Development of a high-throughput assay for monitoring adipogenesis in vitro

Development of a high-throughput assay for monitoring adipogenesis in vitro | 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 Development of a high-throughput assay for monitoring adipogenesis in vitro Rachel Giles, Chrisna Durandt, Melvin Ambele, Michael Pepper This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4349556/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The obesity pandemic is listed as a chronic disease by the World Health Organization, and is underpinned by an increase in fat cell formation (adipogenesis). This study aims to develop high-throughput screening assays (flow cytometry, spectrophotometry and RT-qPCR) for cost-effective and rapid monitoring of adipogenesis using a human adipose-derived stem cell differentiation model. Adipogenesis was successfully upscaled from 6- to 96-well plates. We found that the most efficient well size differs between the assays investigated. Adipogenic differentiation could best be assessed in a 48-well plate (flow cytometry and spectrophotometry), while a 12-well plate was more efficient for obtaining reliable RT-qPCR results. The cost associated with hypothetically screening 100 compounds (in a high-throughput setting) using the method with the lowest degree of variability and the highest degree of cost-effectiveness was spectrophotometry (48-well plate), followed by flow cytometry (48-well plate) and RT-qPCR (12-well plate). This study provides valuable information for designing and selecting the most efficient high-throughput adipogenesis assays to screen for potential drug candidates in the continuous search of novel and more effective pharmacological agents to combatting obesity and its related comorbidities. adipogenesis high-throughput screening flow cytometry RT-qPCR spectrophotometry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The continued increase in the global prevalence of obesity has led to obesity being classified as a pandemic with 30% of the world’s population either overweight or obese. According to the most recent statistics released by the World Health Organization, in 2016 more than 1.9 billion adults were considered overweight, of which more than 650 million were classified as obese [ 1 ]. The first step in treating obesity is to adopt a healthier and more active lifestyle. However, there are a variety of pharmacological treatments, such as orlistat, naltrexone-bupropion and phentermine-topiramate available for treating obesity if adopting a healthier lifestyle is not possible and/or unsuccessful. Adverse drug reactions, such as liver damage, diarrhea, insomnia, increased heart rate, increased blood pressure, and lack of long-term efficacy are some of the main challenges associated with current pharmaceutical treatments. Thus, there is a continuous need to identify novel, more effective pharmacological agents to treat obesity when pharmaceutical intervention is required. Currently, most models used to study fat cell formation or adipogenesis make use of murine cell lines, such as the immortalized 3T3-L1 cell line, which is a well-established, preadipocyte cell line from mouse embryos [ 2 , 3 ]. Even though animal models are often used, human cells such as mesenchymal stromal/stem cells (MSCs) has sparked more interest due to their reliability, physiological compatibility, and relevance to human disease. Adipose tissue is a popular source from which MSCs can be isolated and in this context are referred to as adipose derived stromal/stem cells (ASCs). ASCs have multi-lineage differentiation capacity and their enhanced ability to differentiate into adipocytes serves as an excellent in vitro model to study human adipogenesis using cells of human origin. Adipogenesis refers to the process of fat cell formation and involves undifferentiated multipotent MSCs differentiating into preadipocytes, which subsequently mature into lipid-filled adipocytes [ 4 ]. Several different techniques are used to quantify adipogenesis, and these can be divided into two categories, namely (i) quantifying adipocyte formation based on the presence of intracellular lipid droplet accumulation, i.e. morphological features; and/or (ii) measuring levels of expression of genes associated with adipogenesis [ 5 , 6 ]. Adipogenesis has been extensively researched in the last two decades in order to find new molecular targets that regulate intracellular lipid accumulation and to test novel anti-obesity therapeutic agents on modulating this process. New anti-obesity drugs being investigated include leptin analogues, lipase inhibitors, cannabinoid type-1 receptor blockers and insulinotropic polypeptide analogues, to name a few [ 7 ]. Some of the key considerations in screening potential anti-obesity drug libraries is the cost involved in effectively screening a large number of potential candidates. Creating high-throughput screening assays using a human-derived model of adipogenic differentiation will allow for a cost-effective and rapid approach to monitoring the effect of anti-obesity agents on human adipogenesis. The aim of this project was to develop assays that will allow for high-throughput screening of adipogenesis. Our data showed differentiation of ASCs into adipocytes was successfully upscaled from 6-well plates to 96-well plates resulting in a high-throughput assay, which may be used as a screening tool to identify novel adipogenic regulators to combat obesity and its related comorbidities. Materials and Methods Isolation of ASCs from adipose tissue Adipose tissue was obtained from donors undergoing liposuction procedures after informed consent was obtained. The study was approved by the Research Ethics Committee (REC), Faculty of Health Sciences, University of Pretoria (Ethics Reference No.: 237/2019). Adipose-derived stromal/stem cells (ASCs) were isolated using a standardized protocol optimized in our laboratory and cryopreserved in complete growth medium (CGM) supplemented with 10% DMSO [ 8 – 10 ]. Culture maintenance ASCs at passage 2 were used. ASCs were expanded and maintained in complete growth medium (CGM) consisting of Dulbecco’s Modified Eagle’s Medium (DMEM), supplemented with 10% FBS and 2% p/s in a 37°C/5% CO 2 water jacketed incubator (Thermo Forma 311TF, Thermo Fisher Scientific, Waltham, Massachusetts, USA). CGM was replaced with fresh CGM twice a week. At 80–90% confluency, the cells were trypsinized using 3 mL of 0.25% Trypsin-EDTA solution (Gibco, Life Technologies™, Grand Island, New York, USA) for 7 minutes in a 37°C/5% CO 2 water jacketed incubator. The trypsinized ASCs in suspension were washed twice with PBS and replated at a density of 5 000 cells/cm 2 in T75 culture flasks for further expansion. The cells used for the ASC differentiation studies were phenotyped using flow cytometer and were positive for CD44, CD73, CD90 and negative for CD34, CD45 and CD105. Adipogenic differentiation ASCs were seeded in 6-, 12-, 24-, 48- and 96-well plates. Seeding densities were adjusted according to the relative well sizes (Table 1 ). ASC cultures were maintained and expanded in CGM to approximately 80–90% confluence and then induced to undergo adipogenic differentiation over a 21-day period, by replacing CGM with adipogenic induction medium [CGM supplemented with 10% FBS, 2% pen/strep, IBMX (0.5 M), insulin (10 µg/mL), indomethacin (200 µM) and dexamethasone (1 µM)]. Non-induced ASC cultures in CGM served as undifferentiated controls. Intracellular lipid accumulation was assessed on days 0, 14 and 21 as a proxy to quantify adipocyte differentiation using fluorescence microscopy, flow cytometry, RT-qPCR and spectrophotometry. Table 1 Plating density for each well size Plating density (number of cells/cm 2 ) 6-well plate 5 000 12-well plate 5 000 24-well plate 2 500 48-well plate 1 250 96-well plate 750 Fluorescence microscopy Cell culture medium was removed from the wells designated for fluorescence microscopy and rinsed with PBS to remove non-adherent cells before adding fresh CGM to the wells. The induced and non-induced cultures were stained with DAPI (2.5 µg/mL) and incubated overnight in a 37°C/5% CO 2 incubator. The cultures were then stained with Nile Red (50 ng/mL) followed by incubation for 20 minutes at room temperature (RT), after which they were rinsed with PBS to remove unbound dye. PBS was added to the wells and fluorescence images were captured using the Zeiss AxioVert A1 inverted fluorescence microscope with the AxioCam Cm1 camera (Carl Zeiss Werke, Göttingen, Germany). Single images were captured using two different fluorescence channels to capture DAPI [Filter Set 49 (excitation G 365 nm; emission BP 445/50 nm)] and Nile Red [Filter set 9 (excitation BP 450–490 nm; emission LP 515 nm)] staining, and then converted into overlay images. The images were enhanced (adjusting brightness and contrast) using ImageJ software (Version 1.8.0). Quantification of adipogenic differentiation using flow cytometry The proportion of ASCs that underwent adipogenic differentiation was quantified as described by Durandt et al. (2016). Briefly, the cells were dissociated from the well surfaces (0.25% Trypsin-EDTA) and transferred into flow cytometry tubes (one tube/well) using sterile Pasteur pipettes. Cells in the flow cytometry tubes were stained with Nile Red (50 ng/mL) and DAPI (2.5 µg/mL) (Table 2 ), incubated for 20 minutes at room RT followed by acquisition on the CytoFLEX flow cytometer (Beckman Coulter, Miami, USA). The acquired events were visualized on a forward scatter (FS) Lin vs. side scatter (SS) Log plot to exclude debris. A two-parameter plot, SS Log vs. FL9 Log plot (DAPI PB450) was used to select DAPI-stained nucleated cells. Next, a FL9 Height (H) log plot (DAPI PB450) vs. FL9 Area 9 (A) log plot (DAPI PB450) was generated to exclude aggregates. ASC intracellular lipid content was assessed using a FL2 Log (Nile Red) vs. FL6 Log (CD36) plot. Post-acquisition data was analysed using the Kaluza Flow Cytometry analysis software (Version 2.1.1; Beckman Coulter). Table 2 Volume of trypsin, Nile Red and DAPI added to the different well sizes 6-well plate 12-well plate 24-well plate 48-well plate 96-well plate Trypsin 500 µL 250 µL 150 µL 100 µL 50 µL CGM 500 µL 250 µL 150 µL 100 µL 50 µL Nile Red (50 ng/mL) 10 µL 5 µL 3 µL 2 µL 1 µL DAPI (2.5 µg/mL) 1 µL 1 µL 1 µL 1 µL 1 µL RNA isolation and RT-qPCR Total cellular RNA was extracted using either the RNeasy Mini kit (Qiagen, Hilden, Germany) or the RNeasy Micro kit (Qiagen, Hilden, Germany) according to the manufacturer’s guidelines. The RNeasy Mini Kit was used when the total number of cells was more than 500 000 while the RNeasy Micro Kit was used when total cell number was less than 500 000 cells. RNA integrity and quality were assessed on an Agilent TapeStation ® 2200 using RNA ScreenTape ® and Sample buffer kit (Agilent Technologies, California, USA) after which cDNA synthesis was performed. RNA integrity was compared to 18S and 28S ribosomal RNA and expressed as an RNA integrity number (RIN). cDNA synthesis was performed using the SensiFast™ cDNA synthesis kit (Bioline, London, England) according to manufacturer’s guidelines. RT-qPCR reactions were performed in final volumes of 10 µL, which included forward and reverse primer concentrations of 400 nM (Integrated DNA Technologies, IDT, Coralville, IA, USA), LightCycler ® 480 SYBR Green I Master Mix (Roche, Basel, Switzerland) and cDNA template (25 ng/µL; 2 µL per reaction). The following conditions were used on the Roche LightCycler 480 II: denaturation for 5 minutes at 95°C, 45 cycles for amplification for 30 seconds at 95°C, 30 seconds at 62°C and 30 seconds at 72°C. A melt curve was performed for 30 seconds at 95°C, 30 seconds at 40°C, ramped at 0.1°C/s. Genes of interest and reference genes are listed in Table 3 . Table 3 Primers used to detect the genes of interest [ 10 ] Gene Forward (3’ to 5’) Reverse (3’ to 5’) Genes of interest CEBPA PPARG FABP4 CD36 GTCTCTGCTAAACCACCA CGTGGATCTCTCCGTAAT ATCAACCACCATAAAGAGAAA CTTTGCCTCTCCAGTTGAA AAAGGAAAGGGAGTCTCAG TGGATCTGTTCTTGTGAATG AACTTCAGTCCAGGTCAA ACACAGGTCTCCCTTCTT Reference genes GUSB PPIA TBP YWHAZ GATCGCTCACACCAAATC GAGTTAAGAGTGTTGATGTAGG CCGAAACGCCGAATATAA TGACATTGGGTAGCATTAAC TCGTGATACCAAGAGTAGTAG CCTGGGACTGGAAAGTAA GGACTGTTCTTCACTCTTG GCACCTGACAAATAGAAAGA Spectrophotometry A 0.5% (w/v) Oil Red O stock solution was prepared (0.2 g of Oil Red O to 40 mL 2-propanol). An Oil Red O working solution was prepared by diluting the stock solution (2:3 ratio; 2-propanol: dH 2 O) to produce a 0.2% Oil Red O solution in 40% 2-propanol. A fresh working solution of 0.2% Oil Red O in 40% 2-propanol was prepared for each experiment and filtered using a 0.22 µM syringe filter before use. Cells were washed with PBS and fixed with 4% paraformaldehyde for 30 minutes (Table 