Graphical abstract By repeatedly selecting and pooling the most rapidly outgrowing clones during multiple single cell subcloning rounds, the time required for subcloning was reduced from 3 to 2 weeks and the cloning efficiency significantly increased. Transcriptome analyses of the resulting host cell lines revealed a diverse set of pathways differentially enriched in each host cell line treated, with the only shared DE pathway related to changes in extracellular matrix. This indicates that cells struggle with the lack of cell-to-cell communication in isolation during subcloning. graphic file with name ga1.jpg [35]Open in a new tab Abbreviations: CHO, Chinese hamster ovary; CoI, clusters of interest; DE, directed evolved; ECM, extracellular matrix; ES, enrichment score; FACS, fluorescent-activated cell sorting; GSEA, gene set analysis; LDC, limiting dilution cloning; POI, product of interest; NES, negative enrichment score; PCA, principal component analysis; PC, principal component; RNA-Seq, RNA sequencing; SCC, single cell cloning; lfcSE, logfoldstandard error Keywords: Chinese Hamster Ovary Cells, CHO cells, CHO, Cell line development, Single Cell Cloning, Subcloning, Single Cell Subcloning, Directed Evolution, RNA Sequencing, RNA-Seq, Growth enhancement, Growth improvement, FACS, Fluorescent-activated cell sorting, Cell sorting Abstract Chinese Hamster Ovary (CHO) cells are the working horse of the pharmaceutical industry. To obtain high producing cell clones and to satisfy regulatory requirements single cell cloning is a necessary step in cell line development. However, it is also a tedious, labor intensive and expensive process. Here we show an easy way to enhance subclonability using subcloning by single cell sorting itself as the selection pressure, resulting in improved subcloning performance of three different host cell lines. These improvements in subclonability also lead to an enhanced cellular growth behavior during standard batch culture. RNA-seq was performed to shed light on the underlying mechanisms, showing that there is little overlap in differentially expressed genes or associated pathways between the cell lines, each finding their individual strategy for optimization. However, in all three cell lines pathways associated with the extracellular matrix were found to be enriched, indicating that cells struggle predominantly with their microenvironment and possibly lack of cell-to-cell contact. The observed small overlap may hint that there are multiple ways for a cell line to achieve a certain phenotype due to numerous genetic and subsequently metabolic redundancies. 1. Introduction Chinese Hamster Ovary (CHO) cells are one of the most important and commonly used industrial production organisms since the late 1980ies [36][1]. Advantages of using CHO as a production system are that cells can be grown in chemically defined media and in suspension, that few human viruses propagate in CHO, that several non-antibiotic selection and amplification systems are available and that proteins produced in CHO possess human-like glycosylation [37][1] Due to these advantages CHO established itself as the predominant production system for biological therapeutics, mainly for monoclonal antibodies (mAb), where 84% of currently approved mAbs are produced in CHO [38][2]. An important step to achieve commercial production of therapeutic proteins is the generation of a production cell line able to produce the product at high yields and of satisfactory quality. While it is in the interest of the industry and the patient to speed this process up as far as possible, there are also safety and regulatory considerations and rules that require confirmation of monoclonality. Furthermore, subcloning inherently is a limiting factor in promoting rapid cell line development, as both the cloning efficiency, that is the percentage of surviving clones per plate, and the speed of their outgrowth are low [39][3]. Single Cell Cloning (SCC) is performed during cell line development to ensure that selected cells are producing the POI in a reproducible manner and with the required quality attributes [40][4]. A typical process of cell line establishment involves random integration of the gene of interest and amplification by a selection system, both steps which cause clonal heterogeneity. In addition, the genome of CHO, as a rapidly growing cell line, exhibits rapid genetic changes, due to random mutations, genetic drifts and chromosomal rearrangements [41][5], [42][6], [43][7], [44][8], [45][9]. As high-producing clones occur rarely in heterogenous cell populations, and as they often have a