Highly selective, reversible water activation by P,N-cooperativity in pyridyl-functionalized phosphinines.

Tetrapyridyl-functionalized phosphinines were prepared and structurally characterized. The donor-functionalized aromatic phosphorus heterocycles react highly selectively and even reversibly with water. Calculations reveal P,N-cooperativity for this process, with the flanking pyridyl groups serving to kinetically enhance the formal oxidative addition process of H2O to the low-coordinate phosphorus atom via H-bonding. Subsequent tautomerization forms 1,2-dihydrophosphinine derivatives, which can be quantitatively converted back to the phosphinine by applying vacuum, even at room temperature. This process can be repeated numerous times, without any sign of decomposition of the phosphinine. In the presence of CuI·SMe2, dimeric species of the type ([Cu2I2(phosphinine)]2) are formed, in which each phosphorus atom shows the less common µ2-bridging 2e--lone-pair-donation to two Cu(i)-centres. Our results demonstrate that fully unsaturated phosphorus heterocycles, containing reactive P[double bond, length as m-dash]C double bonds, are interesting candidates for the activation of E-H bonds, while the aromaticity of such compounds plays an appreciable role in the reversibility of the reaction, supported by NICS calculations.

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.

Brain eQTLs of European, African American, and Asian ancestry improve interpretation of schizophrenia GWAS.

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet, the majority of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n=158), Europeans (EUR, n=408), and East Asians (EAS, n=217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs (representing ∼17% of all eQTLs pairs) linked to 1,276 genes (about 10% of all eGenes) and 198,769 SNPs (approximately 16% of all eSNPs) were identified only in the non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare (MAF < 0.05) in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified seven new risk genes ( SFXN2 , RP11-282018.3 , CYP17A1 , VPS37B , DENR , FTCDNL1 , and NT5DC2 ), and three potential novel regulatory variants in known risk genes ( CNNM2 , C12orf65 , and MPHOSPH9 ) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of novel risk genes in SCZ.