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Dynamic Protein Pathway Activation Mapping of Adipose-Derived Stem Cell Differentiation Implicates Novel Regulators of Adipocyte Differentiation

2013; Elsevier BV; Volume: 12; Issue: 9 Linguagem: Inglês

10.1074/mcp.m112.025346

1535-9484

Bridget S. Wilson, Lance A. Liotta, Emanuel PetricoinIII,

Advanced Biosensing Techniques and Applications

Next to embryonic stem cell research, adult stem cell research is providing a promising alternative for enhanced tissue regeneration and transplantation. The key biochemical networks controlling the differentiation processes regulating stem cell biology remain largely disputed and or undefined, contributing to a lack of knowledge of the principle phosphoregulatory events propagating signal transduction. To effectively monitor these events relative to adipocyte differentiation, this study utilized a high throughput reverse phase protein microarray platform and characterized adult adipose-derived stem cell (ASC) differentiation through the monitoring of ∼100 phosphospecific endpoints with 33 distinct time points examined across 14 days. This kinetic-based analysis showed time ordered signal transduction ultimately implicating pathways correlated with adipogenic differentiation. To further validate the causal significance of these network activations, pharmacological targeting was implemented to include the chemical inhibitors MAPK inhibitor PD169316, rapamycin, and HNMPA-(AM)3 yielding partial or complete disruption of adipocytic differentiation, as noted by a decrease or lack of lipid formation within the mature adipocytes. Based on this analysis, v-crk sarcoma virus CT10 oncogene homolog (CRKII) and c-abl oncogene 1, non-receptor tyrosine kinase (c-ABL) were implicated as novel key regulators of adipocyte differentiation, with v-akt murine thymoma viral oncogene (AKT), mammalian target of rapamycin (mTOR), and SMAD family member (SMAD) pathways being implicated as secondary regulators. This dynamic molecular profiling provides a novel insight into the signaling architecture of mesenchymal stem cell differentiation and may be useful in the development of therapeutic modulators for clinical applications; in addition to advancing the collective understanding of key cellular processes, ultimately contributing to more confident stem cell manipulation. Next to embryonic stem cell research, adult stem cell research is providing a promising alternative for enhanced tissue regeneration and transplantation. The key biochemical networks controlling the differentiation processes regulating stem cell biology remain largely disputed and or undefined, contributing to a lack of knowledge of the principle phosphoregulatory events propagating signal transduction. To effectively monitor these events relative to adipocyte differentiation, this study utilized a high throughput reverse phase protein microarray platform and characterized adult adipose-derived stem cell (ASC) differentiation through the monitoring of ∼100 phosphospecific endpoints with 33 distinct time points examined across 14 days. This kinetic-based analysis showed time ordered signal transduction ultimately implicating pathways correlated with adipogenic differentiation. To further validate the causal significance of these network activations, pharmacological targeting was implemented to include the chemical inhibitors MAPK inhibitor PD169316, rapamycin, and HNMPA-(AM)3 yielding partial or complete disruption of adipocytic differentiation, as noted by a decrease or lack of lipid formation within the mature adipocytes. Based on this analysis, v-crk sarcoma virus CT10 oncogene homolog (CRKII) and c-abl oncogene 1, non-receptor tyrosine kinase (c-ABL) were implicated as novel key regulators of adipocyte differentiation, with v-akt murine thymoma viral oncogene (AKT), mammalian target of rapamycin (mTOR), and SMAD family member (SMAD) pathways being implicated as secondary regulators. This dynamic molecular profiling provides a novel insight into the signaling architecture of mesenchymal stem cell differentiation and may be useful in the development of therapeutic modulators for clinical