growth disadvantage due to the large amount of cellular resources that is used for recombinant protein production [46][10], SCC is an essential step to prevent overgrowth of low- or non-producers and to enable stable survival of the high producers. For these reasons, a cell line development campaign can involve the screening of thousands of clones, a process that is labor, time and cost intensive, and may take up to 6 months even in streamlined industrial pipelines that use robotics [47][1], [48][3]. These are all strong motivations to improve SCC, where one aim is to increase the percentage of outgrowing clones and the speed at which outgrowth is achieved and in turn to streamline the cell line development. The simplest way to perform SCC for cell culture is limiting dilution cloning (LDC) where cells are diluted so that statistically less than one cell is seeded per well. Although easy to perform without any dedicated equipment, LDC is a random approach to obtain high producers and thus requires a large number of cells to be screened, to ensure the presence of a sufficient number of high producing clones that can be further screened for product quality and stability. Moreover, using LDC the risk that the obtained cell line originated from more than one single cell is higher compared to the alternative methods. Using fluorescent-activated cell sorting (FACS) in combination with a suitable staining protocol [49][11] it is possible to focus specifically on the producers, by removing low or non-producers, thus reducing the total number of cells that need to be screened [50][12]. Deposition of single cells into microtiter plates by FACS has also been used simply for proof of monoclonality [51][3]. The main disadvantage of cell sorting is that the procedure may further reduce the outgrowth of subclones, due to the shear stress cells are exposed to during sorting and deposition into wells. To improve clone outgrowth, multiple strategies can be used. One is the use of conditioned media or media additives that promote cell growth [52][13], [53][14]. Such additives are commercially available, but may be expensive and not chemically defined. Other options are the use of equipment such as cell printers or microfluidic systems. In addition, robotics and automation may enable the rapid handling of large numbers of clones and plates to help reliable identification of stable high producers [54][15], [55][16], [56][17], [57][18], [58][19], [59][20]. Recent research is focusing on improving the methods for SCC [60][21], [61][22] or ensuring the statistical evaluation of clonality [62][23], [63][24], [64][25] to meet regulatory requirements. However, very few if any published studies have systematically addressed the generation of a better host cell line with improved SCC properties. Here we show a simple and straightforward way to improve the ability of CHO to rapidly and efficiently grow from the single cell stage using a directed evolution approach using rapid outgrowth as the evolutionary pressure ([65]Fig. 1). Three different host cell lines were exposed to this treatment, with all three cell lines showing improvement in outgrowth of visible colonies within a 2-week period. Further experiments showed that improvements in growth behavior were also obtained in standard batch cultures. RNA Sequencing (RNA-Seq) demonstrated that each cell line achieved its observed phenotype by regulating individual genes, highlighting the ability of mammalian cells to achieve the same end via multiple routes. Fig. 1. [66]Fig. 1 [67]Open in a new tab Process of directed evolution for SCC enhancement. Cells are single cell sorted by FACS and after outgrowth, the 10 fastest growing clones are pooled to minimize possible clonal effects. The whole process was performed twice to accumulate positive cells. Afterwards the evolved cells were compared to the starting cell line with (1) another round of single cell sorting to compare colony outgrowth, (2) growth characterization in batch cultures and (3) RNA sequencing to determine possible genetic mechanism for future rational cell line design. 2. Material & methods 2.1. Cell culture All cell lines were routinely cultivated as suspension cultures in TPP TubeSpin® bioreactors (TPP Techno Plastic Products, Switzerland) in CD CHO media (Thermo Fisher Scientific, USA) with supplements as described below at 37 °C, 7% CO[2], 85% humidity and 220 rpm shaking with a shaking diameter of 25 mm. Cells were passaged every 3–4 days. CHO-K1 cells (ECACC-CCL61) (“K1 8 mM”) were adapted to serum-free and suspension growth in house [68][26]. Cultures of these cells were supplemented with 8 mM GlutaMAX (Thermo Fisher Scientific) and 1:500 Anti-clumping agent (Thermo Fisher Scientific). Media for CHO-K1 cells adapted to 0 mM l-Glutamine, isolated from afore-mentioned CHO K1 [69][27], (“K1 0 mM”) were supplemented only with 1:500 Anti-clumping agent. CHO-K1 Hy (“K1 Hy”) cells were received from Cytiva and grown in medium supplemented with 6 mM GlutaMAX. 