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ItemDecoding Disease Persistence in Pediatric Acute Lymphoblastic Leukemia One Single Cell at a Time(Georgia Institute of Technology, 2020-12) Imbach, Kathleen JaneIn order to understand the biological and molecular mechanisms underlying disease resistance to therapy in pediatric acute lymphoblastic leukemia (ALL), we performed an investigation utilizing single-cell RNA-sequencing (scRNA-seq) on the 10X Genomics Chromium platform. Bone marrow samples from seven patients were collected, four of whom exhibited measurable residual disease (MRD) after induction therapy, and three patients who did not. Cells from bone marrow tissue were extracted from each patient at the time of diagnosis, prior to treatment efforts. Leukemic cells were separated from peripheral immune cells using flow cytometry and ~1000 single cells were sequenced from each patients’ cell populations. The goal of this study was to discern how the immune and leukemic cell populations and gene expression therein vary at the time of diagnosis between patients who do or do not respond to induction. Our results demonstrate a comparative increase in immune exhaustion signatures in the immune cells of MRD-positive patients, corroborating previous findings that implicate the role of exhaustion in resistant disease. We also show a discrepancy of cell cycle states in the leukemic cell compartment according to disease outcome, with an enrichment of blasts from MRD-negative patients exhibiting genetic signatures of S- and G2/M-phase.
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ItemSingle Cell Analysis of Patients with Perianal Chron’s Disease(Georgia Institute of Technology, 2020-05) Ferguson, Katherine ElizabethPerianal Chron’s Disease is a debilitating form of Chron’s Disease that often involves rectal tissue in its manifestation in the body. There is a greater need for understanding of the role of the epithelial tissue in the rectum in the pathology of Chron’s Disease. This study aims to determine cellular make-up of rectal-derived organoids grown from Chron’s Disease patients. Through the use of single-cell transcriptomics, this study will determine the cell types present in four rectal-derived organoids and use differential expression to analyze differences in epithelial tissue of Chron’s Disease patients in comparison to a healthy individual.
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ItemscRNA-seq dropouts serve as a signal for tissue heterogeneity in autism spectrum disorder(Georgia Institute of Technology, 2020-05) Spencer, CollinAnalysis of single-cell RNA-sequencing (scRNA-seq) data is plagued by dropouts, zero counts for mRNA transcripts due to low mRNA in individual cells and inefficient mRNA capture. Dropouts are traditionally treated as an error to be corrected through normalization while performing unsupervised clustering of single cells based on highly expressed, variable transcripts. A novel algorithm, co-occurrence clustering, treats dropouts as a signal and binarizes scRNA-seq data for cell clustering, producing the same clusters as Seurat. Previous application of Seurat to single nuclear RNA-sequencing (snRNA-seq) data taken from the prefrontal cortex (PFC) and anterior cingulate cortex (ACC) of patients with autism spectrum disorder (ASD) found no difference in clusters between brain regions. This seems at odds with literature suggesting tissue-specific emergence of co-expression networks and regional specialization in the brain. We applied co-occurrence clustering to ASD samples to parse interregional heterogeneity between the PFC and ACC and identify novel cell clusters.
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ItemTime of day predicts the activity budget in the Emerald Tree Boa (Boidae: Corallus batesii) across multiple behaviors(Georgia Institute of Technology, 2020-05) Berry, Savannah MargaretExploring the spatial ecology and behavior of organisms is essential to understand an organism’s niche and how to better accommodate animals that are kept in captivity (Ross et al., 2011; Plowman, 2003). Emerald Tree Boas (Corallus batesii) are commonly kept as pets and are important predators in the Amazon; however, despite their commercial exploitation and ecological importance, little is known about their behavior. This paper attempts to fill this literature gap by quantifying the behavior of a juvenile Corallus batesii. The activity budget of the snake was video recorded over a period of three months and was coded using the event logging software, BORIS (Friard & Gamba, 2016). After conducting individual regression analyses on five behaviors (hunting, moving, resting, other: stationary, and out of view), it was found that time of day significantly predicted the behaviors of hunting (p=0.0178, df=23, t=8.50) and resting (p=0.00337, df=23, t=3.52). Hunting behavior was observed between the hours of 2200 h and 700 h and resting between 800 h and 2100 h.