Method for researching molecular interaction between different organelles and application thereof

Abstract

The invention discloses a method for researching molecular interaction between different organelles and application of the method. According to the device for researching molecular interaction between different organelles, provided by the scheme of the invention, joint correlation analysis can be effectively performed on proteomes and lipidomes of different subcellular components, a subcellular level protein-lipid molecule interaction association network is obtained, and key molecules of association interaction between organelles are mined.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to a method for studying molecular interactions between different organelles and its applications. Background Art

[0002] Eukaryotic cells have membrane-covered sub-compartments that form complex organelles and an endomembrane system, capable of performing different cellular processes. Among these cellular processes, in addition to the subcellular localization of proteins playing a decisive role, lipids also play an important role.

[0003] Currently, the main ways to find molecular interactions between organelles are experimental research methods such as fluorescence microscopy, electron microscopy, and biochemical organelle separation. These methods can only analyze the molecular interactions of a single organelle and cannot fully analyze the dynamic molecular interaction network among multiple organelles in the cell. At the same time, the previous global spatial multi-omics was still very rough and could only explore the distribution of molecules at the subcellular level based on existing databases and machine learning methods, unable to study large-scale and precise molecular interactions between multiple organelles. In addition, for organelle separation, the current parallel separation method for high-purity organelles is not perfect. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the above-mentioned prior art. For this purpose, the present invention provides a device for studying molecular interactions between different organelles.

[0005] The present invention also provides a method for studying molecular interactions between different organelles.

[0006] The present invention also provides an application of the above method.

[0007] The present invention also provides a method for separating organelles.

[0008] The present invention also provides the organelles separated by the above method for separating organelles.

[0009] The present invention also provides an application of the above organelles.

[0010] According to one aspect of the present invention, there is provided a device for studying molecular interactions between different organelles, comprising:

[0011] An identification device for extracting and identifying proteins and lipids from an organelle sample to obtain the original characterization of the organelle proteome and lipidome;

[0012] A preprocessing analysis device for analyzing the original characterization of the proteomes and lipidomes of different organelles to obtain the organelles to be analyzed;

[0013] A data analysis device for performing organelle dimensionality reduction clustering and molecular interaction relationship analysis between organelles on organelles to be analyzed;

[0014] A storage device;

[0015] A display;

[0016] Connect the identification device, the preprocessing analysis device, and the data analysis device in sequence from front to back; the storage device is respectively connected to the preprocessing analysis device and the data analysis device; the display is connected to the data analysis device.

[0017] In some embodiments of the present invention, the study of molecular interactions between organelles includes at least one of constructing an organelle - protein interaction network, constructing an organelle - lipid interaction network, and constructing an organelle - lipid and protein interaction network.

[0018] According to the second aspect of the present invention, a method for studying molecular interactions between different organelles based on the above - mentioned device is proposed. The method includes the following steps:

[0019] (1) Pretreatment of original characterization data of organelle proteomes and lipidomes: After performing data missing value processing on the original characterization data of different organelle proteomes and lipidomes, perform normalization processing, and perform batch effect removal on the data obtained from the normalization processing to remove the batch effect between samples, and obtain the pretreated original characterization data of organelle proteomes and lipidomes;

[0020] (2) Organelle clustering and dimensionality reduction calculation: Use the t - SNE dimensionality reduction visualization algorithm on the pretreated original characterization data of organelle proteomes and lipidomes to perform dimensionality reduction and clustering processing on the high - dimensional data, obtain a dimensionality reduction clustering map, and obtain the coordinate positions of each organelle after dimensionality reduction;

[0021] (3) Calculation of interaction relationships between organelles: Perform correlation analysis on the pretreated original characterization data of organelle proteomes and lipidomes by using HALLA software or the R package "psych", with the correlation coefficient |r|>0.7.

[0022] In some embodiments of the present invention, the acquisition of the original characterization data of organelle proteomes and lipidomes includes the following steps: Use protein mass spectrometry and lipid mass spectrometry to identify proteins and lipids in organelles, and use database search software to perform protein and lipid quantitative analysis respectively to obtain the original characterization data of organelle proteomes and lipidomes.

[0023] In some embodiments of the present invention, protein mass spectrometry identification is performed using EvosepOne + ZenoTOF 7600 liquid chromatography - tandem mass spectrometry.

[0024] In some embodiments of the present invention, lipid mass spectrometry identification is performed using an UltiMate-3000 + Orbitrap Exploris 480 liquid chromatography tandem mass spectrometry.

[0025] In some embodiments of the present invention, protein quantification analysis is performed using the database search software DIANN 1.8.

[0026] In some embodiments of the present invention, lipid quantification analysis is performed using the database search software LipidSearch (v5.0).

[0027] In some embodiments of the present invention, the proteins in the organelles further include a pretreatment step before protein mass spectrometry identification, and the steps are as follows: Pretreatment is performed using a Barvo high-throughput sample preparation system.

[0028] In some embodiments of the present invention, the specific steps of performing pretreatment using the Barvo high-throughput sample preparation system are as follows:

[0029] (1) After adding the protein to a 96-well plate, add 15 - 25 μL of protein lysate and heat at 90 - 96 °C for 8 - 12 min to obtain treatment solution A;

[0030] (2) Add 4 - 6 μL of 4 - 6 μg / μL protein enrichment magnetic beads and 40 - 50 μL of acetonitrile to the treatment solution A, and then perform an oscillation treatment for 12 - 17 min to obtain treatment solution B;

[0031] (3) Collect the magnetic beads in the treatment solution B and remove the supernatant, and wash the collected magnetic beads;

[0032] (4) Add 30 - 40 μL of trypsin buffer and 8 - 12 μL of trypsin solution to the washed magnetic beads, and then treat at 35 - 38 °C for 13 - 18 h to obtain a trypsinized protein sample;

[0033] (5) Load the trypsinized protein sample onto an activated C18 solid-phase extraction membrane, centrifuge at 600 - 1000 g for 1 - 3 min; add 40 - 60 μL of 0.05% - 0.2% FA, centrifuge at 600 - 1000 g for 1 - 3 min; add 90 - 110 μL of 0.05% - 0.2% FA to obtain a pretreated sample.

