Defining the “Health Benefit Basket” in nine European countries

This article identifies and analyses a framework for “health baskets,” the taxonomy of benefit catalogues for curative services, and the criteria for the in- or exclusion of benefits in nine EU member states (Denmark, England, France, Germany, Hungary, Italy, The Netherlands, Poland and Spain). Focusing on services of curative care, it is found that the explicitness of benefit catalogues varies largely between the countries. In the absence of explicitly defined benefit catalogues, in- and outpatient remuneration schemes have the character of benefit catalogues. The criteria for the in- or exclusion into benefit catalogues are often not transparent and (cost-)effectiveness is applied only for certain sectors. An EU-wide harmonization of benefit baskets does not seem realistic in the short or medium term as the variation in criteria and the taxonomies of benefit catalogues are large but not insurmountable. There may be scope for a European core basket.
The Kohll/Decker judgments of the European Court of Justice and more recently the Geraets-Smits/Peerbooms cases, have demonstrated that health services can no longer be regarded as operating in isolation from other EU member states [1]. Increasingly there are flows of patients from one member state to another, sometimes due to individual choice, sometimes induced by sickness funds or even by health ministries. Such movements have the potential to stimulate competition between health care systems of the member states. However, the average volume of imported health care services in the EU has been rather small, with approx. €1.99 per person as yet (1998) [2]. One major reason for this low volume of cross country flows is the lack of accurate information as the basis for competitive behavior. Actors in one health care system, for example, sickness funds, do not have sufficient information on benefit catalogues and prices for benefits in other health care systems in order to induce in- or exports of health services. So far research has mainly focused on health services for selected indications or on the comprehensiveness of services, but information on the benefit catalogues, their taxonomy, and inclusion criteria in each country is widely lacking [3, 4, 5, 6, 7].
As one result of the project “Health Benefits and Service Costs in Europe–Health BASKET” funded by the European Commission, this article identifies and analyses the framework for health baskets, the taxonomy of benefit catalogues for curative services, and the criteria for in- or exclusion of benefits in nine EU member states (Denmark, England, France, Germany, Hungary, Italy, The Netherlands, Poland and Spain). It focuses on services of curative care, although an in-depth analysis of all other health sectors is also available as part of the project results (Reports of the EU Health BASKET project on health benefit baskets in each of the nine countries are available at: http://www.ehma.org/projects/healthbasket.asp). One of the main hypotheses of the study is that in the absence of explicit benefit catalogues, inpatient, and outpatient remuneration schemes have the character of (less explicit) benefit catalogues. After introducing the terminology and methodology of the analysis, different regulatory frameworks for the definition of the overall benefit basket in each country are compared and analyzed. Finally the taxonomy and the inclusion criteria of benefit catalogues and their substitutes for curative services are compared and analyzed.
Methodology
In general we differentiate between the terms “benefit basket” (also “benefit package”) and “benefit catalogues.” The benefit basket refers to the totality of services, activities, and goods covered by publicly funded statutory/mandatory insurance schemes (social health insurance, SHI) or by National Health Services (NHS). In contrast, we define benefit catalogues as the document(s) in which the different components of the benefit basket are stated in detail, i.e., which enumerate the services, activities, and goods in a more detailed way, listing single interventions (i.e., specific technologies). Thus a benefit basket may be further defined by one or more benefit catalogues.
Basically, the coverage of a given population for health services can be characterized in three dimensions: “breadth” as the extent of covered population, “depth” as the number and character of covered services, and “height” as the extent to which costs of the defined services are covered by prepaid financial resources as opposed to cost-sharing requirements. Services or goods which are not covered at all (i.e., with a “copayment” of 100%) are not considered to be part of the benefit basket. Figure 1 summarizes the three dimensions of a benefit basket. This article mainly focuses on the coverage of services (depth of benefit coverage) [8].Fig. 1 Three dimensions of coverage
An open questionnaire was developed to explore the different benefit baskets and their underlying benefit catalogues of the participating nine countries, and this served as guide in compiling standardized country reports. To scan the different health systems in search for existing benefit catalogues we followed the framework of functional categories of “health care services and goods” proposed by the OECD in its “System of Health Accounts” (Table 1) [9]. As an initial step the researchers in each country were asked to identify the decision-making processes on the benefit basket as a whole in their country. As a second step they were asked to provide a detailed description of the existing benefit catalogues for each functional category, the actors involved in decision making, and the decision criteria.Table 1 Framework of health care functional categories, system of health accountsHC.1 Services of curative care  HC.1.1 In-patient curative care  HC.1.2 Day cases of curative care  HC.1.3 Out-patient care    HC.1.3.1 Basic medical and diagnostic services (primary health care)    HC.1.3.2 Out-patient dental care    HC.1.3.3 All other specialized care    HC.1.3.9 All other out-patient curative care  HC.1.4 Services of curative home careHC.2 Services of rehabilitative care  HC.2.1 In-patient rehabilitative care  HC.2.2 Day cases of rehabilitative care  HC.2.3 Out-patient rehabilitative care  HC.2.4 Services of rehabilitative home careHC.3 Services of long-term nursing care  HC.3.1 In-patient long-term nursing care  HC.3.2 Day cases of long-term nursing care  HC.3.3 Long-term nursing care at homeHC.4 Ancillary services to health care  HC.4.1 Clinical laboratory  HC.4.2 Diagnostic imaging  HC.4.3 Patient transport and emergency rescue  HC.4.9 All other miscellaneous servicesHC.5 Medical goods dispensed to out-patients  HC.5.1 Pharmaceuticals and other medical non-durables    HC.5.1.1 Prescription medicines    HC.5.1.2 Over-the-counter medicines  HC.5.2 Therapeutic appliances and other medical durables    HC.5.2.1 Glasses and vision products    HC.5.2.2 Orthopedic appliances and other prosthetics    HC.5.2.3 Hearing aids    HC.5.2.4 Medico-technical devices    HC.5.2.9 All other miscellaneous medical durablesHC.6 Prevention and public health services  HC.6.1 Maternal and child health; family planning and counseling  HC.6.2 School health services  HC.6.3 Prevention of communicable diseases  HC.6.4 Prevention of non-communicable diseases  HC.6.5 Occupational health care  HC.6.9 All other miscellaneous public health services
Establishing and shaping health baskets
A generic pattern of establishing and shaping health baskets is found in most of the countries consisting of two levels. At the higher level, legislation passed by the national parliaments (sometimes even rooted in the country’s constitution, e.g., Poland [10]) establishes the general framework by enumerating the areas of health care, often similar to the OECD health care categories, included in the benefit basket. At the lower level, the benefit basket is further shaped by specifying certain procedures provided within each area of health care as part of the benefit catalogues. These catalogues contain recommendations as well as explicit in- or exclusions of services. The extent to and the way in which this shaping actually takes place varies considerably from country to country and within each country from sector to sector of health care. There are several ways used to establish benefit catalogues such as legislations passed by central or regional parliaments, decrees issued by national or regional governments, directives issued by self-governing bodies or by national and/or local authorities, and other documents considered as “quasilaws” (rules without legal character, e.g., clinical guidelines).
In all countries studied a general definition of a benefit basket could be identified at the higher level. Depending on the organization of the system (NHS or SHI), the logic underlying the general definition of the benefit basket differs. In NHS countries the definition of a benefit basket refers to the specification of the duties and obligations of the purchasing organization (regional health authorities), and in SHI countries the issue of the benefit basket is more related to the specification of entitlements of the insured persons. However, it cannot be said that either NHS or SHI countries generally define their benefits more explicitly than the other.
In most NHS countries a coherent legislation contains a list of the areas of care to be provided by the respective NHS, including “regional health services” as part of the benefit basket of regions in decentralized NHS systems, for example, Italy and Spain. Denmark represents an exception from this as the legislation consists of separate acts concerning the categories of hospital, primary and long-term care, and pharmaceuticals [11]. The level of explicitness varies considerably from country to country. The vaguest definition of a benefit basket may be that of the NHS Foundation Act (1946) in England and related posterior documents, where the Secretary of State for Health is legally required to provide services “to such extent as he considers necessary to meet all reasonable requirements” [12]. The responsibility for making available general practitioner, dental, ophthalmic, and pharmaceutical services lay until 2003 with health authorities and since then with Primary Care Trusts. In contrast, the framework of the Italian and Spanish benefit baskets, each of which was established in recently implemented legal documents, is structured in more detail [13, 14]. Common to all four, however, is the differentiation between hospital care and primary care, including specialist outpatient services, as well as preventive or health promotion services.
