DE102022131674A1 - Diagnosis of metabolic changes in the liver using metabolic analysis of erythrocytes by mass spectrometry - Google Patents

Abstract

The invention relates to methods for analyzing a biological sample for changes in signatures of key metabolites in a liver disease, comprising (a) isolating erythrocytes from the biological sample, (b) identifying and optionally quantifying lipids from the erythrocytes as key metabolites for a liver disease, (c) detecting changes in the key metabolites of the liver disease, and (d) diagnosing a liver disease.The invention further relates to the use of the results of the method in personalized medicine.

Description

1. (a) die Isolierung von Erythrozyten aus der biologischen Probe,
2. (b) die Identifikation und gegebenenfalls Quantifizierung von Lipiden aus den Erythrozyten als Schlüsselmetaboliten für eine Lebererkrankung,
3. (c) die Erfassung von Änderungen der Schlüsselmetaboliten der Lebererkrankung, und
4. (d) die Diagnose einer Erkrankung der Leber.

The invention relates to methods for analyzing a biological sample for changes in signatures of bowl metabolites in a liver disease, comprising

1. (a) the isolation of erythrocytes from the biological sample,
2. (b) the identification and, where appropriate, quantification of erythrocyte lipids as key metabolites for liver disease,
3. (c) the detection of changes in key liver disease metabolites, and
4. (d) the diagnosis of liver disease.

The invention further relates to the use of the results of the method in personalized medicine.

Background of the invention

The invention lies in the field of individualized medicine. This plays a particularly important role in complex disease processes. The concept of "individualized" or "personalized" care, which implies stratification of the patient based on a better assessment of the complex disease process, is challenging but promising for seriously ill patients. More comprehensive monitoring of metabolism using mass spectrometry to assess the "metabolome", i.e. "metabolomics", is currently entering clinical routine (Kiehntopf et al., 2013). This technique can be carried out in an "untargeted" approach or to monitor specific groups of analytes. The metabolome as a complex reaction network summarizes all the characteristic metabolic properties of a cell, tissue or organism. The term metabolome was coined as the "sum of all metabolic products (metabolites)" in analogy to the terms genome, transcriptome and proteome and named accordingly. The word is derived from metabolism. Metabolome is sometimes also referred to as metabonomics, particularly in connection with the toxicity assessment of active substances.

• • die Durchflussraten (= Umsatzraten), Metabolit-Spiegel und Enzymaktivitäten der einzelnen Stoffwechselwege,
• • die Interaktionen zwischen den verschiedenen Stoffwechselwegen sowie
• • die Kompartimentierung der verschiedenen Stoffwechselwege innerhalb der Zellen.

The metabolome includes:

• • the flow rates (= turnover rates), metabolite levels and enzyme activities of the individual metabolic pathways,
• • the interactions between the different metabolic pathways and
• • the compartmentalization of the different metabolic pathways within the cells.

Another aspect is the influence of the nutrient supply as well as the effect of active substances on the metabolism and the various functions of the cells, such as cell proliferation, differentiation and apoptosis.

The study of the metabolome is called metabolomics (or metabonomics). This includes the interaction of the metabolites contained therein, their identification and quantification (cf. proteome and genome). The main analysis methods used in metabolomics are GC/MS and LC/MS as well as NMR spectroscopy and ion mobility spectrometers. The methods can be split into separation techniques, ionization techniques and detection techniques. Since the metabolites can vary greatly in their chemical composition and structure, it is often not enough to use just one analysis method to fully elucidate the metabolome of an organism. In addition, it is still not known how many metabolic products there are. The most common sample types are body fluids such as plasma or serum, but also urine, cerebrospinal fluid, synovial fluid, sputum or lavage. Tissue homogenates or cells or supernatants from cell cultures can also be considered.

In the field of liver disease, mass spectrometry is used to measure analytes such as unconjugated and glycine/taurine-conjugated primary bile acids, as in the study by Vanwijngarden et al. While bilirubin has traditionally only been used to monitor excretory liver function in the intensive care unit, e.g. in the most widely used scoring system for multiple organ dysfunction, the sepsis-related (sometimes sequential) organ failure assessment (SOFA) score (Vincent et al., 1996), recent findings suggest that bile acids may indicate liver dysfunction. with higher sensitivity and specificity (Recknagel et al., 2012) or at least might be elevated largely independently of bilirubin (Vanwijngaerden et al., 2014). In any case, even taking into account the inaccuracy of current clinical methods for assessing the smallest changes, these modest increases in bilirubin can be associated with a pronounced impairment of phase I and II metabolism, which is associated with a negative prognosis in these patients (Kruger et al., 2009). In addition to the analysis of metabolites from plasma and serum using mass spectrometry, numerous different methods have been used in clinical functional diagnostics to diagnose metabolic disorders. These include biochemical assays (so-called clinical chemistry), breath gas analysis and pulse densitometry. Metabolic disorders of the liver are investigated using endogenous clearance assays. Biochemical measurements of enzyme activities (transaminases, alkaline phosphatase) are used to assess liver damage and combined with liver function-associated metabolites (cholesterol, bilirubin, albumin) to examine liver function.

• - der Indocyaningrün-Test (fluoreszenzbasierte Messung der exkretorischen Funktion);
• - der Galaktosebelastungstest (Injektion von Galaktose und Messung des Anteils, der über die Niere ausgeschieden wird);
• - der C¹⁴-Aminopyrin oder ¹⁴C-Galaktose Atemtest; und
• - die Koffein-Clearance.

Furthermore, exogenous clearance assays are used clinically. These include:

• - the indocyanine green test (fluorescence-based measurement of excretory function);
• - the galactose tolerance test (injection of galactose and measurement of the portion excreted via the kidneys);
• - the C ¹⁴ -aminopyrine or ¹⁴ C-galactose breath test; and
• - caffeine clearance.

These assays are supported in the experimental area with imaging methods based on matrix-assisted laser desorption ionization (MALDI) imaging.

However, the conventionally used methods have numerous disadvantages. Biochemical assays often require complex sample preparation and pre-analytics. The transport of the usually sensitive biological samples is critical. Breath gas analysis is very complex. There are also numerous clinical disadvantages. Biochemical assays are often imprecise, i.e. they have low specificity, and do not provide precise information about the level of dysfunction. The assessment is often based on liver damage parameters, which only partially indicate the loss of function and the type of loss of function. Differentiation of liver damage is not possible with these assays. Breath gas analysis is usually time-consuming and offers little added value. Only individual analytes are examined. The informative value here, as with fluorescence angiography (indocyanine clearance test), is limited to the statement on liver function. The type of dysfunction can only be assessed indirectly. When imaging metabolites (research) using MALDI, sample preparation is very complex: Special coated (matrix) slides are used. Different coatings must be used for different metabolite groups. Certain metabolite groups cannot be measured on a sample at the same time. The sample is usually destroyed by imaging, and subsequent analysis is not possible. The maximum achievable resolution is relatively low (micrometer scale).

