Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.

Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, it has proven difficult to generate functional hematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we explored single-cell RNA sequencing (scRNAseq) in combination with single-cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors and erythroid-, megakaryocyte-, and leukocyte-committed progenitors, and we identified CD44, CD326, ICAM2/CD9, and CD18, respectively, as markers of these progenitors. Using an artificial neural network that we trained on scRNAseq derived from human fetal liver, we identified a wide range of hPSC-derived HSPCs phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded, and was completely missing in the data set that had been generated using cells selected on the basis of CD43 expression. By comparing the single-cell transcriptome of in vitro-generated HSC-like cells with those generated within the fetal liver, we identified transcription factors and molecular pathways that can be explored in the future to improve the in vitro production of HSCs.

Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions.

Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through development and adopt specialized cell fates. In this chapter we describe in detail how to use our network inference algorithm (PIDC)-and the associated software package NetworkInference.jl-to infer functional interactions between genes from the observed gene expression patterns. We exploit the large sample sizes and inherent variability of single cell data to detect statistical dependencies between genes that indicate putative (co-)regulatory relationships, using multivariate information measures that can capture complex statistical relationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation.

Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher-order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell datasets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

Stem Cell Differentiation as a Non-Markov Stochastic Process.

Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular "macrostates" and functionally similar molecular "microstates" and propose a model of stem cell differentiation as a non-Markov stochastic process.

How to deal with parameters for whole-cell modelling.

Dynamical systems describing whole cells are on the verge of becoming a reality. But as models of reality, they are only useful if we have realistic parameters for the molecular reaction rates and cell physiological processes. There is currently no suitable framework to reliably estimate hundreds, let alone thousands, of reaction rate parameters. Here, we map out the relative weaknesses and promises of different approaches aimed at redressing this issue. While suitable procedures for estimation or inference of the whole (vast) set of parameters will, in all likelihood, remain elusive, some hope can be drawn from the fact that much of the cellular behaviour may be explained in terms of smaller sets of parameters. Identifying such parameter sets and assessing their behaviour is now becoming possible even for very large systems of equations, and we expect such methods to become central tools in the development and analysis of whole-cell models.

Model of Host-Pathogen Interaction Dynamics Links In Vivo Optical Imaging and Immune Responses.

Tracking disease progression in vivo is essential for the development of treatments against bacterial infection. Optical imaging has become a central tool for in vivo tracking of bacterial population development and therapeutic response. For a precise understanding of in vivo imaging results in terms of disease mechanisms derived from detailed postmortem observations, however, a link between the two is needed. Here, we develop a model that provides that link for the investigation of Citrobacter rodentium infection, a mouse model for enteropathogenic Escherichia coli (EPEC). We connect in vivo disease progression of C57BL/6 mice infected with bioluminescent bacteria, imaged using optical tomography and X-ray computed tomography, to postmortem measurements of colonic immune cell infiltration. We use the model to explore changes to both the host immune response and the bacteria and to evaluate the response to antibiotic treatment. The developed model serves as a novel tool for the identification and development of new therapeutic interventions.

MEANS: python package for Moment Expansion Approximation, iNference and Simulation.

MOTIVATION: Many biochemical systems require stochastic descriptions. Unfortunately these can only be solved for the simplest cases and their direct simulation can become prohibitively expensive, precluding thorough analysis. As an alternative, moment closure approximation methods generate equations for the time-evolution of the system's moments and apply a closure ansatz to obtain a closed set of differential equations; that can become the basis for the deterministic analysis of the moments of the outputs of stochastic systems. RESULTS: We present a free, user-friendly tool implementing an efficient moment expansion approximation with parametric closures that integrates well with the IPython interactive environment. Our package enables the analysis of complex stochastic systems without any constraints on the number of species and moments studied and the type of rate laws in the system. In addition to the approximation method our package provides numerous tools to help non-expert users in stochastic analysis. AVAILABILITY AND IMPLEMENTATION: https://github.com/theosysbio/means CONTACTS: m.stumpf@imperial.ac.uk or e.lakatos13@imperial.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Topological sensitivity analysis for systems biology.

