Critical Evaluation of Adipogenic Cell Models: Impact of the Receptor Toolkit on Adipogenic Potential

Abstract

Adipocyte cell models are essential for investigating adipogenesis, yet methodological inconsistencies pose challenges to obtaining reproducible and physiologically relevant results. Murine cell lines, such as 3T3-L1 and OP9, are commonly utilized due to their established adipogenic capabilities. However, differences in its metabolic, genetic regulation, and receptor signaling raise concerns about their applicability to human adipose biology. Human-derived models, including mesenchymal stem cells (hMSCs) and preadipocyte cell lines, offer a closer approximation to in vivo adipogenesis but display significant variability in differentiation efficiency. This variability is often compounded by heterogeneous differentiation protocols, variations in cell confluence, and unstandardized pharmacological induction strategies. A pivotal factor influencing adipogenic potential is the receptor toolkit, which dictates cellular responses to differentiation stimuli. This study systematically evaluates key receptors—PPARγ, glucocorticoid receptors (GR), insulin receptor (IR), thyroid hormone receptors (TR), estrogen receptors (ER), and adenosine receptors (AR)—across commonly used adipocyte models to assess their roles in adipogenic regulation. Additionally, we examine the impact of pharmacological agents capable of inducing adipogenesis (adipogens) and the methodological inconsistencies that contribute to variations in adipocyte differentiation. By addressing these factors, we aim to elucidate the extent to which receptor variability influences experimental outcomes and propose a more structured approach to interpreting adipogenesis research. This critical assessment underscores the need for greater methodological transparency and receptor profiling to enhance the reliability of adipocyte models in metabolic research. Standardizing differentiation methodologies while accounting for receptor diversity will be essential for refining in vitro models and improving their translational potential in the study of obesity, diabetes, and other metabolic disorders.

1. Introduction

Adipogenesis is the physiological process through which precursor cells differentiate into mature adipocytes, playing a fundamental role in energy storage, endocrine function, and metabolic homeostasis. Given its central regulatory role, adipose tissue is closely linked to the onset and progression of metabolic disorders, including obesity, insulin resistance, type 2 diabetes mellitus, and cardiovascular disease [1]. The differentiation of adipocytes is controlled by a complex interplay of transcription factors, signaling pathways, and extracellular regulators that collectively determine cellular fate and function [2]. Although critical regulators such as PPARγ, C/EBPα, and insulin-mediated pathways have been extensively studied, a key gap persists in understanding how variability in receptor expression modulates adipogenic potential. A more refined characterization of the molecular determinants governing adipogenesis is essential to develop targeted therapeutic strategies aimed at addressing the underlying causes of metabolic disease.

While several comprehensive reviews have provided a foundational framework for the study of adipogenic models—notably addressing their utility and limitations [3,4,5]—emerging evidence continues to expand and enrich the field. In particular, there is growing interest in investigating additional variables, such as receptor expression profiles, which may critically influence the efficacy of the induction protocols. Our review aims to integrate this evolving knowledge and highlight receptor-driven mechanisms as a means to address methodological challenges that limit the reproducibility and clinical applicability of in vitro adipogenic models.

Historically, murine-derived preadipocyte cell lines, such as 3T3-L1 and OP9, have been widely used due to their well-characterized adipogenic responses and differentiation capacity. However, species–specific differences in metabolic regulation, transcriptional activity, and receptor-mediated signaling impose significant limitations on their relevance to human adipose tissue biology [6]. While human MSCs and preadipocyte-derived cell lines provide a more physiologically relevant alternative, they face several limitations: many adipogenic human cell lines origin from pathologies, their differentiation potential is highly variable and lacks standardization. Furthermore, commonly employed adipogenic differentiation protocols rely on a diverse set of pharmacological inducers—including insulin, dexamethasone (DEX), isobutyl-1-methylxanthine (IBMX), and peroxisome proliferator-activated receptor gamma (PPARγ) agonists—without adequately considering the extent to which receptor expression influences differentiation efficiency [7]. The assumption that all preadipocyte models share comparable receptor profiles has led to discrepancies in lipid accumulation, gene expression patterns, and overall differentiation outcomes, complicating cross-study comparability and hindering mechanistic insights into adipogenesis.

To address key methodological limitations in adipogenesis research, this study systematically profiles the expression of critical receptors—PPARγ, glucocorticoid receptors (GR), thyroid receptors (TR), estrogen receptors (ER), insulin receptors (IR), retinoid receptors (RXR), and adrenergic receptors (AR)—across widely used adipogenic models. By evaluating how receptor expression influences differentiation dynamics, we aim to optimize adipocyte differentiation protocols and enhance their reproducibility. To strengthen this framework, we also critically assess existing literature on receptor-mediated regulation, incorporating comparative data from studies using techniques such as flow cytometry, Western blotting, RNA sequencing, and PCR. This integrated approach seeks to clarify current inconsistencies, improve the physiological relevance of in vitro models, and advance the translational potential of adipogenesis research.

2. Critical Evaluation of In Vitro Adipogenesis Models: Methodological Considerations and Regulatory Mechanisms

2.1. Human and Murine Models of Adipogenesis: Implications and Limitations

Numerous scientific publications report the generation of mature adipocytes from precursor cells of both human and murine origin. However, a major complication with these primary models lies in the inherent limitations of accessibility and the high cost and challenges associated with culturing primary cells. To address this need, commercial providers offer human mesenchymal stem cells (hMSCs) or human multipotent adipose-derived stem cells (hMADS) sourced from various tissues, including adipose tissue (AT), bone marrow (BM), and umbilical cord matrix from individual donors. However, the source of each sample can vary, and crucial aspects such as the donor’s race, ethnicity, gender, age, or health status remain undetermined, as these details are protected. Research has shown that these factors can significantly influence the phenotype of the cells being studied, leading to variability in results. Consequently, the lack of control over these variables makes it challenging to draw consistent and reproducible conclusions across studies. Furthermore, while these commercial providers claim that the cells are tested to demonstrate their differentiation capacity, the efficiency of differentiation can vary significantly between batches, even when cultured under identical conditions.

Additionally, many suppliers suggest differentiation protocols based on their proprietary products, which typically claim to improve differentiation efficiency [8]. However, the composition of these protocols is often confidential for commercial reasons. This situation complicates the reproducibility of results, as researchers worldwide are unable to replicate the experiments due to the lack of standardized, well-characterized cellular models.

Therefore, it is imperative that adipocyte research be conducted using more universally applicable and reproducible models, such as well-characterized cell lines. To identify the most commonly used and commercially available models, we conducted a comprehensive search through multiple databases, including Google Scholar, Scopus, and PubMed, using a Boolean strategy with “adipogenesis” as the primary keyword combined with cell line names as search variants. The subsequent results, analysis and discussion in this work focused on the most prominent models with sufficient characterization to allow for meaningful comparison and interpretation. This selection criterion was arbitrarily defined by the authors but is supported by the frequency and popularity of use of these models in the scientific literature. While this may be considered a source of bias and a limitation, the contribution of alternative models to the field remains disproportionately low, which reinforces the relevance and focus of our approach. As part of our exclusion criteria, we deliberately omitted non-commercially available cells and hMSCs and hMADS, acknowledging that although some of these models are available and valuable for adipogenesis research, they are derived from individual donors and thus inherently subject to inter-individual, source, racial, genetic, age, and sex-related variability. Such donor-specific information is often inaccessible to researchers, particularly when obtained from commercial sources, which imposes significant limitations for studies focused on receptor-dependent mechanisms.

The analysis of the adipogenic cells lines revealed that approximately 75% of the models used in adipocyte research are murine, with the minority derived from human sources (Figure 1A). Additionally, we observed significant disparities in the tissue origins and the phenotypic and functional characteristics of these cell lines. Among human cell lines, only HS-5 is derived from BM, while the others originate from subcutaneous/abdominal adipose tissue (SGBS, PAZ6, LS14, ASC52telo) and the retroperitoneal region (LiSa-2).

Regarding murine cell lines, most are derived from various tissues such as BM, subcutaneous and epididymal AT, calvaria, with a smaller number originating from embryonic or perinatal sources (Figure 1A, right). Among human-derived cell lines, the SGBS cell line is the most widely used for adipogenesis. However, its origin, from a pediatric donor with Berardinelli–Seip syndrome, raises questions about the representativeness of this model. On the other hand, LiSa-2 and LS14 cells, which are derived from liposarcoma, further highlight that 50% of the most commonly used human cell lines in adipogenesis research originate from pathological conditions.

Furthermore, the database analysis, revealed that the 3T3 cell line (parental and derivatives such as 3T3-L1 and 3T3-F442A) is by far the most widely used for adipogenesis studies. Despite discrepancies in the number of results across databases (due to varying search algorithms), the trend remained consistent across all platforms, clearly indicating that murine (embryionic) 3T3 cells are overwhelmingly the predominant cell line used for adipogenesis research. It also highlights the need for a more standardized and reproducible approach using well-characterized human cell lines to address the limitations and inconsistencies in current adipogenesis models.

In this regard, efforts have been made to develop human-derived systems such as hMADS cells (hMADS 1–6), which exhibit high proliferative capacity and can maintain their adipogenic potential for up to 200 passages. However, these cells are not commercially available through major international suppliers, which significantly restricts their accessibility and global reproducibility. Moreover, hMADS cells were established from pediatric donors between 4 months and 4 years old, undergoing various surgical procedures, and not necessarily classified as healthy [9]. The adipose tissue was obtained from umbilical cord and pubic regions, which, combined with the donor age, confers these cells biological characteristics distinct from adult-derived adipose progenitors.

Indeed, infant-derived MSCs display enhanced resistance to senescence and replicative stress, as well as constitutively elevated expression of genes such as SOD1-3, c-MYC, p16, p21, p53, PPARγ, and LPL [10]. These features, while experimentally advantageous, limit the extrapolation of findings to adult adipogenesis and raise concerns about the physiological relevance of this model in contexts seeking to emulate adult adipose tissue behavior.

The overreliance on models with unknown or pathological origins complicates the comparability and applicability of findings across different research groups. Moreover, it is imperative to consider the limitations associated with using cell lines that harbor diverse chromosomal abnormalities when compared to primary cells. For instance, the 3T3-L1 cell line, which is widely employed in adipogenesis studies, possesses an aneuploid and unstable karyotype. Such aberrations may potentially influence cellular behavior and differentiation capacity, theoretically favoring or limiting adipogenesis, and thereby affecting the accurate extrapolation of results to a healthy physiological context. Detailed karyotypic information and other key cellular observations are provided and will be discussed later in Table S1.

Moreover, the source from which adipocytes are isolated plays a crucial role, as these cells exhibit distinct gene expression patterns and demonstrate variable responses to signaling pathways in vitro. The microenvironmental context in which these cells were originally present has been shown to significantly influence their differentiation capacity. For instance, adipocytes derived from subcutaneous tissue exhibit a much higher differentiation potential compared to those from omental adipose tissue. This distinction is often overlooked by many researchers who use and compare cell lines without considering that adipogenesis efficiency may vary depending on the tissue source [11,12,13].

