OSCR

Cell-type-specific m1A dynamics are associated with microglial phenotypic transition and neuronal metabolic adaptation during spinal cord injury.

Code ↔ Paper

15 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 15 matches
  1. [1] § 4. Methods › 4.10. Construction of SuperCell Metacells ↔ snRNA-seq_process_scripts/4_metacell_enhance_cor.R, lines 1–43 · score 0.98 · k_knn, nb_var_genes, supercell_GE, SCimplify, nb pc, normalized gene expression
  2. [2] § 4. Methods › 4.9. Pseudotime analysis ↔ scRNA-seq_process_scripts/4_microglia_monocle.R, lines 48–94 · score 0.82 · lowerDetectionLimit, min_expr, expressed genes, GSE162610, matrix, microglial
  3. [3] § 4. Methods › 4.8. Transcription factor inference ↔ scRNA-seq_process_scripts/3_SCI_affected_cell_types.R, lines 50–90 · score 0.80 · TF activity scores, min.size, AUCell, pySCENIC, regulon, Module
  4. [4] § 4. Methods › 4.3. Data processing ↔ snRNA-seq_process_scripts/2_neuron_subcluster.R, lines 48–106 · score 0.78 · FindIntegrationAnchors, IntegrateData, ScaleData, DimPlot, PCA, UMAP
  5. [5] § 4. Methods › 4.4. Cell annotation ↔ snRNA-seq_process_scripts/2_neuron_subcluster.R, lines 109–197 · score 0.76 · P2ry12, Cdk1, Igf1, Lgals3, Siglech, Vim
  6. [6] § 4. Methods › 4.3. Data processing ↔ snRNA-seq_process_scripts/4_metacell_enhance_cor.R, lines 1–43 · score 0.71 · Principal Component, variable genes, dimensionality reduction, vars, Seurat, single cell
  7. [7] § 2. Results › 2.5. The m1A score is associated with neuronal energy metabolism after SCI ↔ Bulk RNA-seq_process_scripts/GO_BP.R, lines 45–106 · score 0.61 · oxidative phosphorylation, aerobic respiration, mitochondrial, ATP, GO, energy
  8. [8] § 4. Methods › 4.7. Functional annotation ↔ Bulk RNA-seq_process_scripts/bulk_code/1.KEGG_GO.R, lines 55–114 · score 0.61 · enrichGO, enrichKEGG, CC, enriched, Gene
  9. [9] § 2. Results › 2.3. Dysregulation of transcriptional networks in myeloid cells and its close relationship with m1A score after SCI ↔ scRNA-seq_process_scripts/3_SCI_affected_cell_types.R, lines 92–132 · score 0.60 · pcaRAS_1, encoded, shift, UMAP, regulon, transcriptional
  10. [10] § 4. Methods › 4.4. Cell annotation ↔ snRNA-seq_process_scripts/1_m1A score and expression.R, lines 1–42 · score 0.58 · Schwann, hematopoietic, leptomeningeal, pericytes, precursor, ependymal
  11. [11] § 2. Results › 2.1. The dynamic changes of m1A score at the bulk level post-SCI and its significant correlation with energy metabolism pathways ↔ scRNA-seq_process_scripts/1_m1A score and expression.R, lines 158–206 · score 0.56 · Trmt61a, Trmt10c, Ythdf3, Alkbh1, Ythdf2, Ythdc1
  12. [12] § 2. Results › 2.1. The dynamic changes of m1A score at the bulk level post-SCI and its significant correlation with energy metabolism pathways ↔ snRNA-seq_process_scripts/1_m1A score and expression.R, lines 77–133 · score 0.56 · Trmt61a, Trmt10c, Ythdf3, Alkbh1, Ythdf2, Ythdc1
  13. [13] § 4. Methods › 4.6. m1A Score Calculation ↔ snRNA-seq_process_scripts/5_2_diff_metacell_m1a_cor.R, lines 1–47 · score 0.54 · AddModuleScore_UCell, ncores, ranking, RNA, m1A
  14. [14] § 4. Methods › 4.6. m1A Score Calculation ↔ snRNA-seq_process_scripts/5_diff_function_m1a_cor.R, lines 288–334 · score 0.53 · AddModuleScore_UCell, ncores, ranking, RNA, m1A, regulatory
  15. [15] § 2. Results › 2.5. The m1A score is associated with neuronal energy metabolism after SCI ↔ snRNA-seq_process_scripts/5_2_diff_metacell_m1a_cor.R, lines 537–579 · score 0.52 · aerobic respiration, mRNA, Seurat, metacells, metabolism, UCell

