ABSTRACT In this community effort, we compare measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass

spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as a set of

candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method and/or their method of choice. Mean concentration values, and intra- and

inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively. These results are the most precise (intra-laboratory CVs ≤

biases and help in harmonizing lipidomics. Collectively, the results from the present study provide a significant knowledge base for translation of lipidomic technologies to future clinical

applications that might require the determination of reference intervals (RIs) in various human populations or might need to estimate reference change values (RCV), when analytical

variability is a key factor for recall during multiple testing of individuals. SIMILAR CONTENT BEING VIEWED BY OTHERS IMPUTATION OF PLASMA LIPID SPECIES TO FACILITATE INTEGRATION OF

LIPIDOMIC DATASETS Article Open access 20 February 2024 ANALYSIS OF PLASMA METABOLOMES FROM 11 309 SUBJECTS IN FIVE POPULATION-BASED COHORTS Article Open access 18 April 2024

INTER-LABORATORY REPRODUCIBILITY OF AN UNTARGETED METABOLOMICS GC–MS ASSAY FOR ANALYSIS OF HUMAN PLASMA Article Open access 02 July 2020 INTRODUCTION Mass spectrometry (MS) is a powerful

technology that allows the qualitative and quantitative analysis of circulating metabolites, including lipids, in blood plasma1. Challenges in the translation of MS-based analytical methods

stem from technical aspects (e.g., dependency on stringent and consistent sampling procedures, reproducibility between different laboratories, control of bias and noise)2,3,4. Beyond that,

introducing and adopting new MS-based workflows into existing fields will require additional efforts, such as demonstrating cost-benefit effectiveness, scale, and pertinent turn-around times

from sampling to reporting. In the case of clinical applications, demonstration of utility to individuals and populations will be required in addition to economic benefit5. Once

established, through future efforts by the scientific community, reference intervals (RI, aka reference ranges) will be an important tool for the convincing communication of lipidomic

measurements and clinical adoption of laboratory-developed tests (LDT) within established clinical practices. RI represent lower and higher concentration boundaries of analytes, lipids, and

metabolites, in the case of lipidomics and metabolomics, respectively. Clinicians use and rely on established RI for interpretation of laboratory results, medical diagnosis, and evaluation

of treatment options for patients within a given reference group (e.g., total cholesterol, LDL-cholesterol RI according to different sex, age, and ethnicity). Appropriate standardization,

which is the focus of this manuscript, is a pre-requisite for (i) establishment and (ii) transportability of RI. This inter-laboratory and cross-platform study is an extension of a

community-initiated effort that started in 2017 and is now hosted by the International Lipidomics Society. As detailed and recommended in the initial proposal, reporting of molar

concentrations of individual lipids by different laboratories should be regarded as a first step in harmonizing plasma lipidomics. The main goals of this study were to: (i) evaluate the

degree of accuracy and concordance obtained in a large inter-laboratory trial using the same shared samples and custom-tailored calibrant materials; (ii) highlight technical issues

contributing to variability and technical outliers to avoid in future; (iii) document as precisely as possible the absolute concentrations of four circulating lipids in a publicly available

standard reference material and (iv) lay the foundation for determination of MS-based lipidomic RI in diverse human populations across the world in a standardized fashion. The evaluation of

utility of population RIs for the interpretation of lipidomic results is an important step that needs evaluation. If the analyte shows low individuality and the value dispersion in each

individual covers most of the dispersion among individuals, then the RI value is a useful parameter6. However, for analytes showing high individuality the use of RI is not recommended, while

monitoring longitudinal changes of concentrations by the reference change values (RCV) application is a better strategy. The RCV is defined as the change needed between two serial results

from the same individual to be statistically significantly different7. The RIs are descriptive of a specific population, derived from a reference distribution (usually 95% interval) and are

different from the decision limits above or below which a specific medical decision is recommended. And because we must consider that, for many analytes, a continuous variation may have

diagnostic significance, the concept of RCV is useful, although not much used in everyday practice. Why ceramides? The initial group of investigators involved in the human blood plasma

guidelines initiative8 identified ceramides, among other circulating lipids, as a particularly well-suited test case for a number of reasons including: (i) _Disease association_: Ceramides

are associated with major adverse cardiovascular events in individuals with coronary artery disease, Type 2 Diabetes, renal impairment, and age9,10,11,12,13 as well as major depressive

disorders14. (ii) _Clinical utility:_ Ceramides are used clinically to identify the risk of adverse cardiovascular events within 5 years15,16. (iii) _Actionability:_ Circulating ceramide

levels can be modified by long-term lifestyle interventions17,18,19, and the sphingolipid synthetic pathway can be targeted therapeutically (e.g. via several naturally occurring enzyme

inhibitors and derivatives thereof such as Fingolimod)20,21. (iv) _Genetic heritability:_ Ceramides display among the highest reported heritability values of blood lipids in different

populations, and accordingly, sphingolipids are strongly influenced by ethnicity22. (v) _Technical considerations:_ Analyte stability, detectable concentration levels in the circulation with

currently used analytical platforms, and availability of standards were additional important considerations. RESULTS The main goal of this community effort was to compare MS measurements

between a large number of laboratories (_n_ = 34) using the (i) same human blood plasma reference materials (four NIST plasma RMs)23, utilizing (ii) the same synthetic standards (a mixture

of deuterated ceramides at a pre-defined concentration prepared by Avanti Polar Lipids), and (iii) to report absolute concentrations of four ceramide species (Cer 18:1;O2/16:0, Cer

18:1;O2/18:0, Cer 18:1;O2/24:0 and Cer 18:1;O2/24:1) in the different RMs. The choice of the NIST SRM 1950 as one of the RMs was deliberate given its commercial accessibility offering

widespread access and utility. Consequently, this material has been used in numerous lipidomic and metabolomic community-driven studies24,25,26,27,28,29. Furthermore, NIST SRM 1950 is a

relatively well-characterized standard reference material (e.g., sterol and fatty acid concentrations are certified by NIST). It is increasingly being adopted in the lipidomic and

metabolomic communities for inter-laboratory comparisons and as a shared reference material for human plasma studies30,31,32. The other three RMs23 used in this ring trial allowed us to test

different matrix effects which might be useful in future clinical research33. As ceramides are implicated in cardio-metabolic diseases, we included pooled samples from populations with

hypertriglyceridemia (hTAG) and diabetes (DB), respectively. Ethnicity is another factor affecting plasma sphingolipid levels34. For this reason, we also analysed the Young African American

(YAA) blood plasma as one of the RMs. To help standardize experimental steps between laboratories experienced in lipidomic analysis and those newer to this field, a “Standard” operating

protocol (referred to as SOP throughout, see Supplementary Information) was prepared by the study coordinators, according to a published quantitative method35. This SOP included detailed,

step-by-step procedures covering ceramide extraction from human blood plasma, preparation of external calibration curves using deuterated and non-deuterated synthetic standards, and use of

MRM-based LC-MS/MS for measurement on triple quadrupole instruments. A template to design the sample analytical sequence and data reporting in a standardized format was also included (see

the SOP in Supplementary Information). To minimize variability-associated procedures, peak areas were chosen as the raw data report format rather than concentration values derived by

normalization to internal standards. The structured data reports facilitated integration with a specially designed pipeline for automated downstream processing, including conversion of peak

areas to concentration values. The pipeline was designed to simplify reproducibility of both tabular and graphical results. The structured reporting format contained sections to report peak

areas for all four ceramides and their labelled counterparts in columns, with three replicates each. The report rows contained the peak areas of all dilution steps of both calibration line

samples for multi-point calibration, the areas for every aliquot of each RM, the areas for each concentration step of the QC samples, and finally for the ceramide peak areas measured in the

matrix and total blank samples (see the SOP in the Supplementary Information for the definitions of different sample types). The calibration lines were calculated using R’s ‘lm‘ method,

regressing the ceramide area to labelled standard ratio on the known concentration, weighted by 1/x2, following the method used by Kauhanen et al.35. We then evaluated the average of the

calibration lines for each lab to calculate the adjusted concentrations from the reported areas of each ceramide and corresponding labelled standard within the QC and RM samples

