Skip to main content
NCBI home page
Biophysics Reports logoLink to Biophysics Reports
. 2023 Dec 31;9(6):299–308. doi: 10.52601/bpr.2023.230042

Rapid quantification of 50 fatty acids in small amounts of biological samples for population molecular phenotyping

Pinghui Liu 1, Qinsheng Chen 1, Lianglong Zhang 1, Chengcheng Ren 1, Biru Shi 1, Jingxian Zhang 1, Shuaiyao Wang 1, Ziliang Chen 2, Qi Wang 1, Hui Xie 1,*, Qingxia Huang 1,*, Huiru Tang 1,*
PMCID: PMC10960574  PMID: 38524698

Abstract

Efficient quantification of fatty-acid (FA) composition (fatty-acidome) in biological samples is crucial for understanding physiology and pathophysiology in large population cohorts. Here, we report a rapid GC-FID/MS method for simultaneous quantification of all FAs in numerous biological matrices. Within eight minutes, this method enabled simultaneous quantification of 50 FAs as fatty-acid methyl esters (FAMEs) in femtomole levels following the efficient transformation of FAs in all lipids including FFAs, cholesterol-esters, glycerides, phospholipids and sphingolipids. The method showed satisfactory inter-day and intra-day precision, stability and linearity (R2 > 0.994) within a concentration range of 2–3 orders of magnitude. FAs were then quantified in typical multiple biological matrices including human biofluids (urine, plasma) and cells, animal intestinal content and tissue samples. We also established a quantitative structure-retention relationship (QSRR) for analytes to accurately predict their retention time and aid their reliable identification. We further developed a novel no-additive retention index (NARI) with endogenous FAMEs reducing inter-batch variations to 15 seconds; such NARI performed better than the alkanes-based classical RI, making meta-analysis possible for data obtained from different batches and platforms. Collectively, this provides an inexpensive high-throughput analytical system for quantitative phenotyping of all FAs in 8-minutes multiple biological matrices in large cohort studies of pathophysiological effects.

Keywords: Fatty-acidomics, High-throughput quantification, Structure-retention relationship, No-additive retention index

INTRODUCTION

Fatty acids (FAs) are important nutrients for all organisms with numerous physiological functions (Bazinet and Layé 2014; Dalile et al. 2019; Röhrig and Schulze 2016) and quantitative analysis of FA composition (fatty-acidome) in small amounts of biological samples in large population cohorts is essential for understanding such functions (Losito et al. 2018; Wang et al. 2013). FAs can generally be categorized into saturated fatty-acids (SFAs) and unsaturated ones (UFAs) including monounsaturated fatty-acids (MUFAs) and polyunsaturated ones (PUFAs); the latter are essential FAs for mammals including both n3 and n6 ones (Wall et al. 2010). In biological samples, these FAs are present in the forms of free fatty-acids (FFAs) and esterified FAs such as phospholipids, sphingolipids (SPs), glycerides, cholesterol-esters (CEs), FA-esters of hydroxyl-FAs (FAHFAs) and acylcarnitines (ACars) (Fahy et al. 2005; Han 2016; Zhang et al. 2023). In human blood plasma and serum, for example, FAs are present as FFAs and lipoproteins implicated in numerous pathophysiological processes (Chen et al. 2020, 2023; Li et al. 2015b, 2023a; Loo et al. 2021; Nicholson 2021; Wu et al. 2021b; Xia et al. 2021, 2022). The biological functions of FAs and their metabolites are diverse including cellular membrane homeostasis (Romero et al. 2019), energy generation (Lopaschuk et al. 2010), regulation of transcription-factor activity (Zhang et al. 2021) and signaling (Dalile et al. 2019). For instance, short-chain FAs play vital roles in not only anti-inflammation but also regulation of glucose and lipid homeostasis (Frampton et al. 2020; Hummasti and Hotamisligil 2010; Wang et al. 2018; Wu et al. 2021a) whilst FA acylation of proteins had vital pathophysiological implications (Dai et al. 2020; Zhu et al. 2021). FAs in various forms were further considered as indicators for nutritional status, cardiometabolic and neurodegenerative diseases as well as cancer (Kim 2018; Li et al. 2023a; Lin et al. 2010; Nicholson 2021; Wu et al. 2021b; Xia et al. 2021, 2022). For example, a biomarker set consisting of eight fatty acids effectively indicated cell viability and characterized the hepatotoxicity of amiodarone (Li et al. 2023b). Biological functions of FAs are continuously emerging but far from fully understood whilst only small amounts of biological samples are often available. Therefore, high-throughput methods for quantifying fatty-acid composition (fatty-acidomics) in small amounts of samples are still essentially required especially for large cohort studies.

REVIEW

Quantification of FAs is classically done using GC-FID/MS (Agnew et al. 2019; An et al. 2013; Ecker et al. 2012; Han et al. 2011; Huang et al. 2019b; Ichihara and Fukubayashi 2010; Jiang et al. 2017; Masood et al. 2005; Tremblay-Franco et al. 2015; Xia et al. 2019; Xu et al. 2010; Zhang et al. 2015; Zhao et al. 2017), LC-MS (Han et al. 2015; Jiang et al. 2017; Mattarozzi et al. 2021; Wang et al. 2013) and NMR (Cai et al. 2017) methods. GC-FID/MS is clearly the technology of choice with its high sensitivity, capability of separating positional and cis-trans isomers, excellent stability and availability of databases for fatty-acid-methyl-esters (FAMEs). This is achieved by measuring FAMEs resulting from transesterification (Ostermann et al. 2014) catalyzed with bases or acids. The former include potassium hydroxide (KOH-MeOH) (Huang et al. 2019b) and sodium methoxide (NaOCH3) (Glaser et al. 2010) whereas the latter include boron trifluoride (BF3-MeOH) (Bondia-Pons et al. 2007), methanolic hydrochloric acid (HCl-MeOH) (Ichihara and Fukubayashi 2010), methanolic sulfuric acid (H2SO4-MeOH) (Agnew et al. 2019) and acetyl-chloride (CH3COCl-MeOH) (An et al. 2013; Li et al. 2015a; Xu et al. 2010; Zhang et al. 2015). However, bases are ineffective for methylation of FFAs and SMs whilst BF3-MeOH is unstable and toxic (Bondia-Pons et al. 2007); HCl-MeOH and H2SO4-MeOH are inconvenient to prepare and by-products can be formed at high-temperature (Agnew et al. 2019).

