CN113075282A - In-situ analysis method for endogenous metabolites in brain tissue - Google Patents
In-situ analysis method for endogenous metabolites in brain tissue Download PDFInfo
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Abstract
The invention discloses an in-situ analysis method of endogenous metabolites in brain tissues. The in-situ analysis method of endogenous metabolites in brain tissues comprises the following steps: and performing mass spectrum imaging on the isolated brain tissue slice sample to be detected to obtain the species, ionic strength and/or spatial distribution characteristics of endogenous metabolites in the brain tissue. The invention discloses an in-situ metabonomics analysis method suitable for comprehensively analyzing various endogenous metabolites in brain tissues based on an AFADESI-MSI mass spectrometry imaging technology of ultra-high resolution mass spectrometry, and aims to systematically research the distribution characteristics of various endogenous metabolites in different areas of the brain tissues, obtain biomarkers related to the occurrence of diabetic encephalopathy and deeply discuss the pathogenesis of the diabetic encephalopathy. The invention provides a new research strategy and technical means for the pathogenesis and the drug development of the diabetic encephalopathy and provides important reference information for the diagnosis, the prevention and the treatment of the diabetic encephalopathy.
Description
Technical Field
The invention belongs to the technical field of biological analysis, and particularly relates to an in-situ analysis method of endogenous metabolites in brain tissues.
Background
The number of the existing diabetics in China exceeds 9000 ten thousand, 90 percent of the existing diabetics are type 2 diabetics, and the number of the diabetics in the world is nearly 1/3, and the existing diabetics have a continuously increasing trend. Metabolic disorder and poor blood sugar control in a diabetic patient can cause various macrovascular and microvascular complications and damage target organs such as kidneys, eyes, peripheral nerves, brain and the like. Brain tissue consumes oxygen and glucose in an amount that is one fifth of the total body consumption, and thus, various acute and chronic hyperglycemia or hypoglycemia may cause damage to brain tissue. As early as 1922, researchers recognized varying degrees of cognitive dysfunction in Diabetic patients and proposed the concept of Diabetic Encephalopathy (DE) in the 60's of the 20 th century, which is used to describe diabetes-induced central nervous system dysfunction, and was continuously demonstrated in later clinical and experimental animal models. Studies have shown that about 40% of diabetic patients with long-term or poor glycemic control develop diabetic encephalopathy. Diabetic encephalopathy patients mainly show mild and moderate cognitive dysfunction, type 1 diabetic encephalopathy patients mainly show slow thinking and movement, such as inattention, slow movement and the like, and type 2 diabetic encephalopathy patients mainly show impaired learning and memory functions and possibly have defects in the memory extraction process. These symptoms, although not so obvious as to be regarded as important, significantly affect the quality of life of the patient and place a heavy burden on the family and society. In addition, the diabetic encephalopathy is a chronic progressive disease, is closely related to the onset of dementia, particularly Alzheimer's Disease (AD), and has many similarities on a molecular level, so that the diabetes is considered as an independent risk factor of AD, and the incidence rate of dementia of diabetic patients is 1.5-2.5 times that of non-diabetic patients.
The pathogenesis of the diabetic encephalopathy comprises a plurality of factors, such as insulin resistance, chronic inflammation, vascular injury, mitochondrial dysfunction, blood brain barrier damage, oxidative stress, neurotransmitter metabolic disorder, beta-amyloid protein expression abnormality and the like, and the factors are mutually linked and mutually influenced, so that the pathophysiological mechanism of the diabetic encephalopathy is extremely complex. At present, the understanding of human beings on diabetic encephalopathy is limited, the pathogenesis of the diabetic encephalopathy cannot be completely clarified, and effective diagnosis, prevention and treatment measures are lacked. In view of the huge base number of diabetic patients and the increasing number of patients in China, the diabetic encephalopathy may cause serious health and social problems. Therefore, the research on the pathogenesis and the prevention and treatment method of the diabetic encephalopathy has important significance.
Diabetic encephalopathy, a complex metabolic disease, is closely related to the abnormal metabolism of small molecule compounds. The metabolic pathways currently found to be involved in diabetic encephalopathy, moderate cognitive impairment and dementia are mainly sugar metabolism (pentose phosphate pathway), amino acid metabolism (glutamic acid, tryptophan), choline metabolism (acetylcholine), lipid metabolism (glycerophospholipids, plasmalogens, sphingomyelins), coenzyme metabolism (NAD, NADP), hormone metabolism (enolone, progesterone, testosterone, etc.), organic acid metabolism (taurine, 2, 4-dihydroxybutyric acid), etc. For example, nicotinic acid and nicotinamide are important components of coenzymes NAD and NADP, have antioxidant stress, anti-inflammatory and neuroprotective effects, Song and the like adopt a metabonomics method to research the change of metabolite levels in diabetic mice in the development process of moderate cognitive dysfunction, and find that the nicotinic acid and nicotinamide in urine are closely developed with the moderate cognitive dysfunction and the content of the nicotinic acid and nicotinamide is continuously reduced along with the development of diseases.
