| Literature DB >> 34227318 |
Mengting Zhang1, Yulu Zhang1, Haojiang Wang1, Ning Li2, Bo Li1, Hong Xiao2, Wei Bian1, Zongwei Cai3.
Abstract
The incidence of breast cancer, one of the most common malignancies affecting women, is increasing significantly worldwide. Given the rapid development of medical technology, early and effective diagnostic methods should be able to improve the survival rate and quality of life of patients suffering from disease. However, although existing treatment options, including chemotherapy and endocrine therapies, have greatly improved the survival of patients, disease recurrence in the long term remains a challenge. Because breast cancer is a heterogeneous and complex disease, which includes several subtypes with different responses to treatment, the continual acquisition of spatial information on related biomolecules is important for accurate tracking of the tumor heterogeneity and microenvironment. At present, prognostic and predictive biomarkers, such as human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), Ki-67, progesterone receptor (PR), and programmed death-ligand 1 (PD-L1), are validated for use in the decision-making over breast cancer therapies. Mass spectrometry imaging (MSI) is a useful technique for acquiring molecular information about biological tissues, including qualitative, quantitative, and spatial distribution information, because it is based on the ion mass-to-charge ratio of the biomolecules and avoids the need for their labeling and staining. MSI can also acquire molecular information on drugs and their metabolites, as well as that on molecules related to endogenous metabolism, such as lipids, peptides, and proteins. Of the various ion sources available for MSI, the most popular are matrix-assisted laser desorption ionization, secondary ion mass spectrometry, and desorption electrospray ionization, and modifications or derivatives of these sources are still emerging. MSI-based techniques provide new ideas and directions for the molecular typing of tumors, as well as knowledge on the metabolism of related antitumor drugs. The process of MSI analysis generally involves tissue acquisition, section preparation, mass spectrometry ionization, map acquisition, and data analysis, with the most crucial step being sample handling to preserve the original chemical and location information of the analytes. The sample preparation steps are sample collection, storage, and slicing, tissue pretreatment, and matrix spraying. This review focuses mainly on the preparation of biological specimens for MSI analysis and the recent progress made in breast cancer research with this technology. With regard to sample preparation, four aspects are discussed: small-molecule samples, macromolecular samples, paraffin-embedded samples, and matrix spraying methods. To solve the difficulties associated with small-molecule sample processing, including the low extraction efficiency for certain lipids and matrix interference in the low-molecular-weight region, the addition of a cationic reagent to the extractant, the use of a new matrix, and tissue derivatization have been used. In the review of macromolecular sample processing, several different washing protocols are summarized. With regard to paraffin-embedded samples, the solutions to several common problems are reviewed. Additionally, the application of MSI to three models associated with breast cancer research is discussed, viz. cell models, animal models, and clinical tumor samples. For these models, MSI technology is used to evaluate the penetration and metabolism of antitumor agents in breast cancer, which can better reflect the malignant transformation of cells and changes in the microenvironment. With regard to lipid molecules, the use of MSI to study differences in their spatial distribution may provide a better understanding of the relationship between lipid metabolism and cancer. This review also provides important information for accurate molecular typing and drug screening in cancer research. Analytically, the tissue preparation method, tissue storage conditions, instrumentation choice, and experimental parameters have all been associated with variability in the imaging and mass-spectral qualities of MSI, thereby affecting the performance of the method. Large-scale studies using diverse sample cohorts are therefore needed to properly evaluate the robustness of MSI molecular markers and workflows for the clinical diagnosis and characterization of breast cancer variants. Our review provides strong evidence that MSI is a reliable, highly reproducible, and rapid technique for the diagnosis of breast cancer biopsies and may be useful in clinical application.Entities:
Keywords: breast cancer; mass spectrometry imaging technology; sample preparation
Mesh:
Year: 2021 PMID: 34227318 PMCID: PMC9404019 DOI: 10.3724/SP.J.1123.2020.10005
Source DB: PubMed Journal: Se Pu ISSN: 1000-8713
不同电离方式在检测小分子时的难点及解决方法
| Ionization methods | Advantages | Difficulties | Solutions | Refs. |
|---|---|---|---|---|
| DESI & nano-DESI | requires less sample pretreatment, no matrix, high resolution of nano-DESI | low extraction efficiency for certain lipids | add cationic reagent to extractant | [ |
| MALDI | need matrix, can realize the analysis from lipids and other small biological molecules to proteins and other biological macromolecules, high resolution | matrix interference in the low molecular weight region | new matrix: BNDM, Gly-3AQ | [ |
| carbon-carbon double bond positional isomer | Paternò-Büchi reaction | [ | ||
| detection of certain specific lipids | on situ tissue derivatization | [ |
DESI: desorption electrospray ionization; MALDI: matrix-assisted laser desorption/ionization; BNDM: 1,1'-binaphthyl-2,2'-diamine; Gly-3AQ: glycosyl-3-aminoquinoline.
