Summer Student Research on the Application of Boolean Analysis

项目总结/文摘

Project Summary/Abstract Despite 50 years of extensive investigations to characterize the stem cell population in human colon crypts, we still do not have a clear definition of cells that maintain the colon crypts. We attempt to answer this question with emerging technology of single-cell RNA sequencing (scRNA-seq), which can provide insight into detailed transcriptomes of individual stem and progenitor cells. We have a new method that can predict differentiation hierarchy using unbiased systems biology perspective and mathematical models of large patient-derived gene expression datasets. We have mathematical models that can predict terminally differentiated cells. The mathematical principle we use is based on Boolean implication logic that has not been commonly applied to study tissue cell populations. Boolean analysis assigns a parameter (e.g. RNA level of a gene) with only two values, i.e., high/low, 1/0, or positive/negative. Applying the Boolean principle, it is possible to determine the relationship between the expression levels of any pair of genes. Boolean analysis enables identification of fundamental gene expression relationships in human tissues and across species. The primary goal of this proposal is to use Boolean implication relationships to enable single-cell analysis of human colon tissue. Based on our preliminary data, the overall hypothesis is that Boolean principles can be used to specifically characterize the population of cell types in human colon tissue. Undergraduate involvement in this stem cell and computational biology research can expose them to the benefits of interdisciplinary research in biomedical fields and advance their professional careers.