Identifying and Optimizing Critical Process Parameters for Large-Scale Manufacturing of iPSC Derived Insulin-Producing β-cells

Identifying and Optimizing Critical Process Parameters for Large-Scale Manufacturing of iPSC Derived Insulin-Producing β-cells | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying and Optimizing Critical Process Parameters for Large-Scale Manufacturing of iPSC Derived Insulin-Producing β-cells Haneen Yehya, Alexandra Wells, Michael Majcher, Dhruv Nakhwa, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4244002/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Nov, 2024 Read the published version in Stem Cell Research & Therapy → Version 1 posted 5 You are reading this latest preprint version Abstract Background Type 1 diabetes, an autoimmune disorder leading to the destruction of pancreatic β-cells, requires lifelong insulin therapy. Islet transplantation offers a promising solution but faces challenges such as limited availability and the need for immunosuppression. Induced pluripotent stem cells (iPSCs) provide a potential alternative source of functional β-cells and have the capability for large-scale production. However, current differentiation protocols, predominantly conducted in hybrid or 2D settings, lack scalability and optimal conditions for suspension culture. Methods We examined a range of bioreactor scaleup process parameters and quality target product profiles that might affect the differentiation process. This investigation was conducted using an optimized HD-DoE protocol designed for scalability and implemented in 0.5L (PBS-0.5 Mini) vertical wheel bioreactors. Results A three stage suspension manufacturing process is developed, transitioning from adherent to suspension culture, with TB2 media supporting iPSC growth during scaling. Stage-wise optimization approaches and extended differentiation times are used to enhance marker expression and maturation of iPSC-derived islet-like clusters. Continuous bioreactor runs were used to study nutrient and growth limitations and impact on differentiation. The continuous bioreactors were compared to a Control media change bioreactor showing metabolic shifts and a more bcell-like differentiation profile. Cryopreserved aggregates harvested from the runs were recovered and showed maintenance of viability and insulin secretion capacity post-recovery, indicating their potential for storage and future transplantation therapies. Conclusion This study demonstrated that stage time increase and limited media replenishing with lactate accumulation can increase the differentiation capacity of insulin producing cells cultured in a large-scale suspension environment. Diabetes human induced pluripotent stem cell Insulin Producing Cells Bioreactor DoE β-cells Pancreatic cells bioprocess development optimization islets iPSCs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Type I diabetes is a chronic autoimmune destruction disorder of the pancreatic β-cells. It affects ~ 540 millions of patients worldwide and is expected to rise to 783.2 million in the next two decades with a projected health expenditures of 1,054 billion USD [ 1 , 2 ]. Life-long insulin therapy is needed for patients suffering from this disease. Islet transplantation that replaces lost insulin secreting cells is a promising therapy solution [ 3 , 4 ]. However, it has been tempered by the lack of islets available and the requirement of life-long immunosuppression. This has led to the investigation of induced human pluripotent stem cells (iPSCs) as an available source of functional β-cells [ 5 ]. Cell therapies derived from (iPSCs) hold promise in treating a variety of clinical indications [ 6 ]. They have the ability of replacing damaged or lost cells and are capable of high in vitro proliferation and differentiation into all three germ layers [ 7 , 8 ]. This makes them ideal for applications that require a large sustainable source of clinical grade cells. To meet the required clinical cell dosage, a large-batch manufacturing system must be established. Thus, a scaleup from a planar adherent culture to a 3D culture that generates aggregates is required. However, most research and established differentiation protocols are done in a 2D environment that lack suspension environment factors and considerations [ 9 , 10 ]. Adherent culture methods are impractical for scaling due to the limitation of surface area. To increase production beyond these constraints, suspension culture becomes necessary. Recent research indicates that a bioreactor scale-up approach can effectively expand and differentiate induced pluripotent stem cells (iPSCs) while reducing the need for manual interventions in static cultures [ 10 – 16 ]. Already established differentiation protocols were conducted side by side in adherent and suspension environments. The results indicated that the cells produced from both methods are different. In our novel (HD-DoE) protocol for pancreatic β-cell differentiation optimized on adherent plates [ 17 ], optimal conditions required BMP antagonism and retinoid input, resulting in the induction of dorsal foregut endoderm (DFE). The study demonstrated that pancreatic identity can be rapidly and robustly induced from DFE, and these cells exhibit a dorsal pancreatic identity [ 17 ]. Other published methods for differentiating human induced pluripotent stem cells (iPSCs) into β cells involve a series of developmental stages, including definitive endoderm, primitive gut tube, pancreatic progenitor, endocrine progenitor, and insulin-producing β-cells [ 18 ]. These methods typically entail the sequential addition of growth factors and small molecules to mimic pancreatic development [ 17 , 19 – 28 ]. The initial step in differentiating iPSCs into pancreatic cells often begins with definitive endoderm commitment. D'Amour et al. established an efficient method using activin A and low serum to direct up to 80% of iPSCs towards the definitive endoderm lineage [ 29 ]. Subsequent protocols have further specified and differentiated iPSCs into PDX1 andNKX6.1 positive pancreatic progenitors, leading to the generation of β-cells [ 20 , 30 , 31 ]. For instance, Rezania et al. and Pagliuca et al. successfully generated iPSC-derived β-cells with varying functionality [ 19 , 22 ]. These cells, while exhibiting reduced glucose-stimulated insulin secretion (GSIS) functionality compared to human islets, have shown promise in reversing diabetes in diabetic mice post-transplant. Recent advancements have led to the development of methods capable of producing cells with more robust and dynamic GSIS [ 21 , 32 , 33 ]. Nair et al. demonstrated that isolating INS positive cells at an early immature stage followed by reaggregation into clusters enhances β-cell maturation, although sustained second-phase response remains a challenge [ 18 ]. Velazco-Cruz et al. reported that selective modulation of transforming growth factor-β signaling, combined with resizing of cell clusters, improves dynamic GSIS with a more sustained second-phase response [ 33 ]. While GSIS functionality is of great importance, in vivo transplantation has been the most favorable method for gaining full β-cell maturation and function. A clinical trial presented at the 83rd Scientific Sessions of the American Diabetes Association showcased VX-880, a stem cell-derived islet cell therapy, as a potential treatment for type 1 diabetes (T1D). Results demonstrated restored insulin secretion, improved glycemic control, and elimination of severe hypoglycemic events in all six treated patients [ 34 ]. Despite these advancements, a significant limitation of current differentiation methods is the scalability recovery yield and the use of serum-containing or xeno-containing components, which may pose translational challenges in the future. Efforts to address these limitations are crucial for the clinical development of iPSC-derived β-cells. Methods Differentiation of human induced pluripotent stem cell culture Cells were maintained at 37°C and 5% CO 2 . Prior to the experiment, cells were grown to 70–80% confluency on vitronectin coated flasks. Cells were dissociated with TrypLE for 3 minutes at 37°C, resuspended in E8 medium, transferred to 50 ml conical tubes, and centrifuged at 400 x g for 6 minutes. The pellet was resuspended in E8 and 10 µM Y-27632 ROCK inhibitor. 90 million cells were seeded in a 500ml bioreactor with the TB10 media (Table 1). After 1 day of culture, 50% of the media was replenished. After an additional day, differentiation was started using the differentiation medium as indicated in (Table 1). All reagents used are listed in (Table 2). Cell counting and aggregate size Bioreactor samples were taken after mixing using a pipette to avoid sampling bias and gradient formation after settling. 3 ml total was sampled daily for 3 samples of 1 ml for cell count. The cells were dissociated using Accutase (Sigma A6964) and incubated for 10 minutes. Cells were then quenched with E8 then centrifuged at 400 x g for 6 minutes. Pellets were resuspended in an equivalent volume of PBS and analyzed for total cell count using Attune flow. Triplicate counts were collected for each bioreactor at each stage of culture. Duplicate 500 ul samples were collected for aggregate imaging on a 24 well plate. EVOS M7000 was used for bright field images of the aggregates. ImageJ was used to analyze aggregate size and distribution. Flow cytometry and qPCR Triplicate samples were taken from each bioreactor for qPCR testing at different stages throughout the culture time. RNA samples were dissolved in Lysis Buffer and extracted using MagMAX™-96 Total RNA Isolation Kit (cat # AM1830) according to manufacturer’s protocol. Quantification of RNA was performed on epoch reader. A high-Capacity cDNA RT Kit (cat #4368813) was used for reverse transcription of RNA triplicate samples. A 10 ml sample was taken from each bioreactor at the end of each stage of differentiation for flow testing. The aggregates were dissociated with Accutase for 10 minutes into single cells. Cells were resuspended in PBS and divided into samples for intracellular staining and cells for extracellular staining. Samples for intracellular staining were fixed with a live/dead stain FVS 780, then permeabilized and stained for antibodies corresponding for each stage using Attune™ Flow Cytometry (Table 3). QuantStudio Data Analysis Data collection was performed using an Open Array (QuantStudio, Life Technologies) with custom design gene cards (Table 4). The design focused on endodermal lineages and pancreatic fates. cDNA samples were loaded onto a custom design Quant Studio Card using an Open Array AccuFill System and ran on a QuantStudio 12k Flex Real-Time PCR System. QuantStudio runs were performed according to manufacturer’s protocol. The resulting QuantStudio gene expression data was analyzed using Expression Suite™ software (Life Tech). The data set was then exported to Excel and normalized against three internal standard housekeeping genes present on the QS card GAPDH, EEF1A1 and 18S. Final expression levels were expressed as 1/(2 DCrt ) x 10000 where Crt is the relative threshold cycle. Immunofluorescent staining iPSC and differentiated bioreactor aggregates were dissociated with Accutase and plated onto a Vitronectin treated 24 well plate and grown for 1 day. Cells were fixed with 5% formalin in DPBS for 15 minutes at room temperature, permeabilized and blocked with a blocking buffer solution (1% BSA and 0.1% Tween-20) for 60 minutes at room temperature and stained with primary antibodies at 1:250 (Table 3) overnight. Cells were then washed with DPBS and stained with secondary antibodies for 60 minutes. Cells were washed three times with DPBS (5 minutes each) and stained with DAPI. Plates were imagined using EVOS M7000 Imaging System microscope. RNA isolation, library preparation and RNA-seq The RNA was qualified and quantified using Qubit™ RNA BR Assay Kit (ThermoFisher- Catalogue No- Q10211). Total RNA was isolated using MagMAX™-96 Total RNA Isolation Kit (ThermoFisher Scientific) according to the manufacturer’s instructions. RNA quality was validated using 4200 TapeStation System (Agilent Technologies). Enrichment of polyadenylated RNA and library preparation were performed using Illumina Stranded mRNA Prep (illumina) using the reagents provided in an Illumina® TruSeq® Stranded mRNA library prep workflow. The library underwent a final cleanup using the Agencourt AMPure XP system (Beckman Coulter) after which the library’s quality was assessed using a 4200 TapeStation System (Agilent Technologies). For all samples, the sequencing was done at Genewiz from Azenta Life Sciences. The quality trimming and alignment of the samples were conducted using the nextflow nf-core/rnaseq pipeline (version 3.6). The pipeline incorporated Trim Galore (v.0.6.7) for adaptor trimming and quality control. The trimmed RNAseq reads were then mapped to the Homo sapiens GRCh38 genome annotation utilizing STAR (v 2.6.1). Datasets underwent filtration to eliminate low counts (< 10 reads). The heatmap was created using the package “pheatmap” (v1.0.12). Dithizone staining Dithizone (DTZ) (Sigma, Cat# 194832) powder was reconstituted in DMSO, diluted in PBS and filtered to eliminate sediments. Clusters were stained in 5 mg/mL DTZ for 2–3 min and washed with PBS. Images were acquired using an EVOS XL Core. Human C‑peptide content Stage 3 endocrine clusters were lysed in Tissue Protein Extraction Reagent (ThermoFisher Scientific, Cat# 78510). The cell suspension was centrifuged (1 min, 1000 rcf, 4°C) to remove cell debris. Cell lysates were stored at -20°C until assayed using the human Ultrasensitive C-peptide ELISA kit (mercodia, Cat# 10-1141-01). Content was normalized to aggregate number or protein content using a Bradford kit (ThermoFisher Scientific, Cat #23200) per the manufacture’s instruction. Oxygen consumption rate (OCR) OCR and extracellular acidification rate (ECAR) were measured using a Seahorse XFe96 analyzer. For stage 3, the cells were tested as clusters in the test plate. The cells were lysed and tested using a Bradford kit for protein normalization. The seeded plates were left for 24 hours in the incubator. For the Mito Stress Test, cells were incubated in a non-CO 2 incubator for 1 h in serum free Seahorse XF Base minimal DMEM media (Cat # 103335-100) supplemented with 3 mM glucose, 1 mM sodium pyruvate and 2 mM L-glutamine. Following measurement of basal respiration, the cells were treated with sequential injections of 14.5 µM glucose, 1.5 µM oligomycin, 0.5 µM carbonyl cyanide-4-(trifuoromethoxy) phenyl hydrazone (FCCP) and 0.5 µM rotenone/antimycin A. Metabolite assessment of spent media Spent media collected from several time points were centrifuged to remove cell debris and frozen at − 80°C until being assayed. Glucose, lactate, Gln, Glu, NH4+, Na+, K+, Ca++, pH, PCO2, and PO2 were measured using a Flex2 analyzer (Nova Biomedical). Amino acid concentrations were measured using REBEL cell culture media analyzer (908devices) from spent media samples loaded onto a round bottom plate and diluted 1:10 with the provided REBEL diluent. Glucose response For the glucose stimulated insulin secretion assay, approximately 15–20 stem cell-derived clusters were collected in a 24 well plate. The clusters were equilibrated in 2.8 mM glucose in RPMI medium (ThermoFisher Scientific, Cat# 11879020) for 30 minutes at 37°C, and then they were washed and incubated again for 1 hour. Aggregates were washed again and were exposed to 2.8 mM glucose for 1 hour and then challenged for another hour with 17.5 mM glucose, and then tested for depolarization with KCL. Media samples were collected after each incubation of 1 hour and tested using the human Ultrasensitive C-peptide ELISA kit (Mercodia, Cat# 10-1141-01). The aggregates were counted and used for normalization. Cryopreservation and recovery Harvest aggregates were pelleted and resuspended in 1ml of CS10 in cryotubes to achieve a density from 1–5 million/ml. Tubes are then placed in a 9X9 freezer box and placed into the 4°C (pre-chilled) ThermoFisher CryoMed Controlled-Rate Freezer. After the controlled rate freezing was completed, cells are moved directly into liquid nitrogen. Aggregates are recovered in a TB2 solution with 10 µM Y-27632 ROCK and washed to be resuspended again in the same solution. Generating DoE designs All experimental designs based on Design of Experiments (DoE) were computer-generated using D-optimal interaction designs in MODDE software (Sartorius Stedim Data Analytical Solutions, SSDAS). In the Design Wizard within MODDE software, all factors tested and genes measured were manually input, and the screening option was selected within the Objective window. Factors known to initiate insulin secretion were incorporated into our design. The design runs were set to include up to 93 reaction conditions, along with the addition of 3 center point conditions. Following the generation of DoE designs, the design with the highest G-efficiency was selected. This chosen DoE design served as a template for the creation of perturbation media matrices. The perturbation matrices, consisting of 96 independent experimental runs, were generated using a Freedom Evo150 liquid handling robot (TECAN, CH). Generating Computer Gene Models The differentiation space was modeled using MODDE software. After importing the data into MODDE, primary 'Summary of Fit Plots' were automatically generated, providing R2 and Q2 measurements for each gene model. R2 assesses how well the data fits the gene model, while Q2 estimates the precision of the model's prediction. Both metrics range from 0 to 1, with values above 0.5 indicating a significant gene model. Statistical analysis Data were analyzed and graphed in Excel and GraphPad Prism9. Comparisons were conducted via ANOVA and T-Test with a significant difference defined as P < 0.05. Results Impact of culture system (2D vs. 3D) on differentiation and metabolism The protocol was originally developed using adherent cultures and derived from design of experiments [ 17 ]. Suspension protocol was initiated in TB10 media (Table 1) to support aggregate formation [ 35 ]. This media was designed to stabilize aggregate formation while limiting aggregate growth. CDM2 medium [ 36 , 37 ] poorly sustained aggregate growth during iPSC differentiation in bioreactors (Data not shown), so a descendent media was developed (TB2) specifically for sustaining iPSC differentiation in bioreactor suspension conditions (Table 1). Initial evaluation of the protocol transfer was performed in two culture conditions: adherent and in suspension bioreactors. Stage 3 cells from a suspension environment and adherent culture were compared and evaluated through RNA sequencing (Figure S1). RNA sequencing data in heatmap (Figure S1-A) and volcano plot (Figure S1-B-C) evaluation of core endocrine genes showed that expression levels in adherent cells were lower than those expressed in 3D environment (Figure S1-A). Several genes that are essential for insulin transcription such as NEUROD1, MAFA, PPARGC1A and NKX2.2 [ 18 ] along with INS were expressed with higher transcript levels in bioreactor samples. It was observed that genes usually expressed in earlier stages such as FOXA2 and HNF1b had higher expression levels in adherent culture indicating that 2D environment differentiation might be lagging. Since the cell architecture of human islets is in 3D and not 2D, dissociating and reaggregating the cells can more closely mimic conditions during embryonic islet development [ 21 ]. Thus, endocrine stage adherent cells were dissociated and aggregated in flasks (Figure S1-D) as a means of additional comparison of the final material as aggregates. ELISA assay was used for quantifying C-peptide content per aggregate in this comparison. The results indicated that the C-peptide per aggregate content observed in suspension environment was significantly higher compared to pseudoiselets generated from the adherent cultures (Figure S1-E). These results were consistent with the data from RNA sequencing despite reaggregation. Thus, there are discrepancies between culture characteristics requiring custom differentiation optimization. Aggregate size and density might be potential factors impacting the differentiation process and cell fate [ 35 ]. Expansion and differentiation of iPSCs into insulin-producing cells To develop and solidify the protocol transferred from adherent to suspension environment, an iterative strategy of stage-wise optimization was used to determine and control critical process parameters