The population context is a driver of the heterogeneous response of epithelial cells to interferons.

Isogenic cells respond in a heterogeneous manner to interferon. Using a micropatterning approach combined with high-content imaging and spatial analyses, we characterized how the population context (position of a cell with respect to neighboring cells) of epithelial cells affects their response to interferons. We identified that cells at the edge of cellular colonies are more responsive than cells embedded within colonies. We determined that this spatial heterogeneity in interferon response resulted from the polarized basolateral interferon receptor distribution, making cells located in the center of cellular colonies less responsive to ectopic interferon stimulation. This was conserved across cell lines and primary cells originating from epithelial tissues. Importantly, cells embedded within cellular colonies were not protected from viral infection by apical interferon treatment, demonstrating that the population context-driven heterogeneous response to interferon influences the outcome of viral infection. Our data highlights that the behavior of isolated cells does not directly translate to their behavior in a population, placing the population context as one important factor influencing heterogeneity during interferon response in epithelial cells.

FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes.

MOTIVATION: Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. RESULTS: Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. AVAILABILITY AND IMPLEMENTATION: FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.

A Hepatitis C virus genotype 1b post-transplant isolate with high replication efficiency in cell culture and its adaptation to infectious virus production in vitro and in vivo.

Hepatitis C virus (HCV) is highly diverse and grouped into eight genotypes (gts). Infectious cell culture models are limited to a few subtypes and isolates, hampering the development of prophylactic vaccines. A consensus gt1b genome (termed GLT1) was generated from an HCV infected liver-transplanted patient. GLT1 replicated to an outstanding efficiency in Huh7 cells upon SEC14L2 expression, by use of replication enhancing mutations or with a previously developed inhibitor-based regimen. RNA replication levels almost reached JFH-1, but full-length genomes failed to produce detectable amounts of infectious virus. Long-term passaging led to the adaptation of a genome carrying 21 mutations and concomitant production of high levels of transmissible infectivity (GLT1cc). During the adaptation, GLT1 spread in the culture even in absence of detectable amounts of free virus, likely due to cell-to-cell transmission, which appeared to substantially contribute to spreading of other isolates as well. Mechanistically, genome replication and particle production efficiency were enhanced by adaptation, while cell entry competence of HCV pseudoparticles was not affected. Furthermore, GLT1cc retained the ability to replicate in human liver chimeric mice, which was critically dependent on a mutation in domain 3 of nonstructural protein NS5A. Over the course of infection, only one mutation in the surface glycoprotein E2 consistently reverted to wildtype, facilitating assembly in cell culture but potentially affecting CD81 interaction in vivo. Overall, GLT1cc is an efficient gt1b infectious cell culture model, paving the road to a rationale-based establishment of new infectious HCV isolates and represents an important novel tool for the development of prophylactic HCV vaccines.

Influence of cell type specific infectivity and tissue composition on SARS-CoV-2 infection dynamics within human airway epithelium.

Human airway epithelium (HAE) represents the primary site of viral infection for SARS-CoV-2. Comprising different cell populations, a lot of research has been aimed at deciphering the major cell types and infection dynamics that determine disease progression and severity. However, the cell type-specific replication kinetics, as well as the contribution of cellular composition of the respiratory epithelium to infection and pathology are still not fully understood. Although experimental advances, including Air-liquid interface (ALI) cultures of reconstituted pseudostratified HAE, as well as lung organoid systems, allow the observation of infection dynamics under physiological conditions in unprecedented level of detail, disentangling and quantifying the contribution of individual processes and cells to these dynamics remains challenging. Here, we present how a combination of experimental data and mathematical modelling can be used to infer and address the influence of cell type specific infectivity and tissue composition on SARS-CoV-2 infection dynamics. Using a stepwise approach that integrates various experimental data on HAE culture systems with regard to tissue differentiation and infection dynamics, we develop an individual cell-based model that enables investigation of infection and regeneration dynamics within pseudostratified HAE. In addition, we present a novel method to quantify tissue integrity based on image data related to the standard measures of transepithelial electrical resistance measurements. Our analysis provides a first aim of quantitatively assessing cell type specific infection kinetics and shows how tissue composition and changes in regeneration capacity, as e.g. in smokers, can influence disease progression and pathology. Furthermore, we identified key measurements that still need to be assessed in order to improve inference of cell type specific infection kinetics and disease progression. Our approach provides a method that, in combination with additional experimental data, can be used to disentangle the complex dynamics of viral infection and immunity within human airway epithelial culture systems.