4 ). Cells were once again washed and stained with 1 µL DAPI (2.5 µg/mL) followed by incubation for 15 minutes at RT. PBS was removed and the wells rinsed with dH 2 O. Oil Red O (Table 4 ) was added, followed by incubation for 30 minutes at RT. After incubation, Oil Red O was removed and the wells washed five times with dH 2 O to ensure unbound dye was removed. The plates were air-dried before adding 100% 2-propanol to the wells (Table 4 ). The plate was placed on an orbital shaker for 10 minutes at RT to dissolve the Oil Red O in 100% 2-propanol. An aliquot (200 µL) of the supernatant from each well was transferred to a 96-well plate for spectrophotometric readings and 2-Propanol was used as a blank. Absorbance was measured at 510 nm on the Bio-Tek PowerWave X microplate reader (Bio-Tek, Winooski, Vermont, USA). Table 4 Reagent volumes added to the different well sizes (spectrophotometry) 6-well plate 12-well plate 24-well plate 48-well plate 96-well plate PBS 2 mL 1 mL 500 µL 250 µL 100 µL 4% paraformaldehyde 1 mL 1 mL 500 µL 250 µL 100 µL Oil Red O working solution 1 mL 500 µL 300 µL 200 µL 100 µL 100% 2-propanol 1 mL 500 µL 300 µL 200 µL 100 µL Statistical modelling Bland-Altman multiple comparison plots were used to determine whether there was sufficient discrimination within the assays to detect adipogenic differentiation of ASCs. The data values for each well were compared to the respective well for each method, i.e. well 1 flow cytometry (day 14) was compared to well 1 spectrophotometry (day 14) using the same culture. Bland-Altman plots were generated using the MedCalc ® statistical software (Version 20.026) (MedCalc Software Ltd, Ostend, Belgium; https://www.medcalc.org ; 2022). Variability was based on the limits of agreement (the distance between the upper and lower limit). Cost analysis The well size which yielded an acceptable level of agreement (low degree of variability and mean difference close to zero) for each method (from the Bland-Altman multiple method comparison) was determined, and the cost of consumables (plates) and reagents required to screen 100 compounds (in triplicate) was calculated to determine the most efficient well size for flow cytometry, spectrophotometry and RT-qPCR respectively. A positive, negative and non-induced control (all done in triplicate) were included in the cost calculations. Statistical analysis All experiments were performed in triplicate. The data is reported as mean ± SEM for each time point. Two-way ANOVA and Tukey’s multiple comparisons test was used to determine statistical differences in adipogenic differentiation at each time point between the 6-, 12-, 24-, 48- and 96-well plates. A non-parametric Mann-Whitney test was used to compare statistical differences of gene expression between the non-induced and induced samples. Data was considered statistically significant when p < 0.05. GraphPad Prism 9 (Version 9.3.1) was used for all statistical analyses. Results Intracellular accumulation of lipid droplets No intracellular lipid accumulation was observed in non-induced (control) cells on both days 14 and 21 (Fig. 1 a, c, e, g, i and Fig. 2 a, c, e, g, i). Adipocytes containing multiple intracellular lipid droplets were observed in the induced samples (on both days 14 and 21), indicating the ASCs successfully underwent adipogenic differentiation (Fig. 1 b, d, f, h, j and Fig. 2 b, d, f, h, j). The majority of the cells displayed multiple intracellular lipid droplets, with larger droplets observed in some cells (Fig. 2 j). Progression of adipogenic differentiation is associated with the merging of intracellular lipid droplets, resulting in a single unilocular lipid droplet in fully mature adipocytes in vivo [ 10 ]. Detection of Nile Red-positive cells using flow cytometry The proportion of ASCs that underwent adipogenic differentiation was highest in the 6-well plates (41.85% ± 6.08% and 38.38% ± 7.87% on days 14 and 21 respectively) (Fig. 3 a). The average proportion of ASCs that underwent adipogenic differentiation when seeded in the other well sizes were 32.96% ± 5.61% (day 14) and 29.76% ± 8.26% (day 21) in the 12-well plates (Fig. 3 b), 24.63% ± 3.95% (day 14) and 30.51% ± 7.56% (day 21) in the 24-well plates (Fig. 3 c), and 20.92% ± 6.26% (day 14) and 25.07% ± 3.64% (day 21) in the 48-well plates (Fig. 3 d), and 31.02% ± 7.74% and 30.02% ± 7.52% on days 14 and 21 in the 96-well plates (Fig. 3 e). The observed differences in adipogenic differentiation between days 14 and 21 were not statistically significant. When comparing the different well sizes, the only significant differences observed amongst the induced samples were between the 6- and 24-well plate and the 6- and 48-well plate on day 14 (Fig. 3 g). Quantification of Oil Red O using spectrophotometry The optical density (OD) of Oil Red O extracted from non-induced and induced cells was measured. The OD values were normalized to the average cell count determined for each well size (example in Table S13). The highest optical density (OD) value, i.e. the highest concentration of Oil Red O per well was observed in the induced cultures seeded in the 6-well plates on day 14. The OD values were 23.20 ± 6.85 and 10.47 ± 2.77 on day 14 and 21 respectively (Fig. 4 a). The OD values obtained on day 14 for the induced ASCs were 22.16 ± 6.31 (12-well plate), 11.29 ± 1.48 (24-well plate), 16.87 ± 1.97 (48-well plate) and 22.07 ± 4.94 (96-well plate) (Fig. 4 a-e) with no statistical difference between well sizes. Relative quantification of adipogenic-associated genes using RT-qPCR On day 14, no statistically significant differences were observed for the relative expression of PPARG, CEBPA , CD36 and FABP4 between the well sizes (Fig. 5 a, b, c and d respectively). However, the CEBPA expression on day 21 differed significantly between the 6- and 24-well plates (p = 0.0005), 12- and 24-well plates (p = 0.0005) as well as the 24-well plates compared to the 48- (p = 0.0010) and 96-well plates (p = 0.0220) (Fig. 5 b). No statistically significant differences were observed for PPARG , CD36 and FABP4 on day 21 (Fig. 5 a, c and d respectively). Due to the low RNA yield using 96-well plates, we were only able to obtain results for one culture of the two cultures analysed (day 21 for CD36) (Fig. 5 c). Although 6 biological replicates (independent donors) were plated for the RT-qPCR experiments, acceptable mRNA concentrations were only consistently obtained for the 6-, 12-well plates. Obtaining sufficient mRNA concentrations were inconsistent for the 24-, 48- and 96-well plates, i.e. the RNA yield obtained was insufficient in some of the wells. The low number of ASCs obtained in the smaller well sizes was thus a limitation when using the smaller well sizes for RT-qPCR anaylsis. Statistical modelling For flow cytometry, the lowest degree of variability was present in the 24-well plate on days 14 and 21, and the highest degree of variability was present in the 96-well plate on days 14 and 21. The degree of variability is based on the size of the 95% confidence interval (Table 5 ; Fig. S1 and S2, respectively). Negative biases [data values below the assay bias (blue line); smaller well size minus 6-well plate] were observed as the proportion of differentiated ASCs reported in the 6-well plate increased, suggesting that ASC differentiation is favored more in the 6-well plate. The opposite was observed when low levels of adipogenic differentiation were measured in the 6-well plate, where a positive difference suggests that ASC differentiation is less favored in the smaller well sizes (positive bias; data values above the blue line). On day 14, the lowest variation (narrow 95% confident interval range) for spectrophotometry was observed in the 48-well plate and the highest (broadest 95% confident interval range) in the 24-well plate (Table 5 ). However, low variation was observed in general indicating a tight level of agreement (difference close to zero with low level of variation) between the well sizes (Fig. S3). Large error bars (95% confidence intervals) in the comparison between the 6- and 96-well plate on day 21 (Fig S4d) are due to notable differences in the OD readings obtained from the different cultures during spectrophotometry analysis. On day 21, the lowest variation was seen in the 12-well plate and the highest in the 96-well plate (Fig. S4a and d, respectively). Table 5 Summary of the Bland-Altman plots for the well size which produced the least variability per condition using the 6-well plate as the reference well size Well size (least variability) Day 14 Day 21 Flow cytometry 24-well plate 24-well plate Spectrophotometry 48-well plate 12-well plate RT-qPCR PPARG 24-well plate 24-well plate RT-qPCR FABP4 24-well plate 24-well plate Next, the methods used in this study (flow cytometry, spectrophotometry and RT-qPCR) were compared to determine which method produced results with the least variability when the different well sizes were compared (Table 6 ; Fig. S9-S18). Flow cytometry was used as the reference method as it produced the most reliable and consistent results for all well sizes. Due to insufficient RT-qPCR data being generated for the 48- (day 21) and 96-well plates (days 14 and 21) (Fig. S16-S18 respectively), these plates (48- and 96-well plates) were not considered for analysis. Only flow cytometry and spectrophotometry were compared in the 48- (day 21) and 96-well plates (days 14 and 21) (Fig. S16-S18). For all well sizes, spectrophotometry produced the least amount of variability when compared to flow cytometry on both days 14 and 21 (Table 6 ; Fig. S9-S18). The variability in the RT-qPCR data was noticeably higher (size of the green and blue error bars) compared to the variability observed when spectrophotometry was compared to flow cytometry (Fig. S9-S15). It needs to be mentioned that there were less than 3 biological repeats for some analysis, specifically in the 24-well plate days 14 and 21 for RT-qPCR PPARG and FABP4 (Fig. S13b and c; S14b and c respectively). For qPCR analysis, the lowest variability relating to PPARG gene expression was present in the 24-well plate on days 14 and 21 (Table 5 ), although on day 14 low variability was seen in all well sizes (Fig. S5 and S6). The 96-well plate (days 14) (Fig. S5d) data should be interpreted with caution as only two data values were available for use in this comparison. Due to availability of only one data point for analysis, the 48- and 96-well plate data on day 21 was not considered. Similarly, to PPARG , the lowest variability for FABP4 gene expression was observed in the 24-well plate on days 14 and 21 (Table 5 ; Fig. S7 and S8) and the highest variation was seen in the 12-well plate on days 14 and 21. The 96-well plate (day 21) had one data value and was therefore not considered. Additionally, the 96-well plate (day 14) (Fig. S7d) and 48-well plate (day 21) (Fig. S8c) only had two data values and therefore the interpretation should be considered with caution. Overall, the Bland-Altman plots revealed the 24-well plate produced results with the least amount of variability while the 96-well plate had the highest degree of variability. In summary, the flow cytometry data corresponded more closely to the spectrophotometry data as can be seen from the excellent agreement (mean difference close to 0) and low variability (Fig. S9-S18a). Additionally, spectrophotometry and RT-qPCR had tight levels of agreement (small mean difference) indicating the close similarity in the measurements (except day 21 PPARG ). In conclusion, the Bland-Altman statistical analysis showed that all well sizes were comparable and the ultimate decision of the assay of choice will likely depend on other factors such as reliability in obtaining results and the cost effectiveness of the assay. Table 6 Summary of the Bland-Altman plots for the method which produced the least variability per condition using flow cytometry as the reference methods Method (least variability – flow cytometry reference method) Day 14 Day 21 6-well plate Spectrophotometry Spectrophotometry 12-well plate Spectrophotometry Spectrophotometry 24-well plate Spectrophotometry Spectrophotometry 48-well plate Spectrophotometry - 96-well plate - - - Insufficient data. Unable to conclude which method produced the least variability Cost analysis A cost analysis was undertaken according to the well size established to yield an acceptable level of agreement for each method (Table S1 and S2). Our data suggests that flow cytometry and spectrophotometry assays can be upscaled with relative confidence to a 48-well plate (all data values fell within the upper and lower limits of agreement (± 1.96SD). The 12-well plate was the smallest well size, which consistently yielded a sufficient amount of mRNA for all primary cultures used in this study. The costs associated with hypothetically