applications; in addition to advancing the collective understanding of key cellular processes, ultimately contributing to more confident stem cell manipulation. Recent breakthroughs in adult stem cell research have increased industry focus on the potency and availability of these cells. Adult stem cells exist in many tissue types, but adult adipose-derived stem cells (ASCs) 1The abbreviations used are:ASCAdipose-derived stromal/stem cellAKTv-akt murine thymoma viral oncogenec-ABLand c-abl oncogene 1 non-receptor tyrosine kinaseCRKIIv-crk sarcoma virus CT10 oncogene homologERKextracellular-signal-regulated kinasesIGFRinsulin-like growth factor receptorIRinsulin receptormTORmammalian target of rapamycinOROOil red OPTENphosphatase and tensin homologRPMAReverse phase protein microarraySMADSMAD family member. 1The abbreviations used are:ASCAdipose-derived stromal/stem cellAKTv-akt murine thymoma viral oncogenec-ABLand c-abl oncogene 1 non-receptor tyrosine kinaseCRKIIv-crk sarcoma virus CT10 oncogene homologERKextracellular-signal-regulated kinasesIGFRinsulin-like growth factor receptorIRinsulin receptormTORmammalian target of rapamycinOROOil red OPTENphosphatase and tensin homologRPMAReverse phase protein microarraySMADSMAD family member. provide a particularly abundant source obtained via through minimally invasive techniques (liposuction) (1Colicelli J. Abl tyrosine kinases: evolution of function, regulation, and specificity.Sci. Signal. 2010; 3: 1-26Crossref Scopus (234) Google Scholar, 2Katz A. Tholpady A. Tholpady S.S. Shang H. Ogle R. Cell surface and transcriptional characterization of human adipose-derived adherent stromal (hADAS) Cells.Stem Cells. 2005; 23: 412-423Crossref PubMed Scopus (432) Google Scholar, 3Wu L. Wang T. Ge Y. Cai X. Wang J. Lin Y. Secreted factors from adipose tissue increase adipogenic differentiation of mesenchymal stem cells.Cell Prolif. 2012; 45: 311-319Crossref PubMed Scopus (17) Google Scholar). This plastic-adherent, multipotent cell population is also known as adipose-derived stromal cells among other names, with the International Fat Applied Technology Society reaching a consensus to refer to them as ASCs (4Gimble J. Katz A. Bunnell B. Adipose-derived stem cells for regenerative medicine.Circ. Res. 2007; 100: 1249-1260Crossref PubMed Scopus (1847) Google Scholar). Despite extensive research on the differentiation of stem cells, the underpinning molecular mechanisms remain an enigma. During adipogenesis, terminal differentiation from a mesenchymal stem cell to a mature adipocyte is characterized by the ability to store triglycerides that can be mobilized as fuel for other organs, but the cellular signaling actuating this transformation remains predominately undefined (5Bost F. Aouadi M. Caron L. Binétruy B. The role of MAPKs in adipocyte differentiation and obesity.Biochimie. 2004; 87: 51-56Crossref Scopus (424) Google Scholar, 6Gonzalez F. Getting fat: Two new players in molecular adipogenesis.Cell Metab. 2005; 1: 85-86Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar). A few well-established factors inducing differentiation are: high concentrations of insulin (resulting in stimulation of the insulin-like growth factor 1 receptor), glucocorticoid agonists, peroxisome proliferator-activated receptor γ (PPARγ) agonist, and agents that elevate cAMP (7Liu J. DeYoung S.M. Zhang M. Zhang M. Cheng A. Saltiel A.R. Changes in integrin expression during adipocyte differentiation.Cell. Metab. 2005; 2: 165-176Abstract Full Text Full Text PDF PubMed Scopus (139) Google Scholar, 8Rodriguez A. Elabd C. Delteil F. Astier J. Vernochet C. Saint-Marc P. Guesnet J. Guezennec A. Amri E.Z. Dani C. Ailhaud G. Adipocyte differentiation of multipotent cells established from human adipose tissue.Bio. Biophys. Res. Commun. 