143 moderate cognitive dysfunction patients are followed up, and 52 serum samples which later develop into Alzheimer disease are analyzed by adopting a metabonomics and lipidomics analysis platform based on a mass spectrum technology, and the results show that pentose phosphate pathway in the moderate cognitive dysfunction patients is obviously up-regulated, and metabolism of glycerophospholipid, phosphatidylcholine and sphingomyelin is obviously abnormal. Acetylcholine is an important neurotransmitter in brain, participates in regulation and control of important functions such as movement, sleep, emotion and memory, and researches show that metabolic abnormalities of acetylcholine in hippocampus and cerebral cortex are closely related to cognitive disorder and senile dementia. The research provides abundant information for understanding the metabolic mechanism in the diabetic encephalopathy occurrence process, but cannot provide the spatial distribution information of the small molecule metabolites in the brain tissue for people, and the knowledge of the information has important value for accurately explaining the complex pathophysiological process of the diabetic encephalopathy and finding specific biomarkers.
Mass Spectrometry Imaging (MSI) is a leading-edge and hot spot field in Mass Spectrometry research at present as a novel label-free molecular Imaging technology. The technology combines imaging processing software with an ion scanning technology of mass spectrometry, can perform visual analysis on biological tissues on a molecular level, realizes simultaneous detection of various molecules and obtains structure, content and spatial distribution information of the molecules on the tissues, and has very important significance on monitoring of animal pathophysiological states, discovery of disease markers, in-situ characterization of effective components of medicaments and the like. Currently, MSI technology mainly includes the following three major types: secondary Ion Mass Spectrometry (SIMS) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry, which require ionization under vacuum conditions, are used. Among them, the SIMS imaging technique is mainly applied to elemental analysis of a sample surface. MALDI-MSI is the most mature and widely applied mass spectrum imaging technology, and is particularly suitable for imaging analysis of biomacromolecules such as protein, polypeptide and the like. Since the low mass end is susceptible to interference from matrix peaks, its application in small molecule metabolite analysis is somewhat limited.
How to acquire the spatial distribution information of small molecule metabolites in brain tissue through mass spectrometry imaging and establish an in-situ metabonomics analysis method suitable for comprehensively analyzing various endogenous metabolites in the brain tissue is the problem to be solved at present.
Disclosure of Invention
The invention aims to provide an in-situ analysis method for endogenous metabolites in brain tissues, which is based on an open Air Flow Assisted Ionization (AFADESI) -MSI mass spectrometry imaging technology and is used for acquiring spatial distribution information of various endogenous metabolites in different areas of the brain tissues.
The invention provides an in-situ analysis method of endogenous metabolites in brain tissues, which comprises the following steps: and performing mass spectrum imaging on the isolated brain tissue slice sample to be detected to obtain the species, ionic strength and/or spatial distribution characteristics of endogenous metabolites in the brain tissue.
In the above method, the ion source used for mass spectrometry imaging may be an aerodynamic assisted desorption electrospray (AFA DESI) ion source.
The slide glass used for attaching the brain tissue section sample to be detected in the mass spectrometry imaging can be a positive charge anti-dropping slide glass.
The thickness of the brain tissue slice sample to be tested can be 8 μm.
The spray solvent used for mass spectrometry imaging can be prepared from a solvent of 8:2 acetonitrile and water; the flow rate of the spray solvent may be 7. mu.L/min.
The mass resolution of the mass spectral imaging may be 120,000.
In the above method, the spatial distribution of the endogenous metabolites in the brain tissue is characterized by the spatial distribution of the endogenous metabolites in the brain tissue in the whole brain and in different regions, such as eight regions divided according to the following histological structures: amygdala (AM), Corpus Callosum (CC), cerebral Cortex (CTX), Hippocampus (HP), Thalamus (TH), Internal Capsule (IC), mid-abdominal nucleus (MN), hypothalamus and substantia nigra (HY-SN).
The method for in situ analysis of endogenous metabolites in brain tissue of the present invention is a method for in situ analysis of endogenous metabolites in brain tissue for non-diagnostic purposes. The in-situ analysis method of endogenous metabolites in brain tissue of the invention takes an isolated brain tissue sample as an object, and directly aims at obtaining the species, ionic strength and spatial distribution characteristics of the endogenous metabolites in the brain tissue, so that the disease diagnosis result or health condition can not be directly obtained.
The invention further provides an in situ analysis system of endogenous metabolites in brain tissue, comprising an instrument, a reagent and a readability carrier;
the instrument comprises an instrument for mass spectrometry imaging involved in the readability carrier;
the reagents include reagents involved in the readability carrier for mass spectrometry imaging;
the readability vector is described with the following steps for the in situ analysis of endogenous metabolites in brain tissue: and carrying out mass spectrum imaging on the isolated brain tissue slice to be detected to obtain the species, ionic strength and/or spatial distribution characteristics of endogenous metabolites in the brain tissue.
In the above system, the readable carrier may be instructions, and the step of performing in situ analysis of endogenous metabolites in brain tissue is printed on a card;
the readable carrier may be a computer readable carrier.
In the above system, the instrument comprises a mass spectrometer; the ion source of the mass spectrometer may be an aerodynamic assisted desorption electrospray (afadisi) ion source.
In the system, the instrument comprises a glass slide for attaching the brain tissue slice sample to be measured; the slide is a positive charge anti-dropping slide.
In the above system, the reagent comprises a spray reagent; the spraying solvent is prepared from the following components in a volume ratio of 8:2 acetonitrile and water.
In the above system, the readability carrier further records the following conditions for mass spectrometry imaging: the thickness of the brain tissue slice sample is 8 μm; the flow rate of the spray solvent is 7 mu L/min; the mass resolution was 120,000.