2.2. Single cell sorting enhancement For each cell line, fifteen 96-well-plates (Greiner Bio-One, Austria) were prepared with 200 µL per well of the appropriate medium supplemented with Penicillin-Streptomycin (10.000 U/mL Penicillin, 10 mg/mL Streptomycin, VWR Chemicals, USA). Cell sorting was performed on a MoFlo® Astrios™ (Beckmann Coulter, USA), using a 488 nm laser to determine forward (FSC) and side scatter (SSC). Cells were live gated based on a FSC height to 488 SSC area gate to identify single cells and exclude doublets. The sorted single cells were incubated static at 37 °C, 7% CO[2] and 85% humidity for 3 weeks. After three weeks the 10 colonies that had shown fastest outgrowth were pooled, centrifuged at 180×g for 8 min and the supernatant removed. Cells were resuspended in 1 mL of fresh medium and the suspension was transferred to 9 mL TPP TubeSpin® bioreactors and then incubated as described above. After 2 passages, the entire process was repeated. 2.3. Colony counting Colonies were counted between 13 and 21 days after SCC. Colonies visible by eye were denoted further as “Big” and all colonies visible by microscope were denoted as “All”, where a colony had to consist of at least 5 cells. Moreover, the microscopy data was used to confirm data acquired by visual inspection. Statistical Analysis was performed in the statistical software R version 3.6.2 [70][28] using a student’s t-test. “*” indicates a p-value <0.05, “**” a p-value <0.01 and “***” a p-value <0.001. 2.4. Batch experiments Cell lines were seeded at 0.2·10^6 cells/mL in 20 mL media in TPP TubeSpin® bioreactors and incubated as described above. Viable cell concentration and viability was measured each day by ViCell XR 2.04 (Beckman Coulter) based on the Trypan blue exclusion method. RNA for sequencing was taken on day 2, around 45 h after the start of the batch experiment. 5·10^6 cells were spun down at 200×g for 8 min, supernatant removed and the cell pellet dissolved in 600 µL TRI Reagent® (Merck KGaA, Germany) and stored at −80 °C for later isolation. To investigate transient productivity, cells (passage 6) were transfected to express EPO-Fc using the Neon® transfection system with the Neon® transfection system 100 µL kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. In short, 5.5·10^6 cells were spun down (200×g for 8 min) and resuspended in 100 µL buffer R. After the addition of 15 µg of plasmid ([71]Supplementary Fig. 1), cells were transfected by applying one pulse with 1700 V and 20 ms. A mock transfection was used as control. Cells were allowed to recover for 1.5 h at static 37 °C, 85% humidified air and 7% CO[2]. Afterwards cells were incubated as described above. Viability and product titer were quantified each day. Cells were spun down at 180×g for 8 min and the supernatant frozen at −20 °C for later quantification. Batches were characterized using an in-house R package vicellR version 0.1.9 [72][29]. 2.5. EPO-Fc quantification EPO-Fc concentration was quantified using the Octet® RED96e (FORTÉBIO, USA), equipped with Dip and Read™ Protein A Biosensors (Pall Corp, USA) according to the manufacturer’s recommendations. Samples were diluted 1:2 in non-supplemented CD-CHO media before measurement. Quantification was performed relatively to Trastuzumab (BioVison, USA), as no EPO-Fc standard was commercially available. 2.6. RNA sequencing Total RNA was isolated using a Direct-zol™ RNA mini prep kit (Zymo Research, USA) according to the manufacturer’s instruction. rRNA depletion and library preparation of 2 replicates per sample was done with the in-house protocol established by the Vienna Biocenter Core Facility NGS Unit. Samples were sequenced as single end 100 bp reads on the HiSeq 2500 system (Ilumina, USA). Data is available under PRJEB37009. 