Forward single-cell sequencing into clinical application: Understanding of ageing and rejuvenation from clinical observation to single-cell solution

Single-cell and spatial alterations of neural cells and circuits in clinical and translational medicine

The spatiotemporal heterogeneity of neurons, circuits and regulators is being uncovered at a single-cell level, from single-cell gene expression to functional regulations. The classifications, architectonics and functional communications amongst neural cells and circuits within the brain can be clearly delineated using single-cell multiomics and transomics. This Editorial highlights the spatiotemporal heterogeneity of neurons and circuits as well as regulators, initiates the translation of neuronal diversity and spatial organisation at single-cell levels into clinical considerations, and enables the discovery and development of new therapies for neurological diseases. It is predicted that single-cell and spatial multiomics will be integrated with metabolomic profiles and corresponding gene epigenetic modifications. The interactions amongst DNAs, RNAs and proteins in a cell provide details of intracellular functional regulations and new opportunities for the translation of temporospatial diversity of neural cell subtypes/states into clinical practice. The application of single-cell multiomics with four-dimensional genome to the human pathological brain will lead us to a new milestone of the diagnosis and treatment.

Construction and analysis of a joint diagnostic model of machine learning for cryptorchidism based on single-cell sequencing

BACKGROUND

Cryptorchidism is a condition in which one or both of a baby's testicles do not fully descend into the bottom of the scrotum. Newborns with cryptorchidism are at increased risk of developing infertility later in life. The aim of this study was to develop a novel diagnostic model for cryptorchidism and to identify new biomarkers associated with cryptorchidism.

METHODS

The study data were obtained from RNA sequencing data of cryptorchid patients from Nantong University Hospital and the Gene Expression Omnibus (GEO) database. Differential expression analysis was used to obtain differentially expressed genes (DEGs) between the control and cryptorchid groups. These DEGs were analyzed for their functions by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment using GSEA software. Random Forest algorithm was used to screen central genes based on these DEGs. Neuralnet software package was used to develop artificial neural network models. Based on clinical data, receiver operating characteristic (ROC) was used to validate the models. Single-cell sequencing analysis was used for the pathogenesis of cryptorchidism.

RESULTS

We obtained a total of 525 important DEGs related to cryptorchidism, which are mainly associated with biological functions such as supramolecular complexes and microtubule cytoskeleton. Random forest approach screening obtained eight hub genes. A neural network based on the hub genes showed a 100% success rate of the model. Finally, single-cell sequencing analysis validated the hub genes.

CONCLUSION

We developed a novel diagnostic model for cryptorchidism using artificial neural networks and validated its utility as an effective diagnostic tool.