using two cell lines. Prior to initiating the differentiation protocol, 90 million adherent cells were collected from flasks of two different cell lines, RCRP5005N and NCRM-1. Each batch was then reseeded into a 500 ml vertical wheel bioreactor at a density of approximately 1.8 x 10^5 cells/ml in TB10 media which consists of Essential 8 (E8) medium supplemented with polyethylene glycol (PEG) and Heparin Sodium Salt (HS). On the 3rd day, differentiation was initiated as previously described [ 17 ]. The protocol, originally optimized for adherent culture using a HD-DoE approach, aimed to induce dorsal foregut endoderm from pluripotent stem cells. This protocol (TB-beta) was modified as depicted in (Fig. 1 A) with the basal media TB2 (Table 1). Modifications in the basal media supplements were made to enhance aggregate stability and limit aggregate fusion events, thus minimizing cluster diameter. During the initial 3 days of differentiation toward definitive endoderm, cells were optimized for FOXA2 expression. The morphology and growth of the clusters were characterized at all stages of differentiation, and the aggregate diameter measured in the range of (300 ± 100) µm for both cell lines (Fig. 1 B-C). Retinoic acid, LDN3189, A8301, and PD0325901 were identified as differentiation factors for stage 1 (DFE) based on previous optimization efforts for HNF1β and PDX1 [ 17 ]. By the end of this stage, bioreactors contained approximately 325 million cells of NCRM-1 and 420 million cells of RCRP5005N, representing ~ 4x fold expansion throughout the process. Following the DFE stage, aggregates from each bioreactor were split into two additional 500 ml bioreactors to control cellular density. One half of the NCRM-1 cells remained in their original spent medium in a continuous bioreactor run with additional Pancreatic Progenitor (PP) stage factors being spiked in (Fig. 1 A). It was also noted that aggregate diameter remained consistent for both cell lines throughout PP induction (Fig. 1 B-C). Subsequently, cells were cultured in endocrine inducing media for 10–30 days, consisting of TB2 media supplemented with gamma secretase and A8301. Media changes occurred for all bioreactors except the NCRM-1 cells that had never received a media exchange. NCRM-1 kept the same medium with spiking in additional factors needed for endocrine induction. In this study, several parameters and changes were identified and deemed essential for optimizing efficient large-scale manufacturing of islet-like clusters. Some of these parameters include controlling aggregate size without dissociation, altering basal media to support aggregation, conducting a 100% suspension protocol, extending the PP induction and endocrine induction stages differentiation periods, and limiting basal media changes throughout the process. Optimization of time through a stage-wise approach Cell evaluation occurred at the end of each differentiation stage. Prior to bioreactor seeding, cells were assessed for pluripotency markers SSEA4 and TRA-1-60 (Figure S2A). Populations exceeded 90% for both markers and over 85% co-expression. At the conclusion of DFE stage, FOXA2 expression ranged from 50–85%, with no detectable OCT4 expression, signifying the absence of undifferentiated cells (Figure S2B). Immunostaining on bioreactor clusters, which were plated on a vitronectin coated 24 well plate, revealed co-expression of HNF1β and FOXA2 (Figure S2C). HNF1β was originally optimized as a marker in the adherent protocol for directing dorsal foregut endoderm differentiation and subsequent endocrine cell fates [ 17 ]. To optimize PDX1 expression, cells remained as clusters in PP stage media for 4–8 days. This time study on PP stage cells determined the optimal duration for differentiation in a suspension environment. Bioreactor PP stage cells were sampled at various time points. While the control sample remained in stage 2 (PP) for only 4 days, two additional samples were kept in PP induction stage media for 6 and 8 days, respectively. Subsequently, the cells were subjected to endocrine induction medium for 10 days and then analyzed for the expression of several endocrine specific markers including GCG, INS, SST, NKX6.1, NKX2.2, and FOXA2 (Fig. 2 A). Prolonging the exposure of the PP inducing media led to increased expression of all the afore-mentioned markers except for SST. Notably, the extra two days in the PP inducing media resulted in increased expression of GCG, SST, NKX6.1, and NKX2.2 as compared to both the 4- and 8-day induction periods. It was noted that the expression of INS and FOXA2 continued to increase over time. During the endocrine induction period, marker expression analysis was performed weekly. Prolonging the exposure to the endocrine inducing medium resulted in increased expression of INS, MAFA, MFN1, NKX6.1, and PDX1.Only SST and NEUROD1 expression decreased over time (Fig. 2 B). Additionally, daily sampling after 10 days in endocrine induction media revealed that extending this time by at least an additional five days resulted in a 20-fold increase in C-PEP expression. Glucose stimulated insulin release assays conducted over this 5-day time-period showed a minimal increase in C-PEP on day 10, and the largest increase was observed on day 13. Further increases following depolarization with KCL on day 15 showed a significantly higher C-PEP content (~ 10-fold) as compared to the control samples performed on day 10 of endocrine inducing stage 3 media (Fig. 2 D). Characterization of stage-specific markers throughout the protocol The transition of β-cells during maturation involves notable alterations in the expression levels of HK2 (Hexokinase 2), LDHA (Lactate Dehydrogenase A), and SLC16A1 (Monocarboxylate Transporter 1) [ 18 , 38 , 39 ]. These genes play crucial roles in glucose metabolism and lactate production within cells. As b-cells mature, LDHA expression declines, leading to decreased lactate production. Additionally, SLC16A1 expression decreases, limiting the capacity for lactate transport further contributing to the metabolic shift from lactate generation to pyruvate utilization. This causes a b-cell maturation event by facilitating functional adaptation to full glucose utilization [ 18 ]. Comparing the original adherent protocol to the bioreactor production of b-cells over time focusing on these three genes (Fig. 3 A) showed that these ‘functional disallowed genes’ are repressed throughout differentiation process within bioreactors. The difference between adherent and bioreactor culture becomes especially significant during the PP-induction stage and throughout the endocrine induction stage, where suspension cultures show significant repression of these glycolytic genes. These results match the findings shown in our RNA sequencing analysis presented in (Figure S1) which suggested that adherent culture lags in differentiation. To determine the shift in gene expression occurring throughout the differentiation protocol, key insulin transcription factors such as NEUROD1, NEUROG3, NKX6.1, NKX2.2, and MAFA, which play crucial roles in β-cell maturation [ 18 , 40 – 43 ], were monitored throughout the entire process (Fig. 3 B). NKX6.1 expression increased after the media changed into the endocrine induction media. Conversely, NEUROD1, NEUROG3, NKX2.2, and MAFA expression began to significantly increase as the duration of the endocrine induction media was lengthened, suggesting a maturation benefit for extended differentiation time. Throughout the culture, FOXA2 and HNF1b were also tracked. FOXA2 regulates gene expression crucial for b-cell development during embryonic pancreatic development [ 17 ], and HNF1b dives exocrine development [ 17 ]. HNF1b exhibited a significant increase in expression levels between the differentiation from DFE to a PP but had a diminishing expression after endocrine induction. This was expected since its continued expression leads to exocrine development. In contrast, FOXA2 continued to rise post endocrine induction since it governs the expression of key genes like PDX1 and maintains the differentiated state and functionality of mature b-cells. Additionally, the expression levels of INS and GCG were mapped throughout the process, both showing significant increases during endocrine induction, in agreement with the discussed results. The iPSC-derived islet-like clusters generated from the bioreactors were assessed for endocrine marker expression through immunostaining of the clusters. It was revealed that the aggregates expressed several known pancreatic markers, including PDX1, C-PEP, CGA, GCG, and SST, indicating the induction of a genuine pancreatic endocrine state (Figure S3A). Some co-expression of endocrine products was noted with the presence of CPEP+/SST + and CPEP+/GCG + cells being observed. Continuous culture dramatically impacts the differentiation towards pancreatic progenitors Maintaining stable cellular homeostasis, characterized by minimal fluctuations, is a prerequisite for optimal cellular function and environment understanding which leads to better system control [ 44 ]. This stability ensures that processes such as metabolism, signaling, and gene expression remain finely regulated within narrow ranges conducive to cellular function. Multiple factors contribute to the fluctuation and homeostasis and not just a small number of regulatory enzymes [ 45 – 47 ]. Factors include growth conditions, glucose concentration, cellular signaling pathways like AKT1, enzymatic regulation by LDH, PFK, and PEP, oxygen availability, cellular metabolism, tissue-specific functions, metabolic shifts, all collectively shaping the balance between lactate production and consumption [ 44 ]. To better understand the full bioprocess and nutrient limitations in maintaining a cellular homeostatic environment within the culture, a continuous media study was conducted throughout the differentiation protocol (Figure S3B). The stage-specific utilization of glucose and lactate generation throughout the culture time was assessed. A comparison was made between the Control media change bioreactor and the continuous bioreactor at the end of each stage and every week after endocrine induction. Spent media samples collected throughout the differentiation protocol were evaluated (Fig. 4 A). Initially, the glucose concentration in the Control media change bioreactor decreased by approximately 30%. By around day 17 of the protocol, this decrease became less than approximately 16%, eventually reaching around 11% at harvest. With each media change, a steady increase in lactate concentration in the basal medium was observed. Toward the end of the control protocol which received full media changes throughout the process, an accumulation of (6 mM) lactate was measured. Comparing the profile of glucose and lactate change over time between the Control media change bioreactor and the continuous condition revealed significant differences. The glucose consumption rate decreased from approximately 30% before the initiation of differentiation to negligible levels toward the end of the protocol, stabilizing at around 7mM after approximately 5 days from the start of differentiation (Fig. 4 A). Similarly, lactate accumulation increased only during the first 3–5 days of the protocol, reaching a steady state after 5 days in culture at around 17mM. The impact of not changing the basal medium and spiking differentiation factors in a continuous culture of cells was assessed alongside the Control media change bioreactors that underwent the process previously described (Fig. 1 A). A comparison of expression levels at late endocrine induction stage revealed significant differences in the levels of PDX1, NEUROD1, and SST between the control cells and the cells that remained in the same medium with only spiking in stage-specific differentiation factors (Fig. 4 B). The cells that were not subjected to basal media change exhibited higher expressions of PDX1, NEUROD1, INS, and NKX2.2 but lower expression of SST and GCG. This suggests an increased preference for b-cells throughout the differentiation process. To explore the metabolic phenotype of the islet aggregates generated by our suspension protocol, we examined the growth rate of cells in both cultures (Fig. 4 C) along with the consumption rate of glucose relative to lactate production over the culture period (Fig. 4 D). The results further suggest that the cells are potentially primarily relying on glycolysis during the early stages of differentiation (S1-2) and transition to oxidative phosphorylation towards endocrine induction during the protocol (Fig. 4 D). This transition occurs earlier in the continuous bioreactor because the glucose consumption rate reached a steady state faster. Endocrine cells produced from a continuous bioreactor run were assessed for INS expression using a high dimensional design of experiment (HD-DoE) assay that included multiple secretagogues (Figure S4A). This demonstrated that a 5-fold increase in insulin levels can be attained within a 3-hour incubation using the combinatorial influences of high glucose Rapamycin, DCA, oxytocin, and arginine (Figure S4B). The baseline medium on the cells was at low glucose whereas high glucose (17.5mM) was incorporated as an additive in the design. Nutrient consumption and metabolite measurements Examining potential alternative fuel and nutrients sources for the cell, the amino acid profile throughout the culture of both the control and the continuous bioreactors was analyzed (Figure S5A-B). Amino acid concentrations were measured using the REBEL Cell Culture Analyzer (908 Devices). Essential amino acids such as histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine were maintained throughout the culture period in both bioreactors. However, some amino acids were completely depleted in both culture mediums, including L-aspartic acid after 5 days and L-glutamic acid after 16 days. Amino acid metabolism is crucial for normal pancreatic β-cell function, and alanine and glutamine are known for their role in regulating β-cell function and insulin secretion [ 48 ]. At the end of culture, glutamine and alanine concentrations were at a higher concentration than they occur in fresh media, suggesting they were not growth-limiting (Figure S5A-B). However, the source of their increase remains unknown, unlike previous observations attributing their increase to the GlutaMAX™ additive [ 49 ]. This increased level of alanine and glutamine compared to the starting medium was not observed in the Control bioreactor that had frequent media changes between stages and throughout the extended endocrine induction stage. No other significant differences were observed between the two bioreactors. As previously noted, the bioreactor with limited media replenishment showed better differentiation than the Control media change bioreactor. Dietary manipulations of amino acids and serum deprivation have been linked to promoting adult-like traits in pancreas β-cells derived from human stem cells [ 50 – 53 ]. In addition, both culture bio-profiles were assessed using FLEX2 (Nova Biomedical), analyzing Gln, Glu, NH4+, Na+, K+, Ca++, pH, PCO2, and PO2 throughout the culture period for both reactors (Figure S6A-B). The osmolarity of the culture medium steadily increased in the continuous bioreactor but remained within the range of 280–320 mOsm/kg. This increase can be attributed to the accumulation of solutes from nutrient metabolism and other waste products. In contrast, the osmolarity of the Control media change bioreactor fluctuated as the media was replenished at different stages of the differentiation process. Glutamine and glutamate levels were also assessed, and both showed depletion over time. This was consistent with measurements taken using the REBEL analyzer. Both bioreactors were comparable in their bio-profile, except for major differences observed in the continuous decrease of pH in the continuous bioreactor, as expected, and the rate of oxygen consumption. The gases measured in the media may have been impacted by the time between collection and measurement, however, the overall impact is the same for all samples. The overall data profile showed that the PO2 level began steadily decreasing after 10 days of culture or PP induction stage of differentiation. Although the interface of the media with the gas in the headspace of both bioreactors is the same since they are both the same size (500ml), the near-equivalent flux of oxygen into the media may not be sufficient to replenish increased oxygen consumption in the 0.5L continuous vessel as compared to the Control media change bioreactor that simply has all the media replaced at regular intervals. Validation and scaling After resuming iterative process optimization and improvement efforts and implementing them as needed to achieve the desired culture outcomes, the process was validated using multiple bioreactors at different densities. Three different seeding densities were used to seed 0.5L bioreactors (75M, 90M and 120M). The aggregates generated were monitored throughout the experiment and harvested at the end of the process (Fig. 5 A). The cell clusters were evaluated for staining with dithizone (DTZ, which binds zinc within insulin granules), flow cytometry and protein levels of selected markers, viability, and oxygen consumption rate. There was a DTZ intensity observed on all clusters (Fig. 5 B). Peak intensity and retention appeared to be in Endo90A and Endo120A-B clusters. The aggregates were then analyzed for viability (Fig. 5 C) and size (Fig. 5 D). The cells were viable after the induction of endocrine cells, and the culture aggregate diameter average was below 500µm. The growth rate of the bioreactors followed a profile of rapid proliferation during the early stages of differentiation (Generation of PP) and plateauing after endocrine induction (Fig. 5 E). A sample of the clusters was digested to be further tested for counts (Fig. 5 F) and flow cytometry (Figure S7-10). As expected, the largest yield was observed in bioreactors with the largest seeding density. These bioreactors also had the largest aggregate diameter; however, this didn’t impact viability (Fig. 5 E). The continuous and Control media change bioreactors were tested for oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) (Fig. 6 A). Consistent with our findings, iPSCs-derived pancreatic progenitors produced with constant medium changes have lower OCR compared to those produced in a continuous bioreactor. iPSC-islets generated with our protocol have a higher aerobic respiration capacity. Mature function is dependent on obligatory aerobic metabolism and an increase in aerobic capacity of iPSC-derived islets is suggestive of an increased functionality, though full function has not been obtained. Cryopreservation and recovery The harvested aggregates were cryopreserved in CS10 solution with 10% DMSO in liquid nitrogen. Some vials were then recovered in TB2 medium + 10 µM Y-27632 ROCK as aggregates. The clusters were checked for viability using FDA/PI stain immediately, and after 5 days of recovery, (Fig. 6 B) no change was observed. The cells were > 85% viable after recovery. In addition, the cells were evaluated for C-PEP content in high glucose medium (17.5mM) over time using a C-PEP Elisa kit. The concentration was then normalized per aggregate number (Fig. 6 C). The C-PEP content was ~ 22 pmol per aggregate before cryopreservation. It decreased immediately after recovery but then reached a steady state after 5 days and stayed at 5 pmol per aggregate for the next 10 days. Discussion The translation of a developed large-scale process for β-cell production into the clinic applications is much needed [ 54 , 55 ]. In this study, the process development and improvement for transitioning adherent cell culture protocols to a bioreactor suspension culture system involved several sequential steps (Table 5). Firstly, an assessment of the current cell culture parameters, including medium composition, cell seeding density, differentiation factors, and culture vessel, is needed. Next is the identification of key performance indicators and desired process outcomes to establish the goals of the optimization process. Before this study, feasibility studies were performed to evaluate the suitability of bioreactor culture vessels and impeller speed for the specific cell line or type. This was followed by small-scale bioreactor trials (100ml) to assess cell expression, growth kinetics, and overall culture performance [ 35 ]. In this study, adjustments were made to bioreactor operating parameters