Taken together, the observations discussed throughout this section underscore the need to refine the selection of adipogenic cell models toward systems that more accurately reflect the biology of adult human adipocytes. The most widely used models today—whether murine, pediatric, or tumor-derived—present significant limitations, including genetic instability, donor-related variability, and restricted physiological relevance. These factors contribute to marked differences in adipogenic capacity that do not necessarily correspond to the behavior of adipose tissue in adult humans. Furthermore, inconsistencies in tissue origin, undefined donor metadata, and non-standardized differentiation protocols hinder reproducibility and limit the extrapolation of results. Moving forward, it is essential that the field prioritize models with well-characterized origins and stable phenotypes, derived from adult sources, in order to strengthen the translational value of adipogenesis research and reduce variability across studies.

2.2. From Precursor Cells to Adipocytes: Stages and Molecular Regulation of Adipogenesis

Adipogenesis is a multistage process by which precursor cells (MSC, fibroblasts or preadipocytes) progressively acquire the characteristics required to become mature adipocytes. This differentiation follows a defined sequence of genetic, morphological, and functional changes, which can be broadly divided into the following stages: commitment, induction, early differentiation, and late differentiation (Figure 2). During the commitment phase, MSCs, identified by the expression of surface markers such as CD73, CD90, and CD105, while lacking CD14, CD11b, CD34, CD45, CD79a, CD19, and HLA-DR [14], transition into preadipocytes. MSCs exhibit self-renewal potential and the ability to differentiate into multiple lineages (multipotency), including adipocytes, chondrocytes, and osteoblasts. Specific lineage regulators dictate MSC fate, such as Runx2 for osteoblastogenesis [15], Sox9 for chondrogenesis [16], and for adipogenesis, the CAAT/enhancer-binding protein (C/EBP) family—particularly C/EBPα—and the peroxisome proliferator-activated receptor gamma (PPARγ), both of which are essential positive regulators.

The transformation of MSCs into adipocytes is not direct but involves the generation of an intermediate cell type called the preadipocyte. Compared to MSCs, preadipocytes lack multipotency and their differentiation potential is restricted to adipocytes under optimal conditions. Morphologically, both MSCs and preadipocytes are heterogeneous fusiform cells, devoid of significant cytoplasmic complexity, and exhibit high proliferative potential. As a result, markers such as Ki67 are commonly used to identify preadipocytes before their terminal differentiation, along with other early differentiation markers such as C/EBPβ, C/EBPδ, Pref-1, and ZFP423 [17].

While the specific molecular events driving the differentiation of MSCs into preadipocytes are not fully understood, recent reviews have discussed the role of several pathways and microenvironmental factors in this process [14]. During the commitment phase, preadipocytes proliferate extensively, and this step is considered critical for adipocyte generation, as reported by multiple independent groups, despite the variability in their adipogenic models. It is important to note that high cell confluence promotes cell cycle arrest in adherent cells when the culture surface is saturated, leading to inhibition of proliferation upon cell-to-cell contact. Once at high confluence, preadipocytes, restricted to a specific lineage and arrested in the cell cycle, can be supplemented with an adipogenic cocktail to initiate early differentiation. This stage is characterized by the appearance of small lipid droplets in the cytoplasm and an increase in metabolic activity. The duration of this stage is highly dependent on the culture conditions and models used, but it typically lasts for the first 7 days of culture. Subsequently, adipocyte maturation involves further specialization, during which the cell demonstrates its capacity for lipid storage, leading to noticeable changes in morphology. The cell becomes ellipsoid with larger lipid droplets, which can be single or multiple, depending on the type of adipocyte generated. It is important to recognize that the classical classification of unilocular white adipocytes and multilocular brown adipocytes does not reliably recapitulate in vitro. Rather than serving as a clear distinction, this morphological criterion often complicates the interpretation of results, as many immortalized and even primary white adipocyte models display a multilocular phenotype. Therefore, studies claiming to generate specific adipocyte types should validate their identity through a more precise and systematic approach using well-established markers. Functional markers (e.g., enzymes and regulatory proteins), as well as specific surface markers, have been proposed for decades and continue to be refined to improve the accuracy of adipocyte characterization in vitro [18,19,20].

The molecular determinants driving adipogenic differentiation from pre-adipocytes are graphically shown in Figure 2B. PPARγ was initially identified as the essential factor for adipogenesis. It is considered the master regulator because it is necessary for differentiation and, in some models, sufficient by itself to induce differentiation. Various transcription factors and receptors work together to collaborate with PPARγ, including members of the C/EBP family, such as C/EBPβ and C/EBPδ, which are early factors associated with early differentiation. These are activated in pre-adipocytes to promote the transcription of PPARγ itself, which in turn enhances the expression of other C/EBP family members, particularly C/EBPα. This creates a positive feedback loop, further enhancing the expression of PPARγ. Adipogenesis research has been focused on PPARγ, as it has been experimentally shown that pre-adipocytes can differentiate into adipocytes in the absence of C/EBPα, but not without PPARγ [21]. The loop generated by C/EBPα is considered a later-stage process and a means to consolidate differentiation by regulating genes involved in adipocyte specialization and function. The activity of PPARγ is mediated by ligands but is also enhanced by interaction with other nuclear receptor members, such as retinoid receptors (RXR) [22]. The formation of the PPARγ/RXR heterodimer induces conformational changes that facilitate ligand binding and interaction with PPAR response elements (PPRE) in the promoters of target genes, promoting gene expression and mediating the transition from pre-adipocyte to mature adipocyte. Other nuclear receptors have been linked to the adipogenic differentiation process, such as TR; however, their role in differentiation is not fully understood and may vary positively or negatively depending on the experimental context (discussed later). Nonetheless, TR ligands often promote PPARγ expression. Finally, other receptors, such as GR and insulin receptors, are responsible for promoting the expression of early differentiation molecules, such as C/EBPδ and C/EBPβ, which cooperatively favor PPAR γ expression. These are, in general, the primary regulatory factors of the adipogenic program (Figure 2B).

2.3. Pharmacological Regulators of Adipogenesis In Vitro

It was initially shown that PPAR receptors could be physiologically activated by micromolar concentrations of fatty acids. However, three decades ago, a family of antidiabetic compounds known as thiazolidinediones (TZDs), including rosiglitazone, pioglitazone, and troglitazone, was identified as potent PPARγ activators, with nanomolar affinity for its isoforms [23]. Since then, the inclusion of TZDs in adipogenic cocktails has become widely used. Additionally, insulin is employed in the adipogenic cocktails, as it regulates early expression of key transcription factors such as C/EBPβ and C/EBPδ, while downregulating C/EBPα through the insulin receptor (InsR) [24]. Insulin also promotes glucose uptake via GLUT4 expression, facilitating triglyceride accumulation in adipocytes. Notably, insulin sensitivity varies across models, with OP9 cells being more sensitive and effective than 3T3-L1 cells [25]. This is significant because high, chronic insulin concentrations can lead to reprogramming that negatively impacts insulin signaling, glucose uptake, lipid droplet size, and induces basal glucose uptake and lipolysis disruptions [26]. Isobutylmethylxanthine (IBMX), a non-selective, non-competitive phosphodiesterase (PDE) inhibitor, promotes increased cAMP levels, thereby activating protein kinase A, which is essential for the transcriptional activation of PPARγ and C/EBPβ [27,28]. Finally, glucocorticoids (GCs) have proven to be effective inducers of early differentiation, contributing to the expression of PPARγ and various C/EBP family members, such as C/EBPβ, C/EBPδ, and C/EBPα [29,30,31]. In some studies, triiodothyronine (T3) has been added to adipogenic cocktails, although its use is less common. It has been shown to promote not differentiation, but rather the transition from white adipocytes to brown adipocytes (browning), enhancing mitochondrial biogenesis and the expression of thermogenic proteins like UCP-1 [32,33,34]. Similarly, indomethacin has been used as an adjunct in adipogenic cocktails, as it has been demonstrated to partially activate the PPARγ receptor directly [35]. More recently, indomethacin has been shown to promote browning during adipogenesis, warranting cautious use and thorough evaluation of the phenotypic outcomes of the generated adipocytes [36].

2.4. Current Protocols for Adipocyte Differentiation: Methodological Approaches and Key Limitations

The differentiation of adipocytes in vitro has become a crucial model for understanding adipogenesis and related metabolic processes. Various protocols have been developed, utilizing a range of pharmacological agents to induce differentiation. A comprehensive search of protocols related to adipocyte differentiation was conducted using the terms “Adipocytes differentiation AND…” across several adipogenic cell lines, without temporal restrictions. This approach aimed to provide a broad understanding of the key factors influencing adipogenic differentiation. A total of 55 protocols were included, from which essential parameters were extracted, including starting cell confluence, the composition of the adipogenic cocktail, protocol duration, and differentiation confirmation methods (Table 1).

Table 1. Characteristics of the protocols employed for adipocyte differentiation from several cell lines.