CH3COCl-MeOH appeared to be the choice of method. However, the existing methods often required a dozen hours for methylation and dozens of minutes for each spectral acquisition (An et al. 2013; Li et al. 2015a; Xu et al. 2010; Zhang et al. 2015) limiting their applications in large cohort studies. Even the fastest GC-FID/MS method required 15-min for data acquisition but detected only 24 endogenous FAs in human plasma samples (Ecker et al. 2012) without covering some important FAs (e.g., C11:0, C14:1n5c, C16:1n9c, C17:1n7c, C20:2n6c, C22:1n9c, C22:3n3 and C23:0). For instance, the levels of C14:1n5c and C16:1n9c in plasma were reported as important diagnostic indicators for long-chain fatty-acid oxidation defects (Cecatto et al. 2020) and Meniere's disease (Coon et al. 2023); C22:1n9 is a possible therapeutic agent for neurodegenerative diseases (Goyal et al. 2023). Some other methods with derivatization at high temperatures (95–100 °C) might impair analytical sensitivity and accuracy due to isomerization and instability of UFAs (Chiu and Kuo 2020; Liu et al. 2018). These methods also demand sizable samples which are not always available and transesterification efficiency for all fatty-acid forms needs clarifying together with the temperature-dependent isomerization for UFAs. Furthermore, additives (alkanes) were classically used to generate a retention index (RI) (Kovats 1958) to correct the batch effects universally present in chromatographic separation. Unfortunately, such additive-based RI methods inevitably introduce extra unwanted matrix effects and ionization interferences, especially for analytes coeluted with the additives. Moreover, quantitative structure-retention relationships (QSRR) remain to be established for predicting the retention time (tR) of analytes when their standards are unavailable.

To address these, here, we developed a parameter-optimized transesterification method using CH3COCl-MeOH and ensured complete methylation of FAs in all important forms of lipids. We then developed a rapid GC-FID/MS method for simultaneous quantification of all FAs in multiple biological matrices with an 8-min data acquisition. We further established a no-additive retention index (NARI) for FAMEs to correct the batch effects and a QSRR model for predicting tR of FAMEs with standards unavailable.

RESULTS AND DISCUSSION

Large cohort studies of lipid metabolism require high-throughput quantification of fatty acid composition in different biological samples with minimum inter-batch variations. To meet this demand, we established an 8-min GC-FID/MS method for simultaneously quantifying 50 FAs in multiple biological matrices with coverage of all lipid types. This has much higher coverage and is more rapid than previous methods (An et al. 2013; Li et al. 2015a; Xu et al. 2010; Zhang et al. 2015). We also developed a novel no-additive retention index (NARI) for correcting inter-batch variations with better performance than the alkanes-based classical RI (Kovats 1958). We have further established a quantitative structure-retention relationship (QSRR) for predicting tR of FAMEs when their standards are unavailable.

Efficient transesterification of fatty acids in different lipids and GC-FID/MS quantification

The efficient transformation of FAs in all lipids into FAMEs is the prerequisite for their high coverage quantification using GC-FID/MS. MeOH-CH3COCl was proven as a mild and safe transesterification reagent (An et al. 2013; Li et al. 2015a; Xu et al. 2010). However, the transesterification efficiency for fatty acids in different types of lipids remains to be clarified. Therefore, we here systematically optimized parameters including CH3COCl concentration, reaction time and temperature using human blood plasma samples. Differing from what was reported previously (Ecker et al. 2012; Lepage and Roy 1986), our optimal reaction parameters were 12.5% CH3COCl, 73 °C, 3 h and 6:1 for MeOH/hexane ratio (V/V) enabling methylation of the above 95% lipids (Fig. 1A). Only <2% isomerization by-products were detected for some UFAs with different carbon chain-lengths having one and two double bonds (Fig. 1B). Notably, our results also showed that the levels of isomerization by-product from C18:2n6c was obviously lower than those from C18:1n9c (Fig. 1B) being consistent with previously reported results that PUFAs with cis-fatty acids were less prone to isomerization than monosaturated FAs under same derivatization conditions (Agnew et al. 2019). This avoided the temperature-induced isomerization for UFAs (Liu et al. 2018) and acid-hydrolysis of FAMEs (Chu et al. 2015) above 80 °C thus clearly had better methylation performance than the reported methods (Ichihara and Fukubayashi 2010; Xu et al. 2010) (supplementary Fig. S1–S3). Such was confirmed here with an LC-MS method (Huang et al. 2019a; Loo et al. 2021) by monitoring 13 lipid subclasses including TG, PC, LPC, PE, LPE, PG, FFAs, sphingolipids (SM), CE, DG, ceramides (Cer), FAHFA and acyl-carnitines (Fig. 1A, supplementary Figs. S2 and S3) unlike previous studies monitoring only TG, phospholipids, sphingolipids, FFAs and CE (Ichihara and Fukubayashi 2010; Xu et al. 2010). Notably, ceramides showed about 55% derivatization probably due to the resistance of their amide-bonds to acid-catalyzed transmethylation (Masood et al. 2005). However, ceramides only account for less than 1% of fatty acids in biological samples.

Figure 1.