There is therefore a need to provide improved methods for a more comprehensive assessment of metabolism and the metabolome. In the field of liver disease, such improved methods are likely to challenge the preconceived notion of liver dysfunction reflecting a rather late and rare event in the ICU and may be suitable tools to better monitor hepatobiliary function and personalize metabolic needs and nutritional support in critically ill patients (Bauer & Kiehntopf, 2014).

• (a) die Ermittlung gewünschter Messareale in der biologischen Probe anhand eines, mittels optischer Methoden erzeugten Bildes der biologischen Probe,
• (b) ein bild-basiertes Screening der in Schritt (a) ermittelten gewünschten Messareale mittels mindestens einer spektroskopischen Methode, umfassend
  • ◯ die Identifikation und Quantifizierung von Schlüssel-Metaboliten der Stoffwechselerkrankung,
  • ◯ die Erfassung von Änderungen der Schlüssel-Metaboliten der Stoffwechselerkrankung, und
• (c) die Diagnose einer Erkrankung oder die Bestimmung der Verteilung der Schlüssel-Metaboliten in der biologischen Probe.

The WO 2022/167521 A1 relates to a method for analyzing a biological sample for changes in signatures of bowl metabolites in a disease, comprising

• (a) the determination of desired measurement areas in the biological sample using an image of the biological sample generated by optical methods,
• (b) an image-based screening of the desired measurement areas determined in step (a) by means of at least one spectroscopic method, comprising
  • ◯ the identification and quantification of key metabolites of the metabolic disease,
  • ◯ the detection of changes in the key metabolites of the metabolic disease, and
• (c) diagnosing a disease or determining the distribution of key metabolites in the biological sample.

The method according to the invention is particularly suitable for the diagnosis of metabolic diseases and proliferative diseases, but is technically complex and corresponding machines are not yet fully established and certified for routine clinical use.

Description of the invention

The object of the invention was to provide methods for a more comprehensive assessment of metabolism and the metabolome, which do not have the disadvantages of the state of the art and can be established in clinically established infrastructures using the measurement method of mass spectrometry.

To achieve this object, the invention provides a method for analyzing a biological sample for changes in signatures of bowl metabolites in a liver disease according to claim 1.

According to the invention, a “biological sample” is understood to mean any type of biological material in which erythrocytes are present or from which erythrocytes can be isolated.

In a preferred embodiment, the amount of key metabolites is detected in a body fluid sample derived from a mammal, most preferably a human. The term "body fluid" refers to all fluids present in the human body in which erythrocytes are present or from which erythrocytes can be isolated. Preferably, body fluids within the meaning of the invention are selected from blood and urine. A blood sample is particularly preferred. The blood sample can be treated before use, e.g. with an anticoagulant such as EDTA.

The biological sample can also be a tissue sample or a biopsy that contains traces of blood and therefore erythrocytes.

The biological sample can also be a sample from a cell culture of erythrocytes.

In a particularly preferred embodiment, the biological sample is a sample selected from the group comprising blood, urine, a tissue sample, a biopsy, and a cell culture, wherein the biological sample contains erythrocytes.

In one embodiment of the invention, the method for analyzing biological samples for changes in signatures of key metabolites in a disease includes the step of taking the biological sample from a test subject. However, it is particularly preferred if the method according to the invention is carried out on a biological sample that has previously been taken from a test subject.

The term "subject" refers to a mammal that suffers from, or is suspected of suffering from, a disease such as a metabolic disease or cancer. Preferably, the term "subject" refers to a human.

The method according to the invention is based in particular on using erythrocytes as a cellular compartment to reveal potential differences in the lipid profile and to enable early diagnosis and treatment of liver diseases. The lipids present in erythrocytes are the key metabolites that are analyzed using the method of the invention.

Erythrocytes represent the largest cellular fraction of blood, with 4-5 million erythrocytes per microliter of blood. In terms of their morphology, erythrocytes are biconcave, circular cells with a diameter of about 7.5 µm. Hemoglobin is the molecular equivalent that causes the red color. It is the iron-containing metalloprotein for oxygen transport that enables the main function of erythrocytes. As they pass through the pulmonary capillaries, red blood cells bind oxygen to hemoglobin, forming oxyhemoglobin for peripheral transport. (Kuhn et al, 2917) Erythrocytes but are also involved in other physiological processes, such as regulating blood vessel tone caused by shear stress. (Sun et al, 2021) The release of adenosine triphosphate (ATP) or S-nitrosothiols can directly relax vessel walls, thereby improving microvascular transfusion. (Barvitenko et al,., 2005, Kuhn et al., 2017) Interestingly, red blood cells lack a nucleus, ribosomes, and an endoplasmic reticulum. (Smith, JE, 1987) Erythrocytes are unable to perform protein synthesis or aerobic cellular respiration. Anaerobic glycolysis therefore remains the only possible strategy for ATP production. Another conclusion from erythrocyte physiology is that proteomic changes cannot be adapted to changes in the microenvironment due to the inappropriate protein synthesis machinery, making erythrocytes an interesting target for the detection of molecular changes.

The surrounding cell membrane consists mainly of a typical lipid bilayer. Cholesterol and phospholipids are the essential elements of the inner and outer layers of the membrane. Comparing both structures, differences in the lipid distribution become apparent. While phosphatidylcholines (PC) and sphingomyelins (SM) are more common on the surface, phosphatidylethanolamines (PE) and phosphatidylserines (PS) predominate in the intracellular layer (see also 1 ). This defined lipid distribution is crucial for cell integrity and is maintained by the enzymes flippase and scramblase. (Mohandas et al., 2008) A network of more than 50 membrane proteins enables the cell to fulfill the above-mentioned functions in all their diversity.

Nowadays, when diagnosing erythrocytes, attention is mainly paid to a quantitative and morphological pattern. The characterization of the properties of the erythrocytes is essentially based on the number and volumetric characteristics in general. The microscopic appearance of the erythrocytes in a blood smear can show abstract formations such as acanthocytes, fragmentocytes or spherocytes, which can facilitate the diagnostic procedure in many cases. (Düzenli et al., 2020, Ford, J., 2013) Within the scope of the method of the invention, the composition of the erythrocytes is now used as an independent compartment, in particular the lipid composition, for the detection of various organic diseases, preferably liver diseases.

The analysis of erythrocyte lipids is carried out using spectroscopic methods, particularly preferably using mass spectrometry (MS).

Mass spectrometry (MS) is a widely used and well-established method for quantifying various molecular changes in different samples. The principle of mass spectrometry is to analyze the mass charge quotient for a single molecule, which can be easily compared to a standard mixture or a worldwide database technology. Molecules passing through the spectrometer must be desorbed in a first step, followed by an ionization step to introduce a specific molecular charge. Powerful turbo pumps create a high vacuum in the MS so that the molecules can pass without friction. The induced ions can then be accelerated and separated in electric fields to guide them to the surface of the detector. (Loos et al., 2016) The previous high pressure liquid chromatography (HPLC) allows the effective separation of molecules in complex sample matrices by fractionation. The combination of both techniques enables valuable measurements in various application areas. (Wilson et al., 2005)

Lipidomics is a subfield of metabolomics that mainly focuses on different lipid subgroups involved in signaling, membrane or metabolic pathways. (Luque et al., 2020, Züllig et al., 2020) Many recent studies show that lipids play a central role in the pathophysiology of sepsis and could become the central component of potential diagnostic and therapeutic targets. (Amungama et al., 2021)

Phospholipids, which are involved in various important molecular processes, can be divided into glycerophospholipids and sphingolipids. Both molecules are bound to phosphate, but differ in the structure of the lipid backbone. While glycerophospholipids contain glycerol and two fatty acid chains, sphingolipids contain sphingosine and an additional fatty acid chain. (see also 2 ).

