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.

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.

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.

Differential regulation of β-catenin-mediated transcription via N- and C-terminal co-factors governs identity of murine intestinal epithelial stem cells.

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.

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.

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.

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 . We combine experiments and computer simulation to show that a simple ecological model of population dispersal can describe the emergence of branching patterns.

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.

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.

Parameterizing state-space models for infectious disease dynamics by generalized profiling: measles in Ontario.

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.