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20 March 2026

20 Pages

Toward Comprehensive In Vitro Evaluation of Serum Albumin Binding of Per- and Polyfluoroalkyl Substances

and
Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
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Author to whom correspondence should be addressed.
†
Current address: ICF Corporation, Reston, VA 20190, USA.
J. Xenobiotics2026, 16(2), 54;https://doi.org/10.3390/jox16020054
This article belongs to the Section Emerging Chemicals
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Abstract

Per- and polyfluoroalkyl substances (PFAS) constitute a large and chemically diverse class of synthetic compounds characterized by one or more fully fluorinated methyl or methylene groups. Many PFAS are toxic, environmentally persistent, bioaccumulative, and highly mobile, resulting in widespread contamination and biological exposure. Across taxa PFAS exhibit affinity for proteins and preferentially accumulate in protein-rich, highly perfused tissues. Protein binding critically influences PFAS distribution, bioaccumulation, toxicity, and elimination. A variety of different approaches for determining binding affinity have existed for decades; however, depending on experimental conditions, calculated affinities can vary over multiple orders of magnitude which limits comparison of protein–PFAS binding affinities across studies and across PFAS chemical space. Addressing this limitation requires robust and standardized experimental platforms capable of rapidly generating quantitative binding data. Among the most important targets is serum albumin—the principal transport protein in vertebrate blood—which plays a central role in governing PFAS toxicokinetics. This review summarizes current methodologies for measuring protein–PFAS binding affinities, evaluates the strengths and limitations of each approach, synthesizes the existing literature on serum albumin–PFAS interactions, and highlights differential scanning fluorimetry as a rapid, reproducible, and sensitive technique for in vitro assessment of relative protein–PFAS binding.

1. Introduction

1.1. What Are PFAS?

Per- and polyfluoroalkyl substances (PFAS) are a structurally diverse class of thousands of synthetic chemicals characterized by the presence of at least one fully fluorinated methyl or methylene group [1,2]. Among these, the most extensively studied are the perfluoroalkyl acids (PFAAs)—amphiphilic compounds composed of a fluorinated hydrophobic region and a hydrophilic head group (Figure 1A). This amphiphilic nature imparts commercially valuable water- and oil-repellent properties, a key driver of PFAS use across a broad range of consumer and industrial products, including non-stick coatings, lubricants, firefighting foams, cosmetics, food packaging, textiles, cleaning products, pesticides, electronics, and medical appliances [3,4].
Figure 1. Surfactant properties of PFAAs at air–liquid and liquid–liquid interfaces. (A) Structural representation of PFOA (CAS 335-67-1), with oxygens in the hydrophilic headgroup represented in red and fluorine atoms in the hydrophobic tail represented in blue. (B) Representation of surfactant properties of PFOA, with hydrophilic headgroup represented in red and fluorinated hydrophobic tail represented in blue. The PFOA molecules will orient at the air–water or octanol–water interfaces, with the hydrophilic head oriented towards the water phase, and the hydrophobic tail oriented toward the air or octanol phase.
The exceptional strength of the carbon–fluorine bond renders PFAS extremely stable and resistant to degradation, leading to widespread environmental persistence [5]. Consequently, PFAS are ubiquitously detected in environmental and biological samples worldwide and are present in more than 98% of human serum samples in the United States [6]. Beyond their persistence and bioaccumulative potential, global PFAS contamination poses significant health risks. Exposure has been linked to toxicity across multiple organ systems, including immune, renal, hepatic, reproductive, endocrine, and nervous systems [7,8,9,10,11]. Reflecting these risks, the International Agency for Research on Cancer (IARC) in 2023 classified perfluorooctanoic acid (PFOA) as carcinogenic to humans (Group 1) and perfluorosulfonic acid (PFOS) as possibly carcinogenic (Group 2B) [12].

