Method for classifying tissue metabolic stress based on quantitative analysis of lipid and protein components

Abstract

The present invention relates to a method for classifying tissue metabolic stress based on quantitative analysis of lipid and protein components, belonging to the field of biomedical technology. The specific steps include slicing a biological tissue sample to be measured to prepare a tissue slice sample, performing label-free imaging on the protein signal and / or lipid signal of the tissue slice sample through nonlinear optical imaging technology, quantitatively analyzing the protein signal and / or lipid signal based on the label-free imaging result, and classifying the biological tissue based on the quantitative analysis result to distinguish the metabolic stress type of the biological tissue sample as sensitive type or resistant type. Using this method to classify the metabolic stress of the tissue sample to be measured not only avoids the complex operations and long detection cycles of traditional labeling methods, but also provides comprehensive information on tissue metabolic components, enabling a deeper understanding of the metabolic process and the impact after metabolic intervention, thereby developing new treatment strategies to intervene in metabolic-related diseases, and having broad application prospects and significant beneficial effects.

Description

Technical Field

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

[0002] For the detection of biological tissue metabolic stress, it is necessary to quickly classify the metabolic and metabolic stress response types of the research tissue samples, which helps researchers deeply understand the metabolic process and the impact after metabolic intervention, study the differences and connections between normal and disease from the perspective of tissue and cell metabolism, so as to develop new treatment strategies to intervene in metabolic-related diseases. The research and detection of biological tissue metabolic stress not only help to understand how organisms respond and adapt to external interventions and tissue microenvironment changes, but also have important research significance and application prospects for the development of new treatment strategies, especially for the treatment of tumors, metabolic diseases and aging-related diseases.

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

[0004] Currently, the methods for tumor or other biological tissue metabolic research and stress detection are mainly divided into metabolomics analysis and single-cell resolution metabolic analysis. The main metabolomics analysis methods include nuclear magnetic resonance (NMR) technology and mass spectrometry imaging technology. Using nuclear magnetic resonance (NMR) technology for tumor metabolomics analysis can identify the metabolite changes in tumor tissues and biological fluid samples, thereby revealing the metabolic characteristics of tumors. Using MALDI-TOF MS technology can perform molecular imaging of biological samples, locate peptides and proteins, and perform in-situ imaging of metabolic heterogeneity. However, both metabolic detection methods based on nuclear magnetic resonance (NMR) and mass spectrometry technology face insurmountable obstacles such as low sensitivity, high equipment cost, and complex technical operations. In addition, single-cell resolution metabolic analysis technologies mainly include 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] Further, in step S1, the tissue section sample includes one or more of formalin-fixed tissue sections, agarose gel sections, frozen sections, ex vivo living tissue sections, 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] Further, in step S1, the thickness range of the tissue section is 20 - 200 μm.

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

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

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

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

[0020] Further, in step S2, the acquisition range of the protein signal and / or lipid signal is 2800 cm -1 to 3100 cm -1 band.

[0021] CH P is the carbon-hydrogen bond signal in proteins, and CH L is the carbon-hydrogen bond signal in lipids. Their distinction is based on the different vibration energy levels of carbon-hydrogen bonds in different biological chemical components. The vibration energy levels of carbon-hydrogen bonds in proteins and lipids respectively have equal energy differences with Raman shifts at 2930 cm -1 and 2850 cm -1 to generate resonance coherent signals. Since carbon-hydrogen bonds are widely distributed in both proteins and lipids, the content and distribution of protein and lipid components in biological tissue samples can be imaged and quantitatively analyzed at 2930 cm -1 and 2850 cm -1 wave numbers respectively.

[0022] Further, 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 several 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 accounting for the total cell area ratio, and protein component accounting for the total cell area ratio;

[0024] Further, in step S4, the inhibition of the metabolic activity of the biological tissue includes affecting the stability of cell DNA, intervening in the replication of cell DNA, affecting and blocking cell division, disrupting the energy and material metabolic pathways of cells, affecting the properties of cell proteins, and intervening in the sugar and lipid phenotypes in cells and the extracellular matrix, among one or more of them.

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

[0026] Further, in step S4, the average value of the lipid signal and protein signal area ratio in different imaging regions is taken. When the average value > 0.9, it is regarded as a tissue with a metabolic inhibition resistance classification. When the average value < 0.75, it is a metabolically inhibited sensitive classification tissue. When the average value is between 0.75 - 0.9, it is regarded as a metabolically inhibited intermediate tissue.

[0027] Further, in step S4, the average value of the ratio of the lipid signal and protein signal accounting for the total cell area ratio in different imaging regions is taken. When the average value > 0.9, it is regarded as a tissue with a metabolic inhibition resistance classification. When the average value < 0.5, it is a metabolically inhibited sensitive classification tissue. When the average value is between 0.5 - 0.9, it is regarded as a metabolically inhibited intermediate tissue.