The level of explicitness is not only heterogeneous across different countries, but also within the same country. For instance, in Spain some areas of health care may be further shaped by mentioning specific services or in some cases even specific procedures or technologies being in- or excluded from the benefit basket. Thus the definition of the benefit basket within NHS countries does not always follow a systematic approach of going into further detail. It rather addresses shortcomings perceived by decision makers of each health care system, not necessarily being based on evidence. Concerning the origin of more detailed benefit catalogues some similarities are observed across countries with similar organization of the health care system or with similar organizational demands (i.e., the need to reimburse providers in free practice). In the two countries with regionalized NHS, Italy and Spain, the motivation for defining a detailed benefit catalogue is to be seen in the autonomous status of regions. The national benefit basket can be regarded as a minimum basket of health services that must be provided by the regional health authorities. Therefore the regions in both countries are free to offer additional services not included in the national benefit basket.
Health baskets in SHI countries stem from two different roots. On the one hand, SHI countries formulate the basket as an entitlement for persons insured under the respective statutory health insurance schemes. The main reason for this lies in the fact that SHI schemes have evolved from fragmented voluntary/statutory health insurance schemes not covering the whole population and only covering certain services, for example, sickness benefits. Statutory health insurance in Germany as well as in The Netherlands does still not cover the whole population [15, 16]. Therefore in SHI countries the health basket is also used to indicate the boundaries between those insured under the statutory health insurance and those insured under other schemes. The second root are fee catalogues which were originally more prevalent in SHI than in NHS countries due to the frequently used fee-for-service reimbursement system, at least in ambulatory specialist care.
Another common characteristic of decision making on health baskets in most SHI countries is the role of the self-governing institutions. Within a general framework stipulated by laws, self-governing institutions (e.g., sickness funds, physician associations) specify the rules to explicit benefit catalogues or remuneration schemes with the character of benefit catalogues, limiting the scope of service provision.
As observed in NHS countries, the level of explicitness also varies considerably among SHI countries. Poland has by far the most explicit benefit basket, which is even rooted in the constitution. Different legal acts define benefit catalogues specifying detailed procedures or even technologies being provided. At the other extreme, Germany probably has the vaguest legal definition of the benefit basket among the SHI countries, with the Social Code Book as a general framework. For instance, inpatient services in Germany are limited to only a certain extent by the diagnosis-related group (DRG) reimbursement system because they can be provided unless they are explicitly excluded by directives [17]. There is a trend in all SHI countries towards more explicit benefit baskets. In The Netherlands a system of diagnosis-procedure combinations has been introduced in 2005 defining procedures provided in inpatient care and by specialists in outpatient care [18]. In France a similar list is currently being prepared, while Germany and Hungary recently updated their outpatient remuneration schemes specifying certain procedures that are reimbursed by the statutory health insurance [17, 19].
Definition of benefit catalogues for curative services
Benefit catalogues for inpatient care
As in other sectors of the health care system, the provided inpatient services can either be listed as procedures being part of an explicit benefit catalogue or indirectly determined by grouping systems that serve remuneration purposes, for example, DRGs. France, Poland, and Spain have defined explicit benefit catalogues, grouped according to medical specialties, for inpatient services listing detailed procedures serving as positive lists [10, 14, 20]. While there are clear decision criteria for the inclusion of benefits in the benefit catalogue in France and Spain, no transparent criteria are applied in Poland. In all other countries DRG and other grouping systems serve as a tool for estimating resource consumption supporting budget assignations or providing the basis for remuneration. In general, they classify a single episode of care according to main diagnoses, comorbidities and main surgical interventions into one of a limited number of groups. Thus each classified episode is assumed to require more or less homogeneous resource consumption, independently of whether exactly the same items (e.g., drugs, diagnostics) are used. As, finally, monetary values are attached to the different groups, the use of drugs, diagnostics etc. is confined to the given monetary scope of each group.
In our study four countries have introduced DRG systems: Italy [13], Germany [17], Hungary [19], and Denmark [11]. England and The Netherlands have DRG-like grouping systems called, respectively, health care resource groups and Diagnose Behandeling Combinaties. The main features of the DRG and other grouping systems are very similar (Table 2) [12, 18]. In each country the groups have been developed from data collected on resource consumption, clustering in homogeneous resource consumption groups. The observed variability in the number of classified groups in each system may be explained by the different criteria used to classify them (Table 2). Another possible explanation is the creation of additional groups in certain countries to increase the scope for the use of new innovative technologies (devices, procedures, and even drugs theoretically) which are considered to be worth the promotion (i.e., because of higher efficacy). This has been the case in Italy, where the regional health authority of Lombardy added three new DRGs to its system in order to specifically consider the use of drug-eluting stents and to encourage its utilisation [13].Table 2 Inpatient benefit catalogues or substitutesCountryName of taxonomy, year of introductionApplied geographical areaTaxonomy (and grouping criteria)Actors involved in decision makingCriteria for in-/ exclusion of benefitsDenmarkDRG system (Nordic-DRG); 1998National25 MDC (anatomical, etiological, other) with 589 DRG, special category for chemotherapy and radiotherapy. Grouping criteria: main diagnosis, procedures, sex, age, cause of dischargeLegislation at the national level (law, general framework)Need, budgetMinistry for the Interior and Health (approval)National Board of Health (DRG catalogue)County level (budgeting, hospital plan)Clinicians (priority setting in hospital)FranceCommon Classification of Medical Procedures (CCAM); 2005NationalCCAM lists reimbursable and excluded medical procedures thus being a positive and negative list; Grouping criteria: anatomic classification, medical specialtiesNational level (law, general framework)Effectiveness, safetyMinistry of Health (approval)National Union of Health Insurance Funds (in- and exclusion of services)High Health Authority (advisory body on in- and exclusion of services)GermanyG-DRG system (based on AR-DRG 4.1); stepwise 2003–2009National25 MDC (anatomical/ etiological/ other) with 876 DRG, 71 extra remunerations for special services (in 2005). Grouping criteria: main diagnosis, procedures, age, comorbidity, cause of dischargeLegislation at the national level (law, general framework)Services can be provided as long as they are not explicitly excluded. Adequate, expedient and cost-effectiveMinistry of Health (approval)Federal Joint Committee (exclusion of benefits)Institute for Hospital Reimbursement with assistance of the Committee of on HospitalPayment (DRG catalogue)Clinicians (priority setting in hospital)HungaryDRG system (US-DRG); 1993National26 MDC (anatomical, etiological, other) with 786 DRG. Grouping criteria: main diagnosis, procedures, age, comorbidityLegislation at the national level (law, general framework, budgeting)Costs, effectivenessMinistry of Welfare, Health division (DRG catalogue)National Health Insurance FundAdministration, especially (prepares decisions)Clinicians (priority setting in hospitals)ItalyDRG system (HCFA no. 10); 1995National reference list with regional differences23 MDC (anatomical, etiological, other) with 489 to 506 DRG, exceptional DRGs (e.g. liver and bone marrow transplantation). Grouping criteria: main diagnosis, procedures, age, comorbidity, cause of dischargeLegislation at the national level (law, general framework)Effectiveness, costsCentral level (national DRG catalogue)Regional level (redefines DRG catalogue, sets tariffs)Clinicians (priority setting in hospitals)The NetherlandsDiagnose Behandeling Combinaties (DBC) (DRG-like system); Jan. 2005National111,527 procedures regarding diagnosis and therapy (DBCs) are combined to 641 product groups. DBCs are on three different lists determining the status for tariff negotiations or excluding DBCs from the benefit package. Grouping criteria: medical specialty, product groupLegislation at the national level (law, general framework)Costs, effectiveness, Ministry of Health (decrees)DBC Maintenance Organization (DBC system)Clinicians (priority setting in hospitals)PolandGovernmental decrees and catalogue of benefitsNationalCatalogue lists all services covered under social health insurance scheme; services are linked to the respective regulation/law. Grouping criteria: area of care (e.g., hospital care), medical specialtyLegislation at the national level (law, general framework)/Ministry of Health (regulations decrees)National Health Fund (catalogue)SpainRoyal Decree 63/1995 / Law 16/2003 on cohesion and qualtiy of the National Health SystemNational with regional differencesServices are listed explicitly in decree. In some cases services are restricted to specific patient groups. Grouping criteria: area of care, medical specialtyLegislation at the national level (law, general framework)Safety, efficacy, efficiencyFederal Government (decree)Inter-territorial Council and Council of the State (inclusion of new benefits)Clinicians (provision of services relating to entitlements defined by decree)EnglandHealth care resource group (DRG-like system); stepwise 2004–2009NationalIn April 2004 there were only 48 HRGs in use. Grouping criteria: diagnosis, complexity, procedureLegislation at the national level (law, general framework)Costs, budgetMinistry of Health (catalogue)Primary Care Trusts (negotiate with providers on quantity and tariffs)
The fact that specific procedures and technologies drive the development of DRG systems confirms our hypothesis that they serve as some kind of benefit catalogues. Technologies specifically mentioned in certain groups may not be used if less resource consuming alternatives are available. In this way a less specific DRG system may act as a hidden negative list of technologies which de facto are not available for beneficiaries of publicly financed care since the monetary value assigned to certain groups does not cover the actual resource consumption associated with its use. Furthermore, in most applied grouping systems certain groups (e.g., in DRG systems so-called surgical DRGs) are even defined by specific procedures or specific technologies, such as drug-eluting stents. These groups can therefore be considered as a kind of explicitly defined benefits, similar to a positive list. Thus it can be assumed that grouping systems are applied as substitutes for benefit catalogues, containing incentives to limit the provision of benefits for those services not being explicitly mentioned.