2004; 315: 255-263Crossref PubMed Scopus (254) Google Scholar). Adipose-derived stromal/stem cell v-akt murine thymoma viral oncogene and c-abl oncogene 1 non-receptor tyrosine kinase v-crk sarcoma virus CT10 oncogene homolog extracellular-signal-regulated kinases insulin-like growth factor receptor insulin receptor mammalian target of rapamycin Oil red O phosphatase and tensin homolog Reverse phase protein microarray SMAD family member. Adipose-derived stromal/stem cell v-akt murine thymoma viral oncogene and c-abl oncogene 1 non-receptor tyrosine kinase v-crk sarcoma virus CT10 oncogene homolog extracellular-signal-regulated kinases insulin-like growth factor receptor insulin receptor mammalian target of rapamycin Oil red O phosphatase and tensin homolog Reverse phase protein microarray SMAD family member. Primarily research into the process of adipogenesis has focused on gene expression profiling with proteomic technologies being used only more recently. Despite the benefits of gene expression profiling (9Menssen A. Häup T. Sittinger M. Delorme B. Charbord P. Ringe J. Differential gene expression profiling of human bone marrow-derived mesenchymal stem cells during adipogenic development.BMC Genomics. 2011; 12: 461Crossref PubMed Scopus (83) Google Scholar, 10Sibov T. Severino P. Marti L. Pavon L.F. Oliveira D.M. Tobo P.R. Campos A.H. Paes A.T. Amaro Jr, E. Gamarra L. Moreira-Filho C.A. Mesenchymal stem cells from umbilical cord blood: parameters for isolation, characterization and adipogenic differentiation.Cytotechnology. 2012; 64: 511-521Crossref PubMed Scopus (67) Google Scholar), it shows little correlation to protein levels and provides no insight into the timing of protein activation. This simple fact highlights the need for a systems biology approach. More specifically, the monitoring of key post-translational modification events such as phosphorylation is necessary to characterize the signaling architecture regulating differentiation (11Waters K.M. Pounds J.G. Thrall B.D. Data merging for integrated microarray and proteomic analysis.Brief. Funct. Genomic. Proteomic. 2006; 5: 261-272Crossref PubMed Scopus (88) Google Scholar, 12Williamson A.J. Whetton A.D. The requirement for proteomics to unravel stem cell regulatory mechanisms.J. Cell. Physiol. 2011; 226: 2478-2483Crossref PubMed Scopus (13) Google Scholar). These reversible kinase-driven phosphorylation events alter protein confirmation, ultimately affecting enzymatic activity and protein–protein interactions leading to an array of cellular events from differentiation to gene expression, thus encompassing signal transduction. Characterization of this broad-scale signaling architecture is necessary to provide a more finite depiction of the complex signaling events directing a given cellular phenotype and aid in the understanding of the repercussions of alterations in regulation (13Arsenault R. Griebel P. Napper S. 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Phosphoproteomic analysis of human embryonic stem cells.Cell Stem Cell. 2009; 5: 204-213Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar, 16Van Hoof D. Munoz J. Braam S.R. Pinkse M.W. Linding R. Heck A.J. Mummery C.L. Krijgsveld J. Phosphorylation dynamics during early differentiation of human embryonic stem cells.Cell Stem Cell. 2009; 5: 214-226Abstract Full Text Full Text PDF PubMed Scopus (271) Google Scholar). This study however, utilized the reverse phase protein microarray (RPMA) to maximize both the number of time points and phosphoproteins able to be examined simultaneously. RPMAs enable the quantitative interrogation of the phosphorylation state of hundreds of signaling proteins simultaneously for hundreds of cell lysates allowing for broad-scale pathway activation mapping analysis (17Gallager R.I. Silvestri A. Petricoin 3rd, E.F. Liotta L.A. Espina V. Reverse phase protein microarrays: fluorometric and colorimetric detection.Methods Mol. Biol. 2011; 723: 275-301Crossref PubMed Scopus (15) Google Scholar). This ultrasensitive platform has demonstrated sensitivity of as little as 1000 molecules per spot with less than 1/10th of a cell equivalent volume analyzed per spot and intraslide and interslide CV between 3–10% (18Liotta L.A. Espina V. Mehta A.I. Calvert V. Rosenblatt K. Geho D. Munson P.J. Young L. Wulfkuhle J. Petricoin 3rd., E.F. Protein microarrays: meeting analytical challenges for clinical applications.Cancer Cell. 2003; 3: 317-325Abstract Full Text Full Text PDF PubMed Scopus (391) Google Scholar, 19Rapkiewicz A. Espina V. Zujewski J.A. Lebowitz P.F. Filie A. Wulfkuhle J. Camphausen K. Petricoin 3rd, E.F. Liotta L.A. Abati A. The needle in the haystack: application of breast fine-needle aspirate samples to quantitative protein microarray technology.Cancer. 