Tissue metabolism stress typing method based on quantitative analysis of lipid and protein components

Abstract

The invention relates to a tissue metabolism stress typing method based on quantitative analysis of lipid and protein components, which belongs to the technical field of biomedicine and comprises the following specific steps: slicing a biological tissue sample to be detected to prepare a tissue slice sample; the method comprises the following steps: carrying out label-free imaging on a protein signal and / or a lipid signal of a tissue slice sample through a nonlinear optical imaging technology, carrying out quantitative analysis on the protein signal and / or the lipid signal based on a label-free imaging result, and typing the biological tissue based on a quantitative analysis result. And distinguishing the metabolic stress type of the biological tissue sample as a sensitive type or a resistant type. When the method is used for typing the metabolic stress of the to-be-detected tissue sample, complex operation and long detection period of a traditional marking method are avoided, comprehensive tissue metabolic component information is provided, the metabolic process and the influence after metabolic intervention can be deeply understood, and the detection accuracy is improved. Therefore, a new treatment strategy is developed to interfere with metabolism-related diseases, and wide application prospects and remarkable beneficial effects are achieved.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a tissue metabolic stress typing method based on quantitative analysis of lipid and protein components. Background Art

[0002] Metabolic stress detection of biological tissues requires rapid classification of the metabolism and metabolic stress response types of research tissue samples, helping researchers to gain a deeper understanding of metabolic processes and the effects of metabolic interventions, and to study the differences and connections between normal and disease from the perspective of tissue and cell metabolism, thereby developing new therapeutic strategies to intervene in metabolic-related diseases. The study and detection of metabolic stress in biological tissues not only helps to understand how organisms respond to and adapt to external interventions and changes in the tissue microenvironment, but also has important research significance and application prospects for the development of new therapeutic strategies, especially for the treatment of tumors, metabolic diseases, and aging-related diseases.

[0003] Among them, tumor is a complex disease. The tumor type, stage, genetic background and in vivo environment of each patient are unique. Targeting different cancer types and specific subtypes of tumors, conducting efficacy studies and guiding the development of new anti-tumor drugs can more effectively kill or inhibit the growth of tumor cells and tumor-related cells, delay the progression of the disease and prolong the survival of patients. Many studies have found that the metabolic pattern change of tumor cells is one of its important characteristics. For example, as shown by the Warburg effect, tumor cells tend to provide energy through glycolysis rather than oxidative phosphorylation. For example, lipid accumulation in tumor cells and tissues will increase to provide energy supply in a hypoxic environment, thereby improving the tolerance of tumor cells. These findings all show that determining the metabolic stress types of tumors and tumor-related cells of different cancer types and stages, and predicting their responses to specific metabolic inhibitors, can help researchers more specifically discover new targets and develop new drugs to meet the treatment needs of different tumor patients.

[0004] At present, the methods for studying the metabolism and stress detection of tumors or other biological tissues are mainly divided into metabolomics analysis and single-cell resolution metabolic analysis. The main methods of metabolomics analysis are nuclear magnetic resonance (NMR) technology and mass spectrometry imaging technology. The use of nuclear magnetic resonance (NMR) technology for tumor metabolomics analysis can identify changes in metabolites in tumor tissues and biological fluid samples, thereby revealing the metabolic characteristics of tumors. MALDI-TOF MS technology can be used to perform molecular imaging of biological samples, locate peptides and proteins, and perform in situ imaging of metabolic heterogeneity. However, whether it is a metabolic detection method based on nuclear magnetic resonance (NMR) or mass spectrometry technology, it faces difficult-to-overcome obstacles such as low sensitivity, high equipment cost, and complex technical operation. In addition, single-cell resolution metabolic analysis technology mainly includes isotope labeling (such as 13C) Methods such as metabolic flux analysis and ATP cell viability detection. Through isotope-labeled metabolic flux and ATP viability analysis, the relationship between the metabolic flux and cell viability of tumor cells can be studied, so as to understand how tumor cells use nutrients for growth and survival, which helps to gain a deeper understanding of the metabolic interactions between tumor cells and other cells, reveal the molecular connection between cancer genotype and metabolic dependence, and guide the personalized treatment of metabolic inhibitors. However, single-cell resolution metabolic analysis technology cannot measure the metabolic intervention response of multiple target cells and the scenario of overall tissue evaluation, and the detection and analysis cycle is relatively long, the technical requirements are high, and the data analysis is complex.

