Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19.

Background: Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown.

CITEMO: A flexible single-cell multimodal omics analysis framework to reveal the heterogeneity of immune cells.

Simultaneous measurement of multiple modalities in single-cell analysis, represented by CITE-seq, is a promising approach to link transcriptional changes to cellular phenotype and function, requiring new computational methods to define cellular subtypes and states based on multiple data types. Here, we design a flexible single-cell multimodal analysis framework, called CITEMO, to integrate the transcriptome and antibody-derived tags (ADT) data to capture cell heterogeneity from the multi omics perspective. CITEMO uses Principal Component Analysis (PCA) to obtain a low-dimensional representation of the transcriptome and ADT, respectively, and then employs PCA again to integrate these low-dimensional multimodal data for downstream analysis.

Cas12a Base Editors Induce Efficient and Specific Editing with Low DNA Damage Response.

The advent of base editors (BEs) holds great potential for correcting pathogenic-related point mutations to treat relevant diseases. However, Cas9 nickase (nCas9)-derived BEs lead to DNA double-strand breaks, which can trigger unwanted DNA damage response (DDR). Here, we show that the original version of catalytically dead Cas12a (dCas12a)-conjugated BEs induce a basal level of DNA breaks and minimally activate DDR proteins, including H2AX, ATM, ATR, and p53.

Comparison of cytosine base editors and development of the BEable-GPS database for targeting pathogenic SNVs.

A variety of base editors have been developed to achieve C-to-T editing in different genomic contexts. Here, we compare a panel of five base editors on their C-to-T editing efficiencies and product purity at commonly editable sites, including some human pathogenic C-to-T mutations. We further profile the accessibilities of 20 base editors to all possible pathogenic mutations in silico.

DiseaseMeth version 2.0: a major expansion and update of the human disease methylation database.

The human disease methylation database (DiseaseMeth, http://bioinfo.hrbmu.edu.