Single-cell morphometrics reveals T-box gene-dependent patterns of epithelial tension in the Second Heart field.

The vertebrate heart tube extends by progressive addition of epithelial second heart field (SHF) progenitor cells from the dorsal pericardial wall. The interplay between epithelial mechanics and genetic mechanisms during SHF deployment is unknown. Here, we present a quantitative single-cell morphometric analysis of SHF cells during heart tube extension, including force inference analysis of epithelial stress.

Left/right asymmetrically expressed ephrin and Flamingo proteins regulate lateralized axon growth in C. elegans.

While the nervous system of bilaterian animals is mainly left-right (L-R) symmetric at the anatomical level, some molecular and functional L-R asymmetries exist. However, the extent of these molecular asymmetries and their functional consequences remain poorly characterized. C.

Limited column formation in the embryonic growth plate implies divergent growth mechanisms during pre- and postnatal bone development.

Chondrocyte columns, which are a hallmark of growth plate architecture, play a central role in bone elongation. Columns are formed by clonal expansion following rotation of the division plane, resulting in a stack of cells oriented parallel to the growth direction. In this work, we analyzed hundreds of Confetti multicolor clones in growth plates of mouse embryos using a pipeline comprising 3D imaging and algorithms for morphometric analysis.

Partial label learning for automated classification of single-cell transcriptomic profiles.

Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand.

Random walk informed heterogeneity detection reveals how the lymph node conduit network influences T cells collective exploration behavior.

Random walks on networks are widely used to model stochastic processes such as search strategies, transportation problems or disease propagation. A prominent example of such process is the dynamics of naive T cells within the lymph node while they are scanning for antigens. The observed T cells trajectories in small sub-volumes of the lymph node are well modeled as a random walk and they have been shown to follow the lymphatic conduit network as substrate for migration.

Application of 3D MAPs pipeline identifies the morphological sequence chondrocytes undergo and the regulatory role of GDF5 in this process.

The activity of epiphyseal growth plates, which drives long bone elongation, depends on extensive changes in chondrocyte size and shape during differentiation. Here, we develop a pipeline called 3D Morphometric Analysis for Phenotypic significance (3D MAPs), which combines light-sheet microscopy, segmentation algorithms and 3D morphometric analysis to characterize morphogenetic cellular behaviors while maintaining the spatial context of the growth plate. Using 3D MAPs, we create a 3D image database of hundreds of thousands of chondrocytes.

What machine learning can do for developmental biology.

Developmental biology has grown into a data intensive science with the development of high-throughput imaging and multi-omics approaches. Machine learning is a versatile set of techniques that can help make sense of these large datasets with minimal human intervention, through tasks such as image segmentation, super-resolution microscopy and cell clustering. In this Spotlight, I introduce the key concepts, advantages and limitations of machine learning, and discuss how these methods are being applied to problems in developmental biology.

Synthesizing developmental trajectories.

Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets.

Collective Growth in a Small Cell Network.

Theoretical studies suggest that many of the emergent properties associated with multicellular systems arise already in small networks [1, 2]. However, the number of experimental models that can be used to explore collective dynamics in well-defined cell networks is still very limited. Here we focus on collective cell behavior in the female germline cyst in Drosophila melanogaster, a stereotypically wired network of 16 cells that grows by ∼4 orders of magnitude with unequal distribution of volume among its constituents.

An integrated modelling framework from cells to organism based on a cohort of digital embryos.

We conducted a quantitative comparison of developing sea urchin embryos based on the analysis of five digital specimens obtained by automatic processing of in toto 3D+ time image data. These measurements served the reconstruction of a prototypical cell lineage tree able to predict the spatiotemporal cellular organisation of a normal sea urchin blastula. The reconstruction was achieved by designing and tuning a multi-level probabilistic model that reproduced embryo-level dynamics from a small number of statistical parameters characterising cell proliferation, cell surface area and cell volume evolution along the cell lineage.