Benefit catalogues for outpatient care
In the outpatient sector benefit catalogues are, again, often replaced by grouping systems serving remuneration purposes. Although they generally seem to be more explicit than the inpatient catalogues, the explicitness varies even more than in the inpatient sector (Table 3). These different degrees of explicitness are due mainly to the applied remuneration schemes in each country. If physicians receive fixed budgets or capitations, the benefit “catalogue” (i.e., the procedures that they can offer) is indirectly restricted by the amount of money allocated to them. Therefore in these countries the benefit package for outpatient care is regulated rather implicitly through decrees issued by national or regional health authorities describing the obligation of physicians to provide those benefits that are considered necessary. Examples of these kind of implicit benefit catalogues are the “Health Insurance Treatment and Services Decree” for care provided by general practitioners in The Netherlands and the “General Medical Services Contract” in England. These decrees do not mention specific procedures, although in the case of The Netherlands, the general practitioners’ association, the Landelijke Huisartsen Vereniging, defined a basic general practitioner benefit package in the 1980s [21].Table 3 Outpatient benefit catalogues or substitutesCountryName of taxonomyApplied geographical areaTaxonomy (and grouping criteria)Actors involved in decision makingCriteria for in-/exclusion of benefitsBenefits, procedures explicitly excludedDenmarkHealth Care Reimbursement Scheme Fee ScheduleNationalServices are grouped according to medical specialty and for GPs additionally in basic, supplementary, laboratory and miscellaneous services. Each service has an item number. It is referred to the respective legislation decree specifying the benefit, certain goods, procedures or in rare cases indicationsNational level (law, general framework)NeedAlternative careMinistry for the Interior and Health (approval)Counties (budgeting, health plan)Health care Reimbursement Negotiating Committee and health professional associations (negotiate catalogue)FranceCommon Classification of Medical Procedures (CCAM)NationalLists all medical procedures reimbursable and excluded. Grouping criteria: anatomic classification, medical specialtiesNational level (law, general framework)Effectiveness, safetySpa treatments; cosmetic surgeryMinistry of Health (approval)National Union of Health Insurance Funds (in- and exclusion of services)High Health Authority (advisory body on in- and exclusion of services)GermanySHI-EBM, SHI-BEMA, SHI-BEL-IINationalServices are grouped according to the medical specialty allowed to provide the service. Each service is assigned a numeric code in accordance with the subjection of the catalogueNational level (law, general framework)Diagnostic and therapeutic expedience, medical necessity and cost-effectivenessOrthopedic services after the age of 18 yearsFederal Joint Committee (approval of new benefits)Valuation Committee (negotiates EBM)Dental Valuation Committee (negotiates BEMA, BEL-II)HungaryGovernmental decrees and reimbursement cataloguesNationalSimilar services are listed in groups. Governmental decrees relate to different areas of care (e.g., dental care, specialist services). Items in reimbursement catalogues are listed with the respective ICPM code and a point valueLegislation at the national level (law, general framework, budgeting)Costs, effectiveness–Ministry of Welfare (decrees, approval)National Health Insurance FundAdministration, especially (prepares decisions)Payment Codes Updating Committee (reimbursement catalogues)ItalyNational contract for primary care; decree on specialist outpatient servicesNational benefit package, regions include additional servicesContract for primary care describes obligations of GP. Individual services are not further itemized. Decree on specialist outpatient services lists services in three sections: available, availability restricted to specific indications, excludedGovernment at national level (sets decree, negotiates contract)Effectiveness, costsNonconventional treatments (e.g., acupuncture, phytotherapy); vaccination for traveling purposesRepresentatives of GPs (negotiate contract)Ministry of Health (transfers contract into law)Government at regional level (negotiates additional contracts)The NetherlandsHealth Insurance (Treatment and Services) Decree; Diagnose Behandeling Combinaties (DBC; DRG-like system); Jan. 2005NationalGP services are regulated in generic terms only by decree, DBC catalogue (111,527 DBCs) combine information on diagnosis and treatment for medical specialists. DBCs are on three different lists determining the status for tariff negotiations or excluding DBCs from the benefit package. Grouping criteria: medical specialty, product groupLegislation at the national level (law, general framework)Costs, effectiveness–Ministry of Health (decrees)DBC-Maintenance Organization (DBC-System)Physicians (priority setting)PolandGovernmental decrees and catalogue of benefitsNationalCatalogue lists all services covered under social health insurance scheme. Services are linked to the respective regulation/law. Grouping criteria: area of care, medical specialtyLegislation at the national level (law, general framework)–Vaccination; acupuncture, unless part of chronic pain managementMinistry of Health (regulations)National Health Fund (catalogue)SpainRoyal Decree 63/1995National with regional differencesServices are listed explicitly in decree. In some cases, services are restricted to specific patient groups. Decree lists services in 5 areas of care (e.g., primary care, specialized care, pharmaceutical care) which are further subdivided.Legislation at the national level (law, general framework)Safety, efficacy, efficiencyCosmetic surgery (transplantation of hair and nails); sex changeFederal Government (decree)Inter-territorial Council and Council of the State (inclusion of new benefits)Clinicians (provision of services relating to entitlements defined by decree)EnglandNational Service Framework NationalHealth Resource Groups are linked to procedures. Currently only 48 HRGs are in use. Guidelines recommend services to be used on certain indicationsLegislator at national level (law, general framework)Need, effectivenessCosmetic dental treatmentsGeneral Medical Services ContractNational, with possible variation at PCT-levelNHS Confederation and General Practitioners Committee (negotiate contract)Need, costsClinical GuidelinesNationalPrimary Care Trusts (PCT) (negotiate additional contracts)Need, costs, effectivenessNICE (clinical guidelines)
In contrast, the countries remunerating providers on the basis of fee-for-service schemes need detailed lists of procedures or at least of service complexes (aggregated multiple procedures) to be able to negotiate on price and/or volumes. These lists can therefore be interpreted as substitutes for benefit catalogues, as physicians are usually reimbursed only for those items listed. The explicitness of these lists differs largely. Some countries issue detailed lists of all procedures to be performed by physicians (e.g. the “Common Classification of Medical Procedures” in France [20]) while other countries list service complexes making physicians responsible for the priority setting within such a service complex (e.g., SHI-EBM or SHI-BEMA in Germany [17] and the Health Care Reimbursement Scheme Fee Schedule in Denmark [11]).
Interestingly, taxonomy and structure are very similar in all countries. For example, in Denmark, France, Germany, Hungary, and The Netherlands services are grouped according to medical specialty. Certain outpatient benefits are also linked to indications or special patient groups in Poland and Spain [10, 14]. The high degree of explicitness regarding the definition of the benefit package is also underlined by the diverse lists of excluded services, common in all countries. Exclusion practices vary from issuing negative lists (e.g., Spain, Poland) and directives of self-governmental institutions with the character of negative lists (e.g., Germany) to excluded services within the framework of the national law (e.g., England). However, most countries exclude similar benefits such as cosmetic surgery (if not closely linked to certain treatments), vaccination for nonstandard diseases (e.g., for traveling purposes), and certain nonconventional treatments (e.g., acupuncture). Benefits of dental care are either restricted to specific treatment methods or age groups (e.g., Germany).