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 273 lines · 10 KB · MIT · 2 matches

  1. # 设置工作目录
  2. setwd('/home/bio/Projects/NC2022/neuro_sub/')
  3. # 清空变量 加载包
  4. # rm(list = ls())
  5. library(Seurat)
  6. # packageVersion('sub_seurat_obj1')
  7. library(DESeq2)
  8. library(tidyverse)
  9. library(ggthemes)
  11. # 加载数据
  12. m1Aset <- read.csv("../m1A_genesets.csv")
  13. #load("./data/neuron_subcluster_20250622.Rda")
  14. sub_seurat_obj1 <- qs::qread("./data/neuron_subcluster_20250622.qs")
  15. # 使用的数据就是神经元的亚分群数据 sub_seurat_obj1
  16. # 使用SuperCell包进行
  17. # if (!requireNamespace("remotes")) install.packages("remotes")
  18. # remotes::install_github("GfellerLab/SuperCell")
  20. library(SuperCell)
  22. # 定义参数
  23. MC_tool = "SuperCell"
  24. proj_name <- "subneuron"
  25. gamma = 20 # the requested graining level.
  26. k_knn = 30 # the number of neighbors considered to build the knn network.
  27. nb_var_genes = 2000 # number of the top variable genes to use for dimensionality reduction
  28. nb_pc = 30 # the number of principal components to use.
  30. # Metacells identification
  31. MC <- SuperCell::SCimplify(Seurat::GetAssayData(sub_seurat_obj1, slot = "data"), # single-cell log-normalized gene expression data
  32. k.knn = k_knn,
  33. gamma = gamma,
  34. # n.var.genes = nb_var_genes,
  35. n.pc = nb_pc
  36. #genes.use = Seurat::VariableFeatures(sub_seurat_obj1)
  37. )
  39. MC.GE <- supercell_GE(Seurat::GetAssayData(sub_seurat_obj1, slot = "counts"),
  40. MC$membership,
  41. mode = "sum"
  42. )
  43. dim(MC.GE)
  45. # 注释细胞
  46. print(annotation_label)
  47. #> [1] "celltype_simplified"
  48. MC$annotation <- supercell_assign(clusters = [email hidden][, "celltype"], # single-cell annotation
  49. supercell_membership = MC$membership, # single-cell assignment to metacells
  50. method = "absolute"
  51. )
  53. MC$condition <- supercell_assign(clusters = [email hidden][, "condition"], # single-cell annotation
  54. supercell_membership = MC$membership, # single-cell assignment to metacells
  55. method = "absolute"
  56. )
  58. head(MC$annotation)
  59. head(MC$condition)
  60. # 可以先画个图看看
  61. # plot network of metacells
  62. supercell_plot(
  63. MC$graph.supercells,
  64. group = MC$annotation,
  65. lay.method = 'drl',
  66. seed = 1,
  67. alpha = -pi/2,
  68. main = "Metacells colored by sc assignment"
  69. )
  71. pdf("./figure/Figure_4_supercell/A_spercell_origin_celltype.pdf",height = 6,width = 10)
  72. mysupercell(
  73. MC$graph.supercells,
  74. group = MC$annotation,
  75. lay.method = 'drl',
  76. seed = 1,
  77. alpha = -pi/2,
  78. main = "Metacells colored by sc_celltype assignment"
  79. )
  80. dev.off()
  82. pdf("./figure/Figure_4_supercell/A_spercell_origin_group.pdf",height = 6,width = 10)
  83. mysupercell(
  84. MC$graph.supercells,
  85. group = MC$condition,
  86. lay.method = 'drl',
  87. seed = 1,
  88. alpha = -pi/2,
  89. main = "Metacells colored by sc_group assignment"
  90. )
  91. dev.off()
  94. # 查看每个元细胞含有的细胞系类别的纯度
  95. purity <- supercell_purity(clusters = [email hidden][, "celltype"],
  96. supercell_membership = MC$membership, method = 'entropy')
  98. pdf("./figure/Figure_4_supercell/B_metacell_purity.pdf",height = 5,width = 6)
  99. hist(purity, main = "Purity of metacells in terms of composition")
  100. dev.off()
  102. # MC$purity <- purity
  104. # 直接看基因表达
