Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes.

BACKGROUND: RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial. RESULTS: We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples. CONCLUSIONS: We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.

Author Correction: Benchmarking of cell type deconvolution pipelines for transcriptomics data.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20288-9.

A neuronal enhancer network upstream of MEF2C is compromised in patients with Rett-like characteristics.

Mutations in myocyte enhancer factor 2C (MEF2C), an important transcription factor in neurodevelopment, are associated with a Rett-like syndrome. Structural variants (SVs) upstream of MEF2C, which do not disrupt the gene itself, have also been found in patients with a similar phenotype, suggesting that disruption of MEF2C regulatory elements can also cause a Rett-like phenotype. To characterize those elements that regulate MEF2C during neural development and that are affected by these SVs, we used genomic tools coupled with both in vitro and in vivo functional assays. Through circularized chromosome conformation capture sequencing (4C-seq) and the assay for transposase-accessible chromatin using sequencing (ATAC-seq), we revealed a complex interaction network in which the MEF2C promoter physically contacts several distal enhancers that are deleted or translocated by disease-associated SVs. A total of 16 selected candidate regulatory sequences were tested for enhancer activity in vitro, with 14 found to be functional enhancers. Further analyses of their in vivo activity in zebrafish showed that each of these enhancers has a distinct activity pattern during development, with eight enhancers displaying neuronal activity. In summary, our results disentangle a complex regulatory network governing neuronal MEF2C expression that involves multiple distal enhancers. In addition, the characterized neuronal enhancers pose as novel candidates to screen for mutations in neurodevelopmental disorders, such as Rett-like syndrome.

Asthma inflammatory phenotypes show differential microRNA expression in sputum.

BACKGROUND: Asthma is classified according to severity and inflammatory phenotype and is likely to be distinguished by specific microRNA (miRNA) expression profiles. OBJECTIVE: We sought to associate miRNA expression in sputum supernatants with the inflammatory cell profile and disease severity in asthmatic patients. METHODS: We investigated miRNA expression in sputum supernatants of 10 healthy subjects, 17 patients with mild-to-moderate asthma, and 9 patients with severe asthma using high-throughput, stem-loop, reverse transcriptase quantitative real-time PCR miRNA expression profiling (screening cohort, n = 36). Differentially expressed miRNAs were validated in an independent cohort (n = 60; 10 healthy subjects and 50 asthmatic patients). Cellular miRNA origin was examined by using in situ hybridization and reverse transcriptase quantitative real-time PCR. The functional role of miRNAs was assessed by using in silico analysis and in vitro transfecting miRNA mimics in human bronchial epithelial cells. RESULTS: In 2 independent cohorts expression of miR-629-3p, miR-223-3p, and miR-142-3p was significantly upregulated in sputum of patients with severe asthma compared with that in healthy control subjects and was highest in patients with neutrophilic asthma. Expression of the 3 miRNAs was associated with sputum neutrophilia, and miR-223-3p and miR-142-3p expression was associated also with airway obstruction (FEV1/forced vital capacity). Expression of miR-629-3p was localized in the bronchial epithelium, whereas miR-223-3p and miR-142-3p were expressed in neutrophils, monocytes, and macrophages. Transfecting human bronchial epithelial cells with miR-629-3p mimic induced epithelial IL-8 mRNA and protein expression. IL-1ß and IL-8 protein levels were significantly increased in sputum of patients with severe asthma and were positively associated with sputum neutrophilia. CONCLUSIONS: Expression of miR-223-3p, miR-142-3p, and miR-629-3p is increased in sputum of patients with severe asthma and is linked to neutrophilic airway inflammation, suggesting that these miRNAs contribute to this asthma inflammatory phenotype.