Discussion
The analysis of benefits defined in the countries under study reveals that there is a clear trend towards a more explicit definition of benefit baskets and their benefit catalogues. Those countries which recently introduced new health care legislations, such as Italy, Poland, and Spain, have more explicitly defined benefit catalogues. Other countries with older health care legislations, for example, the UK’s English-NHS Foundation Act (1946) and Germany’s Social Code Book (1988) have rather implicitly defined benefit catalogues, but increasingly work with negative lists, based on evidence provided by independent institutions such as the Englisch NICE and the German Institute for Quality and Efficiency (IQWiG) [12, 17]. Apart from negative lists remuneration schemes, for example, DRGs and procedures catalogues used for grouping are more and more used as benefit catalogues. These developments indicate that all included countries move towards a more explicit definition of benefit catalogues. Explicitly defined benefit catalogues, however, require clear and transparent decision criteria for the in- or exclusion of benefits.
Most countries officially state that (cost)-effectiveness is an important decision criteria. However, further inquiries often demonstrate that there is no rational process of reviewing the available evidence on specific procedures or technologies [22]. In reality the decision-making process is rather guided by lobbying activities of certain actors in the system. Especially those countries with very explicit benefit baskets, e.g. Poland, often lack transparency of decision criteria [10]. In contrast to this, countries with rather implicitly defined benefit baskets, such as England and Germany, define very transparent criteria for benefit exclusion, although lists with excluded services are minor compared to explicitly oriented countries. In addition, criteria such as cost-effectiveness and even effectiveness are often restricted to one or few sectors of the health care system, for example, pharmaceuticals or medical devices, and are not generalizable to all products or services [23]. In general the transparency of decision criteria must be improved in all countries in order to achieve accountability for all actors of the health care systems as well as consumers.
This contribution, as well as the overall EU Health BASKET project, provides useful information for health care providers and industrial companies willing to invest in EU countries but do not have the necessary information on benefit baskets and their underlying decision-making processes. However, to improve the environment for investments and to provide confidence for foreign investors, public documents should be regularly prepared by each country giving a transparent overview of the health baskets and the decision-making criteria.
The information provided will be beneficial especially to decision makers at all levels of health policy enabling them to compare different approaches of benefit definitions in order to develop their own position. The need for benchmarking will grow in line with the further development of cross boarder flows and the establishment of coherent benchmark criteria as part of the “Open Method of Coordination” initiated by EU policy makers [24, 25]. However, the project results also demonstrate that a harmonization of health baskets of EU countries, which in the view of certain decision makers could be the final stage after identifying best practice in benchmarking, is not realistic in the short or medium term since the definitions of benefit baskets vary substantially. Additionally, as shown in the cases of Italy and Spain, especially in NHS countries there is rather a trend towards more decentralization of decision making on benefits, delegating to regions the autonomy to offer certain benefits in addition to nationally defined health baskets [26, 27]. On the other hand, this could also mean that in future a minimum basket of health benefits may be defined by all countries on the national level, which could be harmonized on the EU level at a certain stage due to systems’ competition as a result of increased cross-border flows. Beyond this minimum basket, there could be regional variations reflecting differences in wealth and of preferences.

health services
health benefit plans
health priorities
national health programs
insurance benefits


Sensitive Spectroscopic Detection of Large and Denatured Protein Aggregates in Solution by Use of the Fluorescent Dye Nile Red

The fluorescent dye Nile red was used as a probe for the sensitive detection of large, denatured aggregates of the model protein β-galactosidase (E. coli) in solution. Aggregates were formed by irreversible heat denaturation of β-galactosidase below and above the protein’s unfolding temperature of 57.4°C, and the presence of aggregates in heated solutions was confirmed by static light scattering. Interaction of Nile red with β-galactosidase aggregates led to a shift of the emission maximum (λmax) from 660 to 611 nm, and to an increase of fluorescence intensity. Time-resolved fluorescence and fluorescence correlation spectroscopy (FCS) measurements showed that Nile red detected large aggregates with hydrodynamic radii around 130 nm. By steady-state fluorescence measurements, it was possible to detect 1 nM of denatured and aggregated β-galactosidase in solution. The comparison with size exclusion chromatography (SEC) showed that native β-galactosidase and small aggregates thereof had no substantial effect on the fluorescence of Nile red. Large aggregates were not detected by SEC, because they were excluded from the column. The results with β-galactosidase demonstrate the potential of Nile red for developing complementary analytical methods that overcome the size limitations of SEC, and can detect the formation of large protein aggregates at early stages.
Introduction
Among the biological macromolecules, proteins have become an important source of active substances for medical purposes. Insulin and growth hormone are two prominent examples of the many proteins used in medicine nowadays [1]. Many protein formulations can cause immune reactions in patients, and the presence of protein aggregates, even in minute amounts, has been identified as an important factor leading to the breaking of immune tolerance [2, 3]. Large protein aggregates are regarded as particularly immunogenic [4], and for this reason, analytical procedures for their sensitive and selective detection are important. The most widely used technique for analyzing aggregates in protein formulations is size exclusion chromatography (SEC) [2]. However, large protein aggregates of more than about 5 million Da are out of the separation range of SEC and can even be excluded from separation columns. Moreover, protein-column interactions may cause aberrant elution leading to erroneous interpretations. Field flow fractionation and analytical ultracentrifugation have a greater capacity than SEC for detecting high molecular weight protein aggregates [5, 6], but until now, these techniques are not readily available in every laboratory. Analysis by light scattering techniques [7] and Fourier transform infrared spectroscopy (FTIR) [8–10] is experimentally easy in the sense that these techniques do not rely on physical separation steps. However, the former is prone to artifacts when aggregates are present at low concentrations, and the latter is not very sensitive. Fluorescence spectroscopy with probes covalently labeled to proteins has been used for the direct detection of aggregated species [11], but this approach suffers from the problem that the modified protein may exhibit an altered aggregation behavior. Methods without protein labeling are rarer, but environment sensitive fluorescent dyes can be useful for detecting aggregated structures. For example, Congo red is used for the detection and identification of amyloid fibrils [12, 13]. Another potentially interesting dye is Nile red, which is established for the detection of hydrophobic areas on the surfaces of proteins [14–17]. Recently, this dye has been used for the detection of aggregates of an IgG1 recombinant humanized monoclonal antibody, as well as human calcitonin fibrils by fluorescence microscopy [18].
The fluorescence of Nile red is strongly dependent on the polarity of its environment. Decreasing polarity leads to a substantial blue shift of the absorption and emission maxima, as well as a marked increase of quantum yield and fluorescence lifetime. This has been related to the large dipole moment of the molecules in the excited state and the existence of a non-emissive twisted intramolecular charge transfer (TICT) state, which is more likely to be formed in polar than apolar environments [19, 20]. It has been proposed that the TICT state is non-emissive because of fast, non-radiative transition to the triplet state [19]. Nile red is a hydrophobic molecule with low solubility in water of ∼1 μM [21]. Therefore, it has the tendency to partition into hydrophobic phases [22].
β-Galactosidase from Escherichia coli is used as a model protein in the present study. This tetrameric enzyme (MW = 465 kDa) consists of four equal subunits. It contains four catalytic sites for the hydrolysis of lactose or other β-galactosides to monosaccharides. For maximal enzymatic activity, the presence of Mg2+ or, alternatively, Mn2+ is required [23, 24]. It has been shown that Mg2+ increases the unfolding temperature of β-galactosidase in aqueous solution. In the presence of Mg2+, heat induced unfolding is irreversible. It coincides with activity loss and leads to the formation of denatured monomers, dimers, and aggregates [25].
In this study, steady-state and time-resolved fluorescence spectroscopy experiments with Nile red are used for the sensitive detection of large β-galactosidase aggregates, present in solution at concentrations in the low nanomolar range. Denaturation and aggregation of native protein is induced by incubating aqueous solutions below and above the experimentally determined unfolding temperature of 57.4°C. The presence of aggregates in heat-treated solutions is confirmed by light scattering. Time-resolved fluorescence anisotropy and fluorescence correlation spectroscopy (FCS) are used for analyzing the size of the aggregates detected by Nile red. The results obtained with Nile red are compared with results from SEC analysis.
