Here is the translation in English, maintaining the Markdown format:

The authors of this article include Brady G. Anderson (Research Interests: P-Nitroanisole/Pyridine Actinometers, P-Nitroacetophenone, Quantum Yield, Column Packing, Omics, Institution: Biomed Res Core Facil Metabol Core, Univ Michigan), Pavlo Popov (Institution: Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA), Amanda R. Cicali (Institution: Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA), Adanna Nwamba (Institution: Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA), Evans Charles R (Research Interests: Metabolomics, Sepsis, L-Carnitine, Acetyl-Carnitine, Clinical Trial, Institution: Department of Internal Medicine, University of Michigan) and Robert T. Kennedy (Research Interests: Analytical Chemistry and its applications in Neuroscience, Endocrinology, and Biotechnology, Institution: College of Engineering, University of Michigan; Department of Pharmacology, Medical School, University of Michigan; Department of Chemistry, College of Literature, Science, and the Arts, University of Michigan).

Outline of the Paper

Abstract

• Introduces a method using microdialysis sampling combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) for in-depth analysis of the extracellular space of the brain
• This method can identify and monitor the chemical constituents in the extracellular space of the brain, improving the understanding of brain status

Introduction

• The extracellular space of the brain is the chemical environment for communication between neurons and glial cells
• Microdialysis sampling can obtain samples from the extracellular space of the brain, but sample volume and low concentration limit the analysis
• Liquid chromatography-tandem mass spectrometry technology can enhance the depth of analysis

Experimental Section

Chemical Reagents and Materials

• Lists the chemical reagents and materials used in the experiment

Sample Collection and Preparation

• Samples were collected from the rat striatum using microdialysis technology
• Samples were processed through concentration and derivatization

LC-MS/MS Conditions

• Describes the specific conditions for liquid chromatography and tandem mass spectrometry

Data Processing and Compound Identification

• MetIDTracker software was used for compound identification
• Compounds were identified through spectral library matching and retention time prediction

Results

• Using this method, 479 compounds were identified in non-derivatized samples
• In 5 μL samples, 60% of the identified compounds could be detected
• After BzCl derivatization, more neurotransmitters and metabolites could be detected

Discussion

• Discusses the limitations of the method and possible directions for improvement
• Proposes that this method can be used to monitor the chemical changes in the brain status

Conclusion

• This strategy can deeply annotate and monitor the chemical composition of the extracellular space of the brain
• It helps to define the chemical basis under different brain states

Q: What specific research methods were used in the paper?

• In vivo microdialysis sampling: Collection of interstitial fluid samples from the rat striatum.
• Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS): Separation of samples using reversed-phase and hydrophilic interaction liquid chromatography (RPLC and HILIC), combined with tandem mass spectrometry for compound identification.
• Data-dependent acquisition: Enhancement of the detection capability for low abundance compounds through iterative acquisition and rolling precursor ion exclusion.
• Chemical derivatization: Derivatization of samples with benzoyl chloride (BzCl) to increase the detection sensitivity of polar compounds.
• Database matching and retention time matching: Matching MS/MS spectra with databases and combining retention time information to improve the accuracy of compound identification.
• Retention time prediction model: Construction of a retention time prediction model using Retip software to assist in identifying compounds not present in the database.

Q: What are the main research findings and achievements?

• In-depth annotation of the interstitial fluid metabolome: Identification of 479 unique compounds using LC-MS/MS, including lipids, neurotransmitters, neuromodulators, hormones, precursors and metabolites, energy molecules, etc.
• Detection in small volume samples: 60% of the identified compounds can be detected in 5 μL of interstitial fluid by LC-MS, providing possibilities for routine monitoring.
• Derivatization enhances detection range: Detection of 872 unique benzoylated features using BzCl derivatization technology, including most small-molecule neurotransmitters.
• Rich metabolome in interstitial fluid: The study results indicate that interstitial fluid is a complex environment rich in metabolic and neurochemical information.

Q: What are the current limitations of this research?

• False positive identifications: The use of database matching and retention time matching methods still carries the possibility of false positive identifications, which require validation with standards.
• Unidentified compounds: There are many unidentified compounds in the LC-MS/MS data, which need to be further analyzed using other techniques or methods.
• Limitations of derivatization technology: The BzCl derivatization technique may not derivatize all compounds, necessitating the exploration of other derivatization methods.