Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
Cluster Analysis ; Transcriptome

With wide applications of genomics, transcriptomics, proteomics and metabolomics in the post-genome era, functional explanation has become the central task in life science research, and multi-omics data integrative analysis has become an indispensable strategy for uncovering the underlying biological mechanism. This special issue aimed to introduce the related research advances and applications in multi-omics by inviting the domestic experts. In total, 28 papers have been collected in this issue, for researcher's reference in multi-omics.
Genomics ; Proteomics ; Metabolomics ; Transcriptome

No abstract available.
Gene Expression Profiling* ; RNA ; Transcriptome*

Humans ; Transcriptome ; Gene Expression Profiling

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
Drug Delivery Systems ; Proteins ; Transcriptome

Hemoglobin A2 ; genetics ; Humans ; Thalassemia ; genetics ; Transcriptome

Plasmodium culture in vitro is often used as an antimalarial drug evaluation model, but the lifecycle of P. falciparum culture in vitro tends to be disordered, which affects the research and evaluation of antimalarial drug mechanism in vitro. By combining magnetic bead separation method with sorbitol synchronization method, a synchronization method was constructed to quickly acquire different lifecycles of P. falciparum and obtain large amounts of parasite with a narrow synchronization window in a short period. Furthermore, the dihydroartemisinin(DHA) was used to treat the early trophozoite phase of P. falciparum 3 D7 for 4 h. Then mRNA was extracted and RNA-seq was conducted to analyze the differential expression of mRNA after drug treatment and obtain the differential gene expression profile. Differential expression of up-regulated genes and down-regulated genes was analyzed according to the screening criteria of |log_2FC|>1 and P<0.05. There, 262 genes were up-regulated and 77 genes were down-regulated. GO functional enrichment analysis of all the differentially expressed genes showed that the enrichment items mainly included cell membrane components, transporter activity, serine/threonine kinase activity, Maurer's clefts(MCs), rhoptry, antigen variation and immune evasion. The enrichment of KEGG pathway included malaria, fatty acid metabolism and peroxisome. Protein-protein interaction(PPI) analysis showed that the down-regulated genes in the modules with high degree of association included rhoptry, myosin complex, transporter and other genes related to the important life activities of malaria invasion and immune escape; the up-regulated genes were mainly related to various toxic exportins of malaria, such as PfSBP1 of MCs. qRT-PCR was used to verify the expression level of some genes, and most of the results were the same as the sequencing results. SBP1 was significantly up-regulated, while some antigenic protein expression levels were down-regulated. Above all, key molecules of DHA therapy were mainly involved in the parasites' rhoptry, transporter, antigenic variation, plasmodium exportin. These results offer us many hints to guide the further studies on mechanism of artemisinin and provide a new way for development of new antimalarial drugs.
Animals ; Antimalarials ; Artemisinins ; Erythrocytes ; Plasmodium falciparum ; Transcriptome

No abstract available.
Gene Expression ; Humans ; Keratinocytes ; Skin ; Transcriptome

Acclimatization ; Acinetobacter ; metabolism ; Cold Temperature ; Transcriptome

Immune Checkpoint Inhibitors ; Transcriptome ; Immunotherapy ; Biomarkers, Tumor