  105. genes.to.plot <- c("Alkbh1","Alkbh3","Fto","Trmt10c","Trmt6")
  107. # 导出成seurat对象
  108. colnames(MC.GE) <- as.character(1:ncol(MC.GE))
  109. MC.seurat <- CreateSeuratObject(counts = MC.GE,
  110. meta.data = data.frame(size = as.vector(table(MC$membership)))
  111. )
  112. # 添加细胞类型
  113. MC.seurat[["celltype_simplified"]] <- MC$annotation
  114. MC.seurat[["celltype"]] <- MC$annotation
  115. # 添加分组信息
  116. MC.seurat[["condition"]] <- MC$condition
  118. # save single-cell membership to metacells in the MC.seurat object
  119. MC.seurat@misc$cell_membership <- data.frame(row.names = names(MC$membership), membership = MC$membership)
  120. MC.seurat@misc$var_features <- MC$genes.use
  122. # Save the PCA components and genes used in SCimplify
  123. PCA.res <- irlba::irlba(scale(Matrix::t(sub_seurat_obj1@assays$RNA@data[MC$genes.use, ])), nv = nb_pc)
  124. pca.x <- PCA.res$u %*% diag(PCA.res$d)
  125. rownames(pca.x) <- colnames(sub_seurat_obj1@assays$RNA@data)
  126. MC.seurat@misc$sc.pca <- CreateDimReducObject(
  127. embeddings = pca.x,
  128. loadings = PCA.res$v,
  129. key = "PC_",
  130. assay = "RNA"
  131. )
  132. if(packageVersion("Seurat") >= 5) {
  133. MC.seurat[["RNA"]] <- as(object = MC.seurat[["RNA"]], Class = "Assay")
  134. }
  135. print(paste0("Saving metacell object for the ", proj_name, " dataset using ", MC_tool))
  136. #> [1] "Saving metacell object for the bmcite dataset using SuperCell"
  138. # 保存数据
  139. save(MC.seurat, file = './figure/Figure_4_supercell/data/subneuron_MC.seurat.Rda')
  141. #### 上面构建了metacell，接下来就是继续处理，然后按照seurat流程走
  142. # 先来看看 这个时候是count，所以画图都是平行的
  143. FeatureScatter(object = MC.seurat, feature1 = 'Il6', feature2 = 'Ythdc1')
  145. MC_tool = "SuperCell"
  146. proj_name = "subneuron"
  147. annotation_column = "celltype"
  149. celltypes <- c("Slit2_IN", "Npy_IN","Gal_IN", "Cck_EN", "Sox5_EN", "Pde11a_EN", "Tac2_EN")
  151. celltype_colors <- c("#3477a9", "#96c3d8","#1E88E5", "#d62e2d", "#f47d2f",
  152. "#F06292", "#4a9d47")
  153. # c("#1E88E5", "#FFC107", "#004D40", "#9E9D24",
  154. # "#F06292", "#546E7A", "#D4E157", "#76FF03",
  155. # "#26A69A", "#AB47BC", "#D81B60", "#42A5F5",
  156. # "#2E7D32", "#FFA726", "#5E35B1", "#EF5350","#6D4C41")
  157. names(celltype_colors) <- celltypes
  159. # 定义好细胞ident
  160. [email hidden]$celltype <- [email hidden]$celltype_simplified
  162. [email hidden]$celltype <- factor([email hidden]$celltype,
  163. levels = celltypes )
  164. Idents(MC.seurat) <- 'celltype'
  166. ##### Seurat降维流程#####
  167. MC.seurat <- NormalizeData(MC.seurat)
  168. MC.seurat <- FindVariableFeatures(MC.seurat, selection.method = "vst", nfeatures = 2000)
  169. MC.seurat <- ScaleData(MC.seurat)
  170. #> Centering and scaling data matrix
  171. MC.seurat <- RunPCA(MC.seurat, verbose = F)
  172. MC.seurat <- RunUMAP(MC.seurat, dims = 1:30, verbose = F, min.dist = 1)
  173. #> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
  174. #> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
  175. #> This message will be shown once per session
  177. data <- cbind(Embeddings(MC.seurat, reduction = "umap"),
  178. data.frame(size = MC.seurat$size,
  179. cell_type = [email hidden][, annotation_column]))
  180. colnames(data)[1:2] <- c("umap_1", "umap_2")
  182. data$cell_type <- factor(data$cell_type,levels = celltypes)
  183. p_annot <- ggplot(data, aes(x= umap_1, y=umap_2, color = cell_type)) + geom_point(aes(size=size)) +
