Simultaneous deep transcriptome and proteome profiling in a single mouse oocyte.

Although single-cell multi-omics technologies are undergoing rapid development, simultaneous transcriptome and proteome analysis of a single-cell individual still faces great challenges. Here, we developed a single-cell simultaneous transcriptome and proteome (scSTAP) analysis platform based on microfluidics, high-throughput sequencing, and mass spectrometry technology to achieve deep and joint quantitative analysis of transcriptome and proteome at the single-cell level, providing an important resource for understanding the relationship between transcription and translation in cells. This platform was applied to analyze single mouse oocytes at different meiotic maturation stages, reaching an average quantification depth of 19,948 genes and 2,663 protein groups in single mouse oocytes. In particular, we analyzed the correlation of individual RNA and protein pairs, as well as the meiosis regulatory network with unprecedented depth, and identified 30 transcript-protein pairs as specific oocyte maturational signatures, which could be productive for exploring transcriptional and translational regulatory features during oocyte meiosis.

Development and validation of a preoperative nomogram for predicting positive surgical margins after laparoscopic radical prostatectomy.

BACKGROUND: Positive surgical margins are independent risk factor for biochemical recurrence, local recurrence, and distant metastasis after radical prostatectomy. However, limited predictive tools are available. This study aimed to develop and validate a preoperative nomogram for predicting positive surgical margins after laparoscopic radical prostatectomy (LRP). METHODS: From January 2010 to March 2016, a total of 418 patients who underwent LRP without receiving neoadjuvant therapy at Peking University Third Hospital were retrospectively involved in this study. Clinical and pathological results of each patient were collected for further analysis. Univariable and multivariable logistic regression (backward stepwise method) were used for the nomogram development. The concordance index (CI), calibration curve analysis and decision curve analysis were used to evaluate the performance of our model. RESULTS: Of 418 patients involved in this study, 142 patients (34.0%) had a positive surgical margin on final pathology. Based on the backward selection, four variables were included in the final multivariable regression model, including the percentage of positive cores in preoperative biopsy, clinical stage, free prostate specific antigen (fPSA)/total PSA (tPSA), and age. A nomogram was developed using these four variables. The concordance index (C-index) of the nomogram was 0.722 in the development cohort and 0.700 in the bootstrap validations. The bias-corrected calibration plot showed a limited departure from the ideal line with a mean absolute error of 2.0%. In decision curve analyses, the nomogram showed net benefits in the range from 0.2 to 0.7. CONCLUSION: A nomogram to predict positive surgical margins after LRP was developed and validated, which could help urologists plan surgical procedures.