[0034] In some embodiments of the present invention, the collection of the magnetic beads in the treatment solution B is performed by adsorption.

[0035] In some embodiments of the present invention, the washing of the collected magnetic beads includes the following steps: Add 80% ethanol to wash the magnetic beads, then adsorb the magnetic beads and remove the supernatant; add acetonitrile to wash the magnetic beads, then adsorb the magnetic beads and remove the supernatant, and collect the magnetic beads.

[0036] In some embodiments of the present invention, the trypsin buffer is prepared from ddH2O, 90 - 110 mM Tris-HCl, and ACN in a volume ratio of (12 - 14):(4 - 6):(1 - 3).

[0037] In some embodiments of the present invention, in the trypsin solution, the concentration of trypsin is 4 - 5 μg / mL.

[0038] In some embodiments of the present invention, before loading the enzymatically digested protein sample into the activated C18 solid-phase extraction membrane, the step of adding 5 - 7 μL of 4% - 6% TFA to the enzymatically digested protein sample and oscillating for 0.5 - 2 min is further included.

[0039] In some embodiments of the present invention, the specific steps for pretreatment using the Barvo high-throughput sample preparation system are as follows:

[0040] (1) After adding the protein to the 96-well plate, add 20 μL of protein lysate and heat at 95 °C for 10 min to obtain treatment solution A;

[0041] (2) Add 5 μL of 5 μg / μL protein enrichment magnetic beads and 45 μL of acetonitrile to the treatment solution A, and then oscillate for 15 min to obtain treatment solution B;

[0042] (3) Collect the magnetic beads in the treatment solution B, remove the supernatant, and wash the collected magnetic beads;

[0043] (4) Add 35 μL of trypsin buffer and 10 μL of trypsin solution to the washed magnetic beads, and then treat at 37 °C for 16 h to obtain the enzymatically digested protein sample;

[0044] (5) Load the enzymatically digested protein sample into the activated C18 solid-phase extraction membrane, centrifuge at 800 g for 2 min; add 50 μL of 0.1% FA, centrifuge at 800 g for 2 min; add 100 μL of 0.1% FA to obtain the pretreated sample.

[0045] In some embodiments of the present invention, the processing of data missing values includes the following steps: removing the eigenvalue with a missing value greater than 50% from the original characterization data of different organelle proteomes and lipidomes respectively, and replacing the remaining missing values with 0 values.

[0046] In some embodiments of the present invention, the normalization processing includes sum normalization and Pareto scaling.

[0047] In some embodiments of the present invention, the batch removal processing includes the following steps: setting different parameters for batch removal processing according to different organelles and the omics studied.

[0048] In some embodiments of the present invention, the omics includes proteome and lipidome.

[0049] In some embodiments of the present invention, when the omics is proteome, the parameters for de-batching processing of different organelles are as follows:

[0050]

[0051] In some embodiments of the present invention, when the omics is lipidome, the parameters for de-batching processing of different organelles are as follows:

[0052]

[0053] In some embodiments of the present invention, the study of intermolecular interactions between organelles includes at least one of constructing an inter-organelle protein interaction network, constructing an inter-organelle lipid interaction network, and constructing an inter-organelle lipid and protein interaction network.

[0054] In some embodiments of the present invention, the organelles are obtained by preparing cells after different drug treatments.

[0055] In some embodiments of the present invention, the drugs include paclitaxel, lenalidomide, pomalidomide, enzalutamide, ibrutinib, acalabrutinib, panobinostat, afatinib, osimertinib, idelalisib, palbociclib, ruxolitinib, tofacitinib, aspirin, paracetamol, tolmetin, acarbose, metformin, rosiglitazone, empagliflozin, sitagliptin, linagliptin, fenofibrate, pravastatin, simvastatin, rosuvastatin, ezetimibe, nifedipine, amlodipine, metoprolol, nebivolol, azilsartan medoxomil, captopril, ramipril, ambrisentan, macitentan, clopidogrel, ticagrelor, rivaroxaban, apixaban, ranolazine, budesonide, montelukast, zileuton, roflumilast, omeprazole, lansoprazole, pantoprazole, ranitidine, galantamine, donepezil, venlafaxine, duloxetine.

[0056] In some embodiments of the present invention, before performing analysis using the HALLA software or the R package "psych", it further includes the step of grouping the original characterization data of the preprocessed organelle proteome and lipidome according to the drugs and calculating the average value.

[0057] In some embodiments of the present invention, the statistical method for the correlation analysis includes Spearman.

[0058] In some embodiments of the present invention, the P-value correction method for the correlation analysis includes Benjamini-Hochberg.

[0059] In some embodiments of the present invention, after the analysis using the HALLA software or the R package "psych" is completed and the results are output, it further includes a step of performing FDR correction with FDR < 0.05.

[0060] In some embodiments of the present invention, the FDR correction is performed using fdrtool.

[0061] In some embodiments of the present invention, the analysis of the R package "psych" is performed using the corr.test function.

[0062] According to the third aspect of the present invention, there is provided an application of the above device for studying molecular interactions between different organelles or a method for studying molecular interactions between different organelles based on the above device in any one of the following:

[0063] (1) Studying molecular interactions between organelles;

[0064] (2) Organelle dimensionality reduction and clustering;

[0065] (3) Screening for molecules involved in molecular interactions between organelles.