Materials and methods
Materials
Freeze-dried β-Galactosidase from Escherichia coli (protein content ∼80%) was purchased from Fluka (Buchs, Switzerland) and stored at −20°C. Phosphate buffer (pH 7.4) containing 55 mM sodium-phosphate and 10 mM MgCl2 was prepared with reverse osmosis water, and rendered isotonic by the addition of NaCl. Upon preparation, buffer solution was filtered through a 0.2 μm cellulose acetate filter (Schleicher and Schuell, Dassel, Germany), and stored at 4°C for at most 4 weeks. 0.2 μm HPLC filters were obtained from Alltech (Deerfield, IL). Nile red (9-diethylamino-5H-benzo[α]phenoxazine-5-one) was obtained from Fluka. A stock solution containing 0.25 mM Nile red was prepared with water-free dimethylsulfoxide (DMSO) and stored at −20°C.
Methods
Preparation of β-galactosidase solution
A fresh solution of β-galactosidase in phosphate buffer was prepared before each experiment. The protein was dissolved in the buffer and subsequently, the solution was filtered through a 0.2 μm HPLC filter. Protein concentration after filtration was determined by measuring absorption at 280 nm, assuming an absorption coefficient of 2.09 cm2/mg [26]. Reproducibly, a protein concentration close to 0.10 μM was found after filtration.
For incubation at elevated temperatures, 1 ml aliquots of β-galactosidase solution containing 0.10 μM protein were filled into 1.5 ml polypropylene Eppendorf tubes. They were heated for a defined period of time without shaking using an Eppendorf Thermomixer (Eppendorf, Hamburg, Germany), and then cooled at room temperature.
Steady-state fluorescence measurements
Instrument and measurement settings
Steady-state fluorescence measurements were performed with a Fluorolog FL3-21 spectrofluorometer (Jobin Yvon – Horiba, Edison, NJ), equipped with a short-arc xenon lamp. The slit openings of the excitation and emission monochromators were set to a bandwidth of 3 nm. The integration time was 0.05 s, and the signals were corrected for lamp intensity fluctuations by a simultaneously recorded reference signal. Each sample was measured 3–5 times, and the average spectrum was calculated. The sample temperature was controlled by a water bath with a temperature sensor connected to the sample holder. Samples were measured in quartz cuvettes (Hellma GmbH, Muellheim, Germany).
Intrinsic tryptophan fluorescence
Measurements of intrinsic tryptophan fluorescence of β-galactosidase were performed by exciting 0.10 μM protein solutions at 298 nm and scanning emission between 310 and 450 nm. To establish a protein denaturation curve, the sample temperature was increased from 25 to 70°C in one degree steps. At every temperature, the sample was equilibrated for 5 min and then measured.
Nile red fluorescence
Before every measurement, a fresh solution of 25 μM dye in phosphate buffer was prepared by dilution of Nile red stock solution. This solution was immediately added to heated 0.10 μM β-galactosidase solution, non-heated 0.10 μM β-galactosidase solution or phosphate buffer in a cuvette, and mixed well. The final concentration of Nile red was 18.75 nM, well below the solubility limit of Nile red in water of 1 μM [21]. Nile red fluorescence was measured by exciting at 550 nm and scanning emission between 565 and 750 nm. All measurements were performed at 25°C. Fluorescence spectra were measured immediately after addition of Nile red. However, Nile red emission in water remained unchanged for at least 25 min upon sample preparation.
Time-resolved fluorescence measurements
Instrument and measurement settings
Time-resolved fluorescence measurements were performed by time-correlated single photon counting, as described in detail elsewhere [27]. Excitation source was a mode-locked titanium:sapphire laser (model Verdi V10, Coherent Inc., Santa Clara, CA), pumped by a CW diode-pumped, frequency doubled Nd:YVO4 laser (model Mira 900-D in fs mode, Coherent Inc., Santa Clara, CA), and tuned at 980 nm. An excitation wavelength of 490 nm was obtained by frequency doubling, using a second harmonic generation system (model 5-050 Ultrafast Harmonic Generation System, Inrad Inc., Northvale, NJ). The repetition rate of the excitation pulses was 3.8 MHz. The emission filters were a Schott 3 mm OG 530 nm cut-off filter (Schott AG, Mainz, Germany), combined with a Schott IL 611.6 nm (Δλ=11.0 nm) interference filter.
Measurements were performed at 25°C, and consisted of repeated 10 s sequences of measuring parallel and perpendicularly polarized fluorescence emission until a maximum peak content of at least 50,000 counts in the data files was reached. Samples of β-galactosidase solution without Nile red were measured for background correction. To minimize background luminescence, all solutions, including buffer, were prepared with fluorescence spectroscopy grade water (Fluka, Buchs, Switzerland). For the performance of a deconvolution procedure in data analysis, the dynamic instrumental response of the experimental setup was recorded using the fast and single-exponential fluorescence decay of the reference compound erythrosine B in water.
Data analysis was performed using the “TRFA Data Processing Package” of the Scientific Software Technologies Center (Belarusian State University, Minsk, Belarus) [28]. The decay of fluorescence intensity with time, I(t), could be described by an exponential model with three independent fluorescence lifetimes (τi) [29, 30]:
where αi are the pre-exponential factors, which were normalized. I(t) itself was calculated from the measured parallel and perpendicular polarized fluorescence emission, III(t) and I⊥(t):
G in the above equation represents the g-factor, which had a value of 1 for the used experimental setup.
The course of fluorescence anisotropy with time r(t) was calculated from the measured intensities III(t) and I⊥(t):
Fluorescence correlation spectroscopy measurements
Instrument and measurement settings
Fluorescence correlation spectroscopy measurements were performed with a system composed of a krypton-argon laser and an MRC1024 confocal laser-scanning microscope (Biorad, Hercules, CA), a TE300D inverted microscope (Nikon, Tokyo, Japan) with a water immersion objective lens (Plan Apo 60×, NA 1.2, collar rim correction, Nikon), and ALV-5000/E avalanche photodiode detectors (ALV GmbH, Langen, Germany). The 568 nm line of the krypton-argon laser was used for excitation of Nile red samples. Fluorescence emission above 585 nm was detected.
Samples were prepared by mixing heated 0.10 μM β-galactosidase solution, non-heated 0.10 μM β-galactosidase solution or phosphate buffer with diluted Nile red stock solution in polypropylene Eppendorf tubes. Heated β-galactosidase solution was mixed with Nile red in the same tubes that were used for heating. The final concentration of Nile red in the samples was 18.75 nM. One hundred μl sample was transferred into a well of a 96 glass bottom well plate (Bio-one, Greiner, Frickenhausen, Germany). For measurements, the focal volume was positioned 100 μm above the bottom of the well. Raw data, i.e. fluctuations of fluorescence intensity with time, were collected during 30 s. The laser power was minimized to prevent photobleaching during this period. Every sample was measured ten times, and ten experimental autocorrelation functions were subsequently obtained from the raw data.
Analysis of the experimental autocorrelation functions was performed by non-linear regression, based on a theoretical expression that describes the decay of an autocorrelation function, G(τ), in consequence of diffusion and fast, non-diffusive processes, such as excitation of fluorescent molecules to the triplet state [31–33]:
Here, Ei is the relative emission rate of species i, and 〈Ni〉 the average number of molecules of species i in the focal volume. Mi(τ) is the mobility term for free, 3-dimensional diffusion:
where τD,i is the diffusion time of fluorescent species i. The coefficients  and z0 are defined, respectively, as the distances from and along the optical axis at which the excitation light intensity has dropped by 1/e2. These coefficients were determined from calibration of the focal volume with rhodamine green of known diffusion coefficient (2.8×10−6cm2 s−1).
The coefficient F in Eq. (4) represents the fraction of molecules involved in a non-diffusive process, and τf is the decay time of this process. It is justified to assume the existence of a non-diffusive decay process, because the transition of excited state Nile red molecules to a non-emissive TICT state and to the triplet state has been proposed [19]. G(∞) is the value of the autocorrelation function at long time intervals (τ).
