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Proteomic Comparison of Various Hepatic Cell Cultures for Preclinical Safety Pharmacology

Proteomic Comparison of Various Hepatic Cell Cultures for Preclinical Safety Pharmacology Abstract Experimental drugs need to be screened for safety within time constraints. Hepatotoxicity is one concerning contributor to the failure of investigational new drugs and a major rationale for postmarketing withdrawal decisions. Ethical considerations in preclinical research force the requirement for highly predictive in vitro assays using human tissue which retains functionality reflective of primary tissue. Here, the proteome of cells commonly used to assess preclinical hepatotoxicity was compared. Primary human hepatocytes (PHHs), hepatocyte-like cells (HLCs) differentiated from human pluripotent stem cells, HepG2 cell monolayers and HepG2 cell 3D spheroids were cultured and collected as whole cell lysates. Over 6000 proteins were identified and quantified in terms of relative abundance in replicate proteomic experiments using isobaric tagging methods. Comparison of these quantitative data provides biological insight into the feasibility of using HLCs, HepG2 monolayers, and HepG2 3D spheroids for hepatotoxicity testing. Collectively these data reveal how HLCs differentiated for 35 days and HepG2 cells proteomes differ from one another and that of PHHs. HepG2 cells possess a strong cancer cell signature and do not adequately express key metabolic proteins which mark the hepatic phenotype, this was not substantially altered by culturing as 3D spheroids. These data suggest that while no single hepatic model reflects the diverse array of outcomes required to mimic the in vivo liver functions, that HLCs are the most suitable investigational avenue for replacing PHHs in vitro. hepatotoxicity, labeled proteomics, systems biology, hepatocyte-like cells, HepG2 cells, spheroids Disappointing drug approval rates suggest that the pharmaceutical golden era is over. Unprecedented challenges facing the pharmaceutical industry include high preclinical and clinical termination and attrition rates, patent expirations as well as regulatory and other governing policies (Cai et al., 2012; DiMasi et al., 2003). Safety Pharmacology Studies for Human Pharmaceuticals (ICH-S7A and ICH-S7B) govern policies for the identification of undesirable pharmacodynamic effects on the “core battery” of vital organ systems: central nervous, respiratory and cardiovascular (Redfern et al., 2002). Studies of other organ systems are based on the nature of the candidate drug which then less rigorously assesses organ toxicities such the liver. The number of drugs resulting in drug-induced liver injury (DILI) is a noteworthy burden for the pharmaceutical industry as there are no universal approaches for early identification of hepatotoxic potential prior to R&D commitment (Ballet, 1997). In vitro models for assessing drug metabolism and safety need to be sufficiently sensitive and reproducible to extrapolate to in vivo counterparts. Beyond the cellular diversity of the liver, the biophysical and biochemical properties of extracellular matrices influence cellular behavior (LeCluyse et al., 2012). Successful prediction of in vitro hepatotoxicity relies on the state of hepatocyte differentiation, degree of cellular functionality, duration of exposure and type of investigational drug (Knasmüller et al., 2004; Xu et al., 2004). Freshly isolated primary human hepatocytes (PHHs) remain the “gold standard”. PHHs are lifespan restricted and unable to proliferate ex vivo but express all major metabolizing enzymes and transporter proteins. Decreased cytochrome-related functions and liver-specific gene expression have historically been of concern (LeCluyse et al., 2012; Mills et al., 2004) but can be circumvented or minimized under the appropriate culture conditions (Bell et al., 2016; Vorrink et al., 2017). Other hepatocyte sources include immortalized cell lines and hepatocytes derived from human-induced pluripotent stem cells (hiPSCs), which possess bipotent differentiation potential. Immortalized cell lines, such as HepG2 cells, are still commonly used as surrogates for hepatocytes in vitro (Mingard et al., 2018; Paech et al., 2018; Ramirez et al., 2018; Shah et al., 2018) despite being associated with unreliable expression of bio-transforming enzymes and a discontinuous phenotype which reduces functionality (Duret et al., 2007; LeCluyse et al., 2012). Adhesive cues, growth factors, intercellular contact, mechanical forces, cell shape, extracellular matrix, spatial organization and other environmental mechanics are reported to dictate cellular functionality (Bhadriraju and Chen, 2002). Monolayer cell cultures oversimplify the complexity of organ systems which misrepresent the original phenotype (Bhadriraju and Chen, 2002; Peters, 2005). Cellular dependence on “community behavior” has put an emphasis on 3D cultures which spatially organize and better resemble the in vivo cellular architecture (Fey and Wrzesinski, 2012). To determine which cell model approximates the proteome of PHHs with the greatest fidelity, thereby implying a relevant pharmaceutical screening platform, the proteomes of pooled donor PHHs, differentiated hepatocyte-like cells (HLCs) as well as monolayer and 3D spheroid cultured human hepatocyte-derived cell lines were compared using stabled isotope labeled mass spectrometry. The results suggest that the proteome was limited in all cell models investigated but that HLCs appear to be a more suitable replacement for PHHs under the conditions in this study. MATERIALS AND METHODS Pooled PHHs Cryopreserved pooled PHHs (10 donors; Lot number HUE50D, Gibco Lifeline Cell Technology) were purchased. Hepatocytes were thawed in prewarmed Hepatocyte Recovery Medium (Gibco Lifeline Cell Technology). Cells were resuspended in plating medium (Williams E Medium containing 5% fetal bovine serum (FBS), 1 µM dexamethasone, 1% penicillin-streptomycin, 4 µg/ml human recombinant insulin, 2 mM GlutaMAX and 15 mM HEPES; pH 7.4). PHHs were cultured in suspension at 2 × 106 cells/well for 4 h at 37°C in 5% CO2 to remove cellular debris and minimize the effects of dedifferentiation on the proteome. HepG2 cell monolayers and 3D spheroids Human hepatoma cells (85011430-1VL) were obtained from the European Collection of Cell Cultures (Wiltshire, UK). HepG2 cells were cultured in EMEM supplemented with 10% FBS, 1% penicillin-streptomycin and 2 mM L-glutamine and incubated at 37°C in 5% CO2. For HepG2 3D spheroids, cells were seeded into Perfecta3D 96-well hanging drop plates (3D Biomatrix; Michigan) at 10 000 cells/well in 45 µl medium. Cells aggregated under gravity with partial exchange of growth medium every alternate day. Cells were seeded from the same stock and then harvested appropriately for each culture format. HepG2 monolayers were seeded at a moderate to high density and harvested once confluence was reached at day 3. Seeding density of HepG2 3D spheroids to obtain a high protein yield was titrated to viability and were therefore cultured for 10 days. HLC differentiation hiPSCs were generated as previously reported (Hannan et al., 2013; Rashid et al., 2010; Yusa et al., 2011). An α1-antitrypsin deficient hiPSC line was wild-type corrected (Glu342Lys; SERPINA 1) using a targeted biallelic gene correction of the homozygous Z mutation. Corrected hiPSCs had 29 mutations in protein-coding exons, 22 were which were splice site mutations or nonsynonymous and not found to alter differentiation (Yusa et al., 2011). Stable hiPSC colonies, from a single clone, were cultured in chemically defined medium with polyvinyl alcohol (CDM-PVA: 250 ml Iscove’s Modified Dulbecco’s Media, 250 ml Ham’s F12 + GlutaMAX, 1% concentrated lipids, 0.7% insulin, 0.14% transferrin, 0.1% PVA, 1% penicillin/streptomycin) supplemented with Activin A (10 ng/ml) and FGF-2 (12 ng/ml) in a tri-gas incubator (5% O2, 5% CO2, 90% N2), maintained at 37°C. Differentiation (Figure 1) was conducted as previously reported in Hannan et al. (2013) and samples were collected after 35 days where a peak in functional activity was observed (data not shown). Figure 1. View largeDownload slide Phase contrast images (EVOS FL Cell Imaging System) showing differentiation of hiPSCs into HLCs. (A) hiPSCs organized in tightly packed colonies. (B) Definitive endoderm specification with cellular migration from the colony. (C) Foregut or anterior definitive endoderm cells. (D) Hepatic endoderm. (E) and (F) Hepatocyte-like cell maturation. CDM-PVA, chemically defined medium with polyvinyl alcohol; FGF, fibroblast growth factor; BMP, bone morphogenic protein; HGF, hepatocyte growth factor. Scale bar: 400 μm (reprinted/redrawn from open access journal with permission, Hannan et al., 2013). Figure 1. View largeDownload slide Phase contrast images (EVOS FL Cell Imaging System) showing differentiation of hiPSCs into HLCs. (A) hiPSCs organized in tightly packed colonies. (B) Definitive endoderm specification with cellular migration from the colony. (C) Foregut or anterior definitive endoderm cells. (D) Hepatic endoderm. (E) and (F) Hepatocyte-like cell maturation. CDM-PVA, chemically defined medium with polyvinyl alcohol; FGF, fibroblast growth factor; BMP, bone morphogenic protein; HGF, hepatocyte growth factor. Scale bar: 400 μm (reprinted/redrawn from open access journal with permission, Hannan et al., 2013). Sample collection, protein quantitation, and SDS-PAGE Cells were lysed, on ice, using a buffer containing 10 mM Tris-HCL, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% SDS, 0.1% sodium deoxycholate, 140 mM sodium chloride and cOmplete protease inhibitor cocktail (Roche Pharmaceuticals; Basel, Switzerland). Cellular disruption was performed using an ultrasonic bath (120 W) for 5 min with 30 s pulses. Supernatant protein was quantified using the bicinchoninic acid (BCA) assay with a 1:50 ratio of Reagent B (4% copper II sulfate pentahydrate) to Reagent A (2% sodium carbonate, 0.16% sodium tartrate, 0.9% sodium bicarbonate and 1% BCA; pH 11.25). Protein (20 µg) was mixed 1:1 with Laemmli sample buffer (0.125 M Tris-HCL (pH 6.8), 4% SDS, 20% glycerol, 5% β-mercaptoethanol, 0.004% bromophenol blue) and loaded onto a precast Mini-PROTEAN TGX polyacrylamide gel (4-15%). Proteins were separated using a Mini-PROTEAN Tetra System at 80 V (15 min) to 160 V. Gels were stained using 0.1% Coomassie brilliant blue and scanned using a Bio-Rad Gel-Doc EZ Imager. Protein digestion and isobaric tagging Replicates of each sample (n = 6 for HLCs, n = 4 for PHHs, n = 4 for HepG2 monolayers, n = 3 for HepG2 3D spheroids) were labeled with different 6-plex tandem mass tags (TMTs; Thermo Fischer Scientific Inc.; Maryland). Fifty micrograms of protein was reduced with 10 mM dithiothreitol at 37°C and then alkylated with 25 mM iodoacetamide for 2 h at room temperature. Proteins were precipitated overnight with acetone at 4°C, harvested by centrifugation at 16 000 × g and resuspended in 100 mM HEPES (pH 8.5). Samples were digested with 1.25 µg (1:40) sequence-grade modified trypsin for 1 h at 37°C. Additional trypsin (1:40) was added and digestion continued overnight at 37°C. Tags were resuspended in mass spectrometry-grade acetonitrile. Digested peptides were clarified for 20 min at 16 000 × g and the supernatant labeled for 2 h at room temperature under constant agitation. Labeling was quenched with 5% hydroxylamine for 1 h and further quenched overnight at 4°C with dH2O. Labeled samples were combined to contain all 6-plex labeled samples and reduced to dryness. Solid phase extraction and peptide fractionation Labeled peptides were solubilized in dH2O with 0.1% trifluoroacetic acid (TFA) and loaded onto a conditioned SepPak C18 cartridge (100 mg). Desalting was conducted by washing with 0.1% TFA and 0.5% acetic acid and peptides were eluted in 70% acetonitrile with 0.05% acetic acid. Eluents were vacuum dried and resuspended in 100 µl of 20 mM ammonium formate (pH 10) with 4% acetonitrile. Sample complexity was reduced by peptide fractionation using a Waters ACQUITY system. Peptides were loaded via a single partial loop injection, onto a Waters ACQUITY UPLC BEH C18 column (130 Å, 2.1 × 150 mm, 1.7 µm). Peptides were profiled at 0.25 ml/min using an initial isocratic low organic phase (mobile phase A: 20 mM ammonium formate; pH 10 and mobile phase B: 80% acetonitrile, 20 mM ammonium formate; pH 10) followed by a 50-min linear gradient of increasing percentage (5%–60%) mobile phase B. Chromatography was monitored using a diode array detector scanning between 200 and 400 nm. Fractions with eluted peptides were dried and pooled using 0.1% formic acid, into 15 samples for liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis. Mass spectrometry Samples were analyzed using a Dionex Ultimate 3000 RSLCnano LC system and a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer. Peptides (1–2 µg) were loaded onto an Acclaim PepMap 100 C18 precolumn (100 Å, 300 µm × 5 mm, 5 µm) using an Ultimate 3000 auto-sampler with 0.1% formic acid for 3 min at a flow rate of 10 µl/min. Switching the column valve eluted peptides onto a PepMap C18, EASY-Spray LC analytical column (100 Å, 75 µm × 500 mm, 2 µm). Peptide separation was profiled at 300 nl/min by applying a 100-min linear gradient of 4%–40% using mobile phase A (H2O with 0.1% formic acid) and mobile phase B (80% acetonitrile, 20% H2O with 0.1% formic acid) over a 120 minute total run time. Mass spectrometry measured the mass-to-charge ratio (m/z) in positive ion data-dependent mode. Full MS scans were performed in the range of 380–1500 m/z at a mass resolution of 70 000 with an automatic gain control (AGC) of 5 × 106 at a maximum injection time of 250 ms. Data-dependent scans of the top 20 most abundant ions, with charge states between 2+ and 5+, were automatically