screening 100 compounds in triplicate was determined. Inclusion of a positive, negative and non-induced controls were taken into account when performing the calculations (all calculations can be found in supplementary data; Table S3-S12). The positive control would include a drug/compound that is known to inhibit adipogenesis, the negative control would include induced ASCs that receive no drug treatment and non-induced (undifferentiated) cells will be used as a baseline control. The ASCs would first need to be expanded in T75 flasks to obtain the required number of cells needed for seeding (supplementary data), followed by the expansion in the designated well sizes until adipogenic differentiation is induced. The assumption (based on data generated in our laboratory) was made that one T75 culture vessel will yield approximately one million cells (at 80–90% confluency; time period 2 weeks). The 12-well plate (used for RT-qPCR) requires the largest volume of reagents [3 bottles (500 mL) of DMEM to prepare CGM and 4 bottles (500 mL) of DMEM to prepare the adipogenic induction medium]. Next, the total volume for each reagent required to screen 100 compounds in triplicate using flow cytometry, spectrophotometry and RT-qPCR was calculated (Table S1 and S2). Calculations were based on the standard approach of inducing ASCs for 21 days before assessing adipogenic differentiation and these calculations can be found in supplementary data (Table S3 and S6) (10,11). The following reagents would be sufficient to screen 100 compounds (in triplicate) in the 48-well plate for flow cytometry and spectrophotometry: 2 vials of CD36-APC monoclonal antibody, 1 vial/bottle of DAPI (powder), Nile Red (powder), Oil Red O (powder), 4% formaldehyde (solution) and Trypsin-EDTA (solution) and PBS (solution) (Table S1 ). The screening of 100 compounds in triplicate in the 12-well plate for RT-qPCR requires 3 RNeasy Plus Micro Kits and one of each of the following: SensiFast cDNA synthesis kit, Light Cycler 96 well plate, RNA ScreenTape Ladder, RNA ScreenTape Sample Buffer, Light Cycler 480 SYBR Green I Master Mix and primers (Table S2). Therefore, when comparing the costs associated with the most effective well size for each method, spectrophotometry (48-well plate) (R30 998; $ 1 658.53, dollar rate taken on 05/04/2024 at $ 1 to R18.69) was the most cost-effective method to screen 100 compounds (in triplicate), followed by flow cytometry (48-well plate) (R39 234.88; $ 2 099.24) (Table S1 ) and RT-qPCR (12-well plate) (R97 612.73; $ 5 222.72) (Table S2). RT-qPCR cost analysis was based on one primer pair (forward and reverse) for a gene of interest and 3 primer sets (forward and reverse) for the reference genes. Additionally, a standard, no template controls (primer pair of gene of interest and 3 reference gene primers) as well as the controls (positive, negative and non-induced) in triplicate were considered. Discussion and conclusion The rise in obesity has become a serious concern resulting in the WHO classifying it as a world-wide pandemic, where more than 30% of the world’s population are obese [ 1 ]. The process of adipogenesis has been extensively studied to determine the molecular players which could serve as potential targets to control fat accumulation. The effective screening of large libraries of potential anti-obesity drugs can be costly. Therefore, the creation of a high-throughput screening adipogenesis assay will provide a cost effective and rapid approach for identifying anti-obesity agents. In this study, we set out to investigate the feasibility of upscaling the differentiation of primary ASCs into adipocytes (process known as adipogenesis) from the commonly used 6- and 12-well plates to 24-, 48- and 96-well plates. The enhanced ability of ASCs to differentiate into adipocytes serves as an excellent in vitro model to study human adipogenesis using cells of human origin. Using cells of human origin is likely to result in more reliable translation of findings to the clinical setting. The primary ASC cultures used in this study adhered to the MSC characterization criteria, except for CD105 expression, recommended by the International Society for Cell and Gene Therapy (ISCT) and International Federation for Adipose Therapeutics and Science (IFATS) [ 12 , 13 ]. Several studies have suggested that the expression of CD105 may vary between different culture conditions [ 14 – 16 ] and that ASCs contain a sub-population of CD105 negative cells. Mark et al. (2013) reported that the CD105 expression by bone marrow (BM)-derived MSCs (obtained from sternal bone marrow aspirates) cultured with serum-free growth media (MSCGM-CD™) was significantly lower than BM-derived MSCs cultured with serum-containing media (MSCGM™) (51.70% ± 14.53 and 95.83% ± 2.35 respectively). Despite the difference in CD105 expression, the BM-derived MSCs in both culture conditions showed differentiation potential in all three mesodermal lineages. The findings by Mark et al. (2013) are consistent with findings from our laboratory, in which we observed that ASCs cultured in pooled human platelet lysate (pHPL) express CD105, in contrast to the lack of CD105 expression in ASCs cultured in medium supplemented with FBS (unpublished data). Similar to the findings by Mark et al . (2013), the primary ASC cultures used in this study, despite not expressing CD105, still successfully underwent adipogenic differentiation when induced (Figs. 2 and 3 ). The most common techniques used to monitor adipogenic differentiation include RT-qPCR (gene expression), microscopy, spectrophotometry, and flow cytometry. As mentioned earlier, adipogenic differentiation is commonly assessed using either 6- or 12-well plates. It is well accepted that higher-throughput screening assays are needed to select for potential anti-obesity pharmaceutical agents. However, a very limited number of studies have investigated the feasibility of monitoring adipogenic differentiation in smaller well sizes. Sottile and Seuwen (2001) used a 96-well plate to measure, using spectrophotometry, glycerol-3-phosphate dehydrogenase (GPDH) activity during adipogenic differentiation of murine 3T3-L1 preadipocytes. Glycerol-3-phosphate dehydrogenase (GPDH) is highly expressed in terminally differentiated (mature) adipocytes. Yuan et al. (2019) developed an algorithm, referred to as the Fast Adipogenesis Tracking System (FATS), to quantify adipogenic differentiation observed in microscopy images (after staining with Nile Red or Oil Red O) of cells cultured (differentiated) in 96-well plate wells. To the best of our knowledge, this is the first study that compared data generated using different well sizes to assess if data generated using smaller well sizes are comparable with data obtained using the standard 6-well plates. This is also the first study, to our knowledge, that has compared the different techniques (RT-qPCR, spectrophotometry, flow cytometry and microscopy) using all the different plate sizes. In both fluorescence microscopy and flow cytometry, Nile Red staining was used to determine the proportion of cells that underwent adipogenesis. Nile Red stains neutral lipids, the predominant components in the lipid droplet cores [ 17 ]. Consequently, the proportion of Nile Red-positive ASCs is proportionate to the number of ASCs that underwent adipogenesis. Intracellular lipid droplets have been visually observed (fluorescence microscopy) in induced cultures seeded in all well sizes (6-, 12-, 24-, 48- and 96-well plates), suggesting that the detection of adipogenesis can successfully be upscaled to smaller well sizes. The fluorescence microscopy observations were confirmed using flow cytometry. Significant variation in the proportion of ASCs that underwent adipogenic differentiation (flow cytometric analysis) was observed between the 24- (p = 0.0386) and 48-well (p = 0.0071) plates compared to the 6-well plates. The variation observed is likely due to biological variation (different donors) and technical variation (experimental variation). Spectrophotometry also clearly showed an increase in lipid content (based on an increased OD value due to increased Oil Red O staining) in the induced samples compared to the non-induced samples in all well sizes. No significant differences were observed between the well sizes on days 14 and 21 for the induced samples. The main challenge we encountered using spectrophotometry was the tendency of unbound Oil Red O to stick to the plastic, which resulted in increased OD readings in the smaller well sizes (clearly seen with the increase in the OD readings of the non-induced samples as the well sizes decreased; Fig. 5 f). Adipogenesis is regulated by a cascade of transcription factors of which peroxisome proliferator-activated receptor gamma (PPARγ) is the master regulator [ 18 ]. CD36 and FABP4 are proteins associated with terminally differentiated adipocytes and are known to be PPARγ responsive genes [ 10 ]. Upregulation of the adipogenic related genes of interest ( PPARG, CEBPA, CD36 and FABP4 ) was observed in the induced cultures for all well sizes with no statistically significant difference between plate sizes (6-, 12-, 24-well plates), and the expression levels were consistent with findings published by other studies [ 10 , 19 – 23 ]. The significant differences observed on day 21 (6- vs. 24-well plate and 12- vs. 24-well plate) for CEBPA expression are likely due to the variability between biological replicates. The major limitation encountered using smaller well sizes for RT-qPCR experiments was the low number of cells present, resulting in the extraction of insufficient mRNA for the RT-qPCR protocol used in our laboratory. It is important to note that fluorescence microscopy, flow cytometry and spectrophotometry made use of individual wells (in triplicate), while in this this study wells were pooled for RT-qPCR (to obtain sufficient RNA). In order to overcome this limitation of small cell numbers in the smaller wells, the RT-qPCR method should be optimized in future to allow for accurate detection using low cell concentrations in individual wells. In summary, the least amount of variation (6 independent donors done in triplicate; from the 6-well plate used as the reference standard) was observed in 24-well plates; however, adipogenic differentiation can also be quantified with confidence in 48-well plates (especially for flow cytometry and spectrophotometry). Increased variability was observed in 96-well plates. Our data suggest that data generated using 24- and 48-well plates is likely to be more reproducible than data obtained using 96-well plates. Intra-assay variation within the assays showed that spectrophotometry performed the best amongst all the methods compared (tightest level of agreement). The cost analysis showed that screening a large library of compounds will be more cost-effective in the smaller wells compared to the more standard 6- or 12-well plates. Spectrophotometry (48-well plate) was the most cost-effective method (R30 998; $ 1 658.53), followed by flow cytometry (48-well plate) (R39 234.88; $ 2 099.24), while the cost of RT-qPCR (12-well plate) was high (R97 612.73; $ 5 222.72). In conclusion, adipogenic differentiation of ASCs was achieved in all well sizes. Spectrophotometry was the most cost-effective and may serve as a valuable initial screening tool to select for agents with enhanced anti-adipogenic activity. Each of the assays have strengths and weaknesses and may be used at specific stages of the screening process. Assays like flow cytometry are highly quantitative and provide more information regarding the impact of the agents at a single-cell level, while RT-qPCR will play an important role in assessing the impact of potential agents on gene expression. This study has demonstrated that flow cytometry, spectrophotometry and RT-qPCR can successfully be used to assess adipogenic differentiation in smaller well sizes, resulting in a higher-throughput approach. The use of smaller well sizes is also likely be more time-efficient. Declarations Ethics Approval Approval was obtained from the Research Ethics Committee (REC), Faculty of Health Sciences, University of Pretoria (Ethics Reference No.: 237/2019). Consent to participate Not applicable. Consent for Publication Consent for publication was taken from all individuals. Competing interests The authors declare no competing interests. Funding The research leading to these results received funding from the South African Medical Research Council Extramural Unit for Stem Cell Research and Therapy under Grant Agreement No SAMRC-RFA-UFSP-01-2013/STEM CELLS, and with the support of a National Research Foundation Freestanding Innovation and Scarce Skills Bursary (PR_SFH201124576219). Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis was performed by Rachel Giles. The first draft of the manuscript was written by Rachel Giles and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability Not applicable. References World Health Organization Obesity and overweight [Internet]. 2019 [cited 2019 Feb 5]. https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight . Morrison, S., & McGee, S. L. (2015). 3T3-L1 adipocytes display phenotypic characteristics of multiple adipocyte lineages. Adipocyte , 4 (4), 295–302. Sadowski, H. B., Wheeler, T. T., & Young, D. A. (1992). Gene expression during 3T3-L1 adipocyte differentiation. Characterization of initial responses to the inducing agents and changes during commitment to differentiation. Journal Of Biological Chemistry , 267 (7), 4722–4731. Ali, A. T., Hochfeld, W. E., Myburgh, R., & Pepper, M. S. (2013). Adipocyte and adipogenesis. European Journal Of Cell Biology , 92 (6–7), 229–236. Gerhold, D. L., Franklin, L. I. U., Jiang, G., Zhihua, L. I., Jian, X. U., Meiqing, L. U., et al. (2002). Gene expression profile of adipocyte differentiation and its regulation by peroxisome proliferator-activated receptor-γ agonists. Endocrinology , 143 (6), 2106–2118. Hristov, I., Mocanu, V., Zugun-Eloae, F., Labusca, L., Cretu-Silivestru, I., Oboroceanu, T., et al. (2019). Association of intracellular lipid accumulation in subcutaneous adipocyte precursors and plasma adipokines in bariatric surgery candidates. Lipids In Health And Disease , 18 (1), 1–8. Srivastava, G., & Apovian, C. (2018). Future Pharmacotherapy for Obesity: New Anti-obesity Drugs on the Horizon. Curr Obes Rep , 7 (2), 147–161. Dessels, C., Ambele, M. A., & Pepper, M. S. (2019). The effect of medium supplementation and serial passaging on the transcriptome of human adipose-derived stromal cells expanded in vitro. Stem Cell Research & Therapy , 10 (1), 1–17. Dessels, C., & Pepper, M. S. (2019). Reference Gene Expression in Adipose-Derived Stromal Cells Undergoing Adipogenic Differentiation. Tissue Eng - Part C Methods , 25 (6), 353–366. Durandt, C., Vollenstee, F. A., Van, Dessels, C., Kallmeyer, K., Villiers, D., De, Murdoch, C., et al. (2016). Novel flow cytometric approach for the detection of adipocyte subpopulations during adipogenesis. Methods , 57 (January), 729–742. Durandt, C., Dessels, C., da Silva, C., Murdoch, C., & Pepper, M. S. (2019). The Effect of Early Rounds of ex vivo Expansion and Cryopreservation on the Adipogenic Differentiation Capacity of Adipose-Derived Stromal/Stem Cells. Scientific Reports , 9 (1), 1–13. Bourin, P., Bunnell, B. A., Casteilla, L., Dominici, M., Katz, A. J., March, K. L. (2013). Stromal cells from the adipose tissue-derived stromal vascular fraction and culture expanded adipose tissue-derived stromal/stem cells: A joint statement of the International Federation for Adipose Therapeutics and Science (IFATS) and the International So. Cytotherapy [Internet]. ;15(6):641–8. http://dx.doi.org/10.1016/j.jcyt.2013.02.006 . Dominici, M., Le Blanc, K., Mueller, I., Slaper-Cortenbach, I., Marini, F. C., Krause, D. S., et al. (2006). Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for Cellular Therapy position statement. Cytotherapy , 8 (4), 315–317. Mark, P., Kleinsorge, M., Gaebel, R., Lux, C. A., Toelk, A., Pittermann, E. (2013). Human mesenchymal stem cells display reduced expression of CD105 after culture in serum-free medium. Stem Cells Int ;2013:Article ID 698076. Pham, L. H., Vu, N. B., & Van Pham, P. (2019). The subpopulation of CD105 negative mesenchymal stem cells show strong immunomodulation capacity compared to CD105 positive mesenchymal stem cells. Biomed Res Ther , 6 (4), 3131–3140. Lv, X. J., Zhou, G. D., Liu, Y., Liu, X., Chen, J. N., Luo, X. S., et al. (2012). In vitro proliferation and differentiation of adipose-derived stem cells isolated using anti-CD105 magnetic beads. International Journal Of Molecular Medicine , 30 (4), 826–834. Greenspan, P., Mayer, E. P., & Fowler, S. D. (1985). Nile red: A selective fluorescent stain for intracellular lipid droplets. Journal Of Cell Biology , 100 (3), 965–973. Ricote, M., & Glass, C. K. (2007). PPARs and molecular mechanisms of transrepression. Biochim Biophys Acta - Mol Cell Biol Lipids , 1771 (8), 926–935. Ambele, M. A., & Pepper, M. S. (2017). Identification of transcription factors potentially involved in human adipogenesis in vitro. Mol Genet Genomic Med , 5 (3), 210–222. Ambele, M. A., Dessels, C., Durandt, C., & Pepper, M. S. (2016). Genome-wide analysis of gene expression during adipogenesis in human adipose-derived stromal cells reveals novel patterns of gene expression during adipocyte differentiation. Stem Cell Res , 16 (3), 725–734. Bieback, K., Hecker, A., Schlechter, T., Hofmann, I., Brousos, N., Redmer, T., et al. (2012). Replicative aging and differentiation potential of human adipose tissue-derived mesenchymal stromal cells expanded in pooled human or fetal bovine serum. Cytotherapy , 14 (5), 570–583. Yu, G., Wu, X., Dietrich, M. A., Polk, P., Scott, L. K., Ptitsyn, A. A., et al. (2010). Yield and characterization of subcutaneous human adipose- derived stem cells by flow cytometric and adipogenic mRNA analyses. Cytotherapy , 12 (4), 538–546. Al-Ghadban, S., Diaz, Z. T., Singer, H. J., Mert, K. B., & Bunnell, B. A. (2020). Increase in Leptin and PPAR-γ Gene Expression in Lipedema Adipocytes Differentiated in vitro from Adipose-Derived Stem Cells. Cells , 9 (2), 1–14. Supplementary Files Graphicalabstract.pdf Supplementarydata.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4349556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376356173,"identity":"88f015d6-5db3-47c3-b00b-d5750489faac","order_by":0,"name":"Rachel Giles","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie2PsWqDUBSGjxRulktdLQb6CicIN4vog3QxCHax4NjBBksHlz6AL9G184ULySJ1DXQxFMzSwdEhQ48hZFM7Fnq/4XA4/B8/B0Cj+ZswgAT4NYDx1aFLB+NZTisInOaVA0nUK9mvlH4yG1p1Oo0qy1w1dYtPczbLazvBynvLFbWk7t2QMi+j5aLALWe8RKfAz/C9XJGyiR6yAcWCmNkcN5xZMQScFCFJMTI1rJjfzD72yu2hlhw/QlHtJxSLWgBTaoHFC0fpid1Ui9WIm1eU9EvsGAWGgdhRSzD2ixk2Vve49s3Z9tC1R88X1f2+blN3UDmjLtvqlAzG4z3ry+ZPhzUajea/8QNMw1vrL4V9qwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8403-6603","institution":"University of Pretoria Faculty of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Giles","suffix":""},{"id":376356174,"identity":"f0f01b49-a584-4967-98fa-80efedc5cf2b","order_by":1,"name":"Chrisna Durandt","email":"","orcid":"","institution":"University of Pretoria Faculty of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chrisna","middleName":"","lastName":"Durandt","suffix":""},{"id":376356175,"identity":"18abc861-2dce-4373-a0ed-babc036bb2d6","order_by":2,"name":"Melvin Ambele","email":"","orcid":"","institution":"University of Pretoria Faculty of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Melvin","middleName":"","lastName":"Ambele","suffix":""},{"id":376356176,"identity":"d40cf0e2-fd68-49bd-b2a8-f1949e239ddf","order_by":3,"name":"Michael Pepper","email":"","orcid":"https://orcid.org/0000-0001-6406-2380","institution":"University of Pretoria Faculty of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Pepper","suffix":""}],"badges":[],"createdAt":"2024-04-30 13:54:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4349556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4349556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71556403,"identity":"769e601a-f777-410c-aa4f-ced1d5385b34","added_by":"auto","created_at":"2024-12-16 16:24:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":877837,"visible":true,"origin":"","legend":"\u003cp\u003eFluorescence micrographs o non-induced and induced ASCs captured on day 14.\u003cstrong\u003e a\u003c/strong\u003e Non-induced 6-well plate, \u003cstrong\u003eb\u003c/strong\u003e Induced 6-well plate, \u003cstrong\u003ec\u003c/strong\u003e Non-induced 12-well plate, \u003cstrong\u003ed\u003c/strong\u003e Induced 12-well plate, \u003cstrong\u003ee\u003c/strong\u003eNon-induced 24-well plate, \u003cstrong\u003ef\u003c/strong\u003e Induced 24-well plate, \u003cstrong\u003eg\u003c/strong\u003e Non-induced 48-well plate, \u003cstrong\u003eh\u003c/strong\u003e Induced 48-well plate, \u003cstrong\u003ei\u003c/strong\u003e Non-induced 96-well plate and \u003cstrong\u003ej\u003c/strong\u003e Induced 96-well plate. Images were taken at 10x magnification. The blue fluorescence represents the DAPI-stained nuclei and the green fluorescence represents the Nile Red-stained lipid droplets.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4349556/v1/2b3e76bfa85d15a702ebac6c.png"},{"id":71556407,"identity":"a993da2e-448b-4cd8-a4d3-404b26df033b","added_by":"auto","created_at":"2024-12-16 16:24:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":816579,"visible":true,"origin":"","legend":"\u003cp\u003eFluorescence micrographs of non-induced and induced ASCs captured on day 21. \u003cstrong\u003ea\u003c/strong\u003e Non-induced 6-well plate, \u003cstrong\u003eb\u003c/strong\u003e Induced 6-well plate, \u003cstrong\u003ec\u003c/strong\u003e Non-induced 12-well plate, \u003cstrong\u003ed\u003c/strong\u003e Induced 12-well plate, \u003cstrong\u003ee \u003c/strong\u003eNon-induced 24-well plate, \u003cstrong\u003ef\u003c/strong\u003e Induced 24-well plate, \u003cstrong\u003eg\u003c/strong\u003eNon-induced 48-well plate, \u003cstrong\u003eh\u003c/strong\u003e Induced 48-well plate, \u003cstrong\u003ei\u003c/strong\u003e Non-induced 96-well plate and \u003cstrong\u003ej\u003c/strong\u003e Induced 96-well plate. Images were taken at 10x magnification. The blue fluorescence represents the DAPI-stained nuclei and the green fluorescence represents the Nile Red-stained lipid droplets. The white arrow indicates larger lipid droplets, likely due to the merging of the intracellular lipid droplets.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4349556/v1/070ddd65b925856aa9d9aaa6.png"},{"id":71556409,"identity":"70947ff2-db9e-4d3c-9b3d-947cdc03f335","added_by":"auto","created_at":"2024-12-16 16:24:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":283950,"visible":true,"origin":"","legend":"\u003cp\u003eQuantification of Nile Red-positive ASCs.\u003cstrong\u003e \u003c/strong\u003eTukey box-and-whisker plots were used to display the percentage of cells that stained positive for Nile Red. Error bars represent the minimum and maximum values. The top and bottom boundaries of the boxes represent the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles. The horizontal line in the middle of the boxplot represents the median and the dots represents the outliers.\u003cstrong\u003e a\u003c/strong\u003e Represents the 6-well plate (light purple non-induced and dark purple induced), \u003cstrong\u003eb\u003c/strong\u003e the 12-well plate (light green non-induced and dark green induced), \u003cstrong\u003ec \u003c/strong\u003ethe 24-well plate (light blue non-induced and dark blue induced), \u003cstrong\u003ed\u003c/strong\u003e the 48-well plate (light pink non-induced and dark pink induced), and \u003cstrong\u003ee\u003c/strong\u003e the 96-well plate (light orange non-induced and dark orange induced). \u003cstrong\u003ef\u003c/strong\u003e A summary of the percentage of Nile Red-positive cells detected in the non-induced cultures in the different well sizes (Colors as indicated in Fig. 3a-e for the different plate sizes) and \u003cstrong\u003eg\u003c/strong\u003e A summary of the percentage of Nile Red-positive cells detected in the induced cultures in the different well sizes (Colors as indicated in Fig. 3a-e for the different plate sizes). The number of biological replicates (n) (independent donors assessed in triplicate, i.e., three technical repeats) for the different well sizes are as follows: 6-, 12- and 24-well plates n=6; 48-well plate n=5, and 96-well plate n=5 (days 0 and 14), n=4 (day 21). Statistical significance: *p\u0026lt;0.05, **p\u0026lt;0.01 and ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4349556/v1/b63d5ec374c9c77572e2d392.png"},{"id":71556404,"identity":"0d15f86c-a7ff-42dc-bd09-0f9dacc08514","added_by":"auto","created_at":"2024-12-16 16:24:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":189822,"visible":true,"origin":"","legend":"\u003cp\u003eOptical density values normalized to average cell count.