2.7. Analysis of RNA sequencing data and differential gene expression Raw sequences were trimmed of low quality reads and adapters using Trimmomatic 0.36 [73][30]. HiSat2, version 2.1.0 [74][31], was used to map processed reads to the Chinese hamster genome [75][32]. Reads mapped to coding genes were counted using the HTSeq python package [76][33]. Read counts were analyzed using the DESeq2 R package, version 1.24.0 [77][34]. Differential expression analysis was performed using the DESeq function of the package. Differentially expressed genes between samples were analyzed using the log2 fold change threshold 0 and BH adjusted p‐value 0.05 Genes with a foldchange difference of ≥1.5 and BH < 0.05 were deemed significantly differentially expressed. For further analysis, counts were normalized using the DESeq2′s variance stabilizing transformation (vst-normalisation). Gene Set analysis (GSEA) was performed using GSEA 4.0.3 [78][35], [79][36]. GSEAPreranked settings were 1000 permutations, use of c2.cp.v7.0.symbols.gmt as geneset and no collapse. For visualization, Cytoscape 3.7.2 was used [80][37]. EnrichmentMap v3.2.1 [81][38] was used to generate the network and AutoAnnotate v1.3.2 [82][39] was used for grouping of pathways. 2.8. KEGG profiling of gene clusters Hardclustering of genes according to their z-scores was done using the command heatmap of the R package ComplexHeatmap v2.0.0 [83][40] via the row_split option. Z-scores were calculated according to: [MATH: Z-scor e=x-μ σ :MATH] where x is the count value of a gene of a cell line replicate, µ the mean of all count values of this gene in all cell line replicates of interest and σ the corresponding standard deviation. Identified genes were mapped to the KEGG pathways [84][41], [85][42], using R package KEGGprofile [86][43], as annotated to identify significantly enriched pathways. Significance was tested using a hypergeometric test with q < 0.05 using a conservative Benjamini-Yekutieli correction. Differentially expressed genes were indicated on the provided pathway maps according to their log[2] fold change. 2.9. Metastudy analysis Genes with a reported correlation to growth were obtained from [87][44]. Genes with a reported frequency of ≥2 were considered for analysis. The Mouse Genome Information database batch gene lookup tool ([88]http://www.informatics.jax.org/batch) was used to obtain gene names. Upset plot was generated with the R package UpsetR v1.4.0 [89][45]. 3. Results 3.1. Directed evolution improves clone outgrowth during single cell cloning To improve the SCC ability of the three cell lines, single cells were deposited into microtiter wells by FACS and subsequently allowed to develop into colonies ([90]Fig. 1). To avoid biased clonal effects of the resulting evolved cell line, the 10 biggest colonies of fifteen 96-well plates by visual inspection were pooled roughly 3 weeks after sorting, and again subcloned. After two rounds of this selection for rapid outgrowth, the effect of directed evolution on SCC performance was determined. To monitor SCC improvement, parental cell lines and pools generated by directed evolution (DE) were evaluated by again seeding fifteen 96-well plates per cell line. This approach led to a significant increase in the number of directly visible colonies per plate approximately 2 weeks after sorting in all 3 cell lines ([91]Fig. 2a – “Big”). K1 8 mM showed the best starting SCC, followed by K1 0 mM and K1 Hy. This is inversely represented in the number of colonies, which improved 1.3-fold for K1 8 mM, 2.1-fold for K1 0 mM and 4.8-fold for K1 Hy, respectively. Due to further outgrowth of microscopic colonies, the differences in directly visible colonies are less a week later, however still significant with 1.1-fold more counted colonies for K1 8 mM, 1.4-fold for K1 0 mM and 2.7-fold increase for K1 Hy. Apart from the faster outgrowth of colonies to visible size, the total number of surviving colonies is also significantly improved in K1 8 mM and K1 Hy ([92]Fig. 2a – “All”). For K1 0 mM the difference is not significant due to 4 outlier plates with lower numbers of colonies. Thus, for each DE cell line, a comparable or higher number of directly visible colonies is found 2 weeks after sorting as for the parental cell line after 3 weeks. This corresponds to a significant reduction in the timelines required for the different subcloning steps during a cell line development campaign. Fig. 2. [93]Fig. 2 [94]Open in a new tab Results of SCC improvement (a) Boxplots of colony outgrowth of parental cell lines and DE cell lines. X-axis displays day of analysis after single cell sorting. (b) VCD (blue) and viability (red) of the standard batch experiment with corresponding growth rates (c) and max VCD reached (d). To obtain results in (d) VCD values of K1 8 mM replicate 3 at 190 h was excluded, being an outlier in the measurement. Error bars represent 95% confidence interval. (e) Mean specific productivity (qP) against mean growth rate (µ) DE at the end of the cell line name indicate DE pools. (For interpretation of the references to colour in