8 subcluster

Yearly citation counts for the publication titled "Subclustering among Local Group Galaxies".

Scripts used for analysis of V1 and V2 Datasets.seurat_v1.R - initialize seurat object from 10X Genomics cellranger outputs. Includes filtering, normalization, regression, variable gene identification, PCA analysis, clustering, tSNE visualization. Used for v1 datasets. merge_seurat.R - merge two or more seurat objects into one seurat object. Perform linear regression to remove batch effects from separate objects. Used for v1 datasets. subcluster_seurat_v1.R - subcluster clusters of interest from Seurat object. Determine variable genes, perform regression and PCA. Used for v1 datasets.seurat_v2.R - initialize seurat object from 10X Genomics cellranger outputs. Includes filtering, normalization, regression, variable gene identification, and PCA analysis. Used for v2 datasets. clustering_markers_v2.R - clustering and tSNE visualization for v2 datasets. subcluster_seurat_v2.R - subcluster clusters of interest from Seurat object. Determine variable genes, perform regression and PCA analysis. Used for v2 datasets.seurat_object_analysis_v1_and_v2.R - downstream analysis and plotting functions for seurat object created by seurat_v1.R or seurat_v2.R. merge_clusters.R - merge clusters that do not meet gene threshold. Used for both v1 and v2 datasets. prepare_for_monocle_v1.R - subcluster cells of interest and perform linear regression, but not scaling in order to input normalized, regressed values into monocle with monocle_seurat_input_v1.R monocle_seurat_input_v1.R - monocle script using seurat batch corrected values as input for v1 merged timecourse datasets. monocle_lineage_trace.R - monocle script using nUMI as input for v2 lineage traced dataset. monocle_object_analysis.R - downstream analysis for monocle object - BEAM and plotting. CCA_merging_v2.R - script for merging v2 endocrine datasets with canonical correlation analysis and determining the number of CCs to include in downstream analysis. CCA_alignment_v2.R - script for downstream alignment, clustering, tSNE visualization, and differential gene expression analysis.

This short report describes the characteristics of actinobacteriophages assigned to subcluster AZ3.

Top 30 enriched genes per supporting cell subcluster in P0 control cochlear duct.

This short report describes the characteristics of actinobacteriophages assigned to subcluster AZ4.

aAverage synonymous substitution per codon (dS) for each branch in the gene tree of individual subgroups was calculated based on the alignment of paralogs in the subgroup.bThe ratio of the average dS per branch calculated based on alignment of the most divergent (dSECDhigh) and the least divergent (dSECDlow) ectodomains in each protocadherin subgroup.

Markers specifically expressed in sub-clusters of trophoblast cells (log2FC>1, FDR<0.01), selected from pairwise comparison among the seven sub-clusters are ranked by normalized UMI counts from the most abundant to the least. No markers emerged for sub-cluster 1 and 7 from pairwise comparison, therefore markers of sub-clusters 2, 3, 4, 5, 6 are included in the table. Human first trimester placenta single cell RNA-sequencing.

Comparison of ScRDAVis and other popular single cell data analysis tools.

Subcluster-enriched genes identified by DVB scRNA-seq data.

Background: Ischemic stroke (IS) is a fatal cerebrovascular disease involving several pathological mechanisms. Modification of 7-methylguanosine (m7G) has multiple regulatory functions. However, the expression pattern and mechanism of m7G in IS remain unknown. Herein, we aimed to explore the effect of m7G modification on IS.Methods: We screened significantly different m7G-regulated genes in Gene Expression Omnibus datasets, GSE58294 and GSE22255. The random forest (RF) algorithm was selected to identify key m7G-regulated genes that were subsequently validated using the middle cerebral artery occlusion (MCAO) model and quantitative polymerase chain reaction (qPCR). A risk model was subsequently generated using key m7G-regulated genes. Then, “ConsensusClusterPlus” package was used to distinguish different m7G clusters of patients with IS. Simultaneously, between two m7G clusters, differentially expressed genes (DEGs) and immune infiltration differences were also explored. Finally, we investigated functional enrichment and the mRNA–miRNA–transcription factor network of DEGs.Results: RF and qPCR confirmed that EIF3D, CYFIP2, NCBP2, DCPS, and NUDT1 were key m7G-related genes in IS that could accurately predict clinical risk (area under the curve = 0.967). NCBP2 was the most significantly associated gene with immune infiltration. Based on the expression profiles of these key m7G-related genes, the IS group could be divided into two clusters. According to the single-sample gene set enrichment analysis algorithm, four types of immune cells (immature dendritic cells, macrophages, natural killer T cells, and TH1 cells) were significantly different in the two m7G clusters. The functional enrichment of 282 DEGs between the two clusters was mainly concentrated in the “regulation of apoptotic signaling pathway,” “cellular response to DNA damage stimulus,” “adaptive immune system,” and “pyroptosis.” The miR-214–LTF–FOXJ1 axis may be a key regulatory pathway for IS.Conclusion: Our findings suggest that EIF3D, CYFIP2, NCBP2, DCPS, and NUDT1 may serve as potential diagnostic biomarkers for IS and that the m7G clusters developed by these genes provide more evidence for the regulation of m7G in IS.