2007; 111: 173-184Crossref PubMed Scopus (74) Google Scholar). RPMAs greatly reduce the required sample size with spot deposits averaging between 0.3–2 nL, and yet show concurrent findings when parallel processing of samples via Western blot is performed (20Espina V. Mehta A.I. Winters M.E. Calvert V. Wulfkuhle J. Petricoin 3rd, E.F. Liotta L.A. Protein microarrays: molecular profiling technologies for clinical specimens.Proteomics. 2003; 3: 2091-2100Crossref PubMed Scopus (212) Google Scholar, 21Gulmann C. Sheehan K.M. Conroy R.M. Wulfkuhle J.D. Espina V. Mullarkey M.J. Kay E.W. Liotta L.A. Petricoin 3rd., E.F. Quantitative cell signalling analysis reveals down-regulation of MAPK pathway activation in colorectal cancer.J. Pathol. 2009; 218: 514-519Crossref PubMed Scopus (50) Google Scholar, 22Herrmann P.C. Gillespie J.W. Charboneau L. Bichsel V.E. Paweletz C.P. Calvert V.S. Kohn E.C. Emmert-Buck M.R. Liotta L.A. Petricoin 3rd., E.F. Mitochondrial proteome: altered cytochrome c oxidase subunit levels in prostate cancer.Proteomics. 2003; 3: 1801-1810Crossref PubMed Scopus (130) Google Scholar, 23Frederick M.J. VanMeter A.J. Gadhikar M.A. Henderson Y.C. Yao H. Pickering C.C. Williams M.D. El-Naggar A.K. Sandulache V. Tarco E. Myers J.N. Clayman G.L. Liotta L.A. Petricoin 3rd, E.F. Calvert V.S. Fodale V. Wang J. Weber R.S. Phosphoproteomic analysis of signaling pathways in head and neck squamous cell carcinoma patient samples.Am. J. Pathol. 2011; 178: 548-571Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar, 24Silvestri A. Calvert V. Belluco C. Lipsky M. De Maria R. Deng J. Colombatti A. De Marchi F. Nitti D. Mammano E. Liotta L. Petricoin E. Pierobon M. Protein pathway activation mapping of colorectal metastatic progression reveals metastasis-specific network alterations.Clin, Exp, Metastasis. 2013; 30: 309-316Crossref PubMed Scopus (16) Google Scholar). Despite the implication of various markers of adipogenesis, there remains little consensus on the overarching signal transduction governing the differentiation process. The objective of this research was to better characterize this process through utilization of ASCs combined with broad-scale protein pathway activation mapping, using a dynamic experimental design. Lineage signal transduction profiles were established through the monitoring of protein network activation during the course of differentiation into adipocyte, osteoblast, and chondrocyte lineages. This multilineage kinetic experimentation allowed for global examination of signal transduction through the monitoring of ∼100 phosphospecific endpoints, across 33 consecutive time points that spanned a 14 day period to reach terminal differentiation demonstrating time-specific and lineage-specific signaling. This experimental design allowed for isolation of a subset of time-specific endpoints unique to adipogenesis relative to the other lineages that could then be further tested for causal significance using pharmacologic knock-out analysis. ASCs were purchased from Zen-Bio and then cultured according to the manufacture's protocols. Cells were comprised of a mixed lot of five donors and were shown to be positive for CD29, CD44, and CD 105, while negative for CD14, CD31, CD34, CD45, and CD133 (performed by Zen-bio). Cells were passaged twice in a 75 cm2 flask, then transferred to a 24-well plate, and seeded at a concentration of 39,140 cells/well. 24 h post-transfer, cells had media changed to lineage-specific media per the manufacture specifications, with media being replaced every other day. Samples for the multilineage differentiation time-course (Study Set #1) were performed in triplicate and began with time zero (no differentiation media added), followed by 33 time points for adipocytic and osteoblastic lineages (terminal differentiation reached at day 14), and 39 for the chondrocytic lineage (terminal differentiation reached at 20 days) (Fig. 1). To lyse samples, the media was removed, cells were rinsed three times with 1 × phosphate-buffered saline (PBS), lysed in 70 μl lysis buffer, and stored at −80 °C. The lysis buffer was comprised of 50% T-PER® tissue protein extraction reagent (Thermo Scientific), 47.5% Novex® Tris-glycine SDS sample buffer (Invitrogen, Carlsbad, CA), and 2.5% 2-mercaptoethanol. Based on results from inhibitor pilot experiments, three inhibitors were selected that completely or partially disrupted adipocyte differentiation. A time-course using 15 μm of HNMPA-(AM)3 (Calbiochem), 500 nm of mitogen-activated protein kinase (MAPK) inhibitor PD169316 (Calbiochem), and 250 nm of rapamycin (BIOSOURCE) was performed with inhibitors having a final concentration of 0.1% dimethyl sulfoxide (DMSO). Study Set #3 samples were cultured in triplicate, as previously describe, with treatments beginning at the start of differentiation and continuing through day 14 with associated untreated and DMSO controls. Fresh inhibitor was added with each media change and sample lysates were taken every other day through day 14. To further characterize the role of the timing of signaling events, additional inhibitor experiments were performed with inhibitor administered at delayed time-points. Three delayed treatment sets were performed in triplicate; Study Set #4: inhibitor/DMSO treatment was started on day 2 and continued until day 14, Study Set #5: inhibitor/DMSO treatment was started on day 4 and continued until day 14, and Study Set #6: inhibitor/DMSO treatment was started on day 8 and continued until day 14 (Fig. 1A). In all cases, differentiation was monitored through the quantification of Oil Red O (ORO) staining to determine the impact on lipid formation. At various points in the differentiation process, lipid formation was visualized and quantified utilizing ORO. Briefly, a stock solution of ORO (0.35g ORO and 100 mls isopropanol) was stirred overnight and filtered through a 0.2 μ filter. Before use, a working stock comprising 6 mls of the ORO stock and 4 mls of ddH2O was prepared and filtered (0.2 μ). Cells were rinsed two times with PBS prior to a 10 min rinse with 10% phosphate buffered formalin, followed by a 1 hour incubation period. Following fixation, cells were washed with ddH2O, incubated with 60% isopropanol for 5 min, and dried in an oven at 37 °C. This step was followed by incubation with 500 μl of the working ORO working stock for 10 min, rinsed four times with 2 mls of ddH2O, images taken, and allowed to air dry. The ORO was then eluted using 1 ml of 100% isopropanol and incubated on an orbital shaker for 10 min. An optical density (OD) was then obtained using a spectrophotometer at 500 nm and values normalized to a negative control sample. Lipid based differentiation was established using a time course with lipid quantification performed every other day until day 14 (Study Set #2, Fig. 1A). Samples were heated at 100 °C for 7 min to melt the protein into its primary structure. Sample lysates were loaded into 384-well plates and a two-point dilution curve (neat and 1:4) was generated for each sample. Lysates were printed in duplicate on a customized FAST® nitrocellulose coated slide (Whatman®) using the 2470 Arrayer (Aushon Biosystems) with controls lysates to include jurkat treated with etoposide, jurkat treated with calyculin, HeLa, and HeLa treated with pervanadate printed in a ten point dilution curve (neat, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64, etc.). Slides were stored at −20 °C until further use. Prior to staining, slides were incubated in 1 × Re-Blot (Chemicon) for 14 min on an agitator to relax protein structure. Re-Blot was removed, and slides were washed two times for 5 min in PBS. Slides were then placed into a blocking solution [1g I-block (Tropix, Applied Biosystems), 0.5% Tween-20 in 500 ml PBS] for at least 2 h with agitation before staining. To ensure printing quality and to provide protein quantification, a total protein assay was performed with selected slides using SYPRO® Ruby as previously described and visualized with a NovaRay® (Alpha Innotech) imager (17Gallager R.I. Silvestri A. Petricoin 3rd, E.F. Liotta L.A. Espina V. Reverse phase protein microarrays: fluorometric and colorimetric detection.Methods Mol. Biol. 2011; 723: 