isolated, selected and fragmented by higher-energy collisional dissociation (HCD) in the quadrupole mass analyzer. Dynamic exclusion was set at 60 s. HCD fragmentation was performed at a normalized collision energy (NCE) of 32.5% and a stepped NCE of 10% and monitored at a resolution of 17 500. The AGC, maximum injection time, first fixed mass, and isolation window for MS2 scans was 5 × 104, 150 ms, 100 and 1.2 m/z, respectively. Data processing Raw files were converted using ProteoWizard MSConvertGUI (Kessner et al., 2008) with peak picking and a threshold count of 150 used as conversion filters. Peak lists were searched against a UniProtKB/Swiss-Prot human database (Homo sapiens, Canonical sequences, January 2016, Sequences: 20 194) using SearchGUI version 2.3.1 (Vaudel et al., 2011) with X! Tandem, MS-GF+ and Comet search engines. Postprocessing of peptide-spectrum matches for protein identification was done using Peptide Shaker version 1.7.3 (Vaudel et al., 2015). Search parameters included: minimum and maximum precursor mass of 300 and 900 Da, respectively, precursor mass tolerance of 10 ppm, fragment mass tolerance of 0.2 Da and a maximum number of 2 missed cleavages. Fixed modifications were set to include S-carbamidomethyl cysteine, TMT 6-plex modification of lysine and peptide N-termini with variable modifications including oxidation of methionine and deamidation of asparagine or glutamine. Deisotoping using label specific purity coefficients and relative quantification of TMT reporter ions was conducted in Reporter version 0.2.13 (http://compomics.github.io/projects/reporter.html). Data analysis and visualization Proteins present in all replicates, identified with 2 unique peptides and 100% confidence, were then analyzed in Perseus version 1.5.3.1 (Max Planck Institute of Biochemistry). Average protein ratios, with the associated standard deviation, were calculated and generic protein clusters identified using Euclidean distances from reference profiles. K-mean preprocessing and average linkages were used for hierarchical clustering. Multi-sample testing was conducted, on log2(x) transformed relative abundance ratios, using ANOVA with a permutation-based false discovery rate (FDR) for truncation at an FDR of 0.01 with results reported as q-values. Volcano plots were generated using 2-tailed t tests and stringency of analysis controlled with an FDR of 0.01 with 250 randomizations, mean weighting and the difference and −Log(p-value) were used to assign significance (q-value). In addition, proteins were annotated for gene ontology biological processes, molecular functions, cellular components as well as Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway identifiers. RESULTS This study includes proteomic comparisons which, to date, are absent from literature. Here, PHHs were compared with hiPSC-derived HLC monolayers, HepG2 monolayers and HepG2 3D spheroids (Figs. 2A–C) using replicates of quantitative proteomics. Protein-mass profiles of the various hepatocyte lysates clearly demonstrated proteomic differences (Figure 2D). The stabled isotope labeled proteomics workflow applied to these lysates identified and quantified 6682, 6285, and 6449 proteins for replicates 1, 2 and 3 respectively. Filtering proteins for those identified by at least 2 unique peptides at 100% identification confidence reduced the cohort to 5231 proteins across triplicate TMT experiments. Figure 2. View largeDownload slide Phase contrast images (EVOS FL Cell Imaging System) of (A) Hepatocyte-like cell monolayers differentiation at day 35, (B) HepG2 cell monolayers on day 1 after seeding which were harvested once at confluence, (C) HepG2 cell 3D spheroids at day 10 of culture (scale bar: 200 µm), (D) Coomassie stained 4%–15% Mini-PROTEAN TGX gel of hepatocyte lysates. Lane 1 and 10, St: Precision Plus Protein Dual Color standard (2–250 kDa); lanes 2 and 4, HepG2 cell 3D spheroids collected on day 10; lane 3, HepG2 cell spheroids collected at day 14; lanes 5 to 7, HepG2 cell monolayers at various passages; lanes 8 and 9: primary human hepatocytes. Figure 2. View largeDownload slide Phase contrast images (EVOS FL Cell Imaging System) of (A) Hepatocyte-like cell monolayers differentiation at day 35, (B) HepG2 cell monolayers on day 1 after seeding which were harvested once at confluence, (C) HepG2 cell 3D spheroids at day 10 of culture (scale bar: 200 µm), (D) Coomassie stained 4%–15% Mini-PROTEAN TGX gel of hepatocyte lysates. Lane 1 and 10, St: Precision Plus Protein Dual Color standard (2–250 kDa); lanes 2 and 4, HepG2 cell 3D spheroids collected on day 10; lane 3, HepG2 cell spheroids collected at day 14; lanes 5 to 7, HepG2 cell monolayers at various passages; lanes 8 and 9: primary human hepatocytes. Hierarchical Clustering of PHHs, HLCs, HepG2 Monolayers, and HepG2 3D Spheroids Hierarchical clustering, of the 5231 proteins, predominantly grouped samples according to cell type with distinctive grouping of PHHs, HLCs and HepG2 monolayers and HepG2 3D spheroids (Figure 3A). PHHs and HLCs cosegregated separately from HepG2 cells with the exception of PHH3 which clustered with HepG2 3D spheroids. Identification of PHH3 as an outlier could be due to biological variance induced when thawing, quantity of labeled protein or reduced labeling efficiency. Despite this behavior of PHH3, hierarchical clustering of biological replicates, which identified protein groupings with abundance trends, was unique to the specific cell types (Figure 3B). Here, cluster 4 (254 proteins) contained proteins which were increased in HLCs, cluster 5 (44 proteins) increased in both HLCs and PHHs, while cluster 6 (30 proteins) and cluster 8 (10 proteins) both increased in HLCs only (Supplementary Material 2: Hierarchical clustering). In addition, generic clustering provided 100 groups of more tightly regulated trends to investigate more conservative protein relationships (Supplementary Material: Generic clustering_100). Figure 3. View largeDownload slide (A) Hierarchical clustering of proteomic data for individual samples. (B) Protein profile trends produced from hierarchical clustering (10 row clusters) with the corresponding number of grouped proteins. Clusters 9 and 10 (not included) contained a single protein only. Figure 3. View largeDownload slide (A) Hierarchical clustering of proteomic data for individual samples. (B) Protein profile trends produced from hierarchical clustering (10 row clusters) with the corresponding number of grouped proteins. Clusters 9 and 10 (not included) contained a single protein only. Principal Component Analysis of PHHs, HLCs, HepG2 Monolayers, and HepG2 3D Spheroids Principal component analysis (PCA) clustered PHHs, with the exception of PHH3, and HLCs distinctly in Component 1 (40.5%) and Component 2 (33.2%) of principal component space (Figure 4A). In contrast, HepG2 cells, regardless of culturing strategy, clustered together in these components. This suggests that when collapsing the data into new linear combinations that HepG2 monolayers and 3D spheroids are seemingly indistinguishable when compared with the proteome of either PHHs or HLCs. However, comparing Components 1 and 3 (4.8%) of the PCA (Figure 4B) spatially resolved HepG2 monolayers and HepG2 3D spheroids suggesting that differences based on culture technique do potentially alter the proteome but are confounded by the degree to which these cells differ from PHHs. Lower components, accounting for less overall variance (Component 4: 4.3% to Component 16: 0.6%) did not provide additional insight into clustering (data not shown). Figure 4. View largeDownload slide Principal components analysis scatter plots of (A) Component 1 versus 2 (B) Component 1 versus 3. PHHs (n = 4, triangle), HLCs (n = 6, square), HepG2 monolayers (n = 4, circle), HepG2 spheroids (n = 3, diamond). Figure 4. View largeDownload slide Principal components analysis scatter plots of (A) Component 1 versus 2 (B) Component 1 versus 3. PHHs (n = 4, triangle), HLCs (n = 6, square), HepG2 monolayers (n = 4, circle), HepG2 spheroids (n = 3, diamond). Comparison of PHHs Versus HLCs Direct comparison, using significance and fold change, to the PHH proteome identified 961 proteins increased and 1020 decreased in abundance in HLCs (Figure 5A). These 1981 proteins which differ in abundance, with the corresponding difference and q-value were reported (Supplementary Material 2: Volcano plot_PHHvHLCs). Of these, many proteins with higher abundance in PHHs were involved in metabolism including aldoketo reductase family 1 member C2, CYP4V2, mitochondrial aldehyde dehydrogenase X, alcohol dehydrogenase 6, mitochondrial dimethylglycine dehydrogenase and, solute carrier family 22 member 7 responsible for organic anion transport. Abundant proteins in HLCs included Kunitz-type protease inhibitor 2 (−log p-value: 5.042 and difference: −3.104) an inhibitor of hepatocyte growth factor activator. Other abundant proteins were involved in cytoskeletal arrangement such as coactosin-like protein, microtubule-associated protein 1B responsible for tyrosination of alpha-tubulin, macrophage-capping protein and, serine/threonine-protein kinase PAK 1 with roles in cytoskeleton dynamics, cell adhesion, migration, proliferation, apoptosis and mitosis (The UniProt Consortium, 2014). Figure 5. View largeDownload slide Volcano plots comparing PHHs to HLCs, PHHs to HepG2 monolayers and PHHs to HepG2 3D spheroids. Proteins plotted above and to the left or right of the significance discriminant line are differentially increased or decreased abundance relative to PHHs, respectively using an FDR of 0.01. Proteins which displayed significant differences between PHH and each model are provided (Supplementary Material 2: volcano plots for PHHvHLCs, PHHvHepG2, and PHHvHepG2 [3D]). Figure 5. View largeDownload slide Volcano plots comparing PHHs to HLCs, PHHs to HepG2 monolayers and PHHs to HepG2 3D spheroids. Proteins plotted above and to the left or right of the significance discriminant line are differentially increased or decreased abundance relative to PHHs, respectively using an FDR of 0.01. Proteins which displayed significant differences between PHH and each model are provided (Supplementary Material 2: volcano plots for PHHvHLCs, PHHvHepG2, and PHHvHepG2 [3D]). Comparison of PHHs Versus HepG2 Monolayers When compared with PHH protein abundance, 956 and 1327 proteins were increased and decreased in HepG2 monolayers, respectively (Figure 5B). These 2283 proteins which differed in abundance (Supplementary Material 2: Volcano plot_PHHvHepG2) accounted for approximately 40% of the quantified proteins making HepG2 monolayers the most divergent from PHHs in direct comparison. Many proteins with greater abundance in PHHs were mitochondrial in origin and essential to facilitate hepatic functions such as energy production and catalysis. These included mitochondrial cytochrome b-c1 complex subunit which generates electrochemical potential coupled to ATP synthesis, mitochondrial aminomethyltransferase which catalyzes glycine degradation, glycogenin-2 which serves as substrate for glycogen synthase, nicotinamide N-methyltransferase involved in xenobiotic metabolism, and solute carrier organic anion transporter family member 1B1 (The UniProt Consortium, 2014). The decreased abundance in mitochondrial proteins could also be associated with the transformed phenotype of cancer cell lines which alters overall metabolic, glycolytic, and anaerobic activity. Proteins with greater abundance in HepG2 monolayers than PHHs, included multiple ubiquitous actin-associated proteins. This could be as a result of the unnatural microenvironment produced in monolayer cultures where HepG2 cells display cell protrusions for motility and migration not seen in vivo or in suspensions of PHHs. Actin-associated proteins which function as intracellular anchors, scaffolds and signaling proteins, included protein enabled homolog, microtubule-associated protein 1B, fascin which organizes filamentous actin into bundles and filamin-A required for orthogonal branching and linking of actin filaments to membrane glycoproteins (The UniProt Consortium, 2014). Comparison of PHHs Versus HepG2 3D Spheroids When compared with PHH protein abundance, 180 proteins increased and 683 decreased in abundance in HepG2 3D spheroids (Figure 5C). These 862 proteins accounted for only 18.8% (180 vs 956) and 51.4% (682 vs 1327) of the variance seen in HepG2 monolayers (Supplementary Material 2: Volcano plot_PHHvHepG2[3D]). Here approximately 2.5-fold less proteins meets statistical cut-offs compared with HepG2 monolayers. Abundant proteins in PHHs compared with HepG2 3D spheroids, again included many mitochondrial proteins including cytochrome b-c1 complex subunit, ATP synthase subunit and cytochrome c oxidase subunit 6C. Proteins associated with absorption, distribution, metabolism and excretion (ADME) were more abundant in PHHs and included dimethylaniline monooxygenase [N-oxide] 4 involved in oxidative metabolism of xenobiotics, retinol dehydrogenase 16, CYP2D6, alpha-1-antichymotrypsin, alpha-1-acid glycoprotein 1, sideroflexin-5 a transmembrane citrate transport, and StAR-related lipid transfer protein 5 responsible for intracellular transport of sterols or other lipids (The UniProt Consortium, 2014). These data suggest that despite less proteins which differ in abundance that the source of these differences may not be related to enhancing the hepatic phenotype. Comparison of Overall Differences in Protein Expression Multiple sample testing (ANOVA) reported 684 of the 5231 proteins (13%) with nonsignificant q-values (Supplementary Material 2: ANOVA, nonsignificant). These proteins were predominantly ribosomal, endoplasmic reticulum (ER), mitochondrial, transcriptional, or translational associated proteins. However, side-by-side comparison of PHHs compared with HLCs, HepG2 monolayers or HepG2 3D spheroids