Applications
Lipidomics at cohort scale
Lipidomics produces large feature counts with a heavy isomer and alignment burden. As cohorts grow, keeping lipid features matched and complete across samples becomes the limiting step long before annotation does.
The problem
The gap in lipidomics
Lipids are numerous, structurally similar, and prone to coelution, so a lipidomics run generates many features that must be kept distinct and correctly matched across samples. Isomeric and isobaric species crowd narrow regions of the map, retention shifts move them relative to each other, and low abundance species sit near the noise. Across a large cohort these pressures combine, and the feature table fills with splits, misalignments, and missing values that obscure the biology the study was meant to reveal.
How Metablify fits
Recovery and alignment on data you already have
Metablify recovers reproducible lipid features and aligns them across the full cohort using agreement between injections, which stabilizes matching for crowded isomeric regions and recovers low abundance species that a fixed threshold would drop. The output is a cleaner, more complete lipid feature table that flows into the annotation tools with strong lipid libraries, so recovery and annotation each play to their strengths rather than competing.
Who this is for
Where the need is real
Research groups and core facilities running lipidomics at scale in areas such as metabolic disease, nutrition, and translational studies, where large cohorts and isomer complexity make alignment the bottleneck.
What matters
Where this makes a difference
Crowded maps, many isomers
Isomeric and isobaric lipids crowd narrow regions of the map, and retention shifts move them relative to each other, so keeping features distinct and matched is the hard part.
Alignment before annotation
As cohorts grow, matching lipid features across samples limits results long before annotation does. Metablify stabilizes that layer, then hands features to your lipid libraries.
Low abundance species recovered
Reproducibility across the cohort recovers low abundance lipids that a fixed threshold would drop, without inflating the table with noise.
Questions
Common questions
Does Metablify annotate lipids?
Metablify focuses on recovery, alignment, and quantification at the feature layer. Its output is designed to feed annotation tools with strong lipid libraries, so you keep the annotation you trust while improving the features that reach it.
How does it handle isomers?
Metablify aligns and recovers features that were measured. Isomers separated by your method are kept distinct and matched across samples. Isomers that fully coeluted at the instrument still require an orthogonal separation, which processing cannot add after the fact.
Keep reading
Related
Ready to talk about your study?
Bring us your samples, LC/MS data, or workflow challenge and we will map the right path forward.
Discuss a lipidomics cohort where alignment is the bottleneck.