\u003cstrong\u003e \u003c/strong\u003eBar graphs were used to display the Oil Red O OD value, with \u003cstrong\u003ea\u003c/strong\u003e representing the 6-well plate (light purple non-induced and dark purple induced), \u003cstrong\u003eb\u003c/strong\u003e the 12-well plate (light green non-induced and dark green induced), \u003cstrong\u003ec\u003c/strong\u003e the 24-well plate (light blue non-induced and dark blue induced), \u003cstrong\u003ed\u003c/strong\u003e the 48 well plate (light pink non-induced and dark pink induced), and \u003cstrong\u003ee\u003c/strong\u003e the 96-well plate (light orange non-induced and dark orange induced). \u003cstrong\u003ef \u003c/strong\u003enon-induced and induced (purple 6-well plate, green 12-well plate, blue 24-well plate, pink 48-well plate and orange 96-well plate). The number of biological replicates (n) (each biological repeat was assessed in triplicate, i.e., three technical repeats) for the different well sizes are as follows: 6-well plate n=2 (day 0), n=3 (day 14), n=4 (day 21); 12-well plate n=3 (days 0 and 14), n=5 (day 21); 24-well plate n=3 (days 0 and 21), n=2 (day 14); 48-well plate n=2 (days 0 and 14), n=5 (day 21) and 96-well plate n=5 (day 0), n=4 (days 14 and 21). Statistical significance: *p\u0026lt;0.05, **p\u0026lt;0.01 and ****p\u0026lt;0.0001. Data represented as mean ± SEM.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4349556/v1/ca65f456499a4d1a5605580b.png"},{"id":71556405,"identity":"d0c4bf94-6558-400f-85f4-b007bf4c28b1","added_by":"auto","created_at":"2024-12-16 16:24:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":238673,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the relative fold increase of mRNA expression normalized to the non-induced samples.\u003cstrong\u003e a \u003c/strong\u003eRepresents PPARG, \u003cstrong\u003eb\u003c/strong\u003e CEBPA, \u003cstrong\u003ec\u003c/strong\u003e CD36 and \u003cstrong\u003ed\u003c/strong\u003e FABP4. The black bar graph represents day 14 and the grey represents day 21. The number of biological replicates (n) (independent donors assessed in triplicate, i.e., three technical repeats for the different well sizes) are as follows: 6-, 12-well plates n=4; 24-well plate PPARg, CD36 and FABP4 n=3; 24-well plate C/EBPA n=1 (day 14) and n=2 (day 21); 48-well plate n=3 (day 14) and n=3 (day 21, PPARG n=0); 96-well plate n=2 (day 14) and n=1 (day 21). Statistical significance: *p\u0026lt;0.05, **p\u0026lt;0.01 and ***p\u0026lt;0.001. Data represented as mean ± SEM.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4349556/v1/f087028de649f46890aa89d1.png"},{"id":75091310,"identity":"ca033540-e4f0-4c32-a04e-64311c3a21e9","added_by":"auto","created_at":"2025-01-30 11:00:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2980539,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349556/v1/49820a2c-b63d-4dac-9219-1b5e35a2acbe.pdf"},{"id":71557505,"identity":"ff2cc2d3-0503-4075-b2c2-e5e60068e3ee","added_by":"auto","created_at":"2024-12-16 16:32:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":288027,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349556/v1/b38b04f7ae7e6ddfa2398e58.pdf"},{"id":71556410,"identity":"61a8e561-f462-4c47-a409-a970a7acac55","added_by":"auto","created_at":"2024-12-16 16:24:15","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2675585,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349556/v1/42dddcb802f2bf5b704804bb.pdf"}],"financialInterests":"","formattedTitle":"Development of a high-throughput assay for monitoring adipogenesis in vitro","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe continued increase in the global prevalence of obesity has led to obesity being classified as a pandemic with 30% of the world\u0026rsquo;s population either overweight or obese. According to the most recent statistics released by the World Health Organization, in 2016 more than 1.9\u0026nbsp;billion adults were considered overweight, of which more than 650\u0026nbsp;million were classified as obese [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe first step in treating obesity is to adopt a healthier and more active lifestyle. However, there are a variety of pharmacological treatments, such as orlistat, naltrexone-bupropion and phentermine-topiramate available for treating obesity if adopting a healthier lifestyle is not possible and/or unsuccessful. Adverse drug reactions, such as liver damage, diarrhea, insomnia, increased heart rate, increased blood pressure, and lack of long-term efficacy are some of the main challenges associated with current pharmaceutical treatments. Thus, there is a continuous need to identify novel, more effective pharmacological agents to treat obesity when pharmaceutical intervention is required.\u003c/p\u003e \u003cp\u003eCurrently, most models used to study fat cell formation or adipogenesis make use of murine cell lines, such as the immortalized 3T3-L1 cell line, which is a well-established, preadipocyte cell line from mouse embryos [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Even though animal models are often used, human cells such as mesenchymal stromal/stem cells (MSCs) has sparked more interest due to their reliability, physiological compatibility, and relevance to human disease. Adipose tissue is a popular source from which MSCs can be isolated and in this context are referred to as adipose derived stromal/stem cells (ASCs). ASCs have multi-lineage differentiation capacity and their enhanced ability to differentiate into adipocytes serves as an excellent \u003cem\u003ein vitro\u003c/em\u003e model to study human adipogenesis using cells of human origin.\u003c/p\u003e \u003cp\u003eAdipogenesis refers to the process of fat cell formation and involves undifferentiated multipotent MSCs differentiating into preadipocytes, which subsequently mature into lipid-filled adipocytes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Several different techniques are used to quantify adipogenesis, and these can be divided into two categories, namely (i) quantifying adipocyte formation based on the presence of intracellular lipid droplet accumulation, i.e. morphological features; and/or (ii) measuring levels of expression of genes associated with adipogenesis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdipogenesis has been extensively researched in the last two decades in order to find new molecular targets that regulate intracellular lipid accumulation and to test novel anti-obesity therapeutic agents on modulating this process. New anti-obesity drugs being investigated include leptin analogues, lipase inhibitors, cannabinoid type-1 receptor blockers and insulinotropic polypeptide analogues, to name a few [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Some of the key considerations in screening potential anti-obesity drug libraries is the cost involved in effectively screening a large number of potential candidates. Creating high-throughput screening assays using a human-derived model of adipogenic differentiation will allow for a cost-effective and rapid approach to monitoring the effect of anti-obesity agents on human adipogenesis.\u003c/p\u003e \u003cp\u003eThe aim of this project was to develop assays that will allow for high-throughput screening of adipogenesis. Our data showed differentiation of ASCs into adipocytes was successfully upscaled from 6-well plates to 96-well plates resulting in a high-throughput assay, which may be used as a screening tool to identify novel adipogenic regulators to combat obesity and its related comorbidities.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eIsolation of ASCs from adipose tissue\u003c/h2\u003e\n \u003cp\u003eAdipose tissue was obtained from donors undergoing liposuction procedures after informed consent was obtained. The study was approved by the Research Ethics Committee (REC), Faculty of Health Sciences, University of Pretoria (Ethics Reference No.: 237/2019). Adipose-derived stromal/stem cells (ASCs) were isolated using a standardized protocol optimized in our laboratory and cryopreserved in complete growth medium (CGM) supplemented with 10% DMSO [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eCulture maintenance\u003c/h2\u003e\n \u003cp\u003eASCs at passage 2 were used. ASCs were expanded and maintained in complete growth medium (CGM) consisting of Dulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s Medium (DMEM), supplemented with 10% FBS and 2% p/s in a 37\u0026deg;C/5% CO\u003csub\u003e2\u003c/sub\u003e water jacketed incubator (Thermo Forma 311TF, Thermo Fisher Scientific, Waltham, Massachusetts, USA). CGM was replaced with fresh CGM twice a week. At 80\u0026ndash;90% confluency, the cells were trypsinized using 3 mL of 0.25% Trypsin-EDTA solution (Gibco, Life Technologies\u0026trade;, Grand Island, New York, USA) for 7 minutes in a 37\u0026deg;C/5% CO\u003csub\u003e2\u003c/sub\u003e water jacketed incubator. The trypsinized ASCs in suspension were washed twice with PBS and replated at a density of 5 000 cells/cm\u003csup\u003e2\u003c/sup\u003e in T75 culture flasks for further expansion. The cells used for the ASC differentiation studies were phenotyped using flow cytometer and were positive for CD44, CD73, CD90 and negative for CD34, CD45 and CD105.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eAdipogenic differentiation\u003c/h2\u003e\n \u003cp\u003eASCs were seeded in 6-, 12-, 24-, 48- and 96-well plates. Seeding densities were adjusted according to the relative well sizes (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). ASC cultures were maintained and expanded in CGM to approximately 80\u0026ndash;90% confluence and then induced to undergo adipogenic differentiation over a 21-day period, by replacing CGM with adipogenic induction medium [CGM supplemented with 10% FBS, 2% pen/strep, IBMX (0.5 M), insulin (10 \u0026micro;g/mL), indomethacin (200 \u0026micro;M) and dexamethasone (1 \u0026micro;M)]. Non-induced ASC cultures in CGM served as undifferentiated controls. Intracellular lipid accumulation was assessed on days 0, 14 and 21 as a proxy to quantify adipocyte differentiation using fluorescence microscopy, flow cytometry, RT-qPCR and spectrophotometry.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePlating density for each well size\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlating density (number of cells/cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6-well plate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12-well plate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e24-well plate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e48-well plate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e96-well plate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eFluorescence microscopy\u003c/h2\u003e\n \u003cp\u003eCell culture medium was removed from the wells designated for fluorescence microscopy and rinsed with PBS to remove non-adherent cells before adding fresh CGM to the wells. The induced and non-induced cultures were stained with DAPI (2.5 \u0026micro;g/mL) and incubated overnight in a 37\u0026deg;C/5% CO\u003csub\u003e2\u003c/sub\u003e incubator. The cultures were then stained with Nile Red (50 ng/mL) followed by incubation for 20 minutes at room temperature (RT), after which they were rinsed with PBS to remove unbound dye. PBS was added to the wells and fluorescence images were captured using the Zeiss AxioVert A1 inverted fluorescence microscope with the AxioCam Cm1 camera (Carl Zeiss Werke, G\u0026ouml;ttingen, Germany). Single images were captured using two different fluorescence channels to capture DAPI [Filter Set 49 (excitation G 365 nm; emission BP 445/50 nm)] and Nile Red [Filter set 9 (excitation BP 450\u0026ndash;490 nm; emission LP 515 nm)] staining, and then converted into overlay images. The images were enhanced (adjusting brightness and contrast) using ImageJ software (Version 1.8.0).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eQuantification of adipogenic differentiation using flow cytometry\u003c/h2\u003e\n \u003cp\u003eThe proportion of ASCs that underwent adipogenic differentiation was quantified as described by Durandt \u003cem\u003eet al.\u003c/em\u003e (2016). Briefly, the cells were dissociated from the well surfaces (0.25% Trypsin-EDTA) and transferred into flow cytometry tubes (one tube/well) using sterile Pasteur pipettes. Cells in the flow cytometry tubes were stained with Nile Red (50 ng/mL) and DAPI (2.5 \u0026micro;g/mL) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), incubated for 20 minutes at room RT followed by acquisition on the CytoFLEX flow cytometer (Beckman Coulter, Miami, USA). The acquired events were visualized on a forward scatter (FS) Lin vs. side scatter (SS) Log plot to exclude debris. A two-parameter plot, SS Log vs. FL9 Log plot (DAPI PB450) was used to select DAPI-stained nucleated cells. Next, a FL9 Height (H) log plot (DAPI PB450) vs. FL9 Area 9 (A) log plot (DAPI PB450) was generated to exclude aggregates. ASC intracellular lipid content was assessed using a FL2 Log (Nile Red) vs. FL6 Log (CD36) plot. Post-acquisition data was analysed using the Kaluza Flow Cytometry analysis software (Version 2.1.1; Beckman Coulter).