scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation - PubMed (original) (raw)

doi: 10.1186/s13059-019-1906-x.

A Wilbrey-Clark 1, R J Miragaia 1, K Saeb-Parsy 3, K T Mahbubani 3, N Georgakopoulos 3, P Harding 1, K Polanski 1, N Huang 1, K Nowicki-Osuch 4, R C Fitzgerald 4, K W Loudon 5, J R Ferdinand 5, M R Clatworthy 5, A Tsingene 1, S van Dongen 1, M Dabrowska 1, M Patel 1, M J T Stubbington 1 6, S A Teichmann 1, O Stegle 2, K B Meyer 7

Affiliations

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

E Madissoon et al. Genome Biol. 2019.

Abstract

Background: The 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.

Results: This 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.

Conclusions: In 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.

Keywords: Esophagus; Human; Ischemic time; Lung; Single-cell RNA sequencing; Spleen.

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Conflict of interest statement

MJTS has been employed by 10x Genomics since April 2018; this employment had no bearing on this work. RJM has been employed by MedImmune/AstraZeneca since October 2018; this employment had no bearing on this work. The other authors declare that they have no competing interests.

Figures

Fig. 1

Fig. 1

scRNA-seq quality metrics remain stable for at least 24 h of cold storage. a Experimental design: samples from the lung, esophagus, and spleen were collected from 5 or 6 donors each and stored as whole organ pieces at 4 °C for different time points prior to tissue processing for scRNA-seq and bulk RNA-seq. b–e Change of quality metrics of scRNA-seq data obtained with time, showing the b number of reads per sample, c number of cells per sample, d median number of genes detected per cell, and e number of genes confidently mapped to the transcriptome

Fig. 2

Fig. 2

Exploration of loss of data quality with time in the spleen compared to other organs. a Violin plot of good quality reads mapped to exons in the spleen, b mean percentage of good quality exonic reads in all organs, c violin plot of good quality reads per exon in the spleen, d mean percentage of intronic reads across all organs, e box plot of percentage of mitochondrial reads in the spleen, f mean percentage of mitochondrial reads across all organs, and e percentage of cells with greater than 10% mitochondrial reads. The tissue of origin is indicated by color

Fig. 3

Fig. 3

Loss of data quality is associated with increased “ambient RNA” and “debris” reads in the data. a Average spread of normalized UMI counts per droplet in the spleen, which were classified into ambient RNA, debris, and cellular material. b Mean values of normalized UMI in droplets containing debris or c cellular material. Individual sample means are shown for each donor with corresponding shape; color represents tissue. Means across donors per time point are shown by filled circles; whiskers represent standard deviation. p values were gained by Student’s paired (T0 vs 72 h) and non-paired (T0 vs 24 h) t test

Fig. 4

Fig. 4

Cell types identified in different organs with time a UMAP projections of scRNA-seq data for the lung (n = 57,020), b esophagus (n = 87,947), and c spleen (n = 94,257). d–f Proportions of cells identified per donor and per time point for the d lung, e esophagus, and f spleen. g–j The single-cell UMAP plots for each organ with length of storage time highlighted. j Percent variance explained in the combined dataset by cell types, n counts, donor, tissue, and time points

Fig. 5

Fig. 5

Cell type-specific changes in transcriptome. a Proportion of mitochondrial reads relative to T0 calculated for the spleen, esophagus, and lung. The fold change (FC) of mitochondrial percentage is measured in every cell type between T0 and 12 h, 24 h, and 72 h. FC is indicated by color with white indicating no fold change (FC = 1), blue indicating a drop in mitochondrial percentage, and red indicating an increase in mitochondrial percentage compared to T0 (FC > 1). The Benjamini and Hochberg (BH)-adjusted p values are indicated by asterisk as follows: *p value < 0.01, **_p_ value < 0.00001, and ***_p_ value < 0.00000001. All cells are used including those with high mitochondrial percentage (> 10%), annotated via scmap tool. Gray indicated time points with fewer than 5 cells. Missing values (no sample) are shown by a cross. b Percentage of variance in gene expression explained by time for cell type groups in the lung, esophagus, and spleen. Cell type groups in the lung are Endothelial (Blood vessel, Lymph vessel), Alveolar (Alveolar Type 1 and Type 2), Mono_macro (Monocyte, Macrophage_MARCOneg, Macrophage_MARCOpos), and T_cell (T_CD4, T_CD8_Cyt, T_regulatory). Cell type groups in the spleen are Mono_macro (Monocyte, Macrophage), NK (NK_FCGR3Apos, NK_CD160pos), T_cell (T_CD4_conv, T_CD4_fh, T_CD4_naive, T_CD4_reg, T_CD8_activated, T_CD8_CTL, T_CD8_gd, T_CD8_MAIT-like, T_cell_dividing), and B_cell (B_follicular, B_Hypermutation, B_mantle). c Hierarchical clustering of cell types of up to 10 cells per cell type per tissue per donor and time. Cell attributes (cell type, organ, time, and donor ID) are indicated by color

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