VisuStatR: visualizing motility and morphology statistics on images in R.

MOTIVATION: Live-cell microscopy has become an essential tool for analyzing dynamic processes in various biological applications. Thereby, high-throughput and automated tracking analyses allow the simultaneous evaluation of large numbers of objects. However, to critically assess the influence of individual objects on calculated summary statistics, and to detect heterogeneous dynamics or possible artifacts, such as misclassified or -tracked objects, a direct mapping of gained statistical information onto the actual image data would be necessary. RESULTS: We present VisuStatR as a platform independent software package that allows the direct visualization of time-resolved summary statistics of morphological characteristics or motility dynamics onto raw images. The software contains several display modes to compare user-defined summary statistics and the underlying image data in various levels of detail. AVAILABILITY AND IMPLEMENTATION: VisuStatR is a free and open-source R-package, containing a user-friendly graphical-user interface and is available via GitHub at https://github.com/grrchrr/VisuStatR/ under the MIT+ license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany.

Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventional measures during an ongoing outbreak. However, reliably inferring the epidemical dynamics by connecting complex models to real data is still hard and requires either laborious manual parameter fitting or expensive optimization methods which have to be repeated from scratch for every application of a given model. In this work, we address this problem with a novel combination of epidemiological modeling with specialized neural networks. Our approach entails two computational phases: In an initial training phase, a mathematical model describing the epidemic is used as a coach for a neural network, which acquires global knowledge about the full range of possible disease dynamics. In the subsequent inference phase, the trained neural network processes the observed data of an actual outbreak and infers the parameters of the model in order to realistically reproduce the observed dynamics and reliably predict future progression. With its flexible framework, our simulation-based approach is applicable to a variety of epidemiological models. Moreover, since our method is fully Bayesian, it is designed to incorporate all available prior knowledge about plausible parameter values and returns complete joint posterior distributions over these parameters. Application of our method to the early Covid-19 outbreak phase in Germany demonstrates that we are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.

FAMoS: A Flexible and dynamic Algorithm for Model Selection to analyse complex systems dynamics.

Most biological systems are difficult to analyse due to a multitude of interacting components and the concomitant lack of information about the essential dynamics. Finding appropriate models that provide a systematic description of such biological systems and that help to identify their relevant factors and processes can be challenging given the sheer number of possibilities. Model selection algorithms that evaluate the performance of a multitude of different models against experimental data provide a useful tool to identify appropriate model structures. However, many algorithms addressing the analysis of complex dynamical systems, as they are often used in biology, compare a preselected number of models or rely on exhaustive searches of the total model space which might be unfeasible dependent on the number of possibilities. Therefore, we developed an algorithm that is able to perform model selection on complex systems and searches large model spaces in a dynamical way. Our algorithm includes local and newly developed non-local search methods that can prevent the algorithm from ending up in local minima of the model space by accounting for structurally similar processes. We tested and validated the algorithm based on simulated data and showed its flexibility for handling different model structures. We also used the algorithm to analyse experimental data on the cell proliferation dynamics of CD4+ and CD8+ T cells that were cultured under different conditions. Our analyses indicated dynamical changes within the proliferation potential of cells that was reduced within tissue-like 3D ex vivo cultures compared to suspension. Due to the flexibility in handling various model structures, the algorithm is applicable to a large variety of different biological problems and represents a useful tool for the data-oriented evaluation of complex model spaces.

