The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Background: Although clozapine is an effective option for treatment-resistant schizophrenia (TRS), there are still 1/3 to 1/2 of TRS patients who do not respond to clozapine. The main purpose of this randomized, double-blind, placebocontrolled trial was to explore the amisulpride augmentation efficacy on the psychopathological symptoms and cognitive function of clozapine-resistant treatment-refractory schizophrenia (CTRS) patients.

Methods: A total of 80 patients were recruited and randomly assigned to receive initial clozapine plus amisulpride (amisulpride group) or clozapine plus placebo (placebo group). Positive and Negative Syndrome Scale (PANSS), Scale for the Assessment of Negative Symptoms (SANS), Clinical Global Impression (CGI) scale scores, Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Treatment Emergent Symptom Scale (TESS), laboratory measurements, and electrocardiograms (ECG) were performed at baseline, week 6, and week 12.

Results: Compared with the placebo group, amisulpride group had a lower PANSS total score, positive subscore, and general psychopathology subscore at week 6 and week 12 (P_Bonferroni<0.01). Furthermore, compared with the placebo group, the amisulpride group showed an improved RBANS language score at week 12 (P_Bonferroni<0.001). Amisulpride group had a higher treatment response rate (P=0.04), lower scores of CGI severity and CGI efficacy at week 6 and week 12 than placebo group (P_Bonferroni<0.05). There were no differences between the groups in body mass index (BMI), corrected QT (QTc) intervals, and laboratory measurements. This study demonstrates that amisulpride augmentation therapy can safely improve the psychiatric symptoms and cognitive performance of CTRS patients.

Conclusions: This study indicates that amisulpride augmentation therapy has important clinical significance for treating CTRS to improve clinical symptoms and cognitive function with tolerability and safety.

Clinicaltrials.gov identifier- NCT03652974. Registered August 31, 2018, https://clinicaltrials.gov/ct2/show/NCT03652974

Background: The cell cycle is at the center of cellular activities and is orchestrated by complex regulatory mechanisms, among which transcriptional regulation is one of the most important components. Alternative splicing dramatically expands the regulatory network by producing transcript isoforms of genes to exquisitely control the cell cycle. However, the patterns of transcript isoform expression in the cell cycle are unclear. Therapies targeting cell cycle checkpoints are commonly used as anticancer therapies, but none of them have been designed or evaluated at the alternative splicing transcript level. The utility of these transcripts as markers of cell cycle-related drug sensitivity is still unknown, and studies on the expression patterns of cell cycle-targeting drug-related transcripts are also rare.

Methods: To explore alternative splicing patterns during cell cycle progression, we performed sequential transcriptomic assays following cell cycle synchronization in colon cancer HCT116 and breast cancer MDA-MB-231 cell lines, using flow cytometry and reference cell cycle transcripts to confirm the cell cycle phases of samples, and we developed a new algorithm to describe the periodic patterns of transcripts fluctuating during the cell cycle. Genomics of Drug Sensitivity in Cancer (GDSC) drug sensitivity datasets and Cancer Cell Line Encyclopedia (CCLE) transcript datasets were used to assess the correlation of genes and their transcript isoforms with drug sensitivity. We identified transcripts associated with typical drugs targeting cell cycle by determining correlation coefficients. Cytotoxicity assays were used to confirm the effect of ENST00000257904 against cyclin dependent kinase 4/6 (CDK4/6) inhibitors. Finally, alternative splicing transcripts associated with mitotic (M) phase arrest were analyzed using an RNA synthesis inhibition assay and transcriptome analysis.

Results: We established high-resolution transcriptome datasets of synchronized cell cycle samples from colon cancer HCT116 and breast cancer MDA-MB-231 cells. The results of the cell cycle assessment showed that 43,326, 41,578 and 29,244 transcripts were found to be periodically expressed in HeLa, HCT116 and MDA-MB-231 cells, respectively, among which 1280 transcripts showed this expression pattern in all three cancer cell lines. Drug sensitivity assessments showed that a large number of these transcripts displayed a higher correlation with drug sensitivity than their corresponding genes. Cell cycle-related drug screening showed that the level of the CDK4 transcript ENST00000547281 was more significantly associated with the resistance of cells to CDK4/6 inhibitors than the level of the CDK4 reference transcript ENST00000257904. The transcriptional inhibition assay following M phase arrest further confirmed the M-phase-specific expression of the splicing transcripts. Combined with the cell cycle-related drug screening, the results also showed that a set of periodic transcripts, for example, ENST00000314392 (a dolichyl-phosphate mannosyltransferase polypeptide 2 isoform transcript), was more associated with drug sensitivity than the levels of their corresponding gene transcripts.

Conclusions: In summary, we identified a panel of cell cycle-related periodic transcripts and found that the levels of transcripts of drug target genes showed different values for predicting drug sensitivity, providing novel insights into alternative splicing-related drug development and evaluation.

Bone, cartilage, and soft tissue regeneration is a complex spatiotemporal process recruiting a variety of cell types, whose activity and interplay must be precisely mediated for effective healing post-injury. Although extensive strides have been made in the understanding of the immune microenvironment processes governing bone, cartilage, and soft tissue regeneration, effective clinical translation of these mechanisms remains a challenge. Regulation of the immune microenvironment is increasingly becoming a favorable target for bone, cartilage, and soft tissue regeneration; therefore, an in-depth understanding of the communication between immune cells and functional tissue cells would be valuable. Herein, we review the regulatory role of the immune microenvironment in the promotion and maintenance of stem cell states in the context of bone, cartilage, and soft tissue repair and regeneration. We discuss the roles of various immune cell subsets in bone, cartilage, and soft tissue repair and regeneration processes and introduce novel strategies, for example, biomaterial-targeting of immune cell activity, aimed at regulating healing. Understanding the mechanisms of the crosstalk between the immune microenvironment and regeneration pathways may shed light on new therapeutic opportunities for enhancing bone, cartilage, and soft tissue regeneration through regulation of the immune microenvironment.