(Supplementary Fig. 1). As a commonly adopted procedure in lipidomics, the concentration was also calculated using a single-point calibration, based on the ratio of each ceramide and its

corresponding labelled standard, multiplied by the known internal standard concentration. The individual concentrations were then combined and enriched with lab-specific metadata, e.g., the

methodology and MS-platform used, to calculate CV values for the inter-lab comparison. The participating laboratories were also invited to use their preferred protocol (from extraction to

detection to the type of instrument used) with which they might be more familiar, either as an alternative or an addition to the SOP (referred to as OTHER below). However, the same set of

synthetic standards were to be used in either case. At the end of the study, we collected 23 datasets generated with the SOP method and 15 with OTHER methods. The latter included a

combination of different techniques, such as nano or capillary-LC, flow injection analysis (FIA), different types of LC columns and gradients, as well as different detection methods (Table 

1). The breadth of different approaches allowed us to observe the influence of a prescribed methodological procedure (SOP) in comparison to performing the measurements in different ways

(OTHER) and, importantly, the effect that a common set of carefully adjusted standards might have on the quantitation performances when different methodologies are used. All the final

annotated data are available as Supplementary Data. It should be noted that for simplicity, since all four ceramides share the same sphingoid base moiety, i.e., 18:1;O2, we will refer to Cer

18:1;O2/16:0 as Cer16, Cer 18:1;O2/18:0 as Cer18, Cer 18:1;O2/24:0 as Cer24:0, and Cer 18:1;O2/24:1 as Cer24:1 in the text, but always according to the updated LIPID MAPS nomenclature in

the displays and tables36. In addition, and again for simplicity, we limit the report of the results in the next section to those obtained for NIST SRM 1950 plasma. A separate paragraph will

discuss the comparison of ceramide concentrations between the four RMs. Before estimating consensus values for the concentration of the ceramides in SRM 1950, we analysed the data

contributed by all the participant laboratories (Fig. 1). This allowed us to compare the reproducibility of the results between different methods and instruments, starting from the same

plasma sample and internal standard mixtures and excluding possible effects derived from different ways of estimating concentration values. Please note that the results shown in Fig. 1 and

Supplementary Fig. 2 represent the full dataset, with outlier removal thresholds based on Tukey’s 1.5 × IQR fences37 represented and determined separately for each of the four ceramides and

quantification methods, i.e., single vs. multi-point calibration. The outliers filtering resulted in the exclusion of 4 (10% of the total contributions) for Cer16 and Cer24, and 8 sets (21%)

for Cer18 and Cer24:1, respectively, when considering multi-point calibration (Supplementary Table 1 and Supplementary Data). Outliers were not represented in Figs. 2 and 3 and in specific

Supplementary Figs. (please refer to the corresponding legends). The medians of the intra-lab CVs ranged from 3.7 to 4.2% for the concentration of the four ceramides when using the

multi-point calibration, confirming good reproducibility between the measurements acquired by the individual participants (Fig. 1, Table 2). The level of concordance of the measurements

between the different contributors was estimated by the inter-lab CVs, ranging from 9 % to 14% when excluding outliers, and 25% to 31% for the unfiltered dataset (Table 2 and Supplementary

Table 1). The concordance of the results between the participants was not affected by the use of SOP or OTHER procedures, as the values generated from these two groups were not significantly

different (_P_ > 0.05) for any of the analytes in all RMs (Supplementary Table 2 and Fig. 1). Considering the disparity between the number of contributions obtained with low

(quadrupole/linear ion traps, _n_ = 33) and high resolution (Orbitrap and Time of flight, _n_ = 6, Table 1) instruments, we are not reporting any statistics for this comparison, although we

did not find significant differences between these two sets of results. In addition, the CV did not show any obvious dependency on the signal intensity of the measured molecules, except for

measurements acquired via FIA, where the sensitivity of the detection was lower for low abundant ceramides (e.g., Cer18 in Fig. 1, Lab ♯22). Collectively, these results demonstrate that

precise and concordant (low intra- and inter-lab CVs, respectively) values can be obtained independently of different methods and MS platforms in many laboratories and establish the

concentrations based on calibration curve quantification for the four ceramides in SRM 1950 as 0.244 ± 0.006 µmol/L for Cer 18:1;O2/16:0 (mean ± SEM; _n_ = 35), 0.0835 ± 0.0017 µmol/L for

Cer 18:1;O2/18:0 (_n_ = 31), 2.42 ± 0.04 µmol/L for Cer 18:1;O2/24:0 (_n_ = 35) and 0.855 ± 0.013 µmol/L Cer 18:1;O2/24:1 (_n_ = 31) (Table 1). ESTIMATION OF CERAMIDES CONCENTRATIONS IN SRM

1950 BY SINGLE-POINT INTERNAL STANDARD VS. MULTI-POINT CALIBRATION CURVE To generate reference values and absolute concentrations for the four ceramides in SRM 1950 plasma, we chose to

quantify the analytes in two different ways, namely (i) via the use of calibration curves (multi-point, Fig. 1 and Supplementary Fig. 1) and (ii) by using the labelled internal standards

(single-point calibration in a verified linear range, Supplementary Fig. 2). While the second approach is common when performing large lipidomic studies utilizing a small set of internal

standards representative of each of the lipid classes measured and their concentrations, the use of a calibration curve built with both endogenous and labelled molecules is characteristic of

targeted approaches. It should aid in accurate quantitation, minimizing the errors that could occur when using a single-point calibration. As both methods are currently part of lipidomic

workflows, we tried to clarify if, in this specific case, a significant benefit was conferred by either of them. As previously mentioned, we used only the dataset without outliers for this

comparison. This exercise aimed to estimate if the two procedures generate the same results with the same variability between laboratories. When considering all the participating

laboratories independently of the analytical method used (SOP vs. OTHER methods), we obtained inter-lab CVs for concentrations derived using single-point normalization of 9.2% for Cer16:0,

9.8% for C18:0, 11.4% for C24:0, and 14.9% for C24:1, respectively, while the corresponding CVs for concentrations derived using the multi-point calibration (external calibration curve) were

13.8%, 11.6%, 10.1% and 8.5% (Table 2). When comparing the mean absolute concentrations determined via single- or multi-point calibration, we observed different values for Cer16 and Cer18,

but not Cer24:0 and Cer24:1 (Fig. 2, Table 2). In the case of Cer16 we note a 1.4-fold higher concentration (for single-point) when compared with the multi-point calibration results. The

opposite is true for Cer18. We do not have a conclusive explanation for this discrepancy other than a possible disagreement in amount between labelled and unlabelled standards in the case of

Cer16 and Cer18, also observed in the calibration curves prepared in bovine serum albumin solution (Supplementary Fig. 1). Given the excellent agreement between single-point and multi-point

quantifications in the case of Cer24:0 and Cer24:1, it can be argued that the absolute levels of endogenous Cer24:0 and Cer24:1 in NIST SRM 1950 are close to 2.48 and 0.85 µmol/L (average

of single-point and multi-point calibration), respectively, unless both unlabelled and labelled ceramide standards employed for calibration were affected by the same concentration bias.