Early HCV viral kinetics under DAAs may optimize duration of therapy in patients with compensated cirrhosis.

BACKGROUND & AIMS: Detailed hepatitis C virus (HCV) kinetics modelling is scarce in patients with advanced liver disease receiving direct-acting antivirals (DAAs). Due to budget restrictions, patients and health systems would benefit from the shortest possible treatment course. We investigated whether modelling very early HCV kinetics in cirrhotic patients under DAAs therapy could be used to individualize care and reduce treatment duration to achieve cure. METHODS: We included 74 patients with HCV-related cirrhosis who received interferon-free treatments for 12-24 weeks. HCV genotype, liver disease stage and treatment regimen were recorded. Viral load was determined prospectively at very frequent intervals until target not detected (TND, <15 IU/mL). A viral kinetic model was used to predict time to cure based on HCV clearance in extracellular body fluid (CL-EF). RESULTS: Sixty-eight patients (92%) achieved cure. Thirteen (18%) had MELD ≥15, 35 (47%) were Child-Pugh (CTP) ≥7. Median time to reach TND was 2 weeks (IQR: 1-4 weeks). Modelling indicated an average DAAs efficacy in blocking viral production of ε = 99.1%. HCV half-life (t1/2 ) was significantly shorter in patients with CTP <7, LSM <21 kPa or MELD <15 (1.5 vs 2.5 hours; P = 0.0057). The overall median CL-EF was 5.6 weeks (4.1-7.8). A CTP >7 and a LSM ≥21 kPa were significantly (P = 0.016) associated with longer CL-EF. CONCLUSIONS: The study provides insights into HCV dynamics during DAAs therapy in patients with compensated and decompensated cirrhosis. Viral kinetics modelling suggests that treatment duration may be optimized in patients with compensated cirrhosis.

Protective efficacy of individual CD8+ T cell specificities in chronic viral infection.

Specific CD8(+) T cells (CTLs) play an important role in resolving protracted infection with hepatitis B and C virus in humans and lymphocytic choriomeningitis virus (LCMV) in mice. The contribution of individual CTL specificities to chronic virus control, as well as epitope-specific patterns in timing and persistence of antiviral selection pressure, remain, however, incompletely defined. To monitor and characterize the antiviral efficacy of individual CTL specificities throughout the course of chronic infection, we coinoculated mice with a mixture of wild-type LCMV and genetically engineered CTL epitope-deficient mutant virus. A quantitative longitudinal assessment of viral competition revealed that mice continuously exerted CTL selection pressure on the persisting virus population. The timing of selection pressure characterized individual epitope specificities, and its magnitude varied considerably between individual mice. This longitudinal assessment of "antiviral efficacy" provides a novel parameter to characterize CTL responses in chronic viral infection. It demonstrates remarkable perseverance of all antiviral CTL specificities studied, thus raising hope for therapeutic vaccination in the treatment of persistent viral diseases.

HCV kinetic and modeling analyses indicate similar time to cure among sofosbuvir combination regimens with daclatasvir, simeprevir or ledipasvir.

