Electron tomography using energy loss and X-ray spectroscopy in the electron microscope continues to develop in rapidly evolving and diverse directions, enabling new insight into the three-dimensional chemistry and physics of nanoscale volumes. Progress has been made recently in improving reconstructions from EELS and EDS signals in electron tomography by applying compressed sensing methods, characterizing new detector technologies in detail, deriving improved models of signal generation, and exploring machine learning approaches to signal processing. These disparate threads can be brought together in a cohesive framework in terms of a model-based approach to analytical electron tomography. Models incorporate information on signal generation and detection as well as prior knowledge of structures in the spectrum image data. Many recent examples illustrate the flexibility of this approach and its feasibility for addressing challenges in non-linear or limited signals in EELS and EDS tomography. Further work in combining multiple imaging and spectroscopy modalities, developing synergistic data acquisition, processing, and reconstruction approaches, and improving the precision of quantitative spectroscopic tomography will expand the frontiers of spatial resolution, dose limits, and maximal information recovery.