Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network

Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework

The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (IL)-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified cytokine-specific genes associated with late pSTAT3 time frames and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems. A record of this paper's transparent peer review process is included in the supplemental information.

Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework

The JAK-STAT pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational workflow to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to IL-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified select cytokine-induced gene sets associated with late pSTAT3 timeframes and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying dynamically regulated genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems.

SelectBCM tool: a batch evaluation framework to select the most appropriate batch-correction methods for bulk transcriptome analysis

Bulk transcriptomes are an essential data resource for understanding basic and disease biology. However, integrating information from different experiments remains challenging because of the batch effect generated by various technological and biological variations in the transcriptome. Numerous batch-correction methods to deal with this batch effect have been developed in the past. However, a user-friendly workflow to select the most appropriate batch-correction method for the given set of experiments is still missing. We present the SelectBCM tool that prioritizes the most appropriate batch-correction method for a given set of bulk transcriptomic experiments, improving biological clustering and gene differential expression analysis. We demonstrate the applicability of the SelectBCM tool on analyses of real data for two common diseases, rheumatoid arthritis and osteoarthritis, and one example to characterize a biological state, where we performed a meta-analysis of the macrophage activation state. The R package is available at https://github.com/ebi-gene-expression-group/selectBCM.

Multi-Phenotypic Exosome Secretion Profiling Microfluidic Platform for Exploring Single-Cell Heterogeneity

Cellular phenotypic and functional heterogeneities have advanced cancer evolution and treatment resistance. Although exosome-bound proteins reflect cellular functions, single-cell exosomes are rarely profiled owing to the lack of effective platforms. Herein, the authors developed an integrated microfluidic platform consisting of a single-cell trapping chip and a spatially coded antibody barcode chip for the multiplexed outline of exosome secretion by single cells. Using this platform, five phenotypic exosomes of over 1 000 single cells are simultaneously profiled, in addition to inflammatory factor secretion from the same single cell. Also, a robust analysis workflow for single-cell secretion profiling is proposed to explore the intercellular heterogeneity, which integrated unsupervised clustering and linear clustering. When applied to the tumor cell lines of epithelial-origin and normal epithelial cell lines, the strategy identifies functionally heterogeneous subpopulations with unique secretion patterns. Notably, special functional cell subsets for unique phenotypic exosomes (HSP70⁺ , EPCAM⁺ ) are found within ovarian tumor cells. The strategy proposed offers a new analysis approach for cellular differential exosome secretion at single-cell resolution using inflammatory factors, ultimately reinforcing the understanding of cell-to-cell heterogeneity and tumor landscape, and providing a valuable universal platform for single-cell biomarker exploration in biological and clinical research.

A novel approach to single-cell analysis reveals intrinsic differences in immune marker expression in unstimulated BALB/c and C57BL/6 macrophages

The origin of functional heterogeneity among macrophages, key innate immune system components, is still debated. While mouse strains differ in their immune responses, the range of gene expression variation among their pre-stimulation macrophages is unknown. With a novel approach to scRNA-seq analysis, we reveal the gene expression variation in unstimulated macrophage populations from BALB/c and C57BL/6 mice. We show that intrinsic strain-to-strain differences are detectable before stimulation and we place the unstimulated single cells within the gene expression landscape of stimulated macrophages. C57BL/6 mice show stronger evidence of macrophage polarization than BALB/c mice, which may contribute to their relative resistance to pathogens. Our computational methods can be generally adopted to uncover biological variation between cell populations.

Functional Hallmarks of Healthy Macrophage Responses: Their Regulatory Basis and Disease Relevance

Macrophages are first responders for the immune system. In this role, they have both effector functions for neutralizing pathogens and sentinel functions for alerting other immune cells of diverse pathologic threats, thereby initiating and coordinating a multipronged immune response. Macrophages are distributed throughout the body-they circulate in the blood, line the mucosal membranes, reside within organs, and survey the connective tissue. Several reviews have summarized their diverse roles in different physiological scenarios and in the initiation or amplification of different pathologies. In this review, we propose that both the effector and the sentinel functions of healthy macrophages rely on three hallmark properties: response specificity, context dependence, and stimulus memory. When these hallmark properties are diminished, the macrophage's biological functions are impaired, which in turn results in increased risk for immune dysregulation, manifested by immune deficiency or autoimmunity. We review the evidence and the molecular mechanisms supporting these functional hallmarks.

Stimulus-specific responses in innate immunity: Multilayered regulatory circuits

Immune sentinel cells initiate immune responses to pathogens and tissue injury and are capable of producing highly stimulus-specific responses. Insight into the mechanisms underlying such specificity has come from the identification of regulatory factors and biochemical pathways, as well as the definition of signaling circuits that enable combinatorial and temporal coding of information. Here, we review the multi-layered molecular mechanisms that underlie stimulus-specific gene expression in macrophages. We categorize components of inflammatory and anti-pathogenic signaling pathways into five layers of regulatory control and discuss unifying mechanisms determining signaling characteristics at each layer. In this context, we review mechanisms that enable combinatorial and temporal encoding of information, identify recurring regulatory motifs and principles, and present strategies for integrating experimental and computational approaches toward the understanding of signaling specificity in innate immunity.