Figure 1

Open in a new tab

High-throughput quantitative fatty-acidomics analysis. A Transmethylation efficiency (12.5% CH3COCl, 73 °C, 3 h) for different lipids including cholesteryl-esters (CE), triglycerides (TG), diglycerides (DG), lysophosphatidylcholines (LPC), phosphatidylcholines (PC), lysophosphatidylethanolamines (LPE), phosphatidylethanolamines (PE), phosphatidylglycerol (PG), free fatty acids (FFA), fatty acid esters of hydroxyl-fatty-acids (FAHFA), acylcarnitines (ACar), sphingomyelins (SM) and ceramides (Cer). B Isomerization of some representative unsaturated fatty acids as a function of temperature and CH3COCl concentration. C concentrations (c, μmol/L) of fatty acids in 10 μL human fluids (urine and plasma) and 10 mg of other biological matrices (H1299 cells, mouse cecum content and rabbit liver tissue)

Furthermore, simultaneous quantification of FAs was achieved here with an 8-min acquisition with our comprehensively optimized GC-FID/MS parameters (supplementary Tables S1 and S2) showing good separation of UFA isomers such as C18:3n6-C18:3n3 and C18:2n6t-C18:2n6c (Fig. 2). This enables measurements of FA ratios as indicators of enzymes such as SCD1 (C16:1/C16:0) and elongases (C18:0/C16:0). This method also has higher throughput and coverage than all previous ones (An et al. 2013; Ecker et al. 2012; Gao et al. 2009; Garlito et al. 2019; Han et al. 2011, 2015; Jiang et al. 2017; Lepage and Roy 1986; Li et al. 2015a; Masood et al. 2005; Tremblay-Franco et al. 2015; Xia et al. 2019; Xu et al. 2010; Zhao et al. 2017) (supplementary Table S3). Since data acquisition is the speed-limiting step, this rapid analysis provided a feasible approach for large cohort studies when combined with automatic sample-preparation robotics. Moreover, this method had excellent linearity (R2 > 0.994) for all analytes in a concentration range of 2–3 orders of magnitude with LOD (S/N~3) and LOQ (S/N~10) below 20 and 40 fmol (as low as 0.5 and 2 fmol) on the column, respectively (supplementary Table S4). For all three concentration levels, the intra- and inter-day CV values were below 20% (below 15% for 95% analytes) with recoveries of 80%–120% (supplementary Table S5) and good stability (CV < 15%) in autosampler (20 °C) and storage at 4 °C and –80 °C (supplementary Table S6) except few compounds whose concentrations were close to LOQ.

Figure 2.

Figure 2

Open in a new tab

Total ion chromatogram (TIC) from GC-FID/MS analysis of 45 methylated FAs (Keys for analytes are listed in supplementary Table S3)

Applicability for quantifying fatty-acids in multiple typical biological matrices

We confirmed the applicability of this method for quantifying fatty acids in multiple typical biological matrices including human urine, plasma, cells, animal intestinal content and liver tissue samples (supplementary Table S7). The results showed significant differences in FA composition hence molecular phenotypes for these biological samples (Fig. 1C). The composition of FAs in human plasma was broadly consistent with what was reported (Masood et al. 2005). However, our method managed to quantify 30 FAs in healthy human plasma (supplementary Table S7) including some low-level ones (C11:0, C17:1n7c, C22:3n3 and C23:0) compared to less than 22 FAs with other methods (Han et al. 2011; Tremblay-Franco et al. 2015).

Some interesting molecular phenotypic features were observable for fatty acids in these biological samples. Whilst mouse cecum contents contained rich SFAs, human non-small cell lung cancer cells H1299 had more MUFAs whereas rabbit liver samples contained rich SFAs and PUFAs. Much fewer fatty acids were detectable in urine with only a few saturated (C10:0, C12:0, C14:0, C16:0, C18:0) and unsaturated fatty acids (C18:1n9c, C18:2n6c, C22:1n9c). In contrast, cecum content samples contained noticeably more odd-carbon SFAs (C15:0, C17:0, C19:0, C23:0, C25:0) than plasma and mammalian cell samples (supplementary Table S7). This is understandable and probably due to gut microbial contributions. However, these odd-carbon SFAs also had higher levels in rabbit liver tissue than human plasma and cells implying some profound effects of dietary and gut microbial FAs on the mammal physiology since conventional wisdom believed that mammals were only capable of synthesizing the even-carbon FAs. If this conventional belief were correct, then the odd-carbon fatty acids in human plasma detected here (C11:0, C15:0, C17:0, C23:0) should also be from dietary and gut microbiota whereas these in human cells (C15:0, C17:0, C23:0, C25:0) should be from their culture media, requiring further investigation. Such arguments about the origins and functions of odd-carbon fatty acids are clearly beyond the scope of this study but warrant further investigations. Nevertheless, these verified the applicability of the current method to multiple biological matrices for new discoveries in terms of nutritional and physiological effects. Since not all fatty acids have commercial standards, it is essential to establish empirical models for quantitative structure-retention relationships to assist reliable identification of analytes without standards and for correcting inter-batch variations in order to compare data from different batches, especially for large cohort studies.

Quantitative structure-retention relationship and no-additive retention index for FAMEs

Quantitative structure-retention relationship (QSRR) for analytes is vital for predicting their retention time hence assisting their reliable identification for chromatography-based analysis (Héberger 2007; Hellmuth et al. 2012; Hu et al. 2022; Kaliszan 2007; Ovčačíková et al. 2016; Vu et al. 2019); numerous calculated structural descriptors were considered in literature (Héberger 2007; Kaliszan 2007) though such methods were less practical. Interestingly, our experimental tR results clearly showed dependence on the carbon-chain length (CL), double-bond number (DB) and positions (DBP) for FAMEs (supplementary Fig. S4). By using MOMR and best-subset selection approaches, we employed data from 45 representative FAMEs to have established an empirical mathematical model for tR as a function of CL (c), DB (d) and DBP (p), Inline graphicInline graphic. Five-fold cross-validation indicated the model reliability with good correlation (R2, 0.9871) and small residual standard error (0.16 min). The model-calculated (tRC) and experimental retention-time (tRE) showed nice correlation (R2 ~0.9899) for these 45 FAMEs (supplementary Fig. 3A). For five test analytes which were not included in the model-building but detected in biological samples, their predicted tR values were consistent with the measured ones with ΔtR < 0.38 min (supplementary Table S8, Fig. 3B). Gratifyingly, their measured tR values from biological samples and analyte standards showed ΔtR < 0.02 min. Another analyte C22:2n9c was also detected here from some biological samples and assigned to the NIST database though commercial standards were unavailable. Nevertheless, its model-calculated tR values were consistent (ΔtR < 0.38 min) with the experimental one as well (supplementary Table S8). Such model-calculation values are expected to be useful for MS quantification in SIM mode and identification of FAs especially when commercial standards are unavailable.

Figure 3.

