Spatial Transcriptome for the Molecular Annotation of Lineage Fates and Cell Identity in Mid-gastrula Mouse Embryo

Abstract

During preparation of Figure 1A, the left and right sides all of tissue images were labeled in reverse and were noted in this way in the figure and figure legend in the originally published version of this article. The originally published paper reported that there was no leftright asymmetry at E7.0 stage, so the L-R inversion did not change our results, and the overall conclusion is unaffected. However, the authors have noticed this error and are correcting their paper. The corrected Figure 1 and Figure 1A legend are below. The authors apologize for any confusion generated by the original figure.

Imbalance of Excitatory/Inhibitory Neuron Differentiation in Neurodevelopmental Disorders with an NR2F1 Point Mutation

“…For functional enrichment analysis, DEGs' functional enrichment analysis, including GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment, were performed using clusterProfiler (version 3.4.4) (Yu et al, 2012). For target gene enrichment analysis for different signaling pathways, in addition to the signaling pathways (TGF-b, Hedgehog, BMP, FGF, Nodal, and WNT) analyzed previously (Li et al, 2017a;Peng et al, 2016), two other signaling pathways (Notch and Hippo/Yap) were included in this analysis. Rank Prod (p < 0.001 for Notch and p < 0.01 for Hippo/Yap) results were generated using data from public datasets GSE15268 and GSE69669, respectively.…”

“…For gene set enrichment analysis (GSEA), the published GEO dataset (GSE) for the Hedgehog signaling pathway perturbations was assessed by comparing control samples with treatment samples using RankProd (version 3.2.0, p value % 0.001) (Hong et al, 2006) to identify the potential signaling target genes of Hedgehog pathway. Conserved genes in humans were selected as the target genes of the Hedgehog signaling pathway (Li et al, 2017a;Peng et al, 2016). To determine the enrichment of the pathway target genes in WT or Mut cells, GSEA (Mootha et al, 2003;Subramanian et al, 2005) was used to evaluate whether a specific signaling pathway target gene set is significantly enriched in WT or Mut cells.…”

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

“…1a). These approaches have been applied to assess the homeostasis and development of healthy tissue in the liver [21][22][23][24] , intestine 25,26 , bone marrow 27 , mouse embryo 28 , brain 13,29 , reproductive system 30 and heart 31,32 . The widest application of spatial transcriptomics to studying disease has been the tumour microenvironment of cancers [33][34][35] .…”

“…Immunofluorescence based on gene markers specific to the cell types validated deconvolution findings, thereby providing insight into resident cell subpopulations active in normal bone marrow. LCM on single mid-gastrulation mouse embryos followed by RNA-seq was performed to render a 3D understanding of the regionalization of cell fates in the embryo 28 .…”

Robust Acquisition of High Resolution Spatial Transcriptomes from Preserved Tissues with Immunofluorescence Based Laser Capture Microdissection

“…In this regard, laser capture microdissection (LCM) (9) combined with RNAseq is a powerful approach with its own unique strength for the characterization of spatial transcriptomes, since cells are directly isolated from the tissue with complete knowledge of their spatial location for an unbiased delineation of their transcriptome. Although identifying cells for LCM based on cell morphology alone is possible for some specific cell types under certain conditions (10)(11), the vast majority of cell types in a tissue cannot be distinguished by morphology alone (12). Therefore, use of specific phenotype markers, especially immunofluorescence (IF) based cell type identification, remains the most attractive for LCM to acquire comprehensive transcriptomes in situ (13)(14).…”