Murine Cell Lines
White Adipocyte Cells
Cell line	Organism	Initial Cell Confluency (n Well in Plate/cm2)	Culture Medium Employed	Differentiation Cocktail Employed	Differentiation Days	Differentiation Efficiency	Validation Method	Reference
3T3-L1	Mouse	50,000 (24)	DMEM-HG 10% BCS 100 U/mL penicillin 100 μg/mL streptomycin 0.25 μg/mL amphotericin B	1 μg/mL insulin 0.025 µM DEX 0.5 mM IBMX	7	ND	Nile Red staining mRNA expression of Pparg, Fabp4 & Plin1	[37]
		NS	DMEM-HG 10% FBS 100 U/mL penicillin 100 mg/mL streptomycin	0.02 μg/mL insulin 0.250 µM DEX 0.5 mM IBMX	7	ND	Oxygen consumption	[38]
		NS	DMEM-HG 110 mg/L sodium pyruvate 10% BCS	6 μg/mL insulin 0.1 µM DEX 0.5 mM IBMX 1 µM ROSI 250 µM INDO	10	80%	Oil Red O staining	[39]
		2000–3000 (cm2)	DMEM-HG 10% BCS 100 U/mL penicillin 100 mg/mL streptomycin	5 μg/mL insulin 1 μM of DEX 0.5 μM IBMX 2 μM ROSI	14	90%	Oil Red O staining	[40]
		30,000 (96)	DMEM-HG 10% BCS 100 U/mL penicillin 100 mg/mL streptomycin	1.0 μg/mL insulin 0.5 mM IBMX	10	ND	Nile Red staining	[41]
		10,000 (24)	DMEM/F12 100 U/mL penicillin 100 mg/mL streptomycin 10% FBS	10 μg/mL insulin 1 μM DEX 0.5 mM IBMX 20 μM rosiglitazone	8	95%	Oil Red O staining	[42]
		NS	DMEM 100 mg/mL of kanamycin 10% FBS	10 µg/mL insulin 1µM DEX 0.5 mM IBMX	5	70%	Oil Red O staining	[43]
		30,000 (cm2)	DMEM with 1 g/L glucose 10% FBS 100 U/mL penicillin 100 mg/mL streptomycin 10 g/L-glutamine	1 μg/mL insulin 0.25 μM DEX 0.5 mM IBMX	14	90%	Oil Red O staining PLIN-1 expression FABP expression	[44]
		NS	DMEM F-12 10% BCS 100 U/mL penicillin 100 mg/mL streptomycin	0.5 μg/mL insulin 5 μM DEX 0.5 mM IBMX 1 μM ROSI 1 nM T3	7	100%	Lipid quantification	[45]
		30,000 (96)	DMEM-HG 10% BCS 100 U/mL penicillin 100 mg/mL streptomycin	1.0 μg/mL insulin 0.5 mM IBMX	10	80%	Triglyceride Content	[46]
		NS	DMEM-HG L-Glutamine 10% BCS 100 U/mL penicillin	2 µM insulin 1 µM DEX 0.025 mM IBMX	16–20	90%	Oil Red O staining	[47]
		NS	DMEM-HG 10% BCS 100 U/mL penicillin	10 μg/mL insulin 1 μM DEX 0.5 mM IBMX	6	100%	Oil Red O staining Triglyceride accumulation assay	[48]
		NS	DMEM 10% FBS 100 μg/mL streptomycin 100 U/mL penicillin 250 ng/mL fungizone	10 μg/mL insulin 0.1 μM DEX 0.5 mM IBMX	8	90%	Oil Red O staining	[49]
		NS	DMEM 10% BCS 2 mM l-glutamine 100 U/mL penicillin 100 U/mL streptomycin	0.01 µg/mL insulin 1 μM DEX 0.25 mM IBMX	2	55%	PPARG expression	[50]
		NS	DMEM 10% FBS	500 mM IBMX 200 mM indomethacin 1 μM DEX 10 μM Insulin	10	>50%	Oil Red O staining	[51]
3T3-F442A	Mouse	5000 (6)	DMEM-HG 4 mmol/L L-glutamine 1.5 g/L sodium bicarbonate 10% BCS 100 μg/mL streptomycin 100 U/mL penicillin	10 μg/mL insulin 1 μM rosiglitazone	7	80%	Oil Red O staining	[52]
		NS (48)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	10 μg/mL insulin	2	ND	Triglyceride accumulation assay	[53]
		3300 (cm2)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 μg/mL insulin 1 μM DEX 5 mM IBMX	6–8	90%	Oil Red O staining	[54]
		NS (48)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	10 μg/mL insulin	3	ND	LipidTox staining Upregulation of PPARγ and C/EBP-α	[55]
		70,000 (6)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL amphotericin	5 μg/mL insulin	8	ND	Oil Red O staining Cygb expression	[56]
		NS	DMEM-HG 10% FBS 50 μg/mL streptomycin 100 U/mL penicillin	58 μg/mL insulin 1 μM DEX 0.5 mM IBMX	12	ND	Oil Red O staining	[57]
OP9	Mouse	50,000 (24)	αMEM 20% FBS 26 mM sodium bicarbonate 100 U/mL penicillin 100 μg/mL streptomycin 0.25 μg/mL amphotericin B	1 μg/mL insulin 0.025 μM DEX 0.5 mM IBMX	7	100%	Nile Red staining mRNA expression of Pparg, Fabp4 & Plin1	[37]
		NS	αMEM 5% FBS 100 U/mL penicillin 100 μg/mL streptomycin	1 mM rosiglitazone	10	80%	Oil Red O staining	[58]
		60,000 (48)	αMEM 20% FBS 100/mL penicillin 100 µg/mL streptomycin 2 M L-glutamine	10 µg/mL insulin 0.25 µM DEX 0.25 mM IBMX	6	ND	Oil Red O staining	[59]
		2000 (384)	αMEM 10% FBS 100/mL penicillin 100 µg/mL streptomycin GLUTAMAX	5 µg/mL insulin 10 µM DEX 0.5 mM IBMX	6	ND	Nile Red imaging Trygliceride content assay	[60]
		NS	DMEM 5% FBS 1 mM rosiglitazone	10 µg/mL insulin 1 μM DEX 0.500 mM IBMX 1 μM rosiglitazone	8	ND	Oil Red O staining mRNA expression of Pparg, Fabp4 & Plin1	[61]
		NS	αMEM 20% FBS 100 U/mL Penicillin 100 mg/mL Streptomycin 292 mg/mL L-glutamate	10 µg/mL insulin 1 µM DEX 0.25 mM IBMX	2	80%	PPARG expression	[50]
		NS	αMEM 20% FBS 100 U/mL Penicillin 100 mg/mL Streptomycin 292 mg/mL L-glutamate	10 µg/mL insulin 1 µM DEX 250 mM IBMX	2	ND	PPARG expression	[62]
		400,000 (24)	DMEM 10% FBS	10 µg/mL insulin	5	70%	Oil Red O staining	[63]
		NS	αMEM 20% FBS	1 μg/mL of insulin 1 μM DEX 0.5 mM IBMX	5	80%	NS	[64]
D1-ORL-UVA	Mouse	500 (24)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 0.01 μM DEXA 50 µM INDO	9	40%	Oil Red O staining	[65]
		1000 (24)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 0.01 μM DEXA 50 µM INDO	15	ND	NS	[66]
BMS2	Mouse	50,000 (NS)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	1 µg/mL insulin 0.25 μM DEXA 0.5 mM IBMX	8	80%	Oil Red O staining	[67]
		20,000 (24)	DMEM-HG 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µM hydrocortisone 0.5 mM IBMX 5 μM Rosiglitazone 25 μM Pioglitazone 60 µM INDO	6	80%	Oil Red O staining	[68]
OB17	Mouse	NS	DMEM 10% FBS 33 μM biotin 17 μM sodium pantothenate Penicilin Streptomycin	17 nM insulin 1.5 nM T3	14	ND	NS	[69]
		3000/cm2	DMEM 200 U/mL penicilin 50 μg Streptomycin 33 μM biotin 17 μM pantothenate	17 nM Insulin 2 nM T3	19	ND	ND	[70]
		NS	DMEM antibiotics 33μM biotin 17 μM pantothenate	170 nM Insulin 1.5 nM T3	18	ND	ND	[71]
		NS	DMEM antibiotics 33 μM biotin 17 μM pantothenate	170 nM Insulin 1.5 nM T3	16	ND	ND	[72]
MS5		NS	aMEM 10% FCS	5 µg/mL insulin	15	ND	ND	[73]
		19,000–38,000 (12)	DMEM 10% FBS 100 U/mL Penicilin 100 μg/mL streptomycin 2 mM L-glutamine 10 mM HEPES 1 mM sodium piruvate 50 µM mercaptoethanol	STEMPro adipogenesis Kit	14–21	95%	Oil Red O staining	[74]
Brown adipocyte cells
C3H/10T1/2	Mouse	20,000 (cm2)	DMEM 10% FBS	5 µg/mL insulin 0.25 µM DEX 0.5 mM IBMX	12	90%	Oil Red O staining	[75]
		NS	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin 1% L-glutamine	5 µg/mL insulin 0.2 µM DEX 0.5 mM IBMX 5 µM rosiglitazone	6–8	80%	Oil Red O staining	[76]
		NS	DMEM 10% FBS	5 μg/mL of insulin 0.5 μM DEX 0.25 mM IBMX 50 μM INDO	5	35%	Oil Red O staining	[51]
		5000 (96)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin 1% L-glutamine	10 µM insulin 1 μM DEX 0.5 mM IBMX 0.5 µM rosiglitazone 0.25 μM INDO	7	80%	Oil Red O staining	[77]
		NS	DMEM 10% FBS	500 mM IBMX 200 mM INDO 1 μM DEX 10 μM Insulin	10	>50%	Oil Red O staining	[51]
T37i	Mouse	NS	DMEM/Ham’s F12 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	0.058 µg/mL insulin 2 nM T3	8	ND	mRNA expression	[78]
		10,000 (24)	DMEM/Ham’s F12 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	0.6 µg/mL insulin 2 nM T3	9	95%	Oil Red O staining Trygliceride content assay	[79]
		NS	DMEM/Ham’s F12 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	0.12 µg/mL insulin 2 nM T3	7	35%	mRNA expression	[80]
HIB1B	Mouse	NS	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	10 µg/mL insulin 0.25 μM DEX 0.5 mM IBMX	8	ND	lipolytic protein markers	[81]
		NS	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	0.12 µg/mL insulin 0.25 μM DEX 0.5 mM IBMX 1 nM T3	7	90%	Oil Red O staining	[82]
BFC-1	Mouse	NS	DMEM 10% FBS 200 U/mL penicillin 50 μg/mL streptomycin	0.058 µg/mL insulin 2 nM T3	NS	ND	mRNA expression	[83]
Human in vitro cellular model
White adipocyte cells
Cell line	Organism	Initial cell confluency (n well in plate/cm2)	Culture medium employed	Differentiation cocktail employed	Differentiation days	Differentiation Efficiency (based on ORO staining and microscopic analysis)	Validation Method	Reference
HS-5	Human	NS	10% FBS 100 U/mL penicillin 100 μg/mL streptomycin 1% L-glutamine	OriCellTM Supplement For Human Related Stem Cells Adipogenic Differentiation A-I, A-II & B	NS	80%	Oil Red O staining	[84]
		20,000 (75 cm2)	DMEM 10% FBS 1% antibiotic/antimycotic	10 µg/mL insulin 500 µM DEX 0.5 mM IBMX 100 µM INDO	19	90%	Oil Red O staining	[85]
		300,000 (cm2)	αMEM 10% FBS	5 µg/mL insulin 0.5 µM DEX 0.25 mM IBMX 50 µM INDO	14	80%	Oil Red O staining	[86]
LiSa-2	Human	10,000 (cm2)	DMEM/Ham’s F12 10 mg/mL transferrin 15 mM NaHCO3 15 mM HEPES 33 mM biotin 17 mM pantothenate 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 0.02 nM T3 1 µM cortisol	20	30%	Sudan red and hematoxylin	[87]
		NS	DMEM/Ham’s F12 2% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 1 mM DEX 1 nM T3	9	ND	Oil Red O staining	[88]
		NS	DMEM-F12 33 µM biotin 17 µM pantothenic acid 100 U/mL penicillin/streptomycin 10% FBS	0.005 µg/mL insulin 0.02 nM T3 1 µM cortisol	NS	ND	Microscopy	[89]
SGBS	Human	NS	DMEM-F12 33 µM biotin 17 µM pantothenic acid 100 U/mL penicillin/streptomycin 10% FBS	0.1 µg/mL insulin 0.025 μM DEX 0.5 mM IBMX 2 µM rosiglitazone 0.02 nM T3 0.1 µM cortisol	28	ND	Microscopy	[89]
		800,000 (15 cm2)	DMEM-F12 66 µM biotin 33 nM pantothenic acid 2% penicillin/streptomycin antibiotics	0.5 µg/mL insulin 0.095 µM DEX 1.9 mM IBMX 7.6 µM rosiglitazone 0.76 nM T3 380 nM cortisol	12	90%	Secretome	[90]
		NS (6)	DMEM-F12 3 µM biotin 17 µM pantothenic acid 0.5% penicillin 0.5% streptomycin 0.5% amphotericin B	0.116 µg/mL insulin 0.25 µM DEX 0.5 mM IBMX 2 µM troglitazone 0.2 nM T3 100 nM cortisol	7	ND	Triglyceride accumulation assay	[91]
		NS (12)	DMEM-F12 33 µM biotin 17 µM pantothenic acid 100 U/mL penicillin/streptomycin 10% FBS	0.1 µg/mL insulin 0.025 µM DEX 0.5 mM IBMX 2 µM rosiglitazone 0.02 nM T3 100 nM cortisol	12	ND	RNAm and Oxygen consumption	[92]
LS14	Human	500 (96)	DMEM-F-12 33 µM biotin 17 µM pantothenic acid 100 U/mL penicillin/streptomycin	1 µM insulin 0.25 mM IBMX 2 µM rosiglitazone 1 nM T3 No glucocorticoids	10	58%	Oil Red O staining	[93]
	Human	100,000 (24)	DMEM/F12 (1:1) 33 µM biotin 0.5% penicillin 0.5% streptomycin 17 µM pantothenic acid 10 µg/mL apotransferrin 200 µM ascorbate phosphate 4 µM oleic acid/BSA 4 µM linoleic acid/BSA	1 µM human insulin 1 nM T3 2 µM rosiglitazone 1 µM methoprene acid (RXR ligand) 1 µM T0901317 (LXR ligand) 250 µM IBMX No glucocorticoids	10	70–80% mentioned in the text.	ND	[94]
ASC52Telo	Human	ND	DMEM/F12 (1:1) GlutaMAX 1x 250 mM sodium piruvate 10% FBS	100 nM insulin 500 µM DEX 0.25 mM IBMX 2 µM rosiglitazone	21	<10%	Oil red O staining	[95]
		ND (12)	DMEM Low glucose 10% FBS	10 µg/mL insulin 1 µM DEX 0.5 mM IBMX	21	28%	PPARy activation in comparison to MSCs	[96]
Brown adipocyte cells
PAZ6	Human	NS	DMEM/Ham’s F12 2% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 1 μM DEX 1 nM T3	9	ND	Oil Red O staining	[88]
		NS	DMEM-Ham’s F-12 8% FBS 15 mmol/l HEPES	5 µg/mL insulin 0.1 μM DEX 0.25 mM IBMX 1 mM pioglitazone 1 nM T3	14	ND	Oil Red O staining	[97]
		1,500,000 (NS)	DMEM-Ham’s F-12 8% FBS 15 mmol/l HEPES	5 µg/mL insulin 0.1 μM DEX 0.25 mM IBMX 1 mM pioglitazone 1 nM T3	14	ND	Oil Red O staining	[98]
		10,000 (cm2)	DMEM-Ham’s F-12 8% FBS 15 mmol/l HEPES 100 U/mL penicillin 100 μg/mL streptomycin	0.25 mM IBMX 0.1 nM T3	14	ND	NS	[99]
		NS	DMEM-Ham’s F-12 8% FBS 15 mmol/l HEPES 100 U/mL penicillin 100 μg/mL streptomycin	3 µg/mL insulin 0.1 μM DEX 0.25 mM IBMX 1000 nM pioglitazone 1 nM T3	14	ND	NS	[100]
WT-1	Human	NS	DMEM 10% FBS penicillin streptomycin	20 nM insulin 1 μM DEX 0.5 mM IBMX 0.125 mM INDO 1 nM T3	6	ND	NS	[101]
			DMEM 10% FBS	5 μg/mL insulin 1 μM DEX 0.5 mM IBMX 0.5 μM rosiglitazone	8	80–90%	Oil Red O Staining	[102]