In the above system, in the readability vector, the spatial distribution of the endogenous metabolites in the brain tissue is characterized by the spatial distribution of the endogenous metabolites in the brain tissue in the whole brain and in different regions, such as eight regions divided according to the following histological structures: amygdala (AM), Corpus Callosum (CC), cerebral Cortex (CTX), Hippocampus (HP), Thalamus (TH), Internal Capsule (IC), mid-abdominal nucleus (MN), hypothalamus and substantia nigra (HY-SN).
The invention also provides application of the in-situ analysis system of the endogenous metabolites in the brain tissue in at least one of the following A1-A8:
a1, preparing a central nervous system disease in-situ analysis product;
a2, preparing a product for obtaining biomarkers of central nervous system diseases;
a3, preparing products for diagnosing or assisting in diagnosing central nervous system diseases;
a4, preparing products for distinguishing or identifying central nervous system diseases;
a5, preparing a diabetic encephalopathy in-situ analysis product;
a6, preparing a product for acquiring biomarkers of diabetic encephalopathy;
a7, preparing products for diagnosing or assisting in diagnosing diabetic encephalopathy;
a8, preparing products for distinguishing or identifying diabetic encephalopathy.
The invention has the following beneficial effects:
the invention discloses an in-situ metabonomics analysis method suitable for comprehensively analyzing various endogenous metabolites in brain tissues based on an AFADESI-MSI mass spectrometry imaging technology of ultra-high resolution mass spectrometry, and aims to systematically research the distribution characteristics of various endogenous metabolites in different areas of the brain tissues, obtain biomarkers related to the occurrence of diabetic encephalopathy and deeply discuss the pathogenesis of the diabetic encephalopathy. The invention provides a new research strategy and technical means for the pathogenesis and the drug development of the diabetic encephalopathy and provides important reference information for the diagnosis, the prevention and the treatment of the diabetic encephalopathy.
Drawings
FIG. 1 is a graph of the effect of different slide types on brain AFADESI-MSI positive ion detection, a normal slide I, a positive charge prevention slide II, and a SuperFrost plus slide III; wherein, fig. 1A is a bar chart of total ion intensity of different kinds of glass slides, and fig. 1B is a bar chart of total ion number of different kinds of glass slides.
FIG. 2 is an AFADESI-MSI positive ion detection image and ion intensity bar graph of representative metabolites in the brain under different types of slides (I, II, III), a normal slide I, a positive charge-retaining slide II and a SuperFrost plus slide III; wherein FIG. 2A is a graph representing metabolites, FIG. 2B is a graph of ion intensity histogram of glutamine, FIG. 2C is a graph of ion intensity histogram of L-Carnitine 16:1(L-Carnitine C16:1), and FIG. 2D is a graph of ion intensity histogram of phosphatidylcholine (40:6) (PC (40:6)) at a scale of 2 mm.
Fig. 3 is a graph of the effect of different slice thicknesses on AFADESI-MSI positive ion detection in brain tissue, wherein fig. 3A is a histogram of total ion intensity for different slice thicknesses and fig. 3B is a histogram of total ion number for different slice thicknesses.
FIG. 4 is an image of AFADESI-MSI positive ion detection and ion intensity bar graph of representative metabolites in the brain at different slice thicknesses (8 μm, 10 μm and 12 μm), wherein FIG. 4A is an image of representative metabolites, FIG. 4B is a bar graph of the ion intensity of glutamine, FIG. 4C is a bar graph of the ion intensity of L-Carnitine 16:1(L-Carnitine C16:1), FIG. 4D is a bar graph of the ion intensity of phosphatidylcholine (40:6) (PC (40:6)), scale bar: 2 mm.
Fig. 5 is a graph of the effect of different spray solvent systems on brain tissue AFADESI-MSI positive ion detection, wherein fig. 5A is a histogram of total ion intensity and fig. 5B is a histogram of total ion number.
FIG. 6 is an AFADESI-MSI positive ion detection image and ion intensity bar graph of representative metabolites in the brain under different spray solvent systems, wherein FIG. 6A is an image of representative metabolites, FIG. 6B is an ion intensity bar graph of glutamine, FIG. 6C is an ion intensity bar graph of L-Carnitine 16:1(L-Carnitine C16:1), FIG. 6D is an ion intensity bar graph of phosphatidylcholine (40:6) (PC (40:6)), and the scale bar: 2 mm.
FIG. 7 is a graph of the effect of different spray solvent flow rates on brain tissue AFADESI-MSI positive ion detection, wherein FIG. 7A is a histogram of total ion intensity and FIG. 7B is a histogram of total ion number.
FIG. 8 is an AFADESI-MSI positive ion detection histogram and an ionic strength histogram of representative metabolites in the brain at different spray solvent flow rates, wherein FIG. 8A is a representative metabolite histogram, FIG. 8B is an ionic strength histogram of glutamine, FIG. 8C is an ionic strength histogram of L-Carnitine 16:1(L-Carnitine C16:1), FIG. 8D is an ionic strength histogram of phosphatidylcholine (40:6) (PC (40:6)), scale bar: 2 mm.
FIG. 9 is a graph of total ion intensity histogram in FIG. 9A and total ion population histogram in FIG. 9B, showing the effect of different mass resolutions on AFADESI-MSI positive ion detection in brain tissue.
FIG. 10 Mass resolution of AFADESI-MSI increased from 15,000 to 240,000 imaging plots for analysis of m/z-proximal metabolites (m/z 788.4637 and m/z 788.4999) in brain tissue, R, resolution, scale bar: 2 mm.