1.2. Challenges in PFAS Research

Although PFAS are recognized as persistent organic pollutants (POPs) of global concern, most toxicological and exposure research has focused on a limited subset of PFAA congeners—chiefly PFOA and PFOS. These studies have informed regulatory actions restricting the production and use of a few PFAS chemicals [13,14,15]. However, such restrictions have unintentionally driven the development of structurally related replacement PFAS, designed with shorter alkyl chains or ether linkages to reduce bioaccumulation. Many of these substitutes have proven to be “regrettable substitutions,” remaining highly persistent, widely used, and poorly characterized toxicologically [16,17].
The scope of the issue is illustrated by the U.S. Environmental Protection Agency’s PFASSTRUCTV5 database, which currently lists more than 14,000 distinct PFAS chemical structures [18]. Most PFAS chemicals remain unregulated, with limited information available on their toxicity, bioaccumulative potential, or basic physicochemical properties.
Researchers also face substantial challenges in characterizing PFAS behavior due to their chemical diversity and unique physiochemical properties. PFAAs act as surfactants with strong affinities for air–liquid and liquid–liquid interfaces, orienting hydrophilic heads toward water and hydrophobic tails toward the opposing phase (air, octanol, etc.; Figure 1) [19,20,21,22]. Consequently, conventional methods for determining the octanol–water partition coefficient (KOW)—a standard predictor of bioavailability and distribution for organic pollutants—are not suitable for PFAS [23,24]. Although alternative approaches have been explored, KOW is not an appropriate descriptor for PFAS toxicokinetics because these compounds preferentially bind to proteins rather than partitioning into lipid-rich tissues [24,25,26,27]. Thus, data describing protein–PFAS interactions are far more informative for understanding PFAS distribution and accumulation.

1.3. PFAS and Protein Binding

The proteophilic nature of PFAS distinguishes them from lipophilic POPs [28,29]. PFAS bind to proteins and accumulate predominantly in protein-rich, highly perfused tissues such as blood, liver, and kidney. Investigating protein binding is critical for understanding PFAS toxicokinetics and biological impacts, as well as the determination of key molecular mechanisms related to the nature and structure of PFAS binding sites, the resulting PFAS binding-induced conformational changes, and the competitive displacement of endogenous ligands [30,31,32,33,34,35,36].
Circulatory protein binding is a fundamental determinant of PFAS bioaccumulation and distribution [29,37,38,39,40]. Serum albumin is the most abundant protein in vertebrate blood and accounts for >50% of total plasma protein. It is a major carrier of endogenous and exogenous ligands including fatty acids, bile acids, vitamins, hormones, metals, ions, steroids, and pharmaceuticals [41,42,43,44]. Serum albumin is also the predominant blood transport protein for many PFAS congeners, that bind with affinities comparable to those of endogenous and exogenous albumin ligands [28,39,41,45,46,47,48,49]. Albumin binding substantially prolongs circulatory half-life and delays elimination for many of its ligands—including PFAS—and is thus a major pharmaco- and toxicokinetic determinant [38,50,51].
While the protein-bound fraction of PFAS tends to persist longer in serum, the unbound fraction is more bioavailable for tissue transport, and may exhibit increased reactivity and toxicity [36,38,40,52,53,54,55]. Physiologically based pharmacokinetic (PBPK) modeling studies indicate that the plasma-free fractions of PFOA and PFOS are most likely to distribute throughout the body and enter tissues and cells [38,56,57,58,59,60]. The PBPK models developed by Ng and Hungerbühler and Cheng and Ng highlight serum albumin as a key predictive parameter for PFAA concentrations and tissue distributions (Figure 2) [38,60]. The permeability limited PBPK model of Cheng and Ng which was trained with in vitro toxicokinetic data, emphasized the role of systemic circulation and albumin binding within interstitial spaces in determining organ uptake of PFOA [38,61,62]. The model’s high predictive accuracy for PFOA tissue distribution in rats—validated against in vivo data—was attributed to the inclusion of protein interactions [38,63,64,65].
Figure 2. Simplified PBPK model schematic for PFAA transport. The presence of serum albumin in the blood plasma and vascular space of organs is shown. Blood flow rate, cell membrane permeability, and protein binding are all critical parameters in the determination of tissue concentrations of PFAAs.
Given the immense structural diversity of PFAS, traditional in vivo toxicokinetic testing for every congener is infeasible due to cost and time constraints. The U.S. National Academy of Sciences identified the need for high-throughput new-approach methodologies (NAMs) to address these and other critical data gaps and limitations in chemical toxicity testing [66,67,68]. Computational tools such as PBPK and quantitative structure–activity relationship (QSAR) models show promise for elucidating PFAS toxicokinetics, including the influence of protein binding on half-life, tissue distribution, and elimination [29,35,37,38,40,69]. However, model accuracy remains limited by the paucity of experimentally derived physicochemical data, particularly protein binding affinities. Although well-studied PFAS such as PFOA can be modeled accurately, data for most congeners remain insufficient [38,69,70,71].
Our laboratory’s previous work on albumin–PFAS interactions identified certain PFAS that do not bind serum albumin, emphasizing the need for empirical data across structurally diverse PFAS classes [45,72]. Models built on narrow congener sets cannot accurately represent the full chemical diversity of PFAS. The lack of comprehensive PFAS–protein binding data has been widely recognized as a key limitation in improving predictive accuracy [38,56,69,70,71]. Furthermore, across published studies, substantial methodological differences have yielded albumin–PFAS binding affinity estimates that vary by several orders of magnitude for the same congener (see Table 1). This variability complicates data interpretation and precludes cross-study comparison.
Table 1. Reported serum albumin–PFAS association constant (Ka) values in the literature.
The goal of this review is to: (1) summarize major experimental approaches used to assess albumin–PFAS binding, (2) review the current state of the literature, and (3) highlight the strengths and limitations of differential scanning fluorimetry (DSF)—a rapid, cost-effective assay for quantifying albumin–PFAS binding. DSF provides a powerful high-throughput platform for screening many of the diverse PFAS chemistries and generating essential physicochemical data to close decades long knowledge gaps in PFAS toxicokinetics.