NS: Not Specified; ND: Not determined; FBS: Bovine Fetal Serum; BCS: Bovine Calf Serum; DEX: dexamethasone; DMEM: Dulbecco’s Modified Eagle Medium; DMEM-HG: Dulbecco’s Modified Eagle Medium with high glucose; HEPES: 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid; INDO: indomethacine; IBX: isobutylmethylxanthin; Pparγ: PPARγ1/2; T3: triiodothyronine.

Table 1. Characteristics of the protocols employed for adipocyte differentiation from several cell lines.

Murine Cell Lines
White Adipocyte Cells
Cell line	Organism	Initial Cell Confluency (n Well in Plate/cm2)	Culture Medium Employed	Differentiation Cocktail Employed	Differentiation Days	Differentiation Efficiency	Validation Method	Reference
3T3-L1	Mouse	50,000 (24)	DMEM-HG 10% BCS 100 U/mL penicillin 100 μg/mL streptomycin 0.25 μg/mL amphotericin B	1 μg/mL insulin 0.025 µM DEX 0.5 mM IBMX	7	ND	Nile Red staining mRNA expression of Pparg, Fabp4 & Plin1	[37]
		NS	DMEM-HG 10% FBS 100 U/mL penicillin 100 mg/mL streptomycin	0.02 μg/mL insulin 0.250 µM DEX 0.5 mM IBMX	7	ND	Oxygen consumption	[38]
		NS	DMEM-HG 110 mg/L sodium pyruvate 10% BCS	6 μg/mL insulin 0.1 µM DEX 0.5 mM IBMX 1 µM ROSI 250 µM INDO	10	80%	Oil Red O staining	[39]
		2000–3000 (cm2)	DMEM-HG 10% BCS 100 U/mL penicillin 100 mg/mL streptomycin	5 μg/mL insulin 1 μM of DEX 0.5 μM IBMX 2 μM ROSI	14	90%	Oil Red O staining	[40]
		30,000 (96)	DMEM-HG 10% BCS 100 U/mL penicillin 100 mg/mL streptomycin	1.0 μg/mL insulin 0.5 mM IBMX	10	ND	Nile Red staining	[41]
		10,000 (24)	DMEM/F12 100 U/mL penicillin 100 mg/mL streptomycin 10% FBS	10 μg/mL insulin 1 μM DEX 0.5 mM IBMX 20 μM rosiglitazone	8	95%	Oil Red O staining	[42]
		NS	DMEM 100 mg/mL of kanamycin 10% FBS	10 µg/mL insulin 1µM DEX 0.5 mM IBMX	5	70%	Oil Red O staining	[43]
		30,000 (cm2)	DMEM with 1 g/L glucose 10% FBS 100 U/mL penicillin 100 mg/mL streptomycin 10 g/L-glutamine	1 μg/mL insulin 0.25 μM DEX 0.5 mM IBMX	14	90%	Oil Red O staining PLIN-1 expression FABP expression	[44]
		NS	DMEM F-12 10% BCS 100 U/mL penicillin 100 mg/mL streptomycin	0.5 μg/mL insulin 5 μM DEX 0.5 mM IBMX 1 μM ROSI 1 nM T3	7	100%	Lipid quantification	[45]
		30,000 (96)	DMEM-HG 10% BCS 100 U/mL penicillin 100 mg/mL streptomycin	1.0 μg/mL insulin 0.5 mM IBMX	10	80%	Triglyceride Content	[46]
		NS	DMEM-HG L-Glutamine 10% BCS 100 U/mL penicillin	2 µM insulin 1 µM DEX 0.025 mM IBMX	16–20	90%	Oil Red O staining	[47]
		NS	DMEM-HG 10% BCS 100 U/mL penicillin	10 μg/mL insulin 1 μM DEX 0.5 mM IBMX	6	100%	Oil Red O staining Triglyceride accumulation assay	[48]
		NS	DMEM 10% FBS 100 μg/mL streptomycin 100 U/mL penicillin 250 ng/mL fungizone	10 μg/mL insulin 0.1 μM DEX 0.5 mM IBMX	8	90%	Oil Red O staining	[49]
		NS	DMEM 10% BCS 2 mM l-glutamine 100 U/mL penicillin 100 U/mL streptomycin	0.01 µg/mL insulin 1 μM DEX 0.25 mM IBMX	2	55%	PPARG expression	[50]
		NS	DMEM 10% FBS	500 mM IBMX 200 mM indomethacin 1 μM DEX 10 μM Insulin	10	>50%	Oil Red O staining	[51]
3T3-F442A	Mouse	5000 (6)	DMEM-HG 4 mmol/L L-glutamine 1.5 g/L sodium bicarbonate 10% BCS 100 μg/mL streptomycin 100 U/mL penicillin	10 μg/mL insulin 1 μM rosiglitazone	7	80%	Oil Red O staining	[52]
		NS (48)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	10 μg/mL insulin	2	ND	Triglyceride accumulation assay	[53]
		3300 (cm2)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 μg/mL insulin 1 μM DEX 5 mM IBMX	6–8	90%	Oil Red O staining	[54]
		NS (48)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	10 μg/mL insulin	3	ND	LipidTox staining Upregulation of PPARγ and C/EBP-α	[55]
		70,000 (6)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL amphotericin	5 μg/mL insulin	8	ND	Oil Red O staining Cygb expression	[56]
		NS	DMEM-HG 10% FBS 50 μg/mL streptomycin 100 U/mL penicillin	58 μg/mL insulin 1 μM DEX 0.5 mM IBMX	12	ND	Oil Red O staining	[57]
OP9	Mouse	50,000 (24)	αMEM 20% FBS 26 mM sodium bicarbonate 100 U/mL penicillin 100 μg/mL streptomycin 0.25 μg/mL amphotericin B	1 μg/mL insulin 0.025 μM DEX 0.5 mM IBMX	7	100%	Nile Red staining mRNA expression of Pparg, Fabp4 & Plin1	[37]
		NS	αMEM 5% FBS 100 U/mL penicillin 100 μg/mL streptomycin	1 mM rosiglitazone	10	80%	Oil Red O staining	[58]
		60,000 (48)	αMEM 20% FBS 100/mL penicillin 100 µg/mL streptomycin 2 M L-glutamine	10 µg/mL insulin 0.25 µM DEX 0.25 mM IBMX	6	ND	Oil Red O staining	[59]
		2000 (384)	αMEM 10% FBS 100/mL penicillin 100 µg/mL streptomycin GLUTAMAX	5 µg/mL insulin 10 µM DEX 0.5 mM IBMX	6	ND	Nile Red imaging Trygliceride content assay	[60]
		NS	DMEM 5% FBS 1 mM rosiglitazone	10 µg/mL insulin 1 μM DEX 0.500 mM IBMX 1 μM rosiglitazone	8	ND	Oil Red O staining mRNA expression of Pparg, Fabp4 & Plin1	[61]
		NS	αMEM 20% FBS 100 U/mL Penicillin 100 mg/mL Streptomycin 292 mg/mL L-glutamate	10 µg/mL insulin 1 µM DEX 0.25 mM IBMX	2	80%	PPARG expression	[50]
		NS	αMEM 20% FBS 100 U/mL Penicillin 100 mg/mL Streptomycin 292 mg/mL L-glutamate	10 µg/mL insulin 1 µM DEX 250 mM IBMX	2	ND	PPARG expression	[62]
		400,000 (24)	DMEM 10% FBS	10 µg/mL insulin	5	70%	Oil Red O staining	[63]
		NS	αMEM 20% FBS	1 μg/mL of insulin 1 μM DEX 0.5 mM IBMX	5	80%	NS	[64]
D1-ORL-UVA	Mouse	500 (24)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 0.01 μM DEXA 50 µM INDO	9	40%	Oil Red O staining	[65]
		1000 (24)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 0.01 μM DEXA 50 µM INDO	15	ND	NS	[66]
BMS2	Mouse	50,000 (NS)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	1 µg/mL insulin 0.25 μM DEXA 0.5 mM IBMX	8	80%	Oil Red O staining	[67]
		20,000 (24)	DMEM-HG 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µM hydrocortisone 0.5 mM IBMX 5 μM Rosiglitazone 25 μM Pioglitazone 60 µM INDO	6	80%	Oil Red O staining	[68]
OB17	Mouse	NS	DMEM 10% FBS 33 μM biotin 17 μM sodium pantothenate Penicilin Streptomycin	17 nM insulin 1.5 nM T3	14	ND	NS	[69]
		3000/cm2	DMEM 200 U/mL penicilin 50 μg Streptomycin 33 μM biotin 17 μM pantothenate	17 nM Insulin 2 nM T3	19	ND	ND	[70]
		NS	DMEM antibiotics 33μM biotin 17 μM pantothenate	170 nM Insulin 1.5 nM T3	18	ND	ND	[71]
		NS	DMEM antibiotics 33 μM biotin 17 μM pantothenate	170 nM Insulin 1.5 nM T3	16	ND	ND	[72]
MS5		NS	aMEM 10% FCS	5 µg/mL insulin	15	ND	ND	[73]
