline mixed‐effects formulation is general enough for application to a broad range of research areas beyond just cell culture metabolomics.

Extracellular vesicles (EVs) contain a variety of proteins with anti-inflammatory and immunomodulatory properties that offer promising benefits in skin therapy applications. An influx of EV proteomic studies in recent years has created the opportunity for a detailed comparison of EV heterogeneity between studies in the context of therapeutic applications. Although several process conditions are known to cause variability in EVs, little has been done to quantify the impact of these factors on the nature of EV protein cargo. This review aims to both compile publicly available EV proteomics data and quantitatively estimate. the impact of process conditions on protein cargo—particularly in the context of skin therapy applications. Of roughly 400 articles, 52 relevant proteomic studies were identified within the last 15 years. Across studies, 40% of the 13,000 observed proteins were identified in only a single study. EVs in general were found to be highly variable, with mixed effects models only able to account for 25–60% of variance when considering factors such as EV source, medium, isolation method, LC-MS ionization, and protein search algorithm. Overall, MSC-derived EVs contained a greater fraction of proteins within pathways associated with wound healing and skin therapy (immune system, hemostasis, extracellular matrix organization, and cellular response to stress) as well as the most number of unique proteins when compared to all other analysed EVs. Although EVs are a promising tool within skin therapeutics, the overall variability in protein cargo underscores the need for standardized methodologies to fully elucidate the impact of process conditions on EV cargo.

Protein hydrolysates have long been touted as effective cell culture additives for improving both cell density and product titer. However, typical media optimization studies rarely consider multiple different products or their interaction (either with each other or with the basal media). This study documents the successful replacement of animal serum in Chinese hamster ovary (CHO) cell culture using a combination of hydrolysate products while elucidating often-overlooked interaction effects. Two design-of-experiments (DOE) mixture designs were used to determine the effect of eight plant and yeast-based hydrolysates and their interactions on cell density. Among the hydrolysates, plants were generally found to have positive effects when utilized in combination, while yeast hydrolysates experienced more negative effects in combination with each other. The addition of either a combination of cotton and wheat or individual Hy-Yest™ 466 to aDMEM was found to achieve similar growth to 5% serum, outperforming other hydrolysates. In contrast, adding the same hydrolysates to chemically defined commercial media resulted in growth inhibition. The combination of observed interactions between the various hydrolysate products and the basal media (as well as general batch-to-batch variability) reinforce the need for robust experimental design in further development of hydrolysate additives for cell culture media optimization.