Gene expression measurements normalized to cell number reveal large scale differences due to cell size changes, transcriptional amplification and transcriptional repression in CHO cells

Homoeologs in Allopolyploids: Navigating Redundancy as Both an Evolutionary Opportunity and a Technical Challenge-A Transcriptomics Perspective

Allopolyploidy in plants involves the merging of two or more distinct parental genomes into a single nucleus, a significant evolutionary process in the plant kingdom. Transcriptomic analysis provides invaluable insights into allopolyploid plants by elucidating the fate of duplicated genes, revealing evolutionary novelties and uncovering their environmental adaptations. By examining gene expression profiles, scientists can discern how duplicated genes have evolved to acquire new functions or regulatory roles. This process often leads to the development of novel traits and adaptive strategies that allopolyploid plants leverage to thrive in diverse ecological niches. Understanding these molecular mechanisms not only enhances our appreciation of the genetic complexity underlying allopolyploidy but also underscores their importance in agriculture and ecosystem resilience. However, transcriptome profiling is challenging due to genomic redundancy, which is further complicated by the presence of multiple chromosomes sets and the variations among homoeologs and allelic genes. Prior to transcriptome analysis, sub-genome phasing and homoeology inference are essential for obtaining a comprehensive view of gene expression. This review aims to clarify the terminology in this field, identify the most challenging aspects of transcriptome analysis, explain their inherent difficulties, and suggest reliable analytic strategies. Furthermore, bulk RNA-seq is highlighted as a primary method for studying allopolyploid gene expression, focusing on critical steps like read mapping and normalization in differential gene expression analysis. This approach effectively captures gene expression from both parental genomes, facilitating a comprehensive analysis of their combined profiles. Its sensitivity in detecting low-abundance transcripts allows for subtle differences between parental genomes to be identified, crucial for understanding regulatory dynamics and gene expression balance in allopolyploids.

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH

Advances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers.

Selection of high-producing clones by a relative titer predictive model using image analysis

Background

The commercial success of monoclonal antibodies (Mabs) has made biological therapeutics attractive to pharmaceutical companies. The priority of biopharmaceutical companies is to acquire and develop cell lines that enable them to manufacture biologics quickly, consistently, and economically. Clone selection is a critical process for cell line development. However, the traditional clone selection process requires the evaluation of large numbers of clones using cell growth rate, cell densities and titer, product quality, and so on.

Methods

To improve efficiency of the clone selection strategies, we developed a relative titer (RT) prediction model by the quantitative information extracted from microscope images during the cell line development process. The performance of this RT prediction model was further evaluated with 50 clones from 5 different cell lines.

Results

The RT prediction model was able to predict high producers from a given data set when the same host cells were used. Although inaccurate prediction occurred when different host cell was used, this RT prediction model may serve as an excellent proof of concept study that quantitative information from cell line development images provides valuable information to facilitate the cell line development process.

Conclusions

Here, we present the first predictive model that can be used to estimate the relative productivity of Chinese hamster ovaries (CHO) clones during the cell line development. Additional experiments are currently in process to further improve the RT predictive model. Nevertheless, our current study will serve as a foundation for more prediction models for cell line development that can facilitate the selection of clones.

Plasmodium Reproduction, Cell Size, and Transcription: How to Cope With Increasing DNA Content?

Plasmodium, the unicellular parasite that causes malaria, evolved a highly unusual mode of reproduction. During its complex life cycle, invasive or transmissive stages alternate with proliferating stages, where a single parasite can produce tens of thousands of progeny. In the clinically relevant blood stage of infection, the parasite replicates its genome up to thirty times and forms a multinucleated cell before daughter cells are assembled. Thus, within a single cell cycle, Plasmodium develops from a haploid to a polypoid cell, harboring multiple copies of its genome. Polyploidy creates several biological challenges, such as imbalances in genome output, and cells can respond to this by changing their size and/or alter the production of RNA species and protein to achieve expression homeostasis. However, the effects and possible adaptations of Plasmodium to the massively increasing DNA content are unknown. Here, we revisit and embed current Plasmodium literature in the context of polyploidy and propose potential mechanisms of the parasite to cope with the increasing gene dosage.