[0005] Therefore, there is currently a lack of a method for detecting and typing metabolic stress in biological samples with a short detection cycle, simple operation, high sensitivity and low cost at home and abroad. Summary of the invention

[0006] To solve the above technical problems, the present invention provides a method for typing tissue metabolic stress based on quantitative analysis of lipid and protein components. The specific steps include slicing a biological tissue sample to be tested, preparing a tissue slice sample, performing label-free imaging of the protein signal and / or lipid signal of the tissue slice sample by nonlinear optical imaging technology, performing quantitative analysis of the protein signal and / or lipid signal based on the label-free imaging result, and typing the biological tissue based on the quantitative analysis result to distinguish the metabolic stress type of the biological tissue sample as sensitive or resistant.

[0007] The first object of the present invention is to provide a method for typing tissue metabolic stress based on quantitative analysis of lipid and protein components, comprising the following steps:

[0008] Step S1, slicing the biological tissue sample to be tested to prepare a tissue slice sample;

[0009] Step S2, performing label-free imaging of protein signals and / or lipid signals of the tissue section sample by nonlinear optical imaging technology;

[0010] Step S3, performing quantitative analysis on protein signals and / or lipid signals based on the label-free imaging results;

[0011] Step S4: Based on the quantitative analysis results, the biological tissue is classified to distinguish the metabolic stress type of the biological tissue sample as sensitive or resistant. The metabolic stress type is the metabolic phenotype of the tissue sample's response to the inhibitor after the metabolic activity of the biological tissue is inhibited.

[0012] Furthermore, the tissue is tumor tissue.

[0013] Furthermore, in step S1, the tissue section sample includes one or more of formalin-fixed tissue sections, agarose gel sections, frozen sections, ex vivo biopsies, organoid smears, cell microspheres and paraffin section samples.

[0014] In one embodiment of the present invention, the tissue section sample is an agarose gel section.

[0015] Furthermore, in step S1, the thickness of the tissue slice is in the range of 20-200 μm.

[0016] Furthermore, in step S2, the nonlinear optical imaging technology includes one or more of coherent Raman microscopy imaging technology, second harmonic generation imaging and two-photon fluorescence imaging.

[0017] In one embodiment of the present invention, the nonlinear optical imaging technology is coherent Raman microscopy imaging technology.

[0018] Furthermore, the coherent Raman microscopy imaging technology includes one or more of stimulated Raman scattering, coherent anti-Stokes microscopy imaging technology, surface enhanced Raman spectroscopy imaging technology and Raman spectroscopy analysis technology.

[0019] In one embodiment of the present invention, the coherent Raman microscopy technique is stimulated Raman scattering.

[0020] Furthermore, in step S2, the collection range of the protein signal and / or lipid signal is 2800 cm -1 Up to 3100cm -1 Band.

[0021] CH P is the carbon-hydrogen bond signal in the protein, CH L The carbon-hydrogen bond signal in lipids is distinguished by the different carbon-hydrogen bond vibration energy levels in different biochemical components. The carbon-hydrogen bond vibration energy levels in proteins and lipids are respectively 2930cm -1 and 2850cm -1 The wave number Raman shift energy difference is equal to produce a resonant coherent signal, and carbon-hydrogen bonds are widely distributed in proteins and lipids, that is, at 2930cm -1 and 2850cm -1 The content and distribution of protein and lipid components in biological tissue samples were imaged and quantitatively analyzed at different wave numbers.

[0022] Furthermore, in step S2, the lipids include one or more of phospholipids, triglycerides, cholesterol, cholesterol esters, glycolipids, saturated fatty acids and unsaturated fatty acids.