A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization

Macrophages are highly plastic immune cells that dynamically integrate microenvironmental signals to shape their own functional phenotypes, a process known as polarization. Here we develop a large-scale mechanistic computational model that for the first time enables a systems-level characterization, from quantitative, temporal, dose-dependent, and single-cell perspectives, of macrophage polarization driven by a complex multi-pathway signaling network. The model was extensively calibrated and validated against literature and focused on in-house experimental data. Using the model, we generated dynamic phenotype maps in response to numerous combinations of polarizing signals; we also probed into an in silico population of model-based macrophages to examine the impact of polarization continuum at the single-cell level. Additionally, we analyzed the model under an in vitro condition of peripheral arterial disease to evaluate strategies that can potentially induce therapeutic macrophage repolarization. Our model is a key step toward the future development of a network-centric, comprehensive "virtual macrophage" simulation platform.

Meta-Analysis of in vitro-Differentiated Macrophages Identifies Transcriptomic Signatures That Classify Disease Macrophages in vivo

Macrophages are heterogeneous leukocytes regulated in a tissue- and disease-specific context. While in vitro macrophage models have been used to study diseases empirically, a systematic analysis of the transcriptome thereof is lacking. Here, we acquired gene expression data from eight commonly-used in vitro macrophage models to perform a meta-analysis. Specifically, we obtained gene expression data from unstimulated macrophages (M0) and macrophages stimulated with lipopolysaccharides (LPS) for 2–4 h (M-LPS_early), LPS for 24 h (M-LPS_late), LPS and interferon-γ (M-LPS+IFNγ), IFNγ (M-IFNγ), interleukin-4 (M-IL4), interleukin-10 (M-IL10), and dexamethasone (M-dex). Our meta-analysis identified consistently differentially expressed genes that have been implicated in inflammatory and metabolic processes. In addition, we built macIDR, a robust classifier capable of distinguishing macrophage activation states with high accuracy (>0.95). We classified in vivo macrophages with macIDR to define their tissue- and disease-specific characteristics. We demonstrate that alveolar macrophages display high resemblance to IL10 activation, but show a drop in IFNγ signature in chronic obstructive pulmonary disease patients. Adipose tissue-derived macrophages were classified as unstimulated macrophages, but acquired LPS-activation features in diabetic-obese patients. Rheumatoid arthritis synovial macrophages exhibit characteristics of IL10- or IFNγ-stimulation. Altogether, we defined consensus transcriptional profiles for the eight in vitro macrophage activation states, built a classification model, and demonstrated the utility of the latter for in vivo macrophages.

Order by chance: origins and benefits of stochasticity in immune cell fate control

To protect against diverse challenges, the immune system must continuously generate an arsenal of specialized cell types, each of which can mount a myriad of effector responses upon detection of potential threats. To do so, it must generate multiple differentiated cell populations with defined sizes and proportions, often from rare starting precursor cells. Here, we discuss the emerging view that inherently probabilistic mechanisms, involving rare, rate-limiting regulatory events in single cells, control fate decisions and population sizes and fractions during immune development and function. We first review growing evidence that key fate control points are gated by stochastic signaling and gene regulatory events that occur infrequently over decision-making timescales, such that initially homogeneous cells can adopt variable outcomes in response to uniform signals. We next discuss how such stochastic control can provide functional capabilities that are harder to achieve with deterministic control strategies, and may be central to robust immune system function.

The Putative Association of TOB1-AS1 Long Non-coding RNA with Immune Tolerance: A Study on Multiple Sclerosis Patients

The hallmark of multiple sclerosis (MS) pathogenesis is the breakdown of peripheral tolerance in the immune system. However, its molecular mechanism is not completely understood. Since long non-coding RNAs (lncRNAs) has played important roles in regulation of immunological pathways, here, we evaluated the expression of a novel lncRNA, TOB1-AS1, and its putative associated coding genes in the mechanism of maintaining immune tolerance in peripheral blood of MS patients to assess their possible roles in MS pathogenesis. In this study, 39 MS patients and 32 healthy matched controls were recruited. Real-time PCR standard curve method was used to quantify transcript levels of TOB1-AS1, TOB1, SKP2, and TSG. In addition, the potential sex hormone receptor binding sites on target genes promoter were analyzed using JASPR software. This work demonstrates a negative correlation between TOB1-AS1 expression and EDSS of patients. Also, a robust dysregulation of co-expression of TOB1-AS1 lncRNA and the coding genes in MS patients compared to controls was observed. Such dysregulation in this pathway may be related to MS pathogenesis and response to interferon treatment.

Comparative transcriptome analysis of trout skin pigment cells

BACKGROUND

Enormous variability in skin colour and patterning is a characteristic of teleost fish, including Salmonidae fishes, which present themselves as a suitable model for studying mechanisms of pigment patterning. In order to screen for candidate genes potentially involved in the specific skin pigment pattern in marble trout (labyrinthine skin pattern) and brown trout (spotted skin pattern), we conducted comparative transcriptome analysis between differently pigmented dermis sections of the adult skin of the two species.