2. Methods for Evaluation of Albumin Binding of PFAS

Serum albumin binding to PFAS is a reversible, non-covalent interaction that can occur at multiple sites on the protein, making albumin a complex system to study relative to traditional receptor–ligand models. Human serum albumin (HSA) contains seven distinct fatty acid binding sites (FA sites 1–7) and two major drug binding regions known as Sudlow sites (Figure 3) [42,95,96,97,98]. Sudlow site I, located in subdomain IIA and overlapping with FA site 7, is the classical warfarin-binding site and binds bulky, heterocyclic anionic compounds. Sudlow site II, found in subdomain IIIA and overlapping with FA sites 3 and 4, is smaller and more rigid, favoring extended aromatic compounds with carboxylic groups such as ibuprofen. Sudlow sites I and II, multiple FA sites, and even interstitial regions between FA sites have all been identified as PFAS binding domains on albumin, with binding driven by hydrophobic, electrostatic, and van der Waals interactions [32,47,54,80,81,83,85,88,98,99,100,101,102]. Structural variation across PFAS congeners can markedly influence binding affinity, resulting in preferential interaction with different albumin sites [85]. Consequently, multiple experimental methodologies have been developed to probe distinct aspects of albumin–PFAS binding.
Figure 3. Three-dimensional structural representation of HSA (PDB 1AO6), with the three homologous domains shown in green (subdomain I), blue (subdomain II), and magenta (subdomain III). The seven major fatty acid (FA) binding sites are shown, as well as the major drug binding sites, Sudlow I and Sudlow II, which overlap the FA7 and FA3/4 sites respectively. Image created with PyMOL v3.1.
Experimental techniques used to evaluate serum albumin binding to PFAS generally fall into several broad categories—separative, spectroscopic, surfactant-based, calorimetric, mass spectrometry (MS)-based, and other methods which yield both qualitative and quantitative binding data [35,103]. This review focuses on in vitro methods that provide experimentally determined binding affinity data allowing calculation of association (Ka) or dissociation (Kd) constants. Common approaches for quantifying albumin–PFAS binding include separative (equilibrium dialysis, ultrafiltration centrifugation, size exclusion chromatography), calorimetric (isothermal titration calorimetry), MS-based (ESI-MS, nanoESI-MS), and spectroscopic (fluorescence quenching, nuclear magnetic resonance) methods.

