Digital microfluidics-based digital counting of single-cell copy number variation (dd-scCNV Seq).

Single-cell copy number variations (CNVs), major dynamic changes in humans, result in differential levels of gene expression and account for adaptive traits or underlying disease. Single-cell sequencing is needed to reveal these CNVs but has been hindered by single-cell whole-genome amplification (scWGA) bias, leading to inaccurate gene copy number counting. In addition, most of the current scWGA methods are labor intensive, time-consuming, and expensive with limited wide application. Here, we report a unique single-cell whole-genome library preparation approach based on digital microfluidics for digital counting of single-cell Copy Number Variation (dd-scCNV Seq). dd-scCNV Seq directly fragments the original single-cell DNA and uses these fragments as templates for amplification. These reduplicative fragments can be filtered computationally to generate the original partitioned unique identified fragments, thereby enabling digital counting of copy number variation. dd-scCNV Seq showed an increase in uniformity in the single-molecule data, leading to more accurate CNV patterns compared to other methods with low-depth sequencing. Benefiting from digital microfluidics, dd-scCNV Seq allows automated liquid handling, precise single-cell isolation, and high-efficiency and low-cost genome library preparation. dd-scCNV Seq will accelerate biological discovery by enabling accurate profiling of copy number variations at single-cell resolution.

LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis.

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for biomedical research by providing a variety of valuable information with the advancement of computational tools. Lineage analysis based on scRNA-seq provides key insights into the fate of individual cells in various systems. However, such analysis is limited by several technical challenges. On top of the considerable computational expertise and resources, these analyses also require specific types of matching data such as exogenous barcode information or bulk assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) data. To overcome these technical challenges, we developed a user-friendly computational algorithm called "LINEAGE" (label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis). Aiming to screen out endogenous markers of lineage located on mitochondrial reads from label-free scRNA-seq data to conduct lineage inference, LINEAGE integrates a marker selection strategy by feature subspace separation and de novo "low cross-entropy subspaces" identification. In this process, the mutation type and subspace-subspace "cross-entropy" of features were both taken into consideration. LINEAGE outperformed three other methods, which were designed for similar tasks as testified with two standard datasets in terms of biological accuracy and computational efficiency. Applied on a label-free scRNA-seq dataset of BRAF-mutated cancer cells, LINEAGE also revealed genes that contribute to BRAF inhibitor resistance. LINEAGE removes most of the technical hurdles of lineage analysis, which will remarkably accelerate the discovery of the important genes or cell-lineage clusters from scRNA-seq data.

Mapping Gene Expression in the Spatial Dimension.

The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.

SARS-CoV-2-Encoded MiRNAs Inhibit Host Type I Interferon Pathway and Mediate Allelic Differential Expression of Susceptible Gene.

Infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the rapid spread of coronavirus disease 2019 (COVID-19), has generated a public health crisis worldwide. The molecular mechanisms of SARS-CoV-2 infection and virus-host interactions are still unclear. In this study, we identified four unique microRNA-like small RNAs encoded by SARS-CoV-2. SCV2-miR-ORF1ab-1-3p and SCV2-miR-ORF1ab-2-5p play an important role in evasion of type I interferon response through targeting several genes in type I interferon signaling pathway. Particularly worth mentioning is that highly expressed SCV2-miR-ORF1ab-2-5p inhibits some key genes in the host innate immune response, such as IRF7, IRF9, STAT2, OAS1, and OAS2. SCV2-miR-ORF1ab-2-5p has also been found to mediate allelic differential expression of COVID-19-susceptible gene OAS1. In conclusion, these results suggest that SARS-CoV-2 uses its miRNAs to evade the type I interferon response and links the functional viral sequence to the susceptible genetic background of the host.