RESULTS

Differentially expressed genes (DEGs) possibly associated with skin pigment pattern were identified. The expression profile of 27 DEGs was further tested with quantitative real-time PCR on a larger number of samples. Expression of a subset of ten of these genes was analysed in hybrid (marble x brown) trout individuals and compared with the complexity of their skin pigment pattern. A correlation between the phenotype and the expression profile assessed for hybrid individuals was detected for four (gja5, clcn2, cdkn1a and tjp1) of the ten candidate genes tested. The potential role of these genes in skin pigment pattern maintenance is discussed.

CONCLUSIONS

Our results indicate that the maintenance of different pigment patterns in trout is dependent upon specific communication-involving gap junctions, tight junctions and ion channels-between chromatophores present in differentially pigmented skin regions.

In situ 10-cell RNA sequencing in tissue and tumor biopsy samples

Single-cell transcriptomic methods classify new and existing cell types very effectively, but alternative approaches are needed to quantify the individual regulatory states of cells in their native tissue context. We combined the tissue preservation and single-cell resolution of laser capture with an improved preamplification procedure enabling RNA sequencing of 10 microdissected cells. This in situ 10-cell RNA sequencing (10cRNA-seq) can exploit fluorescent reporters of cell type in genetically engineered mice and is compatible with freshly cryoembedded clinical biopsies from patients. Through recombinant RNA spike-ins, we estimate dropout-free technical reliability as low as ~250 copies and a 50% detection sensitivity of ~45 copies per 10-cell reaction. By using small pools of microdissected cells, 10cRNA-seq improves technical per-cell reliability and sensitivity beyond existing approaches for single-cell RNA sequencing (scRNA-seq). Detection of low-abundance transcripts by 10cRNA-seq is comparable to random 10-cell groups of scRNA-seq data, suggesting no loss of gene recovery when cells are isolated in situ. Combined with existing approaches to deconvolve small pools of cells, 10cRNA-seq offers a reliable, unbiased, and sensitive way to measure cell-state heterogeneity in tissues and tumors.

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.

Autoinhibition in Ras effectors Raf, PI3Kα, and RASSF5: a comprehensive review underscoring the challenges in pharmacological intervention

Autoinhibition is an effective mechanism that guards proteins against spurious activation. Despite its ubiquity, the distinct organizations of the autoinhibited states and their release mechanisms differ. Signaling is most responsive to the cell environment only if a small shift in the equilibrium is required to switch the system from an inactive (occluded) to an active (exposed) state. Ras signaling follows this paradigm. This underscores the challenge in pharmacological intervention to exploit and enhance autoinhibited states. Here, we review autoinhibition and release mechanisms at the membrane focusing on three representative Ras effectors, Raf protein kinase, PI3Kα lipid kinase, and NORE1A (RASSF5) tumor suppressor, and point to the ramifications to drug discovery. We further touch on Ras upstream and downstream signaling, Ras activation, and the Ras superfamily in this light, altogether providing a broad outlook of the principles and complexities of autoinhibition.

Advancing systems immunology through data-driven statistical analysis

Systems biology provides an effective approach to decipher, predict, and ultimately manipulate the complex and inter-connected networks that regulate the immune system. Advances in high-throughput, multiplexed experimental techniques have increased the availability of proteomic and transcriptomic immunological datasets, and as a result, have also accelerated the development of new data-driven computational algorithms to extract biological insight from these data. This review highlights how data-driven statistical models have been used to characterize immune cell subsets and their functions, to map the signaling and intercellular networks that regulate immune responses, and to connect immune cell states to disease outcomes to generate hypotheses for novel therapeutic strategies. We focus on recent advances in evaluating immune cell responses following viral infection and in the tumor microenvironment, which hold promise for improving vaccines, antiviral and cancer immunotherapy.

Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR

CD4⁺ T regulatory (T_reg) cells are central to immune homeostasis, their phenotypic heterogeneity reflecting the diverse environments and target cells they regulate. To understand this heterogeneity, we combined single-cell RNAseq, activation reporter and TCR analysis to profile thousands of T_regs or T_convs from mouse lymphoid organs or human blood. T_reg and T_conv pools showed areas of overlap, as resting “furtive” T_regs with overall similarity to T_conv, or as a convergence of activated states. All T_regs express a small core of FoxP3-dependent transcripts, onto which additional programs are added less uniformly. Among suppressive functions, Il2ra and Ctla4 were quasi-constant, inhibitory cytokines being more sparsely distributed. TCR signal intensity didn’t affect resting/activated T_reg proportions, but molded activated T_reg programs. The main lines of T_reg heterogeneity in mice were strikingly conserved in human blood. These results reveal unexpected TCR-shaped states of activation, providing a framework to synthesize previous observations about T_reg heterogeneity.

Environments Tune and Select Cellular Diversity

Technical advances in single-cell sequencing data and their application to greater samples is revealing substantial cell-to-cell variation in expression levels and propagation of this variation between molecules across cells. New quantitative approaches that apply mechanistic and statistical models in a systems-wide approach are illuminating the drivers of phenotypic diversity.