To reduce the number of fitting parameters, Eq. (4) was simplified. For one diffusing species, it can readily be written as:
The assumption of two or more diffusing species gives:
where
Before data analysis, the experimental autocorrelation functions were truncated at very short time intervals below 0.001 ms and at very long time intervals above 600–1000 ms. The autocorrelation functions were then normalized [34]:
where G(0) is the start value of the autocorrelation function and G(∞) is its value at very long time intervals. G(0) and G(∞) were estimated individually for every experimental autocorrelation function by non-linear regression, using Eqs. (6) and (7) [34]. Normalization eliminated possible differences in experimental autocorrelation functions that were related to systematic errors, such as adsorption of dye molecules to the surfaces of the well plate during experiments.
The above described normalization changes Eq. (6) to:
Here, Gnorm(0) is the start value of the normalized autocorrelation function, which is expected to be one. The value at long time intervals, accounted for by Gnorm(∞), is expected to be zero. Equation (7) changes to:
where
Equation (12) shows that fi,norm represents an intensity-weighted fraction of diffusing species i. Equations (10) and (11) were used for analyzing the normalized, experimental autocorrelation functions by non-linear regression using the Levenberg-Marquardt algorithm. The calculations were performed with IGOR Pro V.4.04 (WaveMetrics, Inc., Lake Oswego, OR). When applicable, up to ten autocorrelation functions obtained from the same sample were concatenated into one data set and analyzed by a global analysis procedure contained in the software. The standard deviation (σ) of experimental autocorrelation functions, estimated from the measurement repeats, was used as the weighting factor during fitting [34].
Solutions with a heterogeneous distribution of particle sizes showed very complex decays of the experimental autocorrelation functions. These could not be adequately fit by the discrete multi-exponential model using a small number of diffusion times, and increasing the number of diffusion times above three in the model resulted in substantially increased error margins of the determined parameters. For such complex data, calculation of the distribution of diffusion times was performed by the maximum entropy method (MEM) using the MEMFCS software [35].
Translation of diffusion time (τD) to a diffusion coefficient (D) was done by the following relationship:
Calculation of the hydrodynamic radius (Rh) was then possible using the Stokes-Einstein equation:
Light scattering
Detection of β-galactosidase aggregates in solution was done with static light scattering measurements using a fluorometer (Fluorolog FL3-21, Jobin Yvon–Horiba, Edison, NJ). The excitation and emission wavelengths were set to 400 nm, and measurements were performed at 25°C.
Furthermore, the hydrodynamic radius of native β-galactosidase was determined by dynamic light scattering measurements of 0.10 μM protein solutions in phosphate buffer. Experiments were performed at 25°C with a CGS-3 goniometer system and an LSE-5003 correlator (ALV, Langen, Germany). The average hydrodynamic radius (Rh) of the β-galactosidase molecules was determined using the method of cumulants.
Size exclusion chromatography (SEC)
SEC measurements were performed using an Alliance 2695 system with a type 2487 dual wavelength detector and a type 2475 fluorescence detector (Waters, Milford, MA). A Superose 6 column with a separation range of 5 × 103 to 5 × 106 Da, and an exclusion limit of 4 × 107 Da (GE Healthcare Europe GmBH, Roosendaal, The Netherlands) was used. Phosphate buffered saline (pH 7.4) was used as an eluent, and the injection volume was 40 μl. Detection was by UV absorption at 210 nm or by fluorescence emission (λex 280 nm and λem 340 nm for intrinsic protein fluorescence; λex 550 nm and λem 611 nm for Nile red fluorescence). Before injection, samples were centrifuged at 10000 RPM for 5 min. A molecular weight calibration curve using chymotrypsin (25 kDa), human serum albumin (66 kDa and 132 kDa dimer), and thyroglobulin (670 kDa) as molecular weight standards was established.
Results and discussion
Heat denaturation of β-galactosidase
The temperature induced denaturation of β-galactosidase was followed by measuring intrinsic tryptophan fluorescence emission of this protein (not shown). Denaturation started between 50 and 55°C and was completed between 60 and 65°C. An unfolding temperature of 57.4°C was obtained by fitting the denaturation curve with a model for a two-state unfolding process [36]. This is in good agreement with DSC experiments and activity assays performed under comparable conditions [25].
Heating β-galactosidase solutions above the unfolding temperature leads to irreversible aggregation of protein molecules [25]. Accordingly, a 0.10 μM β-galactosidase solution incubated at 62°C for 5 min was heavily aggregated and remained so upon cooling back to room temperature, as detected by light scattering (not shown). The aggregates formed under these conditions were too large to be detected by SEC. This is shown in Fig. 1a, which depicts SEC chromatograms of a non-heated β-galactosidase solution and of a β-galactosidase solutions that was heated at 62°C for 5 min. The former consists of peaks representing native, tetrameric β-galactosidase, oligomers of two and three β-galactosidase molecules, and even some aggregates of higher molecular weight. The latter consists only of a peak representing native, tetrameric β-galactosidase. However, the area under the curve of this peak is reduced to about 10% of the area under the curve of the corresponding peak of non-heated β-galactosidase solution. The apparent loss of protein was caused by the formation of large aggregates that were excluded from the SEC column.Fig. 1(a) SEC chromatograms of non-heated 0.10 μM β-galactosidase solution (solid line) and β-galactosidase solution incubated at 62°C for 5 min (dotted line). The peaks represent native (i.e. tetrameric) β-galactosidase (1) and oligomers of two (2) and three (3) β-galactosidase molecules. (b) Plot of the increase of scattered light intensity at 400 nm against incubation time of 0.10 μM β-galactosidase solution at 49°C. Data points are averages of 5–10 individual samples, and the error bars indicate ±1 SD. (c) SEC chromatograms of non-heated 0.10 μM β-galactosidase solution and solutions heated at 49°C for 15 and 60 min. The peaks represent native, tetrameric β-galactosidase (1) and oligomers of two (2) and three (3) β-galactosidase molecules. The insert shows that after 60 min incubation, high-order aggregates of β-galactosidase appeared, with a retention time of 29 min. There was no detectable difference between non-heated solutions and solutions heated at 49°C for 15 min
Static light scattering measurements using a fluorometer indicated that heating β-galactosidase solutions at 49°C also led to the formation of aggregates. Figure 1b shows the intensity of scattered light of 0.10 μM β-galactosidase solutions heated for different times between 15 min and 6 h. The increase of scattered light intensity indicates the formation of protein aggregates. This is clearly an effect of heating, because scattered light intensity did not increase upon storage at 25°C (not shown). Figure 1b also shows that a maximum of scattered light intensity was reached after about 120 min of incubation. This is possibly related to the sedimentation of very large aggregates that had been formed after long incubation times, which would also explain the increasing error margins of the long time-points.Fig. 2(a) Fluorescence emission spectra of Nile red added to phosphate buffer (broken line; λmax = 662 nm), non-heated 0.10 μM β-galactosidase solution (dotted line; λmax = 659 nm), and 0.1 μM β-galactosidase solution that was previously heated at 62°C for 5 min (solid line; λmax = 611 nm). Excitation wavelength was 550 nm. (b) Fit of two Voigt-shaped peaks (broken lines) to an emission spectrum (circles) of Nile red added to β-galactosidase solution that was heated at 49°C for 360 min. The solid black line is the fitted spectrum, and the residuals are indicated. The positions of the fitted peaks are 605.0 ± 2.2 nm and 658.3 ± 1.4 nm. (c) Plot of Nile red fluorescence intensity at 611 nm against incubation time at 49°C of 0.10 μM β-galactosidase solution. Data points are averages of 5 individual samples, and the error bars indicate ±1 SD
The SEC chromatograms in Fig. 1c show the formation of large β-galactosidase aggregates by heating solutions at 49°C. Non-heated solution mainly contained the native, tetrameric β-galactosidase, along with some dimer, trimer, and even higher order aggregates. Heating for 15 min at 49°C did not lead to a change of the SEC chromatogram. However, after 60 min incubation an additional peak with a retention time of 29 min appeared, representing large β-galactosidase aggregates. The area of this peak was 1.5% of the total area under the curve of the protein and increased with increasing incubation time at 49°C (not shown). The chromatograms shown in Fig. 1c were obtained by measuring absorbance at 210 nm. The use of an on-line fluorescence detector system (λex 280 nm and λem 340 nm) led to corresponding results, i.e. aggregation was only detected in β-galactosidase solutions that were heated at 49°C for at least 60 min.