[0023] Further, in step S3, the quantitative analysis includes one or more of lipid content, lipid concentration, lipid spatial distribution, protein concentration, protein content, protein spatial distribution, lipid / protein content ratio, lipid component / protein component area ratio, lipid component to total cell area ratio, and protein component to total cell area ratio;

[0024] Furthermore, in step S4, the inhibition of metabolic activity of biological tissues includes one or more of affecting the stability of cell DNA, interfering with the replication of cell DNA, affecting and blocking cell division, destroying the energy and material metabolic pathways of cells, affecting the properties of cell proteins, and interfering with the sugar and lipid phenotypes in cells and intercellular matrix.

[0025] Furthermore, in step S4, the average value of the lipid signal and protein signal intensity ratio in different imaging areas is taken. When the average value is >0.7, it is regarded as metabolic inhibition resistance classification tissue, when the average value is <0.4, it is metabolic inhibition sensitive classification tissue, and when the average value is between 0.4-0.7, it is regarded as metabolic inhibition intermediate classification tissue.

[0026] Furthermore, in step S4, the average of the lipid signal and protein signal area ratios in different imaging areas is taken. When the average value is >0.9, it is regarded as metabolic inhibition resistance classification tissue, when the average value is <0.75, it is metabolic inhibition sensitive classification tissue, and when the average value is between 0.75-0.9, it is regarded as metabolic inhibition intermediate classification tissue.

[0027] Furthermore, in step S4, the ratio of the lipid signal and protein signal to the total cell area in different imaging areas is averaged. When the average value is >0.9, it is regarded as metabolic inhibition resistance tissue, when the average value is <0.5, it is metabolic inhibition sensitive tissue, and when the average value is between 0.5-0.9, it is regarded as metabolic inhibition intermediate tissue.

[0028] Beneficial effects of the present invention:

[0029] The nonlinear optical imaging technology is used to perform label-free imaging of protein signals and lipid signals of tissue slice samples, avoiding the complex operations and long detection cycles that may be caused by traditional labeling methods, and achieving non-invasive and efficient detection. Based on the label-free imaging results, the protein signals and lipid signals are quantitatively analyzed, covering multiple dimensions and various proportional relationships between lipids and proteins, providing comprehensive tissue metabolism information, and metabolic stress typing of biological tissues based on the quantitative analysis results. It can accurately distinguish the sensitive or resistant response of biological tissue samples to metabolic stress, providing an important reference for the diagnosis, treatment and prognosis of the disease, helping to reveal the response mechanism of tumor tissue under metabolic stress, and providing new ideas and methods for tumor treatment and drug development. At the same time, this method can also be applied to the study of metabolic stress in other biological tissues, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein

[0031] Figure 1 These are stimulated Raman scattering lipid imaging and lipid droplet area analysis diagrams of biological tissue sections resistant (R) and sensitive (S) to DNA toxicity-related metabolic activity inhibitors in Example 2 of the present invention, wherein A is the imaging result and B is the lipid droplet area difference analysis result;

[0032] Figure 2 is the analysis result of lipid unsaturation of lipid droplets in biological tissue slice cells resistant and sensitive to DNA toxicity-related and antibiotic metabolic activity inhibitors in Example 2 of the present invention, wherein A is the Raman signal collection result of different cells, and B is the result of quantitative analysis of lipid unsaturation;

[0033] Figure 3 is the CH of different regions of tissue sections resistant to DNA toxicity and inhibitors of metabolic activity related to cell division arrest in Example 3 of the present invention (R). P Protein signaling and CH L Stimulated Raman scattering imaging of lipid signals;

[0034] Figure 4 The CH in different regions of the tissue sections sensitive to DNA toxicity and inhibitors of metabolic activity related to cell division arrest in Example 3 of the present invention (S) P Protein signaling and CH L Stimulated Raman scattering imaging of lipid signals;

[0035] Figure 5 The tissue sections CH resistant (R) and sensitive (S) to inhibitors of DNA toxicity and metabolic activity related to cell division arrest in Example 3 of the present invention are L Lipid signaling and CH P Quantitative analysis results of protein signal intensity and intensity ratio;

