Profiling Cell Signaling Networks at Single-cell Resolution

Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

PUFFFIN: an ultra-bright, customisable, single-plasmid system for labelling cell neighbourhoods

Cell communication coordinates developmental processes, maintains homeostasis, and contributes to disease. Therefore, understanding the relationship between cells in a shared environment is crucial. Here we introduce Positive Ultra-bright Fluorescent Fusion For Identifying Neighbours (PUFFFIN), a cell neighbour-labelling system based upon secretion and uptake of positively supercharged fluorescent protein s36GFP. We fused s36GFP to mNeonGreen or to a HaloTag, facilitating ultra-bright, sensitive, colour-of-choice labelling. Secretor cells transfer PUFFFIN to neighbours while retaining nuclear mCherry, making identification, isolation, and investigation of live neighbours straightforward. PUFFFIN can be delivered to cells, tissues, or embryos on a customisable single-plasmid construct composed of interchangeable components with the option to incorporate any transgene. This versatility enables the manipulation of cell properties, while simultaneously labelling surrounding cells, in cell culture or in vivo. We use PUFFFIN to ask whether pluripotent cells adjust the pace of differentiation to synchronise with their neighbours during exit from naïve pluripotency. PUFFFIN offers a simple, sensitive, customisable approach to profile non-cell-autonomous responses to natural or induced changes in cell identity or behaviour.

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

Deciphering the aging process through single-cell cytometric technologies

The advent of single-cell cytometric technologies, in conjunction with advances in single-cell biology, has significantly propelled forward the field of geroscience, enhancing our comprehension of the mechanisms underlying age-related diseases. Given that aging is a primary risk factor for numerous chronic health conditions, investigating the dynamic changes within the physiological landscape at the granularity of single cells is crucial for elucidating the molecular foundations of biological aging. Utilizing hallmarks of aging as a conceptual framework, we review current literature to delineate the progression of single-cell cytometric techniques and their pivotal applications in the exploration of molecular alterations associated with aging. We next discuss recent advancements in single-cell cytometry in terms of the development in instrument, software, and reagents, highlighting its promising and critical role in driving future breakthrough discoveries in aging research.

Genetic Tools for Cell Lineage Tracing and Profiling Developmental Trajectories in the Skin

The epidermis is the body's first line of protection against dehydration and pathogens, continually regenerating the outermost protective skin layers throughout life. During both embryonic development and wound healing, epidermal stem and progenitor cells must respond to external stimuli and insults to build, maintain, and repair the cutaneous barrier. Recent advances in CRISPR-based methods for cell lineage tracing have remarkably expanded the potential for experiments that track stem and progenitor cell proliferation and differentiation over the course of tissue and even organismal development. Additional tools for DNA-based recording of cellular signaling cues promise to deepen our understanding of the mechanisms driving normal skin morphogenesis and response to stressors as well as the dysregulation of cell proliferation and differentiation in skin diseases and cancer. In this review, we highlight cutting-edge methods for cell lineage tracing, including in organoids and model organisms, and explore how cutaneous biology researchers might leverage these techniques to elucidate the developmental programs that support the regenerative capacity and plasticity of the skin.

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.

EZH2-mediated epigenetic silencing of tumor-suppressive let-7c/miR-99a cluster by hepatitis B virus X antigen enhances hepatocellular carcinoma progression and metastasis

BACKGROUND

Hepatitis B virus (HBV)-encoded X antigen, HBx, assists in the development of hepatocellular carcinoma (HCC) through complex mechanisms. Our results provide new insights into the EZH2 epigenetic repression of let-7c that promotes HCC migration induced by HBx. Thus, let-7c and HMGA2 represent key diagnostic markers and potential therapeutic targets for the treatment of HBV-related HCC.

RESULTS

We investigated the epigenetic regulation of let-7c, an important representative miRNA in liver tumor metastasis, in human HCC cells to verify the effect of HBx. Based on quantitative PCR (qPCR) of mRNA isolated from tumor and adjacent non-tumor liver tissues of 24 patients with HBV-related HCC, EZH2 expression was significantly overexpressed in most HCC tissues (87.5%). We executed a miRNA microarray analysis in paired HBV-related HCC tumor and adjacent non-tumorous liver tissue from six of these patients and identified let-7c, miR-199a-3p, and miR-99a as being downregulated in the tumor tissue. Real-time PCR analysis verified significant downregulation of let-7c and miR-99a in both HepG2X and Hep3BX cells, which stably overexpress HBx, relative to parental cells. HBX enhanced EZH2 expression and attenuated let-7c expression to induce HMGA2 expression in the HCC cells. Knockdown of HMGA2 significantly downregulated the metastatic potential of HCC cells induced by HBx.