demonstrated 1981, 2283, and 862 proteins, respectively which were altered in abundance. This posed a series of questions: Were the same proteins increased or decreased in abundance in HLCs, HepG2 monolayers and HepG2 3D spheroids? Were some proteins increased in one model yet decreased in another? Therefore, proteins above significance thresholds in volcano plots were compared to determine whether protein differences were conserved between HLCs, HepG2 monolayers, and HepG2 3D spheroids (Table 1). These comparisons (HLCs vs HepG2 monolayers, HLCs vs HepG2 3D spheroids or HepG2 monolayers vs HepG2 3D spheroids) suggest that where abundance was lower compared with PHHs it was common to HLCs, HepG2 monolayers and 3D spheroids. However, when proteins were increased in abundance compared with PHHs it was specific to HLCs, HepG2 cell monolayers or HepG2 3D spheroids. Further comparisons identified 62 proteins that displayed contrary abundance between HLCs and HepG2 monolayers and only 11 proteins for HepG2 3D spheroids. In total, for HLCs and HepG2 cells, only 73 proteins were differential in their comparison to PHHs which accounted for only 1.4% (73 of 5231) of the total proteins analyzed (Supplementary Material 2: Model comparison). Proteins with higher abundance in HepG2 cells compared with HLCs were principally associated with DNA replication, transcription, and translation. Cell proliferation does not occur in HLCs during maturation which is in contrast to HepG2 cells which propagate indefinitely. This notably high abundance of proliferation associated proteins in HepG2 cells suggests that continual replication is a key factor restricting the mimicry of terminally differentiated PHHs and mature hiPSC-HLCs. Table 1. Comparison of Proteins With the Same or Different Abundance Between Models HLCs HepG2 Monolayers HepG2 3D Spheroids Proteins with higher abundance in PHHs compared with each model 1020 1327 682 Proteins with lower abundance in PHHs compared with each model 961 956 180 HLCs Versus HepG2 Monolayers HLCs Versus HepG2 3D Spheroids HepG2 Monolayers Versus 3D Spheroids Comparison of the overlap of proteins with higher abundance in PHHs 751 500 653 Comparison of the overlap of proteins with lower abundance in PHHs 282 66 142 Variable differences 62 11 0 HLCs HepG2 Monolayers HepG2 3D Spheroids Proteins with higher abundance in PHHs compared with each model 1020 1327 682 Proteins with lower abundance in PHHs compared with each model 961 956 180 HLCs Versus HepG2 Monolayers HLCs Versus HepG2 3D Spheroids HepG2 Monolayers Versus 3D Spheroids Comparison of the overlap of proteins with higher abundance in PHHs 751 500 653 Comparison of the overlap of proteins with lower abundance in PHHs 282 66 142 Variable differences 62 11 0 Proteins with higher abundance in PHHs (right of volcano plot) or proteins with lower abundance in PHHs (left of volcano plot) for HLCs, HepG2 monolayers, or HepG2 3D spheroids were extracted. These proteins were then compared between HLCs, HepG2 monolayers, and HepG2 3D spheroids to determine if the overlap of proteins meeting the same cut-off values on the volcano plots were similar. For instance, 1020 proteins were higher in PHHs compared with HLCs and this was compared with the 1327 proteins for HepG2 monolayers and 751 of these were the same proteins. Table 1. Comparison of Proteins With the Same or Different Abundance Between Models HLCs HepG2 Monolayers HepG2 3D Spheroids Proteins with higher abundance in PHHs compared with each model 1020 1327 682 Proteins with lower abundance in PHHs compared with each model 961 956 180 HLCs Versus HepG2 Monolayers HLCs Versus HepG2 3D Spheroids HepG2 Monolayers Versus 3D Spheroids Comparison of the overlap of proteins with higher abundance in PHHs 751 500 653 Comparison of the overlap of proteins with lower abundance in PHHs 282 66 142 Variable differences 62 11 0 HLCs HepG2 Monolayers HepG2 3D Spheroids Proteins with higher abundance in PHHs compared with each model 1020 1327 682 Proteins with lower abundance in PHHs compared with each model 961 956 180 HLCs Versus HepG2 Monolayers HLCs Versus HepG2 3D Spheroids HepG2 Monolayers Versus 3D Spheroids Comparison of the overlap of proteins with higher abundance in PHHs 751 500 653 Comparison of the overlap of proteins with lower abundance in PHHs 282 66 142 Variable differences 62 11 0 Proteins with higher abundance in PHHs (right of volcano plot) or proteins with lower abundance in PHHs (left of volcano plot) for HLCs, HepG2 monolayers, or HepG2 3D spheroids were extracted. These proteins were then compared between HLCs, HepG2 monolayers, and HepG2 3D spheroids to determine if the overlap of proteins meeting the same cut-off values on the volcano plots were similar. For instance, 1020 proteins were higher in PHHs compared with HLCs and this was compared with the 1327 proteins for HepG2 monolayers and 751 of these were the same proteins. Comparison of Liver Enriched and Metabolic Proteins Correlating the abundance of proteins suggested throughout literature (Baxter et al., 2015; Lemaigre, 2010; Schwartz et al., 2014; Si‐Tayeb et al., 2010; Uhlén et al., 2015) to describe the liver enriched proteome (Supplementary Table 1) and those integral to hepatic metabolic function was done (Table 2). Data were interrogated for phases I and II metabolic enzymes, CYP450s, monoamine oxidases, flavin-containing oxygenases, esterases, UGTs, sulfotransferases, N-acetyl transferases, glutathione S-transferases, and transmembrane transporters. Relationships were tabulated as ANOVA significant as well as t test significant to highlight the difference across each cell model. All of the major CYP isoforms (CYP3A4/5, CYP2D6, CYP2C8/9, CYP1A2, CYP2C19, and CYP2B6) were identified in PHHs but other cell models were limited in expression of these enzymes. HLCs and HepG2 cells mostly displayed difference in t test significance across the extracted protein cohorts. Differences in relative protein abundance of some alcohol and aldehyde dehydrogenases, uridine 5′-diphospho-glucuronosyltransferase (UDP)-glucuronosyltransferases (UGTs), glutathione S-transferases and cytochromes (CYP2C8, CYP2B6, CYP2S1, and CYP4F12) was observed in HepG2 3D spheroids compared with equivalent monolayer cultures. Table 2. Statistical Significance of Proteins Involved in Metabolism Compared Using Multiple Sample Testing (ANOVA) or 2-Tailed t Test Multiple Sample Testing Significance Following 2-Tailed t Tests Protein Description Main Accession ANOVA Significance PHHs Versus HLCs PHHs Versus HepG2 Monolayers PHHs Versus HepG2 3D Spheroids Alcohol dehydrogenase [nicotinamide adenine dinucleotide phosphate NADP(+)] P14550 + − + + Alcohol dehydrogenase 1A P07327 + + + + Alcohol dehydrogenase 1B P00325 + + + + Alcohol dehydrogenase 1C P00326 + + + + Alcohol dehydrogenase 4 P08319 + + + + Alcohol dehydrogenase 6 P28332 + + + − Alcohol dehydrogenase class-3 P11766 + − + − Aldehyde dehydrogenase family 16 member A1 Q8IZ83 + + + − Aldehyde dehydrogenase family 8 member Q9H2A2 + + + + Aldehyde dehydrogenase X, mitochondrial P30837 + + + + Aldehyde dehydrogenase, mitochondrial P05091 + + + + Aldehyde oxidase Q06278 + + + + Aldo-keto reductase family 1 member B10 O60218 + − − + Aldo-keto reductase family 1 member C1 Q04828 + + − − Aldo-keto reductase family 1 member C2 P52895 + + + − Aldo-keto reductase family 1 member C3 P42330 + + + − Aldo-keto reductase family 1 member C4 P17516 + + + − Cytochrome b5 P00167 + + + + Cytochrome P450 1A1 P04798 + + + + Cytochrome P450 1A2 P05177 + + + + Cytochrome P450 20A1 Q6UW02 + + + + Cytochrome P450 2A6 P11509 + + + + Cytochrome P450 2B6 P20813 + + + − Cytochrome P450 2C19 P33261 + + + + Cytochrome P450 2C8 P10632 + + + − Cytochrome P450 2C9 P11712 + + + + Cytochrome P450 2D6 P10635 + + + + Cytochrome P450 2E1 P05181 + + + + Cytochrome P450 2J2 P51589 + + + + Cytochrome P450 2S1 Q96SQ9 + + + − Cytochrome P450 2W1 Q8TAV3 + − − − Cytochrome P450 3A4 P08684 + + + + Cytochrome P450 3A5 P20815 + + + + Cytochrome P450 4A11 Q02928 + + + + Cytochrome P450 4F12 Q9HCS2 + + + − Cytochrome P450 4V2 Q6ZWL3 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 3 P31513 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 4 P31512 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 5 P49326 + + + + Gamma-glutamyltranspeptidase 1 P19440 + + + − Glutathione reductase, mitochondrial P00390 + − + − Glutathione S-transferase A2 P09210 + − + + Glutathione S-transferase C-terminal domain-containing protein Q8NEC7 + − + − Glutathione S-transferase kappa 1 Q9Y2Q3 + − + + Glutathione S-transferase Mu 1 P09488 + + + + Glutathione S-transferase Mu 2 P28161 − − − − Glutathione S-transferase Mu 3 P21266 + + – – Glutathione S-transferase omega-1 P78417 + – + – Glutathione S-transferase P P09211 + + – – Glutathione S-transferase theta-1 P30711 + + + – Glutathione S-transferase theta-2 P0CG29 + + + + Glutathione synthetase P48637 – – – – Multidrug resistance protein 1 P08183 + + – – Multidrug resistance-associated protein 1 P33527 + + + – Multidrug resistance-associated protein 6 O95255 + + + + NADPH–cytochrome P450 reductase P16435 + – + + UDP-glucuronosyltransferase 1-1 P22309 + + + + UDP-glucuronosyltransferase 1-4 P22310 + + + + UDP-glucuronosyltransferase 1-6 P19224 + + + + UDP-glucuronosyltransferase 2A3 Q6UWM9 + + + – UDP-glucuronosyltransferase 2B10 P36537 + + + + UDP-glucuronosyltransferase 2B15 P54855 + + + + UDP-glucuronosyltransferase 2B17 O75795 + + + + UDP-glucuronosyltransferase 2B4 P06133 + + + – UDP-glucuronosyltransferase 2B7 P16662 + + + + Multiple Sample Testing Significance Following 2-Tailed t Tests Protein Description Main Accession ANOVA Significance PHHs Versus HLCs PHHs Versus HepG2 Monolayers PHHs Versus HepG2 3D Spheroids Alcohol dehydrogenase [nicotinamide adenine dinucleotide phosphate NADP(+)] P14550 + − + + Alcohol dehydrogenase 1A P07327 + + + + Alcohol dehydrogenase 1B P00325 + + + + Alcohol dehydrogenase 1C P00326 + + + + Alcohol dehydrogenase 4 P08319 + + + + Alcohol dehydrogenase 6 P28332 + + + − Alcohol dehydrogenase class-3 P11766 + − + − Aldehyde dehydrogenase family 16 member A1 Q8IZ83 + + + − Aldehyde dehydrogenase family 8 member Q9H2A2 + + + + Aldehyde dehydrogenase X, mitochondrial P30837 + + + + Aldehyde dehydrogenase, mitochondrial P05091 + + + + Aldehyde oxidase Q06278 + + + + Aldo-keto reductase family 1 member B10 O60218 + − − + Aldo-keto reductase family 1 member C1 Q04828 + + − − Aldo-keto reductase family 1 member C2 P52895 + + + − Aldo-keto reductase family 1 member C3 P42330 + + + − Aldo-keto reductase family 1 member C4 P17516 + + + − Cytochrome b5 P00167 + + + + Cytochrome P450 1A1 P04798 + + + + Cytochrome P450 1A2 P05177 + + + + Cytochrome P450 20A1 Q6UW02 + + + + Cytochrome P450 2A6 P11509 + + + + Cytochrome P450 2B6 P20813 + + + − Cytochrome P450 2C19 P33261 + + + + Cytochrome P450 2C8 P10632 + + + − Cytochrome P450 2C9 P11712 + + + + Cytochrome P450 2D6 P10635 + + + + Cytochrome P450 2E1 P05181 + + + + Cytochrome P450 2J2 P51589 + + + + Cytochrome P450 2S1 Q96SQ9 + + + − Cytochrome P450 2W1 Q8TAV3 + − − − Cytochrome P450 3A4 P08684 + + + + Cytochrome P450 3A5 P20815 + + + + Cytochrome P450 4A11 Q02928 + + + + Cytochrome P450 4F12 Q9HCS2 + + + − Cytochrome P450 4V2 Q6ZWL3 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 3 P31513 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 4 P31512 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 5 P49326 + + + + Gamma-glutamyltranspeptidase 1 P19440 + + + − Glutathione reductase, mitochondrial P00390 + − + − Glutathione S-transferase A2 P09210 + − + + Glutathione S-transferase C-terminal domain-containing protein Q8NEC7 + − + − Glutathione S-transferase kappa 1 Q9Y2Q3 + − + + Glutathione S-transferase Mu 1 P09488 + + + + Glutathione S-transferase Mu 2 P28161 − − − − Glutathione S-transferase Mu 3 P21266 + + – – Glutathione S-transferase omega-1 P78417 + – + – Glutathione S-transferase P P09211 + + – – Glutathione S-transferase theta-1 P30711 + + + – Glutathione S-transferase theta-2 P0CG29 + + + + Glutathione synthetase P48637 – – – – Multidrug resistance protein 1 P08183 + + – – Multidrug resistance-associated protein 1 P33527 + + + – Multidrug resistance-associated protein 6 O95255 + + + + NADPH–cytochrome P450 reductase P16435 + – + + UDP-glucuronosyltransferase 1-1 P22309 + + + + UDP-glucuronosyltransferase 1-4 P22310 + + + + UDP-glucuronosyltransferase 1-6 P19224 + + + + UDP-glucuronosyltransferase 2A3 Q6UWM9 + + + – UDP-glucuronosyltransferase 2B10 P36537 + + + + UDP-glucuronosyltransferase 2B15 P54855 + + + + UDP-glucuronosyltransferase 2B17 O75795 + + + + UDP-glucuronosyltransferase 2B4 P06133 + + + – UDP-glucuronosyltransferase 2B7 P16662 + + + + The average relative abundance and standard deviation across replicates is provided in Supplementary Material 2: Metabolic proteins. Table 2. Statistical Significance of Proteins Involved in Metabolism Compared Using Multiple Sample Testing (ANOVA) or 2-Tailed t Test Multiple Sample Testing Significance Following 2-Tailed t Tests Protein Description Main Accession ANOVA Significance PHHs Versus HLCs PHHs Versus HepG2 Monolayers PHHs Versus HepG2 3D Spheroids Alcohol dehydrogenase [nicotinamide adenine dinucleotide phosphate NADP(+)] P14550 + − + + Alcohol dehydrogenase 1A P07327 + + + + Alcohol dehydrogenase 1B P00325 + + + + Alcohol dehydrogenase 1C P00326 + + + + Alcohol dehydrogenase 4 P08319 + + + + Alcohol dehydrogenase 6 P28332 + + + − Alcohol dehydrogenase class-3 P11766 + − + − Aldehyde dehydrogenase family 16 member A1 Q8IZ83 + + + − Aldehyde dehydrogenase family 8 member Q9H2A2 + + + + Aldehyde dehydrogenase X, mitochondrial P30837 + + + + Aldehyde dehydrogenase, mitochondrial P05091 + + + + Aldehyde oxidase Q06278 + + + + Aldo-keto reductase family 1 member B10 O60218 + − − + Aldo-keto reductase family 1 member C1 Q04828 + + − − Aldo-keto reductase family 1 member C2 P52895 + + + − Aldo-keto