Automated single-cell omics end-to-end framework with data-driven batch inference.

To facilitate single cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease.

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

A methylation clock model of mild SARS-CoV-2 infection provides insight into immune dysregulation.

DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.

Benchmarking transcriptional host response signatures for infection diagnosis.

Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.

Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature.

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Asymptomatic SARS-CoV-2 Infection Is Associated With Higher Levels of Serum IL-17C, Matrix Metalloproteinase 10 and Fibroblast Growth Factors Than Mild Symptomatic COVID-19.

Young adults infected with SARS-CoV-2 are frequently asymptomatic or develop only mild disease. Because capturing representative mild and asymptomatic cases require active surveillance, they are less characterized than moderate or severe cases of COVID-19. However, a better understanding of SARS-CoV-2 asymptomatic infections might shed light into the immune mechanisms associated with the control of symptoms and protection. To this aim, we have determined the temporal dynamics of the humoral immune response, as well as the serum inflammatory profile, of mild and asymptomatic SARS-CoV-2 infections in a cohort of 172 initially seronegative prospectively studied United States Marine recruits, 149 of whom were subsequently found to be SARS-CoV-2 infected. The participants had blood samples taken, symptoms surveyed and PCR tests for SARS-CoV-2 performed periodically for up to 105 days. We found similar dynamics in the profiles of viral load and in the generation of specific antibody responses in asymptomatic and mild symptomatic participants. A proteomic analysis using an inflammatory panel including 92 analytes revealed a pattern of three temporal waves of inflammatory and immunoregulatory mediators, and a return to baseline for most of the inflammatory markers by 35 days post-infection. We found that 23 analytes were significantly higher in those participants that reported symptoms at the time of the first positive SARS-CoV-2 PCR compared with asymptomatic participants, including mostly chemokines and cytokines associated with inflammatory response or immune activation (i.e., TNF-α, TNF-ß, CXCL10, IL-8). Notably, we detected 7 analytes (IL-17C, MMP-10, FGF-19, FGF-21, FGF-23, CXCL5 and CCL23) that were higher in asymptomatic participants than in participants with symptoms; these are known to be involved in tissue repair and may be related to the control of symptoms. Overall, we found a serum proteomic signature that differentiates asymptomatic and mild symptomatic infections in young adults, including potential targets for developing new therapies and prognostic tests.

Advances in the computational landscape for repurposed drugs against COVID-19.

The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.

Dissection of intercellular communication using the transcriptome-based framework ICELLNET.

Cell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We develop ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand-receptor interactions accounting for multiple subunits expression; 2) quantification of communication scores; 3) the possibility to connect a cell population of interest with 31 reference human cell types; and 4) three visualization modes to facilitate biological interpretation. We apply ICELLNET to three datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveals autocrine IL-10 control of human dendritic cell communication with up to 12 cell types. Four of them (T cells, keratinocytes, neutrophils, pDC) are further tested and experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles.

Filling the gaps in the global prevalence map of clinical antimicrobial resistance.

Surveillance is critical in containing globally increasing antimicrobial resistance (AMR). Affordable methodologies to prioritize AMR surveillance efforts are urgently needed, especially in low- and middle-income countries (LMICs), where resources are limited. While socioeconomic characteristics correlate with clinical AMR prevalence, this correlation has not yet been used to estimate AMR prevalence in countries lacking surveillance. We captured the statistical relationship between AMR prevalence and socioeconomic characteristics in a suite of beta-binomial principal component regression models for nine pathogens resistant to 19 (classes of) antibiotics. Prevalence data from ResistanceMap were combined with socioeconomic profiles constructed from 5,595 World Bank indicators. Cross-validated models were used to estimate clinical AMR prevalence and temporal trends for countries lacking data. Our approach provides robust estimates of clinical AMR prevalence in LMICs for most priority pathogens (cross-validated q2 > 0.78 for six out of nine pathogens). By supplementing surveillance data, 87% of all countries worldwide, which represent 99% of the global population, are now informed. Depending on priority pathogen, our estimates benefit 2.1 to 4.9 billion people living in countries with currently insufficient diagnostic capacity. By estimating AMR prevalence worldwide, our approach allows for a data-driven prioritization of surveillance efforts. For carbapenem-resistant Acinetobacter baumannii and third-generation cephalosporin-resistant Escherichia coli, specific countries of interest are located in the Middle East, based on the magnitude of estimates; sub-Saharan Africa, based on the relative prevalence increase over 1998 to 2017; and the Pacific Islands, based on improving overall model coverage and performance.

Deciphering the combinatorial landscape of immunity.

