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Lipidomic profiling reveals age-dependent changes in plasma membrane lipids that affect neural stem cell aging.

Code ↔ Paper

25 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 25 matches · 13 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › OPLS-DA and assessment of model performance ↔ Scripts/DESI_MSI/OPLS-DA_in_DESI.R, lines 2–53 · score 0.95 · OPLS DA, crossvalI, orthoI, permI, predI, scaleC
  2. [2] § MATERIALS AND METHODS › OPLS-DA and assessment of model performance ↔ Scripts/DESI_MSI/OPLS-DA_in_DESI.R, lines 2–53 · score 0.77 · OPLS DA, aNSCs, DESI MSI, S7D, Q2, permutated
  3. [3] § MATERIALS AND METHODS › Comparison between the effect of young plasma membrane lipid supplementation on recipient qNSCs and age-related changes in GPMV lipids ↔ Scripts/Mboat2_OE_and_PM_lipid_supplementation_lipidomics/M2PM_MsDial_8_Effect_of_PM_lipid_supplementation_Pie.chart.R, the whole file · a weak match · score 0.71 · pie chart, young GPMVs, quadrants, lipidomic, age
  4. [4] § MATERIALS AND METHODS › Lipid quantification ↔ Scripts/In_vivo_lipidomics/Invivo_MsDial_1_IS_concentration.R, the whole file · a weak match · score 0.68 · lipid concentration, internal standards, MG, dilution, PG, DG
  5. [5] § RESULTS › Spatial lipidomic profiling shows changes in membrane lipids in qNSCs in situ ↔ Scripts/DESI_MSI/OPLS-DA_in_DESI.R, lines 55–111 · score 0.67 · OPLS DA, aNSCs, DESI MSI, NPCs, orthogonal, models
  6. [6] § MATERIALS AND METHODS › Lipid quantification ↔ Scripts/In_vitro_#1/Invitro_MsDial_1_IS_concentration.R, the whole file · a weak match · score 0.62 · internal standards, MG, PG, deuterated, DG, PS
  7. [7] § MATERIALS AND METHODS › Evaluation of cell type–specific lipidomic deconvolution accuracy ↔ Scripts/DESI_MSI/In_silico_admixture_deconvolution.R, lines 249–288 · score 0.62 · csSAM, lipid intensity, DESI MSI, silico, oligodendrocytes, deconvolution
  8. [8] § MATERIALS AND METHODS › Quantification of substrate and product level in Mboat2, Elovl5, Agpat3, Fads2, and Pla2g4e knockouts in vitro ↔ Scripts/In_vitro_#2/InvitroExp2_MsDial_12_KO_efficiency_Elovl5_Fads2.R, lines 1–55 · score 0.62 · free fatty acids, efficiency, substrates, Elovl5, Fads2, products
  9. [9] § MATERIALS AND METHODS › Choice of lipid-modifying enzymes for CRISPR-Cas9–based functional perturbations ↔ Scripts/In_vitro_#2/InvitroExp2_MsDial_8_KO_efficiency_Mboat2_Pla2g4e_Agpat3.R, lines 52–139 · score 0.60 · acyltransferase activity, Pla2g4e, phospholipase, age
  10. [10] § MATERIALS AND METHODS › Cell type–specific deconvolution of DESI-MSI mass spectra ↔ Scripts/DESI_MSI/mass_spec_deconvolution_run.R, lines 1–85 · score 0.60 · csSAM, DESI MSI, rounding, mass, aggregated, deconvolution
  11. [11] § RESULTS › Identification of enzymes that affect the lipidome of young and old qNSCs ↔ Scripts/In_vitro_#2/InvitroExp2_MsDial_10_Lipidomic_profile_by_KO.R, lines 60–146 · score 0.57 · Pla2g4e, lipidomic profile, primary cultures, Agpat3, Elovl5, Fads2
  12. [12] § MATERIALS AND METHODS › Nomenclature ↔ Scripts/In_vitro_#2/InvitroExp2_MsDial_12_KO_efficiency_Elovl5_Fads2.R, lines 58–92 · score 0.56 · double bond, fatty acids, MAPS, position