FIG. 11 is a graph of mass spectra images of similar m/z, scaled-bar, metabolites whose AFADESI-MSI distinguishes between similar m/z (m/z790.5380 and m/z 790.5536) and complementary spatial distributions at a mass resolution of 120000: 2 mm.
FIG. 12 is a plot of 6-day mass spectra images of six representative metabolites with different m/z values in the stability study of the AFADESI-MSI method, scale bar: 2 mm.
FIG. 13 biochemical marker levels of diabetic and control groups, wherein FIG. 13A is a histogram of serum FBG, FIG. 13B is a GLU histogram, FIG. 13C is a HbA1C histogram, FIG. 13D is a CHOL histogram, FIG. 13E is a TG histogram, and the level of diabetic model group is elevated; calculating P value by t test of independent students, comparing with control group, P is less than or equal to 0.05; p is less than or equal to 0.01; CHOL: cholesterol; FBG: fasting blood glucose; GLU: blood glucose; HbA1 c: glycated hemoglobin; TG: a triglyceride.
FIG. 14 is a bar graph of the dynamic change of body weight over time of two groups of rats, FIG. 14B is a bar graph of the brain tissue weight of rats, FIG. 14C is a bar graph of the brain/body weight ratio, FIG. 14D is a photomicrograph of the stained tissue of hematoxylin-eosin (H & E) in the brain tissue, FIG. 14E is a histogramming pattern of the brain on the coronal plane, and FIGS. 14F and 14G are the escape latency and characteristic swimming trajectories, respectively, of rats in the MWM experiment; p is less than or equal to 0.05 compared to control group; p is less than or equal to 0.01. AM, amygdala; CC, corpus callosum; CTX, cerebral cortex; HP, hippocampus; HY-SN, hypothalamus-substantia nigra, IC, inner capsule; MN, middle sniff core; TH, thalamus; scale bar: 2 mm.
FIG. 15 is an OPLS-DA model score map constructed from AFADESI-MSI positive (C, D) and negative ion (A, B) data obtained from different brain regions of rats in the control and diabetic groups.
FIG. 16 is an OPLS-DA model score plot constructed from AFADESI-MSI positive ion data obtained from different brain regions of rats in the control group and the diabetic group.
FIG. 17 is an OPLS-DA model score map constructed from AFADESI-MSI anion data obtained from different brain regions of rats in the control group and the diabetic group.
FIG. 18 is the spatial distribution characteristics and trend chart of 19 different metabolites closely related to diabetic encephalopathy in rat brain tissue.
Detailed Description
In view of the above, the method for in situ analysis of endogenous metabolites in brain tissue according to the present invention comprises the following steps: and performing mass spectrum imaging on the isolated brain tissue slice sample to be detected to obtain the species, ionic strength and/or spatial distribution characteristics of endogenous metabolites in the brain tissue.
The in-situ analysis system of endogenous metabolites in brain tissues provided by the invention comprises an instrument, a reagent and a readable carrier; the instrument comprises an instrument for mass spectrometry imaging involved in a readability carrier; the reagent comprises a reagent involved in the readability carrier for mass spectrometry imaging; the readability vector describes the following steps for the in situ analysis of endogenous metabolites in brain tissue: and performing mass spectrum imaging on the isolated brain tissue slice sample to be detected to obtain the species, ionic strength and/or spatial distribution characteristics of endogenous metabolites in the brain tissue.
The invention discloses an in-situ metabonomics analysis method and an analysis system suitable for comprehensively analyzing various endogenous metabolites in brain tissues based on an AFADESI-MSI mass spectrometry imaging technology of ultra-high resolution mass spectrometry, and aims to systematically research the distribution characteristics of various endogenous metabolites in different areas of the brain tissues, obtain biomarkers related to the occurrence of diabetic encephalopathy and deeply discuss the pathogenesis of the diabetic encephalopathy. The invention provides a new research strategy and technical means for the pathogenesis and the drug development of the diabetic encephalopathy and provides important reference information for the diagnosis, the prevention and the treatment of the diabetic encephalopathy.
The experimental procedures used in the following examples are all conventional procedures unless otherwise specified.
Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.
Example 1 establishment and optimization of AFADESI-MSI analysis method
Establishing of AFADESI-MSI analysis method
Endogenous metabolites in rat brain tissue were analyzed as follows:
preparing a brain tissue sample: rat brain tissue was transferred from a-80 ℃ freezer to a CM1860 cryomicrotome (operating temperature-20 ℃) and rat brain tissue sections of 8 μm thickness were prepared and adhered to a glass slide, dried in a vacuum desiccator for 30min and then subjected to AFADESI-MSI analysis.
AFADESI-MSI experiments were performed on a Q-OT-qIT hybrid mass spectrometer (Orbitrap Fusion Lumos; Thermo Fisher Scientific, USA) equipped with an AFADESI ion source (Rapid Commun. Mass Spectrum.2011, 25, 843-. AFADESI-MSI analysis brain tissue sections were continuously scanned at a rate of 200 μm/s in the x-direction with an interval step of 200 μm in the y-direction. Adopting a positive and negative ion full scanning mode, wherein the scanning range is 100-1000 Da, the mass resolution is 120000, and the AGC target is 1 multiplied by 106The maximum injection time is 100 ms. The spray voltage was 7.0/-7.0kV, the transmission line voltage was 3.0/-3.0kV, the spray gas (nitrogen) pressure was 0.5MP, the spray solvent (acetonitrile-water, 8:2, V/V) flow rate was 7. mu.L/min, and the air assist gas flow rate was 45L/min. The data was collected using Xcalibur 2.2 software (Thermo Scientific, usa).