[0066] In some embodiments of the present invention, the study of molecular interactions between organelles includes at least one of constructing a protein interaction network between organelles, constructing a lipid interaction network between organelles, and constructing a lipid - protein interaction network between organelles.

[0067] According to the fourth aspect of the present invention, there is provided a method for separating organelles, the method comprising the following steps:

[0068] S1. Centrifuge the cell homogenate to obtain a solid phase A and a liquid phase A;

[0069] S2. Perform density gradient centrifugation on the solid phase A using an iodixanol solution to obtain cell nuclei;

[0070] S3. Centrifuge the liquid phase A at 800 - 1200 g for 8 - 12 min to obtain a liquid phase B;

[0071] S4. Centrifuge the liquid phase B at 12000 - 14000 g for 8 - 12 min, collect the solid phase C, add 400 - 600 μL of KPBS to resuspend the solid phase C, add Tom20 antibody and incubate for 1 - 3 h, then add magnetic beads and incubate for 0.5 - 2 h, collect the magnetic beads, wash the magnetic beads with a washing solution, and collect the magnetic beads, which are the mitochondria;

[0072] S5. Centrifuge the liquid phase B at 7000 - 9000g for 8 - 12 min, collect the liquid phase D, add calcium chloride solution to the liquid phase D and incubate for 0.3 - 0.8 h, then remove the liquid phase to obtain the endoplasmic reticulum;

[0073] S6. Add the liquid phase B to a sucrose density gradient, with a 1 M sucrose solution, a 0.5 M sucrose solution, and the liquid phase B from bottom to top in sequence, perform density gradient centrifugation. After centrifugation, the top layer is the cytoplasm, the white band between the 0.5 M - 1 M sucrose buffer is the Golgi apparatus, and the precipitate is the cell membrane. Through the above organelle separation method, multiple organelles can be separated in parallel, with a simple method, short time consumption, and high purity of the separated organelles.

[0074] In some embodiments of the present invention, the cells include 293T cells.

[0075] In some embodiments of the present invention, it further includes the step of drug treatment for the cells.

[0076] In some embodiments of the present invention, the drugs include paclitaxel, lenalidomide, pomalidomide, enzalutamide, ibrutinib, acalabrutinib, panobinostat, afatinib, osimertinib, idelalisib, palbociclib, ruxolitinib, tofacitinib, aspirin, paracetamol, tolmetin, acarbose, metformin, rosiglitazone, empagliflozin, sitagliptin, linagliptin, fenofibrate, pravastatin, simvastatin, rosuvastatin, ezetimibe, nifedipine, amlodipine, metoprolol, nebivolol, azilsartan medoxomil, captopril, ramipril, ambrisentan, macitentan, clopidogrel, ticagrelor, rivaroxaban, apixaban, ranolazine, budesonide, montelukast, zileuton, roflumilast, omeprazole, lansoprazole, pantoprazole, ranitidine, galantamine, donepezil, venlafaxine, duloxetine.

[0077] In some embodiments of the present invention, the cell amount used for preparing the cell homogenate is 1×10 7 - 3×10 7 cells.

[0078] In some embodiments of the present invention, the cell amount used for preparing the cell homogenate is 2×10 7 cells.

[0079] In some embodiments of the present invention, the cell homogenate is prepared through the following steps: mix the cells and the homogenate to obtain a cell suspension; homogenize the cell suspension to obtain the cell homogenate.

[0080] In some embodiments of the present invention, the homogenate includes a KPBS solution.

[0081] In some embodiments of the present invention, the addition amount of the homogenate is 450-550 μL.

[0082] In some embodiments of the present invention, the addition amount of the homogenate is 500 μL.

[0083] In some embodiments of the present invention, the instruments used for homogenization include a Dounce homogenizer and a pestle.

[0084] In some embodiments of the present invention, before use, the instruments used for homogenization further include a step of cleaning with a homogenate at 2-6 °C.

[0085] In some embodiments of the present invention, the number of strokes of the homogenizer used for homogenization is 45-55 times, and the time for each stroke is 0.8-1.2 s.

[0086] In some embodiments of the present invention, the conditions for centrifugation in step S1 are: centrifugation at 2-6 °C and 800-1200 g for 8-12 min.

[0087] In some embodiments of the present invention, the specific steps for density gradient centrifugation of the solid phase A using an iodixanol solution include: mixing the solid phase A with an iodixanol solution to prepare an iodixanol mixed solution with a concentration of 23-27%; adding an iodixanol solution with a concentration of 27-31% below the 23-27% iodixanol mixed solution; adding an iodixanol solution with a concentration of 33-37% below the 27-31% iodixanol solution, and centrifuging at 2-6 °C and 2800-3200 g for 28-32 min; taking the white nuclear band between the 27-31% iodixanol solution and the 23-27% iodixanol mixed solution, adding a buffer solution, mixing, centrifuging, removing the supernatant, resuspending with a buffer solution, and filtering the buffer solution through a filter to collect the cell nuclei.

[0088] In some embodiments of the present invention, the buffer solution includes an HB solution.

[0089] In some embodiments of the present invention, the HB solution contains calcium chloride with a final concentration of 3-6 mM, magnesium acetate with a final concentration of 2-4 mM, Tris with a final concentration of 8-12 mM, sucrose with a final concentration of 300-340 mM, and a protease inhibitor mixture solution.

[0090] In some embodiments of the present invention, the protease inhibitor mixed solution comprises aprotinin at a final concentration of 350 - 450 nM, bestatin at 8 - 12 μM, leupeptin at 8 - 12 μM, pepstatin A at 1 - 3 μM, and PMSF at 0.5 - 2 mM.

[0091] In some embodiments of the present invention, the protease inhibitor mixed solution comprises aprotinin at a final concentration of 400 nM, bestatin at 10 μM, leupeptin at 10 μM, pepstatin A at 2 μM, and PMSF at 1 mM.