2.1. Separative Methods

Separative methods physically separate free and bound ligand, enabling quantification of each fraction and facilitating straightforward analysis of protein–ligand interactions [103,104]. The most widely used separative approach is equilibrium dialysis [103,105]. This technique employs a dual-chamber apparatus divided by a semi-permeable membrane that is impermeable to protein but permeable to small PFAS molecules (Figure 4). One chamber contains serum albumin, and the other holds buffer with the PFAS of interest. During incubation, PFAS diffuse across the membrane until equilibrium is reached, allowing measurement of free PFAS in the protein-free chamber [35,103]. Although equilibrium dialysis is simple and low-cost, equilibration is slow, typically requiring 4–120 h [35,55,73,74,76,77,78,79,103,105]. Rapid equilibrium dialysis (RED) reduces equilibration time but still requires about 4 h [105,106]. The assay often demands preliminary testing to determine the time to equilibrium and can be limited by poor solubility of large or hydrophobic PFAS, necessitating higher concentrations [54,103]. Quantification of PFAS following equilibration usually relies on quantitative MS methods that introduce added expense and time.
Figure 4. Graphical representation of the experimental set-up used for equilibrium dialysis.
Ultrafiltration centrifugation, another separative approach, uses a two-chamber setup but applies centrifugal forces to accelerate fluid transfer [103,107]. This reduces equilibration time compared to dialysis but often results in nonspecific adsorption of PFAS to the membrane, reducing reproducibility [35,51,105,108]. Analysis also requires MS analysis which again increases cost and lowers throughput.
Another related separative method, the micro-desalting column technique, uses size-exclusion chromatography to separate free PFAS from albumin-bound complexes [51]. In this approach, small PFAS molecules penetrate porous solid phase particles and elute later than the larger albumin–PFAS complexes [51,103]. Han et al. achieved over 98% recovery of PFOA, resolving adsorption issues seen with ultrafiltration [51]. However, low protein recovery and inefficiency limit this method’s usefulness [35,103,108]. Overall, separative methods are simple and reliable but slow, labor-intensive, and unsuitable for high-throughput PFAS screening.

2.2. Calorimetric Methods

Calorimetric methods measure heat changes during molecular interactions to quantify binding kinetics [109,110]. Isothermal titration calorimetry (ITC), the most common calorimetric approach, has been used to assess HSA–PFAS binding [80,81]. ITC titrates PFAS into albumin samples while monitoring heat exchange [109]. It provides a comprehensive thermodynamic profile, including estimates of binding constants (Kd), stoichiometry, enthalpy (ΔH), entropy (ΔS), heat capacity change (ΔCp), and Gibbs free energy (ΔG) [35,109,110,111,112]. Despite its detail, ITC has low throughput, requires large protein concentrations, and is expensive, making it impractical for large-scale PFAS screening.

2.3. Mass Spectrometry Approaches

MS-based approaches ionize mixed protein–PFAS samples and detect complexes by their mass spectra [75,82,111,113]. Native MS studies commonly couple electrospray ionization (ESI) with MS to transition samples to the gas phase while preserving non-covalent interactions. The highly sensitive ESI-MS and nanoESI-MS approaches require small sample volumes and provide stoichiometric and affinity data. However, instrumentation costs are high which limits availability, and technical expertise is required to select the capillary temperature, cone voltage, and collision energy that preserve albumin–PFAS complexes [31,35,114]. Variability, lack of reproducibility, and differences in sensitivity across congeners (e.g., short chain and long chain) are also common due to buffer composition effects on protein stability and MS sensitivity [31,35]. While powerful, MS approaches lack the simplicity, repeatability, and accessibility of other techniques.

2.4. Spectroscopic Methods

Spectroscopic methods monitor changes in light emission or absorption, usually fluorescence, to infer protein–ligand binding. They are the most commonly applied techniques for evaluating albumin–PFAS interactions and can include analysis of fluorescence quenching, fluorine nuclear magnetic resonance (19F NMR), fluorescence displacement, microscale thermophoresis (MST), and differential scanning fluorimetry (DSF). Fluorescence-based assays are advantageous for their sensitivity, flexibility across molar ratios, and high signal-to-noise ratios.
Fluorescence quenching is a frequently used technique that measures reduced intrinsic fluorescence from albumin tryptophan residues (Trp134 and Trp213 in BSA; Trp214 in HSA) upon PFAS binding [83,85,86,87,88,89,90,91]. Thus, this approach relates the degree of fluorescence quenching induced by conformational changes to the structure of albumin, resulting from PFAS binding [115]. However, its dependence on protein conformational change limits its applicability to smaller PFAS that fail to induce detectable structural shifts. Short-chain PFAS, such as PFBA, PFPeA, PFHxA, and PFBS, often show minimal fluorescence quenching [85,86,88,116].
For fluorine nuclear magnetic resonance (19F NMR) spectroscopy, the resonance shifts of fluorine nuclei during PFAS binding is quantified, allowing high-resolution, site-specific assessment of binding stoichiometry and kinetics [51,83,84]. Although powerful, it is best suited to high-affinity interactions, requires large protein volumes, is costly, and has low-throughput.
Fluorescence displacement assays use site-specific probes that fluoresce when bound and are quenched upon displacement by PFAS [47,85]. Chen and Guo quantified PFAS binding to HSA at Sudlow sites I and II using this approach, and while capable of site-specific analysis, interpretation is complicated by simultaneous binding at other albumin regions, leading to complex fluorescence patterns [85]. Microscale thermophoresis (MST) measures the movement of fluorescent molecules across a temperature gradient to quantify binding-induced changes [92,111,117]. MST offers high sensitivity and requires small volumes but is low-throughput and not scalable [111,118].
Across all experimental approaches, PFAS–albumin binding measurements are sensitive to experimental differences in temperature, pH, buffer composition, and protein and ligand concentrations. Consequently, reported affinities can differ by several orders of magnitude across studies based on experimental approach and conditions (Table 1). Quantitative values are most meaningful when interpreted as relative affinities derived under identical experimental conditions. While all these approaches have advanced understanding of PFAS–albumin interactions, the field still lacks high-throughput, directly comparable quantitative data.

