Spatial mapping of SARS-CoV-2 and H1N1 Lung Injury Identifies Differential Transcriptional Signatures
Here, we analyzed specific ARDS regions (lower left lung lobe) of interest utilizing a new spatial transcriptomic platform (Nanostring GeoMx) on autopsy-derived lung tissue from patients with SARS-CoV-2 (n=3), H1N1 (n=3), and a unique dual- infected individual (n=1). Paraffin embedded tissues were processed and analyzed at NanoString techonology laboratories using a combination of fluorescently labeled antibodies, anti-CD68 (Santa Cruz, sc-20060 AF647, RRID: KP1), anti-EpCAM (Abcam, ab213500, RRID:EPR20532-222), anti-smooth muscle actin (Invitrogen, 53-9760-82, RRID:1A4) and the GeoMX COVID-19 Immune Response Atlas gene set with custom probe set specific for SARS-CoV-2 lung infection and tissue responses. Selection of regions of interests (ROI, 12 per patient) was performed based on the immunofluorescent viral staining, the cellular immunofluorescent profile and the pathological features of ARDS (i.e. presence of hyaline membranes and diffused alveolar damage) observed in the H&E stained sections. To ensure even and representative selection of ROIs, only patients lower left lung tissue was analyzed. Each patient had 2-3 total lung areas selected in regions of ARDS (confirmed by Dr. Benson, pathologist), 2-4 ROIs for epithelial cells (normal epithelium vs hyperplastic), 2-3 vascular beds selected, and 2-4 macrophage populations (infiltrate and clusters). For cell-specific profiling, at least 50 cells per ROI were utilized for analyses. The 1860 gene profile not only identified increased regional coagulopathy, but also robust transcriptional signatures of enhanced extracellular remodeling, alternative macrophage activation, and squamous metaplasia of type II pneumocytes in SARS- CoV-2. These gene signatures were expressed and enhanced in alveolar epithelium, vascular tissue, and lung macrophages. Both the H1N1 and dual infected transcriptome demonstrated an enhanced antiviral response compared to SARS-CoV-2.
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Counts from each area of interest are analyzed and under-sequenced samples are dropped (field of view percentage of 75% and Binding density from 0.1 to 2.25), and a probe QC, where mRNAs are targeted by multiple probes and outlier probes are dropped from downstream data analysis (positive spike-in normalization factor between 0.3 and 3). Then RNA counts were normalized using a signal-based normalization, in which individual counts are normalized against the 75th percentile of signal from their own area of interest, which gives the data presented in this file.The final list of detectable genes was then obtained by dropping genes in each specific group (ARDS regions, vascular, epithelium, macrophages) by using a limit of quantification (LOQ) of 20% coverage within replicates. The LOQ was calculated using the geometric mean and geometric standard deviation of negative probes in the dataset. Counts were normalized to log2 and statistical comparisons were performed using a two sample t-test upon normality testing and ComBat correction for batch effect (H1N1_3 and COVID_3 are part of batch 2). Comparison of SARS-CoV-2 to H1N1 was performed by averaging the technical replicates and by comparing biological replicates (n=3 per group). Comparison of the double infected patient to the single infection was done using technical replicates (ROIs) as unique samples for statistical reasons. P-value threshold for differential gene expression were set at p=0.02 and log2 fold change of 0.5.