[0092] In some embodiments of the present invention, the HB solution contains a calcium chloride solution at a final concentration of 5 mM, a magnesium acetate solution at a final concentration of 3 mM, a Tris solution at a final concentration of 10 mM, a sucrose solution at a final concentration of 320 mM, and a protease inhibitor mixed solution.

[0093] In some embodiments of the present invention, the specification of the filter is 18 - 22 μm.

[0094] In some embodiments of the present invention, it further includes the step of adding a buffer to the filtered filter and centrifuging.

[0095] In some embodiments of the present invention, the conditions for centrifugation are: centrifuging at 800 - 1200 g for 8 - 12 min at 2 - 6°C.

[0096] In some embodiments of the present invention, the temperature of centrifugation in step S3 is 2 - 6°C.

[0097] In some embodiments of the present invention, the temperature of incubation in step S4 is 2 - 6°C.

[0098] In some embodiments of the present invention, the magnetic beads include Pierce A / G protein magnetic beads.

[0099] In some embodiments of the present invention, the temperature of the magnetic beads is 2 - 6°C.

[0100] In some embodiments of the present invention, before using the magnetic beads, it further includes a pre - cleaning step, including: sequentially washing the magnetic beads with 100 - 150 μL of TBST, 0.5 - 2 mL of TBST, 0.8 - 1.2 mL of homogenate, then adding 45 - 55 μL of homogenate to resuspend the magnetic beads and placing them on ice.

[0101] In some embodiments of the present invention, the method of collecting the magnetic beads includes collecting them using a magnetic stand.

[0102] In some embodiments of the present invention, the cleaning solution comprises a homogenizing solution.

[0103] In some embodiments of the present invention, when Tom20 antibody is used, before adding Tom20 antibody, the step of centrifuging liquid phase B is further included, and the centrifugation conditions are: 2-6°C, 12000-14000g, and centrifugation for 8-12 min.

[0104] In some embodiments of the present invention, the centrifugation temperature in step S5 is 2-6°C.

[0105] In some embodiments of the present invention, the volume ratio of the calcium chloride solution to the liquid phase C is (6-8): 1

[0106] In some embodiments of the present invention, the incubation temperature in step S5 is 2-6°C.

[0107] In some embodiments of the present invention, the method of removing the liquid phase includes centrifugation, and the centrifugation conditions are 2-6°C, 2000-4000g, centrifugation for 8-12 minutes, and then centrifugation again at 2-6°C, 2000-4000g, for 4-6 minutes.

[0108] In some embodiments of the present invention, the conditions of density gradient centrifugation in S6 are 2-6°C, 90,000-110,000 g, and centrifugation for 50-70 min.

[0109] According to a fifth aspect of the present invention, a cell organelle separated by the above cell organelle separation method is provided.

[0110] In some embodiments of the present invention, the cell organelles include cell nucleus, cytoplasm, cell membrane, mitochondria, Golgi apparatus, and endoplasmic reticulum.

[0111] According to a sixth aspect of the present invention, the application of the above-mentioned organelles in studying the molecular interactions between different organelles is proposed.

[0112] In some embodiments of the present invention, the study of molecular interactions between organelles includes at least one of constructing a protein interaction network between organelles, constructing a lipid interaction network between organelles, and constructing a lipid and protein interaction network between organelles.

[0113] According to a preferred (specific) embodiment of the present invention, at least the following beneficial effects are achieved: by using the device provided by the present invention for studying the molecular interactions between different organelles, it is possible to effectively conduct joint correlation analysis on the proteomes and lipidomes of different subcellular components, obtain the subcellular protein-lipid molecular interaction network, and explore the key molecules of the interactions between organelles. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, where:

[0115] Figure 1 It is a schematic diagram of the organelle parallel separation and purification scheme in Example 1 of the present invention;

[0116] Figure 2 It is a t-SNE dimensionality reduction clustering result diagram of the changes of each organelle sample in the proteome and lipidome. Among them, A is the t-SNE dimensionality reduction clustering of the proteome changes of each organelle sample without removing batch effects, B is the t-SNE dimensionality reduction clustering of the proteome changes of the drug-induced organelle samples with batch effects removed, C is the t-SNE dimensionality reduction clustering of the lipidome changes of the drug-induced organelle samples without removing batch effects, and D is the t-SNE dimensionality reduction clustering of the lipidome changes of the drug-induced organelle samples with batch effects removed;

[0117] Figure 3 It is a protein correlation interaction network diagram;

[0118] Figure 4 It is a lipid correlation interaction network diagram;

[0119] Figure 5 It is a correlation interaction analysis diagram of proteins and lipids. Detailed implementation manners

[0120] The concept of the present invention and the technical effects produced will be clearly and completely described below in conjunction with the embodiments to fully understand the purpose, features, and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0121] Example 1 Method for fractionating organelles

[0122] In this example, a method for fractionating organelles was prepared. The schematic diagram is as Figure 1 shown, and the specific process is as follows:

[0123] 1. Cell homogenization

[0124] (1) After removing the complete medium from 2×10 7 293T cells (which can be treated with a drug (any one of the drugs shown in Table 1) or not. In this example, the organelles were prepared using drug treatment, and the treatment condition was to incubate the cells with a final concentration of 10 mM of the drug (any one of the drugs shown in Table 1) for 24 h), add 2 mL of PBS to wash the cells once, and discard the PBS;

[0125] Table 1

[0126]

[0127]

[0128]

[0129] (2) Add 1 mL of PBS and collect the cells using a cell scraper. Centrifuge at 1000 g for 1 min, discard the supernatant, and collect the cells.

[0130] (3) Resuspend the cells in 1 mL of pre-cooled KPBS homogenate. Centrifuge at 1000 g for 1 min, discard the supernatant, and collect the cells.

