Project description:Recently, combining single-cell RNA sequencing (scRNA-seq) with upstream cell preservation procedures such as cryopreservation or methanol fixation has become more common. By separating cell handling and preparation, from downstream library generation, scRNA-seq workflows are more flexible and manageable. However, the inherent transcriptomics changes associated with cell preservation and how they may bias further downstream analysis remain unknown. Here, we present a side-by-side droplet-based scRNA-seq analysis, comparing the gold standard – fresh cells – to three different cell preservation workflows: dimethyl sulfoxide based cryopreservation, methanol fixation and CellCover reagent. Cryopreservation proved to be the most robust protocol, maximizing both cell integrity and low background ambient RNA. Importantly, gene expression profiles from fresh cells correlated most with those of cryopreserved cells. Such similarities were consistently observed across the tested cell lines (R ≥ 0.97), monocyte-derived macrophages (R = 0.97) and immune cells (R = 0.99). In contrast, both methanol fixation and CellCover preservation showed an increased ambient RNA background and an overall lower gene expression correlation to fresh cells. Thus, our results demonstrate the superiority of cryopreservation over other cell preservation methods. We expect our comparative study to provide single cell omics researchers invaluable support when integrating cell preservation into their scRNA-seq studies.
Project description:Recently, combining single-cell RNA sequencing (scRNA-seq) with upstream cell preservation procedures such as cryopreservation or methanol fixation has become more common. By separating cell handling and preparation, from downstream library generation, scRNA-seq workflows are more flexible and manageable. However, the inherent transcriptomics changes associated with cell preservation and how they may bias further downstream analysis remain unknown. Here, we present a side-by-side droplet-based scRNA-seq analysis, comparing the gold standard – fresh cells – to three different cell preservation workflows: dimethyl sulfoxide based cryopreservation, methanol fixation and CellCover reagent. Cryopreservation proved to be the most robust protocol, maximizing both cell integrity and low background ambient RNA. Importantly, gene expression profiles from fresh cells correlated most with those of cryopreserved cells. Such similarities were consistently observed across the tested cell lines (R ≥ 0.97), monocyte-derived macrophages (R = 0.97) and immune cells (R = 0.99). In contrast, both methanol fixation and CellCover preservation showed an increased ambient RNA background and an overall lower gene expression correlation to fresh cells. Thus, our results demonstrate the superiority of cryopreservation over other cell preservation methods. We expect our comparative study to provide single cell omics researchers invaluable support when integrating cell preservation into their scRNA-seq studies.
Project description:Longitudinal analysis of Salmonella typhimurium mRNA from superspeader mouse cecal content and stool compared to in vitro Salmonella typhimurium mRNA.
Project description:Cardioviruses are a genus of picornaviruses that cause severe illnesses in rodents, but little is known about the prevalence, diversity, or spectrum of disease of such agents among humans. We report the identification of a group of human cardioviruses that have been detected and cloned directly from patient specimens (Chiu and DeRisi, et al, PNAS, 2008). This series includes 9 arrays (both raw and normalized data) used to detect cardioviruses in human respiratory and stool specimens. The arrays employed here are capable of pan-viral detection (Wang and DeRisi, et al., PNAS, 2002). Keywords: viral detection, cardiovirus, TMEV, gastroenteritis The series includes 3 arrays from respiratory samples and 6 arrays from stool samples. Among the 3 arrays from respiratory sample, 1 array has a signature for an adenovirus, 1 array has a signature for human metapneumovirus, and 1 array has a signature for cardiovirus UC1 (see Chiu and DeRisi, et al., PNAS, in 2008). All 6 arrays from stool samples are cardiovirus-positive; some show evidence of dual infection with other gastroenteritis viruses (i.e. norovirus, rotavirus, etc.). Data in Sample records fed to E-Predict (Urisman, et al, Genome Biology, 2005) E-Predict normalization metrics Array Normalization: Sum E-Matrix Normalization: Quadratic Distance Metric: Pearson Uncentered