reductase family 1 member C3 P42330 + + + − Aldo-keto reductase family 1 member C4 P17516 + + + − Cytochrome b5 P00167 + + + + Cytochrome P450 1A1 P04798 + + + + Cytochrome P450 1A2 P05177 + + + + Cytochrome P450 20A1 Q6UW02 + + + + Cytochrome P450 2A6 P11509 + + + + Cytochrome P450 2B6 P20813 + + + − Cytochrome P450 2C19 P33261 + + + + Cytochrome P450 2C8 P10632 + + + − Cytochrome P450 2C9 P11712 + + + + Cytochrome P450 2D6 P10635 + + + + Cytochrome P450 2E1 P05181 + + + + Cytochrome P450 2J2 P51589 + + + + Cytochrome P450 2S1 Q96SQ9 + + + − Cytochrome P450 2W1 Q8TAV3 + − − − Cytochrome P450 3A4 P08684 + + + + Cytochrome P450 3A5 P20815 + + + + Cytochrome P450 4A11 Q02928 + + + + Cytochrome P450 4F12 Q9HCS2 + + + − Cytochrome P450 4V2 Q6ZWL3 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 3 P31513 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 4 P31512 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 5 P49326 + + + + Gamma-glutamyltranspeptidase 1 P19440 + + + − Glutathione reductase, mitochondrial P00390 + − + − Glutathione S-transferase A2 P09210 + − + + Glutathione S-transferase C-terminal domain-containing protein Q8NEC7 + − + − Glutathione S-transferase kappa 1 Q9Y2Q3 + − + + Glutathione S-transferase Mu 1 P09488 + + + + Glutathione S-transferase Mu 2 P28161 − − − − Glutathione S-transferase Mu 3 P21266 + + – – Glutathione S-transferase omega-1 P78417 + – + – Glutathione S-transferase P P09211 + + – – Glutathione S-transferase theta-1 P30711 + + + – Glutathione S-transferase theta-2 P0CG29 + + + + Glutathione synthetase P48637 – – – – Multidrug resistance protein 1 P08183 + + – – Multidrug resistance-associated protein 1 P33527 + + + – Multidrug resistance-associated protein 6 O95255 + + + + NADPH–cytochrome P450 reductase P16435 + – + + UDP-glucuronosyltransferase 1-1 P22309 + + + + UDP-glucuronosyltransferase 1-4 P22310 + + + + UDP-glucuronosyltransferase 1-6 P19224 + + + + UDP-glucuronosyltransferase 2A3 Q6UWM9 + + + – UDP-glucuronosyltransferase 2B10 P36537 + + + + UDP-glucuronosyltransferase 2B15 P54855 + + + + UDP-glucuronosyltransferase 2B17 O75795 + + + + UDP-glucuronosyltransferase 2B4 P06133 + + + – UDP-glucuronosyltransferase 2B7 P16662 + + + + Multiple Sample Testing Significance Following 2-Tailed t Tests Protein Description Main Accession ANOVA Significance PHHs Versus HLCs PHHs Versus HepG2 Monolayers PHHs Versus HepG2 3D Spheroids Alcohol dehydrogenase [nicotinamide adenine dinucleotide phosphate NADP(+)] P14550 + − + + Alcohol dehydrogenase 1A P07327 + + + + Alcohol dehydrogenase 1B P00325 + + + + Alcohol dehydrogenase 1C P00326 + + + + Alcohol dehydrogenase 4 P08319 + + + + Alcohol dehydrogenase 6 P28332 + + + − Alcohol dehydrogenase class-3 P11766 + − + − Aldehyde dehydrogenase family 16 member A1 Q8IZ83 + + + − Aldehyde dehydrogenase family 8 member Q9H2A2 + + + + Aldehyde dehydrogenase X, mitochondrial P30837 + + + + Aldehyde dehydrogenase, mitochondrial P05091 + + + + Aldehyde oxidase Q06278 + + + + Aldo-keto reductase family 1 member B10 O60218 + − − + Aldo-keto reductase family 1 member C1 Q04828 + + − − Aldo-keto reductase family 1 member C2 P52895 + + + − Aldo-keto reductase family 1 member C3 P42330 + + + − Aldo-keto reductase family 1 member C4 P17516 + + + − Cytochrome b5 P00167 + + + + Cytochrome P450 1A1 P04798 + + + + Cytochrome P450 1A2 P05177 + + + + Cytochrome P450 20A1 Q6UW02 + + + + Cytochrome P450 2A6 P11509 + + + + Cytochrome P450 2B6 P20813 + + + − Cytochrome P450 2C19 P33261 + + + + Cytochrome P450 2C8 P10632 + + + − Cytochrome P450 2C9 P11712 + + + + Cytochrome P450 2D6 P10635 + + + + Cytochrome P450 2E1 P05181 + + + + Cytochrome P450 2J2 P51589 + + + + Cytochrome P450 2S1 Q96SQ9 + + + − Cytochrome P450 2W1 Q8TAV3 + − − − Cytochrome P450 3A4 P08684 + + + + Cytochrome P450 3A5 P20815 + + + + Cytochrome P450 4A11 Q02928 + + + + Cytochrome P450 4F12 Q9HCS2 + + + − Cytochrome P450 4V2 Q6ZWL3 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 3 P31513 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 4 P31512 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 5 P49326 + + + + Gamma-glutamyltranspeptidase 1 P19440 + + + − Glutathione reductase, mitochondrial P00390 + − + − Glutathione S-transferase A2 P09210 + − + + Glutathione S-transferase C-terminal domain-containing protein Q8NEC7 + − + − Glutathione S-transferase kappa 1 Q9Y2Q3 + − + + Glutathione S-transferase Mu 1 P09488 + + + + Glutathione S-transferase Mu 2 P28161 − − − − Glutathione S-transferase Mu 3 P21266 + + – – Glutathione S-transferase omega-1 P78417 + – + – Glutathione S-transferase P P09211 + + – – Glutathione S-transferase theta-1 P30711 + + + – Glutathione S-transferase theta-2 P0CG29 + + + + Glutathione synthetase P48637 – – – – Multidrug resistance protein 1 P08183 + + – – Multidrug resistance-associated protein 1 P33527 + + + – Multidrug resistance-associated protein 6 O95255 + + + + NADPH–cytochrome P450 reductase P16435 + – + + UDP-glucuronosyltransferase 1-1 P22309 + + + + UDP-glucuronosyltransferase 1-4 P22310 + + + + UDP-glucuronosyltransferase 1-6 P19224 + + + + UDP-glucuronosyltransferase 2A3 Q6UWM9 + + + – UDP-glucuronosyltransferase 2B10 P36537 + + + + UDP-glucuronosyltransferase 2B15 P54855 + + + + UDP-glucuronosyltransferase 2B17 O75795 + + + + UDP-glucuronosyltransferase 2B4 P06133 + + + – UDP-glucuronosyltransferase 2B7 P16662 + + + + The average relative abundance and standard deviation across replicates is provided in Supplementary Material 2: Metabolic proteins. DISCUSSION Human-derived hepatic cells remain central to the applicability and performance of innovative liver models (Sison-Young et al., 2015). Generating extensive protein expression profiles are required to ascertain that each model systems is “fit-for-purpose”. Comparable reports have previously quantified 2722 proteins in 3 human-derived hepatic cell lines (HepG2, Upcyte, HepaRG) and compared these with cryopreserved PHHs. Hierarchical clustering also segregated PHHs from the other cell types with ADME proteins being an influential factor (Sison-Young et al., 2015). The greater depth of protein coverage reported in this study provides additional insight into hepatic phenotypes. PCA and hierarchical clustering on the full complement of quantified proteins demonstrated that the HLCs proteome are more closely correlated to PHHs. PHHs are nontransformed and maintain the highest differentiation status while HepG2 express a strong cancer cell signature. Considering this, it is suggested that in PCA Component 1 appears capable of separating samples based on whether the cells are transformed (HepG2 cells) or nontransformed (PHHs and HLCs) whereas Component 2 is distinguishing the level of differentiation. Glucocorticoids, such as dexamethasone, through interaction with glucocorticoid receptors modulate gene transcription and cellular function. As a major regulator of cellular function, the absence of glucocorticoids in the HepG2 cell culture medium, while present in HLCs differentiation medium could contribute to the PCA clustering. Psarra and Sekeris (2011) suggested that dexamethasone induces mitochondrial transcription factors including transcription factors A, B1 and B2 as well as oxidative phosphorylation genes such as cytochrome b. Mitochondrial transcription factor A (Q00059) was nonsignificant for each t test comparison whereas mitochondrial dimethyladenosine transferase 1 (Q8WVM0) and 2 (Q9H5Q4) differed in HepG2 monolayers and HLCs, respectively. Furthermore, cytochrome b5 (P00167) differed in all comparisons to PHHs regardless of the presence or absence of glucocorticoids. This illustrates that while glucocorticoids may be a contributor to the magnitude of difference between cell models, it is not the sole source of the variance observed. HepG2 cells and HLCs were maintained in cell culture medium containing glucose in supra-physiological levels which consequently enables cells to generate energy through glycolysis as opposed to oxidative phosphorylation. The use of protocols which do not maintain hepatocytes ex vivo in a physiologically applicable environment could underpin many of the differences observed across the proteome. Irrespective of the limitations of the cell culture microenvironment, clustering of HLCs and PHHs in Component 1 suggests that despite HLCs being cultured as monolayers that the proteomes of PHHs and HLCs are most closely related. Successful differentiation of hiPSCs to HLCs is a complex interplay of cell proliferation and maturation. The duration of differentiation and resultant degree of maturity influences the degree to which HLCs mimic PHHs. Therefore, differentiation beyond a critical threshold could underestimate the proteomic correlation between hiPSC-HLCs and PHHs. Recent work by Asai et al. (2017) found that HLCs with spatial proximity to human umbilical vein endothelial or mesenchymal stem cells that provide paracrine signals showed a proteome more closely aligned with that of PHHs. This suggests that using a differentiation protocol with additional paracrine signaling could enhance the HLCs protein complement to be more representative of PHHs. These proteomic data described may contain noteworthy insights of essential contributors to the HLC proteome. Major differences between PHHs and HepG2 cells include morphology, the transformed and discontinuous phenotype of cancer cells as well as maintenance of cells in culture (Wilkening et al., 2003). In addition, HepG2 cells vary in their overall metabolic, glycolytic, and anaerobic activity functions with the low number of mRNA copies and protein expression of essential phase I enzymes (Duret et al., 2007; Wilkening et al., 2003). This study further reiterates the risk of using short-term HepG2 cell monolayer cultures as a hepatocyte model for metabolic studies. Comparing the proteomes of PHHs to HepG2 3D spheroids identified fewer abundance variances than in HepG2 monolayer counterparts. Luckert et al. (2017) observed that metabolic competence of HepG2 cells grown as monolayers for 21 days had comparable biochemical characteristics to HepG2 cells grown in various 3D culture formats (Luckert et al., 2017). This suggests that the differences in metabolic proteins observed could be as a result of culturing HepG2 cells as 3D spheroids for 10 days as opposed to only 3 days in monolayer culture. Despite the extended culture duration, hierarchical clustering and PCA were unable to distinguish HepG2 monolayers and 3D spheroid cultures from one another. Here, under the conditions investigated, HepG2 cells as 3D spheroids would not confer major alteration in biochemical characteristics. Instead, culturing HepG2 cells without adherence to artificial substrates which augmented cell morphology was considered as a main contributor to the reduced number of significantly different proteins compared with PHHs. CONCLUSIONS Here, the use of isobaric-labeled quantitative proteomics and careful preparation steps resulted in over 6000 proteins being positively identified per replicate with high confidence for comparison of PHHs, HLCs, and HepG2 cells. This quantitative data provides biological insight into the feasibility of using HLCs, HepG2 monolayers and HepG2 3D spheroids as hepatotoxicity models. Since cellular homeostasis, cellular growth and hepatic maturity are dynamic processes the cellular proteome data described in this study represents a “snapshot” of hepatocyte proteomes. These data supports the concept that there is, no single hepatic model that can accurately reflect the diverse array of outcomes required to mimic the in vivo liver functions as of yet. None of the models are considered a feasible substitute to PHHs, however, these data reveals how differentiated HLCs and HepG2 cells under different culturing techniques differ in the relative abundance of proteins compared with PHHs. The proteome of the HLCs, under the conditions investigated, better correlated to PHHs than the HepG2 monolayers or 3D spheroid counterparts. It is proposed that these data could provide insights into improving hepatocyte models and aid in enhancing the mimicry of the in vivo hepatic phenotype. SUPPLEMENTARY DATA Supplementary data are available at Toxicological Sciences online. AUTHOR’S CONTRIBUTION T.H., A.D.C., and K.S.L. designed and planned the experiments. T.H. and C.P.S. performed the cell culture and differentiation procedures. T.H. performed protein sample preparation and data analysis. All authors contributed to the data interpretation and to the writing of the manuscript. None of the authors declare any competing interests. FUNDING National Research Foundation of South Africa for the grant (Grant No. 87880); UK Commonwealth Split-site PhD Scholarship (ZACS-2014-653) and a Commonwealth, European and International Cambridge Trust Scholarship (USN: 302989247 App No: 10326363) to T.H.; Children Liver Disease Foundation PhD studentship to C.-P.S.; ERC starting Grant Relieve IMD to L.V. Any opinion, finding, conclusion, or recommendation expressed in this material is that of the author(s) and not of the funding agencies mentioned. ACKNOWLEDGMENTS We would like to thank Dr Mike Deery at the Cambridge Centre for Proteomics for assistance with the mass spectrometry and Dr Stoyan Stoychev at the South African Council for Scientific and Industrial Research who provided the infrastructure required for the bioinformatics data analysis. REFERENCES Asai A. , Aihara E. , Watson C. , Mourya R. , Mizuochi T. , Shivakumar P. , Phelan K. , Mayhew C. , Helmrath M. , Takebe T. et al. , . ( 2017 ). 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Google Scholar CrossRef Search ADS PubMed Yusa K. , Rashid S. T. , Strick-Marchand H. , Varela I. , Liu P.-Q. , Paschon D. E. , Miranda E. , Ordóñez A. , Hannan N. R. F. , Rouhani F. J. et al. , . ( 2011 ). Targeted gene correction of alpha-1-antitrypsin deficiency in induced pluripotent stem cells . Nature 478 , 391 – 394 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Toxicological Sciences Oxford University Press