such as stage time, culture medium change, and nutrient feeding strategies based on the results of these trials. Throughout the process, cell viability, proliferation, metabolite assessment and product quality were monitored and evaluated. A gradual transition from adherent cell culture to bioreactor cell culture was then achieved with careful optimization and monitoring at each step (Table 5). As the realization of implementing cellular replacement therapies for Type 1 diabetes in clinical settings advances, it is crucial to refine the current adapted manufacturing methods, their feasibility, and efficiency. This research focuses on identifying and assessing various bioprocess variables in the production of insulin-producing cells derived from human pluripotent stem cells (hPSCs). We examined gene expression profile throughout the process, metabolic behaviors after nutrient limitation, and growth patterns, cell functionalities, and the activation of specific markers associated with maturation and metabolic shifts during differentiation. Our study has shown that 3D culture systems better mimic the in vivo microenvironment, leading to improved differentiation efficiency and functionality of b-cells. We determined that culture platform can affect cell fate and differentiation efficiency. Understanding the effects of culture morphology on b-cell production is crucial for optimizing differentiation protocols. The differentiation towards endocrine fates was improved when transferring the protocol from adherent platform to suspension platform. Crucial for this improvement was developing a media formulation optimized for suspension cultures and is reflected in the changing of the basal culture medium from CDM2 to TB2 to better sustain aggregate stability. While we believe the general advantage of 3D culture on differentiation over 2D cultures, the differentiation protocol and process have factors that could cause variability in results. Some inherent causes of variability throughout the process that were evaluated here are the cell source used, differentiation media, aggregation method and reagents used. In addition, the impact of time on the differentiation process cannot be understated. We show here that prolonged culture durations lead to increased cell differentiation though they have the potential to increase process cost. Here we propose that a compromise between an increased maturation level of a biologic and the overall process cost needs to be fully addressed during process optimization. In this study, extending stage durations was shown to benefit the differentiation capacity of desired markers associated with our target product profile. To counter the associated cost with this increased production time, we evaluated the need for replenishing media throughout the process and how the overall differentiation process was affected. Achieving significant cell quantities is essential for producing insulin-producing cells efficiently. Estimates suggest around 1 billion b-cells would be necessary for the treatment of a type 1 diabetic patient [ 56 – 60 ]. By defining critical quality attributes, we can pinpoint stages for enhancing cell differentiation without compromising process quality and yield. Our study showed an approximate production capability of 1 billion cells per liter of production run. The cell yield was enhanced by eliminating cell disruption and digestion throughout the entire process, increasing seeding density and culturing the cells in an aggregate stability enhanced media [ 35 ].Though increasing the seeding density was shown to have the direct effect of increasing aggregate size, a balance between initial seeding density and product yield must center on defining the desired aggregate size of the final product. Despite observing a large increase in cell number and glucose consumption in the first 5 days indicating proliferation, the actual proliferative capacity was limited as the differentiation continued presumably due to at least a partial metabolic shift away from glycolysis. This shift from proliferation to differentiation occurred faster in continuous bioreactors when compared to the control medium change environment. This change in addition to reduced glucose consumption rate can be partially attributed to insulin degradation in medium and inability to facilitate glucose uptake by thus regulating glucose consumption in cell medium. We propose that glucose utilization and lactate accumulation are critical quality attributes that can influence cell fate. We report in our study that while the intention behind running continuous bioreactors was for media nutrition limitation studies, the cells produced in that environment achieved a more desirable differentiation state. This may result from achieving a level of cellular homeostasis and stability not capable when cultures are continuously shocked by drastic environmental changes. Replenishment of media was shown to drastically change both the glucose and lactate levels while process related changes, such as centrifugation and the complete removal from culture media, are suggested to also have negative effects. The continuous culturing process described here was found to be practically amenable to this protocol since none of the differentiation factors used work directly against each other. A situation unlikely to occur in all directed differentiation protocols. Most small molecules are relatively stable as proteins or metabolites that can be used as differentiation reagents. The latter two naturally degrading over time in culture. Recent studies have reported and support the notion that lactate accumulation contribute to a slightly acidic environment and may be beneficial for differentiation efficiency [ 49 ]. Lactate production rates in our continuous process media were similar to what is observed in physiological fasting levels [ 61 ].Lactate is approximately double glucose concentrations when measured on a molar basis. This equivalence extends to a carbon-atom basis because two lactate molecules equate to one glucose molecule [ 61 ]. The clear implication from these findings is that pyruvate, a product of glycolysis, might not enter the tricarboxylic acid (TCA) cycle directly within cells, but may instead be converted into lactate and released into the bloodstream. This conversion process necessitates the activity of lactate dehydrogenase (LDH) [ 61 ]. The lactate steady state achieved in the continuous bioreactor could be attributed to a lower glucose consumption rate after a metabolic shift away from glycolysis and towards oxidative phosphorylation during our endocrine induction stage. Undifferentiated human pluripotent stem cells (hPSCs) primarily utilize glycolysis for glucose metabolism, whereas during differentiation, they transition to oxidative phosphorylation [ 62 ]. Even when maintained in an undifferentiated state, culturing iPSCs in stirred suspension bioreactors prompts a shift from glycolysis accompanied by increased lactate production compared to differentiated cells [ 63 ]. Adapting to the dynamic metabolic changes and consequential fate decisions of hPSCs in bioreactors will necessitate a departure from conventional cell culture techniques [ 64 ]. Metabolic profiling of the cell culture medium is shown to be an essential parameter. The composition of the medium, including glucose concentration, amino acids, and lactate, influences cellular metabolism and ultimately effects b-cell production. For instance, higher glucose concentrations will stimulate glycolysis, whereas lower concentrations may favor oxidative phosphorylation as supported by data from our continuous bioreactor runs. Understanding these metabolic shifts and their impact on b-cell differentiation is critical for optimizing culture conditions to both enhance cell yield and functionality. These results offer valuable insights for manufacturing processes, though there are limitations to consider such as variations between cell lines and protocols, the need to evaluate robustness and reproducibility of both manufacturing and QC processes. Data suggests that stem cell derived endocrine cells still lack glucose response function in comparison to human islet cells. While our cells were mildly glucose responsive, the high glucose set point is still lacking insulin increase [ 17 , 37 , 39 ]. Previous studies have shown that less mature β-cells exhibit heightened responsiveness to calcium levels when glucose concentrations are low [ 18 ]. This can explain the elevated basal insulin secretion and poor glucose-stimulated insulin secretion (GSIS) [ 18 ]. Our cells were assessed for function using a basic static GSIS (Fig. 2 D) while a more dynamic INS expression assay using our continuous bioreactor endocrine cells incorporated a high dimensional design of experiment (HD-DoE) assay. Mathematical models from MODDE software (Figure S4) have demonstrated an ability to increase insulin secretion levels 5-fold. It is reasonable to assume that our endocrine aggregates have the potential for an insulin secretion profile of human islets, but still lack necessary mechanism for achieving this level of insulin response. Strategic scale-up and design transfer processes are vital for translating laboratory-scale protocols into the large-scale manufacturing needed for commercial for clinical uses. Ensuring scalability, reproducibility, and validation of the manufacturing process is essential for generating consistent high-quality cellular products. This includes properly identifying critical process parameters and defining adequate quality control measures that can establish robust protocols ensuring reproducibility for a manufacturing site. Ultimately, the successful translation of iPSC-derived b-cells into manufacturing and production relies on a comprehensive understanding of various factors, including culture morphology, differentiation time, culture medium metabolic profile, cryopreservation, and scalability of the manufacturing process. By addressing these considerations, researchers can optimize protocols for efficient b-cell production and pave the way for successful clinical trials and ultimately, the treatment of Type 1 Diabetes. Conclusion Our study outlines a large-scale differentiation protocol for generating insulin-producing cells from human pluripotent stem cells (hPSCs), emphasizing the need for extended differentiation periods and minimal culture interventions. We highlight the impact of reducing media changes on process efficiency and differentiation, underscoring the importance of refining manufacturing protocols. These insights advance cell replacement therapy initiatives by iPSC-derived islet-like cluster production, offering valuable insights to the field's understanding and practices. Abbreviations HS Heparin sodium salt PEG polyethylene glycol E8 Essential 8 DMSO Dimethyl Sulfoxide DoE Design of Experiments 3D Three-dimensional iPSC Human Induced pluripotent stem cell PBS Phosphate buffer solution q-PCR Quantitative PCR RPM Revolution per minute ROCK Rho-Kinase PP Pancreatic Progenitor DFE Dorsal Foregut Endoderm Crt Relative threshold cycle OCR Oxygen consumption rate 2D Two-dimensional TCA Tricarboxylic acid T1DM Type 1 diabetes mellitus LDH Lactate Dehydrogenase GSIS Glucose-stimulated insulin secretion PI Propidium iodide FDA Fluorescein diacetate Declarations Acknowledgements We would like to thank Trailhead Biosystems for funding this research. The authors also thank Cleveland State University for their support. Author contributions M.B conceived and supervised the project. H.Y. designed, conducted all experiments, data & samples collection, and analysis, and wrote the full manuscript. A.W. cultured the starting iPSC material for all the experiments and conducted ELISA testing. F.S. assisted in QuantStudio QC. M.M. and D.N. ran the validation bioreactors for large-scale production. R.K. assisted with amino acids testing and cryopreservation. R.P. conducted the RNA Seq analysis and figures. J.J. provided review feedback. All authors approved the manuscript. Funding The work was supported and funded by Trailhead Biosystems Inc. No external funding was used. Data availability Requests for further information or more detailed protocols should be directed to and will be fulfilled by the corresponding author. This study did not generate new unique reagents. The data that support the findings of this study are available on request. Ethics approval and consent to participate This study does not include a clinical trial, therefore a consent to participate and the declaration of Helsinki – Ethical principles for medical research involving human subjects are not applicable. Experiments used human induced pluripotent stem cells purchased from REPROCELL (Cat#RCRP005N) and iXCells (Cat#NCRM-1) that have had their IRB approval documents and informed consents examined by National Institute of Neurological Disorders and Stroke to ensure that voluntary consent is obtained from the donor. The use of donor human islets was approved by PRODO Laboratories (HP-24050-01) by “Donate Life California Organ & Tissue Donor Registry” with a completed “California Document of Gift” authorized by state law. Consent for publication Not applicable. Competing Interests J.J. is founder of and shareholder of Trailhead Biosystems, Inc., Beachwood, OH, USA. M. B. is a shareholder in Trailhead Biosystems, Inc., Beachwood OH. This work has been filed as US Provisional Application No. application pending References Roep BO, Thomaidou S, van Tienhoven R, Zaldumbide A. Type 1 diabetes mellitus as a disease of the β-cell (do not blame the immune system?). Nat Rev Endocrinol. 2021;17:150–61. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183. Gamble A, Pepper AR, Bruni A, Shapiro AMJ. The journey of islet cell transplantation and future development. Islets. 2018;10:80–94. Shapiro AMJ, Lakey JRT, Ryan EA, Korbutt GS, Toth E, Warnock GL, et al. Islet Transplantation in Seven Patients with Type 1 Diabetes Mellitus Using a Glucocorticoid-Free Immunosuppressive Regimen. N Engl J Med. 2000;343:230–8. Takahashi K, Yamanaka S. Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell. 2006;126:663–76. Yamanaka S. Pluripotent Stem Cell-Based Cell Therapy—Promise and Challenges. Cell Stem Cell. 2020;27:523–31. Chen G, Gulbranson DR, Hou Z, Bolin JM, Ruotti V, Probasco MD, et al. Chemically defined conditions for human iPSC derivation and culture. Nat Methods. 2011;8:424–9. Carvalho AM, Nunes R, Sarmento B. From pluripotent stem cells to bioengineered islets: A challenging journey to diabetes treatment. European Journal of Pharmaceutical Sciences. Elsevier B.V.; 2022. Tohyama S, Fujita J, Fujita C, Yamaguchi M, Kanaami S, Ohno R, et al. Efficient Large-Scale 2D Culture System for Human Induced Pluripotent Stem Cells and Differentiated Cardiomyocytes. Stem Cell Rep. 2017;9:1406–14. Lambrechts T, Papantoniou I, Viazzi S, Bovy T, Schrooten J, Luyten FP, et al. Evaluation of a monitored multiplate bioreactor for large-scale expansion of human periosteum derived stem cells for bone tissue engineering applications. Biochem Eng J. 2016;108:58–68. Amini N, Paluh JL, Xie Y, Saxena V, Sharfstein ST. Insulin production from hiPSC-derived pancreatic cells in a novel wicking matrix bioreactor. Biotechnol Bioeng. 2020;117:2247–61. Nogueira DES, Rodrigues CAV, Carvalho MS, Miranda CC, Hashimura Y, Jung S, et al. Strategies for the expansion of human induced pluripotent stem cells as aggregates in single-use Vertical-Wheel™ bioreactors. J Biol Eng. 2019;13:74. Abbasalizadeh S, Larijani MR, Samadian A, Baharvand H. Bioprocess Development for Mass Production of Size-Controlled Human Pluripotent Stem Cell Aggregates in Stirred Suspension Bioreactor. Tissue Eng Part C Methods. 2012;18:831–51. Mihara Y, Matsuura K, Sakamoto Y, Okano T, Kokudo N, Shimizu T. Production of pancreatic progenitor cells from human induced pluripotent stem cells using a three-dimensional suspension bioreactor system. J Tissue Eng Regen Med. 2017;11:3193–201. Borys BS, Dang T, So T, Rohani L, Revay T, Walsh T, et al. Overcoming bioprocess bottlenecks in the large-scale expansion of high-quality hiPSC aggregates in vertical-wheel stirred suspension bioreactors. Stem Cell Res Ther. 2021;12:55. Jeske R, Liu C, Duke L, Canonicco Castro ML, Muok L, Arthur P, et al. Upscaling human mesenchymal stromal cell production in a novel vertical-wheel bioreactor enhances extracellular vesicle secretion and cargo profile. Bioact Mater. 2023;25:732–47. Bukys MA, Mihas A, Finney K, Sears K, Trivedi D, Wang Y, et al. High-Dimensional Design-Of-Experiments Extracts Small-Molecule-Only Induction Conditions for Dorsal Pancreatic Endoderm from Pluripotency. iScience. 2020;23:101346. Nair GG, Tzanakakis ES, Hebrok M. Emerging routes to the generation of functional β-cells for diabetes mellitus cell therapy. Nat Rev Endocrinol Nat Res; 2020. p. 506–18. Rezania A, Bruin JE, Arora P, Rubin A, Batushansky I, Asadi A, et al. Reversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells. Nat Biotechnol. 2014;32:1121–33. Kroon E, Martinson LA, Kadoya K, Bang AG, Kelly OG, Eliazer S, et al. Pancreatic endoderm derived from human embryonic stem cells generates glucose-responsive insulin-secreting cells in vivo. Nat Biotechnol. 2008;26:443–52. Nair GG, Liu JS, Russ HA, Tran S, Saxton MS, Chen R, et al. Recapitulating endocrine cell clustering in culture promotes maturation of human stem-cell-derived β cells. Nat Cell Biol. 2019;21:263–74. Pagliuca FW, Millman JR, Gürtler M, Segel M, Van Dervort A, Ryu JH, et al. Generation of Functional Human Pancreatic β Cells In Vitro. Cell. 2014;159:428–39. Russ HA, Parent AV, Ringler JJ, Hennings TG, Nair GG, Shveygert M, et al. Controlled induction of human pancreatic progenitors produces functional beta-like cells in vitro . EMBO J. 2015;34:1759–72. Millman JR, Xie C, Van Dervort A, Gürtler M, Pagliuca FW, Melton DA. Generation of stem cell-derived β-cells from patients with type 1 diabetes. Nat Commun. 2016;7:11463. Velazco-Cruz L, Goedegebuure MM, Millman JR. Advances Toward Engineering Functionally Mature Human Pluripotent Stem Cell-Derived β Cells. Front Bioeng Biotechnol. 2020;8. Kunisada Y, Tsubooka-Yamazoe N, Shoji M, Hosoya M. Small molecules induce efficient differentiation into insulin-producing cells from human induced pluripotent stem cells. Stem Cell Res. 2012;8:274–84. Ghazizadeh Z, Kao DI, Amin S, Cook B, Rao S, Zhou T et al. ROCKII inhibition promotes the maturation of human pancreatic beta-like cells. Nat Commun. 2017;8. D’Amour KA, Bang AG, Eliazer S, Kelly OG, Agulnick AD, Smart NG, et al. Production of pancreatic hormone–expressing endocrine cells from human embryonic stem cells. Nat Biotechnol. 2006;24:1392–401. D’Amour KA, Agulnick AD, Eliazer S, Kelly OG, Kroon E, Baetge EE. Efficient differentiation of human embryonic stem cells to definitive endoderm. Nat Biotechnol. 2005;23:1534–41. Rezania A, Bruin JE, Riedel MJ, Mojibian M, Asadi A, Xu J, et al. Maturation of Human Embryonic Stem Cell–Derived Pancreatic Progenitors Into Functional Islets Capable of Treating Pre-existing Diabetes in Mice. Diabetes. 2012;61:2016–29. Loo LSW, Lau HH, Jasmen JB, Lim CS, Teo AKK. An arduous journey from human pluripotent stem cells to functional pancreatic β cells. Diabetes Obes Metab. 2018;20:3–13. Hogrebe NJ, Maxwell KG, Augsornworawat P, Millman JR. Generation of insulin-producing pancreatic β cells from multiple human stem cell lines. Nat Protoc. 2021;16:4109–43. Velazco-Cruz L, Song J, Maxwell KG, Goedegebuure MM, Augsornworawat P, Hogrebe NJ, et al. Acquisition of Dynamic Function in Human Stem Cell-Derived β Cells. Stem Cell Rep. 2019;12:351–65. Business Wire. Vertex Presents Positive VX-880 Results From Ongoing Phase 1/2 Study in Type 1 Diabetes at the American Diabetes Association 83rd Scientific Sessions. Business Wire. 2023. Yehya H, Raudins S, Jensen J, Bukys MA. Addressing Bioreactor hiPSC Aggregate Stability, Maintenance and Scaleup Challenges Using a Design of Experiment Approach. Res Sq. 2023. Takao T, Yamada D, Takarada T. A protocol to induce expandable limb-bud mesenchymal cells from human pluripotent stem cells. STAR Protoc. 2022;3:101786. Loh KM, Ang LT, Zhang J, Kumar V, Ang J, Auyeong JQ et al. Efficient endoderm induction from human pluripotent stem cells by logically directing signals controlling lineage bifurcations. Cell Stem Cell. 2014;14. Lemaire K, Thorrez L, Schuit F. Disallowed and Allowed Gene Expression: Two Faces of Mature Islet Beta Cells. Annu Rev Nutr. 2016;36:45–71. Thorrez L, Laudadio I, Van Deun K, Quintens R, Hendrickx N, Granvik M, et al. Tissue-specific disallowance of housekeeping genes: the other face of cell differentiation. Genome Res. 2011;21:95–105. Aguayo-Mazzucato C, Zavacki AM, Marinelarena A, Hollister-Lock J, El Khattabi I, Marsili A, et al. Thyroid hormone promotes postnatal rat pancreatic β-cell development and glucose-responsive insulin secretion through MAFA. Diabetes. 