Nile red steady-state fluorescence
Figure 2a depicts the emission spectra of Nile red in buffer, in non-heated 0.10 μM β-galactosidase solution, and in 0.10 μM β-galactosidase solution that was previously heated at 62°C for 5 min. The emission of Nile red in the presence of non-heated protein was almost identical to the dye’s emission in buffer. This indicates that the surface area of native β-galactosidase is highly polar, and that possible apolar sites are not accessible to Nile red. Storage of unheated β-galactosidase solutions containing Nile red for up to 25 min at room temperature did not lead to a change of the dyes fluorescence, showing that the presence of Nile red does not lead to a change of β-galactosidase surface hydrophobicity over time.
Interaction of Nile red with heat-denatured β-galactosidase resulted in a shift of emission maximum from 660 to 611 nm and in a substantial increase in fluorescence intensity (Fig. 2a). This is typically observed when Nile red molecules bind to hydrophobic, apolar sites on the surface of a protein [14], and shows that irreversible denaturation and aggregation by heating above the unfolding temperature has increased the surface hydrophobicity of β-galactosidase.
Upon 15 min incubation of 0.10 μM β-galactosidase solution at 49°C, the emission spectrum of Nile red reproducibly showed an increased intensity below 640 nm. Upon longer incubation between 1 and 6 h, a shoulder between 605 and 615 nm emerged. Figure 2b shows a two-peak fit of an emission spectrum obtained with Nile red added to β-galactosidase solution that was previously heated at 49°C for 6 h. In Fig. 2b, the λmax of the main peak is 658 nm, whereas the peak responsible for the shoulder is positioned at 605 nm. Emission at 658 nm originates from free Nile red molecules, and emission at 605 nm from Nile red interacting with denatured β-galactosidase. The latter is comparable with the λmax of Nile red in β-galactosidase solution that had been heated above the unfolding temperature (Fig. 2a). This indicates that the surface polarity of protein molecules denatured at different temperatures is similar. The fluorescence of Nile red did not depend on the time that protein solutions were kept at room temperature upon heating at 49°C (not shown), which indicates that irreversibly denatured β-galactosidase species were detected.
The effect of increasing incubation time at 49°C on Nile red fluorescence intensity is shown in Fig. 2c. An observation wavelength of 611 nm was chosen, which corresponds to the average emission wavelength of Nile red interacting with irreversibly denatured β-galactosidase. The profile of Fig. 2c is similar to that of Fig. 1b, suggesting that Nile red interacted with aggregated β-galactosidase. A clear decrease of Nile red emission at 611 nm upon centrifugation at 10000 RPM (not shown) supported this notion. However, it was not possible to confirm this by SEC, neither by adding Nile red to the samples before injection and performing on-line fluorescence detection (λex 550 nm and λem 611 nm), nor by staining fractions from SEC runs with Nile red and performing off-line fluorescence detection. For this reason, time-resolved fluorescence spectroscopy and FCS were used to further investigate the interaction between Nile red and aggregated β-galactosidase.Fig. 3Fluorescence intensity decay, I(t), of Nile red in 0.10 μM β-galactosidase solution that was heated at 49°C for 60 min. The fitted decay curve and the weighted residuals are shown. Fitting gave three lifetimes (τ1 = 0.11, τ2 = 1.11, and τ3 = 4.36 ns) and three (normalized) pre-exponential factors (α1,norm = 0.40, α2,norm = 0.24, and α3,norm = 0.36). The value of χ2 was 1.056Table 1Lifetime analysisSampleχ2 totala〈τ〉bbuffer1.0390.20non-heated1.0110.4215–49°C1.0621.3060–49°C1.0561.89120–49°C1.0642.48240–49°C1.0002.105–62°C1.0321.50aχ2 obtained with Eq. (1) using three fluorescence lifetimes.b
Time-resolved fluorescence spectroscopy with Nile red
Figure 3 shows the analysis of the fluorescence intensity decay, I(t), of Nile red in 0.10 μM β-galactosidase solution that was heated at 49°C for 60 min. The data points could be adequately described using Eq. (1) with three fluorescent lifetimes, as can be seen from the regular distribution of the residuals around zero and the proximity of χ2 to unity. The use of three lifetimes was necessary for the analysis of all intensity decays, and it was not possible to relate particular lifetimes to the free and the protein-bound state of Nile red. In Table 1, the results are summarized by listing 〈τ〉 values, which are proportional to steady state intensities. In agreement with the increase of Nile red steady state intensity at 611 nm, heating of β-galactosidase solution at 49°C for 15 min led to a clear increase of 〈τ〉 as compared to Nile red in buffer or non-heated protein solution. The tendency of 〈τ〉 to further increase with increasing heating time at 49°C corresponds nicely to the increase of fluorescence intensity upon prolonged heating shown in Fig. 2c. Also with β-galactosidase solution that was heated at 62°C for 5 min, 〈τ〉 was substantially increased as compared to non-heated protein solution. However, it was lower than expected from the steady-state fluorescence intensity at 611 nm (Fig. 2a). Possible reasons for this difference are the variation between individual experiments with solutions containing highly aggregated protein and, since there is a lot of light scattering in such samples, different sensitivities of the steady-state and time-resolved instrumentation for scattered light.
The course of fluorescence anisotropy with time, r(t), of Nile red in 0.10 μM β-galactosidase solution that was heated at 49°C for 15, 60, 120, and 240 min was measured (not shown). With all samples, anisotropy decreased at short times, then increased again, and finally remained at an almost constant value. The practically constant value of anisotropy at long times indicated rotational diffusion that was too slow to be detected by Nile red fluorescence. Therefore, although indicating that Nile red was in fact bound to large particles such as aggregates of β-galactosidase, it was not possible to estimate the size of these particles by time-resolved fluorescence anisotropy.
Fluorescence correlation spectroscopy (FCS) measurements with Nile red
Figure 4a depicts an overlay of ten normalized, experimental autocorrelation functions that were obtained with Nile red in buffer. These data were analyzed simultaneously using the one-component model (Eq. (10)). The fitted curve included in Fig. 4a, the regular distribution of the weighted residuals around zero, and the proximity of χ2 to unity show that Eq. (10) was appropriate for describing the experimental data. The determined decay time for non-diffusive process (τf) was 0.022 ms, the fraction of dye molecules involved in this process (F) was 0.45, and the determined diffusion time (τD,1) was 0.309 ms (Table 2). A diffusion time of Nile red around 0.3 ms is in agreement with reported values [37]. It corresponds to a diffusion coefficient of 2.9 × 10−6 cm2 s−1, which is reasonable for a molecule with a molecular weight of 318 Da. Therefore, τD,1 can be associated with the diffusion of free dye molecules. Gnorm(0) deviated slightly from the expected value of unity (Table 2). Forcing it to unity increased χ2 slightly, but had no effect on the determined parameters τD,1, τf, and F. Also Gnorm(∞) deviated somewhat from the expected value of zero (Table 2). This is related to the background subtraction described in the materials and methods section. Leaving out background subtraction did not lead to different values of the coefficients τD,1, τf, and F. Measurement of Nile red in non-heated 0.10 μM β-galactosidase solution gave the same parameters as Nile red in buffer (Table 2), confirming that there was no substantial interaction between the dye and non-heated protein.Table 2Coefficients from autocorrelation function analysisSample withNile redModelτD,1a (ms)f1,normaτD,2a (ms)f2,normaτfa (ms)FaGnorm(0)aGnorm(∞)aPhosphate bufferOne-Component0.309±0.008–––0.022±0.0020.451±0.0100.920±0.016−0.0049±0.00031.03β-Galactosidase non-heatedOne-Component0.291±0.009–––0.018±0.0020.434±0.0150.915±0.021−0.0047±0.00041.08β-Galactosidase 62°C/5 minTwo-Component0.287±0.0170.335±0.00545.7±1.020.690±0.0070.019±0.0010.246±0.008–−0.0368±0.00211.44aEstimates ±1 SD.Fig. 4Analysis of FCS data. (a) Simultaneous fit of 10 normalized autocorrelation functions obtained with Nile red in buffer. Analysis was performed by non-linear regression using Eq. (10). The fitted curve (black) and the weighted residuals for one of the normalized autocorrelation functions are shown. Determined parameters (value±1 SD) were F=0.451±0.010, τf=0.022±0.002 ms, τD,1=0.309±0.008 ms, Gnorm(0)=0.920±0.016, and Gnorm(∞)=−0.0049±0.0003. The value of  was 1.03. (b) Simultaneous fit of 9 normalized autocorrelation functions obtained with Nile red in 0.10 μM β-galactosidase solution that was previously heated at 62°C for 5 min. Analysis was performed by non-linear regression using Eq. (11) for two diffusing species. The fitted curve (black) and the weighted residuals for one of the normalized autocorrelation functions are shown. Determined parameters (value ±1 SD) were F=0.246±0.008, τf=0.019±0.001 ms, f1,norm=0.335±0.005, τD,1=0.287±0.017 ms, f2,norm=0.690±0.007, τD,2=45.70±1.02 ms, and Gnorm(∞)=−0.037±0.0021. The value of  was 1.44Fig. 5Analysis of FCS data. (a) MEM analysis of a single autocorrelation function obtained with Nile red in 0.10 μM β-galactosidase solution that was previously heated at 62°C for 5 min. The non-weighted residuals are shown. (b) Distribution of diffusion times (τD) calculated by MEM analysisFig. 6Raw data of two FCS measurements obtained with 0.10 μM β-galactosidase solution that was heated at 62°C for 5 min (upper curve) and at 49°C for 15 min
