CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data.

Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. To address this, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences.

An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator.

The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.

Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions.

T cells are critically important components of the adaptive immune system primarily responsible for identifying and responding to pathogenic challenges. This recognition of pathogens is driven by the interaction between membrane-bound T cell receptors (TCRs) and antigenic peptides presented on major histocompatibility complex (MHC) molecules. The formation of the TCR-peptide-MHC complex (TCR-pMHC) involves interactions among germline-encoded and hypervariable amino acids. Germline-encoded and hypervariable regions can form contacts critical for complex formation, but only interactions between germline-encoded contacts are likely to be shared across many of all the possible productive TCR-pMHC complexes. Despite this, experimental investigation of these interactions have focused on only a small fraction of the possible interaction space. To address this, we analyzed every possible germline-encoded TCR-MHC contact in humans, thereby generating the first comprehensive characterization of these largely antigen-independent interactions. Our computational analysis suggests that germline-encoded TCR-MHC interactions that are conserved at the sequence level are rare due to the high amino acid diversity of the TCR CDR1 and CDR2 loops, and that such conservation is unlikely to dominate the dynamic protein-protein binding interface. Instead, we propose that binding properties such as the docking orientation are defined by regions of biophysical compatibility between these loops and the MHC surface.

SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain.

The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.

SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain.

The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.

Absolute protein quantitation of the mouse macrophage Toll-like receptor and chemotaxis pathways.

The Toll-like receptor (TLR) and chemotaxis pathways are key components of the innate immune system. Subtle variation in the concentration, timing, and molecular structure of the ligands are known to affect downstream signaling and the resulting immune response. Computational modeling and simulation at the molecular interaction level can be used to study complex biological pathways, but such simulations require protein concentration values as model parameters. Here we report the development and application of targeted mass spectrometry assays to measure the absolute abundance of proteins of the mouse macrophage Toll-like receptor 4 (TLR4) and chemotaxis pathways. Two peptides per protein were quantified, if possible. The protein abundance values ranged from 1,332 to 227,000,000 copies per cell. They moderately correlated with transcript abundance values from a previously published mouse macrophage RNA-seq dataset, and these two datasets were combined to make proteome-wide abundance estimates. The datasets produced during this investigation can be used for pathway modeling and simulation, as well as for other studies of the TLR and chemotaxis pathways.

Space-time histories approach to fast stochastic simulation of bimolecular reactions.

Computational models of reaction-diffusion systems involving low copy numbers or strongly heterogeneous molecular spatial distributions, such as those frequently found in cellular signaling pathways, require approaches that account for the stochastic dynamics of individual particles, as opposed to approaches representing them through their average concentrations. Efforts to remedy the high computational cost associated with particle-based stochastic approaches by taking advantage of Green's functions are hampered by the need to draw random numbers from complicated, and therefore costly, non-standard probability distributions to update particle positions. Here, we introduce an approach that permits the reconstruction of entire molecular trajectories, including bimolecular encounters, in retrospect, after a simulated time step, while avoiding inefficient draws from non-standard distributions. This means that highly accurate stochastic simulations can be performed for system sizes that would be prohibitively costly to simulate with conventional Green's function based methods. The algorithm applies equally well to one, two, and three dimensional systems and can be readily extended to include deterministic forces specified by an interaction potential, such as the Coulomb potential.

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2.

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through "wildcards" representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the "type" concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes a medium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.

A negative-feedback loop maintains optimal chemokine concentrations for directional cell migration.

Chemoattractant gradients frequently guide migrating cells. To achieve the most directional signal, such gradients should be maintained with concentrations around the dissociation constant (Kd)1-6 of the chemoreceptor. Whether this actually occurs in animals is unknown. Here we investigate whether a moving tissue, the zebrafish posterior lateral line primordium, buffers its attractant in this concentration range to achieve robust migration. We find that the Cxcl12 (also known as Sdf1) attractant gradient ranges from 0 to 12 nM, values similar to the 3.4 nM Kd of its receptor Cxcr4. When we increase the Kd of Cxcl12 for Cxcr4, primordium migration is less directional. Furthermore, a negative-feedback loop between Cxcl12 and its clearance receptor Ackr3 (also known as Cxcr7) regulates the Cxcl12 concentrations. Breaking this negative feedback by blocking the phosphorylation of the cytoplasmic tail of Ackr3 also results in less directional primordium migration. Thus, directed migration of the primordium is dependent on a close match between the Cxcl12 concentration and the Kd of Cxcl12 for Cxcr4, which is maintained by buffering of the chemokine levels. Quantitative modelling confirms the plausibility of this mechanism. We anticipate that buffering of attractant concentration is a general mechanism for ensuring robust cell migration.

Mechanistic Models of Cellular Signaling, Cytokine Crosstalk, and Cell-Cell Communication in Immunology.