[0131] (4) Homogenize using a 2 mL Dounce homogenizer and a pestle. Wash with pre-cooled KPBS, remove the KPBS after washing, and place on ice.

[0132] (5) Resuspend the cell pellet in 500 μL of pre-cooled KPBS homogenate in the cells obtained in step (3), and then transfer to a 2 mL pre-cooled Dounce homogenizer.

[0133] (6) Use the Dounce homogenizer to make 50 strokes, about 1 s per stroke. Collect the homogenate into a new 1.5 mL EP tube (denoted as homogenate A).

[0134] (7) Place homogenate A in a 4 °C centrifuge and centrifuge at 1000 g for 10 min. Transfer the supernatant to a new 1.5 mL EP tube (denoted as B); add 200 μL of 1×HB solution to the remaining precipitate in homogenate A (denoted as C).

[0135] (8) Continue to place B in a 4 °C centrifuge and centrifuge at 1000 g for 10 min. Transfer the supernatant to a new 1.5 mL EP tube (the obtained supernatant is the supernatant after enucleation).

[0136] 2. Nucleus isolation and purification

[0137] (1) Add the pre-cooled equal volume of 50% iodixanol solution to C obtained in step (7) of the cell homogenate and mix well to form a 25% iodixanol mixture.

[0138] (2) Add 300 μL of pre-cooled 29% iodixanol solution below the 25% mixture, avoiding mixing of each layer.

[0139] (3) Add 200 μL of pre-cooled 35% iodixanol solution below the 29% mixture, avoiding mixing of each layer.

[0140] (4) Place the three-layered C in a bucket-type horizontal centrifuge and centrifuge at 4 °C and 3000 g for 30 min.

[0141] (5) Carefully take out the centrifuged C, aspirate about 200 μL of the white nuclear band between the second and third layers, and transfer it to a new 1.5 mL EP tube (denoted as D).

[0142] (6) Add 200 μL of 1×HB (5 mM calcium chloride + 3 mM magnesium acetate + 10 mM Tris at pH 7.8 + 320 mM sucrose + protease inhibitor mixture (400 nM aprotinin + 10 μM bestatin + 10 μM leupeptin + 2 μM pepstatin A + 1 mM PMSF)) to D, mix well, centrifuge at 3000 g for 5 min at 4°C, and discard the supernatant.

[0143] (7) Resuspend D with 200 μL of 1×HB, then filter it once through a 20 μm filter pre-wetted with 1×HB, transfer it to a new 1.5 mL EP tube, then add 200 μL of 1×HB to the 20 μm filter to collect the cell nuclei, and finally centrifuge at 1000 g for 10 min in a 4°C centrifuge to collect the enriched and purified cell nuclei.

[0144] 3. Mitochondria isolation and purification

[0145] (1) Use one-fourth volume of the supernatant after removing nuclei obtained in step (8) of the above 1. Cell homogenization, place it in a 4°C centrifuge and centrifuge at 13000 g for 10 min, discard the supernatant, and resuspend the precipitate with 500 μL of KPBS.

[0146] (2) Add 1 μL of Tom20 antibody, and then incubate on a 4°C shaker at 10 rpm for 2 h.

[0147] (3) Take 10 μL of Pierce A / G protein magnetic beads for pre-cleaning: wash the magnetic beads successively with 150 μL of 1×TBST, 1 mL of 1×TBST, and 1 mL of KPBS homogenate, add 50 μL of KPBS homogenate to resuspend the magnetic beads and place them on ice.

[0148] (4) Add the pre-cooled and pre-cleaned Pierce A / G protein magnetic beads, and continue to incubate on a 4°C shaker at 10 rpm for 1 h.

[0149] (5) Collect the magnetic beads through a magnetic stand, discard the supernatant completely for the first time, then add 500 μL of KPBS homogenate and pipette 10 times to wash the magnetic beads, wash them 2 times in total, and finally pipette the last time into a new 1.5 mL EP tube, discard the supernatant completely to obtain the enriched and purified mitochondria.

[0150] 4. Endoplasmic reticulum isolation and purification

[0151] (1) Place one-fourth volume of the supernatant obtained after enucleation in step (8) of the above-mentioned 1. Cell homogenization at 4 °C in a centrifuge and centrifuge at 8000 g for 10 min. Transfer the supernatant to a 15 mL tube.

[0152] (2) Add 7.5 times the volume of pre-cooled 8 mM calcium chloride solution and incubate at 10 rpm on a shaker at 4 °C for half an hour.

[0153] (3) Centrifuge at 3000 g for 10 min at 4 °C and discard the supernatant.

[0154] (4) Centrifuge again at 3000 g for 5 min at 4 °C to completely remove the supernatant. The white precipitate is the enriched and purified endoplasmic reticulum.

[0155] 5. Separation and purification of cytoplasm, Golgi apparatus and plasma membrane

[0156] Set up a density gradient three-layer separation: the bottom layer is 2 mL of 1 M Sucrose buffer, the middle layer is 2 mL of 0.5 M Sucrose buffer, and the top layer is one-half volume of the supernatant obtained after enucleation in step (8) of the above-mentioned 1. Cell homogenization. Use a 4 °C ultracentrifuge to centrifuge at 100000 g for 1 h. After centrifugation, the top layer obtained is the cytoplasm, the white band between 0.5 M - 1 M Sucrose buffer is the Golgi apparatus, and the bottom layer precipitate is resuspended in KPBS to be the cell membrane and stored at -80 °C. Example 2 Research method for molecular interactions between different organelles

[0157] This example provides a research method for molecular interactions between different organelles. The specific process is as follows:

[0158] 1. Extraction and identification of proteome and lipidome

[0159] (1) Extraction and identification of proteome of different organelles (prepared in Example 1)

[0160] 1) Resuspend the nuclear sample with lysis buffer A1 (consisting of 25 μL of CA-630, 500 μL of 1 M Tris-HCl (pH 7.5), and 49.475 mL of ddH2O), and repeatedly aspirate 50 times with a 1 mL syringe on ice to break the nucleus. Then centrifuge at 17000 g for 10 min at 4 °C, transfer the supernatant to a new 1.5 mL EP tube, and measure the protein concentration.

