Florencia Burian

MIT Department: Chemistry
Faculty Mentor: Prof. Alek Shalek
Undergraduate Institution: Kean University
Website: LinkedIn
Research Poster


My name is Florencia Burian and I was born in Montevideo, Uruguay. I am a rising senior studying at Kean University in Union, New Jersey. I am pursuing a bachelor’s degree in Molecular Biology and a master’s degree in Biotechnology. I have done research in drug discovery and genetics since my freshman year of college, and started an independent study on figuring out if novel compounds created in my drug discovery research can cross the blood brain barrier in Drosophila species. I am interested in researching more in immunology, infectious diseases, and computational biology but have a strong drive for bioinformatics, and plan to pursue a PhD is any one of those fields. Outside the lab, I spend my time drawing and painting, hiking, and playing volleyball.

2021 Abstract

Comparison of STARsolo and DropSeq Tools for single cell RNA-sequencing Alignment of Pancreatic Cancer Organoids

Florencia Burian 1, Benjamin Mead 2-7, Conner Kummerlowe 6-9
and Alex Shalek
1NJ Center for Science, Technology and Mathematics, Kean University
2Koch Institute for Integrative Cancer Research,
Massachusetts Institute of Technology
3Harvard Stem Cell Institute
4Department of Chemistry, Massachusetts Institute of Technology
5Institute for Medical Engineering & Science,
Massachusetts Institute of Technology
6Broad Institute of MIT and Harvard
7Ragon Institute of MGH, MIT, and Harvard
8Program in Computational and Systems Biology,
Massachusetts Institute of Technology

Pancreatic ductal adenocarcinoma, also known as PDAC or pancreatic cancer, is a rare and difficult disease to understand and treat. PDAC tumors are poorly described by mutational profiling, and therefore often characterized by phenotypic behavior, such as basal or classical tumors with basal being the most aggressive type. Drug screening is available to screen a number of different drugs that change a cell’s behavior and can be used in cell cultured models to understand PDAC vulnerabilities. These perturbations in cell behavior enables screening of gene expressions of these cell behaviors and can be examined through single cell RNA sequencing (scRNA-seq). Traditional scRNA-seq methods are costly to scale, making impractical, but the development of new technologies with scRNA-seq, such as compressed screening, provides high throughput, high quality data for low-input samples at low cost, making screening possible and more effective. As a result, an equally efficient scRNA data analysis workflow is needed. We used and analyzed two different sequencing alignment tools—STARsolo and DropSeq

Tools—looking at the number of cells detected, cell quality and gene expression of these cells by each aligner. We observed that STARsolo gave much greater cell recovery than DropSeq Tools but found that DropSeq detected more cells in arrays with smaller total of recovered cells. Based on this preliminary analysis, STARsolo performed better than DropSeq in arrays with greater sample sizes. However, there were biases in the genes STARsolo detected compared to the genes DropSeq Tools detected, and different gene expression patterns shown for each aligner. In our next steps, we aim to investigate if these biases extend to all samples or are only context specific. Understanding what genes are responsible for PDAC’s aggressive behavior will enable us to get the most accurate and useful data to learn the fundamental nature of PDAC.