At his desk, graduate student Alex Fine has an iced coffee in hand and his eyes glued to the computer. He analyzes a whole slew of RNA data from a set of soon-to-be sperm cells. The data — transcriptional development data — will determine their stage of meiosis.
In Handel’s lab, researchers analyze the same set of soon-to-be sperm cells with a microscope. This is the cytological development data.
This recent collaborative project between the Carter and Handel labs was published in Molecular Biology of the Cell. Fine helped combine and compare the cytological and transcriptional data sets, collected from the Prdm9-/- infertility mouse model, as an important part of his doctoral thesis. The results led to important implications for both infertility and data science research.
Cytological and transcriptional data, from the same sample, occasionally point to different stages of sperm cell development. Although the two methods are related, neither one tells the full story on its own. Fine urges fertility scientists to do both the wet lab and the computational research to get a complete picture.
“It is very important to integrate multiple measurement types, in order to build more predictive models for biology,” says Fine.
Further, he thinks that this finding will be relevant to multiple fields, from development to cancer. “In other systems, such as cancer, we know that genetic mutations lead to cellular changes,” Fine explains. “Often researchers will use transcriptional data as a proxy for cellular phenotypes, and skip the cytology. But our work suggests that without pairing computational analyses with classical, molecular assays, transcriptomes of perturbed systems may mislead us regarding cell state and therefore limit our understanding of the mutant tissue.”
Fine plans to finish his thesis project, and his time in the joint Jackson Laboratory-Tufts University mammalian genetics Ph.D. program, in 2019.