2013;62:1569–80. Gu C, Stein GH, Pan N, Goebbels S, Hörnberg H, Nave K-A, et al. Pancreatic beta cells require NeuroD to achieve and maintain functional maturity. Cell Metab. 2010;11:298–310. Taylor BL, Liu F-F, Sander M. Nkx6.1 is essential for maintaining the functional state of pancreatic beta cells. Cell Rep. 2013;4:1262–75. Lemaire K, Thorrez L, Schuit F. Disallowed and Allowed Gene Expression: Two Faces of Mature Islet Beta Cells. Annu Rev Nutr. 2016;36:45–71. Mulukutla BC, Gramer M, Hu WS. On metabolic shift to lactate consumption in fed-batch culture of mammalian cells. Metab Eng. 2012;14:138–49. Mulukutla BC, Khan S, Lange A, Hu W-S. Glucose metabolism in mammalian cell culture: new insights for tweaking vintage pathways. Trends Biotechnol. 2010;28:476–84. Levine AJ, Puzio-Kuter AM. The Control of the Metabolic Switch in Cancers by Oncogenes and Tumor Suppressor Genes. Science (1979). 2010;330:1340–4. Cairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nat Rev Cancer. 2011;11:85–95. Newsholme P, Brennan L, Bender K. Amino acid metabolism, β-cell function, and diabetes. Diabetes. 2006;55. Iworima DG, Baker RK, Ellis C, Sherwood C, Zhan L, Rezania A et al. Metabolic switching, growth kinetics and cell yields in the scalable manufacture of stem cell-derived insulin-producing cells. Stem Cell Res Ther [Internet]. 2024;15:1. https://stemcellres.biomedcentral.com/articles/ 10.1186/s13287-023-03574-3 . Veres A, Faust AL, Bushnell HL, Engquist EN, Kenty JH-R, Harb G, et al. Charting cellular identity during human in vitro β-cell differentiation. Nature. 2019;569:368–73. Helman A, Cangelosi AL, Davis JC, Pham Q, Rothman A, Faust AL, et al. A Nutrient-Sensing Transition at Birth Triggers Glucose-Responsive Insulin Secretion. Cell Metab. 2020;31:1004–e10165. Velazco-Cruz L, Song J, Maxwell KG, Goedegebuure MM, Augsornworawat P, Hogrebe NJ, et al. Acquisition of Dynamic Function in Human Stem Cell-Derived β Cells. Stem Cell Rep. 2019;12:351–65. Alvarez-Dominguez JR, Melton DA. Cell maturation: Hallmarks, triggers, and manipulation. Cell. Elsevier B.V.; 2022. pp. 235–49. Iworima DG, Rieck S, Kieffer TJ. Process Parameter Development for the Scaled Generation of Stem Cell-Derived Pancreatic Endocrine Cells. Stem Cells Transl Med. 2021;10:1459–69. Ghorbani-Dalini S, Azarpira N, Sangtarash MH, Urbach V, Yaghobi R, Soleimanpour-Lichaei HR et al. Optimization of 3D islet-like cluster derived from human pluripotent stem cells: An efficient in vitro differentiation protocol. Gene. 2022;845. Pisania A, Weir GC, O’Neil JJ, Omer A, Tchipashvili V, Lei J, et al. Quantitative analysis of cell composition and purity of human pancreatic islet preparations. Lab Invest. 2010;90:1661–75. Shapiro AMJ, Lakey JRT, Ryan EA, Korbutt GS, Toth E, Warnock GL, et al. Islet Transplantation in Seven Patients with Type 1 Diabetes Mellitus Using a Glucocorticoid-Free Immunosuppressive Regimen. N Engl J Med. 2000;343:230–8. Shapiro AMJ. Islet Transplantation in Type 1 Diabetes: Ongoing Challenges, Refined Procedures, and Long-Term Outcome. Rev Diabet Stud. 2012;9:385–406. Docherty K, Bernardo AS, Vallier L. Embryonic stem cell therapy for diabetes mellitus. Semin Cell Dev Biol. 2007;18:827–38. Lock LT, Tzanakakis ES. Stem/Progenitor Cell Sources of Insulin-Producing Cells for the Treatment of Diabetes. Tissue Eng. 2007;13:1399–412. Rabinowitz JD, Enerbäck S. Lactate: the ugly duckling of energy metabolism. Nat Metab. 2020;2:566–71. Cliff TS, Dalton S. Metabolic switching and cell fate decisions: implications for pluripotency, reprogramming and development. Curr Opin Genet Dev. 2017;46:44–9. Kropp C, Kempf H, Halloin C, Robles-Diaz D, Franke A, Scheper T, et al. Impact of Feeding Strategies on the Scalable Expansion of Human Pluripotent Stem Cells in Single-Use Stirred Tank Bioreactors. Stem Cells Transl Med. 2016;5:1289–301. Hakim F, Kaitsuka T, Raeed JM, Wei F-Y, Shiraki N, Akagi T, et al. High oxygen condition facilitates the differentiation of mouse and human pluripotent stem cells into pancreatic progenitors and insulin-producing cells. J Biol Chem. 2014;289:9623–38. Tables Table 1 to 5 are available in the Supplementary Files section. Supplementary Files Table1.png Table 1: Composition and concentration of basal medium and differentiation factors used Table2.png Table 2: List of reagents along with cell lines used in the protocol Table3.png Table 3: List of antibodies used for immunostaining and flow cytometry Table4.png Table 4:List of oligonucleotides used on the QuantStudio chip for gene expression testing Table5.png Table 5: Process Optimization and improvement flowchart Supplementfiles.pdf Figures S1–S10 (Additional files 1-10) Additional File 1: The transfer of the insulin-producing differentiation protocol from adherent to suspension environment. A. RNA sequencing heatmap comparison of selected endocrine specific genes on undifferentiated iPSCs, adherently differentiated cells, and bioreactor endocrine cells. B. Volcano plot comparison between iPSCs and bioreactor differentiated endocrine cells. C. Volcano plot comparison between adherent and bioreactor cells. D. Aggregate images of both culture platforms. E. C-Peptide content normalized by aggregate count comparison between adherent and suspension culture. All bar charts show individual points with mean ± SD. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. Additional File 2: Early-stage differentiation characterization on bioreactor differentiated cells. A. undifferentiated iPSC pluripotency characterization using flowcytometry testing on TRA-1-60 and SSEA-4 markers. B. Flowcytometry characterization on DFE stage cells using OCT-4 marker and FOXA2. C. Immunostaining on DFE stage bioreactor digested aggregates seeded onto plates. Additional File 3: Late-stage differentiation characterization on bioreactor differentiated cells using immunofluorescent staining. A. Immunostaining on stage-3 bioreactor aggregates using PDX1, CPEP, CGA, GCG and SST. Aggregates were transferred to plates and allowed to attach. B. Schematic showing the steps needed for stage change for both a continuous and a control medium change bioreactor. The steps shown are repeated again when going from PP stage to endocrine and after 5 days of last stage. Additional File 4: Characterization of endocrine aggregate function. A. Human insulin gene expression and stimulation fold change response to different secretagogues obtained from an HD-DoE design. B. MODDE insulin optimizer results for the design shown. Additional File 5: Amino acids profile on differentiated cells. A. Amino acid concentration changes over time on a Control media change bioreactor. Lines indicate times of media changes. B. Amino acid concentration changes over time for continuous bioreactor. Additional File 6: Metabolite profile on differentiated cells. A. Gln, Glu, NH4+, Na+, K+, Ca++, pH, PCO2 and PO2concentration change over time for Control media change bioreactor. Red lines indicate time of media changes. B. Gln, Glu, NH4+, Na+, K+, Ca++, pH, PCO2 and PO2 concentration changes over time for continuous bioreactor. Lines indicate times of media changes. Additional File 7: Flow cytometry characterization of endocrine cellular product. A. Flow cytometry of endocrine aggregates on validation bioreactors using GP2 and PDX1 markers. B. Summary table of flow cytometry results on endocrine aggregates of validation bioreactors in comparison to human islets and their respective islet equivalent count. Additional File 8: Flow cytometry characterization of endocrine cells. A. Flow cytometry of endocrine aggregates on validation bioreactors and human islets with GP2, SOX9 and PDX1 markers. Additional File 9: Flow cytometry characterization of endocrine cells. A. Flow cytometry of endocrine aggregates on validation bioreactors and human islets with C-PEP and SST markers. Additional File 10: Flow cytometry characterization of endocrine cells. A. Flow cytometry of endocrine aggregates on validation bioreactors and human islets with NKX2.2 marker. Cite Share Download PDF Status: Published Journal Publication published 09 Nov, 2024 Read the published version in Stem Cell Research & Therapy → Version 1 posted Editorial decision: Major Revision 01 Jun, 2024 Reviewers agreed at journal 15 May, 2024 Reviewers invited by journal 14 May, 2024 Editor assigned by journal 10 Apr, 2024 First submitted to journal 10 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4244002","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":302456297,"identity":"c3865d78-d341-47df-a3fa-647517c94639","order_by":0,"name":"Haneen Yehya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAwYGNiBlA8SMjYdBXGK1pEkAtTSQpOWwBIhzmCiHmUskH3vMU3O+zrz9cMPhggIbBv727gS8WixnpKUb8xy7LSFzJrHh8AyDNAaJM2c34HfYjRwzaR622xISDEAtPAZA70jkEtKS/02a5985CQn+h0RryWGT5m07ICEhQbQtZ56ZSc7tS5acIfEQ7Bcewn45nvxM4s03O34J/vSHjwv+2Mjxt/fi18IgkMDAxIPE58GpEg74DzAw/iCsbBSMglEwCkYyAAD5fkd6+Ln7dAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0006-6117-7442","institution":"Cleveland State University","correspondingAuthor":true,"prefix":"","firstName":"Haneen","middleName":"","lastName":"Yehya","suffix":""},{"id":302456298,"identity":"70add96e-4c5b-473e-98a6-54491cf9d149","order_by":1,"name":"Alexandra Wells","email":"","orcid":"","institution":"Trailhead Biosystems Inc","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Wells","suffix":""},{"id":302456299,"identity":"2db39897-f390-4b6a-903d-3346ee4f5fe1","order_by":2,"name":"Michael Majcher","email":"","orcid":"","institution":"Trailhead Biosystems Inc.","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Majcher","suffix":""},{"id":302456300,"identity":"46d14a72-40f7-40d1-bc10-2960e2f6fab8","order_by":3,"name":"Dhruv Nakhwa","email":"","orcid":"","institution":"Trailhead Biosystems Inc.","correspondingAuthor":false,"prefix":"","firstName":"Dhruv","middleName":"","lastName":"Nakhwa","suffix":""},{"id":302456301,"identity":"2845c4c3-8671-4258-a082-b0da7fce5250","order_by":4,"name":"Ryan King","email":"","orcid":"","institution":"Trailhead Biosystems","correspondingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"King","suffix":""},{"id":302456302,"identity":"fddaba4a-126e-42e9-9c83-c688f8c24521","order_by":5,"name":"Faruk Senturk","email":"","orcid":"","institution":"Trailhead Biosystems Inc.","correspondingAuthor":false,"prefix":"","firstName":"Faruk","middleName":"","lastName":"Senturk","suffix":""},{"id":302456303,"identity":"ed43be34-67c3-42d0-8000-bbc125b926f3","order_by":6,"name":"Roshan Padmanabhan","email":"","orcid":"","institution":"Trailhead Biosystems Inc.","correspondingAuthor":false,"prefix":"","firstName":"Roshan","middleName":"","lastName":"Padmanabhan","suffix":""},{"id":302456304,"identity":"f4ab967b-8bea-43c9-afa9-965b0f4a1e0c","order_by":7,"name":"Jan Jensen","email":"","orcid":"","institution":"Trailhead Biosystems Inc.","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Jensen","suffix":""},{"id":302456305,"identity":"a335becb-3db6-45bf-811d-3bc4ea139db2","order_by":8,"name":"Michael A. Bukys","email":"","orcid":"https://orcid.org/0009-0003-8089-1711","institution":"Trailhead Biosystems Inc","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"A.","lastName":"Bukys","suffix":""}],"badges":[],"createdAt":"2024-04-09 20:59:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4244002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4244002/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13287-024-03973-0","type":"published","date":"2024-11-09T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57021457,"identity":"3c4fad0c-d9c6-4688-8647-47a6afbd362f","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":609725,"visible":true,"origin":"","legend":"\u003cp\u003eBioreactor-based differentiation protocol timeline. A. Schematic of the 3-stage differentiation pancreatic protocol used in bioreactors. B. Images of bioreactor aggregates sampled throughout the different protocol stages. C. Average aggregate size measurements on two different cell lines used throughout the protocol.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/9d9cea3fae9eaeb9d50f1e89.png"},{"id":57021451,"identity":"a614f920-6b46-4db5-adb9-3855a67124d7","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72117,"visible":true,"origin":"","legend":"\u003cp\u003eTime study evaluating differentiation and function. A. Gene expression profile of harvested cells that had different prolonged PP induction timing of 4 days, 6 days, and 8 days B. Gene expression profile of harvested endocrine cells with increasing 1-week intervals. C. C-Peptide concentration per aggregate count as a function of time in endocrine induction media. D. Glucose-Stimulated Insulin Secretion of endocrine aggregates as a function of time. The stimulation index for day 11,12,13,14,15 after the end of endocrine induction (10 days), was 1.4, 1.2, 1.8, 1.2 and 1.4 respectively. All bar charts show individual points with mean ± SD. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001, ****P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/1371a7df2979f971f720cabb.png"},{"id":57021450,"identity":"c04dd441-49e9-4b55-aba2-16b474270d79","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90576,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of glycolytic and stage specific markers throughout the differentiation protocol. A. Glycolytic genes expression comparison between adherent and suspension culture over time. B. Gene expression profile of cells at different stages of the protocol. All bar charts show individual points with mean ± SD. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001, ****P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/423aefe9c1dab7141355bfd0.png"},{"id":57021454,"identity":"4c028565-c2f9-4056-ae91-6b3941bb5369","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65171,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of glucose consumption and lactate accumulation and profile on the differentiation and growth of cells A. Glucose and lactate concentration profile throughout the differentiation protocol on two bioreactors: the control which has frequent media changes at the different stages and the continuous which has no media changes but only spike in of differentiation factors at each stage. B. Gene expression profile of cells for stage specific markers on both bioreactors. C. Cell growth profile throughout the differentiation period on both control and continuous bioreactors. D. The change of glucose over lactate concentration over time for the continuous bioreactor. All bar charts show individual points with mean ± SD. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001, ****P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/cde81105152916d45fb7cd44.png"},{"id":57021459,"identity":"138e9b6c-4042-469c-b079-63a90fece359","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2981359,"visible":true,"origin":"","legend":"\u003cp\u003eScale-up validation on multiple 0.5L bioreactors with different seeding densities. A. An image of all bioreactors runs for this validation. B. Dithizone staining of endocrine aggregates products. C. Live dead staining on endocrine stage aggregates generated from all bioreactors using fluorescein diacetate for live staining and propidium iodide for dead staining. D. Violin plot of the aggregate diameter variance on endocrine stage aggregate from all bioreactors runs including previous control. E. The growth rate of cells over time for a Control media change bioreactor. F. Table summary of the viability of digested endocrine aggregates and their respective total cell count from each 500ml bioreactor.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/1335a67b21253cd12eb3a69f.png"},{"id":57021461,"identity":"284bab99-7686-42ca-9a5d-e7f279b9d834","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":506984,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy map profile and cryopreservation recovery on endocrine cells. A. Seahorse data on oxygen consumption rate (OCR) and their respective extracellular acidification rate (ECAR) profiles on a control run with regular media changes versus a continuous bioreactor run. B. Live dead staining using fluorescein diacetate for live staining and propidium iodide for dead staining. on endocrine aggregates that were recovered from cryopreservation. C-peptide concentration profile normalized to the number of aggregates on cryopreserved and recovered cells from bioreactors. OCR and ECAR charts show individual points with mean ± SD. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001, ****P\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/a97be96f81ef8923e9ea105b.png"},{"id":68750756,"identity":"eac0a6b4-b06a-4110-92fc-454fb9046c15","added_by":"auto","created_at":"2024-11-11 16:12:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5150076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/c9c46317-302b-4a2d-beab-9b1a6cf48634.pdf"},{"id":57021449,"identity":"c682897a-5e96-4ec5-b494-b4bac8860ef5","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":59664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1: \u003c/strong\u003eComposition and concentration of basal medium and differentiation factors used\u003c/p\u003e","description":"","filename":"Table1.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/525bc87f51aa4d8e31612081.png"},{"id":57021453,"identity":"178bc095-c4c4-488a-abdb-bced29810bc5","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":83643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 2: \u003c/strong\u003eList of reagents along with cell lines used in the protocol\u003c/p\u003e","description":"","filename":"Table2.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/37fbda89a09dc841502924d0.png"},{"id":57021455,"identity":"a09d312f-26ae-4775-be57-b96c04164fd8","added_by":"auto","created_at":"2024-05-23 14:02:10","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":111939,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: List of antibodies used for immunostaining and flow cytometry\u003c/p\u003e","description":"","filename":"Table3.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/00481f258e01d14e6ef938e3.png"},{"id":57022665,"identity":"0748502c-0ac3-47ab-bef2-7c26e601f53e","added_by":"auto","created_at":"2024-05-23 14:10:10","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":54576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003eList of oligonucleotides used on the QuantStudio chip for gene expression testing\u003c/p\u003e","description":"","filename":"Table4.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/6f7f40a9f608d45d76a78566.png"},{"id":57022669,"identity":"a2a13666-0cfd-45c6-9061-6b63ca0aaa9d","added_by":"auto","created_at":"2024-05-23 14:10:10","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":65261,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e: Process Optimization and improvement flowchart\u003c/p\u003e","description":"","filename":"Table5.png","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/662256c5a5b29cd14d0a6c00.png"},{"id":57022670,"identity":"da9ebf8a-6aa6-4b46-9ad6-7b12a053d6ba","added_by":"auto","created_at":"2024-05-23 14:10:10","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2014566,"visible":true,"origin":"","legend":"\u003cp\u003eFigures S1–S10 (Additional files 1-10)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 1: \u003c/strong\u003eThe transfer of the insulin-producing differentiation protocol from adherent to suspension environment.\u003cstrong\u003e \u003c/strong\u003eA. RNA sequencing heatmap comparison of selected endocrine specific genes on undifferentiated iPSCs, adherently differentiated cells, and bioreactor endocrine cells. B. Volcano plot comparison between iPSCs and bioreactor differentiated endocrine cells. C. Volcano plot comparison between adherent and bioreactor cells. D. Aggregate images of both culture platforms. E. C-Peptide content normalized by aggregate count comparison between adherent and suspension culture.\u003cstrong\u003e \u003c/strong\u003eAll bar charts show individual points with mean ± SD. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001, ****P\u0026lt;0.0001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 2: \u003c/strong\u003eEarly-stage differentiation characterization on bioreactor differentiated cells. A. undifferentiated iPSC pluripotency characterization using flowcytometry testing on TRA-1-60 and SSEA-4 markers. B. Flowcytometry characterization on DFE stage cells using OCT-4 marker and FOXA2. C. Immunostaining on DFE stage bioreactor digested aggregates seeded onto plates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 3: \u003c/strong\u003eLate-stage differentiation characterization on bioreactor differentiated cells using immunofluorescent staining. A. Immunostaining on stage-3 bioreactor aggregates using PDX1, CPEP, CGA, GCG and SST. Aggregates were transferred to plates and allowed to attach. B. Schematic showing the steps needed for stage change for both a continuous and a control medium change bioreactor. The steps shown are repeated again when going from PP stage to endocrine and after 5 days of last stage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 4: \u003c/strong\u003eCharacterization of endocrine aggregate function. A. Human insulin gene expression and stimulation fold change response to different secretagogues obtained from an HD-DoE design. B. MODDE insulin optimizer results for the design shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 5: \u003c/strong\u003eAmino acids profile on differentiated cells. A. Amino acid concentration changes over time on a Control media change bioreactor. Lines indicate times of media changes. B. Amino acid concentration changes over time for continuous bioreactor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 6: \u003c/strong\u003eMetabolite profile on differentiated cells. A. Gln, Glu, NH4+, Na+, K+, Ca++, pH, PCO2 and PO2concentration change over time for Control media change bioreactor. Red lines indicate time of media changes. B. Gln, Glu, NH4+, Na+, K+, Ca++, pH, PCO2 and PO2 concentration changes over time for continuous bioreactor. Lines indicate times of media changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 7: \u003c/strong\u003eFlow cytometry characterization of endocrine cellular product. A. Flow cytometry of endocrine aggregates on validation bioreactors using GP2 and PDX1 markers. B. Summary table of flow cytometry results on endocrine aggregates of validation bioreactors in comparison to human islets and their respective islet equivalent count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 8: \u003c/strong\u003eFlow cytometry characterization of endocrine cells. A. Flow cytometry of endocrine aggregates on validation bioreactors and human islets with GP2, SOX9 and PDX1 markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 9: \u003c/strong\u003eFlow cytometry characterization of endocrine cells. A. Flow cytometry of endocrine aggregates on validation bioreactors and human islets with C-PEP and SST markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional File 10: \u003c/strong\u003eFlow cytometry characterization of endocrine cells. A. Flow cytometry of endocrine aggregates on validation bioreactors and human islets with NKX2.2 marker.\u003c/p\u003e","description":"","filename":"Supplementfiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4244002/v1/369ff1c01a6d7aa584ce297e.pdf"}],"financialInterests":"","formattedTitle":"Identifying and Optimizing Critical Process Parameters for Large-Scale Manufacturing of iPSC Derived Insulin-Producing β-cells","fulltext":[{"header":"Background","content":"\u003cp\u003eType I diabetes is a chronic autoimmune destruction disorder of the pancreatic β-cells. It affects\u0026thinsp;~\u0026thinsp;540 millions of patients worldwide and is expected to rise to 783.2\u0026nbsp;million in the next two decades with a projected health expenditures of 1,054\u0026nbsp;billion USD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Life-long insulin therapy is needed for patients suffering from this disease. Islet transplantation that replaces lost insulin secreting cells is a promising therapy solution [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, it has been tempered by the lack of islets available and the requirement of life-long immunosuppression. This has led to the investigation of induced human pluripotent stem cells (iPSCs) as an available source of functional β-cells [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Cell therapies derived from (iPSCs) hold promise in treating a variety of clinical indications [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. They have the ability of replacing damaged or lost cells and are capable of high \u003cem\u003ein vitro\u003c/em\u003e proliferation and differentiation into all three germ layers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This makes them ideal for applications that require a large sustainable source of clinical grade cells. To meet the required clinical cell dosage, a large-batch manufacturing system must be established. Thus, a scaleup from a planar adherent culture to a 3D culture that generates aggregates is required. However, most research and established differentiation protocols are done in a 2D environment that lack suspension environment factors and considerations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdherent culture methods are impractical for scaling due to the limitation of surface area. To increase production beyond these constraints, suspension culture becomes necessary. Recent research indicates that a bioreactor scale-up approach can effectively expand and differentiate induced pluripotent stem cells (iPSCs) while reducing the need for manual interventions in static cultures [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Already established differentiation protocols were conducted side by side in adherent and suspension environments. The results indicated that the cells produced from both methods are different. In our novel (HD-DoE) protocol for pancreatic β-cell differentiation optimized on adherent plates [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], optimal conditions required BMP antagonism and retinoid input, resulting in the induction of dorsal foregut endoderm (DFE). The study demonstrated that pancreatic identity can be rapidly and robustly induced from DFE, and these cells exhibit a dorsal pancreatic identity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOther published methods for differentiating human induced pluripotent stem cells (iPSCs) into β cells involve a series of developmental stages, including definitive endoderm, primitive gut tube, pancreatic progenitor, endocrine progenitor, and insulin-producing β-cells [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These methods typically entail the sequential addition of growth factors and small molecules to mimic pancreatic development [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The initial step in differentiating iPSCs into pancreatic cells often begins with definitive endoderm commitment. D'Amour et al. established an efficient method using activin A and low serum to direct up to 80% of iPSCs towards the definitive endoderm lineage [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Subsequent protocols have further specified and differentiated iPSCs into PDX1 andNKX6.1 positive pancreatic progenitors, leading to the generation of β-cells [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For instance, Rezania et al. and Pagliuca et al. successfully generated iPSC-derived β-cells with varying functionality [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These cells, while exhibiting reduced glucose-stimulated insulin secretion (GSIS) functionality compared to human islets, have shown promise in reversing diabetes in diabetic mice post-transplant.\u003c/p\u003e \u003cp\u003eRecent advancements have led to the development of methods capable of producing cells with more robust and dynamic GSIS [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Nair et al. demonstrated that isolating INS positive cells at an early immature stage followed by reaggregation into clusters enhances β-cell maturation, although sustained second-phase response remains a challenge [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Velazco-Cruz et al. reported that selective modulation of transforming growth factor-β signaling, combined with resizing of cell clusters, improves dynamic GSIS with a more sustained second-phase response [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile GSIS functionality is of great importance, in vivo transplantation has been the most favorable method for gaining full β-cell maturation and function. A clinical trial presented at the 83rd Scientific Sessions of the American Diabetes Association showcased VX-880, a stem cell-derived islet cell therapy, as a potential treatment for type 1 diabetes (T1D). Results demonstrated restored insulin secretion, improved glycemic control, and elimination of severe hypoglycemic events in all six treated patients [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these advancements, a significant limitation of current differentiation methods is the scalability recovery yield and the use of serum-containing or xeno-containing components, which may pose translational challenges in the future. Efforts to address these limitations are crucial for the clinical development of iPSC-derived β-cells.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDifferentiation of human induced pluripotent stem cell culture\u003c/h2\u003e \u003cp\u003eCells were maintained at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. Prior to the experiment, cells were grown to 70\u0026ndash;80% confluency on vitronectin coated flasks. Cells were dissociated with TrypLE for 3 minutes at 37\u0026deg;C, resuspended in E8 medium, transferred to 50 ml conical tubes, and centrifuged at 400 x g for 6 minutes. The pellet was resuspended in E8 and 10 \u0026micro;M Y-27632 ROCK inhibitor. 90\u0026nbsp;million cells were seeded in a 500ml bioreactor with the TB10 media (Table\u0026nbsp;1). After 1 day of culture, 50% of the media was replenished. After an additional day, differentiation was started using the differentiation medium as indicated in (Table\u0026nbsp;1). All reagents used are listed in (Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCell counting and aggregate size\u003c/h2\u003e \u003cp\u003eBioreactor samples were taken after mixing using a pipette to avoid sampling bias and gradient formation after settling. 3 ml total was sampled daily for 3 samples of 1 ml for cell count. The cells were dissociated using Accutase (Sigma A6964) and incubated for 10 minutes. Cells were then quenched with E8 then centrifuged at 400 x g for 6 minutes. Pellets were resuspended in an equivalent volume of PBS and analyzed for total cell count using Attune flow. Triplicate counts were collected for each bioreactor at each stage of culture. Duplicate 500 ul samples were collected for aggregate imaging on a 24 well plate. EVOS M7000 was used for bright field images of the aggregates. ImageJ was used to analyze aggregate size and distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry and qPCR\u003c/h2\u003e \u003cp\u003eTriplicate samples were taken from each bioreactor for qPCR testing at different stages throughout the culture time. RNA samples were dissolved in Lysis Buffer and extracted using MagMAX\u0026trade;-96 Total RNA Isolation Kit (cat # AM1830) according to manufacturer\u0026rsquo;s protocol. Quantification of RNA was performed on epoch reader. A high-Capacity cDNA RT Kit (cat #4368813) was used for reverse transcription of RNA triplicate samples. A 10 ml sample was taken from each bioreactor at the end of each stage of differentiation for flow testing. The aggregates were dissociated with Accutase for 10 minutes into single cells. Cells were resuspended in PBS and divided into samples for intracellular staining and cells for extracellular staining. Samples for intracellular staining were fixed with a live/dead stain FVS 780, then permeabilized and stained for antibodies corresponding for each stage using Attune\u0026trade; Flow Cytometry (Table\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuantStudio Data Analysis\u003c/h2\u003e \u003cp\u003eData collection was performed using an Open Array (QuantStudio, Life Technologies) with custom design gene cards (Table\u0026nbsp;4). The design focused on endodermal lineages and pancreatic fates. cDNA samples were loaded onto a custom design Quant Studio Card using an Open Array AccuFill System and ran on a QuantStudio 12k Flex Real-Time PCR System. QuantStudio runs were performed according to manufacturer\u0026rsquo;s protocol. The resulting QuantStudio gene expression data was analyzed using Expression Suite\u0026trade; software (Life Tech). The data set was then exported to Excel and normalized against three internal standard housekeeping genes present on the QS card GAPDH, EEF1A1 and 18S. Final expression levels were expressed as 1/(2\u003csup\u003eDCrt\u003c/sup\u003e) x 10000 where Crt is the relative threshold cycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescent staining\u003c/h2\u003e \u003cp\u003eiPSC and differentiated bioreactor aggregates were dissociated with Accutase and plated onto a Vitronectin treated 24 well plate and grown for 1 day. Cells were fixed with 5% formalin in DPBS for 15 minutes at room temperature, permeabilized and blocked with a blocking buffer solution (1% BSA and 0.1% Tween-20) for 60 minutes at room temperature and stained with primary antibodies at 1:250 (Table\u0026nbsp;3) overnight. Cells were then washed with DPBS and stained with secondary antibodies for 60 minutes. Cells were washed three times with DPBS (5 minutes each) and stained with DAPI. Plates were imagined using EVOS M7000 Imaging System microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation, library preparation and RNA-seq\u003c/h2\u003e \u003cp\u003eThe RNA was qualified and quantified using Qubit\u0026trade; RNA BR Assay Kit (ThermoFisher- Catalogue No- Q10211). Total RNA was isolated using MagMAX\u0026trade;-96 Total RNA Isolation Kit (ThermoFisher Scientific) according to the manufacturer\u0026rsquo;s instructions. RNA quality was validated using 4200 TapeStation System (Agilent Technologies). Enrichment of polyadenylated RNA and library preparation were performed using Illumina Stranded mRNA Prep (illumina) using the reagents provided in an Illumina\u0026reg; TruSeq\u0026reg; Stranded mRNA library prep workflow. The library underwent a final cleanup using the Agencourt AMPure XP system (Beckman Coulter) after which the library\u0026rsquo;s quality was assessed using a 4200 TapeStation System (Agilent Technologies).\u003c/p\u003e \u003cp\u003eFor all samples, the sequencing was done at Genewiz from Azenta Life Sciences. The quality trimming and alignment of the samples were conducted using the nextflow nf-core/rnaseq pipeline (version 3.6). The pipeline incorporated Trim Galore (v.0.6.7) for adaptor trimming and quality control. The trimmed RNAseq reads were then mapped to the Homo sapiens GRCh38 genome annotation utilizing STAR (v 2.6.1). Datasets underwent filtration to eliminate low counts (\u0026lt;\u0026thinsp;10 reads). The heatmap was created using the package \u0026ldquo;pheatmap\u0026rdquo; (v1.0.12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDithizone staining\u003c/h2\u003e \u003cp\u003eDithizone (DTZ) (Sigma, Cat# 194832) powder was reconstituted in DMSO, diluted in PBS and filtered to eliminate sediments. Clusters were stained in 5 mg/mL DTZ for 2\u0026ndash;3 min and washed with PBS. Images were acquired using an EVOS XL Core.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eHuman C‑peptide content\u003c/h2\u003e \u003cp\u003eStage 3 endocrine clusters were lysed in Tissue Protein Extraction Reagent (ThermoFisher Scientific, Cat# 78510). The cell suspension was centrifuged (1 min, 1000 rcf, 4\u0026deg;C) to remove cell debris. Cell lysates were stored at -20\u0026deg;C until assayed using the human Ultrasensitive C-peptide ELISA kit (mercodia, Cat# 10-1141-01). Content was normalized to aggregate number or protein content using a Bradford kit (ThermoFisher Scientific, Cat #23200) per the manufacture\u0026rsquo;s instruction.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOxygen consumption rate (OCR)\u003c/h2\u003e \u003cp\u003eOCR and extracellular acidification rate (ECAR) were measured using a Seahorse XFe96 analyzer. For stage 3, the cells were tested as clusters in the test plate. The cells were lysed and tested using a Bradford kit for protein normalization. The seeded plates were left for 24 hours in the incubator. For the Mito Stress Test, cells were incubated in a non-CO\u003csub\u003e2\u003c/sub\u003e incubator for 1 h in serum free Seahorse XF Base minimal DMEM media (Cat # 103335-100) supplemented with 3 mM glucose, 1 mM sodium pyruvate and 2 mM L-glutamine. Following measurement of basal respiration, the cells were treated with sequential injections of 14.5 \u0026micro;M glucose, 1.5 \u0026micro;M oligomycin, 0.5 \u0026micro;M carbonyl cyanide-4-(trifuoromethoxy) phenyl hydrazone (FCCP) and 0.5 \u0026micro;M rotenone/antimycin A.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetabolite assessment of spent media\u003c/h2\u003e \u003cp\u003eSpent media collected from several time points were centrifuged to remove cell debris and frozen at \u0026minus;\u0026thinsp;80\u0026deg;C until being assayed. Glucose, lactate, Gln, Glu, NH4+, Na+, K+, Ca++, pH, PCO2, and PO2 were measured using a Flex2 analyzer (Nova Biomedical). Amino acid concentrations were measured using REBEL cell culture media analyzer (908devices) from spent media samples loaded onto a round bottom plate and diluted 1:10 with the provided REBEL diluent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGlucose response\u003c/h2\u003e \u003cp\u003eFor the glucose stimulated insulin secretion assay, approximately 15\u0026ndash;20 stem cell-derived clusters were collected in a 24 well plate. The clusters were equilibrated in 2.8 mM glucose in RPMI medium (ThermoFisher Scientific, Cat# 11879020) for 30 minutes at 37\u0026deg;C, and then they were washed and incubated again for 1 hour. Aggregates were washed again and were exposed to 2.8 mM glucose for 1 hour and then challenged for another hour with 17.5 mM glucose, and then tested for depolarization with KCL. Media samples were collected after each incubation of 1 hour and tested using the human Ultrasensitive C-peptide ELISA kit (Mercodia, Cat# 10-1141-01). The aggregates were counted and used for normalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCryopreservation and recovery\u003c/h2\u003e \u003cp\u003eHarvest aggregates were pelleted and resuspended in 1ml of CS10 in cryotubes to achieve a density from 1\u0026ndash;5\u0026nbsp;million/ml. Tubes are then placed in a 9X9 freezer box and placed into the 4\u0026deg;C (pre-chilled) ThermoFisher CryoMed Controlled-Rate Freezer. After the controlled rate freezing was completed, cells are moved directly into liquid nitrogen. Aggregates are recovered in a TB2 solution with 10 \u0026micro;M Y-27632 ROCK and washed to be resuspended again in the same solution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGenerating DoE designs\u003c/h2\u003e \u003cp\u003eAll experimental designs based on Design of Experiments (DoE) were computer-generated using D-optimal interaction designs in MODDE software (Sartorius Stedim Data Analytical Solutions, SSDAS). In the Design Wizard within MODDE software, all factors tested and genes measured were manually input, and the screening option was selected within the Objective window. Factors known to initiate insulin secretion were incorporated into our design. The design runs were set to include up to 93 reaction conditions, along with the addition of 3 center point conditions. Following the generation of DoE designs, the design with the highest G-efficiency was selected. This chosen DoE design served as a template for the creation of perturbation media matrices. The perturbation matrices, consisting of 96 independent experimental runs, were generated using a Freedom Evo150 liquid handling robot (TECAN, CH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGenerating Computer Gene Models\u003c/h2\u003e \u003cp\u003eThe differentiation space was modeled using MODDE software. After importing the data into MODDE, primary 'Summary of Fit Plots' were automatically generated, providing R2 and Q2 measurements for each gene model. R2 assesses how well the data fits the gene model, while Q2 estimates the precision of the model's prediction. Both metrics