		19,000–38,000 (12)	DMEM 10% FBS 100 U/mL Penicilin 100 μg/mL streptomycin 2 mM L-glutamine 10 mM HEPES 1 mM sodium piruvate 50 µM mercaptoethanol	STEMPro adipogenesis Kit	14–21	95%	Oil Red O staining	[74]
Brown adipocyte cells
C3H/10T1/2	Mouse	20,000 (cm2)	DMEM 10% FBS	5 µg/mL insulin 0.25 µM DEX 0.5 mM IBMX	12	90%	Oil Red O staining	[75]
		NS	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin 1% L-glutamine	5 µg/mL insulin 0.2 µM DEX 0.5 mM IBMX 5 µM rosiglitazone	6–8	80%	Oil Red O staining	[76]
		NS	DMEM 10% FBS	5 μg/mL of insulin 0.5 μM DEX 0.25 mM IBMX 50 μM INDO	5	35%	Oil Red O staining	[51]
		5000 (96)	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin 1% L-glutamine	10 µM insulin 1 μM DEX 0.5 mM IBMX 0.5 µM rosiglitazone 0.25 μM INDO	7	80%	Oil Red O staining	[77]
		NS	DMEM 10% FBS	500 mM IBMX 200 mM INDO 1 μM DEX 10 μM Insulin	10	>50%	Oil Red O staining	[51]
T37i	Mouse	NS	DMEM/Ham’s F12 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	0.058 µg/mL insulin 2 nM T3	8	ND	mRNA expression	[78]
		10,000 (24)	DMEM/Ham’s F12 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	0.6 µg/mL insulin 2 nM T3	9	95%	Oil Red O staining Trygliceride content assay	[79]
		NS	DMEM/Ham’s F12 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	0.12 µg/mL insulin 2 nM T3	7	35%	mRNA expression	[80]
HIB1B	Mouse	NS	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	10 µg/mL insulin 0.25 μM DEX 0.5 mM IBMX	8	ND	lipolytic protein markers	[81]
		NS	DMEM 10% FBS 100 U/mL penicillin 100 μg/mL streptomycin	0.12 µg/mL insulin 0.25 μM DEX 0.5 mM IBMX 1 nM T3	7	90%	Oil Red O staining	[82]
BFC-1	Mouse	NS	DMEM 10% FBS 200 U/mL penicillin 50 μg/mL streptomycin	0.058 µg/mL insulin 2 nM T3	NS	ND	mRNA expression	[83]
Human in vitro cellular model
White adipocyte cells
Cell line	Organism	Initial cell confluency (n well in plate/cm2)	Culture medium employed	Differentiation cocktail employed	Differentiation days	Differentiation Efficiency (based on ORO staining and microscopic analysis)	Validation Method	Reference
HS-5	Human	NS	10% FBS 100 U/mL penicillin 100 μg/mL streptomycin 1% L-glutamine	OriCellTM Supplement For Human Related Stem Cells Adipogenic Differentiation A-I, A-II & B	NS	80%	Oil Red O staining	[84]
		20,000 (75 cm2)	DMEM 10% FBS 1% antibiotic/antimycotic	10 µg/mL insulin 500 µM DEX 0.5 mM IBMX 100 µM INDO	19	90%	Oil Red O staining	[85]
		300,000 (cm2)	αMEM 10% FBS	5 µg/mL insulin 0.5 µM DEX 0.25 mM IBMX 50 µM INDO	14	80%	Oil Red O staining	[86]
LiSa-2	Human	10,000 (cm2)	DMEM/Ham’s F12 10 mg/mL transferrin 15 mM NaHCO3 15 mM HEPES 33 mM biotin 17 mM pantothenate 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 0.02 nM T3 1 µM cortisol	20	30%	Sudan red and hematoxylin	[87]
		NS	DMEM/Ham’s F12 2% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 1 mM DEX 1 nM T3	9	ND	Oil Red O staining	[88]
		NS	DMEM-F12 33 µM biotin 17 µM pantothenic acid 100 U/mL penicillin/streptomycin 10% FBS	0.005 µg/mL insulin 0.02 nM T3 1 µM cortisol	NS	ND	Microscopy	[89]
SGBS	Human	NS	DMEM-F12 33 µM biotin 17 µM pantothenic acid 100 U/mL penicillin/streptomycin 10% FBS	0.1 µg/mL insulin 0.025 μM DEX 0.5 mM IBMX 2 µM rosiglitazone 0.02 nM T3 0.1 µM cortisol	28	ND	Microscopy	[89]
		800,000 (15 cm2)	DMEM-F12 66 µM biotin 33 nM pantothenic acid 2% penicillin/streptomycin antibiotics	0.5 µg/mL insulin 0.095 µM DEX 1.9 mM IBMX 7.6 µM rosiglitazone 0.76 nM T3 380 nM cortisol	12	90%	Secretome	[90]
		NS (6)	DMEM-F12 3 µM biotin 17 µM pantothenic acid 0.5% penicillin 0.5% streptomycin 0.5% amphotericin B	0.116 µg/mL insulin 0.25 µM DEX 0.5 mM IBMX 2 µM troglitazone 0.2 nM T3 100 nM cortisol	7	ND	Triglyceride accumulation assay	[91]
		NS (12)	DMEM-F12 33 µM biotin 17 µM pantothenic acid 100 U/mL penicillin/streptomycin 10% FBS	0.1 µg/mL insulin 0.025 µM DEX 0.5 mM IBMX 2 µM rosiglitazone 0.02 nM T3 100 nM cortisol	12	ND	RNAm and Oxygen consumption	[92]
LS14	Human	500 (96)	DMEM-F-12 33 µM biotin 17 µM pantothenic acid 100 U/mL penicillin/streptomycin	1 µM insulin 0.25 mM IBMX 2 µM rosiglitazone 1 nM T3 No glucocorticoids	10	58%	Oil Red O staining	[93]
	Human	100,000 (24)	DMEM/F12 (1:1) 33 µM biotin 0.5% penicillin 0.5% streptomycin 17 µM pantothenic acid 10 µg/mL apotransferrin 200 µM ascorbate phosphate 4 µM oleic acid/BSA 4 µM linoleic acid/BSA	1 µM human insulin 1 nM T3 2 µM rosiglitazone 1 µM methoprene acid (RXR ligand) 1 µM T0901317 (LXR ligand) 250 µM IBMX No glucocorticoids	10	70–80% mentioned in the text.	ND	[94]
ASC52Telo	Human	ND	DMEM/F12 (1:1) GlutaMAX 1x 250 mM sodium piruvate 10% FBS	100 nM insulin 500 µM DEX 0.25 mM IBMX 2 µM rosiglitazone	21	<10%	Oil red O staining	[95]
		ND (12)	DMEM Low glucose 10% FBS	10 µg/mL insulin 1 µM DEX 0.5 mM IBMX	21	28%	PPARy activation in comparison to MSCs	[96]
Brown adipocyte cells
PAZ6	Human	NS	DMEM/Ham’s F12 2% FBS 100 U/mL penicillin 100 μg/mL streptomycin	5 µg/mL insulin 1 μM DEX 1 nM T3	9	ND	Oil Red O staining	[88]
		NS	DMEM-Ham’s F-12 8% FBS 15 mmol/l HEPES	5 µg/mL insulin 0.1 μM DEX 0.25 mM IBMX 1 mM pioglitazone 1 nM T3	14	ND	Oil Red O staining	[97]
		1,500,000 (NS)	DMEM-Ham’s F-12 8% FBS 15 mmol/l HEPES	5 µg/mL insulin 0.1 μM DEX 0.25 mM IBMX 1 mM pioglitazone 1 nM T3	14	ND	Oil Red O staining	[98]
		10,000 (cm2)	DMEM-Ham’s F-12 8% FBS 15 mmol/l HEPES 100 U/mL penicillin 100 μg/mL streptomycin	0.25 mM IBMX 0.1 nM T3	14	ND	NS	[99]
		NS	DMEM-Ham’s F-12 8% FBS 15 mmol/l HEPES 100 U/mL penicillin 100 μg/mL streptomycin	3 µg/mL insulin 0.1 μM DEX 0.25 mM IBMX 1000 nM pioglitazone 1 nM T3	14	ND	NS	[100]
WT-1	Human	NS	DMEM 10% FBS penicillin streptomycin	20 nM insulin 1 μM DEX 0.5 mM IBMX 0.125 mM INDO 1 nM T3	6	ND	NS	[101]
			DMEM 10% FBS	5 μg/mL insulin 1 μM DEX 0.5 mM IBMX 0.5 μM rosiglitazone	8	80–90%	Oil Red O Staining	[102]

NS: Not Specified; ND: Not determined; FBS: Bovine Fetal Serum; BCS: Bovine Calf Serum; DEX: dexamethasone; DMEM: Dulbecco’s Modified Eagle Medium; DMEM-HG: Dulbecco’s Modified Eagle Medium with high glucose; HEPES: 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid; INDO: indomethacine; IBX: isobutylmethylxanthin; Pparγ: PPARγ1/2; T3: triiodothyronine.

The analysis of adipogenic differentiation protocols across multiple murine and human cell lines reveals substantial heterogeneity in both the composition and concentrations of the compounds used—even among protocols applied to the same cell line. While insulin, DEX, and IBMX remain core components (Figure 2C), their concentrations vary dramatically, ranging from nanomolar to millimolar levels, often without clear pharmacological justification. Additional agents such as rosiglitazone, pioglitazone, indomethacin (INDO), T3, and cortisol are selectively included, further diversifying differentiation strategies. No species–specific trends in compound selection or protocol design were consistently observed, with the notable exception of T3, which appears almost exclusively in protocols targeting brown or beige adipogenesis rather than classical white adipocytes. Interestingly, some protocols—such as those used for the SGBS or LS14 cell line—employ complex cocktails containing up to six non-redundant pharmacological agents. In contrast, in other models like 3T3-F442A, OP9, or MS5 effective adipogenic differentiation has been achieved even with minimalistic approach, often relying on a single modulator such as insulin or, in the case of OP9, rosiglitazone alone (Table 1). These observations highlight not only the diversity of strategies employed but also the adaptability of certain cell lines to simplified induction conditions.

It is imperative to objectively justify the use of different drug concentrations in adipogenic differentiation protocols. While many compounds are indispensable for the induction and early or late stages of differentiation, their use at excessively high concentrations or for prolonged periods can lead to cytotoxic effects. This issue becomes particularly evident in protocols that feature atypical combinations or unreferenced concentrations of adipogenic inducers. For instance, IBMX, commonly used at concentrations between 250 and 500 µM, has been reported at a questionably high concentration of 500 mM in 3T3-L1 and C3H/10T1/2 cells [51]. Similarly, indomethacin has been used across a wide range—from 250 nM [77] to 250 µM [39], and even up to 200 mM [51]. DEX, typically used at low micromolar concentrations (0.01–1 µM), has been employed at significantly higher levels, such as 10 µM [60], 100 µM [85], and up to 500 µM [95], which can be cytotoxic depending on the cell type. Due to such toxicity, some researchers have even chosen to exclude DEX from their protocols [93]. In addition, PPARγ agonists, which have nanomolar affinity, are sometimes used at unnecessarily high micromolar concentrations and in redundant combinations, such as 5 µM rosiglitazone with 25 µM pioglitazone [42]. Or even 1 mM for OP9 cells [58]. These inconsistencies highlight the need for careful optimization and justification of experimental conditions to ensure reproducibility and facilitate meaningful comparisons across different cellular models.

2.5. Impact of the Culture Conditions on Adipogenic Differentiation

Considering that confluence is a determinant factor for the commitment of progenitor cells to specific differentiation lineages, understanding and standardizing confluence-related conditions is essential. High cell density alone has been shown to induce transcriptomic changes affecting thousands of genes relevant to differentiation potential [103], including those involved in membrane receptor expression, signal transduction, metabolic activity, and secretome composition. Also, it has been found that confluence levels directly influence lineage commitment in primary BM MSCs [104]. These findings highlight that confluence is not merely a preparatory condition, but a biological signal that can critically shape cell fate.

In this sense, a significant heterogeneity in adipogenic differentiation conditions was identified among multiple studies, particularly regarding the number of cells seeded, which ranged from 200 to 280,000 cells/cm² (Table 1 and Figure 3A). Although one might assume that authors wait for cultures to reach confluence before initiating adipogenesis, this is not always the case. In several studies, the adipogenic cocktail was applied immediately after seeding, without allowing time for cells to reach confluence. This is relevant for two reasons: first, it may limit cell cycle arrest—a critical step in preadipocyte commitment to the adipogenic lineage—and second, it alters the theoretical drug-to-cell ratio, introducing an additional layer of variability to the differentiation outcome. While our comparative analysis of seeding density and differentiation efficiency (estimated from representative microscopic images) did not reveal a consistent correlation, these findings highlight the need to better define the specific dependencies of each cell type. For example, 3T3-L1 cells lose adipogenic potential at high confluence or after repeated passaging [25], whereas OP9 cells retain this capacity, regardless of confluence level. These intrinsic differences suggest that although high confluence is often a prerequisite, it must be carefully standardized. Overconfluent cultures, for instance, may trigger stress responses or lead to cell death, thereby compromising differentiation.

We strongly recommend that future studies report both the seeding density and the conditions under which adipogenic induction is initiated—whether by indicating the confluence level at day 0 or the number of days post-confluence. Standardizing and clearly documenting these parameters would greatly enhance the reproducibility and comparability of results across different models.

In this context, it is also important to highlight that even basic experimental conditions—such as the type of culture plate used—can substantially influence adipogenic differentiation efficiency. Mehra and colleagues, demonstrated that the surface properties of culture plates, despite being often overlooked, can alter differentiation outcomes by as much as 50%, even under otherwise optimal conditions. This finding underscores the critical role of seemingly minor technical details in shaping experimental results. We therefore consider it not only relevant but necessary to include such parameters in future protocols and discussions aimed at optimizing adipogenic differentiation strategies [105]. Furthermore, in line with this, adipogenic induction media itself can regulate cell cycle genes to promote arrest and facilitate differentiation, which may explain why, under appropriate induction conditions, high differentiation efficiencies can still be achieved regardless of initial cell number [106]. These findings underscore the need for further research on each cell line, focusing on the transcriptional and proteomic mechanisms governing adipogenesis, as a significant knowledge gap remains in this field.

2.6. Impact of the Receptor Toolkit on the Adipogenic Potential of Cellular Models

From a critical perspective, the rationale behind the considerable disparity in the number of compounds included in the adipogenic cocktails and the highly variable concentrations used remains unclear. In our view, the use and replication of differentiation protocols have been passed down over decades, adapting and transposing them from model to model without due consideration of the underlying principles governing each of the components and their molecular targets. There is a wrong assumption that all models—whether murine or human, normal or disease-derived, primary or cell line-based—possess comparable adipogenic machinery, proliferate equally, reach confluence at the same time, and exhibit identical affinities for ligands.

A thorough analysis of publications employing adipogenic cocktails reveals that most studies use the full array of adipogenic compounds without exploring the presence of their molecular targets, assuming their existence (Table S1). While many of the receptors discussed earlier (Section 2.3; Figure 2) are widely expressed across various tissues, the analysis conducted shows that, for the cell lines used in adipogenesis studies, characterization is often limited, and extremely scarce for comparative studies across different cell models. In general, it is evident that the most thoroughly characterized cell line in terms of adipogenic receptor profiling is the murine 3T3-L1 line, followed by the OP9 cells, which partly explains their popularity and consideration as useful and effective models for differentiation. However, other well-characterized models, such as the murine C3H/10T1/2 cells, have not gained comparable popularity. For the remaining cell lines, the situation is largely underexplored, and the available evidence is either nonexistent or extremely limited, as is the case for MS-5, D1 ORL UVA, BMS2, LiSa-2, PAZ 6, T37i, or WT-1 cells (Figure 3B).

2.6.1. PPARγ Receptor

PPARγ is unequivocally recognized as the master regulator of adipogenic differentiation, as it is both necessary and sufficient, in most of the cases, to drive the commitment of precursor cells into adipocytes. This pivotal role is underscored by its ability to orchestrate a complex transcriptional program, binding to thousands of sites as revealed by genome-wide profiling studies [107,108]. The essential requirement of PPARγ has been rigorously demonstrated through both in vivo and in vitro knockout models, where its absence results in a complete failure of adipogenic differentiation [109].

In line with this, our receptor expression analysis (Figure 3B) confirms that PPARγ is the most extensively studied and documented receptor across all adipogenic cellular models, except for MS-5 cells, where a targeted knockout (KO) of PPARγ has been reported. Notably, authors describe the MS-5 PPARγ KO as incapable of differentiation, reinforcing its indispensable nature. However, they fail to present comparative data from wild-type and KO conditions, limiting a comprehensive evaluation [110]. Given its prominence, numerous recent reviews continue to explore the role of PPARγ in adipogenesis. While PPARγ binding sites exhibit remarkably low conservation between human and murine models, the genes regulated by PPARγ remain conserved at approximately 50–60% [107]. While this suggests a significant evolutionary conservation of PPARγ function, it raises critical questions about the validity of widely used adipogenic models such as 3T3 or OP9 in fully capturing human adipocyte biology.

Despite the extensive documentation of PPARγ, it is imperative to acknowledge that its transcriptional activity depends on its heterodimerization with RXRs. Our analysis (Figure 3B) highlights that only a limited number of studies have thoroughly investigated RXR isoform expression in adipogenic models. This is important, as PPARγ-mediated transcriptional regulation is highly dependent upon RXR interactions, which may be model-dependent. For instance, in MSCs, RXRs act as pro-adipogenic co-regulators through PPARγ activation. However, in preadipocyte models such as 3T3 cells, RXR agonists paradoxically inhibit adipogenesis [111]. These recent findings suggest that while PPARγ expression is well-documented, its mere presence should not be interpreted as a definitive indicator of positive adipogenic regulation. Instead, deeper investigations into the interplay between nuclear receptors in specific cellular contexts are required to accurately predict a model’s responsiveness to adipogenic stimuli and its differentiation potential in response to agonists.

2.6.2. Glucocorticoid Receptors

The role of GRs in cellular differentiation has been firmly established in both 3T3-L1 cells and embryonic murine fibroblasts with GR silencing [112]. The absence of GRs significantly compromises the ability to activate the adipogenic program, reducing CEBPα by 66%, CEBPβ by 50%, CEBPδ by 66%, and PPARγ by 55%, even in the presence of insulin and IBMX. While these fibroblasts retain a residual capacity for differentiation, which can be rescued via PPARγ inducers, the notion that GR is non-essential for adipogenesis in vitro is misleading. Instead, it is clear that GR plays a substantial and non-redundant role in orchestrating the adipogenic cascade, favoring adipogenic fate instead of osteogenic differentiation [112,113].

Our previous analyses reinforce this notion (Figure 3B and Table 1), demonstrating a direct correlation between GR expression, DEX sensitivity, and differentiation capacity. Notably, comparative studies reveal that 3T3-L1 cells exhibit twice the GR abundance compared to OP9 cells, a distinction that aligns with the differential GCs concentrations required for adipogenesis induction in each cell line (Table 1).

Understanding the mechanisms by which GR mediates adipogenic responses is critical, as adipogenic induction efficiency is not merely dictated by receptor presence or abundance but by a deeper assessment of its functional state. GRs are typically sequestered in the cytoplasm by chaperone proteins, awaiting ligand binding to facilitate nuclear translocation. However, emerging evidence suggests that multiple cell types harbor membrane-bound GRs capable of engaging GCs to trigger rapid, non-genomic signaling events [114,115]. The presence and functional significance of these membrane GRs in preadipocytes or MSCs remain unexplored, yet their potential role as glucocorticoid buffers warrants further investigation.

A similar paradigm may explain glucocorticoid sensitivity discrepancies across cell models, particularly in the context of human GR isoforms. The GRα isoform exists in multiple splice variants (A, B, C1-3, D1-3), whereas GRβ is an alternative, permanently nuclear isoform that cannot bind glucocorticoids yet exerts dominant-negative effects by interfering with the transcriptional activity of GRα isoforms [116]. However, the ratio between GRs isoforms in adipose precursors and its significance in adipogenesis remains unexplored.

