Size exclusion chromatography for screening yeastolate used in cell culture media.

Yeastolate is often used as a media supplement in industrial mammalian cell culture or as a major media component for microbial fermentations. Yeastolate variability can significantly affect process performance, but analysis is technically challenging because of its compositional complexity. However, what may be adequate for manufacturing purposes is a fast, inexpensive screening method to identify molecular variance and provide sufficient information for quality control purposes, without characterizing all the molecular components. Here we used Size Exclusion Chromatography (SEC) and chemometrics as a relatively fast screening method for identifying lot-to-lot variance (with Principal Component Analysis, PCA) and investigated if Partial Least Squares, PLS, predictive models which correlated SEC data with process titer could be obtained. SEC provided a relatively fast measure of gross molecular size hydrolysate variability with minimal sample preparation and relatively simple data analysis. The sample set comprised of 18 samples from 12 unique source lots of an ultra-filtered yeastolate (10 kDa molecular weight cut-off) used in a mammalian cell culture process. SEC showed significant lot-to-lot variation, at 214 and 280 nm detection, with the most significant variation, that correlated with process performance, occurring at a retention time of ∼6 min. PCA and PLS regression correlation models provided fast identification of yeastolate variance and its process impact. The primary drawback is the limited column lifetime (<300 injections) caused by the complex nature of yeastolate and the presence of zinc. This limited long term reproducibility because these age-related, non-linear changes in chromatogram peak positions and shapes were very significant.

A User-Friendly Platform for Single-Cell Raman Spectroscopy Analysis.

The optimization of Raman instruments greatly expands our understanding of single-cell Raman spectroscopy. The improvement in the speed and sensitivity of the instrument and the implementation of advanced data mining methods help to reveal the complex chemical and biological information within the Raman spectral data. Here we introduce a new Matlab Graphical User-Friendly Interface (GUI), named "CELL IMAGE" for the analysis of cellular Raman spectroscopy data. The three main steps of data analysis embedded in the GUI include spectral processing, pattern recognition and model validation. Various well-known methods are available to the user of the GUI at each step of the analysis. Herein, a new subsampling optimization method is integrated into the GUI to estimate the minimum number of spectral collection points. The introduction of the signal-to-noise ratio (SNR) of the analyte in the binomial statistical model means the new subsampling model is more sophisticated and suitable for complicated Raman cell data. These embedded methods allow "CELL IMAGE" to transform spectral information into biological information, including single-cell visualization, cell classification and biomolecular/ drug quantification.

Data mining in Raman imaging in a cellular biological system.

The distribution and dynamics of biomolecules in the cell is of critical interest in biological research. Raman imaging techniques have expanded our knowledge of cellular biological systems significantly. The technological developments that have led to the optimization of Raman instrumentation have helped to improve the speed of the measurement and the sensitivity. As well as instrumental developments, data mining plays a significant role in revealing the complicated chemical information contained within the spectral data. A number of data mining methods have been applied to extract the spectral information and translate them into biological information. Single-cell visualization, cell classification and biomolecular/drug quantification have all been achieved by the application of data mining to Raman imaging data. Herein we summarize the framework for Raman imaging data analysis, which involves preprocessing, pattern recognition and validation. There are multiple methods developed for each stage of analysis. The characteristics of these methods are described in relation to their application in Raman imaging of the cell. Furthermore, we summarize the software that can facilitate the implementation of these methods. Through its careful selection and application, data mining can act as an essential tool in the exploration of information-rich Raman spectral data.