LIPID CHEMISTRY 79
Beyond plant oils: exploring microbial lipid diversity
Heykel Trabelsi - Smey
Most lipid ingredients used in personal care still trace back to a handful of crops. Palm, shea, coconut, jojoba and cocoa supply the bulk of the oils and butters that go into creams, serums and lip balms, and all of them share the same constraint: a growing season, a patch of land, a climate. Palm oil production has driven well- documented deforestation in southeast Asia.1 Shea yields swing by as much as 50% from
one year to the next depending on rainfall in the Sahel.2
Cocoa prices have grown more volatile
as aging plantations in West Africa contend with disease and shifting weather.3
Formulators have
known this for years; what has changed is the urgency of finding alternatives. Yeasts are an obvious place to look but a
poorly explored one. A handful of species are known to accumulate lipids inside the cell, in some cases above 60% of dry biomass, yet most potentially oleaginous yeasts have never been characterized for fatty acid composition, let alone screened for cosmetic relevance.4 What characterization exists tends to come
from biofuel research, where the primary metric is total lipid yield, not the fine structure of the fatty acid profile.5
Chain length, double-bond position,
and minor lipid components that govern oxidative stability and skin feel: none of that shows up in a biofuel screen, and all of it matters to a skin care formulator.
This is the knowledge gap Smey set out to
address, working from a wide panel of yeast strains rather than a single target. Instead of choosing a fatty acid and then hunting for an organism that makes it, the company built a discovery platform to map lipid diversity broadly and let the data point to the most interesting candidates for skin and hair applications. What follows is how that platform works, what it has turned up so far, and what that means for the pace of ingredient discovery in personal care.
Leveraging the hidden diversity of microbial lipids The starting point was a strain library: hundreds of yeast species from different natural environments were brought to the lab. For most of them, this was the first time anyone had evaluated their lipid production at all; oleaginous yeasts outside a handful of model organisms simply have not received much attention. Screening at this scale required a single, standardized growth medium, engineered to
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push every strain towards its maximum lipid accumulation regardless of that species’ particular nutritional preferences. Without that, a strain that merely tolerated
the test conditions well would have looked like a better lipid producer than one that genuinely was, but happened to be less suited to whatever medium it was screened in. Hundreds of yeasts have been brought into the
programme so far. To date, more than 350 of them have been confirmed as oleaginous. Genome sequencing ran alongside this work.
Each strain’s genome was sequenced in full while its lipid production was being evaluated, so fermentation results and genomic identity could later be matched up strain by strain. Building that paired record had a second motivation. Training a machine-learning predictive model
on genomic sequences from strains with known lipid production should, in time, allow oleaginous candidates to be identified from sequence data alone, before a single fermentation is run. That would change the economics of library
expansion considerably: rather than culturing and screening every new yeast isolate to find out whether it accumulates lipids, genome analysis coupled with machine learning performs the pre- screening. The confirmed strains we have now are, in that sense, as much a training set as a product pipeline. Confirmation has a specific meaning here, and for skin care it is the part that actually matters.
Each candidate strain is fermented, and its lipid fraction analysed by gas chromatography with flame ionization detection (GC-FID)6
to give a
quantitative fatty acid profile, not an estimate. Iodine value and saponification value follow from that profile by standard, deterministic chemistry. Oxidative stability index is the exception: it
cannot be measured directly at screening scale, so it is estimated computationally from the fatty acid profile instead, and it is the one number in the dataset that carries model uncertainty rather than a direct reading. A formulator comparing two candidate lipids
for a barrier or sebum-mimicking claim should know which figures came off a chromatogram and which came out of a model. They are not the same kind of evidence, and skin compatibility claims should rest on the former. As the confirmed list grew, it became obvious
the data was useful beyond the company’s own pipeline. What started as an internal strain-selection tool became a proprietary lipid discovery platform available through subscription, searchable by fatty acid composition, including the chain-length and unsaturation patterns most relevant to skin and hair performance. It is not trying to compete with the large public sequence repositories on size. Its point is different: every entry corresponds to a named, taxonomically identified organism with a fatty acid profile that was actually measured, under the same screening conditions as every other entry,
October 2026 PERSONAL CARE MAGAZINE
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