AFADESI-MSI data processing: raw files are obtained after AFADESI-MSI analysis, converted into a cdf format by using Xcalibur software, and then imported into MassImager software for image reconstruction and background subtraction. According to H of adjacent slices&E staining graph, in the imaging picture of certain ion, the region of interest (ROI) is circled by mouse, and the mass spectrogram of the ROI, including the information of endogenous metabolites from the region in the tissue section, is accurately extracted. This mass spectral data is further saved as a txt formatted file, which is a two-dimensional data matrix (m/z-intensity). Additionally, an average mass spectrum of the selected region may be obtained simultaneously, which may be saved in the. tif or. jpg format. Importing data files of a plurality of extracted ROI areas into MarkerViewTMPeak alignment was achieved in 1.2.1(AB SCIEX) software. After the background and isotope ions are manually deleted, the background and isotope ions are exported to be a multidimensional data matrix and saved as a txt file. Data statistics using metabolomicsThe analysis method comprises the steps of carrying out supervised Orthogonal partial least squares (OPLS-DA) analysis and t-test multivariate statistical analysis on the obtained multidimensional data matrix by using SIMCA-P15.0 and Excel software, and carrying out VIP (variable input on project) value based on an OPLS-DA model>1.0 and two-sided t-test P<0.05, screening differential metabolites related to diabetic encephalopathy. The structural formula of the differential metabolite is determined according to a method of accurate molecular weight, isotopic pattern binding database (METLIN) retrieval.
Second, optimization of AFADESI-MSI analysis method
Selection of the loading fragments: three different slides were examined, including a common slide I (Cat #1027105P, Shitao, China Jiangsu province), a positive charge loss prevention slide II (Cat #188105, Shitao, China Jiangsu province), and a SuperFrost PLUS slide III (Cat #4951PLUS-001, Thermo Scientific, N.H.A.). Adjacent brain sections (8 μm) were attached to three slides and subjected to AFADESI-MSI analysis. As shown in fig. 1, the total ion number detected by the three slides was substantially consistent in AFADESI-MSI positive ion detection mode, but the total ion intensity detected by positive charge removal prevention slide II was significantly higher than the other two. As can be seen from FIG. 2, three representative metabolites, Glutamine (Glutamine, m/z169.0584), L-Carnitine (L-Carnitine C16:1, m/z 398.3265) and phosphatidylcholine (PC (40:6), m/z872.5566), all had the highest ionic strength and better spatial resolution of the ion image under positive charge anti-shedding slide II, so positive charge anti-shedding slide (Cat #188105, Shitao, Jiangsu province, China) was finally selected as the slide used in this study.
Examination and optimization of slice thickness: in this study, frozen brain tissue sections of 8 μm, 10 μm and 12 μm were prepared, and slice thicknesses were examined and optimized. As shown in FIG. 3, in the AFADESI-MSI positive ion detection mode, the total ion signal intensity was highest for tissue sections with a thickness of 8 μm compared to the thicknesses of 10 μm and 12 μm, and the number of total ions detected was the largest, and it can be seen from FIG. 4 that, at a section thickness of 8 μm, the ion intensities of the three representative metabolites Glutamine (Glutamine, m/z169.0584), L-Carnitine (L-Carnitine C16:1, m/z 398.3265), phosphatidylcholine (PC (40:6), m/z872.5566) were the highest and the ion image spatial resolution was also better, which is probably due to the fact that the metabolites could be completely exposed when the tissue section thickness was thinner.
Since it is difficult to prepare frozen tissue sections having good physical morphology when the thickness is less than 8 μm, tissue sections having a thickness of less than 8 μm are not further evaluated. The final frozen section of brain tissue selected was 8 μm thick.
Investigation and optimization of spray solvent system: in the research, an AFADESI-MSI system is adopted to prepare adjacent rat brain slices (8 mu m), the analysis capability and ionization effect of acetonitrile-water systems with different proportions on endogenous metabolites in rat brain tissues are considered, and six spray solvent systems are as follows: ACN/H2O(5:5,v/v),ACN/H2O(6:4,v/v),ACN/H2O(8:2,v/v),ACN/H2O (8:2, v/v) + 0.1% FA and ACN/H2O (8:2, v/v) + 0.3% FA. As shown in FIG. 5, the spray solvent system is ACN/H2O (8:2, v/v), the total ion intensity and the number of total ions detected were the highest, as can be seen from FIG. 6, except that L-Carnitine (L-Carnitine C16:1, m/z 398.3265) was ACN/H in the spray solvent system2O (8:2, v/v) + 0.1% FA, the ionic strength is highest, and the ionic strengths of the other two representative metabolites Glutamine (Glutamine, m/z169.0584) and phosphatidylcholine (PC (40:6), m/z872.5566) are ACN/H in the spray solvent system2The highest O (8:2, v/v) and better ion image spatial resolution, and the spray solvent system is selected to be ACN/H2O (8:2, v/v), which indicates the function of the nebulized solvent in desorbing and transporting most metabolites in brain tissue.
