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The Notch signaling pathway is a fundamental regulator of cell fate determination in homeostasis and regeneration. In this work, we aimed to determine how Notch signaling mediates the interactions between perivascular stem cells and their niches in human dental mesenchymal tissues, both in homeostatic and regenerative conditions. By single cell RNA sequencing analysis, we showed that perivascular cells across the dental pulp and periodontal human tissues all express NOTCH3, and that these cells are important for the response to traumatic injuries in vivo in a transgenic mouse model. We further showed that the behavior of perivascular NOTCH3‐expressing stem cells could be modulated by cellular and molecular cues deriving from their microenvironments. Taken together, the present studies, reinforced by single‐cell analysis, reveal the pivotal importance of Notch signaling in the crosstalk between perivascular stem cells and their niches in tissue homeostasis and regeneration., In the dental pulp and the periodontium, mesenchymal stem cells (MSCs) are highly similar, are mostly located around blood vessels, and express NOTCH3. These NOTCH3 + MSCs are important for the reaction of dental tissues to injury. In both tissues, endothelial cells adjacent to NOTCH3 + MSCs express NOTCH ligands, which could thus mediate the molecular crosstalk between MSCs and their perivascular niche.

The homeostasis of the gut epithelium relies upon continuous renewal and proliferation of crypt-resident intestinal epithelial stem cells (IESCs). Wnt/β-catenin signaling is required for IESC maintenance, however, it remains unclear how this pathway selectively governs the identity and proliferative decisions of IESCs. Here, we took advantage of knock-in mice harboring transgenic β-catenin alleles with mutations that specifically impair the recruitment of N- or C-terminal transcriptional co-factors. We show that C-terminally-recruited transcriptional co-factors of β-catenin act as all-or-nothing regulators of Wnt-target gene expression. Blocking their interactions with β-catenin rapidly induces loss of IESCs and intestinal homeostasis. Conversely, N-terminally recruited co-factors fine-tune β-catenin’s transcriptional output to ensure proper self-renewal and proliferative behaviour of IESCs. Impairment of N-terminal interactions triggers transient hyperproliferation of IESCs, eventually resulting in exhaustion of the self-renewing stem cell pool. IESC mis-differentiation, accompanied by unfolded protein response stress and immune infiltration, results in a process resembling aberrant “villisation” of intestinal crypts. Our data suggest that IESC-specific Wnt/β-catenin output requires selective modulation of gene expression by transcriptional co-factors., Nature Communications, 12 (1), ISSN:2041-1723

Summary Teeth exert fundamental functions related to mastication and speech. Despite their great biomedical importance, an overall picture of their cellular and molecular composition is still missing. In this study, we have mapped the transcriptional landscape of the various cell populations that compose human teeth at single-cell resolution, and we analyzed in deeper detail their stem cell populations and their microenvironment. Our study identified great cellular heterogeneity in the dental pulp and the periodontium. Unexpectedly, we found that the molecular signatures of the stem cell populations were very similar, while their respective microenvironments strongly diverged. Our findings suggest that the microenvironmental specificity is a potential source for functional differences between highly similar stem cells located in the various tooth compartments and open new perspectives toward cell-based dental therapeutic approaches., Graphical abstract, Highlights • Dental atlas of the pulp and periodontal tissues of human teeth • Identification of three common MSC subclusters between dental pulp and periodontium • Dental pulp and periodontal MSCs are similar, and their niches diverge, Cell Biology; Stem Cells Research; Omics; Transcriptomics

We introduce TreeTop, an algorithm for single-cell data analysis to identify and assess statistical significance of branch points in biological processes with possibly multi-level branching hierarchies. We demonstrate branch point identification for processes with varying topologies, including T cell maturation, B cell differentiation and hematopoiesis. Our analyses are consistent with recent experimental studies suggesting a shallow hierarchy of differentiation events in hematopoiesis, rather than the classical multi-level hierarchy.

Knowledge about the clonal evolution of each tumor can inform driver-alteration discovery by pointing out initiating genetic events as well as events that contribute to the selective advantage of proliferative, and potentially drug-resistant tumor subclones. A necessary building block to the reconstruction of clonal evolution from tumor profiles is the estimation of the cellular composition of each tumor subclone (cellularity), and these, in turn, are based on estimates of the relative abundance (frequency) of subclone-specific genetic alterations in tumor biopsies. Estimating the frequency of genetic alterations is complicated by the high genomic instability that characterizes many tumor types.