Figure 3

Open in a new tab

Correlations for experimental (tRE) and calculated values (tRC) from the quantitative structure-retention relationship (A) and prediction accuracy for six test analytes (B)

To correct tR drifts resulting from different temperature gradients, flow rates and acquisition batches, furthermore, we developed a no-additive retention index (NARI) using saturated fatty-acids methyl-esters (SFAMEs) endogenously present in samples instead of spiking alkanes for traditional RI. This is because adding alkanes will inevitably change sample matrices and co-eluted alkanes will inevitably interfere with analyte ionization limiting the use of FID and ion-selection in SIM mode. In this study, minor retention-time variations (ΔtR < 0.04 min) were observed for all analytes in multiple different matrices (mixed FAME standards, human urine, plasma, H1299C cells, mouse feces and rabbit liver tissue) analyzed in the same batch (supplementary Table S9). In contrast, retention-time variations between different temperature gradients, flow rates and batches were much larger (ΔtR ~1.6 min) as expected (supplementary Figs. S5A and S6A). Apparently, three different NARI schemes derived from three sets of SFAMEs all showed their powerfulness to correct tR variations from different temperature gradients, flow rates and batches (supplementary Figs. S5, and S6). Remarkably, NARI from seven FAMEs (C8:0, C16:0, C20:0, C22:0, C24:0, C25:0, C30:0) reduced all above inter-experimental ΔtR to 0.025 min (supplementary Fig. S6B) which performed better than the alkanes-based RI (ΔtR < 0.033 min) (supplementary Fig. S6E). Some of these seven analytes might not be detectable, such as C25:0 and C30:0 in urine (supplementary Table S9), or below the limit of quantification in some biological samples (supplementary Table S7). Nevertheless, the tR data for these FAMEs from standard calibration curves within the same batch are still useable with very small intra-batch tR variations (ΔtR < 0.02 min) resulting from different matrices (Chen et al. 2024).

CONCLUSION

We developed an 8-min GC-FID/MS method for simultaneously quantifying FA composition in biological samples with femtomole-level sensitivity, good accuracy, precision, stability and applicability to biofluids, cells, intestinal contents and tissues. Molecular phenotypes of these biological samples were quantitatively characterized showing unusually high levels of numerous odd-carbon fatty acids in mammalian cells, liver and plasma samples with dietary and gut microbial implications. We also established a no-additive retention index with endogenous analytes to enable comparison of tR data from different batches. We further established a quantitative structure-retention relationship (QSRR) for FAMEs with variable chain length, double-bond number and positions. To the best of our knowledge, such models have not been reported so far and are useful to predict tR values for FAMEs with standards unavailable. A number of applications of this rapid quantitative method to large population studies are now undergoing in this lab.

MATERIALS AND METHODS

Materials and reagents

All FA standards were purchased from commercial sources with details listed in supplementary Table S10. Potassium carbonate (K2CO3, 99.5%) was obtained from Aladdin Biochemical Technology Co. Ltd. (Shanghai, China). HPLC-grade methanol (MeOH) and n-hexane were purchased from Sigma-Aldrich (MO, USA) together with acetyl chloride (CH3COCl, 99.0%) and butylated hydroxytoluene (BHT, 99.0%). Ultrapure water was prepared by a Milli-Q purification system (Millipore, MA, USA).

Collection of biological samples

Human blood plasma and urine samples were obtained from Chinese adult volunteers recruited for the Human Phenome Project approved by the Ethics Committee of Fudan University (FE21087) with informed consent from all participants. Human cells, H1299, were acquired from the China Center for Type Culture Collection (CCTCC). Liver tissue samples of New Zealand rabbits were acquired from the Song Lian Experimental Animal Center in Songjiang District (Shanghai, China). Cecum contents of C57BL/6 mice from the Zi Yuan Experimental Animal Science and Technology (Zhejiang, China) were collected according to the National Guidelines for Experimental Animal Welfare (MOST of PR China, 2006). Rabbit liver tissue and cecum contents of C57BL/6 mice were approved by the Experimental Animal Ethics Committee, School of Pharmacy, Fudan University (2018-03-YL-GW-01). All samples were snap-frozen with liquid nitrogen and stored at –80 °C prior to analysis.

Preparation of stock and working solutions

A stock solution of 45 FA standards was prepared with MeOH at concentrations around 0.17–25.37 mmol/L (supplementary Table S10) with BHT added as an antioxidant. The solution for each of the five test analytes was prepared similarly. The stock solution was then sequentially diluted to obtain standard working solutions for calibration curves and these from 50-, 10- and 2-fold dilution were used as low, medium and high concentration quality control (QC) samples, respectively. A MeOH solution of C17:0-d33 (0.10 mmol/L) and C19:0-d37 (1.00 mmol/L) was used as internal standards (IS).

Transesterification of fatty acids in different lipids

We first systematically optimized the MeOH-to-hexane ratio, reaction time (1–3 h) and temperature (60–90 °C) from the previous methods (An et al. 2013; Li et al. 2015a) to establish an effective FA methylation method. In brief, 15 μL working solutions of FA standards were added with 15 μL IS and 500 μL CH3COCl solution (12.5%, V/V) in mixed MeOH-hexane (6:1, V/V). After 3 h incubation (73 ± 3 °C, 500 r/min) using a TUS-200P incubator (Yi Heng Technology, Shanghai), the mixture was cooled down to room temperature and slowly added with 500 μL K2CO3 aqueous solution (10%) under ultrasonic condition. An extra 100 μL hexane was then added, vortex-mixed for 2 min and centrifugated (4,500 g, 10 min) to obtain the upper layer for GC-FID/MS analysis. For biological samples, the working solution was replaced by 15 μL biofluids (plasma, urine) or about 10 mg intestinal contents or cells or homogenized tissues, respectively. The above procedures were conducted in glass vials to avoid plastic contamination. The efficiency of the above transesterification was monitored with an LC-MS method (Huang et al. 2019a; Loo et al. 2021) for all 13 lipid subclasses.