3. Differential Scanning Fluorimetry (DSF)

3.1. Principles and Uses of DSF

The folding status of native proteins is a thermodynamically driven process, and protein stability is directly linked to the Gibbs free energy (ΔG) of the system, which is influenced by environmental factors such as temperature. As temperature increases, protein stability decreases, leading to denaturation and a corresponding increase in Gibbs free energy. The protein melting temperature (Tm) is defined as the temperature at which equilibrium is achieved, with 50% of the protein remaining folded and 50% denatured.
The differential scanning fluorimetry (DSF) or thermal melt assay was introduced in the 1990s as a preliminary drug discovery screening method to identify bioactive molecules that interact with specific target proteins [111,119]. The core principle of DSF is that both specific and non-specific binding interactions can alter protein stability. Although important exceptions exist, protein–ligand complex formation often reduces the Gibbs free energy (ΔG), proportional to the DG of ligand binding, resulting in an increase in protein thermal stability that is observed as a change to the melting temperature (ΔTm). Under controlled conditions (e.g., same ionic strength and protein concentration) DSF directly measures these stability changes by quantifying shifts in protein melting temperature (ΔTm) in response to differing ligand concentrations [118,119,120,121]. While widely used to identify stable buffer conditions for biological pharmaceuticals, a relevant application for binding of toxicants is assessing thermal stabilization due to protein–ligand binding by calculating ΔTm across a range of ligand concentrations (Figure 5B). Moreover, through non-linear regression modeling of the concentration response relationship, the resulting ΔTm data from DSF can be used to calculate the binding affinity (Kd) of the protein–ligand interactions, following the protocol described by Starnes and Belcher [120].
Figure 5. Characteristic fluorescence profiles in the DSF assay. (A) Graph depicting the typical DSF fluorescence profile for serum albumin, represented as RFU data normalized to percent maximum fluorescence, from 60 to 85 °C. Panel A depicts changes in fluorescence emission as (1) the native protein minimally interacts with the fluorescent dye, (2) protein unfolding occurs and dye fluorescence increases, and Tm is defined at the temperature in which 50% of proteins are unfolded, (3) all proteins are completely denatured, and (4) fluorescence decreases due to protein aggregation at high temperature. (B) Graph depicting the typical DSF fluorescence profile, represented as RFU data normalized to percent maximum fluorescence, from 60 to 85 °C of the native protein in black, and the ligand-bound protein in pink. ΔTm is calculated by subtracting the Tm of the native protein (black) from the Tm of the ligand-bound protein (pink).
Using the DSF assay, thermal denaturation of a protein across a specified temperature range is monitored by measuring either intrinsic or extrinsic fluorescence. For intrinsic fluorescence-based DSF, fluorescence emitted by the protein itself is tracked to evaluate signal changes associated with environmental variations such as ligand binding. In extrinsic fluorescence-based DSF, a fluorescent environmental sensing dye is incorporated into the assay medium [120]. These dyes are typically quenched when free in solution but exhibit increased fluorescence as protein unfold. While not suited for amphiphilic PFAS, SYPRO Orange is a commonly used environmental sensing fluorescent dye with high signal-to-noise ratio that binds to hydrophobic residues which become exposed as the protein is thermally denatured.