CONCLUSIONS

The deregulation of let-7c expression by HBx may indicate a potential novel pathway through deregulating cell metastasis and imply that HMGA2 might be used as a new prognostic marker and/or as an effective therapeutic target for HCC.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Multiplexed Detection of Molecular Interactions with DNA Origami Engineered Cells in 3D Collagen Matrices

The interactions of cells with signaling molecules present in their local microenvironment maintain cell proliferation, differentiation, and spatial organization and mediate progression of diseases such as metabolic disorders and cancer. Real-time monitoring of the interactions between cells and their extracellular ligands in a three-dimensional (3D) microenvironment can inform detection and understanding of cell processes and the development of effective therapeutic agents. DNA origami technology allows for the design and fabrication of biocompatible and 3D functional nanodevices via molecular self-assembly for various applications including molecular sensing. Here, we report a robust method to monitor live cell interactions with molecules in their surrounding environment in a 3D tissue model using a microfluidic device. We used a DNA origami cell sensing platform (CSP) to detect two specific nucleic acid sequences on the membrane of B cells and dendritic cells. We further demonstrated real-time detection of biomolecules with the DNA sensing platform on the surface of dendritic cells in a 3D microfluidic tissue model. Our results establish the integration of live cells with membranes engineered with DNA nanodevices into microfluidic chips as a highly capable biosensor approach to investigate subcellular interactions in physiologically relevant 3D environments under controlled biomolecular transport.

Network-based approaches for modeling disease regulation and progression

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.

Protein phosphatase 2A in the healthy and failing heart: New insights and therapeutic opportunities

Protein phosphatases have emerged as critical regulators of phosphoprotein homeostasis in settings of health and disease. Protein phosphatase 2A (PP2A) encompasses a large subfamily of enzymes that remove phosphate groups from serine/threonine residues within phosphoproteins. The heterogeneity in PP2A structure, which arises from the grouping of different catalytic, scaffolding and regulatory subunit isoforms, creates distinct populations of catalytically active enzymes (i.e. holoenzymes) that localise to different parts of the cell. This structural complexity, combined with other regulatory mechanisms, such as interaction of PP2A heterotrimers with accessory proteins and post-translational modification of the catalytic and/or regulatory subunits, enables PP2A holoenzymes to target phosphoprotein substrates in a highly specific manner. In this review, we summarise the roles of PP2A in cardiac physiology and disease. PP2A modulates numerous processes that are vital for heart function including calcium handling, contractility, β-adrenergic signalling, metabolism and transcription. Dysregulation of PP2A has been observed in human cardiac disease settings, including heart failure and atrial fibrillation. Efforts are underway, particularly in the cancer field, to develop therapeutics targeting PP2A activity. The development of small molecule activators of PP2A (SMAPs) and other compounds that selectively target specific PP2A holoenzymes (e.g. PP2A/B56α and PP2A/B56ε) will improve understanding of the function of different PP2A species in the heart, and may lead to the development of therapeutics for normalising aberrant protein phosphorylation in settings of cardiac remodelling and dysfunction.

Evaluation of FOXO1 Target Engagement Using a Single-Cell Microfluidic Platform

The cellular thermal shift assay (CETSA) has been used extensively since its introduction to study drug-target engagement within both live cells and cellular lysate. This has proven to be a useful tool in early stage drug discovery and is used to study a wide range of protein classes. We describe the application of a single-cell CETSA workflow within a microfluidic affinity capture (MAC) chip. This has enabled us to quantitatively determine the active FOXO1 single-molecule count and observe FOXO1 stabilization and destabilization in the presence of three small molecule inhibitors, including demonstrating the determination of EC₅₀. The successful use of the MAC chip for single-cell CETSA paves the way for the study of precious clinical samples owing to the low number of cells needed by the chip. It also provides a useful tool for studying any underlying population heterogeneity that exists within a cellular system, a feature that is usually masked when conducting ensemble measurements.

An Integrated Strategy for Mass Spectrometry-Based Multiomics Analysis of Single Cells

Single-cell-based genomics and transcriptomics analysis have revealed substantial cellular heterogeneity among seemingly identical cells. Knowledge of the cellular heterogeneity at multiomics levels is vital for a better understanding of tumor metastasis and drug resistance, stem cell differentiation, and embryonic development. However, unlike genomics and transcriptomics studies, single-cell characterization of metabolites, proteins, and post-translational modifications at the omics level remains challenging due to the lack of amplification methods and the wide diversity of these biomolecules. Therefore, new tools that are capable of investigating these unamplifiable "omes" from the same single cells are in high demand. In this work, a microwell chip was prepared and the internal surface was modified for hydrophilic interaction liquid chromatography-based tandem extraction of metabolites and proteins and subsequent protein digestion. Next, direct electrospray ionization mass spectrometry was adopted for single-cell metabolome identification, and a data-independent acquisition-mass spectrometry approach was established for simultaneous proteome profiling and phosphoproteome analysis without phosphopeptide enrichment. This integrated strategy resulted in 132 putatively annotated compounds, more than 1200 proteins, and the first large-scale phosphorylation data set from single-cell analysis. Application of this strategy in chemical perturbation studies provides a multiomics view of cellular changes, demonstrating its capability for more comprehensive investigation of cellular heterogeneity.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Dissecting intercellular and intracellular signaling networks with barcoded genetic tools

