Mice are some of the widely used experimental animal models for studying human diseases. Defining the compositions of immune cell populations in various tissues from experimental mouse models is critical to understanding the involvement of immune responses in various physiological and patho-physiological conditions. However, non-lymphoid tissues are normally composed of vast and diverse cellular components, which make it difficult to quantify the relative proportions of immune cell types. Here we report the development of a computational algorithm, ImmuCC, to infer the relative compositions of 25 immune cell types in mouse tissues using microarray-based mRNA expression data. The ImmuCC algorithm showed good performance and robustness in many simulated datasets. Remarkable concordances were observed when ImmuCC was used on three public datasets, one including enriched immune cells, one with normal single positive T cells, and one with leukemia cell samples. To validate the performance of ImmuCC objectively, thorough cross-comparison of ImmuCC predicted compositions and flow cytometry results was done with in-house generated datasets collected from four distinct mouse lymphoid tissues and three different types of tumor tissues. The good correlation and biologically meaningful results demonstrate the broad utility of ImmuCC for assessing immune cell composition in diverse mouse tissues under various conditions.

Monkeypox virus (MPXV) has generally circulated in West and Central Africa since its emergence. Recently, sporadic MPXV infections in several nonendemic countries have attracted widespread attention. Here, we conducted a systematic analysis of the recent outbreak of MPXV‐2022, including its genomic annotation and molecular evolution. The phylogenetic analysis indicated that the MPXV‐2022 strains belong to the same lineage of the MPXV strain isolated in 2018. However, compared with the MPXV strain in 2018, in total 46 new consensus mutations were observed in the MPXV‐2022 strains, including 24 nonsynonymous mutations. By assigning mutations to 187 proteins encoded by the MPXV genome, we found that 10 proteins in the MPXV are more prone to mutation, including D2L‐like, OPG023, OPG047, OPG071, OPG105, OPG109, A27L‐like, OPG153, OPG188, and OPG210 proteins. In the MPXV‐2022 strains, four and three nucleotide substitutions are observed in OPG105 and OPG210, respectively. Overall, our studies illustrated the genome evolution of the ongoing MPXV outbreak and pointed out novel mutations as a reference for further studies.

The RNA sequencing approach has been broadly used to provide gene-, pathway-, and network-centric analyses for various cell and tissue samples. However, thus far, rich cellular information carried in tissue samples has not been thoroughly characterized from RNA-Seq data. Therefore, it would expand our horizons to better understand the biological processes of the body by incorporating a cell-centric view of tissue transcriptome. Here, a computational model named seq-ImmuCC was developed to infer the relative proportions of 10 major immune cells in mouse tissues from RNA-Seq data. The performance of seq-ImmuCC was evaluated among multiple computational algorithms, transcriptional platforms, and simulated and experimental datasets. The test results showed its stable performance and superb consistency with experimental observations under different conditions. With seq-ImmuCC, we generated the comprehensive landscape of immune cell compositions in 27 normal mouse tissues and extracted the distinct signatures of immune cell proportion among various tissue types. Furthermore, we quantitatively characterized and compared 18 different types of mouse tumor tissues of distinct cell origins with their immune cell compositions, which provided a comprehensive and informative measurement for the immune microenvironment inside tumor tissues. The online server of seq-ImmuCC are freely available at http://wap-lab.org:3200/immune/.

The influenza A (H1N1) virus causes seasonal epidemics that result in severe illnesses and deaths almost every year. A deep understanding of the antigenic patterns and evolution of human influenza A (H1N1) virus is extremely important for its effective surveillance and prevention. Through development of antigenicity inference method for human influenza A (H1N1), named PREDAC-H1, we systematically mapped the antigenic patterns and evolution of the human influenza A (H1N1) virus. Eight dominant antigenic clusters have been inferred for seasonal H1N1 viruses since 1977, which demonstrated sequential replacements over time with a similar pattern in Asia, Europe and North America. Among them, six clusters emerged first in Asia. As for China, three of the eight antigenic clusters were detected in South China earlier than in North China, indicating the leading role of South China in H1N1 transmission. The comprehensive view of the antigenic evolution of human influenza A (H1N1) virus can help formulate better strategy for its prevention and control.

Zika virus (ZIKV) infection during pregnancy threatens pregnancy and fetal health. However, the infectivity and pathological effects of ZIKV on placental trophoblast progenitor cells in early human embryos remain largely unknown. Here, using human trophoblast stem cells (hTSCs), we demonstrated that hTSCs were permissive to ZIKV infection, and resistance to ZIKV increased with hTSC differentiation. Combining gene knockout and transcriptome analysis, we demonstrated that the intrinsic expression of AXL and TIM-1, and the absence of potent interferon (IFN)-stimulated genes (ISGs) and IFNs contributed to the high sensitivity of hTSCs to ZIKV. Furthermore, using our newly developed hTSC-derived trophoblast organoid (hTSC-organoid), we demonstrated that ZIKV infection disrupted the structure of mature hTSC-organoids and inhibited syncytialization. Single-cell RNA sequencing (scRNA-seq) further demonstrated that ZIKV infection of hTSC-organoids disrupted the stemness of hTSCs and the proliferation of cytotrophoblast cells (CTBs) and probably led to a preeclampsia (PE) phenotype. Overall, our results clearly demonstrate that hTSCs represent the major target cells of ZIKV, and a reduced syncytialization may result from ZIKV infection of early developing placenta. These findings deepen our understanding of the characteristics and consequences of ZIKV infection of hTSCs in early human embryos.

Glioblastoma (GBM) is the deadliest primary brain tumor and is generally resistant to immunotherapy because of severe dysfunction of T cells. Novel treatment options are critically needed to overcome the immunotherapy resistance of GBM. Here we demonstrate that Zika virus (ZIKV) treatment improves the efficacy of anti-PD ligand 1 (PD-L1) immunotherapy in GBM. We found that ZIKV induces a strong pro-inflammatory response and increases CD4⁺ and CD8⁺ T cell intratumoral infiltration and activation in GBM mouse models. ZIKV treatment of mice bearing GBM tumors inhibits tumor growth and prolongs survival. These therapeutic effects of ZIKV on GBM tumors are negated in mice depleted of T cells. Moreover, ZIKV dramatically promotes activation of the type I interferon signaling pathway in GBM cells. ZIKV treatment potently sensitizes GBM to PD-L1 blockade and provides significant and durable survival benefits. Our findings reveal that ZIKV overcomes the resistance of GBM to immune checkpoint blockade, which may lead to therapeutic applications of ZIKV in individuals with GBM receiving immunotherapy.