Biology subjects

Chen, J.-B.

Publications and source records attributed to Chen, J.-B..

4 recordsLinked to original sources

bioRxiv2022

Simultaneous transcriptome and proteome profiling in a single mouse oocyte with a deep single-cell multi-omics approach

Nowadays, 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 for the first time. This platform was applied to analyze single mouse oocytes at different meiotic maturation stages, reaching an average quantification depth of 19948 genes and 2663 protein groups in single mouse oocytes. This reliable quantitative two-omics dataset of single cells provided an important resource for understanding the relationship between the transcriptome and the proteome in cells. Based on the correlation analysis of RNAs and proteins in the same single cell, we demonstrated the expressive heterogeneity of transcriptome and proteome during the cellular biological process. Specially, we analyzed the meiosis regulatory network during oocyte maturation with an unprecedented depth at the single-cell level, and identified 30 transcript-protein pairs as specific oocyte maturational signatures, providing crucial insights into the regulatory features of transcription and translation during oocyte meiotic maturation.

cell biology↗

bioRxiv2022

Pick-up Single-Cell Proteomic Analysis for Quantifying up to 3000 Proteins in a Tumor Cell

The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of [~]1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a tumor cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 1804-3349, 1778-3049 and 1074-2487 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27), respectively. Benefiting from the flexible cell picking-up ability, we study tumor cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.

cancer biology↗

bioRxiv2020

Phenome-wide investigation of the causal associations between childhood BMI and adult outcomes: A two-sample Mendelian randomization study

BackgroundCompelling observational studies have reported that childhood obesity is associated with the risk of many complex diseases in adulthood. However, results from observational studies are unable to fully account for confounding factors. The causal effects of childhood obesity have not been systematically characterized. We aimed to assess the causal associations between childhood body mass index (BMI) and various adult traits/diseases using two-sample Mendelian randomization (MR). Methods and findingsOver 5,000 datasets for adult outcomes were obtained from various resources. After data filtering, 269 adult traits genetically correlated with childhood BMI (P < 0.05) were subjected to MR analyses. The number of independent outcomes was 148, setting the significant threshold as P < 3.38 x 10-4. Inverse-variance weighted method, MR-Egger, weighted median method, and weighted mode method were used to estimate the causal effects. We identified potential causal effects of childhood obesity on 60 adult traits (27 disease-related traits, 27 lifestyle factors, and 6 other traits). Higher childhood BMI was associated with a reduced overall health rating ({beta} = -0.10, 95% CI: -0.13 to -0.07, P = 6.26 x 10-11). Findings on diseases included some novel effects, such as the adverse effects of higher childhood BMI on cholelithiasis (OR = 1.26, 95% CI: 1.18 to 1.35, P = 3.29 x 10-5). For dietary habits, we found that higher childhood BMI was positively associated adult diet portion size ({beta} = 0.26, 95% CI: 0.18 to 0.34, P = 7.34 x 10-11). Different from the conventional impression, our results showed that higher childhood BMI was positively associated with low calorie density food intake. With 76 adult BMI single-nucleotide polymorphisms (SNPs) as instruments, we confirmed that adulthood BMI was positively associated with heel bone mineral density. However, the association no longer present after excluding the SNPs existing in or in linkage disequilibrium (LD) with childhood BMI. Network MR analyses suggested that past tobacco smoking and portion size mediated 6.39% and 10.90% of the associations between childhood BMI and type 2 diabetes, respectively. The main study limitation is that it is difficult to tease out the independent effects of childhood BMI due to the strong correlation between childhood and adulthood BMI. ConclusionsIn summary, we provided a phenome-wide view of the effects of childhood BMI on adult traits. Our results highlight the need to intervene in childhood to reduce obesity from a young age and its later-life effects. Author summaryWhy was this study done? O_LIChildhood obesity is a worldwide public health problem. The prevalence has increased at an alarming rate. C_LIO_LIObservational epidemiological studies have reported that childhood obesity is associated with the risk of many complex diseases in adulthood, such as coronary artery disease (CAD) and diabetes. However, observational studies are limited in explaining causality because of possible bias from unmeasured confounding factors. C_LI What did the researchers do and find? O_LIA Mendelian randomization (MR) approach was used to provide a phenome-wide view of the causal associations between childhood BMI and adult outcomes. Potential causal effects of childhood obesity on 60 adult traits were identified. C_LIO_LIHigher childhood BMI was associated with reduced overall health rating, and caused increased risk of some diseases, such as cholelithiasis, hypothyroidism, CAD, and type 2 diabetes (T2D). In contrast, childhood BMI was positively associated with adult heel bone mineral density and low calorie density food intake. C_LIO_LIPortion size and smoking behavior might mediate the association between childhood BMI and T2D risk. C_LI What do these findings mean? O_LIOur results highlight the importance to address rising childhood obesity prevalence rate and early interventions on obesity might help to promote health equity in later life. C_LI

genetics↗

bioRxiv2018

Modeling circRNAs expression pattern with integrated sequence and epigenetic features identifies H3K79me2 as regulators for circRNAs expression

Circular RNAs (circRNAs) are an abundant class of noncoding RNAs with widespread, cell/tissue specific pattern. Because of their involvement in the pathogenesis of multiple disease, they are receiving increasing attention. Previous work suggested that epigenetic features might be related to circRNA expression. However, current algorithms for circRNAs prediction neglect these features, leading to constant results across different cells.\n\nHere we built a machine learning framework named CIRCScan, to predict expression status and expression levels of circRNAs in various cell lines based on sequence and epigenetic features. Both expression status and expression levels can be accurately predicted by different groups of features. For expression status, the top features were similar in different cells. However, the top features for predicting expression levels were different in different cells. Noteworthy, the importance of H3K79me2 ranked high in predicting both circRNAs expression status and levels across different cells, indicating its important role in regulating circRNAs expression. Further validation experiment in K562 confirmed that knock down of H3K79me2 did result in reduction of circRNA production.\n\nOur study offers new insights into the regulation of circRNAs by incorporating epigenetic features in prediction models in different cellular contexts.

bioinformatics↗