The cells of the immune system respond to a great variety of different signals that frequently reach them simultaneously. Computational models of signaling pathways and cellular behavior can help us explore the biochemical mechanisms at play during such responses, in particular when those models aim at incorporating molecular details of intracellular reaction networks. Such detailed models can encompass hypotheses about the interactions among molecular binding domains and how these interactions are modulated by, for instance, post-translational modifications, or steric constraints in multi-molecular complexes. In this way, the models become formal representations of mechanistic immunological hypotheses that can be tested through quantitative simulations. Due to the large number of parameters (molecular abundances, association-, dissociation-, and enzymatic transformation rates) the goal of simulating the models can, however, in many cases no longer be the fitting of particular parameter values. Rather, the simulations perform sweeps through parameter space to test whether a model can account for certain experimentally observed features when allowing the parameter values to vary within experimentally determined or physiologically reasonable ranges. We illustrate how this approach can be used to explore possible mechanisms of immunological pathway crosstalk. Probing the input-output behavior of mechanistic pathway models through systematic simulated variations of receptor stimuli will soon allow us to derive cell population behavior from single-cell models, thereby bridging a scale gap that currently still is frequently addressed through heuristic phenomenological multi-scale models.

Cadherin-Mediated Cell Coupling Coordinates Chemokine Sensing across Collectively Migrating Cells.

The directed migration of cells sculpts the embryo, contributes to homeostasis in the adult, and, when dysregulated, underlies many diseases [1, 2]. During these processes, cells move singly or as a collective. In both cases, they follow guidance cues, which direct them to their destination [3-6]. In contrast to single cells, collectively migrating cells need to coordinate with their neighbors to move together in the same direction. Recent studies suggest that leader cells in the front sense the guidance cue, relay the directional information to the follower cells in the back, and can pull the follower cells along [7-19]. In this manner, leader cells steer the collective and set the collective's overall speed. However, whether follower cells also participate in steering and speed setting of the collective is largely unclear. Using chimeras, we analyzed the role of leader and follower cells in the collectively migrating zebrafish posterior lateral line primordium. This tissue expresses the chemokine receptor Cxcr4 and is guided by the chemokine Cxcl12a [20-23]. We find that leader and follower cells need to sense the attractant Cxcl12a for efficient migration, are coupled to each other through cadherins, and require coupling to pull Cxcl12a-insensitive cells along. Analysis of cell dynamics in chimeric and protein-depleted primordia shows that Cxcl12a-sensing and cadherin-mediated adhesion contribute jointly to direct migration at both single-cell and tissue levels. These results suggest that all cells in the primordium need to sense the attractant and adhere to each other to coordinate their movements and migrate with robust directionality.

Using Python for Spatially Resolved Modeling with Simmune.

Mechanistic models are an important tool to gain insights about the quantitative behavior of cell-biological signal transduction networks. Here we show how Simmune can be used in conjunction with IPython to create repeatable, self-contained analyses of signal transduction processes in spatially inhomogeneous environments.

Anosmin1 Shuttles Fgf to Facilitate Its Diffusion, Increase Its Local Concentration, and Induce Sensory Organs.

Growth factors induce and pattern sensory organs, but how their distribution is regulated by the extracellular matrix (ECM) is largely unclear. To address this question, we analyzed the diffusion behavior of Fgf10 molecules during sensory organ formation in the zebrafish posterior lateral line primordium. In this tissue, secreted Fgf10 induces organ formation at a distance from its source. We find that most Fgf10 molecules are highly diffusive and move rapidly through the ECM. We identify Anosmin1, which when mutated in humans causes Kallmann Syndrome, as an ECM protein that binds to Fgf10 and facilitates its diffusivity by increasing the pool of fast-moving Fgf10 molecules. In the absence of Anosmin1, Fgf10 levels are reduced and organ formation is impaired. Global overexpression of Anosmin1 slows the fast-moving Fgf10 molecules and results in Fgf10 dispersal. These results suggest that Anosmin1 liberates ECM-bound Fgf10 and shuttles it to increase its signaling range.

A hierarchy of affinities between cytokine receptors and the common gamma chain leads to pathway cross-talk.

Cytokines belonging to the common gamma chain (γc) family depend on the shared γc receptor subunit for signaling. We report the existence of a fast, cytokine-induced pathway cross-talk acting at the receptor level, resulting from a limiting amount of γc on the surface of T cells. We found that this limited abundance of γc reduced interleukin-4 (IL-4) and IL-21 responses after IL-7 preexposure but not vice versa. Computational modeling combined with quantitative experimental assays indicated that the asymmetric cross-talk resulted from the ability of the "private" IL-7 receptor subunits (IL-7Rα) to bind to many of the γc molecules even before stimulation with cytokine. Upon exposure of T cells to IL-7, the high affinity of the IL-7Rα:IL-7 complex for γc further reduced the amount of free γc in a manner dependent on the concentration of IL-7. Measurements of bioluminescence resonance energy transfer (BRET) between IL-4Rα and γc were reduced when IL-7Rα was overexpressed. Furthermore, in a system expressing IL-7Rα, IL-4Rα, and γc, BRET between IL-4Rα and γc increased after IL-4 binding and decreased when cells were preexposed to IL-7, supporting the assumption that IL-7Rα and the IL-7Rα:IL-7 complex limit the accessibility of γc for other cytokine receptor complexes. We propose that in complex inflammatory environments, such asymmetric cross-talk establishes a hierarchy of cytokine responsiveness.

SBML Level 3 package: Multistate, Multicomponent and Multicompartment Species, Version 1, Release 1.

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains (components) and the states such as phosphorylation and binding status of the involved components. Fine-grained spatial information such as the locations of the molecular components relative to a membrane (e.g. whether a modeled molecular domain is embedded into the inner leaflet of the cellular plasma membrane) can also be provided. Through wildcards representing component states entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states and multiple compartments. The SBML Level 3 Multi Package (Multistate, Multicomponent and Multicompartment Species Package for SBML Level 3) extends the SBML Level 3 core with the "type" concept in the Species and Compartment classes and therefore reaction rules may contain species that can be patterns and be in multiple locations in reaction rules. Multiple software tools such as Simmune and BioNetGen support the SBML Level 3 Multi package that thus also becomes a medium for exchanging rule-based models.

Migrating Myeloid Cells Sense Temporal Dynamics of Chemoattractant Concentrations.

Chemoattractant-mediated recruitment of hematopoietic cells to sites of pathogen growth or tissue damage is critical to host defense and organ homeostasis. Chemotaxis is typically considered to rely on spatial sensing, with cells following concentration gradients as long as these are present. Utilizing a microfluidic approach, we found that stable gradients of intermediate chemokines (CCL19 and CXCL12) failed to promote persistent directional migration of dendritic cells or neutrophils. Instead, rising chemokine concentrations were needed, implying that temporal sensing mechanisms controlled prolonged responses to these ligands. This behavior was found to depend on G-coupled receptor kinase-mediated negative regulation of receptor signaling and contrasted with responses to an end agonist chemoattractant (C5a), for which a stable gradient led to persistent migration. These findings identify temporal sensing as a key requirement for long-range myeloid cell migration to intermediate chemokines and provide insights into the mechanisms controlling immune cell motility in complex tissue environments.

Unified path integral approach to theories of diffusion-influenced reactions.

Building on mathematical similarities between quantum mechanics and theories of diffusion-influenced reactions, we develop a general approach for computational modeling of diffusion-influenced reactions that is capable of capturing not only the classical Smoluchowski picture but also alternative theories, as is here exemplified by a volume reactivity model. In particular, we prove the path decomposition expansion of various Green's functions describing the irreversible and reversible reaction of an isolated pair of molecules. To this end, we exploit a connection between boundary value and interaction potential problems with δ- and δ^{'}-function perturbation. We employ a known path-integral-based summation of a perturbation series to derive a number of exact identities relating propagators and survival probabilities satisfying different boundary conditions in a unified and systematic manner. Furthermore, we show how the path decomposition expansion represents the propagator as a product of three factors in the Laplace domain that correspond to quantities figuring prominently in stochastic spatially resolved simulation algorithms. This analysis will thus be useful for the interpretation of current and the design of future algorithms. Finally, we discuss the relation between the general approach and the theory of Brownian functionals and calculate the mean residence time for the case of irreversible and reversible reactions.

Distinct NF-κB and MAPK Activation Thresholds Uncouple Steady-State Microbe Sensing from Anti-pathogen Inflammatory Responses.

The innate immune system distinguishes low-level homeostatic microbial stimuli from those of invasive pathogens, yet we lack understanding of how qualitatively similar microbial products yield context-specific macrophage functional responses. Using quantitative approaches, we found that NF-κB and MAPK signaling was activated at different concentrations of a stimulatory TLR4 ligand in both mouse and human macrophages. Above a threshold of ligand, MAPK were activated in a switch-like manner, facilitating production of inflammatory mediators. At ligand concentrations below this threshold, NF-κB signaling occurred, promoting expression of a restricted set of genes and macrophage priming. Among TLR-induced genes, we observed an inverse correlation between MAPK dependence and ligand sensitivity, highlighting the role of this signaling dichotomy in partitioning innate responses downstream of a single receptor. Our study reveals an evolutionarily conserved innate immune response system in which danger discrimination is enforced by distinct thresholds for NF-κB and MAPK activation, which provide sequential barriers to inflammatory mediator production.

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing.

Osteoclasts are monocyte-derived multinuclear cells that directly attach to and resorb bone. Sphingosine-1-phosphate (S1P)(1) regulates bone resorption by functioning as both a chemoattractant and chemorepellent of osteoclast precursors through two G-protein coupled receptors that antagonize each other in an S1P-concentration-dependent manner. To quantitatively explore the behavior of this chemosensing pathway, we applied targeted proteomics, transcriptomics, and rule-based pathway modeling using the Simmune toolset. RAW264.7 cells (a mouse monocyte/macrophage cell line) were used as model osteoclast precursors, RNA-seq was used to identify expressed target proteins, and selected reaction monitoring (SRM) mass spectrometry using internal peptide standards was used to perform absolute abundance measurements of pathway proteins. The resulting transcript and protein abundance values were strongly correlated. Measured protein abundance values, used as simulation input parameters, led to in silico pathway behavior matching in vitro measurements. Moreover, once model parameters were established, even simulated responses toward stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrate the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight.