Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.

BACKGROUND: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. METHODS: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. RESULTS: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. CONCLUSIONS: In our opinion, SCA represents the bioinformatics version of a universal "Swiss-knife" for the extraction of hidden knowledgeable features from single cell omics data.

A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity.

Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.

Identifying Gene Markers Associated with Cell Subpopulations.

An important point of the analysis of a single-cell RNA experiment is the identification of the key elements, i.e., genes, characterizing each cell subpopulation cluster. In this chapter, we describe the use of sparsely connected autoencoder, as a tool to convert single-cell clusters in pseudo-RNAseq experiments to be used as input for differential expression analysis, and the use of COMET, as a tool to depict cluster-specific gene markers.

Manipulating and studying gene function in human pluripotent stem cell models.

Human pluripotent stem cells (hPSCs) are uniquely suited to study human development and disease and promise to revolutionize regenerative medicine. These applications rely on robust methods to manipulate gene function in hPSC models. This comprehensive review aims to both empower scientists approaching the field and update experienced stem cell biologists. We begin by highlighting challenges with manipulating gene expression in hPSCs and their differentiated derivatives, and relevant solutions (transfection, transduction, transposition, and genomic safe harbor editing). We then outline how to perform robust constitutive or inducible loss-, gain-, and change-of-function experiments in hPSCs models, both using historical methods (RNA interference, transgenesis, and homologous recombination) and modern programmable nucleases (particularly CRISPR/Cas9 and its derivatives, i.e., CRISPR interference, activation, base editing, and prime editing). We further describe extension of these approaches for arrayed or pooled functional studies, including emerging single-cell genomic methods, and the related design and analytical bioinformatic tools. Finally, we suggest some directions for future advancements in all of these areas. Mastering the combination of these transformative technologies will empower unprecedented advances in human biology and medicine.