Liquid biopsies using cell-free RNA (cfRNA) can noninvasively measure dynamic physiological changes throughout the body. While there is much effort in the liquid biopsy field to determine disease tissue-of-origin, pathophysiology occurs at the cellular level. Here, we show that it is possible to determine cell type-of-origin from cfRNA by leveraging single cell transcriptomic atlases to perform computational deconvolution. We derived cell type gene signatures by combining the whole-body single cell atlas Tabula Sapiens, individual tissue single cell atlases, and bulk tissue atlases. Using deconvolution, we identified cell types-of-origin in the healthy human cell-free transcriptome, including contributions from multiple cell types in the brain, liver, lung, intestine, kidney, and pancreas in addition to hematopoietic cell types. We further showed that it is possible not only to detect cell types implicated in the pathology of chronic kidney disease (CKD) and Alzheimer’s disease (AD), but also to measure changes in these cell types as a function of disease state. Altogether, our results show that cfRNA measurements reflect cellular contributions in health and disease from diverse tissue-specific cell types. These findings underscore the resolution at which one can monitor pathophysiological changes and the broad potential prognostic utility afforded by non-invasive transcriptomic measurement.