  184. ggplot2::scale_size_continuous(range = c(0.5, 0.5*max(log((data$size))))) +
  185. ggplot2::scale_color_manual(values = celltype_colors) +
  186. theme_classic() + guides(color=guide_legend(ncol=1))+
  187. theme(axis.title = element_text(size = 12, colour = "black"),
  188. axis.text = element_text(size = 10, colour = "black"))+
  189. guides(color=guide_legend(override.aes = list(size=3,alpha=1))) # 注意fill和color的变化
  190. p_annot
  192. ggsave(
  193. file.path( "./figure/Figure_4_supercell/C_Supercell_Umap.pdf"),
  194. plot = p_annot,
  195. height = 5,
  196. width = 6.5)
  198. ##### Seurat聚类流程#####
  199. MC.seurat <- FindNeighbors(MC.seurat, reduction = "pca", dims = 1:30)
  201. MC.seurat <- FindClusters(MC.seurat, resolution = 0.1)
  203. data <- cbind(Embeddings(MC.seurat, reduction = "umap"),
  204. data.frame(size = MC.seurat$size,
  205. cluster = MC.seurat$seurat_clusters))
  207. colnames(data)[1:2] <- c("umap_1", "umap_2")
  208. p_cluster <- ggplot(data, aes(x= umap_1, y=umap_2, color = cluster)) + geom_point(aes(size=size)) +
  209. ggplot2::scale_size_continuous(range = c(0.5, 0.5*max(log1p((data$size))))) +
  210. theme_classic() + guides(color=guide_legend(ncol=1))+
  211. theme(axis.title = element_text(size = 12, colour = "black"),
  212. axis.text = element_text(size = 10, colour = "black"))+
  213. guides(color=guide_legend(override.aes = list(size=3,alpha=1))) # 注意fill和color的变化
  214. p_cluster
  216. ##### 差异分析 ####
  217. # Set idents to metacell clusters
  219. Idents(MC.seurat) <- "seurat_clusters"
  220. cells_markers <- FindMarkers(MC.seurat, ident.1 = "0", only.pos = TRUE,
  221. logfc.threshold = 0.25, min.pct = 0.1,
  222. test.use = 'wilcox', pseudocount.use = 1)
  223. #genes.to.plot <- c("Alkbh1","Alkbh3","Fto","Trmt10c","Trmt6")
  224. test_marker <- c("Slit2","Esrrg","Cdh18",'Snrpn')
  225. cells_markers[test_marker, ]
  227. VlnPlot(MC.seurat, test_marker, ncol = 3, pt.size = 0.0)
  228. p_cluster + p_annot
  230. ##### Visualize gene-gene correlation ####
  231. # 单细胞水平相关性
  232. # cells_markers <- cells_markers[order(cells_markers$avg_log2FC, decreasing = T),]
  233. gene_x <- test_marker[1:3]
  234. gene_y <- test_marker[4]
  236. alpha <- 0.7
  238. p.sc <- SuperCell::supercell_GeneGenePlot(
  239. GetAssayData(sub_seurat_obj1, slot = "data"),
  240. gene_x = gene_x,
  241. gene_y = gene_y,
  242. clusters = [email hidden][, annotation_column],
  243. sort.by.corr = F,
  244. alpha = alpha,
  245. color.use = celltype_colors
  246. )
  247. p.sc$p
  249. # metacell水平相关性
  251. p.MC <- SuperCell::supercell_GeneGenePlot(GetAssayData(MC.seurat, slot = "data"),
  252. gene_x = gene_x,
  253. gene_y = gene_y,
  254. clusters = [email hidden][, annotation_column],
  255. sort.by.corr = F, supercell_size = MC.seurat$size,
  256. alpha = alpha,
  257. color.use = celltype_colors)
  258. p.MC$p
  260. # 只看一个细胞类型
  261. Slit2_IN <- subset(MC.seurat,celltype =='Slit2_IN')
  263. p.MC <- SuperCell::supercell_GeneGenePlot(GetAssayData(Slit2_IN, slot = "data"),
  264. gene_x = 'Trmt10c',
  265. gene_y = 'Fto',
  266. # clusters = [email hidden][, annotation_column],
  267. # sort.by.corr = F, supercell_size = MC.seurat$size,
  268. alpha = alpha,
  269. color.use = celltype_colors)
  270. p.MC$p
  272. # 保存数据
  273. save(MC.seurat, file = './figure/Figure_4_supercell/data/subneuron_MC.seurat.Rda')