275-301Crossref PubMed Scopus (15) Google Scholar). Each antibody staining procedure was completed using the Autostainer DAKO Cytomation, which uses a catalyzed signal amplification (CSA) system, according to the manufacturer's protocol. Every antibody used was commercially purchased and specificity was confirmed via Western blot. Antibody staining employed the use of multiple phosphospecific primary antibodys, as well an appropriate secondary antibody (mouse or rabbit), followed by visualization with the fluorophore IR-Dye® 680 streptavidin (Li-Cor® Biosystems). Additionally, every staining run had an associated negative control slide treated with antibody diluent in the place of the primary antibody. Phospho-specific antibodies selected pertained to various areas of cellular functioning, with some being implicated in differentiation and others having no known affiliations. Incubation sequences, reagent dilutions, and incubation times were performed as previously described (17Gallager R.I. Silvestri A. Petricoin 3rd, E.F. Liotta L.A. Espina V. Reverse phase protein microarrays: fluorometric and colorimetric detection.Methods Mol. Biol. 2011; 723: 275-301Crossref PubMed Scopus (15) Google Scholar). Slides were scanned with the NovaRay for a maximum of 500 milliseconds or until no sample or control saturation was noted. Both antibody stained and SYPRO Ruby stained slides were analyzed using the Microvigene® software package, with local background subtraction and the averaging of duplicated spots resulting in a single data point per sample. Each spot was then normalized to the total protein slides as well as the negative control slide such that the values obtained for any phosphoprotein are independent of the total amount of protein on a per/cell basis. This data was then subjected to unsupervised hierarchical clustering analysis and Spearman's Rho correlation analysis using JMP 5.0 (SAS). To establish statistical significance, the math library (Apache Foundation Software) was utilized to perform Mann-Whitney U tests. These tests consisted of experiment specific time point and end point ("time point-end point") comparisons. Statistically significant time point-endpoints were further correlated between endpoints during specific time windows ("time-window-end point") using a Spearman's Rho analysis, and further paired down to significant at the same time point. During analysis of Study Set #1, two significance-filtering-methods were used. Filter 1) all time point–end point samples for a given lineage were compared with the corresponding untreated time zero end point sample and were required to have a Mann-Whitney p value of ≤ 0.05 and 2) Spearman's Rho time-window-end point correlation samples were required to have a p value of ≤ 0.05 (Fig. 1B). Similarly three significance-filtering-methods were used for Study Sets #3 - 6 with Filter 1) being the same as above. Filter 2) each time point–end point untreated sample was compared with its corresponding time point-end point inhibitor sample and required to have a Mann-Whitney p value of ≤ 0.05, 3) Spearman's Rho time-window-end point correlations of inhibitor samples were required to have a p value of ≤ 0.05 (Fig. 1C). Data was further visualized using Microsoft Access to create composite data tables and histograms. In Study Set #1, a Mann-Whitney U test table was viewed in histogram format showing the frequency of significant time-points for a given end point within each lineage for a specific time-window. Endpoints that were unique to a treatment or those having a frequency minimally 2 higher than other inhibitor treatments were highlighted for further examination. Similarly for Study Sets #3–6, a table was created linking the Mann-Whitney U table for the time zero sample compared with an inhibitor treated sample and the Mann-Whitney U table for the untreated to treated comparison, with each linked by time point, inhibitor treatment, and end point. Spearman's Rho correlations were also taken into consideration for each experimental set, with experimental sets ultimately being compared with each other to isolate endpoints unique to adipogenesis. Alterations in signaling for inhibitor treated