Translational feedback on the cellular proteome has been seen historically as a combination of post-transcriptional modifications and ribosome associated factors influencing the translational status of a transcript. Thus, leaving the ribosome itself as a static non-tuneable machine (Genuth and Barna, 2018b). Consequently, specialized ribosomal sub-populations that would be capable of selective translation remain a controversial premise (Haag and Dinman, 2019). Functional specialization of plant ribosomes is supported by current research (Genuth and Barna, 2018a), and could partially rely on the usage of non-canonical ribosomal RNAs and proteins (RPs) (Martinez-Seidel, et al. 2019 - manuscript in interactive review). Many RP paralogs in plants appear to have neo or subfunctionalized. Hence, the usage of specific RPs, or paralogs, to assemble new ribosomes could ultimately influence the functional features that newly synthesized ribosomes use to constrain translation. We have determined that during cold acclimation, Arabidopsis is able to reprogram its RP composition independently from growth, confirming that a non-canonical RP composition characterizes stress-induced heterogeneous and possibly specialized ribosomes. Furthermore, the reprogramming is not random but significantly constrained to specific ribosomal regions. The RPs populating the significantly remodelled regions are of major importance to understand the functional aspects of this phenomenon. A combination of spatio-temporal mass spectrometry, i.e., stable isotope label-assisted mass spectrometry imaging will allow us to determine the synthesis of cytosolic ribosomal proteins in root meristems. The meristematic zones in plant roots represent the main regions responsible for plant growth and as such are a hotspot for the appearance of new and potentially specialized ribosomal populations. To access this information we have developed and optimized a mass spectrometry imaging method from sample embedding steps up until data analysis. Results show that we are able to discriminate root meristematic tissue as the largest variance influencing factor based on a K-means clustering partition of extracted ion counts.