  13. [13] § MATERIALS AND METHODS › Lentiviral production and transduction for Mboat2 overexpression in vitro ↔ Scripts/Mboat2_OE_and_PM_lipid_supplementation_lipidomics/M2PM_MsDial_13_M2OE_PCA.R, the whole file · a weak match · score 0.56 · Mboat2 OE, Mboat2 overexpression, qNSC, EGFP, cultures, lipidomic
  14. [14] § MATERIALS AND METHODS › Lentiviral production for in vivo Mboat2 overexpression ↔ Scripts/Mboat2_OE_and_PM_lipid_supplementation_lipidomics/M2PM_MsDial_6_effect.size.calc.R, the whole file · a weak match · score 0.55 · Mboat2 OE, Mboat2 overexpression, treatments, EGFP
  15. [15] § MATERIALS AND METHODS › PCA of lipidomic datasets ↔ Scripts/In_vitro_#1/Invitro_MsDial_4_PCA.R, lines 1–61 · score 0.54 · log2 transformed, PCA, PC2, PC1, prcomp, activated
  16. [16] § MATERIALS AND METHODS › Lipid identification ↔ Scripts/Mboat2_OE_and_PM_lipid_supplementation_lipidomics/M2PM_MsDial_1_IS_concentration.R, the whole file · a weak match · score 0.54 · MG, PG, molecular, DG, PS, TG
  17. [17] § MATERIALS AND METHODS › Lipid identification ↔ Scripts/GPMV/GPMV_MsDial_1_IS_concentration.R, the whole file · a weak match · score 0.54 · MG, PG, molecular, DG, PS, TG
  18. [18] § MATERIALS AND METHODS › Lentiviral production for in vivo Mboat2 overexpression ↔ Scripts/Mboat2_OE_and_PM_lipid_supplementation_lipidomics/M2PM_MsDial_13_M2OE_PCA.R, the whole file · a weak match · score 0.54 · Mboat2 OE, Mboat2 overexpression, EGFP, culture, vitro
  19. [19] § MATERIALS AND METHODS › PCA of lipidomic datasets ↔ Scripts/Overlap_or_across_studies/Fig1.Lipid_zscore_for_PCA_2.invitro.studies.R, the whole file · a weak match · score 0.52 · log2 transformed, vitro lipidomic, PCA, score, overlapping, lipids
  20. [20] § MATERIALS AND METHODS › Side chain composition analysis ↔ Scripts/Mboat2_OE_and_PM_lipid_supplementation_lipidomics/M2PM_MsDial_4_SideChain.calc.R, the whole file · a weak match · score 0.52 · side chain composition, double bond, class, lipid
  21. [21] § MATERIALS AND METHODS › Side chain composition analysis ↔ Scripts/In_vivo_lipidomics/Invivo_MsDial_5_SideChain.calc.R, the whole file · a weak match · score 0.52 · side chain composition, double bond, class, lipid
  22. [22] § MATERIALS AND METHODS › Plotting effect size means and SEM and confidence intervals of specific lipids ↔ Scripts/Function_scripts/Effect_size_functions.R, lines 28–57 · score 0.51 · confidence interval, standard error, Age
  23. [23] § MATERIALS AND METHODS › Lentiviral production and transduction for Mboat2 overexpression in vitro ↔ Scripts/Mboat2_OE_and_PM_lipid_supplementation_lipidomics/M2PM_MsDial_6_effect.size.calc.R, the whole file · a weak match · score 0.51 · Mboat2 OE, Mboat2 overexpression, EGFP, young, lipidomic
  24. [24] § MATERIALS AND METHODS › PCA of lipidomic datasets ↔ Scripts/In_vitro_#2/InvitroExp2_MsDial_9_PCA_by_KO.R, lines 1–44 · score 0.50 · log2 transformed, PCA, PC2, PC1, prcomp, cell
  25. [25] § MATERIALS AND METHODS › Comparison of lipid concentrations between in vitro lipidomic datasets ↔ Scripts/Overlap_or_across_studies/Fig1.Lipid.concentration.correlation.2.invitro.studies.R, the whole file · a weak match · score 0.50 · Correlation coefficients, lipid concentration, Pearson, overlapping, vitro

Paper

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The authors' code

R · 158 lines · 5.6 KB · no license · 3 matches

  2. setwd(rstudioapi::getActiveProject())
  3. rm(list=ls())
  5. library(tidyverse)
  6. library(ropls)
  7. library(data.table)
  8. ssDT <- fread('./Output_Data/20210512_DESI_decomposition_single_sample.csv')
  10. ##====OPLS-DA between aNSCs and qNSCs from DESI====
  11. all.DESI.Cell<- ssDT %>%
  12. filter(!cell == "other") %>%
  13. filter(!cell == "ki67") %>%
  14. mutate(sample_cell = paste0(sampleID, "_", cell)) %>%
  15. dplyr::select(peak, mean, sample_cell) %>%
  16. pivot_wider(names_from = peak, values_from = mean) %>%
  17. mutate(CellType = case_when(
  18. grepl("_gfap", sample_cell) ~ "qNSCs",
  19. grepl("_both", sample_cell) ~ "aNSCs",
  20. )) %>%
  21. relocate(CellType, .after = "sample_cell")
  23. cell.df <- all.DESI.Cell %>%
  24. dplyr::select(CellType)
  26. cell.mtx <- data.matrix(cell.df)
  28. data.mtx <- all.DESI.Cell %>%
  29. column_to_rownames(var = "sample_cell") %>%
  30. dplyr::select(-CellType)
  32. data.mtx <- data.matrix(data.mtx)
  33. ### ====Permutation test====
  34. set.seed(12345)
  35. Cell.oplsda <- opls(data.mtx, cell.mtx,
  36. predI = 1, orthoI = 1, crossvalI = 6, permI = 100, scaleC = 'standard')
  37. plot(Cell.oplsda, typeVc = "permutation")
  39. pR2Y <- Cell.oplsda@summaryDF$`pR2Y`
  40. pQ2 <- Cell.oplsda@summaryDF$`pQ2`
  41. cat("p-value for R²Y:", pR2Y, "\np-value for Q²:", pQ2)
  42. # p-value for R²Y: 0.01
  43. # p-value for Q²: 0.01>
  45. perm_df <- as.data.frame(Cell.oplsda@suppLs$permMN)
  47. ggplot(perm_df, aes(x = sim, y = `Q2(cum)`)) +
  48. geom_point(alpha = 0.65, size = 3) +
  49. geom_hline(yintercept = perm_df$`Q2(cum)`[perm_df$sim == 1], linetype = "dashed", color = "red") +
  50. labs(x = "Similarity", y = "Q²", title = "Permutation Test Results") +
  51. theme_classic() +
  52. theme(axis.text = element_text(colour = "black"))
  53. ggsave(filename = "./Figure_Panels/fig.S7D.pdf", width = 5, height = 5, useDingbats=FALSE)
  55. ### ====OPLS-DA====
  56. o1 = as.data.frame(Cell.oplsda@orthoScoreMN)
  57. p1 = as.data.frame(Cell.oplsda@scoreMN)
  59. df.p <- bind_cols(o1, p1) %>%
  60. rownames_to_column(var = "Sample") %>%
  61. mutate(CellType = case_when(
  62. grepl("_gfap", Sample) ~ "qNSCs",
  63. # grepl("_ki67", Sample) ~ "NPCs",
  64. grepl("_both", Sample) ~ "aNSCs",
  65. )) %>%
  66. mutate(Age = ifelse(grepl("^Y", Sample), "Young", "Old"))
  68. df.p$CellType <- factor(df.p$CellType, levels = c("aNSCs", "qNSCs"))
  69. df.p$Age <- factor(df.p$Age, levels = c("Young", "Old"))
  71. A <- ggplot(df.p, aes(p1, o1))
  72. A + geom_point(aes( color = CellType), shape = 18, size = 4, alpha = 0.8)+
  73. scale_color_manual(values = c("#664692", "#63A66C"))+
  74. theme_classic() +
  75. theme(text=element_text(size = 13, face = "plain", colour = "black"))+
  76. theme(axis.text = element_text(colour = "black"))+
  77. ggtitle("DESI aNSC vs. qNSC - OPLS-DA")+
  78. xlab(paste0("Component 1: ", format(Cell.oplsda@modelDF["p1", "R2X"] * 100,
  79. digits = 3), " % variance"))+
  80. ylab(paste0("Orthogonal Component 1: ", format(Cell.oplsda@modelDF["o1", "R2X"] * 100,
  81. digits = 3), " % variance")) +
  82. theme(legend.position = "bottom")
  83. ggsave(filename = "./Figure_Panels/fig.S7C.pdf", width = 5, height = 5, useDingbats=FALSE)
  86. ##====OPLS-DA between young and old qNSCs from DESI====
  88. Q.all.DESI<- ssDT %>%
  89. filter(cell == "gfap") %>%
  90. mutate(sample_cell = paste0(sampleID, "_", cell)) %>%
  91. dplyr::select(peak, mean, sample_cell) %>%
  92. pivot_wider(names_from = peak, values_from = mean) %>%
  93. mutate(Age = case_when(
  94. grepl("^Y", sample_cell) ~ "Young",
  95. grepl("^O", sample_cell) ~ "Old"
  96. )) %>%
  97. mutate(CellType = case_when(
  98. grepl("_gfap", sample_cell) ~ "qNSCs",
  99. )) %>%
  100. relocate(c(Age, CellType), .after = "sample_cell")
  102. var.mtx <- Q.all.DESI %>%
  103. dplyr::select(Age)
  105. var.mtx <- data.matrix(var.mtx)
  107. data.mtx <- Q.all.DESI %>%
  108. column_to_rownames(var = "sample_cell") %>%
  109. dplyr::select(-c(Age, CellType))
  111. Q.data.mtx <- data.matrix(data.mtx)
  113. ### ====Permutation test====
  114. set.seed(1111)
  115. Q.DESI.pls <- opls(Q.data.mtx, var.mtx, , predI = 1, orthoI = 1, crossvalI = 6, permI = 100, scaleC = 'standard')
  117. plot(Q.DESI.pls, typeVc = "permutation")
  119. pR2Y <- Q.DESI.pls@summaryDF$`pR2Y`
  120. pQ2 <- Q.DESI.pls@summaryDF$`pQ2`
  121. cat("p-value for R²Y:", pR2Y, "\np-value for Q²:", pQ2)
  122. # p-value for R²Y: 0.42
  123. # p-value for Q²: 0.98
  125. perm_df <- as.data.frame(Q.DESI.pls@suppLs$permMN)
  127. ggplot(perm_df, aes(x = sim, y = `Q2(cum)`)) +
  128. geom_point(alpha = 0.65, size = 3) +
  129. geom_hline(yintercept = perm_df$`Q2(cum)`[perm_df$sim == 1], linetype = "dashed", color = "red") +
  130. labs(x = "Similarity", y = "Q²", title = "Permutation Test Results") +
  131. theme_classic()
  132. ggsave(filename = "./Figure_Panels/fig.S7F.pdf", width = 5, height = 5, useDingbats=FALSE)
  134. ### ====OPLS-DA====
  135. o1 = as.data.frame(Q.DESI.pls@orthoScoreMN)
  136. p1 = as.data.frame(Q.DESI.pls@scoreMN)
  138. df.p <- bind_cols(o1, p1) %>%
  139. rownames_to_column(var = "Sample") %>%
  140. mutate(Age = ifelse(grepl("^Y", Sample), "Young", "Old"))
  142. df.p$Age <- factor(df.p$Age, levels = c("Young", "Old"))
  144. pal4 <- c("cyan3", "magenta3")
  145. A <- ggplot(df.p, aes(p1, o1))
  146. A + geom_point(aes( color = Age), shape = 18, size = 4, alpha = 0.8)+
  147. scale_color_manual(values = pal4)+
  148. # scale_shape_manual(values = c(16, 17, 10)) +
  149. theme_classic() +
  150. theme(text=element_text(size = 13, face = "plain", colour = "black"))+
  151. theme(axis.text = element_text(colour = "black"))+
  152. ggtitle("DESI old vs. young qNSC - OPLS-DA")+
  153. xlab(paste0("Component 1: ", format(Q.DESI.pls@modelDF["p1", "R2X"] * 100,
  154. digits = 3), " % variance"))+
  155. ylab(paste0("Orthogonal Component 1: ", format(Q.DESI.pls@modelDF["o1", "R2X"] * 100,
  156. digits = 3), " % variance")) +
  157. theme(legend.position = "bottom")
  158. ggsave(filename = "./Figure_Panels/fig.S7E.pdf", width = 5, height = 5, useDingbats=FALSE)

OPLS-DA_in_DESI.R at commit 5255d95, no license · at the source

Overview

Authors: Xiaoai Zhao1,2,3, Ryan M Feitzinger1, Jeeyoon Na1,4, Xin Yan5, Andrew Erickson2,3, Kévin Contrepois1, Olivia Y Zhou1,6,7, Francesco Vallania8,9, Mathew Ellenberger1, Chloe M Kashiwagi1, Stephanie D Gagnon1, Cynthia J Siebrand1, Matias Cabruja1, Gavin M Traber1, Andrew McKay1, Daniel Hornburg1, Purvesh Khatri8,9, Michael P Snyder1, Richard N Zare5, Anne Brunet1,10,11
ORCID iDs: Xiaoai Zhao, Ryan M Feitzinger, Olivia Y Zhou, Francesco Vallania, Cynthia J Siebrand, Matias Cabruja, Gavin M Traber, Andrew McKay, Daniel Hornburg, Purvesh Khatri, Michael P Snyder, Richard N Zare, Anne Brunet
  1. Department of Genetics, Stanford University, Stanford, CA, USA
  2. Department of Comparative Medicine, Yale University, New Haven, CT, USA
  3. Yale Center for Molecular and Systems Metabolism, Yale University, New Haven, CT, USA
  4. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
  5. Department of Chemistry, Stanford University, Stanford, CA, USA
  6. Stanford Biophysics Program, Stanford University, Stanford, CA, USA
  7. Stanford Medical Scientist Training Program, Stanford University, Stanford, CA, USA
  8. Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, CA, USA
  9. Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
  10. Glenn Laboratories for the Biology of Aging, Stanford University, Stanford, CA, USA
  11. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
Institutions: Yale University (United States); Stanford University (United States); Institute for Stem Cell Biology and Regenerative Medicine
Journal: Science advances, volume 12, issue 31, article eaeh9771
Dates: received 13 April 2026; accepted 23 June 2026; published online 29 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aeh9771 · PMID 42525742 · PMCID PMC13418745 · OpenAlex W7171718022
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, Preprocessing, Evoked potentials, fMRI & imaging
MeSH: Aging*, Cell Membrane*, Cellular Senescence*, Lipidomics*, Membrane Lipids*, Neural Stem Cells*, Animals, Mice (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: NIAID NIH HHS (U19 AI057229, U19 AI167903); NIA NIH HHS (P01 AG036695)
Citations: not cited yet (Europe PMC); 173 references in the paper
Research resources: antibodies against Ki67 RRID:AB_10854564, RRID:AB_10953628, IRDye 680RD goat anti-rat IgG RRID:AB_10956590, RRID:AB_141844, 1:5000 Calnexin RRID:AB_2069006, RRID:AB_2096607, 000 Beta-actin RRID:AB_2223210, RRID:AB_2534017, anti-Goat Alexa Fluor 488 RRID:AB_2534102, anti-Rabbit Alexa Fluor 488 RRID:AB_2535792, RRID:AB_2535794, anti-Rat Alexa Fluor 594 RRID:AB_2535795, anti-Rabbit Alexa Fluor 647 RRID:AB_2536183, mCherry RRID:AB_2536611, Ki67-APC RRID:AB_2688057, 1:5000 Cox4 RRID:AB_443304, IRDye 800CW goat anti-rabbit IgG RRID:AB_621843, Antibodies against GFAP RRID:AB_880202, Trono RRID:Addgene_12260, Zhang RRID:Addgene_52961, Weinberg RRID:Addgene_8454