Preferred key metabolites for the method of the invention are therefore lipids from erythrocytes, preferably phospholipids and sphingolipids, particularly preferably lipids which independently selected from the group comprising phosphatidylcholines (PC), ceramides (Cer), sphingomyelins (SM), lysophosphatidylethanolamines (LPE), and lysophosphatidylcholines (LPC).

Phosphatidylcholines suitable for the process according to the invention are selected, for example, from
PC (28:0), PC (30:0), PC (30:1), PC (32:0), PC (32:1), PC (34:1), PC (34:2), PC (36:0), PC (36:1), PC (36:2), PC (36:3), PC (36:4), PC (38:0), PC (38:1), PC (38:2), PC (38:3) and PC (38:4).

Sphingomyelins suitable for the method according to the invention are selected, for example, from
SM (14:0), SM (16:0), SM (18:0), SM (18:1), SM (20:0), SM (22:0), SM (24:0), SM (24:1), SM (26:0) and SM (26:1).

Lysophosphatidylethanolamines suitable for the process according to the invention are selected, for example, from
LPE (16:0), LPE (16:1), LPE (18:0), LPE (18:1), LPE (18:2) and LPE (20:4).

Ceramides suitable for the process according to the invention are selected, for example, from Cer (14:0), Cer (16:0), Cer (18:0), Cer (18:1), Cer (20:0), Cer (22:0), Cer (24:0) and Cer (24:1)

Lysophosphatidylcholines suitable for the process according to the invention are selected, for example, from
LPC (16:0), LPC (16:1), LPC (18:0), LPC (18:1), LPC (18:2), LPC (20:0), LPC (20:1), LPC (20:3) and LPC (20:4).

In the example compounds listed here, the numbers in brackets symbolize the chain length of the lipid and the number of double bonds in the molecule (chain length of the lipid : number of double bonds in the molecule).

A particular advantage of the invention is that the method provided can be used to analyze a combination of a large number of different lipids as key metabolites of erythrocytes. This allows changes in signatures of key metabolites in biological samples to be recognized in the case of a disease and used to diagnose a metabolic disease or metabolic disorder, particularly in the liver. This is not yet known from the prior art.

According to the invention, at least one mass spectrum of the biological sample is measured with at least one key metabolite.

In one embodiment of the invention, more than one mass spectrum is measured for more than one key metabolite of the biological sample. Preferably, between two to ten mass spectra, particularly preferably between two to five mass spectra are measured for the corresponding number of key metabolites.

Advantageously, mass spectrometry requires only small amounts of samples to be carried out. Accordingly, in one embodiment of the invention, between 1 µl and 150 µl, preferably between 30 µl and 100 µl, most preferably 50 µl of the biological sample is used for measurement by mass spectrometry. Since biological samples from the field of medical diagnostics have a limited volume, it is a great advantage that only a small part of the biological sample is necessary to carry out the method according to the invention. This enables general, untargeted screening diagnostics for all samples before or in parallel with targeted conventional laboratory diagnostics. Nevertheless, a predominant part of a biological sample is available for carrying out further diagnostics, such as routine medical diagnostics in the laboratory.

Based on the identified differences in the type, amount or pattern of key metabolites (collectively referred to herein as the key metabolite signature) present in a biological sample obtained from a subject compared to a normal control sample, a correlation can be made between the type or amount of one or more key metabolites and a probable diagnosis of a disease. A statistically significant increase in the amount of one or more key metabolites compared to the control samples is sufficiently predictive that the subject has a disease specific to the key metabolite(s) or a particular pattern of key metabolites. metabolites. A normal amount of one or more key metabolites or a normal pattern of key metabolites, as characteristic of a control sample isolated from a normal population, indicates that the patient does not have a disease typical of the key metabolite(s) or a particular pattern of key metabolites. A positive indication of disease based on an increased or decreased amount of one or more key metabolites or an altered pattern of key metabolites compared to a normal control will generally be considered along with other factors in making a final determination of a particular disease. Therefore, the increased or decreased amounts of one or more key metabolites or an altered pattern of key metabolites in the subject being tested will generally be considered along with other accepted clinical signs of the particular disease in making a definitive diagnosis of such a disease.

Key metabolites are disease-specific and may only be identified and detected using the method according to the invention.

1. (a) die Isolierung von Erythrozyten aus der biologischen Probe,
2. (b) die Identifikation und gegebenenfalls Quantifizierung von Lipiden aus Erythrozyten als Schlüsselmetaboliten für eine Lebererkrankung,
3. (c) die Erfassung von Änderungen der Schlüsselmetaboliten der Lebererkrankung, und
4. (d) die Diagnose einer Erkrankung der Leber.

In a particularly preferred embodiment, the method for analyzing a biological sample for changes in signatures of bowl metabolites in a liver disease comprises

1. (a) the isolation of erythrocytes from the biological sample,
2. (b) the identification and, where appropriate, quantification of erythrocyte lipids as key metabolites for liver disease,
3. (c) the detection of changes in key liver disease metabolites, and
4. (d) the diagnosis of liver disease.

The method according to the invention can further comprise conventional steps for sample preparation for mass spectrometry which are familiar to the person skilled in the art.

The invention further relates to the use of the method for analyzing a biological sample for changes in signatures of key metabolites in a disease for diagnosing a metabolic disease, metabolic disorder or a proliferative disease.

This concerns, for example, the isolation of erythrocytes from a biological sample. Whole blood from a test subject is preferably used for the isolation. When the sample is taken, agents to prevent blood clotting, preferably ethylenediaminetetraacetic acid (EDTA), are added to the whole blood for the further processing steps. Further steps for isolating erythrocytes from a biological sample usually include sedimenting the erythrocytes by centrifugation (e.g. at 1,000 g for 10 minutes at 20 °C) to separate the cellular blood components, washing the sedimented erythrocytes with a buffer solution (e.g. PBS) and centrifuging again (at 1,000 g for 10 minutes at 20 °C), and storing them at -80 °C.

Further sample preparation steps are usually necessary for the analysis of key metabolites using mass spectrometry. It is particularly preferred if sample preparation is carried out under standardized conditions in order to comply with the requirements of Good Laboratory Practice (GLP) and to achieve reproducible results.

For example, a stored red blood cell sample is thawed, a predefined volume is removed and mixed with a predefined volume of solvent and a predefined volume of an internal standard mixture (see Table 1).

In a particularly preferred embodiment of the method according to the invention, 80 µl of isolated erythrocytes are mixed with 790 µl of methanol and 10 µl of the internal standard mixture.