Given that the osteoblast–adipocyte balance in MSC differentiation is profoundly influenced by GC dosage and GR signaling dynamics [117], precise characterization of GR expression levels and isoform distribution in each cellular model is highly encouraged. This knowledge is essential for optimizing adipogenic differentiation protocols and advancing our understanding of GC-mediated lineage commitment in MSCs.

2.6.3. Thyroid Hormone Receptors

Thyroid hormones (predominantly T3) play a critical role in regulating the adipogenic program and the specialization of adipocytes toward thermogenesis, particularly in the formation of brown adipose tissue. T3 exerts its effects through both direct action on thyroid response elements and through diverse thyroid receptor isoforms, as well as by regulating accessory genes such as PPARγ and C/EBPα [118,119,120]. The variability and accessibility of these determinants across different cell lines may influence the capacity of T3 to promote adipogenesis and adipocyte specialization.

Recent studies have demonstrated the role of T3 in white adipocytes derived from mice. Specifically, knockout (KO) and expression of unique isoforms of TRα or TRβ have shown that TRβ is the predominant isoform mediating T3 action on genes involved in metabolism and the adipogenic program [121]. This highlights the importance of receptor isoforms in mediating T3’s effects on adipogenesis.

In line with this, independent research has demonstrated in standard cell lines such as 3T3-L1 that dominant negative mutant isoforms of TRα or TRβ limit adipogenesis, in 94% or 54%, respectively, compared to wild-type 3T3 cells, indicating a pro-adipogenic role for both receptors [119]. Interestingly, TRα appears to play a more dominant role in this model. It is noteworthy that the abundance of TR isoforms in specific cell lines may determine their differentiation efficiency, particularly when using isoform-specific ligands.

As discussed previously, thyroid receptors are not only essential for promoting adipogenesis but also play a pivotal role in the browning of white adipose tissue, which involves the adaptive thermogenic program [122]. This is an important consideration when adding thyroid hormone to adipogenic cocktails, as while T3 enhances adipogenesis through PPARγ and C/EBPα, its effects on browning cannot be overlooked.

The expression of TRα and TRβ in commonly used models such as OP9 and 3T3-L1 further underscores the relevance of these receptors in regulating key genes and factors essential for the adipogenic program. The differential expression of these receptors may offer greater or more direct regulation over the adipogenic process. However, whether this provides an advantage or redundancy with other receptors requires further investigation, as does the role of these receptors in other cellular models that remain underexplored in the context of adipogenesis.

2.6.4. Adrenoreceptors (AdrR)

Adrenoceptors constitute central regulators of adipose tissue biology, particularly in the control of lipolysis, thermogenesis, and the sympathetic adaptation of energy metabolism [123]. Yet, their role in adipocyte differentiation itself remains elusive. The evidence presents conflicting data depending on the model used, the differentiation stage, the adrenergic receptor subtype, and the experimental conditions.

Among the β-adrenoceptors, β₃-AR is the most frequently associated with adipogenesis, particularly in murine white adipocyte models. In 3T3-L1 cells, β3-AR expression increases robustly during terminal differentiation, and its activation enhances triglyceride accumulation and promotes transcriptional programs involving PPARγ and C/EBPα, partly via phosphorylation and nuclear translocation of STAT-5. [124,125]. This suggests a possibly instructive role of β₃-AR signaling in late-stage adipogenesis. In contrast, β₁- and β₂-ARs are expressed at lower levels, and their functional relevance in adipogenesis remains less defined. While both β₁- and β₂-adrenoceptors are constitutively expressed in adipocyte models, their regulation during differentiation diverges significantly across systems. While β₁-AR expression tends to decrease during adipogenesis—a pattern observed both in murine models and primary human adipocytes—β₂-AR displays a more complex profile. In primary adipocytes, differentiation is typically accompanied by upregulation of both β₂-AR and β₃-AR, supporting their role in metabolic maturation and responsiveness to catecholamines. In contrast, LS14 cells, derived from human liposarcoma, exhibit a decline in β₂-AR expression during differentiation and do not express β₃-AR at all. This deviation underscores the limitations of tumor-derived adipocyte models in recapitulating canonical adrenergic signaling patterns observed in healthy adipose tissue [93,94]. What does appear consistent across several models is that β₃-AR activity is more closely linked to metabolic maturation and lipid storage, while β₁- and β₂-ARs are better characterized for their roles in lipolysis and acute stress response.

In human white adipocyte cell models, particularly in LS14 and ASC52telo cells, β₃-AR is either absent or expressed in a small, functionally unresponsive subpopulation. This casts doubt on the translational relevance of murine findings to human adipogenesis and indicates a need for more refined, single-cell resolution analyses of receptor functionality. Intriguingly, in ASC52telo, less than 2% of the cells responded to β-AR agonists in calcium mobilization assays, despite molecular detection of the receptors—suggesting a disconnection between expression and downstream signaling [93,94,126].

Additionally, in the HS-5 human stromal cell line, pharmacological studies have documented the presence of functional β₂-ARs, which appear to mediate prosurvival signaling. However, their specific role in adipogenesis remains undefined and requires further experimental exploration. These findings raise the possibility that adrenergic signaling in stromal-derived adipogenic models may influence not only differentiation but also cell viability.

The role of β-AR expression in adipogenesis posses another layer of complexity. Given that some components of the adipogenic cocktails, such as T3, enhance β₃-AR expression in murine adipocytes [127], GC, present in nearly all adipogenic differentiation cocktails, induces β₂-AR while repressing both β₃-AR and UCP1 [128]. This glucocorticoid-driven reprogramming may interfere with the browning potential of adipocytes in vitro, a relevant confounder considering that β₃-AR agonism is a well-established inducer of UCP1 expression and beige adipocyte recruitment. Yet, experimental validation of this effect has been inconsistent and limited to a minority of cell lines, further challenging the assumption of a conserved β-AR–UCP1 axis across models.

In stromal and bone marrow–derived cells (e.g., MS-5), β₂- and β₃-ARs are functionally present but their role in adipogenesis remains ambiguous. However, activation of these receptors leads to downregulation of CXCL12, a chemokine crucial for hematopoietic cell retention. Given that mature adipocytes also secrete significant amounts of CXCL12 [129], this link may be particularly relevant when adipogenic models are employed to study cancer–adipocyte interactions.

Taken together, current evidence suggests that adrenoceptor expression and function in adipogenesis are model- and context-dependent. The role of β₃-AR appears most closely tied to browning or terminal lipid filling and metabolic maturation, rather than commitment or early differentiation per se. Meanwhile, β₂-AR activity may reflect a glucocorticoid-influenced lipolytic program rather than an adipogenic one. Functional validation remains scarce; for instance, in ASC52telo cells, although molecular detection confirms the presence of β₁-, β₂-, and β₃-ARs, only about 20% of the population expresses each isoform, and fewer than 2% of cells respond to non-selective β-AR agonists in single-cell calcium imaging assays. This indicates that receptor presence does not guarantee functional responsiveness, underscoring the need for combined molecular and functional assessments when interpreting adrenergic signaling in adipogenesis especially in human systems, and the widespread use of GCs in differentiation protocols complicates the intepretation of the possible role of adrenoreceptors in adipogenesis.

2.6.5. Liver X Receptors (LxRs)

LxRs are members of the nuclear receptor superfamily and exist in two isoforms: LxRα and LxRβ. They act as sensors of cholesterol and oxidized lipids, regulating genes involved in lipid metabolism and energy homeostasis. In the context of adipogenesis, both isoforms are considered permissive, as their activation enhances the activity of other nuclear receptors, such as RxRs, through the formation of functional heterodimers.

In murine cells, the role of LxR in adipogenesis has been documented more extensively, particularly in the OP9 and 3T3 (3T3-L1 and 3T3-F442A) cell lines. In OP9, LxRα is expressed approximately fourfold higher than in 3T3-L1, while LxRβ is expressed at similar levels in both lines [130]. LxRα has been shown to promote the expression of PPARγ; however, LxRα agonism for two days does not significantly increase PPARγ levels in OP9 cells, in contrast to what is observed in 3T3-L1. This difference may be attributed to the fact that OP9 cells represent a different stage of adipocyte differentiation and already express high basal levels of PPARγ. Nevertheless, LxRα activation increases triglyceride accumulation in OP9, indicating a relevant metabolic role independent of further PPARγ induction [130].

In contrast, in 3T3-L1 and 3T3-F442A cells—where LxRα expression is similar to LxRβ levels—LxRα agonism clearly promotes adipogenic differentiation. This can be explained by the receptor’s ability to induce PPARγ, whose basal levels are lower in these cells compared to OP9, and by its synergistic action with RxRs. Although retinoid receptors are also expressed in OP9, some isoforms are more abundant in 3T3 cells (Figure 3B), highlighting the importance of receptor abundance and cooperative interactions in the final outcome of adipogenesis. This distinction supports the idea that LxR activation does not act independently, but rather in coordination with the nuclear receptor network.

In murine brown adipocytes, LxR’s role has not been associated with the induction of adipogenesis per se, but rather with their functional specialization. In BAT the receptor acts as a transcriptional repressor of adrenergic receptors and, consequently, of cAMP-mediated UCP1 expression [131]. Thus, LxR functions as a browning repressor in this context, underscoring a functional distinction between adipocyte types.

In human models, cell lines remain poorly characterized with respect to LxR. However, administration of LxR ligands has been shown to increase insulin sensitivity in both the LiSa-2 cell line and in primary human adipocytes [132], which may partially explain the observed increase in triglyceride content in other models. Whether this effect is specific to LiSa-2 or can be generalized to other human adipocyte models remains unknown.

2.6.6. Adenosin Receptors (AdoRs)

AdoRs belong to the family of G protein-coupled receptors (GPCRs) and are involved in multiple physiological processes, including inflammation, energy metabolism, and cellular differentiation. In mammals and rodents, four subtypes have been identified: A1, A2A, A2B, and A3 with similar homology. These are encoded by distinct genes and differ in their affinity for adenosine as well as their coupling to different G proteins. Subtypes A1R and A3R primarily couple to Gαi/o proteins, which inhibit adenylate cyclase and reduce intracellular cyclic AMP (cAMP) levels. Conversely, A2AR and A2BR couple to Gαs proteins, stimulating adenylate cyclase and increasing cAMP, which is a key second messenger and regulator of adipogenesis, and modulation of its intracellular levels by AdoRs influences adipocyte differentiation depending on receptor subtype, cellular context, and expression patterns.

In hMSCs, the A2BR subtype is most abundantly expressed under basal conditions [133]. During adipogenic differentiation, the expression of A1R and A2AR increases, facilitating the induction of transcription factors C/EBPα and PPARγ, which are critical for adipocyte commitment and lipid accumulation. Further studies of the same group employing adipogenic cell lines suggest a differential role between AdoR types, whereas A2AR plays a more prominent role in early differentiation stages, A1R predominantly promotes lipogenesis [134,135].