Abstract

The aging brain exhibits a decline in the regenerative populations of neural stem cells (NSCs). While mechanisms that restore old NSC function have started to be identified, the role of lipids—especially complex lipids—in NSC aging remains largely unclear. Using lipidomic profiling by mass spectrometry, we identify age-related changes in complex lipids in quiescent NSCs in vitro and in vivo. Moreover, several polyunsaturated fatty acids increase across lipid classes in quiescent NSCs during aging. Using spatial lipidomics, we find that some of the changes in complex lipids are also observed in situ. Several age-related changes in complex lipids and side chain composition are occurring at the plasma membrane, as revealed by lipidomic profiling of isolated plasma membrane vesicles. Experimentally, we show that aging is accompanied by a decrease in plasma membrane order, a key membrane biophysical property, in old quiescent NSCs in vitro and in vivo. To determine the functional role of plasma membrane lipids in aging NSCs, we performed genetic and supplementation studies. Knocking out the phospholipid acyltransferase MBOAT2 exacerbates age-related lipidomic changes in old quiescent NSCs and impedes their ability to activate. Mboat2 overexpression reverses age-related lipidomic changes in old quiescent NSCs and boosts their ability to activate in vitro and in vivo. Moreover, supplementation of plasma membrane lipids from young NSCs improves the ability of old quiescent NSCs to activate. Our work could lead to lipid-based strategies for restoring the regenerative potential of NSCs, which has important implications for countering brain decline during aging.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Its files are read in the Code ↔ Paper reader above, with 25 matches between paragraphs and lines of code.

xiaoaizhao/Neural-stem-cell-NSC-aging-lipidomics

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5255d958b21158884458252edd6f57c3434e785e, 27 May 2026
Languages: R (88)
Size: 515 files, 88 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, environment (renv.lock), 2 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (83 files), ggpubr (31 files), data.table (5 files), patchwork (2 files), rstatix (2 files), ComplexHeatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
89 files
At the source: github.com/xiaoaizhao/Neural-stem-cell-NSC-aging-lipidomics

Zenodo 20416165

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (83 files), ggpubr (31 files), data.table (5 files), patchwork (2 files), rstatix (2 files), ComplexHeatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
89 files
At the source: doi.org/10.5281/zenodo.20416165

The paper's code and data availability statement is in the Data section.