Mathematical models of natural systems are abstractions of much more complicated processes. Developing informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise, and a modicum of luck. Except for cases where physical principles provide sufficient guidance, it will also be generally possible to come up with a large number of potential models that are compatible with a given natural system and any finite amount of data generated from experiments on that system. Here we develop a computational framework to systematically evaluate potentially vast sets of candidate differential equation models in light of experimental and prior knowledge about biological systems. This topological sensitivity analysis enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions.

Kinetic and computational evidence for an intermediate in the hydrolysis of sulfonate esters.

The hydrolytic reactions of sulfonate esters have previously been considered to occur by concerted mechanisms. We now report the observation of a break in a Brønsted correlation for the alkaline hydrolysis of aryl benzenesulfonates. On either side of a break-point, ß(leaving group) values of -0.27 (pK(a) < 8.5) and -0.97 (pK(a) > 8.5) are measured. These data are consistent with a two-step mechanism involving a pentavalent intermediate that is also supported by QM/MM calculations. The emerging scenario can be explained by the combined effect of a strong nucleophile with a poor leaving group that compel a usually concerted reaction to favour a stepwise process.

An efficient, multiply promiscuous hydrolase in the alkaline phosphatase superfamily.

We report a catalytically promiscuous enzyme able to efficiently promote the hydrolysis of six different substrate classes. Originally assigned as a phosphonate monoester hydrolase (PMH) this enzyme exhibits substantial second-order rate accelerations ((k(cat)/K(M))/k(w)), ranging from 10(7) to as high as 10(19), for the hydrolyses of phosphate mono-, di-, and triesters, phosphonate monoesters, sulfate monoesters, and sulfonate monoesters. This substrate collection encompasses a range of substrate charges between 0 and -2, transition states of a different nature, and involves attack at two different reaction centers (P and S). Intrinsic reactivities (half-lives) range from 200 days to 10(5) years under near neutrality. The substantial rate accelerations for a set of relatively difficult reactions suggest that efficient catalysis is not necessarily limited to efficient stabilization of just one transition state. The crystal structure of PMH identifies it as a member of the alkaline phosphatase superfamily. PMH encompasses four of the native activities previously observed in this superfamily and extends its repertoire by two further activities, one of which, sulfonate monoesterase, has not been observed previously for a natural enzyme. PMH is thus one of the most promiscuous hydrolases described to date. The functional links between superfamily activities can be presumed to have played a role in functional evolution by gene duplication.

What makes an enzyme promiscuous?

Kinetic analyses of promiscuous enzymes reveal rate accelerations, (k(cat)/K(M))/k(2), of up to 10(18) for their secondary activities. Such large values suggest that binding and catalysis can be highly efficient for more than one reaction, challenging the notion that proficient catalysis requires specificity. Growing numbers of reported promiscuous activities indicate that catalytic versatility is an inherent property of many enzymes. The examples discussed here illustrate promiscuous molecular recognition mechanisms that, together with knowledge from structural and computational analysis, might be used for the identification or development of catalysts for new reactions.

Simultaneous determination of gene expression and enzymatic activity in individual bacterial cells in microdroplet compartments.

A microfluidic device capable of storing picoliter droplets containing single bacteria at constant volumes has been fabricated in PDMS. Once captured in droplets that remain static in the device, bacteria express both a red fluorescent protein (mRFP1) and the enzyme, alkaline phosphatase (AP), from a biscistronic construct. By measuring the fluorescence intensity of both the mRFP1 inside the cells and a fluorescent product formed as a result of the enzymatic activity outside the cells, gene expression and enzymatic activity can be simultaneously and continuously monitored. By collecting data from many individual cells, the distribution of activities in a cell is quantified and the difference in activity between two AP mutants is measured.

Efficient catalytic promiscuity for chemically distinct reactions.

High catalytic proficiencies observed for the native and promiscuous reaction of the Pseudomonas aeruginosa arylsulfatase (PAS; the picture shows transition states of the two substrates with corresponding binding constants K(tx)) suggest that the trade-off between high activity and tight specificity can be substantially relaxed.