Inter-laboratory CVs of SOP-based results were marginally lower than those obtained by OTHER methods, collectively confirming that appropriately titrated authentic labelled standards

significantly reduce variability, irrespective of the degree of standardization of the procedures. COMPARISON OF CERAMIDES LEVELS IN DIFFERENT REFERENCE MATERIALS The concentration values of

the four ceramides were obtained for each of the RMs (i.e., SRM 1950 and the three materials within RM 8231) with the two different procedures described above. Also here, we removed

outliers for each RM and ceramide species, using the Tukey’s 1.5×IQR approach as previously described. As expected, the values generated by single-point and multi-point calibration for

Cer24:0 and Cer24:1 fall along the y = x line of equality (5% lower and 3% higher, respectively, when using multi-point compared to single-point calibration), while Cer16 and Cer18 show a

clear bias away from the y = x line, with 30.1% lower and 51.5% higher values, respectively (Fig. 2 and Table 2). The mean concentrations of all ceramide species were elevated in the RM

8231-2 (hypertriglyceridemic, hTAG) when compared with SRM 1950, most strikingly for Cer24:0 and Cer24:1 (1.56 fold and 1.47 fold, respectively) confirming previous results of 1.5-2 fold

changes from the same comparison using different MS-platforms23,33. These results are consistent with many independent human plasma lipidomic studies that reported a positive correlation

between TAG and ceramides. Genetic and environmental conditions such as diet and exercise influence circulating sphingolipids, including ceramides. Ceramides display high genetic variability

compared to other circulating lipids38,39 and vary across different ethnicities, age and sex40. The levels of all four ceramide species are significantly lower in RM 8231-3 (Young African

American, YAA, Fig. 3, Supplementary Table 1), again in agreement with previous characterizations of these RM23,33, thus calling for determination of stratified reference intervals for

applications in clinical testing. The concentrations of all four ceramides in the diabetic RM (RM 8231-1) were not significantly different from SRM 1950, consistent with previous

studies23,33. We did not observe any consistent difference in inter-lab variation of concentration values between the four RM (Fig. 3, Table 2 and Supplementary Table 1). This suggests that

the measurements had comparable concordance among the laboratories for different matrices, despite their potentially different properties, e.g., the hTAG plasma sample being of different

viscosity and YAA having lower ceramide concentrations, compared to SRM 1950. COMPARISON WITH PREVIOUS INTER-LABORATORY AND/OR CROSS MS-PLATFORM STUDIES MEASURING CERAMIDES IN NIST SRM 1950

We next compared our results to previous inter-laboratory studies using MS platforms which reported concentration values of ceramides in NIST SRM 195024,27,29,33. A summary of the comparison

is presented in Fig. 4. Note that in this figure we included all datasets from our study, i.e., no outliers were removed, as in other published datasets. Details of the previous trials and

this current study are tabulated in Supplementary Table 3, i.e., year of publication, usage of internal standards, type of extraction and MS-platforms, and the number of participating

laboratories. As we only measured four endogenous molecules, the most distinguishing feature of our study is the use of authentic labelled standards for each of the quantified analytes.

Furthermore, the choice of extraction protocol and MS platforms in our study were at the discretion of the participants compared with previous trials, with the notable exception of

reference24. The current study is also the largest based on the number of participating laboratories and returned datasets. We would also like to highlight and acknowledge that several

laboratories participated in more than one of these trials, underlying the broad common efforts put forward by our communities to advance the field through improvement and harmonization of

procedures. We compared the ceramide concentration values and their variability across the five studies wherever possible. For example, the distinct cores within the original LIPID MAPS

consortium reported results in a lipid class-specific fashion, thus limiting the laboratory reporting sphingolipids/ceramides to _n_ = 1, without an option to calculate inter-laboratory

CVs27. Another complicating factor in the comparison of diverse studies is the precise identity of the reported analytes. For example, three out of the five studies reported to the levels of

lipid species identities (i.e., Cer 18:1;O2/16:0) while others were based on sum composition (e.g., Cer 34:1;O2) due to technological limitations. Finally, the mean absolute deviations

(MAD) in our study are among the smallest despite the large number of participants and diverse methods and MS platforms utilized to generate the results. USE OF AUTHENTIC VS. CLASS-SPECIFIC

INTERNAL STANDARDS FOR CALIBRATION The inclusion of authentic labelled standards, prepared to approximate the levels of endogenous ceramides in human plasma (Supplementary Fig. 1), was an

early design choice of our trial. It is grounded in the belief that appropriately matching each analyte with its closest stable-isotope derivative would overcome complications, such as

different extraction efficiencies, ionization and fragmentation properties, chain-length dependencies, lack of co-elution in reversed-phase LC. To evaluate the relevance of this choice, we

normalized the peak areas returned for each endogenous ceramide species (from each participant laboratory) with either the authentic standard (i.e., the corresponding stable isotope

derivative) or one of the other three available labelled standards with differing fatty acid moieties (Fig. 5). For this analysis we excluded the outlier datasets (based on authentic

internal standard) as described before (Fig. 1). In the case of the endogenous concentrations of Cer24:0 we obtained 2.41 ± 0.24 μmol/L (mean ± SD) when using the authentic internal standard

for normalization, i.e., D7-Cer24:0, and 3.07 ± 1.86 μmol/L when normalizing with the D7-Cer16 internal standard, corresponding to a 1.27-fold difference in absolute concentrations. In

addition, and importantly, the corresponding variabilities were also significantly affected by the choice of the IS and resulted in inter-lab CVs of 10% and 61%, respectively, when using the

authentic labelled analyte or not. Similar disparities were found in other permutations of this procedure when testing the use of different labelled standards (Supplementary Fig. 6).

RECALIBRATION OF RMS USING SRM 1950 AS SHARED REFERENCE The use of shared reference samples can correct systematic method-specific quantitative biases and help in harmonising lipidomic

data30,31. Harmonization with shared materials is quite accepted in clinical settings, where accurate results over time and location are achieved by standardising measurements and

establishing traceability to a reference system41,42,43,44,45. Since NIST SRM 1950 is commercially available and one of the most popular references for human plasma, we harmonised the data

between all participants by using the following procedure: the four ceramides concentration values obtained from the different RMs (hTAG, DB and YAA) by each participating laboratory were

normalised by the mean concentration of the corresponding ceramide obtained by the same lab for SRM 1950, according to the following formula (i). $${c}_{{Sample\; k}}^{{Analyte\; i},{Lab\;

j}}\left({{{\rm{recalibrated}}}}\right)=\frac{{c}_{{Sample\; k}}^{{Analyte\; i},{Lab\; j}}}{{\bar{c}}_{{SRM}1950}^{{Analyte\; i},{Lab\; j}}} * {c\left({Ref\; SRM}1950\right)}^{{Analyte\;

i}}$$ (i) where the sample’s measured concentration is divided by the mean concentration of SRM 1950 replicates measured by the corresponding lab. This unitless ratio is rescaled by

multiplying by the concentration assigned to the SRM1950, which corresponds to the consensus value established in this study. After this recalibration, the inter-laboratory variability

(inter-lab CV) decreased for all the reference materials, with the number of outliers significantly reduced (Fig. 6 and Supplementary Figs. 4–6). For the DB RM, inter-lab CVs were reduced