BACKGROUND & AIMS: Recent clinical trials of direct-acting-antiviral agents (DAAs) against hepatitis C virus (HCV) achieved >90% sustained virological response (SVR) rates, suggesting that cure often took place before the end of treatment (EOT). We sought to evaluate retrospectively whether early response kinetics can provide the basis to individualize therapy to achieve optimal results while reducing duration and cost. METHODS: 58 chronic HCV patients were treated with 12-week sofosbuvir+simeprevir (n=19), sofosbuvir+daclatasvir (n=19), or sofosbuvir+ledipasvir in three French referral centers. HCV was measured at baseline, day 2, every other week, EOT and 12weeks post EOT. Mathematical modeling was used to predict the time to cure, i.e., <1 virus copy in the entire extracellular body fluid. RESULTS: All but one patient who relapsed achieved SVR. Mean age was 60±11years, 53% were male, 86% HCV genotype-1, 9% HIV coinfected, 43% advanced fibrosis (F3), and 57% had cirrhosis. At weeks 2, 4 and 6, 48%, 88% and 100% of patients had HCV<15IU/ml, with 27%, 74% and 91% of observations having target not detected, respectively. Modeling results predicted that 23 (43%), 16 (30%), 7 (13%), 5 (9%) and 3 (5%) subjects were predicted to reach cure within 6, 8, 10, 12 and 13weeks of therapy, respectively. The modeling suggested that the patient who relapsed would have benefitted from an additional week of sofosbuvir+ledipasvir. Adjusting duration of treatment according to the modeling predicts reduced medication costs of 43-45% and 17-30% in subjects who had HCV<15IU/ml at weeks 2 and 4, respectively. CONCLUSIONS: The use of early viral kinetic analysis has the potential to individualize duration of DAA therapy with a projected average cost saving of 16-20% per 100-treated persons.

Quantification of Hepatitis C Virus Cell-to-Cell Spread Using a Stochastic Modeling Approach.

UNLABELLED: It has been proposed that viral cell-to-cell transmission plays a role in establishing and maintaining chronic infections. Thus, understanding the mechanisms and kinetics of cell-to-cell spread is fundamental to elucidating the dynamics of infection and may provide insight into factors that determine chronicity. Because hepatitis C virus (HCV) spreads from cell to cell and has a chronicity rate of up to 80% in exposed individuals, we examined the dynamics of HCV cell-to-cell spread in vitro and quantified the effect of inhibiting individual host factors. Using a multidisciplinary approach, we performed HCV spread assays and assessed the appropriateness of different stochastic models for describing HCV focus expansion. To evaluate the effect of blocking specific host cell factors on HCV cell-to-cell transmission, assays were performed in the presence of blocking antibodies and/or small-molecule inhibitors targeting different cellular HCV entry factors. In all experiments, HCV-positive cells were identified by immunohistochemical staining and the number of HCV-positive cells per focus was assessed to determine focus size. We found that HCV focus expansion can best be explained by mathematical models assuming focus size-dependent growth. Consistent with previous reports suggesting that some factors impact HCV cell-to-cell spread to different extents, modeling results estimate a hierarchy of efficacies for blocking HCV cell-to-cell spread when targeting different host factors (e.g., CLDN1 > NPC1L1 > TfR1). This approach can be adapted to describe focus expansion dynamics under a variety of experimental conditions as a means to quantify cell-to-cell transmission and assess the impact of cellular factors, viral factors, and antivirals. IMPORTANCE: The ability of viruses to efficiently spread by direct cell-to-cell transmission is thought to play an important role in the establishment and maintenance of viral persistence. As such, elucidating the dynamics of cell-to-cell spread and quantifying the effect of blocking the factors involved has important implications for the design of potent antiviral strategies and controlling viral escape. Mathematical modeling has been widely used to understand HCV infection dynamics and treatment response; however, these models typically assume only cell-free virus infection mechanisms. Here, we used stochastic models describing focus expansion as a means to understand and quantify the dynamics of HCV cell-to-cell spread in vitro and determined the degree to which cell-to-cell spread is reduced when individual HCV entry factors are blocked. The results demonstrate the ability of this approach to recapitulate and quantify cell-to-cell transmission, as well as the impact of specific factors and potential antivirals.

Estimating the In Vivo Killing Efficacy of Cytotoxic T Lymphocytes across Different Peptide-MHC Complex Densities.

Cytotoxic T lymphocytes (CTLs) are important agents in the control of intracellular pathogens, which specifically recognize and kill infected cells. Recently developed experimental methods allow the estimation of the CTL's efficacy in detecting and clearing infected host cells. One method, the in vivo killing assay, utilizes the adoptive transfer of antigen displaying target cells into the bloodstream of mice. Surprisingly, killing efficacies measured by this method are often much higher than estimates obtained by other methods based on, for instance, the dynamics of escape mutations. In this study, we investigated what fraction of this variation can be explained by differences in peptide loads employed in in vivo killing assays. We addressed this question in mice immunized with lymphocytic choriomeningitis virus (LCMV). We conducted in vivo killing assays varying the loads of the immunodominant epitope GP33 on target cells. Using a mathematical model, we determined the efficacy of effector and memory CTL, as well as CTL in chronically infected mice. We found that the killing efficacy is substantially reduced at lower peptide loads. For physiological peptide loads, our analysis predicts more than a factor 10 lower CTL efficacies than at maximum peptide loads. Assuming that the efficacy scales linearly with the frequency of CTL, a clear hierarchy emerges among the groups across all peptide antigen concentrations. The group of mice with chronic LCMV infections shows a consistently higher killing efficacy per CTL than the acutely infected mouse group, which in turn has a consistently larger efficacy than the memory mouse group. We conclude that CTL killing efficacy dependence on surface epitope frequencies can only partially explain the variation in in vivo killing efficacy estimates across experimental methods and viral systems, which vary about four orders of magnitude. In contrast, peptide load differences can explain at most two orders of magnitude.

CD8+ T cells are activated in an antigen-independent manner in HIV-infected individuals.

Hyperactivation of T cells, particularly of CD8(+) T cells, is a hallmark of chronic HIV 1 (HIV-1) infection. Little is known about the antigenic specificities and the mechanisms by which HIV-1 causes activation of CD8(+) T cells during chronic infection. We report that CD8(+) T cells were activated during in vivo HIV-1 replication irrespective of their Ag specificity. Cytokines present during untreated HIV-1 infection, most prominently IL-15, triggered proliferation and expression of activation markers in CD8(+) T cells, but not CD4(+) T cells, in the absence of TCR stimulation. Moreover, LPS or HIV-1-activated dendritic cells (DCs) stimulated CD8(+) T cells in an IL-15-dependent but Ag-independent manner, and IL-15 expression was highly increased in DCs isolated from viremic HIV-1 patients, suggesting that CD8(+) T cells are activated by inflammatory cytokines in untreated HIV-1 patients independent of Ag specificity. This finding contrasts with CD4(+) T cells whose in vivo activation seems biased toward specificities for persistent Ags. These observations explain the higher abundance of activated CD8(+) T cells compared with CD4(+) T cells in untreated HIV-1 infection.

Predicting the impact of CD8+ T cell polyfunctionality on HIV disease progression.

UNLABELLED: During the chronic phase of HIV-1 infection, polyfunctional CD8+ T cell responses, which are characterized by a high frequency of cells able to secrete multiple cytokines simultaneously, are associated with lower virus loads and slower disease progression. This relationship may arise for different reasons. Polyfunctional responses may simply be stronger. Alternatively, it could be that the increased functional diversity in polyfunctional responses leads to lower virus loads and slower disease progression. Lastly, polyfunctional responses could contain more CD8+ T cells that mediate a specific key function that is primarily responsible for viral control. Disentangling the influences of overall strength, functional diversity, and specific function on viral control and disease progression is very relevant for the rational design of vaccines and immunotherapy using cellular immune responses. We developed a mathematical model to study how polyfunctional CD8+ T cell responses mediating lytic and nonlytic effector functions affect the CD4+ T cell count and plasma viral load. We based our model on in vitro data on the efficacy of gamma interferon (IFN-γ) and macrophage inflammatory protein 1ß (MIP-1ß)/RANTES against HIV. We find that the strength of the response is a good predictor of disease progression, while functional diversity has only a minor influence. In addition, our model predicts for realistic levels of cytotoxicity that immune responses dominated by nonlytic effector functions most positively influence disease outcome. IMPORTANCE: It is an open question in HIV research why polyfunctional CD8+ T cell responses are associated with better viral control, while individual functional correlates of protection have not been identified so far. Identifying the role of CD8+ T cells in HIV-1 infection has important implications for the potential development of effective T cell-based vaccines. Our analysis provides new ways to think about a causative role of CD8+ T cells by studying different hypotheses regarding why polyfunctional CD8+ T cells might be more advantageous. We identify measurements that have to be obtained in order to evaluate the role of CD8+ T cells in HIV-1 infection. In addition, our method shows how individual cell functionality data can be used in population-based virus dynamics models.

Can non-lytic CD8+ T cells drive HIV-1 escape?

The CD8+ T cell effector mechanisms that mediate control of HIV-1 and SIV infections remain poorly understood. Recent work suggests that the mechanism may be primarily non-lytic. This is in apparent conflict with the observation that SIV and HIV-1 variants that escape CD8+ T cell surveillance are frequently selected. Whilst it is clear that a variant that has escaped a lytic response can have a fitness advantage compared to the wild-type, it is less obvious that this holds in the face of non-lytic control where both wild-type and variant infected cells would be affected by soluble factors. In particular, the high motility of T cells in lymphoid tissue would be expected to rapidly destroy local effects making selection of escape variants by non-lytic responses unlikely. The observation of frequent HIV-1 and SIV escape poses a number of questions. Most importantly, is the consistent observation of viral escape proof that HIV-1- and SIV-specific CD8+ T cells lyse infected cells or can this also be the result of non-lytic control? Additionally, the rate at which a variant strain escapes a lytic CD8+ T cell response is related to the strength of the response. Is the same relationship true for a non-lytic response? Finally, the potential anti-viral control mediated by non-lytic mechanisms compared to lytic mechanisms is unknown. These questions cannot be addressed with current experimental techniques nor with the standard mathematical models. Instead we have developed a 3D cellular automaton model of HIV-1 which captures spatial and temporal dynamics. The model reproduces in vivo HIV-1 dynamics at the cellular and population level. Using this model we demonstrate that non-lytic effector mechanisms can select for escape variants but that outgrowth of the variant is slower and less frequent than from a lytic response so that non-lytic responses can potentially offer more durable control.

Inferring viral dynamics in chronically HCV infected patients from the spatial distribution of infected hepatocytes.

Chronic liver infection by hepatitis C virus (HCV) is a major public health concern. Despite partly successful treatment options, several aspects of intrahepatic HCV infection dynamics are still poorly understood, including the preferred mode of viral propagation, as well as the proportion of infected hepatocytes. Answers to these questions have important implications for the development of therapeutic interventions. In this study, we present methods to analyze the spatial distribution of infected hepatocytes obtained by single cell laser capture microdissection from liver biopsy samples of patients chronically infected with HCV. By characterizing the internal structure of clusters of infected cells, we are able to evaluate hypotheses about intrahepatic infection dynamics. We found that individual clusters on biopsy samples range in size from 4-50 infected cells. In addition, the HCV RNA content in a cluster declines from the cell that presumably founded the cluster to cells at the maximal cluster extension. These observations support the idea that HCV infection in the liver is seeded randomly (e.g. from the blood) and then spreads locally. Assuming that the amount of intracellular HCV RNA is a proxy for how long a cell has been infected, we estimate based on models of intracellular HCV RNA replication and accumulation that cells in clusters have been infected on average for less than a week. Further, we do not find a relationship between the cluster size and the estimated cluster expansion time. Our method represents a novel approach to make inferences about infection dynamics in solid tissues from static spatial data.

Use of laser capture microdissection to map hepatitis C virus-positive hepatocytes in human liver.

