Radially patterned morphogenesis of murine hair follicle placodes ensures robust epithelial budding.

The bending of simple cellular sheets into complex three-dimensional (3D) forms requires developmental patterning cues to specify where deformations occur, but how positional information directs morphological change is poorly understood. Here, we investigate how morphogen signaling and cell fate diversification contribute to the morphogenesis of murine hair placodes, in which collective cell movements transform radially symmetric primordia into bilaterally symmetric tubes. Through live imaging and 3D volumetric reconstructions, we demonstrate that Wnt and Shh establish radial patterns of cell fate, cell morphology, and movement within developing placodes.

SPIDER: constructing cell-type-specific protein-protein interaction networks.

Motivation: Protein-protein interactions (PPIs) play essential roles in the buildup of cellular machinery and provide the skeleton for cellular signaling. However, these biochemical roles are context dependent and interactions may change across cell type, time, and space. In contrast, PPI detection assays are run in a single condition that may not even be an endogenous condition of the organism, resulting in static networks that do not reflect full cellular complexity.

Concurrent TB and HIV therapies effectively control clinical reactivation of TB during co-infection but fail to eliminate chronic immune activation.

The majority of Human Immunodeficiency Virus (HIV) negative individuals exposed to () control the bacillary infection as latent TB infection (LTBI). Co-infection with HIV, however, drastically increases the risk to progression to tuberculosis (TB) disease. TB is therefore the leading cause of death in people living with HIV (PLWH) globally.

Multi-modal contrastive learning of subcellular organization using DICE.

The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information.