Single-cell proteomics defines the cellular heterogeneity of localized prostate cancer.

Localized prostate cancer exhibits multiple genomic alterations and heterogeneity at the proteomic level. Single-cell technologies capture important cell-to-cell variability responsible for heterogeneity in biomarker expression that may be overlooked when molecular alterations are based on bulk tissue samples. This study aims to identify prognostic biomarkers and describe the heterogeneity of prostate cancer and the associated microenvironment by simultaneously quantifying 36 proteins using single-cell mass cytometry analysis of over 1.6 million cells from 58 men with localized prostate cancer. We perform this task, using a high-dimensional clustering pipeline named Franken to describe subpopulations of immune, stromal, and prostate cells, including changes occurring in tumor tissues and high-grade disease that provide insights into the coordinated progression of prostate cancer. Our results further indicate that men with localized disease already harbor rare subpopulations that typically occur in castration-resistant and metastatic disease.

Notch signaling in the dynamics of perivascular stem cells and their niches.

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.

A single-cell atlas of human teeth.

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.

Inferring clonal composition from multiple tumor biopsies.

Knowledge about the clonal evolution of a tumor can help to interpret the function of its genetic alterations by identifying initiating events and events that contribute to the selective advantage of proliferative, metastatic, and drug-resistant subclones. Clonal evolution can be reconstructed from estimates of the relative abundance (frequency) of subclone-specific alterations in tumor biopsies, which, in turn, inform on its composition. However, estimating these frequencies is complicated by the high genetic instability that characterizes many cancers. Models for genetic instability suggest that copy number alterations (CNAs) can influence mutation-frequency estimates and thus impede efforts to reconstruct tumor phylogenies. Our analysis suggested that accurate mutation frequency estimates require accounting for CNAs-a challenging endeavour using the genetic profile of a single tumor biopsy. Instead, we propose an optimization algorithm, Chimæra, to account for the effects of CNAs using profiles of multiple biopsies per tumor. Analyses of simulated data and tumor profiles suggested that Chimæra estimates are consistently more accurate than those of previously proposed methods and resulted in improved phylogeny reconstructions and subclone characterizations. Our analyses inferred recurrent initiating mutations in hepatocellular carcinomas, resolved the clonal composition of Wilms' tumors, and characterized the acquisition of mutations in drug-resistant prostate cancers.

Tree-ensemble analysis assesses presence of multifurcations in single cell data.

We introduce TreeTop, an algorithm for single cell data analysis to identify and assign a branching score to branch points in biological processes which may have 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 shallower hierarchy of differentiation events in hematopoiesis, rather than the classical multi-level hierarchy.

Computational and experimental single cell biology techniques for the definition of cell type heterogeneity, interplay and intracellular dynamics.

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 ecological basis of morphogenesis: branching patterns in swarming colonies of bacteria.

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: (1) colony expansion, which increases the likelihood of colonizing a patch at a close distance and (2) 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.

Multilevel selection analysis of a microbial social trait.

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.

Evolutionary dynamics of BRCA1 alterations in breast tumorigenesis.

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.