Investigation of cell line specific responses to pH inhomogeneity and consequences for process design

With increasing bioreactor volumes, the mixing time of the reactor increases as well, which creates an inhomogeneous environment for the cells. This can result in impaired process performance in large-scale production reactors. Particularly the addition of base through the reactor headspace can be problematic, since it creates an area, where cells are repeatedly exposed to an increased pH. The aim of this study is to simulate this large-scale phenomenon at lab-scale and investigate its impact. Two different cell lines were exposed to pH amplitudes of a maximal magnitude of 0.05 units (pH of 6.95). Both cell lines showed similar responses, like decreased viable cell counts, but unaffected lactate levels. However, cell line B showed an initially increased specific productivity in response to the introduced amplitudes, whereas cell line A showed a consistently lower specific productivity. Furthermore, the time point at which base addition is started influences the impact, which pH amplitudes have on process performance. When pH control was started earlier in the process, maximal viable cell counts decreased and the lactate metabolic shift was less pronounced. These results show that the potential negative impact of pH amplitudes can be minimized by strategic process design.

Divergent gene expression levels between diploid and autotetraploid Tolmiea relative to the total transcriptome, the cell, and biomass

PREMISE OF THE STUDY

Studies of gene expression and polyploidy are typically restricted to characterizing differences in transcript concentration. Using diploid and autotetraploid Tolmiea, we present an integrated approach for cross-ploidy comparisons that account for differences in transcriptome size and cell density and make multiple comparisons of transcript abundance.

METHODS

We use RNA spike-in standards in concert with cell size and density to identify and correct for differences in transcriptome size and compare levels of gene expression across multiple scales: per transcriptome, per cell, and per biomass.

KEY RESULTS

In total, ~17% of all loci were identified as differentially expressed (DEGs) between the diploid and autopolyploid species. The per-transcriptome normalization, the method researchers typically use, captured the fewest DEGs (58% of total DEGs) and failed to detect any DEGs not found by the alternative normalizations. When transcript abundance was normalized per biomass and per cell, ~66% and ~82% of the total DEGs were recovered, respectively. The discrepancy between per-transcriptome and per-cell recovery of DEGs occurs because per-transcriptome normalizations are concentration-based and therefore blind to differences in transcriptome size.

CONCLUSIONS

While each normalization enables valid comparisons at biologically relevant scales, a holistic comparison of multiple normalizations provides additional explanatory power not available from any single approach. Notably, autotetraploid loci tend to conserve diploid-like transcript abundance per biomass through increased gene expression per cell, and these loci are enriched for photosynthesis-related functions.

Transcriptome Analysis of CHO Cell Size Increase During a Fed-Batch Process

In a Chinese Hamster Ovary (CHO) cell fed-batch process, arrest of cell proliferation and an almost threefold increase in cell size occurred, which is associated with an increase in cell-specific productivity. In this study, transcriptome analysis is performed to identify the molecular mechanisms associated with this. Cell cycle analysis reveals that the cells are arrested mainly in the G₀ /G₁ phase. The cell cycle arrest is associated with significant up-regulation of cyclin-dependent kinases inhibitors (CDKNs) and down-regulation of cyclin-dependent kinases (CDKs) and cyclins. During the cell size increase phase, the gene expression of the upstream pathways of mechanistic target of rapamycin (mTOR), which is related to the extracellular growth factor, cytokine, and amino acid conditions, shows a strongly synchronized pattern to promote the mTOR activity. The downstream genes of mTOR also show a synchronized pattern to stimulate protein translation and lipid synthesis. The results demonstrate that cell cycle inhibition and stimulated mTOR activity at the transcriptome level are related to CHO cell size increase. The cell size increase is related to the extracellular nutrient conditions through a number of cascade pathways, indicating that by rational design of media and feeds, CHO cell size can be manipulated during culture processes, which may further improve cell growth and specific productivity.

Electrophoretic cytometry of adherent cells

Cell-matrix and cell-cell interactions influence intracellular signalling and play an important role in physiologic and pathologic processes. Detachment of cells from the surrounding microenvironment alters intracellular signalling. Here, we demonstrate and characterise an integrated microfluidic device to culture single and clustered cells in tuneable microenvironments and then directly analyse the lysate of each cell in situ, thereby eliminating the need to detach cells prior to analysis. First, we utilise microcontact printing to pattern cells in confined geometries. We then utilise a microscale isoelectric focusing (IEF) module to separate, detect, and analyse lamin A/C from substrate-adhered cells seeded and cultured at varying (500, 2000, and 9000 cells per cm2) densities. We report separation performance (minimum resolvable pI difference of 0.11) that is on par with capillary IEF and independent of cell density. Moreover, we map lamin A/C and β-tubulin protein expression to morphometric information (cell area, circumference, eccentricity, form factor, and cell area factor) of single cells and observe poor correlation with each of these parameters. By eliminating the need for cell detachment from substrates, we enhance detection of cell receptor proteins (CD44 and β-integrin) and dynamic phosphorylation events (pMLCS19) that are rendered undetectable or disrupted by enzymatic treatments. Finally, we optimise protein solubilisation and separation performance by tuning lysis and electrofocusing (EF) durations. We observe enhanced separation performance (decreased peak width) with longer EF durations by 25.1% and improved protein solubilisation with longer lysis durations. Overall, the combination of morphometric analyses of substrate-adhered cells, with minimised handling, will yield important insights into our understanding of adhesion-mediated signalling processes.

Metabolic characterization of a CHO cell size increase phase in fed-batch cultures

Normally, the growth profile of a CHO cell fed-batch process can be divided into two main phases based on changes in cell concentration, being an exponential growth phase and a stationary (non-growth) phase. In this study, an additional phase is observed during which the cell division comes to a halt but the cell growth continues in the form of an increase in cell size. The cell size increase (SI) phase occurs between the exponential proliferation phase (also called the number increase or NI phase) and the stationary phase. During the SI phase, the average volume and dry weight per cell increase threefold linearly with time. The average mAb specific productivity per cell increases linearly with the cell volume and therefore is on average two times higher in the SI phase than in the NI phase. The specific essential amino acids consumption rates per cell remain fairly constant between the NI and the SI phase, which agrees with the similar biomass production rate per cell between these two phases. Accumulation of fatty acids and formation of lipid droplets in the cells are observed during the SI phase, indicating that the fatty acids synthesis rate exceeds the demand for the synthesis of membrane lipids. A metabolic comparison between NI and SI phase shows that the cells with a larger size produce more mAb per unit of O2 and nutrient consumed, which can be used for further process optimization.

Metabolic characterization of a CHO cell size increase phase in fed-batch cultures

Normally, the growth profile of a CHO cell fed-batch process can be divided into two main phases based on changes in cell concentration, being an exponential growth phase and a stationary (non-growth) phase. In this study, an additional phase is observed during which the cell division comes to a halt but the cell growth continues in the form of an increase in cell size. The cell size increase (SI) phase occurs between the exponential proliferation phase (also called the number increase or NI phase) and the stationary phase. During the SI phase, the average volume and dry weight per cell increase threefold linearly with time. The average mAb specific productivity per cell increases linearly with the cell volume and therefore is on average two times higher in the SI phase than in the NI phase. The specific essential amino acids consumption rates per cell remain fairly constant between the NI and the SI phase, which agrees with the similar biomass production rate per cell between these two phases. Accumulation of fatty acids and formation of lipid droplets in the cells are observed during the SI phase, indicating that the fatty acids synthesis rate exceeds the demand for the synthesis of membrane lipids. A metabolic comparison between NI and SI phase shows that the cells with a larger size produce more mAb per unit of O₂ and nutrient consumed, which can be used for further process optimization.