GC-FID/MS analysis

An Agilent 9000 gas-chromatography (GC) system was used for quantification with a flame-ionization detector (FID) and an Agilent 5977B electron-ionization (EI) mass spectrometer (Agilent Technologies, USA). FID and MS signals (at 70 eV) were acquired simultaneously for all analytes. GC separation was accomplished on an Agilent DB-FastFAME capillary column (20 m × 0.18 mm × 0.20 μm) with helium as carrier gas (1.0 mL/min); 1 μL sample was injected and analyzed with the split-ratio of 20:1 and the optimized temperature-gradient listed in supplementary Table S1. The temperature was set to 260, 250, 150 and 230 °C for the injection port, MSD transfer line, quadrupole and ion source, respectively. FID was used for quantifying FAs with more than 24 carbons and MS in selected ion monitoring (SIM) mode for the rest FAs to obtain the best responses.

Data processing and statistical analyses

Agilent MassHunter Workstation Qualitative and Quantitative Analysis software (v10.1) was used for peak-deconvolution and identification. For MS, one quantifying ion and at least two identifying ions were used for each analyte (supplementary Table S2) to avoid false positives. Fatty acids in biological samples were identified using tR, quantifying and identifying ions together with their abundance ratios obtained from FA standards. The above method was validated for sensitivity, linearity, precision, accuracy and stability (supplementary information notes). All statistical analyses were conducted using R-platform (v 4.1.0) with p < 0.05 considered as significant.

Quantitative structure-retention relationship for FAMES in GC-FID/MS analysis

Experimental tR values for FAMES from 45 FA standards were employed to construct multivariate-orthogonal multinomial-regression (MOMR) models against their structural characteristics including carbon-chain length (CL), double-bond (DB) number and DB position (DBP). LM function of R-software was used for evaluation with 5-fold cross-validation performed to obtain the model having minimized prediction error. Another six FAs were used as test analytes for assessing prediction accuracy. This enabled coverage of 50 fatty acids with commercial standards.

No-additive retention index (NARI) for GC-FID/MS analysis of FAMES

Experimental tR values for the FAMES from selected endogenous FAs were used to establish a retention index (supplementary information notes) to correct tR variations resulting from different experimental conditions such as temperature-gradient, flow-rate and inter-batch variations. This was done without any exogenous additives. We also built a classical alkanes-based RI in the classical way (Kovats 1958) as a comparison.

Conflict of interest

Pinghui Liu, Qinsheng Chen, Lianglong Zhang, Chengcheng Ren, Biru Shi, Jingxian Zhang, Shuaiyao Wang, Ziliang Chen, Qi Wang, Hui Xie, Qingxia Huang and Huiru Tang declare that they have no conflict of interest.

Compliance with Ethical Standards

Human and animal rights and informed consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Contributor Information

Hui Xie, Email: xiehui@fudan.edu.cn.

Qingxia Huang, Email: qingxhuang@fudan.edu.cn.

Huiru Tang, Email: huiru_tang@fudan.edu.cn.

