biologists continue to mistake single-cell technical sampling noise for meaningful cellular heterogeneity. the apparent sparsity of single-cell data is due largely to technical artifacts like transcript capture and sequencing depth--if there are hundreds of thousands of transcripts per cell but you sequence only 5-10k unique molecules, of course you get mostly zeroes in any given cell. pseudo-bulking makes this clear: when you aggregate these noisy single cell samples, the vast majority of the genome shows expression greater than zero this is because the entire genome is being pervasively (yet stochastically) transcribed across nearly all cell types, including terminally differentiated cells (with a few obvious exceptions, e.g. enucleated red blood cells, spermatozoa, etc). hence you should expect to find at least one copy of nearly every transcript if you sample a single cell deeply enough over time it is the *relative* or *ranked* differences in expression compared to this low-level baseline that dictate cellular identity and function yet this pervasive transcription extends to retrotransposons, heterochromatin, and other supposedly "silenced" regions of the genome--all of which are systematically discarded in most standard processing pipelines. stare at the raw .fastq for long enough and this becomes obvious thus, this pervasive genome-wide transcription has implications not only for how we train virtual cell models on single-cell RNA count data, but for our understanding of cellular biology as a whole: to truly plumb the depths of the cell, we must venture beyond the reference genome into transcriptional terra incognita, where we will encounter eldritch species of non-coding RNAs that may throw into question much of what we know about the biology of the cell