• - einen Schritt des Rotierens der Mischung für eine vordefinierte Zeitdauer bei Raumtemperatur;
• - einen Schritt des Präzipitierens über Nacht bei einer Temperatur von -80°C;
• - einen Schritt des Zentrifugierens;
• - einen Schritt des Verdampfens des Überstands nach der Zentrifugation;
• - einen Schritt des Lösens der eingedampften Probe in einem Lösungsmittel; und
• - das Einlagern der Probe bei einer Temperatur von -80°C.

Sample preparation for mass spectrometry typically includes further steps known to those skilled in the art, such as:

• - a step of rotating the mixture for a predefined period of time at room temperature;
• - a precipitation step overnight at a temperature of -80°C;
• - a centrifugation step;
• - a step of evaporation of the supernatant after centrifugation;
• - a step of dissolving the evaporated sample in a solvent; and
• - storing the sample at a temperature of -80°C.

The step of rotating the mixture for a predefined period of time at room temperature can, for example, be carried out in a vortex mixer at high speed for 1 min. and then in a rotary mixer for 20 min. at 15 revolutions per minute.

Centrifugation can be carried out, for example, at 10,000 g for 10 min. at 4°C.

The step of evaporating the supernatant can be carried out, for example, at 50°C for 90 min. in a vacuum centrifuge.

The sample can then be resuspended in a solvent such as methanol, preferably 100 µl methanol.

The resuspended sample can then be directly analyzed by mass spectrometry or temporarily stored at a temperature of -80 °C.

• - einen Schritt des Rotierens einer Mischung aus 80 µl isolierte Erythrozyten mit 790 µl Methanol und 10 µl der internen Standardmischung bei Raumtemperatur für 1 min. bei Hochgeschwindigkeit in einem Vortexmischer und anschließend in einem Rotationsmischer für 20 min. bei 15 Umdrehungen pro Minute;
• - einen Schritt des Präzipitierens über Nacht bei einer Temperatur von -80°C;
• - einen Schritt des Zentrifugierens bei 10.000 g für 10 min. bei 4°C;
• - einen Schritt des Verdampfens des Überstands nach der Zentrifugation bei 50°C für 90 min. in einer Vakuumzentrifuge;
• - einen Schritt des Lösens der eingedampften Probe in 100 µl Methanol; und
• - optional das Einlagern der Probe bei einer Temperatur von -80°C.

In a particularly preferred embodiment, sample preparation for mass spectrometry comprises the following steps:

• - a step of rotating a mixture of 80 µl of isolated red blood cells with 790 µl of methanol and 10 µl of the internal standard mixture at room temperature for 1 min. at high speed in a vortex mixer and then in a rotary mixer for 20 min. at 15 revolutions per minute;
• - a precipitation step overnight at a temperature of -80°C;
• - a centrifugation step at 10,000 g for 10 min. at 4°C;
• - a step of evaporation of the supernatant after centrifugation at 50°C for 90 min. in a vacuum centrifuge;
• - a step of dissolving the evaporated sample in 100 µl methanol; and
• - optionally storing the sample at a temperature of -80°C.

The invention further relates to the use of the method for analyzing a biological sample for changes in signatures of key metabolites in a disease for diagnosing a metabolic disease or metabolic disorder, in particular of the liver.

• • Vergleichen der nachgewiesenen Menge eines oder mehrerer Schlüsselmetabolite in der biologischen Probe mit einer für eine normale Kontrollprobe charakteristischen Menge der Schlüsselmetabolite (Referenzbereich); und/oder
• • Vergleichen der Identität eines oder mehrerer Schlüsselmetabolite in der biologischen Probe mit den in einer normalen Kontrollprobe bzw. Referenzbereich vorhandenen Schlüsselmetaboliten; wobei die Anwesenheit zusätzlicher Schlüsselmetabolite oder die Abwesenheit von Schlüsselmetaboliten, vorzugsweise die Anwesenheit zusätzlicher Schlüsselmetabolite in der biologischen Probe im Vergleich zur normalen Kontrollprobe ein positiver Indikator für die Erkrankung ist; und/oder
• • Vergleichen des Musters an Schlüsselmetaboliten in der biologischen Probe mit dem Muster an Schlüsselmetaboliten in einer normalen Kontrollprobe bzw. mit einem Referenzbereich; wobei die Änderungen des Musters an Schlüsselmetaboliten in der biologischen Probe im Vergleich zur normalen Kontrollprobe ein positiver Indikator für die Erkrankung ist.

The step “diagnosis of the disease” of the method according to the invention then further comprises the steps:

• • comparing the detected amount of one or more key metabolites in the biological sample with an amount of the key metabolites characteristic of a normal control sample (reference range); and/or
• • comparing the identity of one or more key metabolites in the biological sample with the key metabolites present in a normal control sample or reference range; the presence of additional key metabolites or the absence of key metabolites, preferably the presence of additional key metabolites in the biological sample compared to the normal control sample is a positive indicator of the disease; and/or
• • Comparing the pattern of key metabolites in the biological sample with the pattern of key metabolites in a normal control sample or with a reference range; where the change in the pattern of key metabolites in the biological sample compared to the normal control sample is a positive indicator of the disease.

In a particularly preferred variant, the method according to the invention is based on the examination of blood samples, whereby the analysis of the signatures of lipids or the lipid profile of erythrocytes is carried out.

This makes it possible to determine, based on the signature of the lipids, whether the tissue being examined, in particular the liver, and thus the test subject, is diseased or not.

• - Datenvalidierung;
• - Interpretation;
• - Vergleich mit Referenzen; und
• - Kategorisierung.

The data evaluation of the method according to the invention usually comprises steps such as:

• - Data validation;
• - Interpretations;
• - Comparison with references; and
• - Categorization.

In a particularly preferred embodiment of the method according to the invention, the diagnosis of metabolic changes in the liver according to step v. is carried out on the basis of the changes in predefined lipid fractions, i.e. on the basis of key metabolites/lipids that are typical for a particular liver disease.

The key metabolites are preferably identified by correlation with a spectral reference library of pure substances. Such a procedure is known to the person skilled in the art. The step of analyzing and evaluating the metabolic signature is preferably carried out using statistical methods. The spectroscopy data can be analyzed by purely visual comparison in order to extract the disease-relevant information, or by applying suitable statistical methods that are known to the person skilled in the art and are selected. All unsupervised and supervised statistical methods are suitable for this. Supervised statistical methods include, for example, principal component analysis. Supervised statistical models include, for example, linear discriminant analysis, support vector machine, partial least squares regression, random forest, neural network analysis and machine learning. Furthermore, methods for sample classification and visualization of the component distribution (alone or in combination with supervised and unsupervised statistical methods) can be used. Suitable methods for sample classification and visualization are, for example: abundance analyses and cluster analysis methods. These include, for example: vector component analysis, NFINDR and k-means clustering.

Particularly preferably, principal component analysis is used in the method according to the invention to analyze and evaluate the metabolic signature. The evaluation of data using principal component analysis is known to the person skilled in the art, see e.g. https://de.wikipedia.org/wiki/Hauptkomponentenanalyse.