References

  1. Agnew MP, Craigie CR, Weralupitiya G, Reis MM, Johnson PL, Reis MG Comprehensive evaluation of parameters affecting one-step method for quantitative analysis of fatty acids in meat. Metabolites. 2019;9(9):189. doi: 10.3390/metabo9090189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. An Y, Xu W, Li H, Lei H, Zhang L, Hao F, Duan Y, Yan X, Zhao Y, Wu J, Wang Y, Tang H High-fat diet induces dynamic metabolic alterations in multiple biological matrices of rats. J Proteome Res. 2013;12(8):3755–3768. doi: 10.1021/pr400398b. [DOI] [PubMed] [Google Scholar]
  3. Bazinet RP, Layé S Polyunsaturated fatty acids and their metabolites in brain function and disease. Nat Rev Neurosci. 2014;15(12):771–785. doi: 10.1038/nrn3820. [DOI] [PubMed] [Google Scholar]
  4. Bondia-Pons I, Moltó-Puigmartí C, Castellote AI, López-Sabater MC Determination of conjugated linoleic acid in human plasma by fast gas chromatography. J Chromatogr A. 2007;1157(1-2):422–429. doi: 10.1016/j.chroma.2007.05.020. [DOI] [PubMed] [Google Scholar]
  5. Cai J, Zhang J, Tian Y, Zhang L, Hatzakis E, Krausz KW, Smith PB, Gonzalez FJ, Patterson AD Orthogonal comparison of GC-MS and 1H NMR spectroscopy for short chain fatty acid quantitation. Anal Chem. 2017;89(15):7900–7906. doi: 10.1021/acs.analchem.7b00848. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cecatto C, Amaral AU, Wajner A, Wajner SM, Castilho RF, Wajner M Disturbance of mitochondrial functions associated with permeability transition pore opening induced by cis-5-tetradecenoic and myristic acids in liver of adolescent rats. Mitochondrion. 2020;50:1–13. doi: 10.1016/j.mito.2019.09.008. [DOI] [PubMed] [Google Scholar]
  7. Chen J, Dan L, Tu X, Sun Y, Deng M, Chen X, Hesketh T, Li R, Wang X, Li X Metabolic dysfunction-associated fatty liver disease and liver function markers are associated with Crohn's disease but not Ulcerative Colitis: a prospective cohort study. Hepatol Int. 2023;17(1):202–214. doi: 10.1007/s12072-022-10424-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chen Q, Lu Q, Zhang L, Zhang C, Zhang J, Gu Y, Huang Q, Tang H (2024) A novel endogenous retention-index for minimizing retention-time variations in metabolomic analysis with reversed-phase ultrahigh-performance liquid-chromatography and mass spectrometry. Talanta 268(Pt 1): 125318. https://doi.org/10.1016/j.talanta.2023.125318
  9. Chen YM, Zheng Y, Yu Y, Wang Y, Huang Q, Qian F, Sun L, Song ZG, Chen Z, Feng J, An Y, Yang J, Su Z, Sun S, Dai F, Chen Q, Lu Q, Li P, Ling Y, Yang Z, Tang H, Shi L, Jin L, Holmes EC, Ding C, Zhu TY, Zhang YZ Blood molecular markers associated with COVID-19 immunopathology and multi-organ damage. EMBO J. 2020;39(24):e105896. doi: 10.15252/embj.2020105896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chiu HH, Kuo CH Gas chromatography-mass spectrometry-based analytical strategies for fatty acid analysis in biological samples. J Food Drug Anal. 2020;28(1):60–73. doi: 10.1016/j.jfda.2019.10.003. [DOI] [PubMed] [Google Scholar]
  11. Chu PN, Chu FF, Zhang Y, Wu C, Zeng RJ A robust direct-transesterification method for microalgae. Energy Sources Part A. 2015;37(23):2583–2590. doi: 10.1080/15567036.2012.733481. [DOI] [Google Scholar]
  12. Coon AM, Setzen G, Musah RA Mass spectrometric interrogation of earwax: toward the detection of Ménière's disease. ACS Omega. 2023;8(30):27010–27023. doi: 10.1021/acsomega.3c01943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dai X, Hou H, Zhang W, Liu T, Li Y, Wang S, Wang B, Cao H Microbial metabolites: critical regulators in NAFLD. Front Microbiol. 2020;11:567654. doi: 10.3389/fmicb.2020.567654. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dalile B, Van Oudenhove L, Vervliet B, Verbeke K The role of short-chain fatty acids in microbiota-gut-brain communication. Nat Rev Gastroenterol hepatolo. 2019;16(8):461–478. doi: 10.1038/s41575-019-0157-3. [DOI] [PubMed] [Google Scholar]
  15. Ecker J, Scherer M, Schmitz G, Liebisch G A rapid GC-MS method for quantification of positional and geometric isomers of fatty acid methyl esters. J Chromatogr B Analyt Technol Biomed Life Sci. 2012;897:98–104. doi: 10.1016/j.jchromb.2012.04.015. [DOI] [PubMed] [Google Scholar]
  16. Fahy E, Subramaniam S, Brown HA, Glass CK, Merrill AH, Jr., Murphy RC, Raetz CR, Russell DW, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, VanNieuwenhze MS, White SH, Witztum JL, Dennis EA A comprehensive classification system for lipids. J Lipid Res. 2005;46(5):839–861. doi: 10.1194/jlr.E400004-JLR200. [DOI] [PubMed] [Google Scholar]
  17. Frampton J, Murphy KG, Frost G, Chambers ES Short-chain fatty acids as potential regulators of skeletal muscle metabolism and function. Nat Metab. 2020;2(9):840–848. doi: 10.1038/s42255-020-0188-7. [DOI] [PubMed] [Google Scholar]
  18. Gao X, Pujos-Guillot E, Martin JF, Galan P, Juste C, Jia W, Sebedio JL Metabolite analysis of human fecal water by gas chromatography/mass spectrometry with ethyl chloroformate derivatization. Anal Biochem. 2009;393(2):163–175. doi: 10.1016/j.ab.2009.06.036. [DOI] [PubMed] [Google Scholar]
  19. Garlito B, Portolés T, Niessen WMA, Navarro JC, Hontoria F, Monroig Ó, Varó I, Serrano R Identification of very long-chain (>C24) fatty acid methyl esters using gas chromatography coupled to quadrupole/time-of-flight mass spectrometry with atmospheric pressure chemical ionization source. Anal Chim Acta. 2019;1051:103–109. doi: 10.1016/j.aca.2018.11.001. [DOI] [PubMed] [Google Scholar]
  20. Glaser C, Demmelmair H, Koletzko B High-throughput analysis of fatty acid composition of plasma glycerophospholipids. J Lipid Res. 2010;51(1):216–221. doi: 10.1194/jlr.D000547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Goyal A, Dubey N, Verma A, Agrawal A (2023) Erucic acid: a possible therapeutic agent for neurodegenerative diseases. Curr Mol Med.
  22. Han J, Lin K, Sequeira C, Borchers CH An isotope-labeled chemical derivatization method for the quantitation of short-chain fatty acids in human feces by liquid chromatography-tandem mass spectrometry. Anal Chim Acta. 2015;854:86–94. doi: 10.1016/j.aca.2014.11.015. [DOI] [PubMed] [Google Scholar]