range from 0 to 1, with values above 0.5 indicating a significant gene model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed and graphed in Excel and GraphPad Prism9. Comparisons were conducted via ANOVA and T-Test with a significant difference defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImpact of culture system (2D vs. 3D) on differentiation and metabolism\u003c/h2\u003e \u003cp\u003eThe protocol was originally developed using adherent cultures and derived from design of experiments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Suspension protocol was initiated in TB10 media (Table\u0026nbsp;1) to support aggregate formation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This media was designed to stabilize aggregate formation while limiting aggregate growth. CDM2 medium [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] poorly sustained aggregate growth during iPSC differentiation in bioreactors (Data not shown), so a descendent media was developed (TB2) specifically for sustaining iPSC differentiation in bioreactor suspension conditions (Table\u0026nbsp;1). Initial evaluation of the protocol transfer was performed in two culture conditions: adherent and in suspension bioreactors. Stage 3 cells from a suspension environment and adherent culture were compared and evaluated through RNA sequencing (Figure S1). RNA sequencing data in heatmap (Figure S1-A) and volcano plot (Figure S1-B-C) evaluation of core endocrine genes showed that expression levels in adherent cells were lower than those expressed in 3D environment (Figure S1-A). Several genes that are essential for insulin transcription such as NEUROD1, MAFA, PPARGC1A and NKX2.2 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] along with INS were expressed with higher transcript levels in bioreactor samples. It was observed that genes usually expressed in earlier stages such as FOXA2 and HNF1b had higher expression levels in adherent culture indicating that 2D environment differentiation might be lagging. Since the cell architecture of human islets is in 3D and not 2D, dissociating and reaggregating the cells can more closely mimic conditions during embryonic islet development [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, endocrine stage adherent cells were dissociated and aggregated in flasks (Figure S1-D) as a means of additional comparison of the final material as aggregates. ELISA assay was used for quantifying C-peptide content per aggregate in this comparison. The results indicated that the C-peptide per aggregate content observed in suspension environment was significantly higher compared to pseudoiselets generated from the adherent cultures (Figure S1-E). These results were consistent with the data from RNA sequencing despite reaggregation. Thus, there are discrepancies between culture characteristics requiring custom differentiation optimization. Aggregate size and density might be potential factors impacting the differentiation process and cell fate [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eExpansion and differentiation of iPSCs into insulin-producing cells\u003c/h2\u003e \u003cp\u003eTo develop and solidify the protocol transferred from adherent to suspension environment, an iterative strategy of stage-wise optimization was used to determine and control critical process parameters using two cell lines. Prior to initiating the differentiation protocol, 90\u0026nbsp;million adherent cells were collected from flasks of two different cell lines, RCRP5005N and NCRM-1. Each batch was then reseeded into a 500 ml vertical wheel bioreactor at a density of approximately 1.8 x 10^5 cells/ml in TB10 media which consists of Essential 8 (E8) medium supplemented with polyethylene glycol (PEG) and Heparin Sodium Salt (HS). On the 3rd day, differentiation was initiated as previously described [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The protocol, originally optimized for adherent culture using a HD-DoE approach, aimed to induce dorsal foregut endoderm from pluripotent stem cells. This protocol (TB-beta) was modified as depicted in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) with the basal media TB2 (Table\u0026nbsp;1). Modifications in the basal media supplements were made to enhance aggregate stability and limit aggregate fusion events, thus minimizing cluster diameter.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the initial 3 days of differentiation toward definitive endoderm, cells were optimized for FOXA2 expression. The morphology and growth of the clusters were characterized at all stages of differentiation, and the aggregate diameter measured in the range of (300\u0026thinsp;\u0026plusmn;\u0026thinsp;100) \u0026micro;m for both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). Retinoic acid, LDN3189, A8301, and PD0325901 were identified as differentiation factors for stage 1 (DFE) based on previous optimization efforts for HNF1β and PDX1 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. By the end of this stage, bioreactors contained approximately 325\u0026nbsp;million cells of NCRM-1 and 420\u0026nbsp;million cells of RCRP5005N, representing\u0026thinsp;~\u0026thinsp;4x fold expansion throughout the process. Following the DFE stage, aggregates from each bioreactor were split into two additional 500 ml bioreactors to control cellular density. One half of the NCRM-1 cells remained in their original spent medium in a continuous bioreactor run with additional Pancreatic Progenitor (PP) stage factors being spiked in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). It was also noted that aggregate diameter remained consistent for both cell lines throughout PP induction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C).\u003c/p\u003e \u003cp\u003eSubsequently, cells were cultured in endocrine inducing media for 10\u0026ndash;30 days, consisting of TB2 media supplemented with gamma secretase and A8301. Media changes occurred for all bioreactors except the NCRM-1 cells that had never received a media exchange. NCRM-1 kept the same medium with spiking in additional factors needed for endocrine induction. In this study, several parameters and changes were identified and deemed essential for optimizing efficient large-scale manufacturing of islet-like clusters. Some of these parameters include controlling aggregate size without dissociation, altering basal media to support aggregation, conducting a 100% suspension protocol, extending the PP induction and endocrine induction stages differentiation periods, and limiting basal media changes throughout the process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eOptimization of time through a stage-wise approach\u003c/h2\u003e \u003cp\u003eCell evaluation occurred at the end of each differentiation stage. Prior to bioreactor seeding, cells were assessed for pluripotency markers SSEA4 and TRA-1-60 (Figure S2A). Populations exceeded 90% for both markers and over 85% co-expression. At the conclusion of DFE stage, FOXA2 expression ranged from 50\u0026ndash;85%, with no detectable OCT4 expression, signifying the absence of undifferentiated cells (Figure S2B). Immunostaining on bioreactor clusters, which were plated on a vitronectin coated 24 well plate, revealed co-expression of HNF1β and FOXA2 (Figure S2C). HNF1β was originally optimized as a marker in the adherent protocol for directing dorsal foregut endoderm differentiation and subsequent endocrine cell fates [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo optimize PDX1 expression, cells remained as clusters in PP stage media for 4\u0026ndash;8 days. This time study on PP stage cells determined the optimal duration for differentiation in a suspension environment. Bioreactor PP stage cells were sampled at various time points. While the control sample remained in stage 2 (PP) for only 4 days, two additional samples were kept in PP induction stage media for 6 and 8 days, respectively. Subsequently, the cells were subjected to endocrine induction medium for 10 days and then analyzed for the expression of several endocrine specific markers including GCG, INS, SST, NKX6.1, NKX2.2, and FOXA2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Prolonging the exposure of the PP inducing media led to increased expression of all the afore-mentioned markers except for SST. Notably, the extra two days in the PP inducing media resulted in increased expression of GCG, SST, NKX6.1, and NKX2.2 as compared to both the 4- and 8-day induction periods. It was noted that the expression of INS and FOXA2 continued to increase over time. During the endocrine induction period, marker expression analysis was performed weekly. Prolonging the exposure to the endocrine inducing medium resulted in increased expression of INS, MAFA, MFN1, NKX6.1, and PDX1.Only SST and NEUROD1 expression decreased over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Additionally, daily sampling after 10 days in endocrine induction media revealed that extending this time by at least an additional five days resulted in a 20-fold increase in C-PEP expression. Glucose stimulated insulin release assays conducted over this 5-day time-period showed a minimal increase in C-PEP on day 10, and the largest increase was observed on day 13. Further increases following depolarization with KCL on day 15 showed a significantly higher C-PEP content (~\u0026thinsp;10-fold) as compared to the control samples performed on day 10 of endocrine inducing stage 3 media (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of stage-specific markers throughout the protocol\u003c/h2\u003e \u003cp\u003eThe transition of β-cells during maturation involves notable alterations in the expression levels of HK2 (Hexokinase 2), LDHA (Lactate Dehydrogenase A), and SLC16A1 (Monocarboxylate Transporter 1) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These genes play crucial roles in glucose metabolism and lactate production within cells. As b-cells mature, LDHA expression declines, leading to decreased lactate production. Additionally, SLC16A1 expression decreases, limiting the capacity for lactate transport further contributing to the metabolic shift from lactate generation to pyruvate utilization. This causes a b-cell maturation event by facilitating functional adaptation to full glucose utilization [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComparing the original adherent protocol to the bioreactor production of b-cells over time focusing on these three genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) showed that these \u0026lsquo;functional disallowed genes\u0026rsquo; are repressed throughout differentiation process within bioreactors. The difference between adherent and bioreactor culture becomes especially significant during the PP-induction stage and throughout the endocrine induction stage, where suspension cultures show significant repression of these glycolytic genes. These results match the findings shown in our RNA sequencing analysis presented in (Figure S1) which suggested that adherent culture lags in differentiation. To determine the shift in gene expression occurring throughout the differentiation protocol, key insulin transcription factors such as NEUROD1, NEUROG3, NKX6.1, NKX2.2, and MAFA, which play crucial roles in β-cell maturation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], were monitored throughout the entire process (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). NKX6.1 expression increased after the media changed into the endocrine induction media. Conversely, NEUROD1, NEUROG3, NKX2.2, and MAFA expression began to significantly increase as the duration of the endocrine induction media was lengthened, suggesting a maturation benefit for extended differentiation time. Throughout the culture, FOXA2 and HNF1b were also tracked. FOXA2 regulates gene expression crucial for b-cell development during embryonic pancreatic development [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and HNF1b dives exocrine development [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. HNF1b exhibited a significant increase in expression levels between the differentiation from DFE to a PP but had a diminishing expression after endocrine induction. This was expected since its continued expression leads to exocrine development. In contrast, FOXA2 continued to rise post endocrine induction since it governs the expression of key genes like PDX1 and maintains the differentiated state and functionality of mature b-cells. Additionally, the expression levels of INS and GCG were mapped throughout the process, both showing significant increases during endocrine induction, in agreement with the discussed results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe iPSC-derived islet-like clusters generated from the bioreactors were assessed for endocrine marker expression through immunostaining of the clusters. It was revealed that the aggregates expressed several known pancreatic markers, including PDX1, C-PEP, CGA, GCG, and SST, indicating the induction of a genuine pancreatic endocrine state (Figure S3A). Some co-expression of endocrine products was noted with the presence of CPEP+/SST\u0026thinsp;+\u0026thinsp;and CPEP+/GCG\u0026thinsp;+\u0026thinsp;cells being observed.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eContinuous culture dramatically impacts the differentiation towards pancreatic progenitors\u003c/h2\u003e \u003cp\u003eMaintaining stable cellular homeostasis, characterized by minimal fluctuations, is a prerequisite for optimal cellular function and environment understanding which leads to better system control [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This stability ensures that processes such as metabolism, signaling, and gene expression remain finely regulated within narrow ranges conducive to cellular function. Multiple factors contribute to the fluctuation and homeostasis and not just a small number of regulatory enzymes [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Factors include growth conditions, glucose concentration, cellular signaling pathways like AKT1, enzymatic regulation by LDH, PFK, and PEP, oxygen availability, cellular metabolism, tissue-specific functions, metabolic shifts, all collectively shaping the balance between lactate production and consumption [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. To better understand the full bioprocess and nutrient limitations in maintaining a cellular homeostatic environment within the culture, a continuous media study was conducted throughout the differentiation protocol (Figure S3B). The stage-specific utilization of glucose and lactate generation throughout the culture time was assessed. A comparison was made between the Control media change bioreactor and the continuous bioreactor at the end of each stage and every week after endocrine induction. Spent media samples collected throughout the differentiation protocol were evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Initially, the glucose concentration in the Control media change bioreactor decreased by approximately 30%. By around day 17 of the protocol, this decrease became less than approximately 16%, eventually reaching around 11% at harvest. With each media change, a steady increase in lactate concentration in the basal medium was observed. Toward the end of the control protocol which received full media changes throughout the process, an accumulation of (6 mM) lactate was measured. Comparing the profile of glucose and lactate change over time between the Control media change bioreactor and the continuous condition revealed significant differences. The glucose consumption rate decreased from approximately 30% before the initiation of differentiation to negligible levels toward the end of the protocol, stabilizing at around 7mM after approximately 5 days from the start of differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Similarly, lactate accumulation increased only during the first 3\u0026ndash;5 days of the protocol, reaching a steady state after 5 days in culture at around 17mM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe impact of not changing the basal medium and spiking differentiation factors in a continuous culture of cells was assessed alongside the Control media change bioreactors that underwent the process previously described (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). A comparison of expression levels at late endocrine induction stage revealed significant differences in the levels of PDX1, NEUROD1, and SST between the control cells and the cells that remained in the same medium with only spiking in stage-specific differentiation factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The cells that were not subjected to basal media change exhibited higher expressions of PDX1, NEUROD1, INS, and NKX2.2 but lower expression of SST and GCG. This suggests an increased preference for b-cells throughout the differentiation process.\u003c/p\u003e \u003cp\u003eTo explore the metabolic phenotype of the islet aggregates generated by our suspension protocol, we examined the growth rate of cells in both cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) along with the consumption rate of glucose relative to lactate production over the culture period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The results further suggest that the cells are potentially primarily relying on glycolysis during the early stages of differentiation (S1-2) and transition to oxidative phosphorylation towards endocrine induction during the protocol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). This transition occurs earlier in the continuous bioreactor because the glucose consumption rate reached a steady state faster. Endocrine cells produced from a continuous bioreactor run were assessed for INS expression using a high dimensional design of experiment (HD-DoE) assay that included multiple secretagogues (Figure S4A). This demonstrated that a 5-fold increase in insulin levels can be attained within a 3-hour incubation using the combinatorial influences of high glucose Rapamycin, DCA, oxytocin, and arginine (Figure S4B). The baseline medium on the cells was at low glucose whereas high glucose (17.5mM) was incorporated as an additive in the design.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eNutrient consumption and metabolite measurements\u003c/h2\u003e \u003cp\u003eExamining potential alternative fuel and nutrients sources for the cell, the amino acid profile throughout the culture of both the control and the continuous bioreactors was analyzed (Figure S5A-B). Amino acid concentrations were measured using the REBEL Cell Culture Analyzer (908 Devices). Essential amino acids such as histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine were maintained throughout the culture period in both bioreactors. However, some amino acids were completely depleted in both culture mediums, including L-aspartic acid after 5 days and L-glutamic acid after 16 days. Amino acid metabolism is crucial for normal pancreatic β-cell function, and alanine and glutamine are known for their role in regulating β-cell function and insulin secretion [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. At the end of culture, glutamine and alanine concentrations were at a higher concentration than they occur in fresh media, suggesting they were not growth-limiting (Figure S5A-B). However, the source of their increase remains unknown, unlike previous observations attributing their increase to the GlutaMAX\u0026trade; additive [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This increased level of alanine and glutamine compared to the starting medium was not observed in the Control bioreactor that had frequent media changes between stages and throughout the extended endocrine induction stage. No other significant differences were observed between the two bioreactors. As previously noted, the bioreactor with limited media replenishment showed better differentiation than the Control media change bioreactor. Dietary manipulations of amino acids and serum deprivation have been linked to promoting adult-like traits in pancreas β-cells derived from human stem cells [\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, both culture bio-profiles were assessed using FLEX2 (Nova Biomedical), analyzing Gln, Glu, NH4+, Na+, K+, Ca++, pH, PCO2, and PO2 throughout the culture period for both reactors (Figure S6A-B). The osmolarity of the culture medium steadily increased in the continuous bioreactor but remained within the range of 280\u0026ndash;320 mOsm/kg. This increase can be attributed to the accumulation of solutes from nutrient metabolism and other waste products. In contrast, the osmolarity of the Control media change bioreactor fluctuated as the media was replenished at different stages of the differentiation process. Glutamine and glutamate levels were also assessed, and both showed depletion over time. This was consistent with measurements taken using the REBEL analyzer. Both bioreactors were comparable in their bio-profile, except for major differences observed in the continuous decrease of pH in the continuous bioreactor, as expected, and the rate of oxygen consumption. The gases measured in the media may have been impacted by the time between collection and measurement, however, the overall impact is the same for all samples. The overall data profile showed that the PO2 level began steadily decreasing after 10 days of culture or PP induction stage of differentiation. Although the interface of the media with the gas in the headspace of both bioreactors is the same since they are both the same size (500ml), the near-equivalent flux of oxygen into the media may not be sufficient to replenish increased oxygen consumption in the 0.5L continuous vessel as compared to the Control media change bioreactor that simply has all the media replaced at regular intervals.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eValidation and scaling\u003c/h2\u003e \u003cp\u003eAfter resuming iterative process optimization and improvement efforts and implementing them as needed to achieve the desired culture outcomes, the process was validated using multiple bioreactors at different densities. Three different seeding densities were used to seed 0.5L bioreactors (75M, 90M and 120M). The aggregates generated were monitored throughout the experiment and harvested at the end of the process (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The cell clusters were evaluated for staining with dithizone (DTZ, which binds zinc within insulin granules), flow cytometry and protein levels of selected markers, viability, and oxygen consumption rate. There was a DTZ intensity observed on all clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Peak intensity and retention appeared to be in Endo90A and Endo120A-B clusters. The aggregates were then analyzed for viability (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and size (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The cells were viable after the induction of endocrine cells, and the culture aggregate diameter average was below 500\u0026micro;m. The growth rate of the bioreactors followed a profile of rapid proliferation during the early stages of differentiation (Generation of PP) and plateauing after endocrine induction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). A sample of the clusters was digested to be further tested for counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eF) and flow cytometry (Figure S7-10). As expected, the largest yield was observed in bioreactors with the largest seeding density. These bioreactors also had the largest aggregate diameter; however, this didn\u0026rsquo;t impact viability (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe continuous and Control media change bioreactors were tested for oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Consistent with our findings, iPSCs-derived pancreatic progenitors produced with constant medium changes have lower OCR compared to those produced in a continuous bioreactor. iPSC-islets generated with our protocol have a higher aerobic respiration capacity. Mature function is dependent on obligatory aerobic metabolism and an increase in aerobic capacity of iPSC-derived islets is suggestive of an increased functionality, though full function has not been obtained.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eCryopreservation and recovery\u003c/h2\u003e \u003cp\u003eThe harvested aggregates were cryopreserved in CS10 solution with 10% DMSO in liquid nitrogen. Some vials were then recovered in TB2 medium\u0026thinsp;+\u0026thinsp;10 \u0026micro;M Y-27632 ROCK as aggregates. The clusters were checked for viability using FDA/PI stain immediately, and after 5 days of recovery, (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) no change was observed. The cells were \u0026gt;\u0026thinsp;85% viable after recovery. In addition, the cells were evaluated for C-PEP content in high glucose medium (17.5mM) over time using a C-PEP Elisa kit. The concentration was then normalized per aggregate number (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The C-PEP content was ~\u0026thinsp;22 pmol per aggregate before cryopreservation. It decreased immediately after recovery but then reached a steady state after 5 days and stayed at 5 pmol per aggregate for the next 10 days.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe translation of a developed large-scale process for β-cell production into the clinic applications is much needed [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In this study, the process development and improvement for transitioning adherent cell culture protocols to a bioreactor suspension culture system involved several sequential steps (Table\u0026nbsp;5). Firstly, an assessment of the current cell culture parameters, including medium composition, cell seeding density, differentiation factors, and culture vessel, is needed. Next is the identification of key performance indicators and desired process outcomes to establish the goals of the optimization process. Before this study, feasibility studies were performed to evaluate the suitability of bioreactor culture vessels and impeller speed for the specific cell line or type. This was followed by small-scale bioreactor trials (100ml) to assess cell expression, growth kinetics, and overall culture performance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, adjustments were made to bioreactor operating parameters such as stage time, culture medium change, and nutrient feeding strategies based on the results of these trials. Throughout the process, cell viability, proliferation, metabolite assessment and product quality were monitored and evaluated. A gradual transition from adherent cell culture to bioreactor cell culture was then achieved with careful optimization and monitoring at each step (Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eAs the realization of implementing cellular replacement therapies for Type 1 diabetes in clinical settings advances, it is crucial to refine the current adapted manufacturing methods, their feasibility, and efficiency. This research focuses on identifying and assessing various bioprocess variables in the production of insulin-producing cells derived from human pluripotent stem cells (hPSCs). We examined gene expression profile throughout the process, metabolic behaviors after nutrient limitation, and growth patterns, cell functionalities, and the activation of specific markers associated with maturation and metabolic shifts during differentiation.\u003c/p\u003e \u003cp\u003eOur study has shown that 3D culture systems better mimic the \u003cem\u003ein vivo\u003c/em\u003e microenvironment, leading to improved differentiation efficiency and functionality of b-cells. We determined that culture platform can affect cell fate and differentiation efficiency. Understanding the effects of culture morphology on b-cell production is crucial for optimizing differentiation protocols. The differentiation towards endocrine fates was improved when transferring the protocol from adherent platform to suspension platform. Crucial for this improvement was developing a media formulation optimized for suspension cultures and is reflected in the changing of the basal culture medium from CDM2 to TB2 to better sustain aggregate stability. While we believe the general advantage of 3D culture on differentiation over 2D cultures, the differentiation protocol and process have factors that could cause variability in results. Some inherent causes of variability throughout the process that were evaluated here are the cell source used, differentiation media, aggregation method and reagents used. In addition, the impact of time on the differentiation process cannot be understated. We show here that prolonged culture durations lead to increased cell differentiation though they have the potential to increase process cost. Here we propose that a compromise between an increased maturation level of a biologic and the overall process cost needs to be fully addressed during process optimization. In this study, extending stage durations was shown to benefit the differentiation capacity of desired markers associated with our target product profile. To counter the associated cost with this increased production time, we evaluated the need for replenishing media throughout the process and how the overall differentiation process was affected.\u003c/p\u003e \u003cp\u003eAchieving significant cell quantities is essential for producing insulin-producing cells efficiently. Estimates suggest around 1\u0026nbsp;billion b-cells would be necessary for the treatment of a type 1 diabetic patient [\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. By defining critical quality attributes, we can pinpoint stages for enhancing cell differentiation without compromising process quality and yield. Our study showed an approximate production capability of 1\u0026nbsp;billion cells per liter of production run. The cell yield was enhanced by eliminating cell disruption and digestion throughout the entire process, increasing seeding density and culturing the cells in an aggregate stability enhanced media [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].Though increasing the seeding density was shown to have the direct effect of increasing aggregate size, a balance between initial seeding density and product yield must center on defining the desired aggregate size of the final product.\u003c/p\u003e \u003cp\u003eDespite observing a large increase in cell number and glucose consumption in the first 5 days indicating proliferation, the actual proliferative capacity was limited as the differentiation continued presumably due to at least a partial metabolic shift away from glycolysis. This shift from proliferation to differentiation occurred faster in continuous bioreactors when compared to the control medium change environment. This change in addition to reduced glucose consumption rate can be partially attributed to insulin degradation in medium and inability to facilitate glucose uptake by thus regulating glucose consumption in cell medium. We propose that glucose utilization and lactate accumulation are critical quality attributes that can influence cell fate. We report in our study that while the intention behind running continuous bioreactors was for media nutrition limitation studies, the cells produced in that environment achieved a more desirable differentiation state. This may result from achieving a level of cellular homeostasis and stability not capable when cultures are continuously shocked by drastic environmental changes. Replenishment of media was shown to drastically change both the glucose and lactate levels while process related changes, such as centrifugation and the complete removal from culture media, are suggested to also have negative effects. The continuous culturing process described here was found to be practically amenable to this protocol since none of the differentiation factors used work directly against each other. A situation unlikely to occur in all directed differentiation protocols. Most small molecules are relatively stable as proteins or metabolites that can be used as differentiation reagents. The latter two naturally degrading over time in culture. Recent studies have reported and support the notion that lactate accumulation contribute to a slightly acidic environment and may be beneficial for differentiation efficiency [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Lactate production rates in our continuous process media were similar to what is observed in physiological fasting levels [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].Lactate is approximately double glucose concentrations when measured on a molar basis. This equivalence extends to a carbon-atom basis because two lactate molecules equate to one glucose molecule [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The clear implication from these findings is that pyruvate, a product of glycolysis, might not enter the tricarboxylic acid (TCA) cycle directly within cells, but may instead be converted into lactate and released into the bloodstream. This conversion process necessitates the activity of lactate dehydrogenase (LDH) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The lactate steady state achieved in the continuous bioreactor could be attributed to a lower glucose consumption rate after a metabolic shift away from glycolysis and towards oxidative phosphorylation during our endocrine induction stage.\u003c/p\u003e \u003cp\u003eUndifferentiated human pluripotent stem cells (hPSCs) primarily utilize glycolysis for glucose metabolism, whereas during differentiation, they transition to oxidative phosphorylation [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Even when maintained in an undifferentiated state, culturing iPSCs in stirred suspension bioreactors prompts a shift from glycolysis accompanied by increased lactate production compared to differentiated cells [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Adapting to the dynamic metabolic changes and consequential fate decisions of hPSCs in bioreactors will necessitate a departure from conventional cell culture techniques [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolic profiling of the cell culture medium is shown to be an essential parameter. The composition of the medium, including glucose concentration, amino acids, and lactate, influences cellular metabolism and ultimately effects b-cell production. For instance, higher glucose concentrations will stimulate glycolysis, whereas lower concentrations may favor oxidative phosphorylation as supported by data from our continuous bioreactor runs. Understanding these metabolic shifts and their impact on b-cell differentiation is critical for optimizing culture conditions to both enhance cell yield and functionality. These results offer valuable insights for manufacturing processes, though there are limitations to consider such as variations between cell lines and protocols, the need to evaluate robustness and reproducibility of both manufacturing and QC processes.\u003c/p\u003e \u003cp\u003eData suggests that stem cell derived endocrine cells still lack glucose response function in comparison to human islet cells. While our cells were mildly glucose responsive, the high glucose set point is still lacking insulin increase [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Previous studies have shown that less mature β-cells exhibit heightened responsiveness to calcium levels when glucose concentrations are low [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This can explain the elevated basal insulin secretion and poor glucose-stimulated insulin secretion (GSIS) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our cells were assessed for function using a basic static GSIS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) while a more dynamic INS expression assay using our continuous bioreactor endocrine cells incorporated a high dimensional design of experiment (HD-DoE) assay. Mathematical models from MODDE software (Figure S4) have demonstrated an ability to increase insulin secretion levels 5-fold. It is reasonable to assume that our endocrine aggregates have the potential for an insulin secretion profile of human islets, but still lack necessary mechanism for achieving this level of insulin response.\u003c/p\u003e \u003cp\u003eStrategic scale-up and design transfer processes are vital for translating laboratory-scale protocols into the large-scale manufacturing needed for commercial for clinical uses. Ensuring scalability, reproducibility, and validation of the manufacturing process is essential for generating consistent high-quality cellular products. This includes properly identifying critical process parameters and defining adequate quality control measures that can establish robust protocols ensuring reproducibility for a manufacturing site.\u003c/p\u003e \u003cp\u003eUltimately, the successful translation of iPSC-derived b-cells into manufacturing and production relies on a comprehensive understanding of various factors, including culture morphology, differentiation time, culture medium metabolic profile, cryopreservation, and scalability of the manufacturing process. By addressing these considerations, researchers can optimize protocols for efficient b-cell production and pave the way for successful clinical trials and ultimately, the treatment of Type 1 Diabetes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study outlines a large-scale differentiation protocol for generating insulin-producing cells from human pluripotent stem cells (hPSCs), emphasizing the need for extended differentiation periods and minimal culture interventions. We highlight the impact of reducing media changes on process efficiency and differentiation, underscoring the importance of refining manufacturing protocols. These insights advance cell replacement therapy initiatives by iPSC-derived islet-like cluster production, offering valuable insights to the field's understanding and practices.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeparin sodium salt\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePEG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolyethylene glycol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEssential 8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDimethyl Sulfoxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDoE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDesign of Experiments\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e3D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThree-dimensional\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eiPSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Induced pluripotent stem cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphate buffer solution\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eq-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative PCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRevolution per minute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROCK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRho-Kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePancreatic Progenitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDorsal Foregut Endoderm\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCrt\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative threshold cycle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOxygen consumption rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e2D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTwo-dimensional\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTricarboxylic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 1 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLactate Dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucose-stimulated insulin secretion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropidium iodide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFluorescein diacetate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Trailhead Biosystems for funding this research. The authors also thank Cleveland State University for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.B conceived and supervised the project. H.Y. designed, conducted all experiments, data \u0026amp; samples collection, and analysis, and wrote the full manuscript. A.W. cultured the starting iPSC material for all the experiments and conducted ELISA testing. F.S. assisted in QuantStudio QC. M.M. and D.N. ran the validation bioreactors for large-scale production. R.K. assisted with amino acids testing and cryopreservation. R.P. conducted the RNA Seq analysis and figures. J.J. provided review feedback. All authors approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported and funded by Trailhead Biosystems Inc. No external funding was used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRequests for further information or more detailed protocols should be directed to and will be fulfilled by the corresponding author. This study did not generate new unique reagents. The data that support the findings of this study are available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not include a clinical trial, therefore a consent to participate and the declaration of Helsinki \u0026ndash; Ethical principles for medical research involving human subjects are not applicable. Experiments used human induced pluripotent stem cells purchased from REPROCELL (Cat#RCRP005N) and iXCells (Cat#NCRM-1) that have had their IRB approval documents and informed consents examined by National Institute of Neurological Disorders and Stroke to ensure that voluntary consent is obtained from the donor. The use of donor human islets was approved by PRODO Laboratories (HP-24050-01) by \u0026ldquo;Donate Life California Organ \u0026amp; Tissue Donor Registry\u0026rdquo; with a completed \u0026ldquo;California Document of Gift\u0026rdquo; authorized by state law.