[0028] Advantages of the present invention:

[0029] By using non - linear optical imaging technology to perform label - free imaging on protein signals and lipid signals of tissue section samples, it avoids the complex operations and long detection cycles that may be brought by traditional labeling methods, and realizes non - invasive and efficient detection. Based on the label - free imaging results, quantitative analysis of protein signals and lipid signals is carried out, covering multiple dimensions and various proportional relationships between lipids and proteins, providing comprehensive tissue metabolic information. According to the quantitative analysis results, metabolic stress typing of biological tissues is performed, which can accurately distinguish the sensitive or resistant responses of biological tissue samples to metabolic stress, providing important reference basis for the diagnosis, treatment, and prognosis of diseases, helping to reveal the response mechanism of tumor tissues under metabolic stress, and providing new ideas and methods for the treatment of tumors and drug research and development. At the same time, this method can also be applied to the metabolic stress research of other biological tissues and has broad application prospects. Description of the Drawings

[0030] To make the content of the present invention easier to be clearly understood, the following further describes the present invention in detail according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein

[0031] Figure 1 is the stimulated Raman scattering lipid imaging and lipid droplet area analysis diagram of DNA toxicity-related metabolic activity inhibitor-resistant (R) and sensitive (S) biological tissue sections in Example 2 of the present invention, where 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 DNA toxicity-related and antibiotic metabolic activity inhibitor-resistant and sensitive biological tissue section cells in Example 2 of the present invention, where A is the Raman signal acquisition result of different cells and B is the quantitative analysis result of lipid unsaturation;

[0033] Figure 3 is the stimulated Raman scattering imaging diagram of CH P protein signal and CH L lipid signal in different regions of the DNA toxicity and cell division arrest-related metabolic activity inhibitor-resistant (R) tissue section in Example 3 of the present invention;

[0034] Figure 4 is the stimulated Raman scattering imaging diagram of CH P protein signal and CH L lipid signal in different regions of the DNA toxicity and cell division arrest-related metabolic activity inhibitor-sensitive (S) tissue section in Example 3 of the present invention;

[0035] Figure 5 is the quantitative analysis result diagram of CH L lipid signal and CH P protein signal intensity and intensity ratio in the DNA toxicity and cell division arrest-related metabolic activity inhibitor-resistant (R) and sensitive (S) tissue sections in Example 3 of the present invention;

[0036] Figure 6 is the quantitative analysis result diagram of CH L lipid signal and CH P protein signal area and area ratio in the DNA toxicity and cell division arrest-related metabolic activity inhibitor-resistant (R) and sensitive (S) tissue sections in Example 3 of the present invention;

[0037] Figure 7 is the cell area and CH in cells of the DNA toxicity and cell division arrest-related metabolic activity inhibitor-resistant (R) and sensitive (S) tissue sections in Example 3 of the present invention LLipid Signaling and CH P Quantitative analysis result diagram of the proportion of cell area occupied by protein signals Specific Embodiments

[0038] The present invention will be 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 be able to implement it, but the exemplified embodiments are not intended to limit the present invention.

[0039] Example 1

[0040] I. Preparation of Tissue Agar Clots

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

[0042] (2) Add triple-distilled water to a conical flask, weigh and add agarose powder with a mass ratio of 3% and stir, heat the agarose solution in a microwave oven for 1 - 2 min, observe the dissolution situation until the solution becomes clear;

[0043] (2) Take out the conical flask and cool it to 40 °C. During this period, cut the excised tumor tissue into small pieces with side lengths of 3 - 5 mm; pour the agarose solution into a plastic sectioning tray, immerse the tissue pieces into the agarose solution until the tissue pieces wrapped in agarose solidify; it should be noted that the tissue is placed in the agarose solution at about 40 °C in the plastic sectioning tray to ensure that it does not overheat and burn the tissue while having fluidity. After placing the tissue, the tissue can sink but will not sink to the bottom, and at the same time, the upper part can be wrapped by the slurry;

[0044] (3) Take out the solidified block and cut it into agarose cuboid clots with lengths of 0.5 - 1 cm.

[0045] II. Fixation of Tissue Clots on the Microtome

[0046] Put glue with a diameter of 1 mm at the center position on the front side of the cutting slot of the microtome, and take about 2 cm of double-sided tape and paste it vertically on the cutting slot with the help of the glue against the blade edge; then put glue with a diameter of 1 mm 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 firmly; submerge the tissue block with PBS, and cut the tissue wrapped in agarose into slices with a thickness of about 100 μm (as thin and flat as possible); then fix it with tissue fixative as a sample to be detected by nonlinear optical detection.

