scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation

Abstract

BackgroundThe Human Cell Atlas is a large international collaborative effort to map all cell types of the human body. Single-cell RNA sequencing can generate high-quality data for the delivery of such an atlas. However, delays between fresh sample collection and processing may lead to poor data and difficulties in experimental design.ResultsThis study assesses the effect of cold storage on fresh healthy spleen, esophagus, and lung from ≥ 5 donors over 72 h. We collect 240,000 high-quality single-cell transcriptomes with detailed cell type annotations and whole genome sequences of donors, enabling future eQTL studies. Our data provide a valuable resource for the study of these 3 organs and will allow cross-organ comparison of cell types.We see little effect of cold ischemic time on cell yield, total number of reads per cell, and other quality control metrics in any of the tissues within the first 24 h. However, we observe a decrease in the proportions of lung T cells at 72 h, higher percentage of mitochondrial reads, and increased contamination by background ambient RNA reads in the 72-h samples in the spleen, which is cell type specific.ConclusionsIn conclusion, we present robust protocols for tissue preservation for up to 24 h prior to scRNA-seq analysis. This greatly facilitates the logistics of sample collection for Human Cell Atlas or clinical studies since it increases the time frames for sample processing.

Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection

“…Here, we explored the published scRNA-seq datasets from various tissues and organs of different human body systems, including the respiratory system [4,5] (nasal mucosa, respiratory track, bronchus, and lung), the cardiovascular system [6] (heart), the digestive system [7][8][9][10] (esophagus, stomach, ileum, and liver), and the urinary system [11,12] (kidney and bladder). The lung scRNA-seq data were acquired from the Gene Expression Omnibus (GEO) database under the series number GSE122960; the nasal mucosa, respiratory track, bronchus scRNA-seq data were from GSE121600; the heart data were from GSE106118; the esophagus data were downloaded from https://www.tissuestabilitycellatlas.org/; the ileum data were from GSE134809 sample GSM3972018; the stomach data were from GSE134520 sample GSM3954949; the liver data were from GSE115469; the kidney data were from GSE109564; and the bladder data were from GSE129845 sample GSM3723358.…”

Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses

“…GEO (https://www.ncbi.nlm.nih.gov/geo/) or Human Cell Atlas (https://www.humancellatlas.org). Totally, we curated single cell gene expression matrices of 13 human tissues, including lung [8], liver [9], ileum [10], rectum [10], blood [11], bone marrow [12], skin [13], spleen [14], esophagus [14], colon [15], eye [16], stomach [17] and kidney [18] (Table S1). For each tissue, we performed cell clustering and dimension reduction on the scaled gene expression matrix using Seurat package [19].…”

Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

“…3a). These included 6 published datasets [63][64][65][66][67][68] and 16 datasets that are not yet published [69][70][71][72][73] . In the case of unpublished data, we only obtained singlecell expression counts for the three genes, as well as the total UMI counts per cell, cell identity annotations, and the relevant anonymous clinical variables (age and sex, as well as smoking status when ascertained).…”

“…Aggregation of these datasets was enabled by harmonizing the cell type labels of individual datasets within Scanpy 165 (version 1.4.5.1). We harmonized annotations together with data contributors using a preliminary ontology generated on the basis of 5 published datasets [63][64][65][66]174 with 3 levels of annotations (level 1 -lowest resolution; Supplementary Table 2). We further harmonized metadata by collapsing the smoking covariate into "has smoked" and "has never smoked" and by taking mean ages where only age ranges were given.…”