4_metacell_enhance_cor.R at commit 4b24961, under MIT · at the source

Overview

Authors: Chi Zhang1, Shaolong Li2, Ruizhi Jiang2, Heng Duan2, Chuang Li1, Enlin Qi2, Mingxin Wu3, Xueying Li4, Shiqing Feng1,2,3, Hengxing Zhou2
ORCID iDs: Hengxing Zhou
  1. Department of Orthopaedics, The Second Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Cheeloo College of Medicine, Shandong University, Jinan, China
  2. Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
  3. Department of Orthopaedics, Tianjin Medical University General Hospital, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin, China
  4. Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
Institutions: Shandong University (China); Qilu Hospital of Shandong University (China); Tianjin Medical University General Hospital (China); Tianjin Medical University (China)
Journal: PLoS computational biology, volume 22, issue 7, article e1014573
Dates: received 9 November 2025; accepted 13 July 2026; published online 24 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014573 · PMID 42497212 · PMCID PMC13423174 · OpenAlex W7170835326
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
MeSH: Adenosine*, Microglia*, Neurons*, Spinal Cord Injuries*, Adaptation, Physiological, Animals, Computational Biology, Epigenesis, Genetic, Epitranscriptome, Gene Expression Profiling, Mice, Phenotype, RNA Methylation, Transcriptome (* major topic)
Journal subjects: Medicine and Health Sciences, Critical Care and Emergency Medicine, Trauma Medicine, Traumatic Injury, Neurotrauma, Spinal Cord Injury, Neurology, Biology and Life Sciences, Cell Biology, Cellular Types, Animal Cells, Neurons, Neuroscience, Cellular Neuroscience, Glial Cells, Microglial Cells, Bone Marrow Cells, Biochemistry, Metabolism, Energy Metabolism, Physiology, Physiological Processes, Physical Sciences, Chemistry, Chemical Reactions, Methylation, Proteins, DNA-binding proteins, Transcription Factors, Genetics, Gene Expression, Gene Regulation, Regulatory Proteins, Anatomy, Nervous System, Neuroanatomy, Spinal Cord
Topic: RNA modifications and cancer (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Shandong Provincial Natural Science Foundation (ZR2024MH032); Postdoctoral Innovation Program of Shandong Province (SDCX-ZG-202503112); Major Basic Research Project of the Shandong Provincial Natural Science Foundation (ZR2024ZD13, ZR2023ZD16); National Natural Science Foundation of China (82372413, 82220108005); Taishan Scholars Program of Shandong Province-Pandeng Taishan Scholars (tspd20210320)
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