Cancer results from the accumulation of alterations in oncogenes and tumor suppressor genes. Tumor suppressors are classically defined as genes which contribute to tumorigenesis if their function is lost. Genetic or epigenetic alterations inactivating such genes may arise during somatic cell divisions or alternatively may be inherited from a parent. One notable exception to this rule is the BRCA1 tumor suppressor that predisposes to hereditary breast cancer when lost. Genetic alterations of this gene are hardly ever observed in sporadic breast cancer, while individuals harboring a germline mutation readily accumulate a second alteration inactivating the remaining allele--a finding which represents a conundrum in cancer genetics. In this paper, we present a novel mathematical framework of sporadic and hereditary breast tumorigenesis. We study the dynamics of genetic alterations driving breast tumorigenesis and explore those scenarios which can explain the absence of somatic BRCA1 alterations while replicating all other disease statistics. Our results support the existence of a heterozygous phenotype of BRCA1 and suggest that the loss of one BRCA1 allele may suppress the fitness advantage caused by the inactivation of other tumor suppressor genes. This paper contributes to the mathematical investigation of breast tumorigenesis.

Single cell sequencing and proteome profiling efforts in the past few years have revealed widespread genetic and proteomic heterogeneity among tumor cells. However, sensible cell-type definition of such heterogeneous cell populations has so far been a challenging task. Single cell technologies such as RNA sequencing and mass cytometry provide information precluded by conventional bulk measurements and have achieved significant improvements in multiparametricity at high cellular throughput. By combining these technologies with computational and mathematical techniques it is possible to quantitatively define cellular heterogeneity, uncovering distinct phenotypic profiles that can be utilized to, for example, characterize tumor heterogeneity with the potential to develop and improve therapeutic strategies.

Novel technological developments enable single cell population profiling with respect to their spatial and molecular setup. These include single cell sequencing, flow cytometry and multiparametric imaging approaches and open unprecedented possibilities to learn about the heterogeneity, dynamics and interplay of the different cell types which constitute tissues and multicellular organisms. Statistical and dynamic systems theory approaches have been applied to quantitatively describe a variety of cellular processes, such as transcription and cell signaling. Machine learning approaches have been developed to define cell types, their mutual relationships, and differentiation hierarchies shaping heterogeneous cell populations, yielding insights into topics such as, for example, immune cell differentiation and tumor cell type composition. This combination of experimental and computational advances has opened perspectives towards learning predictive multi-scale models of heterogeneous cell populations.

The evolution of cooperation in colonies of swarming bacteria is analyzed by manipulating the cost-to-benefit ratio of cooperation to show that ‘constitutive' cooperation is favored only when relatedness is high, in contrast to ‘prudent' cooperation., Swarming in the bacterium Pseudomonas aeruginosa is a cooperative trait that is beneficial for the group, as it allows colony expansion. Constitutive swarming cooperation is costly to cooperating individuals and has diminishing returns, but can still be favored by multilevel selection if relatedness is high. Swarming cooperation is favored in a wider range of conditions when regulated by metabolic prudence., The study of microbial communities often leads to arguments for the evolution of cooperation due to group benefits. However, multilevel selection models caution against the uncritical assumption that group benefits will lead to the evolution of cooperation. We analyze a microbial social trait to precisely define the conditions favoring cooperation. We combine the multilevel partition of the Price equation with a laboratory model system: swarming in Pseudomonas aeruginosa. We parameterize a population dynamics model using competition experiments where we manipulate expression, and therefore the cost-to-benefit ratio of swarming cooperation. Our analysis shows that multilevel selection can favor costly swarming cooperation because it causes population expansion. However, due to high costs and diminishing returns constitutive cooperation can only be favored by natural selection when relatedness is high. Regulated expression of cooperative genes is a more robust strategy because it provides the benefits of swarming expansion without the high cost or the diminishing returns. Our analysis supports the key prediction that strong group selection does not necessarily mean that microbial cooperation will always emerge.

Parameter estimation for infectious disease models is important for basic understanding (e.g. to identify major transmission pathways), for forecasting emerging epidemics, and for designing control measures. Differential equation models are often used, but statistical inference for differential equations suffers from numerical challenges and poor agreement between observational data and deterministic models. Accounting for these departures via stochastic model terms requires full specification of the probabilistic dynamics, and computationally demanding estimation methods. Here, we demonstrate the utility of an alternative approach, generalized profiling, which provides robustness to violations of a deterministic model without needing to specify a complete probabilistic model. We introduce novel means for estimating the robustness parameters and for statistical inference in this framework. The methods are applied to a model for pre-vaccination measles incidence in Ontario, and we demonstrate the statistical validity of our inference through extensive simulation. The results confirm that school term versus summer drives seasonality of transmission, but we find no effects of short school breaks and the estimated basic reproductive ratio ℛ 0 greatly exceeds previous estimates. The approach applies naturally to any system for which candidate differential equations are available, and avoids many challenges that have limited Monte Carlo inference for state–space models.