Investigation and optimization of spray solvent flow rate: in the research, rat brain tissue slices (8 mu m) are taken as research objects, and the detection sensitivity of an AFADESI-MSI system on endogenous metabolites in the brain tissue under 4 different spray solvent flow rates of 5 mu L/min, 7 mu L/min, 10 mu L/min and 15 mu L/min is examined. As can be seen from FIG. 7, the total ion intensity is highest when the flow rate of the spray solvent is 7. mu.L/min, and although more ions are detected than 7. mu.L/min at the flow rates of 10. mu.L/min and 15. mu.L/min, the size and speed of the spray droplets and thus the resolution and ionization efficiency are directly influenced by the flow rate of the spray solvent and the intensity of the atomizing gas. The higher the spray solvent flow rate, the lower the spatial resolution, as shown in fig. 8, when the spray solvent flow rate is 7 μ L/min, the ionic strength of three representative metabolites, Glutamine (Glutamine, m/z169.0584), L-Carnitine (L-Carnitine C16:1, m/z 398.3265), and phosphatidylcholine (PC (40:6), m/z872.5566), were all the highest and the spatial resolution of the ion image was also the best, and the spray solvent flow rate was finally selected to be 7 μ L/min by taking into account the factors of solvent saving and spatial resolution.
The investigation and optimization of mass resolution, namely the separation capability, sensitivity and spatial resolution of mass resolution AFADESI-MSI analysis have great influence. As shown in fig. 9, as the mass resolution increased from 15,000 to 240,000, the number of ions detected by AFADESI-MS analysis increased, but the intensity of the total ion current relatively decreased. This is probably because an increase in mass resolution would enhance the accurate mass-near metabolite spectral separation, but would also result in a decrease in overall sensitivity due to a decrease in scan speed. In addition, as the mass resolution is increased, the separation capability of the MS image is also significantly improved. As shown in FIG. 10, AFADESI-MSI failed to isolate the metabolites at m/z 788.4637 and m/z 788.4999 when the mass resolution was below 60,000, but above 120,000 at mass resolution, it was observed that the spatial distributions of these two metabolites are complementary in the rat brain. Therefore, a mass resolution of 120,000 was finally selected for AFADESI-MSI analysis. As can be seen in FIG. 11, a mass resolution of 120,000 can accurately distinguish metabolites with similar m/z values and sensitivity is high.
Repeatability investigation of the method: to evaluate the stability of the AFADESI-MSI analysis brain tissue slice method, 6 adjacent brain tissue slices of the same rat brain were prepared and subjected to six consecutive days of AFADESI-MSI analysis in the negative ion detection mode. FIG. 12 shows the mass spectrometric images of six representative metabolites with different m/z values, including Taurine (Taurine, m/z 124.0075), Ascorbic acid (Ascorbic acid, m/z 175.0254), Glucose (Glucose, m/z 215.0335), fatty acids (FA (22:6), m/z 327.2343), phosphatidic acid (PA (P-36:2/PA (O-36:3), m/z 726.5487) and phosphatidylserine PS (40:6) (m/z 834.5381), normalized by Total Ion Current (TIC), the spatial distribution and signal intensity of these metabolites are consistent in 6 days, which indicates that the AFADESI-MSI method established for frozen sections of brain tissue in this study is stable and can be used for analysis of spatially resolved metabolism.
Through the investigation and optimization, the AFADESI-MSI analysis method suitable for endogenous metabolites in rat brain tissues is finally established. Finally, the thickness of the brain tissue section is determined to be 8 μm, the brain tissue section is attached to a positive charge anti-shedding slide glass, and the key parameters of the AFADESI-MSI analysis system are shown in Table 1.
TABLE 1 AFADESI analysis of Key parameters
Example 2 application of brain tissue endogenous metabolite as biomarker in preparation of product for diagnosing diabetic encephalopathy
Animal experiments: selecting 7-9-week-old male Wistar rats of 180-240 g, feeding 12 normal feeds to a normal control group (blank control group), feeding 4-week-old high-fat feeds (HFD12492 high-fat feeds, American Research Diets) to the other animals as a diabetes model group, fasting (free drinking water) postperitoneal injection of streptozotocin STZ (purchased from Sigma Aldrich, St.Louis, Mo., 35mg/kg), intraperitoneally injecting a freshly prepared sodium citrate solution (0.1mol/L, PH 4.4) to the normal control group, and molding for 2h and then administering 10% sucrose solution for 2 days. Blood was taken from the tail vein on day 7 and blood glucose concentration was determined to be greater than 16.7MMOL/L for 3 consecutive days. And the molding is successful when the urine glucose is strong positive and the urine volume is more than 50% of the control group. The breeding is continued for 12 weeks. The physical signs of animals are continuously observed in the experiment, the indexes such as weight, food intake, water intake, blood sugar, blood biochemistry and the like are regularly measured, the Morris water maze experiment is carried out in 12 weeks, and the change of the cognitive function of rats is observed. After the water maze experiment is finished, the rat is anesthetized by injecting 2.0% sodium pentobarbital into the abdominal cavity (the anesthetic dose is 40-50 mg/kg), and after the rat is completely anesthetized, the abdominal aorta is exsanguinated and killed, and then dissection is carried out. The brains of the rats were carefully removed, rinsed with normal saline, and then excess water was absorbed by filter paper, quickly fixed in liquid nitrogen, and then frozen in a freezer at-80 ℃.
