Phenotypes and Multi-omics Reveal Changes and Molecular Mechanism of Suspension Adaptation of HEK293 Cells: Structural Remodelling, Metabolic Reconstruction and Stress Resistance

Phenotypes and Multi-omics Reveal Changes and Molecular Mechanism of Suspension Adaptation of HEK293 Cells: Structural Remodelling, Metabolic Reconstruction and Stress Resistance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Phenotypes and Multi-omics Reveal Changes and Molecular Mechanism of Suspension Adaptation of HEK293 Cells: Structural Remodelling, Metabolic Reconstruction and Stress Resistance Benyao Zhang, Shishi Li, Jingjing Liu, Wenhao Su, Xiaohuan Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8498955/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Human embryonic kidney 293 (HEK293) cells have been successfully adapted from adherent to suspension culture and have been widely applied in both scientific research and the pharmaceutical industry. However, the alterations in cells during the adaptation have not been well described, which raise some uncertainties and concerns regarding the underlying changes and cell behavior. In this work, we adapted adherent HEK293 to suspension culture with desirable cell growth and high production titers for recombinant adenoviral vectors, and cells at several stages throughout the process were characterized. First, we obtained three strains of suspension cells from adherent parental HEK293 cells by gradually phasing out fetal bovine serum in original Dulbecco’s modified essential medium with a simultaneous medium replacement with four serum-free suspension culture media, and one strain was chosen as the preferred candidate for further studies due to its satisfying cell conditions and adenoviral vector productivity. Slower cell growth rate, lower glucose uptake, increased lactate production, weaker cell-surface adhesion, and prolonged S phase in the cell cycle were observed in suspension cells compared to their adherent counterparts. We further performed transcriptomics, proteomics, and metabolomics analysis to identify key switches in cells. A total of 2476 differential genes were found, including 1218 up-regulated genes and 1258 down-regulated genes in suspension cells. A similar and correlated pattern was observed in the proteomic study: an almost balanced up-down regulation between suspension and adherent cells, and 702 differentially expressed metabolites were identified by untargeted metabolomics. By virtue of enrichment analysis on differentially expressed genes, proteins and metabolites, we summarized that HEK293 adherent cells survived and adapted to suspension culture by structural remodelling, metabolic network reconstruction and inherent stress resistance. Additionally, we identified claudin7 as a key player involved in suspension transformation in both transcriptomic and proteomic aspects. Our results provide a molecular enlightenment for the mechanism of suspension adaptation and new directions for the rational design of genetically engineered HEK293-derived cell lines for viral-vectored vaccine production. HEK293 suspension adaptation adenoviral vector transcriptomics proteomics metabolomics claudin 7 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Cell culture is a biological process in which cells are carefully cultivated in an artificial, controlled in-vitro environment favoring their proliferation and other biological activities [ 1 ]. According to the ‘location’ of cell residence, cell culture can be classified into two types: adherent and suspension cell culture. The most fundamental difference between these two types is that adherent cells require a solid surface or substrate to attach and then function, and such cell property is referred to as anchorage dependency [ 2 ]. Alternatively, suspension cells grow in a floating status without supporting material and move freely in the culture liquid. Both types of cell culture are widely used in basic research and the pharmaceutical industry, particularly in the bioproduction of proteins, viruses, and cells per se [ 3 , 4 , 5 ]. In the pharmaceutical industry, large-scale manufacture in specific facilities is always required to achieve a cost-effective yield and to meet potential clinical needs. To fulfil the productivity, multi-layered cell factory, micro-carrier, and carrier disk are popular choices for large-scale adherent cell culture [ 6 , 7 , 8 ]. Repeated rounds of an ‘attach-detach’ process by detaching agents, e.g., trypsin, are usually necessary to handle and expand adherent cells, and extra animal-derived supplements, such as fetal bovine serum (FBS), are also indispensable in the traditional adherent culture mode. In addition to the extra expense and workload, the use of trypsin and FBS also introduces difficulties in downstream purification and poses a threat to product safety and batch-to-batch stability [ 9 ]. Comparatively, suspension cell culture offers scalability, operational simplicity, and the avoidance of FBS and trypsin [ 10 ]. Some popular production cells are inherently in suspension status, such as Spodoptera frugiperda clone 9 cells from insects, while others are naturally adherent, including Chinese hamster ovary (CHO), Madin Darby canine kidney (MDCK), Vero cells, and human embryonic kidney 293 (HEK293). Numerous attempts have been made to transform these adherent cells into suspension to take advantage of the suspension culture, but not all adherent cells are amenable to such a change [ 11 , 12 , 13 , 14 ]. A commonly used technique for such a transformation is suspension adaptation, and suspension adaptation of HEK293 is a success [ 15 , 16 ]. The HEK293 cell line was first established in 1973, and several derivatives have been developed from this parental lineage for various purposes. Initially transfected with E1 gene from type 5 adenovirus, all HEK293 cell lines were endowed with an immortalization feature and a capacity to provide E1 gene in trans [ 17 ], which makes them E1-complementary cells and desirable cell factories for the packaging of adenoviral vectors (AdV) and adenovirus-associated viral vectors [ 18 ]. Adenovirus (Ad), a pathogen that commonly affects human respiratory system and causes flu-like symptoms, has been rendered replication-defective by deletion of its replication-related genes and a safe delivery vehicle for exogenous genes, usually referred to as recombinant adenoviral vector (rAdV) [ 19 ]. AdV has proven its competence as a gene vector for the development of vaccines and gene therapies, thanks to its excellence in safety, wide host range spectrum, minimum risk of gene integration into host genome, suitability for large-scale production, and an instinctive nature to trigger host immune response alike to a vaccine adjuvant [ 20 ]. AdV plays an important role in the field of virus-vectored vaccines with many preclinical and clinical applications aiming at several pathogens, including influenza virus [ 21 ], Ebola [ 22 ], Zika [ 23 ], human immunodeficiency virus [ 24 ], tuberculosis [ 25 ], and the most widely distributed adenoviral vaccine hitherto -ChAdOx1-nCoV-19 against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [ 26 , 27 , 28 ]. Suspension HEK293 has long been a favourable production cell line for AdV. Many of the suspension HEK293 cells originate from adherent parental cells by suspension adaptation. Interestingly, although most research and applications employed a universal strategy by gradually weaning off the serum in the medium, they varied widely in the gradient of serum reduction and selection of the replacement suspension culture medium (SCM), and these differences resulted in variance in cell performance and the length of the adaptation process. Furthermore, the majority of these works focused more on the cell growth and the yield of their target product, whilst only a few sought a deeper insight into the underlying alterations occurring in the suspension-adapted cells. Here, we followed a similar approach to adapt the HEK293 adherent cells to suspension cells by gradual removal of the serum from the culture medium, with a simultaneous medium substitution with four different SCMs. We compared the cell growth and the titer of rAdV produced by these cells. More importantly, we performed transcriptomic, proteomic and metabolomic analysis to depict a more comprehensive picture of the changes in the adapted cells versus their original adherent counterpart, and tried to find a hint to elucidate the mechanism of the suspension adaptation. Materials and Methods Adherent HEK293 cell culture HEK293 cells were purchased from the American Type Culture Collection (CRL-1573, ATCC). Adherent cultures were maintained in tissue culture–treated T75 flasks (430641, Corning) in a humidified incubator (BPN-240RHP, Yiheng) at 37°C in a 5% CO₂, 80% relative humidity (RH) atmosphere. Cells were fed in Dulbecco’s Modified Eagle Medium (DMEM, 10566-016, Gibco) supplemented with 10% (v.\v.) FBS (13011 − 8611, Tianhang) and 1% (v.\v.) penicillin–streptomycin (PS, GNM15140-1, Genom). When cultures reached 80%–90% confluence, cells were transferred into new flasks and seeded at (4 ± 0.5) ×10 4 cells/cm² for subculture. An optical microscope (IX51, Olympus) was used to view and capture the cells with an accessory software (cellSens Standard, Olympus) from its manufacturer. Sequential adaptation of adherent cells Adherent cells were adapted to serum-free suspension culture by gradually reducing the serum in a 10%, 7%, 3%, 1%, 0% stepwise gradient. Culture media with different serum concentrations were prepared by mixing the complete DMEM with the SCMs of our selection at defined ratios (v/v): 100%DMEM, 70%DMEM/30%SCM, 30%DMEM/70%SCM, 10%DMEM/90%SCM, 100%SCM, accordingly. In this way, serum elimination and medium substitution were implemented at a synchronized pace. The ratio was determined based on our in-house unpublished results, and the SCMs to be screened were FreeStyle™293 (12338-018, Gibco), BalanCD HEK293 (91165, FUJIFILM), CD293 (11913-019, Gibco) and Celer-S001 (FG0104003, Bioenine). At each serum concentration, cells were maintained for at least three consecutive passages to fully accommodate the current condition. Cells were advanced to the next lower serum concentration when they reached approximately 80% confluence within 72h as an indicator that cells had likely recovered their growth capacity; otherwise, they were maintained at the current serum level until this criterion was satisfied. Suspension adaptation and suspension cell culture When HEK293 cells performed stably in the serum-free adherent culture, they were transferred into 125 mL Erlenmeyer flasks (781011, Nest) at a seeding density of (0.8 ± 0.2) ×10⁶ cells/mL in 30mL working volume. Suspension cultures were incubated in an orbital shaker (ZWYC-290A, Zhicheng) at 130rpm (25mm shaking diameter) in a 37°C, 8%CO₂, 80%RH environment. Cells were passaged every three to four days at the preseted seeding density. The population doubling time (PDT) was calculated as follows for each passage: $$\:PDT=\frac{t\times\:ln\left(2\right)}{ln\left(\frac{{N}_{t}}{{N}_{0}}\right)}$$ Where PDT is the population doubling time; t is the culture duration; N t is the cell density at time point t; N 0 is the initial cell density. When the PDT became settled for three consecutive passages, the cells were considered successfully adapted to suspension culture. In some studies, a HEK293-SUS-CD374M (CD374M) cell strain was introduced as a reference. It is a well-adapted suspension cell from HEK293 in our lab by a direct adaptation approach, which will be explained later in the discussion section. Cell counting, viability, clumping and diameter Cell density and viability for both adherent and suspension cultures were determined using an automated cell counter (IC1000, CountStar). Adherent cells were counted after digestion and resuspension, while suspension cells were sampled and counted directly. 10µL cell suspension was mixed evenly with 10µL trypan blue stain solution (T10282, Invitrogen) and loaded onto a counting chamber (M17, CountStar). Cell number, viability, clumping and diameter were analyzed automatically using the instrument’s proprietary software. Extracellular metabolite analysis The culture supernatants, which were collected from cell cultures after centrifugation at 2,000g, 10min, were examined using a biochemical analyzer (M900, Sieman) to determine the concentrations of glucose and lactate by glucose oxidase enzyme electrode-membrane method and lactate oxidase enzyme electrode membrane method, respectively. The glucose consumption rate and lactate accumulation rate were calculated using following equations: $$\:qGlc=\frac{1}{VCD}\times\:\frac{dGlc}{dt}$$ $$\:qLac=\frac{1}{VCD}\times\:\frac{dLac}{dt}$$ where gGlc is the glucose consumption rate; qLac is the lactate accumulation rate; VCD is the viable cell density; dGlc and dLac are the concentration changes of glucose and lactate in a given time period; and t is the time period. Cell cycle distribution by flow cytometry Cell cycle distribution was sorted using the Cell Cycle and Apoptosis Analysis Kit (C1052, Beyotime). Briefly, 1×10⁶ cells collected in their logarithmic growth phase were centrifuged at 1,000g for 5 min and washed three times with Dulbecco’s phosphate buffered saline (DPBS, 14190144, Gibco). Cells were then fixed in 1 mL of 70% prechilled ethanol at 4°C for 2h. After fixation, cells were pelleted again at 1,000g for 5 min, washed once with DPBS to remove residual ethanol, and resuspended in the staining buffer containing propidium iodide (PI). Samples were analyzed using a flow cytometer (CytoFLEX LX, Beckman Coulter), and 10,000 events were collected per sample. The DNA content histogram was analyzed using FlowJo software to discriminate the percentage of cells in Gap 0 phase (G0), Gap 1 phase (G1), Synthesis phase (S), Gap 2 phase (G2) and Mitosis phase (M). Cell adhesion assay To quantify the adhesion capacity of cells, adherent cells in 10% serum concentration cultured for 72h were used to establish a standard curve. Cells were seeded into 96-well plates at an initial density of 4×10⁴ cells/well, followed by 1:2 serial dilution to generate five concentrations, each with two replicates. After incubation at 37°C for 4h, the cells were considered tightly attached to the surfaces and ready to construct a standard curve. For adhesion analysis, both adherent and suspension HEK293 cells were seeded at 1×10⁴ cells/well in DMEM containing their respective serum concentrations or suspension culture medium with another two parallel wells per group. After incubation at 37°C for 1h, the supernatant was carefully removed, and non-adherent cells were therewith cleared away after two repeats of DPBS wash. Each well was then supplemented with 90µL fresh DMEM and 10µL WST-8 from a cell count kit-8 (40203ES60, Yeasen) and incubated for 2h at 37°C. The absorbance at 450nm (OD 450 ) was measured using a microplate reader (SpectraMax M5, Molecular Devices). The number of adhered cells was calculated based on the standard curve to quantify the adhesion capacity. Adenovirus infection and viral tittering The productivity of rAdV by suspension-adapted HEK293 cells was compared by getting infected with rAdV working stocks, which were established and preserved in in a -70°C cryogenic freezer for long-term storage in Weijiangbo Laboratory, National Vaccine and Serum Institute, at a multiplicity of infection (MOI) of 3, with a cell density of 1.5×10⁶ cells/mL in a 30 mL culture volume. The MOI is the quantitative ratio of infectious viruses against cells to be infected, which can be calculated by: $$\:MOI=\frac{virus\:titer\times\:Vv}{VCD\times\:V}$$ where MOI is the abbreviation of multiplicity of infection; V v is the volume of viruses; VCD is the viable cell density; and V represents the volume of cell suspension. Cultures were maintained on an orbital shaker at 37°C, 8%CO₂, 80%RH, and 110 rpm with two replicates. The parameters for MOI, seeding density, harvest time, and cultivation conditions were based on design of experiment (DOE)-powered, unpublished data in our laboratory. Two rAdVs were tested here, specifically, a rAdV encoding herpes simplex virus type 2 (HSV) glycoprotein D (rAdV-gD2) as an HSV-2 vaccine and another encoding rabies virus glycoprotein (rAdV-G) as a rabies vaccine. They were constructed on the basis of an Adeno-X™ Adenoviral System 3 (632269, Takara) and antigenic inserts, whose sequence information were gathered from the Uniprot database ( https://www.uniprot.org/uniprotkb/Q69467/entry for HSV-gD2 sequence and https://www.uniprot.org/uniprotkb/P03524/entry for rabies-G sequence). The detailed construction process is not discussed here. After 48h post-infection, the cultures were transferred into 50mL centrifuge tubes and then subjected to three repeated cycles of ‘freeze-thaw’ in liquid nitrogen and 37°C water bath to release viral particles. The crude rAdVs were harvested by collecting the supernatant after a 2,000g, 10min centrifugation of cell lysates. Recombinant viruses were tittered using Adeno-X™ Rapid Titer Kit (632250, TaKaRa). Briefly, HEK293 cells in the logarithmic growth phase were digested, centrifuged, resuspended then seeded into 24-well plates at 2.5×10⁵ cells/mL in 1mL DMEM per well. While cells were maintained in the 37°C incubator for 1 h, the virus samples were prepared by ten-fold serial dilution in DPBS, and 50µL of each dilution was added to per well with two replicates in the 24-well plates. The supernatant was discarded after 48h incubation and cells were fixed with 0.5 mL methanol. The fixed cells were sequentially incubated with Mouse Anti-Hexon Antibody and Rat Anti-Mouse Antibody one and another for 1h each, with repeated washes by 100µL PBS + 1% bovine serum albumin (BSA) between the two steps. An aliquot of 250µL 3,3' diaminobenzidine (DAB) working solution, formulated by 90% stable peroxidase buffer and 10% DAB substrate (10×), was added into each well for 10min at room temperature (RT) for coloration. Following color development, positive cells were observed and counted under a light microscope, and the titer was calculated. Transcriptomics: RNA extraction, library preparation, sequencing and data analysis Samples containing 6×10⁶ cells were collected in the logarithmic growth phase from serum-free suspension-adapted cells, 0% serum adherent cells, 1% serum adherent cells, and 10% serum adherent cells. Independent biological triplicates were prepared for each condition. Total RNA was extracted using Trizol reagent (R0016, Beyotime) according to the manufacturer’s instructions. RNA integrity was assessed using an Agilent 5400 Bioanalyzer (Agilent Technologies) and agarose gel electrophoresis. RNA-seq libraries were prepared using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (E7770, New England Biolabs). Library construction comprised mRNA enrichment with oligo(dT) magnetic beads, random fragmentation, first-strand and second-strand cDNA synthesis, end repair and A-tailing, adapter ligation, and PCR amplification. Libraries were quantified using a Qubit 2.0 Fluorometer and diluted to 1.5ng/µL. Insert size distributions were assessed on an Agilent 2100 Bioanalyzer. Libraries with expected insert sizes were further quantified by RT-qPCR to determine the effective library concentration (≥ 1.5nM). Qualified libraries were sequenced using an Illumina NovaSeq 6000 platform using paired-end 150 bp reads, yielding at least 6 G clean data per sample. Raw reads were quality-controlled with fastp (v0.19.7) using the parameters (-g-q5-u50-n15-l150) to obtain Clean Reads, which were then aligned to the human reference genome GRCh38/hg38 using HISAT2 (v2.2.1). Gene counts were summarized with featureCounts and normalized to FPKM for expression estimation. Differential expression analysis was performed using DESeq2 (R package); Differentially expressed genes (DEG) were defined as |log₂FoldChange|≥1 and padj < 0.05. Identified DEGs were subjected to functional enrichment and pathway analysis using clusterProfiler (v4.8.1) with a significance threshold of p < 0.05. Gene set enrichment analysis (GSEA) was conducted using GSEA software (v4.3.2) with parameters (-permute phenotype -metric Signal2Noise set_min 15-set_max 5000-plot_top_x50). Proteomics: Protein extraction, proteomics analysis and data processing 2×10⁶ cell samples were collected in their logarithmic growth phase from serum-free suspension-adapted cells, 0% serum adherent cells, 1% serum adherent cells, and 10% serum adherent cells, respectively, with independent biological triplicates per condition. Cells were lysed by adding an appropriate volume of protein lysis buffer (555899, DB biosciences), vortexed gently, and sonicated in an ice–water bath for 5min to ensure complete disruption. Lysates were centrifuged at 12,000g for 15min at 4°C, and the supernatants were collected. An appropriate amount of 1M dithiothreitol (DTT) was added to the supernatant, and samples were incubated at 56°C for 1h for reduction, followed by a 2-minute ice bath. Iodoacetamide (IAM) was then added, and samples were kept at RT in the dark for 1h for alkylation. Protein concentrations were determined using a Bradford protein assay kit (P0006C, Beyotime) according to the manufacturer’s instructions. For SDS–PAGE, 20µg of each sample was loaded per lane on a 12% gel. Electrophoresis conditions were 120V for 20 min for the stacking gel and 150V for 50 min for the resolving gel. Gels were stained with Coomassie Brilliant Blue R250 and destained until bands were clear. For digestion, protein samples were adjusted to a final volume of 100µL with DB protein lysis buffer. Trypsin and 100 mM tetraethylammonium bromide (TEAB) buffer were added, mixed, and incubated at 37°C for 4h for proteolytic digestion. The digestion was quenched by lowering the pH to < 3 with formic acid, mixed, and centrifuged at 12,000g for 5min at RT. The supernatant was slowly passed through a C18 desalting column. The column was washed three times with wash solution (0.1% formic acid, 3% acetonitrile), and peptides were eluted with elution solution (0.1% formic acid, 70% acetonitrile). Eluates were collected and lyophilized for later analysis. Mobile phase A (99.9% water, 0.1% formic acid) and mobile phase B (80% acetonitrile, 0.1% formic acid) were prepared before the analysis. Lyophilized powder was dissolved in 10µL of mobile phase A, centrifuged at 4°C and 14,000 g for 20 minutes. 200ng of the supernatant was taken for sample loading and further liquid chromatography-mass spectrometry (LC-MS) analysis. Vanquish Neo UHPLC system with a C18 precolumn (174500, 5 mm×300 µm, 5µm, Thermo Fisher Scientific) was heated at 50°C in the column oven. The C18 analytical column was ES906 (PepMap™ Neo UHPLC, 150µm×15cm, 2µm, Thermo Fisher Scientific). LC elution conditions were not described here. An Orbitrap astral mass spectrometer with an easy-spray (ESI) ion source was employed, set at 2.0 kV ion spray voltage and 290°C ion transfer tube temperature. Data-independent acquisition (DIA) mode was chosen with primary mass spectrometry full-scan m/z acquisition range set to 380–980 and resolution of 240,000 (at 200 m/z). Automatic generation control (AGC), parent ion window size, DIA window count, and normalized collision energy (NCE) were set to 500%, 2 Th, 300, and 25% respectively. The secondary mass spectrometer m/z range was 150–2000, with a secondary mass spectrometer resolution of 80,000 and a maximum injection time of 3ms. Raw mass spectrometry detection data were produced as (.raw). Raw data file analysis was executed using the DIA-NN database search software. Database search parameters were set as follows: automatic determination and correction of mass deviations for precursor and fragment ions; fixed modifications set to alkylation at cysteine residues; N-terminal methionine loss as a variable modification; and a maximum of two missing sites allowed. To enhance analytical quality, DIA-NN further filtered results: retaining only peptides with Global.Q.Value < 0.01 and proteins with PG.Q.Value 1.5 and P-value < 0.05, and down-regulated proteins were identified when FC < 0.67 and P-value < 0.05. InterProScan software performs gene ontology (GO) and InterPro functional annotation (including Pfam, PRINTS, ProDom, SMART, ProSite, and PANTHER databases), while clusters of orthologous groups (COG) and Kyoto Encyclopedia of genes and genomes (KEGG) analyzed identified proteins for functional protein families and pathways. Volcano plot analysis, clustering heatmap analysis, and GO, InterPro, and KEGG enrichment analysis are conducted for differential enrichment analysis (DEA). Metabolomics: Metabolites extraction, data processing, identification, and analysis Cell samples from serum-free suspension-adapted cells, 0% serum adherent cells, 1% serum adherent cells, and 10% serum adherent cells were collected in their logarithmic growth phase and frozen in liquid nitrogen, with another two independent replicates per condition. The samples (1.5×10⁶ cells) were resuspended with pre-cooled 80% methanol by vortex, and then melted on ice and vortexed again for 30s. After a 6-min sonification and centrifugation at 5,000 rpm, 4°C for 1 min, the supernatant was freeze-dried and redissolved with 10% methanol for test. The sample preparation was finished, and the solution was transferred into the LC-MS/MS system for following analysis. A Vanquish UHPLC system (ThermoFisher, Germany), in conjunction with an Orbitrap Q ExactiveTM HF mass spectrometer or Orbitrap Q ExactiveTMHF-X mass spectrometer (Thermo Fisher, Germany) were used for UHPLC-MS/MS analysis. Samples were injected into a Hypersil Goldcolumn (100×2.1mm, 1.9µm) in a 12-min linear gradient at 0.2 mL/min. The eluents were eluent A (0.1% FA in Water) for positive polarity mode and eluent B (Methanol) for negative polarity mode. The solvent gradient was set as: 2% B, 1.5 min; 2–85% B, 3 min; 85–100% B, 10 min; 100-2% B, 10.1 min; 2% B, 12 min. Q ExactiveTM HF mass spectrometer was performed in positive/negative polarity mode with spray voltage on 3.5 kV, capillary temperature at 320°C, sheath gas flow rate at 35 psi/aux gas flow rate of 10 L/min, S-lens RF level at 60, 350°C aux gas heater temperature. The data were later processed by XCMS to perform peak alignment, picking, and metabolite quantitation. Based on adduct ions and setting mass deviation to 10 ppm, high-quality secondary spectrum database was used for metabolite comparison and identification. Following background noise elimination, the preliminary quantitative results were normalized to obtain relative peak areas by: $$\:Relative\:peak\:areas=Raw\:quantitative\:value\:of\:samples\times\:\frac{The\:sum\:of\:quantitative\:value\:of\:QC1}{The\:sum\:of\:quantitative\:value\:of\:samples}$$ Compounds whose coefficient of variation (CV) of relative peak areas in QC samples were larger than 30% were removed. Final data handling was performed on Linux operating system (CentOS version 6.6), using R and Python. Metabolites were annotated using the KEGG database, human metabolome database (HMDB) and LIPIDMaps database. Principal components analysis (PCA) and Partial least squares discriminant analysis (PLS-DA) were performed for intergroup comparison. The metabolites with variable importance in projection (VIP) value > 1, P-value < 0.05 (t-tests) and foldchange ≥ 2 or FC ≤ 0.5 were considered statistically differential. Volcano plots were used to filter metabolites of our interest based on log2(Fold Change) and -log10(p-value) of metabolites by ggplot2 in R language. For clustering heat maps, the data were normalized using z-scores of the intensity areas of differential metabolites and were plotted by Pheatmap package in R language. The correlation between differential metabolites were analyzed in R language. P-value < 0.05 was considered as statistically significant and correlation plots were plotted. Functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathways enrichment of differential metabolites was performed and when P-value of metabolic pathway < 0.05, metabolic pathway was considered as statistically significant enriched. Transcriptome–proteome integrated analysis Raw RNA-seq count data were processed with DESeq2 to obtain normalized expression values and gene-level log 2 fold-changes and adjusted p-values. Protein intensity matrices (DIA quantification) were log 2 -transformed and median-normalized prior to analysis. Proteins quantified in fewer than two biological replicates per group were excluded from downstream analysis. For both datasets, principal component analysis (PCA) and hierarchical clustering were used to assess sample quality and identify outliers. Transcript identifiers were mapped to gene symbols/UniProt accessions using biomaRt. Proteomics protein groups were mapped to the corresponding gene symbols when possible. The intersection set was defined and used as the basis for pairwise comparisons and joint modelling. Differential expression for transcripts was determined by DESeq2; differential proteins were identified using the statistical test described in the proteomics analysis pipeline. For consistency across omics layers, features were categorized as upregulated, downregulated or not significant using the aforementioned thresholds. For the intersection set we computed pairwise correlations between transcript and protein. Pearson correlation coefficient and associated p-value were reported to assess linear concordance; Spearman rank correlation was calculated as a nonparametric robustness check. To summarize concordance/discordance at the single-feature level, we implemented a nine-quadrant classification: transcript log 2 FC (x-axis) and protein log 2 FC (y-axis) were partitioned and generated nine sectors that captured concordant up/down, discordant, and nonresponsive categories. Counts per sector were reported and the distribution was tested where appropriate to assess enrichment of concordant vs discordant. To decompose shared and dataset-specific variation and to nominate joint molecular drivers, two-way orthogonal partial least-squares (O2PLS) modelling was applied to the intersection matrices. O2PLS modelling was performed with the OmicsPLS implementation (R) using cross-validation to select the number of joint and orthogonal components. Model performance metrics (R2X, R2Y, Q2) and permutation testing were used to evaluate model robustness and to avoid overfitting. Features were ranked by their absolute joint loadings and the top contributors were reported as candidate drivers of suspension adaptation. Gene Ontology (GO) and KEGG enrichment analyses were carried out on sets derived from (i) concordant features, (ii) discordant features, and (iii) top O2PLS drivers to probe biological processes and pathways mostly associated with suspension adaptation. Results HEK293 adherent cells were successfully adapted to suspension culture and showed different growth, glucose intake and lactate generation HEK293 cells were originally cultured in DMEM complemented with 10% FBS plus 1%PS in tissue culture-treated flat-bottom flasks. We employed the sequential adaptation strategy to adapt adherent cells to suspension cells by decreasing the serum content from 10%, 7%, 3%, 1% to 0% gradually with medium replacement at the same time and thereafter transferred the cells to shaking flasks. We screened four commercially available suspension culture media for medium replacement, and cells survived in three of them, which were FreeStyle™293, BalanCD HEK293 and CD293. Survived cells were named in correspondence to their culture medium as: HEK293-Sus-Freestyle (FS293), HEK293-Sus-BalanCD (BLCD293), and HEK293-Sus-CD293 (CD293). These cells behaved differently in growth performance, and adherent cells in different serum gradients in the adaptation process also exhibited different growth rates, as shown in Fig. 1 . All cells, either adherent or suspension, were maintained to keep the same passage number to ensure comparability. Adherent cells manifested a similar growth pattern that aligned with typical cell growth kinetics. They were initially seeded at 1×10 5 cells/well in 6-well plates and stayed in the latent phase for nearly three days. From day 3, cells entered a logarithmic growth phase until day 7, when they reached a plateau phase due to nutrient depletion or density-dependent contact inhibition, characterized by a comparatively slower, even stationary growth. The density of serum-free adherent cells declined swiftly on day 8 and day 9, indicating a programmed cell death due to nutrient deprivation. In comparison to the adherent cells, adapted suspension cells were prone to starting a logarithmic growth phase earlier, from day 1 until day 5. Results also showed that the control cells-CD374M achieved maximum cell density at approximately 7×10⁶ cells/mL, higher than the density of FS293, BLCD293 and CD293, around 4×10⁶ cells/mL. The PDTs in 72h of FS293, BalanCD293, and CD293 were close to each other, which were 38.01h, 39.14h and 39.22h, respectively. Albeit lower than our previously established CD374M cell strain, their PDT and their maximum density satisfied our production requirements. The lower density might result from cell aggregation and incomplete adaptation to shear force, which could possibly be solved in the future by the addition of anti-clumping agents, longer adaptation culture and refinement of culture parameters. Figure 1. HEK293 adherent cells were successfully adapted to suspension culture and showed different growth, glucose intake and lactate generation. Figure 1A, the cell growth curve of adherent cells in different serum concentrations and proportions of DMEM in the culture media. The upright legend of percentage referred to the serum concentration and also indicated the percentage of DMEM (multiply by ten) in the total culture medium, specifically, 10%:100%DMEM (10% serum), 70%:70%DMEM/30%SCM (7% serum); 30%: 30%DMEM/70%SCM (3% serum), 10%:10%DMEM/90%SCM (1% serum), 0%:100%SCM (0% serum). Figure 1B, the cell growth curve of suspension-adapted cells in three different suspension culture media, plus one control cell, CD374M, as a reference. Figure 1C, glucose consumption and lactate production of adherent cells in different serum concentration groups. Figure 1D, glucose consumption and lactate production of suspension-adapted FS293 cells. Figure 1E, glucose consumption rate of adherent cells in different groups after normalization to standardize the initial glucose concentration. The percentage referred to the serum concentration and also indicated the percentage of DMEM (multiply by ten) in the total culture medium, specifically, 10%:100%DMEM (10% serum), 70%:70%DMEM/30%SCM (7% serum); 30%: 30%DMEM/70%SCM (3% serum), 10%:10%DMEM/90%SCM (1% serum), 0%:100%SCM (0% serum). Glucose consumption and lactate accumulation are strictly monitored metabolites during large-scale, cell-based bioproduction because glucose is a necessary carbon source for cells to build the cytoskeleton and proliferate, while lactate is an undesirable chemical generated from the glycolysis pathway and is recognized as a hazard to cell function and yield of the target product. Therefore, we were also interested in the concentration of glucose and lactate during the suspension adaptation as well as in the adapted suspension cells. We dynamically monitored the concentrations of glucose and lactate in the culture supernatant. As the cells were adapted using a gradual serum replacement method with medium substitution, the initial glucose concentrations in the culture media differed across the experimental groups. The glucose concentration data for each group were normalized to eliminate this variability and enable an accurate comparison of glucose consumption. As illustrated in Fig. 1 , the rate of glucose consumption demonstrated a significantly positive correlation with the serum concentration in the culture medium. The qGlc values for adherent cells in 10%, 7%, 3%, 1%, 0% serum groups and suspension cells (FS293) were 0.27, 0.185, 0.134, 0.135, 0.266 and 0.15 \(\:\text{mg∕}{\text{10}}^{\text{6}}\text{cells}\text{×}{\text{day}}^{\text{-1}}\) , respectively. In other words, given the same seeding density, the cells exhibited a gradually slower glucose uptake as adaptation proceeded. Specifically, the 10% serum group (10%-GLU) exhibited the fastest glucose consumption rate, with glucose being nearly used up by day 9. As the serum concentration decreased, the rate of glucose consumption progressively slowed down, with the slowest rate observed in the serum-free group (0%-GLU). Meanwhile, the lactate accumulation rate showed a trend that was highly in a reverse manner as the glucose consumption rate. The gLac calculations were 0.256, 0.160, 0.107, 0.101, 0.198 and 0.072 \(\:\text{mg∕}{\text{10}}^{\text{6}}\text{cells}\text{×}{\text{day}}^{\text{-1}}\) , respectively. The 10% serum group had the highest rate of lactate production. As the serum concentration was reduced, the rate of lactate generation decelerated correspondingly, with the serum-free group producing the lowest amount of lactate. The qGlc and qLac data suggest that HEK293 cells cultured in 10% serum concentration were more active in metabolic activities. It was interesting to observe that suspension-adapted cells without serum nourishment had relatively high qGlc and qLac rates ( Fig. 1D) . It might be reasoned that cells without serum supplement need to promote their metabolism to accustom themselves to the serum-free environment, but it requires further investigation. Cells in each serum condition and suspension manifested differently in morphology, cell cycle distribution and adhesion ability Adapting and adapted cells underwent morphological changes during suspension adaptation. Figure 2 A demonstrated that adherent HEK293 cells in DMEM containing 10% serum maintained an epithelium-like morphology, identified as irregular polygons with distinct boundaries. As the adaptation went in progress, some cells could not tolerate the serum-reducing culture condition, and as a consequence, they detached from the surface and turned a spherical appearance floating in the medium. We tried to collect these floating cells and subculture them, but the cell viability was less than 80% at the time of collection then even became lower than 10% after passage, indicating that these floating cells could not adapt and survive. The epithelial appearance of adherent cells was kept identifiable until the complete eradication of the serum component. In the serum-free condition, adherent cells seemed unable to maintain their normal morphology. Instead, they became more rounded, aggregated as a cluster, and loosened in intracellular and cell-surface connection with blurred boundaries observed under an optical microscope (Fig. 2 A). When transferred and sufficiently adapted to suspension culture from 0% serum adherent culture, most cells existed as spherical individuals, along with some cell clumps. There was also an obvious change in cell diameter before and after the suspension adaptation, as shown in the size distribution charts in Supplementary Fig. 1 , with a 15.65µm in diameter of the original adherent cells versus the adapted suspension cells, which had an average diameter of 16.07µm. Cell cycle distribution provides information on the exact percentage of different cycle phases of a group of cells and ultimately helps to understand cell dynamics and activities. Therefore, we analyzed the cell cycle distribution as an explanation for the changes in cell growth rate, as shown in Fig. 2 B. We observed a relationship between serum concentration and the population proportion of G2/M phase, during which cells grew and divided. The results revealed that as the serum concentration in the medium was gradually reduced from 10% to 0%, the total proportion of cells in the DNA synthesis phase and G2/M phase also decreased in degrees. Under serum-free conditions, this proportion dropped to a minimum level at approximately 31.92%. This might indicate that growth factors present in the serum are critical signals affecting the cell cycle. The absence of serum leads to G0/G1 phase arrest in the majority of the cell population, severely inhibiting their proliferative capacity. However, a shift was observed in the cells that had been successfully adapted to serum-free suspension culture. The proportion of these suspension cells in the S + G2/M phases rebounded substantially to 46.97%. This figure is not only significantly higher than that of adherent cells cultured in serum-free medium (31.92%) but also surpasses that of adherent cells grown under optimal conditions with 10% serum (44.36%). Based on our perception of the suspension adaptation, a change in the adhesive strength of cells to the surface might occur. Therefore, we performed CCK-8-based adhesion assays on adherent cells cultured in media containing 10%, 7%, 3%, 1% or 0% serum and suspension-adapted cells (each group, n = 3) to evaluate cell adhesion ability. The adhesion ability was defined as the number of cells that attached to a surface over a given time period and was calculated using a high-quality, linear-correlation standard curve whose coefficient of determination, R-squared, was greater than 0.99, as shown in Fig. 2 C after calculation. Cells routinely cultured with 10% serum showed the highest adhesion (13655.6 ± 3000.3), followed by 1% (10095.6 ± 1382.8). The 3% serum group showed intermediate adhesion (8976.0 ± 735.4), whereas the 0% serum group exhibited lower adhesion (6988.9 ± 389.9), which was comparable to the value of the suspension cells (7396.5 ± 1892.0). Overall, cells cultured in 10% serum exhibited markedly greater adhesive capacity than cells cultured in low or serum-free conditions, and a loss of adhesive capacity was seen in the suspension cells and in the adherent cells with less serum component. Suspension cells were capable of producing recombinant adenoviral vectors encoding different antigens We compared the adenoviral production capacity of the suspension-adapted cell lines using two rAdVs. These two rAdVs had a similar adenoviral type 5 backbone but were inserted with different transgenes, rabies virus glycoprotein and HSV-2 glycoprotein D, respectively. The reason why two rAdVs were tested here was that the size and properties of the insertion sequence as well as the final translated product would influence the cell function and cell productivity of the viral vectors, and we would like to screen out a preferable cell line that was universally capable of producing rAdV effectively regardless of the insertion sequence. Cell density was adjusted to 1.5×10 6 cells/mL and infected with rAdVs at an MOI of 3. The viruses were harvested 48h post-infection and tittered. The results showed that the FS293 cell line achieved the highest viral yield for both rAdVs, approximately tenfold higher than that of the control CD374M cells, reaching about 9×10⁷ IFU/mL (Fig. 3 ). Adherent HEK293 cells and suspension-adapted cells had different gene transcription Principal component analysis (PCA) revealed a clear separation of samples according to serum concentration, indicating a progressive shift in the transcriptome as serum was gradually reduced. Adherent cells cultured with 10% serum (A10) formed a tight cluster, and fully suspension-adapted cells (SA_F) also clustered distinctly. Notably, A0 (adherent, 0% serum) samples localized closer to the SA_F cluster, suggesting that A0 represents a key transitional state from adherent to suspension phenotypes. We observed heterogeneity within the A1 group: two replicates (A1_1 and A1_3) clustered closely together, while A1_2 was displaced in PCA space (Fig. 4 B). Pairwise correlation analysis showed that most within-group correlations exceeded 0.97, whereas correlation of A1_2 with A1_1/A1_3 was ~ 0.94, lower than in other groups (Fig. 4 A). These findings indicated that cells cultured at 1% serum occupied a highly dynamic transitional state with elevated transcriptional heterogeneity. Consistently, hierarchical clustering of differentially expressed genes placed A0 in closer adjacency to SA_F, whereas A10 clustered with one A1 sample and the remaining two A1 replicates formed a separate cluster (Fig. 4 C). Overall, complete serum withdrawal drove adherent cells toward a transcriptomic state similar to suspension cells, while intermediate low-serum conditions produce a bifurcating population in which some cells retain a serum-like profile and others begin to acquire suspension-like features. To elucidate the transcriptional reprogramming underlying adaptation of HEK293 cells from adherent culture to suspension, we performed RNA-seq on three biological replicates per condition and conducted differential expression analysis. Using DESeq2 with thresholds of adjusted p ≤ 0.05 and |log₂FC| ≥ 1, we identified 2,476 DEGs, of which 1,218 were upregulated in suspension (49.1%) and 1,258 downregulated (50.9%), as illustrated in Fig. 4 H. The balanced distribution of up-regulated and down-regulated genes indicates bidirectional transcriptional remodelling rather than a unidirectional activation or repression. Differential gene enrichment analysis can relate these DEGs to specific biological functions, processes and pathways, and contribute to our understanding of how these DEGs influence cell function and phenotype. There are several tools to study and annotate the DEGs, each relying on particular databases and reflecting different aspects of genes. The gene analytical tools utilized in this article were GO, KEGG and GSEA enrichment analysis to highlight the functional switches in cells and to elucidate the mechanism of suspension adaptation. GO enrichment analysis categorizes all genes into three groups based on their functions: cellular component (CC), biological process (BP) and molecular function (MF). As the GO analysis indicated, the up-regulated genes in suspension cells versus adherent cells were enriched in cell-cell adhesion via plasma-membrane adhesion molecules (GO:0098742), synapse organization (GO:0050808), and regulation of monoatomic ion transmembrane transport (GO:0034765). There were also upregulations in the pathways that involved in neural functions, such as regulation of amine transport (GO:0015837), modulation of chemical synaptic transmission (GO:0050804) and axon development (GO:0061564) ( Fig. 4 D ). On the opposite side, down-regulated genes were annotated to antigen binding (GO:0003823), receptor ligand activity (GO:0048018), embryonic organ development (GO:0048568) and response to nutrient levels (GO:0031667) ( Fig. 4 E ). KEGG offered another perspective to identify these DEGs by their biological pathways. The KEGG results showed that up-regulated DEPs were significantly enriched in pathways related to signal transduction and neural activities, and down-regulated genes were mainly enriched in cytokine-cytokine receptor interaction (hsa04060), TGF-beta signalling pathway (hsa04350), and biosynthesis of amino acids Fig. 4 (F,G) . GSEA, on the other hand, provided a more comprehensive gene analysis beyond just examining individual genes, but considering the collective actions of functionally related genes, as a supplement to GO and KEGG enrichment study. In light of the GSEA method, up-regulated genes in suspension-adapted cells were enriched mainly in the regulation of mitotic nuclear division pathway (GO:0007088) and intermediate filament cytoskeleton organization pathway (GO:0045104). Meanwhile, down-regulated gene networks found in suspension-adapted cells were cellular response to nutrient pathway (GO:0031670), and intrinsic apoptotic signalling pathway (GO:0097193). During our intergroup comparisons, we identified an intriguing gene, claudin 7 (CLDN7), whose expression levels in cells declined progressively from adherent culture to final adaptation to suspension. Significant differences between groups are detailed in the Fig. 4 I. We subsequently paid particular attention to the downstream product of the expressed gene- the CLDN7 protein, in our following proteomic analysis. Adherent HEK293 cells and suspension-adapted cells had different protein expression To characterize proteomic changes associated with the transition of HEK293 cells from adherent culture (A10; 10% FBS) to serum-free suspension culture (SA_F), we performed DIA-based quantitative proteomics. Principal component analysis (PCA, Fig. 5 . A ) showed that A10 and SA_F samples were clearly separated along PC1 (accounting for 32.72% of the variance) and exhibited good within-group reproducibility. In total, 9,991 proteins were identified, of which 806 were considered differentially expressed proteins (DEPs): 414 proteins were upregulated and 392 downregulated in SA_F relative to A10 |log2Fold Change| ≥ 0.58, P-value ≤ 0.05, representing approximately 8.07% of the identified proteome (Fig. 5 . B ). Hierarchical clustering (heatmap; Fig. 5 . C ) further demonstrated consistent expression patterns of these DEPs within groups, with samples clustering into two primary branches corresponding to the treatments. Likewise, DEPS can be interpreted more clearly by enrichment analysis, as demonstrated in Fig. 5 (D-G) . DEPs were significantly enriched in all three aspects of GO classification: BP, CC and MF. In terms of BP, up-regulated DEPS were mainly annotated to specific biological processes including regulation of cell growth, cell adhesion, oxidation–reduction process, lipid biosynthetic process. Proteins that associated to cellular component were differentially expressed, particularly those structural proteins in extracellular matrix, extracellular region, integral component of membrane and intermediate filament. There were also enrichments in glycosaminoglycan binding, metal ion binding and endopeptidase activity to imply that suspension-adapted cells acclimatized themselves to suspension culture by differential expression of some proteins to affect molecular functions. Protein downregulations were clustered into several functions, namely, transport, localization, transmembrane transport, ion transport and immune response related processes in BP; plasma membrane, integral component of membrane, extracellular region and membrane protein complex in CC; and transporter activity, substrate-specific transmembrane transporter activity, ion binding and metal ion transmembrane transporter activity in MF. Tracking down these DEGs via KEGG approach to biological pathways where they play an active role, proteins participating in cell adhesion molecules, ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway were differentially expressed in an upregulated manner. From the other side, down-expressed proteins were significantly clustered in arrhythmogenic right ventricular cardiomyopathy (ARVC), hypertrophic cardiomyopathy (HCM), bile secretion and salivary secretion. These pathways were seemingly irrelevant to cell suspension adaptation; however, it is judicious to notice that ARVC and HCM pathways are involved with cellular skeletons and connexins, and consequently regulate the cell morphology and resistance to mechanical forces; bile secretion and salivary secretion pathways play a regulatory role in membrane transport, secretion and endocytosis of chemicals and nutrients. Regarding the intriguing CLDN7 gene identified in the transcriptomic analysis, we observed that its protein expression pattern mirrored its gene expression: intracellular protein levels progressively diminished as the cells adapted to the suspension culture ( Fig. 5 H ) . We shall elaborate on this finding in the subsequent discussion section, given its biological function. Adherent HEK293 cells and suspension-adapted cells were different in metabolic activities To further characterize the metabolic reprogramming of HEK293 cells following adaptation to serum-free suspension culture, we performed untargeted metabolomics analysis on SA-F and A10 cells (n = 3 per group). Principal Component Analysis (PCA) revealed a distinct separation between the SA-F and A10 groups in both negative (NEG) (Fig. 6 A) and positive (POS) (Fig. 6 B) ion modes, indicating a significant shift in the global metabolic profile. A total of 2,742 metabolites were identified using LC-MS/MS. Differential accumulated metabolites (DEMs) were screened based on the criteria of P 1.0, and FC > 1.2 or < 0.833. Under these thresholds, 275 DEMs were identified in NEG mode (118 up-regulated, 157 down-regulated; Fig. 6 C) and 427 DEMs in POS mode (123 up-regulated, 304 down-regulated; Fig. 6 D). When enriched via KEGG database, five pathways differed in different degrees between SA-F and A10 cells, as shown in Table.1 . Table.1. Altered metabolic pathways between suspension-adapted cells and parental adherent cells by KEGG enrichment. MapID, the ID of the enriched KEGG pathway; MapTitle, the name of the enriched KEGG pathway; Pvalue, the P-value of the enrichment analysis; x, the number of differentially expressed metabolites associated with this pathway; y, the number of background (all) metabolites associated with this pathway; n, the number of KEGG-annotated differentially expressed metabolites; N, the number of KEGG-annotated background (all) metabolites. Multi-omics integration analysis of differentially expressed genes and differentially expressed proteins of adherent HEK293 cells and suspension-adapted cells Linking transcriptomics and proteomics depicts a more comprehensive picture of the complete process and mechanism of gene transcription to its downstream expression of the actual participant in biological process-proteins, and pinpoints how the RNAs and proteins influence cell functions. To comprehensively characterize the regulatory relationship between gene transcription and protein expression during the adaptation process from adherent cells (A10) to suspension cells (SA_F), we performed an integrated analysis of the transcriptomic and proteomic datasets. Initially, we assessed the differential expression statistics across both omics layers. A substantial number of genes and proteins were quantified. Through intersection analysis of the quantified proteins and genes, we identified a set of co-quantified molecules, among which 130 exhibited significant differential expression in both datasets (Fig. 7 B). Figure 7 A provides a detailed breakdown of these differentially expressed molecules: the transcriptomic analysis identified 1,258 downregulated and 1,218 upregulated genes, whereas the proteomic analysis revealed 392 downregulated and 414 upregulated proteins. In accordance with the Central Dogma, we performed a Pearson correlation analysis on the Log2 Fold Changes of the co-quantified molecules to evaluate the extent to which gene transcription influences protein expression. As illustrated in Fig. 7 C, the transcriptome and proteome profiles for the SA_F vs. A10 comparison exhibited a weak positive correlation trend This relatively low correlation coefficient suggested that, while transcriptional changes served as the basis for protein expression, extensive and significant post-transcriptional or translational regulations occurred during the suspension adaptation of HEK293 cells. Consequently, changes in mRNA abundance did not linearly translate into changes in protein abundance. The Nine-Quadrant Plot (Fig. 7 D) offers a more detailed characterization of the relationship between gene transcription and protein expression: Co-regulation were presented as dots located in the red (Quadrant 3) and dark blue (Quadrant 7) regions represent genes with consistent changing trends in both mRNA and protein levels. These genes were primarily transcriptionally driven, and the previously mentioned enzymes related to amino acid metabolism were largely distributed within these regions. Dots located in the green (Quadrants 1, 2, 4) and orange (Quadrants 6, 8, 9) regions represented genes where mRNA and protein changes are inconsistent, as discordant regulation. Notably, the presence of the orange region (mRNA downregulated or unchanged, but protein upregulated) suggest that cells were likely to maintain the homeostasis of key proteins, such as certain stress-response proteins, by enhancing translation efficiency or inhibiting protein degradation. Conversely, the green region (mRNA upregulated, but protein unchanged or downregulated) implied potential translational stalling or accelerated protein degradation, which aligned with the rapid clearance of obsolete structures during cytoskeletal remodelling. To further identify the key driving factors contributing to the joint variation between the transcriptome and proteome, we employed two-way Orthogonal Partial Least Squares (O2PLS) analysis. Figure 7 E displayed the top molecules with the highest contribution to the covariance between the two omics layers. At the proteome level, we found that proteins such as Hsp10, mitochondrial chaperonin indicating energy metabolism and stress response, Na+/K + ATPase alpha 1, indicating changes in ion transport function and macrophage migration inhibitory factor (MIF) exhibited extremely high loading values, indicating that they were core proteins distinguishing the SA_F state from the A10 state. At the transcriptome level, multiple genes identified by ENSG IDs (such as ENSG00000211459) also demonstrated high contribution weights. These high-loading molecules might serve as key regulatory nodes, driving the metabolic and structural remodelling networks during the suspension adaptation process. We then performed a joint GO and KEGG enrichment analysis on the co-differentially expressed molecules to elucidate how transcriptional regulation translated into final protein functional execution. The joint GO Analysis (Fig. 7 F) revealed key features of suspension adaptation across three dimensions: BP, CC and MF. In terms of BP, terms such as "cellular amino acid biosynthetic process" and "glutamine family amino acid biosynthetic process" exhibited significant enrichment in the proteome (represented by orange circles) with high enrichment ratios. This confirmed that suspension cells effectively execute metabolic reprogramming at the protein level to sustain proliferation. Furthermore, the enrichment of ion transport processes, such as "sodium ion transport," implies alterations in the mechanisms regulating membrane potential and osmotic pressure. Regarding cellular component, the analysis highlighted profound changes in cellular structure involving the remodelling of cytoskeleton and membrane complexes. Actin cytoskeleton and intermediate filament were significantly enriched in the proteome, which possibly accounted for the morphological transition of suspension cells from an adherent, flattened shape to a spherical form. Of particular note, the sodium-potassium-exchanging ATPase complex displayed an extremely high enrichment ratio and significance in the proteome. This corroborated our previous identification of the key driver protein Na+/K + ATPase alpha 1 in the O2PLS analysis. In terms of molecular function, the analysis revealed an adaptive strategy characterized by the remarkable upregulation of protease inhibitor activity. Peptidase inhibitor activity and endopeptidase inhibitor activity exhibited extremely high statistical significance in the proteome (dark red circles, P < 10 − 5 ), whereas the significance at the transcriptional level was weaker. This strongly suggested that SA_F cells accumulated large amounts of protease inhibitors via post-transcriptional regulation to resist proteolytic stress in the serum-free suspension environment or to protect autocrine factors. The joint KEGG analysis (Fig. 7 G) further validated these findings. Pathways such as cell adhesion molecules (CAMs) and ECM-receptor interaction exhibited the highest significance in the proteomic data (dark red/orange circles), with enrichment ratios significantly higher than those in the transcriptome. This hinted that, although gene transcription was altered, cells adapted to the suspension state through more profound adjustments in protein abundance, e.g., downregulation of specific integrins or switching of adhesion molecules. Regarding the retention of metabolic and secretory functions, glycine, serine and threonine metabolism was enriched in both omics layers, yet the significance at the protein level (orange) was stronger than at the transcriptional level (grey/blue), again underscoring the stable accumulation of metabolic enzymes. Furthermore, pathways closely related to the cytoskeleton, such as HCM and ARVC, which were essentially abundant in the actin/myosin system, also displayed high significance in the proteome, further corroborating that cytoskeletal rearrangement was a core event in suspension adaptation. Taken together, the joint enrichment analysis demonstrated that the suspension adaptation of HEK293 cells was a multi-level regulatory process. While the transcriptome set the blueprint for gene expression, the proteome exhibited more significant and direct functional execution characteristics regarding metabolic enhancement, cytoskeletal remodelling, and protection against proteolysis. Discussion In general, there are three commonly followed routes for suspension adaptation starting from adherent cells and ending with well-adapted suspension cells: direct adaptation, sequential adaptation, and gradual serum reduction [ 29 ]. All approaches obey an unchanged principle: elimination of serum component, and the divergence lies in the speed of serum elimination. Cells in the direct adaptation experience a sudden eradication of serum. It would be length and struggling for cells to adapt to this instant change, and there is uncertainty on whether the cell would survive this new condition. The difference between sequential adaptation and gradual serum reduction is in the medium substitution process. Serum reduction and suspension medium substitution are carried out at the same time in sequential adaptation, while suspension medium replaces the original adherent culture medium after finishing the serum reduction. To the best of the author’s knowledge, the sequential adaptation is the comparatively preferable choice since it is time-saving, labour-saving, and it allows the cells to adapt to the changing medium simultaneously with the serum reduction. Regardless of the approach of suspension adaptation, there is a limited amount of work focusing on the changes of cells during suspension adaptation. Here, we measured and identified some phenotypic changes in several aspects, including cell growth, cell morphology, cell cycle distribution, cell adhesion strength and productivity of rAdV. First, deduced from the cell growth curve, the cell growth rate decreased during the adaptation to serum-free culture as a result of serum reduction. This suggests that adherent cells may rely on the additional nutrient content and signalling molecules from the serum to maintain growth and division. Serum-rich conditions can provide more growth factors and nutrients, thereby promoting rapid cell expansion [ 30 ]. It is also interesting to see that cell average diameter increased in suspension-adapted cells compared to adherent parental cells. Loss of contact inhibition, comparatively spacious volume for cell activities might lead to the diameter increase [ 31 ]. Cell adhesion assay showed that cells cultured in 10% serum exhibited the strongest adhesion strength, while both serum-free adapted cells and suspension-adapted cells demonstrated the poorest adhesion with no significant difference between these two groups. A conclusion is reached that serum existence and concentration have an impact on cell adhesion capacity. Such phenomenon and conclusion are also reported in a study concerning the adhesion ability of THP-1 cells [ 32 ], and a study on human osteosarcoma MG63 cells and human hepatic stellate LX-2 cells [ 33 ]. When no serum exists in the culture medium, cells adapt to the culture environment by adjusting their dependence on adhesion and reprogramming their own structure. Transcriptomic and proteomic analyses also evidence this conclusion: many of the differentially expressed genes and proteins are associated with cell structure and cell adhesion. Of note, at the metabolic level, we detected a noticeable retard in both glucose consumption and lactate generation rates in suspension-adapted cells. As mentioned earlier, slower lactate generation is considered a useful feature of cell particularly in large-scale bioproduction since lactate is regarded as a waste from cell metabolic fluxes. This waste product can create a toxic environment for cells by disturbing pH level, changing osmotic pressure and affecting normal metabolism [ 34 ], and lactate can also adversely influence the productivity of cells to produce viral vectors [ 35 ]. With respect to cell cycle, adherent cells displayed a progressive decline in the S/G2/M phase proportion as serum levels decreased, culminating in a minimal proportion of approximately 31.92% under serum-free conditions. This observation is consistent with established literature documenting that acute serum withdrawal induces G₁ arrest and S-phase diminution-a well-characterized cellular response [ 36 , 37 ]. In striking contrast, suspension-adapted cells re-established a substantially elevated S and G2/M fraction (46.97%), surpassing even the 44.36% seen in adherent cultures maintained in 10% serum. This rebound implies that, notwithstanding the loss of structural and adhesive integrity entailed by suspension adaptation, cells retain a relatively normal cell cycle distribution through extensive metabolic and signalling rewiring. These findings are congruent with the upregulation of gene sets associated with the "regulation of mitotic nuclear division" identified in our transcriptome-wide GSEA. In addition to characterizing cell phenotype and providing corresponding interpretations, we also sought a more in-depth exploration into the changes in the molecular level during the adhesion turnover. Hereon, we conclude from the huge amount number of multi-omics data and enrichment analysis, and then propose an omics study-based hypothesis, attempting to offer a possible explanation for the mechanism of suspension adaptation: adherent HEK293 cells adapt to suspension culture via cell reprogramming in three aspects - structural remodelling, metabolic network reconstruction and intensification of resistance against external stress. The morphological change from a flattened adherent form to a spherical suspension form is the most striking and determining feature of adaptation, and it is an apparent compass to indicate cell structural changes. Our multi-omics data provide molecular evidence that this shift is governed by a coordinated restructuring of adhesion complexes and the internal cytoskeleton. On the one hand, cell selectively suppresses anchorage-dependent signaling. Joint enrichment analysis showed significant downregulation of pathways related to cell adhesion molecules, ECM-receptor interaction, and collagen-containing extracellular matrix. This suppression effect is consistent with the observed reduction in adhesion capacity and is essential for the cells to acquire anoikis resistance-the ability to survive