[0036] Figure 6 The tissue sections CH resistant (R) and sensitive (S) to inhibitors of DNA toxicity and metabolic activity related to cell division arrest in Example 3 of the present invention are L Lipid signaling and CH P Quantitative analysis results of protein signal area and area ratio;

[0037] Figure 7 The cell area of ​​tissue sections and the CH in cells resistant (R) and sensitive (S) to inhibitors of DNA toxicity and metabolic activity related to cell division arrest in Example 3 of the present invention are LLipid signaling and CH P Quantitative analysis results of the proportion of cell area occupied by protein signals. DETAILED DESCRIPTION

[0038] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0039] Example 1

[0040] 1. Preparation of tissue agar clots

[0041] (1) Wash the tumor tissue with PBS buffer and cut the tumor tissue into small pieces in PBS buffer with dissecting scissors;

[0042] (2) Add triple distilled water to a conical flask, and add 3% agarose powder weighed by a balance and stirred. Heat the agarose solution in a microwave oven for 1-2 minutes and observe the dissolution until the solution becomes clear.

[0043] (2) Take out the conical flask and cool it to 40°C. During this time, cut the ex vivo tumor tissue into small pieces with a side length of 3-5 mm. Pour the agarose solution into a plastic slice holder and immerse the tissue block into the agarose solution until the agarose-coated tissue block solidifies. Care should be taken to place the tissue in the plastic slice holder when the agarose solution is around 40°C to ensure that it does not overheat and burn the tissue and that it has fluidity. After placing the tissue, the tissue can sink but will not sink to the bottom and can be covered by the slurry on the top.

[0044] (3) Take out the solidified block and cut it into 0.5-1 cm long agarose cuboid blocks.

[0045] 2. Fixation of tissue clots on the slicer

[0046] Apply 1mm diameter glue at the center of the front side of the slicer groove, and take about 2cm of double-sided tape perpendicular to the blade and stick it on the groove with the help of glue; then apply 1mm diameter glue at the same position as the previous glue, stick the agarose clot on the double-sided tape at the glue point, press and fix it to make it firmly cut; submerge the tissue block with PBS, cut the agarose-wrapped tissue into slices about 100μm thick (as thin and flat as possible); then fix it with tissue fixative as a sample to be tested for nonlinear optical detection.

[0047] Example 2

[0048] The biological tissue samples were sliced ​​using the method of Example 1 to obtain sensitive cells BIU87, cisplatin-resistant cells CisR, and doxorubicin-resistant cells AdmR at 2850 cm -1 Wave number collection CH LThe lipid signal and the 1902 cm -1 The wave number is used to collect off-resonance data to remove off-resonance signals in later data processing.

[0049] The difference in lipid distribution and content between cells and cells was analyzed by stimulated Raman scattering (SRS) system imaging. Figure 1 As shown, CH L Channel signals reflect lipid hydrocarbon signals in tissues and cells. The red-orange color without a black background in the figure is the hydrocarbon signal. The brighter the signal, the stronger the signal. Figure 1 The higher the brightness, the higher the lipid content density of the area. The lipid droplets are high-density lipid content areas, which appear bright yellow in the figure. The percentage of lipid droplet area in sensitive and resistant tissue cells was quantified and statistically analyzed at the single-cell level. The average percentage of lipid droplet area in sensitive tissues was 1.9%, and the average percentage of lipid droplet area in resistant tissues was 5.0%. The average percentage of lipid droplet area in resistant tissues was 2.6 times that of sensitive tissues. That is, the percentage of lipid droplet (LD) cell area in tissues resistant to inhibitors of metabolic activity of metal platinum complexes was significantly higher than that in sensitive tissues, indicating that a higher level of lipid droplet accumulation is a metabolic characteristic of tissues resistant to inhibitors of metabolic activity related to DNA toxicity, and such tissues or cells can be typed and identified by lipid chemical composition detection.