  23. Han LD, Xia JF, Liang QL, Wang Y, Wang YM, Hu P, Li P, Luo GA Plasma esterified and non-esterified fatty acids metabolic profiling using gas chromatography-mass spectrometry and its application in the study of diabetic mellitus and diabetic nephropathy. Anal Chim Acta. 2011;689(1):85–91. doi: 10.1016/j.aca.2011.01.034. [DOI] [PubMed] [Google Scholar]
  24. Han X Lipidomics for studying metabolism. Nat Rev Endocrinol. 2016;12(11):668–679. doi: 10.1038/nrendo.2016.98. [DOI] [PubMed] [Google Scholar]
  25. Héberger K Quantitative structure-(chromatographic) retention relationships. J Chromatogr A. 2007;1158(1-2):273–305. doi: 10.1016/j.chroma.2007.03.108. [DOI] [PubMed] [Google Scholar]
  26. Hellmuth C, Weber M, Koletzko B, Peissner W Nonesterified fatty acid determination for functional lipidomics: comprehensive ultrahigh performance liquid chromatography-tandem mass spectrometry quantitation, qualification, and parameter prediction. Anal Chem. 2012;84(3):1483–1490. doi: 10.1021/ac202602u. [DOI] [PubMed] [Google Scholar]
  27. Hu Q, Sun Y, Yuan P, Lei H, Zhong H, Wang Y, Tang H Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry. Talanta. 2022;238(2):123059. doi: 10.1016/j.talanta.2021.123059. [DOI] [PubMed] [Google Scholar]
  28. Huang Q, Lei H, Dong M, Tang H, Wang Y Quantitative analysis of 10 classes of phospholipids by ultrahigh-performance liquid chromatography tandem triple-quadrupole mass spectrometry. Analyst. 2019a;144(13):3980–3987. doi: 10.1039/C9AN00676A. [DOI] [PubMed] [Google Scholar]
  29. Huang WW, Hong BH, Sun JP, Tan R, Bai KK, Yang T, Wu H, Yi RZ Comparing the simultaneous determination of cis- and trans-palmitoleic acid in fish oil using HPLC and GC. Lipids Health Dis. 2019b;18(1):86. doi: 10.1186/s12944-019-1033-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hummasti S, Hotamisligil GS Endoplasmic reticulum stress and inflammation in obesity and diabetes. Circ Res. 2010;107(5):579–591. doi: 10.1161/CIRCRESAHA.110.225698. [DOI] [PubMed] [Google Scholar]
  31. Ichihara K, Fukubayashi Y Preparation of fatty acid methyl esters for gas-liquid chromatography. J Lipid Res. 2010;51(3):635–640. doi: 10.1194/jlr.D001065. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jiang R, Jiao Y, Zhang P, Liu Y, Wang X, Huang Y, Zhang Z, Xu F Twin derivatization strategy for high-coverage quantification of free fatty acids by liquid chromatography-tandem mass spectrometry. Anal Chem. 2017;89(22):12223–12230. doi: 10.1021/acs.analchem.7b03020. [DOI] [PubMed] [Google Scholar]
  33. Kaliszan R QSRR: quantitative structure-(chromatographic) retention relationships. Chem Rev. 2007;107(7):3212–3246. doi: 10.1021/cr068412z. [DOI] [PubMed] [Google Scholar]
  34. Kim CH (2018) Microbiota or short-chain fatty acids: which regulates diabetes? Cell Mol Immunol 15(2): 88−91
  35. Kovats E Gas chromatographic characterization of organic compounds, I. retention indices of aliphatic halides, alcohols, aldehydes and ketones. Helv Chim Acta. 1958;3(41):1915–1932. [Google Scholar]
  36. Lepage G, Roy CC Direct transesterification of all classes of lipids in a one-step reaction. J Lipid Res. 1986;27(1):114–120. doi: 10.1016/S0022-2275(20)38861-1. [DOI] [PubMed] [Google Scholar]
  37. Li D, Zhang L, Dong F, Liu Y, Li N, Li H, Lei H, Hao F, Wang Y, Zhu Y, Tang H Metabonomic changes associated with atherosclerosis progression for LDLR-/- mice. J Proteome Res. 2015a;14(5):2237–2254. doi: 10.1021/acs.jproteome.5b00032. [DOI] [PubMed] [Google Scholar]
  38. Li J, Huang Q, Wang Y, Cui M, Xu K, Suo C, Liu Z, An Y, Jin L, Tang H, Chen X, Jiang Y Circulating lipoproteins mediate the association between cardiovascular risk factors and cognitive decline: a community-based cohort study. Phenomics. 2023a doi: 10.1007/s43657-023-00120-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Li L, Zang Q, Li X, Zhu Y, Wen S, He J, Zhang R, Abliz Z Spatiotemporal pharmacometabolomics based on ambient mass spectrometry imaging to evaluate the metabolism and hepatotoxicity of amiodarone in HepG2 spheroids. J Pharm Anal. 2023b;13(5):483–493. doi: 10.1016/j.jpha.2023.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Li Y, Xu S, Zhang X, Yi Z, Cichello S Skeletal intramyocellular lipid metabolism and insulin resistance. Biophys Rep. 2015b;1:90–98. doi: 10.1007/s41048-015-0013-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lin PY, Huang SY, Su KP A meta-analytic review of polyunsaturated fatty acid compositions in patients with depression. Biol Psychiatry. 2010;68(2):140–147. doi: 10.1016/j.biopsych.2010.03.018. [DOI] [PubMed] [Google Scholar]
  42. Liu Z, Ezernieks V, Rochfort S, Cocks B Comparison of methylation methods for fatty acid analysis of milk fat. Food Chem. 2018;261:210–215. doi: 10.1016/j.foodchem.2018.04.053. [DOI] [PubMed] [Google Scholar]
  43. Loo RL, Lu Q, Carter EM, Liu S, Clark S, Wang Y, Baumgartner J, Tang H, Chan Q A feasibility study of metabolic phenotyping of dried blood spot specimens in rural Chinese women exposed to household air pollution. J Expo Sci Environ Epidemiol. 2021;31(2):328–344. doi: 10.1038/s41370-020-0252-0. [DOI] [PubMed] [Google Scholar]
  44. Lopaschuk GD, Ussher JR, Folmes CD, Jaswal JS, Stanley WC Myocardial fatty acid metabolism in health and disease. Physiol Rev. 2010;90(1):207–258. doi: 10.1152/physrev.00015.2009. [DOI] [PubMed] [Google Scholar]
  45. Losito I, Facchini L, Valentini A, Cataldi TRI, Palmisano F Fatty acidomics: Evaluation of the effects of thermal treatments on commercial mussels through an extended characterization of their free fatty acids by liquid chromatography - Fourier transform mass spectrometry. Food Chem. 2018;255:309–322. doi: 10.1016/j.foodchem.2018.02.073. [DOI] [PubMed] [Google Scholar]
  46. Masood A, Stark KD, Salem N, Jr. A simplified and efficient method for the analysis of fatty acid methyl esters suitable for large clinical studies. J Lipid Res. 2005;46(10):2299–2305. doi: 10.1194/jlr.D500022-JLR200. [DOI] [PubMed] [Google Scholar]