It is also preferred if the principal component analysis of the measured lipid profiles of the erythrocytes is subsequently used in a cluster analysis. The implementation of cluster analyses is also known to the person skilled in the art, see e.g. https://de.wikipedia.org/wiki/Clusteranalyse.

• - toxischen, akuten Leberschäden;
• - obstruktiv, cholestatischen Leberschäden;
• - ischämischen Leberschäden;
• - fibrotisch, chronischen Leberschäden; und
• - septischen Leberschäden.

A liver disease that can be diagnosed using the method according to the invention is, for example, a disease selected from

• - toxic, acute liver damage;
• - obstructive, cholestatic liver damage;
• - ischemic liver damage;
• - fibrotic, chronic liver damage; and
• - septic liver damage.

Typical key metabolites/lipids for toxic, acute liver damage are gamma-glutaryltransferase, alanine aminotransferase and aspartate aminotransferase, glutamate dehydrogenase.

Typical key metabolites/lipids for obstructive, cholestatic liver damage are gamma-glutaryltransferase, alkaline phosphatase, direct and indirect bilirubin, taurine- or glycine-conjugated and unconjugated bile acids (such as litocholic acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, obeticholic acid, dehydrocholic acid).

Typical key metabolites/lipids for ischemic liver damage are alanine aminotransferase and aspartate aminotransferase.

Typical key metabolites/lipids for fibrotic, chronic liver damage are synthesis parameters (such as albumin, coagulation factors, haptoglobin), alanine aminotransferase, aspartate aminotransferase and cholinesterase.

Typical key metabolites/lipids for septic liver damage are alanine aminotransferase, aspartate aminotransferase and glutamate dehydrogenase.

The invention has numerous advantages. It makes it possible to identify metabolites in erythrocytes using statistical methods such as abundance mapping and cluster analyses. The erythrocytes can thus be used as a "metabolic marker cell" for identifying pathophysiological metabolic changes. The method according to the invention now allows targeted metabolomics in erythrocytes based on mass spectrometry. The method also allows the analysis of the lipid profile in erythrocytes, which allows statements to be made about the severity of a disease progression or the stage of a disease, particularly of the liver.

As part of functional diagnostics in everyday clinical practice, so-called metabolic fingerprints can be created and used to identify liver diseases. Due to the much larger amount of information processed in the method according to the invention, more precise diagnostics are possible than in laboratory diagnostics.

The results of the method according to the invention can be used advantageously in individualized medicine. Early diagnosis is made possible. In addition, the results of the method according to the invention can be used to prevent progressive organ failure. Therapies can be individually adapted and used in a more targeted manner.

The invention is explained in more detail below with reference to 4 drawings and 5 embodiments.

• 1 die Isolierung der Erythrozyten und Zusammensetzung der Membran (erstellt mit BioRender.com);
• 2 die Grundstruktur verschiedener Phospho- und Sphingolipide, wobei R, R₁ und R₂ Fettsäurereste unterschiedlicher Kettenlänge sein können (geändert nach Cui, L., & Decker, E. A. (2016). Phospholipids in foods: prooxidants or antioxidants? Journal of the science of food and agriculture, 96(1), 18-31 ;
• 3 die Hauptkomponentenanalyse zur Auswertung der massenspektrometrisch gewonnenen Lipiddaten aus einem vorausgegangenen Tierversuch. Es bedeuten:

  CONTROL

  Gesunde Kontrollgruppe

  APAP

  Modell des toxischen, akuten Leberschadens

  BDL

  Modell zum obstruktiv, cholestatischen Leberschaden

  IR

  Modell zum ischämischen Leberschaden

  TAA

  Modell zum fibrotisch, chronischen Leberschaden

  PCI

  Modell zum septischen Leberschaden

• 4 den schematischen Ablauf des erfindungsgemäßen Verfahrens

  110

  Probengewinnung, umfassend Bereitstellung von EDTA-Vollblut, Isolation der Erythrozyten, Aufreinigung der Erythrozyten;

  120

  Massenspektrometriemessungen, umfassend Probenaufreinigung nach standardisierter SOP, Durchführung der Messungen mit definierten Standards;

  130

  Datenauswertung, umfassend Datenvalidierung, Interpretation, Vergleich mit Referenzen, Kategorisierung; und

  140

  Individualisierte Medizin, umfassend Frühe Diagnosestellung Prävention von progredientem Organversagen Adaptierte und zielgerichtete Therapie

Show it:

• 1 the isolation of erythrocytes and composition of the membrane (created with BioRender.com);
• 2 the basic structure of various phospho- and sphingolipids, where R, R ₁ and R ₂ can be fatty acid residues of different chain lengths (modified after Cui, L., & Decker, E. A. (2016). Phospholipids in foods: prooxidants or antioxidants? Journal of the science of food and agriculture, 96(1), 18-31 ;
• 3 the principal component analysis for evaluating the lipid data obtained by mass spectrometry from a previous animal experiment. This means:

  CONTROL

  Healthy control group

  APAP

  Model of toxic, acute liver damage

  BDL

  Model of obstructive, cholestatic liver damage

  IR

  Model of ischemic liver damage

  TAA

  Model of fibrotic, chronic liver damage

  PCI

  Model of septic liver damage

• 4 the schematic flow of the method according to the invention

  110

  Sample collection, including provision of EDTA whole blood, isolation of erythrocytes, purification of erythrocytes;

  120

  Mass spectrometry measurements, including sample purification according to standardized SOP, carrying out the measurements with defined standards;

  130

  Data evaluation, including data validation, interpretation, comparison with references, categorization; and

  140

  Individualized medicine, comprehensive Early diagnosis Prevention of progressive organ failure Adapted and targeted therapy

Example 1: Isolation of erythrocytes from a whole blood sample

Whole blood samples were transferred into 1.2 ml ethylenediaminetetraacetic acid (EDTA) Monovettes (Sarstedt, Germany) to prevent blood clotting for further processing steps. After initial centrifugation of the Monovettes at 1,000 g for 10 minutes at 20°C, the plasma fraction was transferred to fresh polypropylene tubes and stored at -80°C for further analyses. The remaining red blood cells were washed twice by replacing the amount of extracted plasma with PBS (Biozym, Germany). After addition of approximately 200 µl PBS, the samples were centrifuged again at 1,000 g for 10 minutes at 20°C. This supernatant exchange step was performed twice. After final removal of the supernatant, red blood cells were aliquoted into polypropylene tubes (Eppendorf, Germany), weighed, and stored at -80°C.

Example 2: Sample preparation for mass spectrometry

Sample preparation for mass spectrometry was performed according to a standard operating procedure.

used material

• - Methanol (>99.9%)
• - Internal standard mixture (see Table 1)
• - Glass bottles

Devices used

• - Rotator IKA Loopster
• - Tissuelyzer Qiagen
• - Centrifuge
• - SpeedVac
• - Horizontal shaker
• - Vortex mixer

Test procedure

A weighted aliquot of approximately 80 µl of erythrocytes was used for the measurements. Preparation for lipid quantification began with the addition of 790 µl of methanol (>99.9%, Honeywell Riedel-de-Haen, Germany) and 10 µl of internal standard mixture (each 30 pmol per injection volume, for details see Table 1 in Example 4) as a reference for normalization and quantification) to the tubes containing the erythrocyte samples. After precipitation overnight at -80°C, the samples were centrifuged at 10,000 g for 1 hour.