The power of next-generation sequencing has stimulated the development of many analysis techniques for transcriptomics and genomics. More recently, the concept of 'molecular barcoding' has broadened the spectrum of sequencing-based applications to dissect different aspects of intracellular and intercellular signaling. In these assay formats, barcode reporters replace standard reporter genes. The virtually infinitive number of expressed barcode sequences allows high levels of multiplexing, hence accelerating experimental progress. Furthermore, reporter barcodes are used to quantitatively monitor a variety of biological events in living cells which has already provided much insight into complex cellular signaling and will further increase our knowledge in the future.

Computational quantification of global effects induced by mutations and drugs in signaling networks of colorectal cancer cells

Colorectal cancer (CRC) is one of the most deadly and commonly diagnosed tumors worldwide. Several genes are involved in its development and progression. The most frequent mutations concern APC, KRAS, SMAD4, and TP53 genes, suggesting that CRC relies on the concomitant alteration of the related pathways. However, with classic molecular approaches, it is not easy to simultaneously analyze the interconnections between these pathways. To overcome this limitation, recently these pathways have been included in a huge chemical reaction network (CRN) describing how information sensed from the environment by growth factors is processed by healthy colorectal cells. Starting from this CRN, we propose a computational model which simulates the effects induced by single or multiple concurrent mutations on the global signaling network. The model has been tested in three scenarios. First, we have quantified the changes induced on the concentration of the proteins of the network by a mutation in APC, KRAS, SMAD4, or TP53. Second, we have computed the changes in the concentration of p53 induced by up to two concurrent mutations affecting proteins upstreams in the network. Third, we have considered a mutated cell affected by a gain of function of KRAS, and we have simulated the action of Dabrafenib, showing that the proposed model can be used to determine the most effective amount of drug to be delivered to the cell. In general, the proposed approach displays several advantages, in that it allows to quantify the alteration in the concentration of the proteins resulting from a single or multiple given mutations. Moreover, simulations of the global signaling network of CRC may be used to identify new therapeutic targets, or to disclose unexpected interactions between the involved pathways.

Integration of FRET and sequencing to engineer kinase biosensors from mammalian cell libraries

The limited sensitivity of Förster Resonance Energy Transfer (FRET) biosensors hinders their broader applications. Here, we develop an approach integrating high-throughput FRET sorting and next-generation sequencing (FRET-Seq) to identify sensitive biosensors with varying substrate sequences from large-scale libraries directly in mammalian cells, utilizing the design of self-activating FRET (saFRET) biosensor. The resulting biosensors of Fyn and ZAP70 kinases exhibit enhanced performance and enable the dynamic imaging of T-cell activation mediated by T cell receptor (TCR) or chimeric antigen receptor (CAR), revealing a highly organized ZAP70 subcellular activity pattern upon TCR but not CAR engagement. The ZAP70 biosensor elucidates the role of immunoreceptor tyrosine-based activation motif (ITAM) in affecting ZAP70 activation to regulate CAR functions. A saFRET biosensor-based high-throughput drug screening (saFRET-HTDS) assay further enables the identification of an FDA-approved cancer drug, Sunitinib, that can be repurposed to inhibit ZAP70 activity and autoimmune-disease-related T-cell activation.

Breast cancer heterogeneity through the lens of single-cell analysis and spatial pathologies

Breast cancer ecosystems are composed of complex cell types, including tumor, stromal and immune cells, each of which can assume diverse phenotypes. Both the heterogeneous composition and spatially distinct tumor microenvironment impact breast cancer progression, treatment response and therapeutic resistance. Thus, a deeper understanding of breast cancer heterogeneity may help facilitate the development of novel therapies and improve outcomes for patients. The advent of paradigm shifting single-cell analysis and spatial pathologies allows for a comprehensive analysis of the tumor ecosystem as well as the interactions between its components at unprecedented resolution. In this review, we discuss the insights gained through single-cell analysis and spatial pathologies on breast cancer heterogeneity.