[0161] 2) Add 100 μL of 0.1 M glycine (pH 2.0) to Mito (mitochondria), resuspend and mix well. After standing for 10 min, add 15 μL of 1 M Tris-HCl (pH 7.5), mix well, adsorb the magnetic beads with a magnetic stand, collect the supernatant, repeat 2 times, for a total of 3 collections. Then add 115 μL of lysis buffer A2 (consisting of 25 μL of CA-630, 2 mL of 1 M Tris-HCl (pH 7.5), and 47.975 mL of ddH2O), resuspend and mix well, adsorb the magnetic beads with a magnetic stand, collect the supernatant, and measure the protein concentration.

[0162] 3) Add 100 μL of protein lysis buffer (6 M guanidine hydrochloride, 10 mM tris(2-carboxyethyl)phosphine hydrochloride (TCEP), 40 mM chloroacetamide, dissolved in 100 mM Tris-HCl (pH 8.5)) to ER (endoplasmic reticulum), resuspend and mix well, and then measure the protein concentration.

[0163] 4) Directly measure the protein concentration of the samples of Cyto (cytoplasm), Golgi (Golgi apparatus), and PM (cell membrane)

[0164] After measuring the protein concentration of the protein samples of different organelles in 1)-4) above, take 200 ng of the total protein amount for each and add it to a Bravo special 96-well plate. Use the Barvo high-throughput sample preparation system to prepare the samples before mass spectrometry detection: Briefly, the sequence is to add 20 μL of protein lysis buffer, heat at 95 °C for 10 min; add 5 μL of 5 μg / μL protein enrichment magnetic beads; add 45 μL of acetonitrile and shake for 15 min, adsorb the magnetic beads and remove the supernatant, add 80% ethanol to wash the magnetic beads, adsorb the magnetic beads and remove the supernatant, add acetonitrile to wash the magnetic beads; adsorb the magnetic beads and remove the supernatant, add 35 μL of trypsin buffer (prepared from ddH2O, 100 mM Tris-HCl (pH 8.5), and ACN in a volume ratio of 13:5:2), add 10 μL of trypsin working solution (20 μg of trypsin (Promega) dissolved in 4 mL of trypsin buffer), place in a thermostatic shaker at 37 °C for 16 h; the next day, activate the C18 solid-phase extraction membrane (add 20 μL of 80% CAN containing 0.1% FA, centrifuge at 800 g for 2 min, soak in chromatographic-grade isopropanol for 5 min, add 20 μL of 0.1% FA, centrifuge at 800 g for 1 min, soak in 0.1% FA before use); after adding 6 μL of 5% TFA to the enzymatically digested protein sample and shaking for 1 min, load the sample onto the activated C18 solid-phase extraction membrane, centrifuge at 800 g for 2 min, add 50 μL of 0.1% FA, centrifuge at 800 g for 2 min, add 100 μL of 0.1% FA, and after transient centrifugation, use EvosepOne + ZenoTOF 7600 for protein mass spectrometry identification, and use the database search software DIANN1.8 for protein quantitative analysis to obtain the original data of the identification.

[0165] (2) Extraction and Identification of Organelle Lipidome

[0166] Lipid extraction: Add 200 μL of a 3:1 chloroform-methanol mixture to different organelles (including the nucleus, cytoplasm, cell membrane, mitochondria, Golgi apparatus, and endoplasmic reticulum). After shaking for 2 min, centrifuge at 16,000 g at 4 °C for 15 min. Transfer the supernatant to a new 1.5 mL EP tube and then concentrate it under vacuum. After concentration, add 50 μL of a lipid reconstitution solution (prepared with chloroform and methanol in a volume ratio of 5:13), shake for 2 min, centrifuge at 16,000 g at 4 °C for 15 min, take 40 μL of the supernatant and transfer it to an injection vial. Mix the remaining supernatant into the same new 1.5 mL EP tube, and then take a portion and transfer it to an injection vial as QC quality control.

[0167] Use UltiMate-3000 + Orbitrap Exploris 480 for lipid mass spectrometry identification and use the database search software LipidSearch (v5.0) for lipid quantitative analysis to obtain the original identification data.

[0168] 2. Data preprocessing

[0169] For the original data obtained from the extraction and identification of the proteome and lipidome of organelles, remove the eigenvalues with missing values greater than 50%. Replace the remaining missing values with 0, perform total normalization, scale according to the Pareto of the samples, and use the R package "WaveICA" (V0.1.0) for batch effect removal. The parameter settings for batch effect removal are set to different values according to different organelles and omics (the parameter settings for different organelles are shown in Table 2). Group the data after batch effect removal according to the drug and calculate the average value, and use this average value for subsequent correlation analysis.

[0170] Table 2 WaveICA Parameter Settings

[0171]

[0172] 3. t-Distribution - Stochastic Neighbor Embedding Dimensionality Reduction Clustering

[0173] Perform visual dimensionality reduction clustering on the preprocessed high-dimensional data. Use the R package "Rtsne" (V0.1) to generate a t-distribution - stochastic neighbor embedding (t-SNE) scatter plot, with the perplexity value set to 10, the dimension set to 2, and the remaining parameters as default values. Set the seed number to 123, and set the seed number to 123 for all subsequent data analyses that require a seed number.