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.J. is founder of and shareholder of Trailhead Biosystems, Inc., Beachwood, OH, USA. M. B. is a shareholder in Trailhead Biosystems, Inc., Beachwood OH. This work has been filed as US Provisional Application No. application pending\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoep BO, Thomaidou S, van Tienhoven R, Zaldumbide A. Type 1 diabetes mellitus as a disease of the β-cell (do not blame the immune system?). Nat Rev Endocrinol. 2021;17:150\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGamble A, Pepper AR, Bruni A, Shapiro AMJ. The journey of islet cell transplantation and future development. Islets. 2018;10:80\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapiro AMJ, Lakey JRT, Ryan EA, Korbutt GS, Toth E, Warnock GL, et al. Islet Transplantation in Seven Patients with Type 1 Diabetes Mellitus Using a Glucocorticoid-Free Immunosuppressive Regimen. N Engl J Med. 2000;343:230\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi K, Yamanaka S. Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell. 2006;126:663\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamanaka S. Pluripotent Stem Cell-Based Cell Therapy\u0026mdash;Promise and Challenges. Cell Stem Cell. 2020;27:523\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen G, Gulbranson DR, Hou Z, Bolin JM, Ruotti V, Probasco MD, et al. Chemically defined conditions for human iPSC derivation and culture. Nat Methods. 2011;8:424\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho AM, Nunes R, Sarmento B. From pluripotent stem cells to bioengineered islets: A challenging journey to diabetes treatment. European Journal of Pharmaceutical Sciences. Elsevier B.V.; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTohyama S, Fujita J, Fujita C, Yamaguchi M, Kanaami S, Ohno R, et al. Efficient Large-Scale 2D Culture System for Human Induced Pluripotent Stem Cells and Differentiated Cardiomyocytes. Stem Cell Rep. 2017;9:1406\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambrechts T, Papantoniou I, Viazzi S, Bovy T, Schrooten J, Luyten FP, et al. Evaluation of a monitored multiplate bioreactor for large-scale expansion of human periosteum derived stem cells for bone tissue engineering applications. Biochem Eng J. 2016;108:58\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmini N, Paluh JL, Xie Y, Saxena V, Sharfstein ST. Insulin production from hiPSC-derived pancreatic cells in a novel wicking matrix bioreactor. Biotechnol Bioeng. 2020;117:2247\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNogueira DES, Rodrigues CAV, Carvalho MS, Miranda CC, Hashimura Y, Jung S, et al. Strategies for the expansion of human induced pluripotent stem cells as aggregates in single-use Vertical-Wheel\u0026trade; bioreactors. J Biol Eng. 2019;13:74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbasalizadeh S, Larijani MR, Samadian A, Baharvand H. Bioprocess Development for Mass Production of Size-Controlled Human Pluripotent Stem Cell Aggregates in Stirred Suspension Bioreactor. Tissue Eng Part C Methods. 2012;18:831\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMihara Y, Matsuura K, Sakamoto Y, Okano T, Kokudo N, Shimizu T. Production of pancreatic progenitor cells from human induced pluripotent stem cells using a three-dimensional suspension bioreactor system. J Tissue Eng Regen Med. 2017;11:3193\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorys BS, Dang T, So T, Rohani L, Revay T, Walsh T, et al. Overcoming bioprocess bottlenecks in the large-scale expansion of high-quality hiPSC aggregates in vertical-wheel stirred suspension bioreactors. Stem Cell Res Ther. 2021;12:55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeske R, Liu C, Duke L, Canonicco Castro ML, Muok L, Arthur P, et al. Upscaling human mesenchymal stromal cell production in a novel vertical-wheel bioreactor enhances extracellular vesicle secretion and cargo profile. Bioact Mater. 2023;25:732\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBukys MA, Mihas A, Finney K, Sears K, Trivedi D, Wang Y, et al. High-Dimensional Design-Of-Experiments Extracts Small-Molecule-Only Induction Conditions for Dorsal Pancreatic Endoderm from Pluripotency. iScience. 2020;23:101346.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair GG, Tzanakakis ES, Hebrok M. Emerging routes to the generation of functional β-cells for diabetes mellitus cell therapy. Nat Rev Endocrinol Nat Res; 2020. p. 506\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezania A, Bruin JE, Arora P, Rubin A, Batushansky I, Asadi A, et al. Reversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells. Nat Biotechnol. 2014;32:1121\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroon E, Martinson LA, Kadoya K, Bang AG, Kelly OG, Eliazer S, et al. Pancreatic endoderm derived from human embryonic stem cells generates glucose-responsive insulin-secreting cells in vivo. Nat Biotechnol. 2008;26:443\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair GG, Liu JS, Russ HA, Tran S, Saxton MS, Chen R, et al. Recapitulating endocrine cell clustering in culture promotes maturation of human stem-cell-derived β cells. Nat Cell Biol. 2019;21:263\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePagliuca FW, Millman JR, G\u0026uuml;rtler M, Segel M, Van Dervort A, Ryu JH, et al. Generation of Functional Human Pancreatic β Cells In Vitro. Cell. 2014;159:428\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuss HA, Parent AV, Ringler JJ, Hennings TG, Nair GG, Shveygert M, et al. Controlled induction of human pancreatic progenitors produces functional beta-like cells \u003cem\u003ein vitro\u003c/em\u003e. EMBO J. 2015;34:1759\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillman JR, Xie C, Van Dervort A, G\u0026uuml;rtler M, Pagliuca FW, Melton DA. Generation of stem cell-derived β-cells from patients with type 1 diabetes. Nat Commun. 2016;7:11463.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelazco-Cruz L, Goedegebuure MM, Millman JR. Advances Toward Engineering Functionally Mature Human Pluripotent Stem Cell-Derived β Cells. Front Bioeng Biotechnol. 2020;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunisada Y, Tsubooka-Yamazoe N, Shoji M, Hosoya M. Small molecules induce efficient differentiation into insulin-producing cells from human induced pluripotent stem cells. Stem Cell Res. 2012;8:274\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhazizadeh Z, Kao DI, Amin S, Cook B, Rao S, Zhou T et al. ROCKII inhibition promotes the maturation of human pancreatic beta-like cells. Nat Commun. 2017;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Amour KA, Bang AG, Eliazer S, Kelly OG, Agulnick AD, Smart NG, et al. Production of pancreatic hormone\u0026ndash;expressing endocrine cells from human embryonic stem cells. Nat Biotechnol. 2006;24:1392\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Amour KA, Agulnick AD, Eliazer S, Kelly OG, Kroon E, Baetge EE. Efficient differentiation of human embryonic stem cells to definitive endoderm. Nat Biotechnol. 2005;23:1534\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezania A, Bruin JE, Riedel MJ, Mojibian M, Asadi A, Xu J, et al. Maturation of Human Embryonic Stem Cell\u0026ndash;Derived Pancreatic Progenitors Into Functional Islets Capable of Treating Pre-existing Diabetes in Mice. Diabetes. 2012;61:2016\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoo LSW, Lau HH, Jasmen JB, Lim CS, Teo AKK. An arduous journey from human pluripotent stem cells to functional pancreatic β cells. Diabetes Obes Metab. 2018;20:3\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHogrebe NJ, Maxwell KG, Augsornworawat P, Millman JR. Generation of insulin-producing pancreatic β cells from multiple human stem cell lines. Nat Protoc. 2021;16:4109\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelazco-Cruz L, Song J, Maxwell KG, Goedegebuure MM, Augsornworawat P, Hogrebe NJ, et al. Acquisition of Dynamic Function in Human Stem Cell-Derived β Cells. Stem Cell Rep. 2019;12:351\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusiness Wire. Vertex Presents Positive VX-880 Results From Ongoing Phase 1/2 Study in Type 1 Diabetes at the American Diabetes Association 83rd Scientific Sessions. Business Wire. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYehya H, Raudins S, Jensen J, Bukys MA. Addressing Bioreactor hiPSC Aggregate Stability, Maintenance and Scaleup Challenges Using a Design of Experiment Approach. Res Sq. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakao T, Yamada D, Takarada T. A protocol to induce expandable limb-bud mesenchymal cells from human pluripotent stem cells. STAR Protoc. 2022;3:101786.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoh KM, Ang LT, Zhang J, Kumar V, Ang J, Auyeong JQ et al. Efficient endoderm induction from human pluripotent stem cells by logically directing signals controlling lineage bifurcations. Cell Stem Cell. 2014;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemaire K, Thorrez L, Schuit F. Disallowed and Allowed Gene Expression: Two Faces of Mature Islet Beta Cells. Annu Rev Nutr. 2016;36:45\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThorrez L, Laudadio I, Van Deun K, Quintens R, Hendrickx N, Granvik M, et al. Tissue-specific disallowance of housekeeping genes: the other face of cell differentiation. Genome Res. 2011;21:95\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAguayo-Mazzucato C, Zavacki AM, Marinelarena A, Hollister-Lock J, El Khattabi I, Marsili A, et al. Thyroid hormone promotes postnatal rat pancreatic β-cell development and glucose-responsive insulin secretion through MAFA. Diabetes. 2013;62:1569\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu C, Stein GH, Pan N, Goebbels S, H\u0026ouml;rnberg H, Nave K-A, et al. Pancreatic beta cells require NeuroD to achieve and maintain functional maturity. Cell Metab. 2010;11:298\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor BL, Liu F-F, Sander M. Nkx6.1 is essential for maintaining the functional state of pancreatic beta cells. Cell Rep. 2013;4:1262\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemaire K, Thorrez L, Schuit F. Disallowed and Allowed Gene Expression: Two Faces of Mature Islet Beta Cells. Annu Rev Nutr. 2016;36:45\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulukutla BC, Gramer M, Hu WS. On metabolic shift to lactate consumption in fed-batch culture of mammalian cells. Metab Eng. 2012;14:138\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulukutla BC, Khan S, Lange A, Hu W-S. Glucose metabolism in mammalian cell culture: new insights for tweaking vintage pathways. Trends Biotechnol. 2010;28:476\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine AJ, Puzio-Kuter AM. The Control of the Metabolic Switch in Cancers by Oncogenes and Tumor Suppressor Genes. Science (1979). 2010;330:1340\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nat Rev Cancer. 2011;11:85\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewsholme P, Brennan L, Bender K. Amino acid metabolism, β-cell function, and diabetes. Diabetes. 2006;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIworima DG, Baker RK, Ellis C, Sherwood C, Zhan L, Rezania A et al. Metabolic switching, growth kinetics and cell yields in the scalable manufacture of stem cell-derived insulin-producing cells. Stem Cell Res Ther [Internet]. 2024;15:1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stemcellres.biomedcentral.com/articles/\u003c/span\u003e\u003cspan address=\"https://stemcellres.biomedcentral.com/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13287-023-03574-3\u003c/span\u003e\u003cspan address=\"10.1186/s13287-023-03574-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeres A, Faust AL, Bushnell HL, Engquist EN, Kenty JH-R, Harb G, et al. Charting cellular identity during human in vitro β-cell differentiation. Nature. 2019;569:368\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelman A, Cangelosi AL, Davis JC, Pham Q, Rothman A, Faust AL, et al. A Nutrient-Sensing Transition at Birth Triggers Glucose-Responsive Insulin Secretion. Cell Metab. 2020;31:1004\u0026ndash;e10165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelazco-Cruz L, Song J, Maxwell KG, Goedegebuure MM, Augsornworawat P, Hogrebe NJ, et al. Acquisition of Dynamic Function in Human Stem Cell-Derived β Cells. Stem Cell Rep. 2019;12:351\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlvarez-Dominguez JR, Melton DA. Cell maturation: Hallmarks, triggers, and manipulation. Cell. Elsevier B.V.; 2022. pp. 235\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIworima DG, Rieck S, Kieffer TJ. Process Parameter Development for the Scaled Generation of Stem Cell-Derived Pancreatic Endocrine Cells. Stem Cells Transl Med. 2021;10:1459\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhorbani-Dalini S, Azarpira N, Sangtarash MH, Urbach V, Yaghobi R, Soleimanpour-Lichaei HR et al. Optimization of 3D islet-like cluster derived from human pluripotent stem cells: An efficient in vitro differentiation protocol. Gene. 2022;845.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePisania A, Weir GC, O\u0026rsquo;Neil JJ, Omer A, Tchipashvili V, Lei J, et al. Quantitative analysis of cell composition and purity of human pancreatic islet preparations. Lab Invest. 2010;90:1661\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapiro AMJ, Lakey JRT, Ryan EA, Korbutt GS, Toth E, Warnock GL, et al. Islet Transplantation in Seven Patients with Type 1 Diabetes Mellitus Using a Glucocorticoid-Free Immunosuppressive Regimen. N Engl J Med. 2000;343:230\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapiro AMJ. Islet Transplantation in Type 1 Diabetes: Ongoing Challenges, Refined Procedures, and Long-Term Outcome. Rev Diabet Stud. 2012;9:385\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDocherty K, Bernardo AS, Vallier L. Embryonic stem cell therapy for diabetes mellitus. Semin Cell Dev Biol. 2007;18:827\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLock LT, Tzanakakis ES. Stem/Progenitor Cell Sources of Insulin-Producing Cells for the Treatment of Diabetes. Tissue Eng. 2007;13:1399\u0026ndash;412.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabinowitz JD, Enerb\u0026auml;ck S. Lactate: the ugly duckling of energy metabolism. Nat Metab. 2020;2:566\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCliff TS, Dalton S. Metabolic switching and cell fate decisions: implications for pluripotency, reprogramming and development. Curr Opin Genet Dev. 2017;46:44\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKropp C, Kempf H, Halloin C, Robles-Diaz D, Franke A, Scheper T, et al. Impact of Feeding Strategies on the Scalable Expansion of Human Pluripotent Stem Cells in Single-Use Stirred Tank Bioreactors. Stem Cells Transl Med. 2016;5:1289\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHakim F, Kaitsuka T, Raeed JM, Wei F-Y, Shiraki N, Akagi T, et al. High oxygen condition facilitates the differentiation of mouse and human pluripotent stem cells into pancreatic progenitors and insulin-producing cells. J Biol Chem. 2014;289:9623\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"stem-cell-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scrt","sideBox":"Learn more about [Stem Cell Research \u0026 Therapy](http://stemcellres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/scrt/default.aspx","title":"Stem Cell Research \u0026 Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, human induced pluripotent stem cell, Insulin Producing Cells, Bioreactor, DoE, β-cells, Pancreatic cells, bioprocess development, optimization, islets, iPSCs","lastPublishedDoi":"10.21203/rs.3.rs-4244002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4244002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eType 1 diabetes, an autoimmune disorder leading to the destruction of pancreatic β-cells, requires lifelong insulin therapy. Islet transplantation offers a promising solution but faces challenges such as limited availability and the need for immunosuppression. Induced pluripotent stem cells (iPSCs) provide a potential alternative source of functional β-cells and have the capability for large-scale production. However, current differentiation protocols, predominantly conducted in hybrid or 2D settings, lack scalability and optimal conditions for suspension culture.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe examined a range of bioreactor scaleup process parameters and quality target product profiles that might affect the differentiation process. This investigation was conducted using an optimized HD-DoE protocol designed for scalability and implemented in 0.5L (PBS-0.5 Mini) vertical wheel bioreactors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA three stage suspension manufacturing process is developed, transitioning from adherent to suspension culture, with TB2 media supporting iPSC growth during scaling. Stage-wise optimization approaches and extended differentiation times are used to enhance marker expression and maturation of iPSC-derived islet-like clusters. Continuous bioreactor runs were used to study nutrient and growth limitations and impact on differentiation. The continuous bioreactors were compared to a Control media change bioreactor showing metabolic shifts and a more bcell-like differentiation profile. Cryopreserved aggregates harvested from the runs were recovered and showed maintenance of viability and insulin secretion capacity post-recovery, indicating their potential for storage and future transplantation therapies.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study demonstrated that stage time increase and limited media replenishing with lactate accumulation can increase the differentiation capacity of insulin producing cells cultured in a large-scale suspension environment.\u003c/p\u003e","manuscriptTitle":"Identifying and Optimizing Critical Process Parameters for Large-Scale Manufacturing of iPSC Derived Insulin-Producing β-cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 14:02:05","doi":"10.21203/rs.3.rs-4244002/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-06-01T04:40:43+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-05-15T06:22:05+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-14T16:56:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-11T01:17:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Stem Cell Research \u0026 Therapy","date":"2024-04-10T14:04:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"stem-cell-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scrt","sideBox":"Learn more about [Stem Cell Research \u0026 Therapy](http://stemcellres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/scrt/default.aspx","title":"Stem Cell Research \u0026 Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15c7e54e-9746-40ad-9879-fb7b926b5982","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T16:08:16+00:00","versionOfRecord":{"articleIdentity":"rs-4244002","link":"https://doi.org/10.1186/s13287-024-03973-0","journal":{"identity":"stem-cell-research-and-therapy","isVorOnly":false,"title":"Stem Cell Research \u0026 Therapy"},"publishedOn":"2024-11-09 15:57:56","publishedOnDateReadable":"November 9th, 2024"},"versionCreatedAt":"2024-05-23 14:02:05","video":"","vorDoi":"10.1186/s13287-024-03973-0","vorDoiUrl":"https://doi.org/10.1186/s13287-024-03973-0","workflowStages":[]},"version":"v1","identity":"rs-4244002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4244002","identity":"rs-4244002","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}