5-fold for all four ceramide species. For hTAG and YAA RM the CV value decreased 2-fold. However, the final mean values did not show any significant difference after harmonization. Although

it delivers final concentration values relative to the shared reference (SRM 1950), this approach improves _comparability_ between measurements generated with different methods or

instruments. The differences between the normalised inter-lab CV values for the various SRMs possibly reflect the influence of matrix effects. Similarly, matrix effects of individual plasma

samples in clinical studies will affect the reported concentrations, something that may need to be investigated further. While we consider the use of authentic labelled standards essential

to yield quantitative results with low inter-laboratory and cross-platform CV (above), we show here that adding this normalisation approach can reduce systematic errors significantly. Even

rather severe outlier values (e.g., #34) are realigned with the rest of the participants when using this approach. This last example illustrates well that for better transparency and for

future applications, we suggest reporting not only the harmonized concentration values but the ones generated before and after recalibration. DISCUSSION Our results demonstrate that,

independent of the pre-analytical steps, separation method and type of mass spectrometer used for measurements, our community was able to provide reproducible data from a targeted lipidomic

approach, when provided normalization procedures using corresponding labelled standards46,47. This inter-laboratory and cross-platform trial yielded excellent reproducibility, albeit only

for a small number of analytes. This good reproducibility was achieved even if we did not recommend any system suitability test procedures to optimize the performances of the analytical

systems prior to analysis. We highlight the importance of using labelled standards authentic to the endogenous analyte of interest to improve the accuracy of the reported concentrations in a

sample and reduce the variability associated with such measurements (Fig. 5, Supplementary Fig. 3). The reasons for the findings illustrated in Fig. 5, where increased variability between

participants is associated with the use of class-specific standards instead of authentic ones, were inconclusive. However, one could consider matrix effects (e.g., at different retention

times and different in-source ionization efficiency), as well as fragmentation conditions/efficiencies for the different analytes, to exert a relevant effect. Some of these factors could

possibly be excluded by using HILIC/SFC separations or FIA, allowing co-elution of the analyte with the surrogate standard, improving on the homogeneity of matrix effects with co-elution,

and by the optimization of the ionization, transport, and fragmentation of each single lipid species, although the number of such datasets in our study was too small to draw a definitive

conclusion. In this study, most analytes were fragmented with the same or very comparable collision energies despite displaying different chain lengths. In the absence of authentic labelled

standards, it has been shown for different lipid species that accurate quantitation requires the use of individual response factors, determined for each molecular species48. Structural

features (chain length and number of double bonds) and coelution with other analytes can influence ionization and in-source fragmentation, when present, which also depends on the individual

setup of the mass spectrometer used. A second notable aspect included in this work is the comparison of single-point calibration vs. standard curves to generate final concentration values.

While the use of calibration curves is an accepted requirement in clinical chemistry, many research laboratories rely on single-point calibration after testing for linearity under the

experimental conditions used. Specifically, we compared the reference values generated with the two methodologies and their associated CV. While the variability (CV) between participants was

comparable, when using the two methods we did not obtain the same concentration values for Cer16 and Cer18 (Fig. 2). This might be because in our calibration standard mix for Cer16 and

Cer18 equimolar solutions of labelled and non-labelled standards did not produce identical areas. This might point to a deuterium effect during ionization and fragmentation or small

differences in the concentrations of standards. This was noticed during the calibration curve measurement and affected the calculated final values. We can also highlight that according to

the use of fit-for-purpose calibration curves, we would now design differently the concentration intervals for the standards used to build the calibration curves used here. After measuring

the highest and lowest values in the reference materials and in order to increase the accuracy of the results, a dilution series spanning a smaller concentration range, and with a median

close to the value we report here for each ceramide, would be a preferable choice. ANALYSIS OF OUTLIERS We found concordant final concentrations (inter-laboratory CV < 30%) between

participants. However, to find potential causes of variability and improve future studies, we dedicated some attention to understanding the most divergent values. We decided to classify

“outliers” based on Tukey’s 1.5× IQR fences, corresponding to approximately 3× SD37,49. This outlier removal method was used to filter the dataset before calculating the average consensus

concentrations of the four ceramides in NIST SRM 1950; see also Fig. 1. As we systematically reported results based on both single-point and multi-point calibrations, we noticed that some of

the outliers resulting from the use of the single-point calibration were ‘corrected’ and re-aligned to the average value when using multi-point calibration instead (e.g., #04 and #20; Fig. 

1, Supplementary Fig. 2). However, as the opposite was observed in specific cases (#17), we conclude that while in principle the use of a calibration curve can help generate more accurate

values by developing a response function in the expected range of concentrations of the analytes in unknown samples, sometimes it might also introduce variations due to more tedious

protocols. In addition, some measurements were identified as outliers with both calibration methods. When considering the outliers, we did not notice any specific bias towards datasets

generated with the recommended SOP or OTHER methods and in terms of the instrument used. We next carefully inspected several of these outliers (Fig. 1). We contacted participants to manually

check raw data to exclude possible errors due to peak integration and/or calculations. This in-depth analysis provided satisfactory explanations for some of the original discrepancies,

while we can highlight several useful observations for others. As an example, several participants returned higher values for Cer18 and Cer24:1 when compared to the average of all

measurements. These values were generated using a preferred method that included acquisition on a high-resolution instrument, in MS1 only, and identification based on accurate mass of the

precursor ion, retention time and co-elution with the labelled standard. It is possible that when measuring the intact mass of ceramides in high-resolution MS (but without sufficient

chromatographic separation, e.g., via HILIC or reversed phase with short gradients) without using any correction during quantitation, the resulting peak area might include contributions from

isomeric or isobaric compounds present in solution or generated during ionization. With chromatographic conditions unable to separate possible isomers of the target analytes (such as Cer

16:1;O2/20:0 or Cer 18:2;O2/24:0), a partial contribution to the signal of Cer18 and Cer24:1 would have been included with the reported value. When we independently estimated the levels of

Cer 16:1;O2/20:0 or Cer 18:2;O2/24:0 in SRM 1950 plasma by LC-MRM, we confirmed the existence of a substantial contribution (10–15% and 30%, respectively) to the MS1 signal of Cer18 and Cer

24:1. After this was taken into account, the values could be aligned with the rest of the participants. Another source of error was due to copy/paste mistakes (e.g., swapping values between

two different ceramide species when generating calibration curves or preparing the final data report). These are quite common errors that lead to the recommendation of using automated

procedures, even for data management. At the same time, an outlier characterized by significantly lower values for all ceramides (#34) was likely the result of the erroneous addition of

internal standard volumes. In this case, all analytes showed 3-fold lower values compared to the average of participants. Although each individual outlier value was examined and possible

reasons were discussed with the corresponding participants, we could not clarify all the cases reported. Possible factors could include LC-related suppression by coeluting analytes and

ionization factors that are MS platform-dependent and that we described earlier in this section, even considering that isotopically labelled analogues are the best solution to counteract

matrix effects50. Two laboratories were not able to receive plasma samples of human origin, but only organic lipid extracts thereof. As a result, we had the opportunity to evaluate extract

stability for this kind of analysis. Human plasma lipid extracts were prepared in Singapore, following the procedure described in the SOP, and were subsequently shipped to the respective

participants. Upon package arrival, one of the recipients noticed that some of the vials containing the lipid extracts were not properly sealed any longer, with significant portions being

spilled resulting in smaller volumes of samples available. Nevertheless, it was decided to proceed with the analysis as planned. Interestingly, the results from both participants receiving

extracts instead of plasma fully aligned with the mean of the other values. This underscores again the importance of adding isotopically labelled authentic standards in such complex mixtures

at the beginning of sample processing to compensate for possible variations during the entire procedure. VALUE OF THIS STUDY FOR THE DEVELOPMENT OF REFERENCE MATERIALS AND CALIBRATION

STANDARDS NIST SRM 1950 (“Metabolites in Frozen Human Plasma”) was prepared from 100 individuals representing the demographic diversity in the United States. A primary utilization purpose

was to serve as a reference material of ‘normal’ human blood plasma for validation studies. The original three layers of content characterization provided by NIST are (i) certified values,