新基质及其优势
| Matrix | Advantages | Refs. |
|---|---|---|
| BNDM & Gly-3AQ | overcome background interference in the low-quality range, and enhance the detection intensity of small molecule metabolites in the range of m/z<500; BNDM can be used for positive and negative ion mode detection; Gly-3AQ has acid response, and the optimum pH range is 2-7. | [ |
| Polyvinylpyrrolidone capped silver | enhanced comprehensive imaging of lipids | [ |
| Polydopamine-capped AgNPs | decrease the strength of phosphatidylcholine, and increase the strength of glycerophospholipid and sphingomyelin | [ |
| Combination of sodium doping | detection of neutral lipids | [ |
图 1BNDM作为MALDI-MSI的新基质检测代谢物的示意图[
图 2脂肪酸与衍生试剂反应示意图[
大分子成像前的切片洗涤方案
| Protocol | Step 1 | Step 2 | Step 3 | Step 4 | Step 5 | Step 6 | Sample | Ref. |
|---|---|---|---|---|---|---|---|---|
| 1 | 75% isopropanol 1 min | 90% isopropanol 1 min | neurodegenerative disease human skin sample | [ | ||||
| 2 | 70% ethanol 2 min | 70% ethanol 2 min | 100% ethanol 2 min | several washings with cold ACS-grade water | ethanol | animal with multiple skin nodular melanomas | [ | |
| 3 | 70% ethanol 15s | 96% ethanol 15s | multiple sclerosis brain tissue | [ | ||||
| 4 | 70% ethanol | 99% ethanol | Carnoy’s fluid (ethanol, | 99.9% ethanol | double-distilled water | 99.9% ethanol | APP transgenic mice (APPPS1-21) | [ |
| 5 | 70% ethanol 30 s | 100% ethanol 30 s | deionized water | 70% ethanol 30 s | 100% ethanol 30 s | human pancreas tissue sample | [ |
图 3基质沉积方法示意图[
图 4对FFPE组织进行MALDI MSI肽分析的工作流程[
图 5MALDI-MSI检测西妥昔单抗的示意图[
不同离子源质谱、3D模型及其检测内容
| Different ion source | 3D model (cancer cells) | Test content | Refs. |
|---|---|---|---|
| NanoLC-MS/MS | HT29 | proteomics, phosphorylated proteomics | [ |
| LC-MS/MS | MCF10A | lipid metabolism | [ |
| MALDI-MSI | HCT 116, HT-29, DLD-1 | penetration and distribution of drugs in cancer cells | [ |
| MALDI-MSI | MCF-7 | distribution of adenosine phosphate and glutathione | [ |
| MALDI-MSI | HCT116 | epigenetic drug UNC1999 | [ |
| LA-ICPMS | HCT116 | phosphorus and platinum in cancer cells | [ |
LA-ICPMS: laser ablation inductively coupled plasma mass spectrometry.