The supernatant was transferred to a new polypropylene tube and evaporated in the SPD SpeedVac (Thermo Scientific Savant) at 50°C for 90 min. Afterwards, reconstitution was carried out in 100 µL methanol (> 99.9%) on a plate shaker at maximum speed for 1 min. Finally, the samples were transferred to glass vials (Dr. R. Forche Chromatographie, Germany) and sealed.

Depending on the availability of the mass spectrometer, the sample was then measured immediately or temporarily stored at -80°C.

Example 3: Metabolic analysis

Metabolomic analyses were performed using mass spectrometry to measure the lipid composition of red blood cells and to demonstrate the changes in the different lipid fractions to further characterize disease at a molecular level.

Hepatic excretory function was assessed mainly by bile acid measurements in spleen and liver samples.

Therefore, mass spectrometry was the most efficient method to identify the overall changes in different organic compartments.

Example 4: Lipid quantification

The lipid profile was measured on erythrocytes. Sample preparation for lipid quantification was carried out as described in Example 2. For the internal standard mixture (30 pmol per injection volume) as a reference for normalization and quantification, see Table 1 below.

For the measurements, an autosampler L-7250, a Peltier sample cooler from Merck for L-7250, an L-2130 liquid chromatograph with the column oven L-2300 with a 60x2 mm MultoHigh 100 RP 18 column with 3 µm particle size (CS Chromatographie Service, Germany) were combined with an Elite LaChrom LC system (Hitachi Europe GmbH, Germany) and kept at 50 °C. The mobile phase A consisted of 1.0% (v/v) formic acid in ddH2O, the mobile phase B of 100% methanol (Chromasolv, Honeywell Riedel-de Haën, Seelze, Germany). The column was run at a flow rate of 0.5 ml min ^-1 in 10% B. The mobile phase changed to 100% B after sample injection. The flow rate increased linearly from 0.5 ml min ^-1 at 5 min to 1.0 ml min ^-1 at 7 min and remained constant until 10 min. Thereafter, the mobile phase was switched to 10% B and the flow rate decreased linearly again from 1.0 ml min ^-1 at 10 min to 0.5 ml min ^-1 at 10.5 min and remained constant until the end of the program at 11.3 min. Detection was performed between 2 and 10 min.

All samples were cooled to 4 °C and 10.0 µL per sample was injected. The high pressure liquid chromatography system equipped with an ESI or APCI source was combined with the API 2000 triple quadrupole mass spectrometer (Sciex, Foster City, CA, USA), both operating in positive mode under specific source parameters: source temperature 450 °C, curtain gas 40, collision gas low, ion spray voltage 5500, ion source gas1 60, ion source gas2 30. The target ions Q1>Q3, the internal standard assignment and the type of analysis are given in Table 1 below. All results were quantified using Analyst 1.6.2 (AB Sciex, Forster City, CA, USA) based on internal standard samples and the external standard curve. Table 1: List of target ions/MRM transitions, internal standard assignment and analysis mode Surname Internal standard mode Target ion Q1>Q3 Cerium(15:0) APCI 524.4>264.3 Cerium(12:0) >Cerium(15:0)< APCI 482.6>264.4 Cerium(14:0 >Cerium(15:0)< APCI 510.7>264.4 Cerium(16:0) >Cerium(15:0)< APCI 538.7>264.4 Cerium(18:1) >Cerium(15:0)< APCI 564.7>264.4 Cerium(18:0) >Cerium(15:0)< APCI 566.7>264.4 Cerium(20:0) >Cerium(15:0)< APCI 594.7>264.4 Cerium(22:0) >Cerium(15:0)< APCI 622.8>264.4 Cerium(24: 1) >Cerium(15:0)< APCI 648.9>264.4 Cerium(24:0) >Cerium(15:0)< APCI 650.9>264.4 SM (17:0) IT I 717.483>184.1 SM (12:0) >SM (17:0)< IT I 647.7>184.1 SM (14:0) >SM (17:0)< IT I 675.7>184.1 SM (16:0) >SM (17:0)< IT I 703.8>184.1 SM (18:1) >SM (17:0)< IT I 729.8>184.1 SM (18:0) >SM (17:0)< IT I 731.8>184.1 SM (20:0) >SM (17:0)< IT I 759.9>184.1 SM (22:0) >SM (17:0)< IT I 797.9>184.1 SM (24:1) >SM (17:0)< IT I 813.9>184.1 SM (24:0) >SM (17:0)< IT I 815.9>184.1 SM (26:1) >SM (17:0)< IT I 841.9>181.1 SM (26:0) >SM (17:0)< IT I 843.9>181.1 PC (28:0) >PC (34:0)< IT I 678.5>184.1 PC (30:0) >PC (34:0)< IT I 706.55>184.1 PC (30:1) >PC (34:0)< IT I 704.5>184.1 PC (32:0) >PC (34:0)< IT I 734.55>184.1 PC (32:1) >PC (34:0)< IT I 732.55>184.1 PC (34:0) IT I 762.455>184.1 PC (34:1) >PC (34:0)< IT I 760.6>184.1 PC (34:2) >PC (34:0)< IT I 758.629>184.1 PC (36:0) >PC (34:0)< IT I 790.65>184.1 PC (36:1) >PC (34:0)< IT I 788.6>184.1 PC (36:2) >PC (34:0)< IT I 786.6>184.1 PC (36:3) >PC (34:0)< IT I 784.6>184.1 PC (36:4) >PC (34:0)< IT I 782.55>184.1 PC (38:0) >PC (34:0)< IT I 818.65>184.1 PC (38:1) >PC (34:0)< IT I 816.65>184.1 PC (38:2) >PC (34:0)< IT I 814.65>184.1 PC (38:3) >PC (34:0)< IT I 812.60>184.1 PC (38:4) >PC (34:0)< IT I 810.60>184.1 LPC (16:0) >LPC (17:0)< IT I 496.278>184.1 LPC (16:1) >LPC (17:0)< IT I 494.3>184.1 LPC (17:0) IT I 510.222>184.1 LPC (18:0) >LPC (17:0)< IT I 524.35>184.1 LPC (18:1) >LPC (17:0)< IT I 522.35>184.1 LPC (18:2) >LPC (17:0)< IT I 520.35>184.1 LPC (20:0) >LPC (17:0)< IT I 552.40>184.1 LPC (20:1) >LPC (17:0)< IT I 550.4>184.1 LPC (20:3) >LPC (17:0)< IT I 546.35>184.1 LPC (20:4) >LPC (17:0)< IT I 544.35>184.1 LPE (16:0) >LPE (17:1)< IT I 454.3>313.25 LPE (16:1) >LPE (17:1)< IT I 452.3>311.25 LPE (17: 1) IT I 466.3>325.3 LPE (18:0) >LPE (17:1)< IT I 482.3>341.3 LPE (18:1) >LPE (17:1)< IT I 480.3>339.3 LPE (18:2) >LPE (17:1)< IT I 478.3>337.25 LPE (20:4) >LPE (17:1)< IT I 502.3>361.25

Example 5: Principal component analysis and cluster analysis of the results of lipid measurements using mass spectrometry

The principal component analysis in is based on the experimentally collected data on the lipid composition of erythrocytes. The principal component analysis was created using R. The dimensions 1 and 2 are shown, with each point shown corresponding to an individual. The assignment to the corresponding animal model is made using the symbols shown.