[0050] In addition, Raman spectroscopy signals were collected for lipid droplets in cisplatin (Cis) and doxorubicin (Adm) sensitive and resistant tissue cells (different lipid components will present different characteristic peaks in the Raman spectrum). Figure 2 The height ratio of the second peak to the first peak in A can be used to quantitatively analyze the unsaturation of lipids stored in lipid droplets in cells. Figure 2 As shown in B, the lipid unsaturation stored in lipid droplets of cells resistant to cisplatin Cis and doxorubicin Adm is significantly lower than that of sensitive cells.

[0051] From the above example, we can see that by analyzing the content and distribution of lipid components in tissues and cells, as well as the specific composition of lipids, metabolic stress detection and typing can be performed on biological samples under the intervention of different inhibitors.

[0052] Example 3

[0053] According to the method of Example 1, sections of bladder cancer tumor tissues of co-resistant (R) and co-sensitive (S) types to inhibitors of DNA toxicity and metabolic activity related to cell division arrest were prepared respectively. -1 and 2930cm -1 The bands are collected separately for CH L (lipid) and CH P (protein) signal and through 1902cm -1Off resonance data were collected to remove non-resonance signals in the subsequent data processing. Stimulated Raman scattering (SRS) system imaging was used to analyze the differences in the distribution and content of lipids and proteins within and between cells.

[0054] Figure 3 and Figure 4 These are stimulated Raman scattering (SRS) microscopic images of resistant and sensitive tissue sections. P and CH L The channel signals reflect the carbon and hydrogen signal intensity of proteins and lipids in cells respectively. The brighter the signal, the stronger the signal. The data analysis results are shown in Table 1 and Table 2 respectively. P Protein signaling and CH L The lipid signal intensity is similar, while CH P The protein signal intensity is significantly higher than that of CH L Lipid signal. The signal intensity data was quantitatively analyzed, and the analysis results were as follows: Figure 5 As shown, the ratio of lipid signal to protein signal intensity in cells from resistant (R) tissue sections was significantly higher than that in cells from sensitive (S) tissue sections.

[0055] Table 1 Resistance (R) tissue sections CH L and CH P Signal strength and strength ratio

[0056]

[0057]

[0058] Table 2 Sensitive (S) tissue sections CHL and CH P Signal strength and strength ratio

[0059]

[0060] Analysis of CH in resistant (R) and sensitive (S) tissue sections P Signaling response protein component area and CH L The lipid component area data of the signal response are shown in Table 3. P and CH L Signal average, resistance (R) CH in tissue sections L Lipid signaling and CH P The area ratios of protein signals are similar, while CH P The protein signal area is higher than CH L Lipid signal area. Quantitative analysis of protein and lipid component area data is performed, and the analysis results are as follows: Figure 6As shown, the area ratio of lipid signal to protein signal in resistant (R) tissue sections was significantly higher than that in sensitive (S) tissue sections.

[0061] Table 3 Resistant (R) / Sensitive (S) tissue sections CH L and CH P Signal area and area ratio

[0062]

[0063]

[0064] Analysis of CH in cells from resistant (R) and sensitive (S) tissue sections P Protein signaling and CH L The lipid signal accounts for the cell area ratio of the corresponding slice. The results are shown in Tables 4 and 5. P and CH L Signal average, resistance (R) CH in tissue sections L Lipid signaling and CH P The protein signal occupies a similar proportion of the cell area, while the CH P The protein signal occupies a higher proportion of cell area than CH L The ratio of lipid signal to cell area. L and CH P The data of lipid and protein components in the cells represented by the signal were quantitatively analyzed. The analysis results are as follows: Figure 7 As shown, the proportion of lipid signals and protein signals in the cells of resistance (R) tissue sections was significantly higher than that in the cells of resistance (R) tissue sections.

[0065] Table 4 Resistance (R) tissue section cell area and CH in cells L and CH P Signal ratio of cell area

[0066]

[0067] Table 5. Cell area of ​​sensitive (S) tissue sections and CH in cells L and CH P Signal ratio of cell area

[0068]

[0069]

[0070] The above experimental results indicate that the lipid-to-protein content ratio, area ratio and proportion of cell area occupied can all be used as detection indicators for metabolic stress typing of biological samples.

