Reprogramming efficiency in PIWI-deficient MEFs is not compromised.

<p>(A) Correlation between GFP+ colony number and percentage 12 days post transduction. Each dot represents a single well of the reprogramming experiments. (B) Representative FACS plot for SSEA1/Oct4-GFP at 12 days post-viral transduction. (C, D) Relative reprogramming efficiencies are shown, with the fold changes indicated. (C) Fully reprogrammed efficiency, assessed by the percentage of SSEA1+Oct4-GFP+ cells; (D) Intermediately reprogrammed efficiency, assessed by percentage of SSEA1+Oct4-GFP- cells; Student's t-test (two-tailed) is used for statistics. Error bars, standard error. n = experiments with independent MEFs. Ctrl, wild type or heterozygous littermate controls; TKO, triple knockout of piwi.</p

Differential expressed genes (DEGs) in PIWI-deficient iPSCs.

<p>(A) Venn diagram of the number of DEGs in 4 statistical methods. For the robustness of DEGs detection, we chose 90 DEGs which were identified at least two methods for further downstream analysis. (B) MA plot of all genes tested. X-axis represents average gene expression and Y-axis represents log fold changes. Red or blue dots represent up or down regulated genes, respectively. (C) Heatmap of 90 DEGs clustered by expression patterns. Each gene expression was standardized and the color represents z-score of each expression. Red or green color represent up or down regulation of the genes. (D) Correlation analysis of gene expression level between TKO and Ctrl iPSC (<i>n</i> = 4).</p

Genotypes of 120 offspring at age 10 months from male triple heterozygous–female triple knockout mating.

<p>Genotypes of 120 offspring at age 10 months from male triple heterozygous–female triple knockout mating.</p

Expression of <i>Piwi</i> transcripts.

<p>qRT-PCR comparison of <i>piwi</i> expression in mouse cells (A) and human cells (B). RNA was isolated from mouse ESCs (CCE) and embryonic fibroblasts (MEF) and human ESCs (H1 and H7), human foreskin keratinocytes, human foreskin fibroblasts. The ratios of individual <i>piwi</i> genes/eukaryotic 18S rRNA are shown for both panels.</p

Differentially expressed genes in triple knockout iPS cells.

<p>Differentially expressed genes in triple knockout iPS cells.</p

PIWI-deficient iPSCs form teratomas normally.

<p>Hematoxylin and eosin staining of teratoma sections showed differentiation of TKO iPS cells to tissues derived from all three germ layers, including the gut (endoderm), cartilage (mesoderm), and neural epithelium (ectoderm).</p

Piwi-deficiency does not affect reprogramming of MEFs.

<p>(A) iPS colonies exhibited typical ES cell morphology and expressed Oct4-GFP homogeneously. (B) Representative figure showing TKO iPS cells expressed comparable level of Oct4-GFP as Ctrl cells. (<i>n</i> = 20). Relative expression levels of (C) Oct4-GFP and (D) SOX2 proteins, as denoted by quantitative mean fluorescence intensity (MFI) shown by FACS analysis (<i>n</i> = 3 and 8, respectively). (E) Both TKO and Ctrl cells remained pluripotent and express Oct4-GFP⁺ SSEA1⁺ (>99%) over 30 passages. (F) A competition strategy was designed to determine if Piwi depletion compromises iPSC self-renewal. GFP+ cells (marked the iPSCs) were mixed in a 1-to-1 ratio with normal mouse ESCs (GFP-) cells, and cultured in the presence of LIF. With each passage the ratio of GFP/total cells was measured by FACS. The proportion of GFP+ cells with TKO were indiscernible from Ctrl cells over five passages (n = 3). Ctrl, wildtype or heterozygous littermate controls; TKO, triple knockout of piwi.</p

Additional file 1: of Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes

Text S1. Materials and data collection. Text S2. Details of smoothing and Feature Topology Plots (FTP). Text S3. Simulation setting to evaluate iPF. Text S4. Comprehensive validation scheme for iPF. Figure S5. (A) An illustration of integrated omics data sets, (B) A workflow to generate future topology plot (FTP). Figure S6. Flowchart of validation scheme for Integrative phenotyping framework for multiple omics data sets. Figure S7. An example of iPF that utilizes fused multiple data sets at the stage (vi). Figure S8. Examples of iPF using various combinations of the omics data sets (pooled analysis). Figure S9A. The gap statistics and its scree plot to choose the optimal number of clustering (clinical and miRNA data). Figure S9B. The gap statistics and its scree plot to choose the optimal number of clustering (mRNA and miRNA data). Figure S9C. The gap statistics and its scree plot to choose the optimal number of clustering (mRNA and clincal data). Figure S9D. The gap statistics and its scree plot to choose the optimal number of clustering (clincal data and combined data of mRNA and miRNA). Figure S10. The best choice of the number of feature modules. Figure S11. Simulation study shows robust true feature discovery in “Feature Fusion”. The x-axis represents multiplication levels of noise features. The y-axis represents average ARIs from 100 simulations. Each figure is generated based on simulation scenarios of the different number of true features (e.g., 200, 400, and 600, respectively). Figure S12. Immunomodulating drugs target overexpressed genes in module two. Table S13. The description of mRNA and miRNA lung disease data. Table S14. Various correlation types depending on variable attributes. Table S15. The demographic summary of clinical features in each sub-cluster. Table S16. Target gene enrichment analysis (via Fisher exact test) related to twelve. Table S17. Regression analysis on target miRNA features, and coefficient of determination significant miRNA features. Table S18. The top disease or functional annotations associated with genes in module two in Cluster E patients. Figure S19. Basic consensus clustering using only gene expression data. (DOCX 6398 kb

Additional file 3: of Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes

This file includes 669 clinical variables analyzed in this paper. (XLSX 45 kb

Long, high-quality, consensus sequences accurately benchmark transcript diversity.

<p><b>a</b>, Length distributions of full-length (FL) input reads, high-quality CCS reads, and ToFU transcript sequences. <b>b</b>, Histogram of percent nucleotide identity of ToFU transcript sequences aligned to the reference genome. <b>c</b>, Accumulative histogram of number of reference annotations that have a ToFU transcript that completely covers each annotated junction (transcript-covered) or only partially covers the annotated gene (loci-covered). Reference annotations that were not assayed (blue stack) are also shown. <b>d</b>, Distribution of distinct isoforms per loci for the reference annotation and ToFU transcript set. <b>e</b>. Illumina short-read coverage (grey) and junction support (red lines, associated numbers indicate Illumina reads that support each splice junction) aligned along the reference annotated transcript (blue) for a glycosyl hydrolase gene with 120 distinct PacBio isoforms aligned below (splice junctions are shown in red and exon sequences are shown in green). <b>f</b>, An enlarged view of the region between two starts in <b>2e</b>.</p