without physical attachment to the extracellular matrix [ 38 ]. On the other hand, the internal structural integrity is actively maintained. Although adhesion structures are compromised, GO analysis highlights significant enrichment of the actin cytoskeleton and intermediate filament components in the proteome. This reorganization suggests that the cells reinforce their internal mechanical support to sustain a spherical shape and resist the hydrodynamic shear stress inherent to suspension culture. Differential expression of macrophage migration inhibitory factor between adherent and adapted cells identified by O2PLS analysis further confirms this structural adaptation. While known for its role as an immune modulator in inflammation, several diseases and cancers [ 39 , 40 ], MIF has been reported to regulate cell adhesion, migration, spreading, and morphogenesis, partly by modulating Rho GTPase activity [ 41 , 42 ], which directly interacts with cytoskeletal components like F-actin, myosin [ 43 , 44 , 45 , 46 ]. Thus, the elevated expression of MIF likely reflects its role in turning its cytoskeletal dynamics suitable in a continuously motional environment. In addition to structural remodelling, cells take metabolic network reconstruction to achieve nutritional homeostasis and proliferative capacity. The turnover from a serum-rich, adherent culture system to a serum-free suspension environment imposes dual metabolic challenges: compensating for the loss of exogenous nutrients, and meeting the demands of normal cell function and rapid proliferation under such circumstances. Our results highlight key adaptive strategies of cells in lipid and amino acid metabolism, as well as ion transport regulation. First, the capacity for amino acid biosynthesis is significantly enhanced, as evidenced by joint transcriptomic and proteomic enrichment analysis of cellular amino acid biosynthetic process and glycine, serine, and threonine metabolism. The upregulation of these bioprocesses reveals that cells make a systemic effort to secure the necessary precursors for protein synthesis and cell function, supporting the observed recovery of a high S/G2/M phase ratio in adapted suspension cells. Second, lipid synthesis is re-directed. Metabolomic data showed enrichment of fatty acid biosynthesis and linoleic acid metabolism, corroborated by transcriptomic upregulation of lipid biosynthetic process. This enhancement of endogenous lipid production is crucial for three conceivable reasons: compensating for the removal of serum-derived lipids [ 47 , 48 ]; potentially alteration of cell membrane composition thereby enhancing resistance in response to the shear stress [ 49 ]; and alteration of cell membrane composition to incur the metabolic shifts by adjusting the chemical transportation gating [ 50 ]. Third, ion homeostasis is actively managed. The identification of Na+/K + ATPase alpha 1 as a core O2PLS driver, combined with the strong GO enrichment for sodium ion transport, points to a critical adjustment in membrane transport function. Na+/K + ATPase alpha 1 is a key regulator of cell volume and osmotic pressure, and its upregulation suggests the cells are actively reshaping their membrane potential and osmotic balance to cope with the altered physicochemical properties of the suspension culture medium [ 51 , 52 , 53 ]. Over and above, cells adapted to the suspension mode by another additional strategy: leveling up stress response to maintain homeostasis. The gradual progression of suspension adaptation represents a chronic stress situation where cells must survive oxidative damage, nutrient limitation, and mechanical forces, the last two of which have been discussed as aforementioned. To survive oxidative stress, it is important for cells to alter its metabolic profile and to maintain protein homeostasis, bolstered by protease inhibition [ 54 , 55 ]. The dramatic enrichment and high statistical significance of peptidase inhibitor activity in the proteome is a salient adaptive feature. This strongly suggests that suspension-adapted cells employ a mechanism, likely involving translational control, to accumulate protease inhibitors such as Serpins or TIMPs. This strategy is vital in serum-free conditions to protect cell-surface receptors and autocrine growth factors from degradation by endogenous or exogenous proteases, thereby ensuring survival signaling and microenvironmental stability [ 56 ]. Besides, cells need to manage mitochondrial stress. The high loading of the mitochondrial chaperone Hsp10 in the O2PLS analysis indicates activation of the mitochondrial unfolded protein response [ 57 , 58 ]. The elevated expression of Hsp10 helps maintain protein folding integrity and function within the mitochondria and ensures the high ATP output required for the adapted cells to keep their metabolic activities and proliferative state. This is also complemented by the enrichment of antioxidant pathways observed in the metabolome to form a comprehensive defense mechanism against oxidative stress. Conclusively, the successful suspension adaptation of HEK293 cells is achieved through a multitiered regulatory process. The cells overcome physical constraints via cytoskeletal remodelling, ensure viability through metabolic adaptation, and establish survival stability by activating sophisticated stress defense systems. These three aspects of cell responses interact and interweave as a complex network, assisting the cells to live through the suspension adaptation, and these mechanistic insights provide a molecular roadmap for the rational design and engineering of excellent industrial cell lines. The massive quantity of omics data can also be interpreted in other ways for different purposes, such as exploring key genes and proteins that regulate the production of viral vectors, unveiling the relationship between the suspension adaptation and tumorigenicity, et cetera. There is also a single gene, or protein, that attract our attention in particular, namely claudin 7 (CLDN7). CLDN7 protein belongs to the claudin family, a protein family with 27 members found in mammalian cells. Claudins are tetraspan transmembrane proteins that form tight junction strands between epithelial or endothelial cell sheets and serve as a basic structural component as a physical barrier [ 59 ]. They also serve crucial rules in cell permeability, selective ion transportation, maintenance of cell polarity, signal transduction, and notably, cell adhesion and migration [ 60 , 61 ]. Back to cell suspension adaptation, it is a process wherein adherent cells are progressively selected and acclimatized to grow and proliferate as suspension cells in a free-floating state, devoid of any solid surface attachment. This process necessitates a profound phenotypic reprogramming, which possibly includes loss of contact inhibition, rearrangement of the cytoskeleton, regulation of cell adhesion molecule expression, acquisition of anti-apoptotic capabilities and metabolic adaptations. CLDN7 is presumed to take an active part in these processes. In adherent Cells, high CLDN7 expression is a hallmark of a mature epithelial phenotype, facilitating cell-cell connections, the formation of cohesive monolayers and proliferation [ 62 , 63 ], and aberrant expression of CLDN7 brings alterations in cell integrity, cell polarity and intercellular contact [ 64 ]. Research has found different expression of claudins in canine mammary cells from primary tissue culture to establishment of cell line [ 65 ], and loss of tight junction usually happens in such course. Similarly, during adaptation, cells must dissociate from tight junctions to exist as single cells in suspension, and a plausible mechanism to elucidate this is the significant downregulation or functional inhibition of CLDN7. Such reduction weakens intercellular tethering forces thus benefits cell dissociation and promotes cell survival [ 66 ]. All these theories and researches have set up a rationale and provided an idea that in future work, we can utilize gene-editing tools, e.g., CRISPR-Cas9, to knock down/out CLDN7 in adherent HEK293 cells for suspension adaptation. If feasible, suspension HEK293 cells, originated from adherent status by gene editing, rather than suspension adaptation, will be more homogeneous in cell genome and more stable in cell performance, and ultimately accelerate cell line development for cell-based pharmaceutical applications. In conclusion, we successfully adapted adherent HEK293 cells to suspension culture with preferable cell growth and productivity for recombinant adenoviral vector. We observed alterations in cell growth rate, glucose consumption and lactate generation, cell-surface adhesion, and cell cycle distribution between suspension-adapted cells and the adherent, parental counterpart. Transcriptomics, proteomics, and metabolomics analysis and following GO, KEGG and GSEA enrichment analysis were performed to provide a molecular investigation into the key switches in cells. Based on the differentially expressed genes, proteins, and metabolic pathways, we hypothesize and summarize that suspension adaptation occurs not only in a phenotypic aspect, but also at molecular levels, including gene expression, protein expression and metabolic activities, and all these cellular changes aim to mediate structural components, metabolic activities and stress resistance to survive and adapt in the turnover environment. Declarations Author Contributions B.Zhang and S.Li contributed equally to this work. J.Wei, S.Li and B.Zhang designed and initiated the research. S.Li and B.Zhang performed the experiments with the help of J.Liu and W.Su for cell culture, viral production and cell characterization. S.Li and B.Zhang performed the multi-omics experiments, data analysis and visualization. X.Zhang, X.Ren, Z.Ge, T.Zhao and Q.Huang determined viral titers. S.Li and B.Zhang drafted the manuscript, which was revised by J.Wei. All authors read and approved the final manuscript. Data Availability The RNA sequencing data during the current study are available in the SRA, PRJNA1381743. Proteomics data during the current study are available in the iProX, IPX0014727001 Metabolomics data during the current study are available in the MetaboLights, MTBLS13517 Funding This research did not receive funding. Competing Interests I declare that the authors have no competing interests , or other interests that might be perceived to influence the results and/or discussion reported in this paper. Ethical Approval This study does not involve human participants or animals. The HEK293 cell line used in this study was obtained from a commercial source (ATCC), and therefore, specific ethical approval for human tissue use was not required. All experiments were conducted in accordance with the institutional biosafety guidelines. 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12:16:08","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":239442,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/8d7d20be6af1b3c018689215.html"},{"id":100367746,"identity":"8a3ff73d-c7d5-423b-b1aa-d5bd9cf6220a","added_by":"auto","created_at":"2026-01-16 07:57:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244106,"visible":true,"origin":"","legend":"\u003cp\u003eHEK293 adherent cells were successfully adapted to suspension culture and showed different growth, glucose intake and lactate generation. \u003cstrong\u003eFig.1A\u003c/strong\u003e, the cell growth curve of adherent cells in different serum concentrations and proportions of DMEM in the culture media. The upright legend of percentage referred to the serum concentration and also indicated the percentage of DMEM (multiply by ten) in the total culture medium, specifically, 10%:100%DMEM (10% serum), 70%:70%DMEM/30%SCM (7% serum); 30%: 30%DMEM/70%SCM (3% serum), 10%:10%DMEM/90%SCM (1% serum), 0%:100%SCM (0% serum). \u003cstrong\u003eFig.1B\u003c/strong\u003e, the cell growth curve of suspension-adapted cells in three different suspension culture media, plus one control cell, CD374M, as a reference. \u003cstrong\u003eFig.1C\u003c/strong\u003e, glucose consumption and lactate production of adherent cells in different serum concentration groups. \u003cstrong\u003eFig.1D\u003c/strong\u003e, glucose consumption and lactate production of suspension-adapted FS293 cells. \u003cstrong\u003eFig.1E\u003c/strong\u003e, glucose consumption rate of adherent cells in different groups after normalization to standardize the initial glucose concentration. The percentage referred to the serum concentration and also indicated the percentage of DMEM (multiply by ten) in the total culture medium, specifically, 10%:100%DMEM (10% serum), 70%:70%DMEM/30%SCM (7% serum); 30%: 30%DMEM/70%SCM (3% serum), 10%:10%DMEM/90%SCM (1% serum), 0%:100%SCM (0% serum).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/cd8612c45c4973c18bc4db10.png"},{"id":100144721,"identity":"adf687cb-0b84-4df8-bfd5-0b8aef710b87","added_by":"auto","created_at":"2026-01-13 12:16:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":300848,"visible":true,"origin":"","legend":"\u003cp\u003eCells in each serum condition and suspension manifested differently in morphology, cell cycle distribution and adhesion ability. \u003cstrong\u003eFig.2A\u003c/strong\u003e, Cell observations under a light microscope. From left to right, adherent cells in complete DMEM containing 10% FBS; adherent cells in 70% complete DMEM/30%SCM (7%FBS); adherent cells in 30% complete DMEM/70%SCM (3%FBS); adherent cells in 10% complete DMEM/90%SCM (1%FBS); adherent cells in 1000%SCM (0%FBS);\u003cstrong\u003e \u003c/strong\u003esuspension adapted cells in serum-free SCM. \u003cstrong\u003eFig.2B, \u003c/strong\u003eCell cycle distribution of different cells. All cells were collected and measured in the logistic growth phase. G1: Gap phase1; S: Synthesis phase; G2: Gap phase 2; M: Mitosis phase; adherent cells in complete DMEM containing 10% FBS; adherent cells in 70% complete DMEM/30%SCM (7%FBS); adherent cells in 30% complete DMEM/70%SCM (3%FBS); adherent cells in 10% complete DMEM/90%SCM (1%FBS); adherent cells in 1000%SCM (0%FBS); suspension-adapted cells in serum-free SCM. \u003cstrong\u003eFig.2C, \u003c/strong\u003eCell adhesion capacity of different cells, as characterized by the number of cells that firmly attached to the plate surface after a set time period and rounds of PBS wash. Cells were numbered via a CCK-8 kit and a standard curve, which was created by a linear series of adequately adhered cells in different cell numbers. 10%: adherent cells in 100%DMEM (10% serum), 70%: adherent cells in 70%DMEM/30%SCM (7% serum); 30%: adherent cells in 30%DMEM/70%SCM (3% serum), 10%: adherent cells in 10%DMEM/90%SCM (1% serum), 0%: adherent cells in 100%SCM (0% serum); FS293: well-adapted suspension cells in Freestyle suspension culture medium.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/3ee364016dd6413f772ab5a7.png"},{"id":100144701,"identity":"9b79adf2-aa82-42fe-a1c3-bdbf17458f02","added_by":"auto","created_at":"2026-01-13 12:16:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26476,"visible":true,"origin":"","legend":"\u003cp\u003eProductivity of two recombinant adenoviral vectors by three suspension-adapted cells. rAdV-gG2: adenoviral vector encodes herpes simplex virus type 2 glycoprotein D as an HSV-2 vaccine; rAdV-G: adenoviral vector encodes rabies virus glycoprotein as a rabies vaccine.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/3a23dfddb177cdb89616a066.png"},{"id":100144702,"identity":"a65e2670-10d7-4fe9-b0fd-de3523b41b19","added_by":"auto","created_at":"2026-01-13 12:16:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":686586,"visible":true,"origin":"","legend":"\u003cp\u003eAdherent HEK293 cells and suspension-adapted cells had different gene transcription.\u003cstrong\u003e Fig.4A\u003c/strong\u003e, Pearson correlation heatmap shows high reproducibility among replicates, with slight divergence within the A1 group. \u003cstrong\u003eFig.4B, \u003c/strong\u003eprincipal component analysis (PCA) reveals a progressive transcriptomic shift with decreasing serum concentration, with A0 clustering closer to FS, indicating a transition toward a suspension-like state. \u003cstrong\u003eFig.4C, \u003c/strong\u003ehierarchical clustering of DEGs shows A10 clustering with part of the A1 group, while A0 clusters with SA_F, reflecting transcriptomic reprogramming and heterogeneity during serum withdrawal. \u003cstrong\u003eFig.4(D, E),\u003c/strong\u003e Dot plots of GO (Gene Ontology) enrichment analysis for up-regulated (A) and down-regulated (B) genes, showing the top 20 significantly enriched biological processes or cellular components. \u003cstrong\u003eFig.4(F, G), \u003c/strong\u003eDot plots of KEGG pathway enrichment analysis for up-regulated (C) and down-regulated (D) genes. Note: The size of the dot represents the number of DEGs enriched in the pathway/term (Count). The color of the dot corresponds to the adjusted p-value (padj), with redder colors indicating higher significance. The x-axis (GeneRatio) represents the ratio of DEGs to the total number of genes in the specific pathway. \u003cstrong\u003eFig.4H,\u003c/strong\u003e Differentially expressed genes between suspension-adapted cells and parental adherent cells Using DESeq2 with thresholds of adjusted p ≤ 0.05 and |log₂FC| ≥ 1. SA_F, suspension-adapted HEK293-SA-FS cells; A10, parental adherent cells in 10% serum environment. \u003cstrong\u003eFig.4I, \u003c/strong\u003eCLDN7 gene expression levels in cells cultured at different serum concentrations. A10: adherent, 10% serum; A1: adherent, 1% serum; A0: adherent, 0% serum; SA_F: suspension-adapted cells.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/184353558fcfbc58ef68ccfc.png"},{"id":100144724,"identity":"f63592de-6ef6-437c-8832-00252f177f80","added_by":"auto","created_at":"2026-01-13 12:16:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":359576,"visible":true,"origin":"","legend":"\u003cp\u003eProteomic analysis of suspension-adapted cells versus adherent cells. \u003cstrong\u003eFig.5A\u003c/strong\u003e, PCA results of three parallel samples for each cell type with good reproducibility. \u003cstrong\u003eFig.5B, \u003c/strong\u003eoverall illustration of differentially expressed proteins between two cell types. \u003cstrong\u003eFig.5C, \u003c/strong\u003ehierarchical clustering heatmap of within-group samples. SA_F, suspension-adapted HEK293-SA-FS cells; A10, parental adherent cells in 10% serum environment. \u003cstrong\u003eFig.5(D, E), \u003c/strong\u003eup- \u003cstrong\u003e(Fig.5D) \u003c/strong\u003eand down-\u003cstrong\u003e(Fig.5E)\u003c/strong\u003e regulations of differentially expressed proteins in GO enrichment. \u003cstrong\u003eFig.5(F, G), \u003c/strong\u003eup- \u003cstrong\u003e(Fig.5F)\u003c/strong\u003e and down-\u003cstrong\u003e(Fig.5G)\u003c/strong\u003eregulations of differentially expressed proteins in KEGG enrichment. SA_F, suspension-adapted HEK293-SA-FS cells; A10, parental adherent cells in 10% serum environment. \u003cstrong\u003eFig.5H, \u003c/strong\u003eCLDN7 protein expression levels in cells cultured at different serum concentrations. A10 : adherent, 10% serum; A1: adherent, 1% serum; A0: adherent, 0% serum; SA_F: suspension-adapted cells\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/ff762086427a4f0e9ae8a4b8.png"},{"id":100368524,"identity":"229b434c-42f2-4635-b2b5-1609028a1e01","added_by":"auto","created_at":"2026-01-16 07:58:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":291611,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomics analysis of suspension-adapted cells and parental adherent cells. \u003cstrong\u003eFig.6 (A,B)\u003c/strong\u003e, PCA analysis in NEG\u003cstrong\u003e (Fig.6A)\u003c/strong\u003e and POS \u003cstrong\u003e(Fig.6B)\u003c/strong\u003e modes. \u003cstrong\u003eFig.6 (C,D)\u003c/strong\u003e, differential accumulated metabolites in NEG mode \u003cstrong\u003e(Fig.6C) \u003c/strong\u003eand POS mode\u003cstrong\u003e (Fig.6D).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/cd370d587cea896c2588d002.png"},{"id":100144703,"identity":"9c5ced3d-ae3c-4451-94ec-b3de3c89d31a","added_by":"auto","created_at":"2026-01-13 12:16:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":309825,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated transcriptomic and proteomic analysis reveals the regulatory mechanisms of HEK293 suspension adaptation. \u003cstrong\u003eFig.7A, \u003c/strong\u003eBar chart summarizing the number of up-regulated and down-regulated genes and proteins in SA_F versus A10 comparison. \u003cstrong\u003eFig.7B\u003c/strong\u003e, Venn diagram displaying the overlap of identified and differentially expressed molecules between the transcriptome (Tran) and proteome (Prot). \u003cstrong\u003eFig.7C,\u003c/strong\u003e scatter plot showing the correlation of Log2Fold Change between the transcriptome (Y-axis) and proteome (X-axis) for co-quantified molecules. The red line represents the linear regression fit, and the Pearson correlation coefficient (R) is 0.249. \u003cstrong\u003eFig.7D,\u003c/strong\u003e nine-quadrant plot characterizing the correlation of expression changes. Red/Dark Blue dots (Quadrants 3 \u0026amp; 7) indicate co-regulated genes (consistent trend in both omics); Orange/Green dots (Quadrants 1, 2, 4, 6, 8, 9) indicate discordant regulation (e.g., Orange: protein-level upregulation only; Green: transcript-level upregulation only); Grey dots (Quadrant 5) indicate non-significant molecules. Dashed lines represent the fold-change thresholds. \u003cstrong\u003eFig.7E\u003c/strong\u003e, O2PLS joint loadings plot identifying key drivers contributing to the covariance between the two omics layers. Triangles represent proteins, and circles represent transcripts; markers far from the origin indicate high contribution weight. \u003cstrong\u003eFig.7(F-G), \u003c/strong\u003eJoint enrichment analysis of GO terms (\u003cstrong\u003eFig.7F\u003c/strong\u003e) and KEGG pathways (\u003cstrong\u003eFig.7G\u003c/strong\u003e) for co-differentially expressed molecules. Shapes: Triangles blacktriangle represent transcriptomic data; Circles (bullet) represent proteomic data. X-axis: The ratio of differentially expressed molecules to total molecules in the pathway. Color: Represents statistical significance (Log10P-value); warmer colors (red/orange) indicate higher significance. Size: Represents the count of differentially expressed molecules.