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Feller, K. J.

Publications and source records attributed to Feller, K. J..

2 recordsLinked to original sources

Identification of inter- and intra-tumoral molecular phenotypes steering temozolomide resistance in patient-derived glioblastoma cells.

BackgroundRadiation therapy and chemotherapy using Temozolomide are the standard adjuvant treatments for patients with glioblastoma. Despite maximal treatment prognosis is still poor largely due to the emergence of Temozolomide resistance. This resistance is closely linked to the widely recognized inter- and intra-tumoral heterogeneity in glioblastoma, although the underlying mechanisms are not yet fully understood. This study aims to investigate the diverse molecular mechanisms involved in temozolomide resistance. MethodsTo induce temozolomide resistance, we subjected 21 patient-derived glioblastoma cell cultures to Temozolomide treatment for a period of up to 90 days. Prior to treatment, the cells molecular characteristics were analyzed using bulk RNA sequencing. Additionally, we performed single-cell sequencing on four of the cell cultures to track the evolution of temozolomide resistance. ResultsThe induced temozolomide resistance was associated with two distinct phenotypic behaviors, classified as "adaptive" (ADA) or "non-adaptive" (N-ADA) to temozolomide. The ADA phenotype displayed neurodevelopmental and metabolic gene signatures, whereas the N-ADA phenotype expressed genes related to cell cycle regulation, DNA repair, and protein synthesis. Single-cell RNA sequencing revealed that in ADA cell cultures, one or more subpopulations emerged as dominant in the resistant samples, whereas N-ADA cell cultures remained relatively stable. ConclusionsThe adaptability and heterogeneity of glioblastoma cells play pivotal roles in temozolomide treatment and contribute to the tumors ability to survive. Depending on the tumors adaptability potential, subpopulations with acquired resistance mechanisms may arise. Further research is necessary to deepen our understanding of these mechanisms and develop strategies to overcome them.

cancer biology↗

Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image data

Quantitative characterizations of cellular dynamics and features of individual cells from a large heterogenous population is essential to identify rare, disease-driving cells, which often exhibit aberrant cellular behaviors like abnormal division, aggressive migration or irregular phylogenetic cell lineages. A recent development in the combination of high-throughput screening microscopy with single cell profiling provides an unprecedented opportunity to decipher the underlying mechanisms of disease-driving phenotypes observed under a microscope. However, accurately and instantly processing large amounts of image data like longitudinal time lapse movies remains a technical challenge when an immediate analysis output (in minutes) of quantitative characterizations is required after data acquisition. Here we present a Fast and Accurate real-time Cell Tracking (FACT) algorithm, which combines GPU-based, ground truth-assisted trainable Weka segmentation and real-time Gaussian mixture model-based cell linking. FACT also implements an automatic cell track correction function to improve the tracking accuracy. With FACT, we can segment [~]20,000 cells in 2 seconds ([~]4.5-27.5 times faster than state-of-the-art), and can export quantifiable features from the cell tracking results minutes after data acquisition (independent of the number of acquired image frames) with average 90-95% tracking precision. Such performance is not feasible with state-of-the-art cell tracking algorithms. We applied FACT to real-time identify directionally migrating glioblastoma cells with 96% precision and to identify rare, irregular cell lineages in a population of [~]10,000 cells from a 24hr-time lapse movie with an average 91% F1 score, results from both were exported instantly, mere minutes after image acquisition.

bioengineering↗