Autopsies #4 and #5 were collected from rapid postmortem lung biopsies. Lungs were only partially fixed at this time (about 50% fixed in thicker segments) and were sectioned further into small 2-4cm chunks and immersed in 10% formalin for further investigation. After 48 hours, lungs were removed and immersion fixed whole in 10% formalin for 48 hours and then processed further. The patient expired and his family consented for autopsy. Autopsy #2 was a standard autopsy performed by anatomical pathology in the BS元 autopsy suite. Lung specimens were collected in 10% Zinc-formalin and stored for 72 h before processing for histology. Violin, Swarm and Bubble plots are created using python seaborn package version 0.10.1. The statistical significance of KM plots was assessed by log-rank test. Kaplan- Meier analysis is performed using lifelines python package version 0.14.6. Reactome identifies signaling and metabolic molecules and organizes their relations into biological pathways and processes. Pathway analysis of gene lists were carried out via the Reactome database and CluGo algorithm. Sample number of each analysis is provided with associated plots beside each GSE ID no. Multiple hypothesis correction was performed by adjusting p values with (fdr_bh: Benjamini/Hochberg principles). Standard t-tests were performed using python _ind package (version 0.19.0) with Welch's Two Sample t-test (unpaired, unequal variance (equal_var=False), and unequal sample size) parameters. All statistical tests were performed using R version 3.2.3 (). A color-coded bar plot is combined with a density plot to visualize the gene signature-based classification. Gene signature is used to classify sample categories and the performance of the multi- class classification is measured by ROC-AUC (Receiver Operating Characteristics Area Under the Curve) values. The downstream BoNE composite score analyses were performed similarly to regular bulk RNASeq dataset. All other single-cell RNA Seq datasets GSE159354, GSE132914, GSE146981, and GSE149878 were processed using scanpy python package (v1.5.1) and converted to pseudo-bulk samples by adding counts from all cells and normalized using log2(CPM+1). Pseudo bulk datasets were prepared by adding counts from the different cell subtypes and normalized using log2(CPM+1). B cells (CD19, MS4A1, CD79A), T cells (CD3D, CD3E, CD3G), CD4 T cells (CCR7, CD4, IL7R, FOXP3, IL2RA), CD8 T cells (CD8A, CD8B), Natural killer cells (KLRF1), Macrophages, Monocytes and DCs (TYROBP, FCER1G), Epithelial (SFTPA1, SFTPB, AGER, AQP4, SFTPC, SCGB3A2, KRT5, CYP2F1, CCDC153, TPPP3) cells were identified using relevant gene markers using SCINA algorithm. The filtered barcode data matrix was processed using Seurat v3 R package. Single Cell RNASeq data from GSE145926 was downloaded from Gene Expression Omnibus (GEO) in the HDF5 Feature Barcode Matrix Format. Pathway analysis of gene lists were carried out via the reactome database and algorithm. Welch's Two Sample t-test (unpaired, unequal variance (equal_var=False), and unequal sample size) parameters were used to compare the differential signature score in different sample categories. Classification of sample categories using this ordering is measured by ROC-AUC (Receiver Operating Characteristics Area Under The Curve) values. The samples were ordered based on the final signature score. Third, the normalized expression values for every genes were added together to create the final score for the gene signature. Second, Gene expression values were normalized according to a modified Z-score approach centered around StepMiner threshold (formula = (expr - SThr)/3*stddev). ImmPRESS HRP Horse Anti-Rabbit IgG Polymer Detection Kit, Peroxidaseīoolean network explorer (BoNE) provides an integrated platform for the construction, visualization and querying of a gene expression signature underlying a disease or a biological process in three steps ( Figures 2, 4, 6D, 8D, S1): First, the expression levels of all genes in these datasets were converted to binary values (high or low) using the StepMiner algorithm. Leica DMI4000B (Automated Inverted Microscope) The Lancet Regional Health – Western PacificĬlaudin 4-specific Rabbit polyclonal antibodyĪnti-Rabbit IgG Secondary Antibody AlexaFluor 594 nmĪnti-Mouse IgG Secondary Antibody, AlexaFluor 488 nm.The Lancet Regional Health – Southeast Asia.The Lancet Gastroenterology & Hepatology.
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