Biochemical analysis and histopathological staining. FBG concentrations were measured using a glucometer (baserola, switzerland). The glycated hemoglobin (HbA1c) concentration was measured using an Quo-Test HbA1c analyzer (QUOTIENT Diagnostics Ltd. Walton-on-Thames, Surrey, UK). Blood Glucose (GLU), cholesterol and Triglyceride (TG) levels were determined using an AU480 automated chemical analyzer (Beckman Coulter, blareia, california, usa).
Water maze experiment: the SMG-2 water maze apparatus (pharmaceutical institute of Chinese medical sciences, Beijing) was used to evaluate the spatial learning ability and memory of rats. The maze is a black circular pool of water 120cm in diameter and 50cm in height. The water depth in the pool is 30cm, and the water temperature is 24 +/-2 ℃. A circular escape platform with the diameter of 15cm is arranged 2cm below the water surface in the center of the southeast quadrant of the maze, the escape latency of a rat is measured by a tracking system, and a path reaching the platform is recorded.
In this study, rats were acclimatized in the laboratory for 30min after removal from the feeding room. The control condition of the computer is set, the water maze data transmission line is connected with the computer, and the power supply is switched on. The head of the rat is put into the water towards the pool wall, and the putting position randomly takes one of four starting positions of east, west, south and north. The time(s) at which the rat found the underwater platform was recorded. In the first few training sessions, if the rat could not find a hidden platform within a maximum of 60s, the operator would guide it to the platform and record it as 60 s. After the animals were allowed to rest on the platform for 10 seconds, the rats were removed, wiped dry, and returned to their cages. Each rat is trained for 1 time every day for 2-5 days. The next day after the last acquisition training, the platform was removed and the 90s exploratory training was started. Rats were placed in the water from the opposite side of the previous platform quadrant. The time spent by the rat in the target quadrant (the quadrant where the platform was originally placed) and the number of entries into that quadrant were recorded as a measure of spatial memory.
The results for FBG, GLU, HbA1c, TG and CHOL are summarized in fig. 13. FBG levels were significantly elevated (P <0.01) in diabetic rats compared to the control group and remained stable over an observation period of 12 weeks. The HbA1c and TG level of the diabetic rat is obviously higher than that of the control group (P < 0.05). The CHOL level in the diabetic group was slightly higher than that in the control group (P > 0.05). These results are consistent with the clinical features of diabetes, indicating the successful establishment of a diabetic rat model.
Representative micrographs of body weight, brain/body weight ratio, and H & E stained brain tissue are shown in figure 14. Both body mass and brain mass of diabetic rats were significantly lower than the control group (P < 0.05). Histological examination of H & E stained brain tissue indicated brain atrophy in the diabetic group. The escape latency of diabetic rats in the Morris water maze experiment was significantly longer than that of the control group (P < 0.05). These results indicate that hyperglycemia affects brain tissue morphology and learning and memory ability in diabetic rats.
Changes in metabolites in different brain regions of diabetic encephalopathy rats
The brain, the most complex organ of the rat, is highly heterogeneous. Depending on the neural structure, chemistry and tissue connectivity, the brain can be subdivided into thousands of regions. The coronal plane of the brain was roughly divided into eight regions for analysis, and the corresponding histological structures were Amygdala (AM), Corpus Callosum (CC), cerebral Cortex (CTX), Hippocampus (HP), Thalamus (TH), Inner Capsule (IC), Midriff Nucleus (MN), hypothalamus and substantia nigra (HY-SN) as shown in FIG. 14E.
To study metabolic changes in different regions of the brain of rats in the diabetic encephalopathy model, spatially resolved metabolome profiles of coronal sections of the brain of control and diabetic rats were obtained by AFADESI-MSI. In AFADESI-MSI positive ion detection mode, 1158, 1249, 968, 1062, 1157, 1264, 907, 1200 and 1106 features were detected in WHOLE brain (WHOLE), AM, CC, CTX, HP, HY-SN, IC, MN and TH, respectively. In negative ion mode, 1044, 1013, 772, 1043, 956, 978, 749, 964 and 885 features were detected at WHOLE brain (white), AM, CC, CTX, HP, HY-SN, IC, MN and TH, respectively.
To see if the metabolites of different brain regions differ, OPLS-DA analysis was performed on the data obtained for the WHOLE brain (white) as well as for 8 brain regions to achieve maximum separation. The results are shown in FIG. 15, and it can be seen from the scattergram of the OPLS-DA model in the positive ion (C, D) and negative ion (A, B) detection modes that there is a tendency of significant separation between WHOLE, AM, CC, CTX, HP, HY-SN, IC, MN and TH, indicating that the chemical compositions of these morphological regions are significantly different and their metabolic profiles are significantly different. The metabolic profiles of different brain regions were also changed in the diabetic group compared to the control group.
In order to obtain the differential metabolites between the rats of the diabetic group and the rats of the control group, the data of each brain region obtained in the positive and negative ion detection mode was subjected to OPLS-DA analysis, and the results are shown in FIGS. 16 and 17. It can be seen that there was a clear tendency of grouping between different brain regions (WHOLE, AM, CC, CTX, HP, HY-SN, IC, MN and TH) between the diabetic group and the control group, indicating that significant metabolic changes occurred in these regions in the diabetic rats.