The identification of JAK-STAT pathway mutations in the majority of patients with the myeloproliferative neoplasms (MPN) polycythemia vera (PV), essential thrombocytosis (ET), and primary myelofibrosis (PMF) led the to clinical development of JAK inhibitors, and the resultant approval of ruxolitinib for the treatment of PMF. However, despite this important therapeutic advance, there are significant limitations to JAK inhibitor therapy both with respect to efficacy and toxicity. First, although JAK inhibitors reduce splenomegaly, ameliorate symptoms, and improve long-term outcome, they do not achieve molecular or pathologic remission at currently utilized dosing strategies. Second, JAK2 has a role in hematopoiesis and other biological processes, and JAK inhibition leads to significant hematologic toxicities including anemia and thrombocytopenia. We recently used genetic and pharmacologic studies to demonstrate that JAK inhibitor persistent cells which survive JAK inhibitor therapy in vitro and in vivo remain JAK2 dependent, consistent with incomplete target inhibition. As such, we hypothesized that alternate dosing regimens which allow for intermittent, maximal target inhibition might increase efficacy and reduce toxicity. We therefore used experimental and modeling approaches to investigate the potential efficacy of alternate dosing regimens. We first explored the effects of chronic vs intermittent dosing in vitro by altering the treatment regimen in cell lines. To this end, we treated the JAK2 V617F mutant cell line, SET-2, and JAK2-wild-type (control) cell lines with ruxolitinib (1µM vs 0.5 µM) on a chronic or intermittent (alternating 1 week on and 1 week off the drug) basis. We then performed cell viability assays using flow cytometry to estimate the effect of the drug on the cell division and death rates of each cell population. Using this data, we developed a mathematical model to predict responses to varying dose therapy. Cell proliferation was described using an exponential growth model (pt2 = pt1 e(birth rate-death rate)Dt, p=population size). Birth and death rates as a function of the drug concentration was fitted using a simple iterative least squares estimation from the in vitro collected data, where death(c) = 0.0046log(1.5014 + 30.4910c) and birth(c) = 0.0098 + 0.0051e-1.2946c. Treatment cycles were modeled by ton + toff for pulsitile versus chronic (toff = 0) regimens for time on and off drug. We also added a toxicity constraint based on preclinical testing and the mathematical model T(c) = (α/c) –β, where α = 539 and β=5.2, which will inform our in vivo studies. Inputting these rates into a mathematical model to predict optimal treatment schedule, our in silico analysis suggest that high dose pulse treatment of INCB18424 has the same efficacy as chronic dosing and is associated with reduced toxicity. We are currently testing our dosing and administration schedules using in vivo models of MPN, and we will present these data at the meeting. Preliminary studies suggest intermittent JAK inhibition shows similar efficacy as chronic JAK inhibition, with reduced toxicity, suggesting our in silico models inform the development of more optimal dosing regimens. We are now testing higher doses of JAK inhibitors in an intermittent administration regimen in order to maximize efficacy and mitigate hematologic and non-hematologic toxicity. In conclusion, our proof-of-principle studies show that intermittent treatment with JAK kinase inhibitors demonstrates equivalent efficacy in vitro and our in silico data suggests that we will see reduced toxicity with intermittent dosing in the mouse models. Our in vivo data will inform further clinical optimization of treatment regiments for patients with myeloproliferative neoplasms Disclosures Koppikar: Amgen: Employment. Levine:Novartis: Consultancy, Grant support Other.

Understanding how large-scale shapes in tissues, organs and bacterial colonies emerge from local interactions among cells and how these shapes remain stable over time are two fundamental problems in biology. Here we investigate branching morphogenesis in an experimental model system, swarming colonies of the bacterium Pseudomonas aeruginosa. We combine experiments and computer simulation to show that a simple ecological model of population dispersal can describe the emergence of branching patterns. In our system, morphogenesis depends on two counteracting processes that act on different length-scales: (i) colony expansion, which increases the likelihood of colonizing a patch at a close distance and (ii) colony repulsion, which decreases the colonization likelihood over a longer distance. The two processes are included in a kernel-based mathematical model using an integro-differential approach borrowed from ecological theory. Computer simulations show that the model can indeed reproduce branching, but only for a narrow range of parameter values, suggesting that P. aeruginosa has a fine-tuned physiology for branching. Simulations further show that hyperswarming, a process where highly dispersive mutants reproducibly arise within the colony and disrupt branching patterns, can be interpreted as a change in the spatial kernel.