Biology subjects

Alahmari, S.

Publications and source records attributed to Alahmari, S..

3 recordsLinked to original sources

bioRxiv2025

CLONEID: A Framework for Monitoring and Steering Subclonal Dynamics

Molecular assays can resolve clonal structure, but they are expensive and typically sparse in time, whereas phenotypic observations such as imaging can be collected frequently but often are not preserved in the context needed for later interpretation. We present CLONEID, an event-based framework for organizing clone-resolved phenotypic, molecular, and specimen-context records so that genotype-to-phenotype interpretation can be maintained across time. CLONEID links time-stamped Events, assay-specific Perspectives, and reconciled Identities through structured ingestion, provenance-aware retrieval, and reproducible export, complementing upstream clone-calling methods. In a long-term gastric cancer density-selection experiment, CLONEID linked repeated culture events, growth measurements, and late karyotypic profiling within a shared record, supporting longitudinal interpretation of phenotypic adaptation together with underlying chromosomal state.

bioinformatics↗

bioRxiv2024

Cell identity revealed by precise cell cycle state mapping links data modalities

Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.

bioinformatics↗

bioRxiv2023

Mathematical modeling of clonal interference by density-dependent selection in heterogeneous cancer cell lines

Many cancer cell lines are aneuploid and heterogeneous, with multiple karyotypes co-existing within the same cell line. Karyotype heterogeneity has been shown to manifest phenotypically, affecting how cells respond to drugs or to minor differences in culture media. Knowing how to interpret karyotype heterogeneity phenotypically, would give insights into cellular phenotypes before they unfold temporally. Here we reanalyze single cell RNA (scRNA)- and scDNA sequencing data from eight stomach cancer cell lines by placing gene expression programs into a phenotypic context. We quantify differences in growth rate and contact inhibition between the eight cell lines using live-cell imaging, and use these differences to prioritize transcriptomic biomarkers of growth rate and carrying capacity. Using these biomarkers, we find significant differences in the predicted growth rate or carrying capacity between multiple karyotypes detected within the same cell line. We use these predictions to simulate how the clonal composition of a cell line will change depending on the timing of splitting cells. Once validated, these models can aid the design of experiments that steer evolution with density dependent selection.

systems biology↗