The group contributing large variables (VIP >1) were further screened, followed by Student's t test (P <0.05), and 27, 98, 27, 19, 43, 118, 61, and 28 differential variables were screened in WHOLE, AM, CC, CTX, HP, HY-SN, IC, MN, and TH in total in positive and negative ion detection mode. The structural identification of the differential metabolites was then carried out, resulting in the initial identification of 19 differential metabolites (Table 2). These differential metabolites interact with glycolysis, the pentose phosphate pathway, the glutamate/gaba-glutamine and tricarboxylic acid cycles, nucleotide metabolism; lipid metabolism; carnitine metabolism; taurine metabolism, ascorbic acid metabolism, histidine metabolism and choline metabolism are closely related. The distribution of these differential metabolites in the brain tissue of diabetic rats and their trend are shown in FIG. 18. As can be seen from the figure, these metabolites have unique distribution characteristics in rat brain tissue, and the distribution of the rat brain tissue in the diabetic encephalopathy model is significantly different from that of the control group rats. These results suggest that glycolytic and pentose phosphate pathway activity enhancement, mitochondrial metabolic disorder, adenine-ergic, glutamatergic, dopaminergic, cholinergic and histaminergic nerve conduction system disorder and osmotic regulation, antioxidant system disorder and lipid metabolic disorder occur in brain tissue of rats of diabetic encephalopathy model, and that these changes have regiospecificity. These differential metabolites are expected to develop into biomarkers for diabetic encephalopathy with tissue specificity.
TABLE 2 differential metabolites of diabetic and control groups by AFADESI-MSI positive and negative ion analysis
aAbbreviations: glucose 6-P is Glucose-6-phosphate; glyceraldehyde 3-P is Glyceraldehyde-3-phosphate; UMP is Urridine monophosphate; ADP is Adenosine diphosphate; AMP, Adenosine monophosphophosphate; PE (36:2) Phosphatidetholamine (36:2).
The invention discloses an in-situ metabonomics analysis method suitable for comprehensively analyzing various endogenous metabolites in brain tissues based on an AFADESI-MSI mass spectrometry imaging technology of ultra-high resolution mass spectrometry, and aims to systematically research the distribution characteristics of various endogenous metabolites in different areas of the brain tissues, obtain biomarkers related to the occurrence of diabetic encephalopathy and deeply discuss the pathogenesis of the diabetic encephalopathy. The invention provides a new research strategy and technical means for the pathogenesis and the drug development of the diabetic encephalopathy and provides important reference information for the diagnosis, the prevention and the treatment of the diabetic encephalopathy.
Claims (6)
1. A method for in situ analysis of endogenous metabolites in brain tissue comprising the steps of: and performing mass spectrum imaging on the isolated brain tissue slice sample to be detected to obtain the species, ionic strength and/or spatial distribution characteristics of endogenous metabolites in the brain tissue.
2. The method of claim 1, wherein: the ion source used for mass spectrometry imaging is an aerodynamic assisted desorption electrospray ion source; and/or the presence of a gas in the gas,
the glass slide used for attaching the brain tissue slice sample to be detected in the mass spectrometry imaging is a positive charge anti-falling glass slide; and/or the presence of a gas in the gas,
the thickness of the brain tissue slice sample to be detected is 8 mu m; and/or the presence of a gas in the gas,
the spray solvent used for mass spectrometry imaging is prepared from the following components in a volume ratio of 8:2 acetonitrile and water; the flow rate of the spray solvent is 7 muL/min; and/or the presence of a gas in the gas,
the mass resolution of the mass spectral imaging was 120,000.
3. An in situ analysis system for endogenous metabolites in brain tissue comprising an instrument, a reagent and a readability carrier;
the instrument comprises an instrument for mass spectrometry imaging involved in the readability carrier;
the reagents include reagents involved in the readability carrier for mass spectrometry imaging;
the readability vector is described with the following steps for the in situ analysis of endogenous metabolites in brain tissue: and performing mass spectrum imaging on the isolated brain tissue slice sample to be detected to obtain the species, ionic strength and/or spatial distribution characteristics of endogenous metabolites in the brain tissue.
4. The system of claim 3, wherein: the readable carrier is instructions, and the step of in situ analysis of endogenous metabolites in brain tissue is printed on a card;
the readable carrier is a computer readable carrier.
5. The system according to claim 3 or 4, characterized in that: the instrument comprises a mass spectrometer; the ion source in the mass spectrometer is an aerodynamic assisted desorption electrospray ion source; and/or the presence of a gas in the gas,
the instrument comprises a glass slide for attaching the brain tissue slice sample to be detected; the glass slide is a positive charge anti-dropping glass slide; and/or the presence of a gas in the gas,
the reagent comprises a spray solvent; the spraying solvent is prepared from the following components in a volume ratio of 8:2 acetonitrile and water; and/or the presence of a gas in the gas,
the readability carrier also describes the following conditions for mass spectrometric imaging: the thickness of the brain tissue slice sample is 8 μm; the flow rate of the spray solvent is 7 mu L/min; the mass resolution was 120,000.
6. Use of the system for in situ analysis of endogenous metabolites in brain tissue according to any one of claims 3 to 5 for the preparation of at least one of the following a 1-A8:
a1, preparing a central nervous system disease in-situ analysis product;
a2, preparing a product for obtaining biomarkers of central nervous system diseases;
a3, preparing products for diagnosing or assisting in diagnosing central nervous system diseases;
a4, preparing products for distinguishing or identifying central nervous system diseases;
a5, preparing a diabetic encephalopathy in-situ analysis product;
a6, preparing a product for acquiring biomarkers of diabetic encephalopathy;
a7, preparing products for diagnosing or assisting in diagnosing diabetic encephalopathy;
a8, preparing products for distinguishing or identifying diabetic encephalopathy.
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