3.2. DSF for the Measurement of Albumin–PFAS Binding

The differential scanning fluorimetry (DSF) assay represents a relatively novel approach for measuring serum albumin binding to PFAS, providing significant advantages for rapid, large-scale characterization across diverse PFAS structures. However, the fluorescent dye commonly used in DSF—SYPRO Orange—is incompatible with assays containing surfactants or detergents [45,121,122]. Because many PFAS display surfactant properties due to their amphiphilic structure, SYPRO Orange cannot be used to assess albumin–PFAS interactions. Furthermore, the multiple hydrophobic binding sites of serum albumin bind this hydrophobic dye to result in an unacceptably high background fluorescence. Thus, DSF-based measurement of albumin–PFAS binding requires alternative fluorescence detection strategies.
Two suitable alternatives that have demonstrated compatibility with albumin–PFAS binding assays are intrinsic tryptophan fluorescence and environmentally sensitive molecular rotor dyes. Alesio and Bothun used a label-free intrinsic fluorescence DSF approach to measure BSA–PFAS binding [93]. That approach relied on PFAS-induced conformational changes in albumin that quenched intrinsic fluorescence of tryptophan residues and was able to quantify albumin affinities for all PFAS tested (Table 1). Advantages of the label-free design include reduced reagent requirements and elimination of any potential binding interference from extrinsic dyes [93,123]. However, this approach may share the limitations of other fluorescence quenching-based techniques, where some PFAS including short and ultrashort chain PFAS like trifluoroacetic acid do not induce sufficient conformational change to produce detectable quenching [88,116].
Environmentally sensitive fluorescent molecular rotor dyes offer a second DSF-compatible alternative. These dyes fluoresce when changes in environmental conditions—such as local viscosity—restrict intramolecular rotation of the fluorophore [124,125]. Several molecular rotor dyes, including julolidine-based dyes and 4-(4-(Dimethylamino)styryl)-N-methylpyridinium iodide (DASPMI) have been validated for use in DSF assays measuring albumin–PFAS interactions [45]. Molecular rotor dyes consist of three key components: an electron donor, an electron-acceptor, and a rotatable spacer that separates the donor and acceptor (Figure 6).
Figure 6. Structural composition of molecular rotor dyes. (A) The generic structure of a molecular rotor dye and (B) the structure of common julolidine-based molecular rotor dye 9-(dicyanovinyl) julolidine (DCVJ), compatible with albumin–PFAS binding analysis in the DSF assay. The electron donor is shown in red, spacer subunit gray, and electron acceptor is shown in blue. Curved arrows indicate bonds around which intramolecular rotation occurs. Adapted from [125].
At baseline, molecular rotor dyes exhibit low fluorescence output due to intramolecular rotation of the electron acceptor and donor around the central spacer. When the viscosity of the surrounding microenvironment increases, this rotation becomes restricted, resulting in quantity increases in fluorescent emissions. Studies have demonstrated that albumin–PFAS binding produces sufficient changes in environmental viscosity to spatially restrict molecular rotor dyes and generate measurable fluorescence increases during DSF assays [45,72,122,126].
The differential scanning fluorimetry (DSF) assay represents a relatively novel approach with substantial advantages for large-scale and rapid characterization of protein binding to PFAS, particularly serum albumin–PFAS interactions [53]. Compared with other methods described here, DSF is rapid, accessible, robust, and reproducible [111,121,127,128]. Many of the other protein–ligand binding methods described here are time-consuming, labor intensive, and inaccessible whereas a standard DSF experiment typically requires only one to three hours using universally available instrumentation, and straight forward data analysis that can be completed the same day. Furthermore it is flexible in its ability to assess binding of any available soluble protein and ligand combination.
During the assay the sample is heated in a real-time polymerase chain reaction (PCR) thermal cycler, fluorescence intensity increases as the microenvironment of the dye/protein interaction is altered as the native protein structure is lost as temperature increases. In some cases, the denatured protein can aggregate at even higher temperatures causing a reduction in fluorescent output (Figure 5A). The assay is conventionally run in 96- or 384-well plate formats, allowing for simultaneous analysis of multiple experimental conditions. Not only is the assay high-throughput, but the ability to have each run in a high-capacity format enables simultaneous measurement of protein melting across multiple controls and experimental ligands and concentrations in a single run. This compatibility makes DSF well-suited for high-throughput screening. The method’s accessibility is another major advantage, as DSF assays use widely available and relatively inexpensive real-time PCR instrumentation. Furthermore, DSF requires relatively small protein volumes, is comparatively inexpensive, and accessible to laboratories in the global south and other low- and middle-income countries.
In addition to accessibility and rapid assay time—qualities that facilitate high-throughput screening—DSF is also a robust and reliable method for protein–ligand binding analysis and is especially useful for detecting low-affinity interactions. Unlike some conventional methods, DSF is well-suited. Other methodologies have failed to quantify or even detect binding between serum albumin and low-molecular-weight PFAS congeners; in contrast, DSF reliably detects and quantifies binding for short and ultra-short chain PFAS such as PFBA and trifluoroacetic acid [45,72,94].
Beyond determining binding affinities, DSF provides valuable insight into the thermodynamic nature of protein–ligand interactions. Changes in protein melting temperature (ΔTm) observed during albumin–PFAS binding offer information about the stabilizing or destabilizing effects of the ligand. Although most protein–ligand interactions stabilize the protein, some PFAS produce destabilizing interactions, lowering the melting temperature relative to the native protein [129,130,131]. Identifying PFAS congeners with destabilizing interactions—indicated by negative ΔTm values—may provide clues to mechanisms of PFAS-induced protein disruption and guide additional mechanistic investigations.
The magnitude of ΔTm is also informative. The ΔTm reflects ligand-induced shifts in melting temperature, arising from the combination of enthalpic and entropic factors that govern protein–ligand interactions. Albumin binding to many PFAS is driven largely by non-covalent hydrophobic, electrostatic, and van der Waals interactions, with large ΔTm shifts being characteristic of entropically driven, hydrophobic interactions [129,132]. Thus, when interpreted alongside dissociation constants (Kd), considering the ΔTm contributes to a more holistic understanding of the thermodynamics of binding. However, careful interpretation of ΔTm is essential. For example, because ΔTm is determined by both enthalpic and entropic contributions, it is not directly correlated with binding affinity [132,133].