experimental sets were further examined using CScape pathway maps to reflect signaling pathway changes relative to the related untreated samples (25Einspahr J.G. Calvert V. Alberts D.S. Curiel-Lewandrowski C. Warneke J. Krouse R. Stratton S.P. Liotta L. Longo C. Pellacani G. Prasad A. Sagerman P. Bermudez Y. Deng J. Bowden G.T. Petricoin 3rd., E.F. Functional protein pathway activation mapping of the progression of normal skin to squamous cell carcinoma.Cancer Prev. Res. 2012; 5: 403-413Crossref PubMed Scopus (74) Google Scholar). These maps examined alterations in the early and late signaling events by analysis of day 4 and day 8 data. The intensity values were normalized with unaltered endpoints displaying a value of 1, decreased phosphorylation displaying a value closer to 0 and increased phosphorylation showing a value closer to 2. Dynamic time course RPMA data from each lineage was visualized via unsupervised hierarchical clustering, which facilitated end point clustering and established time windows used for histogram based analysis. The analysis displayed time ordered signal transduction (Fig. 2A) that revealed a near-perfect ordering of signaling dynamics that produced a distinct series of unique dynamic up-and-down signaling activation patterns following a defined time series. The reproducibility of the signaling dynamics was seen through the independent analysis of differentiated independent triplicate samples. This portrayed kinetic activation portraits that were cleanly reproducible across all three samples, including even subtle and time-dependent changes (Fig. 2B and 2C) and interesting oscillations in phosphorylation levels seen for AKT at the later time points (Fig. 2B). The cause and/or consequence of these reproducible phosphorylation level oscillations is unknown and could be because of cell cycling feedback and further investigation is warranted. The unsupervised clustering revealed activation of the insulin-like growth factor receptor (IGFR)/IR, SMAD, and mTOR pathways within the first few hours, with AKT pathway activation a few days post-differentiation. Time-windows were visualized with histograms to isolate lineage unique endpoints per time-window (Supplemental Data Table ST1). This analysis showed extracellular-signal-regulated kinases (ERK), phosphatase and tensin homolog (PTEN) and c-ABL signaling activation to be unique to adipogenesis in the early time points, with SMAD and CRK activation unique in later time points. This outcome suggested lineage-time point-specificity and enabled the implication of potential pathways to be targeted pharmacologically for further detailed molecular analysis on adipocyte differentiation and the associated signaling network activation correlates. Adipocyte differentiation was monitored through the quantification of ORO to validate increased lipid formation throughout differentiation and to generate a quantifiable numeric measurement of functional biology (lipid accumulation). To ensure methodological validity, ASCs were differentiated until day 12, with ORO quantification and cellular imaging performed (supplemental Fig. S1). This showed ORO staining levels to correspond to visual lipid accumulations enabling a quantifiable inhibitor time course to be pursued. Inhibitor screening was performed with inhibitor added at the start of differentiation and maintained until day 14, when cultures were imaged (Fig. 3). The results showed that the HNMPA-(AM)3 inhibitor (15 μm concentration) completely inhibited lipid formation, while rapamycin (250 nm concentration) and the MAPK inhibitor PD169316 (500 nm concentration) partially inhibited lipid formation. To evaluate inhibitor affect for Study Set #3, specific endpoints that should have been targeted by the inhibitor treatment were examined. In the case of the HNMPA-(AM)3 (IR inhibitor), two insulin receptor autophosphorylation sites were examined to validate inhibitor targeting. The end point type I insulin-like growth factor receptor/IR [IGF-1R (Y1135/36)/IR (Y1150/51)] showed inhibition relative to the untreated sample, whereas the end point IGF-1R (Y113)/IR (Y1146) did not (supplemental Fig. S2). ORO values displayed a gradual