What's Luck Got to Do with It: Single Cells, Multiple Fates, and Biological Nondeterminism

Abstract

The field of single-cell biology has morphed from a philosophical digression at its inception, to a playground for quantitative biologists, to a major area of biomedical research. The last several years have witnessed an explosion of new technologies, allowing us to apply even more of the modern molecular biology toolkit to single cells. Conceptual progress, however, has been comparatively slow. Here, we provide a framework for classifying both the origins of the differences between individual cells and the consequences of those differences. We discuss how the concept of "random" differences is context dependent, and propose that rigorous definitions of inputs and outputs may bring clarity to the discussion. We also categorize ways in which probabilistic behavior may influence cellular function, highlighting studies that point to exciting future directions in the field.

Figure 1. Variable phenotypic interpretations of genomic information

(A) Position-effect variegation gives rise to red and white patches in the Drosophila eye. The phenotype is due to imperfect spreading of pericentric heterochromatin, the white gene (red box) is silenced in some cells (white patches), but remains expressed in others (red patches). (B) Calico cats have patches on differently coloured fur (in this case black and orange, on a white background). The animals are heterozygous for a gene, with one one allele causing the orange tabby and the other the black colour. Random inactivation of either one or the other copy of the X chromosome in individual cells gives Calico cats their characteristic patches of colour. (C) Nine-banded armadillos are a polyembryonic species, with a single fertilised egg typically giving rise to quadruplets. While these genetically identical individuals seem very similar at first glance, some traits, such as the patterning of the head shield (h, with two different patterns shown in the red circles) and the banded shield (b) can be highly variable. Sources: (A) Image of eye from Elgin and Reuter: Position effect variegation, heterochromatin formation, and gene silencing in drosophila. Cold Spring Harb Perspect Biol 2013 Aug 1; 5(8):a017780 - http://cshperspectives.cshlp.org/cgi/pmidlookup?view=long&pmid=23906716 (B) Picture of Calico cat from Flickr, public domain dedication: https://www.flickr.com/photos/136594255@N06/24275144860/in/photolist-CZ7sQm-8hm5EF-9dVtJS-5QBizK-pqgXVF-9rYbVz-jWJT9X-8f7gz1-7R8ofB-8YS77x-nZpe8r-7Q4jpi-7HerfD-8i9WKU-7oyZxC-fwdnBA-oowW96-4jfk89-8q2j1o-8y3GUC-8UuXFw-nSSooh-8U9Yty-8BUSAK-8vMXzr-aX9Jwc-dUs5n4-7BfkNn-dML6tc-znkqQy-9KdnuE-83qdTU-czSjnY-5uEAin-7RTp6C-7wZXGY-6JatrC-p5i6vt-8MZ2Uo-9fLKRk-7S88pj-7Rrk5Q-8SNP4h-8PWpJT-9wRby8-gYD1U9-bcBNyk-m7fUdC-gKJLzf-8MhTDP/ (C) Image of Armadillos from Vogt: Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences, J. Biosci. 40(1), March 2015, 159–204 http://link.springer.com/article/10.1007%2Fs12038-015-9506-8

Figure 2. Mappings of inputs to outputs can either be deterministically specified, diversity generating, or buffering

A. Illustration of the three types of mappings. B. Illustration of revealing hidden variables that explain a seemingly probabilistic mapping, with an example of cell size as a hidden variable (from Padovan-Merhar et al. Mol. Cell 2015). C. Diagram of layers of specification in the retinal mosaic in Drosophila melanogaster (adapted from Wernet et al. Nature 2006). Individual ommatidia contain R7 and R8 cells, which either express Rh3 and Rh5, respectively (pale ommatidia) or Rh4 and Rh6, respectively (yellow ommatidia). The specification results from stochastic expression of the spineless transcription factor. Ultimately, the organismal phenotype is ~30% pale vs. ~70% yellow ommatidia.