A multifunctional locus controls motor neuron differentiation through short and long noncoding RNAs.

The transition from dividing progenitors to postmitotic motor neurons (MNs) is orchestrated by a series of events, which are mainly studied at the transcriptional level by analyzing the activity of specific programming transcription factors. Here, we identify a post-transcriptional role of a MN-specific transcriptional unit (MN2) harboring a lncRNA (lncMN2-203) and two miRNAs (miR-325-3p and miR-384-5p) in this transition. Through the use of in vitro mESC differentiation and single-cell sequencing of CRISPR/Cas9 mutants, we demonstrate that lncMN2-203 affects MN differentiation by sponging miR-466i-5p and upregulating its targets, including several factors involved in neuronal differentiation and function. In parallel, miR-325-3p and miR-384-5p, co-transcribed with lncMN2-203, act by repressing proliferation-related factors. These findings indicate the functional relevance of the MN2 locus and exemplify additional layers of specificity regulation in MN differentiation.

A KO mouse model for the lncRNA Lhx1os produces motor neuron alterations and locomotor impairment.

Here, we describe a conserved motor neuron-specific long non-coding RNA, Lhx1os, whose knockout in mice produces motor impairment and postnatal reduction of mature motor neurons (MNs). The ER stress-response pathway result specifically altered with the downregulation of factors involved in the unfolded protein response (UPR). Lhx1os was found to bind the ER-associated PDIA3 disulfide isomerase and to affect the expression of the same set of genes controlled by this protein, indicating that the two factors act in conjunction to modulate the UPR. Altogether, the observed phenotype and function of Lhx1os indicate its important role in the control of MN homeostasis and function.

COTAN: scRNA-seq data analysis based on gene co-expression.

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN.

TAMGeS: a Three-array Method for Genotyping of SNPs by a dual-colour approach.

BACKGROUND: Many of the most effective high-throughput protocols for SNP genotyping employ microarrays. Genotypes are assessed by comparing the signal intensities that derive from the hybridization of different allele-specific probes labelled either by using four fluorescent dyes, one for each base, or by using only two dyes and investigating the polymorphic alleles two by two on separate arrays. The employment of only two dyes makes it possible to use a dual-laser scanner, which has the advantage of being present in every microarray laboratory. However, this protocol may present some drawbacks. To infer all the six possible genotypes it is necessary to compare signals from two arrays, but this comparison not always is successful. A number of systematic errors in the experimental protocol, in fact, may differently affect signal intensities on separate arrays. Here we present TAMGeS (Three-Array Method for Genotyping of SNPs), an exhaustive method for SNP genotyping through SBE (Single Base Extension) and dual-colour microarrays, which makes the comparison of signals on distinct arrays reliable by using a third array and a data handling method for signal normalization based on bilinear regression theory. RESULTS: We tested the effectiveness of the proposed method by evaluating the results obtained from the direct comparison of the two arrays or by applying TAMGeS, both on experimental and synthetic data. With synthetic data, TAMGeS reduced the frequency of errors by an order of magnitude, when the incidence of systematic errors was not negligible. With the experimental data, produced by genotyping 25 SNPs in 437 subjects, TAMGeS reduced the percentage of missing genotypes from 54% (Two-Array Method) to 14.5%. Allelic and genotypic call rates were 99.3% and 99.5%, respectively. The normalization procedure takes into account also systematic errors, which can be generated by a time-delayed assay, thus making the protocol more flexible. CONCLUSION: TAMGeS represents an innovative method, which proved to be very effective in producing reliable SNP genotyping data by dual-colour microarrays. The requirement of a third array is well balanced by the strong enhancement in data quality and by the greater flexibility of the experimental protocol.