Abstract
Lattice light-sheet microscopy provides large amounts of high-dimensional, high-spatiotemporal resolution imaging data of cell surface receptors across the 3D surface of live cells, but user-friendly analysis pipelines are lacking. Here, we introduce lattice light-sheet microscopy multi-dimensional analyses (LaMDA), an end-to-end pipeline comprised of publicly available software packages that combines machine learning, dimensionality reduction, and diffusion maps to analyze surface receptor dynamics and classify cellular signaling states without the need for complex biochemical measurements or other prior information. We use LaMDA to analyze images of T-cell receptor (TCR) microclusters on the surface of live primary T cells under resting and stimulated conditions. We observe global spatial and temporal changes of TCRs across the 3D cell surface, accurately differentiate stimulated cells from unstimulated cells, precisely predict attenuated T-cell signaling after CD4 and CD28 receptor blockades, and reliably discriminate between structurally similar TCR ligands. All instructions needed to implement LaMDA are included in this paper.