The non-book component contains supplementary data to support Chapters 3, 4, and 5.Moreover, the thesis abstract is attached.Abstract.docxFiles containing the code (R Scripts) used to run the analyses:Subclustering analysis.rmd (subclustering analysis of neuronal, skeletal, immune and apical plate cell types)Sp_2dpf_clustering analysis.rmd (clustering analysis of the 2 dpf scRNA-seq dataset)Sp_3dpf_clustering analysis.rmd (clustering analysis of the 3 dpf scRNA-seq dataset)Sp_5dpf_clustering analysis.rmd (clustering analysis of the 5 dpf scRNA-seq dataset)Sp_2_3_dpf_clustering analysis.rmd (clustering analysis of the 2 and 3 dpf integrated scRNA-seq datasets)Data discussed in Chapter 3 "Embryonic and larval cell types at a single cell resolution"1.Tables containing all the S. purpuratus genes expressed in different time-points and cell types. The average expression and the average scaled expression is reported. Annotated by WHL ID.Sp_2dpf_all_genes.xlsxSp_3dpf_all_genes.xlsSp_5dpf_all_genes.xlsx2.Tables containing all the S. purpuratus marker genes (highly differentially expressed genes) in different time-points and cell types. P_value and adjusted P_value are reported. Annotated by WHL ID.Sp_2dpf_marker_genes.xlsSp_3dpf_marker_genes.xlsSp_5dpf_marker_genes.xls3.Table containing additional information on the genes identified in this thesis (NCBI annotation, go annotation, PFAM).Sp_genes_annotation_NCBI_GO_PFAM_FUNCTIONAL.xlsData discussed in Chapter 4 "Neuronal diversity during sea urchin development"1.Table containing all the S. purpuratus genes expressed in different neuronal types of the 3dpf pluteus larva. The average expression and the average scaled expression is reported. Annotated by WHL ID.Sp_3 dpf neuronal subclustering_all genesData discussed in Chapter 5 "Pancreatic-like cell types in sea urchin"1.Tables containing all the S. purpuratus affected genes after Sp-Pdx1 knockdown at 3dpf pluteus larva and the ones affected only in the PPE neurons. (wild type condition vs. Sp-Pdx1 knockdown larvae at 3 dpf). Annotated by WHL ID.Sp-Pdx1 differential RNA-seq.xlsxDifferentially expressed genes in the PPE neurons.xls

Using 23-months old mice of a inducible expression of human a-syn constructs based Parkinson mouse model, we produced a single nucleus RNA dataset by cutting 0mm Bregma to -5mm Bregma. The Chromium 3’ Single Cell Library Kit (10x Genomics) was used and Sequencing was performed on a NovaSeq 6000. From the same model we also used 20-months old mice with the Visium Spatial V1 platform (10x Genomics). Sequencing was performed on a NovaSeq 6000. Both were PE150.