BACKGROUND & AIMS: Hepatitis C virus (HCV) predominantly infects hepatocytes, but many hepatocytes are not infected; studies have shown that HCV antigens cluster within the liver. We investigated spatial distribution and determinants of HCV replication in human liver samples. METHODS: We analyzed liver samples from 4 patients with chronic HCV infection (genotype 1, Metavir scores 0-1) to estimate the proportion of infected hepatocytes and the amount of HCV viral RNA (vRNA) per cell. Single-cell laser capture microdissection was used to capture more than 1000 hepatocytes in grids, to preserve geometric relationships. HCV vRNA and interferon-induced transmembrane protein 3 (IFITM3) messenger RNA (the transcript of an interferon-stimulated gene) were measured in the same hepatocytes by quantitative polymerase chain reaction and assembled in maps to identify areas of high and low HCV replication. RESULTS: Patients' serum levels of HCV RNA ranged from 6.87 to 7.40 log10 IU/mL; the proportion of HCV-infected hepatocytes per person ranged from 21% to 45%, and the level of vRNA ranged from 1 to 50 IU/hepatocyte. Infection was not random; we identified clustering of HCV-positive hepatocytes using infected-neighbor analysis (P < .0005) and distance to the kth nearest neighbor compared with random distributions, obtained by bootstrap simulations (P < .02). Hepatocytes that expressed IFITM3 did not appear to cluster and were largely HCV negative. CONCLUSIONS: We used single-cell laser capture and high-resolution analysis to show that in human liver HCV infects hepatocytes in nonrandom clusters, whereas expression of antiviral molecules is scattered among hepatocytes. These findings show that quantitative single-cell RNA measurements can be used to estimate the abundance of HCV vRNA per infected human hepatocyte and are consistent with cell-cell propagation of infection in the absence of clustered IFITM3.

Dynamics of CD4(+) T cell responses against Listeria monocytogenes.

The generation of CD4(+) T cell memory cells is poorly understood. Recently, two different murine CD4(+) TCR transgenic T cell lines, LLO118 and LLO56, both specific for the same epitope but differing in their expression level of the cell surface protein CD5, were generated. Notably, these cell lines showed different behavior upon primary and secondary exposure to Listeria monocytogenes. Whereas LLO118 showed a stronger primary response and generated more robust CD8(+) T cell help upon secondary exposure, LLO56 CD4(+) T cells had a dramatically better recall response. Using different mathematical models, we analyzed the dynamics of the two CD4(+) T cell lines in mice during infection with L. monocytogenes. Our models allowed the quantitative comparison of the two T cell lines and provided predictions for the conversion of naive T cells into memory cells. LLO118 CD4(+) T cells are estimated to have a higher proliferation rate than LLO56 CD4(+) T cells upon primary exposure. This difference can be explained by the lower expression level of CD5 on LLO118 CD4(+) T cells. Furthermore, LLO56 memory cells are predicted to have a 3-fold longer half-life than LLO118 memory cells ($${t}_{1/2}^{\hbox{ LLO }118}$$ ≈ 4.3 to 5 d and $${t}_{1/2}^{\hbox{ LLO }56}$$ ≈ 11.5 to 13.9 d). Although both cell lines differ in their memory capabilities, our analysis indicates no difference in the rate at which memory cells are generated. Our results show that different CD5 expression levels influence the proliferation dynamics of activated naive CD4(+) T cells while leaving the conversion rate of those cells into memory cells unaffected.

A wall-time minimizing parallelization strategy for approximate Bayesian computation.

Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often necessary. However, the existing parallelization strategies leave computing resources unused at times and thus do not optimally leverage them yet. We present look-ahead scheduling, a wall-time minimizing parallelization strategy for ABC Sequential Monte Carlo algorithms, which avoids idle times of computing units by preemptive sampling of subsequent generations. This allows to utilize all available resources. The strategy can be integrated with e.g. adaptive distance function and summary statistic selection schemes, which is essential in practice. Our key contribution is the theoretical assessment of the strategy of preemptive sampling and the proof of unbiasedness. Complementary, we provide an implementation and evaluate the strategy on different problems and numbers of parallel cores, showing speed-ups of typically 10-20% and up to 50% compared to the best established approach, with some variability. Thus, the proposed strategy allows to improve the cost and run-time efficiency of ABC methods on high-performance infrastructure.