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/2accf0f4cdcd186fd9413eef.png"},{"id":103703411,"identity":"b78a420b-b8db-433e-8227-04c1eac9eedf","added_by":"auto","created_at":"2026-03-01 22:39:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3633474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/00dca42c-ae36-4c08-95a1-6a29993dcf90.pdf"},{"id":100144698,"identity":"8424b819-74d3-4dd8-af26-08e2ea436698","added_by":"auto","created_at":"2026-01-13 12:16:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":116258,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/8ca85fb9c6048c1f0fc04f38.docx"},{"id":100367175,"identity":"18aed100-cd43-4a17-a15a-193201264353","added_by":"auto","created_at":"2026-01-16 07:56:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1478375,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8498955/v1/6d9bce35a357df6fdaab8dcd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Phenotypes and Multi-omics Reveal Changes and Molecular Mechanism of Suspension Adaptation of HEK293 Cells: Structural Remodelling, Metabolic Reconstruction and Stress Resistance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCell culture is a biological process in which cells are carefully cultivated in an artificial, controlled in-vitro environment favoring their proliferation and other biological activities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the \u0026lsquo;location\u0026rsquo; of cell residence, cell culture can be classified into two types: adherent and suspension cell culture. The most fundamental difference between these two types is that adherent cells require a solid surface or substrate to attach and then function, and such cell property is referred to as anchorage dependency [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Alternatively, suspension cells grow in a floating status without supporting material and move freely in the culture liquid. Both types of cell culture are widely used in basic research and the pharmaceutical industry, particularly in the bioproduction of proteins, viruses, and cells per se [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the pharmaceutical industry, large-scale manufacture in specific facilities is always required to achieve a cost-effective yield and to meet potential clinical needs. To fulfil the productivity, multi-layered cell factory, micro-carrier, and carrier disk are popular choices for large-scale adherent cell culture [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Repeated rounds of an \u0026lsquo;attach-detach\u0026rsquo; process by detaching agents, e.g., trypsin, are usually necessary to handle and expand adherent cells, and extra animal-derived supplements, such as fetal bovine serum (FBS), are also indispensable in the traditional adherent culture mode. In addition to the extra expense and workload, the use of trypsin and FBS also introduces difficulties in downstream purification and poses a threat to product safety and batch-to-batch stability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Comparatively, suspension cell culture offers scalability, operational simplicity, and the avoidance of FBS and trypsin [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Some popular production cells are inherently in suspension status, such as Spodoptera frugiperda clone 9 cells from insects, while others are naturally adherent, including Chinese hamster ovary (CHO), Madin Darby canine kidney (MDCK), Vero cells, and human embryonic kidney 293 (HEK293). Numerous attempts have been made to transform these adherent cells into suspension to take advantage of the suspension culture, but not all adherent cells are amenable to such a change [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A commonly used technique for such a transformation is suspension adaptation, and suspension adaptation of HEK293 is a success [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe HEK293 cell line was first established in 1973, and several derivatives have been developed from this parental lineage for various purposes. Initially transfected with E1 gene from type 5 adenovirus, all HEK293 cell lines were endowed with an immortalization feature and a capacity to provide E1 gene in trans [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which makes them E1-complementary cells and desirable cell factories for the packaging of adenoviral vectors (AdV) and adenovirus-associated viral vectors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Adenovirus (Ad), a pathogen that commonly affects human respiratory system and causes flu-like symptoms, has been rendered replication-defective by deletion of its replication-related genes and a safe delivery vehicle for exogenous genes, usually referred to as recombinant adenoviral vector (rAdV) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. AdV has proven its competence as a gene vector for the development of vaccines and gene therapies, thanks to its excellence in safety, wide host range spectrum, minimum risk of gene integration into host genome, suitability for large-scale production, and an instinctive nature to trigger host immune response alike to a vaccine adjuvant [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. AdV plays an important role in the field of virus-vectored vaccines with many preclinical and clinical applications aiming at several pathogens, including influenza virus [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], Ebola [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Zika [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], human immunodeficiency virus [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], tuberculosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and the most widely distributed adenoviral vaccine hitherto -ChAdOx1-nCoV-19 against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSuspension HEK293 has long been a favourable production cell line for AdV. Many of the suspension HEK293 cells originate from adherent parental cells by suspension adaptation. Interestingly, although most research and applications employed a universal strategy by gradually weaning off the serum in the medium, they varied widely in the gradient of serum reduction and selection of the replacement suspension culture medium (SCM), and these differences resulted in variance in cell performance and the length of the adaptation process. Furthermore, the majority of these works focused more on the cell growth and the yield of their target product, whilst only a few sought a deeper insight into the underlying alterations occurring in the suspension-adapted cells.\u003c/p\u003e \u003cp\u003eHere, we followed a similar approach to adapt the HEK293 adherent cells to suspension cells by gradual removal of the serum from the culture medium, with a simultaneous medium substitution with four different SCMs. We compared the cell growth and the titer of rAdV produced by these cells. More importantly, we performed transcriptomic, proteomic and metabolomic analysis to depict a more comprehensive picture of the changes in the adapted cells versus their original adherent counterpart, and tried to find a hint to elucidate the mechanism of the suspension adaptation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAdherent HEK293 cell culture\u003c/h2\u003e \u003cp\u003eHEK293 cells were purchased from the American Type Culture Collection (CRL-1573, ATCC). Adherent cultures were maintained in tissue culture\u0026ndash;treated T75 flasks (430641, Corning) in a humidified incubator (BPN-240RHP, Yiheng) at 37\u0026deg;C in a 5% CO₂, 80% relative humidity (RH) atmosphere. Cells were fed in Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM, 10566-016, Gibco) supplemented with 10% (v.\\v.) FBS (13011\u0026thinsp;\u0026minus;\u0026thinsp;8611, Tianhang) and 1% (v.\\v.) penicillin\u0026ndash;streptomycin (PS, GNM15140-1, Genom). When cultures reached 80%\u0026ndash;90% confluence, cells were transferred into new flasks and seeded at (4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5) \u0026times;10\u003csup\u003e4\u003c/sup\u003e cells/cm\u0026sup2; for subculture. An optical microscope (IX51, Olympus) was used to view and capture the cells with an accessory software (cellSens Standard, Olympus) from its manufacturer.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSequential adaptation of adherent cells\u003c/h3\u003e\n\u003cp\u003eAdherent cells were adapted to serum-free suspension culture by gradually reducing the serum in a 10%, 7%, 3%, 1%, 0% stepwise gradient. Culture media with different serum concentrations were prepared by mixing the complete DMEM with the SCMs of our selection at defined ratios (v/v): 100%DMEM, 70%DMEM/30%SCM, 30%DMEM/70%SCM, 10%DMEM/90%SCM, 100%SCM, accordingly. In this way, serum elimination and medium substitution were implemented at a synchronized pace. The ratio was determined based on our in-house unpublished results, and the SCMs to be screened were FreeStyle\u0026trade;293 (12338-018, Gibco), BalanCD HEK293 (91165, FUJIFILM), CD293 (11913-019, Gibco) and Celer-S001 (FG0104003, Bioenine). At each serum concentration, cells were maintained for at least three consecutive passages to fully accommodate the current condition. Cells were advanced to the next lower serum concentration when they reached approximately 80% confluence within 72h as an indicator that cells had likely recovered their growth capacity; otherwise, they were maintained at the current serum level until this criterion was satisfied.\u003c/p\u003e\n\u003ch3\u003eSuspension adaptation and suspension cell culture\u003c/h3\u003e\n\u003cp\u003eWhen HEK293 cells performed stably in the serum-free adherent culture, they were transferred into 125 mL Erlenmeyer flasks (781011, Nest) at a seeding density of (0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) \u0026times;10⁶ cells/mL in 30mL working volume. Suspension cultures were incubated in an orbital shaker (ZWYC-290A, Zhicheng) at 130rpm (25mm shaking diameter) in a 37\u0026deg;C, 8%CO₂, 80%RH environment. Cells were passaged every three to four days at the preseted seeding density. The population doubling time (PDT) was calculated as follows for each passage:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:PDT=\\frac{t\\times\\:ln\\left(2\\right)}{ln\\left(\\frac{{N}_{t}}{{N}_{0}}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere PDT is the population doubling time; t is the culture duration; N\u003csub\u003et\u003c/sub\u003e is the cell density at time point t; N\u003csub\u003e0\u003c/sub\u003e is the initial cell density.\u003c/p\u003e \u003cp\u003eWhen the PDT became settled for three consecutive passages, the cells were considered successfully adapted to suspension culture. In some studies, a HEK293-SUS-CD374M (CD374M) cell strain was introduced as a reference. It is a well-adapted suspension cell from HEK293 in our lab by a direct adaptation approach, which will be explained later in the discussion section.\u003c/p\u003e\n\u003ch3\u003eCell counting, viability, clumping and diameter\u003c/h3\u003e\n\u003cp\u003eCell density and viability for both adherent and suspension cultures were determined using an automated cell counter (IC1000, CountStar). Adherent cells were counted after digestion and resuspension, while suspension cells were sampled and counted directly. 10\u0026micro;L cell suspension was mixed evenly with 10\u0026micro;L trypan blue stain solution (T10282, Invitrogen) and loaded onto a counting chamber (M17, CountStar). Cell number, viability, clumping and diameter were analyzed automatically using the instrument\u0026rsquo;s proprietary software.\u003c/p\u003e\n\u003ch3\u003eExtracellular metabolite analysis\u003c/h3\u003e\n\u003cp\u003eThe culture supernatants, which were collected from cell cultures after centrifugation at 2,000g, 10min, were examined using a biochemical analyzer (M900, Sieman) to determine the concentrations of glucose and lactate by glucose oxidase enzyme electrode-membrane method and lactate oxidase enzyme electrode membrane method, respectively.\u003c/p\u003e \u003cp\u003eThe glucose consumption rate and lactate accumulation rate were calculated using following equations:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:qGlc=\\frac{1}{VCD}\\times\\:\\frac{dGlc}{dt}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:qLac=\\frac{1}{VCD}\\times\\:\\frac{dLac}{dt}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere gGlc is the glucose consumption rate; qLac is the lactate accumulation rate; VCD is the viable cell density; dGlc and dLac are the concentration changes of glucose and lactate in a given time period; and t is the time period.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell cycle distribution by flow cytometry\u003c/h2\u003e \u003cp\u003eCell cycle distribution was sorted using the Cell Cycle and Apoptosis Analysis Kit (C1052, Beyotime). Briefly, 1\u0026times;10⁶ cells collected in their logarithmic growth phase were centrifuged at 1,000g for 5 min and washed three times with Dulbecco\u0026rsquo;s phosphate buffered saline (DPBS, 14190144, Gibco). Cells were then fixed in 1 mL of 70% prechilled ethanol at 4\u0026deg;C for 2h. After fixation, cells were pelleted again at 1,000g for 5 min, washed once with DPBS to remove residual ethanol, and resuspended in the staining buffer containing propidium iodide (PI). Samples were analyzed using a flow cytometer (CytoFLEX LX, Beckman Coulter), and 10,000 events were collected per sample. The DNA content histogram was analyzed using FlowJo software to discriminate the percentage of cells in Gap 0 phase (G0), Gap 1 phase (G1), Synthesis phase (S), Gap 2 phase (G2) and Mitosis phase (M).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell adhesion assay\u003c/h3\u003e\n\u003cp\u003eTo quantify the adhesion capacity of cells, adherent cells in 10% serum concentration cultured for 72h were used to establish a standard curve. Cells were seeded into 96-well plates at an initial density of 4\u0026times;10⁴ cells/well, followed by 1:2 serial dilution to generate five concentrations, each with two replicates. After incubation at 37\u0026deg;C for 4h, the cells were considered tightly attached to the surfaces and ready to construct a standard curve.\u003c/p\u003e \u003cp\u003eFor adhesion analysis, both adherent and suspension HEK293 cells were seeded at 1\u0026times;10⁴ cells/well in DMEM containing their respective serum concentrations or suspension culture medium with another two parallel wells per group. After incubation at 37\u0026deg;C for 1h, the supernatant was carefully removed, and non-adherent cells were therewith cleared away after two repeats of DPBS wash. Each well was then supplemented with 90\u0026micro;L fresh DMEM and 10\u0026micro;L WST-8 from a cell count kit-8 (40203ES60, Yeasen) and incubated for 2h at 37\u0026deg;C. The absorbance at 450nm (OD\u003csub\u003e450\u003c/sub\u003e) was measured using a microplate reader (SpectraMax M5, Molecular Devices). The number of adhered cells was calculated based on the standard curve to quantify the adhesion capacity.\u003c/p\u003e\n\u003ch3\u003eAdenovirus infection and viral tittering\u003c/h3\u003e\n\u003cp\u003eThe productivity of rAdV by suspension-adapted HEK293 cells was compared by getting infected with rAdV working stocks, which were established and preserved in in a -70\u0026deg;C cryogenic freezer for long-term storage in Weijiangbo Laboratory, National Vaccine and Serum Institute, at a multiplicity of infection (MOI) of 3, with a cell density of 1.5\u0026times;10⁶ cells/mL in a 30 mL culture volume. The MOI is the quantitative ratio of infectious viruses against cells to be infected, which can be calculated by:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:MOI=\\frac{virus\\:titer\\times\\:Vv}{VCD\\times\\:V}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere MOI is the abbreviation of multiplicity of infection; V\u003csub\u003ev\u003c/sub\u003e is the volume of viruses; VCD is the viable cell density; and V represents the volume of cell suspension.\u003c/p\u003e \u003cp\u003eCultures were maintained on an orbital shaker at 37\u0026deg;C, 8%CO₂, 80%RH, and 110 rpm with two replicates. The parameters for MOI, seeding density, harvest time, and cultivation conditions were based on design of experiment (DOE)-powered, unpublished data in our laboratory. Two rAdVs were tested here, specifically, a rAdV encoding herpes simplex virus type 2 (HSV) glycoprotein D (rAdV-gD2) as an HSV-2 vaccine and another encoding rabies virus glycoprotein (rAdV-G) as a rabies vaccine. They were constructed on the basis of an Adeno-X\u0026trade; Adenoviral System 3 (632269, Takara) and antigenic inserts, whose sequence information were gathered from the Uniprot database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/uniprotkb/Q69467/entry\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/uniprotkb/Q69467/entry\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e for HSV-gD2 sequence and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/uniprotkb/P03524/entry\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/uniprotkb/P03524/entry\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e for rabies-G sequence). The detailed construction process is not discussed here. After 48h post-infection, the cultures were transferred into 50mL centrifuge tubes and then subjected to three repeated cycles of \u0026lsquo;freeze-thaw\u0026rsquo; in liquid nitrogen and 37\u0026deg;C water bath to release viral particles. The crude rAdVs were harvested by collecting the supernatant after a 2,000g, 10min centrifugation of cell lysates.\u003c/p\u003e \u003cp\u003eRecombinant viruses were tittered using Adeno-X\u0026trade; Rapid Titer Kit (632250, TaKaRa). Briefly, HEK293 cells in the logarithmic growth phase were digested, centrifuged, resuspended then seeded into 24-well plates at 2.5\u0026times;10⁵ cells/mL in 1mL DMEM per well. While cells were maintained in the 37\u0026deg;C incubator for 1 h, the virus samples were prepared by ten-fold serial dilution in DPBS, and 50\u0026micro;L of each dilution was added to per well with two replicates in the 24-well plates. The supernatant was discarded after 48h incubation and cells were fixed with 0.5 mL methanol. The fixed cells were sequentially incubated with Mouse Anti-Hexon Antibody and Rat Anti-Mouse Antibody one and another for 1h each, with repeated washes by 100\u0026micro;L PBS\u0026thinsp;+\u0026thinsp;1% bovine serum albumin (BSA) between the two steps. An aliquot of 250\u0026micro;L 3,3' diaminobenzidine (DAB) working solution, formulated by 90% stable peroxidase buffer and 10% DAB substrate (10\u0026times;), was added into each well for 10min at room temperature (RT) for coloration. Following color development, positive cells were observed and counted under a light microscope, and the titer was calculated.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomics: RNA extraction, library preparation, sequencing and data analysis\u003c/h2\u003e \u003cp\u003eSamples containing 6\u0026times;10⁶ cells were collected in the logarithmic growth phase from serum-free suspension-adapted cells, 0% serum adherent cells, 1% serum adherent cells, and 10% serum adherent cells. Independent biological triplicates were prepared for each condition. Total RNA was extracted using Trizol reagent (R0016, Beyotime) according to the manufacturer\u0026rsquo;s instructions. RNA integrity was assessed using an Agilent 5400 Bioanalyzer (Agilent Technologies) and agarose gel electrophoresis.\u003c/p\u003e \u003cp\u003eRNA-seq libraries were prepared using the NEBNext\u0026reg; Ultra\u0026trade; RNA Library Prep Kit for Illumina\u0026reg; (E7770, New England Biolabs). Library construction comprised mRNA enrichment with oligo(dT) magnetic beads, random fragmentation, first-strand and second-strand cDNA synthesis, end repair and A-tailing, adapter ligation, and PCR amplification. Libraries were quantified using a Qubit 2.0 Fluorometer and diluted to 1.5ng/\u0026micro;L. Insert size distributions were assessed on an Agilent 2100 Bioanalyzer. Libraries with expected insert sizes were further quantified by RT-qPCR to determine the effective library concentration (\u0026ge;\u0026thinsp;1.5nM). Qualified libraries were sequenced using an Illumina NovaSeq 6000 platform using paired-end 150 bp reads, yielding at least 6 G clean data per sample.\u003c/p\u003e \u003cp\u003eRaw reads were quality-controlled with fastp (v0.19.7) using the parameters (-g-q5-u50-n15-l150) to obtain Clean Reads, which were then aligned to the human reference genome GRCh38/hg38 using HISAT2 (v2.2.1). Gene counts were summarized with featureCounts and normalized to FPKM for expression estimation. Differential expression analysis was performed using DESeq2 (R package); Differentially expressed genes (DEG) were defined as |log₂FoldChange|\u0026ge;1 and padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Identified DEGs were subjected to functional enrichment and pathway analysis using clusterProfiler (v4.8.1) with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Gene set enrichment analysis (GSEA) was conducted using GSEA software (v4.3.2) with parameters (-permute phenotype -metric Signal2Noise set_min 15-set_max 5000-plot_top_x50).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProteomics: Protein extraction, proteomics analysis and data processing\u003c/h2\u003e \u003cp\u003e2\u0026times;10⁶ cell samples were collected in their logarithmic growth phase from serum-free suspension-adapted cells, 0% serum adherent cells, 1% serum adherent cells, and 10% serum adherent cells, respectively, with independent biological triplicates per condition. Cells were lysed by adding an appropriate volume of protein lysis buffer (555899, DB biosciences), vortexed gently, and sonicated in an ice\u0026ndash;water bath for 5min to ensure complete disruption. Lysates were centrifuged at 12,000g for 15min at 4\u0026deg;C, and the supernatants were collected. An appropriate amount of 1M dithiothreitol (DTT) was added to the supernatant, and samples were incubated at 56\u0026deg;C for 1h for reduction, followed by a 2-minute ice bath. Iodoacetamide (IAM) was then added, and samples were kept at RT in the dark for 1h for alkylation. Protein concentrations were determined using a Bradford protein assay kit (P0006C, Beyotime) according to the manufacturer\u0026rsquo;s instructions. For SDS\u0026ndash;PAGE, 20\u0026micro;g of each sample was loaded per lane on a 12% gel. Electrophoresis conditions were 120V for 20 min for the stacking gel and 150V for 50 min for the resolving gel. Gels were stained with Coomassie Brilliant Blue R250 and destained until bands were clear. For digestion, protein samples were adjusted to a final volume of 100\u0026micro;L with DB protein lysis buffer. Trypsin and 100 mM tetraethylammonium bromide (TEAB) buffer were added, mixed, and incubated at 37\u0026deg;C for 4h for proteolytic digestion. The digestion was quenched by lowering the pH to \u0026lt;\u0026thinsp;3 with formic acid, mixed, and centrifuged at 12,000g for 5min at RT. The supernatant was slowly passed through a C18 desalting column. The column was washed three times with wash solution (0.1% formic acid, 3% acetonitrile), and peptides were eluted with elution solution (0.1% formic acid, 70% acetonitrile). Eluates were collected and lyophilized for later analysis.