3.3. Limitations of DSF

Like all methodological approaches, the differential scanning fluorimetry (DSF) assay has limitations that must be considered when designing, conducting and interpreting the results of the binding experiments. One key requirement is that the ligand must be sufficiently soluble in buffered physiologically aqueous solutions—which can only include very low concentrations of chemical solvents such as methanol or DMSO—and must be validated and properly controlled to ensure that solvent addition does not disrupt protein structure [45]. Accordingly, DSF for analysis of PFAS shares a common limitation with other albumin–PFAS binding methods: the poor aqueous solubility of many PFAS congeners, particularly long-chain PFAAs (C12 and above), prevents accurate measurement of their binding in the assay. Thus, DSF measurements are restricted to PFAS congeners with adequate solubility. Additionally, although preliminary analyses indicate that PFAS have not interfered with the assay, PFAS congeners that emit intrinsic fluorescence may prove incompatible if their fluorescence overlaps with that of the reporter dye. Furthermore, environmental sensing dye chemistry must be confirmed to not artifactually alter the protein/PFAS interactions. Such potential problems are easily addressed but require careful assay optimization for each protein of interest and the inclusion of all necessary controls in each replicate experimental assay.
As discussed more broadly for HSA–PFAS binding, direct comparison of absolute binding affinities (Ka or Kd) across different experimental designs can be challenging and must be avoided. This issue is particularly relevant to DSF, which derives affinity estimates from temperature-dependent changes in protein melting, rather than from measurements taken at a constant temperature. As a result, DSF-derived affinity values—although consistently within the range of those produced by other methods—tend to yield lower values than obtained at a fixed physiological temperature [45,121,134]. Therefore, DSF binding affinities should be interpreted as relative measures when comparing to other methodologies or directly compared only with affinities generated under the same DSF experimental conditions. However, DSF is highly amenable to specifically address how changes in the ionic strength or the pH of specific biological compartment may influence binding. For example our lab has ongoing DSF studies examining how PFAS binding by different food and dairy proteins are influenced by the differing pH of the gastrointestinal tract.
The data analysis procedures used to determine protein melting temperatures and relative binding affinities in DSF employ straightforward fitting models, increasing accessibility and ease of use. However, this simplicity also risks using oversimplified models for albumin–PFAS interactions, as serum albumin contains multiple binding sites and binding regions. It is important to appreciate that the affinity values for albumin, and other such proteins with numerous binding sites, represent the average affinity of binding and does not inform on specific binding strength of a specific site. However, as additional proteins are analyzed, DSF data may be fitted using several models depending on understanding of the protein of interest—including single-site, cooperative, or sequential binding models— thus care must be taken to choose a model that accurately reflects both preexisting understanding of protein/ligand interactions and the resulting DSF data [121,135]. Regardless of model selection, DSF alone does not provide binding-site resolution for proteins containing multiple binding sites and is therefore not appropriate for studies aimed at determining PFAS interactions at a specific albumin binding site. This limitation can be overcome by combining DSF-derived data with computational modeling to define the molecular mechanisms of ligand binding.
Another limitation noted in previous studies is that DSF can yield both false positive and false negative results [111,136]. The likelihood of these outcomes depends heavily on the experimental approach. False negatives may arise when competing enthalpic and entropic contributions obscure ligand-induced shifts in protein melting temperature, producing no measurable ΔTm [132,133,137]. Conversely, false positives may occur if the ligand emits intrinsic fluorescence, generating elevated background signal [137,138]. These risks are minimized when binding is evaluated in rigorously controlled experiments across multiple ligand concentrations using a dose-dependent strategy, as employed in recent studies of albumin–PFAS binding [45,120]. Understanding the chemistry of the ligand and the prior evidence of protein binding is also essential for assessing the reliability of DSF results and determining when additional confirmation is warranted.
While DSF is a rapid and robust preliminary screening tool for albumin–PFAS binding, caution is necessary to avoid misclassifying ligands as binding or non-binding. The method is ideally used as a high-throughput screening approach to prioritize PFAS chemicals for further toxicological investigation. Supplementary orthogonal methods should be employed, especially for ligands exhibiting weak binding or no detectable binding in DSF, to ensure accurate characterization of protein–PFAS interactions.