[0174] 4. Correlation analysis

[0175] Perform correlation analysis using "HALLA" (V0.8.20), with the statistical method being Spearman, the P-value correction method being Benjamini-Hochberg (BH), and the remaining parameters being default values. After the results are output, use "fdrtool" to re-perform FDR correction and filter FDR < 0.05.

[0176] For data that cannot use "HALLA" (Cyto_lipid&ER_lipid, Cyto_lipid&Golgi_lipid, Cyto_lipid&Mito_lipid, Cyto_lipid&Nucleus_lipid, Cyto_lipid&PM_lipid), use the corr.test function of the R package "psych" (V2.4.6.26) for correlation analysis, with the statistical method being Spearman, the P-value correction method being Benjamini-Hochberg (BH), and the remaining parameters being default values. After the results are output, use "fdrtool" to re-perform FDR correction and filter FDR < 0.05.

[0177] 5. Data Visualization

[0178] The correlation network diagram is drawn using Cytoscape (V3.10.1). The dimensionality reduction clustering t-SNE diagram is drawn through R and Adobe AI.

[0179] Comparative Example 1

[0180] This comparative example provides a method for studying molecular interactions between different organelles. The specific steps are only different from those of Example 2 in that the batch effect removal treatment is not performed.

[0181] Experimental Example

[0182] 1. t-Distribution - Stochastic Neighbor Embedding Results (t-SNE Dimensionality Reduction Clustering)

[0183] Through the methods for studying molecular interactions between different organelles in Example 2 and Comparative Example 1, the results of visualizing the proteomic data and lipidomic data by t-SNE dimensionality reduction clustering are respectively as Figure 2 shown.

[0184] From Figure 2 it can be seen from Figure A in that after performing t-SNE dimensionality reduction clustering on the common proteins induced by drugs in each organelle without removing the batch effect, there is a certain degree of separation between different organelles, but there is also a certain degree of separation within the same organelle. Reliable results cannot be obtained by directly performing subsequent molecular correlation interaction analysis between organelles.

[0185] From Figure 2As can be seen from Figure B in [reference], after the t-SNE dimensionality reduction clustering of the common proteins in each organelle induced by drugs with batch effects removed, the samples belonging to different organelles have a high degree of separation from each other, and reliable results can be obtained by performing subsequent molecular correlation interaction analysis between organelles.

[0186] From Figure 2 As can be seen from Figure C in [reference], after the t-SNE dimensionality reduction clustering of the common lipids in each organelle induced by drugs without batch effects removed, there is no separation degree between different organelles, and reliable results cannot be obtained by directly performing subsequent molecular correlation interaction analysis between organelles.

[0187] From Figure 2 As can be seen from Figure D in [reference], after the t-SNE dimensionality reduction clustering of the common lipids in each organelle induced by drugs with batch effects removed, there is a high degree of separation between different organelles, and reliable results can be obtained by performing subsequent molecular correlation interaction analysis between organelles. The results show that the data of the six organelles separated and purified after batch processing are all reproducible, and the interference between organelles is relatively small.

[0188] 2. Correlation interaction analysis between proteome and proteome

[0189] By using the molecular interaction research method between different organelles in Example 2, the correlation analysis data between the proteomes of different organelles were obtained. The analysis results with the correlation coefficient |r|>0.7 were used to draw a protein correlation interaction network diagram with cytoscape. The results are as Figure 3 shown. As can be seen from the figure, when the correlation coefficient |r|>0.7, the two organelles with the largest number of proteins in the protein correlation interaction network diagram are the nucleus and mitochondria, followed by the endoplasmic reticulum. These three organelles play a dominant role in the organelle protein interaction association network induced by drugs. It was also found that the core proteins are ATIC, PSMB5, and PYGL in the endoplasmic reticulum, POGZ, EIF4A1, and CCT6A in the nucleus, MSN in the cytoplasm, and PUS1 in the mitochondria. In addition, the correlation network diagram is mainly based on positive correlation interaction associations. Among them, the proteins in the nucleus and mitochondria are mainly based on positive correlation interaction associations, while the proteins in the endoplasmic reticulum coexist with positive correlation interaction associations and negative correlation interaction associations.

[0190] 3. Correlation interaction analysis between lipidome and lipidome

[0191] By using the molecular interaction research method between different organelles in Example 2, the correlation analysis data between the lipidomes of different organelles were obtained. The analysis results with the correlation coefficient |r|>0.72 were used to draw a lipid correlation interaction network diagram with cytoscape. The results are as Figure 4As shown, it can be seen from the figure that when the correlation coefficient |r| > 0.72, the core lipid with the most associated lipids in the lipid correlation interaction network diagram is the polyunsaturated fatty acid linolenic acid FA(18:3)_2.32 in the cell membrane. Followed by the saturated fatty acid stearic acid FA(18:0)_3.91 in the cell membrane, the triglyceride TG(14:0_16:0_18:0)_7.95 in the cell nucleus, and the wax ester WE(O-5:0_17:1)_5.49 in the mitochondria, etc. It is also found that the polyunsaturated fatty acid linolenic acid FA(18:3)_2.32 has positive correlation interaction associations with various cytoplasmic lipids, while the saturated fatty acid stearic acid FA(18:0)_3.91 has negative correlation interaction associations with various cytoplasmic lipids.