Expression and function of AdoRs vary across experimental models. Murine cell lines such as 3T3-L1 and 3T3-F442A are the most well documented. In such models the overexpression of A1R and A2AR enhances the expression of adipocyte genes like lipoprotein lipase (LPL) and glycerol-3-phosphate dehydrogenase, while suppressing osteoblastic markers, thereby promoting adipogenesis. In contrast, A2BR activation inhibits adipogenesis and favors osteogenesis [134,136,137].

As previously mentioned, functional divergence among subtypes is attributed not only to their differential G protein coupling, but also to the efficiency to activate intracellular pathways, e.g., both A2AR and A2BR elevate cAMP, generally favoring adipogenesis. However, in 3T3-F442A cells, forskolin-induced cAMP elevation shows a biphasic effect: low concentrations (1 nM) stimulate adipogenesis, while high concentrations (1 µM) inhibit it [138]. These observations imply that although some receptor subtypes may share redundant signaling pathways—such as the Gαs-cAMP axis—their affinity for adenosine and their intrinsic ability to effectively transduce signaling can be critical determinants in adipogenic outcomes. Subtypes with high ligand affinity and efficient coupling may promote sustained activation of key transcriptional regulators, whereas others may trigger transient or insufficient signaling, leading to divergent cell fate decisions.

Additionally, for Ob17 cells, the stable transfection of A1R expression has been linked to reduced proliferation and enhanced lipid accumulation, suggesting that AdoRs may play dual roles in promoting adipocyte maturation and arresting cell growth. However, these functions remain understudied across most models [137].

In human models the expression of AdoRs is limited and mostly restricted to the SGBS cell line, where functional expression of ARs has been detected and indicates that A1R and A2AR contribute to regulation of differentiation and lipid accumulation, similar to hMSCs [126].

2.6.7. Estrogen Receptors (ERs)

ERs have been extensively studied for their roles in the epigenetic and metabolic regulation of adipocytes, as well as in adipose tissue function. However, their involvement in adipogenic precursor cells remains relatively underexplored. Among these receptors, ERα, ERβ, and GPER have been implicated in modulating adipogenic processes. In murine models, estrogens acting through these receptors have been shown to negatively regulate the differentiation of MSCs by suppressing the expression of key adipogenic transcription factors such as PPARγ and C/EBPα [139]. In contrast, in human MSCs, estrogens not only inhibit adipogenesis but also promote osteogenic differentiation programs [140].

The presence of estrogens in the culture medium has been associated with altered expression of specific receptor isoforms. For instance, in murine MSCs, estrogen exposure enhances ERα expression [139], whereas in human adipocyte precursors, ERβ expression is upregulated approximately threefold, while GPER expression is downregulated [141]. In the widely used 3T3-L1 cell line, ERα is the predominant isoform, followed by ERβ, although variations in receptor expression levels have been reported depending on the cell source (e.g., ATCC vs. ZenBio). Higher receptor expression has also been observed in OP9 cells, suggesting these cells may be particularly susceptible to estrogen-mediated inhibition of adipogenesis [130].

In C3H/10T1/2 cells, both ERα and ERβ are expressed, and estrogen treatment leads to a significant upregulation of bone morphogenetic protein-2 (BMP-2), with ERα producing a ninefold increase compared to a threefold increase by ERβ [142]. This finding is notable because BMP-2 has been shown to act as a repressor of adipogenesis in this cell model. It promotes osteogenic differentiation while limiting lipid accumulation and downregulating adipocyte-specific markers [143,144]. mRNA expression of both ER isoforms has also been reported in other cell lines, including LiSa-2, SGBS, and HS-5 [89,145]. However, the functional roles of these receptors in MSCs and their involvement in adipogenic differentiation remain hypothetical and largely untested.

Overall, current evidence suggests a predominantly suppressive role of ERs in the adipogenic models studied. A theoretical exception may exist in the BMS2 cell line, where estrogens have been shown to regulate 85 genes through ERα, including the upregulation of SFRP1–3 [73]. These secreted frizzled-related proteins are known to act as osteogenic inhibitors, thereby potentially favoring adipogenic differentiation. However, the direct role of ERα in the differentiation of BMS2 cells has not yet been experimentally confirmed.

When designing adipogenic experiments, it is important to consider the potential influence of culture components such as serum and phenol red, both of which have been reported to exhibit estrogenic activity [146,147]. This may partly explain the variability observed in adipogenic outcomes across different cell lines. Given these considerations, estrogen should be viewed not only as a physiological regulator but also as a potential experimental confounder or control in adipogenesis studies.

3. Perspectives and Critical Considerations

Current adipogenesis models provide critical insights into human adipose tissue biology, yet their relevance is highly dependent on the specific metabolic and physiological processes being investigated. Treating adipocyte models as universally representative of adipose biology overlooks key differences in species-specific regulation, depot-specific functions, and metabolic properties. For example, murine 3T3-L1 cells have long been the standard for studying adipocyte differentiation due to their well-characterized adipogenic pathways, yet their transcriptional regulation, insulin signaling, and receptor expression differ significantly from human adipocytes. In contrast, SGBS cells, derived from human subcutaneous fat, exhibit higher insulin sensitivity, greater GLUT4 expression, and a metabolic profile closer to human physiology. However, their differentiation is highly dependent on pharmacological activation of PPARγ, which may not accurately reflect adipogenesis in vivo [148]. These differences underscore the importance of selecting adipocyte models that align with the specific research question rather than assuming a single model can universally represent adipose tissue function.

Each adipocyte model offers a distinct perspective on adipose biology based on its tissue origin and metabolic properties. Subcutaneous-derived models (e.g., SGBS) provide insight into insulin-sensitive adipose depots, as they display reduced lipolysis and an anti-inflammatory cytokine profile [149,150]. In contrast, while also evaluating subcutaneous-derived murine models (e.g., 3T3-L1), they exhibit greater insulin resistance, significant increases of leptin expression and overall higher lipolytic activity [148], making them relevant for studying obesity-related metabolic dysfunction. Meanwhile, brown (e.g., T37i) serve as powerful tools for investigating adaptive thermogenesis, mitochondrial function, and energy expenditure, due to their high UCP1 expression and ability to undergo browning [151]. Recognizing these depot-specific and metabolic differences is essential for ensuring that in vitro adipogenesis research produces physiologically relevant findings that can be translated effectively to human metabolic health and disease.

The study of adipogenesis has heavily relied on in vitro models that, while useful, often introduce methodological biases affecting the interpretation and relevance of results to human physiology. One of the most significant sources of bias arises from the overrepresentation of specific cell lines, such as 3T3-L1, which exhibit a strong dependence on PPARγ for differentiation. This has led to an oversimplified view of adipogenesis, where PPARγ is often considered the primary driver of adipocyte differentiation, neglecting the roles of other nuclear receptors. The assumption that all adipocyte models respond uniformly to differentiation stimuli is problematic, as receptor expression and metabolic responses vary significantly between murine and human-derived models.

To compensate for these differences, researchers frequently adjust differentiation protocols by increasing the concentration of adipogenic inducers or prolonging induction times. While these modifications can enhance differentiation efficiency, they artificially override intrinsic cellular differences, potentially leading to non-physiological adipocyte phenotypes. For instance, the use of high doses of rosiglitazone to compensate for the low PPARγ sensitivity in SGBS cells leads to excessive activation of key pathways such as IRS-2, p85-PI3K, and GLUT4 [152], amplifying glucose uptake and lipid accumulation beyond normal physiological levels. This overstimulation not only artificially accelerates adipogenic differentiation but also biases the interpretation of model efficiency, as pharmacologically induced lipid accumulation does not necessarily reflect functionally adequate adipogenesis. Furthermore, by forcing differentiation, intrinsic depot-specific differences may be masked, causing SGBS adipocytes to exhibit characteristics similar to visceral adipocytes despite their subcutaneous origin.

Given that adipogenesis in human tissues is a gradual and highly regulated process, such pharmacological interventions can compromise the translational relevance of the model, leading to misinterpretations regarding adipocyte biology and metabolic function. Therefore, it is crucial to consider the effects of PPARγ supraphysiological activation when evaluating adipogenic differentiation in SGBS cells, avoiding inappropriate extrapolations that could distort our understanding of physiological adipogenesis mechanisms.

Similarly, low-insulin sensitivity models, such as LiSa-2 or ASC52telo, often require prolonged differentiation periods, which may induce metabolic shifts that resemble pathological states rather than normal adipogenesis. As previously demonstrated, during LiSa-2 differentiation, hyperinsulinemia led to insulin resistance and alterations in glucose uptake and lipid metabolism, highlighting that extended differentiation protocols may promote metabolic dysfunction rather than physiologically representative adipocyte characteristics [153].

Other perspectives include the urgent exploration of models such as T37i, which exhibit exceptionally high adipogenic efficiency—even under low confluence conditions—and differentiate rapidly within a short timeframe. These features offer notable experimental advantages, such as reduced resource requirements, lower cell density dependence, and high reproducibility. However, despite their practical appeal, T37i cells remain poorly characterized at the molecular level. Moreover, their origin from a malignant hibernoma raises concerns, as their neoplastic nature may result in atypical receptor profiles or altered signaling pathways. While these characteristics enhance their utility in vitro, they warrant caution when extrapolating findings to healthy brown adipose tissue physiology.

Thus, beyond inducers, factors like cell source, differentiation duration, and receptor profiling significantly influence experimental outcomes. The lack of standardization in these variables can lead to heterogeneous results, making it difficult to compare findings across different studies. This highlights the importance of characterizing receptor expression before inducing differentiation, as well as ensuring that experimental conditions align with the physiological properties of human adipose tissue. Moving forward, the selection of adipocyte models should be guided by the specific research question, rather than relying on a one-size-fits-all approach.

4. Conclusions

Despite decades of research, the field of adipogenesis continues to rely on assumptions that often remain unverified. One of the most prevalent assumptions is the constitutive expression of key receptors targeted by differentiation cocktails. Our review highlights that in most commonly used adipogenic models, the presence and abundance of nuclear and membrane receptors—particularly those mediating insulin, glucocorticoid, thyroid, and PPARγ signaling—is frequently presumed rather than experimentally confirmed. This gap undermines the reproducibility and interpretability of findings across studies and models.

Available evidence suggests that receptor expression levels and subtypes profiles play a decisive role in the differentiation potential of adipose precursors. However, even when receptor proteins are detected, functional characterization is essential, as several examples demonstrate that receptors may be present but fail to signal effectively, or may lead to unexpected anti- or pro-adipogenic effects depending on the cellular context.

It is increasingly evident that no universal differentiation strategy will suit all adipogenic models. Differences in donor origin, species, cell lineage, and intended adipocyte phenotype (e.g., white, beige, or brown) demand tailored approaches. Current adipogenic cocktails—often designed based on historical precedent rather than molecular rationale—should be re-evaluated and adjusted according to the specific receptor toolkit of the selected model. Doing so could significantly improve the efficiency, consistency, and physiological relevance of in vitro adipogenesis studies.

Ultimately, integrating receptor profiling as a standard step in model validation may pave the way for more accurate, reproducible, and translatable adipogenesis research.