Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace (preprint)

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose s ingle- c ell s patial p osition a ssociated c o- e mbeddings (scSpace), an integrative algorithm to distinguish spatially variable cell subclusters by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We demonstrated that scSpace can define biologically meaningful cell subpopulations neglected by single-cell RNA-seq or spatially resolved transcriptomics. The use of scSpace to uncover the spatial association within single-cell data, reproduced, the hierarchical distribution of cells in the brain cortex and liver lobules, and the regional variation of cells in heart ventricles and the intestinal villus. scSpace identified cell subclusters in intratelencephalic neurons, which were confirmed by their biomarkers. The application of scSpace in melanoma and Covid-19 exhibited a broad prospect in the discovery of spatial therapeutic markers.

Examining geographical disparities in the incubation period of the COVID-19 infected cases in Shenzhen and Hefei, China (preprint)

Aim Current studies on the COVID-19 depicted a general incubation period distribution and did not examine whether the incubation period distribution varies across patients living in different geographical locations with varying environmental attributes. Profiling the incubation distributions geographically help to determine the appropriate quarantine duration for different regions.Subject and Methods This retrospective study mainly used publicly-accessible clinical report data for patients (n=543) confirmed as infected in Shenzhen and Heifei, China. Based on 217 patients on whom the incubation period could be identified by the epidemiological survey. Statistical and econometric methods were used to investigate how the incubation distributions varied between infected cases reported in Shenzhen and Hefei. Results The median of incubation periods of the COVID-19 for all 217 infected patients was 8 days (95% CI 7 to 9), while median values were 9 days in Shenzhen and 4 days in Heifei. The incubation period probably has an inverse U-shaped association with the meteorological temperature. The warmer condition in the winter of Shenzhen, average environmental temperature between 10℃ to 15℃, may decrease viral virulence and result in more extended incubation periods.Conclusion Case studies of the COVID-19 outbreak in Shenzhen and Hefei indicated that the incubation period of COVID-19 had exhibited evident geographical disparities, although the pathological causality between meteorological conditions and incubation period deserves further investigation.