The result of the principal component analysis for the evaluation of mass spectrometrically obtained lipid data from a previous animal experiment shows 4 . In 4 mean: CONTROL Healthy control group APAP Model of toxic, acute liver damage BDL Model of obstructive, cholestatic liver damage IR Model of ischemic liver damage TAA Model of fibrotic, chronic liver damage PCI Model of septic liver damage

This analysis already shows the first clear cluster structures and groupings. It therefore seems plausible to detect various entities of liver dysfunction using obtained erythrocytes and subsequent mass spectrometric measurement. The animal experiments carried out accordingly are based on various animal models of different forms of liver dysfunction. The corresponding entities can be found in the list above.

credentials

• Kiehntopf M, Nin N, Bauer M. Metabolism, metabolome, and metabolomics in intensive care: is it time to move beyond monitoring of glucose and lactate? Am JRespir Crit Care Med 2013; 187: 906 □ 907 .
• Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (Sepsis Related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis □ Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996; 22: 707 □ 710 .
• Recknagel P, Gonnert FA, Westermann M, Lambeck S, Lupp A, Rudiger A, et al. Liver dysfunction and phosphatidylinositol □ 3 □ kinase signaling in early sepsis: experimental studies in rodent models of peritonitis. PLoS Med 2012; 9: e1001338 .
• Vanwijngaerden YM, Langouche L, Brunner R, Debayeve Y, Gielen M, Casaer M, et al. Withholding parenteral nutrition during critical illness increases plasma bilirubin but lowers the incidence of biliary sludge. Hepatology 2014; 60: 202 □ 210 .
• Kruger PS, Freir NM, Venkatesh B, Robertson TA, Roberts MS, Jones M. A preliminary study of atorvastatin plasma concentrations in critically ill patients with sepsis. Intensive Care Med 2009; 35: 717 □ 721 .
• Bauer M., Kiehntopf M. Shades of yellow: Monitoring nutritional needs and hepatobiliary function in the critically ill. Hepatology 2014; 60: 26-29
• Cui, L., & Decker, E. A. (2016). Phospholipids in foods: prooxidants or antioxidants?. Journal of the science of food and agriculture, 96(1), 18-31. https://doi.org/10.1002/jsfa.7320
• Kuhn, V., Diederich, L., Keller, T., 4th, Kramer, C. M., Lückstädt, W., Panknin, C., Suvorava, T., Isakson, B. E., Keim, M., & Cortese-Krott, M. M. (2017). Red Blood Cell Function and Dysfunction: Redox Regulation, Nitric Oxide Metabolism, Anemia. Antioxidants & redox signaling, 26(13), 718-742. https://doi.org/10.1089/ars.2016.6954
• Sun, P., Jia, J., Fan, F., Zhao, J., Huo, Y., Ganesh, S. K., & Zhang, Y. (2021). Hemoglobin and erythrocyte count are independently and positively associated with arterial stiffness in a community-based study. Journal of human hypertension, 35(3), 265-273. https://doi.org/10.1038/s41371-020-0332-6
• Smith J.E. (1987). Erythrocyte membrane: structure, function, and pathophysiology. Veterinary pathology, 24(6), 471-476. https://doi.org/10.1177/030098588702400601
• Mohandas, N., & Gallagher, P. G. (2008). Red cell membrane: past, present, and future. Blood, 112(10), 3939-3948. https://doi.org/10.1182/blood-2008-07-161166
• Düzenli Kar, Y., Ozdemir, Z. C., Emir, B., & Bör, O. (2020). Erythrocyte indices as differential diagnostic biomarkers of iron deficiency anemia and thalassemia. Journal of pediatric hematology/oncology, 42(3), 208-213. https://doi.org/10.1097/MPH.0000000000001597
• Ford J. (2013). Red blood cell morphology. Journal of Hematology, 35(3), 351-357. https://doi.org/10.1111/ijlh.12082
• Loos, G., Van Schepdael, A., & Cabooter, D. (2016). Quantitative mass spectrometry methods for pharmaceutical analysis. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 374(2079), 20150366. https://doi.org/10.1098/rsta.2015.0366
• Wilson, I. D., Plumb, R., Granger, J., Major, H., Williams, R., & Lenz, E. M. (2005). HPLC-MS-based methods for the study of metabonomics. Journal of chromatography. B, Analytical technologies in the biomedical and life sciences, 817(1), 67-76. https://doi.org/10.1016/j.jchromb.2004.07.045
• Luque de Castro, M. D., & Quiles-Zafra, R. (2020). Lipidomics: An omics discipline with a key role in nutrition. Talanta, 219, 121197. https://doi.org/10.1016/j.talanta.2020.121197 Züllig, T., Trötzmüller, M., & Köfeler, H. C. (2020). Lipidomics from sample preparation to data analysis: a primer. Analytical and bioanalytical chemistry, 412(10), 2191-2209. https://doi.org/10.1007/s00216-019-02241-y
• Amunugama, K., Pike, D. P., & Ford, D. A. (2021). The lipid biology of sepsis. Journal of lipid research, 62, 100090. https://doi.org/10.1016/j.j1r.2021.100090

QUOTES INCLUDED IN THE DESCRIPTION

This list of documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions.

Cited patent literature

• WO 2022/167521 A1 [0012]

