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Sample GSM5724660

Query DataSets for GSM5724660

Status

Public on Apr 12, 2023

Title

CD68 DSP-1001660005232-A-A11

Sample type

SRA

Source name

C8 COVID LUNG

Organism

Homo sapiens

Characteristics

geomx oligo set: Cancer Transcriptome Atlas, (v1.0) GeoMx COVID-19
roi number: 4
ffpe: FFPE tissue

Extracted molecule

total RNA

Extraction protocol

nanoString GeoMx digital spatial profiling was applied to 3 COVID-19 and 1 normal lung tissues to acquire spatially resolved, quantitative measurements of gene expression in human lung macrophages, granulocytes and epithelium in FFPE tissue sections. Three cases of post-mortem COVID lung tissues and one case of surgically resected normal lung tissue were sectioned, deparaffinized, rehydrated and treated with low pH retrieval solution. nanoString GeoMX Digital Spatial ImmuneOncology and COVID-19 RNA probes were hybridized to sections overnight. Sections were then stained with a cocktail of immunofluorescent antibodies comprised of Alexa fluor 532-labeled anti-CD68 (Clone KP1, 1:100, Novus Biologicals, USA), Alexa fluor 647-labeled Myeloperoxidase (Clone 2C7, 1:500, Novus Biologicals, USA), Alexa fluor 594 labelled anti-PanCytokeratin (C AE-1, AE-3) and Alexa fluor 488 SYTO13 Nuclear stain. Digital scanning of slides was performed on the GeoMX Digital Profiler (nanoString Technologies, Inc.). Regions of Interest (ROI) and cell type with a spatial resolution of approximately 10 mm were selected, exposed to UV light for RNA probe cleavage and oligonucleotide collection.
Samples were sequenced on a NovaSeq6000.

Library strategy

OTHER

Library source

transcriptomic

Library selection

other

Instrument model

Illumina NovaSeq 6000

Description

CD68
RNA (amplified oligo nucleotide probes)

Data processing

Counts from barcodes corresponding to RNA probes were normalized with internal positive and negative controls and then normalized to ROI area.
Data were pre-processed in nanoString DSP Software v 2.2.0.123 with Q3 normalization. ROIs were classified according to predominant cell type (epithelium, granulocyte, macrophage) in one of two conditions (COVID-19 positive and normal). The R BioConductor packages edgeR (Robinson et al., 2010) and limma (Ritchie et al., 2015) were used to implement the limma-voom (Law et al., 2014) method for differential expression analysis within and across cell types and condition. Trimmed mean of M-values (TMM) (Robinson and Oshlack, 2010) normalization was applied. The experimental design was modeled upon cell type and condition (~0 + cellType_condition). The voom method was employed to model the mean-variance relationship, after which lmFit was used to fit per-gene linear models and empirical Bayes moderation was applied with the eBayes function. Significance was defined by using an adjusted p-value cut-off of <0.05 after multiple testing correction (Benjamini and Hochberg, 1995) using a moderated t-statistic in limma.
Genome_build: GCA_000001405.15
Supplementary_files_format_and_content: Nanostring GeoMx Q3 normalized data file as csv file, Included relative expression levels of genes

Submission date

Dec 08, 2021

Last update date

Apr 12, 2023

Contact name

Judith Varner

E-mail(s)

jvarner@ucsd.edu

Phone

8588220086

Organization name

University of California, San Diego