Evaluation of two public genome references for chinese hamster ovary cells in the context of rna-seq based gene expression analysis

RNA-Seq is a powerful transcriptomics tool for mammalian cell culture process development. Successful RNA-Seq data analysis requires a high quality reference for read mapping and gene expression quantification. Currently, there are two public genome references for Chinese hamster ovary (CHO) cells, the predominant mammalian cell line in the biopharmaceutical industry. In this study, we compared these two references by analyzing 60 RNA-Seq samples from a variety of CHO cell culture conditions. Among the 20,891 common genes in both references, we observed that 31.5% have more than 7.1% quantification differences, implying gene definition differences in the two references. We propose a framework to quantify this difference using two metrics, Consistency and Stringency, which account for the average quantification difference between the two references over all samples, and the sample-specific effect on the quantification result, respectively. These two metrics can be used to identify potential genes for future gene model improvement and to understand the reliability of differentially expressed genes identified by RNA-Seq data analysis. Before a more comprehensive genome reference for CHO cells emerges, the strategy proposed in this study can enable more robust transcriptome analysis from CHO cell RNA-Seq data. Biotechnol. Bioeng. 2017;114: 1603-1613. © 2017 Wiley Periodicals, Inc.

Transcriptome analysis of a CHO cell line expressing a recombinant therapeutic protein treated with inducers of protein expression

The search for specific productivity (qP) determinants in Chinese hamster ovary (CHO) cells has been the focus of the biopharmaceutical cell line engineering efforts aimed at creating "super-producer" cell lines. In this study, we evaluated the impact of small-molecule inducers and temperature shift on recombinant protein production, and used transcriptomic analysis to define gene-phenotype correlations for qP in our biological system. Next-generation RNA Sequencing (RNA-Seq) analysis revealed that each individual inducer (caffeine, hexamethylene bisacetamide (HMBA) and sodium butyrate (NaBu)) or a combination treatment had a distinct impact on the gene expression program of the RANK-Fc cell line. Temperature shift to 31 °C impacted inducer action with respect to transcriptional changes and phenotypic cell line parameters. We showed that inducer treatment was able to increase expression level of the Fc- fusion mRNA and the selectable marker mRNA from 16% up to 45% of total mRNA in the cell. We further demonstrated that qP exhibited a strong positive linear correlation to transcript levels of both the RANK-Fc fusion protein and the dihydrofolate reductase (DHFR) selectable marker. In fact, these were 2 out of 7 transcripts with significant positive correlation to qP at both temperatures. Many more transcripts were anti- correlated to qP, and gene set enrichment analysis (GSEA) revealed that those were involved in cell cycle progression, transcription, mRNA processing, translation and protein folding. Therefore, we postulate that the transcript level of the recombinant protein is a major qP determinant in our biological system, while downregulation of routine activity within the cell is necessary to divert cellular resources towards recombinant protein production.

An evaluation of public genomic references for mapping RNA-Seq data from Chinese hamster ovary cells

While RNA-Seq is increasingly used as the method of choice for transcriptome analysis of mammalian cell culture processes, no universal genomic reference for mapping RNA-Seq reads from CHO cells has been reported. In previous publications, de novo transcriptomes assembled using these RNA-Seq reads were subsequently used for mapping. Potential caveats with this approach include the incomplete coverage and the non-universal nature of the de novo assemblies, leading to challenges in comparing results across studies. In order to facilitate future RNA-Seq studies in CHO cells, we performed a comprehensive evaluation of four public genomic references for CHO cells hosted by the NCBI Reference Sequence Database (RefSeq), including two annotated genomes released in 2012 and 2014 and their accompanying transcriptomes. Each genome showed significantly higher mapped rates compared to its accompanying transcriptome. Furthermore, higher mapped rates in deep intra-genic regions, especially within exons, were observed for the more recent genome release (2014) compared to the older one (2012), indicating that the 2014 genome was the preeminent reference among the four. Sequential addition of human and mouse genomes increased the total mapped rate to 87.3 and 89.7%, respectively, from 73.5% using the 2014 Chinese hamster genome alone. Thus, the sequential combination of the 2014 RefSeq Chinese hamster genome, the Ensembl human genome (h38), and the Ensembl mouse genome (m38) was suggested as the most effective strategy for mapping RNA-Seq data from CHO cells.