m1A (N1-methyladenosine) is an important epigenetic mechanism that regulates the onset and progression of many diseases, including spinal cord injury (SCI). To investigate the overall changes in m1A following SCI, we analyzed transcriptomic sequencing data from SCI samples and assigned m1A scores based on the levels of m1A regulatory factors. In this study, the m1A score is an inferred proxy calculated from the expression of m1A regulator genes (writers/erasers/readers). It does not directly measure RNA m1A modification levels. Our results show that the m1A score increased within the first day after SCI and then decreased, falling below baseline by day 3 and day 7. Further analysis revealed that microglia and neurons are the two cell types with the most significant changes in the m1A score. In microglia, m1A score decreased at all time points, whereas in neurons, m1A score increased at all time points. Additionally, pseudotime and functional enrichment analyses suggested that the m1A score is associated with microglial phenotypic transition and neuronal energy metabolism, which was further validated by both in vivo and in vitro experiments. In summary, our study unveils the characteristic changes of m1A at both the bulk and single-cell levels following SCI, and suggests potential links to neuronal function and supports the rationale for further studies exploring m1A-related regulators as therapeutic targets in SCI.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 15 matches between paragraphs and lines of code.

Magetutor/m1A_score_manuscript

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4b2496179df9d5d7c03910a61ee4ef3504d24978, 2 July 2026
Languages: R (27)
Size: 32 files, 27 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (18 files), ggplot2 (16 files), ggpubr (11 files), Seurat (10 files), clusterProfiler (7 files), DESeq2 (6 files), patchwork (6 files), cowplot (5 files), pheatmap (4 files), data.table (3 files), rstatix (3 files), igraph (1 file), limma (1 file), Monocle 3 (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files
At the source: github.com/Magetutor/m1A_score_manuscript

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 27 scripts, each with its path and the digest of its content;
  • 15 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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 Availability

We confirm that all data and code supporting the findings of this study are available. The raw sequencing data used in this study were obtained from the public GEO repository (accession numbers GSE162610 and GSE172167). The custom code used for data analysis is available on GitHub (https://github.com/Magetutor/m1A_score_manuscript). Detailed information can be found in the Methods section. All analysis results are presented within the manuscript.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 14 MeSH terms, 5 funders, 71 references.

Cite

This paper

Zhang, C., Li, S., Jiang, R., Duan, H., Li, C., Qi, E., Wu, M., Li, X., Feng, S., & Zhou, H. (2026). Cell-type-specific m1A dynamics are associated with microglial phenotypic transition and neuronal metabolic adaptation during spinal cord injury. PLoS computational biology, 22(7), e1014573. https://doi.org/10.1371/journal.pcbi.1014573

BibTeX

@article{zhang2026cell,
author = {Zhang, Chi and Li, Shaolong and Jiang, Ruizhi and Duan, Heng and Li, Chuang and Qi, Enlin and Wu, Mingxin and Li, Xueying and Feng, Shiqing and Zhou, Hengxing},
title = {{Cell-type-specific m1A dynamics are associated with microglial phenotypic transition and neuronal metabolic adaptation during spinal cord injury}},
journal = {PLoS computational biology},
year = {2026},
month = jul,
volume = {22},
number = {7},
pages = {e1014573},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014573},
url = {https://doi.org/10.1371/journal.pcbi.1014573},
pmid = {42497212},
pmcid = {PMC13423174}
}