snRNA pipeline: For the alignment of reads, a custom reference was created by adding the sequences of the V1S/SV2 transgene and the Camk2a promoter to the mm10 mouse reference genome. Count matrices generated by cellranger count 7.1 were loaded into an AnnData object and processed using the Python-based framework Scanpy 1.10.2. Integration with R, where needed, was facilitated through the rpy2 package. Raw count matrices were corrected for ambient RNA contamination using the SoupX 1.6.2. To remove potential doublets, scDblFinder 1.18.0 was employed with a fixed seed (123). Nuclei with nUMI and nGenes values exceeding three median absolute deviations (MADs) from the median were excluded. Genes detected in fewer than five nuclei across the dataset were excluded. The resulting dataset was normalized via scanpy.pp.normalize_total and scanpy.pp.log1p. Highly variable genes were identified using the function scanpy.pp.highly_variable_genes with the Seurat v3 flavor, selecting the top 4,000 genes. Dimensionality reduction was performed using principal component analysis (PCA) and batch effects were corrected using the python-implemented version of Harmony via the function scanpy.external.pp.harmony_integrate. Harmony embeddings were then used to construct a k-nearest neighbor (kNN) graph with scanpy.pp.neighbors. Clustering was performed using Leiden clustering with standard parameters via the function scanpy.tl.leiden. Clusters were annotated using literature, the mousebrain.org, and markers identified via the FindConservedMarkers function in Seurat. First, neurons and non-neuronal cells were distinguished using mainly canonical markers, such as but not limited to Rbfox3 (neurons), Mbp (oligodendrocytes), Acsbg1 (astrocytes), Pdgfra (oligodendrocyte precursor cells), Inpp5d (microglia), Colec12 (vascular cells), and Ttr (choroid plexus cells). Neurons were further classified into Vglut1 (Slc17a7), Vglut2 (Slc17a6), GABA (Gad2), cholinergic (Scube1), and dopaminergic (Th) neurons. Vglut1 and GABA neurons were further annotated into subtypes based on subclustering and FindConservedMarkers markers.

visium spatial pipeline: Sequences were fiducially aligned to spots using Loupe Browser ver. 8. All aligned sequences were mapped using spaceranger count 3.0.1 with a custom refence, which included sequences for the promotor and transgene (Camk2aTTA, V1S/SV2) to the mouse genome mm39. We filtered each sample of the Visium Spatial dataset based on the MAD filtering of number of reads (nUMI), number of genes (nGene), and percentage of mitochondrial genes (percent.mt). A spot was filtered out if it was outside of 3x MAD value in at least two metrics. Filtered samples were merged into one Seurat 5.1.0 object and we obtained normalized counts by the SCTransform function of Seurat. Integration was performed using Harmony 1.2.0 on 50 PCA embeddings and clustering was done using Leiden clustering based on 30 harmony embeddings. Integrated clusters were visualized using the UMAP method. Samples that were not successfully integrated (based on similarity measures of the harmony embeddings) and showed high percentage.mt or low nUMI levels compared to other samples, were removed from subsequent analysis. A final integration and clustering were performed after filtering. Regions were first annotated based on a 0.1 resolution clustering to get high level region annotation (Cortex, Hippocampus, Subcortex). Each high-level region was further annotated based on either more granular resolutions or subclustering. Marker genes from mousebrain.org and literature were used in combination with the Allen mouse brain atlas to obtain anatomically relevant annotations.

This short report describes the characteristics of actinobacteriophages assigned to subcluster B1.

DEGs in 5Ht subcluster U1_wt showed open chromatin peaks, expressed in 3M mice, and were also DEGs in subcluster Epi-C11 in a recent mouse aging atlas study.