(ii) reference values and (iii) information values, in decreasing order of metrological uncertainty (https://www-s.nist.gov/srmors/certificates/1950.pdf). For example, mass concentrations of

cholesterol and total glycerides (as triolein) are certified values (category of highest accuracy confidence) determined by isotope dilution gas chromatography mass spectrometry after

hydrolysis and derivatization. Levels of other lipid-related metabolites (e.g., fatty acids, vitamin D) are combinations of certified and reference values. We show here that generating these

values can have a big impact on the normalization of results obtained with different methods and instruments by different laboratories and that harmonization using a shared RM, in addition

to the calibration with authentic standards, decreases significantly inter-lab variability. We are aware that this might not be yet a perfect solution to solve the reproducibility issues in

the field and that more investigations are needed to clarify other aspects of this process, such as the commutability of the SRM 1950 for specific lipid measurements intended “as the

equivalence of the mathematical relationships between the results of different measurement procedures for a reference material and for representative samples from healthy and diseased

individuals”51. However, we think that this approach is valuable in decreasing systematic biases between platforms and our data support this. Our community is also aware that there are

limitations when using SRM 1950 for harmonization, for example its cost and possible limited availability in the future. As alternatives, either a cheaper laboratory-specific Long Term

Reference sample or a complex mixture of labelled lipids could be considered, as explained elsewhere30.To serve the expanding research communities in metabolomics with reference materials

representing different analytical purposes, populations, and diseases, NIST has initiated the development of new lines of RM (e.g., system suitability, human milk, proteomics among others).

RM 8231 is a package of three pooled human plasma materials including RM 8231-1 (DB), RM 8231-2 (hTAG), and RM 8231-3 (YAA)23. Unlike SRM 1950, the above RM 8231s are prepared from a smaller

number of individuals (_n_ = 11, _n_ = 11, and _n_ = 16, respectively) and are not documented with certified or reference values to the level of SRM 1950. As mentioned earlier, a notable

development over the past decade is the expansion of community efforts aimed at standardization. Collaboration between metrological institutions and interest groups in Quality Assurance and

Quality Control is a promising future opportunity for the co-development of RMs, their characterization, validation and eventually certification32,

(https://lipidomicssociety.org/interest_groups/reference-materials-and-biological-reference-ranges/). Here we added value to these efforts by providing the most precise publicly available

absolute concentration values reported to date for four clinically used ceramides, not only in the commonly used SRM 1950 but also the other RMs as well. The standards used in this study

originated from the same synthetic batch. A single vial containing these four ceramides is commercially available as Deuterated Ceramide LIPIDOMIX® Mass Spec Standard (Avanti Polar Lipids,

Cat. #330713). However, the relative concentrations of #330713 differ from those used in this study. It can be expected that our results will encourage the development of precise standard

mixtures similar to the one used within this study, as well as future generation of standards, including other stable isotope derivatives of endogenous species. RELEVANCE FOR THE UTILITY OF

CERAMIDES IN CLINICAL TESTING It should be noted that this study constituted a technical phase to address inter-laboratory and cross-platform variability rather than biological variations.

However, it forms the basis for the latter to be addressed in future initiatives (e.g., RI in populations or RCV in individuals). Recently, Ramos and colleagues at the Mayo Clinic measured

the concentration of these four ceramides in 24 healthy individuals and showed that the variability of ceramide levels at different time points is minimal within individuals52. Moreover,

when comparing the absolute concentrations of ceramides in these 24 individuals with the results of our study, we notice a good agreement. Establishing reference values in RMs representative

of specific disease conditions was also a propaedeutic exercise for the next phase of our study. These could be from cohorts collected for different studies worldwide, including bio-banked

samples. As it is clear from Fig. 3, each specific condition and plasma matrix will require dedicated attention to generate characteristic concentration values that will advance the

understanding of the role that ceramides may play in a range of human health and disease conditions. IN SUMMARY * This study represents the largest, most targeted public inter-laboratory and

cross-platform ring trial for distinct ceramides in human plasma. * Standardization of the analytical process helps decreasing variability, but it is not a fundamental requirement to obtain

concordant results across multiple laboratories, instruments, and operators. * We report mean absolute concentration values rather than medians or means of medians. The research and medical

community can utilize these reference values as quality control and a quantitative benchmark for ceramides in the reference materials. * Usage of authentic stable isotope labelled standards

for each metabolite of interest reduces variability in inter-laboratory comparisons. * Recalibration with a shared reference material decreases inter-laboratory variability even further. *

Comparison of pooled plasma samples provides an estimation of biological variation that can be expected between healthy individuals and those with hyperlipidemia as well as an approximation

of inter-ethnic differences. * Despite the highly systematic approach we cannot rule out potential biases of our results from the ‘true’ endogenous ceramide concentrations in human plasma.

These remain unresolved and depend on details of preparation procedures of the RM and accuracy of the quality and quantity of the utilized calibration standards. METHODS STUDY DESIGN

COORDINATORS The role of the coordination team within the reference materials and biological reference range interest group of the International Lipidomic Society

(https://lipidomicssociety.org/interest_groups/reference-materials-and-biological-reference-ranges/ which included Federico Torta, Nils Hoffmann, Robert Ahrends, John Bowden, Kim Ekroos,

Markus Wenk) was to design, plan and execute the study in consultation with its partners (Avanti Polar Lipids, National Institute of Standards and Technology- NIST) and participants (other

co-authors of the article listed in alphabetical order). Notable activities in the coordination group were establishing a diverse network of participants, negotiating agreements with

institutions and companies that supported the project, preparing recommended protocols and forms for return of results in a standardized format, overseeing the distribution of samples,

answering questions from participants and keeping them informed about the progress of the study, and, finally, consolidating data and disseminating results, including this report. PARTNERS

This project was conducted with the essential support of NIST, which made available the plasma reference materials (RMs), and Avanti Polar Lipids, which prepared customized solutions of

labelled and non-labelled standards for all study participants. Avanti furthermore helped with the logistics of the sample (both standards and NIST RMs) distribution. SAMPLES A set of

samples including four different human plasma RMs and, separately, pre-mixed solutions of ceramide standards (deuterated and non-deuterated) was shipped to all participants. Exceptions were

countries with restrictions on import of human plasma. In these cases, the corresponding RM samples were extracted at the Singapore Lipidomics Incubator, National University of Singapore,

according to the Standard Operating Protocol (SOP) and distributed to the participants as final lipid extracts in polypropylene tubes. Each laboratory was responsible for sample preparation,

instrument setup, data collection and raw data analysis. No requirements were set in terms of instrument performances before starting the experiments. Raw data (chromatographic peak areas

or signal intensities in case of flow injection analysis) were submitted to the coordinators that assigned a random numerical identifier to each lab and redistributed the anonymized data to

the data centre (Forschungszentrum Jülich and University of Vienna) for quality check and analysis. Each laboratory was free to choose between the SOP prepared by the organizers and based on

published data35 (19 participants) or performing the analysis according to a method of choice (OTHER, 12 participants) or using both the SOP and OTHER methods (3 participants). OTHER

methods differed from the SOP in many ways, as they were based on the favourite protocol of each lab that performed the OTHER (preferred) approach. Variations included the use of different

solvents for ceramide extraction, different chromatographic conditions (column type and gradient, direct infusion) and different detection methods by mass spectrometry (MRM vs full scan at

high resolution, for example). The laboratories were informed of the identity of the sample types, and all agreed to remain blinded to laboratory IDs (other than their own) throughout data

acquisition and publication of the results. Identifying information other than the laboratory ID was also removed from the report forms before data processing, integration and visualization

were performed. STANDARD PROTOCOL AND REPORTING FORMS An SOP (included as Supplementary Information) describing the procedures for extraction, with six independent extractions for each

material, each extract analysed in triplicates, and absolute quantification of Cer 18:1;O2/16:0, Cer 18:1;O2/18:0, Cer 18:1;O2/24:0 and Cer 18:1;O2/24:1 in human plasma using LC-MS/MS, in

multiple reaction monitoring (MRM) mode, was sent to all participants, together with the samples to be analysed. Please see the SOP for more details on samples, calibration curve, quality

control (QC) and blanks preparation. In addition, two Excel files for reporting of the results (one for the SOP and one for OTHER method) were prepared by the coordinators and sent to all

participants (Supplementary Information). HUMAN PLASMA SAMPLES SRM 1950 Metabolites in Frozen Human Plasma and candidate RM 8231 Frozen Human Plasma Suite for Metabolomics