\u003c/p\u003e \u003cp\u003eMobile phase A (99.9% water, 0.1% formic acid) and mobile phase B (80% acetonitrile, 0.1% formic acid) were prepared before the analysis. Lyophilized powder was dissolved in 10\u0026micro;L of mobile phase A, centrifuged at 4\u0026deg;C and 14,000 g for 20 minutes. 200ng of the supernatant was taken for sample loading and further liquid chromatography-mass spectrometry (LC-MS) analysis. Vanquish Neo UHPLC system with a C18 precolumn (174500, 5 mm\u0026times;300 \u0026micro;m, 5\u0026micro;m, Thermo Fisher Scientific) was heated at 50\u0026deg;C in the column oven. The C18 analytical column was ES906 (PepMap\u0026trade; Neo UHPLC, 150\u0026micro;m\u0026times;15cm, 2\u0026micro;m, Thermo Fisher Scientific). LC elution conditions were not described here. An Orbitrap astral mass spectrometer with an easy-spray (ESI) ion source was employed, set at 2.0 kV ion spray voltage and 290\u0026deg;C ion transfer tube temperature. Data-independent acquisition (DIA) mode was chosen with primary mass spectrometry full-scan m/z acquisition range set to 380\u0026ndash;980 and resolution of 240,000 (at 200 m/z). Automatic generation control (AGC), parent ion window size, DIA window count, and normalized collision energy (NCE) were set to 500%, 2 Th, 300, and 25% respectively. The secondary mass spectrometer m/z range was 150\u0026ndash;2000, with a secondary mass spectrometer resolution of 80,000 and a maximum injection time of 3ms. Raw mass spectrometry detection data were produced as (.raw).\u003c/p\u003e \u003cp\u003eRaw data file analysis was executed using the DIA-NN database search software. Database search parameters were set as follows: automatic determination and correction of mass deviations for precursor and fragment ions; fixed modifications set to alkylation at cysteine residues; N-terminal methionine loss as a variable modification; and a maximum of two missing sites allowed. To enhance analytical quality, DIA-NN further filtered results: retaining only peptides with Global.Q.Value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and proteins with PG.Q.Value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. For differential protein screening: Up-regulated proteins were identified when FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and down-regulated proteins were identified when FC\u0026thinsp;\u0026lt;\u0026thinsp;0.67 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eInterProScan software performs gene ontology (GO) and InterPro functional annotation (including Pfam, PRINTS, ProDom, SMART, ProSite, and PANTHER databases), while clusters of orthologous groups (COG) and Kyoto Encyclopedia of genes and genomes (KEGG) analyzed identified proteins for functional protein families and pathways. Volcano plot analysis, clustering heatmap analysis, and GO, InterPro, and KEGG enrichment analysis are conducted for differential enrichment analysis (DEA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics: Metabolites extraction, data processing, identification, and analysis\u003c/h2\u003e \u003cp\u003eCell samples from serum-free suspension-adapted cells, 0% serum adherent cells, 1% serum adherent cells, and 10% serum adherent cells were collected in their logarithmic growth phase and frozen in liquid nitrogen, with another two independent replicates per condition. The samples (1.5\u0026times;10⁶ cells) were resuspended with pre-cooled 80% methanol by vortex, and then melted on ice and vortexed again for 30s. After a 6-min sonification and centrifugation at 5,000 rpm, 4\u0026deg;C for 1 min, the supernatant was freeze-dried and redissolved with 10% methanol for test. The sample preparation was finished, and the solution was transferred into the LC-MS/MS system for following analysis. A Vanquish UHPLC system (ThermoFisher, Germany), in conjunction with an Orbitrap Q ExactiveTM HF mass spectrometer or Orbitrap Q ExactiveTMHF-X mass spectrometer (Thermo Fisher, Germany) were used for UHPLC-MS/MS analysis. Samples were injected into a Hypersil Goldcolumn (100\u0026times;2.1mm, 1.9\u0026micro;m) in a 12-min linear gradient at 0.2 mL/min. The eluents were eluent A (0.1% FA in Water) for positive polarity mode and eluent B (Methanol) for negative polarity mode. The solvent gradient was set as: 2% B, 1.5 min; 2\u0026ndash;85% B, 3 min; 85\u0026ndash;100% B, 10 min; 100-2% B, 10.1 min; 2% B, 12 min. Q ExactiveTM HF mass spectrometer was performed in positive/negative polarity mode with spray voltage on 3.5 kV, capillary temperature at 320\u0026deg;C, sheath gas flow rate at 35 psi/aux gas flow rate of 10 L/min, S-lens RF level at 60, 350\u0026deg;C aux gas heater temperature.\u003c/p\u003e \u003cp\u003eThe data were later processed by XCMS to perform peak alignment, picking, and metabolite quantitation. Based on adduct ions and setting mass deviation to 10 ppm, high-quality secondary spectrum database was used for metabolite comparison and identification. Following background noise elimination, the preliminary quantitative results were normalized to obtain relative peak areas by:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:Relative\\:peak\\:areas=Raw\\:quantitative\\:value\\:of\\:samples\\times\\:\\frac{The\\:sum\\:of\\:quantitative\\:value\\:of\\:QC1}{The\\:sum\\:of\\:quantitative\\:value\\:of\\:samples}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCompounds whose coefficient of variation (CV) of relative peak areas in QC samples were larger than 30% were removed. Final data handling was performed on Linux operating system (CentOS version 6.6), using R and Python.\u003c/p\u003e \u003cp\u003eMetabolites were annotated using the KEGG database, human metabolome database (HMDB) and LIPIDMaps database. Principal components analysis (PCA) and Partial least squares discriminant analysis (PLS-DA) were performed for intergroup comparison. The metabolites with variable importance in projection (VIP) value\u0026thinsp;\u0026gt;\u0026thinsp;1, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (t-tests) and foldchange\u0026thinsp;\u0026ge;\u0026thinsp;2 or FC\u0026thinsp;\u0026le;\u0026thinsp;0.5 were considered statistically differential. Volcano plots were used to filter metabolites of our interest based on log2(Fold Change) and -log10(p-value) of metabolites by ggplot2 in R language. For clustering heat maps, the data were normalized using z-scores of the intensity areas of differential metabolites and were plotted by Pheatmap package in R language. The correlation between differential metabolites were analyzed in R language. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant and correlation plots were plotted. Functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathways enrichment of differential metabolites was performed and when P-value of metabolic pathway\u0026thinsp;\u0026lt;\u0026thinsp;0.05, metabolic pathway was considered as statistically significant enriched.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome\u0026ndash;proteome integrated analysis\u003c/h2\u003e \u003cp\u003eRaw RNA-seq count data were processed with DESeq2 to obtain normalized expression values and gene-level log\u003csub\u003e2\u003c/sub\u003efold-changes and adjusted p-values. Protein intensity matrices (DIA quantification) were log\u003csub\u003e2\u003c/sub\u003e-transformed and median-normalized prior to analysis. Proteins quantified in fewer than two biological replicates per group were excluded from downstream analysis. For both datasets, principal component analysis (PCA) and hierarchical clustering were used to assess sample quality and identify outliers. Transcript identifiers were mapped to gene symbols/UniProt accessions using biomaRt. Proteomics protein groups were mapped to the corresponding gene symbols when possible. The intersection set was defined and used as the basis for pairwise comparisons and joint modelling. Differential expression for transcripts was determined by DESeq2; differential proteins were identified using the statistical test described in the proteomics analysis pipeline. For consistency across omics layers, features were categorized as upregulated, downregulated or not significant using the aforementioned thresholds.\u003c/p\u003e \u003cp\u003eFor the intersection set we computed pairwise correlations between transcript and protein. Pearson correlation coefficient and associated p-value were reported to assess linear concordance; Spearman rank correlation was calculated as a nonparametric robustness check. To summarize concordance/discordance at the single-feature level, we implemented a nine-quadrant classification: transcript log\u003csub\u003e2\u003c/sub\u003eFC (x-axis) and protein log\u003csub\u003e2\u003c/sub\u003eFC (y-axis) were partitioned and generated nine sectors that captured concordant up/down, discordant, and nonresponsive categories. Counts per sector were reported and the distribution was tested where appropriate to assess enrichment of concordant vs discordant.\u003c/p\u003e \u003cp\u003eTo decompose shared and dataset-specific variation and to nominate joint molecular drivers, two-way orthogonal partial least-squares (O2PLS) modelling was applied to the intersection matrices. O2PLS modelling was performed with the OmicsPLS implementation (R) using cross-validation to select the number of joint and orthogonal components. Model performance metrics (R2X, R2Y, Q2) and permutation testing were used to evaluate model robustness and to avoid overfitting. Features were ranked by their absolute joint loadings and the top contributors were reported as candidate drivers of suspension adaptation. Gene Ontology (GO) and KEGG enrichment analyses were carried out on sets derived from (i) concordant features, (ii) discordant features, and (iii) top O2PLS drivers to probe biological processes and pathways mostly associated with suspension adaptation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eHEK293 adherent cells were successfully adapted to suspension culture and showed different growth, glucose intake and lactate generation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHEK293 cells were originally cultured in DMEM complemented with 10% FBS plus 1%PS in tissue culture-treated flat-bottom flasks. We employed the sequential adaptation strategy to adapt adherent cells to suspension cells by decreasing the serum content from 10%, 7%, 3%, 1% to 0% gradually with medium replacement at the same time and thereafter transferred the cells to shaking flasks. We screened four commercially available suspension culture media for medium replacement, and cells survived in three of them, which were FreeStyle\u0026trade;293, BalanCD HEK293 and CD293. Survived cells were named in correspondence to their culture medium as: HEK293-Sus-Freestyle (FS293), HEK293-Sus-BalanCD (BLCD293), and HEK293-Sus-CD293 (CD293). These cells behaved differently in growth performance, and adherent cells in different serum gradients in the adaptation process also exhibited different growth rates, as shown in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. All cells, either adherent or suspension, were maintained to keep the same passage number to ensure comparability. Adherent cells manifested a similar growth pattern that aligned with typical cell growth kinetics. They were initially seeded at 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/well in 6-well plates and stayed in the latent phase for nearly three days. From day 3, cells entered a logarithmic growth phase until day 7, when they reached a plateau phase due to nutrient depletion or density-dependent contact inhibition, characterized by a comparatively slower, even stationary growth. The density of serum-free adherent cells declined swiftly on day 8 and day 9, indicating a programmed cell death due to nutrient deprivation.\u003c/p\u003e \u003cp\u003eIn comparison to the adherent cells, adapted suspension cells were prone to starting a logarithmic growth phase earlier, from day 1 until day 5. Results also showed that the control cells-CD374M achieved maximum cell density at approximately 7\u0026times;10⁶ cells/mL, higher than the density of FS293, BLCD293 and CD293, around 4\u0026times;10⁶ cells/mL. The PDTs in 72h of FS293, BalanCD293, and CD293 were close to each other, which were 38.01h, 39.14h and 39.22h, respectively. Albeit lower than our previously established CD374M cell strain, their PDT and their maximum density satisfied our production requirements. The lower density might result from cell aggregation and incomplete adaptation to shear force, which could possibly be solved in the future by the addition of anti-clumping agents, longer adaptation culture and refinement of culture parameters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1.\u003c/b\u003e HEK293 adherent cells were successfully adapted to suspension culture and showed different growth, glucose intake and lactate generation. Figure\u0026nbsp;1A, the cell growth curve of adherent cells in different serum concentrations and proportions of DMEM in the culture media. The upright legend of percentage referred to the serum concentration and also indicated the percentage of DMEM (multiply by ten) in the total culture medium, specifically, 10%:100%DMEM (10% serum), 70%:70%DMEM/30%SCM (7% serum); 30%: 30%DMEM/70%SCM (3% serum), 10%:10%DMEM/90%SCM (1% serum), 0%:100%SCM (0% serum). Figure\u0026nbsp;1B, the cell growth curve of suspension-adapted cells in three different suspension culture media, plus one control cell, CD374M, as a reference. Figure\u0026nbsp;1C, glucose consumption and lactate production of adherent cells in different serum concentration groups. Figure\u0026nbsp;1D, glucose consumption and lactate production of suspension-adapted FS293 cells. Figure\u0026nbsp;1E, glucose consumption rate of adherent cells in different groups after normalization to standardize the initial glucose concentration. The percentage referred to the serum concentration and also indicated the percentage of DMEM (multiply by ten) in the total culture medium, specifically, 10%:100%DMEM (10% serum), 70%:70%DMEM/30%SCM (7% serum); 30%: 30%DMEM/70%SCM (3% serum), 10%:10%DMEM/90%SCM (1% serum), 0%:100%SCM (0% serum).\u003c/p\u003e \u003cp\u003eGlucose consumption and lactate accumulation are strictly monitored metabolites during large-scale, cell-based bioproduction because glucose is a necessary carbon source for cells to build the cytoskeleton and proliferate, while lactate is an undesirable chemical generated from the glycolysis pathway and is recognized as a hazard to cell function and yield of the target product. Therefore, we were also interested in the concentration of glucose and lactate during the suspension adaptation as well as in the adapted suspension cells. We dynamically monitored the concentrations of glucose and lactate in the culture supernatant. As the cells were adapted using a gradual serum replacement method with medium substitution, the initial glucose concentrations in the culture media differed across the experimental groups. The glucose concentration data for each group were normalized to eliminate this variability and enable an accurate comparison of glucose consumption.\u003c/p\u003e \u003cp\u003eAs illustrated in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e, the rate of glucose consumption demonstrated a significantly positive correlation with the serum concentration in the culture medium. The \u003cem\u003eqGlc\u003c/em\u003e values for adherent cells in 10%, 7%, 3%, 1%, 0% serum groups and suspension cells (FS293) were 0.27, 0.185, 0.134, 0.135, 0.266 and 0.15\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{mg∕}{\\text{10}}^{\\text{6}}\\text{cells}\\text{\u0026times;}{\\text{day}}^{\\text{-1}}\\)\u003c/span\u003e\u003c/span\u003e, respectively. In other words, given the same seeding density, the cells exhibited a gradually slower glucose uptake as adaptation proceeded. Specifically, the 10% serum group (10%-GLU) exhibited the fastest glucose consumption rate, with glucose being nearly used up by day 9. As the serum concentration decreased, the rate of glucose consumption progressively slowed down, with the slowest rate observed in the serum-free group (0%-GLU). Meanwhile, the lactate accumulation rate showed a trend that was highly in a reverse manner as the glucose consumption rate. The \u003cem\u003egLac\u003c/em\u003e calculations were 0.256, 0.160, 0.107, 0.101, 0.198 and 0.072 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{mg∕}{\\text{10}}^{\\text{6}}\\text{cells}\\text{\u0026times;}{\\text{day}}^{\\text{-1}}\\)\u003c/span\u003e\u003c/span\u003e, respectively. The 10% serum group had the highest rate of lactate production. As the serum concentration was reduced, the rate of lactate generation decelerated correspondingly, with the serum-free group producing the lowest amount of lactate. The \u003cem\u003eqGlc\u003c/em\u003e and \u003cem\u003eqLac\u003c/em\u003e data suggest that HEK293 cells cultured in 10% serum concentration were more active in metabolic activities. It was interesting to observe that suspension-adapted cells without serum nourishment had relatively high \u003cem\u003eqGlc\u003c/em\u003e and \u003cem\u003eqLac\u003c/em\u003e rates (\u003cb\u003eFig.\u0026nbsp;1D)\u003c/b\u003e. It might be reasoned that cells without serum supplement need to promote their metabolism to accustom themselves to the serum-free environment, but it requires further investigation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCells in each serum condition and suspension manifested differently in morphology, cell cycle distribution and adhesion ability\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAdapting and adapted cells underwent morphological changes during suspension adaptation. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA demonstrated that adherent HEK293 cells in DMEM containing 10% serum maintained an epithelium-like morphology, identified as irregular polygons with distinct boundaries. As the adaptation went in progress, some cells could not tolerate the serum-reducing culture condition, and as a consequence, they detached from the surface and turned a spherical appearance floating in the medium. We tried to collect these floating cells and subculture them, but the cell viability was less than 80% at the time of collection then even became lower than 10% after passage, indicating that these floating cells could not adapt and survive. The epithelial appearance of adherent cells was kept identifiable until the complete eradication of the serum component. In the serum-free condition, adherent cells seemed unable to maintain their normal morphology. Instead, they became more rounded, aggregated as a cluster, and loosened in intracellular and cell-surface connection with blurred boundaries observed under an optical microscope (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). When transferred and sufficiently adapted to suspension culture from 0% serum adherent culture, most cells existed as spherical individuals, along with some cell clumps. There was also an obvious change in cell diameter before and after the suspension adaptation, as shown in the size distribution charts in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e, with a 15.65\u0026micro;m in diameter of the original adherent cells versus the adapted suspension cells, which had an average diameter of 16.07\u0026micro;m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCell cycle distribution provides information on the exact percentage of different cycle phases of a group of cells and ultimately helps to understand cell dynamics and activities. Therefore, we analyzed the cell cycle distribution as an explanation for the changes in cell growth rate, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. We observed a relationship between serum concentration and the population proportion of G2/M phase, during which cells grew and divided. The results revealed that as the serum concentration in the medium was gradually reduced from 10% to 0%, the total proportion of cells in the DNA synthesis phase and G2/M phase also decreased in degrees. Under serum-free conditions, this proportion dropped to a minimum level at approximately 31.92%. This might indicate that growth factors present in the serum are critical signals affecting the cell cycle. The absence of serum leads to G0/G1 phase arrest in the majority of the cell population, severely inhibiting their proliferative capacity. However, a shift was observed in the cells that had been successfully adapted to serum-free suspension culture. The proportion of these suspension cells in the S\u0026thinsp;+\u0026thinsp;G2/M phases rebounded substantially to 46.97%. This figure is not only significantly higher than that of adherent cells cultured in serum-free medium (31.92%) but also surpasses that of adherent cells grown under optimal conditions with 10% serum (44.36%).\u003c/p\u003e \u003cp\u003eBased on our perception of the suspension adaptation, a change in the adhesive strength of cells to the surface might occur. Therefore, we performed CCK-8-based adhesion assays on adherent cells cultured in media containing 10%, 7%, 3%, 1% or 0% serum and suspension-adapted cells (each group, n\u0026thinsp;=\u0026thinsp;3) to evaluate cell adhesion ability. The adhesion ability was defined as the number of cells that attached to a surface over a given time period and was calculated using a high-quality, linear-correlation standard curve whose coefficient of determination, R-squared, was greater than 0.99, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC after calculation. Cells routinely cultured with 10% serum showed the highest adhesion (13655.