Silvia Giulia Galfrè

Single-cell RNA-sequencing (scRNA-seq) now routinely profiles thousands of individual transcriptomes, yet no consensus pipeline exists for either laboratory protocol or downstream analysis. Droplet-based datasets are especially challenging: their count matrices are extremely sparse and many biologically important genes are expressed at very low levels. Standard workflows, borrowed from bulk RNA-seq, apply normalization, log-transformation, and feature filtering, steps that can distort true-zero counts and systematically discard low-expression regulators. A method that natively handles sparsity, avoids imputation, and retains informative low-abundance genes is therefore desirable. We present an extended version of the COTAN workflow, a framework based on gene correlations that models zero counts directly, requires no log-normalization or scaling, and is tailored to UMI droplet data. COTAN’s key features are (i) robust gene–gene correlation matrices, validated on diverse public datasets, (ii) a superior detection of differentially expressed genes, including those with very low expression, consistently outperforming leading tools, (iii) a highly sensitive statistical scoring, able to detect heterogeneous cell groups and to confirm whether a cluster is truly transcriptomically uniform. Together, these advances provide a biologically grounded, end-to-end solution for scRNA-seq analysis. COTAN is available in Bioconductor and on GitHub. Supplementary information is available at this site.

As clinical data are becoming increasingly available, machine learning methods have been employed to extract knowledge from them and predict clinical events. While promising, approaches suffer from at least two main issues: low availability of labelled data and data heterogeneity leading to missing values. This work proposes the use of self-supervised auto-encoders to efficiently address these challenges. We apply our methodology to a clinical dataset from patients with ischaemic heart disease. Patient data is embedded in a latent space, built using unlabelled data, which is then used to train a neural network classifier to predict cardiovascular death. Results show improved balanced accuracy compared to applying the classifier directly to the raw data, demonstrating that this solution is promising, especially in conditions where availability of unlabelled data could increase.

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.

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.