Single-cell profiling of tuberculosis lung granulomas reveals functional lymphocyte signatures of bacterial control [preprint]

In humans and nonhuman primates, Mycobacterium tuberculosis lung infection yields a complex multicellular structure—the tuberculosis granuloma. All granulomas are not equivalent, however, even within the same host: in some, local immune activity promotes bacterial clearance, while in others, it allows persistence or outgrowth. Here, we used single-cell RNA-sequencing to define holistically cellular responses associated with control in cynomolgus macaques. Granulomas that facilitated bacterial killing contained significantly higher proportions of CD4+ and CD8+ T cells expressing hybrid Type1-Type17 immune responses or stem-like features and CD8-enriched T cells with specific cytotoxic functions; failure to control correlated with mast cell, plasma cell and fibroblast abundance. Co-registering these data with serial PET-CT imaging suggests that a degree of early immune control can be achieved through cytotoxic activity, but that more robust restriction only arises after the priming of specific adaptive immune responses, defining new targets for vaccination and treatment

Aggregated Mycobacterium tuberculosis Enhances the Inflammatory Response

Mycobacterium tuberculosis (Mtb) bacilli readily aggregate. We previously reported that Mtb aggregates lead to phagocyte death and subsequent efficient replication in the dead infected cells. Here, we examined the transcriptional response of human monocyte derived macrophages to phagocytosis of aggregated Mtb relative to phagocytosis of non-aggregated single or multiple bacilli. Infection with aggregated Mtb led to an early upregulation of pro-inflammatory associated genes and enhanced TNFα signaling via the NFκB pathway. These pathways were significantly more upregulated relative to infection with single or multiple non-aggregated bacilli per cell. Phagocytosis of aggregates led to a decreased phagosome acidification on a per bacillus basis and increased phagocyte cell death, which was not observed when Mtb aggregates were heat killed prior to phagocytosis. Mtb aggregates, observed in a granuloma from a patient, were found surrounding a lesion cavity. These observations suggest that TB aggregation may be a mechanism for pathogenesis. They raise the possibility that aggregated Mtb, if spread from individual to individual, could facilitate increased inflammation, Mtb growth, and macrophage cell death, potentially leading to active disease, cell necrosis, and additional cycles of transmission

Selective expansion of myeloid and NK cells in humanized mice yields human-like vaccine responses

Mice engrafted with components of a human immune system have become widely-used models for studying aspects of human immunity and disease. However, a defined methodology to objectively measure and compare the quality of the human immune response in different models is lacking. Here, by taking advantage of the highly immunogenic live-attenuated yellow fever virus vaccine YFV-17D, we provide an in-depth comparison of immune responses in human vaccinees, conventional humanized mice, and second generation humanized mice. We demonstrate that selective expansion of human myeloid and natural killer cells promotes transcriptomic responses akin to those of human vaccinees. These enhanced transcriptomic profiles correlate with the development of an antigen-specific cellular and humoral response to YFV-17D. Altogether, our approach provides a robust scoring of the quality of the human immune response in humanized mice and highlights a rational path towards developing better pre-clinical models for studying the human immune response and disease.National Institute of General Medical Sciences (U.S.) (Award T32GM007753)Searle Scholars ProgramArnold and Mabel Beckman Foundation (Young Investigator Program)National Institutes of Health (U.S.) (1DP2OD020839)National Institutes of Health (U.S.) (5U24AI118672)National Institutes of Health (U.S.) (1U54CA217377)National Institutes of Health (U.S.) (1R33CA202820)National Institutes of Health (U.S.) (2U19AI089992)National Institutes of Health (U.S.) (21R01HL134539)National Institutes of Health (U.S.) (2RM1HG006193)National Institutes of Health (U.S.) (2R01HL095791)National Institutes of Health (U.S.) (P01AI039671)Bill & Melinda Gates Foundation (OPP1139972

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST

Regulation of X-linked gene expression during early mouse development by

Mammalian X-linked gene expression is highly regulated as female cells contain two and male one X chromosome (X). To adjust the X gene dosage between genders, female mouse preimplantation embryos undergo an imprinted form of X chromosome inactivation (iXCI) that requires both Rlim (also known as Rnf12) and the long non-coding RNA Xist. Moreover, it is thought that gene expression from the single active X is upregulated to correct for bi-allelic autosomal (A) gene expression. We have combined mouse genetics with RNA-seq on single mouse embryos to investigate functions of Rlim on the temporal regulation of iXCI and Xist. Our results reveal crucial roles of Rlim for the maintenance of high Xist RNA levels, Xist clouds and X-silencing in female embryos at blastocyst stages, while initial Xist expression appears Rlim-independent. We find further that X/A upregulation is initiated in early male and female preimplantation embryos

Single cell profiling of COVID-19 patients: an international data resource from multiple tissues

In late 2019 and through 2020, the COVID-19 pandemic swept the world, presenting both scientific and medical challenges associated with understanding and treating a previously unknown disease. To help address the need for great understanding of COVID-19, the scientific community mobilized and banded together rapidly to characterize SARS-CoV-2 infection, pathogenesis and its distinct disease trajectories. The urgency of COVID-19 provided a pressing use-case for leveraging relatively new tools, technologies, and nascent collaborative networks. Single-cell biology is one such example that has emerged over the last decade as a powerful approach that provides unprecedented resolution to the cellular and molecular underpinnings of biological processes. Early foundational work within the single-cell community, including the Human Cell Atlas, utilized published and unpublished data to characterize the putative target cells of SARS-CoV-2 sampled from diverse organs based on expression of the viral receptor ACE2 and associated entry factors TMPRSS2 and CTSL (Muus et al., 2020; Sungnak et al., 2020; Ziegler et al., 2020). This initial characterization of reference data provided an important foundation for framing infection and pathology in the airway as well as other organs. However, initial community analysis was limited to samples derived from uninfected donors and other previously-sampled disease indications. This report provides an overview of a single-cell data resource derived from samples from COVID-19 patients along with initial observations and guidance on data reuse and exploration

Time-dependent probability density functions and information geometry in stochastic logistic and Gompertz models

A probabilistic description is essential for understanding growth processes in non-stationary states. In this paper, we compute time-dependent probability density functions (PDFs) in order to investigate stochastic logistic and Gompertz models, which are two of the most popular growth models. We consider different types of short-correlated multiplicative and additive noise sources and compare the time-dependent PDFs in the two models, elucidating the effects of the additive and multiplicative noises on the form of PDFs. We demonstrate an interesting transition from a unimodal to a bimodal PDF as the multiplicative noise increases for a fixed value of the additive noise. A much weaker (leaky) attractor in the Gompertz model leads to a significant (singular) growth of the population of a very small size. We point out the limitation of using stationary PDFs, mean value and variance in understanding statistical properties of the growth in non-stationary states, highlighting the importance of time-dependent PDFs. We further compare these two models from the perspective of information change that occurs during the growth process. Specifically, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory quantifies the total number of different states that the system undergoes in time, and is called the information length. We show that the time-evolution of the two models become more similar when measured in units of the information length and point out the merit of using the information length in unifying and understanding the dynamic evolution of different growth processes

Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma

Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.National Institutes of Health (U.S.) (U24 CA180922