(hypertriglyceridemic, diabetic, and African American plasma samples) are reference materials produced by NIST. All NIST plasma samples were collected after informed consent under approved

IRB protocols reviewed by the NIST Human Subjects Protection Office. Details on the preparation of SRM 1950 have been previously reported23,53,54. Briefly, Bioreclamation, Inc. (Hicksville,

NY) collected whole blood using lithium heparin as the anticoagulant from 100 donors (1:1 male to female ratio; 77% White, 12% African American or Black, 4% Asian, 2% American Indian or

Alaskan Native, 5% “other”) between 40 and 50 years of age after an overnight fast and a 72-h abstention from medication. Plasma was obtained by centrifuging the collected blood at 8000 × 

_g_ for 25 min at 4 °C. Plasma from each donor was thawed once and subsequently blended and aliquoted into individual vials to produce the SRM 1950. The diabetic (DB) plasma material (RM

8231-1) was created by Solomon Park Research Laboratories, Inc. from a pool of five female and six male donors (55% White, 36% African American or Black, 9% Asian) between 34 and 68 years of

age after an overnight fast, each meeting the specified ranges for glucose (>126 mg/dL) and triacylglycerols (<150 mg/dL). The hypertriglyceridemic (hTAG) plasma material (RM 8231-2)

was also created by Solomon Park Research Laboratories, Inc. (Kirkland, WA) from a pool of 11 male donors (55% White, 36% African American or Black, 9% Hispanic) between 31 and 72 years of

age after an overnight fast, each meeting the NIST-specified ranges for glucose (<100 mg/dL) and triacylglycerols (>300 mg/dL). Plasma for both the hypertriglyceridemic and diabetic

materials were obtained by centrifuging the respective collected blood at 1718 × _g_ for 10 min, followed by additional centrifugation at 4300 × _g_. Lithium heparin was used as the

anticoagulant, and plasma from each donor was thawed once and blended to make the respective donor pools before aliquoting into individual vials. For the Young African American (YAA) plasma

(RM 8231-3), Bioreclamation Inc. (Westbury, NY) collected whole blood using dipotassium ethylenediaminetetraacetic acid (K2-EDTA) as the anticoagulant from 16 donors (1:1 male to female

ratio) between 20 and 25 years of age after an overnight fast. The 16 units of blood were subsequently thawed and pooled by Solomon Park Research Laboratories in the same manner as the

hypertriglyceridemic and diabetic plasma materials. NIST plasma aliquots were thawed, pooled and re-aliquoted for this project. Thawing time was minimized and all pooling and aliquoting was

performed under inert nitrogen flow using a robotic enclosed system and the samples were returned to the freezer quickly (the entire activity was approximately 1 h). Aliquots were stored at

−80 °C until shipped on dry ice to each laboratory. A lipid extract was shipped to those participants that were not able to receive human plasma materials. Details are described in the next

sections. Since the scope of this trial was not to compare biofluids such as plasma vs serum55 or establish the influence of the type of anticoagulant56 on the measured ceramide levels, we

only report the measurement of the materials described in this section. LIPID STANDARDS AND GENERATION OF LIPID MIXTURES Stable isotope labelled internal standards (referred in the text also

as ISTD or simply internal standards) were synthesized and provided by Avanti Polar Lipids as a mixture solution in ethyl acetate: 2-propanol (EtOAc:IPA) 2:8 (v/v) at 99% purity. The

concentration value of each standard (0.125 μmol/L Cer 18:1;O2[D7]/16:0, 0.050 μmol/L, Cer 18:1;O2[D7]/18:0, 1.500 μmol/L Cer 18:1;O2[D7]/24:0, 0.500 μmol/L Ce r18:1;O2[D7]/24:1, Lot

#792537-01-010) was chosen based on previously measured levels of the corresponding endogenous analytes35. Non-labelled standards (2 μmol/L Cer 18:1;O2/16:0, 2 μmol/L Cer 18:1;O2/18:0, 20 

μmol/L Cer 18:1;O2/24:0, 20 μmol/L Cer 18:1;O2/24:1, Lot #792536-01-010) were also synthesized and provided by Avanti Polar Lipids as a mixture in EtOAc:IPA 2:8 (v/v) at 99% purity and were

used to build a calibration curve through serial dilutions of the starting mixture as described in the SOP included as Supplementary Information. Avanti used quantitative proton nuclear

magnetic resonance (QHNMR) to determine potency value and concentration of the products. During this process, the solvent from a known volume of each component is removed under nitrogen gas

or by centrifugal evaporation. Approximately 10 mg of NIST traceable QHNMR internal standard are added to each along with 1 mL of deuterated solvent. Samples are analysed by a Bruker 400 MHz

NMR spectrometer, with cryoprobe, using a validated quantitative proton method. For data interpretation, the integral response of the internal standard is used to calibrate the response of

each component so that an accurate concentration is determined. The variance of this method is 2%. Standard mixtures were formulated by first creating stock solutions of each individual

component and quantified through QHNMR (variability of the concentrations were determined to be −/+2% during method validation), then diluted to the final concentrations using glass

pipetting with accuracy −/+0.1% of the intended volume. Individual components of unlabelled and deuterated ceramides were then identified via nominal mass measurement using a QQQ MS. In

addition, isotopic purity was determined using a ratio (including isotopic correction) of the fully labelled species to the incompletely labelled species via Q-TOF MS. As part of a Quality

Assurance process, we would recommend that the users should always analyse the pure commercial standards in full scan mode to at least estimate the purity of the labelled compounds before

using them for analysis. Our strategy to improve the accuracy of quantitation involved the use of a pre-mixed solution containing a fixed amount of each of the four corresponding deuterated

(D7) ceramides. This solution was prepared as a single batch by Avanti Polar Lipids after close consultation with the trial coordinators and distributed as aliquots of the same batch to all

participants. This strategy was followed to increase the reproducibility of the absolute concentration values measured by the participants, that were not dependent on potential variations

between different lots of internal standard solutions. The stock concentration of each component was determined by QHNMR so that the mixture was formulated at the desired concentrations. The

subsequently formulated mixture was then analysed via high resolution, accurate mass measurement by Q-TOF MS. As the four ceramides species are present at different concentrations in human

plasma, the group of coordinators established expected average concentration values for each of them before the preparation of the pooled batch. These values, ranging from 0.050 to 1.5 