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVolume of trypsin, Nile Red and DAPI added to the different well sizes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e24-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e48-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e96-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrypsin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCGM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNile Red (50 ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDAPI (2.5 \u0026micro;g/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eRNA isolation and RT-qPCR\u003c/h2\u003e\n \u003cp\u003eTotal cellular RNA was extracted using either the RNeasy Mini kit (Qiagen, Hilden, Germany) or the RNeasy Micro kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s guidelines. The RNeasy Mini Kit was used when the total number of cells was more than 500 000 while the RNeasy Micro Kit was used when total cell number was less than 500 000 cells. RNA integrity and quality were assessed on an Agilent TapeStation\u003csup\u003e\u0026reg;\u003c/sup\u003e 2200 using RNA ScreenTape\u003csup\u003e\u0026reg;\u003c/sup\u003e and Sample buffer kit (Agilent Technologies, California, USA) after which cDNA synthesis was performed. RNA integrity was compared to 18S and 28S ribosomal RNA and expressed as an RNA integrity number (RIN).\u003c/p\u003e\n \u003cp\u003ecDNA synthesis was performed using the SensiFast\u0026trade; cDNA synthesis kit (Bioline, London, England) according to manufacturer\u0026rsquo;s guidelines. RT-qPCR reactions were performed in final volumes of 10 \u0026micro;L, which included forward and reverse primer concentrations of 400 nM (Integrated DNA Technologies, IDT, Coralville, IA, USA), LightCycler\u003csup\u003e\u0026reg;\u003c/sup\u003e 480 SYBR Green I Master Mix (Roche, Basel, Switzerland) and cDNA template (25 ng/\u0026micro;L; 2 \u0026micro;L per reaction). The following conditions were used on the Roche LightCycler 480 II: denaturation for 5 minutes at 95\u0026deg;C, 45 cycles for amplification for 30 seconds at 95\u0026deg;C, 30 seconds at 62\u0026deg;C and 30 seconds at 72\u0026deg;C. A melt curve was performed for 30 seconds at 95\u0026deg;C, 30 seconds at 40\u0026deg;C, ramped at 0.1\u0026deg;C/s. Genes of interest and reference genes are listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrimers used to detect the genes of interest [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eForward (3\u0026rsquo; to 5\u0026rsquo;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReverse (3\u0026rsquo; to 5\u0026rsquo;)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"Underline\"\u003eGenes of interest\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCEBPA\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePPARG\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFABP4\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCD36\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGTCTCTGCTAAACCACCA\u003c/p\u003e\n \u003cp\u003eCGTGGATCTCTCCGTAAT\u003c/p\u003e\n \u003cp\u003eATCAACCACCATAAAGAGAAA\u003c/p\u003e\n \u003cp\u003eCTTTGCCTCTCCAGTTGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAAAGGAAAGGGAGTCTCAG\u003c/p\u003e\n \u003cp\u003eTGGATCTGTTCTTGTGAATG\u003c/p\u003e\n \u003cp\u003eAACTTCAGTCCAGGTCAA\u003c/p\u003e\n \u003cp\u003eACACAGGTCTCCCTTCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"Underline\"\u003eReference genes\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eGUSB\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePPIA\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTBP\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eYWHAZ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGATCGCTCACACCAAATC\u003c/p\u003e\n \u003cp\u003eGAGTTAAGAGTGTTGATGTAGG\u003c/p\u003e\n \u003cp\u003eCCGAAACGCCGAATATAA\u003c/p\u003e\n \u003cp\u003eTGACATTGGGTAGCATTAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTCGTGATACCAAGAGTAGTAG\u003c/p\u003e\n \u003cp\u003eCCTGGGACTGGAAAGTAA\u003c/p\u003e\n \u003cp\u003eGGACTGTTCTTCACTCTTG\u003c/p\u003e\n \u003cp\u003eGCACCTGACAAATAGAAAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eSpectrophotometry\u003c/h2\u003e\n \u003cp\u003eA 0.5% (w/v) Oil Red O stock solution was prepared (0.2 g of Oil Red O to 40 mL 2-propanol). An Oil Red O working solution was prepared by diluting the stock solution (2:3 ratio; 2-propanol: dH\u003csub\u003e2\u003c/sub\u003eO) to produce a 0.2% Oil Red O solution in 40% 2-propanol. A fresh working solution of 0.2% Oil Red O in 40% 2-propanol was prepared for each experiment and filtered using a 0.22 \u0026micro;M syringe filter before use. Cells were washed with PBS and fixed with 4% paraformaldehyde for 30 minutes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Cells were once again washed and stained with 1 \u0026micro;L DAPI (2.5 \u0026micro;g/mL) followed by incubation for 15 minutes at RT. PBS was removed and the wells rinsed with dH\u003csub\u003e2\u003c/sub\u003eO. Oil Red O (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) was added, followed by incubation for 30 minutes at RT. After incubation, Oil Red O was removed and the wells washed five times with dH\u003csub\u003e2\u003c/sub\u003eO to ensure unbound dye was removed. The plates were air-dried before adding 100% 2-propanol to the wells (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The plate was placed on an orbital shaker for 10 minutes at RT to dissolve the Oil Red O in 100% 2-propanol. An aliquot (200 \u0026micro;L) of the supernatant from each well was transferred to a 96-well plate for spectrophotometric readings and 2-Propanol was used as a blank. Absorbance was measured at 510 nm on the Bio-Tek PowerWave X microplate reader (Bio-Tek, Winooski, Vermont, USA).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReagent volumes added to the different well sizes (spectrophotometry)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e24-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e48-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e96-well plate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4% paraformaldehyde\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOil Red O working solution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100% 2-propanol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 \u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical modelling\u003c/h2\u003e\n \u003cp\u003eBland-Altman multiple comparison plots were used to determine whether there was sufficient discrimination within the assays to detect adipogenic differentiation of ASCs. The data values for each well were compared to the respective well for each method, i.e. well 1 flow cytometry (day 14) was compared to well 1 spectrophotometry (day 14) using the same culture. Bland-Altman plots were generated using the MedCalc\u003csup\u003e\u0026reg;\u003c/sup\u003e statistical software (Version 20.026) (MedCalc Software Ltd, Ostend, Belgium; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medcalc.org\u003c/span\u003e\u003c/span\u003e; 2022). Variability was based on the limits of agreement (the distance between the upper and lower limit).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eCost analysis\u003c/h2\u003e\n \u003cp\u003eThe well size which yielded an acceptable level of agreement (low degree of variability and mean difference close to zero) for each method (from the Bland-Altman multiple method comparison) was determined, and the cost of consumables (plates) and reagents required to screen 100 compounds (in triplicate) was calculated to determine the most efficient well size for flow cytometry, spectrophotometry and RT-qPCR respectively. A positive, negative and non-induced control (all done in triplicate) were included in the cost calculations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll experiments were performed in triplicate. The data is reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM for each time point. Two-way ANOVA and Tukey\u0026rsquo;s multiple comparisons test was used to determine statistical differences in adipogenic differentiation at each time point between the 6-, 12-, 24-, 48- and 96-well plates. A non-parametric Mann-Whitney test was used to compare statistical differences of gene expression between the non-induced and induced samples. Data was considered statistically significant when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. GraphPad Prism 9 (Version 9.3.1) was used for all statistical analyses.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIntracellular accumulation of lipid droplets\u003c/h2\u003e \u003cp\u003eNo intracellular lipid accumulation was observed in non-induced (control) cells on both days 14 and 21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, c, e, g, i and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, c, e, g, i). Adipocytes containing multiple intracellular lipid droplets were observed in the induced samples (on both days 14 and 21), indicating the ASCs successfully underwent adipogenic differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, d, f, h, j and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, d, f, h, j). The majority of the cells displayed multiple intracellular lipid droplets, with larger droplets observed in some cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ej). Progression of adipogenic differentiation is associated with the merging of intracellular lipid droplets, resulting in a single unilocular lipid droplet in fully mature adipocytes \u003cem\u003ein vivo\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDetection of Nile Red-positive cells using flow cytometry\u003c/h2\u003e \u003cp\u003eThe proportion of ASCs that underwent adipogenic differentiation was highest in the 6-well plates (41.85% ± 6.08% and 38.38% ± 7.87% on days 14 and 21 respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The average proportion of ASCs that underwent adipogenic differentiation when seeded in the other well sizes were 32.96% ± 5.61% (day 14) and 29.76% ± 8.26% (day 21) in the 12-well plates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), 24.63% ± 3.95% (day 14) and 30.51% ± 7.56% (day 21) in the 24-well plates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), and 20.92% ± 6.26% (day 14) and 25.07% ± 3.64% (day 21) in the 48-well plates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), and 31.02% ± 7.74% and 30.02% ± 7.52% on days 14 and 21 in the 96-well plates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). The observed differences in adipogenic differentiation between days 14 and 21 were not statistically significant. When comparing the different well sizes, the only significant differences observed amongst the induced samples were between the 6- and 24-well plate and the 6- and 48-well plate on day 14 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of Oil Red O using spectrophotometry\u003c/h2\u003e \u003cp\u003eThe optical density (OD) of Oil Red O extracted from non-induced and induced cells was measured. The OD values were normalized to the average cell count determined for each well size (example in Table S13). The highest optical density (OD) value, i.e. the highest concentration of Oil Red O per well was observed in the induced cultures seeded in the 6-well plates on day 14. The OD values were 23.20 ± 6.85 and 10.47 ± 2.77 on day 14 and 21 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The OD values obtained on day 14 for the induced ASCs were 22.16 ± 6.31 (12-well plate), 11.29 ± 1.48 (24-well plate), 16.87 ± 1.97 (48-well plate) and 22.07 ± 4.94 (96-well plate) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-e) with no statistical difference between well sizes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRelative quantification of adipogenic-associated genes using RT-qPCR\u003c/h2\u003e \u003cp\u003eOn day 14, no statistically significant differences were observed for the relative expression of \u003cem\u003ePPARG, CEBPA\u003c/em\u003e, \u003cem\u003eCD36 and FABP4\u003c/em\u003e between the well sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b, c and d respectively). However, the \u003cem\u003eCEBPA\u003c/em\u003e expression on day 21 differed significantly between the 6- and 24-well plates (p = 0.0005), 12- and 24-well plates (p = 0.0005) as well as the 24-well plates compared to the 48- (p = 0.0010) and 96-well plates (p = 0.0220) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). No statistically significant differences were observed for \u003cem\u003ePPARG\u003c/em\u003e, \u003cem\u003eCD36\u003c/em\u003e and \u003cem\u003eFABP4\u003c/em\u003e on day 21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, c and d respectively). Due to the low RNA yield using 96-well plates, we were only able to obtain results for one culture of the two cultures analysed (day 21 for CD36) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eAlthough 6 biological replicates (independent donors) were plated for the RT-qPCR experiments, acceptable mRNA concentrations were only consistently obtained for the 6-, 12-well plates. Obtaining sufficient mRNA concentrations were inconsistent for the 24-, 48- and 96-well plates, i.e. the RNA yield obtained was insufficient in some of the wells. The low number of ASCs obtained in the smaller well sizes was thus a limitation when using the smaller well sizes for RT-qPCR anaylsis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatistical modelling\u003c/h2\u003e \u003cp\u003eFor flow cytometry, the lowest degree of variability was present in the 24-well plate on days 14 and 21, and the highest degree of variability was present in the 96-well plate on days 14 and 21. The degree of variability is based on the size of the 95% confidence interval (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2, respectively). Negative biases [data values below the assay bias (blue line); smaller well size minus 6-well plate] were observed as the proportion of differentiated ASCs reported in the 6-well plate increased, suggesting that ASC differentiation is favored more in the 6-well plate. The opposite was observed when low levels of adipogenic differentiation were measured in the 6-well plate, where a positive difference suggests that ASC differentiation is less favored in the smaller well sizes (positive bias; data values above the blue line).