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Keywords:

  • cell evolution;
  • cell homology;
  • germ layer;
  • neural crest cells;
  • neurons;
  • transdifferentiation

ABSTRACT

Metazoans are composed of a finite number of recognisable cell types. Similar to the relationship between species and ecosystems, knowledge of cell type diversity contributes to studies of complexity and evolution. However, as with other units of evolution, the cell type often resists definition. This review proposes guidelines for characterising cell types and discusses cell homology and the various developmental pathways by which cell types arise, including germ layers, blastemata (secondary development/neurulation), stem cells, and transdifferentiation. An updated list of cell types is presented for a familiar, albeit overlooked model taxon, adult Homo sapiens, with 411 cell types, including 145 types of neurons, recognised. Two methods for organising these cell types are explored. One is the artificial classification technique, clustering cells using commonly accepted criteria of similarity. The second approach, an empirical method modeled after cladistics, resolves the classification in terms of shared features rather than overall similarity. While the results of each scheme differ, both methods address important questions. The artificial classification provides compelling (and independent) support for the neural crest as the fourth germ layer, while the cladistic approach permits the evaluation of cell type evolution. Using the cladistic approach we observe a correlation between the developmental and evolutionary origin of a cell, suggesting that this method is useful for predicting which cell types share common (multipotential) progenitors. Whereas the current effort is restricted by the availability of phenotypic details for most cell types, the present study demonstrates that a comprehensive cladistic classification is practical, attainable, and warranted. The use of cell types and cell type comparative classification schemes has the potential to offer new and alternative models for therapeutic evaluation.