[0071] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A method for tissue metabolic stress typing based on quantitative analysis of lipid and protein components, characterized in that: The following steps are involved: Step S1, slicing the biological tissue sample to be tested to prepare a tissue slice sample; Step S2, performing label-free imaging of protein signals and / or lipid signals of the tissue section sample by nonlinear optical imaging technology; Step S3, performing quantitative analysis on protein signals and / or lipid signals based on the label-free imaging results; Step S4: Based on the quantitative analysis results, the biological tissue is classified to distinguish the metabolic stress type of the biological tissue sample as sensitive or resistant. The metabolic stress type is the metabolic phenotype of the tissue sample's response to the inhibitor after the metabolic activity of the biological tissue is inhibited.

2. The method for tissue metabolic stress typing according to claim 1, characterized in that: In step S1, the tissue section samples include one or more of formalin-fixed tissue sections, agarose gel sections, frozen sections, ex vivo biopsies, organoid smears, cell microspheres and paraffin section samples.

3. The method for tissue metabolic stress typing according to claim 1, characterized in that: In step S1, the thickness of the tissue slice is in the range of 20-200 μm.

4. The method for tissue metabolic stress typing according to claim 1, characterized in that: In step S2, the nonlinear optical imaging technology includes one or more of coherent Raman microscopy imaging technology, second harmonic imaging and two-photon fluorescence imaging.

5. The method for tissue metabolic stress typing according to claim 5, characterized in that: The coherent Raman microscopy imaging technology includes one or more of stimulated Raman scattering, coherent anti-Stokes microscopy imaging technology, surface enhanced Raman spectroscopy imaging technology and Raman spectroscopy analysis technology.

6. The method for tissue metabolic stress typing according to claim 1, characterized in that: In step S2, the protein signal and / or lipid signal is collected in a range of 2800 cm -1 Up to 3100cm -1 Band.

7. The method for tissue metabolic stress typing according to claim 1, characterized in that: In step S2, the lipids include one or more of phospholipids, triglycerides, cholesterol, cholesterol esters, glycolipids, saturated fatty acids and unsaturated fatty acids.

8. The method for tissue metabolic stress typing according to claim 1, characterized in that: In step S3, the quantitative analysis includes one or more of lipid content, lipid concentration, lipid spatial distribution, protein concentration, protein content, protein spatial distribution, lipid / protein content ratio, lipid component / protein component area ratio, lipid component to total cell area ratio, protein component to total cell area ratio, and the ratio of lipid signal to protein signal to total cell area ratio.

9. The method for tissue metabolic stress typing according to claim 1, characterized in that: In step S4, the inhibition of metabolic activity of biological tissues includes one or more of affecting the stability of cell DNA, interfering with the replication of cell DNA, affecting and blocking cell division, destroying the energy and material metabolic pathways of cells, affecting the properties of cell proteins, and interfering with the sugar and lipid phenotypes in cells and intercellular matrix.

10. The method for tissue metabolic stress typing according to claim 1, characterized in that: In step S4, the average value of the lipid signal and protein signal intensity ratio in different imaging areas is taken. When the average value is greater than 0.7, it is regarded as metabolic inhibition resistance typing tissue, the average value is less than 0.4, it is metabolic inhibition sensitive typing tissue, and the average value between 0.4-0.7 is regarded as metabolic inhibition intermediate tissue; the average value of the lipid signal and protein signal area ratio in different imaging areas is taken. When the average value is greater than 0.9, it is regarded as metabolic inhibition resistance typing tissue, the average value is less than 0.75, it is metabolic inhibition sensitive typing tissue, and the average value between 0.75-0.9 is regarded as metabolic inhibition intermediate tissue; the average value of the ratio of lipid signals and protein signals to the total cell area in different imaging areas is taken. When the average value is greater than 0.9, it is regarded as metabolic inhibition resistance typing tissue, the average value is less than 0.5, it is metabolic inhibition sensitive typing tissue, and the average value between 0.5-0.9 is regarded as metabolic inhibition intermediate tissue.

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