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3000.3), followed by 1% (10095.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1382.8). The 3% serum group showed intermediate adhesion (8976.0\u0026thinsp;\u0026plusmn;\u0026thinsp;735.4), whereas the 0% serum group exhibited lower adhesion (6988.9\u0026thinsp;\u0026plusmn;\u0026thinsp;389.9), which was comparable to the value of the suspension cells (7396.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1892.0). Overall, cells cultured in 10% serum exhibited markedly greater adhesive capacity than cells cultured in low or serum-free conditions, and a loss of adhesive capacity was seen in the suspension cells and in the adherent cells with less serum component.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSuspension cells were capable of producing recombinant adenoviral vectors encoding different antigens\u003c/h2\u003e \u003cp\u003eWe compared the adenoviral production capacity of the suspension-adapted cell lines using two rAdVs. These two rAdVs had a similar adenoviral type 5 backbone but were inserted with different transgenes, rabies virus glycoprotein and HSV-2 glycoprotein D, respectively. The reason why two rAdVs were tested here was that the size and properties of the insertion sequence as well as the final translated product would influence the cell function and cell productivity of the viral vectors, and we would like to screen out a preferable cell line that was universally capable of producing rAdV effectively regardless of the insertion sequence.\u003c/p\u003e \u003cp\u003eCell density was adjusted to 1.5\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/mL and infected with rAdVs at an MOI of 3. The viruses were harvested 48h post-infection and tittered. The results showed that the FS293 cell line achieved the highest viral yield for both rAdVs, approximately tenfold higher than that of the control CD374M cells, reaching about 9\u0026times;10⁷ IFU/mL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAdherent HEK293 cells and suspension-adapted cells had different gene transcription\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) revealed a clear separation of samples according to serum concentration, indicating a progressive shift in the transcriptome as serum was gradually reduced. Adherent cells cultured with 10% serum (A10) formed a tight cluster, and fully suspension-adapted cells (SA_F) also clustered distinctly. Notably, A0 (adherent, 0% serum) samples localized closer to the SA_F cluster, suggesting that A0 represents a key transitional state from adherent to suspension phenotypes. We observed heterogeneity within the A1 group: two replicates (A1_1 and A1_3) clustered closely together, while A1_2 was displaced in PCA space (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Pairwise correlation analysis showed that most within-group correlations exceeded 0.97, whereas correlation of A1_2 with A1_1/A1_3 was ~\u0026thinsp;0.94, lower than in other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These findings indicated that cells cultured at 1% serum occupied a highly dynamic transitional state with elevated transcriptional heterogeneity. Consistently, hierarchical clustering of differentially expressed genes placed A0 in closer adjacency to SA_F, whereas A10 clustered with one A1 sample and the remaining two A1 replicates formed a separate cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Overall, complete serum withdrawal drove adherent cells toward a transcriptomic state similar to suspension cells, while intermediate low-serum conditions produce a bifurcating population in which some cells retain a serum-like profile and others begin to acquire suspension-like features.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo elucidate the transcriptional reprogramming underlying adaptation of HEK293 cells from adherent culture to suspension, we performed RNA-seq on three biological replicates per condition and conducted differential expression analysis. Using DESeq2 with thresholds of adjusted p\u0026thinsp;\u0026le;\u0026thinsp;0.05 and |log₂FC| \u0026ge; 1, we identified 2,476 DEGs, of which 1,218 were upregulated in suspension (49.1%) and 1,258 downregulated (50.9%), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eH. The balanced distribution of up-regulated and down-regulated genes indicates bidirectional transcriptional remodelling rather than a unidirectional activation or repression. Differential gene enrichment analysis can relate these DEGs to specific biological functions, processes and pathways, and contribute to our understanding of how these DEGs influence cell function and phenotype. There are several tools to study and annotate the DEGs, each relying on particular databases and reflecting different aspects of genes. The gene analytical tools utilized in this article were GO, KEGG and GSEA enrichment analysis to highlight the functional switches in cells and to elucidate the mechanism of suspension adaptation.\u003c/p\u003e \u003cp\u003eGO enrichment analysis categorizes all genes into three groups based on their functions: cellular component (CC), biological process (BP) and molecular function (MF). As the GO analysis indicated, the up-regulated genes in suspension cells versus adherent cells were enriched in cell-cell adhesion via plasma-membrane adhesion molecules (GO:0098742), synapse organization (GO:0050808), and regulation of monoatomic ion transmembrane transport (GO:0034765). There were also upregulations in the pathways that involved in neural functions, such as regulation of amine transport (GO:0015837), modulation of chemical synaptic transmission (GO:0050804) and axon development (GO:0061564) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e).\u003c/b\u003e On the opposite side, down-regulated genes were annotated to antigen binding (GO:0003823), receptor ligand activity (GO:0048018), embryonic organ development (GO:0048568) and response to nutrient levels (GO:0031667) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eKEGG offered another perspective to identify these DEGs by their biological pathways. The KEGG results showed that up-regulated DEPs were significantly enriched in pathways related to signal transduction and neural activities, and down-regulated genes were mainly enriched in cytokine-cytokine receptor interaction (hsa04060), TGF-beta signalling pathway (hsa04350), and biosynthesis of amino acids Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e(F,G)\u003c/b\u003e. GSEA, on the other hand, provided a more comprehensive gene analysis beyond just examining individual genes, but considering the collective actions of functionally related genes, as a supplement to GO and KEGG enrichment study. In light of the GSEA method, up-regulated genes in suspension-adapted cells were enriched mainly in the regulation of mitotic nuclear division pathway (GO:0007088) and intermediate filament cytoskeleton organization pathway (GO:0045104). Meanwhile, down-regulated gene networks found in suspension-adapted cells were cellular response to nutrient pathway (GO:0031670), and intrinsic apoptotic signalling pathway (GO:0097193).\u003c/p\u003e \u003cp\u003eDuring our intergroup comparisons, we identified an intriguing gene, claudin 7 (CLDN7), whose expression levels in cells declined progressively from adherent culture to final adaptation to suspension. Significant differences between groups are detailed in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eI. We subsequently paid particular attention to the downstream product of the expressed gene- the CLDN7 protein, in our following proteomic analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAdherent HEK293 cells and suspension-adapted cells had different protein expression\u003c/h2\u003e \u003cp\u003eTo characterize proteomic changes associated with the transition of HEK293 cells from adherent culture (A10; 10% FBS) to serum-free suspension culture (SA_F), we performed DIA-based quantitative proteomics. Principal component analysis (PCA, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e) showed that A10 and SA_F samples were clearly separated along PC1 (accounting for 32.72% of the variance) and exhibited good within-group reproducibility. In total, 9,991 proteins were identified, of which 806 were considered differentially expressed proteins (DEPs): 414 proteins were upregulated and 392 downregulated in SA_F relative to A10 |log2Fold Change| \u0026ge; 0.58, P-value\u0026thinsp;\u0026le;\u0026thinsp;0.05, representing approximately 8.07% of the identified proteome (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e). Hierarchical clustering (heatmap; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cb\u003eC\u003c/b\u003e) further demonstrated consistent expression patterns of these DEPs within groups, with samples clustering into two primary branches corresponding to the treatments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLikewise, DEPS can be interpreted more clearly by enrichment analysis, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(D-G)\u003c/b\u003e. DEPs were significantly enriched in all three aspects of GO classification: BP, CC and MF. In terms of BP, up-regulated DEPS were mainly annotated to specific biological processes including regulation of cell growth, cell adhesion, oxidation\u0026ndash;reduction process, lipid biosynthetic process. Proteins that associated to cellular component were differentially expressed, particularly those structural proteins in extracellular matrix, extracellular region, integral component of membrane and intermediate filament. There were also enrichments in glycosaminoglycan binding, metal ion binding and endopeptidase activity to imply that suspension-adapted cells acclimatized themselves to suspension culture by differential expression of some proteins to affect molecular functions. Protein downregulations were clustered into several functions, namely, transport, localization, transmembrane transport, ion transport and immune response related processes in BP; plasma membrane, integral component of membrane, extracellular region and membrane protein complex in CC; and transporter activity, substrate-specific transmembrane transporter activity, ion binding and metal ion transmembrane transporter activity in MF.\u003c/p\u003e \u003cp\u003eTracking down these DEGs via KEGG approach to biological pathways where they play an active role, proteins participating in cell adhesion molecules, ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway were differentially expressed in an upregulated manner. From the other side, down-expressed proteins were significantly clustered in arrhythmogenic right ventricular cardiomyopathy (ARVC), hypertrophic cardiomyopathy (HCM), bile secretion and salivary secretion. These pathways were seemingly irrelevant to cell suspension adaptation; however, it is judicious to notice that ARVC and HCM pathways are involved with cellular skeletons and connexins, and consequently regulate the cell morphology and resistance to mechanical forces; bile secretion and salivary secretion pathways play a regulatory role in membrane transport, secretion and endocytosis of chemicals and nutrients.\u003c/p\u003e \u003cp\u003eRegarding the intriguing CLDN7 gene identified in the transcriptomic analysis, we observed that its protein expression pattern mirrored its gene expression: intracellular protein levels progressively diminished as the cells adapted to the suspension culture \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. We shall elaborate on this finding in the subsequent discussion section, given its biological function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAdherent HEK293 cells and suspension-adapted cells were different in metabolic activities\u003c/h2\u003e \u003cp\u003eTo further characterize the metabolic reprogramming of HEK293 cells following adaptation to serum-free suspension culture, we performed untargeted metabolomics analysis on SA-F and A10 cells (n\u0026thinsp;=\u0026thinsp;3 per group). Principal Component Analysis (PCA) revealed a distinct separation between the SA-F and A10 groups in both negative (NEG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and positive (POS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) ion modes, indicating a significant shift in the global metabolic profile. A total of 2,742 metabolites were identified using LC-MS/MS. Differential accumulated metabolites (DEMs) were screened based on the criteria of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0, and FC\u0026thinsp;\u0026gt;\u0026thinsp;1.2 or \u0026lt;\u0026thinsp;0.833. Under these thresholds, 275 DEMs were identified in NEG mode (118 up-regulated, 157 down-regulated; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) and 427 DEMs in POS mode (123 up-regulated, 304 down-regulated; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). When enriched via KEGG database, five pathways differed in different degrees between SA-F and A10 cells, as shown in \u003cb\u003eTable.1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1.\u003c/b\u003e Altered metabolic pathways between suspension-adapted cells and parental adherent cells by KEGG enrichment. MapID, the ID of the enriched KEGG pathway; MapTitle, the name of the enriched KEGG pathway; Pvalue, the P-value of the enrichment analysis; x, the number of differentially expressed metabolites associated with this pathway; y, the number of background (all) metabolites associated with this pathway; n, the number of KEGG-annotated differentially expressed metabolites; N, the number of KEGG-annotated background (all) metabolites.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMulti-omics integration analysis of differentially expressed genes and differentially expressed proteins of adherent HEK293 cells and suspension-adapted cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLinking transcriptomics and proteomics depicts a more comprehensive picture of the complete process and mechanism of gene transcription to its downstream expression of the actual participant in biological process-proteins, and pinpoints how the RNAs and proteins influence cell functions. To comprehensively characterize the regulatory relationship between gene transcription and protein expression during the adaptation process from adherent cells (A10) to suspension cells (SA_F), we performed an integrated analysis of the transcriptomic and proteomic datasets. Initially, we assessed the differential expression statistics across both omics layers. A substantial number of genes and proteins were quantified. Through intersection analysis of the quantified proteins and genes, we identified a set of co-quantified molecules, among which 130 exhibited significant differential expression in both datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA provides a detailed breakdown of these differentially expressed molecules: the transcriptomic analysis identified 1,258 downregulated and 1,218 upregulated genes, whereas the proteomic analysis revealed 392 downregulated and 414 upregulated proteins. In accordance with the Central Dogma, we performed a Pearson correlation analysis on the Log2 Fold Changes of the co-quantified molecules to evaluate the extent to which gene transcription influences protein expression. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, the transcriptome and proteome profiles for the SA_F vs. A10 comparison exhibited a weak positive correlation trend This relatively low correlation coefficient suggested that, while transcriptional changes served as the basis for protein expression, extensive and significant post-transcriptional or translational regulations occurred during the suspension adaptation of HEK293 cells. Consequently, changes in mRNA abundance did not linearly translate into changes in protein abundance. The Nine-Quadrant Plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eD) offers a more detailed characterization of the relationship between gene transcription and protein expression: Co-regulation were presented as dots located in the red (Quadrant 3) and dark blue (Quadrant 7) regions represent genes with consistent changing trends in both mRNA and protein levels. These genes were primarily transcriptionally driven, and the previously mentioned enzymes related to amino acid metabolism were largely distributed within these regions. Dots located in the green (Quadrants 1, 2, 4) and orange (Quadrants 6, 8, 9) regions represented genes where mRNA and protein changes are inconsistent, as discordant regulation. Notably, the presence of the orange region (mRNA downregulated or unchanged, but protein upregulated) suggest that cells were likely to maintain the homeostasis of key proteins, such as certain stress-response proteins, by enhancing translation efficiency or inhibiting protein degradation. Conversely, the green region (mRNA upregulated, but protein unchanged or downregulated) implied potential translational stalling or accelerated protein degradation, which aligned with the rapid clearance of obsolete structures during cytoskeletal remodelling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further identify the key driving factors contributing to the joint variation between the transcriptome and proteome, we employed two-way Orthogonal Partial Least Squares (O2PLS) analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eE displayed the top molecules with the highest contribution to the covariance between the two omics layers. At the proteome level, we found that proteins such as Hsp10, mitochondrial chaperonin indicating energy metabolism and stress response, Na+/K\u0026thinsp;+\u0026thinsp;ATPase alpha 1, indicating changes in ion transport function and macrophage migration inhibitory factor (MIF) exhibited extremely high loading values, indicating that they were core proteins distinguishing the SA_F state from the A10 state. At the transcriptome level, multiple genes identified by ENSG IDs (such as ENSG00000211459) also demonstrated high contribution weights. These high-loading molecules might serve as key regulatory nodes, driving the metabolic and structural remodelling networks during the suspension adaptation process.\u003c/p\u003e \u003cp\u003eWe then performed a joint GO and KEGG enrichment analysis on the co-differentially expressed molecules to elucidate how transcriptional regulation translated into final protein functional execution. The joint GO Analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eF) revealed key features of suspension adaptation across three dimensions: BP, CC and MF. In terms of BP, terms such as \"cellular amino acid biosynthetic process\" and \"glutamine family amino acid biosynthetic process\" exhibited significant enrichment in the proteome (represented by orange circles) with high enrichment ratios. This confirmed that suspension cells effectively execute metabolic reprogramming at the protein level to sustain proliferation. Furthermore, the enrichment of ion transport processes, such as \"sodium ion transport,\" implies alterations in the mechanisms regulating membrane potential and osmotic pressure.\u003c/p\u003e \u003cp\u003eRegarding cellular component, the analysis highlighted profound changes in cellular structure involving the remodelling of cytoskeleton and membrane complexes. Actin cytoskeleton and intermediate filament were significantly enriched in the proteome, which possibly accounted for the morphological transition of suspension cells from an adherent, flattened shape to a spherical form. Of particular note, the sodium-potassium-exchanging ATPase complex displayed an extremely high enrichment ratio and significance in the proteome. This corroborated our previous identification of the key driver protein Na+/K\u0026thinsp;+\u0026thinsp;ATPase alpha 1 in the O2PLS analysis. In terms of molecular function, the analysis revealed an adaptive strategy characterized by the remarkable upregulation of protease inhibitor activity. Peptidase inhibitor activity and endopeptidase inhibitor activity exhibited extremely high statistical significance in the proteome (dark red circles, P\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), whereas the significance at the transcriptional level was weaker. This strongly suggested that SA_F cells accumulated large amounts of protease inhibitors via post-transcriptional regulation to resist proteolytic stress in the serum-free suspension environment or to protect autocrine factors.\u003c/p\u003e \u003cp\u003eThe joint KEGG analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eG) further validated these findings. Pathways such as cell adhesion molecules (CAMs) and ECM-receptor interaction exhibited the highest significance in the proteomic data (dark red/orange circles), with enrichment ratios significantly higher than those in the transcriptome. This hinted that, although gene transcription was altered, cells adapted to the suspension state through more profound adjustments in protein abundance, e.g., downregulation of specific integrins or switching of adhesion molecules. Regarding the retention of metabolic and secretory functions, glycine, serine and threonine metabolism was enriched in both omics layers, yet the significance at the protein level (orange) was stronger than at the transcriptional level (grey/blue), again underscoring the stable accumulation of metabolic enzymes. Furthermore, pathways closely related to the cytoskeleton, such as HCM and ARVC, which were essentially abundant in the actin/myosin system, also displayed high significance in the proteome, further corroborating that cytoskeletal rearrangement was a core event in suspension adaptation.