μmol/L, were derived from a previous publication35. From this and the fact that the range for the ceramide concentrations is small and linear, it can be expected that single-point

calibration was acceptable for absolute quantitation. However, as it is widely accepted in analytical chemistry, and recommended by the international guidelines for analyte quantitation, to

prepare a series of standards at different concentrations and build a multi-point calibration curve, a second type of pre-mixed solution consisting of the non-deuterated versions of the four

ceramides for the preparation of an external calibration curve was prepared by the provider at higher concentration levels (2 and 20 μmol/L, respectively) and distributed to the

participants. Due to the presence of endogenous lipids in human plasma and unavailability of lipid-free plasma matrix, calibration line standards were prepared in 5% bovine serum albumin (no

specific type was required). As previously reported, curves in plasma and albumin were shown to have identical slopes. Both solutions were kept at −20 °C, equilibrated at room temperature

and sonicated 10 min before use. DATA REPORTING, PROCESSING AND ANALYSIS All data reports were initially checked manually for completeness and conformance with the requested reporting

format. Deviations were recorded and corrected, where apparent (e.g., an inverse order of concentration steps for calibration lines or deviation from the reporting format structure). Remarks

by participants concerning potential issues during sample preparation or measurement steps were recorded and associated to the specific dataset that was manually examined for a potential

bias. Two sets of calibration line model parameters were calculated for each of the four ceramides per dataset, using a 1/x2 weighted linear model between the expected concentration and the

ratio of unlabelled and labelled ceramide. This model was chosen because of the large range of concentrations measured and because the absolute variation is usually larger for higher

concentrations; we therefore tried to limit the error at the bottom of the curve by weighting the data inversely with the concentration, as reported previously. LoD and LoQ concentrations

were calculated as averages of the standard error of the intercept of the calibration lines of the STD 6 samples (lowest concentration of the calibration line samples), divided by the slope

of the respective calibration line and multiplied by 3.3 (LoD) and 10 (LoQ), due to the lack of consistent Matrix Blanks57,58. Both values were then normalized by the slope of the

corresponding calibration curve. Final concentrations for each ceramide were calculated using the averaged output of the two corresponding calibration line models. As a common procedure in

lipidomics, for each dataset and each sample, single-point calibrated concentrations were calculated from the ratio of the corresponding unlabelled and labelled ceramide, multiplied with the

internal standard concentration. To compare different normalizations, adjusted concentrations were calculated using calibration curves built with either the authentic or non-authentic

labelled internal standards in combination with the authentic non-labelled standard, i.e. Cer 16:0 and Cer 24:0, respectively. OUTLIER REMOVAL Calculation of consensus concentrations,

inter-lab CVs and data in Figs. 1, 2, 3, 5 and 6 were based on outlier-filtered datasets. The outlier filtering was applied using Tukey’s 1.5× IQR (inter quantile range) method, which

corresponds to approximately a mean ± 3 × SD37,49. For each lipid species, matrix and quantification approach (single and multi-point calibration), datasets from laboratories with average

concentrations below Q1 (1st quantile) – 1.5 × IQR, or above Q3 + 1.5 × IQR were excluded. CALCULATIONS AND VISUALIZATIONS All calculations were performed in R (version 4.4.1) using R

packages targets (1.2.2), tidyverse (version 2.0.0), ggbeeswarm (0.7.2) and ggh4x (0.2.8)59,60,61. All raw and processed datasets and the R pipeline used to process, calculate, and plot the

data are available from the GitHub repository (https://github.com/lifs-tools/ils-ceramide-ring-trial). A detailed list of all R packages and their versions is available in the ‘renv.lock‘

file within the same repository. The GitHub repository version used to prepare the data and figures for this accepted manuscript was archived to Zenodo

(https://zenodo.org/doi/10.5281/zenodo.10081970). REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. DATA

AVAILABILITY All raw and processed datasets and the R pipeline used to process, calculate, and plot the data are available from the GitHub repository

(https://github.com/lifs-tools/ils-ceramide-ring-trial). The GitHub repository version used for this accepted manuscript was archived to Zenodo under the

https://doi.org/10.5281/zenodo.10081970. All mass spectrometry raw data generated by the participants were deposited to Zenodo under the https://doi.org/10.5281/zenodo.12632989. Source data

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ADS  Google Scholar  Download references ACKNOWLEDGEMENTS 1,2 Work in the MRW laboratory is supported by grants from the National University of Singapore via the Life Sciences Institute

(LSI) and A*STAR IAF-ICP I1901E0040. 4 N.H. acknowledges support by de.NBI and the de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) and ELIXIR-DE

(Forschungszentrum Jülich and W-de.NBI-001, W-de.NBI-004, W-de.NBI-008, W-de.NBI-010, W-de.NBI-013, W-de.NBI-014, W-de.NBI-016, W-de.NBI-022). 5 Bundesministerium für Bildung, Wissenschaft

und Forschung” BMBFW within the project DigiOmics4Austria. 6 Work in the SALB laboratory is supported by grants from the Canadian Institutes of Health Research and the Canadian Consortium on

Neurodegeneration and Aging CAN-CIHR 163902 and PJT 183604. 7,8,55,56 H.T. was supported by JSPS KAKENHI (21K18216 to Hiroshi T.), the National Cancer Centre Research and Development Fund

(2020-A-9, H.T.), and JST ERATO Grant (JPMJER2101 to H.T.). 9 This research is partly supported by AMED-BINDS (JP23ama121055), JST A-STEP (JPMJTR204J), JST-NBDC (JPMJND2305), and the MEXT

Cooperative Research Project Program, Medical Research Center Initiative for High Depth Omics, and CURE:JPMXP1323015486 for the Medical Institute of Bioregulation, Kyushu University. 10 Work

in MetaToul (Toulouse metabolomics & fluxomics facilities, www.mth-metatoul.com) part of the French National Infrastructure for Metabolomics and Fluxomics is supported by

MetaboHUB-ANR-11-INBS-0010 and Inserm (French National Institut for Research in Health). 13 Work at the University of Manchester supported in part by grants from the British Heart Foundation

(PG/2019/34923) and BBSRC (BB/W006022/1). 16 Work at University of Pardubice was supported by grant No. 21-20238S (Czech Science Foundation) and ERC Adv grant No. 101095860 (European

Research Council). 19 Work in the MTS department is supported by the MetaboHUB infrastructure (ANR-11-INBS-0010 grant). 21 BBSRC for funding to the Babraham Institute and to Michael

Wakelam’s memory for his life of contribution to the lipidomics world. 23 Mass spectrometeric analysis was performed at the Turku Metabolomics Centre with the support of Biocenter Finland.

24,25,26 This publication is based upon work from COST Action EpiLipidNET, Pan-European Network in Lipidomics and Epilipidomics (CA19105; https://www.epilipid.net), supported by COST

(European Cooperation in Science and Technology).” 30 M.G. is an early-stage researcher supported by the H2020 ITN consortium ArthritisHeal (#812890). M.G. was partially supported by the NWO

XOmics project #184.034.019. 31 Nanyang Assistant Professorship, Lee Kong Chian School of Medicine, Nanyang Technological University. 36 Supported by grants from the National Natural

Science Foundation of China (21934006 and 22074144). 48 Ser Cymru Project Sepsis grant funded by Welsh Government/EU-ERDF (VJT, VBO). 51,52 Supported by the Tohoku Medical Megabank Project

from MEXT, the Japan Agency for Medical Research and Development (AMED; under grant numbers JP20km0105001 and JP21tm0124005) and KAKENHI Grant Number JP23H02625 [D.S.]. 54 The Christian

Doppler Laboratory for Innovative Gut Health Concepts of Livestock has been funded by the Austrian Federal Ministry for Digital and Economic Affairs, the Austrian National Foundation for

Research, Technology and Development as well as BIOMIN Holding GmbH, which is part of DSM. The authors also thank Marco Reiter for technical support. The participants would like to

acknowledge Clay Davis from the National Institute of Standards and Technology. B.B. would like to acknowledge Christian Luechtenborg for his contribution to this project. AUTHOR INFORMATION

Author notes * These authors contributed equally: Federico Torta, Nils Hoffmann, Bo Burla. AUTHORS AND AFFILIATIONS * Singapore Lipidomics Incubator, Life Sciences Institute, National

University of Singapore, Singapore, 117456, Singapore Federico Torta, Bo Burla, Sneha Muralidharan & Markus R. Wenk * Precision Medicine Translational Research Programme and Department

of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119077, Singapore Federico Torta & Markus R. Wenk * Signature Research Program in