[0192] 4. Correlation Interaction Analysis between Proteome and Lipidome

[0193] By using the molecular interaction research method between different organelles in Example 2, the correlation analysis data between the proteomes and lipidomes of different organelles are obtained. The analysis results with the correlation coefficient |r| > 0.78 are used to draw the correlation interaction network diagram of the proteome and lipidome by cytoscape. The results are as Figure 5 shown. It can be seen from the figure that when the correlation coefficient |r| > 0.78, the core proteins are NME1 in the endoplasmic reticulum and SERBP1 in the mitochondria, etc., and the core lipids are ceramide Cer(m18:0_16:0)_6.69 and diacylglycerol DG(P-41:10)_5.98 in the cell membrane, etc. Among them, the endoplasmic reticulum protein NME1 has negative correlation interaction associations with various cytoplasmic lipids; the mitochondrial protein SERBP1 has positive correlation interaction associations with various cytoplasmic lipids; various lipids in the cell membrane form a correlation network mainly with negative correlation interaction associations with various mitochondrial proteins, and the most core one is the cell membrane lipid ceramide Cer(m18:0_16:0)_6.69.

[0194] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made without departing from the spirit of the present invention within the knowledge scope of those of ordinary skill in the art. In addition, the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

Claims

1. An apparatus for studying molecular interactions between different organelles, characterized in that, Comprising: An identification device for extracting and identifying proteins and lipids from organelle samples to obtain the original characterizations of organelle proteomes and lipidomes; A preprocessing analysis device for analyzing the original characterizations of different organelle proteomes and lipidomes to obtain the organelles to be analyzed; A data analysis device for performing organelle dimensionality reduction clustering and analyzing the molecular interaction relationships between organelles on the organelles to be analyzed; A storage device; A display; Connecting the sequencing device, the preprocessing analysis device, and the data analysis device in sequence from front to back; The storage device is respectively connected to the preprocessing analysis device and the data analysis device; the display is connected to the data analysis device.

2. A method for studying molecular interactions between different organelles based on the device described in claim 1, characterized in that, The method includes the following steps: (1) Preprocessing of the original characterization data of organelle proteomes and lipidomes: After performing missing value processing on the original characterization data of different organelle proteomes and lipidomes, performing normalization processing, and performing batch removal processing on the data obtained by the normalization processing to remove the batch effects between samples, obtaining the preprocessed original characterization data of organelle proteomes and lipidomes; (2) Organelle clustering and dimensionality reduction calculation: Using the t-SNE dimensionality reduction visualization algorithm on the preprocessed original characterization data of organelle proteomes and lipidomes to perform dimensionality reduction and clustering processing on the high-dimensional data, obtaining a dimensionality reduction clustering map and obtaining the coordinate positions of each organelle after dimensionality reduction; (3) Calculation of the interaction relationship between organelles: Using the HALLA software or the R package "psych" to perform correlation analysis on the preprocessed original characterization data of organelle proteomes and lipidomes, and setting the correlation coefficient |r| to >0.

7.

3. The method according to claim 2, wherein The missing value processing includes the following steps: Removing the eigenvalue with a missing value greater than 50% from the original characterization data of different organelle proteomes and lipidomes respectively, and replacing the remaining missing values with 0 values.

4. The method according to claim 2, wherein The batch removal processing includes the following steps: Setting different parameters for batch removal processing according to different organelles and the omics studied; Preferably, the omics includes proteomics and lipidomics; More preferably, when the omics is proteomics, the parameters set for batch removal processing of different organelles are as follows: More preferably, when the omics is lipidomics, the parameters set for batch removal processing of different organelles are as follows:

5. The method according to claim 2, wherein The study of the interaction relationship between organelles includes at least one of constructing an organelle-to-organelle protein interaction network, constructing an organelle-to-organelle lipid interaction network, and constructing an organelle-to-organelle lipid and protein interaction network.

6. The method according to claim 2, characterized in that, The statistical method for the correlation analysis includes Spearman; And / or, the P-value correction method for the correlation analysis includes Benjamini-Hochberg; And / or, after the analysis result is output using the HALLA software or the R package "psych", it further includes the step of performing FDR correction, with FDR < 0.05; And / or, the analysis of the R package "psych" is performed using the corr.test function.

7. Application of the device for studying the molecular interaction between different organelles according to claim 1 or the method according to claim 2 in any one of the following: (1) Study the molecular interactions between organelles; (2) Dimensionality reduction and clustering of organelles; (3) Screen for molecules involved in molecular interactions between organelles; Preferably, the study of the interaction relationship between organelles includes at least one of constructing a protein interaction network between organelles, constructing a lipid interaction network between organelles, and constructing a lipid and protein interaction network between organelles.

8. A method for separating an organelle, characterized in that, The method includes the following steps: S1. Centrifuge the cell homogenate to obtain solid phase A and liquid phase A; S2. Perform density gradient centrifugation on the solid phase A using an iodixanol solution to obtain the cell nucleus; S3. Centrifuge the liquid phase A at 800 - 1200 g for 8 - 12 min to obtain liquid phase B; S4. Centrifuge the liquid phase B at 12000 - 14000 g for 8 - 12 min, collect solid phase C, add 400 - 600 μL of KPBS to resuspend it, add Tom20 antibody and incubate for 1 - 3 h, then add magnetic beads and incubate for 0.5 - 2 h, collect the magnetic beads, wash the magnetic beads with a washing solution, and collect the magnetic beads, which are mitochondria; S5. Centrifuge the liquid phase B at 7000 - 9000 g for 8 - 12 min, collect liquid phase D, add calcium chloride solution and incubate for 0.3 - 0.8 h, then remove the liquid phase to obtain the endoplasmic reticulum; S6. Add the liquid phase B to a sucrose density gradient, with a 1 M sucrose solution, a 0.5 M sucrose solution, and the liquid phase B from bottom to top in sequence, perform density gradient centrifugation, the top layer obtained after centrifugation is the cytoplasm, the white band between the 0.5 M - 1 M Sucrose buffer is the Golgi apparatus, and the precipitate is the cell membrane; Preferably, the homogenate is a KPBS solution.

9. An organelle, characterized in that, Obtained by the separation method described in claim 8.

10. Use of the organelle described in claim 9 in studying molecular interactions between different organelles.

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