Biology subjects

Jost, T. A.

Publications and source records attributed to Jost, T. A..

4 recordsLinked to original sources

bioRxiv2024

Mapping cell-cell fusion at single-cell resolution

Cell-cell fusion is a tightly controlled process in the human body known to be involved in fertilization, placental development, muscle growth, bone remodeling, and viral response. Fusion between cancer cells results first in a whole-genome doubled state, which may be followed by the generation of aneuploidies; these genomic alterations are known drivers of tumor evolution. The role of cell-cell fusion in cancer progression and treatment response has been understudied due to limited experimental systems for tracking and analyzing individual fusion events. To meet this need, we developed a molecular toolkit to map the origins and outcomes of individual cell fusion events within a tumor cell population. This platform, ClonMapper Duo ( CMDuo), identifies cells that have undergone cell-cell fusion through a combination of reporter expression and engineered fluorescence-associated index sequences paired to randomly generated nucleotide barcodes. scRNA-seq of the indexed barcodes enables the mapping of each set of parental cells and fusion progeny throughout the cell population. In triple-negative breast cancer cells CMDuo uncovered subclonal transcriptomic hybridization and unveiled distinct cell-states which arise in direct consequence of homotypic cell-cell fusion. CMDuo is a platform that enables mapping of cell-cell fusion events in high-throughput single cell data and enables the study of cell fusion in disease progression and therapeutic response.

systems biology↗

bioRxiv2024

Deep learning identifies heterogeneous subpopulations in breast cancer cell lines

MotivationCells exhibit a wide array of morphological features, enabling computer vision methods to identify and track relevant parameters. Morphological analysis has long been implemented to identify specific cell types and cell responses. Here we asked whether morphological features might also be used to classify transcriptomic subpopulations within in vitro cancer cell lines. Identifying cell subpopulations furthers our understanding of morphology as a reflection of underlying cell phenotype and could enable a better understanding of how subsets of cells compete and cooperate in disease progression and treatment. ResultsWe demonstrate that cell morphology can reflect underlying transcriptomic differences in vitro using convolutional neural networks. First, we find that changes induced by chemotherapy treatment are highly identifiable in a breast cancer cell line. We then show that the intra cell line subpopulations that comprise breast cancer cell lines under standard growth conditions are also identifiable using cell morphology. We find that cell morphology is influenced by neighborhood effects beyond the cell boundary, and that including image information surrounding the cell can improve model discrimination ability.

cancer biology↗

bioRxiv2024

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

cancer biology↗

bioRxiv2021

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present work characterizing dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600-800 hours, and this data is used to calibrate a family of cancer growth models derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-106%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

cancer biology↗