Cited non-patent literature

• Cui, L., & Decker, E. A. (2016). Phospholipids in foods: prooxidants or antioxidants? Journal of the science of food and agriculture, 96(1), 18-31 [0077]
• M, Nin N, Bauer M. Metabolism, metabolome, and metabolomics in intensive care: is it time to move beyond monitoring of glucose and lactate? Am JRespir Crit Care Med 2013; 187: 906 □ 907 [0091]
• Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (Sepsis Related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis □ Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996; 22: 707 □ 710 [0091]
• Recknagel P, Gonnert FA, Westermann M, Lambeck S, Lupp A, Rudiger A, et al. Liver dysfunction and phosphatidylinositol □ 3 □ kinase signaling in early sepsis: experimental studies in rodent models of peritonitis. PLoS Med 2012; 9: e1001338 [0091]
• Vanwijngaerden YM, Langouche L, Brunner R, Debayeve Y, Gielen M, Casaer M, et al. Withholding parenteral nutrition during critical illness increases plasma bilirubin but lowers the incidence of biliary sludge. Hepatology 2014; 60: 202 □ 210 [0091]
• Kruger PS, Freir NM, Venkatesh B, Robertson TA, Roberts MS, Jones M. A preliminary study of atorvastatin plasma concentrations in critically ill patients with sepsis. Intensive Care Med 2009; 35: 717 □ 721 [0091]
• Bauer M., Kiehntopf M. Shades of yellow: Monitoring nutritional needs and hepatobiliary function in the critically ill. Hepatology 2014; 60: 26-29 [0091]
• Cui, L., & Decker, E. A. (2016). Phospholipids in foods: prooxidants or antioxidants?. Journal of the science of food and agriculture, 96(1), 18-31. https://doi.org/10.1002/jsfa.7320 [0091]
• Kuhn, V., Diederich, L., Keller, T., 4th, Kramer, C. M., Lückstädt, W., Panknin, C., Suvorava, T., Isakson, B. E., Keim, M., & Cortese-Krott, M. M. (2017). Red Blood Cell Function and Dysfunction: Redox Regulation, Nitric Oxide Metabolism, Anemia. Antioxidants & redox signaling, 26(13), 718-742. https://doi.org/10.1089/ars.2016.6954 [0091]
• Sun, P., Jia, J., Fan, F., Zhao, J., Huo, Y., Ganesh, S. K., & Zhang, Y. (2021). Hemoglobin and erythrocyte count are independently and positively associated with arterial stiffness in a community-based study. Journal of human hypertension, 35(3), 265-273. https://doi.org/10.1038/s41371-020-0332-6 [0091]
• Smith J.E. (1987). Erythrocyte membrane: structure, function, and pathophysiology. Veterinary pathology, 24(6), 471-476. https://doi.org/10.1177/030098588702400601 [0091]
• Mohandas, N., & Gallagher, P. G. (2008). Red cell membrane: past, present, and future. Blood, 112(10), 3939-3948. https://doi.org/10.1182/blood-2008-07-161166 [0091]
• Düzenli Kar, Y., Ozdemir, Z. C., Emir, B., & Bör, O. (2020). Erythrocyte indices as differential diagnostic biomarkers of iron deficiency anemia and thalassemia. Journal of pediatric hematology/oncology, 42(3), 208-213. https://doi.org/10.1097/MPH.0000000000001597 [0091]
• Ford J. (2013). Red blood cell morphology. Journal of Hematology, 35(3), 351-357. https://doi.org/10.1111/ijlh.12082 [0091]
• Loos, G., Van Schepdael, A., & Cabooter, D. (2016). Quantitative mass spectrometry methods for pharmaceutical analysis. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 374(2079), 20150366. https://doi.org/10.1098/rsta.2015.0366 [0091]
• Wilson, I. D., Plumb, R., Granger, J., Major, H., Williams, R., & Lenz, E. M. (2005). HPLC-MS-based methods for the study of metabonomics. Journal of chromatography. B, Analytical technologies in the biomedical and life sciences, 817(1), 67-76. https://doi.org/10.1016/j.jchromb.2004.07.045 [0091]
• Luque de Castro, M. D., & Quiles-Zafra, R. (2020). Lipidomics: An omics discipline with a key role in nutrition. Talanta, 219, 121197. https://doi.org/10.1016/j.talanta.2020.121197 [0091]
• Züllig, T., Trötzmüller, M., & Köfeler, H. C. (2020). Lipidomics from sample preparation to data analysis: a primer. Analytical and bioanalytical chemistry, 412(10), 2191-2209. https://doi.org/10.1007/s00216-019-02241-y [0091]
• Amunugama, K., Pike, D. P., & Ford, D. A. (2021). The lipid biology of sepsis. Journal of lipid research, 62, 100090. https://doi.org/10.1016/j.j1r.2021.100090 [0091]

Claims (13)

A method for analyzing a biological sample for changes in key metabolite signatures in liver disease, comprising (a) isolating red blood cells from the biological sample, (b) identifying and optionally quantifying lipids from the red blood cells as key metabolites for liver and infectious disease, (c) detecting changes in key liver disease metabolites, and (d) diagnosing liver disease. Procedure according to Claim 1 , characterized in that the blood sample is EDTA whole blood. Procedure according to Claim 1 or 2 , characterized in that the lipids are independently selected from the group comprising phosphatidylcholines (PC), ceramides (Cer), sphingomyelins (SM), lysophosphatidylethanolamines (LPE), and lysophosphatidylcholines (LPC). Method according to one of the preceding claims, characterized in that the lipids are selected from PC (28:0), PC (30:0), PC (30:1), PC (32:0), PC (32:1), PC (34:1), PC (34:2), PC (36:0), PC (36:1), PC (36:2), PC (36:3), PC (36:4), PC (38:0), PC (38:1), PC (38:2), PC (38:3) and PC (38:4); SM (14:0), SM (16:0), SM (18:0), SM (18:1), SM (20:0), SM (22:0), SM (24:0), SM (24:1), SM (26:0) and SM (26:1); LPE (16:0), LPE (16:1), LPE (18:0), LPE (18:1), LPE (18:2) and LPE (20:4). Cer (14:0), Cer (16:0), Cer (18:0), Cer (18:1), Cer (20:0), Cer (22:0), Cer (24:0) and Cer (24:1); and LPC (16:0), LPC (16:1), LPC (18:0), LPC (18:1), LPC (18:2), LPC (20:0), LPC (20:1), LPC (20:3) and LPC (20:4). Method according to one of the preceding claims, characterized in that the isolation of the erythrocytes from the blood sample according to step a. of Claim 1 centrifuging the blood sample to separate the cellular blood components and washing the isolated erythrocytes with a buffer solution. Method according to one of the preceding claims, characterized in that the sample processing according to step a. of Claim 1 mixing a predefined volume of the isolated red blood cells with a predefined volume of a solvent and a predefined volume of an internal standard mixture. Procedure according to Claim 6 , characterized in that 80 µl of isolated erythrocytes are mixed with 790 µl of methanol and 10 µl of the internal standard mixture. Procedure according to Claim 6 or 7 , characterized in that the sample preparation according to step a. of Claim 1 further comprising: - a step of rotating the mixture for a predefined period of time at room temperature; - a step of precipitating overnight at a temperature of -80°C; - a step of centrifuging; - a step of evaporating the supernatant after centrifugation; - a step of dissolving the evaporated sample in a solvent; and - storing the sample at a temperature of -80°C. Method according to one of the preceding claims, characterized in that the analysis by means of mass spectrometry according to step b. of Claim 1 which includes measuring the lipid composition of red blood cells. Method according to one of the preceding claims, characterized in that the data evaluation comprises the parts of the method according to steps b, c and d. of Claim 1 the steps include: - data analysis - data validation; - modelling - interpretation; - comparison with references; and - categorization. Method according to one of the preceding claims, characterized in that the diagnosis of metabolic changes of the liver according to step c. of Claim 1 based on changes in predefined lipid fractions. Procedure according to Claim 10 or 11 , characterized in that for the data evaluation according to steps b, c and d. of Claim 1 Lipid data obtained by mass spectrometry are subjected to principal component analysis. Procedure according to Claim 10 , characterized in that by means of the principal component analysis of the lipid data obtained by means of mass spectrometry, liver damage selected from - toxic, acute liver damage; - obstructive, cholestatic liver damage; - ischemic liver damage; - fibrotic, chronic liver damage; and - septic liver damage are diagnosed.

Images