Lipidomic Fingerprint
Lipidomic Fingerprint - Related Videos
08:16
Phenol Red Thread-based Sampling Procedure for Untargeted Tear Fluid Lipidomics in Biomarker Discovery
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2025
This protocol describes a unique clinical protocol for collecting human tear fluid samples using phenol red threads and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based workflow to discover the tear lipidomic profile. The simple methyl tert-butyl ether (MTBE)/methanol biphasic separation method enables rapid tear lipid extraction with high recovery for tear biomarker discovery.
11:46
Shotgun Lipidomics of Rodent Tissues
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Cited by 1 •
2022
Shotgun mass spectrometry-based lipidomics delivers a sensitive quantitative snapshot of a broad spectrum of lipid classes simultaneously in a single measurement from various rodent tissues.
01:03
IR Frequency Region: Fingerprint Region
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2024
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1. The...
09:58
Lipidomics and Transcriptomics in Neurological Diseases
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Cited by 13 •
2022
This article presents a modular protocol for tissue lipidomics and transcriptomics, and plasma lipidomics in neurological disease mouse models targeting lipids underlying inflammation and neuronal activity, membrane lipids, downstream messengers, and mRNA-encoding enzymes/receptors underlying lipid function. Sampling, sample processing, extraction, and quantification procedures are outlined.
12:02
Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis
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Cited by 18 •
2016
We present an automated modular high-throughput-method for the identification and characterization of microbial exopolysaccharides in small scale. This method combines a fast preselection to analyze the total amount of secreted polysaccharides with a detailed carbohydrate fingerprint to enable the fast screening of newly isolated bacterial strains or entire strain collections.
FAQs
Relative abundance helps reveal shifts among lipid species, not merely whether a lipid is present. Changes in the balance of molecules can indicate altered lipid metabolism, membrane composition, or signaling. This makes the profile useful for comparing cell or tissue states and identifying molecular patterns associated with disease or normal physiology.
Researchers compare lipid patterns from biological samples representing different states. A profile associated with normal physiology may differ in the presence or relative abundance of lipid species from one associated with disease. These differences can help identify disease-associated biomarkers and provide molecular evidence for separating physiological conditions from pathological ones.
A lipid profile can reflect several biological processes at once. Changes in lipid metabolism may alter molecular abundance, while shifts in membrane composition may indicate changes in cellular structure. Variation in signaling-related lipids can provide additional information about cell state. Considering these dimensions helps researchers interpret fingerprints as patterns of biology rather than isolated measurements.
Analysis begins by extracting lipids from blood, tissue, or another biological specimen. Researchers then separate the extracted molecules according to chromatographic properties before using mass spectrometry to identify and quantify individual molecular species. The resulting measurements are organized as relative-abundance patterns, allowing comparison among samples and interpretation of lipid-related biological changes.
Medical researchers may use these profiles to search for disease-associated biomarkers, distinguish physiological from pathological states, and investigate metabolic, cardiovascular, inflammatory, and other disorders. The approach is especially valuable when molecular patterns may reveal changes that help clarify disease mechanisms or support the development of more precise diagnostic approaches.
Lipidomic patterns may help group patients according to shared molecular features, an approach known as patient stratification. Repeated or comparative profiling can also support treatment monitoring by showing whether lipid-related molecular patterns change during care. These uses connect biochemical measurements with clinical research goals, including more precise diagnosis and assessment of disease-associated states.