Development of an Optimized LC-MS Workflow for Host Cell Protein Characterization to Support Upstream Process Development

Abstract

Host cell proteins (HCPs) coexpressed during the production of biotherapeutics can affect the safety, efficacy, and stability of the final product. As such, monitoring HCP populations and amounts throughout the production and purification process is an essential part of the overall quality control framework. Mass spectrometry (MS) is used as an orthogonal method to enzyme-linked immunosorbent assays (ELISA) for the simultaneous identification and quantification of HCPs, particularly for the analysis of downstream processes. In this study, we present an MS-based analytical protocol with improvements in both speed and identification performance that can be implemented for routine analysis to support upstream process development. The protocol adopts a streamlined sample preparation strategy, combined with a high-throughput MS analysis pipeline. The developed method identifies and quantifies over 1000 HCPs, including 20 proteins listed as high risk in the literature, in a clarified cell culture sample with repeatability and precision shown for digest replicates. In addition, we explore the effects of varying standard spike-ins and changes to the data processing pipeline on absolute quantification estimates of the HCPs, which highlight the importance of standardization for wider use in the industry. Data are available via ProteomeXchange with the identifier PXD053035.

Keywords: Chinese hamster ovary; LC-MS; absolute quantification; bioprocessing; clarified cell culture fluid; data-independent acquisition; hi3 quantification; host cell proteins; process analytical technologies.

Scheme 1. Overview of the Sample Processing Pipeline Including Sample Preparation and Acquisition and Data Processing of the Workflow Evaluated for Method Reproducibility, Performance, and Influences on Relative Absolute Quantification Using the SMART Digest Protocol. Created with BioRender.com.

Figure 1

Development of an optimized sample preparation workflow to identify and quantify HCP in the CCCF. (a) Overview of parameters investigated, namely, digest protocol used, the effect of the precipitation (Prec) step, digest time, and addition of chicken lysozyme at 5000 ng/mg product before or after the Prec, * effect of digestion times has been investigated using the SMART digest including a Prec step, ° influences of addition of chicken lysozyme before or after the Prec step has been investigated using the SMART digest and a digest time of 40 min. (b) Comparison of protein identifications using different digest protocols with and without Prec, where no standards were spiked into the sample. Raw data from injection triplicates were processed by Spectronaut separately for each condition using default settings. Identifications were filtered to include only proteotypic identifications with two or more peptides per protein. (c) Effect of digest times on identified HCP quantity distributions as kernel density estimates based on protein standard spikes and the default Spectronaut setting. All conditions were processed together and normalized. (d) Effect of the protein standard spiking on HCP quantity distribution linearity as evaluated by Pearson correlation. HCP quantity estimations are based on protein standard spikes at 5000 ng/mg product and the default Spectronaut setting. Both conditions were processed together and normalized.

Figure 2

Assessment of repeatability by comparing absolute quantity estimates of the 1017 proteins found and quantified in all digest replicates when processed separately. Quantification in relation to the protein standard spike and processing was performed using standard settings. (a) Kernel density estimation of HCP quantities from three digest replicates. Linear correlation assessment (Pearson) between digest replicates one and two (b) without normalization and (c) with normalization.

Figure 3

Changes in the kernel density distribution of identified HCP quantities depend on calculations based on peptide or protein standards as well as changes in the mathematical estimation approaches (mean vs sum of fragments). (a) Comparison of peptide/protein-based estimates for the standard Spectronaut settings. (b) Comparison of peptide/protein-based estimates based on the top3 sum quantitation settings. (c) Comparison of std and top3 Spectronaut for peptide-based estimation. (d) Comparison of std and top3 Spectronaut for protein-based estimation.