Abstract: Translation regulation is an important layer of gene expression: Generation of genome-wide expression datasets at multi-omics levels in spatial, temporal, and cell-type resolution is essential for deciphering brain complexity. Regulation of gene expression is a highly dynamic process aiming at the production of precise levels of gene products to guarantee optimal cellular function, in response to physiological cues. Speedy advances in nextgeneration sequencing enabled the understanding of epigenomic and transcriptomic dynamic landscapes of different brain regions along development, aging, and disease progression. However, the correlation of the “transcriptome” with protein levels is poor because numerous mRNAs are subjected to manipulation of their translation efficiency, to warrant a favorable result under certain conditions. Hence, it is widely accepted that regulation at the translation level is a vital layer of gene expression. Quantification of actively translated mRNA populations (i.e., “translatome”) is a more reliable predictor of the “proteome” (Wang et al., 2020). Unfortunately, although translation regulation is becoming increasingly appreciated, the transcriptome data (generated by the evaluation of steadystate mRNA abundance) is still widely used as a single measure of gene expression, mainly due to technical reasons. One option to decipher the translatome is to utilize quantitative mass spectrometry for the assessment of newly synthesized polypeptide chains. Another option is to identify and quantify polyribosome-associated mRNAs by RNA sequencing. The latter is based on the understanding that in most cases, the association of an mRNA with more than a single ribosome (i.e., polyribosomes), refers to its status as actively translated.