Journal: Life Medicine

Article Title: Single-cell analysis reveals essential lncRNAs regulating human trophoblast lineage differentiation

doi: 10.1093/lifemedi/lnag010

Figure Lengend Snippet: Transcriptomic landscape of lncRNAs in human placental trophoblasts. (A) UMAP plot showing first-trimester and term placental single-nucleus transcriptomes (CTB, cytotrophoblast; EVT, extravillous trophoblast; STB, syncytiotrophoblast; STR, stromal cell; e, early; l, late). (B) UMAP plot showing the distribution of first-trimester and term placental samples. (C) Pie plot of the fraction of lncRNAs and mRNAs in snRNA-seq and bulk RNA-seq, respectively. The value “ n ” indicates the average number of detected transcripts per cell for both lncRNAs and mRNAs based on the experimentally measured data. (D) Box plots of transcript counts in per cell type. Statistical significance was determined by two-sided Wilcoxon rank-sum tests (**** P < 0.0001). (E) Volcano plot of cell-type-specific lncRNAs across trophoblast subtypes. The x -axis represents the difference in the percentage of cells expressing each gene between the two compared cell types (Δ percentage = pct.1 − pct.2), while the y -axis indicates the log 2 (fold change) in average gene expression. This representation integrates both expression magnitude and expression prevalence at the single-cell level. (F) Heatmap of cell-type-specific lncRNA expression atlas across trophoblast populations. (G) UMAP plots showing the expression distribution of representative lncRNAs (Color gradient represents the average expression level per cell). (H) GO biological process terms enriched in protein-coding genes (PCGs) associated with stage-specific lncRNAs.

Article Snippet: For cross-platform validation: Three independent external datasets were utilized, including two single-cell RNA-seq datasets (accession numbers: HRA003309, GSE214607 ) and one single-nucleus RNA-seq dataset (singlecell.broadinstitute.org/single_cell/study/SCP2601).

Techniques: RNA Sequencing, Expressing, Gene Expression, Single Cell

Journal: Briefings in Bioinformatics

Article Title: Decoding causal m 6 A: a bioinformatics roadmap for psychiatric disorders

doi: 10.1093/bib/bbag251

Figure Lengend Snippet: An end-to-end bioinformatics pipeline identifying causal m 6 A targets in depression. (A) The integrated workflow illustrating two distinct analytical phases: transitioning from descriptive functional convergence (using MeRIP-seq datasets: Study 2, 3, and 5) to causal SMR inference (using MDD GWAS summary statistics from the PGC and brain m 6 A-QTL datasets). (B) Number of enriched GO terms in the mouse studies. (C) Venn diagram showing the convergence of functional themes on “cognition”. (D) The seven specifically screened cognition-associated genes with differential m 6 A peaks. (E) Example IGV plot for the Cacna1e gene (see – for the full set of identified genes), showing differential m 6 A peaks from two of the analyzed studies. Where the regions with visible logFC value corresponds to the significantly DMPs. The log2 fold-change (logFC) values represent the ratio of m 6 A enrichment levels between depression models and their corresponding controls. (F) SMR locus plot, illustrating the causal mediation of MDD genetic risk through brain-specific m 6 A-QTLs at the ADARB1 locus. (G) snRNA-seq validation from the HPA, demonstrating that the prioritized causal target ( ADARB1 ) is highly specific to excitatory and inhibitory neuronal lineages, supporting a “Brain-First” functional etiology.

Article Snippet: To demonstrate Phase 3 of our roadmap and adhere to a cell-type specific annotation strategy, we validated our top causal target, ADARB1 , using single-nucleus RNA-seq (snRNA-seq) data from the Human Protein Atlas (HPA) (database access links are provided in Supplementary Methods).

Techniques: Functional Assay, Biomarker Discovery