4. Conclusions

Using the DSF assay, our lab has demonstrated the successful generation of directly comparable serum albumin–PFAS relative binding affinities for structurally diverse PFAS across chemical classes and albumins from multiple species [45,72]. Generating these directly comparable binding affinity data across a wide range of diverse PFAS structures, provides valuable information to fill numerous data gaps in the characterization of PFAS toxicokinetics and has opened the door to use of machine learning to predict PFAS binding affinity [94].
As many PFAS are proteophilic compounds, protein binding plays a major role in PFAS distribution, bioaccumulation, and toxicity. In addition to serum albumin, several other transport proteins have been highlighted as proteins that influence the distribution and bioaccumulation of PFAS, including organic anion transporters (OATs) and liver fatty acid-binding protein (L-FABP) [39,139,140]. Additionally, several proteins have been highlighted for their potential contributions to mechanisms of PFAS toxicity, including peroxisome proliferator-activated receptors, thyroid hormone receptors, and steroid hormone receptors [39,141,142,143,144]. Along with providing relative binding data describing serum albumin binding to PFAS, the DSF assay holds great potential to define PFAS-protein binding for other proteins of interest, as the assay is amenable to the study of a wide range of purified, soluble proteins or ligand binding domains of proteins, across a variety of assay conditions [45,127,145]. Recent work from our laboratory has shown that this approach, when integrated into a data-driven molecular modeling workflow, can identify and mechanistically characterize binding of many PFAS congeners to the large superfamily of calyx and calyx-like domain proteins which include β-lactoglobulin and fatty acid binding proteins [146].
The DSF assay is a uniquely positioned tool to address major data gaps in which the basic physiochemical properties of thousands of PFAS congeners currently remain undefined. Consequently, there is a critical need for robust methodologies like DSF to facilitate the screening of protein–PFAS interactions on a larger scale than can be offered by most other methods, with the potential to be applied to aqueous soluble PFAS chemicals [69]. As a rapid, accessible, inexpensive, and accurate in vitro approach, that is scalable to high-throughput formats, the DSF assay offers many advantages over other techniques. DSF represents a flexible quantitative experimental approach well-suited to the largescale determination of protein–PFAS binding, which is necessary for the characterization of PFAS behavior and toxicity for thousands of PFAS congeners to aid in the protection of environmental and public health.

Author Contributions

Conceptualization, H.M.S. and S.M.B.; methodology, H.M.S. and S.M.B.; validation, H.M.S. and S.M.B.; formal analysis; resources, S.M.B.; writing—original draft preparation, H.M.S.; writing—review and editing, H.M.S. and S.M.B.; visualization, H.M.S. and S.M.B.; supervision, S.M.B.; project administration, S.M.B.; funding acquisition, S.M.B. All authors have read and agreed to the published version of the manuscript.

Funding

The research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institutes of Health under award numbers P01ES035542, P42ES031009, P30ES025128, T32ES007046, National Science Foundation award number OCE-2414792. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health nor the National Science Foundation.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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