\u003c/p\u003e \u003cp\u003eOn day 14, the lowest variation (narrow 95% confident interval range) for spectrophotometry was observed in the 48-well plate and the highest (broadest 95% confident interval range) in the 24-well plate (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, low variation was observed in general indicating a tight level of agreement (difference close to zero with low level of variation) between the well sizes (Fig. S3). Large error bars (95% confidence intervals) in the comparison between the 6- and 96-well plate on day 21 (Fig S4d) are due to notable differences in the OD readings obtained from the different cultures during spectrophotometry analysis. On day 21, the lowest variation was seen in the 12-well plate and the highest in the 96-well plate (Fig. S4a and d, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the Bland-Altman plots for the well size which produced the least variability per condition using the 6-well plate as the reference well size\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWell size (least variability)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay 14\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 21\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFlow cytometry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24-well plate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24-well plate\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpectrophotometry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48-well plate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12-well plate\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRT-qPCR\u003c/b\u003e \u003cb\u003ePPARG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24-well plate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24-well plate\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRT-qPCR\u003c/b\u003e \u003cb\u003eFABP4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24-well plate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24-well plate\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eNext, the methods used in this study (flow cytometry, spectrophotometry and RT-qPCR) were compared to determine which method produced results with the least variability when the different well sizes were compared (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Fig. S9-S18). Flow cytometry was used as the reference method as it produced the most reliable and consistent results for all well sizes. Due to insufficient RT-qPCR data being generated for the 48- (day 21) and 96-well plates (days 14 and 21) (Fig. S16-S18 respectively), these plates (48- and 96-well plates) were not considered for analysis. Only flow cytometry and spectrophotometry were compared in the 48- (day 21) and 96-well plates (days 14 and 21) (Fig. S16-S18). For all well sizes, spectrophotometry produced the least amount of variability when compared to flow cytometry on both days 14 and 21 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Fig. S9-S18). The variability in the RT-qPCR data was noticeably higher (size of the green and blue error bars) compared to the variability observed when spectrophotometry was compared to flow cytometry (Fig. S9-S15). It needs to be mentioned that there were less than 3 biological repeats for some analysis, specifically in the 24-well plate days 14 and 21 for RT-qPCR \u003cem\u003ePPARG\u003c/em\u003e and \u003cem\u003eFABP4\u003c/em\u003e (Fig. S13b and c; S14b and c respectively).\u003c/p\u003e \u003cp\u003eFor qPCR analysis, the lowest variability relating to \u003cem\u003ePPARG\u003c/em\u003e gene expression was present in the 24-well plate on days 14 and 21 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), although on day 14 low variability was seen in all well sizes (Fig. S5 and S6). The 96-well plate (days 14) (Fig. S5d) data should be interpreted with caution as only two data values were available for use in this comparison. Due to availability of only one data point for analysis, the 48- and 96-well plate data on day 21 was not considered. Similarly, to \u003cem\u003ePPARG\u003c/em\u003e, the lowest variability for \u003cem\u003eFABP4\u003c/em\u003e gene expression was observed in the 24-well plate on days 14 and 21 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Fig. S7 and S8) and the highest variation was seen in the 12-well plate on days 14 and 21. The 96-well plate (day 21) had one data value and was therefore not considered. Additionally, the 96-well plate (day 14) (Fig. S7d) and 48-well plate (day 21) (Fig. S8c) only had two data values and therefore the interpretation should be considered with caution. Overall, the Bland-Altman plots revealed the 24-well plate produced results with the least amount of variability while the 96-well plate had the highest degree of variability.\u003c/p\u003e \u003cp\u003eIn summary, the flow cytometry data corresponded more closely to the spectrophotometry data as can be seen from the excellent agreement (mean difference close to 0) and low variability (Fig. S9-S18a). Additionally, spectrophotometry and RT-qPCR had tight levels of agreement (small mean difference) indicating the close similarity in the measurements (except day 21 \u003cem\u003ePPARG\u003c/em\u003e). In conclusion, the Bland-Altman statistical analysis showed that all well sizes were comparable and the ultimate decision of the assay of choice will likely depend on other factors such as reliability in obtaining results and the cost effectiveness of the assay.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the Bland-Altman plots for the method which produced the least variability per condition using flow cytometry as the reference methods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMethod (least variability – flow cytometry reference method)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay 14\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDay 21\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6-well plate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectrophotometry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpectrophotometry\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12-well plate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectrophotometry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpectrophotometry\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e24-well plate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectrophotometry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpectrophotometry\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e48-well plate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectrophotometry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e96-well plate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e- Insufficient data. Unable to conclude which method produced the least variability\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCost analysis\u003c/h2\u003e \u003cp\u003eA cost analysis was undertaken according to the well size established to yield an acceptable level of agreement for each method (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). Our data suggests that flow cytometry and spectrophotometry assays can be upscaled with relative confidence to a 48-well plate (all data values fell within the upper and lower limits of agreement (± 1.96SD). The 12-well plate was the smallest well size, which consistently yielded a sufficient amount of mRNA for all primary cultures used in this study.\u003c/p\u003e \u003cp\u003eThe costs associated with hypothetically screening 100 compounds in triplicate was determined. Inclusion of a positive, negative and non-induced controls were taken into account when performing the calculations (all calculations can be found in supplementary data; Table S3-S12). The positive control would include a drug/compound that is known to inhibit adipogenesis, the negative control would include induced ASCs that receive no drug treatment and non-induced (undifferentiated) cells will be used as a baseline control. The ASCs would first need to be expanded in T75 flasks to obtain the required number of cells needed for seeding (supplementary data), followed by the expansion in the designated well sizes until adipogenic differentiation is induced. The assumption (based on data generated in our laboratory) was made that one T75 culture vessel will yield approximately one million cells (at 80–90% confluency; time period 2 weeks). The 12-well plate (used for RT-qPCR) requires the largest volume of reagents [3 bottles (500 mL) of DMEM to prepare CGM and 4 bottles (500 mL) of DMEM to prepare the adipogenic induction medium]. Next, the total volume for each reagent required to screen 100 compounds in triplicate using flow cytometry, spectrophotometry and RT-qPCR was calculated (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). Calculations were based on the standard approach of inducing ASCs for 21 days before assessing adipogenic differentiation and these calculations can be found in supplementary data (Table S3 and S6) (10,11). The following reagents would be sufficient to screen 100 compounds (in triplicate) in the 48-well plate for flow cytometry and spectrophotometry: 2 vials of CD36-APC monoclonal antibody, 1 vial/bottle of DAPI (powder), Nile Red (powder), Oil Red O (powder), 4% formaldehyde (solution) and Trypsin-EDTA (solution) and PBS (solution) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The screening of 100 compounds in triplicate in the 12-well plate for RT-qPCR requires 3 RNeasy Plus Micro Kits and one of each of the following: SensiFast cDNA synthesis kit, Light Cycler 96 well plate, RNA ScreenTape Ladder, RNA ScreenTape Sample Buffer, Light Cycler 480 SYBR Green I Master Mix and primers (Table S2).\u003c/p\u003e \u003cp\u003eTherefore, when comparing the costs associated with the most effective well size for each method, spectrophotometry (48-well plate) (R30 998; \u003cspan\u003e$\u003c/span\u003e1 658.53, dollar rate taken on 05/04/2024 at \u003cspan\u003e$\u003c/span\u003e1 to R18.69) was the most cost-effective method to screen 100 compounds (in triplicate), followed by flow cytometry (48-well plate) (R39 234.88; \u003cspan\u003e$\u003c/span\u003e2 099.24) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and RT-qPCR (12-well plate) (R97 612.73; \u003cspan\u003e$\u003c/span\u003e5 222.72) (Table S2). RT-qPCR cost analysis was based on one primer pair (forward and reverse) for a gene of interest and 3 primer sets (forward and reverse) for the reference genes. Additionally, a standard, no template controls (primer pair of gene of interest and 3 reference gene primers) as well as the controls (positive, negative and non-induced) in triplicate were considered.\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion and conclusion","content":"\u003cp\u003eThe rise in obesity has become a serious concern resulting in the WHO classifying it as a world-wide pandemic, where more than 30% of the world’s population are obese [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The process of adipogenesis has been extensively studied to determine the molecular players which could serve as potential targets to control fat accumulation. The effective screening of large libraries of potential anti-obesity drugs can be costly. Therefore, the creation of a high-throughput screening adipogenesis assay will provide a cost effective and rapid approach for identifying anti-obesity agents.