Analyzing signaling activity and function in hematopoietic cells

Cells constantly sense their environment, allowing the adaption of cell behavior to changing needs. Fine-tuned responses to complex inputs are computed by signaling pathways, which are wired in complex connected networks. Their activity is highly context-dependent, dynamic, and heterogeneous even between closely related individual cells. Despite lots of progress, our understanding of the precise implementation, relevance, and possible manipulation of cellular signaling in health and disease therefore remains limited. Here, we discuss the requirements, potential, and limitations of the different current technologies for the analysis of hematopoietic stem and progenitor cell signaling and its effect on cell fates.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

Combining Mass Spectrometry-Based Phosphoproteomics with a Network-Based Approach to Reveal FLT3-Dependent Mechanisms of Chemoresistance

FLT3 mutations are the most frequently identified genetic alterations in acute myeloid leukemia (AML) and are associated with poor clinical outcome, relapse and chemotherapeutic resistance. Elucidating the molecular mechanisms underlying FLT3-dependent pathogenesis and drug resistance is a crucial goal of biomedical research. Given the complexity and intricacy of protein signaling networks, deciphering the molecular basis of FLT3-driven drug resistance requires a systems approach. Here we discuss how the recent advances in mass spectrometry (MS)-based (phospho) proteomics and multiparametric analysis accompanied by emerging computational approaches offer a platform to obtain and systematically analyze cell-specific signaling networks and to identify new potential therapeutic targets.

Post-translational modification control of viral DNA sensors and innate immune signaling

The vertebrate innate immune system confers host cells with mechanisms to protect against both evolutionarily ancient pathogens and newly emerging pathogenic strains. Innate immunity relies on the host cell's ability to distinguish between self and pathogen-derived molecules. To achieve this, the innate immune system uses germline encoded receptors called pattern recognition receptors (PRRs), which recognize various molecular signatures, including nucleic acids, proteins, lipids, glycans and glycolipids. Among these molecules, the recognition of pathogenic, mislocalized, or damaged DNA by cellular protein receptors, commonly called DNA sensors, represents a major surveillance pathway for initiating immune signaling. The ability of cells to temporally regulate DNA sensor activation and subsequent signal termination is critical for effective immune signaling. These same mechanisms are also co-opted by pathogens to promote their replication. Therefore, there is significant interest in understanding DNA sensor regulatory networks during microbial infections and autoimmune disease. One emerging aspect of DNA sensor regulation is through post-translational modifications (PTMs), including phosphorylation, acetylation, ubiquitination, ADP-ribosylation, SUMOylation, methylation, deamidation, glutamylation. In this chapter, we discuss how PTMs have been shown to positively or negatively impact DNA sensor functions via diverse mechanisms, including direct regulation of enzymatic activity, protein-protein and protein-DNA interactions, protein translocations and protein turnover. In addition, we highlight the ability of virus-induced PTMs to promote immune evasion. We also discuss the recent evidence linking PTMs on DNA sensors with human diseases and more broadly, highlight promising directions for future research on PTM-mediated regulation of DNA sensor-dependent immune signaling.

Reflections on the HUPO Human Proteome Project, the Flagship Project of the Human Proteome Organization, at 10 Years

We celebrate the 10th anniversary of the launch of the HUPO Human Proteome Project (HPP) and its major milestone of confident detection of at least one protein from each of 90% of the predicted protein-coding genes, based on the output of the entire proteomics community. The Human Genome Project reached a similar decadal milestone 20 years ago. The HPP has engaged proteomics teams around the world, strongly influenced data-sharing, enhanced quality assurance, and issued stringent guidelines for claims of detecting previously "missing proteins." This invited perspective complements papers on "A High-Stringency Blueprint of the Human Proteome" and "The Human Proteome Reaches a Major Milestone" in special issues of Nature Communications and Journal of Proteome Research, respectively, released in conjunction with the October 2020 virtual HUPO Congress and its celebration of the 10th anniversary of the HUPO HPP.

Understanding cell-cell communication and signaling in the colorectal cancer microenvironment

Carcinomas are complex heterocellular systems containing epithelial cancer cells, stromal fibroblasts, and multiple immune cell-types. Cell-cell communication between these tumor microenvironments (TME) and cells drives cancer progression and influences response to existing therapies. In order to provide better treatments for patients, we must understand how various cell-types collaborate within the TME to drive cancer and consider the multiple signals present between and within different cancer types. To investigate how tissues function, we need a model to measure both how signals are transferred between cells and how that information is processed within cells. The interplay of collaboration between different cell-types requires cell-cell communication. This article aims to review the current in vitro and in vivo mono-cellular and multi-cellular cultures models of colorectal cancer (CRC), and to explore how they can be used for single-cell multi-omics approaches for isolating multiple types of molecules from a single-cell required for cell-cell communication to distinguish cancer cells from normal cells. Integrating the existing single-cell signaling measurements and models, and through understanding the cell identity and how different cell types communicate, will help predict drug sensitivities in tumor cells and between- and within-patients responses.

Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion

The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks.