Two-dimensional electrophoresis: an overview

Analytical Tools for HCP Detection, Identification, and Quantitation

Identifying and quantifying host cell proteins (HCPs) are crucial in developing and manufacturing biopharmaceutical products. ELISA has been used widely for identifying and quantifying HCPs in biopharmaceutical products. Liquid chromatography-mass spectrometry (LC-MS) has recently emerged as an orthogonal tool for HCP analysis, providing comprehensive and quantitative information on individual HCPs. This chapter explores the advancements in LC-MS/MS methodologies for HCP analysis, including sample preparation, data acquisition modes, and data analysis strategies. It also discusses the challenges of HCP identification and quantitation, and recent innovations to overcome these limitations. The application of LC-MS/MS methods in HCP profiling holds significant promise for improving process control and ensuring product safety in biopharmaceutical manufacturing.

Microalgae Biomolecules: Extraction, Separation and Purification Methods

Several microalgae species have been exploited due to their great biotechnological potential for the production of a range of biomolecules that can be applied in a large variety of industrial sectors. However, the major challenge of biotechnological processes is to make them economically viable, through the production of commercially valuable compounds. Most of these compounds are accumulated inside the cells, requiring efficient technologies for their extraction, recovery and purification. Recent improvements approaching physicochemical treatments (e.g., supercritical fluid extraction, ultrasound-assisted extraction, pulsed electric fields, among others) and processes without solvents are seeking to establish sustainable and scalable technologies to obtain target products from microalgae with high efficiency and purity. This article reviews the currently available approaches reported in literature, highlighting some examples covering recent granted patents for the microalgae’s components extraction, recovery and purification, at small and large scales, in accordance with the worldwide trend of transition to bio-based products.

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8-20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10-20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20-14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image.