An overlay of nine normalized, experimental autocorrelation functions, obtained with Nile red in 0.10 μM β-galactosidase solution that was previously heated at 62°C for 5 min, is shown in Fig. 4b. There is an additional decay of the autocorrelation functions between 20 and 200 ms, which was not observed with Nile red in buffer. Data analysis was therefore performed using a two-component model, i.e. Eq. (11) assuming two diffusing species. The fitted line and the weighted residuals in Fig. 4b show that up to time intervals between 1 and 10 ms, the data could be well described by the two component model. Within this range, the decay of the autocorrelation functions is governed by τf and τD,1. The determined values were 0.019 and 0.287 ms, respectively (Table 2), which is in good agreement with the values found for Nile red in buffer. This shows that also in the heated β-galactosidase solution, not all Nile red was bound to denatured protein. The fraction of dye molecules (F) involved in non-diffusive process was 0.25, which is substantially lower than for Nile red in buffer (Table 2). A possible explanation for this is that in solutions with large amounts of denatured protein, less Nile red molecules undergo transition to the TICT and triplet state, because they can interact with hydrophobic surfaces [19]. The second diffusion time (τD,2) of 46 ms (Table 2) governed the decay of autocorrelation functions between 20 and 200 ms. It corresponds to a diffusion coefficient (D) of 1.9 × 10−8 cm2 s−1 and a hydrodynamic radius (Rh) of 130 nm, which deviates substantially from the values of native β-galactosidase (D = 3.1 × 10−7 cm2 s−1; Rh = 8 nm). This shows that incubation at 62°C for 5 min had led to very large, denatured aggregates of β-galactosidase, which could be detected by Nile red. However, the two-component model (Eq. (11)) was not able to fully describe the very complex autocorrelation function decays shown in Fig. 4b, as reflected by the residuals and the relatively high χ2 value of 1.44 (Table 2). For this reason, the distribution of diffusion times was determined by the maximum entropy method (MEM) [35]. Such an analysis is shown in Fig. 5a for an individual experimental autocorrelation function. The distribution of diffusion times (Fig. 5b) shows 3 peaks, centered at 0.02, 0.36, and 39 ms. The 0.02 and 0.36 ms peaks correspond to τf and τD,1 determined with Eq. (11). The peak around 39 ms is very broad and ranges from 5 to 150 ms, which corresponds to hydrodynamic radii between 14 and 420 nm. The peak maximum of 39 ms corresponds to an Rh of 109 nm, in good agreement with τD,2 obtained with Eq. (11). This is well above the hydrodynamic radius of small products of β-galactosidase denaturation, i.e. denatured monomers, dimers, and tetramers [25], and confirms that interaction with large β-galactosidase aggregates was the reason for the change of Nile red fluorescence.
Nile red also detected large aggregates in 0.10 μM β-galactosidase solutions that had been incubated at 49°C. As for protein samples heated at 62°C, these aggregates were detectable by an additional decrease of FCS autocorrelation functions at long times. However, aggregate concentration in samples heated at 49°C was much lower than in samples heated at 62°C. This is shown in Fig. 6, which compares the raw FCS data obtained with Nile red in β-galactosidase solutions heated for 5 min at 62°C and for 15 min at 49°C. The former is full of high-intensity peaks that were caused by large aggregates with bound Nile red crossing the focal volume. The latter contains only one high-intensity peak, representing a single large aggregate crossing the focal volume. As a consequence of the low aggregate concentration in samples heated at 49°C, only 3 out of 100 measurements contained high-intensity peaks. In spite of their rare occurrence, these peaks were not artifacts. No high-intensity peaks were detected in a total of 150 control experiments with Nile red in buffer and in non-heated β-galactosidase solution.
The combined results of steady-state fluorescence spectroscopy, time-resolved fluorescence spectroscopy, and FCS show that Nile red fluorescence gave complementary information to SEC about the aggregation of β-galactosidase. Native protein molecules and small oligomers could be detected by SEC (Fig. 1a and c), but did not substantially affect Nile red fluorescence. It is possible that both the surface hydrophobicity and the surface area of these protein species were not sufficient for an effective interaction with the dye. However, Nile red was able to interact with large, denatured aggregates of β-galactosidase and could therefore be used as a probe for their specific detection. Although some higher order aggregates could be detected by SEC upon heating at 49°C (Fig. 1c), Nile red fluorescence was sensitive to the presence of even larger aggregates, which were missed by SEC (Fig. 1a).
The interaction of Nile red with large aggregates of β-galactosidase makes it very difficult to use the SEC results for estimating the sensitivity of the Nile red method. Figure 1c shows that some large aggregates were first detected by SEC in protein samples that had been heated at 49°C for 60 min. However, the inability of Nile red to stain these aggregates fractionated from SEC runs (see results above) and the possible exclusion from the SEC column of even larger protein aggregates, which are detectable by Nile red, render the use of SEC as a reference method questionable. Furthermore, Nile red fluorescence was clearly more sensitive than SEC, as it already detected irreversible changes in samples that were heated at 49°C for 15 min. Therefore, to estimate the sensitivity of Nile red, non-heated β-galactosidase solutions were doped with defined amounts of β-galactosidase solution that had been irreversibly denatured and aggregated by heating at 62°C for 5 min. The lower limit of detection by Nile red steady-state fluorescence was 1% (v/v) of heated protein solution, corresponding to 1 nM denatured and aggregated β-galactosidase. The fluorescence intensity of this solution at 611 nm corresponded to that of β-galactosidase solution that had been heated at 49°C for 15 min (Fig. 2c). This comparison is justified, because nearly all β-galactosidase molecules had formed large aggregates upon heating at 62°C for 5 min (Fig. 1a), and because the fluorescence intensity of the detected aggregates did not depend on the incubation temperature (Fig. 6), which means that Nile red was equally sensitive to aggregates formed by heating at 49 and 62°C.
Conclusions
Using the model protein β-galactosidase, it was shown that the presence small amounts of large, denatured protein aggregates in solution can be detected by Nile red fluorescence. Aggregates detected by Nile red had hydrodynamic radii around 130 nm with a broad size distribution. Native protein and small aggregates thereof had no substantial effect on Nile red fluorescence intensity. By steady-state fluorescence measurements, it was possible to detect 1 nM denatured and highly aggregated β-galactosidase in solution.
The spectroscopic detection of protein aggregates by Nile red is potentially useful for formulation screening or quality control of protein pharmaceutics. The presence of even minute fractions of aggregates in protein formulations needs to be avoided, because this can cause immune reactions in patients. Since large aggregates are particularly potent for breaking immune tolerance, their analytical detection is very important. SEC, which is the standard method for studying protein aggregation, is not reliable for detecting large aggregates, because they may be excluded from the separation column. In this work we showed that an analytical detection method with Nile red is a possible approach to overcome this shortcoming of SEC. After establishing the method with time-resolved fluorescence spectroscopy and FCS, as was done for β-galactosidase, steady-state fluorescence measurements, which can be performed in most labs, may enable an experimentally simple and sensitive detection of large protein aggregates.

protein aggregation
nile red
fluorescence correlation spectroscopy
size exclusion chromatography
fluorescence spectroscopy