Among single-cell analysis technologies, single-cell RNA-seq (scRNA-seq) has been one of the front runners in technical inventions. Since its induction, scRNA-seq has been well received and undergone many fast-paced technical improvements in cDNA synthesis and amplification, processing and alignment of next generation sequencing reads, differentially expressed gene calling, cell clustering, subpopulation identification, and developmental trajectory prediction. scRNA-seq has been exponentially applied to study global transcriptional profiles in all cell types in humans and animal models, healthy or with diseases, including cancer. Accumulative novel subtypes and rare subpopulations have been discovered as potential underlying mechanisms of stochasticity, differentiation, proliferation, tumorigenesis, and aging. scRNA-seq has gradually revealed the uncharted territory of cellular heterogeneity in transcriptomes and developed novel therapeutic approaches for biomedical applications. This review of the advancement of scRNA-seq methods provides an exploratory guide of the quickly evolving technical landscape and insights of focused features and strengths in each prominent area of progress.

Hydroxyurea is a commonly used drug for the treatment of sickle cell disease. Several studies have demonstrated the efficacy of hydroxyurea in ameliorating disease pathophysiology. However, a lack of consensus on optimal dosing and the need for ongoing toxicity monitoring for myelosuppression limits its utilization. Pharmacokinetic (PK) and pharmacodynamic (PD) studies describe drug-body interactions, and hydroxyurea PK-PD studies have reported wide inter-patient variability. This variability can be explained by a mathematical model taking into consideration different sources of variation such as genetics, epigenetics, phenotypes, and demographics. A PK-PD model provides us with a tool to capture these variant responses of patients to a given drug. The development of an integrated population PK-PD model that can predict individual patient responses and identify optimal dosing would maximize efficacy, limit toxicity, and increase utilization. In this review, we discuss various treatment challenges associated with hydroxyurea. We summarize existing population PK-PD models of hydroxyurea, the gap in the existing models, and the gap in the mechanistic understanding. Lastly, we address how mathematical modeling can be applied to improve our understanding of hydroxyurea’s mechanism of action and to tackle the challenge of inter-patient variability, dose optimization, and non-adherence.

Aim: Analysis of large datasets has become integral to biological studies due to the advent of high throughput technologies such as next generation sequencing. Techniques for analyzing these large datasets are normally developed by bioinformaticists and statisticians, with input from biologists. Frequently, the end-user does not have the training or knowledge to make informed decisions on input parameter settings required to implement the analyses pipelines. Instead, the end-user relies on “default” settings present within the software packages, consultations with in-house bioinformaticists, or on methods described in previous publications. The aim of this study was to explore the effects of altering default parameters on the cell clustering solutions generated by a common pipeline implemented in the Seurat R package that is used to cluster cells based on single cell RNA sequencing (scRNAseq) data.

Methods: We systematically assessed the effect of altering input parameters by performing iterative analyses on a single scRNAseq dataset. We compared the clustering solutions using the different input parameters to determine which parameters have a large effect on cell clustering solutions.

Results: We used a range of input parameters for many, but not all, of the input parameters required by the Seurat R pipeline. We found that some input parameters had a very small effect on the clustering solution, while other parameters had a much larger effect.

Conclusion: We conclude that, when implementing the Seurat R package, the “default” parameters should be used with caution. We identified specific parameters that have a significant effect on clustering solutions.

Aim: Several genomic signatures are available to predict Prostate Cancer (CaP) outcomes based on gene expression in prostate tissue. However, no signature was tailored to predict aggressive CaP in younger men. We attempted to develop a gene signature to predict the development of metastatic CaP in young men.

Methods: We measured genome-wide gene expression for 119 tumor and matched benign tissues from prostatectomies of men diagnosed at ≤ 50 years and > 70 years and identified age-related differentially expressed genes (DEGs) for tissue type and Gleason score. Age-related DEGs were selected using the improved Prediction Analysis of Microarray method (iPAM) to construct and validate a classifier to predict metastasis using gene expression data from 1,232 prostatectomies. Accuracy in predicting early metastasis was quantified by the area under the curve (AUC) of receiver operating characteristic (ROC), and abundance of immune cells in the tissue microenvironment was estimated using gene expression data.

Results: Thirty-six age-related DEGs were selected for the iPAM classifier. The AUC of five-year survival ROC for the iPAM classifier was 0.87 (95%CI: 0.78-0.94) in young (≤ 55 years), 0.82 (95%CI: 0.76-0.88) in middle-aged (56-70 years), and 0.69 (95%CI: 0.55-0.69) in old (> 70 years) patients. Metastasis-associated immune responses in the tumor microenvironment were more pronounced in young and middle-aged patients than in old ones, potentially explaining the difference in accuracy of prediction among the groups.

Conclusion: We developed a genomic classifier with high precision to predict early metastasis for younger CaP patients and identified age-related differences in immune response to metastasis development.

Genetic influence on medication response has been well documented; yet, the clinical integration of pharmacogenetics has been slow. Lack of knowledge on the fundamentals of pharmacogenetic testing among clinicians and the complexities surrounding test selection, results interpretation, and clinical utility, among others, contribute to this slow adoption. In this paper, we describe integration of pharmacogenetics in the clinic, selection of target population and testing laboratories, team of expertise needed, available resources, and critical considerations for accurate interpretation of test results. We also discuss phenoconversion and pharmacogenetic utility in special populations including pediatrics, pregnant women and transplant patients. Ultimately, it should be observed that pharmacogenetics is an additional piece in the prescribers’ toolbox to guide prescribing decisions. Other intrinsic and extrinsic factors should be considered for more accurate and personalized prescribing practice. This paper aims to guide clinicians with preparatory steps and “clinical pearls” necessary to successfully integrate pharmacogenetics into routine practice.