In microbiology, we know that size matters. Your typical bacterium is only ~1 µm3, and thus, the micron scale is the fundamental level at which bacteria interact with their environments, a level that is hard to appreciate as humans (1). This challenge is one of the things that draws me to microbiology. I love the puzzle of understanding how things work that are too small for us to see. Through much of my PhD and postdoctoral training, I used sequencing approaches, including RNA-seq, to show how bacteria behave across different environments. But I always wondered if I had captured the whole story. For example, when I analyzed the gene expression of an oral pathogen from tooth scrapings taken from people with periodontitis (2), the samples were prepared from millions of microbes across millimeters of a tooth surface. I would ask, are all cells of this pathogen producing the same virulence factors and eating the same carbon sources? Likely not. Not all bacteria are in an identical environment, surrounded by the same host and bacterial cells at the micron level. Furthermore, targeted studies have shown repeatedly that bacterial gene expression varies across clonal cells for traits such as growth rate, antibiotic resistance, and competence due to micron-scale environmental differences, but also bistability, stochasticity, and genealogical effects (3, 4). Then, in 2020 and 2021, two papers, by Blattman et al. (5,6) and Kuchina et al. (6,6), opened up a new way to study bacterial gene expression at this single-cell level using bacterial single-cell RNA-seq (scRNA-seq).

The {messy} R package takes a clean dataset, and randomly adds mess to create data more similar to that which you'd find in the real world. This is an easy way for educators to create data sets that g...

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