Kishore R. Mosaliganti

TL;DR: AO-LLSM takes high-resolution live-cell imaging of subcellular processes from the confines of the coverslip to the more physiologically relevant 3D environment within whole transparent organisms and creates new opportunities to study the phenotypic diversity of intracellular dynamics, extracellular communication, and collective cell behavior across different cell types, organisms, and developmental stages.

TL;DR: An approach for high-resolution tracing of neurons, their subassemblies, and their molecular constituents over large volumes is introduced, which can enable high-throughput comparative studies of neural development, circuit stereotypy, and structural correlations to neural activity or behavior.

TL;DR: This work finds that specified progenitors of different fates are spatially mixed following heterogeneous Sonic Hedgehog signaling responses, and reveals that cell sorting acts to correct imprecision of spatial patterning by noisy inductive signals.

TL;DR: By using the ACME software to study somite formation, the method was able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells.

TL;DR: This work identifies an evolvable design logic that enables robust cell-level regulation of tissue-level development and predicts that cell shape is robust to changes of tissue surface area, cell volume, and cell number, which is confirmed in vivo.