  47. Mattarozzi M, Riboni N, Maffini M, Scarpella S, Bianchi F, Careri M Reversed-phase and weak anion-exchange mixed-mode stationary phase for fast separation of medium-, long- and very long chain free fatty acids by ultra-high-performance liquid chromatography-high resolution mass spectrometry. J Chromatogr A. 2021;1648:462209. doi: 10.1016/j.chroma.2021.462209. [DOI] [PubMed] [Google Scholar]
  48. Nicholson JK Molecular phenomic approaches to deconvolving the systemic effects of SARS-CoV-2 infection and post-acute COVID-19 syndrome. Phenomics. 2021;1(4):143–150. doi: 10.1007/s43657-021-00020-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Ostermann AI, Müller M, Willenberg I, Schebb NH Determining the fatty acid composition in plasma and tissues as fatty acid methyl esters using gas chromatography – a comparison of different derivatization and extraction procedures. Prostaglandins, Leukot Essen Fatty Acids. 2014;91(6):235–241. doi: 10.1016/j.plefa.2014.10.002. [DOI] [PubMed] [Google Scholar]
  50. Ovčačíková M, Lísa M, Cífková E, Holčapek M Retention behavior of lipids in reversed-phase ultrahigh-performance liquid chromatography-electrospray ionization mass spectrometry. J Chromatogr A. 2016;1450:76–85. doi: 10.1016/j.chroma.2016.04.082. [DOI] [PubMed] [Google Scholar]
  51. Röhrig F, Schulze A The multifaceted roles of fatty acid synthesis in cancer. Nat Rev Cancer. 2016;16(11):732–749. doi: 10.1038/nrc.2016.89. [DOI] [PubMed] [Google Scholar]
  52. Romero LO, Massey AE, Mata-Daboin AD, Sierra-Valdez FJ, Chauhan SC, Cordero-Morales JF, Vásquez V Dietary fatty acids fine-tune Piezo1 mechanical response. Nat Commun. 2019;10(1):1200. doi: 10.1038/s41467-019-09055-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Tremblay-Franco M, Zerbinati C, Pacelli A, Palmaccio G, Lubrano C, Ducheix S, Guillou H, Iuliano L (2015) Effect of obesity and metabolic syndrome on plasma oxysterols and fatty acids in human. Steroids 99(Pt B): 287−292 doi.org/10.1016/j.steroids.2015.03.019
  54. Vu N, Narvaez-Rivas M, Chen GY, Rewers MJ, Zhang Q Accurate mass and retention time library of serum lipids for type 1 diabetes research. Anal Bioanal Chem. 2019;411(23):5937–5949. doi: 10.1007/s00216-019-01997-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Wall R, Ross RP, Fitzgerald GF, Stanton C Fatty acids from fish: the anti-inflammatory potential of long-chain omega-3 fatty acids. Nutr Rev. 2010;68(5):280–289. doi: 10.1111/j.1753-4887.2010.00287.x. [DOI] [PubMed] [Google Scholar]
  56. Wang M, Han RH, Han X Fatty acidomics: global analysis of lipid species containing a carboxyl group with a charge-remote fragmentation-assisted approach. Anal Chem. 2013;85(19):9312–9320. doi: 10.1021/ac402078p. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, Shao L, Tian Y, Dong Y, Zheng C, Wang J, Zhu M, Weintraub WS, Gao R Status of hypertension in China: results from the China hypertension survey, 2012-2015. Circulation. 2018;137(22):2344–2356. doi: 10.1161/CIRCULATIONAHA.117.032380. [DOI] [PubMed] [Google Scholar]
  58. Wu D, Zhang Y, Dong S, Zhong C Mutual interaction of microbiota and host immunity during health and diseases. Biophys Rep. 2021a;7(4):326–340. doi: 10.52601/bpr.2021.200045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Wu Q, Huang QX, Zeng HL, Ma S, Lin HD, Xia MF, Tang HR, Gao X Prediction of metabolic disorders using NMR-based metabolomics: the Shanghai Changfeng study. Phenomics. 2021b;1(4):186–198. doi: 10.1007/s43657-021-00021-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Xia F, Feng R, Xu FG, Su H, He C, Hu YJ, Wan JB Quantification of phospholipid fatty acids by chemical isotope labeling coupled with atmospheric pressure gas chromatography quadrupole- time-of-flight mass spectrometry (APGC/Q-TOF MS) Anal Chim Acta. 2019;1082:86–97. doi: 10.1016/j.aca.2019.06.065. [DOI] [PubMed] [Google Scholar]
  61. Xia M, Ma S, Huang Q, Zeng H, Ge J, Xu W, Wu Q, Wu L, Li X, Ma H, Chen L, Li Q, Aleteng Q, Hu Y, He W, Pan B, Lin H, Zheng Y, Wang S, Tang H, Gao X NAFLD-related gene polymorphisms and all-cause and cause-specific mortality in an Asian population: the Shanghai Changfeng Study. Aliment Pharmacol Ther. 2022;55(6):705–721. doi: 10.1111/apt.16772. [DOI] [PubMed] [Google Scholar]
  62. Xia M, Zeng H, Wang S, Tang H, Gao X Insights into contribution of genetic variants towards the susceptibility of MAFLD revealed by the NMR-based lipoprotein profiling. J Hepatol. 2021;74(4):974–977. doi: 10.1016/j.jhep.2020.10.019. [DOI] [PubMed] [Google Scholar]
  63. Xu Z, Harvey K, Pavlina T, Dutot G, Zaloga G, Siddiqui R An improved method for determining medium- and long-chain FAMEs using gas chromatography. Lipids. 2010;45(2):199–208. doi: 10.1007/s11745-009-3382-7. [DOI] [PubMed] [Google Scholar]
  64. Zhang H, Wang Z, Liu O Development and validation of a GC-FID method for quantitative analysis of oleic acid and related fatty acids. J Pharm Anal. 2015;5(4):223–230. doi: 10.1016/j.jpha.2015.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Zhang J, Chen Q, Zhang L, Shi B, Yu M, Huang Q, Tang H Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry. J Pharm Anal. 2024;14(1):140–148. doi: 10.1016/j.jpha.2023.10.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zhang S, Zhao J, Xie F, He H, Johnston LJ, Dai X, Wu C, Ma X Dietary fiber-derived short-chain fatty acids: a potential therapeutic target to alleviate obesity-related nonalcoholic fatty liver disease. Obes Rev. 2021;22(11):e13316. doi: 10.1111/obr.13316. [DOI] [PubMed] [Google Scholar]
  67. Zhao L, Ni Y, Su M, Li H, Dong F, Chen W, Wei R, Zhang L, Guiraud SP, Martin FP, Rajani C, Xie G, Jia W High throughput and quantitative measurement of microbial metabolome by gas chromatography/mass spectrometry using automated alkyl ahloroformate derivatization. Anal Chem. 2017;89(10):5565–5577. doi: 10.1021/acs.analchem.7b00660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Zhu Z, Han Z, Halabelian L, Yang X, Ding J, Zhang N, Ngo L, Song J, Zeng H, He M, Zhao Y, Arrowsmith CH, Luo M, Bartlett MG, Zheng YG Identification of lysine isobutyrylation as a new histone modification mark. Nucleic Acids Res. 2021;49(1):177–189. doi: 10.1093/nar/gkaa1176. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Biophysics Reports are provided here courtesy of Biophysical Society of China

RESOURCES