Interactions between immune cell types facilitate the evolution of immune traits.

An essential prerequisite for evolution by natural selection is variation among individuals in traits that affect fitness1. The ability of a system to produce selectable variation, known as evolvability2, thus greatly affects the rate of evolution. The immune system belongs to the fastest evolving components in mammals3, yet the sources of variation in immune traits remain largely unknown4,5. Here, we show that an important determinant of the immune system's evolvability is its organisation into interacting modules represented by different immune cell types. By profiling immune cell variation in bone marrow of 54 genetically diverse mouse strains from the Collaborative Cross6, we found that variation in immune cell frequencies is polygenic and that many associated genes are involved in homeostatic balance through cell-intrinsic functions of proliferation, migration and cell death. However, we also found genes associated with the frequency of a particular cell type, which are expressed in a different cell type, exerting their effect in what we term cyto-trans. Vertebrate evolutionary record shows that genes associated in cyto-trans have faced weaker negative selection, thus increasing the robustness and hence evolvability2,7,8 of the immune system. This phenomenon is similarly observable in human blood. Our findings suggest that interactions between different components of the immune system provide a phenotypic space where mutations can produce variation without much detriment, underscoring the role of modularity in the evolution of complex systems9.

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.

COVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure1-4, but little is known about its pathophysiology. Here we generated single-cell atlases of 24 lung, 16 kidney, 16 liver and 19 heart autopsy tissue samples and spatial atlases of 14 lung samples from donors who died of COVID-19. Integrated computational analysis uncovered substantial remodelling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of fibroblasts and putative TP63+ intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells, which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in heart tissue from donors with COVID-19, and mapped cell types and genes implicated with disease severity based on COVID-19 genome-wide association studies. Our foundational dataset elucidates the biological effect of severe SARS-CoV-2 infection across the body, a key step towards new treatments.

Found In Translation: a machine learning model for mouse-to-human inference.

Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we present Found In Translation (FIT; http://www.mouse2man.org ), a statistical methodology that leverages public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20-50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost.

SARS-CoV-2 infection elucidates unique features of pregnancy-specific immunity.

Pregnancy is a risk factor for increased severity of SARS-CoV-2 and other respiratory infections. The mechanisms underlying this risk have not been well-established, partly due to a limited understanding of how pregnancy shapes immune responses. To gain insight into the role of pregnancy in modulating immune responses at steady state and upon perturbation, we collected peripheral blood mononuclear cells (PBMC), plasma, and stool from 226 women, including 152 pregnant individuals (n = 96 with SARS-CoV-2 infection and n = 56 healthy controls) and 74 non-pregnant women (n = 55 with SARS-CoV-2 and n = 19 healthy controls). We found that SARS-CoV-2 infection was associated with altered T cell responses in pregnant compared to non-pregnant women. Differences included a lower percentage of memory T cells, a distinct clonal expansion of CD4-expressing CD8 + T cells, and the enhanced expression of T cell exhaustion markers, such as programmed cell death-1 (PD-1) and T cell immunoglobulin and mucin domain-3 (Tim-3), in pregnant women. We identified additional evidence of immune dysfunction in severely and critically ill pregnant women, including a lack of expected elevation in regulatory T cell (Treg) levels, diminished interferon responses, and profound suppression of monocyte function. Consistent with earlier data, we found maternal obesity was also associated with altered immune responses to SARS-CoV-2 infection, including enhanced production of inflammatory cytokines by T cells. Certain gut bacterial species were altered in pregnancy and upon SARS-CoV-2 infection in pregnant individuals compared to non-pregnant women. Shifts in cytokine and chemokine levels were also identified in the sera of pregnant individuals, most notably a robust increase of interleukin-27 (IL-27), a cytokine known to drive T cell exhaustion, in the pregnant uninfected control group compared to all non-pregnant groups. IL-27 levels were also significantly higher in uninfected pregnant controls compared to pregnant SARS-CoV-2-infected individuals. Using two different preclinical mouse models of inflammation-induced fetal demise and respiratory influenza viral infection, we found that enhanced IL-27 protects developing fetuses from maternal inflammation but renders adult female mice vulnerable to viral infection. These combined findings from human and murine studies reveal nuanced pregnancy-associated immune responses, suggesting mechanisms underlying the increased susceptibility of pregnant individuals to viral respiratory infections.

IL-6 contributes to metastatic switch via the differentiation of monocytic-dendritic progenitors into prometastatic immune cells.

BACKGROUND: Metastasis is the major cause of death in patients with cancer. Myeloid skewing of hematopoietic cells is a prominent promoter of metastasis. However, the reservoir of these cells in the bone marrow (BM) compartment and their differentiation pattern from hematopoietic stem and progenitor cells (HSPCs) have not been explored. METHODS: We used a unique model system consisting of tumor cell clones with low metastatic potential or high metastatic potential (met-low and met-high, respectively) to investigate the fate of HSPC differentiation using murine melanoma and breast carcinoma. Single-cell RNA sequencing (scRNA-seq) analysis was performed on HSPC obtained from the BM of met-low and met-high tumors. A proteomic screen of tumor-conditioned medium integrated with the scRNA-seq data analysis was performed to analyze the potential cross talk between cancer cells and HSPCs. Adoptive transfer of tumor-educated HSPC subsets obtained from green fluorescent protein (GFP)+ tagged mice was then carried out to identify the contribution of committed HSPCs to tumor spread. Peripheral mononuclear cells obtained from patients with breast and lung cancer were analyzed for HSPC subsets. RESULTS: Mice bearing met-high tumors exhibited a significant increase in the percentage of HSPCs in the BM in comparison with tumor-free mice or mice bearing met-low tumors. ScRNA-seq analysis of these HSPCs revealed that met-high tumors enriched the monocyte-dendritic progenitors (MDPs) but not granulocyte-monocyte progenitors (GMPs). A proteomic screen of tumor- conditioned medium integrated with the scRNA-seq data analysis revealed that the interleukin 6 (IL-6)-IL-6 receptor axis is highly active in HSPC-derived MDP cells. Consequently, loss of function and gain of function of IL-6 in tumor cells resulted in decreased and increased metastasis and corresponding MDP levels, respectively. Importantly, IL-6-educated MDPs induce metastasis within mice bearing met-low tumors-through further differentiation into immunosuppressive macrophages and not dendritic cells. Consistently, MDP but not GMP levels in peripheral blood of breast and lung cancer patients are correlated with tumor aggressiveness. CONCLUSIONS: Our study reveals a new role for tumor-derived IL-6 in hijacking the HSPC differentiation program toward prometastatic MDPs that functionally differentiate into immunosuppressive monocytes to support the metastatic switch.

A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2.

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients' demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.

An introduction to high-throughput sequencing experiments: design and bioinformatics analysis.

The dramatic fall in the cost of DNA sequencing has revolutionized the experiments within reach in the life sciences. Here we provide an introduction for the domains of analyses possible using high-throughput sequencing, distinguishing between "counting" and "reading" applications. We discuss the steps in designing a high-throughput sequencing experiment, introduce the most widely used applications, and describe basic sequencing concepts. We review the various software programs available for many of the bioinformatics analysis required to make sense of the sequencing data. We hope that this introduction will be accessible to biologists with no previous background in bioinformatics, yet with a keen interest in applying the power of high-throughput sequencing in their research.