Harnessing Single-Cell RNA Sequencing to Identify Dendritic Cell Types, Characterize Their Biological States, and Infer Their Activation Trajectory.

Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce the defense mechanisms the best suited to face the threat. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions and how.To decipher the nature, functions, and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single-cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning in the field. In addition, awareness must be raised on the need for specific, robust, and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories, and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on GitHub. We hope that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types and that it will contribute to establishing high standards in the field.

Soil amendments enhanced the growth of Nicotiana alata L. and Petunia hydrida L. by stabilizing heavy metals from wastewater.

Due to the non-degradable nature of heavy metals (HMs), the industrial effluent, whether treated or untreated, carrying HMs, eventually end up into the water bodies, soil, and sediments. Numerous countermeasures were applied, but the use of ornamental plants for the stress mitigation associated with HMs on the environment is a neglected research domain. The composition of wastewater influences bioremediation strategies. As the wastewater is contaminated with multiple HMs, many lab studies, with the plants, failed in the industrial field. This work focuses on the potential of Nicotiana alata L. and Petunia hydrida L. against multiple HMs contaminated synthetic wastewater. To improve plant tolerance, soil amendments (biochar, compost, and moss, each at 5% v/v in soil) were used, individually and in combination. After 6 weeks of the exposure, plant physiological, biochemical and enzymatic parameters, as well as the distribution of HMs, (Cd, Cr, Cu, Pb, Mn, Ni, and Zn) in the plant (flower, leaves, root, and shoot) and soil, were measured. The HMs uptake positivity influenced the malondialdehyde content, hydrogen peroxide content and electrolyte leakage, while negatively to photosynthetic pigments, and resulted in increased catalase, guaiacol peroxidase, glutathione s-transferase, ascorbate peroxidase, while reduced superoxide dismutase activity. It was found that all amendments improved the plant growth by metal stabilization, and best results were obtained with the combined application of biochar + compost + moss. So, HMs stabilization can be achieved by growing ornamental plants, like Nicotiana alata L. and Petunia hydrida L. along with soil amendments.

Population data of 23 Y STRs from Manchu population of Liaoning Province, Northeast China.

Mongol-like-horsemen-turned-merchants from Manchuria are known as Manchus, originally their homeland was centered around what is nowadays the city of Shenyang in Northeast China. Previously, worldwide analysis of Y-chromosomal haplotype diversity for 23 STR loci and Y-STR databases with PowerPlex® Y23 System (Promega Corporation Madison, USA) kit were created with collaborative efforts, but Manchu population data was missing. In current study, PowerPlex® Y23 System loci were examined in 328 unrelated Manchu male individuals from Xiuyan and Huanren Manchu autonomous counties in Liaoning province, to calculate the forensic parameters of the 23 STR loci. A total of 323 different haplotypes were observed on these 23 Y-STR loci. The gene diversities ranged from 0.3820 (DYS391) to 0.9696 (DYS385a, b). The overall haplotype diversity was 0.9999 ± 0.0002 at PowerPlex® Y23 System. Rst pairwise analyses, multidimensional scaling plot, and linear discriminatory analysis showed the genetic structure of Manchu population was significantly different from some of Chinese populations like Tibetan and Uyghur. Results of our study showed that PowerPlex® Y23 System marker set provided substantially stronger discriminatory power in Manchu population of China.