Background Single-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied. Results Here, we systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, alevin-fry, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2, and scruff) using datasets yielding different biological complexity levels generated by CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. While the scRNA-seq preprocessing workflows compared vary in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produce clustering results that agree well with the known cell type labels that provided the ground truth in our analysis. Conclusions In summary, the choice of preprocessing method was found to be less important than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

While some individuals infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) present mild-to-severe disease, many SARS-CoV-2-infected individuals are asymptomatic. We sought to identify the distinction of immune response between asymptomatic and moderate patients. We performed single-cell transcriptome and T-cell/B-cell receptor (TCR/BCR) sequencing in 37 longitudinal collected peripheral blood mononuclear cell samples from asymptomatic, moderate, and severe patients with healthy controls. Asymptomatic patients displayed increased CD56briCD16− natural killer (NK) cells and upregulation of interferon-gamma in effector CD4+ and CD8+ T cells and NK cells. They showed more robust TCR clonal expansion, especially in effector CD4+ T cells, but lack strong BCR clonal expansion compared to moderate patients. Moreover, asymptomatic patients have lower interferon-stimulated genes (ISGs) expression in general but large interpatient variability, whereas moderate patients showed various magnitude and temporal dynamics of the ISGs expression across multiple cell populations but lower than a patient with severe disease. Our data provide evidence of different immune signatures to SARS-CoV-2 in asymptomatic infections.

No abstract

No abstract

Mutant TP53 proteins are thought to drive the development and sustained expansion of cancers at least in part through the loss of the wild-type (wt) TP53 tumour suppressive functions. Therefore, compounds that can restore wt TP53 functions in mutant TP53 proteins are expected to inhibit the expansion of tumours expressing mutant TP53. APR-246 has been reported to exert such effects in malignant cells and is currently undergoing clinical trials in several cancer types. However, there is evidence that APR-246 may also kill malignant cells that do not express mutant TP53. To support the clinical development of APR-246 it is important to understand its mechanism(s) of action. By establishing isogenic background tumour cell lines with different TP53/TRP53 states, we found that APR-246 can kill malignant cells irrespective of their TP53/TRP53 status. Accordingly, RNAseq analysis revealed that treatment with APR-246 induces expression of the same gene set in Eμ-Myc mouse lymphoma cells of all four possible TRP53 states, wt, wt alongside mutant, knockout and knockout alongside mutant. We found that depending on the type of cancer cell and the concentration of APR-246 used, this compound can kill malignant cells through induction of various programmed cell death pathways, including apoptosis, necroptosis and ferroptosis. The sensitivity of non-transformed cells to APR-246 also depended on the cell type. These findings reveal that the clinical testing of APR-246 should not be limited to cancers expressing mutant TP53 but expanded to cancers that express wt TP53 or are TP53-deficient.

No abstract

Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and when not modelled appropriately, its presence can hamper the detection of differentially expressed genes. Most bulk RNA-seq methods assume equal group variances which will under- and/or over-estimate the true variability in such datasets. We present two methods that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold-standard methods that do not account for heteroscedasticity, we show results from simulation studies and various experiments that demonstrate the superior performance of both voomByGroup and voomQWB in error control and power when group variances in pseudo-bulk scRNA-seq data are unequal. We recommend the use of either of these methods over established approaches, with voomByGroup having the advantage of accurate variance estimation since group variance trends can take on different "shapes", whilst voomQWB has the advantage of catering to complex study designs.

No abstract