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 176 scripts, each with its path and the digest of its content;
  • 25 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. Lipidomics data are deposited at Metabolomics Workbench under Study ID ST002258 (https://metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST002258), Study ID ST002259 (https://metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST002259), Study ID ST002260 (https://metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST002260), Study ID ST004116 (https://metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST004116), and Study ID ST004117 (https://metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST004117). Custom analysis scripts are available at https://github.com/xiaoaizhao/Neural-stem-cell-NSC-aging-lipidomics and archived at Zenodo (https://doi.org/10.5281/zenodo.20416165). Plasmids in Addgene are as follows: (i) pLV[Exp]-EF1A>3×FLAG/3×GGGGS/mMboat2[NM_026037.3] (https://addgene.org/258331/); (ii) LentiCrispr V2 Mboat2 sgRNA1 (https://addgene.org/258333/); (iii) LentiCrispr V2 Mboat2 sgRNA2 (https://addgene.org/258336/); (iv) LentiCrispr V2 Elovl5 sgRNA1 (https://addgene.org/258337/); (v) LentiCrispr V2 Fads2 sgRNA1 (https://addgene.org/258338/); (vi) LentiCrispr V2 Pla2g4e sgRNA1 (https://addgene.org/258363/); (vii) LentiCrispr V2 Agpat3 sgRNA1 (https://addgene.org/258364/); (viii) LentiCrispr V2 Safe-targeting sgRNA1 (https://addgene.org/258365/); and (ix) LentiCrispr V2 Safe-targeting sgRNA2 (https://addgene.org/258366/). Other reagents generated in this study are available from the corresponding authors upon request. To obtain the reagents generated in this study, please contact X.Z. at .

Reproduced under the paper's license (CC BY), from the paper cited above.

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Zhao, X., Feitzinger, R. M., Na, J., Yan, X., Erickson, A., Contrepois, K., Zhou, O. Y., Vallania, F., Ellenberger, M., Kashiwagi, C. M., Gagnon, S. D., Siebrand, C. J., Cabruja, M., Traber, G. M., McKay, A., Hornburg, D., Khatri, P., Snyder, M. P., Zare, R. N., & Brunet, A. (2026). Lipidomic profiling reveals age-dependent changes in plasma membrane lipids that affect neural stem cell aging. Science advances, 12(31), eaeh9771. https://doi.org/10.1126/sciadv.aeh9771

BibTeX

@article{zhao2026lipidomic,
author = {Zhao, Xiaoai and Feitzinger, Ryan M and Na, Jeeyoon and Yan, Xin and Erickson, Andrew and Contrepois, Kévin and Zhou, Olivia Y and Vallania, Francesco and Ellenberger, Mathew and Kashiwagi, Chloe M and Gagnon, Stephanie D and Siebrand, Cynthia J and Cabruja, Matias and Traber, Gavin M and McKay, Andrew and Hornburg, Daniel and Khatri, Purvesh and Snyder, Michael P and Zare, Richard N and Brunet, Anne},
title = {{Lipidomic profiling reveals age-dependent changes in plasma membrane lipids that affect neural stem cell aging}},
journal = {Science advances},
year = {2026},
month = jul,
volume = {12},
number = {31},
pages = {eaeh9771},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aeh9771},
url = {https://doi.org/10.1126/sciadv.aeh9771},
pmid = {42525742},
pmcid = {PMC13418745}
}

RIS

TY - JOUR
AU - Zhao, Xiaoai
AU - Feitzinger, Ryan M
AU - Na, Jeeyoon
AU - Yan, Xin
AU - Erickson, Andrew
AU - Contrepois, Kévin
AU - Zhou, Olivia Y
AU - Vallania, Francesco
AU - Ellenberger, Mathew
AU - Kashiwagi, Chloe M
AU - Gagnon, Stephanie D
AU - Siebrand, Cynthia J
AU - Cabruja, Matias
AU - Traber, Gavin M
AU - McKay, Andrew
AU - Hornburg, Daniel
AU - Khatri, Purvesh
AU - Snyder, Michael P
AU - Zare, Richard N
AU - Brunet, Anne
TI - Lipidomic profiling reveals age-dependent changes in plasma membrane lipids that affect neural stem cell aging
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/07/29
VL - 12
IS - 31
SP - eaeh9771
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aeh9771
UR - https://doi.org/10.1126/sciadv.aeh9771
LA - en
ER -

CSL-JSON

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