RIS

TY - JOUR
AU - Zhang, Chi
AU - Li, Shaolong
AU - Jiang, Ruizhi
AU - Duan, Heng
AU - Li, Chuang
AU - Qi, Enlin
AU - Wu, Mingxin
AU - Li, Xueying
AU - Feng, Shiqing
AU - Zhou, Hengxing
TI - Cell-type-specific m1A dynamics are associated with microglial phenotypic transition and neuronal metabolic adaptation during spinal cord injury
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/07/24
VL - 22
IS - 7
SP - e1014573
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014573
UR - https://doi.org/10.1371/journal.pcbi.1014573
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pcbi.1014573",
"type": "article-journal",
"title": "Cell-type-specific m1A dynamics are associated with microglial phenotypic transition and neuronal metabolic adaptation during spinal cord injury",
"container-title": "PLoS computational biology",
"author": [
{
"family": "Zhang",
"given": "Chi"
},
{
"family": "Li",
"given": "Shaolong"
},
{
"family": "Jiang",
"given": "Ruizhi"
},
{
"family": "Duan",
"given": "Heng"
},
{
"family": "Li",
"given": "Chuang"
},
{
"family": "Qi",
"given": "Enlin"
},
{
"family": "Wu",
"given": "Mingxin"
},
{
"family": "Li",
"given": "Xueying"
},
{
"family": "Feng",
"given": "Shiqing"
},
{
"family": "Zhou",
"given": "Hengxing"
}
],
"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "7",
"page": "e1014573",
"DOI": "10.1371/journal.pcbi.1014573",
"PMID": "42497212",
"PMCID": "PMC13423174",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1014573",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: Monocle 3, limma, DESeq2, 11 other tools, genetics / omics, other condition, cellular / molecular, 2 references
[2] doi:10.1002/ctm2.70683 [code]
Niacin promotes motor function recovery after spinal cord injury via Hcar2-dependent microglia immunometabolic regulation.
Journal: Clinical and translational medicine
In common: Monocle 3, limma, DESeq2, 8 other tools, genetics / omics, other condition, mouse, 1 other category, 4 references
[3] doi:10.1002/imt2.70163 [code]
Spatial multi-omics unveils sphingolipid metabolic reprogramming within the retinal pathological niche.
Journal: iMeta
In common: Monocle 3, limma, rstatix, 12 other tools, genetics / omics, mouse, cellular / molecular
[4] doi:10.1038/s41593-026-02367-0 [code]
A reproducible three-dimensional model of human brain tissue to investigate physiological and disease-associated microglia phenotypes.
Journal: Nature neuroscience
In common: Monocle 3, limma, DESeq2, 11 other tools, cellular / molecular, 2 references
[5] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: rstatix, DESeq2, igraph, 9 other tools, genetics / omics, mouse, cellular / molecular, 3 references
[6] doi:10.1038/s41467-026-76675-1 [code]
Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.
Journal: Nature communications
In common: Monocle 3, limma, rstatix, 11 other tools, genetics / omics
[7] doi:10.3390/ijms27104466 [code]
Uncovering the Key Circuit FOSL2/FOS/EGR3/EGR1, Contributing to the Hyperexcitability of Excitatory Neurons in the Epileptic Temporal Cortex and Hippocampus.
Journal: International journal of molecular sciences
In common: Monocle 3, limma, igraph, 10 other tools, genetics / omics, 1 reference
[8] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: Monocle 3, limma, DESeq2, 10 other tools, mouse, cellular / molecular
[9] doi:10.1038/s41598-026-51501-2 [code]
Expanding canonical cortical cell type markers in the era of single-cell transcriptomics.
Journal: Scientific reports
In common: rstatix, DESeq2, igraph, 9 other tools, genetics / omics, cellular / molecular, 2 references
[10] doi:10.1038/s42003-026-10034-0 [code]
Region- and cell type-specific changes in gene expression in the cerebellum after classical fear conditioning.
Journal: Communications biology
In common: limma, rstatix, DESeq2, 8 other tools, genetics / omics, mouse, cellular / molecular, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.