From cellular activation to drug combinations, immunological responses are shaped by the action of multiple stimuli. Synergistic and antagonistic interactions between stimuli play major roles in shaping immune processes. To understand combinatorial regulation, we present the immune Synergistic/Antagonistic Interaction Learner (iSAIL). iSAIL includes a machine learning classifier to map and interpret interactions, a curated compendium of immunological combination treatment datasets, and their global integration into a landscape of ~30,000 interactions. The landscape is mined to reveal combinatorial control of interleukins, checkpoints, and other immune modulators. The resource helps elucidate the modulation of a stimulus by interactions with other cofactors, showing that TNF has strikingly different effects depending on co-stimulators. We discover new functional synergies between TNF and IFNß controlling dendritic cell-T cell crosstalk. Analysis of laboratory or public combination treatment studies with this user-friendly web-based resource will help resolve the complex role of interaction effects on immune processes.

Eleven grand challenges in single-cell data science.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Multiscale modelling in immunology: a review.

One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.

Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease.

Assessing human immune response remains a challenge as it involves multiple cell types in specific tissues. The use of microarray-based expression profiling as a tool for assessing the immune response has grown increasingly over the past decade. Transcriptome analyses provide investigators with a global perspective of the complex molecular and cellular events that unfold during the development of an immune response. In this review, we will detail the broad use of gene expression profiling to decipher the complexity of immune responses from disease biomarkers identification to cell activation, polarisation or functional specialisation. We will also describe how such data-driven strategies revealed the flexibility of immune function with common and specific transcriptional programme under multiple stimuli.

Combinatorial code governing cellular responses to complex stimuli.

Cells adapt to their environment through the integration of complex signals. Multiple signals can induce synergistic or antagonistic interactions, currently considered as homogenous behaviours. Here, we use a systematic theoretical approach to enumerate the possible interaction profiles for outputs measured in the conditions 0 (control), signals X, Y, X+Y. Combinatorial analysis reveals 82 possible interaction profiles, which we biologically and mathematically grouped into five positive and five negative interaction modes. To experimentally validate their use in living cells, we apply an original computational workflow to transcriptomics data of innate immune cells integrating physiopathological signal combinations. Up to 9 of the 10 defined modes coexisted in context-dependent proportions. Each interaction mode was preferentially used in specific biological pathways, suggesting a functional role in the adaptation to multiple signals. Our work defines an exhaustive map of interaction modes for cells integrating pairs of physiopathological and pharmacological stimuli.

Systems approaches to unravel innate immune cell diversity, environmental plasticity and functional specialization.

Innate immune cells are generated through central and peripheral differentiation pathways, and receive multiple signals from tissue microenvironment. The complex interplay between immune cell state and environmental signals is crucial for the adaptation and efficient response to pathogenic threats. Here, we discuss how systems biology approaches have brought global view and high resolution to the characterization of (1) immune cell diversity, (2) phenotypic, transcriptional and functional changes in response to environmental signals, (3) integration of multiple stimuli. We will mostly focus on systems level studies in dendritic cells and macrophages. Generalization of these approaches should elucidate innate immune cell diversity and plasticity, and may be used in the human to generate hypothesis on cell filiation and novel strategies for immunotherapy.

Combinatorial flexibility of cytokine function during human T helper cell differentiation.

In an inflammatory microenvironment, multiple cytokines may act on the same target cell, creating the possibility for combinatorial interactions. How these may influence the system-level function of a given cytokine is unknown. Here we show that a single cytokine, interferon (IFN)-alpha, can generate multiple transcriptional signatures, including distinct functional modules of variable flexibility, when acting in four cytokine environments driving distinct T helper cell differentiation programs (Th0, Th1, Th2 and Th17). We provide experimental validation of a chemokine, cytokine and antiviral modules differentially induced by IFN-α in Th1, Th2 and Th17 environments. Functional impact is demonstrated for the antiviral response, with a lesser IFN-α-induced protection to HIV-1 and HIV-2 infection in a Th17 context. Our results reveal that a single cytokine can induce multiple transcriptional and functional programs in different microenvironments. This combinatorial flexibility creates a previously unrecognized diversity of responses, with potential impact on disease physiopathology and cytokine therapy.

Human inflammatory dendritic cells induce Th17 cell differentiation.

Dendritic cells (DCs) are critical regulators of immune responses. Under noninflammatory conditions, several human DC subsets have been identified. Little is known, however, about the human DC compartment under inflammatory conditions. Here, we characterize a DC population found in human inflammatory fluids that displayed a phenotype distinct from macrophages from the same fluids and from steady-state lymphoid organ and blood DCs. Transcriptome analysis showed that they correspond to a distinct DC subset and share gene signatures with in vitro monocyte-derived DCs. Moreover, human inflammatory DCs, but not inflammatory macrophages, stimulated autologous memory CD4(+) T cells to produce interleukin-17 and induce T helper 17 (Th17) cell differentiation from naive CD4(+) T cells through the selective secretion of Th17 cell-polarizing cytokines. We conclude that inflammatory DCs represent a distinct human DC subset and propose that they are derived from monocytes and are involved in the induction and maintenance of Th17 cell responses.