Cardiovascular and Metabolic Disorders, Duke-National University of Singapore (NUS) Medical School, Singapore, 169857, Singapore Federico Torta * Institute for Bio- and Geosciences (IBG-5),

Forschungszentrum Jülich GmbH, 52428, Jülich, Germany Nils Hoffmann * Department of Analytical Chemistry, University of Vienna, Vienna, Austria Nils Hoffmann, Cristina Coman & Robert

Ahrends * Neural Regeneration Laboratory, Ottawa Institute of Systems Biology, Ottawa Brain and Mind Research Institute, Department of Biochemistry, Microbiology, and Immunology, and

Department of Chemistry, Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, K1H 8M5, Canada Irina Alecu, Steffany A. L. Bennett & Thao Nguyen-Tran * RIKEN Center

for Integrative Medical Sciences, Yokohama, Japan Makoto Arita & Hiroshi Tsugawa * Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan Makoto Arita & 

Hiroshi Tsugawa * Division of Metabolomics Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3‑1‑1, Maidashi, Higashi‑ku, Fukuoka,

812‑8582, Japan Takeshi Bamba, Yoshihiro Izumi, Masatomo Takahashi & Noriyuki Tomiyasu * MetaboHUBMetaToul Facility, I2MC U1297 Inserm, Toulouse, France Justine Bertrand-Michel & 

Anthony Tournadre * Heidelberg University Biochemistry Center (BZH), Im Neuenheimer Feld 328, 69120, Heidelberg, Germany Britta Brügger * Metabolomics Core Facility-MetCore, Universidad de

los Andes, Bogotá, 111711, Colombia Mónica P. Cala * Laboratory for Lipidomics and Lipid Biology, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology Medicine

and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9NT, United Kingdom Dolores Camacho-Muñoz & Anna Nicolaou * Unit of Integrative

Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden Antonio Checa & Craig E. Wheelock * Department of Pathology and Laboratory Medicine,

University of British Columbia, Vancouver, BC, Canada Michael Chen * Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10,

Pardubice, Czech Republic Michaela Chocholoušková, Michal Holčapek & Denise Wolrab * Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia Michelle Cinel, Kevin Huynh &

 Peter J. Meikle * Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Bundoora, VIC, 3086, Australia Michelle Cinel, Kevin Huynh & Peter J.

Meikle * Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191, Gif sur Yvette, France Emeline Chu-Van & Benoit Colsch *

Avanti Polar Lipids, Alabaster, AL, USA Lisa Connell * Babraham Institute, Babraham Research Campus, Cambridge, MA, CB22 3AT, USA Bebiana C. Sousa, Andrea F. Lopez-Clavijo & Michael O.

Wakelam * Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland Alex M. Dickens & Matej Orešič * Department of Chemistry, University of Turku,

Turku, Finland Alex M. Dickens * Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, University of Leipzig, 04013, Leipzig, Germany Maria Fedorova & Mike Lange *

Center for Biotechnology and Biomedicine, University of Leipzig, 04013, Leipzig, Germany Maria Fedorova & Mike Lange * Center of Membrane Biochemistry and Lipid Research, Faculty of

Medicine Carl Gustav Carus of TU Dresden, 01307, Dresden, Germany Maria Fedorova * Faculty of Pharmaceutical Sciences, University of Iceland, Reykjavik, Iceland Finnur Freyr Eiríksson & 

Margrét Thorsteinsdóttir * ArcticMass, Reykjavik, Iceland Finnur Freyr Eiríksson & Margrét Thorsteinsdóttir * Metabolomics Platform, Faculty of Biology and Medicine, University of

Lausanne, Lausanne, Switzerland Hector Gallart-Ayala & Julijana Ivanisevic * Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333ZA, Leiden, The Netherlands

Mohan Ghorasaini & Martin Giera * Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 636921, Singapore Xue Li Guan * Metabolomics and Proteomics Core,

Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany Mark Haid & Fabien Riols * Metabolomics and Analytics Centre, Leiden Academic

Centre for Drug Research, Leiden University, Leiden, The Netherlands Thomas Hankemeier, Amy Harms & Madhulika Singh * University Hospital of Regensburg, Institute of Clinical Chemistry

and Laboratory Medicine, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany Marcus Höring & Gerhard Liebisch * Institute of Clinical Chemistry, University Zurich, 8952, Schlieren,

Switzerland Thorsten Hornemann & Andreas J. Hülsmeier * State Key Laboratory of Medical Proteomics, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of

Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China Chunxiu Hu, Guowang Xu & Tianrun Xu * Chemical Science Division, National Institute of Standards and Technology,

Gaithersburg, MD, 20899, USA Christina M. Jones & Katrice Lippa * Core Facility Mass Spectrometry, Medical University of Graz, 8010, Graz, Austria Harald C. Köfeler * LipidALL

Technologies, Changzhou, 213000, Jiangshu, China Sin Man Lam * State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of

Sciences, Beijing, 100101, China Sin Man Lam & Guanghou Shui * Department of Chemistry, Yonsei University, Seoul, 03722, South Korea Jong Cheol Lee & Myeong Hee Moon * Centro de

Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina Malena Manzi, 

Manuela R. Martinefski & María Eugenia Monge * Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Intendente

Güiraldes, 2160 C1428EGA, Buenos Aires, Argentina Malena Manzi * Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Departamento de Desarrollo Analítico y Control de

Procesos, Instituto Nacional de Tecnología Industrial, Av. General Paz 5445, B1650WAB, Buenos Aires, Argentina Malena Manzi * Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica,

Departamento de Ciencias Químicas, Buenos Aires, Junin 954, Junin, C1113AAD, CABA, Argentina Manuela R. Martinefski * National Centre for Biological Sciences, Tata Institute of Fundamental

Research, Bangalore, Karnataka, 560065, India Raviswamy G. H. Math, Satyajit Mayor & Sneha Muralidharan * Lydia Becker Institute of Immunology and Inflammation; Faculty of Biology,

Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9NT, United Kingdom Anna Nicolaou * Systems Immunity Research Institute, School

of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK Valerie B. O’Donnell & Victoria J. Tyrrell * School of Medical Sciences, Faculty of Medicine and Health, Örebro

University, 702 81, Örebro, Sweden Matej Orešič * Institute for Stem Cell Science and Regenerative Medicine, 560065, Bangalore, India Arvind Ramanathan * Laboratory of Biomedical and

Analytical Sciences, Faculty of Pharma-Science, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-8605, Japan Daisuke Saigusa * Department of Integrative Genomics, Tohoku Medical

Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8573, Japan Daisuke Saigusa * Chemical Science Division, National Institute of Standards and

Technology, Charleston, SC, 29412, USA Tracey B. Schock * Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock, Institute of Bioanalytics and Agro-Metabolomics,

Department of Agrobiotechnology (IFATulln), University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Str. 20, 3430, Tulln, Austria Heidi Schwartz-Zimmermann * RIKEN

Center for Sustainable Resource Science, Yokohama, Japan Hiroshi Tsugawa * Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Tokyo, Japan Hiroshi

Tsugawa * St. Paul’s Hospital, Department of Pathology and Laboratory Medicine, Vancouver, BC, Canada Grace van der Gugten * Department of Respiratory Medicine and Allergy, Karolinska

University Hospital, Stockholm, Sweden Craig E. Wheelock * Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL, 32610, USA John A.

Bowden * Lipidomics Consulting Ltd., Espoo, Finland Kim Ekroos * College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar Markus R. Wenk Authors * Federico Torta View

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ARTICLE CITE THIS ARTICLE Torta, F., Hoffmann, N., Burla, B. _et al._ Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic

standards. _Nat Commun_ 15, 8562 (2024). https://doi.org/10.1038/s41467-024-52087-x Download citation * Received: 26 November 2023 * Accepted: 27 August 2024 * Published: 03 October 2024 *

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