Optimization and comparison of different methods for RNA isolation for cDNA library construction from the reindeer lichen Cladonia rangiferina

<p>Abstract</p> <p>Background</p> <p>The reindeer lichen is the product of a mutualistic relationship between a fungus and an algae. Lichen demonstrate a remarkable capacity to tolerate dehydration. This tolerance is driven by a variety of biochemical processes and the accumulation of specific secondary metabolites that may be of relevance to the pharmaceutical, biotechnology and agriculture industries. These protective metabolites hinder <it>in vitro </it>enzymatic reactions required in cDNA synthesis. Along with the low concentrations of RNA present within lichen tissues, the process of creating a cDNA library is technically challenging.</p> <p>Findings</p> <p>An evaluation of existing commercial and published protocols for RNA extraction from plant or fungal tissues has been performed and experimental conditions have been optimised to balance the need for the highest quality total ribonucleotides and the constraints of budget, time and human resources.</p> <p>Conclusion</p> <p>We present a protocol that balances inexpensive RNA extraction methods with commercial RNA clean-up kits to yield sufficient RNA for cDNA library construction. Evaluation of the protocol and the construction of, and sampling from, a cDNA library is used to demonstrate the suitability of the RNA extraction method for expressed sequence tag production.</p

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naive single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naive single-cell methods that do not model the subjects in any way. While the naive models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naive methods.</p

ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data

Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging.\nWe introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods.\nILoReg is available as an R package at https://bioconductor.org/packages/ILoReg.\nSupplementary data are available at Supplementary Information and Supplementary Files 1 and 2.\nMOTIVATION\nRESULTS\nAVAILABILITY\nSUPPLEMENTARY INFORMATIO

scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data

MotivationComputational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods.ResultsWe introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of trigonometric trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering.Availability and implementationscShaper is available as an R package at https://github.com/elolab/scshaper. The test data are available at https://doi.org/10.5281/zenodo.5734488.</p

Reproducibility-optimized detection of differential DNA methylation

Compared with state-of-the-art methods, ROTS shows competitive sensitivity and specificity in detecting consistently differentially methylated regions

GSK3β Serine 389 Phosphorylation Modulates Cardiomyocyte Hypertrophy and Ischemic Injury

Prior studies show that glycogen synthase kinase 3β (GSK3β) contributes to cardiac ischemic injury and cardiac hypertrophy. GSK3β is constitutionally active and phosphorylation of GSK3β at serine 9 (S9) inactivates the kinase and promotes cellular growth. GSK3β is also phosphorylated at serine 389 (S389), but the significance of this phosphorylation in the heart is not known. We analyzed GSK3β S389 phosphorylation in diseased hearts and utilized overexpression of GSK3β carrying ser→ala mutations at S9 (S9A) and S389 (S389A) to study the biological function of constitutively active GSK3β in primary cardiomyocytes. We found that phosphorylation of GSK3β at S389 was increased in left ventricular samples from patients with dilated cardiomyopathy and ischemic cardiomyopathy, and in hearts of mice subjected to thoracic aortic constriction. Overexpression of either GSK3β S9A or S389A reduced the viability of cardiomyocytes subjected to hypoxia–reoxygenation. Overexpression of double GSK3β mutant (S9A/S389A) further reduced cardiomyocyte viability. Determination of protein synthesis showed that overexpression of GSK3β S389A or GSK3β S9A/S389A increased both basal and agonist-induced cardiomyocyte growth. Mechanistically, GSK3β S389A mutation was associated with activation of mTOR complex 1 signaling. In conclusion, our data suggest that phosphorylation of GSK3β at S389 enhances cardiomyocyte survival and protects from cardiomyocyte hypertrophy

GSK3β Serine 389 Phosphorylation Modulates Cardiomyocyte Hypertrophy and Ischemic Injury

Prior studies show that glycogen synthase kinase 3β (GSK3β) contributes to cardiac ischemic injury and cardiac hypertrophy. GSK3β is constitutionally active and phosphorylation of GSK3β at serine 9 (S9) inactivates the kinase and promotes cellular growth. GSK3β is also phosphorylated at serine 389 (S389), but the significance of this phosphorylation in the heart is not known. We analyzed GSK3β S389 phosphorylation in diseased hearts and utilized overexpression of GSK3β carrying ser→ala mutations at S9 (S9A) and S389 (S389A) to study the biological function of constitutively active GSK3β in primary cardiomyocytes. We found that phosphorylation of GSK3β at S389 was increased in left ventricular samples from patients with dilated cardiomyopathy and ischemic cardiomyopathy, and in hearts of mice subjected to thoracic aortic constriction. Overexpression of either GSK3β S9A or S389A reduced the viability of cardiomyocytes subjected to hypoxia–reoxygenation. Overexpression of double GSK3β mutant (S9A/S389A) further reduced cardiomyocyte viability. Determination of protein synthesis showed that overexpression of GSK3β S389A or GSK3β S9A/S389A increased both basal and agonist-induced cardiomyocyte growth. Mechanistically, GSK3β S389A mutation was associated with activation of mTOR complex 1 signaling. In conclusion, our data suggest that phosphorylation of GSK3β at S389 enhances cardiomyocyte survival and protects from cardiomyocyte hypertrophy

COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels

Differential ATAC-seq and ChIP-seq peak detection using ROTS

Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data. </p