\u003c/p\u003e \u003cp\u003eTaken together, the joint enrichment analysis demonstrated that the suspension adaptation of HEK293 cells was a multi-level regulatory process. While the transcriptome set the blueprint for gene expression, the proteome exhibited more significant and direct functional execution characteristics regarding metabolic enhancement, cytoskeletal remodelling, and protection against proteolysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn general, there are three commonly followed routes for suspension adaptation starting from adherent cells and ending with well-adapted suspension cells: direct adaptation, sequential adaptation, and gradual serum reduction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. All approaches obey an unchanged principle: elimination of serum component, and the divergence lies in the speed of serum elimination. Cells in the direct adaptation experience a sudden eradication of serum. It would be length and struggling for cells to adapt to this instant change, and there is uncertainty on whether the cell would survive this new condition. The difference between sequential adaptation and gradual serum reduction is in the medium substitution process. Serum reduction and suspension medium substitution are carried out at the same time in sequential adaptation, while suspension medium replaces the original adherent culture medium after finishing the serum reduction. To the best of the author\u0026rsquo;s knowledge, the sequential adaptation is the comparatively preferable choice since it is time-saving, labour-saving, and it allows the cells to adapt to the changing medium simultaneously with the serum reduction.\u003c/p\u003e \u003cp\u003eRegardless of the approach of suspension adaptation, there is a limited amount of work focusing on the changes of cells during suspension adaptation. Here, we measured and identified some phenotypic changes in several aspects, including cell growth, cell morphology, cell cycle distribution, cell adhesion strength and productivity of rAdV. First, deduced from the cell growth curve, the cell growth rate decreased during the adaptation to serum-free culture as a result of serum reduction. This suggests that adherent cells may rely on the additional nutrient content and signalling molecules from the serum to maintain growth and division. Serum-rich conditions can provide more growth factors and nutrients, thereby promoting rapid cell expansion [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It is also interesting to see that cell average diameter increased in suspension-adapted cells compared to adherent parental cells. Loss of contact inhibition, comparatively spacious volume for cell activities might lead to the diameter increase [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Cell adhesion assay showed that cells cultured in 10% serum exhibited the strongest adhesion strength, while both serum-free adapted cells and suspension-adapted cells demonstrated the poorest adhesion with no significant difference between these two groups. A conclusion is reached that serum existence and concentration have an impact on cell adhesion capacity. Such phenomenon and conclusion are also reported in a study concerning the adhesion ability of THP-1 cells [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and a study on human osteosarcoma MG63 cells and human hepatic stellate LX-2 cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. When no serum exists in the culture medium, cells adapt to the culture environment by adjusting their dependence on adhesion and reprogramming their own structure. Transcriptomic and proteomic analyses also evidence this conclusion: many of the differentially expressed genes and proteins are associated with cell structure and cell adhesion. Of note, at the metabolic level, we detected a noticeable retard in both glucose consumption and lactate generation rates in suspension-adapted cells. As mentioned earlier, slower lactate generation is considered a useful feature of cell particularly in large-scale bioproduction since lactate is regarded as a waste from cell metabolic fluxes. This waste product can create a toxic environment for cells by disturbing pH level, changing osmotic pressure and affecting normal metabolism [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and lactate can also adversely influence the productivity of cells to produce viral vectors [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. With respect to cell cycle, adherent cells displayed a progressive decline in the S/G2/M phase proportion as serum levels decreased, culminating in a minimal proportion of approximately 31.92% under serum-free conditions. This observation is consistent with established literature documenting that acute serum withdrawal induces G₁ arrest and S-phase diminution-a well-characterized cellular response [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In striking contrast, suspension-adapted cells re-established a substantially elevated S and G2/M fraction (46.97%), surpassing even the 44.36% seen in adherent cultures maintained in 10% serum. This rebound implies that, notwithstanding the loss of structural and adhesive integrity entailed by suspension adaptation, cells retain a relatively normal cell cycle distribution through extensive metabolic and signalling rewiring. These findings are congruent with the upregulation of gene sets associated with the \"regulation of mitotic nuclear division\" identified in our transcriptome-wide GSEA.\u003c/p\u003e \u003cp\u003eIn addition to characterizing cell phenotype and providing corresponding interpretations, we also sought a more in-depth exploration into the changes in the molecular level during the adhesion turnover. Hereon, we conclude from the huge amount number of multi-omics data and enrichment analysis, and then propose an omics study-based hypothesis, attempting to offer a possible explanation for the mechanism of suspension adaptation: adherent HEK293 cells adapt to suspension culture via cell reprogramming in three aspects - structural remodelling, metabolic network reconstruction and intensification of resistance against external stress.\u003c/p\u003e \u003cp\u003eThe morphological change from a flattened adherent form to a spherical suspension form is the most striking and determining feature of adaptation, and it is an apparent compass to indicate cell structural changes. Our multi-omics data provide molecular evidence that this shift is governed by a coordinated restructuring of adhesion complexes and the internal cytoskeleton. On the one hand, cell selectively suppresses anchorage-dependent signaling. Joint enrichment analysis showed significant downregulation of pathways related to cell adhesion molecules, ECM-receptor interaction, and collagen-containing extracellular matrix. This suppression effect is consistent with the observed reduction in adhesion capacity and is essential for the cells to acquire anoikis resistance-the ability to survive without physical attachment to the extracellular matrix [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. On the other hand, the internal structural integrity is actively maintained. Although adhesion structures are compromised, GO analysis highlights significant enrichment of the actin cytoskeleton and intermediate filament components in the proteome. This reorganization suggests that the cells reinforce their internal mechanical support to sustain a spherical shape and resist the hydrodynamic shear stress inherent to suspension culture. Differential expression of macrophage migration inhibitory factor between adherent and adapted cells identified by O2PLS analysis further confirms this structural adaptation. While known for its role as an immune modulator in inflammation, several diseases and cancers [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], MIF has been reported to regulate cell adhesion, migration, spreading, and morphogenesis, partly by modulating Rho GTPase activity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which directly interacts with cytoskeletal components like F-actin, myosin [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Thus, the elevated expression of MIF likely reflects its role in turning its cytoskeletal dynamics suitable in a continuously motional environment.\u003c/p\u003e \u003cp\u003eIn addition to structural remodelling, cells take metabolic network reconstruction to achieve nutritional homeostasis and proliferative capacity. The turnover from a serum-rich, adherent culture system to a serum-free suspension environment imposes dual metabolic challenges: compensating for the loss of exogenous nutrients, and meeting the demands of normal cell function and rapid proliferation under such circumstances. Our results highlight key adaptive strategies of cells in lipid and amino acid metabolism, as well as ion transport regulation. First, the capacity for amino acid biosynthesis is significantly enhanced, as evidenced by joint transcriptomic and proteomic enrichment analysis of cellular amino acid biosynthetic process and glycine, serine, and threonine metabolism. The upregulation of these bioprocesses reveals that cells make a systemic effort to secure the necessary precursors for protein synthesis and cell function, supporting the observed recovery of a high S/G2/M phase ratio in adapted suspension cells. Second, lipid synthesis is re-directed. Metabolomic data showed enrichment of fatty acid biosynthesis and linoleic acid metabolism, corroborated by transcriptomic upregulation of lipid biosynthetic process. This enhancement of endogenous lipid production is crucial for three conceivable reasons: compensating for the removal of serum-derived lipids [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; potentially alteration of cell membrane composition thereby enhancing resistance in response to the shear stress [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]; and alteration of cell membrane composition to incur the metabolic shifts by adjusting the chemical transportation gating [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Third, ion homeostasis is actively managed. The identification of Na+/K\u0026thinsp;+\u0026thinsp;ATPase alpha 1 as a core O2PLS driver, combined with the strong GO enrichment for sodium ion transport, points to a critical adjustment in membrane transport function. Na+/K\u0026thinsp;+\u0026thinsp;ATPase alpha 1 is a key regulator of cell volume and osmotic pressure, and its upregulation suggests the cells are actively reshaping their membrane potential and osmotic balance to cope with the altered physicochemical properties of the suspension culture medium [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver and above, cells adapted to the suspension mode by another additional strategy: leveling up stress response to maintain homeostasis. The gradual progression of suspension adaptation represents a chronic stress situation where cells must survive oxidative damage, nutrient limitation, and mechanical forces, the last two of which have been discussed as aforementioned. To survive oxidative stress, it is important for cells to alter its metabolic profile and to maintain protein homeostasis, bolstered by protease inhibition [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The dramatic enrichment and high statistical significance of peptidase inhibitor activity in the proteome is a salient adaptive feature. This strongly suggests that suspension-adapted cells employ a mechanism, likely involving translational control, to accumulate protease inhibitors such as Serpins or TIMPs. This strategy is vital in serum-free conditions to protect cell-surface receptors and autocrine growth factors from degradation by endogenous or exogenous proteases, thereby ensuring survival signaling and microenvironmental stability [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Besides, cells need to manage mitochondrial stress. The high loading of the mitochondrial chaperone Hsp10 in the O2PLS analysis indicates activation of the mitochondrial unfolded protein response [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The elevated expression of Hsp10 helps maintain protein folding integrity and function within the mitochondria and ensures the high ATP output required for the adapted cells to keep their metabolic activities and proliferative state. This is also complemented by the enrichment of antioxidant pathways observed in the metabolome to form a comprehensive defense mechanism against oxidative stress.\u003c/p\u003e \u003cp\u003eConclusively, the successful suspension adaptation of HEK293 cells is achieved through a multitiered regulatory process. The cells overcome physical constraints via cytoskeletal remodelling, ensure viability through metabolic adaptation, and establish survival stability by activating sophisticated stress defense systems. These three aspects of cell responses interact and interweave as a complex network, assisting the cells to live through the suspension adaptation, and these mechanistic insights provide a molecular roadmap for the rational design and engineering of excellent industrial cell lines. The massive quantity of omics data can also be interpreted in other ways for different purposes, such as exploring key genes and proteins that regulate the production of viral vectors, unveiling the relationship between the suspension adaptation and tumorigenicity, et cetera.\u003c/p\u003e \u003cp\u003eThere is also a single gene, or protein, that attract our attention in particular, namely claudin 7 (CLDN7). CLDN7 protein belongs to the claudin family, a protein family with 27 members found in mammalian cells. Claudins are tetraspan transmembrane proteins that form tight junction strands between epithelial or endothelial cell sheets and serve as a basic structural component as a physical barrier [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. They also serve crucial rules in cell permeability, selective ion transportation, maintenance of cell polarity, signal transduction, and notably, cell adhesion and migration [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Back to cell suspension adaptation, it is a process wherein adherent cells are progressively selected and acclimatized to grow and proliferate as suspension cells in a free-floating state, devoid of any solid surface attachment. This process necessitates a profound phenotypic reprogramming, which possibly includes loss of contact inhibition, rearrangement of the cytoskeleton, regulation of cell adhesion molecule expression, acquisition of anti-apoptotic capabilities and metabolic adaptations. CLDN7 is presumed to take an active part in these processes. In adherent Cells, high CLDN7 expression is a hallmark of a mature epithelial phenotype, facilitating cell-cell connections, the formation of cohesive monolayers and proliferation [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and aberrant expression of CLDN7 brings alterations in cell integrity, cell polarity and intercellular contact [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Research has found different expression of claudins in canine mammary cells from primary tissue culture to establishment of cell line [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and loss of tight junction usually happens in such course. Similarly, during adaptation, cells must dissociate from tight junctions to exist as single cells in suspension, and a plausible mechanism to elucidate this is the significant downregulation or functional inhibition of CLDN7. Such reduction weakens intercellular tethering forces thus benefits cell dissociation and promotes cell survival [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. All these theories and researches have set up a rationale and provided an idea that in future work, we can utilize gene-editing tools, e.g., CRISPR-Cas9, to knock down/out CLDN7 in adherent HEK293 cells for suspension adaptation. If feasible, suspension HEK293 cells, originated from adherent status by gene editing, rather than suspension adaptation, will be more homogeneous in cell genome and more stable in cell performance, and ultimately accelerate cell line development for cell-based pharmaceutical applications.\u003c/p\u003e \u003cp\u003eIn conclusion, we successfully adapted adherent HEK293 cells to suspension culture with preferable cell growth and productivity for recombinant adenoviral vector. We observed alterations in cell growth rate, glucose consumption and lactate generation, cell-surface adhesion, and cell cycle distribution between suspension-adapted cells and the adherent, parental counterpart. Transcriptomics, proteomics, and metabolomics analysis and following GO, KEGG and GSEA enrichment analysis were performed to provide a molecular investigation into the key switches in cells. Based on the differentially expressed genes, proteins, and metabolic pathways, we hypothesize and summarize that suspension adaptation occurs not only in a phenotypic aspect, but also at molecular levels, including gene expression, protein expression and metabolic activities, and all these cellular changes aim to mediate structural components, metabolic activities and stress resistance to survive and adapt in the turnover environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.Zhang and S.Li contributed equally to this work. J.Wei, S.Li and B.Zhang designed and initiated the research. S.Li and B.Zhang performed the experiments with the help of J.Liu and W.Su for cell culture, viral production and cell characterization. S.Li and B.Zhang performed the multi-omics experiments, data analysis and visualization. X.Zhang, X.Ren, Z.Ge, T.Zhao and Q.Huang determined viral titers. S.Li and B.Zhang drafted the manuscript, which was revised by \u0026nbsp;J.Wei. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA sequencing data during the current study are available in the SRA, PRJNA1381743.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProteomics data during the current study are available in the iProX, IPX0014727001\u003c/p\u003e\n\u003cp\u003eMetabolomics data during the current study are available in the MetaboLights, MTBLS13517\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI declare that the authors have no competing interests , or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Approval This study does not involve human participants or animals. The HEK293 cell line used in this study was obtained from a commercial source (ATCC), and therefore, specific ethical approval for human tissue use was not required. All experiments were conducted in accordance with the institutional biosafety guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeiskirchen S, Schr\u0026ouml;der SK, Buhl EM, Weiskirchen R (2023) A Beginner's Guide to Cell Culture: Practical Advice for Preventing Needless Problems. Cells 12(5):682. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cells12050682\u003c/span\u003e\u003cspan address=\"10.3390/cells12050682\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerten OW (2015) Advances in cell culture: anchorage dependence. Philosophical transactions of the Royal Society of London. 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Front cell Dev biology 11:1308082. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2023.1308082\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2023.1308082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HEK293 suspension adaptation, adenoviral vector, transcriptomics, proteomics, metabolomics, claudin 7","lastPublishedDoi":"10.21203/rs.3.rs-8498955/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8498955/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman embryonic kidney 293 (HEK293) cells have been successfully adapted from adherent to suspension culture and have been widely applied in both scientific research and the pharmaceutical industry. However, the alterations in cells during the adaptation have not been well described, which raise some uncertainties and concerns regarding the underlying changes and cell behavior.\u003c/p\u003e \u003cp\u003eIn this work, we adapted adherent HEK293 to suspension culture with desirable cell growth and high production titers for recombinant adenoviral vectors, and cells at several stages throughout the process were characterized. First, we obtained three strains of suspension cells from adherent parental HEK293 cells by gradually phasing out fetal bovine serum in original Dulbecco\u0026rsquo;s modified essential medium with a simultaneous medium replacement with four serum-free suspension culture media, and one strain was chosen as the preferred candidate for further studies due to its satisfying cell conditions and adenoviral vector productivity. Slower cell growth rate, lower glucose uptake, increased lactate production, weaker cell-surface adhesion, and prolonged S phase in the cell cycle were observed in suspension cells compared to their adherent counterparts. We further performed transcriptomics, proteomics, and metabolomics analysis to identify key switches in cells. A total of 2476 differential genes were found, including 1218 up-regulated genes and 1258 down-regulated genes in suspension cells. A similar and correlated pattern was observed in the proteomic study: an almost balanced up-down regulation between suspension and adherent cells, and 702 differentially expressed metabolites were identified by untargeted metabolomics. By virtue of enrichment analysis on differentially expressed genes, proteins and metabolites, we summarized that HEK293 adherent cells survived and adapted to suspension culture by structural remodelling, metabolic network reconstruction and inherent stress resistance. Additionally, we identified claudin7 as a key player involved in suspension transformation in both transcriptomic and proteomic aspects. Our results provide a molecular enlightenment for the mechanism of suspension adaptation and new directions for the rational design of genetically engineered HEK293-derived cell lines for viral-vectored vaccine production.\u003c/p\u003e","manuscriptTitle":"Phenotypes and Multi-omics Reveal Changes and Molecular Mechanism of Suspension Adaptation of HEK293 Cells: Structural Remodelling, Metabolic Reconstruction and Stress Resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 12:15:55","doi":"10.21203/rs.3.rs-8498955/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54f74b4a-bcc3-40e6-8f01-a641cd013641","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-01T22:39:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 12:15:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8498955","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8498955","identity":"rs-8498955","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}