\u003c/p\u003e\u003cp\u003eIn this study, we set out to investigate the feasibility of upscaling the differentiation of primary ASCs into adipocytes (process known as adipogenesis) from the commonly used 6- and 12-well plates to 24-, 48- and 96-well plates. The enhanced ability of ASCs to differentiate into adipocytes serves as an excellent \u003cem\u003ein vitro\u003c/em\u003e model to study human adipogenesis using cells of human origin. Using cells of human origin is likely to result in more reliable translation of findings to the clinical setting. The primary ASC cultures used in this study adhered to the MSC characterization criteria, except for CD105 expression, recommended by the International Society for Cell and Gene Therapy (ISCT) and International Federation for Adipose Therapeutics and Science (IFATS) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Several studies have suggested that the expression of CD105 may vary between different culture conditions [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and that ASCs contain a sub-population of CD105 negative cells. Mark \u003cem\u003eet al.\u003c/em\u003e (2013) reported that the CD105 expression by bone marrow (BM)-derived MSCs (obtained from sternal bone marrow aspirates) cultured with serum-free growth media (MSCGM-CD™) was significantly lower than BM-derived MSCs cultured with serum-containing media (MSCGM™) (51.70% ± 14.53 and 95.83% ± 2.35 respectively). Despite the difference in CD105 expression, the BM-derived MSCs in both culture conditions showed differentiation potential in all three mesodermal lineages. The findings by Mark \u003cem\u003eet al.\u003c/em\u003e (2013) are consistent with findings from our laboratory, in which we observed that ASCs cultured in pooled human platelet lysate (pHPL) express CD105, in contrast to the lack of CD105 expression in ASCs cultured in medium supplemented with FBS (unpublished data). Similar to the findings by Mark \u003cem\u003eet al\u003c/em\u003e. (2013), the primary ASC cultures used in this study, despite not expressing CD105, still successfully underwent adipogenic differentiation when induced (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe most common techniques used to monitor adipogenic differentiation include RT-qPCR (gene expression), microscopy, spectrophotometry, and flow cytometry. As mentioned earlier, adipogenic differentiation is commonly assessed using either 6- or 12-well plates. It is well accepted that higher-throughput screening assays are needed to select for potential anti-obesity pharmaceutical agents. However, a very limited number of studies have investigated the feasibility of monitoring adipogenic differentiation in smaller well sizes. Sottile and Seuwen (2001) used a 96-well plate to measure, using spectrophotometry, glycerol-3-phosphate dehydrogenase (GPDH) activity during adipogenic differentiation of murine 3T3-L1 preadipocytes. Glycerol-3-phosphate dehydrogenase (GPDH) is highly expressed in terminally differentiated (mature) adipocytes. Yuan \u003cem\u003eet al.\u003c/em\u003e (2019) developed an algorithm, referred to as the Fast Adipogenesis Tracking System (FATS), to quantify adipogenic differentiation observed in microscopy images (after staining with Nile Red or Oil Red O) of cells cultured (differentiated) in 96-well plate wells. To the best of our knowledge, this is the first study that compared data generated using different well sizes to assess if data generated using smaller well sizes are comparable with data obtained using the standard 6-well plates. This is also the first study, to our knowledge, that has compared the different techniques (RT-qPCR, spectrophotometry, flow cytometry and microscopy) using all the different plate sizes.\u003c/p\u003e\u003cp\u003eIn both fluorescence microscopy and flow cytometry, Nile Red staining was used to determine the proportion of cells that underwent adipogenesis. Nile Red stains neutral lipids, the predominant components in the lipid droplet cores [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, the proportion of Nile Red-positive ASCs is proportionate to the number of ASCs that underwent adipogenesis. Intracellular lipid droplets have been visually observed (fluorescence microscopy) in induced cultures seeded in all well sizes (6-, 12-, 24-, 48- and 96-well plates), suggesting that the detection of adipogenesis can successfully be upscaled to smaller well sizes. The fluorescence microscopy observations were confirmed using flow cytometry. Significant variation in the proportion of ASCs that underwent adipogenic differentiation (flow cytometric analysis) was observed between the 24- (p = 0.0386) and 48-well (p = 0.0071) plates compared to the 6-well plates. The variation observed is likely due to biological variation (different donors) and technical variation (experimental variation).\u003c/p\u003e\u003cp\u003eSpectrophotometry also clearly showed an increase in lipid content (based on an increased OD value due to increased Oil Red O staining) in the induced samples compared to the non-induced samples in all well sizes. No significant differences were observed between the well sizes on days 14 and 21 for the induced samples. The main challenge we encountered using spectrophotometry was the tendency of unbound Oil Red O to stick to the plastic, which resulted in increased OD readings in the smaller well sizes (clearly seen with the increase in the OD readings of the non-induced samples as the well sizes decreased; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003eAdipogenesis is regulated by a cascade of transcription factors of which peroxisome proliferator-activated receptor gamma (PPARγ) is the master regulator [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. CD36 and FABP4 are proteins associated with terminally differentiated adipocytes and are known to be PPARγ responsive genes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Upregulation of the adipogenic related genes of interest (\u003cem\u003ePPARG, CEBPA, CD36\u003c/em\u003e and \u003cem\u003eFABP4\u003c/em\u003e) was observed in the induced cultures for all well sizes with no statistically significant difference between plate sizes (6-, 12-, 24-well plates), and the expression levels were consistent with findings published by other studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The significant differences observed on day 21 (6- vs. 24-well plate and 12- vs. 24-well plate) for \u003cem\u003eCEBPA\u003c/em\u003e expression are likely due to the variability between biological replicates. The major limitation encountered using smaller well sizes for RT-qPCR experiments was the low number of cells present, resulting in the extraction of insufficient mRNA for the RT-qPCR protocol used in our laboratory. It is important to note that fluorescence microscopy, flow cytometry and spectrophotometry made use of individual wells (in triplicate), while in this this study wells were pooled for RT-qPCR (to obtain sufficient RNA). In order to overcome this limitation of small cell numbers in the smaller wells, the RT-qPCR method should be optimized in future to allow for accurate detection using low cell concentrations in individual wells.\u003c/p\u003e\u003cp\u003eIn summary, the least amount of variation (6 independent donors done in triplicate; from the 6-well plate used as the reference standard) was observed in 24-well plates; however, adipogenic differentiation can also be quantified with confidence in 48-well plates (especially for flow cytometry and spectrophotometry). Increased variability was observed in 96-well plates. Our data suggest that data generated using 24- and 48-well plates is likely to be more reproducible than data obtained using 96-well plates. Intra-assay variation within the assays showed that spectrophotometry performed the best amongst all the methods compared (tightest level of agreement).\u003c/p\u003e\u003cp\u003eThe cost analysis showed that screening a large library of compounds will be more cost-effective in the smaller wells compared to the more standard 6- or 12-well plates. Spectrophotometry (48-well plate) was the most cost-effective method (R30 998; \u003cspan\u003e$\u003c/span\u003e1 658.53), followed by flow cytometry (48-well plate) (R39 234.88; \u003cspan\u003e$\u003c/span\u003e2 099.24), while the cost of RT-qPCR (12-well plate) was high (R97 612.73; \u003cspan\u003e$\u003c/span\u003e5 222.72).\u003c/p\u003e\u003cp\u003eIn conclusion, adipogenic differentiation of ASCs was achieved in all well sizes. Spectrophotometry was the most cost-effective and may serve as a valuable initial screening tool to select for agents with enhanced anti-adipogenic activity. Each of the assays have strengths and weaknesses and may be used at specific stages of the screening process. Assays like flow cytometry are highly quantitative and provide more information regarding the impact of the agents at a single-cell level, while RT-qPCR will play an important role in assessing the impact of potential agents on gene expression. This study has demonstrated that flow cytometry, spectrophotometry and RT-qPCR can successfully be used to assess adipogenic differentiation in smaller well sizes, resulting in a higher-throughput approach. The use of smaller well sizes is also likely be more time-efficient.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003eApproval was obtained from the Research Ethics Committee (REC), Faculty of Health Sciences, University of Pretoria (Ethics Reference No.: 237/2019).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eConsent for publication was taken from all individuals.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe research leading to these results received funding from the South African Medical Research Council Extramural Unit for Stem Cell Research and Therapy under Grant Agreement No SAMRC-RFA-UFSP-01-2013/STEM CELLS, and with the support of a National Research Foundation Freestanding Innovation and Scarce Skills Bursary (PR_SFH201124576219).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis was performed by Rachel Giles. The first draft of the manuscript was written by Rachel Giles and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization Obesity and overweight [Internet]. 2019 [cited 2019 Feb 5]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight\u003c/span\u003e\u003cspan address=\"https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrison, S., \u0026amp; McGee, S. L. 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Increase in Leptin and PPAR-γ Gene Expression in Lipedema Adipocytes Differentiated in vitro from Adipose-Derived Stem Cells. \u003cem\u003eCells\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"adipogenesis, high-throughput screening, flow cytometry, RT-qPCR, spectrophotometry","lastPublishedDoi":"10.21203/rs.3.rs-4349556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4349556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe obesity pandemic is listed as a chronic disease by the World Health Organization, and is underpinned by an increase in fat cell formation (adipogenesis). This study aims to develop high-throughput screening assays (flow cytometry, spectrophotometry and RT-qPCR) for cost-effective and rapid monitoring of adipogenesis using a human adipose-derived stem cell differentiation model. Adipogenesis was successfully upscaled from 6- to 96-well plates. We found that the most efficient well size differs between the assays investigated. Adipogenic differentiation could best be assessed in a 48-well plate (flow cytometry and spectrophotometry), while a 12-well plate was more efficient for obtaining reliable RT-qPCR results. The cost associated with hypothetically screening 100 compounds (in a high-throughput setting) using the method with the lowest degree of variability and the highest degree of cost-effectiveness was spectrophotometry (48-well plate), followed by flow cytometry (48-well plate) and RT-qPCR (12-well plate). This study provides valuable information for designing and selecting the most efficient high-throughput adipogenesis assays to screen for potential drug candidates in the continuous search of novel and more effective pharmacological agents to combatting obesity and its related comorbidities.\u003c/p\u003e","manuscriptTitle":"Development of a high-throughput assay for monitoring adipogenesis in vitro","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 16:24:10","doi":"10.21203/rs.3.rs-4349556/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80869989-777b-46f2-91e5-030f3d07d578","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-30T10:52:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 16:24:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4349556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4349556","identity":"rs-4349556","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}