Proteomic Comparison of Various Hepatic Cell Cultures for Preclinical Safety Pharmacology

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
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10.1093/toxsci/kfy084
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Abstract

Abstract Experimental drugs need to be screened for safety within time constraints. Hepatotoxicity is one concerning contributor to the failure of investigational new drugs and a major rationale for postmarketing withdrawal decisions. Ethical considerations in preclinical research force the requirement for highly predictive in vitro assays using human tissue which retains functionality reflective of primary tissue. Here, the proteome of cells commonly used to assess preclinical hepatotoxicity was compared. Primary human hepatocytes (PHHs), hepatocyte-like cells (HLCs) differentiated from human pluripotent stem cells, HepG2 cell monolayers and HepG2 cell 3D spheroids were cultured and collected as whole cell lysates. Over 6000 proteins were identified and quantified in terms of relative abundance in replicate proteomic experiments using isobaric tagging methods. Comparison of these quantitative data provides biological insight into the feasibility of using HLCs, HepG2 monolayers, and HepG2 3D spheroids for hepatotoxicity testing. Collectively these data reveal how HLCs differentiated for 35 days and HepG2 cells proteomes differ from one another and that of PHHs. HepG2 cells possess a strong cancer cell signature and do not adequately express key metabolic proteins which mark the hepatic phenotype, this was not substantially altered by culturing as 3D spheroids. These data suggest that while no single hepatic model reflects the diverse array of outcomes required to mimic the in vivo liver functions, that HLCs are the most suitable investigational avenue for replacing PHHs in vitro. hepatotoxicity, labeled proteomics, systems biology, hepatocyte-like cells, HepG2 cells, spheroids Disappointing drug approval rates suggest that the pharmaceutical golden era is over. Unprecedented challenges facing the pharmaceutical industry include high preclinical and clinical termination and attrition rates, patent expirations as well as regulatory and other governing policies (Cai et al., 2012; DiMasi et al., 2003). Safety Pharmacology Studies for Human Pharmaceuticals (ICH-S7A and ICH-S7B) govern policies for the identification of undesirable pharmacodynamic effects on the “core battery” of vital organ systems: central nervous, respiratory and cardiovascular (Redfern et al., 2002). Studies of other organ systems are based on the nature of the candidate drug which then less rigorously assesses organ toxicities such the liver. The number of drugs resulting in drug-induced liver injury (DILI) is a noteworthy burden for the pharmaceutical industry as there are no universal approaches for early identification of hepatotoxic potential prior to R&D commitment (Ballet, 1997). In vitro models for assessing drug metabolism and safety need to be sufficiently sensitive and reproducible to extrapolate to in vivo counterparts. Beyond the cellular diversity of the liver, the biophysical and biochemical properties of extracellular matrices influence cellular behavior (LeCluyse et al., 2012). Successful prediction of in vitro hepatotoxicity relies on the state of hepatocyte differentiation, degree of cellular functionality, duration of exposure and type of investigational drug (Knasmüller et al., 2004; Xu et al., 2004). Freshly isolated primary human hepatocytes (PHHs) remain the “gold standard”. PHHs are lifespan restricted and unable to proliferate ex vivo but express all major metabolizing enzymes and transporter proteins. Decreased cytochrome-related functions and liver-specific gene expression have historically been of concern (LeCluyse et al., 2012; Mills et al., 2004) but can be circumvented or minimized under the appropriate culture conditions (Bell et al., 2016; Vorrink et al., 2017). Other hepatocyte sources include immortalized cell lines and hepatocytes derived from human-induced pluripotent stem cells (hiPSCs), which possess bipotent differentiation potential. Immortalized cell lines, such as HepG2 cells, are still commonly used as surrogates for hepatocytes in vitro (Mingard et al., 2018; Paech et al., 2018; Ramirez et al., 2018; Shah et al., 2018) despite being associated with unreliable expression of bio-transforming enzymes and a discontinuous phenotype which reduces functionality (Duret et al., 2007; LeCluyse et al., 2012). Adhesive cues, growth factors, intercellular contact, mechanical forces, cell shape, extracellular matrix, spatial organization and other environmental mechanics are reported to dictate cellular functionality (Bhadriraju and Chen, 2002). Monolayer cell cultures oversimplify the complexity of organ systems which misrepresent the original phenotype (Bhadriraju and Chen, 2002; Peters, 2005). Cellular dependence on “community behavior” has put an emphasis on 3D cultures which spatially organize and better resemble the in vivo cellular architecture (Fey and Wrzesinski, 2012). To determine which cell model approximates the proteome of PHHs with the greatest fidelity, thereby implying a relevant pharmaceutical screening platform, the proteomes of pooled donor PHHs, differentiated hepatocyte-like cells (HLCs) as well as monolayer and 3D spheroid cultured human hepatocyte-derived cell lines were compared using stabled isotope labeled mass spectrometry. The results suggest that the proteome was limited in all cell models investigated but that HLCs appear to be a more suitable replacement for PHHs under the conditions in this study. MATERIALS AND METHODS Pooled PHHs Cryopreserved pooled PHHs (10 donors; Lot number HUE50D, Gibco Lifeline Cell Technology) were purchased. Hepatocytes were thawed in prewarmed Hepatocyte Recovery Medium (Gibco Lifeline Cell Technology). Cells were resuspended in plating medium (Williams E Medium containing 5% fetal bovine serum (FBS), 1 µM dexamethasone, 1% penicillin-streptomycin, 4 µg/ml human recombinant insulin, 2 mM GlutaMAX and 15 mM HEPES; pH 7.4). PHHs were cultured in suspension at 2 × 106 cells/well for 4 h at 37°C in 5% CO2 to remove cellular debris and minimize the effects of dedifferentiation on the proteome. HepG2 cell monolayers and 3D spheroids Human hepatoma cells (85011430-1VL) were obtained from the European Collection of Cell Cultures (Wiltshire, UK). HepG2 cells were cultured in EMEM supplemented with 10% FBS, 1% penicillin-streptomycin and 2 mM L-glutamine and incubated at 37°C in 5% CO2. For HepG2 3D spheroids, cells were seeded into Perfecta3D 96-well hanging drop plates (3D Biomatrix; Michigan) at 10 000 cells/well in 45 µl medium. Cells aggregated under gravity with partial exchange of growth medium every alternate day. Cells were seeded from the same stock and then harvested appropriately for each culture format. HepG2 monolayers were seeded at a moderate to high density and harvested once confluence was reached at day 3. Seeding density of HepG2 3D spheroids to obtain a high protein yield was titrated to viability and were therefore cultured for 10 days. HLC differentiation hiPSCs were generated as previously reported (Hannan et al., 2013; Rashid et al., 2010; Yusa et al., 2011). An α1-antitrypsin deficient hiPSC line was wild-type corrected (Glu342Lys; SERPINA 1) using a targeted biallelic gene correction of the homozygous Z mutation. Corrected hiPSCs had 29 mutations in protein-coding exons, 22 were which were splice site mutations or nonsynonymous and not found to alter differentiation (Yusa et al., 2011). Stable hiPSC colonies, from a single clone, were cultured in chemically defined medium with polyvinyl alcohol (CDM-PVA: 250 ml Iscove’s Modified Dulbecco’s Media, 250 ml Ham’s F12 + GlutaMAX, 1% concentrated lipids, 0.7% insulin, 0.14% transferrin, 0.1% PVA, 1% penicillin/streptomycin) supplemented with Activin A (10 ng/ml) and FGF-2 (12 ng/ml) in a tri-gas incubator (5% O2, 5% CO2, 90% N2), maintained at 37°C. Differentiation (Figure 1) was conducted as previously reported in Hannan et al. (2013) and samples were collected after 35 days where a peak in functional activity was observed (data not shown). Figure 1. View largeDownload slide Phase contrast images (EVOS FL Cell Imaging System) showing differentiation of hiPSCs into HLCs. (A) hiPSCs organized in tightly packed colonies. (B) Definitive endoderm specification with cellular migration from the colony. (C) Foregut or anterior definitive endoderm cells. (D) Hepatic endoderm. (E) and (F) Hepatocyte-like cell maturation. CDM-PVA, chemically defined medium with polyvinyl alcohol; FGF, fibroblast growth factor; BMP, bone morphogenic protein; HGF, hepatocyte growth factor. Scale bar: 400 μm (reprinted/redrawn from open access journal with permission, Hannan et al., 2013). Figure 1. View largeDownload slide Phase contrast images (EVOS FL Cell Imaging System) showing differentiation of hiPSCs into HLCs. (A) hiPSCs organized in tightly packed colonies. (B) Definitive endoderm specification with cellular migration from the colony. (C) Foregut or anterior definitive endoderm cells. (D) Hepatic endoderm. (E) and (F) Hepatocyte-like cell maturation. CDM-PVA, chemically defined medium with polyvinyl alcohol; FGF, fibroblast growth factor; BMP, bone morphogenic protein; HGF, hepatocyte growth factor. Scale bar: 400 μm (reprinted/redrawn from open access journal with permission, Hannan et al., 2013). Sample collection, protein quantitation, and SDS-PAGE Cells were lysed, on ice, using a buffer containing 10 mM Tris-HCL, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% SDS, 0.1% sodium deoxycholate, 140 mM sodium chloride and cOmplete protease inhibitor cocktail (Roche Pharmaceuticals; Basel, Switzerland). Cellular disruption was performed using an ultrasonic bath (120 W) for 5 min with 30 s pulses. Supernatant protein was quantified using the bicinchoninic acid (BCA) assay with a 1:50 ratio of Reagent B (4% copper II sulfate pentahydrate) to Reagent A (2% sodium carbonate, 0.16% sodium tartrate, 0.9% sodium bicarbonate and 1% BCA; pH 11.25). Protein (20 µg) was mixed 1:1 with Laemmli sample buffer (0.125 M Tris-HCL (pH 6.8), 4% SDS, 20% glycerol, 5% β-mercaptoethanol, 0.004% bromophenol blue) and loaded onto a precast Mini-PROTEAN TGX polyacrylamide gel (4-15%). Proteins were separated using a Mini-PROTEAN Tetra System at 80 V (15 min) to 160 V. Gels were stained using 0.1% Coomassie brilliant blue and scanned using a Bio-Rad Gel-Doc EZ Imager. Protein digestion and isobaric tagging Replicates of each sample (n = 6 for HLCs, n = 4 for PHHs, n = 4 for HepG2 monolayers, n = 3 for HepG2 3D spheroids) were labeled with different 6-plex tandem mass tags (TMTs; Thermo Fischer Scientific Inc.; Maryland). Fifty micrograms of protein was reduced with 10 mM dithiothreitol at 37°C and then alkylated with 25 mM iodoacetamide for 2 h at room temperature. Proteins were precipitated overnight with acetone at 4°C, harvested by centrifugation at 16 000 × g and resuspended in 100 mM HEPES (pH 8.5). Samples were digested with 1.25 µg (1:40) sequence-grade modified trypsin for 1 h at 37°C. Additional trypsin (1:40) was added and digestion continued overnight at 37°C. Tags were resuspended in mass spectrometry-grade acetonitrile. Digested peptides were clarified for 20 min at 16 000 × g and the supernatant labeled for 2 h at room temperature under constant agitation. Labeling was quenched with 5% hydroxylamine for 1 h and further quenched overnight at 4°C with dH2O. Labeled samples were combined to contain all 6-plex labeled samples and reduced to dryness. Solid phase extraction and peptide fractionation Labeled peptides were solubilized in dH2O with 0.1% trifluoroacetic acid (TFA) and loaded onto a conditioned SepPak C18 cartridge (100 mg). Desalting was conducted by washing with 0.1% TFA and 0.5% acetic acid and peptides were eluted in 70% acetonitrile with 0.05% acetic acid. Eluents were vacuum dried and resuspended in 100 µl of 20 mM ammonium formate (pH 10) with 4% acetonitrile. Sample complexity was reduced by peptide fractionation using a Waters ACQUITY system. Peptides were loaded via a single partial loop injection, onto a Waters ACQUITY UPLC BEH C18 column (130 Å, 2.1 × 150 mm, 1.7 µm). Peptides were profiled at 0.25 ml/min using an initial isocratic low organic phase (mobile phase A: 20 mM ammonium formate; pH 10 and mobile phase B: 80% acetonitrile, 20 mM ammonium formate; pH 10) followed by a 50-min linear gradient of increasing percentage (5%–60%) mobile phase B. Chromatography was monitored using a diode array detector scanning between 200 and 400 nm. Fractions with eluted peptides were dried and pooled using 0.1% formic acid, into 15 samples for liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis. Mass spectrometry Samples were analyzed using a Dionex Ultimate 3000 RSLCnano LC system and a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer. Peptides (1–2 µg) were loaded onto an Acclaim PepMap 100 C18 precolumn (100 Å, 300 µm × 5 mm, 5 µm) using an Ultimate 3000 auto-sampler with 0.1% formic acid for 3 min at a flow rate of 10 µl/min. Switching the column valve eluted peptides onto a PepMap C18, EASY-Spray LC analytical column (100 Å, 75 µm × 500 mm, 2 µm). Peptide separation was profiled at 300 nl/min by applying a 100-min linear gradient of 4%–40% using mobile phase A (H2O with 0.1% formic acid) and mobile phase B (80% acetonitrile, 20% H2O with 0.1% formic acid) over a 120 minute total run time. Mass spectrometry measured the mass-to-charge ratio (m/z) in positive ion data-dependent mode. Full MS scans were performed in the range of 380–1500 m/z at a mass resolution of 70 000 with an automatic gain control (AGC) of 5 × 106 at a maximum injection time of 250 ms. Data-dependent scans of the top 20 most abundant ions, with charge states between 2+ and 5+, were automatically isolated, selected and fragmented by higher-energy collisional dissociation (HCD) in the quadrupole mass analyzer. Dynamic exclusion was set at 60 s. HCD fragmentation was performed at a normalized collision energy (NCE) of 32.5% and a stepped NCE of 10% and monitored at a resolution of 17 500. The AGC, maximum injection time, first fixed mass, and isolation window for MS2 scans was 5 × 104, 150 ms, 100 and 1.2 m/z, respectively. Data processing Raw files were converted using ProteoWizard MSConvertGUI (Kessner et al., 2008) with peak picking and a threshold count of 150 used as conversion filters. Peak lists were searched against a UniProtKB/Swiss-Prot human database (Homo sapiens, Canonical sequences, January 2016, Sequences: 20 194) using SearchGUI version 2.3.1 (Vaudel et al., 2011) with X! Tandem, MS-GF+ and Comet search engines. Postprocessing of peptide-spectrum matches for protein identification was done using Peptide Shaker version 1.7.3 (Vaudel et al., 2015). Search parameters included: minimum and maximum precursor mass of 300 and 900 Da, respectively, precursor mass tolerance of 10 ppm, fragment mass tolerance of 0.2 Da and a maximum number of 2 missed cleavages. Fixed modifications were set to include S-carbamidomethyl cysteine, TMT 6-plex modification of lysine and peptide N-termini with variable modifications including oxidation of methionine and deamidation of asparagine or glutamine. Deisotoping using label specific purity coefficients and relative quantification of TMT reporter ions was conducted in Reporter version 0.2.13 (http://compomics.github.io/projects/reporter.html). Data analysis and visualization Proteins present in all replicates, identified with 2 unique peptides and 100% confidence, were then analyzed in Perseus version 1.5.3.1 (Max Planck Institute of Biochemistry). Average protein ratios, with the associated standard deviation, were calculated and generic protein clusters identified using Euclidean distances from reference profiles. K-mean preprocessing and average linkages were used for hierarchical clustering. Multi-sample testing was conducted, on log2(x) transformed relative abundance ratios, using ANOVA with a permutation-based false discovery rate (FDR) for truncation at an FDR of 0.01 with results reported as q-values. Volcano plots were generated using 2-tailed t tests and stringency of analysis controlled with an FDR of 0.01 with 250 randomizations, mean weighting and the difference and −Log(p-value) were used to assign significance (q-value). In addition, proteins were annotated for gene ontology biological processes, molecular functions, cellular components as well as Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway identifiers. RESULTS This study includes proteomic comparisons which, to date, are absent from literature. Here, PHHs were compared with hiPSC-derived HLC monolayers, HepG2 monolayers and HepG2 3D spheroids (Figs. 2A–C) using replicates of quantitative proteomics. Protein-mass profiles of the various hepatocyte lysates clearly demonstrated proteomic differences (Figure 2D). The stabled isotope labeled proteomics workflow applied to these lysates identified and quantified 6682, 6285, and 6449 proteins for replicates 1, 2 and 3 respectively. Filtering proteins for those identified by at least 2 unique peptides at 100% identification confidence reduced the cohort to 5231 proteins across triplicate TMT experiments. Figure 2. View largeDownload slide Phase contrast images (EVOS FL Cell Imaging System) of (A) Hepatocyte-like cell monolayers differentiation at day 35, (B) HepG2 cell monolayers on day 1 after seeding which were harvested once at confluence, (C) HepG2 cell 3D spheroids at day 10 of culture (scale bar: 200 µm), (D) Coomassie stained 4%–15% Mini-PROTEAN TGX gel of hepatocyte lysates. Lane 1 and 10, St: Precision Plus Protein Dual Color standard (2–250 kDa); lanes 2 and 4, HepG2 cell 3D spheroids collected on day 10; lane 3, HepG2 cell spheroids collected at day 14; lanes 5 to 7, HepG2 cell monolayers at various passages; lanes 8 and 9: primary human hepatocytes. Figure 2. View largeDownload slide Phase contrast images (EVOS FL Cell Imaging System) of (A) Hepatocyte-like cell monolayers differentiation at day 35, (B) HepG2 cell monolayers on day 1 after seeding which were harvested once at confluence, (C) HepG2 cell 3D spheroids at day 10 of culture (scale bar: 200 µm), (D) Coomassie stained 4%–15% Mini-PROTEAN TGX gel of hepatocyte lysates. Lane 1 and 10, St: Precision Plus Protein Dual Color standard (2–250 kDa); lanes 2 and 4, HepG2 cell 3D spheroids collected on day 10; lane 3, HepG2 cell spheroids collected at day 14; lanes 5 to 7, HepG2 cell monolayers at various passages; lanes 8 and 9: primary human hepatocytes. Hierarchical Clustering of PHHs, HLCs, HepG2 Monolayers, and HepG2 3D Spheroids Hierarchical clustering, of the 5231 proteins, predominantly grouped samples according to cell type with distinctive grouping of PHHs, HLCs and HepG2 monolayers and HepG2 3D spheroids (Figure 3A). PHHs and HLCs cosegregated separately from HepG2 cells with the exception of PHH3 which clustered with HepG2 3D spheroids. Identification of PHH3 as an outlier could be due to biological variance induced when thawing, quantity of labeled protein or reduced labeling efficiency. Despite this behavior of PHH3, hierarchical clustering of biological replicates, which identified protein groupings with abundance trends, was unique to the specific cell types (Figure 3B). Here, cluster 4 (254 proteins) contained proteins which were increased in HLCs, cluster 5 (44 proteins) increased in both HLCs and PHHs, while cluster 6 (30 proteins) and cluster 8 (10 proteins) both increased in HLCs only (Supplementary Material 2: Hierarchical clustering). In addition, generic clustering provided 100 groups of more tightly regulated trends to investigate more conservative protein relationships (Supplementary Material: Generic clustering_100). Figure 3. View largeDownload slide (A) Hierarchical clustering of proteomic data for individual samples. (B) Protein profile trends produced from hierarchical clustering (10 row clusters) with the corresponding number of grouped proteins. Clusters 9 and 10 (not included) contained a single protein only. Figure 3. View largeDownload slide (A) Hierarchical clustering of proteomic data for individual samples. (B) Protein profile trends produced from hierarchical clustering (10 row clusters) with the corresponding number of grouped proteins. Clusters 9 and 10 (not included) contained a single protein only. Principal Component Analysis of PHHs, HLCs, HepG2 Monolayers, and HepG2 3D Spheroids Principal component analysis (PCA) clustered PHHs, with the exception of PHH3, and HLCs distinctly in Component 1 (40.5%) and Component 2 (33.2%) of principal component space (Figure 4A). In contrast, HepG2 cells, regardless of culturing strategy, clustered together in these components. This suggests that when collapsing the data into new linear combinations that HepG2 monolayers and 3D spheroids are seemingly indistinguishable when compared with the proteome of either PHHs or HLCs. However, comparing Components 1 and 3 (4.8%) of the PCA (Figure 4B) spatially resolved HepG2 monolayers and HepG2 3D spheroids suggesting that differences based on culture technique do potentially alter the proteome but are confounded by the degree to which these cells differ from PHHs. Lower components, accounting for less overall variance (Component 4: 4.3% to Component 16: 0.6%) did not provide additional insight into clustering (data not shown). Figure 4. View largeDownload slide Principal components analysis scatter plots of (A) Component 1 versus 2 (B) Component 1 versus 3. PHHs (n = 4, triangle), HLCs (n = 6, square), HepG2 monolayers (n = 4, circle), HepG2 spheroids (n = 3, diamond). Figure 4. View largeDownload slide Principal components analysis scatter plots of (A) Component 1 versus 2 (B) Component 1 versus 3. PHHs (n = 4, triangle), HLCs (n = 6, square), HepG2 monolayers (n = 4, circle), HepG2 spheroids (n = 3, diamond). Comparison of PHHs Versus HLCs Direct comparison, using significance and fold change, to the PHH proteome identified 961 proteins increased and 1020 decreased in abundance in HLCs (Figure 5A). These 1981 proteins which differ in abundance, with the corresponding difference and q-value were reported (Supplementary Material 2: Volcano plot_PHHvHLCs). Of these, many proteins with higher abundance in PHHs were involved in metabolism including aldoketo reductase family 1 member C2, CYP4V2, mitochondrial aldehyde dehydrogenase X, alcohol dehydrogenase 6, mitochondrial dimethylglycine dehydrogenase and, solute carrier family 22 member 7 responsible for organic anion transport. Abundant proteins in HLCs included Kunitz-type protease inhibitor 2 (−log p-value: 5.042 and difference: −3.104) an inhibitor of hepatocyte growth factor activator. Other abundant proteins were involved in cytoskeletal arrangement such as coactosin-like protein, microtubule-associated protein 1B responsible for tyrosination of alpha-tubulin, macrophage-capping protein and, serine/threonine-protein kinase PAK 1 with roles in cytoskeleton dynamics, cell adhesion, migration, proliferation, apoptosis and mitosis (The UniProt Consortium, 2014). Figure 5. View largeDownload slide Volcano plots comparing PHHs to HLCs, PHHs to HepG2 monolayers and PHHs to HepG2 3D spheroids. Proteins plotted above and to the left or right of the significance discriminant line are differentially increased or decreased abundance relative to PHHs, respectively using an FDR of 0.01. Proteins which displayed significant differences between PHH and each model are provided (Supplementary Material 2: volcano plots for PHHvHLCs, PHHvHepG2, and PHHvHepG2 [3D]). Figure 5. View largeDownload slide Volcano plots comparing PHHs to HLCs, PHHs to HepG2 monolayers and PHHs to HepG2 3D spheroids. Proteins plotted above and to the left or right of the significance discriminant line are differentially increased or decreased abundance relative to PHHs, respectively using an FDR of 0.01. Proteins which displayed significant differences between PHH and each model are provided (Supplementary Material 2: volcano plots for PHHvHLCs, PHHvHepG2, and PHHvHepG2 [3D]). Comparison of PHHs Versus HepG2 Monolayers When compared with PHH protein abundance, 956 and 1327 proteins were increased and decreased in HepG2 monolayers, respectively (Figure 5B). These 2283 proteins which differed in abundance (Supplementary Material 2: Volcano plot_PHHvHepG2) accounted for approximately 40% of the quantified proteins making HepG2 monolayers the most divergent from PHHs in direct comparison. Many proteins with greater abundance in PHHs were mitochondrial in origin and essential to facilitate hepatic functions such as energy production and catalysis. These included mitochondrial cytochrome b-c1 complex subunit which generates electrochemical potential coupled to ATP synthesis, mitochondrial aminomethyltransferase which catalyzes glycine degradation, glycogenin-2 which serves as substrate for glycogen synthase, nicotinamide N-methyltransferase involved in xenobiotic metabolism, and solute carrier organic anion transporter family member 1B1 (The UniProt Consortium, 2014). The decreased abundance in mitochondrial proteins could also be associated with the transformed phenotype of cancer cell lines which alters overall metabolic, glycolytic, and anaerobic activity. Proteins with greater abundance in HepG2 monolayers than PHHs, included multiple ubiquitous actin-associated proteins. This could be as a result of the unnatural microenvironment produced in monolayer cultures where HepG2 cells display cell protrusions for motility and migration not seen in vivo or in suspensions of PHHs. Actin-associated proteins which function as intracellular anchors, scaffolds and signaling proteins, included protein enabled homolog, microtubule-associated protein 1B, fascin which organizes filamentous actin into bundles and filamin-A required for orthogonal branching and linking of actin filaments to membrane glycoproteins (The UniProt Consortium, 2014). Comparison of PHHs Versus HepG2 3D Spheroids When compared with PHH protein abundance, 180 proteins increased and 683 decreased in abundance in HepG2 3D spheroids (Figure 5C). These 862 proteins accounted for only 18.8% (180 vs 956) and 51.4% (682 vs 1327) of the variance seen in HepG2 monolayers (Supplementary Material 2: Volcano plot_PHHvHepG2[3D]). Here approximately 2.5-fold less proteins meets statistical cut-offs compared with HepG2 monolayers. Abundant proteins in PHHs compared with HepG2 3D spheroids, again included many mitochondrial proteins including cytochrome b-c1 complex subunit, ATP synthase subunit and cytochrome c oxidase subunit 6C. Proteins associated with absorption, distribution, metabolism and excretion (ADME) were more abundant in PHHs and included dimethylaniline monooxygenase [N-oxide] 4 involved in oxidative metabolism of xenobiotics, retinol dehydrogenase 16, CYP2D6, alpha-1-antichymotrypsin, alpha-1-acid glycoprotein 1, sideroflexin-5 a transmembrane citrate transport, and StAR-related lipid transfer protein 5 responsible for intracellular transport of sterols or other lipids (The UniProt Consortium, 2014). These data suggest that despite less proteins which differ in abundance that the source of these differences may not be related to enhancing the hepatic phenotype. Comparison of Overall Differences in Protein Expression Multiple sample testing (ANOVA) reported 684 of the 5231 proteins (13%) with nonsignificant q-values (Supplementary Material 2: ANOVA, nonsignificant). These proteins were predominantly ribosomal, endoplasmic reticulum (ER), mitochondrial, transcriptional, or translational associated proteins. However, side-by-side comparison of PHHs compared with HLCs, HepG2 monolayers or HepG2 3D spheroids demonstrated 1981, 2283, and 862 proteins, respectively which were altered in abundance. This posed a series of questions: Were the same proteins increased or decreased in abundance in HLCs, HepG2 monolayers and HepG2 3D spheroids? Were some proteins increased in one model yet decreased in another? Therefore, proteins above significance thresholds in volcano plots were compared to determine whether protein differences were conserved between HLCs, HepG2 monolayers, and HepG2 3D spheroids (Table 1). These comparisons (HLCs vs HepG2 monolayers, HLCs vs HepG2 3D spheroids or HepG2 monolayers vs HepG2 3D spheroids) suggest that where abundance was lower compared with PHHs it was common to HLCs, HepG2 monolayers and 3D spheroids. However, when proteins were increased in abundance compared with PHHs it was specific to HLCs, HepG2 cell monolayers or HepG2 3D spheroids. Further comparisons identified 62 proteins that displayed contrary abundance between HLCs and HepG2 monolayers and only 11 proteins for HepG2 3D spheroids. In total, for HLCs and HepG2 cells, only 73 proteins were differential in their comparison to PHHs which accounted for only 1.4% (73 of 5231) of the total proteins analyzed (Supplementary Material 2: Model comparison). Proteins with higher abundance in HepG2 cells compared with HLCs were principally associated with DNA replication, transcription, and translation. Cell proliferation does not occur in HLCs during maturation which is in contrast to HepG2 cells which propagate indefinitely. This notably high abundance of proliferation associated proteins in HepG2 cells suggests that continual replication is a key factor restricting the mimicry of terminally differentiated PHHs and mature hiPSC-HLCs. Table 1. Comparison of Proteins With the Same or Different Abundance Between Models HLCs HepG2 Monolayers HepG2 3D Spheroids Proteins with higher abundance in PHHs compared with each model 1020 1327 682 Proteins with lower abundance in PHHs compared with each model 961 956 180 HLCs Versus HepG2 Monolayers HLCs Versus HepG2 3D Spheroids HepG2 Monolayers Versus 3D Spheroids Comparison of the overlap of proteins with higher abundance in PHHs 751 500 653 Comparison of the overlap of proteins with lower abundance in PHHs 282 66 142 Variable differences 62 11 0 HLCs HepG2 Monolayers HepG2 3D Spheroids Proteins with higher abundance in PHHs compared with each model 1020 1327 682 Proteins with lower abundance in PHHs compared with each model 961 956 180 HLCs Versus HepG2 Monolayers HLCs Versus HepG2 3D Spheroids HepG2 Monolayers Versus 3D Spheroids Comparison of the overlap of proteins with higher abundance in PHHs 751 500 653 Comparison of the overlap of proteins with lower abundance in PHHs 282 66 142 Variable differences 62 11 0 Proteins with higher abundance in PHHs (right of volcano plot) or proteins with lower abundance in PHHs (left of volcano plot) for HLCs, HepG2 monolayers, or HepG2 3D spheroids were extracted. These proteins were then compared between HLCs, HepG2 monolayers, and HepG2 3D spheroids to determine if the overlap of proteins meeting the same cut-off values on the volcano plots were similar. For instance, 1020 proteins were higher in PHHs compared with HLCs and this was compared with the 1327 proteins for HepG2 monolayers and 751 of these were the same proteins. Table 1. Comparison of Proteins With the Same or Different Abundance Between Models HLCs HepG2 Monolayers HepG2 3D Spheroids Proteins with higher abundance in PHHs compared with each model 1020 1327 682 Proteins with lower abundance in PHHs compared with each model 961 956 180 HLCs Versus HepG2 Monolayers HLCs Versus HepG2 3D Spheroids HepG2 Monolayers Versus 3D Spheroids Comparison of the overlap of proteins with higher abundance in PHHs 751 500 653 Comparison of the overlap of proteins with lower abundance in PHHs 282 66 142 Variable differences 62 11 0 HLCs HepG2 Monolayers HepG2 3D Spheroids Proteins with higher abundance in PHHs compared with each model 1020 1327 682 Proteins with lower abundance in PHHs compared with each model 961 956 180 HLCs Versus HepG2 Monolayers HLCs Versus HepG2 3D Spheroids HepG2 Monolayers Versus 3D Spheroids Comparison of the overlap of proteins with higher abundance in PHHs 751 500 653 Comparison of the overlap of proteins with lower abundance in PHHs 282 66 142 Variable differences 62 11 0 Proteins with higher abundance in PHHs (right of volcano plot) or proteins with lower abundance in PHHs (left of volcano plot) for HLCs, HepG2 monolayers, or HepG2 3D spheroids were extracted. These proteins were then compared between HLCs, HepG2 monolayers, and HepG2 3D spheroids to determine if the overlap of proteins meeting the same cut-off values on the volcano plots were similar. For instance, 1020 proteins were higher in PHHs compared with HLCs and this was compared with the 1327 proteins for HepG2 monolayers and 751 of these were the same proteins. Comparison of Liver Enriched and Metabolic Proteins Correlating the abundance of proteins suggested throughout literature (Baxter et al., 2015; Lemaigre, 2010; Schwartz et al., 2014; Si‐Tayeb et al., 2010; Uhlén et al., 2015) to describe the liver enriched proteome (Supplementary Table 1) and those integral to hepatic metabolic function was done (Table 2). Data were interrogated for phases I and II metabolic enzymes, CYP450s, monoamine oxidases, flavin-containing oxygenases, esterases, UGTs, sulfotransferases, N-acetyl transferases, glutathione S-transferases, and transmembrane transporters. Relationships were tabulated as ANOVA significant as well as t test significant to highlight the difference across each cell model. All of the major CYP isoforms (CYP3A4/5, CYP2D6, CYP2C8/9, CYP1A2, CYP2C19, and CYP2B6) were identified in PHHs but other cell models were limited in expression of these enzymes. HLCs and HepG2 cells mostly displayed difference in t test significance across the extracted protein cohorts. Differences in relative protein abundance of some alcohol and aldehyde dehydrogenases, uridine 5′-diphospho-glucuronosyltransferase (UDP)-glucuronosyltransferases (UGTs), glutathione S-transferases and cytochromes (CYP2C8, CYP2B6, CYP2S1, and CYP4F12) was observed in HepG2 3D spheroids compared with equivalent monolayer cultures. Table 2. Statistical Significance of Proteins Involved in Metabolism Compared Using Multiple Sample Testing (ANOVA) or 2-Tailed t Test Multiple Sample Testing Significance Following 2-Tailed t Tests Protein Description Main Accession ANOVA Significance PHHs Versus HLCs PHHs Versus HepG2 Monolayers PHHs Versus HepG2 3D Spheroids Alcohol dehydrogenase [nicotinamide adenine dinucleotide phosphate NADP(+)] P14550 + − + + Alcohol dehydrogenase 1A P07327 + + + + Alcohol dehydrogenase 1B P00325 + + + + Alcohol dehydrogenase 1C P00326 + + + + Alcohol dehydrogenase 4 P08319 + + + + Alcohol dehydrogenase 6 P28332 + + + − Alcohol dehydrogenase class-3 P11766 + − + − Aldehyde dehydrogenase family 16 member A1 Q8IZ83 + + + − Aldehyde dehydrogenase family 8 member Q9H2A2 + + + + Aldehyde dehydrogenase X, mitochondrial P30837 + + + + Aldehyde dehydrogenase, mitochondrial P05091 + + + + Aldehyde oxidase Q06278 + + + + Aldo-keto reductase family 1 member B10 O60218 + − − + Aldo-keto reductase family 1 member C1 Q04828 + + − − Aldo-keto reductase family 1 member C2 P52895 + + + − Aldo-keto reductase family 1 member C3 P42330 + + + − Aldo-keto reductase family 1 member C4 P17516 + + + − Cytochrome b5 P00167 + + + + Cytochrome P450 1A1 P04798 + + + + Cytochrome P450 1A2 P05177 + + + + Cytochrome P450 20A1 Q6UW02 + + + + Cytochrome P450 2A6 P11509 + + + + Cytochrome P450 2B6 P20813 + + + − Cytochrome P450 2C19 P33261 + + + + Cytochrome P450 2C8 P10632 + + + − Cytochrome P450 2C9 P11712 + + + + Cytochrome P450 2D6 P10635 + + + + Cytochrome P450 2E1 P05181 + + + + Cytochrome P450 2J2 P51589 + + + + Cytochrome P450 2S1 Q96SQ9 + + + − Cytochrome P450 2W1 Q8TAV3 + − − − Cytochrome P450 3A4 P08684 + + + + Cytochrome P450 3A5 P20815 + + + + Cytochrome P450 4A11 Q02928 + + + + Cytochrome P450 4F12 Q9HCS2 + + + − Cytochrome P450 4V2 Q6ZWL3 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 3 P31513 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 4 P31512 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 5 P49326 + + + + Gamma-glutamyltranspeptidase 1 P19440 + + + − Glutathione reductase, mitochondrial P00390 + − + − Glutathione S-transferase A2 P09210 + − + + Glutathione S-transferase C-terminal domain-containing protein Q8NEC7 + − + − Glutathione S-transferase kappa 1 Q9Y2Q3 + − + + Glutathione S-transferase Mu 1 P09488 + + + + Glutathione S-transferase Mu 2 P28161 − − − − Glutathione S-transferase Mu 3 P21266 + + – – Glutathione S-transferase omega-1 P78417 + – + – Glutathione S-transferase P P09211 + + – – Glutathione S-transferase theta-1 P30711 + + + – Glutathione S-transferase theta-2 P0CG29 + + + + Glutathione synthetase P48637 – – – – Multidrug resistance protein 1 P08183 + + – – Multidrug resistance-associated protein 1 P33527 + + + – Multidrug resistance-associated protein 6 O95255 + + + + NADPH–cytochrome P450 reductase P16435 + – + + UDP-glucuronosyltransferase 1-1 P22309 + + + + UDP-glucuronosyltransferase 1-4 P22310 + + + + UDP-glucuronosyltransferase 1-6 P19224 + + + + UDP-glucuronosyltransferase 2A3 Q6UWM9 + + + – UDP-glucuronosyltransferase 2B10 P36537 + + + + UDP-glucuronosyltransferase 2B15 P54855 + + + + UDP-glucuronosyltransferase 2B17 O75795 + + + + UDP-glucuronosyltransferase 2B4 P06133 + + + – UDP-glucuronosyltransferase 2B7 P16662 + + + + Multiple Sample Testing Significance Following 2-Tailed t Tests Protein Description Main Accession ANOVA Significance PHHs Versus HLCs PHHs Versus HepG2 Monolayers PHHs Versus HepG2 3D Spheroids Alcohol dehydrogenase [nicotinamide adenine dinucleotide phosphate NADP(+)] P14550 + − + + Alcohol dehydrogenase 1A P07327 + + + + Alcohol dehydrogenase 1B P00325 + + + + Alcohol dehydrogenase 1C P00326 + + + + Alcohol dehydrogenase 4 P08319 + + + + Alcohol dehydrogenase 6 P28332 + + + − Alcohol dehydrogenase class-3 P11766 + − + − Aldehyde dehydrogenase family 16 member A1 Q8IZ83 + + + − Aldehyde dehydrogenase family 8 member Q9H2A2 + + + + Aldehyde dehydrogenase X, mitochondrial P30837 + + + + Aldehyde dehydrogenase, mitochondrial P05091 + + + + Aldehyde oxidase Q06278 + + + + Aldo-keto reductase family 1 member B10 O60218 + − − + Aldo-keto reductase family 1 member C1 Q04828 + + − − Aldo-keto reductase family 1 member C2 P52895 + + + − Aldo-keto reductase family 1 member C3 P42330 + + + − Aldo-keto reductase family 1 member C4 P17516 + + + − Cytochrome b5 P00167 + + + + Cytochrome P450 1A1 P04798 + + + + Cytochrome P450 1A2 P05177 + + + + Cytochrome P450 20A1 Q6UW02 + + + + Cytochrome P450 2A6 P11509 + + + + Cytochrome P450 2B6 P20813 + + + − Cytochrome P450 2C19 P33261 + + + + Cytochrome P450 2C8 P10632 + + + − Cytochrome P450 2C9 P11712 + + + + Cytochrome P450 2D6 P10635 + + + + Cytochrome P450 2E1 P05181 + + + + Cytochrome P450 2J2 P51589 + + + + Cytochrome P450 2S1 Q96SQ9 + + + − Cytochrome P450 2W1 Q8TAV3 + − − − Cytochrome P450 3A4 P08684 + + + + Cytochrome P450 3A5 P20815 + + + + Cytochrome P450 4A11 Q02928 + + + + Cytochrome P450 4F12 Q9HCS2 + + + − Cytochrome P450 4V2 Q6ZWL3 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 3 P31513 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 4 P31512 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 5 P49326 + + + + Gamma-glutamyltranspeptidase 1 P19440 + + + − Glutathione reductase, mitochondrial P00390 + − + − Glutathione S-transferase A2 P09210 + − + + Glutathione S-transferase C-terminal domain-containing protein Q8NEC7 + − + − Glutathione S-transferase kappa 1 Q9Y2Q3 + − + + Glutathione S-transferase Mu 1 P09488 + + + + Glutathione S-transferase Mu 2 P28161 − − − − Glutathione S-transferase Mu 3 P21266 + + – – Glutathione S-transferase omega-1 P78417 + – + – Glutathione S-transferase P P09211 + + – – Glutathione S-transferase theta-1 P30711 + + + – Glutathione S-transferase theta-2 P0CG29 + + + + Glutathione synthetase P48637 – – – – Multidrug resistance protein 1 P08183 + + – – Multidrug resistance-associated protein 1 P33527 + + + – Multidrug resistance-associated protein 6 O95255 + + + + NADPH–cytochrome P450 reductase P16435 + – + + UDP-glucuronosyltransferase 1-1 P22309 + + + + UDP-glucuronosyltransferase 1-4 P22310 + + + + UDP-glucuronosyltransferase 1-6 P19224 + + + + UDP-glucuronosyltransferase 2A3 Q6UWM9 + + + – UDP-glucuronosyltransferase 2B10 P36537 + + + + UDP-glucuronosyltransferase 2B15 P54855 + + + + UDP-glucuronosyltransferase 2B17 O75795 + + + + UDP-glucuronosyltransferase 2B4 P06133 + + + – UDP-glucuronosyltransferase 2B7 P16662 + + + + The average relative abundance and standard deviation across replicates is provided in Supplementary Material 2: Metabolic proteins. Table 2. Statistical Significance of Proteins Involved in Metabolism Compared Using Multiple Sample Testing (ANOVA) or 2-Tailed t Test Multiple Sample Testing Significance Following 2-Tailed t Tests Protein Description Main Accession ANOVA Significance PHHs Versus HLCs PHHs Versus HepG2 Monolayers PHHs Versus HepG2 3D Spheroids Alcohol dehydrogenase [nicotinamide adenine dinucleotide phosphate NADP(+)] P14550 + − + + Alcohol dehydrogenase 1A P07327 + + + + Alcohol dehydrogenase 1B P00325 + + + + Alcohol dehydrogenase 1C P00326 + + + + Alcohol dehydrogenase 4 P08319 + + + + Alcohol dehydrogenase 6 P28332 + + + − Alcohol dehydrogenase class-3 P11766 + − + − Aldehyde dehydrogenase family 16 member A1 Q8IZ83 + + + − Aldehyde dehydrogenase family 8 member Q9H2A2 + + + + Aldehyde dehydrogenase X, mitochondrial P30837 + + + + Aldehyde dehydrogenase, mitochondrial P05091 + + + + Aldehyde oxidase Q06278 + + + + Aldo-keto reductase family 1 member B10 O60218 + − − + Aldo-keto reductase family 1 member C1 Q04828 + + − − Aldo-keto reductase family 1 member C2 P52895 + + + − Aldo-keto reductase family 1 member C3 P42330 + + + − Aldo-keto reductase family 1 member C4 P17516 + + + − Cytochrome b5 P00167 + + + + Cytochrome P450 1A1 P04798 + + + + Cytochrome P450 1A2 P05177 + + + + Cytochrome P450 20A1 Q6UW02 + + + + Cytochrome P450 2A6 P11509 + + + + Cytochrome P450 2B6 P20813 + + + − Cytochrome P450 2C19 P33261 + + + + Cytochrome P450 2C8 P10632 + + + − Cytochrome P450 2C9 P11712 + + + + Cytochrome P450 2D6 P10635 + + + + Cytochrome P450 2E1 P05181 + + + + Cytochrome P450 2J2 P51589 + + + + Cytochrome P450 2S1 Q96SQ9 + + + − Cytochrome P450 2W1 Q8TAV3 + − − − Cytochrome P450 3A4 P08684 + + + + Cytochrome P450 3A5 P20815 + + + + Cytochrome P450 4A11 Q02928 + + + + Cytochrome P450 4F12 Q9HCS2 + + + − Cytochrome P450 4V2 Q6ZWL3 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 3 P31513 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 4 P31512 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 5 P49326 + + + + Gamma-glutamyltranspeptidase 1 P19440 + + + − Glutathione reductase, mitochondrial P00390 + − + − Glutathione S-transferase A2 P09210 + − + + Glutathione S-transferase C-terminal domain-containing protein Q8NEC7 + − + − Glutathione S-transferase kappa 1 Q9Y2Q3 + − + + Glutathione S-transferase Mu 1 P09488 + + + + Glutathione S-transferase Mu 2 P28161 − − − − Glutathione S-transferase Mu 3 P21266 + + – – Glutathione S-transferase omega-1 P78417 + – + – Glutathione S-transferase P P09211 + + – – Glutathione S-transferase theta-1 P30711 + + + – Glutathione S-transferase theta-2 P0CG29 + + + + Glutathione synthetase P48637 – – – – Multidrug resistance protein 1 P08183 + + – – Multidrug resistance-associated protein 1 P33527 + + + – Multidrug resistance-associated protein 6 O95255 + + + + NADPH–cytochrome P450 reductase P16435 + – + + UDP-glucuronosyltransferase 1-1 P22309 + + + + UDP-glucuronosyltransferase 1-4 P22310 + + + + UDP-glucuronosyltransferase 1-6 P19224 + + + + UDP-glucuronosyltransferase 2A3 Q6UWM9 + + + – UDP-glucuronosyltransferase 2B10 P36537 + + + + UDP-glucuronosyltransferase 2B15 P54855 + + + + UDP-glucuronosyltransferase 2B17 O75795 + + + + UDP-glucuronosyltransferase 2B4 P06133 + + + – UDP-glucuronosyltransferase 2B7 P16662 + + + + Multiple Sample Testing Significance Following 2-Tailed t Tests Protein Description Main Accession ANOVA Significance PHHs Versus HLCs PHHs Versus HepG2 Monolayers PHHs Versus HepG2 3D Spheroids Alcohol dehydrogenase [nicotinamide adenine dinucleotide phosphate NADP(+)] P14550 + − + + Alcohol dehydrogenase 1A P07327 + + + + Alcohol dehydrogenase 1B P00325 + + + + Alcohol dehydrogenase 1C P00326 + + + + Alcohol dehydrogenase 4 P08319 + + + + Alcohol dehydrogenase 6 P28332 + + + − Alcohol dehydrogenase class-3 P11766 + − + − Aldehyde dehydrogenase family 16 member A1 Q8IZ83 + + + − Aldehyde dehydrogenase family 8 member Q9H2A2 + + + + Aldehyde dehydrogenase X, mitochondrial P30837 + + + + Aldehyde dehydrogenase, mitochondrial P05091 + + + + Aldehyde oxidase Q06278 + + + + Aldo-keto reductase family 1 member B10 O60218 + − − + Aldo-keto reductase family 1 member C1 Q04828 + + − − Aldo-keto reductase family 1 member C2 P52895 + + + − Aldo-keto reductase family 1 member C3 P42330 + + + − Aldo-keto reductase family 1 member C4 P17516 + + + − Cytochrome b5 P00167 + + + + Cytochrome P450 1A1 P04798 + + + + Cytochrome P450 1A2 P05177 + + + + Cytochrome P450 20A1 Q6UW02 + + + + Cytochrome P450 2A6 P11509 + + + + Cytochrome P450 2B6 P20813 + + + − Cytochrome P450 2C19 P33261 + + + + Cytochrome P450 2C8 P10632 + + + − Cytochrome P450 2C9 P11712 + + + + Cytochrome P450 2D6 P10635 + + + + Cytochrome P450 2E1 P05181 + + + + Cytochrome P450 2J2 P51589 + + + + Cytochrome P450 2S1 Q96SQ9 + + + − Cytochrome P450 2W1 Q8TAV3 + − − − Cytochrome P450 3A4 P08684 + + + + Cytochrome P450 3A5 P20815 + + + + Cytochrome P450 4A11 Q02928 + + + + Cytochrome P450 4F12 Q9HCS2 + + + − Cytochrome P450 4V2 Q6ZWL3 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 3 P31513 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 4 P31512 + + + + Dimethylaniline monooxygenase [N-oxide-forming] 5 P49326 + + + + Gamma-glutamyltranspeptidase 1 P19440 + + + − Glutathione reductase, mitochondrial P00390 + − + − Glutathione S-transferase A2 P09210 + − + + Glutathione S-transferase C-terminal domain-containing protein Q8NEC7 + − + − Glutathione S-transferase kappa 1 Q9Y2Q3 + − + + Glutathione S-transferase Mu 1 P09488 + + + + Glutathione S-transferase Mu 2 P28161 − − − − Glutathione S-transferase Mu 3 P21266 + + – – Glutathione S-transferase omega-1 P78417 + – + – Glutathione S-transferase P P09211 + + – – Glutathione S-transferase theta-1 P30711 + + + – Glutathione S-transferase theta-2 P0CG29 + + + + Glutathione synthetase P48637 – – – – Multidrug resistance protein 1 P08183 + + – – Multidrug resistance-associated protein 1 P33527 + + + – Multidrug resistance-associated protein 6 O95255 + + + + NADPH–cytochrome P450 reductase P16435 + – + + UDP-glucuronosyltransferase 1-1 P22309 + + + + UDP-glucuronosyltransferase 1-4 P22310 + + + + UDP-glucuronosyltransferase 1-6 P19224 + + + + UDP-glucuronosyltransferase 2A3 Q6UWM9 + + + – UDP-glucuronosyltransferase 2B10 P36537 + + + + UDP-glucuronosyltransferase 2B15 P54855 + + + + UDP-glucuronosyltransferase 2B17 O75795 + + + + UDP-glucuronosyltransferase 2B4 P06133 + + + – UDP-glucuronosyltransferase 2B7 P16662 + + + + The average relative abundance and standard deviation across replicates is provided in Supplementary Material 2: Metabolic proteins. DISCUSSION Human-derived hepatic cells remain central to the applicability and performance of innovative liver models (Sison-Young et al., 2015). Generating extensive protein expression profiles are required to ascertain that each model systems is “fit-for-purpose”. Comparable reports have previously quantified 2722 proteins in 3 human-derived hepatic cell lines (HepG2, Upcyte, HepaRG) and compared these with cryopreserved PHHs. Hierarchical clustering also segregated PHHs from the other cell types with ADME proteins being an influential factor (Sison-Young et al., 2015). The greater depth of protein coverage reported in this study provides additional insight into hepatic phenotypes. PCA and hierarchical clustering on the full complement of quantified proteins demonstrated that the HLCs proteome are more closely correlated to PHHs. PHHs are nontransformed and maintain the highest differentiation status while HepG2 express a strong cancer cell signature. Considering this, it is suggested that in PCA Component 1 appears capable of separating samples based on whether the cells are transformed (HepG2 cells) or nontransformed (PHHs and HLCs) whereas Component 2 is distinguishing the level of differentiation. Glucocorticoids, such as dexamethasone, through interaction with glucocorticoid receptors modulate gene transcription and cellular function. As a major regulator of cellular function, the absence of glucocorticoids in the HepG2 cell culture medium, while present in HLCs differentiation medium could contribute to the PCA clustering. Psarra and Sekeris (2011) suggested that dexamethasone induces mitochondrial transcription factors including transcription factors A, B1 and B2 as well as oxidative phosphorylation genes such as cytochrome b. Mitochondrial transcription factor A (Q00059) was nonsignificant for each t test comparison whereas mitochondrial dimethyladenosine transferase 1 (Q8WVM0) and 2 (Q9H5Q4) differed in HepG2 monolayers and HLCs, respectively. Furthermore, cytochrome b5 (P00167) differed in all comparisons to PHHs regardless of the presence or absence of glucocorticoids. This illustrates that while glucocorticoids may be a contributor to the magnitude of difference between cell models, it is not the sole source of the variance observed. HepG2 cells and HLCs were maintained in cell culture medium containing glucose in supra-physiological levels which consequently enables cells to generate energy through glycolysis as opposed to oxidative phosphorylation. The use of protocols which do not maintain hepatocytes ex vivo in a physiologically applicable environment could underpin many of the differences observed across the proteome. Irrespective of the limitations of the cell culture microenvironment, clustering of HLCs and PHHs in Component 1 suggests that despite HLCs being cultured as monolayers that the proteomes of PHHs and HLCs are most closely related. Successful differentiation of hiPSCs to HLCs is a complex interplay of cell proliferation and maturation. The duration of differentiation and resultant degree of maturity influences the degree to which HLCs mimic PHHs. Therefore, differentiation beyond a critical threshold could underestimate the proteomic correlation between hiPSC-HLCs and PHHs. Recent work by Asai et al. (2017) found that HLCs with spatial proximity to human umbilical vein endothelial or mesenchymal stem cells that provide paracrine signals showed a proteome more closely aligned with that of PHHs. This suggests that using a differentiation protocol with additional paracrine signaling could enhance the HLCs protein complement to be more representative of PHHs. These proteomic data described may contain noteworthy insights of essential contributors to the HLC proteome. Major differences between PHHs and HepG2 cells include morphology, the transformed and discontinuous phenotype of cancer cells as well as maintenance of cells in culture (Wilkening et al., 2003). In addition, HepG2 cells vary in their overall metabolic, glycolytic, and anaerobic activity functions with the low number of mRNA copies and protein expression of essential phase I enzymes (Duret et al., 2007; Wilkening et al., 2003). This study further reiterates the risk of using short-term HepG2 cell monolayer cultures as a hepatocyte model for metabolic studies. Comparing the proteomes of PHHs to HepG2 3D spheroids identified fewer abundance variances than in HepG2 monolayer counterparts. Luckert et al. (2017) observed that metabolic competence of HepG2 cells grown as monolayers for 21 days had comparable biochemical characteristics to HepG2 cells grown in various 3D culture formats (Luckert et al., 2017). This suggests that the differences in metabolic proteins observed could be as a result of culturing HepG2 cells as 3D spheroids for 10 days as opposed to only 3 days in monolayer culture. Despite the extended culture duration, hierarchical clustering and PCA were unable to distinguish HepG2 monolayers and 3D spheroid cultures from one another. Here, under the conditions investigated, HepG2 cells as 3D spheroids would not confer major alteration in biochemical characteristics. Instead, culturing HepG2 cells without adherence to artificial substrates which augmented cell morphology was considered as a main contributor to the reduced number of significantly different proteins compared with PHHs. CONCLUSIONS Here, the use of isobaric-labeled quantitative proteomics and careful preparation steps resulted in over 6000 proteins being positively identified per replicate with high confidence for comparison of PHHs, HLCs, and HepG2 cells. This quantitative data provides biological insight into the feasibility of using HLCs, HepG2 monolayers and HepG2 3D spheroids as hepatotoxicity models. Since cellular homeostasis, cellular growth and hepatic maturity are dynamic processes the cellular proteome data described in this study represents a “snapshot” of hepatocyte proteomes. These data supports the concept that there is, no single hepatic model that can accurately reflect the diverse array of outcomes required to mimic the in vivo liver functions as of yet. None of the models are considered a feasible substitute to PHHs, however, these data reveals how differentiated HLCs and HepG2 cells under different culturing techniques differ in the relative abundance of proteins compared with PHHs. The proteome of the HLCs, under the conditions investigated, better correlated to PHHs than the HepG2 monolayers or 3D spheroid counterparts. It is proposed that these data could provide insights into improving hepatocyte models and aid in enhancing the mimicry of the in vivo hepatic phenotype. SUPPLEMENTARY DATA Supplementary data are available at Toxicological Sciences online. AUTHOR’S CONTRIBUTION T.H., A.D.C., and K.S.L. designed and planned the experiments. T.H. and C.P.S. performed the cell culture and differentiation procedures. T.H. performed protein sample preparation and data analysis. All authors contributed to the data interpretation and to the writing of the manuscript. None of the authors declare any competing interests. FUNDING National Research Foundation of South Africa for the grant (Grant No. 87880); UK Commonwealth Split-site PhD Scholarship (ZACS-2014-653) and a Commonwealth, European and International Cambridge Trust Scholarship (USN: 302989247 App No: 10326363) to T.H.; Children Liver Disease Foundation PhD studentship to C.-P.S.; ERC starting Grant Relieve IMD to L.V. Any opinion, finding, conclusion, or recommendation expressed in this material is that of the author(s) and not of the funding agencies mentioned. ACKNOWLEDGMENTS We would like to thank Dr Mike Deery at the Cambridge Centre for Proteomics for assistance with the mass spectrometry and Dr Stoyan Stoychev at the South African Council for Scientific and Industrial Research who provided the infrastructure required for the bioinformatics data analysis. REFERENCES Asai A. , Aihara E. , Watson C. , Mourya R. , Mizuochi T. , Shivakumar P. , Phelan K. , Mayhew C. , Helmrath M. , Takebe T. et al. , . ( 2017 ). 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Toxicological Sciences – Oxford University Press

Published: Apr 4, 2018

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