Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data.

Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

Cardio-cerebrovascular Outcomes in MODY, Type 1 Diabetes, and Type 2 Diabetes: A Prospective Cohort Study.

CONTEXT: Cardio-cerebrovascular events are severe complications of diabetes. OBJECTIVE: We aim to compare the incident risk of cardio-cerebrovascular events in maturity onset diabetes of the young (MODY), type 1 diabetes, and type 2 diabetes. METHODS: Type 1 diabetes, type 2 diabetes, and MODY were diagnosed by whole exome sequencing. The primary endpoint was the occurrence of the first major adverse cardiovascular event (MACE), including acute myocardial infarction, heart failure, stroke, unstable angina pectoris, and cardio-cerebrovascular-related mortality. Cox proportional hazards models were applied and adjusted to calculate hazard ratios (HRs) and 95% CIs for the incident risk of MACE in type 1 diabetes, type 2 diabetes, MODY, and MODY subgroups compared with people without diabetes (control group). RESULTS: Type 1 diabetes, type 2 diabetes, and MODY accounted for 2.7%, 68.1%, and 11.4% of 26 198 participants with diabetes from UK Biobank. During a median follow-up of 13 years, 1028 MACEs occurred in the control group, contrasting with 70 events in patients with type 1 diabetes (HR 2.15, 95% CI 1.69-2.74, P < .05), 5020 events in patients with type 2 diabetes (HR 7.02, 95% CI 6.56-7.51, P < .05), and 717 events in MODY (HR 5.79, 95% CI 5.26-6.37, P < .05). The hazard of MACE in HNF1B-MODY was highest among MODY subgroups (HR 11.00, 95% CI 5.47-22.00, P = 1.5 × 10-11). CONCLUSION: MODY diagnosed by genetic analysis represents higher prevalence than the clinical diagnosis in UK Biobank. The risk of incident cardio-cerebrovascular events in MODY ranks between type 1 diabetes and type 2 diabetes.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.

The Role of KDM2A and H3K36me2 Demethylation in Modulating MAPK Signaling During Neurodevelopment.

Intellectual disability (ID) is a condition characterized by cognitive impairment and difficulties in adaptive functioning. In our research, we identified two de novo mutations (c.955C>T and c.732C>A) at the KDM2A locus in individuals with varying degrees of ID. In addition, by using the Gene4Denovo database, we discovered five additional cases of de novo mutations in KDM2A. The mutations we identified significantly decreased the expression of the KDM2A protein. To investigate the role of KDM2A in neural development, we used both 2D neural stem cell models and 3D cerebral organoids. Our findings demonstrated that the reduced expression of KDM2A impairs the proliferation of neural progenitor cells (NPCs), increases apoptosis, induces premature neuronal differentiation, and affects synapse maturation. Through ChIP-Seq analysis, we found that KDM2A exhibited binding to the transcription start site regions of genes involved in neurogenesis. In addition, the knockdown of KDM2A hindered H3K36me2 binding to the downstream regulatory elements of genes. By integrating ChIP-Seq and RNA-Seq data, we made a significant discovery of the core genes' remarkable enrichment in the MAPK signaling pathway. Importantly, this enrichment was specifically linked to the p38 MAPK pathway. Furthermore, disease enrichment analysis linked the differentially-expressed genes identified from RNA-Seq of NPCs and cerebral organoids to neurodevelopmental disorders such as ID, autism spectrum disorder, and schizophrenia. Overall, our findings suggest that KDM2A plays a crucial role in regulating the H3K36me2 modification of downstream genes, thereby modulating the MAPK signaling pathway and potentially impacting early brain development.