🇺🇸 미국 English This table contains some of the results from a study of the structure of the high-mass star-forming region RCW 38 and the spatial distribution of its young stellar population. Spitzer Infrared Array Camera (IRAC) photometry (3-8 micron) is combined with Two Micron All Sky Survey (2MASS) near-IR data to identify young stellar objects (YSOs) by IR-excess emission from their circumstellar material. Chandra X-ray data are used to identify class III pre-main-sequence stars lacking circumstellar material. The authors identify 624 YSOs: 23 class 0/I and 90 flat spectrum (FS) protostars, 437 class II stars, and 74 class III stars. They also identify 29 (27 new) O star candidates over the IRAC field. Seventy-two stars exhibit IR-variability, including 7 class 0/I and 12 flat spectrum YSOs. A further 177 tentative candidates are identified by their location in the IRAC [3.6] versus [3.6]-[5.8] color-magnitude diagram. The authors find strong evidence of subclustering in the region. Three subclusters were identified surrounding the central cluster, with massive and variable stars in each subcluster. The central region shows evidence of distinct spatial distributions of the protostars and pre-main-sequence stars. A previously detected IR cluster, DB2001_Obj36, has been established as a subcluster of RCW 38. This suggests that star formation in RCW 38 occurs over a more extended area than previously thought. The gas-to-dust ratio is examined using the X-ray derived hydrogen column density, NH and the K-band extinction, and found to be consistent with the diffuse interstellar medium, in contrast with Serpens and NGC 1333. The authors posit that the high photoionizing flux of massive stars in RCW 38 affects the agglomeration of the dust grains. This table contains the list of 624 young stellar objects (given in Tables 3 and 4 of the reference paper) found among the Spitzer sources in the field of RCW 38 using the two selection techniques described in Section 3 of the reference paper: (1) selection of stars with IR excesses in IR color-color diagrams, and (2) identification of X-ray luminous YSOs by comparing X-ray sources with IR detections. The latter technique was used to identify Type III YSOs lacking emission from a dusty disk. This table does NOT contain (i) the 177 candidate YSOs listed in Table 5 of the reference paper which were identified using the [3.6] versus [3.6] - [5.8] color-magnitude diagram, since contamination removal methods could not be utilized for these objects, (ii) the 24 candidate variable YSOs listed in Table 6 of the reference paper, nor (iii) 21 of the 29 candidate O-star cluster members which were listed in table 7 of the reference paper. This table was created by the HEASARC in January 2012 based on an electronic version of Tables 3 and 4 from the reference paper which were obtained from the ApJ web site. This is a service provided by NASA HEASARC .

Seurat objects containing the raw and normalized data for:Normal bone marrow (NBM) atlas: contains all cells obtained through segmentation after filtering and QC. Includes coarse and fine level of annotations that were obtained through an iterative process of subclustering. Neighborhood analysis results are included as a metadata column. Additional Osteo-MSC and Fibro-MSC cells that were manually annotatedAML/NSM CODEX data: contains all cells after filtering for 3 diagnostic and 2 post-therapy AML samples as well as 3 negative staging marrow samples. Cell labels were derived through reciprocal principal component analysis (RPCA) reference mapping onto the normal bone marrow atlas. Neighborhood analysis was conducted separately for AML Diagnostic, AML Post-Therapy, and NSM samples. Neighborhoods were manually annotated for each set. The results of the neighborhood analysis were merged and included in the metadata of the Seurat object. All normalized data is stored in the Seurat assay object. Markers that were not included in normalization and downstream analysis are included with raw values as a metadata column. Full source code used to generate these objects can be found on GitHub: https://github.com/shovikb94/spatial-bonemarrow-atlas/tree/mainSee related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.7174914

This short report describes the characteristics of actinobacteriophages assigned to subcluster AU6.

We also present the Peirce Skill score (PSS) for each subcluster and days. Capital characters in header refer to green (G), neutral (N), red (R) and black (B) days. Parameter settings: pc = 0.08, ϵ = 30 minutes and tmax = 90 minutes.

Diabetes-associated cognitive decline (DCD), is one of the complications of diabetes, which is characterized by a series of neurophysiological and pathological abnormalities. However, the exact pathogenesis of DCD is still unknown. Single-cell RNA sequencing (scRNA-seq) could discover unusual subpopulations, explore functional heterogeneity and identify signaling pathways and potential markers. The aim of this research was to provide deeper opinion into molecular and cellular changes underlying DCD, identify different cellular types of the diabetic mice hippocampus at single-cell level, and elucidate the factors mediating the pathogenesis of DCD. To elucidate cell specific gene expression changes in the hippocampus of diabetic encephalopathy. Single-cell RNA sequencing of hippocampus from db/m and db/db mice was carried out. Subclustering analysis was performed to further describe microglial cell subpopulations. Interestingly using immunohistochemistry, these findings were confirmed at the protein level. Single cell analysis yielded transcriptome data for 14621 hippocampal cells and defined 11 different cell types. Analysis of differentially expressed genes in the microglia compartments indicated that infection- and immune system process- associated terms, oxidative stress and inflammation play vital roles in the progression of DCD. Compared with db/m mouse, experiments at the protein level supported the activation of microglia, increased expression of inflammatory factors and oxidative stress damage in the hippocampus of db/db mouse. In addition, a major finding of our research was the subpopulation of microglia that express genes related to pro-inflammatory disease-associated microglia (DAM). Our research reveals pathological alterations of inflammation and oxidative stress mediated hippocampal damage in the db/db mice, and may provide potential diagnostic biomarkers and therapeutic interventions for DCD.