[0047] Example 2

[0048] Use the method of Example 1 to perform sectioning on biological tissue samples to obtain sensitive cells BIU87, cisplatin-resistant cells CisR, and doxorubicin-resistant cells AdmR respectively. At a wavenumber of 2850 cm -1 Collect CH at the wavenumber Llipid signals, and collect Off resonance data at 1902 cm -1 wavenumber to remove non-resonant signals during later data processing.

[0049] Differential analysis of intracellular and intercellular lipid distribution and content was performed by stimulated Raman scattering (SRS) system imaging. The cell imaging results are as Figure 1 shown, where the CH L channel signal reflects the lipid hydrocarbon signal in tissues and cells. The red-orange color with a non-black background in the figure is the hydrocarbon signal. The brighter the signal, the stronger the signal. For example, Figure 1 in, the higher the brightness, the higher the lipid content density. Among them, lipid droplets are areas with high lipid content density and appear as bright yellow in the figure. The proportion of the lipid droplet area in sensitive and resistant tissue cells was quantitatively statistically analyzed at the single-cell level. The average proportion of the lipid droplet area in sensitive tissue was 1.9%, and that in resistant tissue was 5.0%. The average proportion of the lipid droplet area in resistant tissue was 2.6 times that in sensitive tissue. That is, the proportion of the lipid droplet (LD) cell area in the tissue resistant to metal platinum complex metabolic activity inhibitors is significantly higher than that in sensitive tissue, indicating that a higher level of lipid droplet deposition is a metabolic characteristic of DNA toxicity-related metabolic activity inhibitor-resistant tissue. This type of tissue or cell can be typed and identified through lipid chemical composition detection.

[0050] In addition, Raman spectral signals of lipid droplets in cisplatin (Cis)- and doxorubicin (Adm)-sensitive and resistant tissue cells were collected respectively (different lipid components will show different characteristic peaks in the Raman spectrum). Through Figure 2 the height ratio of the second peak to the first peak in A, the degree of unsaturation of the lipids stored in lipid droplets in cells can be quantitatively analyzed. The results are as Figure 2 shown in B. The degree of unsaturation of the lipids stored in lipid droplets of cells resistant to cisplatin Cis and doxorubicin Adm is significantly lower than that of sensitive cells.

[0051] As can be seen from the above example, 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 of biological samples under different inhibitor interventions can be carried out.

[0052] Example 3

[0053] According to the method of Example 1, sections of bladder cancer tumor tissues of the co-resistant (R) and co-sensitive (S) types to DNA toxicity and cell division arrest-related metabolic activity inhibitors were prepared respectively. At 2850 cm -1 and 2930 cm -1 bands, CH L (lipid) and CH P (protein) signals were collected respectively, and through 1902 cm -1Collect off-resonance data of wave numbers to remove non-resonant signals during later data processing. Analyze the differences in the distribution and content of intracellular and intercellular lipids and proteins through imaging with a stimulated Raman scattering (SRS) system.

[0054] Figure 3 and Figure 4 are respectively the stimulated Raman scattering (SRS) microscopic images of tissue sections of resistant and sensitive types. Among them, the CH P and CH L channel signals respectively reflect the hydrocarbon signal intensities of proteins and lipids in cells, and the brighter the signal, the stronger the signal. The data analysis results are shown in Tables 1 and 2 respectively. The CH P protein signal and the CH L lipid signal intensity in the resistant (R) tissue section are similar, while the CH P protein signal intensity in the sensitive (S) tissue section is significantly higher than the CH L lipid signal. Quantitative analysis is performed on the signal intensity data, and the analysis results are as Figure 5 shown. The ratio of lipid signal to protein signal intensity in cells of the resistant (R) tissue section is significantly higher than that of the sensitive (S) tissue section.

[0055] Table 1 CH L and CH P signal intensities and intensity ratios of the resistant (R) tissue section

[0056]

[0057]

[0058] Table 2 CHL and CH P signal intensities and intensity ratios of the sensitive (S) tissue section

[0059]

[0060] Analyze the area of the protein component reflected by the CH P signal and the area of the lipid component reflected by the CH L signal in the resistant (R) tissue section and the sensitive (S) tissue section. The results are shown in Table 3. Take the average value of the same type of CH P and CH L signals in different regions. The area ratio of the CH L lipid signal to the CH P protein signal in the resistant (R) tissue section is similar, while the area of the CH P protein signal in the sensitive (S) tissue section is higher than the area of the CH L lipid signal. Quantitative analysis is performed on the protein and lipid component area data, and the analysis results are as Figure 6As shown, the area ratio of lipid signal to protein signal in the tissue section of the resistant (R) group was significantly higher than that in the tissue section of the sensitive (S) group.

[0061] Table 3 CH in the tissue sections of resistant (R) / sensitive (S) L and CH P Signal area and area ratio

[0062]

[0063]

[0064] Analysis of the area ratio of CH P protein signal and CH L lipid signal in the cells of the tissue sections of resistant (R) and sensitive (S) groups. The results are shown in Table 4 and Table 5 respectively. For the same type of CH P and CH L signals in different regions, the average values were taken. In the cells of the tissue section of the resistant (R) group, the area ratio of CH L lipid signal to CH P protein signal was similar, while in the cells of the tissue section of the sensitive (S) group, the area ratio of CH P protein signal was higher than that of CH L lipid signal. Quantitative analysis was performed on the data of the area ratio of lipid and protein components in the cells represented by CH L and CH P signals. The analysis results are shown in Figure 7 Figure 38. The proportion of lipid signal to protein signal in the cells of the tissue section of the resistant (R) group was significantly higher than that in the tissue section of the resistant (R) group.

[0065] Table 4 Area of cells in the tissue section of the resistant (R) group and the proportion of CH L and CH P signals in the cells accounting for the cell area

[0066]

[0067] Table 5 Area of cells in the tissue section of the sensitive (S) group and the proportion of CH L and CH P signals in the cells accounting for the cell area

[0068]

[0069]

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

[0071] Obviously, the above embodiments are merely examples for clear illustration and are not limitations on the implementation manners. For those of ordinary skill 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 enumerate all implementation manners here. And the obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims

1. A method for classifying tissue metabolic stress based on quantitative analysis of lipid and protein components, characterized in that It includes the following steps: Step S1: Section the biological tissue sample to be measured to prepare a tissue section sample; Step S2: Perform label-free imaging on the protein signal and lipid signal of the tissue section sample through non-linear optical imaging technology; Step S3: Based on the label-free imaging results, perform quantitative analysis on the protein signal and lipid signal; Step S4: Based on the quantitative analysis results, classify the biological tissue, and distinguish the metabolic stress type of the biological tissue sample as a metabolic inhibition-sensitive classification or a metabolic inhibition-resistant classification. The metabolic stress type is the metabolic phenotype of the tissue sample's response to the inhibitor after metabolic activity inhibition of the biological tissue; Among them, in step S3, the quantitative analysis includes one or several of the lipid / protein content ratio, lipid component / protein component area ratio, lipid component ratio to the total cell area, protein component ratio to the total cell area, and the ratio of the lipid signal and protein signal to the total cell area; In step S4, take the average value of the intensity ratio of the lipid signal and protein signal in different imaging regions. When the average value > 0.7, it is regarded as a tissue with a metabolic inhibition-resistant classification; when the average value < 0.4, it is regarded as a tissue with a metabolic inhibition-sensitive classification; when the average value is between 0.4 - 0.7, it is regarded as a tissue with a metabolic inhibition intermediate classification. Take the average value of the area ratio of the lipid signal and protein signal in different imaging regions. When the average value > 0.9, it is regarded as a tissue with a metabolic inhibition-resistant classification; when the average value < 0.75, it is regarded as a tissue with a metabolic inhibition-sensitive classification; when the average value is between 0.75 - 0.9, it is regarded as a tissue with a metabolic inhibition intermediate classification. Take the average value of the ratio of the lipid signal and protein signal to the total cell area in different imaging regions. When the average value > 0.9, it is regarded as a tissue with a metabolic inhibition-resistant classification; when the average value < 0.5, it is regarded as a tissue with a metabolic inhibition-sensitive classification; when the average value is between 0.5 - 0.9, it is regarded as a tissue with a metabolic inhibition intermediate classification.

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

3. The method for classifying tissue metabolic stress according to claim 1, wherein: In step S1, the tissue section thickness ranges from 20 to 200 μm.

4. The method for classifying tissue metabolic stress according to claim 1, wherein: In step S2, the non-linear optical imaging technology includes one or several of coherent Raman microscopy imaging technology, second harmonic imaging, and two-photon fluorescence imaging.

5. The method for classifying tissue metabolic stress according to claim 4, wherein: The coherent Raman microscopy imaging technology includes one or several 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 classifying tissue metabolic stress according to claim 1, characterized in that: In step S2, the acquisition range of the protein signal and the lipid signal is 2800 cm -1 to 3100 cm -1 band.

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

8. The method for classifying tissue metabolic stress according to claim 1, wherein: In step S4, the metabolic activity inhibition of the biological tissue includes one or more of affecting the stability of cell DNA, intervening in cell DNA replication, affecting and blocking cell division, disrupting the energy and material metabolism pathways of cells, affecting the properties of cell proteins, and intervening in the sugar and lipid phenotypes in cells and the cell interstitium.

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